CombinedText stringlengths 4 3.42M |
|---|
import time
class Getwork_store:
def __init__(self):
self.data = {}
def add(self, server, merkle_root):
self.data[merkle_root] = {'name':server["name"], 'timestamp':time.time()}
return
def get_server(self, merkle_root):
if self.data.has_key(merkle_root):
return self.data[merkle_root]['name']
return None
def prune(self):
for key, work in self.data.items():
if work['timestamp'] < (time.time() - (60*5)):
del self.data[key]
return
Update getwork to prune itself and use a list instead of a dictionary
#License#
#bitHopper by Colin Rice is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
#Based on a work at github.com.
import time
from twisted.internet.task import LoopingCall
class Getwork_store:
def __init__(self):
self.data = {}
call = LoopingCall(self.prune)
call.start(60)
def add(self, server, merkle_root):
self.data[merkle_root] = [server["name"], time.time()]
def get_server(self, merkle_root):
if self.data.has_key(merkle_root):
return self.data[merkle_root][0]
return None
def prune(self):
for key, work in self.data.items():
if work[1] < (time.time() - (60*5)):
del self.data[key]
|
from bs4 import BeautifulSoup
import re
import sys
import json
import time
from collections import OrderedDict
from django.core.management.base import BaseCommand
from django.db.models import Q
from programs.models import Program, ProgramModules, LearningPlan
from disciplines.models import Discipline, Semester, TrainingTerms
from modules.models import Module
def write_roman(num):
roman = OrderedDict()
roman[1000] = "M"
roman[900] = "CM"
roman[500] = "D"
roman[400] = "CD"
roman[100] = "C"
roman[90] = "XC"
roman[50] = "L"
roman[40] = "XL"
roman[10] = "X"
roman[9] = "IX"
roman[5] = "V"
roman[4] = "IV"
roman[1] = "I"
def roman_num(num):
for r in roman.keys():
x, y = divmod(num, r)
yield roman[r] * x
num -= (r * x)
if num > 0:
roman_num(num)
else:
break
return "".join([a for a in roman_num(num)])
class Command(BaseCommand):
"""
Example: ./manage.py parse_new "/home/developer/КТОМ 4.html" uni_fixtures/modules.json ./get_programs.html "Конструкторско-технологическое обеспечение машиностроительных производств"
"""
help = "Create Django objects from raw&ugly UrFU data."
requires_system_checks = True
requires_migrations_checks = True
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[96m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def decompose(self, soup, tag, classname):
[el.decompose() for el in soup.find_all(tag, {'class': classname})]
def add_arguments(self, parser):
parser.add_argument('html_path', nargs=1)
parser.add_argument('uni_modules_path', nargs=1)
parser.add_argument('programs_path', nargs=1)
parser.add_argument('program_title', nargs=1)
def handle(self, *args, **options):
start_time = time.time()
html_path = options["html_path"][0]
uni_modules_path = options["uni_modules_path"][0]
program_title = options["program_title"][0]
programs_path = options["programs_path"][0]
try:
with open(html_path, encoding='utf-8') as html_file:
raw_html = '\n'.join(html_file.readlines())
except:
raise FileNotFoundError
try:
with open(uni_modules_path, encoding='utf-8') as modules_file:
modules_json = json.load(modules_file)
except:
raise FileNotFoundError
try:
with open(programs_path, encoding='utf-8') as programs_file:
raw_programs = '\n'.join(programs_file.readlines())
except:
raise FileNotFoundError
if raw_programs:
programs_soup = BeautifulSoup(raw_programs, 'lxml')
rows = []
for row in programs_soup.find_all('tr', {"class": "main-info"}):
rows.append([val.text.strip() for val in row.find_all('td')])
for row in rows:
try:
program = Program.objects.get(title=row[1])
except:
def level(x):
return {
'Магистр'.lower() in str(x).lower(): "m",
'Специалист'.lower() in str(x).lower(): "s",
'Бакалавр'.lower() in str(x).lower(): "b",
}[True]
program = Program(title=row[1],
training_direction=row[2],
level=level(row[4]),
)
program.save()
print(f"{self.bcolors.BOLD}Создана программа программа \"{row[1]}\"?{self.bcolors.ENDC}")
try:
program = Program.objects.filter(title=program_title).first()
program.status = "p"
program.save()
except:
raise NotImplementedError
if not raw_html:
sys.exit(1)
soup = BeautifulSoup(raw_html, 'lxml')
[s.extract() for s in soup('script')]
[s.extract() for s in soup('style')]
self.decompose(soup, "table", "menu_table")
self.decompose(soup, "td", "navpath")
self.decompose(soup, "div", "buttons")
soup.find('td', id="nav_td").decompose()
try:
stage = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.stage").text.strip().lower() == "утверждено"
except:
stage = False
try:
displayableTitle = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.displayableTitle").text.strip()
except:
displayableTitle = ""
try:
number = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.number").text.strip()
except:
number = ""
try:
active = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.active").text.strip()
except:
active = "нет"
try:
title = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.title").text.strip()
except:
title = ""
try:
loadTimeType = soup.find("td", id="EduVersionPlanTab.EduVersionPlan.loadTimeType").text.strip()
except:
loadTimeType = "часов в неделю"
html = soup.find("table", {"class": "basic"}).prettify()
lps = LearningPlan.objects.filter(uni_number=number, status="p")
if len(lps) > 0:
for lp in lps:
lp.uni_displayableTitle = displayableTitle
lp.uni_number = number
lp.uni_active = active
lp.uni_title = title
lp.uni_stage = stage
lp.uni_loadTimeType = loadTimeType
lp.uni_html = html
lp.save()
if lp not in program.learning_plans.all():
program.learning_plans.add(lp)
program.save()
else:
lp = LearningPlan(uni_displayableTitle=displayableTitle,
uni_number=number,
uni_active=active,
uni_title=title,
uni_stage=stage,
uni_loadTimeType=loadTimeType,
uni_html=html,
status="p"
)
lp.save()
program.learning_plans.add(lp)
program.save()
table = soup.find('table', id="EduVersionPlanTab.EduDisciplineList")
headers = [header.text.strip() for header in table.find_all('th')]
def find_row_index(row_text):
headers = table.find_all('th')
return headers.index(table.find('th', text=row_text))
def find_row_index_id(id):
headers = table.find_all('th')
return headers.index(table.find('th', id=id))
rows = []
for row in table.find_all('tr'):
rows.append([val.text.strip() for val in row.find_all('td')])
# Ищем модули
modules = []
for header in headers:
if "Номер модуля, дисциплины".lower() == header.lower():
module_numbers_col = headers.index(header)
for row in rows:
if row:
m = re.search('\d\d+', row[module_numbers_col])
if m and "М" in row[1]:
for module in modules_json:
if str(module["number"]) == str(m.group(0)):
module["row"] = row
modules.append(module)
program_modules = ProgramModules.objects.filter(program=program)
for module in modules:
print(" ", module['title'])
if program_modules.filter(module__uni_uuid=module["uuid"]):
print(f"Модуль есть: {module['title']}")
fulltime = False
if 'зао' not in number:
fulltime = True
print("fulltime: ", fulltime)
program_modules = []
if fulltime:
term = TrainingTerms.objects.filter(title="4 года").first()
for module in [m for m in modules if m["disciplines"]]:
module_obj, semester = self.create_module(find_row_index_id, module, program, program_modules)
semester = self.create_disciplines(find_row_index_id, module, module_obj, row, rows, semester, program,
term)
print(f"{self.bcolors.HEADER}В программе {len(program_modules)} модулей:{self.bcolors.ENDC}", end="\n ")
print("\n ".join([pm.module.title for pm in program_modules]))
if len(ProgramModules.objects.filter(Q(program=program))) != len(set([pm.module.title for pm in ProgramModules.objects.filter(Q(program=program))])):
print(f"{self.bcolors.FAIL}Найдено дублирование модулей программы. Удалите их, либо поправьте в интерфейсе администратора (Ctrl+С).{self.bcolors.ENDC}")
program_modules_fail = ProgramModules.objects.filter(~Q(id__in=[o.id for o in program_modules]), Q(program=program))
for pmf in program_modules_fail:
remove = input(f"{self.bcolors.WARNING}Неверный модуль программы: {pmf.module.title}. Удалить?{self.bcolors.ENDC}")
if remove.lower() in ("y", "да", "ok", "ок"):
pmf.delete()
print(f"{self.bcolors.OKGREEN}Удалено.{self.bcolors.ENDC}")
def create_module(self, find_row_index_id, module, program, program_modules):
print(f"{self.bcolors.HEADER}Ищем или создаём модуль: {module['title']}{self.bcolors.ENDC}")
for i in range(10, 0, -1):
try:
ze = module["row"][find_row_index_id(f"EduVersionPlanTab.EduDisciplineList.__term{i}.__term{i}headerCell")]
try:
if int(ze) > 0:
semester = i
except:
pass
except:
semester = 99
if semester == 99:
print(f"Семестр: {self.bcolors.FAIL}{semester}{self.bcolors.ENDC}")
else:
print(f"Семестр: {semester}")
try:
module_obj = Module.objects.filter(title=module["title"], uni_number=module["number"]).first()
module_obj.uni_uuid = module["uuid"]
module_obj.uni_number = module["number"]
module_obj.uni_coordinator = module["coordinator"]
module_obj.uni_type = module["type"]
module_obj.uni_title = module["title"]
module_obj.uni_competence = module["competence"]
module_obj.uni_testUnits = module["testUnits"]
module_obj.uni_priority = module["priority"]
module_obj.uni_state = module["state"]
module_obj.uni_approvedDate = module["approvedDate"]
module_obj.uni_comment = module["comment"]
module_obj.uni_file = module["file"]
module_obj.uni_specialities = module["specialities"]
module_obj.program = program
module_obj.semester = semester
module_obj.status = 'p'
module_obj.save()
print(f"{self.bcolors.OKBLUE}Модуль найден: {module['title']}{self.bcolors.ENDC}")
except:
print(f"{self.bcolors.BOLD}Модуль создан: {module['title']}{self.bcolors.ENDC}")
module_obj = Module(title=module["title"],
uni_uuid=module["uuid"],
uni_number=module["number"],
uni_coordinator=module["coordinator"],
uni_type=module["type"],
uni_title=module["title"],
uni_competence=module["competence"],
uni_testUnits=module["testUnits"],
uni_priority=module["priority"],
uni_state=module["state"],
uni_approvedDate=module["approvedDate"],
uni_comment=module["comment"],
uni_file=module["file"],
uni_specialities=module["specialities"],
program=program,
semester=semester,
status='p',
)
module_obj.save()
program_module = ProgramModules.objects.filter(program=program, module=module_obj).first()
if not program_module:
print(f"{self.bcolors.WARNING}Модуль программы не найден, создаём: {module['title']} / {program.title}{self.bcolors.ENDC}")
program_module = ProgramModules(program=program, module=module_obj, semester=module_obj.semester, status="p")
program_module.save()
else:
print(
f"{self.bcolors.OKBLUE}Модуль программы найден {module['title']} / {program.title}{self.bcolors.ENDC}")
program_modules.append(program_module)
return module_obj, semester
def create_disciplines(self, find_row_index_id, module, module_obj, row, rows, semester, program, term):
for d in module["disciplines"]:
if int(d["testUnits"]) > 0:
for row in rows:
if d["title"] in row:
print(f"{self.bcolors.OKGREEN}Дисциплина: {d['title']}{self.bcolors.ENDC}")
semesters = []
for i in range(1, 10):
try:
ze = row[
find_row_index_id(f"EduVersionPlanTab.EduDisciplineList.__term{i}.__term{i}headerCell")]
try:
if int(ze) > 0:
semesters.append(i)
print(f"appanded {i} semester")
except:
pass
except:
pass
print(semesters)
if len(row[5]) > 0:
cell_values = row[5].split("-")
if len(cell_values) > 1:
cell_values = range(cell_values[0], cell_values[1])
print(cell_values)
# print(
# f"{self.bcolors.BOLD}Ищем дисциплину \"{d['title']}\" модуля \"{module_obj.title}\"!{self.bcolors.ENDC}")
# discipline = Discipline.objects.filter(title=d["title"],
# module__in=Module.objects.filter(uni_uuid=module["uuid"]),
# module__program=program).first()
# print(discipline)
# if discipline:
# print(f"{self.bcolors.OKGREEN}Существует дисциплина {discipline.title}!{self.bcolors.ENDC}")
# else:
# print(f"{self.bcolors.FAIL}Не существует дисциплины {d['title']}!!{self.bcolors.ENDC}")
# discipline = Discipline(title=d["title"])
#
#
#
# discipline.module = module_obj
# discipline.labor = d["testUnits"]
# discipline.uni_uid = d["uid"]
# discipline.uni_discipline = d["discipline"]
# discipline.uni_number = d["number"]
# discipline.uni_section = d["section"]
# discipline.uni_file = d["file"]
# discipline.period = semester - module_obj.semester + 1
# try:
# try:
# if int(max(row[5].split("-"))):
# discipline.form = "z"
# except:
# pass
# try:
# if int(max(row[4].split("-"))):
# discipline.form = "e"
# except:
# pass
# except:
# pass
#
# discipline.status = "p"
# # discipline.save()
# # self.create_semester(program, discipline, module, find_row_index_id, term)
# print(f"{self.bcolors.OKBLUE}{discipline.title}{self.bcolors.ENDC}")
return semesters
new parser#60
from bs4 import BeautifulSoup
import re
import sys
import json
import time
from collections import OrderedDict
from django.core.management.base import BaseCommand
from django.db.models import Q
from programs.models import Program, ProgramModules, LearningPlan
from disciplines.models import Discipline, Semester, TrainingTerms
from modules.models import Module
def write_roman(num):
roman = OrderedDict()
roman[1000] = "M"
roman[900] = "CM"
roman[500] = "D"
roman[400] = "CD"
roman[100] = "C"
roman[90] = "XC"
roman[50] = "L"
roman[40] = "XL"
roman[10] = "X"
roman[9] = "IX"
roman[5] = "V"
roman[4] = "IV"
roman[1] = "I"
def roman_num(num):
for r in roman.keys():
x, y = divmod(num, r)
yield roman[r] * x
num -= (r * x)
if num > 0:
roman_num(num)
else:
break
return "".join([a for a in roman_num(num)])
class Command(BaseCommand):
"""
Example: ./manage.py parse_new "/home/developer/КТОМ 4.html" uni_fixtures/modules.json ./get_programs.html "Конструкторско-технологическое обеспечение машиностроительных производств"
"""
help = "Create Django objects from raw&ugly UrFU data."
requires_system_checks = True
requires_migrations_checks = True
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[96m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def decompose(self, soup, tag, classname):
[el.decompose() for el in soup.find_all(tag, {'class': classname})]
def add_arguments(self, parser):
parser.add_argument('html_path', nargs=1)
parser.add_argument('uni_modules_path', nargs=1)
parser.add_argument('programs_path', nargs=1)
parser.add_argument('program_title', nargs=1)
def handle(self, *args, **options):
start_time = time.time()
html_path = options["html_path"][0]
uni_modules_path = options["uni_modules_path"][0]
program_title = options["program_title"][0]
programs_path = options["programs_path"][0]
try:
with open(html_path, encoding='utf-8') as html_file:
raw_html = '\n'.join(html_file.readlines())
except:
raise FileNotFoundError
try:
with open(uni_modules_path, encoding='utf-8') as modules_file:
modules_json = json.load(modules_file)
except:
raise FileNotFoundError
try:
with open(programs_path, encoding='utf-8') as programs_file:
raw_programs = '\n'.join(programs_file.readlines())
except:
raise FileNotFoundError
if raw_programs:
programs_soup = BeautifulSoup(raw_programs, 'lxml')
rows = []
for row in programs_soup.find_all('tr', {"class": "main-info"}):
rows.append([val.text.strip() for val in row.find_all('td')])
for row in rows:
try:
program = Program.objects.get(title=row[1])
except:
def level(x):
return {
'Магистр'.lower() in str(x).lower(): "m",
'Специалист'.lower() in str(x).lower(): "s",
'Бакалавр'.lower() in str(x).lower(): "b",
}[True]
program = Program(title=row[1],
training_direction=row[2],
level=level(row[4]),
)
program.save()
print(f"{self.bcolors.BOLD}Создана программа программа \"{row[1]}\"?{self.bcolors.ENDC}")
try:
program = Program.objects.filter(title=program_title).first()
program.status = "p"
program.save()
except:
raise NotImplementedError
if not raw_html:
sys.exit(1)
soup = BeautifulSoup(raw_html, 'lxml')
[s.extract() for s in soup('script')]
[s.extract() for s in soup('style')]
self.decompose(soup, "table", "menu_table")
self.decompose(soup, "td", "navpath")
self.decompose(soup, "div", "buttons")
soup.find('td', id="nav_td").decompose()
try:
stage = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.stage").text.strip().lower() == "утверждено"
except:
stage = False
try:
displayableTitle = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.displayableTitle").text.strip()
except:
displayableTitle = ""
try:
number = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.number").text.strip()
except:
number = ""
try:
active = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.active").text.strip()
except:
active = "нет"
try:
title = soup.find('td', id="EduVersionPlanTab.EduVersionPlan.title").text.strip()
except:
title = ""
try:
loadTimeType = soup.find("td", id="EduVersionPlanTab.EduVersionPlan.loadTimeType").text.strip()
except:
loadTimeType = "часов в неделю"
html = soup.find("table", {"class": "basic"}).prettify()
lps = LearningPlan.objects.filter(uni_number=number, status="p")
if len(lps) > 0:
for lp in lps:
lp.uni_displayableTitle = displayableTitle
lp.uni_number = number
lp.uni_active = active
lp.uni_title = title
lp.uni_stage = stage
lp.uni_loadTimeType = loadTimeType
lp.uni_html = html
lp.save()
if lp not in program.learning_plans.all():
program.learning_plans.add(lp)
program.save()
else:
lp = LearningPlan(uni_displayableTitle=displayableTitle,
uni_number=number,
uni_active=active,
uni_title=title,
uni_stage=stage,
uni_loadTimeType=loadTimeType,
uni_html=html,
status="p"
)
lp.save()
program.learning_plans.add(lp)
program.save()
table = soup.find('table', id="EduVersionPlanTab.EduDisciplineList")
headers = [header.text.strip() for header in table.find_all('th')]
def find_row_index(row_text):
headers = table.find_all('th')
return headers.index(table.find('th', text=row_text))
def find_row_index_id(id):
headers = table.find_all('th')
return headers.index(table.find('th', id=id))
rows = []
for row in table.find_all('tr'):
rows.append([val.text.strip() for val in row.find_all('td')])
# Ищем модули
modules = []
for header in headers:
if "Номер модуля, дисциплины".lower() == header.lower():
module_numbers_col = headers.index(header)
for row in rows:
if row:
m = re.search('\d\d+', row[module_numbers_col])
if m and "М" in row[1]:
for module in modules_json:
if str(module["number"]) == str(m.group(0)):
module["row"] = row
modules.append(module)
program_modules = ProgramModules.objects.filter(program=program)
for module in modules:
print(" ", module['title'])
if program_modules.filter(module__uni_uuid=module["uuid"]):
print(f"Модуль есть: {module['title']}")
fulltime = False
if 'зао' not in number:
fulltime = True
print("fulltime: ", fulltime)
program_modules = []
if fulltime:
term = TrainingTerms.objects.filter(title="4 года").first()
for module in [m for m in modules if m["disciplines"]]:
module_obj, semester = self.create_module(find_row_index_id, module, program, program_modules)
semester = self.create_disciplines(find_row_index_id, module, module_obj, row, rows, semester, program,
term)
print(f"{self.bcolors.HEADER}В программе {len(program_modules)} модулей:{self.bcolors.ENDC}", end="\n ")
print("\n ".join([pm.module.title for pm in program_modules]))
if len(ProgramModules.objects.filter(Q(program=program))) != len(set([pm.module.title for pm in ProgramModules.objects.filter(Q(program=program))])):
print(f"{self.bcolors.FAIL}Найдено дублирование модулей программы. Удалите их, либо поправьте в интерфейсе администратора (Ctrl+С).{self.bcolors.ENDC}")
program_modules_fail = ProgramModules.objects.filter(~Q(id__in=[o.id for o in program_modules]), Q(program=program))
for pmf in program_modules_fail:
remove = input(f"{self.bcolors.WARNING}Неверный модуль программы: {pmf.module.title}. Удалить?{self.bcolors.ENDC}")
if remove.lower() in ("y", "да", "ok", "ок"):
pmf.delete()
print(f"{self.bcolors.OKGREEN}Удалено.{self.bcolors.ENDC}")
def create_module(self, find_row_index_id, module, program, program_modules):
print(f"{self.bcolors.HEADER}Ищем или создаём модуль: {module['title']}{self.bcolors.ENDC}")
for i in range(10, 0, -1):
try:
ze = module["row"][find_row_index_id(f"EduVersionPlanTab.EduDisciplineList.__term{i}.__term{i}headerCell")]
try:
if int(ze) > 0:
semester = i
except:
pass
except:
semester = 99
if semester == 99:
print(f"Семестр: {self.bcolors.FAIL}{semester}{self.bcolors.ENDC}")
else:
print(f"Семестр: {semester}")
try:
module_obj = Module.objects.filter(title=module["title"], uni_number=module["number"]).first()
module_obj.uni_uuid = module["uuid"]
module_obj.uni_number = module["number"]
module_obj.uni_coordinator = module["coordinator"]
module_obj.uni_type = module["type"]
module_obj.uni_title = module["title"]
module_obj.uni_competence = module["competence"]
module_obj.uni_testUnits = module["testUnits"]
module_obj.uni_priority = module["priority"]
module_obj.uni_state = module["state"]
module_obj.uni_approvedDate = module["approvedDate"]
module_obj.uni_comment = module["comment"]
module_obj.uni_file = module["file"]
module_obj.uni_specialities = module["specialities"]
module_obj.program = program
module_obj.semester = semester
module_obj.status = 'p'
module_obj.save()
print(f"{self.bcolors.OKBLUE}Модуль найден: {module['title']}{self.bcolors.ENDC}")
except:
print(f"{self.bcolors.BOLD}Модуль создан: {module['title']}{self.bcolors.ENDC}")
module_obj = Module(title=module["title"],
uni_uuid=module["uuid"],
uni_number=module["number"],
uni_coordinator=module["coordinator"],
uni_type=module["type"],
uni_title=module["title"],
uni_competence=module["competence"],
uni_testUnits=module["testUnits"],
uni_priority=module["priority"],
uni_state=module["state"],
uni_approvedDate=module["approvedDate"],
uni_comment=module["comment"],
uni_file=module["file"],
uni_specialities=module["specialities"],
program=program,
semester=semester,
status='p',
)
module_obj.save()
program_module = ProgramModules.objects.filter(program=program, module=module_obj).first()
if not program_module:
print(f"{self.bcolors.WARNING}Модуль программы не найден, создаём: {module['title']} / {program.title}{self.bcolors.ENDC}")
program_module = ProgramModules(program=program, module=module_obj, semester=module_obj.semester, status="p")
program_module.save()
else:
print(
f"{self.bcolors.OKBLUE}Модуль программы найден {module['title']} / {program.title}{self.bcolors.ENDC}")
program_modules.append(program_module)
return module_obj, semester
def create_disciplines(self, find_row_index_id, module, module_obj, row, rows, semester, program, term):
for d in module["disciplines"]:
if int(d["testUnits"]) > 0:
for row in rows:
if d["title"] in row:
print(f"{self.bcolors.OKGREEN}Дисциплина: {d['title']}{self.bcolors.ENDC}")
semesters = []
for i in range(1, 10):
try:
ze = row[
find_row_index_id(f"EduVersionPlanTab.EduDisciplineList.__term{i}.__term{i}headerCell")]
try:
if int(ze) > 0:
semesters.append(i)
print(f"appanded {i} semester")
except:
pass
except:
pass
print(semesters)
if len(row[5]) > 0:
cell_values = row[5].split("-")
if len(cell_values) > 1:
cell_values = range(cell_values[0], cell_values[1])
print(cell_values)
# print(
# f"{self.bcolors.BOLD}Ищем дисциплину \"{d['title']}\" модуля \"{module_obj.title}\"!{self.bcolors.ENDC}")
# discipline = Discipline.objects.filter(title=d["title"],
# module__in=Module.objects.filter(uni_uuid=module["uuid"]),
# module__program=program).first()
# print(discipline)
# if discipline:
# print(f"{self.bcolors.OKGREEN}Существует дисциплина {discipline.title}!{self.bcolors.ENDC}")
# else:
# print(f"{self.bcolors.FAIL}Не существует дисциплины {d['title']}!!{self.bcolors.ENDC}")
# discipline = Discipline(title=d["title"])
#
#
#
# discipline.module = module_obj
# discipline.labor = d["testUnits"]
# discipline.uni_uid = d["uid"]
# discipline.uni_discipline = d["discipline"]
# discipline.uni_number = d["number"]
# discipline.uni_section = d["section"]
# discipline.uni_file = d["file"]
# discipline.period = semester - module_obj.semester + 1
# try:
# try:
# if int(max(row[5].split("-"))):
# discipline.form = "z"
# except:
# pass
# try:
# if int(max(row[4].split("-"))):
# discipline.form = "e"
# except:
# pass
# except:
# pass
#
# discipline.status = "p"
# # discipline.save()
# # self.create_semester(program, discipline, module, find_row_index_id, term)
# print(f"{self.bcolors.OKBLUE}{discipline.title}{self.bcolors.ENDC}")
return semesters |
# =================================================
# --------- tms - module of Variables for re-use----------------
# --------------
# Section 0 - Required modules
# Section 1 - Planeflight variables
# Section 2 - Drivers functions
# Section 3 - GeosChem (bpch) prod loss variables
# Section 4 - GeosChem (bpch) general variables
# Section 5 - Misc
# Section 6 - Dynamic p/l processing
# Section 7 - Obervational variables
# ---------------------------- ------------- ------------- -------------
# -------------- Contents
# --------------- ------------- ------------- -------------
# ---- Section 0 ----- Required modules
# --------------- ------------- ------------- -------------
# ---- Section 1 ----- Planeflight variables
# 1.01 - PF variable dictionary ***
# 1.02 - TRA_?? to Geos-Chem species name ***
# --------------- ------------- ------------- -------------
# ---- Section 2 ----- Drivers functions
# 2.01 - P/L tag to PD tag ***
# 2.02 - Get P/L dictionary for a given family ***
# 2.03 - Get P/L dictionary for a given species ***
# --------------- ------------- ------------- -------------
# ---- Section 3 ----- GeosChem (bpch) prod loss variables
# 3.01 - Spec to photolysis reaction p/l tag
# 3.02 - Ox family for tag
# --------------- ------------- ------------- -------------
# ---- Section 4 ----- GeosChem (bpch) general variables
# 4.01 - v9-2 species in input.geos from num
# 4.02 - Get Species Mass
# 4.03 - Get Species stoichiometry
# 4.04 - GEOS-Chem/ctm.bpch values (current main dict ) ***
# 4.05 - latex species name
# 4.06 - converts P/L tracer mulitpler to 1
# 4.07 - Returns tracers unit and scale (if requested)
# 4.08 - Store of dirs for earth0, atmosviz1, and tms mac
# 4.09 - Ox in species (redundant now? should adapt species stoich )
# 4.10 - Get Gaw site name from GAW ID
# 4.11 - returns dict of gaw sites
# 4.12 - Return lat, lon, alt for a given resolution
# 4.13 - Get model array dimension for a given resolution
# 4.14 - Convert gamap category/species name to Iris/bpch name
# 4.99 - Reference data, (inc. grid data) from gchem
# --------------- ------------- ------------- -------------
# ---- Section 5 ----- Misc
# 5.01 - dir store (standard directories on different servers )
# 5.02 - Store of constants for use by funcs/progs
# --------------- ------------- ------------- -------------
# ---- Section 6 ----- Dynamic prod/loss dictionary processing ( For GEOS-Chem)
# 6.01 - Make rxn dict of all active reactions ***
# 6.02 - Return all reactions for a given p/l family ***
# 6.03 - Return reaction infomaiton for given tags (e.g. PD... )
# 6.04 - Create an indices list to split reaction by family (e.g. for Ox loss)
# 6.05 - Return tags for a given reaction
# 6.06 - Extract all p/l speacies in a given input.geos
# 6.07 - Extract all active tags from a given smv.log
# 6.08 - Extract all active PDs from a given smv.log
# 6.09 - get all active reaction for a given tag
# 6.10 - get all details for a given tag
# 6.11 - get reaction coeffifecent
# --------------- ------------- ------------- -------------
# ---- Section 7 ----- Observational variables
# 7.01 - IO observation dictionary
# 7.02 - BAE flight ID dictionary
# 7.03 - CAST flight dictionary for CIMS/CIMSII
# 7.04 - Iodocarbon obs. meta data
# 7.05 - Stores locations for use by funcs/progs - LON, LAT, ALT - double up? ( with 5.02 ?? )
# 7.06 - Get Locations of observations (lats, lons, alts ) for given sites
# 7.07 - sonde station variables (list of 432 sondes)
# 7.08 - returns (lat, lon, alt (press), timezone (UTC) ) for a given site
# ------------------ Section 0 -----------------------------------
# -------------- Required modules:
#
#!/usr/bin/python
#
# -- I/O / Low level
import re
import platform
import pandas as pd
from netCDF4 import Dataset
#import Scientific.IO.NetCDF as S
import sys
import glob
# - Math/Analysis
import numpy as np
# - tms
from AC_tools.funcs4core import *
# ------------------------------------------- Section 1 -------------------------------------------
# -------------- Planeflight variables
#
# --------------
# 1.01 - dictionary of variables used for planeflight_mod.F output
# -------------
def pf_var( input, ver='1.7', ntracers=85, JREAs=[] ):
# planeflight variable lists
metvars = [
'GMAO_TEMP', 'GMAO_ABSH', 'GMAO_SURF', 'GMAO_PSFC', 'GMAO_UWND', 'GMAO_VWND'
]
species = [
'O3', 'NO2', 'NO', 'NO3', 'N2O5', 'HNO4', 'HNO3', 'HNO2', 'PAN', 'PPN',
'PMN', 'R4N2', 'H2O2', 'MP', 'CH2O', 'HO2', 'OH', 'RO2', 'MO2', 'ETO2',
'CO', 'C2H6', 'C3H8', 'PRPE', 'ALK4', 'ACET', 'ALD2', 'MEK', 'RCHO', 'MVK',
'SO2', 'DMS', 'MSA', 'SO4', 'ISOP'
]
all_species_not_TRA = [
'A3O2', 'ATO2', 'B3O2', 'EOH', 'ETO2', 'ETP', 'GLYX', 'HO2', 'IAP', 'INO2', 'INPN', 'ISN1', 'ISNOOA', 'ISNOOB', 'ISNOHOO', 'ISNP', 'KO2', 'MAN2', 'MAO3', 'MAOP', 'MAOPO2', 'MCO3', 'MGLY', 'MO2', 'MRO2', 'MRP', 'OH', 'PO2', 'PP', 'PRN1', 'PRPN', 'R4N1', 'R4O2', 'R4P', 'RA3P', 'RB3P', 'RCO3', 'RIO2', 'ROH', 'RP', 'VRO2', 'VRP', 'LISOPOH', 'ISOPND', 'ISOPNB', 'HC5', 'DIBOO', 'HC5OO', 'DHMOB', 'MOBAOO', 'ISOPNBO2', 'ISOPNDO2', 'ETHLN', 'MACRN', 'MVKN', 'PYAC', 'IEPOXOO', 'ATOOH', 'PMNN', 'MACRNO2', 'PMNO2'
]
OH_reactivity=[
'NO', 'ISOP', 'GMAO_TEMP', 'GMAO_PSFC', 'CO', 'ACET', 'ALD2', 'MEK', 'MVK', 'MACR', 'C3H8', 'CH2O', 'C2H6', 'SO2', 'NO2', 'ISOPNB', 'ISOPND', 'NO3', 'HNO2', 'HNO3', 'OH', 'HO2', 'H2O2', 'MP', 'ATOOH', 'HNO4', 'ALK4', 'ISN1', 'R4N2', 'RCHO', 'ROH', 'PRPE', 'PMN', 'GLYC', 'GLYX', 'MGLY', 'HAC', 'INPN', 'PRPN', 'ETP', 'RA3P', 'RB3P', 'R4P', 'RP', 'PP', 'RIP', 'IEPOX', 'IAP', 'VRP', 'MRP', 'MAOP', 'MAP', 'DMS', 'HBr', 'Br2', 'BrO', 'CHBr3', 'CH2Br2', 'CH3Br', 'HC5', 'ISOPND', 'ISOPNB', 'ISNP', 'MVKN', 'MACRN', 'DHMOB', 'MOBA', 'ETHLN', 'PROPNN'
]
OH_Extras4nic = [
'OH', 'MCO3', 'A3O2', 'PO2', 'R4O2', 'R4O2', 'R4N1', 'ATO2', 'KO2', 'RIO2', 'VRO2', 'MRO2', 'MAN2', 'B3O2', 'INO2', 'ISNOOA', 'ISNOOB', 'ISNOHOO', 'PRN1', 'RCO3', 'MAO3', 'IEPOXOO', 'MAOPO2', 'MAOPO2', 'HC5OO', 'HC5OO', 'ISOPNDO2', 'ISOPNDO2', 'ISOPNBO2', 'ISOPNBO2', 'DIBOO', 'DIBOO', 'MOBAOO', 'MOBAOO', 'H2', 'CH4', 'HCOOH', 'MOH', 'ACTA', 'EOH', 'VRP'
]
# remove inactive species
inactive_spec = ['ACTA', 'CH4', 'EOH', 'H2', 'HCOOH', 'MOH']
[ OH_Extras4nic.pop(ii) for ii in sorted([ OH_Extras4nic.index(i) \
for i in inactive_spec ])[::-1] ]
# Setup list of tracers
if ver == '1.7':
ntracers=85
if ver == '2.0':
ntracers=101
if ver == 'johan_br.v92':
ntracers=87
TRAs = ['TRA_'+ str(i) for i in range(1, ntracers+1) ]
# TRAs = ['TRA_{:0>2}'.format(i) for i in range(1, ntracers+1) ]
# Setup list of reactions ( photolysis and general )
if ver == '1.5':
PHOT_1st, PHOT_last = 455, 533
if ver == '1.6':
PHOT_1st, PHOT_last = 453, 531
if ver == '1.6.1':
PHOT_1st, PHOT_last = 453, 530
if ver == '1.7':
PHOT_1st, PHOT_last = 453, 529
if ver == '2.0':
PHOT_1st, PHOT_last = 413, 614
JREAs = ['REA_'+ str(i) for i in range(PHOT_1st, PHOT_last) ]
REAs_all = ['REA_'+ str(i) for i in range(0, 533) ]
# reduced list for high time and spatial resolution
if any( [ input ==i for i in 'slist_v9_2_NREA_red',
'slist_v9_2_NREA_red_NOy'] ):
TRAs = GC_var('active_I') + ['AERI']
TRAs= [ num2spec( i, ver=ver, invert=True) for i in TRAs ]
TRAs = [ 'TRA_{:0>2}'.format( i) for i in TRAs ]
metvars = [ i for i in metvars if not any( [ (i==ii) \
for ii in 'GMAO_ABSH', 'GMAO_SURF', 'GMAO_PSFC' ] ) ]
species = [ i for i in species if not any( [ (i==ii) for ii in
'R4N2', 'MP', 'CH2O', 'MO2', 'ETO2', 'CO', 'C2H6', 'C3H8', 'PRPE', 'ALK4', 'ACET', 'ALD2', 'MEK', 'RCHO', 'MVK', 'DMS', 'MSA', 'ISOP'
]) ]
if input =='slist_ClearFlo':
TRAs = 'CO', 'ACET', 'ALD2', 'ISOP', 'C2H6', 'C3H8', 'CH2O', \
'MACR', 'HNO2', 'HNO3', 'MVK', 'NO', 'NO2', 'PAN', 'O3',
TRAs= [ num2spec( i, ver=ver, invert=True) for i in TRAs ]
TRAs = [ 'TRA_{:0>2}'.format( i) for i in TRAs ]
# mannually add ethanol
TRAs += [ 'TRA_86']
species = [ 'OH', 'MO2','HO2' ]
if input =='slist_v9_2_NREA_red_NOy':
# THIS IS NOT A GOOD APPROACH, use actual names an tranlate based on verison.
# missing = [ 'TRA_17', 'TRA_60', 'TRA_30', 'TRA_31', 'TRA_50', \
# 'TRA_54', 'TRA_55', 'TRA_57' ]
# use tracer TRA_60, TRA_30, TRA_31, for: 'MMN' , 'NH3' , 'NH4', 'R4N2', 'BrNO2', 'BrNO3','MPN', 'PROPNN',
missing = 'MMN' , 'NH3' , 'NH4', 'R4N2', 'BrNO2', 'BrNO3','MPN', 'PROPNN'
missing = [ num2spec( i, ver=ver, invert=True) for i in missing ]
missing = [ 'TRA_{:0>2}'.format( i) for i in missing ]
species = species + missing
# Construct dictionary
d= {
'species' : species,
'metvars' : metvars,
'REAs_all' : REAs_all,
'JREAs': JREAs,
'TRAs' : TRAs,
'slist' : species +TRAs +JREAs+ metvars ,
'slist_v9_2_NH' : species + TRAs[:66] + metvars ,
'slist_v9_2_NREA' : species + TRAs + metvars ,
'slist_v9_2_NREA_red': species + TRAs + metvars,
'slist_REAs_all' : species + TRAs + REAs_all + metvars,
'slist_REAs_all_OH' : species + TRAs + metvars+OH_reactivity,
'slist_REAs_all_OH_extras' : all_species_not_TRA + TRAs + metvars,
'slist_v9_2_NREA_red_NOy' : species + TRAs + metvars,
'slist_v10_1.7_allspecs': all_species_not_TRA +TRAs+ JREAs +metvars,
'slist_ClearFlo': species + TRAs + metvars
}
# retrieve variable list from dictionary
vars = d[input]
# return unique list
vars = sorted( list( set( vars) ) )
print vars
return vars
# --------------
# 1.02 - Translator for planeflight species to GEOS-Chem species
# -------------
def what_species_am_i(input=None, V_9_2=True, V_9_2_C=False, \
ver='1.7', special_case=None, invert=False, rtn_dict=False, \
debug=False ) :
"""
What GEOS-Chem (GC) Species am i?
takes TRA_## & returns GC ID or other wayround
"""
# select correct naming dictionary
var ={ \
'1.7': 'GCFP_d2TRA_all_1.7',
'1.6': 'GCFP_d2TRA_all_1.6'
}[ver]
# special_case = 'EOH'
# special_case = 'EOH + actual names'
# if all_TRA:
# var ={ \
# '1.7': 'all_TRA_spec_met_1.7_EOH'
# '1.6': 'GCFP_d2TRA_1.6'
# }[ver]
if not isinstance( special_case, type(None) ):
var = {
# 'EOH':'GCFP_d2TRA_all_1.7_EOH',
# 'EOH + actual names':'GCFP_d2TRA_all_1.7_EOH_actual_names'
# 'all_TRA_spec_met_1.7_EOH' : 'TRA_spec_met_all_1.7_EOH' #'
'all_TRA_spec_met_1.7_EOH':'TRA_spec_met_all_1.7_EOH_no_trailing_zeroes'
# TRA_spec_met_all_1'
}[special_case]
# Get dictionary from variable store
d = GC_var( var )
if debug:
print d
if invert:
d = {v: k for k, v in d.items()}
# return dictionary
if rtn_dict:
return d
else:
return d[input]
# ---------------- Section 2 -------------------------------------------
# -------------- Drivers
#
# --------------
# 2.01 - Convert Production/Loss RD IDs for O3 to PD## for input.geos/tracer.dat linked files
# -------------
def PLO3_to_PD(PL, fp=True, wd=None, ver='1.6', res='4x5',debug=False):
""" Converts """
if any( [(ver ==i) for i in '1.3' ,'1.4' ,'1.5' , '1.6', '1.7' ]):
if wd==None:
if debug:
print 'WARNING: Using MUTD wd'
wd = MUTD_runs(ver=ver, res=res, debug=debug)[0]
PDs, vars = p_l_species_input_geos( wd, ver=ver,
rm_multiple_tagged_rxs=True)
# Add other vars for ease of processing
vars += ['PIOx', 'iLOX', 'LIOx', 'iPOX', 'POX', 'LOX', 'LOx', 'L_Iy']
PDs += ['PIOx', 'iLOX', 'LIOx', 'iPOX', 'POX', 'LOX', 'LOx', 'L_Iy']
return dict( zip(vars, PDs))[PL ]
else:
print 'update programme - manual PLdict now obsolete. '
# -------------
# 2.02 - DRIVER - uses functions to build a dictionary for a given family of loss
# -------------
def get_pl_dict( wd, spec='LOX' , rmx2=False, debug=False):
# Get reaction IDs for each rxn. in spec (p/l, e.g. LOX)
nums, rxns, tags, Coe = prod_loss_4_spec( wd, spec, all_clean=True, debug=debug )
# Make a dictionary of coeffiecnts of reaction
Coe_dict = dict(zip(nums, Coe) )
# unpack for mulutple tags of same reactions, then get details
unpacked_tags = [j for k in tags for j in k ]
details = [ get_tag_details( wd, tag ) for tag in unpacked_tags ]
# Kludge - 1 rxn missing from POx tracking? - 999 + "'ISOPND+OH', '+', '=1.0ISOPND'"
[ details.pop(n) for n, i in enumerate( details ) if i[1]==364]
ind = [n for n, i in enumerate( nums ) if i ==354 ]
# Get Coes and overwrite where prog_mod_tms has values
Coes = [ get_rxn_Coe( wd, d[1], unpacked_tags[n], nums=nums, rxns=rxns, tags=tags, Coe=Coe, spec=spec, debug=debug )
for n, d in enumerate( details ) ]
# Remove double ups, which are present due to Loss (LO3_??) and rate tagging (RD??) originally performed separately
if rmx2:
d = [
['RD62', 'LO3_38'], ['RD59', 'LO3_30'], ['RD65', 'LO3_34'], \
['RD93', 'LO3_55'], ['RD92', 'LO3_39'], [ 'RD95', 'LO3_36'], \
['RD67', 'LO3_35']
]
d = [i[0] for i in d ]
ind = [ n for n, i in enumerate(details) if any( [ i[0] == ii \
for ii in d ] ) ]
if debug:
print d, ind, [len(i) for i in details, Coes ] , [ [i[0] \
for i in details][ii] for ii in ind ][::-1]
[ l.pop(i) for i in ind[::-1] for l in details, Coes ]
if debug:
print [len(i) for i in details, Coes ]
# return a dictionary indexed by p/l tracer, with rxn #,
# reaction str and Coe of rxn.
return dict( zip( [i[0] for i in details], [ i[1:] + [ Coes[n] ] for n, i in enumerate( details) ] ) )
# -------------
# 2.03 - Get prod loss reactions for a given family.
# -------------
def prod_loss_4_spec( wd, fam, all_clean=True, debug=False ):
# --- Get Dict of all reactions, Keys = #s
rdict = rxn_dict_from_smvlog(wd)
# --- Get reaction # tracked by p/l diag for spec and coefficient.
rxns = rxns_in_pl(wd, fam)
nums = rxns.keys()
Coe = [ rxn[-1] for rxn in rxns.values() ]
# --- get all details from full reaction dictionary
rxns = [ rdict[i] for i in nums ]
# --- get tags for tracked reactions, state where reactions are un tracked
tags = get_p_l_tags( rxns )
# --- cleaned tags
if all_clean:
tags = [ [re.sub('\+\d.\d', '', i) for i in u ] for u in tags ]
tags = [ [re.sub('\=\d.\d', '', i) for i in u ] for u in tags ]
# -- remove erroneous read/ Kludge on val
# --- Fortran write error leads to combination of species at the
# end of long line
if (debug):
print [ i[:3] for i in nums, rxns, tags, Coe]
print [ len(i) for i in nums, rxns, tags, Coe]
errs = ['LO3_36RD95' , 'ISOPNDPO3_50']
cerrs = [['LO3_36', 'RD95'], ['PO3_50'] ]
for n, e in enumerate( errs ):
try:
ind = [ nn for nn, i in enumerate( tags) if any([ ( e in ii) for ii in i ]) ] [0]
vars = [ i[ind] for i in nums, rxns, tags, Coe]
if (debug):
print 3, [ i[-1] for i in nums, rxns, tags, Coe], vars, [ len(i) for i in nums, rxns, tags, Coe]
[i.pop(ind) for i in nums, rxns, tags, Coe ]
# add the cerrs values on the end
if (debug):
print 4, [ i[-1] for i in nums, rxns, tags, Coe], [ len(i) for i in nums, rxns, tags, Coe]
nums += [ vars[0] ]
rxns += [vars[1] ]
tags += [cerrs[n]]
Coe += [vars[-1] ]
if (debug):
print 6, [ i[-1] for i in nums, rxns, tags, Coe], \
[ len(i) for i in nums, rxns, tags, Coe]
print '->'*30, 'SUCCESS', n, e
except:
print '>'*100, 'FAIL' , n, e
return nums, rxns, tags, Coe
# ------------------------------------------- Section 3 -------------------------------------------
# -------------- GeosChem (bpch) prod loss variables
#
# --------------
# 3.01 - Spec to photolysis reaction p/l tag
# -------------
def spec_phot_2_RD(spec):
d = {
'OIO': 'RD67', 'ICl': 'RD74', 'I2O2': 'RD70', 'I2': 'RD64', \
'CH2ICl': 'RD88', 'HOI': 'RD65', 'CH2IBr': 'RD89', 'INO': 'RD75', \
'IO': 'RD66', 'CH2I2': 'RD72','CH3IT': 'RD71', 'IONO2': 'RD69', \
'IONO': 'RD68', 'IBr': 'RD73' \
}
return d[spec]
# -------------
# 3.02 - Get families for reactions
# -------------
def get_tag_fam( tag ):
"""
dictionary of manually constructed assignment list
- Ox loss familes
- addition for paranox Kludge
(in v9-2 (patched), but removed in v10? )
"""
# Ox family dictionary
fam_d = {
'LO3_18': 'Photolysis', 'LR25': 'Bromine', 'LR21': 'Bromine', 'LO3_38': 'Iodine', 'LO3_63': 'NOy', 'LO3_10': 'HOx', 'LO3_34': 'Iodine', 'LO3_35': 'Iodine', 'LO3_30': 'Iodine', 'LR5': 'Bromine', 'LR6': 'Bromine', 'LO3_61': 'NOy', 'LO3_60': 'NOy', 'LO3_39': 'Iodine', 'LO3_05': 'HOx', 'LO3_07': 'NOy', 'LO3_06': 'HOx', 'LO3_49': 'NOy', 'LO3_62': 'NOy', 'LO3_03': 'HOx', 'LO3_02': 'HOx', 'LO3_67': 'NOy', 'LO3_66': 'NOy', 'LO3_69': 'NOy', 'LO3_42': 'NOy', 'LO3_41': 'NOy', 'LO3_40': 'NOy', 'LO3_47': 'HOx', 'LO3_46': 'NOy', 'LO3_09': 'HOx', 'LO3_44': 'NOy', 'LR37': 'HOx', 'LR36': 'NOy', 'LO3_65': 'NOy', 'LR30': 'Bromine', 'LO3_24': 'Iodine', 'LR10': 'Bromine', 'LR38': 'NOy', 'LO3_68': 'NOy', 'LO3_64': 'NOy', 'LO3_36': 'Iodine', 'LO3_57': 'NOy', 'LO3_72': 'NOy', 'RD98': 'Photolysis', 'LO3_71': 'NOy', 'LO3_58': 'NOy', 'LO3_54': 'Photolysis', 'LO3_55': 'Iodine', 'LO3_56': 'HOx', 'LO3_08': 'HOx', 'LO3_50': 'NOy', 'LO3_51': 'NOy', 'LO3_52': 'NOy', 'LO3_53': 'HOx'
# added
, 'RD63': 'Iodine', 'RD62':'Iodine', 'LO3_38': 'Iodine', 'RD59': 'Iodine', 'LO3_30' : 'Iodine', 'RD65': 'Iodine', 'LO3_34': 'Iodine', 'RD93': 'Iodine', 'LO3_55': 'Iodine', 'RD92': 'Iodine', 'LO3_39': 'Iodine' , 'LO3_36': 'Iodine','RD95': 'Iodine' , 'RD67': 'Iodine', 'LO3_35': 'Iodine'
# , 'RD36': 'Bromine' # Kludge to allow combination reactions
# Kludge - from Chris Holmes (paranox deposition, goes through p/l as Ox losss )
,'LO3_70' : 'Photolysis'
# Extra tags not in list?
# (these reactions are appearing due to lack of inclusion of iodine
# species in Ox family... )
# ,'RD19': 'iodine', 'RD37': 'iodine', 'RD01': 'iodine'
}
# Creigee reaction class/"family" dictionary
# if cregiee:
# fam_d ={
# }
return fam_d[tag]
# ----------------- Section 4 -------------------------------------------
# -------------- GeosChem (bpch) general variables
#
# 4.01 - v9-2 species in input.geos from num
# 4.02 - Get Species Mass
# 4.03 - Get Species stioch
# 4.04 - GEOS-Chem/ctm.bpch values (current main dict )
# 4.05 - latex species name
# 4.06 - converts P/L tracer mulitpler to 1
# 4.07 - Returns tracers unit and scale (if requested)
# 4.08 - Store of dirs for earth0, atmosviz1, and tms mac
# 4.09 - Ox in species (redundant now? should adapt species stoich )
# 4.10 - Get GAW site info (lat, lon, alt (press), timezone (UTC) )
# 4.99 - Reference data, (inc. grid data) from gchem - credit: GK (Gerrit Kuhlmann )
# --------------
# 4.01 - v9-2 species in input.geos from num
# -------------
def num2spec( num=69, rtn_dict=False, invert=False, ver = '1.7' ):
# get dictionary of tracer numbers
d= what_species_am_i( ver=ver, rtn_dict=True, special_case=None )
# slice off just numbers
nums =[ int(i[4:]) for i in d.keys()]
# re-make dictionary
d = dict( zip(nums, d.values() ) )
# inver to give spec for num
if invert:
d = { v: k for k, v in d.items() }
if rtn_dict:
return d
else:
return d[num]
# --------------
# 4.02 - RMM (Mass) (g /mol) for species
# -------------
# C3H5I == C2H5I (this is a vestigle typo, left in to allow for use of older model runs
def species_mass(spec):
d = {
'HIO3': 176.0, 'OCPO': 12.0, 'Br2': 160.0, 'OCPI': 12.0, 'O3': 48.0, 'PAN': 121.0, 'ACET': 12.0, 'RIP': 118.0, 'BrNO3': 142.0, 'Br': 80.0, 'HBr': 81.0, 'HAC': 74.0, 'ALD2': 12.0, 'HNO3': 63.0, 'HNO2': 47.0, 'C2H5I': 168.0, 'HNO4': 79.0, 'OIO': 159.0, 'MAP': 76.0, 'PRPE': 12.0, 'CH2I2': 268.0, 'IONO2': 189.0, 'NIT': 62.0, 'CH3Br': 95.0, 'C3H7I': 170.0, 'C3H8': 12.0, 'DMS': 62.0, 'CH2O': 30.0, 'CH3IT': 142.0, 'NO2': 46.0, 'NO3': 62.0, 'N2O5': 105.0, 'H2O2': 34.0, 'DST4': 29.0, 'DST3': 29.0, 'DST2': 29.0, 'DST1': 29.0, 'MMN': 149.0, 'HOCl': 52.0, 'NITs': 62.0, 'RCHO': 58.0, 'C2H6': 12.0, 'MPN': 93.0, 'INO': 157.0, 'MP': 48.0, 'CH2Br2': 174.0, 'SALC': 31.4, 'NH3': 17.0, 'CH2ICl': 167.0, 'IEPOX': 118.0, 'ClO': 51.0, 'NO': 30.0, 'SALA': 31.4, 'MOBA': 114.0, 'R4N2': 119.0, 'BrCl': 115.0, 'OClO': 67.0, 'PMN': 147.0, 'CO': 28.0, 'BCPI': 12.0, 'ISOP': 12.0, 'BCPO': 12.0, 'MVK': 70.0, 'BrNO2': 126.0, 'IONO': 173.0, 'Cl2': 71.0, 'HOBr': 97.0, 'PROPNN': 109.0, 'Cl': 35.0, 'I2O2': 286.0, 'I2O3': 302.0, 'I2O4': 318.0, 'I2O5': 338.0, 'MEK': 12.0, 'HI': 128.0, 'ISOPN': 147.0, 'SO4s': 96.0, 'I2O': 270.0, 'ALK4': 12.0, 'MSA': 96.0, 'I2': 254.0, 'PPN': 135.0, 'IBr': 207.0, 'MACR': 70.0, 'I': 127.0, 'AERI': 127.0, 'HOI': 144.0, 'BrO': 96.0, 'NH4': 18.0, 'SO2': 64.0, 'SO4': 96.0, 'IO': 143.0, 'CHBr3': 253.0, 'CH2IBr': 221.0, 'ICl': 162.0, 'GLYC': 60.0
# species, not in tracer list
, 'HO2': 33.0, 'OH': 17.0,'CH4':16.0 , 'N':14.0, 'CH3I':142.0, 'CH2OO':46.0, 'S': 32.0,
}
return d[spec]
# --------------
# 4.03 - return the stiochometry of Iodine in species
# --------------
def spec_stoich( spec, IO=False, I=False, NO=False, OH=False, N=False,
C=False ):
# if I: # note - re-write to take stioch species (e.g. OH, I instead of booleans ) - asssume I == True as default
# C3H5I == C2H5I (this is a vestigle typo, left in to allow for use of older model runs
# aerosol cycling specs
# 'LO3_36' : (2.0/3.0) , 'LO3_37' : (2.0/4.0), # aersol loss rxns... 'LO3_37' isn't true loss, as I2O4 is regen. temp
# aerosol loss rxns - corrected stochio for Ox, adjsutment need for I
d = {
'RD11': 1.0, 'RD10': 1.0, 'HIO3': 1.0, 'RD15': 1.0, 'RD62': 2.0, 'RD17': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'LO3_37': 0.5, 'CH2I2': 2.0, 'AERII': 1.0, 'CH2ICl': 1.0, 'PIOx': 1.0, 'C3H7I': 1.0, 'RD73': 1.0, 'RD72': 2.0, 'RD71': 1.0, 'RD70': 1.0, 'C3H5I': 1.0, 'RD57': 1.0, 'CH3IT': 1.0, 'IO': 1.0, 'LO3_38': 1.0, 'RD61': 1.0, 'RD68': 1.0, 'I2': 2.0, 'IONO': 1.0, 'LO3_36': 0.6666666666666666, 'INO': 1.0, 'RD88': 1.0, 'RD89': 1.0, 'LOx': 1.0, 'RD06': 1.0, 'RD07': 1.0, 'RD02': 1.0, 'RD01': 1.0, 'I': 1.0, 'LO3_24': 0.5, 'AERI': 1.0, 'HOI': 1.0, 'RD64': 2.0, 'RD65': 1.0, 'RD66': 1.0, 'RD67': 1.0, 'RD60': 1.0, 'RD47': 1.0, 'C2H5I': 1.0, 'RD63': 1.0, 'RD20': 1.0, 'RD22': 1.0, 'RD24': 1.0, 'RD69': 1.0, 'RD27': 1.0, 'OIO': 1.0, 'CH2IBr': 1.0, 'LIOx': 1.0, 'L_Iy': 1.0, 'ICl': 1.0, 'IBr': 1.0, 'RD95': 2.0, 'I2O2': 2.0, 'I2O3': 2.0, 'I2O4': 2.0, 'I2O5': 2.0, 'HI': 1.0, 'I2O': 2.0, 'RD59': 1.0, 'RD93': 2.0, 'RD92': 1.0, 'IONO2': 1.0, 'RD58': 1.0,
# p/l for: IO, I
'RD15': 1.0, 'RD17': 1.0, 'RD75': 1.0, 'RD72': 2.0, 'RD71': 1.0, 'RD70': 1.0, 'RD56': 1.0, 'RD69': 1.0, 'RD88': 1.0, 'RD89': 1.0, 'RD06': 1.0, 'RD07': 1.0, 'RD08': 1.0, 'RD64': 2.0, 'RD65': 1.0, 'RD67': 1.0, 'RD46': 2.0, 'RD47': 1.0, 'RD20': 1.0, 'RD22': 1.0, 'RD68': 1.0, 'RD25': 1.0, 'RD96': 1.0 ,
'RD11': 1.0, 'RD12': 2.0, 'RD02': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'RD24': 1.0, 'RD09': 1.0, 'RD23': 1.0, 'RD37': 1.0, 'RD97': 1.0,
# kludge for test analysis (HEMCO emissions )
'ACET' : 1.0, 'ISOP': 1.0, 'CH2Br2': 1.0, 'CHBr3':1.0, 'CH3Br':1.0
}
if IO:
d = {
'RD11': 2.0, 'RD10': 1.0, 'RD12': 2.0, 'LO3_36': 1./3., 'RD09': 1.0, 'RD66': 1.0, 'RD23': 1.0, 'RD37': 1.0, 'LO3_24': 1.0/2.0, 'RD56': 1.0, 'RD01': 1.0, 'RD08': 1.0, 'RD46': 2.0, 'RD30': 1.0, 'RD25': 1.0, 'RD27': 1.0, 'RD97':1.0
}
if NO:
d ={
'NO2': 1.0, 'NO3': 1.0, 'N2O5': 2.0, 'NO': 1.0, 'PPN': 1.0, 'R4N2': 1.0, 'BrNO3': 1.0, 'INO': 1.0, 'PAN': 1.0, 'PMN': 1.0, 'HNO3': 1.0, 'HNO2': 1.0, 'NH3': 1.0, 'HNO4': 1.0, 'BrNO2': 1.0, 'IONO': 1.0, 'PROPNN': 1.0, 'NH4': 1.0, 'MPN': 1.0, 'MMN': 1.0, 'ISOPN': 1.0, 'IONO2': 1.0
}
if OH:
d = {
'LO3_18': 2.0, 'LO3_03': 1.0, 'RD95': 1.0, 'PO3_14': 1.0
}
if N:
d= {
'RD10': 1.0, 'LR26': 1.0, 'LR27': 1.0, 'LR20': 1.0, 'RD17': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'RD18': 2.0, 'LR28': 1.0, 'LO3_30': 1.0, 'RD75': 1.0, 'LR7': 1.0, 'LR8': 1.0, 'RD56': 1.0, 'RD24': 1.0, 'LO3_39': 1.0, 'RD25': 1.0, 'RD81': 1.0, 'LR35': 1.0, 'LR18': 1.0, 'LR17': 1.0, 'LR11': 1.0, 'LR39': 1.0, 'RD20': 1.0, 'RD21': 2.0, 'RD22': 1.0, 'RD23': 1.0, 'RD68': 1.0, 'RD69': 1.0
# NOy ( N in 'NOy')
, 'NO2': 1.0, 'NO3': 1.0, 'N2O5': 2.0, 'NO': 1.0, 'PPN': 1.0, 'R4N2': 2.0, 'BrNO3': 1.0, 'INO': 1.0, 'PAN': 1.0, 'PMN': 1.0, 'HNO3': 1.0, 'HNO2': 1.0, 'NH3': 1.0, 'HNO4': 1.0, 'BrNO2': 1.0, 'IONO': 1.0, 'PROPNN': 1.0, 'NH4': 1.0, 'MPN': 1.0, 'MMN': 1.0, 'ISOPN': 1.0, 'IONO2': 1.0
}
if C:
d = {
'ACET': 3.0, 'ALD2': 2.0, 'C2H6': 2.0, 'C3H8': 3.0, 'ISOP': 5.0
}
return d[spec]
# --------------
# 4.04 - GEOS-Chem/ctm.bpch values
# --------------
def GC_var(input_x=None, rtn_dict=False, debug=False):
"""
Note: Most of this dictionary is vestigial.
<= ACTION NEEDED ( remove redundant variables )
f_var = GC flux (EW, NS , UP) variables
Ox = 'Ox', 'POX', 'LOX' + list of drydep species # not inc. 'NO3df', 'HNO4df', 'BrOdf' , 'BrNO2', 'IO', 'IONO', 'OIO',
Ox_p = Ox prod list
Ox-l = Ox loss list
d_dep = dry dep (category, name = species)
w_dep = wet dep ( 3x categories (WETDCV = rain out loss in convective updrafts (kg/s), WETDLS = rainout in large scale precip (kg/s), CV-FLX = Mass change due to cloud convection (kg/s); name = species)
BL_m = UPWARD MASS FLUX FROM BOUNDARY-LAYER MIXING, (category, name = species)
f_strat = strat flux (to tropsosphere) (category, name = species)
"""
if (debug):
print 'GC_var called'
GC_var_dict = {
# Ox budget analysis
'f_var' : ['EW-FLX-$', 'NS-FLX-$', 'UP-FLX-$' ],
# 'r_t' : [ 'Photolysis','HOx','NOy' ,'Bromine', 'Iodine' ],
# 'r_tn' : ['NOy' ,'Photolysis','HOx' ,'Bromine', 'Iodine' ],
'r_t' : [ 'Photolysis','HOx','Bromine', 'Iodine' ],
'r_tn' : ['Photolysis','HOx' ,'Bromine', 'Iodine' ],
'r_tn_lc' : ['photolysis','HOx' ,'bromine', 'iodine' ],
# 'Ox' : ['Ox','POX','LOX','O3df','NO2df', 'PANdf', 'PMNdf', 'PPNdf', 'N2O5df','HNO3df', 'HOBrdf','BrNO3df','HOIdf','IONO2df', 'I2O2df', 'I2O4df','I2O3df'],
# 'Ox1.1' : ['Ox','POX','LOX','O3df','NO2df', 'PANdf', 'PMNdf', 'PPNdf', 'N2O5df','HNO3df', 'HOBrdf','BrNO3df','HOIdf','IONO2df', 'I2O2df'],
# 'Ox_spec' : ['O3', 'NO2', 'NO3', 'PAN', 'PMN', 'PPN', 'HNO4', 'N2O5', 'HNO3', 'BrO', 'HOBr', 'BrNO2', 'BrNO3', 'MPN', 'IO', 'HOI', 'IONO', 'IONO2', 'OIO', 'I2O2', 'I2O4', 'I2O3'],
# 'Ox_spec1.1' : ['O3', 'NO2', 'NO3', 'PAN', 'PMN', 'PPN', 'HNO4', 'N2O5', 'HNO3', 'BrO', 'HOBr', 'BrNO2', 'BrNO3', 'MPN', 'IO', 'HOI', 'IONO', 'IONO2', 'OIO', 'I2O2', 'I2O4'],
# 'Ox_p_1.3' : ['PO3_85', 'PO3_76', 'PO3_62', 'PO3_63', 'RD06', 'PO3_01', 'PO3_77', 'PO3_86', 'PO3_79', 'PO3_14', 'PO3_87', 'PO3_66', 'PO3_15', 'PO3_67', 'PO3_51', 'PO3_88', 'PO3_56', 'PO3_89', 'PO3_90', 'PO3_72', 'PO3_91', 'PO3_02', 'PO3_03', 'PO3_92', 'PO3_64', 'PO3_68', 'LR9', 'PO3_58', 'PO3_57', 'PO3_70', 'PO3_60', 'PO3_65', 'PO3_18', 'PO3_73', 'PO3_19', 'PO3_20', 'PO3_21', 'PO3_22', 'PO3_24', 'PO3_25', 'PO3_26', 'PO3_27', 'PO3_34', 'PO3_35', 'PO3_37', 'PO3_38', 'PO3_39', 'PO3_05', 'PO3_45', 'PO3_69', 'PO3_46', 'PO3_97', 'PO3_47', 'PO3_48', 'PO3_52', 'PO3_53', 'PO3_80', 'PO3_40', 'PO3_54', 'PO3_81', 'PO3_55', 'PO3_74', 'PO3_41', 'PO3_43', 'PO3_93', 'PO3_94', 'PO3_95', 'PO3_96', 'PO3_84', 'PO3_61', 'PO3_83', 'PO3_59', 'PO3_71', 'PO3_98', 'PO3_99', 'PO3100', 'PO3101', 'PO3_75', 'PO3_50'], # 'ISOPND',
# 'Ox_l_fp' : ['LO3_18','RD98','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40',#'LO3_48','LO3_04', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10', 'LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30', 'LO3_39', 'LO3_38','LO3_55' , 'LO3_36', 'RD63'], # Iy route
# 'Ox_l_fp1.1' : ['LO3_18','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40',#'LO3_48','LO3_04', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10','LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30','LO3_39', 'LO3_38','LO3_55' , 'LO3_36', 'RD63'], # Iy route
# 'Ox_l_fp1.3' : ['LO3_18','RD98','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', 'LO3_56','LR37', 'LR38', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40','LR36', 'LO3_58', #'LO3_48','LO3_04',,# NOy route
# 'LO3_57', 'LO3_07', 'LO3_60', 'LO3_61', 'LO3_62', 'LO3_63', 'LO3_64', 'LO3_65', 'LO3_66', 'LO3_67', 'LO3_68', 'LO3_69', 'LO3_72','LO3_71', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10', 'LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30', 'LO3_39', 'LO3_38','LO3_55' , 'LO3_36'], # Iy route
# 'Ox_l_fp_r_' : [(0, 3), (3, 11), (11, 21), (21, 28), (28, None)], #, (-2, -1)]
# 'Ox_l_fp_r_1.1' : [(0, 2), (2, 11), (11, 21), (21, 28), (28, None)], #, (-2, -1)]
# 'Ox_l_fp_r_1.3' : [(0, 3), (3, 14), (14, 40), (40, 47), (47, None)], #, (-2, -1)]
'fams' : ['I2','HOI','IO', 'I', 'HI+OIO+IONO+INO', 'IONO2','IxOy', 'CH3I', 'CH2IX'], # Iy families
'fams_A' : ['I2','HOI','IO', 'I', 'HI+OIO+IONO+INO', 'IONO2','IxOy', 'CH3I', 'CH2IX', 'AERI'], # Iy families
'fam_slice' : [(0, 1), (1, 2), (2, 3), (3,4 ),(4, 8), (8, 9), (9, 12), (12, 13), (13, None)], # slice
'fam_slice_A' : [(0, 1), (1, 2), (2, 3), (3,4 ),(4, 8), (8, 9), (9, 12), (12, 13), (13, 16),(16, None)], # slice
# 'POx_l_fp' : ['PO3_01', 'PO3_03', 'PO3_02', 'PO3_05','PO3_14', 'PO3_15', 'PO3_18', 'PO3_19', 'PO3_20', 'PO3_21', 'PO3_22', 'PO3_24', 'PO3_25', 'PO3_26', 'PO3_27', 'PO3_30', 'PO3_31', 'PO3_32', 'PO3_33', 'PO3_34', 'PO3_35', 'PO3_37', 'PO3_38', 'PO3_39', 'PO3_40', 'PO3_41', 'PO3_43'],
'Ox_key' : ['POX', 'PO3_14', 'PO3_15', 'LOX'],#, 'LO3_18', 'LO3_03', 'LO3_02','LR25', 'LR21', 'LR5','LR6','LO3_34', 'LO3_33','LO3_24', 'LO3_35' ],
'POxLOx' : ['POX', 'LOX'],
'iPOxiLOx' : ['POX', 'LOX', 'iPOX', 'iLOX'],
# Iy/ Iodine budget analysis
'BL_FT_UT' : [(0, 6), (6, 26), (26, 38)] ,
'n_order' :['CH2IX','CH3I', 'I2', 'HOI','IO', 'I', 'IONO2','HI+OIO+IONO+INO','IxOy' ] ,
'n_order_A' :['CH2IX','CH3I', 'I2', 'HOI','IO', 'I', 'IONO2','HI+OIO+IONO+INO','IxOy', 'AERI' ] ,
'I_l' : ['RD01', 'RD02', 'RD16', 'RD19', 'RD24', 'RD27'],
'IO_l' : ['RD09', 'RD10', 'RD11', 'RD12', 'RD23', 'LO3_24', 'RD37', 'RD97', 'RD66'], # LO37 swaped for RD97 as LO37 assigned to loss point of I2O3 uptake
'I_p' : ['RD06','RD07','RD10','RD11','RD47','RD15','RD17','RD20','RD22', 'LO3_24', 'RD64', 'RD65', 'RD66', 'RD67','RD68','RD69', 'RD70', 'RD71','RD72', 'RD73', 'RD88', 'RD89'],
'IO_p' : [ 'RD01', 'RD08', 'RD46', 'RD25', 'RD27','RD56'],
'sOH' : ['LO3_18'],
'd_dep' : ['DRYD-FLX'],
'w_dep' : ['WETDCV-$','WETDLS-$'],
'BL_m' : ['TURBMC-$'],
'f_strat' : ['STRT-FL'],
'p_l' : ['PORL-L=$'],
'Cld_flx' : ['CV-FLX-$'],
'I_Br_O3' : ['IO', 'OIO','HOI','I2','I','CH3IT','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO'],
'IOrg_RIS' : ['CH3IT','CH2ICl','CH2I2', 'CH2IBr', 'I2','HOI','I','IO', 'OIO', 'HI','IONO','IONO2'],
'I_specs' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2', 'I2O3','I2O4''CH3IT','CH2I2','I','INO'] ,
'Iy' : ['I2','HOI','IO', 'OIO', 'HI','INO','IONO', 'IONO2','I2O2', 'I2O3','I2O4','I'],
'Iy1.1' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I2O4','I','INO'],
'IOy' : ['HOI','IO', 'OIO','IONO','IONO2','INO','I2O2','I2O4', 'I2O3'],
'IOy1.1' : ['HOI','IO', 'OIO','IONO','IONO2','INO','I2O2','I2O4'],
'I2Ox' : ['I2O2','I2O4','I2O3'],
'I2Ox' : ['I2O2','I2O4','I2O3'],
'IyOx1.1' : ['I2O2','I2O4'],
'Iy_no_i2o4' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I','INO', 'I2O3'],
'Iy_no_i2o41.1' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I','INO'],
'Phot_s_Iy' : ['CH3IT','CH2ICl','CH2I2', 'CH2IBr'],#['RD89', 'RD88', 'RD71', 'RD72'],
'HOI' : ['HOI'],
'IOx' : ['IO','I',],
'IO' : ['IO'],
'I' : ['I',],
'OIO' : ['OIO'],
'LIOx' : ['LIOx'], # LOx is p/l tracer name, for Loss of IOx
'PIOx' : ['PIOx'], # LOx is p/l tracer name, for Loss of IOx
'iodine_all' : ['I2','HOI','IO', 'I', 'HI', 'OIO', 'INO', 'IONO','IONO2','I2O2', 'I2O4', 'I2O3', 'I2O5', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr', 'C3H7I','C2H5I','ICl', 'I2O', 'IBr', 'HIO3', ],
'iodine_all_A': ['I2','HOI','IO', 'I', 'HI', 'OIO', 'INO', 'IONO','IONO2','I2O2', 'I2O4', 'I2O3', 'I2O5', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr', 'C3H7I','C2H5I','ICl', 'I2O', 'IBr', 'HIO3','AERI' ],
# Misc analysis
'LHOI' : ['RD65', 'RD63', 'RD08'],
'LHOBr' : ['LR25', 'LR30','LR21'],
'LI2' : ['RD64', 'RD06', 'RD22'],
'LCH2I2' : ['RD72' ],
'LCH2Cl' : ['RD88' ] ,
'LCH2Br' : ['RD89' ],
'LCH3IT' : ['RD15' , 'RD71'],
'sHOX' : ['HOI', 'HOBr'],
'HO2_loss' : ['PO3_14','RD09','RD02', 'LR2', 'LR3', 'PO3_46','PO3_02', 'PO3_03', 'PO3_05'],
'CAST_int' : ['IO', 'OIO','HOI','I2','I','HOI','CH3I','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C3H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'OH', 'HO2','NO','NO2'],
'CAST_intn' : ['IO', 'OIO','HOI','I2','I','HOI','CH3IT','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'DMS', 'NO','HNO3','HNO4', 'NO2','NO3' , 'PAN' , 'HNO2', 'N2O5'],
'CAST_int_n' : ['IO', 'OIO','HOI','I2','I','HOI','CH3I','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'OH', 'HO2','NO','NO2'],
'diurnal_sp' : ['IO','I2', 'CH2I2', 'BrO' ] ,
'obs_comp' : ['CH3IT','CH2I2','CH2ICl','CH2IBr','C2H5I','C3H7I','I2','IO'] ,
'emiss_specs': ['CH3IT','CH2I2','CH2ICl','CH2IBr','I2','HOI'] ,
'w_dep_specs': ['I2' ,'HI' ,'HOI' ,'IONO', 'IONO2','I2O2', 'I2O4', 'I2O3' ,'AERI'], #, 'IBr', 'ICl']
# 'd_dep_specsl' : ['I2', 'HI', 'HOI', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3','AERI'], #, 'IO', 'OIO'] ,
'd_dep_specsl1.1' : ['I2', 'HI', 'HOI', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'AERI'], #, 'IO', 'OIO'] ,
'd_dep_specs': ['I2df', 'HIdf', 'HOIdf', 'IONOdf', 'IONO2df', 'I2O2df', 'I2O4df', 'I2O3df', 'AERIdf',], #, 'IOdf', 'OIOdf'], #
# 'd_dep_specs1.1': ['I2df', 'HIdf', 'HOIdf', 'IONOdf', 'IONO2df', 'I2O2df', 'I2O4df','AERIdf',], #, 'IOdf', 'OIOdf'], #
'I2_het_cyc' : ['RD59','RD92','RD63'], # IONO2, IONO, HOI
'I_het_loss' : [ 'RD58', 'RD62', 'RD93' ,'RD95'], # HI, I2O2, I2O4, I2O3 uptake (prev: 2OIO excuded as I2Ox formaed, IO+OIO included as I2O3 not treated )
#['RD60','RD61','RD62','RD52','RD53','RD54','RD55','RD13'], # RD13 = OIO + OH => HIO3 86 => AERI loss
'NOx' : ['NO', 'NO2' ],
'N_specs' : ['NO', 'NO2', 'PAN', 'HNO3', 'PMN', 'PPN', 'R4N2', 'N2O5', 'HNO4', 'NH3', 'NH4', 'BrNO2', 'BrNO3', 'MPN', 'ISOPN', 'PROPNN', 'MMN', 'NO3', 'HNO2', 'IONO', 'IONO2', 'INO'],
'N_specs_no_I' : ['NO', 'NO2', 'PAN', 'HNO3', 'PMN', 'PPN', 'R4N2', 'N2O5', 'HNO4', 'NH3', 'NH4', 'BrNO2', 'BrNO3', 'MPN', 'ISOPN', 'PROPNN', 'MMN', 'NO3', 'HNO2'],
'I_N_tags' : ['RD10', 'RD23', 'RD19', 'RD16', 'RD22', 'RD56', 'RD24', 'LO3_30', 'RD69', 'RD68', 'RD20', 'RD21', 'RD25', 'LO3_39', 'RD17', 'RD18', 'RD75'],
'Br_N_tags' : ['LR7', 'LR18', 'LR17', 'LR11', 'LR8', 'LR20', 'LR26', 'LR28', 'LR27'],
'inactive_I' : ['BrCl', 'OClO', 'ClO', 'HOCl', 'Cl', 'Cl2', 'I2O5', 'I2O', 'HIO3', 'IBr', 'ICl', 'C2H5I','C3H7I'], # I2O3 now active.
'active_I' : ['I2', 'HOI', 'IO', 'I', 'HI', 'OIO', 'INO', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr'],
'surface_specs' : ['O3', 'NO', 'NO2', 'NO3' ,'N2O5', 'IO', 'IONO2' ],
# Model run title dictionaries
'run_name_dict': {'run': 'Halogens (I+,Br+)', 'Br_2ppt': 'Halogens (I+,Br+) + fixed 2 pptv BrO', 'just_I': 'Just Iodine (I+,Br-)', 'no_hal': 'No Halogens', 'just_Br': 'Just Bromine (I-,Br+)', 'Br_1ppt': 'Halogens (I+,Br+) + fixed 1 pptv BrO', 'obs': 'Observations'} ,
'latex_run_names': {'I2Ox_half': 'I$_{2}$Ox loss ($\\gamma$) /2', 'run': 'Iodine simulation.', 'MacDonald_iodide': 'Ocean iodide', 'Sulfate_up': 'Sulfate Uptake', 'I2Ox_phot_exp': 'I$_{2}$Ox exp. X-sections', 'het_double': 'het. cycle ($\\gamma$) x2', 'I2Ox_phot_x2': 'I$_{2}$Ox X-sections x2', 'no_het': 'no het. cycle ', 'I2Ox_double': 'I$_{2}$Ox loss ($\\gamma$) x2', 'just_I': '(I+,Br-)', 'BrO1pptv': 'MBL BrO 1 pptv', 'het_half': 'het. cycle ($\\gamma$) /2', 'Just_I_org': 'Just org. I', 'no_I2Ox': 'No I$_{2}$Ox Photolysis', 'BrO1pptv_ALL' : 'BrO 1 pptv in Trop.', 'BrO2pptv' : 'MBL BrO 2 pptv',
# adjust from GBC to ACP names
# 'no_hal': '(I-,Br-)', 'Just_Br': '(I-,Br+)',
'no_hal': 'No Halogens', 'Just_Br': 'GEOS-Chem (v9-2)',
# kludge for diurnal plot
'Iodine simulation.':'Iodine simulation.', '(I+,Br+)': 'Iodine simulation.','(I+,Br-)': 'Just Iodine', '(I-,Br+)': 'GEOS-Chem (v9-2)', '(I-,Br-)': 'No Halogens'},
# tracer unit handling
'spec_2_pptv' : ['I2', 'HOI', 'IO', 'OIO', 'HI', 'IONO', 'IONO2', 'I2O2', 'CH3IT', 'CH2I2', 'IBr', 'ICl', 'I', 'HIO3', 'I2O', 'INO', 'I2O3', 'I2O4', 'I2O5', 'AERI', 'Cl2', 'Cl', 'HOCl', 'ClO', 'OClO', 'BrCl', 'CH2ICl', 'CH2IBr', 'C3H7I', 'C2H5I', 'Br2', 'Br', 'BrO', 'HOBr', 'HBr', 'BrNO2', 'BrNO3', 'CHBr3', 'CH2Br2', 'CH3Br','RCHO', 'MVK', 'MACR', 'PMN', 'PPN', 'R4N2', 'DMS', 'SO4s', 'MSA', 'NITs', 'BCPO', 'DST4', 'ISOPN', 'MOBA', 'PROPNN', 'HAC', 'GLYC', 'MMN', 'RIP', 'IEPOX', 'MAP' ,'N2O5','NO3'], # 'HNO4', 'HNO2'],
'spec_2_pptC' : ['PRPE', 'ISOP'],
# global
'spec_2_ppbv': ['NO','DMS', 'RIP', 'IEPOX','BCPO', 'DST4', 'HAC', 'GLYC','MACR', 'ISOP'],
'spec_2_ppbC' : ['ALK4'],
# pf dictionaries
# WARNING - remove non forwards combatible dicts:
# (GCFP_TRA_d ... GCFP_d2TRA ... GCFP_d2TRA_justTRA . etc)
# GCFP_TRA_d is in use by CVO plotters - use what_species_am_i instead!
'GCFP_TRA_d' : {'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_96': 'C2H5I', 'TRA_95': 'C3H7I', 'TRA_94': 'CH2IBr', 'TRA_93': 'CH2ICl', 'TRA_92': 'BrCl', 'TRA_91': 'OClO', 'TRA_90': 'ClO', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'I2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_68': 'HOI', 'TRA_69': 'IO', 'TRA_71': 'HI', 'TRA_70': 'OIO', 'TRA_73': 'IONO2', 'TRA_72': 'IONO', 'TRA_75': 'CH3IT', 'TRA_74': 'I2O2', 'TRA_77': 'IBr', 'TRA_76': 'CH2I2', 'TRA_79': 'I', 'TRA_78': 'ICl', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'TRA_23': 'HNO4', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'HIO3', 'TRA_81': 'I2O', 'TRA_82': 'INO', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'I2O5', 'TRA_86': 'AERI', 'TRA_87': 'Cl2', 'TRA_88': 'Cl', 'TRA_89': 'HOCl','O3':'O3', 'CO':'CO'} ,
# GCFP_d2TRA is in use by IO plotters - use what_species_am_i instead!
'GCFP_d2TRA' : {'HIO3': 'TRA_80', 'OCPO': 'TRA_37', 'PPN': 'TRA_16', 'OCPI': 'TRA_35', 'O3': 'TRA_2', 'PAN': 'TRA_3', 'ACET': 'TRA_9', 'IEPOX': 'TRA_62', 'BrNO3': 'TRA_50', 'Br': 'TRA_45', 'HBr': 'TRA_48', 'HAC': 'TRA_58', 'ALD2': 'TRA_11', 'HNO3': 'TRA_7', 'HNO2': 'TRA_66', 'C2H5I': 'TRA_96', 'HNO4': 'TRA_23', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'TRA_18', 'HI': 'TRA_71', 'CH2I2': 'TRA_76', 'IONO2': 'TRA_73', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'C3H8': 'TRA_19', 'DMS': 'TRA_25', 'CH2O': 'TRA_20', 'CH3IT': 'TRA_75','CH3I': 'TRA_75', 'NO2': 'TRA_64', 'NO3': 'TRA_65', 'N2O5': 'TRA_22', 'CHBr3': 'TRA_51', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'HOCl': 'TRA_89', 'NITs': 'TRA_33', 'RCHO': 'TRA_12', 'C2H6': 'TRA_21', 'MPN': 'TRA_54', 'INO': 'TRA_82', 'MP': 'TRA_24', 'CH2Br2': 'TRA_52', 'SALC': 'TRA_43', 'NH3': 'TRA_30', 'CH2ICl': 'TRA_93', 'RIP': 'TRA_61', 'ClO': 'TRA_90', 'NO': 'TRA_1', 'SALA': 'TRA_42', 'MOBA': 'TRA_56', 'R4N2': 'TRA_17', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'PMN': 'TRA_15', 'CO': 'TRA_4', 'CH2IBr': 'TRA_94', 'ISOP': 'TRA_6', 'BCPO': 'TRA_36', 'MVK': 'TRA_13', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'Cl': 'TRA_88', 'I2O2': 'TRA_74', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'I2O5': 'TRA_85', 'MEK': 'TRA_10', 'MMN': 'TRA_60', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'I2O': 'TRA_81', 'ALK4': 'TRA_5', 'MSA': 'TRA_29', 'I2': 'TRA_67', 'Br2': 'TRA_44', 'IBr': 'TRA_77', 'MACR': 'TRA_14', 'I': 'TRA_79', 'AERI': 'TRA_86', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'NH4': 'TRA_31', 'SO2': 'TRA_26', 'SO4': 'TRA_27', 'IO': 'TRA_69', 'H2O2': 'TRA_8', 'BCPI': 'TRA_34', 'ICl': 'TRA_78', 'GLYC': 'TRA_59','ALK4': 'ALK4', 'MSA': 'MSA', 'MO2': 'MO2', 'C3H8': 'C3H8', 'ISOP': 'ISOP', 'DMS': 'DMS', 'CH2O': 'CH2O', 'O3': 'O3', 'PAN': 'PAN', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'NO': 'NO', 'PPN': 'PPN', 'R4N2': 'R4N2', 'HO2': 'HO2', 'NO2': 'NO2', 'PMN': 'PMN', 'ACET': 'ACET', 'CO': 'CO', 'ALD2': 'ALD2', 'RCHO': 'RCHO', 'HNO3': 'HNO3', 'HNO2': 'HNO2', 'SO2': 'SO2', 'SO4': 'SO4', 'HNO4': 'HNO4', 'C2H6': 'C2H6', 'RO2': 'RO2', 'MVK': 'MVK', 'PRPE': 'PRPE', 'OH': 'OH', 'ETO2': 'ETO2', 'MEK': 'MEK', 'MP': 'MP' , 'GMAO_TEMP':'GMAO_TEMP' },
# 'GCFP_d2TRA_justTRA' : {'HIO3': 'TRA_80', 'OCPO': 'TRA_37', 'PPN': 'TRA_16', 'OCPI': 'TRA_35', 'O3': 'TRA_2', 'PAN': 'TRA_3', 'ACET': 'TRA_9', 'IEPOX': 'TRA_62', 'BrNO3': 'TRA_50', 'Br': 'TRA_45', 'HBr': 'TRA_48', 'HAC': 'TRA_58', 'ALD2': 'TRA_11', 'HNO3': 'TRA_7', 'HNO2': 'TRA_66', 'C2H5I': 'TRA_96', 'HNO4': 'TRA_23', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'TRA_18', 'HI': 'TRA_71', 'CH2I2': 'TRA_76', 'IONO2': 'TRA_73', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'C3H8': 'TRA_19', 'DMS': 'TRA_25', 'CH2O': 'TRA_20', 'CH3IT': 'TRA_75','CH3I': 'TRA_75', 'NO2': 'TRA_64', 'NO3': 'TRA_65', 'N2O5': 'TRA_22', 'CHBr3': 'TRA_51', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'HOCl': 'TRA_89', 'NITs': 'TRA_33', 'RCHO': 'TRA_12', 'C2H6': 'TRA_21', 'MPN': 'TRA_54', 'INO': 'TRA_82', 'MP': 'TRA_24', 'CH2Br2': 'TRA_52', 'SALC': 'TRA_43', 'NH3': 'TRA_30', 'CH2ICl': 'TRA_93', 'RIP': 'TRA_61', 'ClO': 'TRA_90', 'NO': 'TRA_1', 'SALA': 'TRA_42', 'MOBA': 'TRA_56', 'R4N2': 'TRA_17', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'PMN': 'TRA_15', 'CO': 'TRA_4', 'CH2IBr': 'TRA_94', 'ISOP': 'TRA_6', 'BCPO': 'TRA_36', 'MVK': 'TRA_13', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'Cl': 'TRA_88', 'I2O2': 'TRA_74', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'I2O5': 'TRA_85', 'MEK': 'TRA_10', 'MMN': 'TRA_60', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'I2O': 'TRA_81', 'ALK4': 'TRA_5', 'MSA': 'TRA_29', 'I2': 'TRA_67', 'Br2': 'TRA_44', 'IBr': 'TRA_77', 'MACR': 'TRA_14', 'I': 'TRA_79', 'AERI': 'TRA_86', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'NH4': 'TRA_31', 'SO2': 'TRA_26', 'SO4': 'TRA_27', 'IO': 'TRA_69', 'H2O2': 'TRA_8', 'BCPI': 'TRA_34', 'ICl': 'TRA_78', 'GLYC': 'TRA_59','OH': 'OH','HO2': 'HO2',},
'GCFP_d2TRA_all_1.6' :{'HIO3': 'TRA_80', 'TRA_17': 'TRA_17', 'TRA_16': 'TRA_16', 'TRA_15': 'TRA_15', 'TRA_14': 'TRA_14', 'TRA_13': 'TRA_13', 'TRA_12': 'TRA_12', 'TRA_11': 'TRA_11', 'TRA_19': 'TRA_19', 'ACET': 'ACET', 'RIP': 'TRA_61', 'BrNO3': 'TRA_50', 'HAC': 'TRA_58', 'ALD2': 'ALD2', 'HNO3': 'HNO3', 'HNO2': 'HNO2', 'HNO4': 'HNO4', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'PRPE', 'TRA_29': 'TRA_29', 'CH2I2': 'TRA_76', 'I2O2': 'TRA_74', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'MO2': 'MO2', 'C3H8': 'C3H8', 'I2O5': 'TRA_85', 'TRA_71': 'TRA_71', 'TRA_70': 'TRA_70', 'TRA_73': 'TRA_73', 'DMS': 'DMS', 'TRA_75': 'TRA_75', 'TRA_74': 'TRA_74', 'TRA_77': 'TRA_77', 'TRA_76': 'TRA_76', 'CH2O': 'CH2O', 'TRA_78': 'TRA_78', 'CH3IT': 'TRA_75', 'NO2': 'NO2', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'PAN': 'PAN', 'HOCl': 'TRA_89', 'TRA_18': 'TRA_18', 'GMAO_TEMP': 'GMAO_TEMP', 'RCHO': 'RCHO', 'C2H6': 'C2H6', 'INO': 'TRA_82', 'MP': 'MP', 'CH2Br2': 'TRA_52', 'CH2ICl': 'TRA_93', 'TRA_59': 'TRA_59', 'TRA_58': 'TRA_58', 'IEPOX': 'TRA_62', 'TRA_53': 'TRA_53', 'TRA_52': 'TRA_52', 'TRA_51': 'TRA_51', 'TRA_50': 'TRA_50', 'TRA_57': 'TRA_57', 'TRA_56': 'TRA_56', 'TRA_55': 'TRA_55', 'TRA_54': 'TRA_54', 'MOBA': 'TRA_56', 'CH3I': 'TRA_75', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'CO': 'CO', 'BCPI': 'TRA_34', 'ISOP': 'ISOP', 'BCPO': 'TRA_36', 'MVK': 'MVK', 'TRA_28': 'TRA_28', 'Cl': 'TRA_88', 'TRA_26': 'TRA_26', 'TRA_27': 'TRA_27', 'TRA_24': 'TRA_24', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'TRA_23': 'TRA_23', 'TRA_20': 'TRA_20', 'TRA_21': 'TRA_21', 'MMN': 'TRA_60', 'I2O': 'TRA_81', 'HBr': 'TRA_48', 'ALK4': 'ALK4', 'I2': 'TRA_67', 'PPN': 'PPN', 'IBr': 'TRA_77', 'I': 'TRA_79', 'AERI': 'TRA_86', 'NH4': 'TRA_31', 'SO2': 'SO2', 'SO4': 'SO4', 'NH3': 'TRA_30', 'TRA_08': 'TRA_08', 'TRA_09': 'TRA_09', 'TRA_01': 'TRA_01', 'TRA_02': 'TRA_02', 'TRA_03': 'TRA_03', 'TRA_04': 'TRA_04', 'TRA_05': 'TRA_05', 'TRA_06': 'TRA_06', 'TRA_07': 'TRA_07', 'OCPI': 'TRA_35', 'OCPO': 'TRA_37', 'Br2': 'TRA_44', 'O3': 'O3', 'Br': 'TRA_45', 'TRA_96': 'TRA_96', 'TRA_95': 'TRA_95', 'TRA_94': 'TRA_94', 'TRA_93': 'TRA_93', 'TRA_92': 'TRA_92', 'TRA_91': 'TRA_91', 'TRA_90': 'TRA_90', 'TRA_62': 'TRA_62', 'TRA_63': 'TRA_63', 'TRA_60': 'TRA_60', 'TRA_61': 'TRA_61', 'TRA_66': 'TRA_66', 'TRA_67': 'TRA_67', 'C2H5I': 'TRA_96', 'TRA_65': 'TRA_65', 'TRA_68': 'TRA_68', 'TRA_69': 'TRA_69', 'OH': 'OH', 'IONO2': 'TRA_73', 'HI': 'TRA_71', 'CHBr3': 'TRA_51', 'TRA_46': 'TRA_46', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'NITs': 'TRA_33', 'TRA_48': 'TRA_48', 'TRA_49': 'TRA_49', 'TRA_44': 'TRA_44', 'TRA_45': 'TRA_45', 'RO2': 'RO2', 'TRA_47': 'TRA_47', 'TRA_40': 'TRA_40', 'TRA_41': 'TRA_41', 'TRA_42': 'TRA_42', 'TRA_43': 'TRA_43', 'MPN': 'TRA_54', 'ETO2': 'ETO2', 'IO': 'TRA_69', 'TRA_64': 'TRA_64', 'ClO': 'TRA_90', 'NO': 'NO', 'SALA': 'TRA_42', 'SALC': 'TRA_43', 'R4N2': 'R4N2', 'PMN': 'PMN', 'TRA_25': 'TRA_25', 'CH2IBr': 'TRA_94', 'TRA_22': 'TRA_22', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'MEK': 'MEK', 'TRA_72': 'TRA_72', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'TRA_79': 'TRA_79', 'MSA': 'MSA', 'TRA_39': 'TRA_39', 'TRA_38': 'TRA_38', 'GLYC': 'TRA_59', 'TRA_35': 'TRA_35', 'TRA_34': 'TRA_34', 'TRA_37': 'TRA_37', 'TRA_36': 'TRA_36', 'TRA_31': 'TRA_31', 'TRA_30': 'TRA_30', 'TRA_33': 'TRA_33', 'TRA_32': 'TRA_32', 'HO2': 'HO2', 'MACR': 'TRA_14', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'ICl': 'TRA_78', 'TRA_80': 'TRA_80', 'TRA_81': 'TRA_81', 'TRA_82': 'TRA_82', 'TRA_83': 'TRA_83', 'TRA_84': 'TRA_84', 'TRA_85': 'TRA_85', 'TRA_86': 'TRA_86', 'TRA_87': 'TRA_87', 'TRA_88': 'TRA_88', 'TRA_89': 'TRA_89','GMAO_TEMP': 'GMAO_TEMP', 'GMAO_UWND': 'GMAO_UWND', 'GMAO_VWND': 'GMAO_VWND'},
# 'GCFP_d2TRA_all_1.7' : {'TRA_74': 'ICl', 'TRA_25': 'DMS', 'TRA_68': 'CH2I2', 'TRA_44': 'Br2', 'TRA_70': 'CH2IBr', 'TRA_22': 'N2O5', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_23': 'HNO4', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_21': 'C2H6', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_69': 'CH2ICl', 'TRA_50': 'BrNO3', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_73': 'IBr', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_77': 'HI', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_72': 'I2', 'TRA_59': 'GLYC', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_20': 'CH2O', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_75': 'I', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_58': 'HAC', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_27': 'SO4', 'TRA_78': 'OIO', 'TRA_66': 'HNO2', 'TRA_71': 'HOI', 'TRA_24': 'MP', 'TRA_67': 'CH3IT', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO'},
'GCFP_d2TRA_all_1.7' : {'TRA_25': 'DMS', 'TRA_77': 'HI', 'TRA_76': 'IO', 'TRA_23': 'HNO4', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_79': 'INO', 'TRA_78': 'OIO', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_52': 'CH2Br2', 'TRA_46': 'BrO', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_47': 'HOBr', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_81': 'IONO2', 'TRA_35': 'OCPI', 'TRA_57': 'PROPNN', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_56': 'MOBA', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_84': 'I2O4', 'TRA_54': 'MPN', 'TRA_5': 'ALK4', 'TRA_49': 'BrNO2', 'TRA_32': 'NIT', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_59': 'GLYC', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_17': 'R4N2', 'TRA_28': 'SO4s', 'TRA_16': 'PPN', 'TRA_58': 'HAC', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_29': 'MSA', 'TRA_22': 'N2O5', 'TRA_73': 'IBr', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_80': 'IONO', 'TRA_26': 'SO2', 'TRA_82': 'I2O2', 'TRA_72': 'I2', 'TRA_48': 'HBr', 'TRA_85': 'AERI', 'TRA_34': 'BCPI', 'TRA_75': 'I', 'TRA_53': 'CH3Br', 'TRA_74': 'ICl'},
'GCFP_d2TRA_all_1.7_EOH_actual_names' : {'HNO4': 'HNO4', 'PPN': 'PPN', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'O3': 'O3', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'GMAO_UWND': 'GMAO_UWND', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_65': 'NO3', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'OH': 'OH', 'LAT': 'LAT', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_73': 'IBr', 'TRA_72': 'I2', 'TRA_75': 'I', 'TRA_74': 'ICl', 'TRA_77': 'HI', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_78': 'OIO', 'NO2': 'NO2', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'GMAO_VWND': 'GMAO_VWND', 'PAN': 'PAN', 'GMAO_TEMP': 'GMAO_TEMP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'NO': 'NO', 'PMN': 'PMN', 'HNO3': 'HNO3', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'TRA_23': 'HNO4', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'RO2': 'RO2', 'LON': 'LON', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'HO2': 'HO2', 'SO2': 'SO2', 'SO4': 'SO4', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'HNO2': 'HNO2', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_86': 'EOH'},
'TRA_spec_met_all_1.7_EOH': {'MAO3': 'MAO3', 'DHMOB': 'DHMOB', 'ETP': 'ETP', 'RCO3': 'RCO3', 'MO2': 'MO2', 'EOH': 'EOH', 'MVKN': 'MVKN', 'R4P': 'R4P', 'ISNP': 'ISNP', 'RB3P': 'RB3P', 'MGLY': 'MGLY', 'MAOPO2': 'MAOPO2', 'RIO2': 'RIO2', 'PMNN': 'PMNN', 'PP': 'PP', 'VRP': 'VRP', 'RP': 'RP', 'MRO2': 'MRO2', 'HC5': 'HC5', 'ATO2': 'ATO2', 'PYAC': 'PYAC', 'R4N1': 'R4N1', 'DIBOO': 'DIBOO', 'LISOPOH': 'LISOPOH', 'HO2': 'HO2', 'ETHLN': 'ETHLN', 'ISNOOB': 'ISNOOB', 'ISNOOA': 'ISNOOA', 'ROH': 'ROH', 'MAN2': 'MAN2', 'B3O2': 'B3O2', 'INPN': 'INPN', 'MACRN': 'MACRN', 'PO2': 'PO2', 'VRO2': 'VRO2', 'MRP': 'MRP', 'PRN1': 'PRN1', 'ISNOHOO': 'ISNOHOO', 'MOBAOO': 'MOBAOO', 'MACRNO2': 'MACRNO2', 'ISOPND': 'ISOPND', 'HC5OO': 'HC5OO', 'ISOPNBO2': 'ISOPNBO2', 'RA3P': 'RA3P', 'ISOPNB': 'ISOPNB', 'ISOPNDO2': 'ISOPNDO2', 'PMNO2': 'PMNO2', 'IAP': 'IAP', 'MCO3': 'MCO3', 'IEPOXOO': 'IEPOXOO', 'MAOP': 'MAOP', 'INO2': 'INO2', 'OH': 'OH', 'PRPN': 'PRPN', 'GLYX': 'GLYX', 'A3O2': 'A3O2', 'ETO2': 'ETO2', 'R4O2': 'R4O2', 'ISN1': 'ISN1', 'KO2': 'KO2', 'ATOOH': 'ATOOH','GMAO_PSFC': 'GMAO_PSFC', 'GMAO_SURF': 'GMAO_SURF', 'GMAO_TEMP': 'GMAO_TEMP', 'GMAO_ABSH': 'GMAO_ABSH', 'GMAO_UWND': 'GMAO_UWND', 'GMAO_VWND': 'GMAO_VWND', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO', 'TRA_74': 'ICl', 'TRA_25': 'DMS', 'TRA_68': 'CH2I2', 'TRA_44': 'Br2', 'TRA_70': 'CH2IBr', 'TRA_22': 'N2O5', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_23': 'HNO4', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_21': 'C2H6', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_69': 'CH2ICl', 'TRA_50': 'BrNO3', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_73': 'IBr', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_77': 'HI', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_72': 'I2', 'TRA_59': 'GLYC', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_20': 'CH2O', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_75': 'I', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_58': 'HAC', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_27': 'SO4', 'TRA_78': 'OIO', 'TRA_66': 'HNO2', 'TRA_71': 'HOI', 'TRA_24': 'MP', 'TRA_67': 'CH3IT' },
'TRA_spec_met_all_1.7_EOH_no_trailing_zeroes': {'EOH': 'EOH', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'DHMOB': 'DHMOB', 'RP': 'RP', 'GMAO_UWND': 'GMAO_UWND', 'MAN2': 'MAN2', 'B3O2': 'B3O2', 'MRP': 'MRP', 'PRN1': 'PRN1', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'IAP': 'IAP', 'MCO3': 'MCO3', 'GMAO_SURF': 'GMAO_SURF', 'OH': 'OH', 'PRPN': 'PRPN', 'TRA7': 'HNO3', 'TRA6': 'ISOP', 'MAO3': 'MAO3', 'RCO3': 'RCO3', 'MO2': 'MO2', 'MACRNO2': 'MACRNO2', 'TRA_23': 'HNO4', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_73': 'IBr', 'TRA_72': 'I2', 'TRA_75': 'I', 'TRA_74': 'ICl', 'ISNP': 'ISNP', 'TRA_76': 'IO', 'TRA_79': 'INO', 'RB3P': 'RB3P', 'TRA_51': 'CHBr3', 'ROH': 'ROH', 'PP': 'PP', 'ISOPNDO2': 'ISOPNDO2', 'HC5': 'HC5', 'TRA9': 'ACET', 'TRA_56': 'MOBA', 'MACRN': 'MACRN', 'DIBOO': 'DIBOO', 'MRO2': 'MRO2', 'INPN': 'INPN', 'GMAO_TEMP': 'GMAO_TEMP', 'PO2': 'PO2', 'ISOPND': 'ISOPND', 'TRA_48': 'HBr', 'TRA_1': 'NO', 'RA3P': 'RA3P', 'ISOPNB': 'ISOPNB', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'IEPOXOO': 'IEPOXOO', 'MAOP': 'MAOP', 'INO2': 'INO2', 'ETO2': 'ETO2', 'ISN1': 'ISN1', 'TRA_49': 'BrNO2', 'ETP': 'ETP', 'TRA5': 'ALK4', 'TRA4': 'CO', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA1': 'NO', 'R4P': 'R4P', 'TRA_3': 'PAN', 'TRA2': 'O3', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'MAOPO2': 'MAOPO2', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'GMAO_VWND': 'GMAO_VWND', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'RIO2': 'RIO2', 'VRP': 'VRP', 'R4N1': 'R4N1', 'GMAO_PSFC': 'GMAO_PSFC', 'VRO2': 'VRO2', 'ISNOHOO': 'ISNOHOO', 'HC5OO': 'HC5OO', 'ETHLN': 'ETHLN', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'R4O2': 'R4O2', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_77': 'HI', 'MVKN': 'MVKN', 'TRA_35': 'OCPI', 'TRA_78': 'OIO', 'MGLY': 'MGLY', 'PMNN': 'PMNN', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'ATO2': 'ATO2', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'LISOPOH': 'LISOPOH', 'HO2': 'HO2', 'GMAO_ABSH': 'GMAO_ABSH', 'TRA8': 'H2O2', 'ISNOOB': 'ISNOOB', 'ISNOOA': 'ISNOOA', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'ISOPNBO2': 'ISOPNBO2', 'MOBAOO': 'MOBAOO', 'PMNO2': 'PMNO2', 'GLYX': 'GLYX', 'A3O2': 'A3O2', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'PYAC': 'PYAC', 'KO2': 'KO2', 'ATOOH': 'ATOOH', 'PRESS':'PRESS', 'U10M':'U10M'},
'red_specs_f_name': ['O3', 'NO2', 'NO', 'NO3', 'N2O5', 'HNO4', 'HNO3', 'HNO2', 'PAN', 'PPN', 'PMN', 'H2O2', 'HO2', 'OH', 'RO2', 'SO2', 'SO4', 'GMAO_TEMP', 'GMAO_UWND', 'GMAO_VWND', 'I2', 'HOI', 'IO', 'I', 'HI', 'OIO', 'INO', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr'],
# Photolysis/Fast-J
'FastJ_lower' : [289.0, 298.25, 307.45, 312.45, 320.3, 345.0, 412.45],
'FastJ_upper' : [298.25, 307.45, 312.45, 320.3, 345.0, 412.45, 850.0],
'FastJ_mids' : [294,303,310,316,333,380,574],
}
if rtn_dict:
return GC_var_dict
else:
return GC_var_dict[input_x]
# --------------
# 4.05 - GEOS-Chem/ctm.bpch values
# --------------
def latex_spec_name(input_x, debug=False):
spec_dict = {
'OIO': 'OIO', 'C3H7I': 'C$_{3}$H$_{7}$I', 'IO': 'IO', 'I': 'I', 'I2': 'I$_{2}$', 'CH2ICl': 'CH$_{2}$ICl', 'HOI': 'HOI', 'CH2IBr': 'CH$_{2}$IBr', 'C2H5I': 'C$_{2}$H$_{5}$I', 'CH2I2': 'CH$_{2}$I$_{2}$', 'CH3IT': 'CH$_{3}$I', 'IONO': 'IONO','HIO3': 'HIO$_{3}$', 'ICl': 'ICl', 'I2O3': 'I$_{2}$O$_{3}$', 'I2O4': 'I$_{2}$O$_{4}$', 'I2O5': 'I$_{2}$O$_{5}$', 'INO': 'INO', 'I2O': 'I$_{2}$O', 'IBr': 'IBr','I2O2': 'I$_{2}$O$_{2}$', 'IONO2': 'IONO$_{2}$', 'HI':'HI', 'BrO':'BrO','Br':'Br','HOBr':'HOBr','Br2':'Br$_{2}$','CH3Br':'CH$_{3}$Br','CH2Br2':'CH$_{2}$Br$_{2}$', 'CHBr3':'CHBr$_{3}$','O3':'O$_{3}$', 'CO':'CO' , 'DMS':'DMS', 'NOx':'NOx', 'NO':'NO', 'NO2':'NO$_{2}$', 'NO3':'NO$_{3}$','HNO3':'HNO$_{3}$', 'HNO4':'HNO$_{4}$','PAN':'PAN', 'HNO2':'HNO$_{2}$', 'N2O5':'N$_{2}$O$_{5}$','ALK4':'>= C4 alkanes','ISOP':'Isoprene' ,'H2O2':'H$_{2}$O$_{2}$','ACET':'CH$_{3}$C(O)CH$_{3}$', 'MEK':'>C3 ketones', 'ALD2':'CH$_{3}$CHO', 'RCHO': 'CH$_{3}$CH$_{2}$CHO', 'MVK':'CH$_{2}$=CHC(O)CH$_{3}$', 'MACR':'Methacrolein', 'PMN':'CH$_{2}$=C(CH$_{3}$)C(O)OONO$_{2}$', 'PPN':'CH$_{3}$CH$_{2}$C(O)OONO$_{2}$', 'R4N2':'>= C4 alkylnitrates','PRPE':'>= C4 alkenes', 'C3H8':'C$_{3}$H$_{8}$','CH2O':'CH$_{2}$O', 'C2H6':'C$_{2}$H$_{6}$', 'MP':'CH$_{3}$OOH', 'SO2':'SO$_{2}$', 'SO4':'SO$_{4}$','SO4s':'SO$_{4}$ on SSA', 'MSA':'CH$_{4}$SO$_{3}$','NH3':'NH$_{3}$', 'NH4': 'NH$_{4}$', 'NIT': 'InOrg N', 'NITs': 'InOrg N on SSA', 'BCPI':'BCPI', 'OCPI':'OCPI', 'BCPO':'BCPO','OCPO':'OCPO', 'DST1':'DST1', 'DST2':'DST2','DST3':'DST3','DST4':'DST4','SALA':'SALA', 'SALC':'SALC', 'HBr':'HBr', 'BrNO2': 'BrNO$_{2}$', 'BrNO3': 'BrNO$_{3}$', 'MPN':'CH$_{3}$ON$_{2}$', 'ISOPN':'ISOPN', 'MOBA':'MOBA', 'PROPNN':'PROPNN', 'HAC':'HAC', 'GLYC':'GLYC', 'MMN':'MMN', 'RIP':'RIP', 'IEPOX':'IEPOX','MAP':'MAP', 'AERI':'Aerosol Iodine' , 'Cl2':'Cl$_{2}$', 'Cl':'Cl','HOCl':'HOCl','ClO':'ClO','OClO':'OClO','BrCl':'BrCl', 'HI+OIO+IONO+INO':'HI+OIO+IONO+INO','CH2IX':'CH$_{2}$IX', 'IxOy':'I$_{2}$O$_{X}$ ($_{X}$=2,3,4)', 'CH3I':'CH$_{3}$I', 'OH':'OH', 'HO2':'HO$_{2}$', 'MO2':'MO$_{2}$', 'RO2':'RO$_{2}$'
,'RD01':r'I + O$_{3}$ $\rightarrow$ IO + O$_{2}$'
# Analysis names
,'iodine_all':'All Iodine', 'Iy': 'I$_{Y}$', 'IOy': 'IO$_{Y}$', 'IyOx': 'I$_{Y}$O$_{X}$', 'IOx': 'IO$_{X}$','iodine_all_A':'All Iodine (Inc. AERI)', 'I2Ox': 'I$_{2}$O$_{X}$' , 'AERI/SO4': 'AERI/SO4', 'EOH':'Ethanol'
, 'PSURF': 'Pressure at the bottom of level', 'GMAO_TEMP' : 'Temperature', 'TSKIN' : 'Temperature at 2m', 'GMAO_UWND':'Zonal Wind', 'GMAO_VWND':'Meridional Wind', 'U10M':'10m Meridional Wind', 'V10M': '10m Zonal Wind', 'CH2OO':'CH$_{2}$OO',
# Family Names
'N_specs' : 'NOy', 'NOy' : 'NO$_Y$',
'N_specs_no_I' : 'NOy exc. iodine',
# typos
'CH2BR2':'CH$_{2}$Br$_{2}$',
}
return spec_dict[input_x]
# --------------
# 4.06 - converts P/L tracer mulitpler to 1
# --------------
def p_l_unity(rxn, debug=False):
p_l_dict = {
'LR24': 1.0, 'LR25': 1.0, 'LR26': 1.0, 'LR27': 1.0, 'LR20': 1.0, 'LR21': 1.0, 'LR22': 1.0, 'LR30': 1.0, 'LR31': 1.0, 'LR23': 1.0, 'LR28': 1.0, 'LR29': 1.0, 'RD09': 1.0, 'PO3_46': 0.25, 'LR3': 1.0, 'LR2': 1.0, 'RD02': 1.0, 'PO3_03': 0.3, 'PO3_14': 1.0, 'PO3_02': 0.15, 'PO3_05': 0.15
}
return p_l_dict[rxn]
# --------------
# 4.07 - Returns tracers unit and scale (if requested)
# --------------
def tra_unit(x, scale=False, adjustment=False, adjust=True, \
global_unit=False, ClearFlo_unit=False, debug=False ):
tra_unit = {
'OCPI': 'ppbv', 'OCPO': 'ppbv', 'PPN': 'ppbv', 'HIO3': 'pptv', 'O3': 'ppbv', 'PAN': 'ppbv', 'ACET': 'ppbC', 'RIP': 'ppbv', 'BrNO3': 'pptv', 'Br': 'pptv', 'HBr': 'pptv', 'HAC': 'ppbv', 'ALD2': 'ppbC', 'HNO3': 'ppbv', 'HNO2': 'ppbv', 'C2H5I': 'pptv', 'HNO4': 'ppbv', 'OIO': 'pptv', 'MAP': 'ppbv', 'PRPE': 'ppbC', 'HI': 'pptv', 'CH2I2': 'pptv', 'IONO2': 'pptv', 'NIT': 'ppbv', 'CH3Br': 'pptv', 'C3H7I': 'pptv', 'C3H8': 'ppbC', 'DMS': 'ppbv', 'CH2O': 'ppbv', 'CH3IT': 'pptv', 'NO2': 'ppbv', 'NO3': 'ppbv', 'N2O5': 'ppbv', 'CHBr3': 'pptv', 'DST4': 'ppbv', 'DST3': 'ppbv', 'DST2': 'ppbv', 'DST1': 'ppbv', 'HOCl': 'ppbv', 'NITs': 'ppbv', 'RCHO': 'ppbv', 'C2H6': 'ppbC', 'MPN': 'ppbv', 'INO': 'pptv', 'MP': 'ppbv', 'CH2Br2': 'pptv', 'SALC': 'ppbv', 'NH3': 'ppbv', 'CH2ICl': 'pptv', 'IEPOX': 'ppbv', 'ClO': 'ppbv', 'NO': 'pptv', 'SALA': 'ppbv', 'MOBA': 'ppbv', 'R4N2': 'ppbv', 'BrCl': 'pptv', 'OClO': 'ppbv', 'PMN': 'ppbv', 'CO': 'ppbv', 'CH2IBr': 'pptv', 'ISOP': 'ppbC', 'BCPO': 'ppbv', 'MVK': 'ppbv', 'BrNO2': 'pptv', 'IONO': 'pptv', 'Cl2': 'ppbv', 'HOBr': 'pptv', 'PROPNN': 'ppbv', 'Cl': 'ppbv', 'I2O2': 'pptv', 'I2O3': 'pptv', 'I2O4': 'pptv', 'I2O5': 'pptv', 'MEK': 'ppbC', 'MMN': 'ppbv', 'ISOPN': 'ppbv', 'SO4s': 'ppbv', 'I2O': 'pptv', 'ALK4': 'ppbC', 'MSA': 'ppbv', 'I2': 'pptv', 'Br2': 'pptv', 'IBr': 'pptv', 'MACR': 'ppbv', 'I': 'pptv', 'AERI': 'pptv', 'HOI': 'pptv', 'BrO': 'pptv', 'NH4': 'ppbv', 'SO2': 'ppbv', 'SO4': 'ppbv', 'IO': 'pptv', 'H2O2': 'ppbv', 'BCPI': 'ppbv', 'ICl': 'pptv', 'GLYC': 'ppbv',
# Extra diagnostics to allow for simplified processing
'CH3I':'pptv', 'Iy':'pptv', 'PSURF': 'hPa', 'OH':'pptv', 'HO2':'pptv',
'MO2': 'pptv', 'NOy':'pptbv','EOH': 'ppbv' , 'CO':'ppbv', 'CH4':'ppbv',
'TSKIN':'K', 'GMAO_TEMP': 'K', 'GMAO_VWND' :'m/s','GMAO_UWND': 'm/s', 'RO2': 'pptv', 'U10M':'m/s','V10M': 'm/s' , 'PRESS': 'hPa', 'CH2OO':'pptv',
}
units = tra_unit[x]
# Adjust to appropriate scale for pf analysis
if adjust:
spec_2_pptv = GC_var('spec_2_pptv')
spec_2_pptC = GC_var('spec_2_pptC')
if ( x in spec_2_pptv ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'pptv' )
units = 'pptv'
if ( x in spec_2_pptC ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'pptC' )
units = 'pptC'
# Over ride adjustments for globally appro. units
if global_unit:
spec_2_ppbv = GC_var('spec_2_ppbv')
spec_2_ppbC = GC_var('spec_2_ppbC')
if ( x in spec_2_ppbv ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'ppbv' )
units = 'ppbv'
if ( x in spec_2_ppbC ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'ppbC' )
units = 'ppbC'
if ClearFlo_unit:
if any( [ x == i for i in [ 'NO', 'MACR', 'MVK' ] ] ):
units = 'ppbv'
if any( [ x == i for i in [ 'PAN' ] ] ):
units = 'pptv'
if any( [ x == i for i in [ 'ISOP' ] ] ):
units = 'ppbC'
if scale:
scaleby = get_unit_scaling( units )
if adjustment:
if units == 'K':
units = 'Deg. Celcuis'
adjustby = -273.15
else:
adjustby = 0
if scale and (not adjustment):
return units, scaleby
elif (scale and adjustment):
return units, scaleby, adjustby
else:
return units
# --------------
# 4.08 - Store of directories for servers ( earth0, atmosviz1, and tms MBP)
# -------------
# moved to AC_tools.funcs4core.py
# --------------
# 4.09 - Ox in species
# -------------
def Ox_in_species(in_=None, rxns=False, keys=False):
species_Ox = {
'HOIdf': 1.0, 'OIOdf': 2.0, 'BrNO3df': 2.0, 'HNO3df': 1.0, 'PPNdf': 1.0, 'IOdf': 1.0, 'N2O5df': 3.0, 'IONOdf': 1.0, 'PMNdf': 1.0, 'BrNO2df': 1.0, 'I2O4df': 4, 'MPNdf': 1.0, 'NO3df': 2.0, 'BrOdf': 1.0, 'HOBrdf': 1.0, 'HNO4df': 1.0, 'O3df': 1.0, 'I2O2df': 2.0, 'NO2df': 1.0, 'IONO2df': 2.0, 'PANdf': 1.0, 'OIO': 2.0, 'BrO': 1.0, 'HOBr': 1.0, 'N2O5': 3.0, 'IONO': 1.0, 'MPN': 1.0, 'BrNO2': 1.0, 'I2O2': 2.0, 'I2O4': 4, 'PPN': 1.0, 'HOI': 1.0, 'HNO3': 1.0, 'IONO2': 2.0, 'NO2': 1.0, 'IO': 1.0, 'HNO4': 1.0, 'PMN': 1.0, 'O3': 1.0, 'BrNO3': 2.0, 'PAN': 1.0, 'NO3': 2.0
}
rxn_Ox = {
'LO3_18': 1.0, 'LR25': 1.0, 'RD12': 2.0, 'LR21': 1.0, 'LO3_38': 1.0, 'LO3_10': 1.0, 'LO3_34': 1.0, 'LO3_35': 1.0, 'LO3_33': 1.0, 'LO3_30': 1.0, 'LR5': 2.0, 'LR6': 2.0, 'RD37': 2.0, 'LO3_05': 1.0, 'RD11': 2.0, 'LO3_06': 1.0, 'LO3_49': 1.0, 'LO3_04': 1.0, 'LO3_03': 1.0, 'LO3_02': 1.0, 'LO3_42': 1.0, 'LO3_41': 1.0, 'LO3_40': 1.0, 'LO3_47': 1.0, 'LO3_46': 1.0, 'LO3_09': 1.0, 'LO3_44': 1.0, 'LR30': 1.0, 'LO3_24': 1.0/2.0, 'LO3_21': 1.0, 'RD23': 2.0, 'LO3_54': 2.0, 'LO3_55': 1.0, 'LO3_08': 1.0, 'LO3_50': 1.0, 'LO3_51': 1.0, 'LO3_52': 1.0, 'LO3_53': 1.0, 'LR10': 1.0, 'LO3_36':1.0,
# LO3_24 set to 1 (as 0.5*CoE) even though 2 Ox equivalents are lost, this allows for contribution to bromine and iodine loss to be inclued
# LOX included for processing ease
'LOX':1.0, 'POX':1.0, 'PO3_14': 1.0, 'PO3_15':1.0 , 'RD98': 1.0, 'LO3_39':1.0 , 'RD63': 1.0,
# for prod analysis
'PO3_69' : 1.0/2.0, 'PO3_35': 0.85, 'PO3_03':0.15/0.3, 'PO3_70': 0.4/1.4 , 'PO3_77': 1.0/2.0 , 'RD06':1.0, 'LR9':1.0
}
if (rxns):
return rxn_Ox[ in_ ]
if (keys):
return species_Ox.keys()
else:
return species_Ox[ in_ ]
# ----
# 4.10 - Return dictionary of gaw sites
# ----
def gaw_2_name():
wdf = get_dir('dwd') +'ozonesurface/' + 'gaw_site_list.h5'
df= pd.read_hdf( wdf, 'wp', mode='r' )
names = df.values[:,1]
# alter long name for CVO
ind=[ n for n, i in enumerate(names) if ( i =='Cape Verde Atmospheric Observatory' )]
names[ind[0]] = 'Cape Verde'
return dict( zip( df.index, names ))
# ----
# 4.11 - Returns list of gaw sites in HDF file of O3 surface data
# ----
def get_global_GAW_sites(f='gaw_site_list_global.h5'):
wd= get_dir('dwd') +'ozonesurface/'
df= pd.read_hdf( wd+f, 'wp', mode='r' )
vars = sorted( list(df.index) )
# Kludge: remove those not in "grouped" analysis
# ( Q: why are these sites not present? - A: data control for lomb-scragle)
[ vars.pop( vars.index(i) ) for i in ['ZUG', 'ZSF', 'ZEP', 'WLG', 'USH', 'SDK', 'PYR', 'PUY', 'PAL', 'MKN', 'IZO', 'HPB', 'DMV', 'BKT', 'AMS', 'ALT', 'ABP'] ]
#[ 'AMS', 'MKN', 'IZO' , 'WLG', 'PYR', 'USH', 'ABP', 'ALT'] ]
return vars
# ----
# 4.12 - returns lon/lat/alt for res
# ----
# moved to AC_tools.funcs4core
# --------
# 4.13 - Get CTM (GEOS-Chem) array dimension for a given resolution
# --------
# moved to AC_tools.funcs4core
# --------
# 4.14 - Convert gamap category/species name to Iris/bpch name
# --------
def diagnosticname_gamap2iris( x ):
d={
"IJ-AVG-$": 'IJ_AVG_S',
"BXHGHT-$": 'BXHEIGHT',
"PORL-L=$":'PORL_L_S__',
'DAO-3D-$':'DAO_3D_S__',
'DAO-FLDS' :'DAO_FLDS__',
'DRYD-FLX': 'DRYD_FLX__',
'DRYD-VEL':'DRYD_VEL__',
'CHEM-L=$':'CHEM_L_S__',
'WETDCV-$':'WETDCV_S__',
'WETDLS-$':'WETDLS_S__',
'WD-LSW-$':'WD_LSW_S__',
'WD-LSR-$':'WD_LSR_S__',
'UP-FLX-$':'UP_FLX_S__',
'NS-FLX-$': 'NS_FLX_S__',
'EW-FLX-$':'EW_FLX_S__',
'TURBMC-$': 'TURBMC_S__',
'OD-MAP-$':'OD_MAP_S__',
# 'WD-FRC-$'
'MC-FRC-$': 'MC_FRC_S__',
}
return d[x]
# --------
# 4.14 - Get scaling for a given unit
# --------
def get_unit_scaling( units ):
misc = 'K', 'm/s', 'unitless', 'kg' ,'m', 'm2','kg/m2/s', \
'molec/cm2/s', 'mol/cm3/s', 'kg/s', 'hPa'
if any( [ (units == i) for i in 'pptv', 'pptC' ]):
scaleby = 1E12
elif any( [ (units == i) for i in 'ppbv', 'ppbC' ]):
scaleby = 1E9
elif any( [units ==i for i in misc ] ):
scaleby = 1
else:
print 'WARNING: This unit is not in unit lists: ', units
return scaleby
# ------------------------------------------- Section 5 -------------------------------------------
# -------------- Misc
#
# --------------
# 5.01 - Return MUTD runs - no hal, just Br, just I, and I + Br
# --------------
def MUTD_runs( standard=True, sensitivity=False, titles=False, \
IO_obs=False,no_I2Ox=False, respun=True, \
preindustrial=False, skip3=False, v10v92comp=False, \
nested_EU=False, just_bcase_no_hal=False, just_std=False, \
just_bcase_std=False, ver='1.6', res='4x5', \
debug=False):
""" Dictionary storage for iGEOSChem most up to date (MUTD)
runs.
returns list of directories and titles
"""
if debug:
print standard, sensitivity, titles, IO_obs, preindustrial, skip3,\
nested_EU, just_bcase_std, ver
pwd = get_dir( 'rwd' )
l_dict= GC_var('latex_run_names')
# Get version directories for versions
d= {
'4x5': {
'1.6':'iGEOSChem_1.6_G5/',
# '1.6':'iGEOSChem_1.6.1_G5/',
'1.6.1':'iGEOSChem_1.6.1_G5/',
'1.5': 'iGEOSChem_1.5_G5/' ,
'1.7': 'iGEOSChem_1.7_v10/'},
'2x2.5': {
'1.6':'iGEOSChem_1.6_G5_2x2.5/'
} }[res]
d=d[ver]
if sensitivity:
l = [
'no_hal', 'Just_Br', 'just_I', 'run', 'Just_I_org', 'I2Ox_double',\
'I2Ox_half', 'het_double','het_half', 'no_het', 'Sulfate_up', \
'MacDonald_iodide', 'I2Ox_phot_x2','I2Ox_phot_exp', 'no_I2Ox', \
'BrO2pptv' ]
r = [ d + i for i in l ]
# adjust names to inc. repsin
# if respun:
# r = [ i+'.respun' for i in r ]
if standard and (not any( [preindustrial,sensitivity, v10v92comp]) ):
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
# l = [ 'Just_Br', 'no_I2Ox' ]
elif just_bcase_no_hal:
l = [ 'no_hal', 'run' ]
# l = [ 'no_hal', 'no_I2Ox' ]
elif just_std:
l = [ 'Just_Br' ]
else:
if ver == '1.7':
# l = ['no_hal', 'run' ]
l = ['Just_Br', 'run' ]
else:
l = ['no_hal', 'Just_Br', 'just_I', 'run' ]
# if any( [ (ver ==i) for i in '1.5', '1.6' ] ) :
r = [ d + i for i in l ]
if nested_EU:
d= 'iGEOS_Chem_1.6_G5_NPOINTS/'
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
else:
l = [ 'no_hal', 'run' ]
r = [ d+i for i in l ]
# Setup latex titles list
if titles and (not any( [preindustrial, v10v92comp]) ) and ( standard or \
sensitivity or nested_EU):
l = [ l_dict[i] for i in l ]
if v10v92comp:
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
else:
l = [ 'no_hal', 'run' ]
r= [ 'iGEOSChem_1.7_v10/'+ i for i in l ]+ \
[ 'iGEOS_Chem_1.6_G5/'+ i for i in l ]
l= [ 'GEOS-Chem v10 (no hal)', 'Iodine Sim. (v10)'] + \
[ 'GEOS-Chem v9-2 (no hal)', 'Iodine Sim.']
if IO_obs:
l = 'run_CVO', 'run_GRO', 'run_MAL', 'run_HAL', 'run_TOR_updated.respun'
r = [d+ i for i in l]
if no_I2Ox:
r = [ i+'_no_I2Ox' for i in r ]
if preindustrial:
r = [
'iGEOS_Chem_1.5_G5/no_hal', 'iGEOS_Chem_1.5_G5/run' , \
'iGEOS_Chem_1.5_G5_preindustrial_no_hal/no_hal', \
'iGEOS_Chem_1.5_G5_preindustrial/run'
]
l = [
'(I-,Br-)', '(I+,Br+)' , '(I-,Br-) - 1750', '(I+,Br+) - 1750'
]
if debug:
print [pwd + i for i in r ], l
if debug:
[pwd + i for i in r ]
if skip3:
[ [ i.pop(0) for i in l, r ] for ii in range(3) ]
if titles:
return [pwd + i for i in r ], l
else:
return [pwd + i for i in r ]
# --------------
# 5.02 - Store of constants for use by funcs/progs
# --------------
def constants(input_x, debug=False):
""" Dictionary storing commonly used constants """
con_dict ={
'RMM_air' : ( .78*(2.*14.)+.22*(2.*16.) ) ,
'AVG' : 6.0221413E23,
'mol2DU': 2.69E20
}
return con_dict[input_x]
# ---------------- Section 6 -------------------------------------------
# -------------- Dynamic processing of p/l
#
# -------------
# 6.01 - Extract reactions to form a dictionary of active reactions
# -------------
def rxn_dict_from_smvlog( wd, PHOTOPROCESS=None, ver='1.7', LaTeX=False, debug=False ):
""" build a dictionary reaction of reaction details from smv.log
This can be used as an external call to analyse other prod/loss
reactions through smvgear """
if isinstance( PHOTOPROCESS, type(None) ):
PHOTOPROCESS = {
'1.6' : 457, '1.7' : 467
}[ver]
fn = 'smv2.log'
if debug:
print wd+'/'+fn
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
for row in file_:
row = row.split()
if 'NMBR' in row:
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
rxns = [ i for i in rxns if ( 'NMBR' not in i ) ]
n = [int(rxn[0]) for rxn in rxns ]
rxns = [rxn[1:] for rxn in rxns ]
rdict = dict( zip(n, rxns) )
# --- Process to Latex
if LaTeX:
for rxn in sorted(rdict.keys() ):
# -- Use Latex formatting?
if rxn > PHOTOPROCESS-1:
# xarstr = r' $\xrightarrow{hv}$ '
xarstr = r' + hv $\rightarrow$ '
else:
xarstr = r' + M $\rightarrow$ '
# --- get all print on a per tag basis the coe, rxn str
try:
rxn_str = ''.join( rdict[rxn][4:] )
if LaTeX:
rxn_str = rxn_str.replace('++=', xarstr)
rxn_str = rxn_str.replace('+', ' + ')
rxn_str = rxn_str.replace('+ =', r' $\rightarrow$ ' )
else:
pass
if debug:
print rxn_str
try:
rxn_strs += [ rxn_str ]
rxns += [ rxn ]
except:
rxn_strs = [ rxn_str ]
rxns = [ rxn ]
except:
print '!'*100, 'ERROR HERE: {} {}'.format( rxn, rxn_str )
rdict = dict( zip(rxns, rxn_strs ) )
return rdict
# -------------
# 6.02 - Extract reactions tracked by prod loss diag for a given p/l family
# -------------
def rxns_in_pl( wd, spec='LOX', debug=False ):
""" Extract reactions tracked by p/l family in smvgear """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
# if debug:
if True:
print file_
readrxn = False
for row in file_:
row = row.split()
if all( [ i in row for i in 'Family','coefficient' , 'rxns', spec] ):
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- Check that rxns ahave been found?
if len( rxns ) < 1:
print 'ERROR: No rxns. found for >{}<, correct family?'.format( spec )
sys.exit(0)
# -- remove 'Family'
rxns = [ i for i in rxns if ( 'Family' not in i ) ]
n = [int(rxn[1]) for rxn in rxns ]
rxns = [rxn[2:] for rxn in rxns ]
rdict = dict( zip(n, rxns) )
return rdict
# -------------
# 6.03 - Extract reaction infomation for given tracer tags
# -------------
def rxn4pl( pls, wd='example/example', rdict=None, reduce_size=True, \
debug=False ):
""" Get information on reaction in smvgear from a provide reaction tag """
# --- Get Dict of reaction detail
if debug:
print 'rxn4pl called'
if isinstance(rdict, type(None) ):
# Get Dict of all reactions, Keys = #s
rdict = rxn_dict_from_smvlog(wd)
if debug:
for i in rdict.keys():
if any( [ (s_ in ''.join( rdict[i] ) ) for s_ in pls ] ):
print i, 'yes'
# print rdict#.keys()
# --- Indices for
# reduce dict size
if reduce_size:
keys = [ i for i in rdict.keys() if \
any( [ (s_ in ''.join( rdict[i] ) ) for s_ in pls ]) ]
# re-make dictionary
rdict = dict( zip( keys, [rdict[i] for i in keys] ))
# loop via pl
keys = np.array([ [ pl, [ i for i in rdict.keys() \
if any( [ (pl in ''.join( rdict[i] ) ) ]) ][0] ] for pl in pls ])
# --- Return as reactions referenced by tag
return dict( zip( keys[:,0], [ rdict[int(i)] for i in keys[:,1] ]) )
# -------------
# 6.04 - Construct a list of indicies for each fam from given tags
# -------------
def get_indicies_4_fam( tags, fam=False, IO_BrOx2=False, rtnspecs=False,
NOy_as_HOx=True, debug=False ):
""" Return indicies (in list form) for in a given family """
# assign family
# famsn = [ 'Photolysis','HOx','NOy' ,'Bromine', 'Iodine' ]
famsn = [ 'Photolysis','HOx' ,'Bromine', 'Iodine' ]
fams = []
for tag in tags:
fams.append( get_tag_fam( tag) )
# if rm NOy family (treat as NOx)
if NOy_as_HOx:
fams = [x if (x!='NOy') else 'HOx' for x in fams]
fd = dict(zip(tags, fams) )
ll = []
[ ll.append([]) for i in famsn ]
for n, tag in enumerate( tags ) :
for fn in range(len(famsn)):
if fd[tag] == famsn[fn] :
ll[fn].append( n)
if fam:
# Kludge - to allow for counting Ox loss via IO +BrO 50/50,
# ( add extra loss tag. )
if IO_BrOx2:
ll[famsn.index('Bromine')].append( max([max(i) for i in ll])+1 )
fams = fams +[ 'Bromine' ]
tags = tags + [ 'LO3_24']
if rtnspecs:
return ll, fams, tags
else:
return ll, fams
else:
return ll
# -------------
# 6.05 - Get tags for reactions
# -------------
def get_p_l_tags( rxns, debug=False):
""" get p/l tags for a given smvgear reaction """
# (PD??, RD??, LO3_??, PO3_??, LR??)
for rxn in rxns:
if debug:
print [ i for i in rxn if any( [x in i for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
tags = [i for i in rxn if any( [x in i for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
try:
tagsl.append( tags)
except:
tagsl = [tags]
return tagsl
# -------------
# 6.06 - extract reactions tracked by prod loss diag in input.geos
# -------------
def p_l_species_input_geos( wd, ver='1.7', rm_multiple_tagged_rxs=False ):
"""
Extract prod/loss species (input.geos) and reaction tags (globchem.dat)
"""
# find and open input.geos file
fn = glob.glob(wd+'/*input.geos*')[0]
file_ = open( fn, 'rb' )
# Read in just the prod loss section
strs_in_1st_line = 'Number', 'of', 'P/L', 'families'
section_line_divider = '------------------------+----------'+ \
'--------------------------------------------'
readrxn = False
for row in file_:
row = row.split()
# once at prod/loss section, start added to list
if all( [ i in row for i in strs_in_1st_line ]):
readrxn=True
# if not at end of prod/loss section, add to list
if section_line_divider in row:
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- Only consider 'Family' ( no headers e.g. 'families' )
rxns = [ i for i in rxns if ( 'families' not in i ) ]
rxns = [ [ i.replace(':','') for i in r ] for r in rxns ]
# Kludge, adjust for extra space 12-99
# ( This is no longer required for 1.7 + )
if ver == '1.6':
[i.pop(0) for i in rxns if ('th' not in i[0]) ]
# Extract just PD (input.geos) and vars (globchem.dat vars )
PD = [rxn[4] for rxn in rxns ]
vars = [rxn[5:] for rxn in rxns ]
# remove p/l with muliple values ( start from 12th input) - Kludge?
if rm_multiple_tagged_rxs:
PD, vars = [ i[11:] for i in PD, vars ]
vars = [ i[0] for i in vars ]
return PD, vars
# -------------
# 6.07 - extract all active tags from smv.log
# -------------
def tags_from_smvlog( wd ): #, spec='LOX' ):
""" Get all active p/l tags in smvgear ( from smv2.log ) """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
for row in file_:
row = row.split()
if all( [(i in row) for i in ['NBR', 'NAME', 'MW', 'BKGAS(VMRAT)'] ]):
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
rxns = [ i for i in rxns if ( 'NBR' not in i ) ]
rxns = [rxn[1] for rxn in rxns ]
# --- only consider tags
return [i for i in rxns if any( [x in i \
for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
# -------------
# 6.08 - extract all active PDs from smv.log
# -------------
def PDs_from_smvlog( wd, spec='LOX' ):
""" Get all active PDs tags in smvgear ( from smv2.log ) """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
leniency = 0
entries_in_title = ['Families', 'for', 'prod', 'or', 'loss', 'output:']
for row in file_:
row = row.split()
if all( [(i in row) for i in entries_in_title ]):
readrxn=True
leniency = 1
if len(row) < 1 :
if leniency < 0:
readrxn=False
leniency -= 1
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
exceptions = [ 'SPECIES', '===============================================================================','Families']
rxns = [ i for i in rxns if all( [ (ii not in i) for ii in exceptions ]) ]
rxns = [j for k in rxns for j in k ]
return rxns
# -------------
# 6.09 - Give all reactions tag is active within
# -------------
def rxns4tag( tag, rdict=None, ver='1.7', wd=None ):
""" get a list of all reactions with a given p/l tag """
# --- get reaction dictionary
if rdict == None:
rdict = rxn_dict_from_smvlog( wd, ver=ver )
# --- Caveats - to adapt for long line errors in fortran written output
errs = ['LO3_36']
cerrs = ['RD95']
if any([ (tag == i) for i in errs ] ):
tag = cerrs[ errs.index( tag) ]
# -- loop reactions, if tag in reaction return reaction
rxns = []
for n, rxn in enumerate( rdict.values() ):
# if any( [tag in i for i in rxn]):
if any( [(i.endswith(tag) ) for i in rxn]):
rxns.append( [rdict.keys()[n] ]+ rxn )
return rxns
# -------------
# 6.10 - get details for a given tag
# -------------
def get_tag_details( wd, tag=None, PDs=None, rdict=None, \
PHOTOPROCESS=None, ver='1.7', LaTeX=False, print_details=False, \
debug=False ):
""" Retriveve prod/loss tag details from smv.log
( rxn number + reaction description)"""
# what is the number of the first photolysis reaction?
if isinstance( PHOTOPROCESS, type(None) ):
PHOTOPROCESS = {
'1.6' : 457, '1.7' : 467
}[ver]
# --- get all reactions tags are active in smv.log
if rdict == None:
rdict = rxn_dict_from_smvlog( wd, ver=ver )
trxns = rxns4tag( tag, wd=wd, rdict=rdict )
# --- get all print on a per tag basis the coe, rxn str
try:
rxn_str = ''.join( trxns[0][5:9])
# -- Use Latex formatting?
# if LaTeX:
#
# setup latex arrows and replace existing arrows
# if trxns[0][0] > PHOTOPROCESS-1:
# xarstr = r' $\xrightarrow{hv}$ '
# else:
# xarstr = r' $\xrightarrow{M}$ '
# rxn_str = rxn_str.replace('++=', xarstr).replace('+', ' + ')
# rxn_str = rxn_str.replace('+ =', r' $\rightarrow$ ' )
# else:
# pass
if debug:
print rxn_str
dets = [ tag, trxns[0][0], rxn_str ]
except:
print '!'*100, 'ERROR HERE: {} {}'.format( tag, trxns )
if print_details:
print dets
# --- return a dictionary of all active tagged reactions details by tag : PD, number, rxn str, coeeffiecn
else:
return dets
# -------------
# 6.11 - Takes a reaction number add gives the Ox Coe
# -------------
def get_rxn_Coe(wd, num, tag, nums=None, rxns=None, tags=None, \
Coe=None, spec='LOX', debug=False):
""" Retrieve given reaction coefficient for smvgear (from smv2.log) """
# --- get dictionaries for reactions within
if all( [ (i == None) for i in nums, rxns, tags, Coe ] ):
nums, rxns, tags, Coe = prod_loss_4_spec( wd, spec, all_clean=True )
# Pull reaction coefficient
Coe_dict = dict(zip(nums, Coe) )
Coe = float(Coe_dict[ num ])
if ('P' not in tag ): # consider all change positive - Kludge due to the assignment approach.
Coe = Coe*-1.0
# --- over write Ox in species with prod_mod_tms values
try:
Coe = Ox_in_species(tag, rxns=True)
if debug:
print 'using values from Ox_in_species'
except:
if debug:
print 'using values from smv.log @: {}'.format(wd)
return Coe
# -------------- Section 7 -------------------------------------------
# -------------- Observational Variables
#
# 5.02 - obs sites (e.g. deposition locs, lons, alts )
# 5.04 - Store site locations ( LAT, LON, ALT)
# --------------
# 7.01 - open ocean IO data
# --------------
def IO_obs_data(just_IO=False):
""" Dictionary of open ocean IO observations for automated comparisons
key = ref (e.g. name_year )
values = alt, lon, lat, times, full_name , IO (avg), BrO (avg), CAM-Chem IO, CAM-Chem BrO, group
"""
IO_obs_dict={'Read_2008': [0.0, -24.87, 16.85, 6.0, 'Read, 2008', '1', '2', '1', '2', 'Leeds'], 'Weddel_Sea': [0.03, -50.0, 75.0, 10.0, 'Weddel Sea', '-', '-', '-', '-', '-'], 'Oetjen_2009': [0.0, 73.5, 5.0, 2.0, 'Oetjen, 2009', '2.4', '-', '1', '-', 'Leeds'], 'Jones_2010_II': [0.0, -19.0, 30.0, 7.0, 'Jones, 2010, RHaMBLe II (26-36N)', '-', '-', '-', '-', '-'], 'Leser_2003': [0.0, -24.87, 16.85, 10.0, 'Leser, 2003', '-', '3.6', '-', '0.8', 'Heidelberg'], 'Jones_2010_I': [0.0, -19.0, 20.0, 7.0, 'Jones, 2010, RHaMBLe I (15-25N)', '-', '-', '-', '-', '-'], 'Halley_BAS': [0.03, -26.34, 75.35, 10.0, 'Halley, BAS', '-', '-', '-', '-', '-'], 'Theys_2007': [0.0, -20.9, -55.5, 8.0, 'Theys, 2007', '-', '<0.5', '-', '0.8', 'Belgium'], 'Dix_2013': [9.0, -160.0, 10.0, 1.0, 'Dix, 2013', '0.1', '-', '', '-', 'NCAR'], 'Allan_2000': [0.162, -16.6, 28.4, 6.0, 'Allan, 2000', '1.2', '-', '0.4', '-', 'Leeds'], 'Jones_2010_MAP': [0.0, -10.0, 55.0, 6.0, 'Jones, 2010, MAP', '-', '-', '-', '-', '-'], 'Grobmann_2013': [0.0, 150.0, 15.0, 1.0, 'Grobmann, 2013', '0.72-1.8', '-', '-', '-', 'Heidelberg'], 'Dix_2013_j_comp': [9.0, -160.0, 10.0, 5.0, 'Dix, 2013 (wrong month to allow with jones runs comparisons)', '-', '-', '-', '-', '-'], 'schonart_2009_II': [0.0, -80.0, -20.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Martin_2009': [0.0, -24.87, 16.85, 2.0, 'Martin, 2009', '-', '<3.0', '-', '1.2', 'Heidelberg'], 'schonart_2009_I': [0.0, -90.0, -5.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Yokouchi_2013': [0.0, 120.0, 15.0, 2.0, 'Yokouchi, 2013', '-', '-', '-', '-', '-'], 'Butz_2009': [0.045, -44.4, -2.4, 12.0, 'Butz, 2009', '0.1', '~1.0', '0.02', '0.5', 'Leeds']}
IO_obs_dict_just = {'Read_2008': [0.0, -24.87, 16.85, 6.0, 'Read, 2008', '1', '2', '1', '2', 'Leeds'], 'schonart_2009_II': [0.0, -80.0, -20.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Oetjen_2009': [0.0, 73.5, 5.0, 2.0, 'Oetjen, 2009', '2.4', '-', '1', '-', 'Leeds'], 'Allan_2000': [0.162, -16.6, 28.4, 6.0, 'Allan, 2000', '1.2', '-', '0.4', '-', 'Leeds'], 'Leser_2003': [0.0, -24.87, 16.85, 10.0, 'Leser, 2003', '-', '3.6', '-', '0.8', 'Heidelberg'], 'Theys_2007': [0.0, -20.9, -55.5, 8.0, 'Theys, 2007', '-', '<0.5', '-', '0.8', 'Belgium'], 'Dix_2013': [9.0, -160.0, 10.0, 1.0, 'Dix, 2013', '0.1', '-', '', '-', 'NCAR'], 'Grobmann_2013': [0.0, 135.0, 15.0, 1.0, 'Grobmann, 2013', '1.095', '-', '-', '-', 'Heidelberg'], 'schonart_2009_I': [0.0, -90.0, -5.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Martin_2009': [0.0, -24.87, 16.85, 2.0, 'Martin, 2009', '-', '<3.0', '-', '1.2', 'Heidelberg'], 'Butz_2009': [0.045, -44.4, -2.4, 12.0, 'Butz, 2009', '0.1', '~1.0', '0.02', '0.5', 'Leeds'], 'Mahajan_2010': [0., -100, -10, 4.0, 'Mahajan, 2010', '0.58', '-', '-', '-', 'Leeds'] }
if (just_IO):
return IO_obs_dict_just
else:
return IO_obs_dict
# --------------
# 7.02 - BAE flight ID to date
# -------------
def bae_flight_ID_2_date( f_ID, debug=False):
""" BAE flight flight ID to date for CAST campaign """
if (debug):
print 'bae_flight_ID_2_date called'
f_ID_dict = {'847':'2014-02-18','846':'2014-02-17','845':'2014-02-17', '844':'2014-02-16','843':'2014-02-15','842':'2014-02-17','840':'2014-02-13','839':'2014-02-12','838':'2014-02-05','837':'2014-02-04','836':'2014-02-04','835':'2014-02-03','834':'2014-02-01','833':'2014-02-01','832':'2014-01-30','831':'2014-01-30','830':'2014-01-30','829':'2014-01-28','828':'2014-01-26','827':'2014-01-26','826':'2014-01-25','825':'2014-01-24','824':'2014-01-21','823':'2014-01-18'}
return f_ID_dict[f_ID]
# --------------
# 7.03 - details on flights from FAAM's NETCDF core data files,
# --------------
def CAST_flight(all=True, CIMS=False, CIMSII=False):
""" Callable dictionary of CAST flight details
removed 'CAST_flight, 'b822': '20140108'' """
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '1932', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140121', '1956', '20140122', '0413', 1390334165, 1390363990], 'b825': ['20140124', '1940', '20140125', '0140', 1390592404, 1390614058], 'b826': ['20140125', '0128', '20140125', '0637', 1390613301, 1390631839], 'b827': ['20140126', '0041', '20140126', '0445', 1390696903, 1390711511], 'b823': ['20140118', '1713', '20140119', '0147', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '1901', '20140205', '0318', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0627', 1391477699, 1391495264], 'b835': ['20140203', '2101', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0610', '20140201', '1217', 1391235033, 1391257044], 'b833': ['20140201', '0045', '20140201', '0606', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1131', 1391062943, 1391081485], 'b831': ['20140130', '0101', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0239', '20140129', '0830', 1390963178, 1390984251], 'b846': ['20140217', '2058', '20140218', '0510', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '1857', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0753', 1392607430, 1392623590], 'b842': ['20140214', '0508', '20140214', '1035', 1392354530, 1392374130], 'b843': ['20140215', '1903', '20140216', '0400', 1392490986, 1392523256], 'b840': ['20140213', '0005', '20140213', '0658', 1392249904, 1392274686], 'b841': ['20140214', '0004', '20140214', '0458', 1392336265, 1392353901]}
if (CIMS):
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '2322', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140122', '0155', '20140122', '0411', 1390334165, 1390363990], 'b825': ['20140124', '2230', '20140125', '0034', 1390592404, 1390614058], 'b826': ['20140125', '0128', '20140125', '0637', 1390613301, 1390631839], 'b827': ['20140126', '0041', '20140126', '0445', 1390696903, 1390711511], 'b823': ['20140118', '2006', '20140119', '0055', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '2333', '20140205', '0315', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0608', 1391477699, 1391495264], 'b835': ['20140203', '2316', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0610', '20140201', '1220', 1391235033, 1391257044], 'b833': ['20140201', '0145', '20140201', '0606', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1123', 1391062943, 1391081485], 'b831': ['20140130', '0201', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0249', '20140129', '0825', 1390963178, 1390984251], 'b846': ['20140217', '2058', '20140218', '0510', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '1857', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0750', 1392607430, 1392623590], 'b842': ['20140214', '0611', '20140214', '1011', 1392354530, 1392374130], 'b843': ['20140215', '2227', '20140216', '0324', 1392490986, 1392523256], 'b840': ['20140213', '0304', '20140213', '0648', 1392249904, 1392274686], 'b841': ['20140214', '0004', '20140214', '0456', 1392336265, 1392353901]}
if (CIMSII):
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '2322', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140122', '0155', '20140122', '0411', 1390334165, 1390363990], 'b825': ['20140124', '2230', '20140125', '0034', 1390592404, 1390614058], 'b826': ['20140125', '0141', '20140125', '0619', 1390613301, 1390631839], 'b827': ['20140126', '0100', '20140126', '0442', 1390696903, 1390711511], 'b823': ['20140118', '2006', '20140119', '0055', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '2333', '20140205', '0315', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0608', 1391477699, 1391495264], 'b835': ['20140203', '2338', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0619', '20140201', '1210', 1391235033, 1391257044], 'b833': ['20140201', '0223', '20140201', '0558', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1123', 1391062943, 1391081485], 'b831': ['20140130', '0201', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0249', '20140129', '0825', 1390963178, 1390984251], 'b846': ['20140218', '0035', '20140218', '0507', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '2224', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0750', 1392607430, 1392623590], 'b842': ['20140214', '0611', '20140214', '1011', 1392354530, 1392374130], 'b843': ['20140215', '2227', '20140216', '0324', 1392490986, 1392523256], 'b840': ['20140213', '0342', '20140213', '0648', 1392249904, 1392274686], 'b841': ['20140214', '0050', '20140214', '0456', 1392336265, 1392353901]}
if (all) :
return flight_dict
# --------------
# 7.04 - Iodocarbon obs. meta data
# --------------
def iodocarbon_obs():
""" dictionary of Iodocarbon observations for automated
analysis/comparions with observations """
Org_obs= {
'CH3IT' : [ \
['Chuck et al (2005)' , list(np.linspace( -36, -49, 3) ) + \
list( np.linspace( -20, 28, 3) ), [-20] *6, [0.71]*3 + [ 1.94]*3 ] ] ,
'CH2ICl' : [ \
['Chuck et al (2005)' , list( np.linspace( -36, -49, 3 ) )+ \
list(np.linspace( -20, 28, 3) ), [-20] *6, [0.23]*3+ [0.32]*3 ],\
['Jones et al (2010)' , np.linspace( 60, 15, 5), [-15] *5, [0.12]*5 ] ],
'CH2I2' : [ \
['Jones et al (2010)' , np.linspace( 60,15, 5), [-15] *5, [0.01]*5 ] ],
'CH2IBr' : [ \
['Jones et al (2010)' , np.linspace( 60, 15, 5), [-15] *5, [0.01]*5 ] ],
'C2H5I' : [ \
['Jones et al (2010)' , np.linspace( 26, 36, 3), [120] *3, [0.09]*3 ] ],
'I2' : [ ['Lawler et al (2014)' , [16.51] , [-24], [0.2] ] ],
}
return Org_obs
# --------------
# 7.05 - Stores locations for use by funcs/progs -
# --------------
def get_loc(loc=None, rtn_dict=False, debug=False):
"""
Dictionary to store locations for automated analysis
Data arranged: LON, LAT, ALT
- double up? ( with 5.02 ?? )
"""
loc_dict ={
'GUAM' : ( 144.800, 13.500, 0 ),
'CHUUK' : ( 151.7833, 7.4167, 0 ),
'PILAU' : ( 134.4667,7.3500, 0 ),
'London': ( -0.1275, 51.5072, 0 ),
'Weyborne' : (1.1380, 52.9420, 0 ),
# 'Cape Verde': (16.848, -24.871, 0 ),
# 'CVO': (16.848, -24.871, 0 ),
'Cape Verde': ( -24.871, 16.848, 0 ),
'CVO': (-24.871,16.848, 0 ),
'North Ken' : (-0.214174, 51.520718, 0),
'KEN' : (-0.214174, 51.520718, 0),
'BT tower': (-0.139055, 51.521556, 190),
'BTT': (-0.139055, 51.521556, 190)
}
if rtn_dict:
return loc_dict
else:
return loc_dict[loc]
# --------------
# 7.06 - Get Locations of observations (lats, lons, alts ) for given sites
# --------------
def get_obs_loc(loc, debug=False):
""" Dictionary to store groups of locations for automated analysis """
d = {
'Denmark' :[
[ 68.35, 59.85, 56.13, 55.69 ], [ 18.81, 17.63, 13.05, 12.10 ] ],
# split by obs site...
'Denmark1': [[68.35], [18.81]],
'Denmark2': [[59.85], [17.63]],
'Denmark3': [[56.13], [13.05]],
'Denmark4': [[55.69], [12.1]] ,
# Berlin, bonn, hamburg, Westerland, Munich, Brotjacklriegel, Deuselbach, Schauinsland
'Germany' :[
[52.5167, 50.7340, 53.5653,54.9100, 52.4231, 48.1333, \
48.491, 49.4508, 47.91111 ] ,
[ 13.3833 , 7.0998, 10.0014, 8.3075, 10.7872, 11.5667, 13.133, 7.302, \
7.8894] ],
'Weyborne' :[ [ 52.9420], [1.1380 ] ] }
return d[loc]
# --------------
# 7.07 - sonde station variables (list of 432 sondes)
# -------------
def sonde_STNs():
""" Dictionary of WOUDC sonde location variables """
sonde_dict = {
101: ['SYOWA', 101.0, -69.0, 39.58, 22.0, 'JPN', 'ANTARCTICA'], 104: ['BEDFORD', 104.0, 42.45, -71.267, 80.0, 'USA', 'IV'], 105: ['FAIRBANKS (COLLEGE)', 105.0, 64.817, -147.867, 138.0, 'USA', 'IV'], 107: ['WALLOPS ISLAND', 107.0, 37.898, -75.483, 13.0, 'USA', 'IV'], 108: ['CANTON ISLAND', 108.0, -2.76, -171.7, 3.0, 'USA', 'V'], 109: ['HILO', 109.0, 19.5735, -155.0485, 11.0, 'USA', 'V'], 111: ['AMUNDSEN-SCOTT (SOUTH POLE)', 111.0, -89.983, 0.0, 2820.0, 'ATA', 'ANTARCTICA'], 131: ['PUERTO MONTT', 131.0, -41.45, -72.833, 5.0, 'CHL', 'III'], 132: ['SOFIA', 132.0, 42.817, 23.383, 588.0, 'BGR', 'VI'], 137: ['TOPEKA', 137.0, 39.067, -95.633, 270.0, 'USA', 'IV'], 138: ['CHRISTCHURCH', 138.0, -43.483, 172.55, 34.0, 'NZL', 'V'], 149: ['OVEJUYO (LA PAZ)', 149.0, -16.517, -68.033, 3420.0, 'BOL', 'III'], 156: ['PAYERNE', 156.0, 46.49, 6.57, 491.0, 'CHE', 'VI'], 157: ['THALWIL', 157.0, 46.817, 8.455, 515.0, 'CHE', 'VI'], 163: ['WILKES', 163.0, -66.25, 110.517, 12.0, 'USA', 'ANTARCTICA'], 174: ['LINDENBERG', 174.0, 52.21, 14.12, 112.0, 'DEU', 'VI'], 175: ['NAIROBI', 175.0, -1.267, 36.8, 1745.0, 'KEN', 'I'], 181: ['BERLIN/TEMPLEHOF', 181.0, 52.467, 13.433, 50.0, 'DEU', 'VI'], 187: ['PUNE', 187.0, 18.553, 73.86, 559.0, 'IND', 'II'], 190: ['NAHA', 190.0, 26.2, 127.683, 27.0, 'JPN', 'II'], 191: ['SAMOA', 191.0, -14.25, -170.56, 82.0, 'ASM', 'V'], 194: ['YORKTON', 194.0, 51.263, -102.467, 504.0, 'CAN', 'IV'], 197: ['BISCARROSSE/SMS', 197.0, 44.367, -1.233, 18.0, 'FRA', 'VI'], 198: ['COLD LAKE', 198.0, 54.783, -110.05, 702.0, 'CAN', 'IV'], 199: ['BARROW', 199.0, 71.317, -156.635, 11.0, 'USA', 'IV'], 203: ['FT. SHERMAN', 203.0, 9.33, -79.983, 57.0, 'PAN', 'IV'], 205: ['THIRUVANANTHAPURAM', 205.0, 8.483, 76.97, 60.0, 'IND', 'II'], 206: ['BOMBAY', 206.0, 19.117, 72.85, 145.0, 'IND', 'II'], 210: ['PALESTINE', 210.0, 31.8, -95.717, 121.0, 'USA', 'IV'], 213: ['EL ARENOSILLO', 213.0, 37.1, -6.733, 41.0, 'ESP', 'VI'], 217: ['POKER FLAT', 217.0, 65.133, -147.45, 357.5, 'USA', 'IV'], 219: ['NATAL', 219.0, -5.71, -35.21, 30.5, 'BRA', 'III'], 221: ['LEGIONOWO', 221.0, 52.4, 20.967, 96.0, 'POL', 'VI'], 224: ['CHILCA', 224.0, -12.5, -76.8, -1.0, 'PER', 'III'], 225: ['KOUROU', 225.0, 5.333, -52.65, 4.0, 'GUF', 'III'], 227: ['MCDONALD OBSERVATORY', 227.0, 30.666, -90.933, 2081.0, 'USA', 'IV'], 228: ['GIMLI', 228.0, 50.633, -97.05, 228.0, 'CAN', 'IV'], 229: ['ALBROOK', 229.0, 8.983, -79.55, 66.0, 'PAN', 'IV'], 231: ['SPOKANE', 231.0, 47.667, -117.417, 576.0, 'USA', 'IV'], 233: ['MARAMBIO', 233.0, -64.233, -56.623, 196.0, 'ATA', 'ANTARCTICA'], 234: ['SAN JUAN', 234.0, 18.483, -66.133, 17.0, 'PRI', 'IV'], 235: ['LONG VIEW', 235.0, 32.5, -94.75, 103.0, 'USA', 'IV'], 236: ['COOLIDGE FIELD', 236.0, 17.283, -61.783, 10.0, 'ATG', 'IV'], 237: ['GREAT FALLS', 237.0, 47.483, -111.35, 1118.0, 'USA', 'IV'], 238: ['DENVER', 238.0, 39.767, -104.883, 1611.0, 'USA', 'IV'], 239: ['SAN DIEGO', 239.0, 32.76, -117.19, 72.5, 'USA', 'IV'], 242: ['PRAHA', 242.0, 50.02, 14.45, 304.0, 'CZE', 'VI'], 254: ['LAVERTON', 254.0, -37.867, 144.75, 21.0, 'AUS', 'V'], 255: ['AINSWORTH (AIRPORT)', 255.0, 42.583, -100.0, 789.0, 'USA', 'IV'], 256: ['LAUDER', 256.0, -45.03, 169.683, 370.0, 'NZL', 'V'], 257: ['VANSCOY', 257.0, 52.115, -107.165, 510.0, 'CAN', 'IV'], 260: ['TABLE MOUNTAIN (CA)', 260.0, 34.4, -117.7, 2286.0, 'USA', 'IV'], 262: ['SODANKYLA', 262.0, 67.335, 26.505, 179.0, 'FIN', 'VI'], 265: ['IRENE', 265.0, -25.91, 28.211, 1524.0, 'ZAF', 'I'], 280: ['NOVOLASAREVSKAYA / FORSTER', 280.0, -70.767, 11.867, 110.0, 'ATA', 'ANTARCTICA'], 297: ['S.PIETRO CAPOFIUME', 297.0, 44.65, 11.617, 11.0, 'ITA', 'VI'], 303: ['IQALUIT', 303.0, 63.75, -68.55, 20.0, 'CAN', 'IV'], 308: ['MADRID / BARAJAS', 308.0, 40.46, -3.65, 650.0, 'ESP', 'VI'], 315: ['EUREKA / EUREKA LAB', 315.0, 80.04, -86.175, 310.0, 'CAN', 'IV'], 316: ['DE BILT', 316.0, 52.1, 5.18, 4.0, 'NLD', 'VI'], 318: ['VALENTIA OBSERVATORY', 318.0, 51.93, -10.25, 14.0, 'IRL', 'VI'], 323: ['NEUMAYER', 323.0, -70.65, -8.25, 42.0, 'ATA', 'ANTARCTICA'], 328: ['ASCENSION ISLAND', 328.0, -7.98, -14.42, 91.0, 'SHN', 'I'], 329: ['BRAZZAVILLE', 329.0, -4.28, 15.25, 314.0, 'COG', 'I'], 330: ['HANOI', 330.0, 21.033, 105.84, 5.0, 'VNM', 'II'], 333: ['PORTO NACIONAL', 333.0, -10.8, -48.4, 240.0, 'BRA', 'III'], 334: ['CUIABA', 334.0, -15.6, -56.1, 990.0, 'BRA', 'III'], 335: ['ETOSHA PAN', 335.0, -19.2, 15.9, 1100.0, 'NAM', 'I'], 336: ['ISFAHAN', 336.0, 32.477, 51.425, 1550.0, 'IRN', 'II'], 338: ['BRATTS LAKE (REGINA)', 338.0, 50.205, -104.705, 592.0, 'CAN', 'IV'], 339: ['USHUAIA', 339.0, -54.85, -68.308, 15.0, 'ARG', 'III'], 344: ['HONG KONG OBSERVATORY', 344.0, 22.31, 114.17, 66.0, 'HKG', 'II'], 348: ['ANKARA', 348.0, 39.95, 32.883, 896.0, 'TUR', 'VI'], 360: ['PELLSTON (MI)', 360.0, 45.56, -84.67, 238.0, 'USA', 'IV'], 361: ['HOLTVILLE (CA)', 361.0, 32.81, -115.42, -18.0, 'USA', 'IV'], 394: ['BROADMEADOWS', 394.0, -37.6914, 144.9467, 108.0, 'AUS', 'V'], 400: ['MAITRI', 400.0, -70.46, 11.45, 223.5, 'ATA', 'ANTARCTICA'], 401: ['SANTA CRUZ', 401.0, 28.42, -16.26, 36.0, 'ESP', 'I'], 404: ['JOKIOINEN', 404.0, 60.81, 23.5, 103.0, 'FIN', 'VI'], 406: ['SCORESBYSUND', 406.0, 70.49, -21.98, 50.0, 'GRL', 'VI'], 418: ['HUNTSVILLE', 418.0, 34.72, -86.64, 196.0, 'USA', 'IV'], 420: ['BELTSVILLE (MD)', 420.0, 39.02, -76.74, 64.0, 'USA', 'IV'], 432: ['PAPEETE (TAHITI)', 432.0, -18.0, -149.0, 2.0, 'PYF', 'V'], 434: ['SAN CRISTOBAL', 434.0, -0.92, -89.6, 8.0, 'ECU', 'III'], 435: ['PARAMARIBO', 435.0, 5.81, -55.21, 22.5, 'SUR', 'III'], 436: ['LA REUNION ISLAND', 436.0, -20.99, 55.48, 61.5, 'REU', 'I'], 437: ['WATUKOSEK (JAVA)', 437.0, -7.57, 112.65, 50.0, 'IDN', 'V'], 438: ['SUVA (FIJI)', 438.0, -18.13, 178.315, 6.0, 'FJI', 'V'], 439: ['KAASHIDHOO', 439.0, 5.0, 73.5, 1.0, 'MDV', 'V'], 441: ['EASTER ISLAND', 441.0, -27.17, -109.42, 62.0, 'CHL', 'III'], 443: ['SEPANG AIRPORT', 443.0, 2.73, 101.7, 17.0, 'MYS', 'V'], 444: ['CHEJU', 444.0, 33.5, 126.5, 300.0, 'KOR', 'II'], 445: ['TRINIDAD HEAD', 445.0, 40.8, -124.16, 55.0, 'USA', 'IV'], 448: ['MALINDI', 448.0, -2.99, 40.19, -6.0, 'KEN', 'I'], 450: ['DAVIS', 450.0, -68.577, 77.973, 16.0, 'ATA', 'ANTARCTICA'], 456: ['EGBERT', 456.0, 44.23, -79.78, 253.0, 'CAN', 'IV'], 457: ['KELOWNA', 457.0, 49.93, -119.4, 456.0, 'CAN', 'IV'], 458: ['YARMOUTH', 458.0, 43.87, -66.1, 9.0, 'CAN', 'IV'], 459: ['TBD', 459.0, 0.0, 0.0, 0.0, '', 'VI'], 460: ['THULE', 460.0, 76.53, -68.74, 57.0, 'GRL', 'VI'], 466: ['MAXARANGUAPE (SHADOZ-NATAL)', 466.0, -5.445, -35.33, 32.0, 'BRA', 'III'], 472: ['COTONOU', 472.0, 6.21, 2.23, 10.0, 'BEN', 'I'], 477: ['HEREDIA', 477.0, 10.0, -84.11, 1176.0, 'CRI', 'IV'], 480: ['SABLE ISLAND', 480.0, 43.93, -60.02, 4.0, 'CAN', 'IV'], 482: ['WALSINGHAM', 482.0, 42.6, -80.6, 200.0, 'CAN', 'IV'], 483: ['BARBADOS', 483.0, 13.16, -59.43, 32.0, 'BRB', 'III'], 484: ['HOUSTON (TX)', 484.0, 29.72, -95.4, 19.0, 'USA', 'IV'], 485: ['TECAMEC (UNAM)', 485.0, 19.33, -99.18, 2272.0, 'MEX', 'IV'], 487: ['NARRAGANSETT', 487.0, 41.49, -71.42, 21.0, 'USA', 'IV'], 488: ['PARADOX', 488.0, 43.92, -73.64, 284.0, 'USA', 'IV'], 489: ['RICHLAND', 489.0, 46.2, -119.16, 123.0, 'USA', 'IV'], 490: ['VALPARAISO (IN)', 490.0, 41.5, -87.0, 240.0, 'USA', 'IV'], 494: ['ALAJUELA', 494.0, 9.98, -84.21, 899.0, 'CRI', 'IV']
}
return sonde_dict
# ----
# 7.08 - returns (lat, lon, alt (press), timezone (UTC) ) for a given site
# ----
def gaw_2_loc(site, f = 'GLOBAL_SURFACE_O3_2006_2012.nc'):#, f
""" Extract GAW site locations for a given site
Another file is availible with just GAW sites:
'GAW_SURFACE_O3_2006_2012.nc'
"""
from AC_tools.funcs4generic import hPa_to_Km
# Use simple dictionary if site listed
try:
gaw_sites= {
'SMO': (-14.247, -170.565,1002.7885270480558, -11), 'MNM':(24.285, 153.981, 1011.9342452324959, 9), 'BMW':(32.27, -64.88, 1008.6109830510485, -4 ) ,'CVO': (16.848, -24.871, 1011.6679817831093, -1), 'RPB':(13.17000, -59.43000, 1007.0196960034474, -4 ), 'ogasawara': (26.38, 142.10,996.08181619552602, 9 ), 'OGA': (26.38, 142.10,996.08181619552602, 9 ) ,
# Add extras for ease of analysis (e.g. Roscoff ... )
'ROS': (48.433, -3.5904, 1011.6679817831093, +1)
}
return gaw_sites[ site ]
# If not in list then extract details from NetCDF
except:
wd= get_dir('dwd') +'ozonesurface/'
with Dataset(wd+f, 'r', format='NETCDF4') as f:
lon= f.groups[site].longitude
alt = f.groups[site].altitude /1E3
lat = f.groups[site].latitude
print [ (i, type(i) ) for i in lat, lon, alt ]
return (lat, lon, float( hPa_to_Km([alt], reverse=True)[0] ), -9999 )
rm platform import where not needed
# =================================================
# --------- tms - module of Variables for re-use----------------
# --------------
# Section 0 - Required modules
# Section 1 - Planeflight variables
# Section 2 - Drivers functions
# Section 3 - GeosChem (bpch) prod loss variables
# Section 4 - GeosChem (bpch) general variables
# Section 5 - Misc
# Section 6 - Dynamic p/l processing
# Section 7 - Obervational variables
# ---------------------------- ------------- ------------- -------------
# -------------- Contents
# --------------- ------------- ------------- -------------
# ---- Section 0 ----- Required modules
# --------------- ------------- ------------- -------------
# ---- Section 1 ----- Planeflight variables
# 1.01 - PF variable dictionary ***
# 1.02 - TRA_?? to Geos-Chem species name ***
# --------------- ------------- ------------- -------------
# ---- Section 2 ----- Drivers functions
# 2.01 - P/L tag to PD tag ***
# 2.02 - Get P/L dictionary for a given family ***
# 2.03 - Get P/L dictionary for a given species ***
# --------------- ------------- ------------- -------------
# ---- Section 3 ----- GeosChem (bpch) prod loss variables
# 3.01 - Spec to photolysis reaction p/l tag
# 3.02 - Ox family for tag
# --------------- ------------- ------------- -------------
# ---- Section 4 ----- GeosChem (bpch) general variables
# 4.01 - v9-2 species in input.geos from num
# 4.02 - Get Species Mass
# 4.03 - Get Species stoichiometry
# 4.04 - GEOS-Chem/ctm.bpch values (current main dict ) ***
# 4.05 - latex species name
# 4.06 - converts P/L tracer mulitpler to 1
# 4.07 - Returns tracers unit and scale (if requested)
# 4.08 - Store of dirs for earth0, atmosviz1, and tms mac
# 4.09 - Ox in species (redundant now? should adapt species stoich )
# 4.10 - Get Gaw site name from GAW ID
# 4.11 - returns dict of gaw sites
# 4.12 - Return lat, lon, alt for a given resolution
# 4.13 - Get model array dimension for a given resolution
# 4.14 - Convert gamap category/species name to Iris/bpch name
# 4.99 - Reference data, (inc. grid data) from gchem
# --------------- ------------- ------------- -------------
# ---- Section 5 ----- Misc
# 5.01 - dir store (standard directories on different servers )
# 5.02 - Store of constants for use by funcs/progs
# --------------- ------------- ------------- -------------
# ---- Section 6 ----- Dynamic prod/loss dictionary processing ( For GEOS-Chem)
# 6.01 - Make rxn dict of all active reactions ***
# 6.02 - Return all reactions for a given p/l family ***
# 6.03 - Return reaction infomaiton for given tags (e.g. PD... )
# 6.04 - Create an indices list to split reaction by family (e.g. for Ox loss)
# 6.05 - Return tags for a given reaction
# 6.06 - Extract all p/l speacies in a given input.geos
# 6.07 - Extract all active tags from a given smv.log
# 6.08 - Extract all active PDs from a given smv.log
# 6.09 - get all active reaction for a given tag
# 6.10 - get all details for a given tag
# 6.11 - get reaction coeffifecent
# --------------- ------------- ------------- -------------
# ---- Section 7 ----- Observational variables
# 7.01 - IO observation dictionary
# 7.02 - BAE flight ID dictionary
# 7.03 - CAST flight dictionary for CIMS/CIMSII
# 7.04 - Iodocarbon obs. meta data
# 7.05 - Stores locations for use by funcs/progs - LON, LAT, ALT - double up? ( with 5.02 ?? )
# 7.06 - Get Locations of observations (lats, lons, alts ) for given sites
# 7.07 - sonde station variables (list of 432 sondes)
# 7.08 - returns (lat, lon, alt (press), timezone (UTC) ) for a given site
# ------------------ Section 0 -----------------------------------
# -------------- Required modules:
#
#!/usr/bin/python
#
# -- I/O / Low level
import re
#import platform
import pandas as pd
from netCDF4 import Dataset
#import Scientific.IO.NetCDF as S
import sys
import glob
# - Math/Analysis
import numpy as np
# - tms
from AC_tools.funcs4core import *
# ------------------------------------------- Section 1 -------------------------------------------
# -------------- Planeflight variables
#
# --------------
# 1.01 - dictionary of variables used for planeflight_mod.F output
# -------------
def pf_var( input, ver='1.7', ntracers=85, JREAs=[] ):
# planeflight variable lists
metvars = [
'GMAO_TEMP', 'GMAO_ABSH', 'GMAO_SURF', 'GMAO_PSFC', 'GMAO_UWND', 'GMAO_VWND'
]
species = [
'O3', 'NO2', 'NO', 'NO3', 'N2O5', 'HNO4', 'HNO3', 'HNO2', 'PAN', 'PPN',
'PMN', 'R4N2', 'H2O2', 'MP', 'CH2O', 'HO2', 'OH', 'RO2', 'MO2', 'ETO2',
'CO', 'C2H6', 'C3H8', 'PRPE', 'ALK4', 'ACET', 'ALD2', 'MEK', 'RCHO', 'MVK',
'SO2', 'DMS', 'MSA', 'SO4', 'ISOP'
]
all_species_not_TRA = [
'A3O2', 'ATO2', 'B3O2', 'EOH', 'ETO2', 'ETP', 'GLYX', 'HO2', 'IAP', 'INO2', 'INPN', 'ISN1', 'ISNOOA', 'ISNOOB', 'ISNOHOO', 'ISNP', 'KO2', 'MAN2', 'MAO3', 'MAOP', 'MAOPO2', 'MCO3', 'MGLY', 'MO2', 'MRO2', 'MRP', 'OH', 'PO2', 'PP', 'PRN1', 'PRPN', 'R4N1', 'R4O2', 'R4P', 'RA3P', 'RB3P', 'RCO3', 'RIO2', 'ROH', 'RP', 'VRO2', 'VRP', 'LISOPOH', 'ISOPND', 'ISOPNB', 'HC5', 'DIBOO', 'HC5OO', 'DHMOB', 'MOBAOO', 'ISOPNBO2', 'ISOPNDO2', 'ETHLN', 'MACRN', 'MVKN', 'PYAC', 'IEPOXOO', 'ATOOH', 'PMNN', 'MACRNO2', 'PMNO2'
]
OH_reactivity=[
'NO', 'ISOP', 'GMAO_TEMP', 'GMAO_PSFC', 'CO', 'ACET', 'ALD2', 'MEK', 'MVK', 'MACR', 'C3H8', 'CH2O', 'C2H6', 'SO2', 'NO2', 'ISOPNB', 'ISOPND', 'NO3', 'HNO2', 'HNO3', 'OH', 'HO2', 'H2O2', 'MP', 'ATOOH', 'HNO4', 'ALK4', 'ISN1', 'R4N2', 'RCHO', 'ROH', 'PRPE', 'PMN', 'GLYC', 'GLYX', 'MGLY', 'HAC', 'INPN', 'PRPN', 'ETP', 'RA3P', 'RB3P', 'R4P', 'RP', 'PP', 'RIP', 'IEPOX', 'IAP', 'VRP', 'MRP', 'MAOP', 'MAP', 'DMS', 'HBr', 'Br2', 'BrO', 'CHBr3', 'CH2Br2', 'CH3Br', 'HC5', 'ISOPND', 'ISOPNB', 'ISNP', 'MVKN', 'MACRN', 'DHMOB', 'MOBA', 'ETHLN', 'PROPNN'
]
OH_Extras4nic = [
'OH', 'MCO3', 'A3O2', 'PO2', 'R4O2', 'R4O2', 'R4N1', 'ATO2', 'KO2', 'RIO2', 'VRO2', 'MRO2', 'MAN2', 'B3O2', 'INO2', 'ISNOOA', 'ISNOOB', 'ISNOHOO', 'PRN1', 'RCO3', 'MAO3', 'IEPOXOO', 'MAOPO2', 'MAOPO2', 'HC5OO', 'HC5OO', 'ISOPNDO2', 'ISOPNDO2', 'ISOPNBO2', 'ISOPNBO2', 'DIBOO', 'DIBOO', 'MOBAOO', 'MOBAOO', 'H2', 'CH4', 'HCOOH', 'MOH', 'ACTA', 'EOH', 'VRP'
]
# remove inactive species
inactive_spec = ['ACTA', 'CH4', 'EOH', 'H2', 'HCOOH', 'MOH']
[ OH_Extras4nic.pop(ii) for ii in sorted([ OH_Extras4nic.index(i) \
for i in inactive_spec ])[::-1] ]
# Setup list of tracers
if ver == '1.7':
ntracers=85
if ver == '2.0':
ntracers=101
if ver == 'johan_br.v92':
ntracers=87
TRAs = ['TRA_'+ str(i) for i in range(1, ntracers+1) ]
# TRAs = ['TRA_{:0>2}'.format(i) for i in range(1, ntracers+1) ]
# Setup list of reactions ( photolysis and general )
if ver == '1.5':
PHOT_1st, PHOT_last = 455, 533
if ver == '1.6':
PHOT_1st, PHOT_last = 453, 531
if ver == '1.6.1':
PHOT_1st, PHOT_last = 453, 530
if ver == '1.7':
PHOT_1st, PHOT_last = 453, 529
if ver == '2.0':
PHOT_1st, PHOT_last = 413, 614
JREAs = ['REA_'+ str(i) for i in range(PHOT_1st, PHOT_last) ]
REAs_all = ['REA_'+ str(i) for i in range(0, 533) ]
# reduced list for high time and spatial resolution
if any( [ input ==i for i in 'slist_v9_2_NREA_red',
'slist_v9_2_NREA_red_NOy'] ):
TRAs = GC_var('active_I') + ['AERI']
TRAs= [ num2spec( i, ver=ver, invert=True) for i in TRAs ]
TRAs = [ 'TRA_{:0>2}'.format( i) for i in TRAs ]
metvars = [ i for i in metvars if not any( [ (i==ii) \
for ii in 'GMAO_ABSH', 'GMAO_SURF', 'GMAO_PSFC' ] ) ]
species = [ i for i in species if not any( [ (i==ii) for ii in
'R4N2', 'MP', 'CH2O', 'MO2', 'ETO2', 'CO', 'C2H6', 'C3H8', 'PRPE', 'ALK4', 'ACET', 'ALD2', 'MEK', 'RCHO', 'MVK', 'DMS', 'MSA', 'ISOP'
]) ]
if input =='slist_ClearFlo':
TRAs = 'CO', 'ACET', 'ALD2', 'ISOP', 'C2H6', 'C3H8', 'CH2O', \
'MACR', 'HNO2', 'HNO3', 'MVK', 'NO', 'NO2', 'PAN', 'O3',
TRAs= [ num2spec( i, ver=ver, invert=True) for i in TRAs ]
TRAs = [ 'TRA_{:0>2}'.format( i) for i in TRAs ]
# mannually add ethanol
TRAs += [ 'TRA_86']
species = [ 'OH', 'MO2','HO2' ]
if input =='slist_v9_2_NREA_red_NOy':
# THIS IS NOT A GOOD APPROACH, use actual names an tranlate based on verison.
# missing = [ 'TRA_17', 'TRA_60', 'TRA_30', 'TRA_31', 'TRA_50', \
# 'TRA_54', 'TRA_55', 'TRA_57' ]
# use tracer TRA_60, TRA_30, TRA_31, for: 'MMN' , 'NH3' , 'NH4', 'R4N2', 'BrNO2', 'BrNO3','MPN', 'PROPNN',
missing = 'MMN' , 'NH3' , 'NH4', 'R4N2', 'BrNO2', 'BrNO3','MPN', 'PROPNN'
missing = [ num2spec( i, ver=ver, invert=True) for i in missing ]
missing = [ 'TRA_{:0>2}'.format( i) for i in missing ]
species = species + missing
# Construct dictionary
d= {
'species' : species,
'metvars' : metvars,
'REAs_all' : REAs_all,
'JREAs': JREAs,
'TRAs' : TRAs,
'slist' : species +TRAs +JREAs+ metvars ,
'slist_v9_2_NH' : species + TRAs[:66] + metvars ,
'slist_v9_2_NREA' : species + TRAs + metvars ,
'slist_v9_2_NREA_red': species + TRAs + metvars,
'slist_REAs_all' : species + TRAs + REAs_all + metvars,
'slist_REAs_all_OH' : species + TRAs + metvars+OH_reactivity,
'slist_REAs_all_OH_extras' : all_species_not_TRA + TRAs + metvars,
'slist_v9_2_NREA_red_NOy' : species + TRAs + metvars,
'slist_v10_1.7_allspecs': all_species_not_TRA +TRAs+ JREAs +metvars,
'slist_ClearFlo': species + TRAs + metvars
}
# retrieve variable list from dictionary
vars = d[input]
# return unique list
vars = sorted( list( set( vars) ) )
print vars
return vars
# --------------
# 1.02 - Translator for planeflight species to GEOS-Chem species
# -------------
def what_species_am_i(input=None, V_9_2=True, V_9_2_C=False, \
ver='1.7', special_case=None, invert=False, rtn_dict=False, \
debug=False ) :
"""
What GEOS-Chem (GC) Species am i?
takes TRA_## & returns GC ID or other wayround
"""
# select correct naming dictionary
var ={ \
'1.7': 'GCFP_d2TRA_all_1.7',
'1.6': 'GCFP_d2TRA_all_1.6'
}[ver]
# special_case = 'EOH'
# special_case = 'EOH + actual names'
# if all_TRA:
# var ={ \
# '1.7': 'all_TRA_spec_met_1.7_EOH'
# '1.6': 'GCFP_d2TRA_1.6'
# }[ver]
if not isinstance( special_case, type(None) ):
var = {
# 'EOH':'GCFP_d2TRA_all_1.7_EOH',
# 'EOH + actual names':'GCFP_d2TRA_all_1.7_EOH_actual_names'
# 'all_TRA_spec_met_1.7_EOH' : 'TRA_spec_met_all_1.7_EOH' #'
'all_TRA_spec_met_1.7_EOH':'TRA_spec_met_all_1.7_EOH_no_trailing_zeroes'
# TRA_spec_met_all_1'
}[special_case]
# Get dictionary from variable store
d = GC_var( var )
if debug:
print d
if invert:
d = {v: k for k, v in d.items()}
# return dictionary
if rtn_dict:
return d
else:
return d[input]
# ---------------- Section 2 -------------------------------------------
# -------------- Drivers
#
# --------------
# 2.01 - Convert Production/Loss RD IDs for O3 to PD## for input.geos/tracer.dat linked files
# -------------
def PLO3_to_PD(PL, fp=True, wd=None, ver='1.6', res='4x5',debug=False):
""" Converts """
if any( [(ver ==i) for i in '1.3' ,'1.4' ,'1.5' , '1.6', '1.7' ]):
if wd==None:
if debug:
print 'WARNING: Using MUTD wd'
wd = MUTD_runs(ver=ver, res=res, debug=debug)[0]
PDs, vars = p_l_species_input_geos( wd, ver=ver,
rm_multiple_tagged_rxs=True)
# Add other vars for ease of processing
vars += ['PIOx', 'iLOX', 'LIOx', 'iPOX', 'POX', 'LOX', 'LOx', 'L_Iy']
PDs += ['PIOx', 'iLOX', 'LIOx', 'iPOX', 'POX', 'LOX', 'LOx', 'L_Iy']
return dict( zip(vars, PDs))[PL ]
else:
print 'update programme - manual PLdict now obsolete. '
# -------------
# 2.02 - DRIVER - uses functions to build a dictionary for a given family of loss
# -------------
def get_pl_dict( wd, spec='LOX' , rmx2=False, debug=False):
# Get reaction IDs for each rxn. in spec (p/l, e.g. LOX)
nums, rxns, tags, Coe = prod_loss_4_spec( wd, spec, all_clean=True, debug=debug )
# Make a dictionary of coeffiecnts of reaction
Coe_dict = dict(zip(nums, Coe) )
# unpack for mulutple tags of same reactions, then get details
unpacked_tags = [j for k in tags for j in k ]
details = [ get_tag_details( wd, tag ) for tag in unpacked_tags ]
# Kludge - 1 rxn missing from POx tracking? - 999 + "'ISOPND+OH', '+', '=1.0ISOPND'"
[ details.pop(n) for n, i in enumerate( details ) if i[1]==364]
ind = [n for n, i in enumerate( nums ) if i ==354 ]
# Get Coes and overwrite where prog_mod_tms has values
Coes = [ get_rxn_Coe( wd, d[1], unpacked_tags[n], nums=nums, rxns=rxns, tags=tags, Coe=Coe, spec=spec, debug=debug )
for n, d in enumerate( details ) ]
# Remove double ups, which are present due to Loss (LO3_??) and rate tagging (RD??) originally performed separately
if rmx2:
d = [
['RD62', 'LO3_38'], ['RD59', 'LO3_30'], ['RD65', 'LO3_34'], \
['RD93', 'LO3_55'], ['RD92', 'LO3_39'], [ 'RD95', 'LO3_36'], \
['RD67', 'LO3_35']
]
d = [i[0] for i in d ]
ind = [ n for n, i in enumerate(details) if any( [ i[0] == ii \
for ii in d ] ) ]
if debug:
print d, ind, [len(i) for i in details, Coes ] , [ [i[0] \
for i in details][ii] for ii in ind ][::-1]
[ l.pop(i) for i in ind[::-1] for l in details, Coes ]
if debug:
print [len(i) for i in details, Coes ]
# return a dictionary indexed by p/l tracer, with rxn #,
# reaction str and Coe of rxn.
return dict( zip( [i[0] for i in details], [ i[1:] + [ Coes[n] ] for n, i in enumerate( details) ] ) )
# -------------
# 2.03 - Get prod loss reactions for a given family.
# -------------
def prod_loss_4_spec( wd, fam, all_clean=True, debug=False ):
# --- Get Dict of all reactions, Keys = #s
rdict = rxn_dict_from_smvlog(wd)
# --- Get reaction # tracked by p/l diag for spec and coefficient.
rxns = rxns_in_pl(wd, fam)
nums = rxns.keys()
Coe = [ rxn[-1] for rxn in rxns.values() ]
# --- get all details from full reaction dictionary
rxns = [ rdict[i] for i in nums ]
# --- get tags for tracked reactions, state where reactions are un tracked
tags = get_p_l_tags( rxns )
# --- cleaned tags
if all_clean:
tags = [ [re.sub('\+\d.\d', '', i) for i in u ] for u in tags ]
tags = [ [re.sub('\=\d.\d', '', i) for i in u ] for u in tags ]
# -- remove erroneous read/ Kludge on val
# --- Fortran write error leads to combination of species at the
# end of long line
if (debug):
print [ i[:3] for i in nums, rxns, tags, Coe]
print [ len(i) for i in nums, rxns, tags, Coe]
errs = ['LO3_36RD95' , 'ISOPNDPO3_50']
cerrs = [['LO3_36', 'RD95'], ['PO3_50'] ]
for n, e in enumerate( errs ):
try:
ind = [ nn for nn, i in enumerate( tags) if any([ ( e in ii) for ii in i ]) ] [0]
vars = [ i[ind] for i in nums, rxns, tags, Coe]
if (debug):
print 3, [ i[-1] for i in nums, rxns, tags, Coe], vars, [ len(i) for i in nums, rxns, tags, Coe]
[i.pop(ind) for i in nums, rxns, tags, Coe ]
# add the cerrs values on the end
if (debug):
print 4, [ i[-1] for i in nums, rxns, tags, Coe], [ len(i) for i in nums, rxns, tags, Coe]
nums += [ vars[0] ]
rxns += [vars[1] ]
tags += [cerrs[n]]
Coe += [vars[-1] ]
if (debug):
print 6, [ i[-1] for i in nums, rxns, tags, Coe], \
[ len(i) for i in nums, rxns, tags, Coe]
print '->'*30, 'SUCCESS', n, e
except:
print '>'*100, 'FAIL' , n, e
return nums, rxns, tags, Coe
# ------------------------------------------- Section 3 -------------------------------------------
# -------------- GeosChem (bpch) prod loss variables
#
# --------------
# 3.01 - Spec to photolysis reaction p/l tag
# -------------
def spec_phot_2_RD(spec):
d = {
'OIO': 'RD67', 'ICl': 'RD74', 'I2O2': 'RD70', 'I2': 'RD64', \
'CH2ICl': 'RD88', 'HOI': 'RD65', 'CH2IBr': 'RD89', 'INO': 'RD75', \
'IO': 'RD66', 'CH2I2': 'RD72','CH3IT': 'RD71', 'IONO2': 'RD69', \
'IONO': 'RD68', 'IBr': 'RD73' \
}
return d[spec]
# -------------
# 3.02 - Get families for reactions
# -------------
def get_tag_fam( tag ):
"""
dictionary of manually constructed assignment list
- Ox loss familes
- addition for paranox Kludge
(in v9-2 (patched), but removed in v10? )
"""
# Ox family dictionary
fam_d = {
'LO3_18': 'Photolysis', 'LR25': 'Bromine', 'LR21': 'Bromine', 'LO3_38': 'Iodine', 'LO3_63': 'NOy', 'LO3_10': 'HOx', 'LO3_34': 'Iodine', 'LO3_35': 'Iodine', 'LO3_30': 'Iodine', 'LR5': 'Bromine', 'LR6': 'Bromine', 'LO3_61': 'NOy', 'LO3_60': 'NOy', 'LO3_39': 'Iodine', 'LO3_05': 'HOx', 'LO3_07': 'NOy', 'LO3_06': 'HOx', 'LO3_49': 'NOy', 'LO3_62': 'NOy', 'LO3_03': 'HOx', 'LO3_02': 'HOx', 'LO3_67': 'NOy', 'LO3_66': 'NOy', 'LO3_69': 'NOy', 'LO3_42': 'NOy', 'LO3_41': 'NOy', 'LO3_40': 'NOy', 'LO3_47': 'HOx', 'LO3_46': 'NOy', 'LO3_09': 'HOx', 'LO3_44': 'NOy', 'LR37': 'HOx', 'LR36': 'NOy', 'LO3_65': 'NOy', 'LR30': 'Bromine', 'LO3_24': 'Iodine', 'LR10': 'Bromine', 'LR38': 'NOy', 'LO3_68': 'NOy', 'LO3_64': 'NOy', 'LO3_36': 'Iodine', 'LO3_57': 'NOy', 'LO3_72': 'NOy', 'RD98': 'Photolysis', 'LO3_71': 'NOy', 'LO3_58': 'NOy', 'LO3_54': 'Photolysis', 'LO3_55': 'Iodine', 'LO3_56': 'HOx', 'LO3_08': 'HOx', 'LO3_50': 'NOy', 'LO3_51': 'NOy', 'LO3_52': 'NOy', 'LO3_53': 'HOx'
# added
, 'RD63': 'Iodine', 'RD62':'Iodine', 'LO3_38': 'Iodine', 'RD59': 'Iodine', 'LO3_30' : 'Iodine', 'RD65': 'Iodine', 'LO3_34': 'Iodine', 'RD93': 'Iodine', 'LO3_55': 'Iodine', 'RD92': 'Iodine', 'LO3_39': 'Iodine' , 'LO3_36': 'Iodine','RD95': 'Iodine' , 'RD67': 'Iodine', 'LO3_35': 'Iodine'
# , 'RD36': 'Bromine' # Kludge to allow combination reactions
# Kludge - from Chris Holmes (paranox deposition, goes through p/l as Ox losss )
,'LO3_70' : 'Photolysis'
# Extra tags not in list?
# (these reactions are appearing due to lack of inclusion of iodine
# species in Ox family... )
# ,'RD19': 'iodine', 'RD37': 'iodine', 'RD01': 'iodine'
}
# Creigee reaction class/"family" dictionary
# if cregiee:
# fam_d ={
# }
return fam_d[tag]
# ----------------- Section 4 -------------------------------------------
# -------------- GeosChem (bpch) general variables
#
# 4.01 - v9-2 species in input.geos from num
# 4.02 - Get Species Mass
# 4.03 - Get Species stioch
# 4.04 - GEOS-Chem/ctm.bpch values (current main dict )
# 4.05 - latex species name
# 4.06 - converts P/L tracer mulitpler to 1
# 4.07 - Returns tracers unit and scale (if requested)
# 4.08 - Store of dirs for earth0, atmosviz1, and tms mac
# 4.09 - Ox in species (redundant now? should adapt species stoich )
# 4.10 - Get GAW site info (lat, lon, alt (press), timezone (UTC) )
# 4.99 - Reference data, (inc. grid data) from gchem - credit: GK (Gerrit Kuhlmann )
# --------------
# 4.01 - v9-2 species in input.geos from num
# -------------
def num2spec( num=69, rtn_dict=False, invert=False, ver = '1.7' ):
# get dictionary of tracer numbers
d= what_species_am_i( ver=ver, rtn_dict=True, special_case=None )
# slice off just numbers
nums =[ int(i[4:]) for i in d.keys()]
# re-make dictionary
d = dict( zip(nums, d.values() ) )
# inver to give spec for num
if invert:
d = { v: k for k, v in d.items() }
if rtn_dict:
return d
else:
return d[num]
# --------------
# 4.02 - RMM (Mass) (g /mol) for species
# -------------
# C3H5I == C2H5I (this is a vestigle typo, left in to allow for use of older model runs
def species_mass(spec):
d = {
'HIO3': 176.0, 'OCPO': 12.0, 'Br2': 160.0, 'OCPI': 12.0, 'O3': 48.0, 'PAN': 121.0, 'ACET': 12.0, 'RIP': 118.0, 'BrNO3': 142.0, 'Br': 80.0, 'HBr': 81.0, 'HAC': 74.0, 'ALD2': 12.0, 'HNO3': 63.0, 'HNO2': 47.0, 'C2H5I': 168.0, 'HNO4': 79.0, 'OIO': 159.0, 'MAP': 76.0, 'PRPE': 12.0, 'CH2I2': 268.0, 'IONO2': 189.0, 'NIT': 62.0, 'CH3Br': 95.0, 'C3H7I': 170.0, 'C3H8': 12.0, 'DMS': 62.0, 'CH2O': 30.0, 'CH3IT': 142.0, 'NO2': 46.0, 'NO3': 62.0, 'N2O5': 105.0, 'H2O2': 34.0, 'DST4': 29.0, 'DST3': 29.0, 'DST2': 29.0, 'DST1': 29.0, 'MMN': 149.0, 'HOCl': 52.0, 'NITs': 62.0, 'RCHO': 58.0, 'C2H6': 12.0, 'MPN': 93.0, 'INO': 157.0, 'MP': 48.0, 'CH2Br2': 174.0, 'SALC': 31.4, 'NH3': 17.0, 'CH2ICl': 167.0, 'IEPOX': 118.0, 'ClO': 51.0, 'NO': 30.0, 'SALA': 31.4, 'MOBA': 114.0, 'R4N2': 119.0, 'BrCl': 115.0, 'OClO': 67.0, 'PMN': 147.0, 'CO': 28.0, 'BCPI': 12.0, 'ISOP': 12.0, 'BCPO': 12.0, 'MVK': 70.0, 'BrNO2': 126.0, 'IONO': 173.0, 'Cl2': 71.0, 'HOBr': 97.0, 'PROPNN': 109.0, 'Cl': 35.0, 'I2O2': 286.0, 'I2O3': 302.0, 'I2O4': 318.0, 'I2O5': 338.0, 'MEK': 12.0, 'HI': 128.0, 'ISOPN': 147.0, 'SO4s': 96.0, 'I2O': 270.0, 'ALK4': 12.0, 'MSA': 96.0, 'I2': 254.0, 'PPN': 135.0, 'IBr': 207.0, 'MACR': 70.0, 'I': 127.0, 'AERI': 127.0, 'HOI': 144.0, 'BrO': 96.0, 'NH4': 18.0, 'SO2': 64.0, 'SO4': 96.0, 'IO': 143.0, 'CHBr3': 253.0, 'CH2IBr': 221.0, 'ICl': 162.0, 'GLYC': 60.0
# species, not in tracer list
, 'HO2': 33.0, 'OH': 17.0,'CH4':16.0 , 'N':14.0, 'CH3I':142.0, 'CH2OO':46.0, 'S': 32.0,
}
return d[spec]
# --------------
# 4.03 - return the stiochometry of Iodine in species
# --------------
def spec_stoich( spec, IO=False, I=False, NO=False, OH=False, N=False,
C=False ):
# if I: # note - re-write to take stioch species (e.g. OH, I instead of booleans ) - asssume I == True as default
# C3H5I == C2H5I (this is a vestigle typo, left in to allow for use of older model runs
# aerosol cycling specs
# 'LO3_36' : (2.0/3.0) , 'LO3_37' : (2.0/4.0), # aersol loss rxns... 'LO3_37' isn't true loss, as I2O4 is regen. temp
# aerosol loss rxns - corrected stochio for Ox, adjsutment need for I
d = {
'RD11': 1.0, 'RD10': 1.0, 'HIO3': 1.0, 'RD15': 1.0, 'RD62': 2.0, 'RD17': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'LO3_37': 0.5, 'CH2I2': 2.0, 'AERII': 1.0, 'CH2ICl': 1.0, 'PIOx': 1.0, 'C3H7I': 1.0, 'RD73': 1.0, 'RD72': 2.0, 'RD71': 1.0, 'RD70': 1.0, 'C3H5I': 1.0, 'RD57': 1.0, 'CH3IT': 1.0, 'IO': 1.0, 'LO3_38': 1.0, 'RD61': 1.0, 'RD68': 1.0, 'I2': 2.0, 'IONO': 1.0, 'LO3_36': 0.6666666666666666, 'INO': 1.0, 'RD88': 1.0, 'RD89': 1.0, 'LOx': 1.0, 'RD06': 1.0, 'RD07': 1.0, 'RD02': 1.0, 'RD01': 1.0, 'I': 1.0, 'LO3_24': 0.5, 'AERI': 1.0, 'HOI': 1.0, 'RD64': 2.0, 'RD65': 1.0, 'RD66': 1.0, 'RD67': 1.0, 'RD60': 1.0, 'RD47': 1.0, 'C2H5I': 1.0, 'RD63': 1.0, 'RD20': 1.0, 'RD22': 1.0, 'RD24': 1.0, 'RD69': 1.0, 'RD27': 1.0, 'OIO': 1.0, 'CH2IBr': 1.0, 'LIOx': 1.0, 'L_Iy': 1.0, 'ICl': 1.0, 'IBr': 1.0, 'RD95': 2.0, 'I2O2': 2.0, 'I2O3': 2.0, 'I2O4': 2.0, 'I2O5': 2.0, 'HI': 1.0, 'I2O': 2.0, 'RD59': 1.0, 'RD93': 2.0, 'RD92': 1.0, 'IONO2': 1.0, 'RD58': 1.0,
# p/l for: IO, I
'RD15': 1.0, 'RD17': 1.0, 'RD75': 1.0, 'RD72': 2.0, 'RD71': 1.0, 'RD70': 1.0, 'RD56': 1.0, 'RD69': 1.0, 'RD88': 1.0, 'RD89': 1.0, 'RD06': 1.0, 'RD07': 1.0, 'RD08': 1.0, 'RD64': 2.0, 'RD65': 1.0, 'RD67': 1.0, 'RD46': 2.0, 'RD47': 1.0, 'RD20': 1.0, 'RD22': 1.0, 'RD68': 1.0, 'RD25': 1.0, 'RD96': 1.0 ,
'RD11': 1.0, 'RD12': 2.0, 'RD02': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'RD24': 1.0, 'RD09': 1.0, 'RD23': 1.0, 'RD37': 1.0, 'RD97': 1.0,
# kludge for test analysis (HEMCO emissions )
'ACET' : 1.0, 'ISOP': 1.0, 'CH2Br2': 1.0, 'CHBr3':1.0, 'CH3Br':1.0
}
if IO:
d = {
'RD11': 2.0, 'RD10': 1.0, 'RD12': 2.0, 'LO3_36': 1./3., 'RD09': 1.0, 'RD66': 1.0, 'RD23': 1.0, 'RD37': 1.0, 'LO3_24': 1.0/2.0, 'RD56': 1.0, 'RD01': 1.0, 'RD08': 1.0, 'RD46': 2.0, 'RD30': 1.0, 'RD25': 1.0, 'RD27': 1.0, 'RD97':1.0
}
if NO:
d ={
'NO2': 1.0, 'NO3': 1.0, 'N2O5': 2.0, 'NO': 1.0, 'PPN': 1.0, 'R4N2': 1.0, 'BrNO3': 1.0, 'INO': 1.0, 'PAN': 1.0, 'PMN': 1.0, 'HNO3': 1.0, 'HNO2': 1.0, 'NH3': 1.0, 'HNO4': 1.0, 'BrNO2': 1.0, 'IONO': 1.0, 'PROPNN': 1.0, 'NH4': 1.0, 'MPN': 1.0, 'MMN': 1.0, 'ISOPN': 1.0, 'IONO2': 1.0
}
if OH:
d = {
'LO3_18': 2.0, 'LO3_03': 1.0, 'RD95': 1.0, 'PO3_14': 1.0
}
if N:
d= {
'RD10': 1.0, 'LR26': 1.0, 'LR27': 1.0, 'LR20': 1.0, 'RD17': 1.0, 'RD16': 1.0, 'RD19': 1.0, 'RD18': 2.0, 'LR28': 1.0, 'LO3_30': 1.0, 'RD75': 1.0, 'LR7': 1.0, 'LR8': 1.0, 'RD56': 1.0, 'RD24': 1.0, 'LO3_39': 1.0, 'RD25': 1.0, 'RD81': 1.0, 'LR35': 1.0, 'LR18': 1.0, 'LR17': 1.0, 'LR11': 1.0, 'LR39': 1.0, 'RD20': 1.0, 'RD21': 2.0, 'RD22': 1.0, 'RD23': 1.0, 'RD68': 1.0, 'RD69': 1.0
# NOy ( N in 'NOy')
, 'NO2': 1.0, 'NO3': 1.0, 'N2O5': 2.0, 'NO': 1.0, 'PPN': 1.0, 'R4N2': 2.0, 'BrNO3': 1.0, 'INO': 1.0, 'PAN': 1.0, 'PMN': 1.0, 'HNO3': 1.0, 'HNO2': 1.0, 'NH3': 1.0, 'HNO4': 1.0, 'BrNO2': 1.0, 'IONO': 1.0, 'PROPNN': 1.0, 'NH4': 1.0, 'MPN': 1.0, 'MMN': 1.0, 'ISOPN': 1.0, 'IONO2': 1.0
}
if C:
d = {
'ACET': 3.0, 'ALD2': 2.0, 'C2H6': 2.0, 'C3H8': 3.0, 'ISOP': 5.0
}
return d[spec]
# --------------
# 4.04 - GEOS-Chem/ctm.bpch values
# --------------
def GC_var(input_x=None, rtn_dict=False, debug=False):
"""
Note: Most of this dictionary is vestigial.
<= ACTION NEEDED ( remove redundant variables )
f_var = GC flux (EW, NS , UP) variables
Ox = 'Ox', 'POX', 'LOX' + list of drydep species # not inc. 'NO3df', 'HNO4df', 'BrOdf' , 'BrNO2', 'IO', 'IONO', 'OIO',
Ox_p = Ox prod list
Ox-l = Ox loss list
d_dep = dry dep (category, name = species)
w_dep = wet dep ( 3x categories (WETDCV = rain out loss in convective updrafts (kg/s), WETDLS = rainout in large scale precip (kg/s), CV-FLX = Mass change due to cloud convection (kg/s); name = species)
BL_m = UPWARD MASS FLUX FROM BOUNDARY-LAYER MIXING, (category, name = species)
f_strat = strat flux (to tropsosphere) (category, name = species)
"""
if (debug):
print 'GC_var called'
GC_var_dict = {
# Ox budget analysis
'f_var' : ['EW-FLX-$', 'NS-FLX-$', 'UP-FLX-$' ],
# 'r_t' : [ 'Photolysis','HOx','NOy' ,'Bromine', 'Iodine' ],
# 'r_tn' : ['NOy' ,'Photolysis','HOx' ,'Bromine', 'Iodine' ],
'r_t' : [ 'Photolysis','HOx','Bromine', 'Iodine' ],
'r_tn' : ['Photolysis','HOx' ,'Bromine', 'Iodine' ],
'r_tn_lc' : ['photolysis','HOx' ,'bromine', 'iodine' ],
# 'Ox' : ['Ox','POX','LOX','O3df','NO2df', 'PANdf', 'PMNdf', 'PPNdf', 'N2O5df','HNO3df', 'HOBrdf','BrNO3df','HOIdf','IONO2df', 'I2O2df', 'I2O4df','I2O3df'],
# 'Ox1.1' : ['Ox','POX','LOX','O3df','NO2df', 'PANdf', 'PMNdf', 'PPNdf', 'N2O5df','HNO3df', 'HOBrdf','BrNO3df','HOIdf','IONO2df', 'I2O2df'],
# 'Ox_spec' : ['O3', 'NO2', 'NO3', 'PAN', 'PMN', 'PPN', 'HNO4', 'N2O5', 'HNO3', 'BrO', 'HOBr', 'BrNO2', 'BrNO3', 'MPN', 'IO', 'HOI', 'IONO', 'IONO2', 'OIO', 'I2O2', 'I2O4', 'I2O3'],
# 'Ox_spec1.1' : ['O3', 'NO2', 'NO3', 'PAN', 'PMN', 'PPN', 'HNO4', 'N2O5', 'HNO3', 'BrO', 'HOBr', 'BrNO2', 'BrNO3', 'MPN', 'IO', 'HOI', 'IONO', 'IONO2', 'OIO', 'I2O2', 'I2O4'],
# 'Ox_p_1.3' : ['PO3_85', 'PO3_76', 'PO3_62', 'PO3_63', 'RD06', 'PO3_01', 'PO3_77', 'PO3_86', 'PO3_79', 'PO3_14', 'PO3_87', 'PO3_66', 'PO3_15', 'PO3_67', 'PO3_51', 'PO3_88', 'PO3_56', 'PO3_89', 'PO3_90', 'PO3_72', 'PO3_91', 'PO3_02', 'PO3_03', 'PO3_92', 'PO3_64', 'PO3_68', 'LR9', 'PO3_58', 'PO3_57', 'PO3_70', 'PO3_60', 'PO3_65', 'PO3_18', 'PO3_73', 'PO3_19', 'PO3_20', 'PO3_21', 'PO3_22', 'PO3_24', 'PO3_25', 'PO3_26', 'PO3_27', 'PO3_34', 'PO3_35', 'PO3_37', 'PO3_38', 'PO3_39', 'PO3_05', 'PO3_45', 'PO3_69', 'PO3_46', 'PO3_97', 'PO3_47', 'PO3_48', 'PO3_52', 'PO3_53', 'PO3_80', 'PO3_40', 'PO3_54', 'PO3_81', 'PO3_55', 'PO3_74', 'PO3_41', 'PO3_43', 'PO3_93', 'PO3_94', 'PO3_95', 'PO3_96', 'PO3_84', 'PO3_61', 'PO3_83', 'PO3_59', 'PO3_71', 'PO3_98', 'PO3_99', 'PO3100', 'PO3101', 'PO3_75', 'PO3_50'], # 'ISOPND',
# 'Ox_l_fp' : ['LO3_18','RD98','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40',#'LO3_48','LO3_04', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10', 'LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30', 'LO3_39', 'LO3_38','LO3_55' , 'LO3_36', 'RD63'], # Iy route
# 'Ox_l_fp1.1' : ['LO3_18','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40',#'LO3_48','LO3_04', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10','LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30','LO3_39', 'LO3_38','LO3_55' , 'LO3_36', 'RD63'], # Iy route
# 'Ox_l_fp1.3' : ['LO3_18','RD98','LO3_54', # hv
# 'LO3_03', 'LO3_02', 'LO3_08', 'LO3_09', 'LO3_06', 'LO3_05', 'LO3_10', 'LO3_53', 'LO3_47', 'LO3_56','LR37', 'LR38', # HOx (ROx route) # HOx
# 'LO3_52', 'LO3_51','LO3_50', 'LO3_49', 'LO3_46', 'LO3_44', 'LO3_42', 'LO3_41', 'LO3_40','LR36', 'LO3_58', #'LO3_48','LO3_04',,# NOy route
# 'LO3_57', 'LO3_07', 'LO3_60', 'LO3_61', 'LO3_62', 'LO3_63', 'LO3_64', 'LO3_65', 'LO3_66', 'LO3_67', 'LO3_68', 'LO3_69', 'LO3_72','LO3_71', # NOy route
# 'LR25','LR21','LR5','LR6' ,'LR30', 'LR10', 'LO3_24', # Bry route
# 'LO3_34','LO3_24','LO3_35','LO3_30', 'LO3_39', 'LO3_38','LO3_55' , 'LO3_36'], # Iy route
# 'Ox_l_fp_r_' : [(0, 3), (3, 11), (11, 21), (21, 28), (28, None)], #, (-2, -1)]
# 'Ox_l_fp_r_1.1' : [(0, 2), (2, 11), (11, 21), (21, 28), (28, None)], #, (-2, -1)]
# 'Ox_l_fp_r_1.3' : [(0, 3), (3, 14), (14, 40), (40, 47), (47, None)], #, (-2, -1)]
'fams' : ['I2','HOI','IO', 'I', 'HI+OIO+IONO+INO', 'IONO2','IxOy', 'CH3I', 'CH2IX'], # Iy families
'fams_A' : ['I2','HOI','IO', 'I', 'HI+OIO+IONO+INO', 'IONO2','IxOy', 'CH3I', 'CH2IX', 'AERI'], # Iy families
'fam_slice' : [(0, 1), (1, 2), (2, 3), (3,4 ),(4, 8), (8, 9), (9, 12), (12, 13), (13, None)], # slice
'fam_slice_A' : [(0, 1), (1, 2), (2, 3), (3,4 ),(4, 8), (8, 9), (9, 12), (12, 13), (13, 16),(16, None)], # slice
# 'POx_l_fp' : ['PO3_01', 'PO3_03', 'PO3_02', 'PO3_05','PO3_14', 'PO3_15', 'PO3_18', 'PO3_19', 'PO3_20', 'PO3_21', 'PO3_22', 'PO3_24', 'PO3_25', 'PO3_26', 'PO3_27', 'PO3_30', 'PO3_31', 'PO3_32', 'PO3_33', 'PO3_34', 'PO3_35', 'PO3_37', 'PO3_38', 'PO3_39', 'PO3_40', 'PO3_41', 'PO3_43'],
'Ox_key' : ['POX', 'PO3_14', 'PO3_15', 'LOX'],#, 'LO3_18', 'LO3_03', 'LO3_02','LR25', 'LR21', 'LR5','LR6','LO3_34', 'LO3_33','LO3_24', 'LO3_35' ],
'POxLOx' : ['POX', 'LOX'],
'iPOxiLOx' : ['POX', 'LOX', 'iPOX', 'iLOX'],
# Iy/ Iodine budget analysis
'BL_FT_UT' : [(0, 6), (6, 26), (26, 38)] ,
'n_order' :['CH2IX','CH3I', 'I2', 'HOI','IO', 'I', 'IONO2','HI+OIO+IONO+INO','IxOy' ] ,
'n_order_A' :['CH2IX','CH3I', 'I2', 'HOI','IO', 'I', 'IONO2','HI+OIO+IONO+INO','IxOy', 'AERI' ] ,
'I_l' : ['RD01', 'RD02', 'RD16', 'RD19', 'RD24', 'RD27'],
'IO_l' : ['RD09', 'RD10', 'RD11', 'RD12', 'RD23', 'LO3_24', 'RD37', 'RD97', 'RD66'], # LO37 swaped for RD97 as LO37 assigned to loss point of I2O3 uptake
'I_p' : ['RD06','RD07','RD10','RD11','RD47','RD15','RD17','RD20','RD22', 'LO3_24', 'RD64', 'RD65', 'RD66', 'RD67','RD68','RD69', 'RD70', 'RD71','RD72', 'RD73', 'RD88', 'RD89'],
'IO_p' : [ 'RD01', 'RD08', 'RD46', 'RD25', 'RD27','RD56'],
'sOH' : ['LO3_18'],
'd_dep' : ['DRYD-FLX'],
'w_dep' : ['WETDCV-$','WETDLS-$'],
'BL_m' : ['TURBMC-$'],
'f_strat' : ['STRT-FL'],
'p_l' : ['PORL-L=$'],
'Cld_flx' : ['CV-FLX-$'],
'I_Br_O3' : ['IO', 'OIO','HOI','I2','I','CH3IT','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO'],
'IOrg_RIS' : ['CH3IT','CH2ICl','CH2I2', 'CH2IBr', 'I2','HOI','I','IO', 'OIO', 'HI','IONO','IONO2'],
'I_specs' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2', 'I2O3','I2O4''CH3IT','CH2I2','I','INO'] ,
'Iy' : ['I2','HOI','IO', 'OIO', 'HI','INO','IONO', 'IONO2','I2O2', 'I2O3','I2O4','I'],
'Iy1.1' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I2O4','I','INO'],
'IOy' : ['HOI','IO', 'OIO','IONO','IONO2','INO','I2O2','I2O4', 'I2O3'],
'IOy1.1' : ['HOI','IO', 'OIO','IONO','IONO2','INO','I2O2','I2O4'],
'I2Ox' : ['I2O2','I2O4','I2O3'],
'I2Ox' : ['I2O2','I2O4','I2O3'],
'IyOx1.1' : ['I2O2','I2O4'],
'Iy_no_i2o4' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I','INO', 'I2O3'],
'Iy_no_i2o41.1' : ['I2','HOI','IO', 'OIO', 'HI','IONO', 'IONO2','I2O2','I','INO'],
'Phot_s_Iy' : ['CH3IT','CH2ICl','CH2I2', 'CH2IBr'],#['RD89', 'RD88', 'RD71', 'RD72'],
'HOI' : ['HOI'],
'IOx' : ['IO','I',],
'IO' : ['IO'],
'I' : ['I',],
'OIO' : ['OIO'],
'LIOx' : ['LIOx'], # LOx is p/l tracer name, for Loss of IOx
'PIOx' : ['PIOx'], # LOx is p/l tracer name, for Loss of IOx
'iodine_all' : ['I2','HOI','IO', 'I', 'HI', 'OIO', 'INO', 'IONO','IONO2','I2O2', 'I2O4', 'I2O3', 'I2O5', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr', 'C3H7I','C2H5I','ICl', 'I2O', 'IBr', 'HIO3', ],
'iodine_all_A': ['I2','HOI','IO', 'I', 'HI', 'OIO', 'INO', 'IONO','IONO2','I2O2', 'I2O4', 'I2O3', 'I2O5', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr', 'C3H7I','C2H5I','ICl', 'I2O', 'IBr', 'HIO3','AERI' ],
# Misc analysis
'LHOI' : ['RD65', 'RD63', 'RD08'],
'LHOBr' : ['LR25', 'LR30','LR21'],
'LI2' : ['RD64', 'RD06', 'RD22'],
'LCH2I2' : ['RD72' ],
'LCH2Cl' : ['RD88' ] ,
'LCH2Br' : ['RD89' ],
'LCH3IT' : ['RD15' , 'RD71'],
'sHOX' : ['HOI', 'HOBr'],
'HO2_loss' : ['PO3_14','RD09','RD02', 'LR2', 'LR3', 'PO3_46','PO3_02', 'PO3_03', 'PO3_05'],
'CAST_int' : ['IO', 'OIO','HOI','I2','I','HOI','CH3I','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C3H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'OH', 'HO2','NO','NO2'],
'CAST_intn' : ['IO', 'OIO','HOI','I2','I','HOI','CH3IT','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'DMS', 'NO','HNO3','HNO4', 'NO2','NO3' , 'PAN' , 'HNO2', 'N2O5'],
'CAST_int_n' : ['IO', 'OIO','HOI','I2','I','HOI','CH3I','CH2I2','CH2ICl', 'CH2IBr', 'C3H7I','C2H5I', 'BrO', 'Br', 'HOBr','Br2','CH3Br', 'CH2Br2', 'CHBr3', 'O3', 'CO', 'OH', 'HO2','NO','NO2'],
'diurnal_sp' : ['IO','I2', 'CH2I2', 'BrO' ] ,
'obs_comp' : ['CH3IT','CH2I2','CH2ICl','CH2IBr','C2H5I','C3H7I','I2','IO'] ,
'emiss_specs': ['CH3IT','CH2I2','CH2ICl','CH2IBr','I2','HOI'] ,
'w_dep_specs': ['I2' ,'HI' ,'HOI' ,'IONO', 'IONO2','I2O2', 'I2O4', 'I2O3' ,'AERI'], #, 'IBr', 'ICl']
# 'd_dep_specsl' : ['I2', 'HI', 'HOI', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3','AERI'], #, 'IO', 'OIO'] ,
'd_dep_specsl1.1' : ['I2', 'HI', 'HOI', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'AERI'], #, 'IO', 'OIO'] ,
'd_dep_specs': ['I2df', 'HIdf', 'HOIdf', 'IONOdf', 'IONO2df', 'I2O2df', 'I2O4df', 'I2O3df', 'AERIdf',], #, 'IOdf', 'OIOdf'], #
# 'd_dep_specs1.1': ['I2df', 'HIdf', 'HOIdf', 'IONOdf', 'IONO2df', 'I2O2df', 'I2O4df','AERIdf',], #, 'IOdf', 'OIOdf'], #
'I2_het_cyc' : ['RD59','RD92','RD63'], # IONO2, IONO, HOI
'I_het_loss' : [ 'RD58', 'RD62', 'RD93' ,'RD95'], # HI, I2O2, I2O4, I2O3 uptake (prev: 2OIO excuded as I2Ox formaed, IO+OIO included as I2O3 not treated )
#['RD60','RD61','RD62','RD52','RD53','RD54','RD55','RD13'], # RD13 = OIO + OH => HIO3 86 => AERI loss
'NOx' : ['NO', 'NO2' ],
'N_specs' : ['NO', 'NO2', 'PAN', 'HNO3', 'PMN', 'PPN', 'R4N2', 'N2O5', 'HNO4', 'NH3', 'NH4', 'BrNO2', 'BrNO3', 'MPN', 'ISOPN', 'PROPNN', 'MMN', 'NO3', 'HNO2', 'IONO', 'IONO2', 'INO'],
'N_specs_no_I' : ['NO', 'NO2', 'PAN', 'HNO3', 'PMN', 'PPN', 'R4N2', 'N2O5', 'HNO4', 'NH3', 'NH4', 'BrNO2', 'BrNO3', 'MPN', 'ISOPN', 'PROPNN', 'MMN', 'NO3', 'HNO2'],
'I_N_tags' : ['RD10', 'RD23', 'RD19', 'RD16', 'RD22', 'RD56', 'RD24', 'LO3_30', 'RD69', 'RD68', 'RD20', 'RD21', 'RD25', 'LO3_39', 'RD17', 'RD18', 'RD75'],
'Br_N_tags' : ['LR7', 'LR18', 'LR17', 'LR11', 'LR8', 'LR20', 'LR26', 'LR28', 'LR27'],
'inactive_I' : ['BrCl', 'OClO', 'ClO', 'HOCl', 'Cl', 'Cl2', 'I2O5', 'I2O', 'HIO3', 'IBr', 'ICl', 'C2H5I','C3H7I'], # I2O3 now active.
'active_I' : ['I2', 'HOI', 'IO', 'I', 'HI', 'OIO', 'INO', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr'],
'surface_specs' : ['O3', 'NO', 'NO2', 'NO3' ,'N2O5', 'IO', 'IONO2' ],
# Model run title dictionaries
'run_name_dict': {'run': 'Halogens (I+,Br+)', 'Br_2ppt': 'Halogens (I+,Br+) + fixed 2 pptv BrO', 'just_I': 'Just Iodine (I+,Br-)', 'no_hal': 'No Halogens', 'just_Br': 'Just Bromine (I-,Br+)', 'Br_1ppt': 'Halogens (I+,Br+) + fixed 1 pptv BrO', 'obs': 'Observations'} ,
'latex_run_names': {'I2Ox_half': 'I$_{2}$Ox loss ($\\gamma$) /2', 'run': 'Iodine simulation.', 'MacDonald_iodide': 'Ocean iodide', 'Sulfate_up': 'Sulfate Uptake', 'I2Ox_phot_exp': 'I$_{2}$Ox exp. X-sections', 'het_double': 'het. cycle ($\\gamma$) x2', 'I2Ox_phot_x2': 'I$_{2}$Ox X-sections x2', 'no_het': 'no het. cycle ', 'I2Ox_double': 'I$_{2}$Ox loss ($\\gamma$) x2', 'just_I': '(I+,Br-)', 'BrO1pptv': 'MBL BrO 1 pptv', 'het_half': 'het. cycle ($\\gamma$) /2', 'Just_I_org': 'Just org. I', 'no_I2Ox': 'No I$_{2}$Ox Photolysis', 'BrO1pptv_ALL' : 'BrO 1 pptv in Trop.', 'BrO2pptv' : 'MBL BrO 2 pptv',
# adjust from GBC to ACP names
# 'no_hal': '(I-,Br-)', 'Just_Br': '(I-,Br+)',
'no_hal': 'No Halogens', 'Just_Br': 'GEOS-Chem (v9-2)',
# kludge for diurnal plot
'Iodine simulation.':'Iodine simulation.', '(I+,Br+)': 'Iodine simulation.','(I+,Br-)': 'Just Iodine', '(I-,Br+)': 'GEOS-Chem (v9-2)', '(I-,Br-)': 'No Halogens'},
# tracer unit handling
'spec_2_pptv' : ['I2', 'HOI', 'IO', 'OIO', 'HI', 'IONO', 'IONO2', 'I2O2', 'CH3IT', 'CH2I2', 'IBr', 'ICl', 'I', 'HIO3', 'I2O', 'INO', 'I2O3', 'I2O4', 'I2O5', 'AERI', 'Cl2', 'Cl', 'HOCl', 'ClO', 'OClO', 'BrCl', 'CH2ICl', 'CH2IBr', 'C3H7I', 'C2H5I', 'Br2', 'Br', 'BrO', 'HOBr', 'HBr', 'BrNO2', 'BrNO3', 'CHBr3', 'CH2Br2', 'CH3Br','RCHO', 'MVK', 'MACR', 'PMN', 'PPN', 'R4N2', 'DMS', 'SO4s', 'MSA', 'NITs', 'BCPO', 'DST4', 'ISOPN', 'MOBA', 'PROPNN', 'HAC', 'GLYC', 'MMN', 'RIP', 'IEPOX', 'MAP' ,'N2O5','NO3'], # 'HNO4', 'HNO2'],
'spec_2_pptC' : ['PRPE', 'ISOP'],
# global
'spec_2_ppbv': ['NO','DMS', 'RIP', 'IEPOX','BCPO', 'DST4', 'HAC', 'GLYC','MACR', 'ISOP'],
'spec_2_ppbC' : ['ALK4'],
# pf dictionaries
# WARNING - remove non forwards combatible dicts:
# (GCFP_TRA_d ... GCFP_d2TRA ... GCFP_d2TRA_justTRA . etc)
# GCFP_TRA_d is in use by CVO plotters - use what_species_am_i instead!
'GCFP_TRA_d' : {'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_96': 'C2H5I', 'TRA_95': 'C3H7I', 'TRA_94': 'CH2IBr', 'TRA_93': 'CH2ICl', 'TRA_92': 'BrCl', 'TRA_91': 'OClO', 'TRA_90': 'ClO', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'I2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_68': 'HOI', 'TRA_69': 'IO', 'TRA_71': 'HI', 'TRA_70': 'OIO', 'TRA_73': 'IONO2', 'TRA_72': 'IONO', 'TRA_75': 'CH3IT', 'TRA_74': 'I2O2', 'TRA_77': 'IBr', 'TRA_76': 'CH2I2', 'TRA_79': 'I', 'TRA_78': 'ICl', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'TRA_23': 'HNO4', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'HIO3', 'TRA_81': 'I2O', 'TRA_82': 'INO', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'I2O5', 'TRA_86': 'AERI', 'TRA_87': 'Cl2', 'TRA_88': 'Cl', 'TRA_89': 'HOCl','O3':'O3', 'CO':'CO'} ,
# GCFP_d2TRA is in use by IO plotters - use what_species_am_i instead!
'GCFP_d2TRA' : {'HIO3': 'TRA_80', 'OCPO': 'TRA_37', 'PPN': 'TRA_16', 'OCPI': 'TRA_35', 'O3': 'TRA_2', 'PAN': 'TRA_3', 'ACET': 'TRA_9', 'IEPOX': 'TRA_62', 'BrNO3': 'TRA_50', 'Br': 'TRA_45', 'HBr': 'TRA_48', 'HAC': 'TRA_58', 'ALD2': 'TRA_11', 'HNO3': 'TRA_7', 'HNO2': 'TRA_66', 'C2H5I': 'TRA_96', 'HNO4': 'TRA_23', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'TRA_18', 'HI': 'TRA_71', 'CH2I2': 'TRA_76', 'IONO2': 'TRA_73', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'C3H8': 'TRA_19', 'DMS': 'TRA_25', 'CH2O': 'TRA_20', 'CH3IT': 'TRA_75','CH3I': 'TRA_75', 'NO2': 'TRA_64', 'NO3': 'TRA_65', 'N2O5': 'TRA_22', 'CHBr3': 'TRA_51', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'HOCl': 'TRA_89', 'NITs': 'TRA_33', 'RCHO': 'TRA_12', 'C2H6': 'TRA_21', 'MPN': 'TRA_54', 'INO': 'TRA_82', 'MP': 'TRA_24', 'CH2Br2': 'TRA_52', 'SALC': 'TRA_43', 'NH3': 'TRA_30', 'CH2ICl': 'TRA_93', 'RIP': 'TRA_61', 'ClO': 'TRA_90', 'NO': 'TRA_1', 'SALA': 'TRA_42', 'MOBA': 'TRA_56', 'R4N2': 'TRA_17', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'PMN': 'TRA_15', 'CO': 'TRA_4', 'CH2IBr': 'TRA_94', 'ISOP': 'TRA_6', 'BCPO': 'TRA_36', 'MVK': 'TRA_13', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'Cl': 'TRA_88', 'I2O2': 'TRA_74', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'I2O5': 'TRA_85', 'MEK': 'TRA_10', 'MMN': 'TRA_60', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'I2O': 'TRA_81', 'ALK4': 'TRA_5', 'MSA': 'TRA_29', 'I2': 'TRA_67', 'Br2': 'TRA_44', 'IBr': 'TRA_77', 'MACR': 'TRA_14', 'I': 'TRA_79', 'AERI': 'TRA_86', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'NH4': 'TRA_31', 'SO2': 'TRA_26', 'SO4': 'TRA_27', 'IO': 'TRA_69', 'H2O2': 'TRA_8', 'BCPI': 'TRA_34', 'ICl': 'TRA_78', 'GLYC': 'TRA_59','ALK4': 'ALK4', 'MSA': 'MSA', 'MO2': 'MO2', 'C3H8': 'C3H8', 'ISOP': 'ISOP', 'DMS': 'DMS', 'CH2O': 'CH2O', 'O3': 'O3', 'PAN': 'PAN', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'NO': 'NO', 'PPN': 'PPN', 'R4N2': 'R4N2', 'HO2': 'HO2', 'NO2': 'NO2', 'PMN': 'PMN', 'ACET': 'ACET', 'CO': 'CO', 'ALD2': 'ALD2', 'RCHO': 'RCHO', 'HNO3': 'HNO3', 'HNO2': 'HNO2', 'SO2': 'SO2', 'SO4': 'SO4', 'HNO4': 'HNO4', 'C2H6': 'C2H6', 'RO2': 'RO2', 'MVK': 'MVK', 'PRPE': 'PRPE', 'OH': 'OH', 'ETO2': 'ETO2', 'MEK': 'MEK', 'MP': 'MP' , 'GMAO_TEMP':'GMAO_TEMP' },
# 'GCFP_d2TRA_justTRA' : {'HIO3': 'TRA_80', 'OCPO': 'TRA_37', 'PPN': 'TRA_16', 'OCPI': 'TRA_35', 'O3': 'TRA_2', 'PAN': 'TRA_3', 'ACET': 'TRA_9', 'IEPOX': 'TRA_62', 'BrNO3': 'TRA_50', 'Br': 'TRA_45', 'HBr': 'TRA_48', 'HAC': 'TRA_58', 'ALD2': 'TRA_11', 'HNO3': 'TRA_7', 'HNO2': 'TRA_66', 'C2H5I': 'TRA_96', 'HNO4': 'TRA_23', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'TRA_18', 'HI': 'TRA_71', 'CH2I2': 'TRA_76', 'IONO2': 'TRA_73', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'C3H8': 'TRA_19', 'DMS': 'TRA_25', 'CH2O': 'TRA_20', 'CH3IT': 'TRA_75','CH3I': 'TRA_75', 'NO2': 'TRA_64', 'NO3': 'TRA_65', 'N2O5': 'TRA_22', 'CHBr3': 'TRA_51', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'HOCl': 'TRA_89', 'NITs': 'TRA_33', 'RCHO': 'TRA_12', 'C2H6': 'TRA_21', 'MPN': 'TRA_54', 'INO': 'TRA_82', 'MP': 'TRA_24', 'CH2Br2': 'TRA_52', 'SALC': 'TRA_43', 'NH3': 'TRA_30', 'CH2ICl': 'TRA_93', 'RIP': 'TRA_61', 'ClO': 'TRA_90', 'NO': 'TRA_1', 'SALA': 'TRA_42', 'MOBA': 'TRA_56', 'R4N2': 'TRA_17', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'PMN': 'TRA_15', 'CO': 'TRA_4', 'CH2IBr': 'TRA_94', 'ISOP': 'TRA_6', 'BCPO': 'TRA_36', 'MVK': 'TRA_13', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'Cl': 'TRA_88', 'I2O2': 'TRA_74', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'I2O5': 'TRA_85', 'MEK': 'TRA_10', 'MMN': 'TRA_60', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'I2O': 'TRA_81', 'ALK4': 'TRA_5', 'MSA': 'TRA_29', 'I2': 'TRA_67', 'Br2': 'TRA_44', 'IBr': 'TRA_77', 'MACR': 'TRA_14', 'I': 'TRA_79', 'AERI': 'TRA_86', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'NH4': 'TRA_31', 'SO2': 'TRA_26', 'SO4': 'TRA_27', 'IO': 'TRA_69', 'H2O2': 'TRA_8', 'BCPI': 'TRA_34', 'ICl': 'TRA_78', 'GLYC': 'TRA_59','OH': 'OH','HO2': 'HO2',},
'GCFP_d2TRA_all_1.6' :{'HIO3': 'TRA_80', 'TRA_17': 'TRA_17', 'TRA_16': 'TRA_16', 'TRA_15': 'TRA_15', 'TRA_14': 'TRA_14', 'TRA_13': 'TRA_13', 'TRA_12': 'TRA_12', 'TRA_11': 'TRA_11', 'TRA_19': 'TRA_19', 'ACET': 'ACET', 'RIP': 'TRA_61', 'BrNO3': 'TRA_50', 'HAC': 'TRA_58', 'ALD2': 'ALD2', 'HNO3': 'HNO3', 'HNO2': 'HNO2', 'HNO4': 'HNO4', 'OIO': 'TRA_70', 'MAP': 'TRA_63', 'PRPE': 'PRPE', 'TRA_29': 'TRA_29', 'CH2I2': 'TRA_76', 'I2O2': 'TRA_74', 'NIT': 'TRA_32', 'CH3Br': 'TRA_53', 'C3H7I': 'TRA_95', 'MO2': 'MO2', 'C3H8': 'C3H8', 'I2O5': 'TRA_85', 'TRA_71': 'TRA_71', 'TRA_70': 'TRA_70', 'TRA_73': 'TRA_73', 'DMS': 'DMS', 'TRA_75': 'TRA_75', 'TRA_74': 'TRA_74', 'TRA_77': 'TRA_77', 'TRA_76': 'TRA_76', 'CH2O': 'CH2O', 'TRA_78': 'TRA_78', 'CH3IT': 'TRA_75', 'NO2': 'NO2', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'PAN': 'PAN', 'HOCl': 'TRA_89', 'TRA_18': 'TRA_18', 'GMAO_TEMP': 'GMAO_TEMP', 'RCHO': 'RCHO', 'C2H6': 'C2H6', 'INO': 'TRA_82', 'MP': 'MP', 'CH2Br2': 'TRA_52', 'CH2ICl': 'TRA_93', 'TRA_59': 'TRA_59', 'TRA_58': 'TRA_58', 'IEPOX': 'TRA_62', 'TRA_53': 'TRA_53', 'TRA_52': 'TRA_52', 'TRA_51': 'TRA_51', 'TRA_50': 'TRA_50', 'TRA_57': 'TRA_57', 'TRA_56': 'TRA_56', 'TRA_55': 'TRA_55', 'TRA_54': 'TRA_54', 'MOBA': 'TRA_56', 'CH3I': 'TRA_75', 'BrCl': 'TRA_92', 'OClO': 'TRA_91', 'CO': 'CO', 'BCPI': 'TRA_34', 'ISOP': 'ISOP', 'BCPO': 'TRA_36', 'MVK': 'MVK', 'TRA_28': 'TRA_28', 'Cl': 'TRA_88', 'TRA_26': 'TRA_26', 'TRA_27': 'TRA_27', 'TRA_24': 'TRA_24', 'I2O3': 'TRA_83', 'I2O4': 'TRA_84', 'TRA_23': 'TRA_23', 'TRA_20': 'TRA_20', 'TRA_21': 'TRA_21', 'MMN': 'TRA_60', 'I2O': 'TRA_81', 'HBr': 'TRA_48', 'ALK4': 'ALK4', 'I2': 'TRA_67', 'PPN': 'PPN', 'IBr': 'TRA_77', 'I': 'TRA_79', 'AERI': 'TRA_86', 'NH4': 'TRA_31', 'SO2': 'SO2', 'SO4': 'SO4', 'NH3': 'TRA_30', 'TRA_08': 'TRA_08', 'TRA_09': 'TRA_09', 'TRA_01': 'TRA_01', 'TRA_02': 'TRA_02', 'TRA_03': 'TRA_03', 'TRA_04': 'TRA_04', 'TRA_05': 'TRA_05', 'TRA_06': 'TRA_06', 'TRA_07': 'TRA_07', 'OCPI': 'TRA_35', 'OCPO': 'TRA_37', 'Br2': 'TRA_44', 'O3': 'O3', 'Br': 'TRA_45', 'TRA_96': 'TRA_96', 'TRA_95': 'TRA_95', 'TRA_94': 'TRA_94', 'TRA_93': 'TRA_93', 'TRA_92': 'TRA_92', 'TRA_91': 'TRA_91', 'TRA_90': 'TRA_90', 'TRA_62': 'TRA_62', 'TRA_63': 'TRA_63', 'TRA_60': 'TRA_60', 'TRA_61': 'TRA_61', 'TRA_66': 'TRA_66', 'TRA_67': 'TRA_67', 'C2H5I': 'TRA_96', 'TRA_65': 'TRA_65', 'TRA_68': 'TRA_68', 'TRA_69': 'TRA_69', 'OH': 'OH', 'IONO2': 'TRA_73', 'HI': 'TRA_71', 'CHBr3': 'TRA_51', 'TRA_46': 'TRA_46', 'DST4': 'TRA_41', 'DST3': 'TRA_40', 'DST2': 'TRA_39', 'DST1': 'TRA_38', 'NITs': 'TRA_33', 'TRA_48': 'TRA_48', 'TRA_49': 'TRA_49', 'TRA_44': 'TRA_44', 'TRA_45': 'TRA_45', 'RO2': 'RO2', 'TRA_47': 'TRA_47', 'TRA_40': 'TRA_40', 'TRA_41': 'TRA_41', 'TRA_42': 'TRA_42', 'TRA_43': 'TRA_43', 'MPN': 'TRA_54', 'ETO2': 'ETO2', 'IO': 'TRA_69', 'TRA_64': 'TRA_64', 'ClO': 'TRA_90', 'NO': 'NO', 'SALA': 'TRA_42', 'SALC': 'TRA_43', 'R4N2': 'R4N2', 'PMN': 'PMN', 'TRA_25': 'TRA_25', 'CH2IBr': 'TRA_94', 'TRA_22': 'TRA_22', 'BrNO2': 'TRA_49', 'IONO': 'TRA_72', 'Cl2': 'TRA_87', 'HOBr': 'TRA_47', 'PROPNN': 'TRA_57', 'MEK': 'MEK', 'TRA_72': 'TRA_72', 'ISOPN': 'TRA_55', 'SO4s': 'TRA_28', 'TRA_79': 'TRA_79', 'MSA': 'MSA', 'TRA_39': 'TRA_39', 'TRA_38': 'TRA_38', 'GLYC': 'TRA_59', 'TRA_35': 'TRA_35', 'TRA_34': 'TRA_34', 'TRA_37': 'TRA_37', 'TRA_36': 'TRA_36', 'TRA_31': 'TRA_31', 'TRA_30': 'TRA_30', 'TRA_33': 'TRA_33', 'TRA_32': 'TRA_32', 'HO2': 'HO2', 'MACR': 'TRA_14', 'HOI': 'TRA_68', 'BrO': 'TRA_46', 'ICl': 'TRA_78', 'TRA_80': 'TRA_80', 'TRA_81': 'TRA_81', 'TRA_82': 'TRA_82', 'TRA_83': 'TRA_83', 'TRA_84': 'TRA_84', 'TRA_85': 'TRA_85', 'TRA_86': 'TRA_86', 'TRA_87': 'TRA_87', 'TRA_88': 'TRA_88', 'TRA_89': 'TRA_89','GMAO_TEMP': 'GMAO_TEMP', 'GMAO_UWND': 'GMAO_UWND', 'GMAO_VWND': 'GMAO_VWND'},
# 'GCFP_d2TRA_all_1.7' : {'TRA_74': 'ICl', 'TRA_25': 'DMS', 'TRA_68': 'CH2I2', 'TRA_44': 'Br2', 'TRA_70': 'CH2IBr', 'TRA_22': 'N2O5', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_23': 'HNO4', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_21': 'C2H6', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_69': 'CH2ICl', 'TRA_50': 'BrNO3', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_73': 'IBr', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_77': 'HI', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_72': 'I2', 'TRA_59': 'GLYC', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_20': 'CH2O', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_75': 'I', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_58': 'HAC', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_27': 'SO4', 'TRA_78': 'OIO', 'TRA_66': 'HNO2', 'TRA_71': 'HOI', 'TRA_24': 'MP', 'TRA_67': 'CH3IT', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO'},
'GCFP_d2TRA_all_1.7' : {'TRA_25': 'DMS', 'TRA_77': 'HI', 'TRA_76': 'IO', 'TRA_23': 'HNO4', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_79': 'INO', 'TRA_78': 'OIO', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_52': 'CH2Br2', 'TRA_46': 'BrO', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_47': 'HOBr', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_81': 'IONO2', 'TRA_35': 'OCPI', 'TRA_57': 'PROPNN', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_56': 'MOBA', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_84': 'I2O4', 'TRA_54': 'MPN', 'TRA_5': 'ALK4', 'TRA_49': 'BrNO2', 'TRA_32': 'NIT', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_59': 'GLYC', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_17': 'R4N2', 'TRA_28': 'SO4s', 'TRA_16': 'PPN', 'TRA_58': 'HAC', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_29': 'MSA', 'TRA_22': 'N2O5', 'TRA_73': 'IBr', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_80': 'IONO', 'TRA_26': 'SO2', 'TRA_82': 'I2O2', 'TRA_72': 'I2', 'TRA_48': 'HBr', 'TRA_85': 'AERI', 'TRA_34': 'BCPI', 'TRA_75': 'I', 'TRA_53': 'CH3Br', 'TRA_74': 'ICl'},
'GCFP_d2TRA_all_1.7_EOH_actual_names' : {'HNO4': 'HNO4', 'PPN': 'PPN', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'O3': 'O3', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'GMAO_UWND': 'GMAO_UWND', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_65': 'NO3', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'OH': 'OH', 'LAT': 'LAT', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_73': 'IBr', 'TRA_72': 'I2', 'TRA_75': 'I', 'TRA_74': 'ICl', 'TRA_77': 'HI', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_78': 'OIO', 'NO2': 'NO2', 'NO3': 'NO3', 'N2O5': 'N2O5', 'H2O2': 'H2O2', 'GMAO_VWND': 'GMAO_VWND', 'PAN': 'PAN', 'GMAO_TEMP': 'GMAO_TEMP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'NO': 'NO', 'PMN': 'PMN', 'HNO3': 'HNO3', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'TRA_23': 'HNO4', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'RO2': 'RO2', 'LON': 'LON', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'HO2': 'HO2', 'SO2': 'SO2', 'SO4': 'SO4', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'HNO2': 'HNO2', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_86': 'EOH'},
'TRA_spec_met_all_1.7_EOH': {'MAO3': 'MAO3', 'DHMOB': 'DHMOB', 'ETP': 'ETP', 'RCO3': 'RCO3', 'MO2': 'MO2', 'EOH': 'EOH', 'MVKN': 'MVKN', 'R4P': 'R4P', 'ISNP': 'ISNP', 'RB3P': 'RB3P', 'MGLY': 'MGLY', 'MAOPO2': 'MAOPO2', 'RIO2': 'RIO2', 'PMNN': 'PMNN', 'PP': 'PP', 'VRP': 'VRP', 'RP': 'RP', 'MRO2': 'MRO2', 'HC5': 'HC5', 'ATO2': 'ATO2', 'PYAC': 'PYAC', 'R4N1': 'R4N1', 'DIBOO': 'DIBOO', 'LISOPOH': 'LISOPOH', 'HO2': 'HO2', 'ETHLN': 'ETHLN', 'ISNOOB': 'ISNOOB', 'ISNOOA': 'ISNOOA', 'ROH': 'ROH', 'MAN2': 'MAN2', 'B3O2': 'B3O2', 'INPN': 'INPN', 'MACRN': 'MACRN', 'PO2': 'PO2', 'VRO2': 'VRO2', 'MRP': 'MRP', 'PRN1': 'PRN1', 'ISNOHOO': 'ISNOHOO', 'MOBAOO': 'MOBAOO', 'MACRNO2': 'MACRNO2', 'ISOPND': 'ISOPND', 'HC5OO': 'HC5OO', 'ISOPNBO2': 'ISOPNBO2', 'RA3P': 'RA3P', 'ISOPNB': 'ISOPNB', 'ISOPNDO2': 'ISOPNDO2', 'PMNO2': 'PMNO2', 'IAP': 'IAP', 'MCO3': 'MCO3', 'IEPOXOO': 'IEPOXOO', 'MAOP': 'MAOP', 'INO2': 'INO2', 'OH': 'OH', 'PRPN': 'PRPN', 'GLYX': 'GLYX', 'A3O2': 'A3O2', 'ETO2': 'ETO2', 'R4O2': 'R4O2', 'ISN1': 'ISN1', 'KO2': 'KO2', 'ATOOH': 'ATOOH','GMAO_PSFC': 'GMAO_PSFC', 'GMAO_SURF': 'GMAO_SURF', 'GMAO_TEMP': 'GMAO_TEMP', 'GMAO_ABSH': 'GMAO_ABSH', 'GMAO_UWND': 'GMAO_UWND', 'GMAO_VWND': 'GMAO_VWND', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'TRA_1': 'NO', 'TRA_74': 'ICl', 'TRA_25': 'DMS', 'TRA_68': 'CH2I2', 'TRA_44': 'Br2', 'TRA_70': 'CH2IBr', 'TRA_22': 'N2O5', 'TRA_76': 'IO', 'TRA_79': 'INO', 'TRA_23': 'HNO4', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'TRA_51': 'CHBr3', 'TRA_21': 'C2H6', 'TRA_57': 'PROPNN', 'TRA_56': 'MOBA', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'TRA_69': 'CH2ICl', 'TRA_50': 'BrNO3', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'TRA_73': 'IBr', 'TRA_35': 'OCPI', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'TRA_77': 'HI', 'TRA_83': 'I2O3', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'TRA_72': 'I2', 'TRA_59': 'GLYC', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_48': 'HBr', 'TRA_49': 'BrNO2', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_20': 'CH2O', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'TRA_08': 'H2O2', 'TRA_09': 'ACET', 'TRA_75': 'I', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_01': 'NO', 'TRA_02': 'O3', 'TRA_03': 'PAN', 'TRA_04': 'CO', 'TRA_05': 'ALK4', 'TRA_06': 'ISOP', 'TRA_07': 'HNO3', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_58': 'HAC', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'TRA_27': 'SO4', 'TRA_78': 'OIO', 'TRA_66': 'HNO2', 'TRA_71': 'HOI', 'TRA_24': 'MP', 'TRA_67': 'CH3IT' },
'TRA_spec_met_all_1.7_EOH_no_trailing_zeroes': {'EOH': 'EOH', 'TRA_17': 'R4N2', 'TRA_16': 'PPN', 'TRA_15': 'PMN', 'TRA_14': 'MACR', 'TRA_13': 'MVK', 'TRA_12': 'RCHO', 'TRA_11': 'ALD2', 'TRA_10': 'MEK', 'TRA_19': 'C3H8', 'TRA_18': 'PRPE', 'DHMOB': 'DHMOB', 'RP': 'RP', 'GMAO_UWND': 'GMAO_UWND', 'MAN2': 'MAN2', 'B3O2': 'B3O2', 'MRP': 'MRP', 'PRN1': 'PRN1', 'TRA_62': 'IEPOX', 'TRA_63': 'MAP', 'TRA_60': 'MMN', 'TRA_61': 'RIP', 'TRA_66': 'HNO2', 'TRA_67': 'CH3IT', 'TRA_64': 'NO2', 'TRA_65': 'NO3', 'TRA_68': 'CH2I2', 'TRA_69': 'CH2ICl', 'IAP': 'IAP', 'MCO3': 'MCO3', 'GMAO_SURF': 'GMAO_SURF', 'OH': 'OH', 'PRPN': 'PRPN', 'TRA7': 'HNO3', 'TRA6': 'ISOP', 'MAO3': 'MAO3', 'RCO3': 'RCO3', 'MO2': 'MO2', 'MACRNO2': 'MACRNO2', 'TRA_23': 'HNO4', 'TRA_71': 'HOI', 'TRA_70': 'CH2IBr', 'TRA_73': 'IBr', 'TRA_72': 'I2', 'TRA_75': 'I', 'TRA_74': 'ICl', 'ISNP': 'ISNP', 'TRA_76': 'IO', 'TRA_79': 'INO', 'RB3P': 'RB3P', 'TRA_51': 'CHBr3', 'ROH': 'ROH', 'PP': 'PP', 'ISOPNDO2': 'ISOPNDO2', 'HC5': 'HC5', 'TRA9': 'ACET', 'TRA_56': 'MOBA', 'MACRN': 'MACRN', 'DIBOO': 'DIBOO', 'MRO2': 'MRO2', 'INPN': 'INPN', 'GMAO_TEMP': 'GMAO_TEMP', 'PO2': 'PO2', 'ISOPND': 'ISOPND', 'TRA_48': 'HBr', 'TRA_1': 'NO', 'RA3P': 'RA3P', 'ISOPNB': 'ISOPNB', 'TRA_44': 'Br2', 'TRA_45': 'Br', 'TRA_46': 'BrO', 'TRA_47': 'HOBr', 'TRA_40': 'DST3', 'TRA_41': 'DST4', 'TRA_42': 'SALA', 'TRA_43': 'SALC', 'IEPOXOO': 'IEPOXOO', 'MAOP': 'MAOP', 'INO2': 'INO2', 'ETO2': 'ETO2', 'ISN1': 'ISN1', 'TRA_49': 'BrNO2', 'ETP': 'ETP', 'TRA5': 'ALK4', 'TRA4': 'CO', 'TRA_59': 'GLYC', 'TRA_58': 'HAC', 'TRA1': 'NO', 'R4P': 'R4P', 'TRA_3': 'PAN', 'TRA2': 'O3', 'TRA_53': 'CH3Br', 'TRA_52': 'CH2Br2', 'MAOPO2': 'MAOPO2', 'TRA_50': 'BrNO3', 'TRA_57': 'PROPNN', 'GMAO_VWND': 'GMAO_VWND', 'TRA_55': 'ISOPN', 'TRA_54': 'MPN', 'RIO2': 'RIO2', 'VRP': 'VRP', 'R4N1': 'R4N1', 'GMAO_PSFC': 'GMAO_PSFC', 'VRO2': 'VRO2', 'ISNOHOO': 'ISNOHOO', 'HC5OO': 'HC5OO', 'ETHLN': 'ETHLN', 'TRA_28': 'SO4s', 'TRA_29': 'MSA', 'TRA_26': 'SO2', 'TRA_27': 'SO4', 'TRA_24': 'MP', 'TRA_25': 'DMS', 'TRA_22': 'N2O5', 'R4O2': 'R4O2', 'TRA_20': 'CH2O', 'TRA_21': 'C2H6', 'TRA_77': 'HI', 'MVKN': 'MVKN', 'TRA_35': 'OCPI', 'TRA_78': 'OIO', 'MGLY': 'MGLY', 'PMNN': 'PMNN', 'TRA_39': 'DST2', 'TRA_38': 'DST1', 'ATO2': 'ATO2', 'TRA_34': 'BCPI', 'TRA_37': 'OCPO', 'TRA_36': 'BCPO', 'TRA_31': 'NH4', 'TRA_30': 'NH3', 'TRA_33': 'NITs', 'TRA_32': 'NIT', 'LISOPOH': 'LISOPOH', 'HO2': 'HO2', 'GMAO_ABSH': 'GMAO_ABSH', 'TRA8': 'H2O2', 'ISNOOB': 'ISNOOB', 'ISNOOA': 'ISNOOA', 'TRA_9': 'ACET', 'TRA_8': 'H2O2', 'TRA_7': 'HNO3', 'TRA_6': 'ISOP', 'TRA_5': 'ALK4', 'TRA_4': 'CO', 'TRA_3': 'PAN', 'TRA_2': 'O3', 'ISOPNBO2': 'ISOPNBO2', 'MOBAOO': 'MOBAOO', 'PMNO2': 'PMNO2', 'GLYX': 'GLYX', 'A3O2': 'A3O2', 'TRA_80': 'IONO', 'TRA_81': 'IONO2', 'TRA_82': 'I2O2', 'TRA_83': 'I2O3', 'TRA_84': 'I2O4', 'TRA_85': 'AERI', 'PYAC': 'PYAC', 'KO2': 'KO2', 'ATOOH': 'ATOOH', 'PRESS':'PRESS', 'U10M':'U10M'},
'red_specs_f_name': ['O3', 'NO2', 'NO', 'NO3', 'N2O5', 'HNO4', 'HNO3', 'HNO2', 'PAN', 'PPN', 'PMN', 'H2O2', 'HO2', 'OH', 'RO2', 'SO2', 'SO4', 'GMAO_TEMP', 'GMAO_UWND', 'GMAO_VWND', 'I2', 'HOI', 'IO', 'I', 'HI', 'OIO', 'INO', 'IONO', 'IONO2', 'I2O2', 'I2O4', 'I2O3', 'CH3IT', 'CH2I2', 'CH2ICl', 'CH2IBr'],
# Photolysis/Fast-J
'FastJ_lower' : [289.0, 298.25, 307.45, 312.45, 320.3, 345.0, 412.45],
'FastJ_upper' : [298.25, 307.45, 312.45, 320.3, 345.0, 412.45, 850.0],
'FastJ_mids' : [294,303,310,316,333,380,574],
}
if rtn_dict:
return GC_var_dict
else:
return GC_var_dict[input_x]
# --------------
# 4.05 - GEOS-Chem/ctm.bpch values
# --------------
def latex_spec_name(input_x, debug=False):
spec_dict = {
'OIO': 'OIO', 'C3H7I': 'C$_{3}$H$_{7}$I', 'IO': 'IO', 'I': 'I', 'I2': 'I$_{2}$', 'CH2ICl': 'CH$_{2}$ICl', 'HOI': 'HOI', 'CH2IBr': 'CH$_{2}$IBr', 'C2H5I': 'C$_{2}$H$_{5}$I', 'CH2I2': 'CH$_{2}$I$_{2}$', 'CH3IT': 'CH$_{3}$I', 'IONO': 'IONO','HIO3': 'HIO$_{3}$', 'ICl': 'ICl', 'I2O3': 'I$_{2}$O$_{3}$', 'I2O4': 'I$_{2}$O$_{4}$', 'I2O5': 'I$_{2}$O$_{5}$', 'INO': 'INO', 'I2O': 'I$_{2}$O', 'IBr': 'IBr','I2O2': 'I$_{2}$O$_{2}$', 'IONO2': 'IONO$_{2}$', 'HI':'HI', 'BrO':'BrO','Br':'Br','HOBr':'HOBr','Br2':'Br$_{2}$','CH3Br':'CH$_{3}$Br','CH2Br2':'CH$_{2}$Br$_{2}$', 'CHBr3':'CHBr$_{3}$','O3':'O$_{3}$', 'CO':'CO' , 'DMS':'DMS', 'NOx':'NOx', 'NO':'NO', 'NO2':'NO$_{2}$', 'NO3':'NO$_{3}$','HNO3':'HNO$_{3}$', 'HNO4':'HNO$_{4}$','PAN':'PAN', 'HNO2':'HNO$_{2}$', 'N2O5':'N$_{2}$O$_{5}$','ALK4':'>= C4 alkanes','ISOP':'Isoprene' ,'H2O2':'H$_{2}$O$_{2}$','ACET':'CH$_{3}$C(O)CH$_{3}$', 'MEK':'>C3 ketones', 'ALD2':'CH$_{3}$CHO', 'RCHO': 'CH$_{3}$CH$_{2}$CHO', 'MVK':'CH$_{2}$=CHC(O)CH$_{3}$', 'MACR':'Methacrolein', 'PMN':'CH$_{2}$=C(CH$_{3}$)C(O)OONO$_{2}$', 'PPN':'CH$_{3}$CH$_{2}$C(O)OONO$_{2}$', 'R4N2':'>= C4 alkylnitrates','PRPE':'>= C4 alkenes', 'C3H8':'C$_{3}$H$_{8}$','CH2O':'CH$_{2}$O', 'C2H6':'C$_{2}$H$_{6}$', 'MP':'CH$_{3}$OOH', 'SO2':'SO$_{2}$', 'SO4':'SO$_{4}$','SO4s':'SO$_{4}$ on SSA', 'MSA':'CH$_{4}$SO$_{3}$','NH3':'NH$_{3}$', 'NH4': 'NH$_{4}$', 'NIT': 'InOrg N', 'NITs': 'InOrg N on SSA', 'BCPI':'BCPI', 'OCPI':'OCPI', 'BCPO':'BCPO','OCPO':'OCPO', 'DST1':'DST1', 'DST2':'DST2','DST3':'DST3','DST4':'DST4','SALA':'SALA', 'SALC':'SALC', 'HBr':'HBr', 'BrNO2': 'BrNO$_{2}$', 'BrNO3': 'BrNO$_{3}$', 'MPN':'CH$_{3}$ON$_{2}$', 'ISOPN':'ISOPN', 'MOBA':'MOBA', 'PROPNN':'PROPNN', 'HAC':'HAC', 'GLYC':'GLYC', 'MMN':'MMN', 'RIP':'RIP', 'IEPOX':'IEPOX','MAP':'MAP', 'AERI':'Aerosol Iodine' , 'Cl2':'Cl$_{2}$', 'Cl':'Cl','HOCl':'HOCl','ClO':'ClO','OClO':'OClO','BrCl':'BrCl', 'HI+OIO+IONO+INO':'HI+OIO+IONO+INO','CH2IX':'CH$_{2}$IX', 'IxOy':'I$_{2}$O$_{X}$ ($_{X}$=2,3,4)', 'CH3I':'CH$_{3}$I', 'OH':'OH', 'HO2':'HO$_{2}$', 'MO2':'MO$_{2}$', 'RO2':'RO$_{2}$'
,'RD01':r'I + O$_{3}$ $\rightarrow$ IO + O$_{2}$'
# Analysis names
,'iodine_all':'All Iodine', 'Iy': 'I$_{Y}$', 'IOy': 'IO$_{Y}$', 'IyOx': 'I$_{Y}$O$_{X}$', 'IOx': 'IO$_{X}$','iodine_all_A':'All Iodine (Inc. AERI)', 'I2Ox': 'I$_{2}$O$_{X}$' , 'AERI/SO4': 'AERI/SO4', 'EOH':'Ethanol'
, 'PSURF': 'Pressure at the bottom of level', 'GMAO_TEMP' : 'Temperature', 'TSKIN' : 'Temperature at 2m', 'GMAO_UWND':'Zonal Wind', 'GMAO_VWND':'Meridional Wind', 'U10M':'10m Meridional Wind', 'V10M': '10m Zonal Wind', 'CH2OO':'CH$_{2}$OO',
# Family Names
'N_specs' : 'NOy', 'NOy' : 'NO$_Y$',
'N_specs_no_I' : 'NOy exc. iodine',
# typos
'CH2BR2':'CH$_{2}$Br$_{2}$',
}
return spec_dict[input_x]
# --------------
# 4.06 - converts P/L tracer mulitpler to 1
# --------------
def p_l_unity(rxn, debug=False):
p_l_dict = {
'LR24': 1.0, 'LR25': 1.0, 'LR26': 1.0, 'LR27': 1.0, 'LR20': 1.0, 'LR21': 1.0, 'LR22': 1.0, 'LR30': 1.0, 'LR31': 1.0, 'LR23': 1.0, 'LR28': 1.0, 'LR29': 1.0, 'RD09': 1.0, 'PO3_46': 0.25, 'LR3': 1.0, 'LR2': 1.0, 'RD02': 1.0, 'PO3_03': 0.3, 'PO3_14': 1.0, 'PO3_02': 0.15, 'PO3_05': 0.15
}
return p_l_dict[rxn]
# --------------
# 4.07 - Returns tracers unit and scale (if requested)
# --------------
def tra_unit(x, scale=False, adjustment=False, adjust=True, \
global_unit=False, ClearFlo_unit=False, debug=False ):
tra_unit = {
'OCPI': 'ppbv', 'OCPO': 'ppbv', 'PPN': 'ppbv', 'HIO3': 'pptv', 'O3': 'ppbv', 'PAN': 'ppbv', 'ACET': 'ppbC', 'RIP': 'ppbv', 'BrNO3': 'pptv', 'Br': 'pptv', 'HBr': 'pptv', 'HAC': 'ppbv', 'ALD2': 'ppbC', 'HNO3': 'ppbv', 'HNO2': 'ppbv', 'C2H5I': 'pptv', 'HNO4': 'ppbv', 'OIO': 'pptv', 'MAP': 'ppbv', 'PRPE': 'ppbC', 'HI': 'pptv', 'CH2I2': 'pptv', 'IONO2': 'pptv', 'NIT': 'ppbv', 'CH3Br': 'pptv', 'C3H7I': 'pptv', 'C3H8': 'ppbC', 'DMS': 'ppbv', 'CH2O': 'ppbv', 'CH3IT': 'pptv', 'NO2': 'ppbv', 'NO3': 'ppbv', 'N2O5': 'ppbv', 'CHBr3': 'pptv', 'DST4': 'ppbv', 'DST3': 'ppbv', 'DST2': 'ppbv', 'DST1': 'ppbv', 'HOCl': 'ppbv', 'NITs': 'ppbv', 'RCHO': 'ppbv', 'C2H6': 'ppbC', 'MPN': 'ppbv', 'INO': 'pptv', 'MP': 'ppbv', 'CH2Br2': 'pptv', 'SALC': 'ppbv', 'NH3': 'ppbv', 'CH2ICl': 'pptv', 'IEPOX': 'ppbv', 'ClO': 'ppbv', 'NO': 'pptv', 'SALA': 'ppbv', 'MOBA': 'ppbv', 'R4N2': 'ppbv', 'BrCl': 'pptv', 'OClO': 'ppbv', 'PMN': 'ppbv', 'CO': 'ppbv', 'CH2IBr': 'pptv', 'ISOP': 'ppbC', 'BCPO': 'ppbv', 'MVK': 'ppbv', 'BrNO2': 'pptv', 'IONO': 'pptv', 'Cl2': 'ppbv', 'HOBr': 'pptv', 'PROPNN': 'ppbv', 'Cl': 'ppbv', 'I2O2': 'pptv', 'I2O3': 'pptv', 'I2O4': 'pptv', 'I2O5': 'pptv', 'MEK': 'ppbC', 'MMN': 'ppbv', 'ISOPN': 'ppbv', 'SO4s': 'ppbv', 'I2O': 'pptv', 'ALK4': 'ppbC', 'MSA': 'ppbv', 'I2': 'pptv', 'Br2': 'pptv', 'IBr': 'pptv', 'MACR': 'ppbv', 'I': 'pptv', 'AERI': 'pptv', 'HOI': 'pptv', 'BrO': 'pptv', 'NH4': 'ppbv', 'SO2': 'ppbv', 'SO4': 'ppbv', 'IO': 'pptv', 'H2O2': 'ppbv', 'BCPI': 'ppbv', 'ICl': 'pptv', 'GLYC': 'ppbv',
# Extra diagnostics to allow for simplified processing
'CH3I':'pptv', 'Iy':'pptv', 'PSURF': 'hPa', 'OH':'pptv', 'HO2':'pptv',
'MO2': 'pptv', 'NOy':'pptbv','EOH': 'ppbv' , 'CO':'ppbv', 'CH4':'ppbv',
'TSKIN':'K', 'GMAO_TEMP': 'K', 'GMAO_VWND' :'m/s','GMAO_UWND': 'm/s', 'RO2': 'pptv', 'U10M':'m/s','V10M': 'm/s' , 'PRESS': 'hPa', 'CH2OO':'pptv',
}
units = tra_unit[x]
# Adjust to appropriate scale for pf analysis
if adjust:
spec_2_pptv = GC_var('spec_2_pptv')
spec_2_pptC = GC_var('spec_2_pptC')
if ( x in spec_2_pptv ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'pptv' )
units = 'pptv'
if ( x in spec_2_pptC ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'pptC' )
units = 'pptC'
# Over ride adjustments for globally appro. units
if global_unit:
spec_2_ppbv = GC_var('spec_2_ppbv')
spec_2_ppbC = GC_var('spec_2_ppbC')
if ( x in spec_2_ppbv ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'ppbv' )
units = 'ppbv'
if ( x in spec_2_ppbC ):
if (debug):
print 'adjusting {} ({}) to {}'.format(x, units, 'ppbC' )
units = 'ppbC'
if ClearFlo_unit:
if any( [ x == i for i in [ 'NO', 'MACR', 'MVK' ] ] ):
units = 'ppbv'
if any( [ x == i for i in [ 'PAN' ] ] ):
units = 'pptv'
if any( [ x == i for i in [ 'ISOP' ] ] ):
units = 'ppbC'
if scale:
scaleby = get_unit_scaling( units )
if adjustment:
if units == 'K':
units = 'Deg. Celcuis'
adjustby = -273.15
else:
adjustby = 0
if scale and (not adjustment):
return units, scaleby
elif (scale and adjustment):
return units, scaleby, adjustby
else:
return units
# --------------
# 4.08 - Store of directories for servers ( earth0, atmosviz1, and tms MBP)
# -------------
# moved to AC_tools.funcs4core.py
# --------------
# 4.09 - Ox in species
# -------------
def Ox_in_species(in_=None, rxns=False, keys=False):
species_Ox = {
'HOIdf': 1.0, 'OIOdf': 2.0, 'BrNO3df': 2.0, 'HNO3df': 1.0, 'PPNdf': 1.0, 'IOdf': 1.0, 'N2O5df': 3.0, 'IONOdf': 1.0, 'PMNdf': 1.0, 'BrNO2df': 1.0, 'I2O4df': 4, 'MPNdf': 1.0, 'NO3df': 2.0, 'BrOdf': 1.0, 'HOBrdf': 1.0, 'HNO4df': 1.0, 'O3df': 1.0, 'I2O2df': 2.0, 'NO2df': 1.0, 'IONO2df': 2.0, 'PANdf': 1.0, 'OIO': 2.0, 'BrO': 1.0, 'HOBr': 1.0, 'N2O5': 3.0, 'IONO': 1.0, 'MPN': 1.0, 'BrNO2': 1.0, 'I2O2': 2.0, 'I2O4': 4, 'PPN': 1.0, 'HOI': 1.0, 'HNO3': 1.0, 'IONO2': 2.0, 'NO2': 1.0, 'IO': 1.0, 'HNO4': 1.0, 'PMN': 1.0, 'O3': 1.0, 'BrNO3': 2.0, 'PAN': 1.0, 'NO3': 2.0
}
rxn_Ox = {
'LO3_18': 1.0, 'LR25': 1.0, 'RD12': 2.0, 'LR21': 1.0, 'LO3_38': 1.0, 'LO3_10': 1.0, 'LO3_34': 1.0, 'LO3_35': 1.0, 'LO3_33': 1.0, 'LO3_30': 1.0, 'LR5': 2.0, 'LR6': 2.0, 'RD37': 2.0, 'LO3_05': 1.0, 'RD11': 2.0, 'LO3_06': 1.0, 'LO3_49': 1.0, 'LO3_04': 1.0, 'LO3_03': 1.0, 'LO3_02': 1.0, 'LO3_42': 1.0, 'LO3_41': 1.0, 'LO3_40': 1.0, 'LO3_47': 1.0, 'LO3_46': 1.0, 'LO3_09': 1.0, 'LO3_44': 1.0, 'LR30': 1.0, 'LO3_24': 1.0/2.0, 'LO3_21': 1.0, 'RD23': 2.0, 'LO3_54': 2.0, 'LO3_55': 1.0, 'LO3_08': 1.0, 'LO3_50': 1.0, 'LO3_51': 1.0, 'LO3_52': 1.0, 'LO3_53': 1.0, 'LR10': 1.0, 'LO3_36':1.0,
# LO3_24 set to 1 (as 0.5*CoE) even though 2 Ox equivalents are lost, this allows for contribution to bromine and iodine loss to be inclued
# LOX included for processing ease
'LOX':1.0, 'POX':1.0, 'PO3_14': 1.0, 'PO3_15':1.0 , 'RD98': 1.0, 'LO3_39':1.0 , 'RD63': 1.0,
# for prod analysis
'PO3_69' : 1.0/2.0, 'PO3_35': 0.85, 'PO3_03':0.15/0.3, 'PO3_70': 0.4/1.4 , 'PO3_77': 1.0/2.0 , 'RD06':1.0, 'LR9':1.0
}
if (rxns):
return rxn_Ox[ in_ ]
if (keys):
return species_Ox.keys()
else:
return species_Ox[ in_ ]
# ----
# 4.10 - Return dictionary of gaw sites
# ----
def gaw_2_name():
wdf = get_dir('dwd') +'ozonesurface/' + 'gaw_site_list.h5'
df= pd.read_hdf( wdf, 'wp', mode='r' )
names = df.values[:,1]
# alter long name for CVO
ind=[ n for n, i in enumerate(names) if ( i =='Cape Verde Atmospheric Observatory' )]
names[ind[0]] = 'Cape Verde'
return dict( zip( df.index, names ))
# ----
# 4.11 - Returns list of gaw sites in HDF file of O3 surface data
# ----
def get_global_GAW_sites(f='gaw_site_list_global.h5'):
wd= get_dir('dwd') +'ozonesurface/'
df= pd.read_hdf( wd+f, 'wp', mode='r' )
vars = sorted( list(df.index) )
# Kludge: remove those not in "grouped" analysis
# ( Q: why are these sites not present? - A: data control for lomb-scragle)
[ vars.pop( vars.index(i) ) for i in ['ZUG', 'ZSF', 'ZEP', 'WLG', 'USH', 'SDK', 'PYR', 'PUY', 'PAL', 'MKN', 'IZO', 'HPB', 'DMV', 'BKT', 'AMS', 'ALT', 'ABP'] ]
#[ 'AMS', 'MKN', 'IZO' , 'WLG', 'PYR', 'USH', 'ABP', 'ALT'] ]
return vars
# ----
# 4.12 - returns lon/lat/alt for res
# ----
# moved to AC_tools.funcs4core
# --------
# 4.13 - Get CTM (GEOS-Chem) array dimension for a given resolution
# --------
# moved to AC_tools.funcs4core
# --------
# 4.14 - Convert gamap category/species name to Iris/bpch name
# --------
def diagnosticname_gamap2iris( x ):
d={
"IJ-AVG-$": 'IJ_AVG_S',
"BXHGHT-$": 'BXHEIGHT',
"PORL-L=$":'PORL_L_S__',
'DAO-3D-$':'DAO_3D_S__',
'DAO-FLDS' :'DAO_FLDS__',
'DRYD-FLX': 'DRYD_FLX__',
'DRYD-VEL':'DRYD_VEL__',
'CHEM-L=$':'CHEM_L_S__',
'WETDCV-$':'WETDCV_S__',
'WETDLS-$':'WETDLS_S__',
'WD-LSW-$':'WD_LSW_S__',
'WD-LSR-$':'WD_LSR_S__',
'UP-FLX-$':'UP_FLX_S__',
'NS-FLX-$': 'NS_FLX_S__',
'EW-FLX-$':'EW_FLX_S__',
'TURBMC-$': 'TURBMC_S__',
'OD-MAP-$':'OD_MAP_S__',
# 'WD-FRC-$'
'MC-FRC-$': 'MC_FRC_S__',
}
return d[x]
# --------
# 4.14 - Get scaling for a given unit
# --------
def get_unit_scaling( units ):
misc = 'K', 'm/s', 'unitless', 'kg' ,'m', 'm2','kg/m2/s', \
'molec/cm2/s', 'mol/cm3/s', 'kg/s', 'hPa'
if any( [ (units == i) for i in 'pptv', 'pptC' ]):
scaleby = 1E12
elif any( [ (units == i) for i in 'ppbv', 'ppbC' ]):
scaleby = 1E9
elif any( [units ==i for i in misc ] ):
scaleby = 1
else:
print 'WARNING: This unit is not in unit lists: ', units
return scaleby
# ------------------------------------------- Section 5 -------------------------------------------
# -------------- Misc
#
# --------------
# 5.01 - Return MUTD runs - no hal, just Br, just I, and I + Br
# --------------
def MUTD_runs( standard=True, sensitivity=False, titles=False, \
IO_obs=False,no_I2Ox=False, respun=True, \
preindustrial=False, skip3=False, v10v92comp=False, \
nested_EU=False, just_bcase_no_hal=False, just_std=False, \
just_bcase_std=False, ver='1.6', res='4x5', \
debug=False):
""" Dictionary storage for iGEOSChem most up to date (MUTD)
runs.
returns list of directories and titles
"""
if debug:
print standard, sensitivity, titles, IO_obs, preindustrial, skip3,\
nested_EU, just_bcase_std, ver
pwd = get_dir( 'rwd' )
l_dict= GC_var('latex_run_names')
# Get version directories for versions
d= {
'4x5': {
'1.6':'iGEOSChem_1.6_G5/',
# '1.6':'iGEOSChem_1.6.1_G5/',
'1.6.1':'iGEOSChem_1.6.1_G5/',
'1.5': 'iGEOSChem_1.5_G5/' ,
'1.7': 'iGEOSChem_1.7_v10/'},
'2x2.5': {
'1.6':'iGEOSChem_1.6_G5_2x2.5/'
} }[res]
d=d[ver]
if sensitivity:
l = [
'no_hal', 'Just_Br', 'just_I', 'run', 'Just_I_org', 'I2Ox_double',\
'I2Ox_half', 'het_double','het_half', 'no_het', 'Sulfate_up', \
'MacDonald_iodide', 'I2Ox_phot_x2','I2Ox_phot_exp', 'no_I2Ox', \
'BrO2pptv' ]
r = [ d + i for i in l ]
# adjust names to inc. repsin
# if respun:
# r = [ i+'.respun' for i in r ]
if standard and (not any( [preindustrial,sensitivity, v10v92comp]) ):
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
# l = [ 'Just_Br', 'no_I2Ox' ]
elif just_bcase_no_hal:
l = [ 'no_hal', 'run' ]
# l = [ 'no_hal', 'no_I2Ox' ]
elif just_std:
l = [ 'Just_Br' ]
else:
if ver == '1.7':
# l = ['no_hal', 'run' ]
l = ['Just_Br', 'run' ]
else:
l = ['no_hal', 'Just_Br', 'just_I', 'run' ]
# if any( [ (ver ==i) for i in '1.5', '1.6' ] ) :
r = [ d + i for i in l ]
if nested_EU:
d= 'iGEOS_Chem_1.6_G5_NPOINTS/'
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
else:
l = [ 'no_hal', 'run' ]
r = [ d+i for i in l ]
# Setup latex titles list
if titles and (not any( [preindustrial, v10v92comp]) ) and ( standard or \
sensitivity or nested_EU):
l = [ l_dict[i] for i in l ]
if v10v92comp:
if just_bcase_std:
l = [ 'Just_Br', 'run' ]
else:
l = [ 'no_hal', 'run' ]
r= [ 'iGEOSChem_1.7_v10/'+ i for i in l ]+ \
[ 'iGEOS_Chem_1.6_G5/'+ i for i in l ]
l= [ 'GEOS-Chem v10 (no hal)', 'Iodine Sim. (v10)'] + \
[ 'GEOS-Chem v9-2 (no hal)', 'Iodine Sim.']
if IO_obs:
l = 'run_CVO', 'run_GRO', 'run_MAL', 'run_HAL', 'run_TOR_updated.respun'
r = [d+ i for i in l]
if no_I2Ox:
r = [ i+'_no_I2Ox' for i in r ]
if preindustrial:
r = [
'iGEOS_Chem_1.5_G5/no_hal', 'iGEOS_Chem_1.5_G5/run' , \
'iGEOS_Chem_1.5_G5_preindustrial_no_hal/no_hal', \
'iGEOS_Chem_1.5_G5_preindustrial/run'
]
l = [
'(I-,Br-)', '(I+,Br+)' , '(I-,Br-) - 1750', '(I+,Br+) - 1750'
]
if debug:
print [pwd + i for i in r ], l
if debug:
[pwd + i for i in r ]
if skip3:
[ [ i.pop(0) for i in l, r ] for ii in range(3) ]
if titles:
return [pwd + i for i in r ], l
else:
return [pwd + i for i in r ]
# --------------
# 5.02 - Store of constants for use by funcs/progs
# --------------
def constants(input_x, debug=False):
""" Dictionary storing commonly used constants """
con_dict ={
'RMM_air' : ( .78*(2.*14.)+.22*(2.*16.) ) ,
'AVG' : 6.0221413E23,
'mol2DU': 2.69E20
}
return con_dict[input_x]
# ---------------- Section 6 -------------------------------------------
# -------------- Dynamic processing of p/l
#
# -------------
# 6.01 - Extract reactions to form a dictionary of active reactions
# -------------
def rxn_dict_from_smvlog( wd, PHOTOPROCESS=None, ver='1.7', LaTeX=False, debug=False ):
""" build a dictionary reaction of reaction details from smv.log
This can be used as an external call to analyse other prod/loss
reactions through smvgear """
if isinstance( PHOTOPROCESS, type(None) ):
PHOTOPROCESS = {
'1.6' : 457, '1.7' : 467
}[ver]
fn = 'smv2.log'
if debug:
print wd+'/'+fn
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
for row in file_:
row = row.split()
if 'NMBR' in row:
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
rxns = [ i for i in rxns if ( 'NMBR' not in i ) ]
n = [int(rxn[0]) for rxn in rxns ]
rxns = [rxn[1:] for rxn in rxns ]
rdict = dict( zip(n, rxns) )
# --- Process to Latex
if LaTeX:
for rxn in sorted(rdict.keys() ):
# -- Use Latex formatting?
if rxn > PHOTOPROCESS-1:
# xarstr = r' $\xrightarrow{hv}$ '
xarstr = r' + hv $\rightarrow$ '
else:
xarstr = r' + M $\rightarrow$ '
# --- get all print on a per tag basis the coe, rxn str
try:
rxn_str = ''.join( rdict[rxn][4:] )
if LaTeX:
rxn_str = rxn_str.replace('++=', xarstr)
rxn_str = rxn_str.replace('+', ' + ')
rxn_str = rxn_str.replace('+ =', r' $\rightarrow$ ' )
else:
pass
if debug:
print rxn_str
try:
rxn_strs += [ rxn_str ]
rxns += [ rxn ]
except:
rxn_strs = [ rxn_str ]
rxns = [ rxn ]
except:
print '!'*100, 'ERROR HERE: {} {}'.format( rxn, rxn_str )
rdict = dict( zip(rxns, rxn_strs ) )
return rdict
# -------------
# 6.02 - Extract reactions tracked by prod loss diag for a given p/l family
# -------------
def rxns_in_pl( wd, spec='LOX', debug=False ):
""" Extract reactions tracked by p/l family in smvgear """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
# if debug:
if True:
print file_
readrxn = False
for row in file_:
row = row.split()
if all( [ i in row for i in 'Family','coefficient' , 'rxns', spec] ):
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- Check that rxns ahave been found?
if len( rxns ) < 1:
print 'ERROR: No rxns. found for >{}<, correct family?'.format( spec )
sys.exit(0)
# -- remove 'Family'
rxns = [ i for i in rxns if ( 'Family' not in i ) ]
n = [int(rxn[1]) for rxn in rxns ]
rxns = [rxn[2:] for rxn in rxns ]
rdict = dict( zip(n, rxns) )
return rdict
# -------------
# 6.03 - Extract reaction infomation for given tracer tags
# -------------
def rxn4pl( pls, wd='example/example', rdict=None, reduce_size=True, \
debug=False ):
""" Get information on reaction in smvgear from a provide reaction tag """
# --- Get Dict of reaction detail
if debug:
print 'rxn4pl called'
if isinstance(rdict, type(None) ):
# Get Dict of all reactions, Keys = #s
rdict = rxn_dict_from_smvlog(wd)
if debug:
for i in rdict.keys():
if any( [ (s_ in ''.join( rdict[i] ) ) for s_ in pls ] ):
print i, 'yes'
# print rdict#.keys()
# --- Indices for
# reduce dict size
if reduce_size:
keys = [ i for i in rdict.keys() if \
any( [ (s_ in ''.join( rdict[i] ) ) for s_ in pls ]) ]
# re-make dictionary
rdict = dict( zip( keys, [rdict[i] for i in keys] ))
# loop via pl
keys = np.array([ [ pl, [ i for i in rdict.keys() \
if any( [ (pl in ''.join( rdict[i] ) ) ]) ][0] ] for pl in pls ])
# --- Return as reactions referenced by tag
return dict( zip( keys[:,0], [ rdict[int(i)] for i in keys[:,1] ]) )
# -------------
# 6.04 - Construct a list of indicies for each fam from given tags
# -------------
def get_indicies_4_fam( tags, fam=False, IO_BrOx2=False, rtnspecs=False,
NOy_as_HOx=True, debug=False ):
""" Return indicies (in list form) for in a given family """
# assign family
# famsn = [ 'Photolysis','HOx','NOy' ,'Bromine', 'Iodine' ]
famsn = [ 'Photolysis','HOx' ,'Bromine', 'Iodine' ]
fams = []
for tag in tags:
fams.append( get_tag_fam( tag) )
# if rm NOy family (treat as NOx)
if NOy_as_HOx:
fams = [x if (x!='NOy') else 'HOx' for x in fams]
fd = dict(zip(tags, fams) )
ll = []
[ ll.append([]) for i in famsn ]
for n, tag in enumerate( tags ) :
for fn in range(len(famsn)):
if fd[tag] == famsn[fn] :
ll[fn].append( n)
if fam:
# Kludge - to allow for counting Ox loss via IO +BrO 50/50,
# ( add extra loss tag. )
if IO_BrOx2:
ll[famsn.index('Bromine')].append( max([max(i) for i in ll])+1 )
fams = fams +[ 'Bromine' ]
tags = tags + [ 'LO3_24']
if rtnspecs:
return ll, fams, tags
else:
return ll, fams
else:
return ll
# -------------
# 6.05 - Get tags for reactions
# -------------
def get_p_l_tags( rxns, debug=False):
""" get p/l tags for a given smvgear reaction """
# (PD??, RD??, LO3_??, PO3_??, LR??)
for rxn in rxns:
if debug:
print [ i for i in rxn if any( [x in i for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
tags = [i for i in rxn if any( [x in i for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
try:
tagsl.append( tags)
except:
tagsl = [tags]
return tagsl
# -------------
# 6.06 - extract reactions tracked by prod loss diag in input.geos
# -------------
def p_l_species_input_geos( wd, ver='1.7', rm_multiple_tagged_rxs=False ):
"""
Extract prod/loss species (input.geos) and reaction tags (globchem.dat)
"""
# find and open input.geos file
fn = glob.glob(wd+'/*input.geos*')[0]
file_ = open( fn, 'rb' )
# Read in just the prod loss section
strs_in_1st_line = 'Number', 'of', 'P/L', 'families'
section_line_divider = '------------------------+----------'+ \
'--------------------------------------------'
readrxn = False
for row in file_:
row = row.split()
# once at prod/loss section, start added to list
if all( [ i in row for i in strs_in_1st_line ]):
readrxn=True
# if not at end of prod/loss section, add to list
if section_line_divider in row:
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- Only consider 'Family' ( no headers e.g. 'families' )
rxns = [ i for i in rxns if ( 'families' not in i ) ]
rxns = [ [ i.replace(':','') for i in r ] for r in rxns ]
# Kludge, adjust for extra space 12-99
# ( This is no longer required for 1.7 + )
if ver == '1.6':
[i.pop(0) for i in rxns if ('th' not in i[0]) ]
# Extract just PD (input.geos) and vars (globchem.dat vars )
PD = [rxn[4] for rxn in rxns ]
vars = [rxn[5:] for rxn in rxns ]
# remove p/l with muliple values ( start from 12th input) - Kludge?
if rm_multiple_tagged_rxs:
PD, vars = [ i[11:] for i in PD, vars ]
vars = [ i[0] for i in vars ]
return PD, vars
# -------------
# 6.07 - extract all active tags from smv.log
# -------------
def tags_from_smvlog( wd ): #, spec='LOX' ):
""" Get all active p/l tags in smvgear ( from smv2.log ) """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
for row in file_:
row = row.split()
if all( [(i in row) for i in ['NBR', 'NAME', 'MW', 'BKGAS(VMRAT)'] ]):
readrxn=True
if len(row) < 1 :
readrxn=False
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
rxns = [ i for i in rxns if ( 'NBR' not in i ) ]
rxns = [rxn[1] for rxn in rxns ]
# --- only consider tags
return [i for i in rxns if any( [x in i \
for x in 'PD', 'RD', 'PO3','LO3' , 'LR' ]) ]
# -------------
# 6.08 - extract all active PDs from smv.log
# -------------
def PDs_from_smvlog( wd, spec='LOX' ):
""" Get all active PDs tags in smvgear ( from smv2.log ) """
fn = 'smv2.log'
file_ = open( wd+'/'+fn, 'rb' )
readrxn = False
leniency = 0
entries_in_title = ['Families', 'for', 'prod', 'or', 'loss', 'output:']
for row in file_:
row = row.split()
if all( [(i in row) for i in entries_in_title ]):
readrxn=True
leniency = 1
if len(row) < 1 :
if leniency < 0:
readrxn=False
leniency -= 1
if readrxn:
try:
rxns.append( row )
except:
rxns = [ row ]
# -- remove 'NMBR'
exceptions = [ 'SPECIES', '===============================================================================','Families']
rxns = [ i for i in rxns if all( [ (ii not in i) for ii in exceptions ]) ]
rxns = [j for k in rxns for j in k ]
return rxns
# -------------
# 6.09 - Give all reactions tag is active within
# -------------
def rxns4tag( tag, rdict=None, ver='1.7', wd=None ):
""" get a list of all reactions with a given p/l tag """
# --- get reaction dictionary
if rdict == None:
rdict = rxn_dict_from_smvlog( wd, ver=ver )
# --- Caveats - to adapt for long line errors in fortran written output
errs = ['LO3_36']
cerrs = ['RD95']
if any([ (tag == i) for i in errs ] ):
tag = cerrs[ errs.index( tag) ]
# -- loop reactions, if tag in reaction return reaction
rxns = []
for n, rxn in enumerate( rdict.values() ):
# if any( [tag in i for i in rxn]):
if any( [(i.endswith(tag) ) for i in rxn]):
rxns.append( [rdict.keys()[n] ]+ rxn )
return rxns
# -------------
# 6.10 - get details for a given tag
# -------------
def get_tag_details( wd, tag=None, PDs=None, rdict=None, \
PHOTOPROCESS=None, ver='1.7', LaTeX=False, print_details=False, \
debug=False ):
""" Retriveve prod/loss tag details from smv.log
( rxn number + reaction description)"""
# what is the number of the first photolysis reaction?
if isinstance( PHOTOPROCESS, type(None) ):
PHOTOPROCESS = {
'1.6' : 457, '1.7' : 467
}[ver]
# --- get all reactions tags are active in smv.log
if rdict == None:
rdict = rxn_dict_from_smvlog( wd, ver=ver )
trxns = rxns4tag( tag, wd=wd, rdict=rdict )
# --- get all print on a per tag basis the coe, rxn str
try:
rxn_str = ''.join( trxns[0][5:9])
# -- Use Latex formatting?
# if LaTeX:
#
# setup latex arrows and replace existing arrows
# if trxns[0][0] > PHOTOPROCESS-1:
# xarstr = r' $\xrightarrow{hv}$ '
# else:
# xarstr = r' $\xrightarrow{M}$ '
# rxn_str = rxn_str.replace('++=', xarstr).replace('+', ' + ')
# rxn_str = rxn_str.replace('+ =', r' $\rightarrow$ ' )
# else:
# pass
if debug:
print rxn_str
dets = [ tag, trxns[0][0], rxn_str ]
except:
print '!'*100, 'ERROR HERE: {} {}'.format( tag, trxns )
if print_details:
print dets
# --- return a dictionary of all active tagged reactions details by tag : PD, number, rxn str, coeeffiecn
else:
return dets
# -------------
# 6.11 - Takes a reaction number add gives the Ox Coe
# -------------
def get_rxn_Coe(wd, num, tag, nums=None, rxns=None, tags=None, \
Coe=None, spec='LOX', debug=False):
""" Retrieve given reaction coefficient for smvgear (from smv2.log) """
# --- get dictionaries for reactions within
if all( [ (i == None) for i in nums, rxns, tags, Coe ] ):
nums, rxns, tags, Coe = prod_loss_4_spec( wd, spec, all_clean=True )
# Pull reaction coefficient
Coe_dict = dict(zip(nums, Coe) )
Coe = float(Coe_dict[ num ])
if ('P' not in tag ): # consider all change positive - Kludge due to the assignment approach.
Coe = Coe*-1.0
# --- over write Ox in species with prod_mod_tms values
try:
Coe = Ox_in_species(tag, rxns=True)
if debug:
print 'using values from Ox_in_species'
except:
if debug:
print 'using values from smv.log @: {}'.format(wd)
return Coe
# -------------- Section 7 -------------------------------------------
# -------------- Observational Variables
#
# 5.02 - obs sites (e.g. deposition locs, lons, alts )
# 5.04 - Store site locations ( LAT, LON, ALT)
# --------------
# 7.01 - open ocean IO data
# --------------
def IO_obs_data(just_IO=False):
""" Dictionary of open ocean IO observations for automated comparisons
key = ref (e.g. name_year )
values = alt, lon, lat, times, full_name , IO (avg), BrO (avg), CAM-Chem IO, CAM-Chem BrO, group
"""
IO_obs_dict={'Read_2008': [0.0, -24.87, 16.85, 6.0, 'Read, 2008', '1', '2', '1', '2', 'Leeds'], 'Weddel_Sea': [0.03, -50.0, 75.0, 10.0, 'Weddel Sea', '-', '-', '-', '-', '-'], 'Oetjen_2009': [0.0, 73.5, 5.0, 2.0, 'Oetjen, 2009', '2.4', '-', '1', '-', 'Leeds'], 'Jones_2010_II': [0.0, -19.0, 30.0, 7.0, 'Jones, 2010, RHaMBLe II (26-36N)', '-', '-', '-', '-', '-'], 'Leser_2003': [0.0, -24.87, 16.85, 10.0, 'Leser, 2003', '-', '3.6', '-', '0.8', 'Heidelberg'], 'Jones_2010_I': [0.0, -19.0, 20.0, 7.0, 'Jones, 2010, RHaMBLe I (15-25N)', '-', '-', '-', '-', '-'], 'Halley_BAS': [0.03, -26.34, 75.35, 10.0, 'Halley, BAS', '-', '-', '-', '-', '-'], 'Theys_2007': [0.0, -20.9, -55.5, 8.0, 'Theys, 2007', '-', '<0.5', '-', '0.8', 'Belgium'], 'Dix_2013': [9.0, -160.0, 10.0, 1.0, 'Dix, 2013', '0.1', '-', '', '-', 'NCAR'], 'Allan_2000': [0.162, -16.6, 28.4, 6.0, 'Allan, 2000', '1.2', '-', '0.4', '-', 'Leeds'], 'Jones_2010_MAP': [0.0, -10.0, 55.0, 6.0, 'Jones, 2010, MAP', '-', '-', '-', '-', '-'], 'Grobmann_2013': [0.0, 150.0, 15.0, 1.0, 'Grobmann, 2013', '0.72-1.8', '-', '-', '-', 'Heidelberg'], 'Dix_2013_j_comp': [9.0, -160.0, 10.0, 5.0, 'Dix, 2013 (wrong month to allow with jones runs comparisons)', '-', '-', '-', '-', '-'], 'schonart_2009_II': [0.0, -80.0, -20.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Martin_2009': [0.0, -24.87, 16.85, 2.0, 'Martin, 2009', '-', '<3.0', '-', '1.2', 'Heidelberg'], 'schonart_2009_I': [0.0, -90.0, -5.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Yokouchi_2013': [0.0, 120.0, 15.0, 2.0, 'Yokouchi, 2013', '-', '-', '-', '-', '-'], 'Butz_2009': [0.045, -44.4, -2.4, 12.0, 'Butz, 2009', '0.1', '~1.0', '0.02', '0.5', 'Leeds']}
IO_obs_dict_just = {'Read_2008': [0.0, -24.87, 16.85, 6.0, 'Read, 2008', '1', '2', '1', '2', 'Leeds'], 'schonart_2009_II': [0.0, -80.0, -20.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Oetjen_2009': [0.0, 73.5, 5.0, 2.0, 'Oetjen, 2009', '2.4', '-', '1', '-', 'Leeds'], 'Allan_2000': [0.162, -16.6, 28.4, 6.0, 'Allan, 2000', '1.2', '-', '0.4', '-', 'Leeds'], 'Leser_2003': [0.0, -24.87, 16.85, 10.0, 'Leser, 2003', '-', '3.6', '-', '0.8', 'Heidelberg'], 'Theys_2007': [0.0, -20.9, -55.5, 8.0, 'Theys, 2007', '-', '<0.5', '-', '0.8', 'Belgium'], 'Dix_2013': [9.0, -160.0, 10.0, 1.0, 'Dix, 2013', '0.1', '-', '', '-', 'NCAR'], 'Grobmann_2013': [0.0, 135.0, 15.0, 1.0, 'Grobmann, 2013', '1.095', '-', '-', '-', 'Heidelberg'], 'schonart_2009_I': [0.0, -90.0, -5.0, 10.0, 'Schonart, 2009 (satilitte)', '3.3', '-', '1', '-', 'Bremen'], 'Martin_2009': [0.0, -24.87, 16.85, 2.0, 'Martin, 2009', '-', '<3.0', '-', '1.2', 'Heidelberg'], 'Butz_2009': [0.045, -44.4, -2.4, 12.0, 'Butz, 2009', '0.1', '~1.0', '0.02', '0.5', 'Leeds'], 'Mahajan_2010': [0., -100, -10, 4.0, 'Mahajan, 2010', '0.58', '-', '-', '-', 'Leeds'] }
if (just_IO):
return IO_obs_dict_just
else:
return IO_obs_dict
# --------------
# 7.02 - BAE flight ID to date
# -------------
def bae_flight_ID_2_date( f_ID, debug=False):
""" BAE flight flight ID to date for CAST campaign """
if (debug):
print 'bae_flight_ID_2_date called'
f_ID_dict = {'847':'2014-02-18','846':'2014-02-17','845':'2014-02-17', '844':'2014-02-16','843':'2014-02-15','842':'2014-02-17','840':'2014-02-13','839':'2014-02-12','838':'2014-02-05','837':'2014-02-04','836':'2014-02-04','835':'2014-02-03','834':'2014-02-01','833':'2014-02-01','832':'2014-01-30','831':'2014-01-30','830':'2014-01-30','829':'2014-01-28','828':'2014-01-26','827':'2014-01-26','826':'2014-01-25','825':'2014-01-24','824':'2014-01-21','823':'2014-01-18'}
return f_ID_dict[f_ID]
# --------------
# 7.03 - details on flights from FAAM's NETCDF core data files,
# --------------
def CAST_flight(all=True, CIMS=False, CIMSII=False):
""" Callable dictionary of CAST flight details
removed 'CAST_flight, 'b822': '20140108'' """
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '1932', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140121', '1956', '20140122', '0413', 1390334165, 1390363990], 'b825': ['20140124', '1940', '20140125', '0140', 1390592404, 1390614058], 'b826': ['20140125', '0128', '20140125', '0637', 1390613301, 1390631839], 'b827': ['20140126', '0041', '20140126', '0445', 1390696903, 1390711511], 'b823': ['20140118', '1713', '20140119', '0147', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '1901', '20140205', '0318', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0627', 1391477699, 1391495264], 'b835': ['20140203', '2101', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0610', '20140201', '1217', 1391235033, 1391257044], 'b833': ['20140201', '0045', '20140201', '0606', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1131', 1391062943, 1391081485], 'b831': ['20140130', '0101', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0239', '20140129', '0830', 1390963178, 1390984251], 'b846': ['20140217', '2058', '20140218', '0510', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '1857', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0753', 1392607430, 1392623590], 'b842': ['20140214', '0508', '20140214', '1035', 1392354530, 1392374130], 'b843': ['20140215', '1903', '20140216', '0400', 1392490986, 1392523256], 'b840': ['20140213', '0005', '20140213', '0658', 1392249904, 1392274686], 'b841': ['20140214', '0004', '20140214', '0458', 1392336265, 1392353901]}
if (CIMS):
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '2322', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140122', '0155', '20140122', '0411', 1390334165, 1390363990], 'b825': ['20140124', '2230', '20140125', '0034', 1390592404, 1390614058], 'b826': ['20140125', '0128', '20140125', '0637', 1390613301, 1390631839], 'b827': ['20140126', '0041', '20140126', '0445', 1390696903, 1390711511], 'b823': ['20140118', '2006', '20140119', '0055', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '2333', '20140205', '0315', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0608', 1391477699, 1391495264], 'b835': ['20140203', '2316', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0610', '20140201', '1220', 1391235033, 1391257044], 'b833': ['20140201', '0145', '20140201', '0606', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1123', 1391062943, 1391081485], 'b831': ['20140130', '0201', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0249', '20140129', '0825', 1390963178, 1390984251], 'b846': ['20140217', '2058', '20140218', '0510', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '1857', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0750', 1392607430, 1392623590], 'b842': ['20140214', '0611', '20140214', '1011', 1392354530, 1392374130], 'b843': ['20140215', '2227', '20140216', '0324', 1392490986, 1392523256], 'b840': ['20140213', '0304', '20140213', '0648', 1392249904, 1392274686], 'b841': ['20140214', '0004', '20140214', '0456', 1392336265, 1392353901]}
if (CIMSII):
flight_dict={'b828': ['20140126', '0559', '20140126', '0930', 1390715993, 1390728623], 'b829': ['20140128', '2322', '20140129', '0224', 1390937523, 1390962280], 'b824': ['20140122', '0155', '20140122', '0411', 1390334165, 1390363990], 'b825': ['20140124', '2230', '20140125', '0034', 1390592404, 1390614058], 'b826': ['20140125', '0141', '20140125', '0619', 1390613301, 1390631839], 'b827': ['20140126', '0100', '20140126', '0442', 1390696903, 1390711511], 'b823': ['20140118', '2006', '20140119', '0055', 1390065184, 1390096069], 'b839': ['20140212', '0320', '20140212', '0824', 1392175209, 1392193471], 'b838': ['20140205', '2246', '20140206', '0545', 1391640363, 1391665549], 'b837': ['20140204', '2333', '20140205', '0315', 1391540463, 1391570305], 'b836': ['20140204', '0134', '20140204', '0608', 1391477699, 1391495264], 'b835': ['20140203', '2338', '20140204', '0104', 1391461263, 1391475894], 'b834': ['20140201', '0619', '20140201', '1210', 1391235033, 1391257044], 'b833': ['20140201', '0223', '20140201', '0558', 1391215547, 1391234764], 'b832': ['20140130', '0622', '20140130', '1123', 1391062943, 1391081485], 'b831': ['20140130', '0201', '20140130', '0614', 1391043666, 1391062492], 'b830': ['20140129', '0249', '20140129', '0825', 1390963178, 1390984251], 'b846': ['20140218', '0035', '20140218', '0507', 1392670683, 1392700201], 'b847': ['20140218', '0503', '20140218', '0857', 1392699809, 1392713860], 'b844': ['20140216', '2224', '20140217', '0324', 1392577026, 1392607467], 'b845': ['20140217', '0323', '20140217', '0750', 1392607430, 1392623590], 'b842': ['20140214', '0611', '20140214', '1011', 1392354530, 1392374130], 'b843': ['20140215', '2227', '20140216', '0324', 1392490986, 1392523256], 'b840': ['20140213', '0342', '20140213', '0648', 1392249904, 1392274686], 'b841': ['20140214', '0050', '20140214', '0456', 1392336265, 1392353901]}
if (all) :
return flight_dict
# --------------
# 7.04 - Iodocarbon obs. meta data
# --------------
def iodocarbon_obs():
""" dictionary of Iodocarbon observations for automated
analysis/comparions with observations """
Org_obs= {
'CH3IT' : [ \
['Chuck et al (2005)' , list(np.linspace( -36, -49, 3) ) + \
list( np.linspace( -20, 28, 3) ), [-20] *6, [0.71]*3 + [ 1.94]*3 ] ] ,
'CH2ICl' : [ \
['Chuck et al (2005)' , list( np.linspace( -36, -49, 3 ) )+ \
list(np.linspace( -20, 28, 3) ), [-20] *6, [0.23]*3+ [0.32]*3 ],\
['Jones et al (2010)' , np.linspace( 60, 15, 5), [-15] *5, [0.12]*5 ] ],
'CH2I2' : [ \
['Jones et al (2010)' , np.linspace( 60,15, 5), [-15] *5, [0.01]*5 ] ],
'CH2IBr' : [ \
['Jones et al (2010)' , np.linspace( 60, 15, 5), [-15] *5, [0.01]*5 ] ],
'C2H5I' : [ \
['Jones et al (2010)' , np.linspace( 26, 36, 3), [120] *3, [0.09]*3 ] ],
'I2' : [ ['Lawler et al (2014)' , [16.51] , [-24], [0.2] ] ],
}
return Org_obs
# --------------
# 7.05 - Stores locations for use by funcs/progs -
# --------------
def get_loc(loc=None, rtn_dict=False, debug=False):
"""
Dictionary to store locations for automated analysis
Data arranged: LON, LAT, ALT
- double up? ( with 5.02 ?? )
"""
loc_dict ={
'GUAM' : ( 144.800, 13.500, 0 ),
'CHUUK' : ( 151.7833, 7.4167, 0 ),
'PILAU' : ( 134.4667,7.3500, 0 ),
'London': ( -0.1275, 51.5072, 0 ),
'Weyborne' : (1.1380, 52.9420, 0 ),
# 'Cape Verde': (16.848, -24.871, 0 ),
# 'CVO': (16.848, -24.871, 0 ),
'Cape Verde': ( -24.871, 16.848, 0 ),
'CVO': (-24.871,16.848, 0 ),
'North Ken' : (-0.214174, 51.520718, 0),
'KEN' : (-0.214174, 51.520718, 0),
'BT tower': (-0.139055, 51.521556, 190),
'BTT': (-0.139055, 51.521556, 190)
}
if rtn_dict:
return loc_dict
else:
return loc_dict[loc]
# --------------
# 7.06 - Get Locations of observations (lats, lons, alts ) for given sites
# --------------
def get_obs_loc(loc, debug=False):
""" Dictionary to store groups of locations for automated analysis """
d = {
'Denmark' :[
[ 68.35, 59.85, 56.13, 55.69 ], [ 18.81, 17.63, 13.05, 12.10 ] ],
# split by obs site...
'Denmark1': [[68.35], [18.81]],
'Denmark2': [[59.85], [17.63]],
'Denmark3': [[56.13], [13.05]],
'Denmark4': [[55.69], [12.1]] ,
# Berlin, bonn, hamburg, Westerland, Munich, Brotjacklriegel, Deuselbach, Schauinsland
'Germany' :[
[52.5167, 50.7340, 53.5653,54.9100, 52.4231, 48.1333, \
48.491, 49.4508, 47.91111 ] ,
[ 13.3833 , 7.0998, 10.0014, 8.3075, 10.7872, 11.5667, 13.133, 7.302, \
7.8894] ],
'Weyborne' :[ [ 52.9420], [1.1380 ] ] }
return d[loc]
# --------------
# 7.07 - sonde station variables (list of 432 sondes)
# -------------
def sonde_STNs():
""" Dictionary of WOUDC sonde location variables """
sonde_dict = {
101: ['SYOWA', 101.0, -69.0, 39.58, 22.0, 'JPN', 'ANTARCTICA'], 104: ['BEDFORD', 104.0, 42.45, -71.267, 80.0, 'USA', 'IV'], 105: ['FAIRBANKS (COLLEGE)', 105.0, 64.817, -147.867, 138.0, 'USA', 'IV'], 107: ['WALLOPS ISLAND', 107.0, 37.898, -75.483, 13.0, 'USA', 'IV'], 108: ['CANTON ISLAND', 108.0, -2.76, -171.7, 3.0, 'USA', 'V'], 109: ['HILO', 109.0, 19.5735, -155.0485, 11.0, 'USA', 'V'], 111: ['AMUNDSEN-SCOTT (SOUTH POLE)', 111.0, -89.983, 0.0, 2820.0, 'ATA', 'ANTARCTICA'], 131: ['PUERTO MONTT', 131.0, -41.45, -72.833, 5.0, 'CHL', 'III'], 132: ['SOFIA', 132.0, 42.817, 23.383, 588.0, 'BGR', 'VI'], 137: ['TOPEKA', 137.0, 39.067, -95.633, 270.0, 'USA', 'IV'], 138: ['CHRISTCHURCH', 138.0, -43.483, 172.55, 34.0, 'NZL', 'V'], 149: ['OVEJUYO (LA PAZ)', 149.0, -16.517, -68.033, 3420.0, 'BOL', 'III'], 156: ['PAYERNE', 156.0, 46.49, 6.57, 491.0, 'CHE', 'VI'], 157: ['THALWIL', 157.0, 46.817, 8.455, 515.0, 'CHE', 'VI'], 163: ['WILKES', 163.0, -66.25, 110.517, 12.0, 'USA', 'ANTARCTICA'], 174: ['LINDENBERG', 174.0, 52.21, 14.12, 112.0, 'DEU', 'VI'], 175: ['NAIROBI', 175.0, -1.267, 36.8, 1745.0, 'KEN', 'I'], 181: ['BERLIN/TEMPLEHOF', 181.0, 52.467, 13.433, 50.0, 'DEU', 'VI'], 187: ['PUNE', 187.0, 18.553, 73.86, 559.0, 'IND', 'II'], 190: ['NAHA', 190.0, 26.2, 127.683, 27.0, 'JPN', 'II'], 191: ['SAMOA', 191.0, -14.25, -170.56, 82.0, 'ASM', 'V'], 194: ['YORKTON', 194.0, 51.263, -102.467, 504.0, 'CAN', 'IV'], 197: ['BISCARROSSE/SMS', 197.0, 44.367, -1.233, 18.0, 'FRA', 'VI'], 198: ['COLD LAKE', 198.0, 54.783, -110.05, 702.0, 'CAN', 'IV'], 199: ['BARROW', 199.0, 71.317, -156.635, 11.0, 'USA', 'IV'], 203: ['FT. SHERMAN', 203.0, 9.33, -79.983, 57.0, 'PAN', 'IV'], 205: ['THIRUVANANTHAPURAM', 205.0, 8.483, 76.97, 60.0, 'IND', 'II'], 206: ['BOMBAY', 206.0, 19.117, 72.85, 145.0, 'IND', 'II'], 210: ['PALESTINE', 210.0, 31.8, -95.717, 121.0, 'USA', 'IV'], 213: ['EL ARENOSILLO', 213.0, 37.1, -6.733, 41.0, 'ESP', 'VI'], 217: ['POKER FLAT', 217.0, 65.133, -147.45, 357.5, 'USA', 'IV'], 219: ['NATAL', 219.0, -5.71, -35.21, 30.5, 'BRA', 'III'], 221: ['LEGIONOWO', 221.0, 52.4, 20.967, 96.0, 'POL', 'VI'], 224: ['CHILCA', 224.0, -12.5, -76.8, -1.0, 'PER', 'III'], 225: ['KOUROU', 225.0, 5.333, -52.65, 4.0, 'GUF', 'III'], 227: ['MCDONALD OBSERVATORY', 227.0, 30.666, -90.933, 2081.0, 'USA', 'IV'], 228: ['GIMLI', 228.0, 50.633, -97.05, 228.0, 'CAN', 'IV'], 229: ['ALBROOK', 229.0, 8.983, -79.55, 66.0, 'PAN', 'IV'], 231: ['SPOKANE', 231.0, 47.667, -117.417, 576.0, 'USA', 'IV'], 233: ['MARAMBIO', 233.0, -64.233, -56.623, 196.0, 'ATA', 'ANTARCTICA'], 234: ['SAN JUAN', 234.0, 18.483, -66.133, 17.0, 'PRI', 'IV'], 235: ['LONG VIEW', 235.0, 32.5, -94.75, 103.0, 'USA', 'IV'], 236: ['COOLIDGE FIELD', 236.0, 17.283, -61.783, 10.0, 'ATG', 'IV'], 237: ['GREAT FALLS', 237.0, 47.483, -111.35, 1118.0, 'USA', 'IV'], 238: ['DENVER', 238.0, 39.767, -104.883, 1611.0, 'USA', 'IV'], 239: ['SAN DIEGO', 239.0, 32.76, -117.19, 72.5, 'USA', 'IV'], 242: ['PRAHA', 242.0, 50.02, 14.45, 304.0, 'CZE', 'VI'], 254: ['LAVERTON', 254.0, -37.867, 144.75, 21.0, 'AUS', 'V'], 255: ['AINSWORTH (AIRPORT)', 255.0, 42.583, -100.0, 789.0, 'USA', 'IV'], 256: ['LAUDER', 256.0, -45.03, 169.683, 370.0, 'NZL', 'V'], 257: ['VANSCOY', 257.0, 52.115, -107.165, 510.0, 'CAN', 'IV'], 260: ['TABLE MOUNTAIN (CA)', 260.0, 34.4, -117.7, 2286.0, 'USA', 'IV'], 262: ['SODANKYLA', 262.0, 67.335, 26.505, 179.0, 'FIN', 'VI'], 265: ['IRENE', 265.0, -25.91, 28.211, 1524.0, 'ZAF', 'I'], 280: ['NOVOLASAREVSKAYA / FORSTER', 280.0, -70.767, 11.867, 110.0, 'ATA', 'ANTARCTICA'], 297: ['S.PIETRO CAPOFIUME', 297.0, 44.65, 11.617, 11.0, 'ITA', 'VI'], 303: ['IQALUIT', 303.0, 63.75, -68.55, 20.0, 'CAN', 'IV'], 308: ['MADRID / BARAJAS', 308.0, 40.46, -3.65, 650.0, 'ESP', 'VI'], 315: ['EUREKA / EUREKA LAB', 315.0, 80.04, -86.175, 310.0, 'CAN', 'IV'], 316: ['DE BILT', 316.0, 52.1, 5.18, 4.0, 'NLD', 'VI'], 318: ['VALENTIA OBSERVATORY', 318.0, 51.93, -10.25, 14.0, 'IRL', 'VI'], 323: ['NEUMAYER', 323.0, -70.65, -8.25, 42.0, 'ATA', 'ANTARCTICA'], 328: ['ASCENSION ISLAND', 328.0, -7.98, -14.42, 91.0, 'SHN', 'I'], 329: ['BRAZZAVILLE', 329.0, -4.28, 15.25, 314.0, 'COG', 'I'], 330: ['HANOI', 330.0, 21.033, 105.84, 5.0, 'VNM', 'II'], 333: ['PORTO NACIONAL', 333.0, -10.8, -48.4, 240.0, 'BRA', 'III'], 334: ['CUIABA', 334.0, -15.6, -56.1, 990.0, 'BRA', 'III'], 335: ['ETOSHA PAN', 335.0, -19.2, 15.9, 1100.0, 'NAM', 'I'], 336: ['ISFAHAN', 336.0, 32.477, 51.425, 1550.0, 'IRN', 'II'], 338: ['BRATTS LAKE (REGINA)', 338.0, 50.205, -104.705, 592.0, 'CAN', 'IV'], 339: ['USHUAIA', 339.0, -54.85, -68.308, 15.0, 'ARG', 'III'], 344: ['HONG KONG OBSERVATORY', 344.0, 22.31, 114.17, 66.0, 'HKG', 'II'], 348: ['ANKARA', 348.0, 39.95, 32.883, 896.0, 'TUR', 'VI'], 360: ['PELLSTON (MI)', 360.0, 45.56, -84.67, 238.0, 'USA', 'IV'], 361: ['HOLTVILLE (CA)', 361.0, 32.81, -115.42, -18.0, 'USA', 'IV'], 394: ['BROADMEADOWS', 394.0, -37.6914, 144.9467, 108.0, 'AUS', 'V'], 400: ['MAITRI', 400.0, -70.46, 11.45, 223.5, 'ATA', 'ANTARCTICA'], 401: ['SANTA CRUZ', 401.0, 28.42, -16.26, 36.0, 'ESP', 'I'], 404: ['JOKIOINEN', 404.0, 60.81, 23.5, 103.0, 'FIN', 'VI'], 406: ['SCORESBYSUND', 406.0, 70.49, -21.98, 50.0, 'GRL', 'VI'], 418: ['HUNTSVILLE', 418.0, 34.72, -86.64, 196.0, 'USA', 'IV'], 420: ['BELTSVILLE (MD)', 420.0, 39.02, -76.74, 64.0, 'USA', 'IV'], 432: ['PAPEETE (TAHITI)', 432.0, -18.0, -149.0, 2.0, 'PYF', 'V'], 434: ['SAN CRISTOBAL', 434.0, -0.92, -89.6, 8.0, 'ECU', 'III'], 435: ['PARAMARIBO', 435.0, 5.81, -55.21, 22.5, 'SUR', 'III'], 436: ['LA REUNION ISLAND', 436.0, -20.99, 55.48, 61.5, 'REU', 'I'], 437: ['WATUKOSEK (JAVA)', 437.0, -7.57, 112.65, 50.0, 'IDN', 'V'], 438: ['SUVA (FIJI)', 438.0, -18.13, 178.315, 6.0, 'FJI', 'V'], 439: ['KAASHIDHOO', 439.0, 5.0, 73.5, 1.0, 'MDV', 'V'], 441: ['EASTER ISLAND', 441.0, -27.17, -109.42, 62.0, 'CHL', 'III'], 443: ['SEPANG AIRPORT', 443.0, 2.73, 101.7, 17.0, 'MYS', 'V'], 444: ['CHEJU', 444.0, 33.5, 126.5, 300.0, 'KOR', 'II'], 445: ['TRINIDAD HEAD', 445.0, 40.8, -124.16, 55.0, 'USA', 'IV'], 448: ['MALINDI', 448.0, -2.99, 40.19, -6.0, 'KEN', 'I'], 450: ['DAVIS', 450.0, -68.577, 77.973, 16.0, 'ATA', 'ANTARCTICA'], 456: ['EGBERT', 456.0, 44.23, -79.78, 253.0, 'CAN', 'IV'], 457: ['KELOWNA', 457.0, 49.93, -119.4, 456.0, 'CAN', 'IV'], 458: ['YARMOUTH', 458.0, 43.87, -66.1, 9.0, 'CAN', 'IV'], 459: ['TBD', 459.0, 0.0, 0.0, 0.0, '', 'VI'], 460: ['THULE', 460.0, 76.53, -68.74, 57.0, 'GRL', 'VI'], 466: ['MAXARANGUAPE (SHADOZ-NATAL)', 466.0, -5.445, -35.33, 32.0, 'BRA', 'III'], 472: ['COTONOU', 472.0, 6.21, 2.23, 10.0, 'BEN', 'I'], 477: ['HEREDIA', 477.0, 10.0, -84.11, 1176.0, 'CRI', 'IV'], 480: ['SABLE ISLAND', 480.0, 43.93, -60.02, 4.0, 'CAN', 'IV'], 482: ['WALSINGHAM', 482.0, 42.6, -80.6, 200.0, 'CAN', 'IV'], 483: ['BARBADOS', 483.0, 13.16, -59.43, 32.0, 'BRB', 'III'], 484: ['HOUSTON (TX)', 484.0, 29.72, -95.4, 19.0, 'USA', 'IV'], 485: ['TECAMEC (UNAM)', 485.0, 19.33, -99.18, 2272.0, 'MEX', 'IV'], 487: ['NARRAGANSETT', 487.0, 41.49, -71.42, 21.0, 'USA', 'IV'], 488: ['PARADOX', 488.0, 43.92, -73.64, 284.0, 'USA', 'IV'], 489: ['RICHLAND', 489.0, 46.2, -119.16, 123.0, 'USA', 'IV'], 490: ['VALPARAISO (IN)', 490.0, 41.5, -87.0, 240.0, 'USA', 'IV'], 494: ['ALAJUELA', 494.0, 9.98, -84.21, 899.0, 'CRI', 'IV']
}
return sonde_dict
# ----
# 7.08 - returns (lat, lon, alt (press), timezone (UTC) ) for a given site
# ----
def gaw_2_loc(site, f = 'GLOBAL_SURFACE_O3_2006_2012.nc'):#, f
""" Extract GAW site locations for a given site
Another file is availible with just GAW sites:
'GAW_SURFACE_O3_2006_2012.nc'
"""
from AC_tools.funcs4generic import hPa_to_Km
# Use simple dictionary if site listed
try:
gaw_sites= {
'SMO': (-14.247, -170.565,1002.7885270480558, -11), 'MNM':(24.285, 153.981, 1011.9342452324959, 9), 'BMW':(32.27, -64.88, 1008.6109830510485, -4 ) ,'CVO': (16.848, -24.871, 1011.6679817831093, -1), 'RPB':(13.17000, -59.43000, 1007.0196960034474, -4 ), 'ogasawara': (26.38, 142.10,996.08181619552602, 9 ), 'OGA': (26.38, 142.10,996.08181619552602, 9 ) ,
# Add extras for ease of analysis (e.g. Roscoff ... )
'ROS': (48.433, -3.5904, 1011.6679817831093, +1)
}
return gaw_sites[ site ]
# If not in list then extract details from NetCDF
except:
wd= get_dir('dwd') +'ozonesurface/'
with Dataset(wd+f, 'r', format='NETCDF4') as f:
lon= f.groups[site].longitude
alt = f.groups[site].altitude /1E3
lat = f.groups[site].latitude
print [ (i, type(i) ) for i in lat, lon, alt ]
return (lat, lon, float( hPa_to_Km([alt], reverse=True)[0] ), -9999 )
|
import theano
import unittest
from numpy.testing import assert_allclose
import numpy as np
from keras.layers.recurrent import SimpleRNN
from mock import Mock
floatX = theano.config.floatX
__author__ = "Jeff Ye"
class TestSimpleRNN(unittest.TestCase):
left_padding_data = np.array(
[
[ # batch 1
[0], [1], [2], [3]
],
[ # batch 2
[0], [0], [1], [2]
]
], dtype=floatX)
left_padding_mask = np.array( # n_sample x n_time
[
[ # batch 1
0, 1, 1, 1
],
[ # batch 2
0, 0, 1, 1
]
], dtype=np.int32)
def setUp(self):
W = np.array([[1]], dtype=floatX)
U = np.array([[1]], dtype=floatX)
b = np.array([0], dtype=floatX)
weights = [W, U, b]
self.forward = SimpleRNN(output_dim=1, activation='linear', weights=weights)
self.backward = SimpleRNN(output_dim=1, activation='linear', weights=weights)
previous = Mock()
previous.nb_input = 1
previous.nb_output = 1
previous.output_shape = self.left_padding_data.shape
previous.get_output_mask = Mock()
self.previous = previous
def test_left_padding(self):
forward = self.forward
forward.go_backwards = False
forward.return_sequences = True
self.previous.get_output.return_value = theano.shared(value=self.left_padding_data)
self.previous.get_output_mask.return_value = theano.shared(value=self.left_padding_mask)
forward.set_previous(self.previous)
np.testing.assert_allclose(forward.get_output().eval(),
np.array([
[[0], [1], [3], [6]],
[[0], [0], [1], [3]]]))
backward = self.backward
backward.go_backwards = True
backward.return_sequences = True
self.previous.get_output.return_value = theano.shared(value=self.left_padding_data)
self.previous.get_output_mask.return_value = theano.shared(value=self.left_padding_mask)
backward.set_previous(self.previous)
np.testing.assert_allclose(backward.get_output().eval(),
np.array([
[[3], [5], [6], [0]],
[[2], [3], [0], [0]]]))
remove incompatible tests
|
#!/usr/bin/env python
# build_script.py - Build, install, and test XCTest -*- python -*-
#
# This source file is part of the Swift.org open source project
#
# Copyright (c) 2014 - 2016 Apple Inc. and the Swift project authors
# Licensed under Apache License v2.0 with Runtime Library Exception
#
# See http://swift.org/LICENSE.txt for license information
# See http://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
import argparse
import glob
import os
import subprocess
import sys
import tempfile
import textwrap
import platform
import errno
SOURCE_DIR = os.path.dirname(os.path.abspath(__file__))
def note(msg):
print("xctest-build: "+msg)
def run(command):
note(command)
subprocess.check_call(command, shell=True)
def _mkdirp(path):
"""
Creates a directory at the given path if it doesn't already exist.
"""
if not os.path.exists(path):
run("mkdir -p {}".format(path))
def symlink_force(target, link_name):
if os.path.isdir(link_name):
link_name = os.path.join(link_name, os.path.basename(target))
try:
os.symlink(target, link_name)
except OSError as e:
if e.errno == errno.EEXIST:
os.remove(link_name)
os.symlink(target, link_name)
else:
raise e
class DarwinStrategy:
@staticmethod
def requires_foundation_build_dir():
# The Foundation build directory is not required on Darwin because the
# Xcode workspace implicitly builds Foundation when building the XCTest
# schemes.
return False
@staticmethod
def build(args):
"""
Build XCTest and place the built products in the given 'build_dir'.
If 'test' is specified, also executes the 'test' subcommand.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
run("xcodebuild -workspace {source_dir}/XCTest.xcworkspace "
"-scheme SwiftXCTest "
"SWIFT_EXEC=\"{swiftc}\" "
"SWIFT_LINK_OBJC_RUNTIME=YES "
"SYMROOT=\"{build_dir}\" OBJROOT=\"{build_dir}\"".format(
swiftc=swiftc,
build_dir=build_dir,
source_dir=SOURCE_DIR))
if args.test:
# Execute main() using the arguments necessary to run the tests.
main(args=["test",
"--swiftc", swiftc,
build_dir])
@staticmethod
def test(args):
"""
Test SwiftXCTest.framework, using the given 'swiftc' compiler, looking
for it in the given 'build_dir'.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
run("xcodebuild -workspace {source_dir}/XCTest.xcworkspace "
"-scheme SwiftXCTestFunctionalTests "
"SWIFT_EXEC=\"{swiftc}\" "
"SWIFT_LINK_OBJC_RUNTIME=YES "
"SYMROOT=\"{build_dir}\" OBJROOT=\"{build_dir}\" "
"| grep -v \" export\"".format(
swiftc=swiftc,
build_dir=build_dir,
source_dir=SOURCE_DIR))
@staticmethod
def install(args):
"""
Installing XCTest is not supported on Darwin.
"""
note("error: The install command is not supported on this platform")
exit(1)
class GenericUnixStrategy:
@staticmethod
def requires_foundation_build_dir():
# This script does not know how to build Foundation in Unix environments,
# so we need the path to a pre-built Foundation library.
return True
@staticmethod
def build(args):
"""
Build XCTest and place the built products in the given 'build_dir'.
If 'test' is specified, also executes the 'test' subcommand.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
foundation_build_dir = os.path.abspath(args.foundation_build_dir)
core_foundation_build_dir = GenericUnixStrategy.core_foundation_build_dir(
foundation_build_dir, args.foundation_install_prefix)
if args.libdispatch_build_dir:
libdispatch_build_dir = os.path.abspath(args.libdispatch_build_dir)
if args.libdispatch_src_dir:
libdispatch_src_dir = os.path.abspath(args.libdispatch_src_dir)
_mkdirp(build_dir)
sourcePaths = glob.glob(os.path.join(
SOURCE_DIR, 'Sources', 'XCTest', '*', '*.swift'))
if args.build_style == "debug":
style_options = "-g"
else:
style_options = "-O"
# Not incremental..
# Build library
if args.libdispatch_build_dir and args.libdispatch_src_dir:
libdispatch_args = "-I {libdispatch_build_dir}/src -I {libdispatch_src_dir} ".format(
libdispatch_build_dir=libdispatch_build_dir,
libdispatch_src_dir=libdispatch_src_dir)
else:
libdispatch_args = ""
run("{swiftc} -Xcc -fblocks -c {style_options} -emit-object -emit-module "
"-module-name XCTest -module-link-name XCTest -parse-as-library "
"-emit-module-path {build_dir}/XCTest.swiftmodule "
"-force-single-frontend-invocation "
"-I {foundation_build_dir} -I {core_foundation_build_dir} "
"{libdispatch_args} "
"{source_paths} -o {build_dir}/XCTest.o".format(
swiftc=swiftc,
style_options=style_options,
build_dir=build_dir,
foundation_build_dir=foundation_build_dir,
core_foundation_build_dir=core_foundation_build_dir,
libdispatch_args=libdispatch_args,
source_paths=" ".join(sourcePaths)))
run("{swiftc} -emit-library {build_dir}/XCTest.o "
"-L {foundation_build_dir} -lswiftGlibc -lswiftCore -lFoundation -lm "
# We embed an rpath of `$ORIGIN` to ensure other referenced
# libraries (like `Foundation`) can be found solely via XCTest.
"-Xlinker -rpath=\\$ORIGIN "
"-o {build_dir}/libXCTest.so".format(
swiftc=swiftc,
build_dir=build_dir,
foundation_build_dir=foundation_build_dir))
if args.test:
# Execute main() using the arguments necessary to run the tests.
main(args=["test",
"--swiftc", swiftc,
"--foundation-build-dir", foundation_build_dir,
build_dir])
# If --module-install-path and --library-install-path were specified,
# we also install the built XCTest products.
if args.module_path is not None and args.lib_path is not None:
# Execute main() using the arguments necessary for installation.
main(args=["install", build_dir,
"--module-install-path", args.module_path,
"--library-install-path", args.lib_path])
note('Done.')
@staticmethod
def test(args):
"""
Test the built XCTest.so library at the given 'build_dir', using the
given 'swiftc' compiler.
"""
lit_path = os.path.abspath(args.lit)
if not os.path.exists(lit_path):
raise IOError(
'Could not find lit tester tool at path: "{}". This tool is '
'requred to run the test suite. Unless you specified a custom '
'path to the tool using the "--lit" option, the lit tool will be '
'found in the LLVM source tree, which is expected to be checked '
'out in the same directory as swift-corelibs-xctest. If you do '
'not have LLVM checked out at this path, you may follow the '
'instructions for "Getting Sources for Swift and Related '
'Projects" from the Swift project README in order to fix this '
'error.'.format(lit_path))
# FIXME: Allow these to be specified by the Swift build script.
lit_flags = "-sv --no-progress-bar"
tests_path = os.path.join(SOURCE_DIR, "Tests", "Functional")
foundation_build_dir = os.path.abspath(args.foundation_build_dir)
core_foundation_build_dir = GenericUnixStrategy.core_foundation_build_dir(
foundation_build_dir, args.foundation_install_prefix)
if args.libdispatch_build_dir:
libdispatch_build_dir = os.path.abspath(args.libdispatch_build_dir)
symlink_force(os.path.join(args.libdispatch_build_dir, "src", ".libs", "libdispatch.so"),
foundation_build_dir)
if args.libdispatch_src_dir and args.libdispatch_build_dir:
libdispatch_src_args = "LIBDISPATCH_SRC_DIR={libdispatch_src_dir} LIBDISPATCH_BUILD_DIR={libdispatch_build_dir}".format(
libdispatch_src_dir=os.path.abspath(args.libdispatch_src_dir),
libdispatch_build_dir=os.path.join(args.libdispatch_build_dir, 'src', '.libs'))
else:
libdispatch_src_args = ""
run('SWIFT_EXEC={swiftc} '
'BUILT_PRODUCTS_DIR={built_products_dir} '
'FOUNDATION_BUILT_PRODUCTS_DIR={foundation_build_dir} '
'CORE_FOUNDATION_BUILT_PRODUCTS_DIR={core_foundation_build_dir} '
'{libdispatch_src_args} '
'{lit_path} {lit_flags} '
'{tests_path}'.format(
swiftc=os.path.abspath(args.swiftc),
built_products_dir=args.build_dir,
foundation_build_dir=foundation_build_dir,
core_foundation_build_dir=core_foundation_build_dir,
libdispatch_src_args=libdispatch_src_args,
lit_path=lit_path,
lit_flags=lit_flags,
tests_path=tests_path))
@staticmethod
def install(args):
"""
Install the XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc build
products into the given module and library paths.
"""
build_dir = os.path.abspath(args.build_dir)
module_install_path = os.path.abspath(args.module_install_path)
library_install_path = os.path.abspath(args.library_install_path)
_mkdirp(module_install_path)
_mkdirp(library_install_path)
xctest_so = "libXCTest.so"
run("cp {} {}".format(
os.path.join(build_dir, xctest_so),
os.path.join(library_install_path, xctest_so)))
xctest_swiftmodule = "XCTest.swiftmodule"
run("cp {} {}".format(
os.path.join(build_dir, xctest_swiftmodule),
os.path.join(module_install_path, xctest_swiftmodule)))
xctest_swiftdoc = "XCTest.swiftdoc"
run("cp {} {}".format(
os.path.join(build_dir, xctest_swiftdoc),
os.path.join(module_install_path, xctest_swiftdoc)))
@staticmethod
def core_foundation_build_dir(foundation_build_dir, foundation_install_prefix):
"""
Given the path to a swift-corelibs-foundation built product directory,
return the path to CoreFoundation built products.
When specifying a built Foundation dir such as
'/build/foundation-linux-x86_64/Foundation', CoreFoundation dependencies
are placed in 'usr/lib/swift'. Note that it's technically not necessary to
include this extra path when linking the installed Swift's
'usr/lib/swift/linux/libFoundation.so'.
"""
return os.path.join(foundation_build_dir,
foundation_install_prefix.strip("/"), 'lib', 'swift')
def main(args=sys.argv[1:]):
"""
The main entry point for this script. Based on the subcommand given,
delegates building or testing XCTest to a sub-parser and its corresponding
function.
"""
strategy = DarwinStrategy if platform.system() == 'Darwin' else GenericUnixStrategy
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent("""
Build, test, and install XCTest.
NOTE: In general this script should not be invoked directly. The
recommended way to build and test XCTest is via the Swift build
script. See this project's README for details.
The Swift build script invokes this %(prog)s script to build,
test, and install this project. You may invoke it in the same way
to build this project directly. For example, if you are in a Linux
environment, your install of Swift is located at "/swift" and you
wish to install XCTest into that same location, here is a sample
invocation of the build script:
$ %(prog)s \\
--swiftc="/swift/usr/bin/swiftc" \\
--build-dir="/tmp/XCTest_build" \\
--foundation-build-dir "/swift/usr/lib/swift/linux" \\
--library-install-path="/swift/usr/lib/swift/linux" \\
--module-install-path="/swift/usr/lib/swift/linux/x86_64"
Note that installation is not supported on Darwin as this library
is only intended to be used as a dependency in environments where
Apple XCTest is not available.
"""))
subparsers = parser.add_subparsers(
description=textwrap.dedent("""
Use one of these to specify whether to build, test, or install
XCTest. If you don't specify any of these, 'build' is executed as a
default. You may also use 'build' to also test and install the
built products. Pass the -h or --help option to any of the
subcommands for more information."""))
build_parser = subparsers.add_parser(
"build",
description=textwrap.dedent("""
Build XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc using the
given Swift compiler. This command may also test and install the
built products."""))
build_parser.set_defaults(func=strategy.build)
build_parser.add_argument(
"--swiftc",
help="Path to the 'swiftc' compiler that will be used to build "
"XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc. This will "
"also be used to build the tests for those built products if the "
"--test option is specified.",
required=True)
build_parser.add_argument(
"--build-dir",
help="Path to the output build directory. If not specified, a "
"temporary directory is used.",
default=tempfile.mkdtemp())
build_parser.add_argument(
"--foundation-build-dir",
help="Path to swift-corelibs-foundation build products, which "
"the built XCTest.so will be linked against.",
required=strategy.requires_foundation_build_dir())
build_parser.add_argument(
"--foundation-install-prefix",
help="Path to the installation location for swift-corelibs-foundation "
"build products ('%(default)s' by default); CoreFoundation "
"dependencies are expected to be found under "
"FOUNDATION_BUILD_DIR/FOUNDATION_INSTALL_PREFIX.",
default="/usr")
build_parser.add_argument(
"--libdispatch-build-dir",
help="Path to swift-corelibs-libdispatch build products, which "
"the built XCTest.so will be linked against.")
build_parser.add_argument(
"--libdispatch-src-dir",
help="Path to swift-corelibs-libdispatch source tree, which "
"the built XCTest.so will be linked against.")
build_parser.add_argument(
"--module-install-path",
help="Location at which to install XCTest.swiftmodule and "
"XCTest.swiftdoc. This directory will be created if it doesn't "
"already exist.",
dest="module_path")
build_parser.add_argument(
"--library-install-path",
help="Location at which to install XCTest.so. This directory will be "
"created if it doesn't already exist.",
dest="lib_path")
build_parser.add_argument(
"--release",
help="builds for release",
action="store_const",
dest="build_style",
const="release",
default="debug")
build_parser.add_argument(
"--debug",
help="builds for debug (the default)",
action="store_const",
dest="build_style",
const="debug",
default="debug")
build_parser.add_argument(
"--test",
help="Whether to run tests after building. Note that you must have "
"cloned https://github.com/apple/swift-llvm at {} in order to "
"run this command.".format(os.path.join(
os.path.dirname(SOURCE_DIR), 'llvm')),
action="store_true")
test_parser = subparsers.add_parser(
"test",
description="Tests a built XCTest framework at the given path.")
test_parser.set_defaults(func=strategy.test)
test_parser.add_argument(
"build_dir",
help="An absolute path to a directory containing the built XCTest.so "
"library.")
test_parser.add_argument(
"--swiftc",
help="Path to the 'swiftc' compiler used to build and run the tests.",
required=True)
test_parser.add_argument(
"--lit",
help="Path to the 'lit' tester tool used to run the test suite. "
"'%(default)s' by default.",
default=os.path.join(os.path.dirname(SOURCE_DIR),
"llvm", "utils", "lit", "lit.py"))
test_parser.add_argument(
"--foundation-build-dir",
help="Path to swift-corelibs-foundation build products, which the "
"tests will be linked against.",
required=strategy.requires_foundation_build_dir())
test_parser.add_argument(
"--foundation-install-prefix",
help="Path to the installation location for swift-corelibs-foundation "
"build products ('%(default)s' by default); CoreFoundation "
"dependencies are expected to be found under "
"FOUNDATION_BUILD_DIR/FOUNDATION_INSTALL_PREFIX.",
default="/usr")
test_parser.add_argument(
"--libdispatch-build-dir",
help="Path to swift-corelibs-libdispatch build products, which "
"the built XCTest.so will be linked against.")
test_parser.add_argument(
"--libdispatch-src-dir",
help="Path to swift-corelibs-libdispatch source tree, which "
"the built XCTest.so will be linked against.")
install_parser = subparsers.add_parser(
"install",
description="Installs a built XCTest framework.")
install_parser.set_defaults(func=strategy.install)
install_parser.add_argument(
"build_dir",
help="An absolute path to a directory containing a built XCTest.so, "
"XCTest.swiftmodule, and XCTest.swiftdoc.")
install_parser.add_argument(
"-m", "--module-install-path",
help="Location at which to install XCTest.swiftmodule and "
"XCTest.swiftdoc. This directory will be created if it doesn't "
"already exist.")
install_parser.add_argument(
"-l", "--library-install-path",
help="Location at which to install XCTest.so. This directory will be "
"created if it doesn't already exist.")
# Many versions of Python require a subcommand must be specified.
# We handle this here: if no known subcommand (or none of the help options)
# is included in the arguments, then insert the default subcommand
# argument: 'build'.
if any([a in ["build", "test", "install", "-h", "--help"] for a in args]):
parsed_args = parser.parse_args(args=args)
else:
parsed_args = parser.parse_args(args=["build"] + args)
# Execute the function for the subcommand we've been given.
parsed_args.func(parsed_args)
if __name__ == '__main__':
main()
Use build type parameter when building and testing using xcodebuild.
#!/usr/bin/env python
# build_script.py - Build, install, and test XCTest -*- python -*-
#
# This source file is part of the Swift.org open source project
#
# Copyright (c) 2014 - 2016 Apple Inc. and the Swift project authors
# Licensed under Apache License v2.0 with Runtime Library Exception
#
# See http://swift.org/LICENSE.txt for license information
# See http://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
import argparse
import glob
import os
import subprocess
import sys
import tempfile
import textwrap
import platform
import errno
SOURCE_DIR = os.path.dirname(os.path.abspath(__file__))
def note(msg):
print("xctest-build: "+msg)
def run(command):
note(command)
subprocess.check_call(command, shell=True)
def _mkdirp(path):
"""
Creates a directory at the given path if it doesn't already exist.
"""
if not os.path.exists(path):
run("mkdir -p {}".format(path))
def symlink_force(target, link_name):
if os.path.isdir(link_name):
link_name = os.path.join(link_name, os.path.basename(target))
try:
os.symlink(target, link_name)
except OSError as e:
if e.errno == errno.EEXIST:
os.remove(link_name)
os.symlink(target, link_name)
else:
raise e
class DarwinStrategy:
@staticmethod
def requires_foundation_build_dir():
# The Foundation build directory is not required on Darwin because the
# Xcode workspace implicitly builds Foundation when building the XCTest
# schemes.
return False
@staticmethod
def build(args):
"""
Build XCTest and place the built products in the given 'build_dir'.
If 'test' is specified, also executes the 'test' subcommand.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
if args.build_style == "debug":
style_options = "Debug"
else:
style_options = "Release"
run("xcodebuild -workspace {source_dir}/XCTest.xcworkspace "
"-scheme SwiftXCTest "
"-configuration {style_options} "
"SWIFT_EXEC=\"{swiftc}\" "
"SWIFT_LINK_OBJC_RUNTIME=YES "
"SYMROOT=\"{build_dir}\" OBJROOT=\"{build_dir}\"".format(
swiftc=swiftc,
build_dir=build_dir,
style_options=style_options,
source_dir=SOURCE_DIR))
if args.test:
# Execute main() using the arguments necessary to run the tests.
main(args=["test",
"--swiftc", swiftc,
build_dir])
@staticmethod
def test(args):
"""
Test SwiftXCTest.framework, using the given 'swiftc' compiler, looking
for it in the given 'build_dir'.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
if args.build_style == "debug":
style_options = "Debug"
else:
style_options = "Release"
run("xcodebuild -workspace {source_dir}/XCTest.xcworkspace "
"-scheme SwiftXCTestFunctionalTests "
"-configuration {style_options} "
"SWIFT_EXEC=\"{swiftc}\" "
"SWIFT_LINK_OBJC_RUNTIME=YES "
"SYMROOT=\"{build_dir}\" OBJROOT=\"{build_dir}\" "
"| grep -v \" export\"".format(
swiftc=swiftc,
build_dir=build_dir,
style_options=style_options,
source_dir=SOURCE_DIR))
@staticmethod
def install(args):
"""
Installing XCTest is not supported on Darwin.
"""
note("error: The install command is not supported on this platform")
exit(1)
class GenericUnixStrategy:
@staticmethod
def requires_foundation_build_dir():
# This script does not know how to build Foundation in Unix environments,
# so we need the path to a pre-built Foundation library.
return True
@staticmethod
def build(args):
"""
Build XCTest and place the built products in the given 'build_dir'.
If 'test' is specified, also executes the 'test' subcommand.
"""
swiftc = os.path.abspath(args.swiftc)
build_dir = os.path.abspath(args.build_dir)
foundation_build_dir = os.path.abspath(args.foundation_build_dir)
core_foundation_build_dir = GenericUnixStrategy.core_foundation_build_dir(
foundation_build_dir, args.foundation_install_prefix)
if args.libdispatch_build_dir:
libdispatch_build_dir = os.path.abspath(args.libdispatch_build_dir)
if args.libdispatch_src_dir:
libdispatch_src_dir = os.path.abspath(args.libdispatch_src_dir)
_mkdirp(build_dir)
sourcePaths = glob.glob(os.path.join(
SOURCE_DIR, 'Sources', 'XCTest', '*', '*.swift'))
if args.build_style == "debug":
style_options = "-g"
else:
style_options = "-O"
# Not incremental..
# Build library
if args.libdispatch_build_dir and args.libdispatch_src_dir:
libdispatch_args = "-I {libdispatch_build_dir}/src -I {libdispatch_src_dir} ".format(
libdispatch_build_dir=libdispatch_build_dir,
libdispatch_src_dir=libdispatch_src_dir)
else:
libdispatch_args = ""
run("{swiftc} -Xcc -fblocks -c {style_options} -emit-object -emit-module "
"-module-name XCTest -module-link-name XCTest -parse-as-library "
"-emit-module-path {build_dir}/XCTest.swiftmodule "
"-force-single-frontend-invocation "
"-I {foundation_build_dir} -I {core_foundation_build_dir} "
"{libdispatch_args} "
"{source_paths} -o {build_dir}/XCTest.o".format(
swiftc=swiftc,
style_options=style_options,
build_dir=build_dir,
foundation_build_dir=foundation_build_dir,
core_foundation_build_dir=core_foundation_build_dir,
libdispatch_args=libdispatch_args,
source_paths=" ".join(sourcePaths)))
run("{swiftc} -emit-library {build_dir}/XCTest.o "
"-L {foundation_build_dir} -lswiftGlibc -lswiftCore -lFoundation -lm "
# We embed an rpath of `$ORIGIN` to ensure other referenced
# libraries (like `Foundation`) can be found solely via XCTest.
"-Xlinker -rpath=\\$ORIGIN "
"-o {build_dir}/libXCTest.so".format(
swiftc=swiftc,
build_dir=build_dir,
foundation_build_dir=foundation_build_dir))
if args.test:
# Execute main() using the arguments necessary to run the tests.
main(args=["test",
"--swiftc", swiftc,
"--foundation-build-dir", foundation_build_dir,
build_dir])
# If --module-install-path and --library-install-path were specified,
# we also install the built XCTest products.
if args.module_path is not None and args.lib_path is not None:
# Execute main() using the arguments necessary for installation.
main(args=["install", build_dir,
"--module-install-path", args.module_path,
"--library-install-path", args.lib_path])
note('Done.')
@staticmethod
def test(args):
"""
Test the built XCTest.so library at the given 'build_dir', using the
given 'swiftc' compiler.
"""
lit_path = os.path.abspath(args.lit)
if not os.path.exists(lit_path):
raise IOError(
'Could not find lit tester tool at path: "{}". This tool is '
'requred to run the test suite. Unless you specified a custom '
'path to the tool using the "--lit" option, the lit tool will be '
'found in the LLVM source tree, which is expected to be checked '
'out in the same directory as swift-corelibs-xctest. If you do '
'not have LLVM checked out at this path, you may follow the '
'instructions for "Getting Sources for Swift and Related '
'Projects" from the Swift project README in order to fix this '
'error.'.format(lit_path))
# FIXME: Allow these to be specified by the Swift build script.
lit_flags = "-sv --no-progress-bar"
tests_path = os.path.join(SOURCE_DIR, "Tests", "Functional")
foundation_build_dir = os.path.abspath(args.foundation_build_dir)
core_foundation_build_dir = GenericUnixStrategy.core_foundation_build_dir(
foundation_build_dir, args.foundation_install_prefix)
if args.libdispatch_build_dir:
libdispatch_build_dir = os.path.abspath(args.libdispatch_build_dir)
symlink_force(os.path.join(args.libdispatch_build_dir, "src", ".libs", "libdispatch.so"),
foundation_build_dir)
if args.libdispatch_src_dir and args.libdispatch_build_dir:
libdispatch_src_args = "LIBDISPATCH_SRC_DIR={libdispatch_src_dir} LIBDISPATCH_BUILD_DIR={libdispatch_build_dir}".format(
libdispatch_src_dir=os.path.abspath(args.libdispatch_src_dir),
libdispatch_build_dir=os.path.join(args.libdispatch_build_dir, 'src', '.libs'))
else:
libdispatch_src_args = ""
run('SWIFT_EXEC={swiftc} '
'BUILT_PRODUCTS_DIR={built_products_dir} '
'FOUNDATION_BUILT_PRODUCTS_DIR={foundation_build_dir} '
'CORE_FOUNDATION_BUILT_PRODUCTS_DIR={core_foundation_build_dir} '
'{libdispatch_src_args} '
'{lit_path} {lit_flags} '
'{tests_path}'.format(
swiftc=os.path.abspath(args.swiftc),
built_products_dir=args.build_dir,
foundation_build_dir=foundation_build_dir,
core_foundation_build_dir=core_foundation_build_dir,
libdispatch_src_args=libdispatch_src_args,
lit_path=lit_path,
lit_flags=lit_flags,
tests_path=tests_path))
@staticmethod
def install(args):
"""
Install the XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc build
products into the given module and library paths.
"""
build_dir = os.path.abspath(args.build_dir)
module_install_path = os.path.abspath(args.module_install_path)
library_install_path = os.path.abspath(args.library_install_path)
_mkdirp(module_install_path)
_mkdirp(library_install_path)
xctest_so = "libXCTest.so"
run("cp {} {}".format(
os.path.join(build_dir, xctest_so),
os.path.join(library_install_path, xctest_so)))
xctest_swiftmodule = "XCTest.swiftmodule"
run("cp {} {}".format(
os.path.join(build_dir, xctest_swiftmodule),
os.path.join(module_install_path, xctest_swiftmodule)))
xctest_swiftdoc = "XCTest.swiftdoc"
run("cp {} {}".format(
os.path.join(build_dir, xctest_swiftdoc),
os.path.join(module_install_path, xctest_swiftdoc)))
@staticmethod
def core_foundation_build_dir(foundation_build_dir, foundation_install_prefix):
"""
Given the path to a swift-corelibs-foundation built product directory,
return the path to CoreFoundation built products.
When specifying a built Foundation dir such as
'/build/foundation-linux-x86_64/Foundation', CoreFoundation dependencies
are placed in 'usr/lib/swift'. Note that it's technically not necessary to
include this extra path when linking the installed Swift's
'usr/lib/swift/linux/libFoundation.so'.
"""
return os.path.join(foundation_build_dir,
foundation_install_prefix.strip("/"), 'lib', 'swift')
def main(args=sys.argv[1:]):
"""
The main entry point for this script. Based on the subcommand given,
delegates building or testing XCTest to a sub-parser and its corresponding
function.
"""
strategy = DarwinStrategy if platform.system() == 'Darwin' else GenericUnixStrategy
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent("""
Build, test, and install XCTest.
NOTE: In general this script should not be invoked directly. The
recommended way to build and test XCTest is via the Swift build
script. See this project's README for details.
The Swift build script invokes this %(prog)s script to build,
test, and install this project. You may invoke it in the same way
to build this project directly. For example, if you are in a Linux
environment, your install of Swift is located at "/swift" and you
wish to install XCTest into that same location, here is a sample
invocation of the build script:
$ %(prog)s \\
--swiftc="/swift/usr/bin/swiftc" \\
--build-dir="/tmp/XCTest_build" \\
--foundation-build-dir "/swift/usr/lib/swift/linux" \\
--library-install-path="/swift/usr/lib/swift/linux" \\
--module-install-path="/swift/usr/lib/swift/linux/x86_64"
Note that installation is not supported on Darwin as this library
is only intended to be used as a dependency in environments where
Apple XCTest is not available.
"""))
subparsers = parser.add_subparsers(
description=textwrap.dedent("""
Use one of these to specify whether to build, test, or install
XCTest. If you don't specify any of these, 'build' is executed as a
default. You may also use 'build' to also test and install the
built products. Pass the -h or --help option to any of the
subcommands for more information."""))
build_parser = subparsers.add_parser(
"build",
description=textwrap.dedent("""
Build XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc using the
given Swift compiler. This command may also test and install the
built products."""))
build_parser.set_defaults(func=strategy.build)
build_parser.add_argument(
"--swiftc",
help="Path to the 'swiftc' compiler that will be used to build "
"XCTest.so, XCTest.swiftmodule, and XCTest.swiftdoc. This will "
"also be used to build the tests for those built products if the "
"--test option is specified.",
required=True)
build_parser.add_argument(
"--build-dir",
help="Path to the output build directory. If not specified, a "
"temporary directory is used.",
default=tempfile.mkdtemp())
build_parser.add_argument(
"--foundation-build-dir",
help="Path to swift-corelibs-foundation build products, which "
"the built XCTest.so will be linked against.",
required=strategy.requires_foundation_build_dir())
build_parser.add_argument(
"--foundation-install-prefix",
help="Path to the installation location for swift-corelibs-foundation "
"build products ('%(default)s' by default); CoreFoundation "
"dependencies are expected to be found under "
"FOUNDATION_BUILD_DIR/FOUNDATION_INSTALL_PREFIX.",
default="/usr")
build_parser.add_argument(
"--libdispatch-build-dir",
help="Path to swift-corelibs-libdispatch build products, which "
"the built XCTest.so will be linked against.")
build_parser.add_argument(
"--libdispatch-src-dir",
help="Path to swift-corelibs-libdispatch source tree, which "
"the built XCTest.so will be linked against.")
build_parser.add_argument(
"--module-install-path",
help="Location at which to install XCTest.swiftmodule and "
"XCTest.swiftdoc. This directory will be created if it doesn't "
"already exist.",
dest="module_path")
build_parser.add_argument(
"--library-install-path",
help="Location at which to install XCTest.so. This directory will be "
"created if it doesn't already exist.",
dest="lib_path")
build_parser.add_argument(
"--release",
help="builds for release",
action="store_const",
dest="build_style",
const="release",
default="debug")
build_parser.add_argument(
"--debug",
help="builds for debug (the default)",
action="store_const",
dest="build_style",
const="debug",
default="debug")
build_parser.add_argument(
"--test",
help="Whether to run tests after building. Note that you must have "
"cloned https://github.com/apple/swift-llvm at {} in order to "
"run this command.".format(os.path.join(
os.path.dirname(SOURCE_DIR), 'llvm')),
action="store_true")
test_parser = subparsers.add_parser(
"test",
description="Tests a built XCTest framework at the given path.")
test_parser.set_defaults(func=strategy.test)
test_parser.add_argument(
"build_dir",
help="An absolute path to a directory containing the built XCTest.so "
"library.")
test_parser.add_argument(
"--swiftc",
help="Path to the 'swiftc' compiler used to build and run the tests.",
required=True)
test_parser.add_argument(
"--lit",
help="Path to the 'lit' tester tool used to run the test suite. "
"'%(default)s' by default.",
default=os.path.join(os.path.dirname(SOURCE_DIR),
"llvm", "utils", "lit", "lit.py"))
test_parser.add_argument(
"--foundation-build-dir",
help="Path to swift-corelibs-foundation build products, which the "
"tests will be linked against.",
required=strategy.requires_foundation_build_dir())
test_parser.add_argument(
"--foundation-install-prefix",
help="Path to the installation location for swift-corelibs-foundation "
"build products ('%(default)s' by default); CoreFoundation "
"dependencies are expected to be found under "
"FOUNDATION_BUILD_DIR/FOUNDATION_INSTALL_PREFIX.",
default="/usr")
test_parser.add_argument(
"--libdispatch-build-dir",
help="Path to swift-corelibs-libdispatch build products, which "
"the built XCTest.so will be linked against.")
test_parser.add_argument(
"--libdispatch-src-dir",
help="Path to swift-corelibs-libdispatch source tree, which "
"the built XCTest.so will be linked against.")
test_parser.add_argument(
"--release",
help="builds the tests for release",
action="store_const",
dest="build_style",
const="release",
default="debug")
test_parser.add_argument(
"--debug",
help="builds the tests for debug (the default)",
action="store_const",
dest="build_style",
const="debug",
default="debug")
install_parser = subparsers.add_parser(
"install",
description="Installs a built XCTest framework.")
install_parser.set_defaults(func=strategy.install)
install_parser.add_argument(
"build_dir",
help="An absolute path to a directory containing a built XCTest.so, "
"XCTest.swiftmodule, and XCTest.swiftdoc.")
install_parser.add_argument(
"-m", "--module-install-path",
help="Location at which to install XCTest.swiftmodule and "
"XCTest.swiftdoc. This directory will be created if it doesn't "
"already exist.")
install_parser.add_argument(
"-l", "--library-install-path",
help="Location at which to install XCTest.so. This directory will be "
"created if it doesn't already exist.")
# Many versions of Python require a subcommand must be specified.
# We handle this here: if no known subcommand (or none of the help options)
# is included in the arguments, then insert the default subcommand
# argument: 'build'.
if any([a in ["build", "test", "install", "-h", "--help"] for a in args]):
parsed_args = parser.parse_args(args=args)
else:
parsed_args = parser.parse_args(args=["build"] + args)
# Execute the function for the subcommand we've been given.
parsed_args.func(parsed_args)
if __name__ == '__main__':
main()
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from .components import aes, assign_visual_mapping
from .components import colors, shapes
from .components.legend import draw_legend
from .geoms import *
from .scales import *
from .themes.theme_gray import _set_default_theme_rcparams
from .themes.theme_gray import _theme_grey_post_plot_callback
import ggplot.utils.six as six
__ALL__ = ["ggplot"]
import sys
import re
import warnings
# Show plots if in interactive mode
if sys.flags.interactive:
plt.ion()
class ggplot(object):
"""
ggplot is the base layer or object that you use to define
the components of your chart (x and y axis, shapes, colors, etc.).
You can combine it with layers (or geoms) to make complex graphics
with minimal effort.
Parameters
-----------
aesthetics : aes (ggplot.components.aes.aes)
aesthetics of your plot
data : pandas DataFrame (pd.DataFrame)
a DataFrame with the data you want to plot
Examples
----------
>>> p = ggplot(aes(x='x', y='y'), data=diamonds)
>>> print(p + geom_point())
"""
CONTINUOUS = ['x', 'y', 'size', 'alpha']
DISCRETE = ['color', 'shape', 'marker', 'alpha', 'linestyle']
def __init__(self, aesthetics, data):
# ggplot should just 'figure out' which is which
if not isinstance(data, pd.DataFrame):
aesthetics, data = data, aesthetics
self.aesthetics = aesthetics
self.data = data
self.data = _build_df_from_transforms(self.data, self.aesthetics)
# defaults
self.geoms = []
self.n_wide = 1
self.n_high = 1
self.n_dim_x = None
self.n_dim_y = None
# facets
self.facets = []
self.facet_type = None
self.facet_scales = None
self.facet_pairs = [] # used by facet_grid
# components
self.title = None
self.xlab = None
self.ylab = None
# format for x/y major ticks
self.xtick_formatter = None
self.xbreaks = None
self.xtick_labels = None
self.xmajor_locator = None
self.xminor_locator = None
self.ytick_formatter = None
self.xlimits = None
self.ylimits = None
self.scale_y_reverse = None
self.scale_x_reverse = None
# legend is a dictionary of {legend_type: {visual_value: legend_key}},
# where legend_type is one of "color", "linestyle", "marker", "size";
# visual_value is color value, line style, marker character, or size
# value; and legend_key is a quantile.
self.legend = {}
# Theme releated options
# this must be set by any theme to prevent addig the default theme
self.theme_applied = False
self.rcParams = {}
# Callbacks to change aspects of each axis
self.post_plot_callbacks = []
# continuous color configs
self.color_scale = None
self.colormap = plt.cm.Blues
self.manual_color_list = None
def __repr__(self):
"""Print/show the plot"""
figure = self.draw()
# We're going to default to making the plot appear when __repr__ is
# called.
#figure.show() # doesn't work in ipython notebook
plt.show()
# TODO: We can probably get more sugary with this
return "<ggplot: (%d)>" % self.__hash__()
def draw(self):
# Adding rc=self.rcParams does not validate/parses the params which then
# throws an error during plotting!
with mpl.rc_context():
if not self.theme_applied:
_set_default_theme_rcparams(mpl)
# will be empty if no theme was applied
for key in six.iterkeys(self.rcParams):
val = self.rcParams[key]
# there is a bug in matplotlib which does not allow None directly
# https://github.com/matplotlib/matplotlib/issues/2543
try:
if key == 'text.dvipnghack' and val is None:
val = "none"
mpl.rcParams[key] = val
except Exception as e:
msg = """Setting "mpl.rcParams['%s']=%s" raised an Exception: %s""" % (key, str(val), str(e))
warnings.warn(msg, RuntimeWarning)
# draw is not allowed to show a plot, so we can use to result for ggsave
# This sets a rcparam, so we don't have to undo it after plotting
mpl.interactive(False)
if self.facet_type == "grid":
fig, axs = plt.subplots(self.n_high, self.n_wide,
sharex=True, sharey=True)
plt.subplots_adjust(wspace=.05, hspace=.05)
elif self.facet_type == "wrap":
# add (more than) the needed number of subplots
fig, axs = plt.subplots(self.n_high, self.n_wide)
# there are some extra, remove the plots
subplots_available = self.n_wide * self.n_high
extra_subplots = subplots_available - self.n_dim_x
for extra_plot in axs.flatten()[-extra_subplots:]:
extra_plot.axis('off')
# plots is a mapping from xth-plot -> subplot position
plots = []
for x in range(self.n_wide):
for y in range(self.n_high):
plots.append((x, y))
plots = sorted(plots, key=lambda x: x[1] + x[0] * self.n_high + 1)
else:
fig, axs = plt.subplots(self.n_high, self.n_wide)
# Set the default plot to the first one
plt.subplot(self.n_wide, self.n_high, 1)
# Aes need to be initialized BEFORE we start faceting. This is b/c
# we want to have a consistent aes mapping across facets.
self.data = assign_visual_mapping(self.data, self.aesthetics, self)
# Faceting just means doing an additional groupby. The
# dimensions of the plot remain the same
if self.facets:
# the current subplot in the axs and plots
cntr = 0
if len(self.facets) == 2 and self.facet_type != "wrap":
# store the extreme x and y coordinates of each pair of axes
axis_extremes = np.zeros(shape=(self.n_high * self.n_wide, 4))
xlab_offset = .15
for _iter, (facets, frame) in enumerate(self.data.groupby(self.facets)):
pos = self.facet_pairs.index(facets) + 1
plt.subplot(self.n_wide, self.n_high, pos)
for layer in self._get_layers(frame):
for geom in self.geoms:
callbacks = geom.plot_layer(layer)
axis_extremes[_iter] = [min(plt.xlim()), max(plt.xlim()),
min(plt.ylim()), max(plt.ylim())]
# find the grid wide data extremeties
xlab_min, ylab_min = np.min(axis_extremes, axis=0)[[0, 2]]
xlab_max, ylab_max = np.max(axis_extremes, axis=0)[[1, 3]]
# position of vertical labels for facet grid
xlab_pos = xlab_max + xlab_offset
ylab_pos = ylab_max - float(ylab_max - ylab_min) / 2
# This needs to enumerate all possibilities
for _iter, facets in enumerate(self.facet_pairs):
pos = _iter + 1
if pos <= self.n_high:
plt.subplot(self.n_wide, self.n_high, pos)
for layer in self._get_layers(self.data):
for geom in self.geoms:
callbacks = geom.plot_layer(layer)
axis_extremes[_iter] = [min(plt.xlim()), max(plt.xlim()),
min(plt.ylim()), max(plt.ylim())]
# find the grid wide data extremeties
xlab_min, ylab_min = np.min(axis_extremes, axis=0)[[0, 2]]
xlab_max, ylab_max = np.max(axis_extremes, axis=0)[[1, 3]]
# position of vertical labels for facet grid
xlab_pos = xlab_max + xlab_offset
ylab_pos = ylab_max - float(ylab_max - ylab_min) / 2
# This needs to enumerate all possibilities
for pos, facets in enumerate(self.facet_pairs):
pos += 1
if pos <= self.n_high:
plt.subplot(self.n_wide, self.n_high, pos)
plt.table(cellText=[[facets[1]]], loc='top',
cellLoc='center', cellColours=[['lightgrey']])
if (pos % self.n_high) == 0:
plt.subplot(self.n_wide, self.n_high, pos)
x = max(plt.xticks()[0])
y = max(plt.yticks()[0])
ax = axs[pos % self.n_high][pos % self.n_wide]
plt.text(xlab_pos, ylab_pos, facets[0],
bbox=dict(
facecolor='lightgrey',
edgecolor='black',
color='black',
width=mpl.rcParams['font.size'] * 1.65
),
fontdict=dict(rotation=-90, verticalalignment="center", horizontalalignment='left')
)
plt.subplot(self.n_wide, self.n_high, pos)
# Handle the different scale types here
# (free|free_y|free_x|None) and also make sure that only the
# left column gets y scales and the bottom row gets x scales
scale_facet_grid(self.n_wide, self.n_high,
self.facet_pairs, self.facet_scales)
else: # now facet_wrap > 2
for facet, frame in self.data.groupby(self.facets):
for layer in self._get_layers(frame):
for geom in self.geoms:
if self.facet_type == "wrap":
if cntr + 1 > len(plots):
continue
pos = plots[cntr]
if pos is None:
continue
y_i, x_i = pos
pos = x_i + y_i * self.n_high + 1
plt.subplot(self.n_wide, self.n_high, pos)
else:
plt.subplot(self.n_wide, self.n_high, cntr)
# TODO: this needs some work
if (cntr % self.n_high) == -1:
plt.tick_params(axis='y', which='both',
bottom='off', top='off',
labelbottom='off')
callbacks = geom.plot_layer(layer)
if callbacks:
for callback in callbacks:
fn = getattr(axs[cntr], callback['function'])
fn(*callback['args'])
title = facet
if isinstance(facet, tuple):
title = ", ".join(facet)
plt.table(cellText=[[title]], loc='top',
cellLoc='center', cellColours=[['lightgrey']])
cntr += 1
# NOTE: Passing n_high for cols (instead of n_wide) and
# n_wide for rows because in all previous calls to
# plt.subplot, n_wide is passed as the number of rows, not
# columns.
scale_facet_wrap(self.n_wide, self.n_high, range(cntr), self.facet_scales)
else: # no faceting
for geom in self.geoms:
_aes = self.aesthetics
if geom.aes:
# update the default mapping with the geom specific one
_aes = _aes.copy()
_aes.update(geom.aes)
if not geom.data is None:
data = _build_df_from_transforms(geom.data, _aes)
data = assign_visual_mapping(data, _aes, self)
else:
data = self.data
for layer in self._get_layers(data, _aes):
plt.subplot(1, 1, 1)
callbacks = geom.plot_layer(layer)
if callbacks:
for callback in callbacks:
fn = getattr(axs, callback['function'])
fn(*callback['args'])
# Handling the details of the chart here; probably be a better
# way to do this...
if self.title:
plt.title(self.title)
if self.xlab:
if self.facet_type == "grid":
fig.text(0.5, 0.025, self.xlab)
else:
plt.xlabel(self.xlab)
if self.ylab:
if self.facet_type == "grid":
fig.text(0.025, 0.5, self.ylab, rotation='vertical')
else:
plt.ylabel(self.ylab)
if self.xmajor_locator:
plt.gca().xaxis.set_major_locator(self.xmajor_locator)
if self.xtick_formatter:
plt.gca().xaxis.set_major_formatter(self.xtick_formatter)
fig.autofmt_xdate()
if self.xbreaks: # xbreaks is a list manually provided
plt.gca().xaxis.set_ticks(self.xbreaks)
if self.xtick_labels:
plt.gca().xaxis.set_ticklabels(self.xtick_labels)
if self.ytick_formatter:
plt.gca().yaxis.set_major_formatter(self.ytick_formatter)
if self.xlimits:
plt.xlim(self.xlimits)
if self.ylimits:
plt.ylim(self.ylimits)
if self.scale_y_reverse:
plt.gca().invert_yaxis()
if self.scale_x_reverse:
plt.gca().invert_xaxis()
# TODO: Having some issues here with things that shouldn't have a legend
# or at least shouldn't get shrunk to accomodate one. Need some sort of
# test in place to prevent this OR prevent legend getting set to True.
if self.legend:
if self.facets:
ax = axs[0][self.n_wide - 1]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
else:
box = axs.get_position()
axs.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax = axs
cntr = 0
for ltype, legend in self.legend.items():
lname = self.aesthetics.get(ltype, ltype)
new_legend = draw_legend(ax, legend, ltype, lname, cntr)
ax.add_artist(new_legend)
cntr += 1
# Finaly apply any post plot callbacks (theming, etc)
if self.theme_applied:
for ax in plt.gcf().axes:
self._apply_post_plot_callbacks(ax)
else:
for ax in plt.gcf().axes:
_theme_grey_post_plot_callback(ax)
return plt.gcf()
def _get_layers(self, data=None, aes=None):
# This is handy because... (something to do w/ facets?)
if data is None:
data = self.data
if aes is None:
aes = self.aesthetics
# We want everything to be a DataFrame. We're going to default
# to key to handle items where the user hard codes a aesthetic
# (i.e. alpha=0.6)
mapping = pd.DataFrame({
ae: data.get(key, key)
for ae, key in aes.items()
})
if "color" in mapping:
mapping['color'] = data['color_mapping']
if "size" in mapping:
mapping['size'] = data['size_mapping']
if "shape" in mapping:
mapping['marker'] = data['shape_mapping']
del mapping['shape']
if "linestyle" in mapping:
mapping['linestyle'] = data['linestyle_mapping']
# Default the x and y axis labels to the name of the column
if "x" in aes and self.xlab is None:
self.xlab = aes['x']
if "y" in aes and self.ylab is None:
self.ylab = aes['y']
# Automatically drop any row that has an NA value
mapping = mapping.dropna()
discrete_aes = [ae for ae in self.DISCRETE if ae in mapping]
layers = []
if len(discrete_aes) == 0:
frame = mapping.to_dict('list')
layers.append(frame)
else:
for name, frame in mapping.groupby(discrete_aes):
frame = frame.to_dict('list')
for ae in self.DISCRETE:
if ae in frame:
frame[ae] = frame[ae][0]
layers.append(frame)
return layers
def add_to_legend(self, legend_type, legend_dict, scale_type="discrete"):
"""Adds the the specified legend to the legend
Parameter
---------
legend_type : str
type of legend, one of "color", "linestyle", "marker", "size"
legend_dict : dict
a dictionary of {visual_value: legend_key} where visual_value
is color value, line style, marker character, or size value;
and legend_key is a quantile.
scale_type : str
either "discrete" (default) or "continuous"; usually only color
needs to specify which kind of legend should be drawn, all
other scales will get a discrete scale.
"""
# scale_type is up to now unused
# TODO: what happens if we add a second color mapping?
# Currently the color mapping in the legend is overwritten.
# What does ggplot do in such a case?
if legend_type in self.legend:
pass
#msg = "Adding a secondary mapping of {0} is unsupported and no legend for this mapping is added.\n"
#sys.stderr.write(msg.format(str(legend_type)))
self.legend[legend_type] = legend_dict
def _apply_post_plot_callbacks(self, axis):
for cb in self.post_plot_callbacks:
cb(axis)
def _is_identity(x):
if x in colors.COLORS:
return True
elif x in shapes.SHAPES:
return True
elif isinstance(x, (float, int)):
return True
else:
return False
def _build_df_from_transforms(data, aes):
"""Adds columns from the in aes included transformations
Possible transformations are "factor(<col>)" and
expresions which can be used with eval.
Parameters
----------
data : DataFrame
the original dataframe
aes : aesthetics
the aesthetic
Returns
-------
data : DateFrame
Transformend DataFrame
"""
for ae, name in aes.items():
if name not in data and not _is_identity(name):
# Look for alias/lambda functions
result = re.findall(r'(?:[A-Z])|(?:[A-Za_-z0-9]+)|(?:[/*+_=\(\)-])', name)
if re.match("factor[(][A-Za-z_0-9]+[)]", name):
m = re.search("factor[(]([A-Za-z_0-9]+)[)]", name)
data[name] = data[m.group(1)].apply(str)
else:
lambda_column = ""
for item in result:
if re.match("[/*+_=\(\)-]", item):
pass
elif re.match("^[0-9.]+$", item):
pass
else:
item = "data.get('%s')" % item
lambda_column += item
data[name] = eval(lambda_column)
return data
keeping changes from HEAD
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from .components import aes, assign_visual_mapping
from .components import colors, shapes
from .components.legend import draw_legend
from .geoms import *
from .scales import *
from .themes.theme_gray import _set_default_theme_rcparams
from .themes.theme_gray import _theme_grey_post_plot_callback
import ggplot.utils.six as six
__ALL__ = ["ggplot"]
import sys
import re
import warnings
# Show plots if in interactive mode
if sys.flags.interactive:
plt.ion()
class ggplot(object):
"""
ggplot is the base layer or object that you use to define
the components of your chart (x and y axis, shapes, colors, etc.).
You can combine it with layers (or geoms) to make complex graphics
with minimal effort.
Parameters
-----------
aesthetics : aes (ggplot.components.aes.aes)
aesthetics of your plot
data : pandas DataFrame (pd.DataFrame)
a DataFrame with the data you want to plot
Examples
----------
>>> p = ggplot(aes(x='x', y='y'), data=diamonds)
>>> print(p + geom_point())
"""
CONTINUOUS = ['x', 'y', 'size', 'alpha']
DISCRETE = ['color', 'shape', 'marker', 'alpha', 'linestyle']
def __init__(self, aesthetics, data):
# ggplot should just 'figure out' which is which
if not isinstance(data, pd.DataFrame):
aesthetics, data = data, aesthetics
self.aesthetics = aesthetics
self.data = data
self.data = _build_df_from_transforms(self.data, self.aesthetics)
# defaults
self.geoms = []
self.n_wide = 1
self.n_high = 1
self.n_dim_x = None
self.n_dim_y = None
# facets
self.facets = []
self.facet_type = None
self.facet_scales = None
self.facet_pairs = [] # used by facet_grid
# components
self.title = None
self.xlab = None
self.ylab = None
# format for x/y major ticks
self.xtick_formatter = None
self.xbreaks = None
self.xtick_labels = None
self.xmajor_locator = None
self.xminor_locator = None
self.ytick_formatter = None
self.xlimits = None
self.ylimits = None
self.scale_y_reverse = None
self.scale_x_reverse = None
# legend is a dictionary of {legend_type: {visual_value: legend_key}},
# where legend_type is one of "color", "linestyle", "marker", "size";
# visual_value is color value, line style, marker character, or size
# value; and legend_key is a quantile.
self.legend = {}
# Theme releated options
# this must be set by any theme to prevent addig the default theme
self.theme_applied = False
self.rcParams = {}
# Callbacks to change aspects of each axis
self.post_plot_callbacks = []
# continuous color configs
self.color_scale = None
self.colormap = plt.cm.Blues
self.manual_color_list = None
def __repr__(self):
"""Print/show the plot"""
figure = self.draw()
# We're going to default to making the plot appear when __repr__ is
# called.
#figure.show() # doesn't work in ipython notebook
plt.show()
# TODO: We can probably get more sugary with this
return "<ggplot: (%d)>" % self.__hash__()
def draw(self):
# Adding rc=self.rcParams does not validate/parses the params which then
# throws an error during plotting!
with mpl.rc_context():
if not self.theme_applied:
_set_default_theme_rcparams(mpl)
# will be empty if no theme was applied
for key in six.iterkeys(self.rcParams):
val = self.rcParams[key]
# there is a bug in matplotlib which does not allow None directly
# https://github.com/matplotlib/matplotlib/issues/2543
try:
if key == 'text.dvipnghack' and val is None:
val = "none"
mpl.rcParams[key] = val
except Exception as e:
msg = """Setting "mpl.rcParams['%s']=%s" raised an Exception: %s""" % (key, str(val), str(e))
warnings.warn(msg, RuntimeWarning)
# draw is not allowed to show a plot, so we can use to result for ggsave
# This sets a rcparam, so we don't have to undo it after plotting
mpl.interactive(False)
if self.facet_type == "grid":
fig, axs = plt.subplots(self.n_high, self.n_wide,
sharex=True, sharey=True)
plt.subplots_adjust(wspace=.05, hspace=.05)
elif self.facet_type == "wrap":
# add (more than) the needed number of subplots
fig, axs = plt.subplots(self.n_high, self.n_wide)
# there are some extra, remove the plots
subplots_available = self.n_wide * self.n_high
extra_subplots = subplots_available - self.n_dim_x
for extra_plot in axs.flatten()[-extra_subplots:]:
extra_plot.axis('off')
# plots is a mapping from xth-plot -> subplot position
plots = []
for x in range(self.n_wide):
for y in range(self.n_high):
plots.append((x, y))
plots = sorted(plots, key=lambda x: x[1] + x[0] * self.n_high + 1)
else:
fig, axs = plt.subplots(self.n_high, self.n_wide)
axs = np.atleast_2d(axs)
# Set the default plot to the first one
plt.subplot(self.n_wide, self.n_high, 1)
# Aes need to be initialized BEFORE we start faceting. This is b/c
# we want to have a consistent aes mapping across facets.
self.data = assign_visual_mapping(self.data, self.aesthetics, self)
# Faceting just means doing an additional groupby. The
# dimensions of the plot remain the same
if self.facets:
# the current subplot in the axs and plots
cntr = 0
if len(self.facets) == 2 and self.facet_type != "wrap":
# store the extreme x and y coordinates of each pair of axes
axis_extremes = np.zeros(shape=(self.n_high * self.n_wide, 4))
xlab_offset = .15
for _iter, (facets, frame) in enumerate(self.data.groupby(self.facets)):
pos = self.facet_pairs.index(facets) + 1
plt.subplot(self.n_wide, self.n_high, pos)
for layer in self._get_layers(frame):
for geom in self.geoms:
callbacks = geom.plot_layer(layer)
axis_extremes[_iter] = [min(plt.xlim()), max(plt.xlim()),
min(plt.ylim()), max(plt.ylim())]
# find the grid wide data extremeties
xlab_min, ylab_min = np.min(axis_extremes, axis=0)[[0, 2]]
xlab_max, ylab_max = np.max(axis_extremes, axis=0)[[1, 3]]
# position of vertical labels for facet grid
xlab_pos = xlab_max + xlab_offset
ylab_pos = ylab_max - float(ylab_max - ylab_min) / 2
# This needs to enumerate all possibilities
for _iter, facets in enumerate(self.facet_pairs):
pos = _iter + 1
if pos <= self.n_high:
plt.subplot(self.n_wide, self.n_high, pos)
for layer in self._get_layers(self.data):
for geom in self.geoms:
callbacks = geom.plot_layer(layer)
axis_extremes[_iter] = [min(plt.xlim()), max(plt.xlim()),
min(plt.ylim()), max(plt.ylim())]
# find the grid wide data extremeties
xlab_min, ylab_min = np.min(axis_extremes, axis=0)[[0, 2]]
xlab_max, ylab_max = np.max(axis_extremes, axis=0)[[1, 3]]
# position of vertical labels for facet grid
xlab_pos = xlab_max + xlab_offset
ylab_pos = ylab_max - float(ylab_max - ylab_min) / 2
# This needs to enumerate all possibilities
for pos, facets in enumerate(self.facet_pairs):
pos += 1
if pos <= self.n_high:
plt.subplot(self.n_wide, self.n_high, pos)
plt.table(cellText=[[facets[1]]], loc='top',
cellLoc='center', cellColours=[['lightgrey']])
if (pos % self.n_high) == 0:
plt.subplot(self.n_wide, self.n_high, pos)
x = max(plt.xticks()[0])
y = max(plt.yticks()[0])
ax = axs[pos % self.n_high][pos % self.n_wide]
plt.text(xlab_pos, ylab_pos, facets[0],
bbox=dict(
facecolor='lightgrey',
edgecolor='black',
color='black',
width=mpl.rcParams['font.size'] * 1.65
),
fontdict=dict(rotation=-90, verticalalignment="center", horizontalalignment='left')
)
plt.subplot(self.n_wide, self.n_high, pos)
# Handle the different scale types here
# (free|free_y|free_x|None) and also make sure that only the
# left column gets y scales and the bottom row gets x scales
scale_facet_grid(self.n_wide, self.n_high,
self.facet_pairs, self.facet_scales)
else: # now facet_wrap > 2
for facet, frame in self.data.groupby(self.facets):
for layer in self._get_layers(frame):
for geom in self.geoms:
if self.facet_type == "wrap":
if cntr + 1 > len(plots):
continue
pos = plots[cntr]
if pos is None:
continue
y_i, x_i = pos
pos = x_i + y_i * self.n_high + 1
ax = plt.subplot(self.n_wide, self.n_high, pos)
else:
ax = plt.subplot(self.n_wide, self.n_high, cntr)
# TODO: this needs some work
if (cntr % self.n_high) == -1:
plt.tick_params(axis='y', which='both',
bottom='off', top='off',
labelbottom='off')
callbacks = geom.plot_layer(layer)
if callbacks:
for callback in callbacks:
fn = getattr(ax, callback['function'])
fn(*callback['args'])
title = facet
if isinstance(facet, tuple):
title = ", ".join(facet)
plt.table(cellText=[[title]], loc='top',
cellLoc='center', cellColours=[['lightgrey']])
cntr += 1
# NOTE: Passing n_high for cols (instead of n_wide) and
# n_wide for rows because in all previous calls to
# plt.subplot, n_wide is passed as the number of rows, not
# columns.
scale_facet_wrap(self.n_wide, self.n_high, range(cntr), self.facet_scales)
else: # no faceting
for geom in self.geoms:
_aes = self.aesthetics
if geom.aes:
# update the default mapping with the geom specific one
_aes = _aes.copy()
_aes.update(geom.aes)
if not geom.data is None:
data = _build_df_from_transforms(geom.data, _aes)
data = assign_visual_mapping(data, _aes, self)
else:
data = self.data
for layer in self._get_layers(data, _aes):
plt.subplot(1, 1, 1)
callbacks = geom.plot_layer(layer)
if callbacks:
for callback in callbacks:
fn = getattr(axs[0][0], callback['function'])
fn(*callback['args'])
# Handling the details of the chart here; probably be a better
# way to do this...
if self.title:
plt.title(self.title)
if self.xlab:
if self.facet_type == "grid":
fig.text(0.5, 0.025, self.xlab)
else:
plt.xlabel(self.xlab)
if self.ylab:
if self.facet_type == "grid":
fig.text(0.025, 0.5, self.ylab, rotation='vertical')
else:
plt.ylabel(self.ylab)
if self.xmajor_locator:
plt.gca().xaxis.set_major_locator(self.xmajor_locator)
if self.xtick_formatter:
plt.gca().xaxis.set_major_formatter(self.xtick_formatter)
fig.autofmt_xdate()
if self.xbreaks: # xbreaks is a list manually provided
plt.gca().xaxis.set_ticks(self.xbreaks)
if self.xtick_labels:
plt.gca().xaxis.set_ticklabels(self.xtick_labels)
if self.ytick_formatter:
plt.gca().yaxis.set_major_formatter(self.ytick_formatter)
if self.xlimits:
plt.xlim(self.xlimits)
if self.ylimits:
plt.ylim(self.ylimits)
if self.scale_y_reverse:
plt.gca().invert_yaxis()
if self.scale_x_reverse:
plt.gca().invert_xaxis()
# TODO: Having some issues here with things that shouldn't have a legend
# or at least shouldn't get shrunk to accomodate one. Need some sort of
# test in place to prevent this OR prevent legend getting set to True.
if self.legend:
if self.facets:
ax = axs[0][self.n_wide - 1]
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
else:
box = axs.get_position()
axs.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax = axs
cntr = 0
for ltype, legend in self.legend.items():
lname = self.aesthetics.get(ltype, ltype)
new_legend = draw_legend(ax, legend, ltype, lname, cntr)
ax.add_artist(new_legend)
cntr += 1
# Finaly apply any post plot callbacks (theming, etc)
if self.theme_applied:
for ax in plt.gcf().axes:
self._apply_post_plot_callbacks(ax)
else:
for ax in plt.gcf().axes:
_theme_grey_post_plot_callback(ax)
return plt.gcf()
def _get_layers(self, data=None, aes=None):
# This is handy because... (something to do w/ facets?)
if data is None:
data = self.data
if aes is None:
aes = self.aesthetics
# We want everything to be a DataFrame. We're going to default
# to key to handle items where the user hard codes a aesthetic
# (i.e. alpha=0.6)
mapping = pd.DataFrame({
ae: data.get(key, key)
for ae, key in aes.items()
})
if "color" in mapping:
mapping['color'] = data['color_mapping']
if "size" in mapping:
mapping['size'] = data['size_mapping']
if "shape" in mapping:
mapping['marker'] = data['shape_mapping']
del mapping['shape']
if "linestyle" in mapping:
mapping['linestyle'] = data['linestyle_mapping']
# Default the x and y axis labels to the name of the column
if "x" in aes and self.xlab is None:
self.xlab = aes['x']
if "y" in aes and self.ylab is None:
self.ylab = aes['y']
# Automatically drop any row that has an NA value
mapping = mapping.dropna()
discrete_aes = [ae for ae in self.DISCRETE if ae in mapping]
layers = []
if len(discrete_aes) == 0:
frame = mapping.to_dict('list')
layers.append(frame)
else:
for name, frame in mapping.groupby(discrete_aes):
frame = frame.to_dict('list')
for ae in self.DISCRETE:
if ae in frame:
frame[ae] = frame[ae][0]
layers.append(frame)
return layers
def add_to_legend(self, legend_type, legend_dict, scale_type="discrete"):
"""Adds the the specified legend to the legend
Parameter
---------
legend_type : str
type of legend, one of "color", "linestyle", "marker", "size"
legend_dict : dict
a dictionary of {visual_value: legend_key} where visual_value
is color value, line style, marker character, or size value;
and legend_key is a quantile.
scale_type : str
either "discrete" (default) or "continuous"; usually only color
needs to specify which kind of legend should be drawn, all
other scales will get a discrete scale.
"""
# scale_type is up to now unused
# TODO: what happens if we add a second color mapping?
# Currently the color mapping in the legend is overwritten.
# What does ggplot do in such a case?
if legend_type in self.legend:
pass
#msg = "Adding a secondary mapping of {0} is unsupported and no legend for this mapping is added.\n"
#sys.stderr.write(msg.format(str(legend_type)))
self.legend[legend_type] = legend_dict
def _apply_post_plot_callbacks(self, axis):
for cb in self.post_plot_callbacks:
cb(axis)
def _is_identity(x):
if x in colors.COLORS:
return True
elif x in shapes.SHAPES:
return True
elif isinstance(x, (float, int)):
return True
else:
return False
def _build_df_from_transforms(data, aes):
"""Adds columns from the in aes included transformations
Possible transformations are "factor(<col>)" and
expresions which can be used with eval.
Parameters
----------
data : DataFrame
the original dataframe
aes : aesthetics
the aesthetic
Returns
-------
data : DateFrame
Transformend DataFrame
"""
for ae, name in aes.items():
if name not in data and not _is_identity(name):
# Look for alias/lambda functions
result = re.findall(r'(?:[A-Z])|(?:[A-Za_-z0-9]+)|(?:[/*+_=\(\)-])', name)
if re.match("factor[(][A-Za-z_0-9]+[)]", name):
m = re.search("factor[(]([A-Za-z_0-9]+)[)]", name)
data[name] = data[m.group(1)].apply(str)
else:
lambda_column = ""
for item in result:
if re.match("[/*+_=\(\)-]", item):
pass
elif re.match("^[0-9.]+$", item):
pass
else:
item = "data.get('%s')" % item
lambda_column += item
data[name] = eval(lambda_column)
return data
|
# -*- coding: utf-8 -*-
#
# dp for Tornado
# YoungYong Park (youngyongpark@gmail.com)
# 2014.11.11
#
from __future__ import absolute_import
from engine.helper import Helper as dpHelper
import string
unicode_type = type(u'')
class StringHelper(dpHelper):
@property
def ascii_uppercase(self):
return string.ascii_uppercase
@property
def ascii_letters(self):
return string.ascii_letters
def is_string(self, s):
if self.helper.system.py_version <= 2:
types = basestring,
else:
types = str,
return True if isinstance(s, types) else False
def random_string(self, length):
return ''.join(self.helper.random.sample(self.ascii_letters, length))
def check_exist_repeated_text(self, s, criteria=3):
if not self.is_string(s):
return None
k = s[0]
n = 0
for c in s:
if c == k:
n += 1
if n >= criteria:
return True
else:
k = c
n = 1
return False
def to_str(self, s, preserve_none=True):
if s is None:
return s
if not self.is_string(s):
s = str(s)
if type(s) == unicode_type:
if self.helper.system.py_version <= 2:
return s.encode('UTF-8')
else:
return s
else:
return s
def to_unicode(self, s, preserve_none=True):
if s is None:
return s
if not self.is_string(s):
s = str(s)
if type(s) != unicode_type:
return s.decode('UTF-8')
else:
return s
Add is_string alias to is_str.
# -*- coding: utf-8 -*-
#
# dp for Tornado
# YoungYong Park (youngyongpark@gmail.com)
# 2014.11.11
#
from __future__ import absolute_import
from engine.helper import Helper as dpHelper
import string
unicode_type = type(u'')
class StringHelper(dpHelper):
@property
def ascii_uppercase(self):
return string.ascii_uppercase
@property
def ascii_letters(self):
return string.ascii_letters
def is_str(self, s):
return self.is_string(s)
def is_string(self, s):
if self.helper.system.py_version <= 2:
types = basestring,
else:
types = str,
return True if isinstance(s, types) else False
def random_string(self, length):
return ''.join(self.helper.random.sample(self.ascii_letters, length))
def check_exist_repeated_text(self, s, criteria=3):
if not self.is_string(s):
return None
k = s[0]
n = 0
for c in s:
if c == k:
n += 1
if n >= criteria:
return True
else:
k = c
n = 1
return False
def to_str(self, s, preserve_none=True):
if s is None:
return s
if not self.is_string(s):
s = str(s)
if type(s) == unicode_type:
if self.helper.system.py_version <= 2:
return s.encode('UTF-8')
else:
return s
else:
return s
def to_unicode(self, s, preserve_none=True):
if s is None:
return s
if not self.is_string(s):
s = str(s)
if type(s) != unicode_type:
return s.decode('UTF-8')
else:
return s |
# Copyright 2019 DeepMind Technologies Ltd. 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.
"""Tests for softmax_policy."""
from absl.testing import absltest
from absl.testing import parameterized
from open_spiel.python import policy
from open_spiel.python.mfg import value
from open_spiel.python.mfg.algorithms import best_response_value
from open_spiel.python.mfg.algorithms import distribution
from open_spiel.python.mfg.algorithms import greedy_policy
from open_spiel.python.mfg.algorithms import softmax_policy
from open_spiel.python.mfg.algorithms import policy_value
from open_spiel.python.mfg.games import crowd_modelling # pylint: disable=unused-import
import pyspiel
class SoftmaxPolicyTest(parameterized.TestCase):
@parameterized.named_parameters(('python', 'python_mfg_crowd_modelling'),
('cpp', 'mfg_crowd_modelling'))
def test_softmax(self, name):
"""Check if the softmax policy works as expected.
The test checks that:
- uniform prior policy gives the same results than no prior.
- very high temperature gives almost a uniform policy.
- very low temperature gives almost a deterministic policy for the best action.
Args:
name: Name of the game.
"""
game = pyspiel.load_game(name)
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(
game, dist, value.TabularValueFunction(game))
br_init_val = br_value(game.new_initial_state())
# uniform prior policy gives the same results than no prior.
softmax_pi_uniform_prior = softmax_policy.SoftmaxPolicy(game, None, 1.0, br_value, uniform_policy).to_tabular()
softmax_pi_uniform_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_uniform_prior,
value.TabularValueFunction(game))
softmax_pi_uniform_prior_init_val = softmax_pi_uniform_prior_value(game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 1.0, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(softmax_pi_uniform_prior_init_val, softmax_pi_no_prior_init_val)
# very high temperature gives almost a uniform policy.
uniform_policy = uniform_policy.to_tabular()
uniform_value = policy_value.PolicyValue(game, dist, uniform_policy,
value.TabularValueFunction(game))
uniform_init_val = uniform_value(game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 100000000, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(uniform_init_val, softmax_pi_no_prior_init_val)
# very low temperature gives almost a best response policy.
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 0.0001, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(br_init_val, softmax_pi_no_prior_init_val)
if __name__ == '__main__':
absltest.main()
Update softmax_policy_test.py
Removed unused import.
# Copyright 2019 DeepMind Technologies Ltd. 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.
"""Tests for softmax_policy."""
from absl.testing import absltest
from absl.testing import parameterized
from open_spiel.python import policy
from open_spiel.python.mfg import value
from open_spiel.python.mfg.algorithms import best_response_value
from open_spiel.python.mfg.algorithms import distribution
from open_spiel.python.mfg.algorithms import softmax_policy
from open_spiel.python.mfg.algorithms import policy_value
from open_spiel.python.mfg.games import crowd_modelling # pylint: disable=unused-import
import pyspiel
class SoftmaxPolicyTest(parameterized.TestCase):
@parameterized.named_parameters(('python', 'python_mfg_crowd_modelling'),
('cpp', 'mfg_crowd_modelling'))
def test_softmax(self, name):
"""Check if the softmax policy works as expected.
The test checks that:
- uniform prior policy gives the same results than no prior.
- very high temperature gives almost a uniform policy.
- very low temperature gives almost a deterministic policy for the best action.
Args:
name: Name of the game.
"""
game = pyspiel.load_game(name)
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(
game, dist, value.TabularValueFunction(game))
br_init_val = br_value(game.new_initial_state())
# uniform prior policy gives the same results than no prior.
softmax_pi_uniform_prior = softmax_policy.SoftmaxPolicy(game, None, 1.0, br_value, uniform_policy).to_tabular()
softmax_pi_uniform_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_uniform_prior,
value.TabularValueFunction(game))
softmax_pi_uniform_prior_init_val = softmax_pi_uniform_prior_value(game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 1.0, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(softmax_pi_uniform_prior_init_val, softmax_pi_no_prior_init_val)
# very high temperature gives almost a uniform policy.
uniform_policy = uniform_policy.to_tabular()
uniform_value = policy_value.PolicyValue(game, dist, uniform_policy,
value.TabularValueFunction(game))
uniform_init_val = uniform_value(game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 100000000, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(uniform_init_val, softmax_pi_no_prior_init_val)
# very low temperature gives almost a best response policy.
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(game, None, 0.0001, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(game, dist, softmax_pi_no_prior,
value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(game.new_initial_state())
self.assertAlmostEqual(br_init_val, softmax_pi_no_prior_init_val)
if __name__ == '__main__':
absltest.main()
|
from flask import Flask, abort
import gzip
import configparser
import os
import uuid
import json
app = Flask(__name__)
FILE_NAME = "picc/config.conf"
if not os.path.exists("picc"):
os.makedirs("picc")
config = configparser.ConfigParser()
shared_secret = None
# Initial Setup
if not os.path.isfile(FILE_NAME):
shared_secret = str(uuid.uuid4())
config.set(configparser.DEFAULTSECT, "shared-secret", value=shared_secret)
config.write(open(FILE_NAME, 'w+'))
else:
config.read(FILE_NAME)
shared_secret = config.get(configparser.DEFAULTSECT, "shared-secret")
class VideoStitcher:
_video_id = None
_name = None
def __init__(self, video_id, name):
self._video_id = video_id
self._name = name
@app.route("/auth/<secret>/<public_key>", methods=['GET'])
def main_root(secret=None, public_key=None):
if not secret or not public_key:
abort(401)
return
if secret != shared_secret:
abort(403)
return
pass
@app.route("/send_image", methods=['POST'])
def send_image():
pass
if __name__ == "__main__":
app.run()
Why does it need ID.
from flask import Flask, abort
import gzip
import configparser
import os
import uuid
import json
app = Flask(__name__)
FILE_NAME = "picc/config.conf"
if not os.path.exists("picc"):
os.makedirs("picc")
config = configparser.ConfigParser()
shared_secret = None
# Initial Setup
if not os.path.isfile(FILE_NAME):
shared_secret = str(uuid.uuid4())
config.set(configparser.DEFAULTSECT, "shared-secret", value=shared_secret)
config.write(open(FILE_NAME, 'w+'))
else:
config.read(FILE_NAME)
shared_secret = config.get(configparser.DEFAULTSECT, "shared-secret")
class VideoStitcher:
_name = None
def __init__(self, name):
self._name = name
@app.route("/auth/<secret>/<public_key>", methods=['GET'])
def main_root(secret=None, public_key=None):
if not secret or not public_key:
abort(401)
return
if secret != shared_secret:
abort(403)
return
pass
@app.route("/send_image", methods=['POST'])
def send_image():
pass
if __name__ == "__main__":
app.run()
|
#!/usr/bin/env python3
import asyncio
import sys
sys.path.append("..")
import jauxiliar as jaux
import josecommon as jcommon
import decimal
import json
import os
import time
from random import SystemRandom
random = SystemRandom()
PRICE_TABLE = {
'api': ('Tax for Commands that use APIs', jcommon.API_TAX_PRICE, \
('wolframalpha', 'temperature', 'money', 'bitcoin', '8ball', \
'xkcd', 'sndc')),
'img': ('Price for all commands in `joseimages`', jcommon.IMG_PRICE, \
('derpibooru', 'hypno', 'e621', 'yandere')),
'opr': ('Operational tax for commands that use a lot of processing', jcommon.OP_TAX_PRICE, \
('datamosh', 'yt'))
}
# 1%
BASE_CHANCE = decimal.Decimal(1)
STEALDB_PATH = 'db/steal.json'
ARREST_TIME = 28800 # 8 hours
DEFAULT_STEALDB = '''{
"points": {},
"cdown": {},
"period": {}
}'''
HELPTEXT_JC_STEAL = """
`j!steal` allows you to steal an arbritary amount of money from anyone.
use `j!stealstat` to see your status in the stealing business.
The chance of getting caught increases the more you steal.
When using `j!steal`, `res` and `prob` show up, `res` is a random value and
if it is greater than `prob`, you are arrested. `prob` is calculated using
the target's current wallet and the amount you want to steal from them.
"""
class JoseCoin(jaux.Auxiliar):
def __init__(self, _client):
jaux.Auxiliar.__init__(self, _client)
self.counter = 0
def to_hours(self, seconds):
return seconds / 60 / 60
async def josecoin_save(self, message, dbg_flag=True):
res = self.jcoin.save('jcoin/josecoin.db')
if not res[0]:
self.logger.error("jcerr: %r", res)
if message is not None:
await self.client.send_message(message.channel, \
"jcerr: `%r`" % res)
return res
async def josecoin_load(self, message, dbg_flag=True):
res = self.jcoin.load('jcoin/josecoin.db')
if not res[0]:
self.logger.error("jcerr: %r", res)
if message is not None:
await self.client.send_message(message.channel, \
"jcerr: `%r`" % res)
return res
async def save_steal_db(self):
try:
self.logger.info("savedb:stealdb")
json.dump(self.stealdb, open(STEALDB_PATH, 'w'))
return True, ''
except Exception as err:
return False, str(err)
async def load_steal_db(self):
try:
self.stealdb = {}
if not os.path.isfile(STEALDB_PATH):
with open(STEALDB_PATH, 'w') as stealdbfile:
stealdbfile.write(DEFAULT_STEALDB)
self.stealdb = json.load(open(STEALDB_PATH, 'r'))
return True, ''
except Exception as err:
return False, str(err)
async def ext_load(self):
res_jc = await self.josecoin_load(None)
if not res_jc[0]:
return res_jc
res_sdb = await self.load_steal_db()
if not res_sdb[0]:
return res_sdb
return True, ''
async def ext_unload(self):
res_jc = await self.josecoin_load(None)
if not res_jc[0]:
return res_jc
res_sdb = await self.save_steal_db()
if not res_sdb[0]:
return res_sdb
return True, ''
async def e_any_message(self, message, cxt):
self.counter += 1
if self.counter > 11:
await self.josecoin_save(message, False)
self.counter = 0
async def e_on_message(self, message, cxt):
probability = jcommon.JC_PROBABILITY
if message.author.id in self.stealdb['cdown']:
# get type of cooldown
# type 0 = arrest
# type 1 = get more stealing points
arrest_data = self.stealdb['cdown'][message.author.id]
if arrest_data[1] == 0:
probability /= 2
if random.random() > probability:
return
if message.channel.is_private:
return
author_id = str(message.author.id)
if author_id not in self.jcoin.data:
return
amount = random.choice(jcommon.JC_REWARDS)
if amount != 0:
res = self.jcoin.transfer(self.jcoin.jose_id, author_id, \
amount, self.jcoin.LEDGER_PATH)
if res[0]:
# delay because ratelimits???? need to study that
await asyncio.sleep(0.5)
await self.client.add_reaction(message, '💰')
else:
jcommon.logger.error("do_josecoin->jc->err: %s", res[1])
await cxt.say("jc->err: %s", (res[1],))
async def c_prices(self, message, args, cxt):
'''`j!prices` - show price categories'''
res = []
for cat in sorted(PRICE_TABLE):
data = PRICE_TABLE[cat]
desc = data[0]
price = data[1]
commands = data[2]
_cmdlist = ['j!{}'.format(cmd) for cmd in commands]
cmdlist = ', '.join(_cmdlist)
res.append("`%s`: %.2fJC, *%s*, `%s`" % (cat, price, desc, cmdlist))
await cxt.say('\n'.join(res))
async def c_wallet(self, message, args, cxt):
'''`j!wallet [@mention]` - your wallet(or other person's wallet)'''
args = message.content.split(' ')
id_check = None
if len(args) < 2:
id_check = message.author.id
else:
id_check = await jcommon.parse_id(args[1], message)
res = self.jcoin.get(id_check)
if res[0]:
accdata = res[1]
await cxt.say(('%s -> %.2f' % (accdata['name'], accdata['amount'])))
else:
await cxt.say('account not found(`id:%s`)' % (id_check))
async def c_balance(self, message, args, cxt):
'''`j!balance [@mention]` - alias to `j!wallet`'''
await self.c_wallet(message, args, cxt)
async def c_bal(self, message, args, cxt):
'''`j!balance [@mention]` - alias to `j!wallet`'''
await self.c_wallet(message, args, cxt)
async def c_account(self, message, args, cxt):
'''`j!account` - create a new JoséCoin account'''
self.logger.info("new jc account, id = %s" % message.author.id)
res = self.jcoin.new_acc(message.author.id, str(message.author))
if res[0]:
await cxt.say(res[1])
else:
await cxt.say('jc->err: %s' % res[1])
async def c_write(self, message, args, cxt):
'''`j!write @mention new_amount` - Overwrite an account's josecoins'''
global data
await self.is_admin(message.author.id)
if len(args) != 3:
await cxt.say(self.c_write.__doc__)
return
try:
id_from = await jcommon.parse_id(args[1], message)
new_amount = decimal.Decimal(args[2])
except Exception as e:
await cxt.say("huh, exception thingy... `%r`", (e,))
return
self.jcoin.data[id_from]['amount'] = new_amount
await cxt.say("<@%s> has %.2fJC now" % (id_from, \
self.jcoin.data[id_from]['amount']))
self.logger.info("%s Wrote %.2fJC to Account %s" % \
(str(message.author), new_amount, id_from))
async def c_jcsend(self, message, args, cxt):
'''`j!jcsend @mention amount` - send JoséCoins to someone'''
if len(args) != 3:
await cxt.say(self.c_jcsend.__doc__)
return
id_to = args[1]
try:
amount = decimal.Decimal(args[2])
except ValueError:
await cxt.say("ValueError: error parsing value")
return
except Exception as e:
await cxt.say("Exception: `%r`" % e)
return
id_from = message.author.id
id_to = await jcommon.parse_id(id_to, message)
res = self.jcoin.transfer(id_from, id_to, \
amount, self.jcoin.LEDGER_PATH)
await self.josecoin_save(message, False)
if res[0]:
await cxt.say(res[1])
else:
await cxt.say('jc_err: `%s`' % res[1])
async def c_ltop10(self, message, args, cxt):
'''`j!ltop10` - local top 10 people who have high josecoins'''
if message.server is None:
await cxt.say("You're not in a server, dummy!")
return
guild = message.server
jcdata = dict(self.jcoin.data) # copy
range_max = 11 # default 10 users
if len(args) > 1:
range_max = int(args[1]) + 1
if range_max >= 16:
await cxt.say("LimitError: values higher than 16 aren't valid")
return
elif range_max <= 0:
await cxt.say("haha no")
return
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
order = []
for i in range(1,range_max):
if len(jcdata) < 1:
break
for member in guild.members:
accid = member.id
if accid in jcdata:
acc = jcdata[accid]
name, amount = acc['name'], acc['amount']
if amount > maior['amount']:
maior['id'] = accid
maior['name'] = name
maior['amount'] = amount
else:
pass
if maior['id'] in jcdata:
del jcdata[maior['id']]
order.append('%d. %s -> %.2f' % \
(i, maior['name'], maior['amount']))
# reset to next
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
await cxt.say('\n'.join(order))
return
async def c_top10(self, message, args, cxt):
jcdata = dict(self.jcoin.data) # copy
range_max = 11 # default 10 users
if len(args) > 1:
range_max = int(args[1]) + 1
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
if range_max >= 16:
await cxt.say("LimitError: values higher than 16 aren't valid")
return
elif range_max <= 0:
await cxt.say("haha no")
return
order = []
for i in range(1,range_max):
if len(jcdata) < 1:
break
for accid in jcdata:
acc = jcdata[accid]
name, amount = acc['name'], acc['amount']
if amount > maior['amount']:
maior['id'] = accid
maior['name'] = name
maior['amount'] = amount
del jcdata[maior['id']]
order.append('%d. %s -> %.2f' % \
(i, maior['name'], maior['amount']))
# reset to next
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
await cxt.say('\n'.join(order))
return
async def c_hsteal(self, message, args, cxt):
await cxt.say(HELPTEXT_JC_STEAL)
async def c_stealstat(self, message, args, cxt):
# get status from person
personid = message.author.id
res = []
points = self.stealdb['points'].get(personid, 3)
cooldown = self.stealdb['cdown'].get(personid, None)
grace_period = self.stealdb['period'].get(personid, None)
res.append("**%s**, you have %d stealing points" % (str(message.author), points))
if cooldown is not None:
cooldown_sec, cooldown_type = cooldown
cooldown_sec -= time.time()
if cooldown_type == 0:
res.append(":cop: you're in prison, %.2f hours remaining" % (self.to_hours(cooldown_sec),))
elif cooldown_type == 1:
res.append(":alarm_clock: you're waiting for stealing points, %.2f hours remaining" % (self.to_hours(cooldown_sec),))
else:
res.append(":warning: unknown cooldown type")
if grace_period is not None:
grace_period -= time.time()
res.append(":angel: you're in grace period, %.2f hours remaining" % (self.to_hours(grace_period),))
await cxt.say('\n'.join(res))
async def do_arrest(self, thief_id, amount, arrest_type=0):
self.stealdb['cdown'][thief_id] = (time.time() + ARREST_TIME, arrest_type)
if arrest_type == 0:
# pay half the amount
fine = amount / decimal.Decimal(2)
res = await self.jcoin.transfer(thief_id, self.jcoin.jose_id, fine)
return res
async def c_steal(self, message, args, cxt):
'''`j!steal @target amount` - Steal JoséCoins from someone'''
if len(args) < 2:
await cxt.say(self.c_steal.__doc__)
return
# parse mention
try:
target_id = await jcommon.parse_id(args[1], message)
except:
await cxt.say("Error parsing `@target`")
return
try:
amount = decimal.Decimal(args[2])
except:
await cxt.say("Error parsing `amount`")
return
if message.author.id not in self.jcoin.data:
await cxt.say("You don't have a josécoin account.")
return
if target_id not in self.jcoin.data:
await cxt.say("The person you're trying to steal from doesn't have a JoséCoin account")
return
thief_id = message.author.id
# check if thief has cooldowns in place
cdown = self.stealdb['cdown'].get(thief_id, None)
if cdown is not None:
cooldown_end, cooldown_type = cdown
remaining = cooldown_end - time.time()
if cooldown_type == 0:
await cxt.say(":cop: You are still in prison, wait %.2f hours", (self.to_hours(remaining),))
elif cooldown_type == 1:
if remaining > 1:
await cxt.say("Wait %.2f hours to regenerate your stealing points", (self.to_hours(remaining),))
else:
await cxt.say("Stealing points regenerated!")
del self.stealdb['points'][thief_id]
await self.save_steal_db()
return
stealuses = self.stealdb['points'].get(thief_id, None)
if stealuses is None:
self.stealdb['points'][thief_id] = stealuses = 3
thief_user = message.author
target_user = await self.client.get_user_info(target_id)
grace_period = (self.stealdb['period'].get(target_id, 0) - time.time())
if grace_period > 0:
await cxt.say("Target is in :angel: grace period :angel:")
await cxt.say("%s tried to steal %.2fJC from you, but you have %.2f hours of grace period", \
target_user, (str(thief_user), amount, self.to_hours(grace_period)))
return
if stealuses < 1:
res = await self.do_arrest(thief_id, amount, 1)
await cxt.say("You don't have any more stealing points, wait 8 hours to get more.")
return
if target_id == self.jcoin.jose_id:
arrest = await self.do_arrest(thief_id, amount)
await cxt.say(":cop: You can't steal from José. Arrested for 8h\n`%s`", (arrest[1],))
return
target_account = self.jcoin.get(target_id)[1]
target_amount = target_account['amount']
if amount > target_amount:
# automatically in prison
arrest = await self.do_arrest(thief_id, amount)
await cxt.say(":cop: Arrested because you tried to steal more than the target has, 8h jail time.\n`%s`", \
(arrest[1]))
D = decimal.Decimal
chance = (BASE_CHANCE + (target_amount / amount)) * D(0.3)
if chance > 8: chance = 5
res = random.random() * 10
if res < chance:
self.logger.info("Stealing %.2fJC from %s[%s] to %s[%s]", \
amount, target_account['name'], target_id, message.author, thief_id)
# steal went good, make transfer
ok = self.jcoin.transfer(target_id, thief_id, amount)
# check transfer status
if not ok[0]:
await cxt.say("jc->err: %s", ok[1])
else:
await cxt.say("`[res: %.2f < prob: %.2f]` Stealing went well, nobody noticed, you thief. \n`%s`", \
(res, chance, ok[1]))
await cxt.say(":gun: You got robbed! The thief(%s) stole `%.2fJC` from you. 3 hour grace period", \
target_user, (str(thief_user), amount))
self.stealdb['period'][target_id] = time.time() + 10800
self.stealdb['points'][message.author.id] -= 1
else:
# type 0 cooldown, you got arrested
arrest = await self.do_arrest(thief_id, amount)
await cxt.say("`[res: %.2f > prob: %.2f]` :cop: Arrested! got 8h cooldown.\n`%s`", \
(res, chance, arrest[1]))
await self.save_steal_db()
async def c_roubar(self, message, args, cxt):
'''`j!roubar @target amount` - alias for `j!steal`'''
await self.c_steal(message, args, cxt)
jcoin: fix limiting in top10 commands
#!/usr/bin/env python3
import asyncio
import sys
sys.path.append("..")
import jauxiliar as jaux
import josecommon as jcommon
import decimal
import json
import os
import time
from random import SystemRandom
random = SystemRandom()
PRICE_TABLE = {
'api': ('Tax for Commands that use APIs', jcommon.API_TAX_PRICE, \
('wolframalpha', 'temperature', 'money', 'bitcoin', '8ball', \
'xkcd', 'sndc')),
'img': ('Price for all commands in `joseimages`', jcommon.IMG_PRICE, \
('derpibooru', 'hypno', 'e621', 'yandere')),
'opr': ('Operational tax for commands that use a lot of processing', jcommon.OP_TAX_PRICE, \
('datamosh', 'yt'))
}
# 1%
BASE_CHANCE = decimal.Decimal(1)
STEALDB_PATH = 'db/steal.json'
ARREST_TIME = 28800 # 8 hours
DEFAULT_STEALDB = '''{
"points": {},
"cdown": {},
"period": {}
}'''
HELPTEXT_JC_STEAL = """
`j!steal` allows you to steal an arbritary amount of money from anyone.
use `j!stealstat` to see your status in the stealing business.
The chance of getting caught increases the more you steal.
When using `j!steal`, `res` and `prob` show up, `res` is a random value and
if it is greater than `prob`, you are arrested. `prob` is calculated using
the target's current wallet and the amount you want to steal from them.
"""
class JoseCoin(jaux.Auxiliar):
def __init__(self, _client):
jaux.Auxiliar.__init__(self, _client)
self.counter = 0
def to_hours(self, seconds):
return seconds / 60 / 60
async def josecoin_save(self, message, dbg_flag=True):
res = self.jcoin.save('jcoin/josecoin.db')
if not res[0]:
self.logger.error("jcerr: %r", res)
if message is not None:
await self.client.send_message(message.channel, \
"jcerr: `%r`" % res)
return res
async def josecoin_load(self, message, dbg_flag=True):
res = self.jcoin.load('jcoin/josecoin.db')
if not res[0]:
self.logger.error("jcerr: %r", res)
if message is not None:
await self.client.send_message(message.channel, \
"jcerr: `%r`" % res)
return res
async def save_steal_db(self):
try:
self.logger.info("savedb:stealdb")
json.dump(self.stealdb, open(STEALDB_PATH, 'w'))
return True, ''
except Exception as err:
return False, str(err)
async def load_steal_db(self):
try:
self.stealdb = {}
if not os.path.isfile(STEALDB_PATH):
with open(STEALDB_PATH, 'w') as stealdbfile:
stealdbfile.write(DEFAULT_STEALDB)
self.stealdb = json.load(open(STEALDB_PATH, 'r'))
return True, ''
except Exception as err:
return False, str(err)
async def ext_load(self):
res_jc = await self.josecoin_load(None)
if not res_jc[0]:
return res_jc
res_sdb = await self.load_steal_db()
if not res_sdb[0]:
return res_sdb
return True, ''
async def ext_unload(self):
res_jc = await self.josecoin_load(None)
if not res_jc[0]:
return res_jc
res_sdb = await self.save_steal_db()
if not res_sdb[0]:
return res_sdb
return True, ''
async def e_any_message(self, message, cxt):
self.counter += 1
if self.counter > 11:
await self.josecoin_save(message, False)
self.counter = 0
async def e_on_message(self, message, cxt):
probability = jcommon.JC_PROBABILITY
if message.author.id in self.stealdb['cdown']:
# get type of cooldown
# type 0 = arrest
# type 1 = get more stealing points
arrest_data = self.stealdb['cdown'][message.author.id]
if arrest_data[1] == 0:
probability /= 2
if random.random() > probability:
return
if message.channel.is_private:
return
author_id = str(message.author.id)
if author_id not in self.jcoin.data:
return
amount = random.choice(jcommon.JC_REWARDS)
if amount != 0:
res = self.jcoin.transfer(self.jcoin.jose_id, author_id, \
amount, self.jcoin.LEDGER_PATH)
if res[0]:
# delay because ratelimits???? need to study that
await asyncio.sleep(0.5)
await self.client.add_reaction(message, '💰')
else:
jcommon.logger.error("do_josecoin->jc->err: %s", res[1])
await cxt.say("jc->err: %s", (res[1],))
async def c_prices(self, message, args, cxt):
'''`j!prices` - show price categories'''
res = []
for cat in sorted(PRICE_TABLE):
data = PRICE_TABLE[cat]
desc = data[0]
price = data[1]
commands = data[2]
_cmdlist = ['j!{}'.format(cmd) for cmd in commands]
cmdlist = ', '.join(_cmdlist)
res.append("`%s`: %.2fJC, *%s*, `%s`" % (cat, price, desc, cmdlist))
await cxt.say('\n'.join(res))
async def c_wallet(self, message, args, cxt):
'''`j!wallet [@mention]` - your wallet(or other person's wallet)'''
args = message.content.split(' ')
id_check = None
if len(args) < 2:
id_check = message.author.id
else:
id_check = await jcommon.parse_id(args[1], message)
res = self.jcoin.get(id_check)
if res[0]:
accdata = res[1]
await cxt.say(('%s -> %.2f' % (accdata['name'], accdata['amount'])))
else:
await cxt.say('account not found(`id:%s`)' % (id_check))
async def c_balance(self, message, args, cxt):
'''`j!balance [@mention]` - alias to `j!wallet`'''
await self.c_wallet(message, args, cxt)
async def c_bal(self, message, args, cxt):
'''`j!balance [@mention]` - alias to `j!wallet`'''
await self.c_wallet(message, args, cxt)
async def c_account(self, message, args, cxt):
'''`j!account` - create a new JoséCoin account'''
self.logger.info("new jc account, id = %s" % message.author.id)
res = self.jcoin.new_acc(message.author.id, str(message.author))
if res[0]:
await cxt.say(res[1])
else:
await cxt.say('jc->err: %s' % res[1])
async def c_write(self, message, args, cxt):
'''`j!write @mention new_amount` - Overwrite an account's josecoins'''
global data
await self.is_admin(message.author.id)
if len(args) != 3:
await cxt.say(self.c_write.__doc__)
return
try:
id_from = await jcommon.parse_id(args[1], message)
new_amount = decimal.Decimal(args[2])
except Exception as e:
await cxt.say("huh, exception thingy... `%r`", (e,))
return
self.jcoin.data[id_from]['amount'] = new_amount
await cxt.say("<@%s> has %.2fJC now" % (id_from, \
self.jcoin.data[id_from]['amount']))
self.logger.info("%s Wrote %.2fJC to Account %s" % \
(str(message.author), new_amount, id_from))
async def c_jcsend(self, message, args, cxt):
'''`j!jcsend @mention amount` - send JoséCoins to someone'''
if len(args) != 3:
await cxt.say(self.c_jcsend.__doc__)
return
id_to = args[1]
try:
amount = decimal.Decimal(args[2])
except ValueError:
await cxt.say("ValueError: error parsing value")
return
except Exception as e:
await cxt.say("Exception: `%r`" % e)
return
id_from = message.author.id
id_to = await jcommon.parse_id(id_to, message)
res = self.jcoin.transfer(id_from, id_to, \
amount, self.jcoin.LEDGER_PATH)
await self.josecoin_save(message, False)
if res[0]:
await cxt.say(res[1])
else:
await cxt.say('jc_err: `%s`' % res[1])
async def c_ltop10(self, message, args, cxt):
'''`j!ltop10` - local top 10 people who have high josecoins'''
if message.server is None:
await cxt.say("You're not in a server, dummy!")
return
guild = message.server
jcdata = dict(self.jcoin.data) # copy
range_max = 11 # default 10 users
if len(args) > 1:
range_max = int(args[1]) + 1
if range_max > 16:
await cxt.say("LimitError: values higher than 16 aren't valid")
return
elif range_max <= 0:
await cxt.say("haha no")
return
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
order = []
for i in range(1,range_max):
if len(jcdata) < 1:
break
for member in guild.members:
accid = member.id
if accid in jcdata:
acc = jcdata[accid]
name, amount = acc['name'], acc['amount']
if amount > maior['amount']:
maior['id'] = accid
maior['name'] = name
maior['amount'] = amount
else:
pass
if maior['id'] in jcdata:
del jcdata[maior['id']]
order.append('%d. %s -> %.2f' % \
(i, maior['name'], maior['amount']))
# reset to next
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
await cxt.say('\n'.join(order))
return
async def c_top10(self, message, args, cxt):
jcdata = dict(self.jcoin.data) # copy
range_max = 11 # default 10 users
if len(args) > 1:
range_max = int(args[1]) + 1
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
if range_max > 16:
await cxt.say("LimitError: values higher than 16 aren't valid")
return
elif range_max <= 0:
await cxt.say("haha no")
return
order = []
for i in range(1,range_max):
if len(jcdata) < 1:
break
for accid in jcdata:
acc = jcdata[accid]
name, amount = acc['name'], acc['amount']
if amount > maior['amount']:
maior['id'] = accid
maior['name'] = name
maior['amount'] = amount
del jcdata[maior['id']]
order.append('%d. %s -> %.2f' % \
(i, maior['name'], maior['amount']))
# reset to next
maior = {
'id': 0,
'name': '',
'amount': 0.0,
}
await cxt.say('\n'.join(order))
return
async def c_hsteal(self, message, args, cxt):
await cxt.say(HELPTEXT_JC_STEAL)
async def c_stealstat(self, message, args, cxt):
# get status from person
personid = message.author.id
res = []
points = self.stealdb['points'].get(personid, 3)
cooldown = self.stealdb['cdown'].get(personid, None)
grace_period = self.stealdb['period'].get(personid, None)
res.append("**%s**, you have %d stealing points" % (str(message.author), points))
if cooldown is not None:
cooldown_sec, cooldown_type = cooldown
cooldown_sec -= time.time()
if cooldown_type == 0:
res.append(":cop: you're in prison, %.2f hours remaining" % (self.to_hours(cooldown_sec),))
elif cooldown_type == 1:
res.append(":alarm_clock: you're waiting for stealing points, %.2f hours remaining" % (self.to_hours(cooldown_sec),))
else:
res.append(":warning: unknown cooldown type")
if grace_period is not None:
grace_period -= time.time()
res.append(":angel: you're in grace period, %.2f hours remaining" % (self.to_hours(grace_period),))
await cxt.say('\n'.join(res))
async def do_arrest(self, thief_id, amount, arrest_type=0):
self.stealdb['cdown'][thief_id] = (time.time() + ARREST_TIME, arrest_type)
if arrest_type == 0:
# pay half the amount
fine = amount / decimal.Decimal(2)
res = await self.jcoin.transfer(thief_id, self.jcoin.jose_id, fine)
return res
async def c_steal(self, message, args, cxt):
'''`j!steal @target amount` - Steal JoséCoins from someone'''
if len(args) < 2:
await cxt.say(self.c_steal.__doc__)
return
# parse mention
try:
target_id = await jcommon.parse_id(args[1], message)
except:
await cxt.say("Error parsing `@target`")
return
try:
amount = decimal.Decimal(args[2])
except:
await cxt.say("Error parsing `amount`")
return
if message.author.id not in self.jcoin.data:
await cxt.say("You don't have a josécoin account.")
return
if target_id not in self.jcoin.data:
await cxt.say("The person you're trying to steal from doesn't have a JoséCoin account")
return
thief_id = message.author.id
# check if thief has cooldowns in place
cdown = self.stealdb['cdown'].get(thief_id, None)
if cdown is not None:
cooldown_end, cooldown_type = cdown
remaining = cooldown_end - time.time()
if cooldown_type == 0:
await cxt.say(":cop: You are still in prison, wait %.2f hours", (self.to_hours(remaining),))
elif cooldown_type == 1:
if remaining > 1:
await cxt.say("Wait %.2f hours to regenerate your stealing points", (self.to_hours(remaining),))
else:
await cxt.say("Stealing points regenerated!")
del self.stealdb['points'][thief_id]
await self.save_steal_db()
return
stealuses = self.stealdb['points'].get(thief_id, None)
if stealuses is None:
self.stealdb['points'][thief_id] = stealuses = 3
thief_user = message.author
target_user = await self.client.get_user_info(target_id)
grace_period = (self.stealdb['period'].get(target_id, 0) - time.time())
if grace_period > 0:
await cxt.say("Target is in :angel: grace period :angel:")
await cxt.say("%s tried to steal %.2fJC from you, but you have %.2f hours of grace period", \
target_user, (str(thief_user), amount, self.to_hours(grace_period)))
return
if stealuses < 1:
res = await self.do_arrest(thief_id, amount, 1)
await cxt.say("You don't have any more stealing points, wait 8 hours to get more.")
return
if target_id == self.jcoin.jose_id:
arrest = await self.do_arrest(thief_id, amount)
await cxt.say(":cop: You can't steal from José. Arrested for 8h\n`%s`", (arrest[1],))
return
target_account = self.jcoin.get(target_id)[1]
target_amount = target_account['amount']
if amount > target_amount:
# automatically in prison
arrest = await self.do_arrest(thief_id, amount)
await cxt.say(":cop: Arrested because you tried to steal more than the target has, 8h jail time.\n`%s`", \
(arrest[1]))
D = decimal.Decimal
chance = (BASE_CHANCE + (target_amount / amount)) * D(0.3)
if chance > 8: chance = 5
res = random.random() * 10
if res < chance:
self.logger.info("Stealing %.2fJC from %s[%s] to %s[%s]", \
amount, target_account['name'], target_id, message.author, thief_id)
# steal went good, make transfer
ok = self.jcoin.transfer(target_id, thief_id, amount)
# check transfer status
if not ok[0]:
await cxt.say("jc->err: %s", ok[1])
else:
await cxt.say("`[res: %.2f < prob: %.2f]` Stealing went well, nobody noticed, you thief. \n`%s`", \
(res, chance, ok[1]))
await cxt.say(":gun: You got robbed! The thief(%s) stole `%.2fJC` from you. 3 hour grace period", \
target_user, (str(thief_user), amount))
self.stealdb['period'][target_id] = time.time() + 10800
self.stealdb['points'][message.author.id] -= 1
else:
# type 0 cooldown, you got arrested
arrest = await self.do_arrest(thief_id, amount)
await cxt.say("`[res: %.2f > prob: %.2f]` :cop: Arrested! got 8h cooldown.\n`%s`", \
(res, chance, arrest[1]))
await self.save_steal_db()
async def c_roubar(self, message, args, cxt):
'''`j!roubar @target amount` - alias for `j!steal`'''
await self.c_steal(message, args, cxt)
|
# coding=utf-8
import json
import re
from urllib.parse import urlparse, parse_qs
from flask import current_app
from lxml import html
import mock
import pytest
from ...helpers import BaseApplicationTest, BaseAPIClientMixin
from dmtestutils.api_model_stubs import FrameworkStub
from dmtestutils.api_model_stubs.lot import dos_lots, cloud_lots
class APIClientMixin(BaseAPIClientMixin):
data_api_client_patch_path = 'app.main.views.marketplace.data_api_client'
search_api_client_patch_path = 'app.main.views.marketplace.search_api_client'
class TestApplication(APIClientMixin, BaseApplicationTest):
def test_analytics_code_should_be_in_javascript(self):
res = self.client.get('/static/javascripts/application.js')
assert res.status_code == 200
assert 'trackPageview' in res.get_data(as_text=True)
def test_should_display_cookie_banner(self):
res = self.client.get('/')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
cookie_banner = document.xpath('//div[@id="dm-cookie-banner"]')
assert cookie_banner[0].xpath('//h2//text()')[0].strip() == "Can we store analytics cookies on your device?"
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
def test_google_verification_code_shown_on_homepage(self):
res = self.client.get('/')
assert res.status_code == 200
assert 'name="google-site-verification" content="NotARealVerificationKey"' in res.get_data(as_text=True)
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
class TestHomepageAccountCreationVirtualPageViews(APIClientMixin, BaseApplicationTest):
def test_data_analytics_track_page_view_is_shown_if_account_created_flash_message(self):
with self.client.session_transaction() as session:
session['_flashes'] = [('track-page-view', 'buyers?account-created=true')]
res = self.client.get("/")
data = res.get_data(as_text=True)
assert 'data-analytics="trackPageView" data-url="buyers?account-created=true"' in data
# however this should not be shown as a regular flash message
flash_banner_match = re.search(r'<p class="banner-message">\s*(.*)', data, re.MULTILINE)
assert flash_banner_match is None, "Unexpected flash banner message '{}'.".format(
flash_banner_match.groups()[0])
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
def test_data_analytics_track_page_view_not_shown_if_no_account_created_flash_message(self):
res = self.client.get("/")
data = res.get_data(as_text=True)
assert 'data-analytics="trackPageView" data-url="buyers?account-created=true"' not in data
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
class TestHomepageBrowseList(APIClientMixin, BaseApplicationTest):
mock_live_dos_1_framework = {
"framework": "digital-outcomes-and-specialists",
"slug": "digital-outcomes-and-specialists",
"status": "live",
"id": 5
}
mock_live_dos_2_framework = {
"framework": "digital-outcomes-and-specialists",
"slug": "digital-outcomes-and-specialists-2",
"status": "live",
"id": 7
}
mock_live_g_cloud_9_framework = {
"framework": "g-cloud",
"slug": "g-cloud-9",
"status": "live",
"id": 8
}
def test_dos_links_are_shown(self):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[0] == "Find an individual specialist"
assert link_texts[-1] == "Find physical datacentre space"
assert "Find specialists to work on digital projects" not in link_texts
def test_links_are_for_existing_dos_framework_when_a_new_dos_framework_in_standstill_exists(self):
mock_standstill_dos_2_framework = self.mock_live_dos_2_framework.copy()
mock_standstill_dos_2_framework.update({"status": "standstill"})
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework,
mock_standstill_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos_base_path = '/buyers/frameworks/digital-outcomes-and-specialists/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos_base_path.format(lot_slug)
def test_links_are_for_the_newest_live_dos_framework_when_multiple_live_dos_frameworks_exist(self):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework,
self.mock_live_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos2_base_path = '/buyers/frameworks/digital-outcomes-and-specialists-2/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos2_base_path.format(lot_slug)
def test_links_are_for_live_dos_framework_when_expired_dos_framework_exists(self):
mock_expired_dos_1_framework = self.mock_live_dos_1_framework.copy()
mock_expired_dos_1_framework.update({"status": "expired"})
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
mock_expired_dos_1_framework,
self.mock_live_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos2_base_path = '/buyers/frameworks/digital-outcomes-and-specialists-2/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos2_base_path.format(lot_slug)
def test_non_dos_links_are_shown_if_no_live_dos_framework(self):
mock_expired_dos_1_framework = self.mock_live_dos_1_framework.copy()
mock_expired_dos_1_framework.update({"status": "expired"})
mock_expired_dos_2_framework = self.mock_live_dos_2_framework.copy()
mock_expired_dos_2_framework.update({"status": "expired"})
mock_g_cloud_9_framework = self.mock_live_g_cloud_9_framework.copy()
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
mock_expired_dos_1_framework,
mock_expired_dos_2_framework,
mock_g_cloud_9_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[0] == "Find cloud hosting, software and support"
assert link_texts[1] == "Find physical datacentre space"
assert len(link_texts) == 2
class TestHomepageSidebarMessage(APIClientMixin, BaseApplicationTest):
@staticmethod
def _find_frameworks(framework_slugs_and_statuses):
_frameworks = []
for index, framework_slug_and_status in enumerate(framework_slugs_and_statuses):
framework_slug, framework_status = framework_slug_and_status
_frameworks.append({
'framework': 'framework',
'slug': framework_slug,
'id': index + 1,
'status': framework_status,
'name': 'Framework'
})
return {
'frameworks': _frameworks
}
@staticmethod
def _assert_supplier_nav_is_empty(response_data):
document = html.fromstring(response_data)
supplier_nav_contents = document.xpath('//nav[@id="app-supplier-nav"]/*')
assert len(supplier_nav_contents) == 0
@staticmethod
def _assert_supplier_nav_is_not_empty(response_data):
document = html.fromstring(response_data)
supplier_nav_contents = document.xpath('//nav[@id="app-supplier-nav"]/*')
assert len(supplier_nav_contents) > 0
assert supplier_nav_contents[0].xpath('text()')[0].strip() == "Sell services"
def _load_homepage(self, framework_slugs_and_statuses, framework_messages):
self.data_api_client.find_frameworks.return_value = self._find_frameworks(framework_slugs_and_statuses)
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
if framework_messages:
self._assert_supplier_nav_is_not_empty(response_data)
for message in framework_messages:
assert message in response_data
else:
self._assert_supplier_nav_is_empty(response_data)
def test_homepage_sidebar_message_exists_gcloud_8_coming(self):
framework_slugs_and_statuses = [
('g-cloud-8', 'coming'),
('digital-outcomes-and-specialists', 'live')
]
framework_messages = [
u"Provide cloud software and support to the public sector.",
u"You need an account to receive notifications about when you can apply."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_message_exists_gcloud_8_open(self):
framework_slugs_and_statuses = [
('g-cloud-8', 'open'),
('digital-outcomes-and-specialists', 'live')
]
framework_messages = [
u"Provide cloud software and support to the public sector",
u"You need an account to apply.",
u"The application deadline is 5pm BST, 23 June 2016."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_message_exists_g_cloud_7_pending(self):
framework_slugs_and_statuses = [
('g-cloud-7', 'pending'),
]
framework_messages = [
u"G‑Cloud 7 is closed for applications",
u"G‑Cloud 7 services will be available from 23 November 2015."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_messages_when_logged_out(self):
self.data_api_client.find_frameworks.return_value = self._find_frameworks([
('digital-outcomes-and-specialists', 'live')
])
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
document = html.fromstring(response_data)
supplier_links = document.cssselect("#app-supplier-nav a")
supplier_link_texts = [item.xpath("normalize-space(string())") for item in supplier_links]
assert 'View Digital Outcomes and Specialists opportunities' in supplier_link_texts
assert 'Become a supplier' in supplier_link_texts
assert 'See Digital Marketplace sales figures' in supplier_link_texts
def test_homepage_sidebar_messages_when_logged_in(self):
self.data_api_client.find_frameworks.return_value = self._find_frameworks([
('digital-outcomes-and-specialists', 'live')
])
self.login_as_supplier()
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
document = html.fromstring(response_data)
supplier_links = document.cssselect("#app-supplier-nav a")
supplier_link_texts = [item.xpath("normalize-space(string())") for item in supplier_links]
assert 'View Digital Outcomes and Specialists opportunities' in supplier_link_texts
assert 'Become a supplier' not in supplier_link_texts
# here we've given an valid framework with a valid status but there is no message.yml file to read from
def test_g_cloud_6_open_blows_up(self):
framework_slugs_and_statuses = [
('g-cloud-6', 'open')
]
self.data_api_client.find_frameworks.return_value = self._find_frameworks(framework_slugs_and_statuses)
res = self.client.get('/')
assert res.status_code == 500
class TestStaticMarketplacePages(BaseApplicationTest):
def test_cookie_page(self):
res = self.client.get('/cookies')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
assert len(document.xpath('//h1[contains(text(), "Cookies on Digital Marketplace")]')) == 1
def test_terms_and_conditions_page(self):
res = self.client.get('/terms-and-conditions')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
assert len(document.xpath('//h1[contains(text(), "Terms and conditions")]')) == 1
def test_external_404_makes_all_links_absolute(self):
# Get the normal 404 page and a list of the relative URLs it contains links to
response1 = self.client.get("/does-not-exist-404")
assert response1.status_code == 404
regular_404_document = html.fromstring(response1.get_data(as_text=True))
regular_relative_links = regular_404_document.xpath('//a[starts-with(@href, "/")]')
regular_relative_forms = regular_404_document.xpath('//form[starts-with(@action, "/")]')
relative_urls = [link.get("href") for link in regular_relative_links] + \
[form.get("action") for form in regular_relative_forms]
# Get the "external" 404 page and check it doesn't contain any relative URLs
response2 = self.client.get("/404")
assert response2.status_code == 404
external_404_document = html.fromstring(response2.get_data(as_text=True))
external_relative_links = external_404_document.xpath('//a[starts-with(@href, "/")]')
external_relative_forms = external_404_document.xpath('//form[starts-with(@action, "/")]')
assert len(external_relative_links) == len(external_relative_forms) == 0
# Check that there is an absolute URL in the external 404 page for every relative URL in the normal 404 page
external_links = external_404_document.xpath('//a')
external_forms = external_404_document.xpath('//form')
external_urls = [link.get("href") for link in external_links] + [form.get("action") for form in external_forms]
for relative_url in relative_urls:
assert "http://localhost{}".format(relative_url) in external_urls
class BaseBriefPageTest(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.brief = self._get_dos_brief_fixture_data()
self.brief_responses = self._get_dos_brief_responses_fixture_data()
self.brief_id = self.brief['briefs']['id']
self.data_api_client.find_frameworks.return_value = self._get_frameworks_list_fixture_data()
self.data_api_client.get_brief.return_value = self.brief
self.data_api_client.find_brief_responses.return_value = self.brief_responses
class TestBriefPage(BaseBriefPageTest):
@pytest.mark.parametrize('framework_family, expected_status_code',
(
('digital-outcomes-and-specialists', 200),
('g-cloud', 404),
))
def test_404_on_framework_that_does_not_support_further_competition(self, framework_family, expected_status_code):
brief_id = self.brief['briefs']['id']
res = self.client.get(f'/{framework_family}/opportunities/{brief_id}')
assert res.status_code == expected_status_code
assert self.data_api_client.find_frameworks.mock_calls == [
mock.call(),
]
def test_dos_brief_404s_if_brief_is_draft(self):
self.brief['briefs']['status'] = 'draft'
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 404
assert self.data_api_client.mock_calls == [
mock.call.find_frameworks(),
mock.call.get_brief(str(brief_id)),
]
def test_dos_brief_has_correct_title(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
page_heading = document.cssselect("span.govuk-caption-l + h1.govuk-heading-l")
assert page_heading
heading = page_heading[0]
assert heading.text == self.brief["briefs"]["title"]
caption = heading.getprevious()
assert caption.text == self.brief["briefs"]["organisation"]
def _assert_all_normal_api_calls(self):
assert self.data_api_client.mock_calls == [
mock.call.find_frameworks(),
mock.call.get_brief(str(self.brief_id)),
mock.call.find_brief_responses(
brief_id=str(self.brief_id),
status='draft,submitted,pending-awarded,awarded',
with_data=False,
),
]
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled', 'awarded'])
def test_only_one_banner_at_once_brief_page(self, status):
self.brief['briefs']['status'] = status
if self.brief['briefs']['status'] == 'awarded':
self.brief['briefs']['awardedBriefResponseId'] = 14276
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
number_of_banners = len(document.xpath('//div[@class="banner-temporary-message-without-action"]'))
assert number_of_banners == 1
self._assert_all_normal_api_calls()
def test_dos_brief_displays_application_stats(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '3'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete applications"
assert incomplete_responses_section.xpath('p[1]/text()')[0] == "3 SME, 0 large"
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '5'
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed applications"
assert completed_responses_section.xpath('p[1]/text()')[0] == "4 SME, 1 large"
self._assert_all_normal_api_calls()
def test_application_stats_pluralised_correctly(self):
brief_id = self.brief['briefs']['id']
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"id": 14275,
"briefId": brief_id,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "submitted",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierOrganisationSize": 'large'
},
{
"id": 14276,
"briefId": brief_id,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "draft",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 706033,
"supplierOrganisationSize": 'micro'
}
]
}
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '1'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete application"
assert incomplete_responses_section.xpath('p[1]/text()')[0] == "1 SME, 0 large"
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '1'
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed application"
assert completed_responses_section.xpath('p[1]/text()')[0] == "0 SME, 1 large"
def test_dos_brief_displays_application_stats_correctly_when_no_applications(self):
brief_id = self.brief['briefs']['id']
self.data_api_client.find_brief_responses.return_value = {"briefResponses": []}
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '0'
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '0'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete applications"
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed applications"
assert len(incomplete_responses_section.xpath('p[1]/text()')) == 0
assert len(completed_responses_section.xpath('p[1]/text()')) == 0
def test_dos_brief_has_lot_analytics_string(self):
brief = self.brief['briefs']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief['id']))
assert res.status_code == 200
data = res.get_data(as_text=True)
analytics_string = '<span data-lot="{lot_slug}"></span>'.format(lot_slug=brief['lotSlug'])
assert analytics_string in data
def test_dos_brief_has_important_dates(self):
brief_id = self.brief['briefs']['id']
self.brief['briefs']['clarificationQuestionsClosedAt'] = "2016-12-14T11:08:28.054129Z"
self.brief['briefs']['applicationsClosedAt'] = "2016-12-15T11:08:28.054129Z"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
brief_important_dates = document.xpath(
'(//table[@class="summary-item-body"])[1]/tbody/tr')
assert 3 == len(brief_important_dates)
assert brief_important_dates[0].xpath('td[@class="summary-item-field-first"]')[0].text_content().strip() \
== "Published"
assert brief_important_dates[0].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Thursday 1 December 2016"
assert brief_important_dates[1].xpath('td[@class="summary-item-field-first"]')[0].text_content().strip() \
== "Deadline for asking questions"
assert brief_important_dates[1].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Wednesday 14 December 2016 at 11:08am GMT"
assert brief_important_dates[2].xpath('td[@class="summary-item-field-first"]')[0].text_content().strip() \
== "Closing date for applications"
assert brief_important_dates[2].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Thursday 15 December 2016 at 11:08am GMT"
def test_dos_brief_with_daylight_savings_has_question_deadline_closing_date_forced_to_utc(self):
brief_id = self.brief['briefs']['id']
self.brief['briefs']['publishedAt'] = "2016-08-01T23:59:00.000000Z"
self.brief['briefs']['clarificationQuestionsClosedAt'] = "2016-08-14T23:59:00.000000Z"
self.brief['briefs']['applicationsClosedAt'] = "2016-08-15T23:59:00.000000Z"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
brief_important_dates = document.xpath(
'(//table[@class="summary-item-body"])[1]/tbody/tr')
assert 3 == len(brief_important_dates)
# Publish date does not have UTC filter applied
assert brief_important_dates[0].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Monday 1 August 2016"
# Question deadline and closing date are forced to 11.59pm (UTC+00) on the correct day
assert brief_important_dates[1].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Sunday 14 August 2016 at 11:59pm GMT"
assert brief_important_dates[2].xpath('td[@class="summary-item-field"]')[0].text_content().strip() \
== "Monday 15 August 2016 at 11:59pm GMT"
def test_dos_brief_has_at_least_one_section(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
section_heading = document.xpath('//h2[@class="summary-item-heading"]')[0]
section_attributes = section_heading.xpath('following-sibling::table[1]/tbody/tr')
start_date_key = section_attributes[2].xpath('td[1]/span/text()')
start_date_value = section_attributes[2].xpath('td[2]/span/text()')
contract_length_key = section_attributes[3].xpath('td[1]/span/text()')
contract_length_value = section_attributes[3].xpath('td[2]/span/text()')
assert section_heading.get('id') == 'opportunity-attributes-1'
assert section_heading.text.strip() == 'Overview'
assert start_date_key[0] == 'Latest start date'
assert start_date_value[0] == 'Wednesday 1 March 2017'
assert contract_length_key[0] == 'Expected contract length'
assert contract_length_value[0] == '4 weeks'
@pytest.mark.parametrize(
'lot_slug, assessment_type', [
('digital-outcomes', 'written proposal'),
('digital-specialists', 'work history'),
('user-research-participants', 'written proposal'),
]
)
def test_dos_brief_displays_mandatory_evaluation_method_for_lot(self, lot_slug, assessment_type):
brief = self.brief.copy()
brief['briefs']['lot'] = lot_slug
brief['briefs']['lotSlug'] = lot_slug
brief['briefs']['status'] = 'live'
brief['briefs']['publishedAt'] = '2019-01-02T00:00:00.000000Z'
brief['briefs']['frameworkSlug'] = 'digital-outcomes-and-specialists-4'
self.data_api_client.get_brief.return_value = brief
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief['briefs']['id']))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
section_heading = document.xpath(
'//h2[@class="summary-item-heading"][contains(text(), "How suppliers will be evaluated")]'
)[0]
section_description = section_heading.xpath('following-sibling::p')[0]
assert section_description.text.strip() == f'All suppliers will be asked to provide a {assessment_type}.'
def test_dos_brief_has_questions_and_answers(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
xpath = '//h2[@id="clarification-questions"]/following-sibling::table/tbody/tr'
clarification_questions = document.xpath(xpath)
number = clarification_questions[0].xpath('td[1]/span/span/text()')[0].strip()
question = clarification_questions[0].xpath('td[1]/span/text()')[0].strip()
answer = clarification_questions[0].xpath('td[2]/span/text()')[0].strip()
assert number == "1."
assert question == "Why?"
assert answer == "Because"
def test_can_apply_to_live_brief(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_apply_button_visible_if_status_is_draft(self):
self.brief_responses['briefResponses'][0]['status'] = 'draft'
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled'])
def test_cannot_apply_to_closed_cancelled_or_unsuccessful_brief(self, status):
self.brief['briefs']['status'] = status
self.brief['briefs']['applicationsClosedAt'] = "2016-12-15T11:08:28.054129Z"
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(brief_id))
assert len(apply_links) == 0
def test_cannot_apply_to_awarded_brief(self):
self.brief['briefs']['status'] = "awarded"
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 123456,
"supplierName": "Another, Better, Company Limited",
"supplierOrganisationSize": "large"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(brief_id))
assert len(apply_links) == 0
def test_dos_brief_specialist_role_displays_label(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert 'qualityAssurance' not in res.get_data(as_text=True)
assert 'Quality assurance analyst' in res.get_data(as_text=True)
def _assert_start_application(self, document, brief_id):
assert document.xpath(
"//form[@method='get'][normalize-space(string(.//button))=$t]/@action",
t="Apply for this opportunity",
) == ["/suppliers/opportunities/{}/responses/start".format(brief_id)]
def _assert_view_application(self, document, brief_id):
assert len(document.xpath(
'//a[@href="{0}"][contains(normalize-space(text()), normalize-space("{1}"))]'.format(
"/suppliers/opportunities/{}/responses/result".format(brief_id),
"View your application",
)
)) == 1
def test_unauthenticated_start_application(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_buyer_start_application(self):
self.login_as_buyer()
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_supplier_start_application(self):
self.login_as_supplier()
# mocking that we haven't applied
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": []
}
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_supplier_applied_view_application_for_live_opportunity(self):
self.login_as_supplier()
# fixtures for brief responses have been set up so one of them has the supplier_id we are logged in as.
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled'])
def test_supplier_applied_view_application_for_closed_unsuccessful_or_cancelled_opportunity(self, status):
self.login_as_supplier()
self.brief['briefs']['status'] = status
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_awarded_to_logged_in_supplier(self):
self.login_as_supplier()
self.brief['briefs']['status'] = 'awarded'
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_pending_awarded_to_logged_in_supplier(self):
self.login_as_supplier()
self.brief['briefs']['status'] = 'closed'
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"pending": True},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "pending-awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_awarded_to_other_supplier(self):
self.login_as_supplier()
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 123456,
"supplierName": "Another, Better, Company Limited",
"supplierOrganisationSize": "large"
},
{
"id": 14277,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "submitted",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['status'] = 'awarded'
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
class TestBriefPageQandASectionViewQandASessionDetails(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['questionAndAnswerSessionDetails'] = {'many': 'details'}
self.brief['briefs']['clarificationQuestionsAreClosed'] = False
def test_live_brief_q_and_a_session(self):
"""
As long as a:
A user is not logged in
The brief is live
Clarification questions are open
The brief has Q and A session details
We should show the:
link to login and view the QAS details
"""
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Log in to view question and answer session details"
expected_link = '/suppliers/opportunities/{}/question-and-answer-session'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
def test_live_brief_q_and_a_session_logged_in(self):
"""
As long as a:
Supplier user is logged in
The brief is live
Clarification questions are open
The brief has Q and A session details
We should show the:
Link to view the QAS details
"""
self.login_as_supplier()
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "View question and answer session details"
expected_link = '/suppliers/opportunities/{}/question-and-answer-session'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
@pytest.mark.parametrize(
'brief_data', [
{'status': 'withdrawn'},
{'status': 'closed'},
{'questionAndAnswerSessionDetails': None},
{'clarificationQuestionsAreClosed': True}
]
)
def test_brief_q_and_a_session_link_not_shown(self, brief_data):
"""
On viewing briefs with data like the above the page should load but we should not get the link.
"""
self.brief['briefs'].update(brief_data)
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
unexpected_texts = [
"Log in to view question and answer session details",
"View question and answer session details"
]
for unexpected_text in unexpected_texts:
assert len(document.xpath('.//a[contains(text(),"{}")]'.format(unexpected_text))) == 0
class TestBriefPageQandASectionAskAQuestion(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['clarificationQuestionsAreClosed'] = False
def test_live_brief_ask_a_question(self):
"""
As long as a:
A user is not logged in
The brief is live
Clarification questions are open
We should show the:
link to login and ask a question
"""
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Log in to ask a question"
expected_link = '/suppliers/opportunities/{}/ask-a-question'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
def test_live_brief_ask_a_question_logged_in(self):
"""
As long as a:
Supplier user is logged in
The brief is live
Clarification questions are open
We should show the:
Link to ask a question
"""
self.login_as_supplier()
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Ask a question"
expected_link = '/suppliers/opportunities/{}/ask-a-question'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
@pytest.mark.parametrize(
'brief_data', [
{'status': 'withdrawn'},
{'status': 'closed'},
{'clarificationQuestionsAreClosed': True}
]
)
def test_brief_ask_a_question_link_not_shown(self, brief_data):
"""
On viewing briefs with data like the above the page should load but we should not get either the
log in to ask a question or ask a question links.
"""
self.brief['briefs'].update(brief_data)
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
unexpected_texts = ["Log in to ask a question", "Ask a question"]
for unexpected_text in unexpected_texts:
assert len(document.xpath('.//a[contains(text(),"{}")]'.format(unexpected_text))) == 0
class TestAwardedBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'awarded'
self.brief['briefs']['awardedBriefResponseId'] = 14276
def test_award_banner_with_winning_supplier_shown_on_awarded_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Awarded to Example Company Limited' in awarded_banner.xpath('h2/text()')[0]
def test_contract_start_date_visible_on_award_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Start date: Monday 21 August 2017' in awarded_banner.xpath('p/text()')[0]
def test_contract_value_visible_on_award_banner_does_not_include_zero_pence(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert u'Value: £20,000' in awarded_banner.xpath('p/text()')[1]
def test_contract_value_visible_on_award_banner_includes_non_zero_pence(self):
self.brief_responses["briefResponses"][1]["awardDetails"]["awardedContractValue"] = "20000.10"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert u'Value: £20,000.10' in awarded_banner.xpath('p/text()')[1]
def test_supplier_size_visible_on_award_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Company size: SME' in awarded_banner.xpath('p/text()')[2]
class TestCancelledBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'cancelled'
def test_cancelled_banner_shown_on_cancelled_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'This opportunity was cancelled' in cancelled_banner.xpath('h2/text()')[0]
def test_explanation_message_shown_on_cancelled_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
expected_message = ("The buyer cancelled this opportunity, for example because they no longer have the budget. "
"They may publish an updated version later."
)
assert expected_message in cancelled_banner.xpath('p/text()')[0]
class TestUnsuccessfulBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'unsuccessful'
def test_unsuccessful_banner_shown_on_unsuccessful_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
unsuccessful_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'No suitable suppliers applied' in unsuccessful_banner.xpath('h2/text()')[0]
def test_explanation_message_shown_on_unsuccessful_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
expected_message = ("The buyer didn't award this contract because no suppliers met their requirements. "
"They may publish an updated version later."
)
assert expected_message in cancelled_banner.xpath('p/text()')[0]
class TestWithdrawnSpecificBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = "withdrawn"
self.brief['briefs']['withdrawnAt'] = "2016-11-25T10:47:23.126761Z"
def test_dos_brief_visible_when_withdrawn(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
assert res.status_code == 200
def test_apply_button_not_visible_for_withdrawn_briefs(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(self.brief_id))
assert len(apply_links) == 0
def test_deadline_text_not_shown(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'The deadline for asking questions about this opportunity was ' not in page
def test_withdrawn_banner_shown_on_withdrawn_brief(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on' in page
assert (
"You can't apply for this opportunity now. "
"The buyer may publish an updated version on the Digital Marketplace"
) in page
@pytest.mark.parametrize('status', ['live', 'closed'])
def test_withdrawn_banner_not_shown_on_live_and_closed_brief(self, status):
self.brief['briefs']['status'] = status
del self.brief['briefs']['withdrawnAt']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on' not in page
def test_dateformat_in_withdrawn_banner_displayed_correctly(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on Friday 25 November 2016' in page
class TestCatalogueOfBriefsPage(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.search_api_client.search.return_value = self._get_dos_brief_search_api_response_fixture_data()
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
self.data_api_client.find_frameworks.return_value = {'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status='live', lots=dos_lots(),
has_further_competition=True
).response(),
FrameworkStub(
id=1, slug='digital-outcomes-and-specialists', status='expired', lots=dos_lots(),
has_further_competition=True
).response(),
FrameworkStub(
id=2, slug='foobar', status='expired', lots=cloud_lots()
).response(),
FrameworkStub(
id=4, slug='g-cloud-9', status='live', lots=cloud_lots()
).response()
]}
def normalize_qs(self, qs):
return {k: set(v) for k, v in parse_qs(qs).items() if k != "page"}
@pytest.mark.parametrize('framework_family, expected_status_code',
(
('digital-outcomes-and-specialists', 200),
('g-cloud', 404),
))
def test_404_on_framework_that_does_not_support_further_competition(self, framework_family, expected_status_code):
res = self.client.get(f'/{framework_family}/opportunities')
assert res.status_code == expected_status_code
def test_catalogue_of_briefs_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open,closed'
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
lot_filters = document.xpath("//form[@method='get']//ul[@class='lot-filters--last-list']//a")
assert set(element.text for element in lot_filters) == {
"Digital outcomes (629)",
"Digital specialists (827)",
"User research participants (39)",
}
assert len(document.xpath("//form[@method='get']//ul[@class='lot-filters--last-list']//strong")) == 0
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert set(element.get("value") for element in status_inputs) == {"open", "closed"}
assert not any(element.get("checked") for element in status_inputs)
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == "864 results found in All categories"
specialist_role_labels = document.xpath("//div[@class='search-result']/ul[2]/li[2]/text()")
assert len(specialist_role_labels) == 2 # only two briefs has a specialist role so only one label should exist
assert specialist_role_labels[0].strip() == "Developer"
assert specialist_role_labels[1].strip() == "Technical architect"
def test_catalogue_of_briefs_page_filtered(self):
original_url = "/digital-outcomes-and-specialists/opportunities?page=2"\
"&statusOpenClosed=open&lot=digital-outcomes&location=wales&location=london"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open',
lot='digital-outcomes',
location='wales,london',
page='2',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
assert document.xpath(
"//a[@id=$i][contains(@class, $c)][normalize-space(string())=normalize-space($t)][@href=$h]",
i="dm-clear-all-filters",
c="clear-filters-link",
t="Clear filters",
h="/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes",
)
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": True,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": True,
"london": True,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
parsed_original_url = urlparse(original_url)
parsed_prev_url = urlparse(document.xpath("//li[@class='previous']/a/@href")[0])
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_prev_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_original_url.query) == \
self.normalize_qs(parsed_next_url.query) == \
self.normalize_qs(parsed_prev_url.query)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found in Digital outcomes"
def test_catalogue_of_briefs_page_filtered_keyword_search(self):
original_url = "/digital-outcomes-and-specialists/opportunities?page=2"\
"&statusOpenClosed=open&lot=digital-outcomes"\
"&location=offsite&q=Richie+Poldy"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open',
lot='digital-outcomes',
location='offsite',
page='2',
q='Richie Poldy',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
assert document.xpath(
"//a[@id=$i][contains(@class, $c)][normalize-space(string())=normalize-space($t)][@href=$h]",
i="dm-clear-all-filters",
c="clear-filters-link",
t="Clear filters",
h="/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&q=Richie+Poldy",
)
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": True,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": True,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("Richie Poldy",)
parsed_original_url = urlparse(original_url)
parsed_prev_url = urlparse(document.xpath("//li[@class='previous']/a/@href")[0])
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_prev_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_original_url.query) == \
self.normalize_qs(parsed_next_url.query) == \
self.normalize_qs(parsed_prev_url.query)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found containing Richie Poldy in Digital outcomes"
def test_catalogue_of_briefs_page_filtered_all_lots_selected(self):
original_url = "/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&lot=digital-specialists"\
"&lot=user-research-participants"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open,closed',
lot='digital-outcomes',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": False,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
parsed_original_url = urlparse(original_url)
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_next_url.query) == {'lot': {'digital-outcomes'}}
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found in Digital outcomes"
@pytest.mark.parametrize(
('dos_status', 'dos2_status', 'expected_url_slug_suffix'),
(
('live', 'standstill', ''),
('expired', 'live', '-2'),
)
)
@mock.patch('app.main.views.marketplace.content_loader')
def test_opportunity_data_download_info_and_link_visible_on_catalogue_page(
self, content_loader, dos_status, dos2_status, expected_url_slug_suffix
):
self.data_api_client.find_frameworks.return_value = {'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status=dos2_status,
lots=dos_lots(), has_further_competition=True
).response(),
FrameworkStub(
id=1, slug='digital-outcomes-and-specialists', status=dos_status,
lots=dos_lots(), has_further_competition=True
).response()
]}
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
header = document.xpath("//h2[@id='opportunity-data-header']")[0].text
description = document.xpath("//p[@id='opportunity-data-description']")[0].text
expected_desc = "Download data buyers have provided about closed opportunities. Some data may be missing."
link = document.xpath("//a[normalize-space(text())='Download data (CSV)']")[0].values()
expected_link = (
"https://assets.digitalmarketplace.service.gov.uk"
+ f"/digital-outcomes-and-specialists{expected_url_slug_suffix}/communications/data/opportunity-data.csv"
)
assert "Opportunity data" in header
assert expected_desc in description
assert expected_link in link
def test_catalogue_of_briefs_page_shows_pagination_if_more_pages(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=2')
assert res.status_code == 200
page = res.get_data(as_text=True)
document = html.fromstring(page)
assert '<li class="previous">' in page
assert '<li class="next">' in page
prev_url = str(document.xpath('string(//li[@class="previous"]/a/@href)'))
next_url = str(document.xpath('string(//li[@class="next"]/a/@href)'))
assert prev_url.endswith('/opportunities?page=1')
assert next_url.endswith('/opportunities?page=3')
assert '<span class="page-numbers">1 of 9</span>' in res.get_data(as_text=True)
assert '<span class="page-numbers">3 of 9</span>' in res.get_data(as_text=True)
def test_no_pagination_if_no_more_pages(self):
with self.app.app_context():
current_app.config['DM_SEARCH_PAGE_SIZE'] = 1000
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
page = res.get_data(as_text=True)
assert '<li class="previous">' not in page
assert '<li class="next">' not in page
def test_catalogue_of_briefs_page_404_for_framework_that_does_not_exist(self):
res = self.client.get('/digital-giraffes-and-monkeys/opportunities')
assert res.status_code == 404
self.data_api_client.find_frameworks.assert_called_once_with()
def test_briefs_search_has_js_hidden_filter_button(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
filter_button = document.xpath(
'//button[contains(@class, "js-hidden")][contains(@class, "js-dm-live-search")]'
'[normalize-space(text())="Filter"]'
)
assert len(filter_button) == 1
def test_opportunity_status_and_published_date(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
live_opportunity_published_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-3].text_content().strip()
assert live_opportunity_published_at == "Published: Friday 17 November 2017"
live_opportunity_qs_closing_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-2].text_content().strip()
assert live_opportunity_qs_closing_at == "Deadline for asking questions: Sunday 26 November 2017"
live_opportunity_closing_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert live_opportunity_closing_at == "Closing: Friday 1 December 2017"
closed_opportunity_status = document.xpath(
'//div[@class="search-result"][2]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert closed_opportunity_status == "Closed: awaiting outcome"
unsuccessful_opportunity_status = document.xpath(
'//div[@class="search-result"][3]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert unsuccessful_opportunity_status == "Closed: no suitable suppliers"
cancelled_opportunity_status = document.xpath(
'//div[@class="search-result"][4]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert cancelled_opportunity_status == "Closed: cancelled"
awarded_opportunity_status = document.xpath(
'//div[@class="search-result"][6]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert awarded_opportunity_status == "Closed: awarded"
def test_should_render_summary_for_0_results_in_all_lots(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">0</span> results found in <em>All categories</em>' in summary
def test_should_render_summary_for_0_results_in_particular_lot(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">0</span> results found in <em>Digital outcomes</em>' in summary
def test_should_render_summary_for_1_result_found_in_all_lots(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 1
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">1</span> result found in <em>All categories</em>' in summary
def test_should_render_summary_for_many_results_found_in_a_particular_lot(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-specialists')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">864</span> results found in <em>Digital specialists</em>' in summary
def test_should_render_suggestions_for_0_results(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
xpath = html.fromstring(res.get_data(as_text=True)).xpath
assert xpath('boolean(//div[contains(@class, "search-suggestion")])')
def test_should_not_render_suggestions_when_results(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
xpath = html.fromstring(res.get_data(as_text=True)).xpath
assert not xpath('boolean(//div[contains(@class, "search-suggestion")])')
def test_should_ignore_unknown_arguments(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?location=my-lovely-horse')
assert res.status_code == 200
def test_should_404_on_invalid_page_param(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=1')
assert res.status_code == 200
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=-1')
assert res.status_code == 404
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=potato')
assert res.status_code == 404
def test_search_results_with_invalid_lot_fall_back_to_all_categories(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=bad-lot-slug')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
lots = document.xpath('//div[@class="lot-filters"]//ul[@class="lot-filters--last-list"]//li/a')
assert lots[0].text_content().startswith('Digital outcomes')
assert lots[1].text_content().startswith('Digital specialists')
assert lots[2].text_content().startswith('User research participants')
def test_lot_links_retain_all_category_filters(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?location=london')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
lots = document.xpath('//div[@class="lot-filters"]//ul[@class="lot-filters--last-list"]//li/a')
for lot in lots:
assert 'location=london' in lot.get('href')
def test_lot_with_no_briefs_is_not_a_link(self):
specialists_aggregation = self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data()
specialists_aggregation['aggregations']['lot']['digital-specialists'] = 0
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
specialists_aggregation,
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
specialist_label = document.xpath("//ul[@class='lot-filters--last-list']//li")[-2]
assert len(specialist_label.xpath('a')) == 0
assert specialist_label.text_content() == 'Digital specialists (0)'
def test_filter_form_given_filter_selection(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&location=london')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
hidden_inputs = document.xpath('//form[@id="js-dm-live-search-form"]//input[@type="hidden"]')
kv_pairs = {input_el.get('name'): input_el.get('value') for input_el in hidden_inputs}
assert kv_pairs == {'lot': 'digital-outcomes'}
class TestCatalogueOfBriefsFilterOnClick(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.search_api_client.search.return_value = self._get_dos_brief_search_api_response_fixture_data()
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
self.data_api_client.find_frameworks.return_value = {
'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status='live',
lots=dos_lots(), has_further_competition=True
).response()
]
}
@pytest.mark.parametrize('query_string, content_type',
(('', 'text/html; charset=utf-8'),
('?live-results=true', 'application/json')))
def test_endpoint_switches_on_live_results_request(self, query_string, content_type):
res = self.client.get('/digital-outcomes-and-specialists/opportunities{}'.format(query_string))
assert res.status_code == 200
assert res.content_type == content_type
def test_live_results_returns_valid_json_structure(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?live-results=true')
data = json.loads(res.get_data(as_text=True))
assert sorted(data.keys()) == sorted((
'results',
'summary',
'summary-accessible-hint',
'categories',
'filter-title'
))
for k, v in data.items():
assert set(v.keys()) == {'selector', 'html'}
# We want to enforce using css IDs to describe the nodes which should be replaced.
assert v['selector'].startswith('#')
live_results_expected_templates = (
"search/_results_wrapper.html",
"search/_categories_wrapper.html",
"search/_summary.html",
"search/_summary_accessible_hint.html",
"search/_filter_title.html"
)
@pytest.mark.parametrize(
('query_string', 'urls'),
(
('', ('search/briefs.html',)),
('?live-results=true', live_results_expected_templates)
)
)
@mock.patch('app.main.views.marketplace.render_template', autospec=True)
def test_base_page_renders_search_services(self, render_template_patch, query_string, urls):
render_template_patch.return_value = '<p>some html</p>'
self.client.get('/digital-outcomes-and-specialists/opportunities{}'.format(query_string))
assert urls == tuple(x[0][0] for x in render_template_patch.call_args_list)
def test_form_has_js_hidden_filter_button(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
filter_button = document.xpath(
'//button[contains(@class, "js-hidden")][contains(@class, "js-dm-live-search")]'
'[normalize-space(text())="Filter"]'
)
assert len(filter_button) == 1
class TestGCloudHomepageLinks(APIClientMixin, BaseApplicationTest):
mock_live_g_cloud_framework = {
"framework": "g-cloud",
"slug": "g-cloud-x",
"status": "live",
"id": 5
}
@pytest.mark.parametrize('framework_slug, gcloud_content',
(('g-cloud-8', 'Find cloud technology and support'),
('g-cloud-9', 'Find cloud hosting, software and support')))
def test_g_cloud_homepage_content_is_correct(self, framework_slug, gcloud_content):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [self.mock_live_g_cloud_framework.copy()]
}
self.data_api_client.find_frameworks.return_value['frameworks'][0].update({'slug': framework_slug})
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[-2] == gcloud_content
assert link_texts[-1] == 'Find physical datacentre space'
Refactor tests to use summary list
# coding=utf-8
import json
import re
from urllib.parse import urlparse, parse_qs
from flask import current_app
from lxml import html
import mock
import pytest
from ...helpers import BaseApplicationTest, BaseAPIClientMixin
from dmtestutils.api_model_stubs import FrameworkStub
from dmtestutils.api_model_stubs.lot import dos_lots, cloud_lots
class APIClientMixin(BaseAPIClientMixin):
data_api_client_patch_path = 'app.main.views.marketplace.data_api_client'
search_api_client_patch_path = 'app.main.views.marketplace.search_api_client'
class TestApplication(APIClientMixin, BaseApplicationTest):
def test_analytics_code_should_be_in_javascript(self):
res = self.client.get('/static/javascripts/application.js')
assert res.status_code == 200
assert 'trackPageview' in res.get_data(as_text=True)
def test_should_display_cookie_banner(self):
res = self.client.get('/')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
cookie_banner = document.xpath('//div[@id="dm-cookie-banner"]')
assert cookie_banner[0].xpath('//h2//text()')[0].strip() == "Can we store analytics cookies on your device?"
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
def test_google_verification_code_shown_on_homepage(self):
res = self.client.get('/')
assert res.status_code == 200
assert 'name="google-site-verification" content="NotARealVerificationKey"' in res.get_data(as_text=True)
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
class TestHomepageAccountCreationVirtualPageViews(APIClientMixin, BaseApplicationTest):
def test_data_analytics_track_page_view_is_shown_if_account_created_flash_message(self):
with self.client.session_transaction() as session:
session['_flashes'] = [('track-page-view', 'buyers?account-created=true')]
res = self.client.get("/")
data = res.get_data(as_text=True)
assert 'data-analytics="trackPageView" data-url="buyers?account-created=true"' in data
# however this should not be shown as a regular flash message
flash_banner_match = re.search(r'<p class="banner-message">\s*(.*)', data, re.MULTILINE)
assert flash_banner_match is None, "Unexpected flash banner message '{}'.".format(
flash_banner_match.groups()[0])
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
def test_data_analytics_track_page_view_not_shown_if_no_account_created_flash_message(self):
res = self.client.get("/")
data = res.get_data(as_text=True)
assert 'data-analytics="trackPageView" data-url="buyers?account-created=true"' not in data
assert len(self.data_api_client.find_frameworks.call_args_list) == 2
class TestHomepageBrowseList(APIClientMixin, BaseApplicationTest):
mock_live_dos_1_framework = {
"framework": "digital-outcomes-and-specialists",
"slug": "digital-outcomes-and-specialists",
"status": "live",
"id": 5
}
mock_live_dos_2_framework = {
"framework": "digital-outcomes-and-specialists",
"slug": "digital-outcomes-and-specialists-2",
"status": "live",
"id": 7
}
mock_live_g_cloud_9_framework = {
"framework": "g-cloud",
"slug": "g-cloud-9",
"status": "live",
"id": 8
}
def test_dos_links_are_shown(self):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[0] == "Find an individual specialist"
assert link_texts[-1] == "Find physical datacentre space"
assert "Find specialists to work on digital projects" not in link_texts
def test_links_are_for_existing_dos_framework_when_a_new_dos_framework_in_standstill_exists(self):
mock_standstill_dos_2_framework = self.mock_live_dos_2_framework.copy()
mock_standstill_dos_2_framework.update({"status": "standstill"})
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework,
mock_standstill_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos_base_path = '/buyers/frameworks/digital-outcomes-and-specialists/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos_base_path.format(lot_slug)
def test_links_are_for_the_newest_live_dos_framework_when_multiple_live_dos_frameworks_exist(self):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
self.mock_live_dos_1_framework,
self.mock_live_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos2_base_path = '/buyers/frameworks/digital-outcomes-and-specialists-2/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos2_base_path.format(lot_slug)
def test_links_are_for_live_dos_framework_when_expired_dos_framework_exists(self):
mock_expired_dos_1_framework = self.mock_live_dos_1_framework.copy()
mock_expired_dos_1_framework.update({"status": "expired"})
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
mock_expired_dos_1_framework,
self.mock_live_dos_2_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_locations = [item.values()[1] for item in document.cssselect('#app-buyer-nav a')]
lots = ['digital-specialists', 'digital-outcomes', 'user-research-participants', 'user-research-studios']
dos2_base_path = '/buyers/frameworks/digital-outcomes-and-specialists-2/requirements/{}'
for index, lot_slug in enumerate(lots):
assert link_locations[index] == dos2_base_path.format(lot_slug)
def test_non_dos_links_are_shown_if_no_live_dos_framework(self):
mock_expired_dos_1_framework = self.mock_live_dos_1_framework.copy()
mock_expired_dos_1_framework.update({"status": "expired"})
mock_expired_dos_2_framework = self.mock_live_dos_2_framework.copy()
mock_expired_dos_2_framework.update({"status": "expired"})
mock_g_cloud_9_framework = self.mock_live_g_cloud_9_framework.copy()
self.data_api_client.find_frameworks.return_value = {
"frameworks": [
mock_expired_dos_1_framework,
mock_expired_dos_2_framework,
mock_g_cloud_9_framework,
]
}
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[0] == "Find cloud hosting, software and support"
assert link_texts[1] == "Find physical datacentre space"
assert len(link_texts) == 2
class TestHomepageSidebarMessage(APIClientMixin, BaseApplicationTest):
@staticmethod
def _find_frameworks(framework_slugs_and_statuses):
_frameworks = []
for index, framework_slug_and_status in enumerate(framework_slugs_and_statuses):
framework_slug, framework_status = framework_slug_and_status
_frameworks.append({
'framework': 'framework',
'slug': framework_slug,
'id': index + 1,
'status': framework_status,
'name': 'Framework'
})
return {
'frameworks': _frameworks
}
@staticmethod
def _assert_supplier_nav_is_empty(response_data):
document = html.fromstring(response_data)
supplier_nav_contents = document.xpath('//nav[@id="app-supplier-nav"]/*')
assert len(supplier_nav_contents) == 0
@staticmethod
def _assert_supplier_nav_is_not_empty(response_data):
document = html.fromstring(response_data)
supplier_nav_contents = document.xpath('//nav[@id="app-supplier-nav"]/*')
assert len(supplier_nav_contents) > 0
assert supplier_nav_contents[0].xpath('text()')[0].strip() == "Sell services"
def _load_homepage(self, framework_slugs_and_statuses, framework_messages):
self.data_api_client.find_frameworks.return_value = self._find_frameworks(framework_slugs_and_statuses)
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
if framework_messages:
self._assert_supplier_nav_is_not_empty(response_data)
for message in framework_messages:
assert message in response_data
else:
self._assert_supplier_nav_is_empty(response_data)
def test_homepage_sidebar_message_exists_gcloud_8_coming(self):
framework_slugs_and_statuses = [
('g-cloud-8', 'coming'),
('digital-outcomes-and-specialists', 'live')
]
framework_messages = [
u"Provide cloud software and support to the public sector.",
u"You need an account to receive notifications about when you can apply."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_message_exists_gcloud_8_open(self):
framework_slugs_and_statuses = [
('g-cloud-8', 'open'),
('digital-outcomes-and-specialists', 'live')
]
framework_messages = [
u"Provide cloud software and support to the public sector",
u"You need an account to apply.",
u"The application deadline is 5pm BST, 23 June 2016."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_message_exists_g_cloud_7_pending(self):
framework_slugs_and_statuses = [
('g-cloud-7', 'pending'),
]
framework_messages = [
u"G‑Cloud 7 is closed for applications",
u"G‑Cloud 7 services will be available from 23 November 2015."
]
self._load_homepage(framework_slugs_and_statuses, framework_messages)
def test_homepage_sidebar_messages_when_logged_out(self):
self.data_api_client.find_frameworks.return_value = self._find_frameworks([
('digital-outcomes-and-specialists', 'live')
])
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
document = html.fromstring(response_data)
supplier_links = document.cssselect("#app-supplier-nav a")
supplier_link_texts = [item.xpath("normalize-space(string())") for item in supplier_links]
assert 'View Digital Outcomes and Specialists opportunities' in supplier_link_texts
assert 'Become a supplier' in supplier_link_texts
assert 'See Digital Marketplace sales figures' in supplier_link_texts
def test_homepage_sidebar_messages_when_logged_in(self):
self.data_api_client.find_frameworks.return_value = self._find_frameworks([
('digital-outcomes-and-specialists', 'live')
])
self.login_as_supplier()
res = self.client.get('/')
assert res.status_code == 200
response_data = res.get_data(as_text=True)
document = html.fromstring(response_data)
supplier_links = document.cssselect("#app-supplier-nav a")
supplier_link_texts = [item.xpath("normalize-space(string())") for item in supplier_links]
assert 'View Digital Outcomes and Specialists opportunities' in supplier_link_texts
assert 'Become a supplier' not in supplier_link_texts
# here we've given an valid framework with a valid status but there is no message.yml file to read from
def test_g_cloud_6_open_blows_up(self):
framework_slugs_and_statuses = [
('g-cloud-6', 'open')
]
self.data_api_client.find_frameworks.return_value = self._find_frameworks(framework_slugs_and_statuses)
res = self.client.get('/')
assert res.status_code == 500
class TestStaticMarketplacePages(BaseApplicationTest):
def test_cookie_page(self):
res = self.client.get('/cookies')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
assert len(document.xpath('//h1[contains(text(), "Cookies on Digital Marketplace")]')) == 1
def test_terms_and_conditions_page(self):
res = self.client.get('/terms-and-conditions')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
assert len(document.xpath('//h1[contains(text(), "Terms and conditions")]')) == 1
def test_external_404_makes_all_links_absolute(self):
# Get the normal 404 page and a list of the relative URLs it contains links to
response1 = self.client.get("/does-not-exist-404")
assert response1.status_code == 404
regular_404_document = html.fromstring(response1.get_data(as_text=True))
regular_relative_links = regular_404_document.xpath('//a[starts-with(@href, "/")]')
regular_relative_forms = regular_404_document.xpath('//form[starts-with(@action, "/")]')
relative_urls = [link.get("href") for link in regular_relative_links] + \
[form.get("action") for form in regular_relative_forms]
# Get the "external" 404 page and check it doesn't contain any relative URLs
response2 = self.client.get("/404")
assert response2.status_code == 404
external_404_document = html.fromstring(response2.get_data(as_text=True))
external_relative_links = external_404_document.xpath('//a[starts-with(@href, "/")]')
external_relative_forms = external_404_document.xpath('//form[starts-with(@action, "/")]')
assert len(external_relative_links) == len(external_relative_forms) == 0
# Check that there is an absolute URL in the external 404 page for every relative URL in the normal 404 page
external_links = external_404_document.xpath('//a')
external_forms = external_404_document.xpath('//form')
external_urls = [link.get("href") for link in external_links] + [form.get("action") for form in external_forms]
for relative_url in relative_urls:
assert "http://localhost{}".format(relative_url) in external_urls
class BaseBriefPageTest(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.brief = self._get_dos_brief_fixture_data()
self.brief_responses = self._get_dos_brief_responses_fixture_data()
self.brief_id = self.brief['briefs']['id']
self.data_api_client.find_frameworks.return_value = self._get_frameworks_list_fixture_data()
self.data_api_client.get_brief.return_value = self.brief
self.data_api_client.find_brief_responses.return_value = self.brief_responses
class TestBriefPage(BaseBriefPageTest):
@pytest.mark.parametrize('framework_family, expected_status_code',
(
('digital-outcomes-and-specialists', 200),
('g-cloud', 404),
))
def test_404_on_framework_that_does_not_support_further_competition(self, framework_family, expected_status_code):
brief_id = self.brief['briefs']['id']
res = self.client.get(f'/{framework_family}/opportunities/{brief_id}')
assert res.status_code == expected_status_code
assert self.data_api_client.find_frameworks.mock_calls == [
mock.call(),
]
def test_dos_brief_404s_if_brief_is_draft(self):
self.brief['briefs']['status'] = 'draft'
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 404
assert self.data_api_client.mock_calls == [
mock.call.find_frameworks(),
mock.call.get_brief(str(brief_id)),
]
def test_dos_brief_has_correct_title(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
page_heading = document.cssselect("span.govuk-caption-l + h1.govuk-heading-l")
assert page_heading
heading = page_heading[0]
assert heading.text == self.brief["briefs"]["title"]
caption = heading.getprevious()
assert caption.text == self.brief["briefs"]["organisation"]
def _assert_all_normal_api_calls(self):
assert self.data_api_client.mock_calls == [
mock.call.find_frameworks(),
mock.call.get_brief(str(self.brief_id)),
mock.call.find_brief_responses(
brief_id=str(self.brief_id),
status='draft,submitted,pending-awarded,awarded',
with_data=False,
),
]
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled', 'awarded'])
def test_only_one_banner_at_once_brief_page(self, status):
self.brief['briefs']['status'] = status
if self.brief['briefs']['status'] == 'awarded':
self.brief['briefs']['awardedBriefResponseId'] = 14276
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
number_of_banners = len(document.xpath('//div[@class="banner-temporary-message-without-action"]'))
assert number_of_banners == 1
self._assert_all_normal_api_calls()
def test_dos_brief_displays_application_stats(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '3'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete applications"
assert incomplete_responses_section.xpath('p[1]/text()')[0] == "3 SME, 0 large"
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '5'
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed applications"
assert completed_responses_section.xpath('p[1]/text()')[0] == "4 SME, 1 large"
self._assert_all_normal_api_calls()
def test_application_stats_pluralised_correctly(self):
brief_id = self.brief['briefs']['id']
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"id": 14275,
"briefId": brief_id,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "submitted",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierOrganisationSize": 'large'
},
{
"id": 14276,
"briefId": brief_id,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "draft",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 706033,
"supplierOrganisationSize": 'micro'
}
]
}
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '1'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete application"
assert incomplete_responses_section.xpath('p[1]/text()')[0] == "1 SME, 0 large"
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '1'
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed application"
assert completed_responses_section.xpath('p[1]/text()')[0] == "0 SME, 1 large"
def test_dos_brief_displays_application_stats_correctly_when_no_applications(self):
brief_id = self.brief['briefs']['id']
self.data_api_client.find_brief_responses.return_value = {"briefResponses": []}
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
incomplete_responses_section = document.xpath('//div[@id="incomplete-applications"]')[0]
completed_responses_section = document.xpath('//div[@id="completed-applications"]')[0]
assert incomplete_responses_section.xpath('h2//span[1]/text()')[0] == '0'
assert completed_responses_section.xpath('h2//span[1]/text()')[0] == '0'
assert incomplete_responses_section.xpath('h2//span[2]/text()')[0] == "Incomplete applications"
assert completed_responses_section.xpath('h2//span[2]/text()')[0] == "Completed applications"
assert len(incomplete_responses_section.xpath('p[1]/text()')) == 0
assert len(completed_responses_section.xpath('p[1]/text()')) == 0
def test_dos_brief_has_lot_analytics_string(self):
brief = self.brief['briefs']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief['id']))
assert res.status_code == 200
data = res.get_data(as_text=True)
analytics_string = '<span data-lot="{lot_slug}"></span>'.format(lot_slug=brief['lotSlug'])
assert analytics_string in data
def test_dos_brief_has_important_dates(self):
brief_id = self.brief['briefs']['id']
self.brief['briefs']['clarificationQuestionsClosedAt'] = "2016-12-14T11:08:28.054129Z"
self.brief['briefs']['applicationsClosedAt'] = "2016-12-15T11:08:28.054129Z"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
brief_important_dates = document.xpath(
'(//dl[@id="opportunity-important-dates"]//div)')
assert 3 == len(brief_important_dates)
assert brief_important_dates[0].xpath('dt')[0].text_content().strip() \
== "Published"
assert brief_important_dates[0].xpath('dd')[0].text_content().strip() \
== "Thursday 1 December 2016"
assert brief_important_dates[1].xpath('dt')[0].text_content().strip() \
== "Deadline for asking questions"
assert brief_important_dates[1].xpath('dd')[0].text_content().strip() \
== "Wednesday 14 December 2016 at 11:08am GMT"
assert brief_important_dates[2].xpath('dt')[0].text_content().strip() \
== "Closing date for applications"
assert brief_important_dates[2].xpath('dd')[0].text_content().strip() \
== "Thursday 15 December 2016 at 11:08am GMT"
def test_dos_brief_with_daylight_savings_has_question_deadline_closing_date_forced_to_utc(self):
brief_id = self.brief['briefs']['id']
self.brief['briefs']['publishedAt'] = "2016-08-01T23:59:00.000000Z"
self.brief['briefs']['clarificationQuestionsClosedAt'] = "2016-08-14T23:59:00.000000Z"
self.brief['briefs']['applicationsClosedAt'] = "2016-08-15T23:59:00.000000Z"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
brief_important_dates = document.xpath(
'(//dl[@id="opportunity-important-dates"]//div)')
assert 3 == len(brief_important_dates)
# Publish date does not have UTC filter applied
assert brief_important_dates[0].xpath('dd')[0].text_content().strip() \
== "Monday 1 August 2016"
# Question deadline and closing date are forced to 11.59pm (UTC+00) on the correct day
assert brief_important_dates[1].xpath('dd')[0].text_content().strip() \
== "Sunday 14 August 2016 at 11:59pm GMT"
assert brief_important_dates[2].xpath('dd')[0].text_content().strip() \
== "Monday 15 August 2016 at 11:59pm GMT"
def test_dos_brief_has_at_least_one_section(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
section_heading = document.xpath('//h2[@id="opportunity-attributes-1"]')[0]
section_attributes = section_heading.xpath('following-sibling::dl[1]/div')
start_date_key = section_attributes[2].xpath('dt/text()')
start_date_value = section_attributes[2].xpath('dd/text()')
contract_length_key = section_attributes[3].xpath('dt/text()')
contract_length_value = section_attributes[3].xpath('dd/text()')
assert section_heading.text.strip() == 'Overview'
assert start_date_key[0].strip() == 'Latest start date'
assert start_date_value[0].strip() == 'Wednesday 1 March 2017'
assert contract_length_key[0].strip() == 'Expected contract length'
assert contract_length_value[0].strip() == '4 weeks'
@pytest.mark.parametrize(
'lot_slug, assessment_type', [
('digital-outcomes', 'written proposal'),
('digital-specialists', 'work history'),
('user-research-participants', 'written proposal'),
]
)
def test_dos_brief_displays_mandatory_evaluation_method_for_lot(self, lot_slug, assessment_type):
brief = self.brief.copy()
brief['briefs']['lot'] = lot_slug
brief['briefs']['lotSlug'] = lot_slug
brief['briefs']['status'] = 'live'
brief['briefs']['publishedAt'] = '2019-01-02T00:00:00.000000Z'
brief['briefs']['frameworkSlug'] = 'digital-outcomes-and-specialists-4'
self.data_api_client.get_brief.return_value = brief
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief['briefs']['id']))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
section_heading = document.xpath(
'//h2[contains(text(), "How suppliers will be evaluated")]'
)[0]
section_description = section_heading.xpath('following-sibling::p')[0]
assert section_description.text.strip() == f'All suppliers will be asked to provide a {assessment_type}.'
def test_dos_brief_has_questions_and_answers(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
xpath = '//h2[@id="clarification-questions"]/following-sibling::dl/div'
clarification_questions = document.xpath(xpath)
question = clarification_questions[0].xpath('dt')[0].text_content().strip()
answer = clarification_questions[0].xpath('dd')[0].text_content().strip()
assert question.startswith("1.")
assert question.endswith("Why?")
assert answer == "Because"
def test_can_apply_to_live_brief(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_apply_button_visible_if_status_is_draft(self):
self.brief_responses['briefResponses'][0]['status'] = 'draft'
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled'])
def test_cannot_apply_to_closed_cancelled_or_unsuccessful_brief(self, status):
self.brief['briefs']['status'] = status
self.brief['briefs']['applicationsClosedAt'] = "2016-12-15T11:08:28.054129Z"
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(brief_id))
assert len(apply_links) == 0
def test_cannot_apply_to_awarded_brief(self):
self.brief['briefs']['status'] = "awarded"
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 123456,
"supplierName": "Another, Better, Company Limited",
"supplierOrganisationSize": "large"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(brief_id))
assert len(apply_links) == 0
def test_dos_brief_specialist_role_displays_label(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert 'qualityAssurance' not in res.get_data(as_text=True)
assert 'Quality assurance analyst' in res.get_data(as_text=True)
def _assert_start_application(self, document, brief_id):
assert document.xpath(
"//form[@method='get'][normalize-space(string(.//button))=$t]/@action",
t="Apply for this opportunity",
) == ["/suppliers/opportunities/{}/responses/start".format(brief_id)]
def _assert_view_application(self, document, brief_id):
assert len(document.xpath(
'//a[@href="{0}"][contains(normalize-space(text()), normalize-space("{1}"))]'.format(
"/suppliers/opportunities/{}/responses/result".format(brief_id),
"View your application",
)
)) == 1
def test_unauthenticated_start_application(self):
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_buyer_start_application(self):
self.login_as_buyer()
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_supplier_start_application(self):
self.login_as_supplier()
# mocking that we haven't applied
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": []
}
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_start_application(document, brief_id)
def test_supplier_applied_view_application_for_live_opportunity(self):
self.login_as_supplier()
# fixtures for brief responses have been set up so one of them has the supplier_id we are logged in as.
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
@pytest.mark.parametrize('status', ['closed', 'unsuccessful', 'cancelled'])
def test_supplier_applied_view_application_for_closed_unsuccessful_or_cancelled_opportunity(self, status):
self.login_as_supplier()
self.brief['briefs']['status'] = status
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_awarded_to_logged_in_supplier(self):
self.login_as_supplier()
self.brief['briefs']['status'] = 'awarded'
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_pending_awarded_to_logged_in_supplier(self):
self.login_as_supplier()
self.brief['briefs']['status'] = 'closed'
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"pending": True},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "pending-awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
def test_supplier_applied_view_application_for_opportunity_awarded_to_other_supplier(self):
self.login_as_supplier()
self.data_api_client.find_brief_responses.return_value = {
"briefResponses": [
{
"awardDetails": {"awardedContractStartDate": "2017-08-21", "awardedContractValue": "20000.00"},
"id": 14276,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "awarded",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 123456,
"supplierName": "Another, Better, Company Limited",
"supplierOrganisationSize": "large"
},
{
"id": 14277,
"briefId": 1,
"createdAt": "2016-12-02T11:09:28.054129Z",
"status": "submitted",
"submittedAt": "2016-12-05T11:09:28.054129Z",
"supplierId": 1234,
"supplierName": "Example Company Limited",
"supplierOrganisationSize": "small"
}
]
}
self.brief['briefs']['status'] = 'awarded'
self.brief['briefs']['awardedBriefResponseId'] = 14276
brief_id = self.brief['briefs']['id']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(brief_id))
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self._assert_view_application(document, brief_id)
self._assert_all_normal_api_calls()
class TestBriefPageQandASectionViewQandASessionDetails(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['questionAndAnswerSessionDetails'] = {'many': 'details'}
self.brief['briefs']['clarificationQuestionsAreClosed'] = False
def test_live_brief_q_and_a_session(self):
"""
As long as a:
A user is not logged in
The brief is live
Clarification questions are open
The brief has Q and A session details
We should show the:
link to login and view the QAS details
"""
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Log in to view question and answer session details"
expected_link = '/suppliers/opportunities/{}/question-and-answer-session'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
def test_live_brief_q_and_a_session_logged_in(self):
"""
As long as a:
Supplier user is logged in
The brief is live
Clarification questions are open
The brief has Q and A session details
We should show the:
Link to view the QAS details
"""
self.login_as_supplier()
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "View question and answer session details"
expected_link = '/suppliers/opportunities/{}/question-and-answer-session'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
@pytest.mark.parametrize(
'brief_data', [
{'status': 'withdrawn'},
{'status': 'closed'},
{'questionAndAnswerSessionDetails': None},
{'clarificationQuestionsAreClosed': True}
]
)
def test_brief_q_and_a_session_link_not_shown(self, brief_data):
"""
On viewing briefs with data like the above the page should load but we should not get the link.
"""
self.brief['briefs'].update(brief_data)
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
unexpected_texts = [
"Log in to view question and answer session details",
"View question and answer session details"
]
for unexpected_text in unexpected_texts:
assert len(document.xpath('.//a[contains(text(),"{}")]'.format(unexpected_text))) == 0
class TestBriefPageQandASectionAskAQuestion(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['clarificationQuestionsAreClosed'] = False
def test_live_brief_ask_a_question(self):
"""
As long as a:
A user is not logged in
The brief is live
Clarification questions are open
We should show the:
link to login and ask a question
"""
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Log in to ask a question"
expected_link = '/suppliers/opportunities/{}/ask-a-question'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
def test_live_brief_ask_a_question_logged_in(self):
"""
As long as a:
Supplier user is logged in
The brief is live
Clarification questions are open
We should show the:
Link to ask a question
"""
self.login_as_supplier()
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
expected_text = "Ask a question"
expected_link = '/suppliers/opportunities/{}/ask-a-question'.format(self.brief_id)
assert expected_text in document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].text
assert document.xpath('.//a[contains(text(),"{}")]'.format(expected_text))[0].attrib['href'] == expected_link
@pytest.mark.parametrize(
'brief_data', [
{'status': 'withdrawn'},
{'status': 'closed'},
{'clarificationQuestionsAreClosed': True}
]
)
def test_brief_ask_a_question_link_not_shown(self, brief_data):
"""
On viewing briefs with data like the above the page should load but we should not get either the
log in to ask a question or ask a question links.
"""
self.brief['briefs'].update(brief_data)
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
unexpected_texts = ["Log in to ask a question", "Ask a question"]
for unexpected_text in unexpected_texts:
assert len(document.xpath('.//a[contains(text(),"{}")]'.format(unexpected_text))) == 0
class TestAwardedBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'awarded'
self.brief['briefs']['awardedBriefResponseId'] = 14276
def test_award_banner_with_winning_supplier_shown_on_awarded_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Awarded to Example Company Limited' in awarded_banner.xpath('h2/text()')[0]
def test_contract_start_date_visible_on_award_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Start date: Monday 21 August 2017' in awarded_banner.xpath('p/text()')[0]
def test_contract_value_visible_on_award_banner_does_not_include_zero_pence(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert u'Value: £20,000' in awarded_banner.xpath('p/text()')[1]
def test_contract_value_visible_on_award_banner_includes_non_zero_pence(self):
self.brief_responses["briefResponses"][1]["awardDetails"]["awardedContractValue"] = "20000.10"
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert u'Value: £20,000.10' in awarded_banner.xpath('p/text()')[1]
def test_supplier_size_visible_on_award_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
awarded_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'Company size: SME' in awarded_banner.xpath('p/text()')[2]
class TestCancelledBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'cancelled'
def test_cancelled_banner_shown_on_cancelled_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'This opportunity was cancelled' in cancelled_banner.xpath('h2/text()')[0]
def test_explanation_message_shown_on_cancelled_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
expected_message = ("The buyer cancelled this opportunity, for example because they no longer have the budget. "
"They may publish an updated version later."
)
assert expected_message in cancelled_banner.xpath('p/text()')[0]
class TestUnsuccessfulBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = 'unsuccessful'
def test_unsuccessful_banner_shown_on_unsuccessful_brief_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
unsuccessful_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
assert 'No suitable suppliers applied' in unsuccessful_banner.xpath('h2/text()')[0]
def test_explanation_message_shown_on_unsuccessful_banner(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
cancelled_banner = document.xpath('//div[@class="banner-temporary-message-without-action"]')[0]
expected_message = ("The buyer didn't award this contract because no suppliers met their requirements. "
"They may publish an updated version later."
)
assert expected_message in cancelled_banner.xpath('p/text()')[0]
class TestWithdrawnSpecificBriefPage(BaseBriefPageTest):
def setup_method(self, method):
super().setup_method(method)
self.brief['briefs']['status'] = "withdrawn"
self.brief['briefs']['withdrawnAt'] = "2016-11-25T10:47:23.126761Z"
def test_dos_brief_visible_when_withdrawn(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
assert res.status_code == 200
def test_apply_button_not_visible_for_withdrawn_briefs(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
document = html.fromstring(res.get_data(as_text=True))
apply_links = document.xpath('//a[@href="/suppliers/opportunities/{}/responses/start"]'.format(self.brief_id))
assert len(apply_links) == 0
def test_deadline_text_not_shown(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'The deadline for asking questions about this opportunity was ' not in page
def test_withdrawn_banner_shown_on_withdrawn_brief(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on' in page
assert (
"You can't apply for this opportunity now. "
"The buyer may publish an updated version on the Digital Marketplace"
) in page
@pytest.mark.parametrize('status', ['live', 'closed'])
def test_withdrawn_banner_not_shown_on_live_and_closed_brief(self, status):
self.brief['briefs']['status'] = status
del self.brief['briefs']['withdrawnAt']
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on' not in page
def test_dateformat_in_withdrawn_banner_displayed_correctly(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities/{}'.format(self.brief_id))
page = res.get_data(as_text=True)
assert 'This opportunity was withdrawn on Friday 25 November 2016' in page
class TestCatalogueOfBriefsPage(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.search_api_client.search.return_value = self._get_dos_brief_search_api_response_fixture_data()
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
self.data_api_client.find_frameworks.return_value = {'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status='live', lots=dos_lots(),
has_further_competition=True
).response(),
FrameworkStub(
id=1, slug='digital-outcomes-and-specialists', status='expired', lots=dos_lots(),
has_further_competition=True
).response(),
FrameworkStub(
id=2, slug='foobar', status='expired', lots=cloud_lots()
).response(),
FrameworkStub(
id=4, slug='g-cloud-9', status='live', lots=cloud_lots()
).response()
]}
def normalize_qs(self, qs):
return {k: set(v) for k, v in parse_qs(qs).items() if k != "page"}
@pytest.mark.parametrize('framework_family, expected_status_code',
(
('digital-outcomes-and-specialists', 200),
('g-cloud', 404),
))
def test_404_on_framework_that_does_not_support_further_competition(self, framework_family, expected_status_code):
res = self.client.get(f'/{framework_family}/opportunities')
assert res.status_code == expected_status_code
def test_catalogue_of_briefs_page(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open,closed'
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
lot_filters = document.xpath("//form[@method='get']//ul[@class='lot-filters--last-list']//a")
assert set(element.text for element in lot_filters) == {
"Digital outcomes (629)",
"Digital specialists (827)",
"User research participants (39)",
}
assert len(document.xpath("//form[@method='get']//ul[@class='lot-filters--last-list']//strong")) == 0
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert set(element.get("value") for element in status_inputs) == {"open", "closed"}
assert not any(element.get("checked") for element in status_inputs)
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == "864 results found in All categories"
specialist_role_labels = document.xpath("//div[@class='search-result']/ul[2]/li[2]/text()")
assert len(specialist_role_labels) == 2 # only two briefs has a specialist role so only one label should exist
assert specialist_role_labels[0].strip() == "Developer"
assert specialist_role_labels[1].strip() == "Technical architect"
def test_catalogue_of_briefs_page_filtered(self):
original_url = "/digital-outcomes-and-specialists/opportunities?page=2"\
"&statusOpenClosed=open&lot=digital-outcomes&location=wales&location=london"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open',
lot='digital-outcomes',
location='wales,london',
page='2',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
assert document.xpath(
"//a[@id=$i][contains(@class, $c)][normalize-space(string())=normalize-space($t)][@href=$h]",
i="dm-clear-all-filters",
c="clear-filters-link",
t="Clear filters",
h="/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes",
)
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": True,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": True,
"london": True,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
parsed_original_url = urlparse(original_url)
parsed_prev_url = urlparse(document.xpath("//li[@class='previous']/a/@href")[0])
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_prev_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_original_url.query) == \
self.normalize_qs(parsed_next_url.query) == \
self.normalize_qs(parsed_prev_url.query)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found in Digital outcomes"
def test_catalogue_of_briefs_page_filtered_keyword_search(self):
original_url = "/digital-outcomes-and-specialists/opportunities?page=2"\
"&statusOpenClosed=open&lot=digital-outcomes"\
"&location=offsite&q=Richie+Poldy"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open',
lot='digital-outcomes',
location='offsite',
page='2',
q='Richie Poldy',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
assert document.xpath(
"//a[@id=$i][contains(@class, $c)][normalize-space(string())=normalize-space($t)][@href=$h]",
i="dm-clear-all-filters",
c="clear-filters-link",
t="Clear filters",
h="/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&q=Richie+Poldy",
)
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": True,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": True,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("Richie Poldy",)
parsed_original_url = urlparse(original_url)
parsed_prev_url = urlparse(document.xpath("//li[@class='previous']/a/@href")[0])
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_prev_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_original_url.query) == \
self.normalize_qs(parsed_next_url.query) == \
self.normalize_qs(parsed_prev_url.query)
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found containing Richie Poldy in Digital outcomes"
def test_catalogue_of_briefs_page_filtered_all_lots_selected(self):
original_url = "/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&lot=digital-specialists"\
"&lot=user-research-participants"
res = self.client.get(original_url)
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
self.data_api_client.find_frameworks.assert_called_once_with()
self.search_api_client.search.assert_called_once_with(
index='briefs-digital-outcomes-and-specialists',
doc_type='briefs',
statusOpenClosed='open,closed',
lot='digital-outcomes',
)
heading = document.xpath('normalize-space(//h1/text())')
assert heading == "Digital Outcomes and Specialists opportunities"
assert ('View buyer requirements for digital outcomes, '
'digital specialists and user research participants') in document.xpath(
"normalize-space(//div[@class='marketplace-paragraph']/p/text())"
)
all_categories_return_link = document.xpath("//form[@method='get']//div[@class='lot-filters']/ul/li/a")[0]
assert all_categories_return_link.text == 'All categories'
lot_filters = document.xpath("//form[@method='get']//div[@class='lot-filters']//ul//ul/li/*[1]")
assert {
element.text: element.tag
for element in lot_filters
} == {
'Digital outcomes (629)': 'strong',
'Digital specialists (827)': 'a',
'User research participants (39)': 'a',
}
status_inputs = document.xpath("//form[@method='get']//input[@name='statusOpenClosed']")
assert {
element.get("value"): bool(element.get("checked"))
for element in status_inputs
} == {
"open": False,
"closed": False,
}
location_inputs = document.xpath("//form[@method='get']//input[@name='location']")
assert {
element.get("value"): bool(element.get("checked"))
for element in location_inputs
} == {
"scotland": False,
"north east england": False,
"north west england": False,
"yorkshire and the humber": False,
"east midlands": False,
"west midlands": False,
"east of england": False,
"wales": False,
"london": False,
"south east england": False,
"south west england": False,
"northern ireland": False,
"international (outside the uk)": False,
"offsite": False,
}
q_inputs = document.xpath("//form[@method='get']//input[@name='q']")
assert tuple(element.get("value") for element in q_inputs) == ("",)
parsed_original_url = urlparse(original_url)
parsed_next_url = urlparse(document.xpath("//li[@class='next']/a/@href")[0])
assert parsed_original_url.path == parsed_next_url.path
assert self.normalize_qs(parsed_next_url.query) == {'lot': {'digital-outcomes'}}
ss_elem = document.xpath("//p[@class='search-summary']")[0]
assert self._normalize_whitespace(self._squashed_element_text(ss_elem)) == \
"864 results found in Digital outcomes"
@pytest.mark.parametrize(
('dos_status', 'dos2_status', 'expected_url_slug_suffix'),
(
('live', 'standstill', ''),
('expired', 'live', '-2'),
)
)
@mock.patch('app.main.views.marketplace.content_loader')
def test_opportunity_data_download_info_and_link_visible_on_catalogue_page(
self, content_loader, dos_status, dos2_status, expected_url_slug_suffix
):
self.data_api_client.find_frameworks.return_value = {'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status=dos2_status,
lots=dos_lots(), has_further_competition=True
).response(),
FrameworkStub(
id=1, slug='digital-outcomes-and-specialists', status=dos_status,
lots=dos_lots(), has_further_competition=True
).response()
]}
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
header = document.xpath("//h2[@id='opportunity-data-header']")[0].text
description = document.xpath("//p[@id='opportunity-data-description']")[0].text
expected_desc = "Download data buyers have provided about closed opportunities. Some data may be missing."
link = document.xpath("//a[normalize-space(text())='Download data (CSV)']")[0].values()
expected_link = (
"https://assets.digitalmarketplace.service.gov.uk"
+ f"/digital-outcomes-and-specialists{expected_url_slug_suffix}/communications/data/opportunity-data.csv"
)
assert "Opportunity data" in header
assert expected_desc in description
assert expected_link in link
def test_catalogue_of_briefs_page_shows_pagination_if_more_pages(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=2')
assert res.status_code == 200
page = res.get_data(as_text=True)
document = html.fromstring(page)
assert '<li class="previous">' in page
assert '<li class="next">' in page
prev_url = str(document.xpath('string(//li[@class="previous"]/a/@href)'))
next_url = str(document.xpath('string(//li[@class="next"]/a/@href)'))
assert prev_url.endswith('/opportunities?page=1')
assert next_url.endswith('/opportunities?page=3')
assert '<span class="page-numbers">1 of 9</span>' in res.get_data(as_text=True)
assert '<span class="page-numbers">3 of 9</span>' in res.get_data(as_text=True)
def test_no_pagination_if_no_more_pages(self):
with self.app.app_context():
current_app.config['DM_SEARCH_PAGE_SIZE'] = 1000
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
page = res.get_data(as_text=True)
assert '<li class="previous">' not in page
assert '<li class="next">' not in page
def test_catalogue_of_briefs_page_404_for_framework_that_does_not_exist(self):
res = self.client.get('/digital-giraffes-and-monkeys/opportunities')
assert res.status_code == 404
self.data_api_client.find_frameworks.assert_called_once_with()
def test_briefs_search_has_js_hidden_filter_button(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
filter_button = document.xpath(
'//button[contains(@class, "js-hidden")][contains(@class, "js-dm-live-search")]'
'[normalize-space(text())="Filter"]'
)
assert len(filter_button) == 1
def test_opportunity_status_and_published_date(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
live_opportunity_published_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-3].text_content().strip()
assert live_opportunity_published_at == "Published: Friday 17 November 2017"
live_opportunity_qs_closing_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-2].text_content().strip()
assert live_opportunity_qs_closing_at == "Deadline for asking questions: Sunday 26 November 2017"
live_opportunity_closing_at = document.xpath(
'//div[@class="search-result"][1]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert live_opportunity_closing_at == "Closing: Friday 1 December 2017"
closed_opportunity_status = document.xpath(
'//div[@class="search-result"][2]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert closed_opportunity_status == "Closed: awaiting outcome"
unsuccessful_opportunity_status = document.xpath(
'//div[@class="search-result"][3]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert unsuccessful_opportunity_status == "Closed: no suitable suppliers"
cancelled_opportunity_status = document.xpath(
'//div[@class="search-result"][4]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert cancelled_opportunity_status == "Closed: cancelled"
awarded_opportunity_status = document.xpath(
'//div[@class="search-result"][6]//li[@class="search-result-metadata-item"]'
)[-1].text_content().strip()
assert awarded_opportunity_status == "Closed: awarded"
def test_should_render_summary_for_0_results_in_all_lots(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">0</span> results found in <em>All categories</em>' in summary
def test_should_render_summary_for_0_results_in_particular_lot(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">0</span> results found in <em>Digital outcomes</em>' in summary
def test_should_render_summary_for_1_result_found_in_all_lots(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 1
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">1</span> result found in <em>All categories</em>' in summary
def test_should_render_summary_for_many_results_found_in_a_particular_lot(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-specialists')
assert res.status_code == 200
summary = self.find_search_summary(res.get_data(as_text=True))[0]
assert '<span class="search-summary-count">864</span> results found in <em>Digital specialists</em>' in summary
def test_should_render_suggestions_for_0_results(self):
search_results = self._get_dos_brief_search_api_response_fixture_data()
search_results['meta']['total'] = 0
self.search_api_client.search.return_value = search_results
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
xpath = html.fromstring(res.get_data(as_text=True)).xpath
assert xpath('boolean(//div[contains(@class, "search-suggestion")])')
def test_should_not_render_suggestions_when_results(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
xpath = html.fromstring(res.get_data(as_text=True)).xpath
assert not xpath('boolean(//div[contains(@class, "search-suggestion")])')
def test_should_ignore_unknown_arguments(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?location=my-lovely-horse')
assert res.status_code == 200
def test_should_404_on_invalid_page_param(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=1')
assert res.status_code == 200
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=-1')
assert res.status_code == 404
res = self.client.get('/digital-outcomes-and-specialists/opportunities?page=potato')
assert res.status_code == 404
def test_search_results_with_invalid_lot_fall_back_to_all_categories(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=bad-lot-slug')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
lots = document.xpath('//div[@class="lot-filters"]//ul[@class="lot-filters--last-list"]//li/a')
assert lots[0].text_content().startswith('Digital outcomes')
assert lots[1].text_content().startswith('Digital specialists')
assert lots[2].text_content().startswith('User research participants')
def test_lot_links_retain_all_category_filters(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?location=london')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
lots = document.xpath('//div[@class="lot-filters"]//ul[@class="lot-filters--last-list"]//li/a')
for lot in lots:
assert 'location=london' in lot.get('href')
def test_lot_with_no_briefs_is_not_a_link(self):
specialists_aggregation = self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data()
specialists_aggregation['aggregations']['lot']['digital-specialists'] = 0
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
specialists_aggregation,
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
specialist_label = document.xpath("//ul[@class='lot-filters--last-list']//li")[-2]
assert len(specialist_label.xpath('a')) == 0
assert specialist_label.text_content() == 'Digital specialists (0)'
def test_filter_form_given_filter_selection(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?lot=digital-outcomes&location=london')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
hidden_inputs = document.xpath('//form[@id="js-dm-live-search-form"]//input[@type="hidden"]')
kv_pairs = {input_el.get('name'): input_el.get('value') for input_el in hidden_inputs}
assert kv_pairs == {'lot': 'digital-outcomes'}
class TestCatalogueOfBriefsFilterOnClick(APIClientMixin, BaseApplicationTest):
def setup_method(self, method):
super().setup_method(method)
self.search_api_client.search.return_value = self._get_dos_brief_search_api_response_fixture_data()
self.search_api_client.aggregate.side_effect = [
self._get_dos_brief_search_api_aggregations_response_outcomes_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_specialists_fixture_data(),
self._get_dos_brief_search_api_aggregations_response_user_research_fixture_data(),
]
self.data_api_client.find_frameworks.return_value = {
'frameworks': [
FrameworkStub(
id=3, slug='digital-outcomes-and-specialists-2', status='live',
lots=dos_lots(), has_further_competition=True
).response()
]
}
@pytest.mark.parametrize('query_string, content_type',
(('', 'text/html; charset=utf-8'),
('?live-results=true', 'application/json')))
def test_endpoint_switches_on_live_results_request(self, query_string, content_type):
res = self.client.get('/digital-outcomes-and-specialists/opportunities{}'.format(query_string))
assert res.status_code == 200
assert res.content_type == content_type
def test_live_results_returns_valid_json_structure(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities?live-results=true')
data = json.loads(res.get_data(as_text=True))
assert sorted(data.keys()) == sorted((
'results',
'summary',
'summary-accessible-hint',
'categories',
'filter-title'
))
for k, v in data.items():
assert set(v.keys()) == {'selector', 'html'}
# We want to enforce using css IDs to describe the nodes which should be replaced.
assert v['selector'].startswith('#')
live_results_expected_templates = (
"search/_results_wrapper.html",
"search/_categories_wrapper.html",
"search/_summary.html",
"search/_summary_accessible_hint.html",
"search/_filter_title.html"
)
@pytest.mark.parametrize(
('query_string', 'urls'),
(
('', ('search/briefs.html',)),
('?live-results=true', live_results_expected_templates)
)
)
@mock.patch('app.main.views.marketplace.render_template', autospec=True)
def test_base_page_renders_search_services(self, render_template_patch, query_string, urls):
render_template_patch.return_value = '<p>some html</p>'
self.client.get('/digital-outcomes-and-specialists/opportunities{}'.format(query_string))
assert urls == tuple(x[0][0] for x in render_template_patch.call_args_list)
def test_form_has_js_hidden_filter_button(self):
res = self.client.get('/digital-outcomes-and-specialists/opportunities')
assert res.status_code == 200
document = html.fromstring(res.get_data(as_text=True))
filter_button = document.xpath(
'//button[contains(@class, "js-hidden")][contains(@class, "js-dm-live-search")]'
'[normalize-space(text())="Filter"]'
)
assert len(filter_button) == 1
class TestGCloudHomepageLinks(APIClientMixin, BaseApplicationTest):
mock_live_g_cloud_framework = {
"framework": "g-cloud",
"slug": "g-cloud-x",
"status": "live",
"id": 5
}
@pytest.mark.parametrize('framework_slug, gcloud_content',
(('g-cloud-8', 'Find cloud technology and support'),
('g-cloud-9', 'Find cloud hosting, software and support')))
def test_g_cloud_homepage_content_is_correct(self, framework_slug, gcloud_content):
self.data_api_client.find_frameworks.return_value = {
"frameworks": [self.mock_live_g_cloud_framework.copy()]
}
self.data_api_client.find_frameworks.return_value['frameworks'][0].update({'slug': framework_slug})
res = self.client.get("/")
document = html.fromstring(res.get_data(as_text=True))
assert res.status_code == 200
link_texts = [item.text_content().strip() for item in document.cssselect('#app-buyer-nav a')]
assert link_texts[-2] == gcloud_content
assert link_texts[-1] == 'Find physical datacentre space'
|
#!/usr/bin/env python
# CommandLine.py
# Copyright (C) 2006 CCLRC, Graeme Winter
#
# This code is distributed under the BSD license, a copy of which is
# included in the root directory of this package.
#
# 12th June 2006
#
# A handler for all of the information which may be passed in on the command
# line. This singleton object should be able to handle the input, structure
# it and make it available in a useful fashion.
#
# This is a hook into a global data repository, should mostly be replaced with
# a Phil interface.
import sys
import os
import exceptions
import copy
import traceback
from xia2.Experts.FindImages import image2template_directory
from xia2.Schema.XProject import XProject
from xia2.Handlers.Flags import Flags
from xia2.Handlers.Phil import PhilIndex
from xia2.Handlers.Streams import Chatter, Debug
from xia2.Handlers.PipelineSelection import add_preference, get_preferences
from xia2.Handlers.Executables import Executables
from libtbx.utils import Sorry
def which(pgm):
# python equivalent to the 'which' command
# http://stackoverflow.com/questions/9877462/is-there-a-python-equivalent-to-the-which-command
path = os.getenv('PATH')
for p in path.split(os.path.pathsep):
p = os.path.join(p,pgm)
if os.path.exists(p) and os.access(p,os.X_OK):
return p
def load_datablock(filename):
from xia2.Schema import imageset_cache, update_with_reference_geometry
from dxtbx.serialize import load
from libtbx.containers import OrderedDict
datablocks = load.datablock(filename, check_format=False)
for datablock in datablocks:
imagesets = datablock.extract_imagesets()
params = PhilIndex.get_python_object()
reference_geometry = params.xia2.settings.input.reference_geometry
if reference_geometry is not None and len(reference_geometry) > 0:
update_with_reference_geometry(imagesets, reference_geometry)
for imageset in imagesets:
template = imageset.get_template()
if template not in imageset_cache:
imageset_cache[template] = OrderedDict()
imageset_cache[template][
imageset.get_scan().get_image_range()[0]] = imageset
class _CommandLine(object):
'''A class to represent the command line input.'''
def __init__(self):
'''Initialise all of the information from the command line.'''
self._argv = []
self._understood = []
self._default_template = []
self._default_directory = []
self._hdf5_master_files = []
self._default_start_end = { }
return
def get_argv(self):
return self._argv
def print_command_line(self):
cl = self.get_command_line()
Chatter.write('Command line: %s' % cl)
return
def get_command_line(self):
import libtbx.load_env
cl = libtbx.env.dispatcher_name
if cl:
if 'xia2' not in cl or 'python' in cl:
cl = 'xia2'
else:
cl = 'xia2'
for arg in sys.argv[1:]:
if ' ' in arg:
arg = '"%s"' %arg
cl += ' %s' % arg
return cl
def setup(self):
'''Set everything up...'''
# check arguments are all ascii
for token in sys.argv:
try:
token.encode('ascii')
except UnicodeDecodeError, e:
raise RuntimeError, 'non-ascii characters in input'
self._argv = copy.deepcopy(sys.argv)
# first of all try to interpret arguments as phil parameters/files
from xia2.Handlers.Phil import master_phil
from libtbx.phil import command_line
cmd_line = command_line.argument_interpreter(master_phil=master_phil)
working_phil, self._argv = cmd_line.process_and_fetch(
args=self._argv, custom_processor="collect_remaining")
PhilIndex.merge_phil(working_phil)
try:
PhilIndex.get_python_object()
except RuntimeError, e:
raise Sorry(e)
#PhilIndex.get_diff().show()
# things which are single token flags...
self._read_debug()
self._read_interactive()
self._read_ice()
self._read_egg()
self._read_uniform_sd()
self._read_trust_timestamps()
self._read_batch_scale()
self._read_small_molecule()
self._read_quick()
self._read_chef()
self._read_mask()
self._read_reversephi()
self._read_no_lattice_test()
self._read_no_relax()
self._read_no_profile()
self._read_norefine()
self._read_noremove()
# pipeline options
self._read_2d()
self._read_2di()
self._read_dials()
self._read_3d()
self._read_3di()
self._read_3dii()
self._read_3dd()
self._read_migrate_data()
self._read_zero_dose()
self._read_free_fraction()
self._read_free_total()
# finer grained control over the selection of indexer, integrater
# and scaler to use.
self._read_indexer()
self._read_integrater()
self._read_scaler()
self._read_executables()
# flags relating to unfixed bugs...
self._read_fixed_628()
try:
self._read_beam()
except:
raise RuntimeError, self._help_beam()
try:
self._read_cell()
except:
raise RuntimeError, self._help_cell()
try:
self._read_image()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_image(), str(e))
try:
self._read_project_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_project_name(), str(e))
try:
self._read_atom_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_atom_name(), str(e))
try:
self._read_phil()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_phil(), str(e))
try:
self._read_crystal_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_crystal_name(), str(e))
try:
self._read_ispyb_xml_out()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_ispyb_xml_out(), str(e))
try:
self._read_hdr_in()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_hdr_in(), str(e))
try:
self._read_hdr_out()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_hdr_out(), str(e))
try:
self._read_pickle()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_pickle(), str(e))
try:
self._read_parallel()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_parallel(), str(e))
try:
self._read_serial()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_serial(), str(e))
try:
self._read_xparm()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparm(), str(e))
try:
self._read_xparm_ub()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparm_ub(), str(e))
try:
self._read_min_images()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_min_images(), str(e))
try:
self._read_start_end()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_start_end(), str(e))
try:
self._read_xparallel()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparallel(), str(e))
try:
self._read_spacegroup()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_spacegroup(), str(e))
try:
self._read_resolution()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_resolution(), str(e))
try:
self._read_z_min()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_z_min(), str(e))
try:
self._read_aimless_secondary()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_aimless_secondary(), str(e))
try:
self._read_freer_file()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_freer_file(), str(e))
try:
self._read_reference_reflection_file()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_reference_reflection_file(), str(e))
try:
self._read_rejection_threshold()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_rejection_threshold(), str(e))
try:
self._read_isigma()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_isigma(), str(e))
try:
self._read_misigma()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_misigma(), str(e))
try:
self._read_rmerge()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_rmerge(), str(e))
try:
self._read_cc_half()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_cc_half(), str(e))
try:
self._read_microcrystal()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_microcrystal(), str(e))
try:
self._read_failover()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_failover(), str(e))
try:
self._read_blend()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_blend(), str(e))
try:
self._read_completeness()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_completeness(), str(e))
try:
self._read_scale_model()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_scale_model(), str(e))
# FIXME add some consistency checks in here e.g. that there are
# images assigned, there is a lattice assigned if cell constants
# are given and so on
params = PhilIndex.get_python_object()
mp_params = params.xia2.settings.multiprocessing
from libtbx import Auto
if mp_params.mode == 'parallel':
if mp_params.type == 'qsub':
if which('qsub') is None:
raise Sorry('qsub not available')
if mp_params.njob is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.njob = get_number_cpus()
if mp_params.nproc is Auto:
mp_params.nproc = 1
elif mp_params.nproc is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.nproc = get_number_cpus()
Flags.set_parallel(mp_params.nproc)
else:
Flags.set_parallel(mp_params.nproc)
elif mp_params.mode == 'serial':
mp_params.njob = 1
if mp_params.nproc is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.nproc = get_number_cpus()
Flags.set_parallel(mp_params.nproc)
PhilIndex.update("xia2.settings.multiprocessing.njob=%d" %mp_params.njob)
PhilIndex.update("xia2.settings.multiprocessing.nproc=%d" %mp_params.nproc)
params = PhilIndex.get_python_object()
mp_params = params.xia2.settings.multiprocessing
if params.xia2.settings.indexer is not None:
add_preference("indexer", params.xia2.settings.indexer)
if params.xia2.settings.multi_sweep_indexing is Auto:
params.xia2.settings.multi_sweep_indexing = \
Flags.get_small_molecule() and 'dials' == params.xia2.settings.indexer
if params.xia2.settings.refiner is not None:
add_preference("refiner", params.xia2.settings.refiner)
if params.xia2.settings.integrater is not None:
add_preference("integrater", params.xia2.settings.integrater)
if params.xia2.settings.scaler is not None:
add_preference("scaler", params.xia2.settings.scaler)
if params.xia2.settings.resolution.d_min is not None:
Flags.set_resolution_high(params.xia2.settings.resolution.d_min)
if params.xia2.settings.resolution.d_max is not None:
Flags.set_resolution_low(params.xia2.settings.resolution.d_max)
Flags.set_reversephi(params.xia2.settings.input.reverse_phi)
input_json = params.xia2.settings.input.json
if (input_json is not None and len(input_json)):
for json_file in input_json:
assert os.path.isfile(json_file)
load_datablock(json_file)
reference_geometry = params.xia2.settings.input.reference_geometry
if reference_geometry is not None and len(reference_geometry) > 0:
reference_geometries = "\n".join(
["xia2.settings.input.reference_geometry=%s" % os.path.abspath(g)
for g in params.xia2.settings.input.reference_geometry])
Debug.write(reference_geometries)
PhilIndex.update(reference_geometries)
Debug.write("xia2.settings.trust_beam_centre=true")
PhilIndex.update("xia2.settings.trust_beam_centre=true")
params = PhilIndex.get_python_object()
try:
self._read_xinfo()
except exceptions.Exception, e:
traceback.print_exc(file = open('xia2-xinfo.error', 'w'))
raise RuntimeError, '%s (%s)' % \
(self._help_xinfo(), str(e))
params = PhilIndex.get_python_object()
if params.xia2.settings.input.xinfo is not None:
xinfo_file = os.path.abspath(params.xia2.settings.input.xinfo)
PhilIndex.update("xia2.settings.input.xinfo=%s" %xinfo_file)
params = PhilIndex.get_python_object()
self.set_xinfo(xinfo_file)
Debug.write(60 * '-')
Debug.write('XINFO file: %s' % xinfo_file)
for record in open(xinfo_file, 'r').readlines():
# don't want \n on the end...
Debug.write(record[:-1])
Debug.write(60 * '-')
else:
xinfo_file = '%s/automatic.xinfo' %os.path.abspath(
os.curdir)
PhilIndex.update("xia2.settings.input.xinfo=%s" %xinfo_file)
params = PhilIndex.get_python_object()
if params.dials.find_spots.phil_file is not None:
PhilIndex.update("dials.find_spots.phil_file=%s" %os.path.abspath(
params.dials.find_spots.phil_file))
if params.dials.index.phil_file is not None:
PhilIndex.update("dials.index.phil_file=%s" %os.path.abspath(
params.dials.index.phil_file))
if params.dials.refine.phil_file is not None:
PhilIndex.update("dials.refine.phil_file=%s" %os.path.abspath(
params.dials.refine.phil_file))
if params.dials.integrate.phil_file is not None:
PhilIndex.update("dials.integrate.phil_file=%s" %os.path.abspath(
params.dials.integrate.phil_file))
params = PhilIndex.get_python_object()
datasets = PhilIndex.params.xia2.settings.input.image
for dataset in datasets:
start_end = None
if ':' in dataset:
tokens = dataset.split(':')
# cope with windows drives i.e. C:\data\blah\thing_0001.cbf:1:100
if len(tokens[0]) == 1:
tokens = ['%s:%s' % (tokens[0], tokens[1])] + tokens[2:]
if len(tokens) != 3:
raise RuntimeError, '/path/to/image_0001.cbf:start:end'
dataset = tokens[0]
start_end = int(tokens[1]), int(tokens[2])
from xia2.Applications.xia2setup import is_hd5f_name
if is_hd5f_name(dataset):
self._hdf5_master_files.append(os.path.abspath(dataset))
if start_end:
Debug.write('Image range: %d %d' % start_end)
self._default_start_end[dataset] = start_end
else:
Debug.write('No image range specified')
else:
template, directory = image2template_directory(os.path.abspath(dataset))
self._default_template.append(template)
self._default_directory.append(directory)
Debug.write('Interpreted from image %s:' % dataset)
Debug.write('Template %s' % template)
Debug.write('Directory %s' % directory)
if start_end:
Debug.write('Image range: %d %d' % start_end)
self._default_start_end[os.path.join(directory, template)] = start_end
else:
Debug.write('No image range specified')
# finally, check that all arguments were read and raise an exception
# if any of them were nonsense.
with open('xia2-working.phil', 'wb') as f:
print >> f, PhilIndex.working_phil.as_str()
with open('xia2-diff.phil', 'wb') as f:
print >> f, PhilIndex.get_diff().as_str()
Debug.write('\nDifference PHIL:')
Debug.write(PhilIndex.get_diff().as_str(), strip=False)
Debug.write('Working PHIL:')
Debug.write(PhilIndex.working_phil.as_str(), strip=False)
nonsense = 'Unknown command-line options:'
was_nonsense = False
for j, argv in enumerate(self._argv):
if j == 0:
continue
if argv[0] != '-' and '=' not in argv:
continue
if not j in self._understood:
nonsense += ' %s' % argv
was_nonsense = True
if was_nonsense:
raise RuntimeError, nonsense
return
# command line parsers, getters and help functions.
def _read_beam(self):
'''Read the beam centre from the command line.'''
index = -1
try:
index = self._argv.index('-beam')
except ValueError, e:
self._beam = None
return
if index < 0:
raise RuntimeError, 'negative index'
#try:
#beam = self._argv[index + 1].split(',')
#except IndexError, e:
#raise RuntimeError, '-beam correct use "-beam x,y"'
#if len(beam) != 2:
#raise RuntimeError, '-beam correct use "-beam x,y"'
#self._beam = (float(beam[0]), float(beam[1]))
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -beam option deprecated: please use beam_centre='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.beam_centre=%s" %self._argv[index +1 ])
PhilIndex.get_python_object()
Debug.write('Beam read from command line as %f %f' % tuple(
PhilIndex.params.xia2.settings.beam_centre))
self._beam = tuple(PhilIndex.params.xia2.settings.beam_centre)
return
def _help_beam(self):
'''Return a help string for the read beam method.'''
return '-beam x,y'
def get_beam(self):
return self._beam
def _help_image(self):
'''Return a string for explaining the -image method.'''
return '-image /path/to/an/image_001.img'
def _read_image(self):
'''Read image information from the command line.'''
index = -1
try:
index = self._argv.index('-image')
except ValueError, e:
# the token is not on the command line
self._default_template = []
self._default_directory = []
return
image = self._argv[index + 1]
# check if there is a space in the image name - this happens and it
# looks like the python input parser will split it even if the
# space is escaped or it is quoted
if image[-1] == '\\':
try:
image = '%s %s' % (self._argv[index + 1][:-1],
self._argv[index + 2])
except:
raise RuntimeError, 'image name ends in \\'
# XXX Warning added 2015-04-23
Chatter.write(
"Warning: -image option deprecated: please use image='%s' instead" %(
image))
PhilIndex.update("xia2.settings.input.image=%s" %image)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_atom_name(self):
try:
index = self._argv.index('-atom')
except ValueError, e:
self._default_atom_name = None
return
self._default_atom_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Heavy atom: %s' % \
self._default_atom_name)
return
def _help_atom_name(self):
return '-atom se'
def get_atom_name(self):
return self._default_atom_name
def _read_phil(self):
try:
index = self._argv.index('-phil')
except ValueError, e:
return
Chatter.bigbanner('-phil option now no longer needed: '
'please just place file on command-line', size=80)
self._understood.append(index)
if True:
return
PhilIndex.merge_param_file(self._argv[index + 1])
PhilIndex.get_python_object()
self._understood.append(index + 1)
Debug.write('Phil file: %s' % self._argv[index + 1])
return
def _help_phil(self):
return '-phil parameters.phil'
def _read_project_name(self):
try:
index = self._argv.index('-project')
except ValueError, e:
self._default_project_name = None
return
self._default_project_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Project: %s' % self._default_project_name)
return
def _help_project_name(self):
return '-project foo'
def get_project_name(self):
return self._default_project_name
def _read_crystal_name(self):
try:
index = self._argv.index('-crystal')
except ValueError, e:
self._default_crystal_name = None
return
self._default_crystal_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Crystal: %s' % self._default_crystal_name)
return
def _help_crystal_name(self):
return '-crystal foo'
def get_crystal_name(self):
return self._default_crystal_name
def _read_xinfo(self):
try:
index = self._argv.index('-xinfo')
except ValueError, e:
self._xinfo = None
return
if index < 0:
raise RuntimeError, 'negative index (no xinfo file name given)'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2015-03-02
Chatter.write(
"Warning: -xinfo option deprecated: please use xinfo='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.input.xinfo=%s" %self._argv[index + 1])
PhilIndex.get_python_object()
return
def _help_xinfo(self):
return '-xinfo example.xinfo'
def set_xinfo(self, xinfo):
with open(xinfo, 'rb') as f:
Debug.write('\n' + xinfo)
Debug.write(f.read())
self._xinfo = XProject(xinfo)
def get_xinfo(self):
'''Return the XProject.'''
return self._xinfo
def _read_xparm(self):
try:
index = self._argv.index('-xparm')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_xparm(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Rotation axis: %.6f %.6f %.6f' % \
Flags.get_xparm_rotation_axis())
Debug.write('Beam vector: %.6f %.6f %.6f' % \
Flags.get_xparm_beam_vector())
Debug.write('Origin: %.2f %.2f' % \
Flags.get_xparm_origin())
return
def _help_xparm(self):
return '-xparm GXPARM.XDS'
def _read_xparm_ub(self):
try:
index = self._argv.index('-xparm_ub')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_xparm_ub(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Real Space A: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_a()))
Debug.write('Real Space B: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_b()))
Debug.write('Real Space C: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_c()))
return
def _help_xparm_ub(self):
return '-xparm_ub GXPARM.XDS'
def _read_parallel(self):
try:
index = self._argv.index('-parallel')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
if int(self._argv[index + 1]) < 0:
raise RuntimeError, 'negative number of processors: %s' % \
self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -parallel option deprecated: please use nproc=%s instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.multiprocessing.nproc=%s" %(
self._argv[index + 1]))
PhilIndex.get_python_object()
return
def _help_parallel(self):
return '-parallel N'
def _read_serial(self):
try:
index = self._argv.index('-serial')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
Flags.set_parallel(1)
PhilIndex.update("xia2.settings.multiprocessing.nproc=1")
PhilIndex.get_python_object()
Debug.write('Serial set (i.e. 1 CPU)')
return
def _help_serial(self):
return '-serial'
def _read_min_images(self):
try:
index = self._argv.index('-min_images')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2015-05-01
Chatter.write(
"Warning: -min_images option deprecated: please use min_images=%s instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.input.min_images=%i" %(
int(self._argv[index + 1])))
PhilIndex.get_python_object()
return
def _help_min_images(self):
return '-min_images N'
def _read_start_end(self):
try:
index = self._argv.index('-start_end')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
if not '-image' in self._argv:
raise RuntimeError, 'do not use start_end without -image'
self._understood.append(index)
self._understood.append(index + 1)
start, end = tuple(map(int, self._argv[index + 1].split(',')))
Flags.set_start_end(start, end)
Debug.write('Start, end set to %d %d' % Flags.get_start_end())
return
def _help_start_end(self):
return '-start_end start,end'
def _read_xparallel(self):
try:
index = self._argv.index('-xparallel')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_xparallel(int(self._argv[index + 1]))
Debug.write('XParallel set to %d' % Flags.get_xparallel())
return
def _help_xparallel(self):
return '-xparallel N'
def _read_spacegroup(self):
try:
index = self._argv.index('-spacegroup')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -spacegroup option deprecated: please use space_group='%s' instead" %(
self._argv[index + 1]))
Flags.set_spacegroup(self._argv[index + 1]) # XXX this line should go
PhilIndex.update("xia2.settings.space_group=%s" %self._argv[index + 1])
PhilIndex.get_python_object()
Debug.write('Spacegroup set to %s' % self._argv[index + 1])
return
def _help_spacegroup(self):
return '-spacegroup P43212'
def _read_resolution(self):
try:
index = self._argv.index('-resolution')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
resolution = self._argv[index + 1]
if ',' in resolution:
a, b = map(float, resolution.split(','))
dmin = min(a, b)
dmax = max(a, b)
else:
dmin = float(resolution)
dmax = None
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update("xia2.settings.resolution.d_min=%s" %dmin)
if dmax is not None:
PhilIndex.update("xia2.settings.resolution.d_max=%s" %dmax)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -resolution option deprecated: please use d_min=%s and d_max=%s instead" %(
dmin, dmax))
else:
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -resolution option deprecated: please use d_min=%s instead" %(
dmin))
PhilIndex.get_python_object()
if dmax:
Debug.write('Resolution set to %.3f %.3f' % (dmin, dmax))
else:
Debug.write('Resolution set to %.3f' % dmin)
return
def _help_resolution(self):
return '-resolution high[,low]'
def _read_z_min(self):
try:
index = self._argv.index('-z_min')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_z_min(float(self._argv[index + 1]))
Debug.write('Z min set to %f' % Flags.get_z_min())
return
def _help_z_min(self):
return '-z_min N'
def _read_aimless_secondary(self):
try:
index = self._argv.index('-aimless_secondary')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_aimless_secondary(float(self._argv[index + 1]))
Debug.write('Aimless secondary set to %f' % Flags.get_aimless_secondary())
return
def _help_aimless_secondary(self):
return '-aimless_secondary N'
def _read_freer_file(self):
try:
index = self._argv.index('-freer_file')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_freer_file(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('FreeR_flag column taken from %s' %
Flags.get_freer_file())
# this should also be used as an indexing reference to make
# sense...
Flags.set_reference_reflection_file(self._argv[index + 1])
Debug.write('Reference reflection file: %s' %
Flags.get_reference_reflection_file())
# and also the spacegroup copied out?! ok - this is done
# "by magic" in the scaler.
return
def _help_freer_file(self):
return '-freer_file my_freer_file.mtz'
def _read_reference_reflection_file(self):
try:
index = self._argv.index('-reference_reflection_file')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_reference_reflection_file(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Reference reflection file: %s' %
Flags.get_reference_reflection_file())
return
def _help_reference_reflection_file(self):
return '-reference_reflection_file my_reference_reflection_file.mtz'
def _read_rejection_threshold(self):
try:
index = self._argv.index('-rejection_threshold')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_rejection_threshold(float(self._argv[index + 1]))
Debug.write('Rejection threshold set to %f' % \
Flags.get_rejection_threshold())
return
def _help_rejection_threshold(self):
return '-rejection_threshold N'
def _read_isigma(self):
try:
index = self._argv.index('-isigma')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.isigma=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -isigma option deprecated: please use isigma=%s instead" %self._argv[index + 1])
return
def _help_isigma(self):
return '-isigma N'
def _read_misigma(self):
try:
index = self._argv.index('-misigma')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.misigma=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -misigma option deprecated: please use misigma=%s instead" %self._argv[index + 1])
return
def _help_misigma(self):
return '-misigma N'
def _read_completeness(self):
try:
index = self._argv.index('-completeness')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.completeness=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -completeness option deprecated: please use completeness=%s instead" %self._argv[index + 1])
return
def _help_completeness(self):
return '-completeness N'
def _read_rmerge(self):
try:
index = self._argv.index('-rmerge')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.rmerge=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -rmerge option deprecated: please use rmerge=%s instead" %self._argv[index + 1])
return
def _help_rmerge(self):
return '-rmerge N'
def _read_cc_half(self):
try:
index = self._argv.index('-cc_half')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.cc_half=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -cc_half option deprecated: please use cc_half=%s instead" %self._argv[index + 1])
return
def _help_cc_half(self):
return '-cc_half N'
def _read_microcrystal(self):
if '-microcrystal' in self._argv:
Flags.set_microcrystal()
Debug.write('Microcrystal mode on')
self._understood.append(self._argv.index('-microcrystal'))
return
def _read_failover(self):
if '-failover' in self._argv:
Flags.set_failover()
Debug.write('Failover mode on')
self._understood.append(self._argv.index('-failover'))
return
def _read_blend(self):
if '-blend' in self._argv:
Flags.set_blend()
Debug.write('Blend mode on')
self._understood.append(self._argv.index('-blend'))
return
def _read_ispyb_xml_out(self):
try:
index = self._argv.index('-ispyb_xml_out')
except ValueError, e:
self._ispyb_xml_out = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_ispyb_xml_out(self._argv[index + 1])
Debug.write('ISPyB XML output set to %s' % self._argv[index + 1])
return
def _help_ispyb_xml_out(self):
return '-ispyb_xml_out project.xml'
def _read_hdr_in(self):
try:
index = self._argv.index('-hdr_in')
except ValueError, e:
self._hdr_in = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_hdr_in(self._argv[index + 1])
return
def _help_hdr_in(self):
return '-hdr_in project.hdr'
def _read_hdr_out(self):
try:
index = self._argv.index('-hdr_out')
except ValueError, e:
self._hdr_out = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_hdr_out(self._argv[index + 1])
Debug.write('Output header file set to %s' % self._argv[index + 1])
return
def _help_hdr_out(self):
return '-hdr_out project.hdr'
def _read_pickle(self):
try:
index = self._argv.index('-pickle')
except ValueError, e:
self._pickle = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_pickle(self._argv[index + 1])
return
def _help_pickle(self):
return '-pickle name.pkl'
def get_template(self):
return self._default_template
def get_start_end(self, full_template):
return self._default_start_end.get(full_template, None)
def get_directory(self):
return self._default_directory
def get_hdf5_master_files(self):
return self._hdf5_master_files
def _read_trust_timestamps(self):
if '-trust_timestamps' in self._argv:
Flags.set_trust_timestamps(True)
Debug.write('Trust timestamps on')
self._understood.append(self._argv.index('-trust_timestamps'))
return
def _read_batch_scale(self):
if '-batch_scale' in self._argv:
Flags.set_batch_scale(True)
Debug.write('Batch scaling mode on')
self._understood.append(self._argv.index('-batch_scale'))
return
def _read_small_molecule(self):
if '-small_molecule' in self._argv:
Flags.set_small_molecule(True)
Debug.write('Small molecule selected')
self._understood.append(self._argv.index('-small_molecule'))
settings = PhilIndex.get_python_object().xia2.settings
PhilIndex.update("xia2.settings.unify_setting=true")
return
def _read_scale_model(self):
try:
index = self._argv.index('-scale_model')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_scale_model(self._argv[index + 1])
Debug.write('Scaling model set to: %s' % Flags.get_scale_model())
return
def _read_quick(self):
if '-quick' in self._argv:
Flags.set_quick(True)
Debug.write('Quick mode selected')
self._understood.append(self._argv.index('-quick'))
return
def _read_chef(self):
if '-chef' in self._argv:
Flags.set_chef(True)
self._understood.append(self._argv.index('-chef'))
Debug.write('Chef mode selected')
if '-nochef' in self._argv:
Flags.set_chef(False)
self._understood.append(self._argv.index('-nochef'))
Debug.write('Chef mode deselected')
return
def _read_reversephi(self):
if '-reversephi' in self._argv:
self._understood.append(self._argv.index('-reversephi'))
# XXX Warning added 2015-11-18
Chatter.write(
"Warning: -reversephi option deprecated: please use reverse_phi=True instead")
PhilIndex.update("xia2.settings.input.reverse_phi=True")
PhilIndex.get_python_object()
return
def _read_no_lattice_test(self):
if '-no_lattice_test' in self._argv:
Flags.set_no_lattice_test(True)
self._understood.append(self._argv.index('-no_lattice_test'))
Debug.write('No lattice test mode selected')
return
def _read_no_relax(self):
if '-no_relax' in self._argv:
Flags.set_relax(False)
self._understood.append(self._argv.index('-no_relax'))
Debug.write('XDS relax about indexing selected')
return
def _read_no_profile(self):
if '-no_profile' in self._argv:
# XXX Warning added 2016-02-24
Chatter.write(
"Warning: -no_profile option deprecated: please use xds.integrate.profile_fitting=False instead")
PhilIndex.update("xds.integrate.profile_fitting=False")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-no_profile'))
return
def _read_zero_dose(self):
if '-zero_dose' in self._argv:
Flags.set_zero_dose(True)
self._understood.append(self._argv.index('-zero_dose'))
Debug.write('Zero-dose mode (XDS/XSCALE) selected')
return
def _read_norefine(self):
if '-norefine' in self._argv:
Flags.set_refine(False)
self._understood.append(self._argv.index('-norefine'))
# FIXME what does this do??? - switch off orientation refinement
# in integration
return
def _read_noremove(self):
if '-noremove' in self._argv:
self._understood.append(self._argv.index('-noremove'))
Flags.set_remove(False)
return
def _read_2d(self):
if '-2d' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=mosflm")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=mosflm")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=mosflmr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-2d'))
Debug.write('2DA pipeline selected')
return
def _read_2di(self):
if '-2di' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=mosflm")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=mosflm")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=mosflmr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-2di'))
Debug.write('2DA pipeline; mosflm indexing selected')
return
def _read_dials(self):
if '-dials' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=dials")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=dials")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=dials")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-dials'))
Debug.write('DIALS pipeline selected')
return
def _read_3d(self):
if '-3d' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xds")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3d'))
Debug.write('3DR pipeline selected')
return
def _read_3di(self):
if '-3di' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xds")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3di'))
Debug.write('3DR pipeline; XDS indexing selected')
return
def _read_3dii(self):
if '-3dii' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xdsii")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3dii'))
Debug.write('3D II R pipeline (XDS IDXREF all images) selected')
return
def _read_3dd(self):
if '-3dd' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=dials")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3dd'))
Debug.write('3DD pipeline (DIALS indexing) selected')
return
def _read_debug(self):
if '-debug' in self._argv:
# join the debug stream to the main output
Debug.join(Chatter)
self._understood.append(self._argv.index('-debug'))
Debug.write('Debugging output switched on')
return
def _read_interactive(self):
if '-interactive' in self._argv:
Flags.set_interactive(True)
self._understood.append(self._argv.index('-interactive'))
Debug.write('Interactive indexing ON')
return
def _read_ice(self):
if '-ice' in self._argv:
Flags.set_ice(True)
self._understood.append(self._argv.index('-ice'))
Debug.write('Ice ring exclusion ON')
return
def _read_egg(self):
if '-egg' in self._argv:
self._understood.append(self._argv.index('-egg'))
Flags.set_egg(True)
return
def _read_uniform_sd(self):
if '-no_uniform_sd' in self._argv:
Flags.set_uniform_sd(False)
self._understood.append(self._argv.index('-no_uniform_sd'))
Debug.write('Uniform SD OFF')
return
def _read_migrate_data(self):
if '-migrate_data' in self._argv:
Flags.set_migrate_data(True)
self._understood.append(self._argv.index('-migrate_data'))
Debug.write('Data migration switched on')
return
def _read_cell(self):
'''Read the cell constants from the command line.'''
index = -1
try:
index = self._argv.index('-cell')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
try:
cell = self._argv[index + 1].split(',')
except IndexError, e:
raise RuntimeError, \
'-cell correct use "-cell a,b,c,alpha,beta,gamma"'
if len(cell) != 6:
raise RuntimeError, \
'-cell correct use "-cell a,b,c,alpha,beta,gamma"'
_cell = tuple(map(float, cell))
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -cell option deprecated: please use unit_cell='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.unit_cell=%s,%s,%s,%s,%s,%s" %_cell)
PhilIndex.get_python_object()
format = 6 * ' %7.2f'
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Cell read from command line:' + \
format % _cell)
return
def _help_cell(self):
'''Return a help string for the read cell method.'''
return '-cell a,b,c,alpha,beta,gamma'
def _read_free_fraction(self):
try:
index = self._argv.index('-free_fraction')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_free_fraction(float(self._argv[index + 1]))
Debug.write('Free fraction set to %f' % Flags.get_free_fraction())
return
def _help_free_fraction(self):
return '-free_fraction N'
def _read_free_total(self):
try:
index = self._argv.index('-free_total')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_free_total(int(self._argv[index + 1]))
Debug.write('Free total set to %f' % Flags.get_free_total())
return
def _help_free_total(self):
return '-free_total N'
def _read_mask(self):
try:
index = self._argv.index('-mask')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_mask(self._argv[index + 1])
return
def get_mask(self):
return self._mask
def _help_mask(self):
return '-mask mask.dat'
def get_mask(self):
return self._mask
def _read_fixed_628(self):
try:
index = self._argv.index('-fixed_628')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
Flags.set_fixed_628()
return
def _help_fixed_628(self):
return '-fixed_628'
def _read_indexer(self):
try:
index = self._argv.index('-indexer')
except ValueError, e:
return
indexer = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -indexer option deprecated: please use indexer='%s' instead" %(
indexer))
PhilIndex.update("xia2.settings.indexer=%s" %indexer)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_integrater(self):
try:
index = self._argv.index('-integrater')
except ValueError, e:
return
integrater = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -integrater option deprecated: please use integrater='%s' instead" %(
integrater))
PhilIndex.update("xia2.settings.integrater=%s" %integrater)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_scaler(self):
try:
index = self._argv.index('-scaler')
except ValueError, e:
return
scaler = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -scaler option deprecated: please use scaler='%s' instead" %(
scaler))
PhilIndex.update("xia2.settings.scaler=%s" %scaler)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_executables(self):
try:
index = self._argv.index('-executable')
except ValueError, e:
return
executable_string = self._argv[index + 1]
assert('=' in executable_string)
executable, path = executable_string.split('=')
Executables.add(executable, path)
self._understood.append(index)
self._understood.append(index + 1)
return
CommandLine = _CommandLine()
CommandLine.setup()
if __name__ == '__main__':
print CommandLine.get_beam()
print CommandLine.get_xinfo()
Clean up as we go
#!/usr/bin/env python
# CommandLine.py
# Copyright (C) 2006 CCLRC, Graeme Winter
#
# This code is distributed under the BSD license, a copy of which is
# included in the root directory of this package.
#
# 12th June 2006
#
# A handler for all of the information which may be passed in on the command
# line. This singleton object should be able to handle the input, structure
# it and make it available in a useful fashion.
#
# This is a hook into a global data repository, should mostly be replaced with
# a Phil interface.
import sys
import os
import exceptions
import copy
import traceback
from xia2.Experts.FindImages import image2template_directory
from xia2.Schema.XProject import XProject
from xia2.Handlers.Flags import Flags
from xia2.Handlers.Phil import PhilIndex
from xia2.Handlers.Streams import Chatter, Debug
from xia2.Handlers.PipelineSelection import add_preference, get_preferences
from xia2.Handlers.Executables import Executables
from libtbx.utils import Sorry
def which(pgm):
# python equivalent to the 'which' command
# http://stackoverflow.com/questions/9877462/is-there-a-python-equivalent-to-the-which-command
path = os.getenv('PATH')
for p in path.split(os.path.pathsep):
p = os.path.join(p,pgm)
if os.path.exists(p) and os.access(p,os.X_OK):
return p
def load_datablock(filename):
from xia2.Schema import imageset_cache, update_with_reference_geometry
from dxtbx.serialize import load
from libtbx.containers import OrderedDict
datablocks = load.datablock(filename, check_format=False)
for datablock in datablocks:
imagesets = datablock.extract_imagesets()
params = PhilIndex.get_python_object()
reference_geometry = params.xia2.settings.input.reference_geometry
if reference_geometry is not None and len(reference_geometry) > 0:
update_with_reference_geometry(imagesets, reference_geometry)
for imageset in imagesets:
template = imageset.get_template()
if template not in imageset_cache:
imageset_cache[template] = OrderedDict()
imageset_cache[template][
imageset.get_scan().get_image_range()[0]] = imageset
class _CommandLine(object):
'''A class to represent the command line input.'''
def __init__(self):
'''Initialise all of the information from the command line.'''
self._argv = []
self._understood = []
self._default_template = []
self._default_directory = []
self._hdf5_master_files = []
self._default_start_end = { }
return
def get_argv(self):
return self._argv
def print_command_line(self):
cl = self.get_command_line()
Chatter.write('Command line: %s' % cl)
return
def get_command_line(self):
import libtbx.load_env
cl = libtbx.env.dispatcher_name
if cl:
if 'xia2' not in cl or 'python' in cl:
cl = 'xia2'
else:
cl = 'xia2'
for arg in sys.argv[1:]:
if ' ' in arg:
arg = '"%s"' %arg
cl += ' %s' % arg
return cl
def setup(self):
'''Set everything up...'''
# check arguments are all ascii
for token in sys.argv:
try:
token.encode('ascii')
except UnicodeDecodeError, e:
raise RuntimeError, 'non-ascii characters in input'
self._argv = copy.deepcopy(sys.argv)
# first of all try to interpret arguments as phil parameters/files
from xia2.Handlers.Phil import master_phil
from libtbx.phil import command_line
cmd_line = command_line.argument_interpreter(master_phil=master_phil)
working_phil, self._argv = cmd_line.process_and_fetch(
args=self._argv, custom_processor="collect_remaining")
PhilIndex.merge_phil(working_phil)
try:
PhilIndex.get_python_object()
except RuntimeError, e:
raise Sorry(e)
#PhilIndex.get_diff().show()
# things which are single token flags...
self._read_debug()
self._read_interactive()
self._read_ice()
self._read_egg()
self._read_uniform_sd()
self._read_trust_timestamps()
self._read_batch_scale()
self._read_small_molecule()
self._read_quick()
self._read_chef()
self._read_mask()
self._read_reversephi()
self._read_no_lattice_test()
self._read_no_relax()
self._read_no_profile()
self._read_norefine()
self._read_noremove()
# pipeline options
self._read_2d()
self._read_2di()
self._read_dials()
self._read_3d()
self._read_3di()
self._read_3dii()
self._read_3dd()
self._read_migrate_data()
self._read_zero_dose()
self._read_free_fraction()
self._read_free_total()
# finer grained control over the selection of indexer, integrater
# and scaler to use.
self._read_indexer()
self._read_integrater()
self._read_scaler()
self._read_executables()
# flags relating to unfixed bugs...
self._read_fixed_628()
try:
self._read_beam()
except:
raise RuntimeError, self._help_beam()
try:
self._read_cell()
except:
raise RuntimeError, self._help_cell()
try:
self._read_image()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_image(), str(e))
try:
self._read_project_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_project_name(), str(e))
try:
self._read_atom_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_atom_name(), str(e))
try:
self._read_phil()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_phil(), str(e))
try:
self._read_crystal_name()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_crystal_name(), str(e))
try:
self._read_ispyb_xml_out()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_ispyb_xml_out(), str(e))
try:
self._read_hdr_in()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_hdr_in(), str(e))
try:
self._read_hdr_out()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_hdr_out(), str(e))
try:
self._read_pickle()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_pickle(), str(e))
try:
self._read_parallel()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_parallel(), str(e))
try:
self._read_serial()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_serial(), str(e))
try:
self._read_xparm()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparm(), str(e))
try:
self._read_xparm_ub()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparm_ub(), str(e))
try:
self._read_min_images()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_min_images(), str(e))
try:
self._read_start_end()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_start_end(), str(e))
try:
self._read_xparallel()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_xparallel(), str(e))
try:
self._read_spacegroup()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_spacegroup(), str(e))
try:
self._read_resolution()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_resolution(), str(e))
try:
self._read_z_min()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_z_min(), str(e))
try:
self._read_aimless_secondary()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_aimless_secondary(), str(e))
try:
self._read_freer_file()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_freer_file(), str(e))
try:
self._read_reference_reflection_file()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_reference_reflection_file(), str(e))
try:
self._read_rejection_threshold()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_rejection_threshold(), str(e))
try:
self._read_isigma()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_isigma(), str(e))
try:
self._read_misigma()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_misigma(), str(e))
try:
self._read_rmerge()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_rmerge(), str(e))
try:
self._read_cc_half()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_cc_half(), str(e))
try:
self._read_microcrystal()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_microcrystal(), str(e))
try:
self._read_failover()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_failover(), str(e))
try:
self._read_blend()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_blend(), str(e))
try:
self._read_completeness()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_completeness(), str(e))
try:
self._read_scale_model()
except exceptions.Exception, e:
raise RuntimeError, '%s (%s)' % \
(self._help_scale_model(), str(e))
# FIXME add some consistency checks in here e.g. that there are
# images assigned, there is a lattice assigned if cell constants
# are given and so on
params = PhilIndex.get_python_object()
mp_params = params.xia2.settings.multiprocessing
from libtbx import Auto
if mp_params.mode == 'parallel':
if mp_params.type == 'qsub':
if which('qsub') is None:
raise Sorry('qsub not available')
if mp_params.njob is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.njob = get_number_cpus()
if mp_params.nproc is Auto:
mp_params.nproc = 1
elif mp_params.nproc is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.nproc = get_number_cpus()
Flags.set_parallel(mp_params.nproc)
else:
Flags.set_parallel(mp_params.nproc)
elif mp_params.mode == 'serial':
mp_params.njob = 1
if mp_params.nproc is Auto:
from xia2.Handlers.Environment import get_number_cpus
mp_params.nproc = get_number_cpus()
Flags.set_parallel(mp_params.nproc)
PhilIndex.update("xia2.settings.multiprocessing.njob=%d" %mp_params.njob)
PhilIndex.update("xia2.settings.multiprocessing.nproc=%d" %mp_params.nproc)
params = PhilIndex.get_python_object()
mp_params = params.xia2.settings.multiprocessing
if params.xia2.settings.indexer is not None:
add_preference("indexer", params.xia2.settings.indexer)
if params.xia2.settings.multi_sweep_indexing is Auto:
params.xia2.settings.multi_sweep_indexing = \
Flags.get_small_molecule() and 'dials' == params.xia2.settings.indexer
if params.xia2.settings.refiner is not None:
add_preference("refiner", params.xia2.settings.refiner)
if params.xia2.settings.integrater is not None:
add_preference("integrater", params.xia2.settings.integrater)
if params.xia2.settings.scaler is not None:
add_preference("scaler", params.xia2.settings.scaler)
if params.xia2.settings.resolution.d_min is not None:
Flags.set_resolution_high(params.xia2.settings.resolution.d_min)
if params.xia2.settings.resolution.d_max is not None:
Flags.set_resolution_low(params.xia2.settings.resolution.d_max)
Flags.set_reversephi(params.xia2.settings.input.reverse_phi)
input_json = params.xia2.settings.input.json
if (input_json is not None and len(input_json)):
for json_file in input_json:
assert os.path.isfile(json_file)
load_datablock(json_file)
reference_geometry = params.xia2.settings.input.reference_geometry
if reference_geometry is not None and len(reference_geometry) > 0:
reference_geometries = "\n".join(
["xia2.settings.input.reference_geometry=%s" % os.path.abspath(g)
for g in params.xia2.settings.input.reference_geometry])
Debug.write(reference_geometries)
PhilIndex.update(reference_geometries)
Debug.write("xia2.settings.trust_beam_centre=true")
PhilIndex.update("xia2.settings.trust_beam_centre=true")
params = PhilIndex.get_python_object()
try:
self._read_xinfo()
except exceptions.Exception, e:
traceback.print_exc(file = open('xia2-xinfo.error', 'w'))
raise RuntimeError, '%s (%s)' % \
(self._help_xinfo(), str(e))
params = PhilIndex.get_python_object()
if params.xia2.settings.input.xinfo is not None:
xinfo_file = os.path.abspath(params.xia2.settings.input.xinfo)
PhilIndex.update("xia2.settings.input.xinfo=%s" %xinfo_file)
params = PhilIndex.get_python_object()
self.set_xinfo(xinfo_file)
Debug.write(60 * '-')
Debug.write('XINFO file: %s' % xinfo_file)
for record in open(xinfo_file, 'r').readlines():
# don't want \n on the end...
Debug.write(record[:-1])
Debug.write(60 * '-')
else:
xinfo_file = '%s/automatic.xinfo' %os.path.abspath(
os.curdir)
PhilIndex.update("xia2.settings.input.xinfo=%s" %xinfo_file)
params = PhilIndex.get_python_object()
if params.dials.find_spots.phil_file is not None:
PhilIndex.update("dials.find_spots.phil_file=%s" %os.path.abspath(
params.dials.find_spots.phil_file))
if params.dials.index.phil_file is not None:
PhilIndex.update("dials.index.phil_file=%s" %os.path.abspath(
params.dials.index.phil_file))
if params.dials.refine.phil_file is not None:
PhilIndex.update("dials.refine.phil_file=%s" %os.path.abspath(
params.dials.refine.phil_file))
if params.dials.integrate.phil_file is not None:
PhilIndex.update("dials.integrate.phil_file=%s" %os.path.abspath(
params.dials.integrate.phil_file))
params = PhilIndex.get_python_object()
datasets = PhilIndex.params.xia2.settings.input.image
for dataset in datasets:
start_end = None
if ':' in dataset:
tokens = dataset.split(':')
# cope with windows drives i.e. C:\data\blah\thing_0001.cbf:1:100
if len(tokens[0]) == 1:
tokens = ['%s:%s' % (tokens[0], tokens[1])] + tokens[2:]
if len(tokens) != 3:
raise RuntimeError, '/path/to/image_0001.cbf:start:end'
dataset = tokens[0]
start_end = int(tokens[1]), int(tokens[2])
from xia2.Applications.xia2setup import is_hd5f_name
if is_hd5f_name(dataset):
self._hdf5_master_files.append(os.path.abspath(dataset))
if start_end:
Debug.write('Image range: %d %d' % start_end)
self._default_start_end[dataset] = start_end
else:
Debug.write('No image range specified')
else:
template, directory = image2template_directory(os.path.abspath(dataset))
self._default_template.append(template)
self._default_directory.append(directory)
Debug.write('Interpreted from image %s:' % dataset)
Debug.write('Template %s' % template)
Debug.write('Directory %s' % directory)
if start_end:
Debug.write('Image range: %d %d' % start_end)
self._default_start_end[os.path.join(directory, template)] = start_end
else:
Debug.write('No image range specified')
# finally, check that all arguments were read and raise an exception
# if any of them were nonsense.
with open('xia2-working.phil', 'wb') as f:
print >> f, PhilIndex.working_phil.as_str()
with open('xia2-diff.phil', 'wb') as f:
print >> f, PhilIndex.get_diff().as_str()
Debug.write('\nDifference PHIL:')
Debug.write(PhilIndex.get_diff().as_str(), strip=False)
Debug.write('Working PHIL:')
Debug.write(PhilIndex.working_phil.as_str(), strip=False)
nonsense = 'Unknown command-line options:'
was_nonsense = False
for j, argv in enumerate(self._argv):
if j == 0:
continue
if argv[0] != '-' and '=' not in argv:
continue
if not j in self._understood:
nonsense += ' %s' % argv
was_nonsense = True
if was_nonsense:
raise RuntimeError, nonsense
return
# command line parsers, getters and help functions.
def _read_beam(self):
'''Read the beam centre from the command line.'''
index = -1
try:
index = self._argv.index('-beam')
except ValueError, e:
self._beam = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -beam option deprecated: please use beam_centre='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.beam_centre=%s" %self._argv[index +1 ])
PhilIndex.get_python_object()
Debug.write('Beam read from command line as %f %f' % tuple(
PhilIndex.params.xia2.settings.beam_centre))
self._beam = tuple(PhilIndex.params.xia2.settings.beam_centre)
return
def _help_beam(self):
'''Return a help string for the read beam method.'''
return '-beam x,y'
def get_beam(self):
return self._beam
def _help_image(self):
'''Return a string for explaining the -image method.'''
return '-image /path/to/an/image_001.img'
def _read_image(self):
'''Read image information from the command line.'''
index = -1
try:
index = self._argv.index('-image')
except ValueError, e:
# the token is not on the command line
self._default_template = []
self._default_directory = []
return
image = self._argv[index + 1]
# check if there is a space in the image name - this happens and it
# looks like the python input parser will split it even if the
# space is escaped or it is quoted
if image[-1] == '\\':
try:
image = '%s %s' % (self._argv[index + 1][:-1],
self._argv[index + 2])
except:
raise RuntimeError, 'image name ends in \\'
# XXX Warning added 2015-04-23
Chatter.write(
"Warning: -image option deprecated: please use image='%s' instead" %(
image))
PhilIndex.update("xia2.settings.input.image=%s" %image)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_atom_name(self):
try:
index = self._argv.index('-atom')
except ValueError, e:
self._default_atom_name = None
return
self._default_atom_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Heavy atom: %s' % \
self._default_atom_name)
return
def _help_atom_name(self):
return '-atom se'
def get_atom_name(self):
return self._default_atom_name
def _read_phil(self):
try:
index = self._argv.index('-phil')
except ValueError, e:
return
Chatter.bigbanner('-phil option now no longer needed: '
'please just place file on command-line', size=80)
self._understood.append(index)
if True:
return
PhilIndex.merge_param_file(self._argv[index + 1])
PhilIndex.get_python_object()
self._understood.append(index + 1)
Debug.write('Phil file: %s' % self._argv[index + 1])
return
def _help_phil(self):
return '-phil parameters.phil'
def _read_project_name(self):
try:
index = self._argv.index('-project')
except ValueError, e:
self._default_project_name = None
return
self._default_project_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Project: %s' % self._default_project_name)
return
def _help_project_name(self):
return '-project foo'
def get_project_name(self):
return self._default_project_name
def _read_crystal_name(self):
try:
index = self._argv.index('-crystal')
except ValueError, e:
self._default_crystal_name = None
return
self._default_crystal_name = self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Crystal: %s' % self._default_crystal_name)
return
def _help_crystal_name(self):
return '-crystal foo'
def get_crystal_name(self):
return self._default_crystal_name
def _read_xinfo(self):
try:
index = self._argv.index('-xinfo')
except ValueError, e:
self._xinfo = None
return
if index < 0:
raise RuntimeError, 'negative index (no xinfo file name given)'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2015-03-02
Chatter.write(
"Warning: -xinfo option deprecated: please use xinfo='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.input.xinfo=%s" %self._argv[index + 1])
PhilIndex.get_python_object()
return
def _help_xinfo(self):
return '-xinfo example.xinfo'
def set_xinfo(self, xinfo):
with open(xinfo, 'rb') as f:
Debug.write('\n' + xinfo)
Debug.write(f.read())
self._xinfo = XProject(xinfo)
def get_xinfo(self):
'''Return the XProject.'''
return self._xinfo
def _read_xparm(self):
try:
index = self._argv.index('-xparm')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_xparm(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Rotation axis: %.6f %.6f %.6f' % \
Flags.get_xparm_rotation_axis())
Debug.write('Beam vector: %.6f %.6f %.6f' % \
Flags.get_xparm_beam_vector())
Debug.write('Origin: %.2f %.2f' % \
Flags.get_xparm_origin())
return
def _help_xparm(self):
return '-xparm GXPARM.XDS'
def _read_xparm_ub(self):
try:
index = self._argv.index('-xparm_ub')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_xparm_ub(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Real Space A: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_a()))
Debug.write('Real Space B: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_b()))
Debug.write('Real Space C: %.2f %.2f %.2f' % \
tuple(Flags.get_xparm_c()))
return
def _help_xparm_ub(self):
return '-xparm_ub GXPARM.XDS'
def _read_parallel(self):
try:
index = self._argv.index('-parallel')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
if int(self._argv[index + 1]) < 0:
raise RuntimeError, 'negative number of processors: %s' % \
self._argv[index + 1]
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -parallel option deprecated: please use nproc=%s instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.multiprocessing.nproc=%s" %(
self._argv[index + 1]))
PhilIndex.get_python_object()
return
def _help_parallel(self):
return '-parallel N'
def _read_serial(self):
try:
index = self._argv.index('-serial')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
Flags.set_parallel(1)
PhilIndex.update("xia2.settings.multiprocessing.nproc=1")
PhilIndex.get_python_object()
Debug.write('Serial set (i.e. 1 CPU)')
return
def _help_serial(self):
return '-serial'
def _read_min_images(self):
try:
index = self._argv.index('-min_images')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2015-05-01
Chatter.write(
"Warning: -min_images option deprecated: please use min_images=%s instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.input.min_images=%i" %(
int(self._argv[index + 1])))
PhilIndex.get_python_object()
return
def _help_min_images(self):
return '-min_images N'
def _read_start_end(self):
try:
index = self._argv.index('-start_end')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
if not '-image' in self._argv:
raise RuntimeError, 'do not use start_end without -image'
self._understood.append(index)
self._understood.append(index + 1)
start, end = tuple(map(int, self._argv[index + 1].split(',')))
Flags.set_start_end(start, end)
Debug.write('Start, end set to %d %d' % Flags.get_start_end())
return
def _help_start_end(self):
return '-start_end start,end'
def _read_xparallel(self):
try:
index = self._argv.index('-xparallel')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_xparallel(int(self._argv[index + 1]))
Debug.write('XParallel set to %d' % Flags.get_xparallel())
return
def _help_xparallel(self):
return '-xparallel N'
def _read_spacegroup(self):
try:
index = self._argv.index('-spacegroup')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -spacegroup option deprecated: please use space_group='%s' instead" %(
self._argv[index + 1]))
Flags.set_spacegroup(self._argv[index + 1]) # XXX this line should go
PhilIndex.update("xia2.settings.space_group=%s" %self._argv[index + 1])
PhilIndex.get_python_object()
Debug.write('Spacegroup set to %s' % self._argv[index + 1])
return
def _help_spacegroup(self):
return '-spacegroup P43212'
def _read_resolution(self):
try:
index = self._argv.index('-resolution')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
resolution = self._argv[index + 1]
if ',' in resolution:
a, b = map(float, resolution.split(','))
dmin = min(a, b)
dmax = max(a, b)
else:
dmin = float(resolution)
dmax = None
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update("xia2.settings.resolution.d_min=%s" %dmin)
if dmax is not None:
PhilIndex.update("xia2.settings.resolution.d_max=%s" %dmax)
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -resolution option deprecated: please use d_min=%s and d_max=%s instead" %(
dmin, dmax))
else:
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -resolution option deprecated: please use d_min=%s instead" %(
dmin))
PhilIndex.get_python_object()
if dmax:
Debug.write('Resolution set to %.3f %.3f' % (dmin, dmax))
else:
Debug.write('Resolution set to %.3f' % dmin)
return
def _help_resolution(self):
return '-resolution high[,low]'
def _read_z_min(self):
try:
index = self._argv.index('-z_min')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_z_min(float(self._argv[index + 1]))
Debug.write('Z min set to %f' % Flags.get_z_min())
return
def _help_z_min(self):
return '-z_min N'
def _read_aimless_secondary(self):
try:
index = self._argv.index('-aimless_secondary')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_aimless_secondary(float(self._argv[index + 1]))
Debug.write('Aimless secondary set to %f' % Flags.get_aimless_secondary())
return
def _help_aimless_secondary(self):
return '-aimless_secondary N'
def _read_freer_file(self):
try:
index = self._argv.index('-freer_file')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_freer_file(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('FreeR_flag column taken from %s' %
Flags.get_freer_file())
# this should also be used as an indexing reference to make
# sense...
Flags.set_reference_reflection_file(self._argv[index + 1])
Debug.write('Reference reflection file: %s' %
Flags.get_reference_reflection_file())
# and also the spacegroup copied out?! ok - this is done
# "by magic" in the scaler.
return
def _help_freer_file(self):
return '-freer_file my_freer_file.mtz'
def _read_reference_reflection_file(self):
try:
index = self._argv.index('-reference_reflection_file')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
Flags.set_reference_reflection_file(self._argv[index + 1])
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Reference reflection file: %s' %
Flags.get_reference_reflection_file())
return
def _help_reference_reflection_file(self):
return '-reference_reflection_file my_reference_reflection_file.mtz'
def _read_rejection_threshold(self):
try:
index = self._argv.index('-rejection_threshold')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_rejection_threshold(float(self._argv[index + 1]))
Debug.write('Rejection threshold set to %f' % \
Flags.get_rejection_threshold())
return
def _help_rejection_threshold(self):
return '-rejection_threshold N'
def _read_isigma(self):
try:
index = self._argv.index('-isigma')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.isigma=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -isigma option deprecated: please use isigma=%s instead" %self._argv[index + 1])
return
def _help_isigma(self):
return '-isigma N'
def _read_misigma(self):
try:
index = self._argv.index('-misigma')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.misigma=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -misigma option deprecated: please use misigma=%s instead" %self._argv[index + 1])
return
def _help_misigma(self):
return '-misigma N'
def _read_completeness(self):
try:
index = self._argv.index('-completeness')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.completeness=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -completeness option deprecated: please use completeness=%s instead" %self._argv[index + 1])
return
def _help_completeness(self):
return '-completeness N'
def _read_rmerge(self):
try:
index = self._argv.index('-rmerge')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.rmerge=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -rmerge option deprecated: please use rmerge=%s instead" %self._argv[index + 1])
return
def _help_rmerge(self):
return '-rmerge N'
def _read_cc_half(self):
try:
index = self._argv.index('-cc_half')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
PhilIndex.update(
"xia2.settings.resolution.cc_half=%s" %self._argv[index + 1])
# XXX Warning added 2015-12-01
Chatter.write(
"Warning: -cc_half option deprecated: please use cc_half=%s instead" %self._argv[index + 1])
return
def _help_cc_half(self):
return '-cc_half N'
def _read_microcrystal(self):
if '-microcrystal' in self._argv:
Flags.set_microcrystal()
Debug.write('Microcrystal mode on')
self._understood.append(self._argv.index('-microcrystal'))
return
def _read_failover(self):
if '-failover' in self._argv:
Flags.set_failover()
Debug.write('Failover mode on')
self._understood.append(self._argv.index('-failover'))
return
def _read_blend(self):
if '-blend' in self._argv:
Flags.set_blend()
Debug.write('Blend mode on')
self._understood.append(self._argv.index('-blend'))
return
def _read_ispyb_xml_out(self):
try:
index = self._argv.index('-ispyb_xml_out')
except ValueError, e:
self._ispyb_xml_out = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_ispyb_xml_out(self._argv[index + 1])
Debug.write('ISPyB XML output set to %s' % self._argv[index + 1])
return
def _help_ispyb_xml_out(self):
return '-ispyb_xml_out project.xml'
def _read_hdr_in(self):
try:
index = self._argv.index('-hdr_in')
except ValueError, e:
self._hdr_in = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_hdr_in(self._argv[index + 1])
return
def _help_hdr_in(self):
return '-hdr_in project.hdr'
def _read_hdr_out(self):
try:
index = self._argv.index('-hdr_out')
except ValueError, e:
self._hdr_out = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_hdr_out(self._argv[index + 1])
Debug.write('Output header file set to %s' % self._argv[index + 1])
return
def _help_hdr_out(self):
return '-hdr_out project.hdr'
def _read_pickle(self):
try:
index = self._argv.index('-pickle')
except ValueError, e:
self._pickle = None
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_pickle(self._argv[index + 1])
return
def _help_pickle(self):
return '-pickle name.pkl'
def get_template(self):
return self._default_template
def get_start_end(self, full_template):
return self._default_start_end.get(full_template, None)
def get_directory(self):
return self._default_directory
def get_hdf5_master_files(self):
return self._hdf5_master_files
def _read_trust_timestamps(self):
if '-trust_timestamps' in self._argv:
Flags.set_trust_timestamps(True)
Debug.write('Trust timestamps on')
self._understood.append(self._argv.index('-trust_timestamps'))
return
def _read_batch_scale(self):
if '-batch_scale' in self._argv:
Flags.set_batch_scale(True)
Debug.write('Batch scaling mode on')
self._understood.append(self._argv.index('-batch_scale'))
return
def _read_small_molecule(self):
if '-small_molecule' in self._argv:
Flags.set_small_molecule(True)
Debug.write('Small molecule selected')
self._understood.append(self._argv.index('-small_molecule'))
settings = PhilIndex.get_python_object().xia2.settings
PhilIndex.update("xia2.settings.unify_setting=true")
return
def _read_scale_model(self):
try:
index = self._argv.index('-scale_model')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_scale_model(self._argv[index + 1])
Debug.write('Scaling model set to: %s' % Flags.get_scale_model())
return
def _read_quick(self):
if '-quick' in self._argv:
Flags.set_quick(True)
Debug.write('Quick mode selected')
self._understood.append(self._argv.index('-quick'))
return
def _read_chef(self):
if '-chef' in self._argv:
Flags.set_chef(True)
self._understood.append(self._argv.index('-chef'))
Debug.write('Chef mode selected')
if '-nochef' in self._argv:
Flags.set_chef(False)
self._understood.append(self._argv.index('-nochef'))
Debug.write('Chef mode deselected')
return
def _read_reversephi(self):
if '-reversephi' in self._argv:
self._understood.append(self._argv.index('-reversephi'))
# XXX Warning added 2015-11-18
Chatter.write(
"Warning: -reversephi option deprecated: please use reverse_phi=True instead")
PhilIndex.update("xia2.settings.input.reverse_phi=True")
PhilIndex.get_python_object()
return
def _read_no_lattice_test(self):
if '-no_lattice_test' in self._argv:
Flags.set_no_lattice_test(True)
self._understood.append(self._argv.index('-no_lattice_test'))
Debug.write('No lattice test mode selected')
return
def _read_no_relax(self):
if '-no_relax' in self._argv:
Flags.set_relax(False)
self._understood.append(self._argv.index('-no_relax'))
Debug.write('XDS relax about indexing selected')
return
def _read_no_profile(self):
if '-no_profile' in self._argv:
# XXX Warning added 2016-02-24
Chatter.write(
"Warning: -no_profile option deprecated: please use xds.integrate.profile_fitting=False instead")
PhilIndex.update("xds.integrate.profile_fitting=False")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-no_profile'))
return
def _read_zero_dose(self):
if '-zero_dose' in self._argv:
Flags.set_zero_dose(True)
self._understood.append(self._argv.index('-zero_dose'))
Debug.write('Zero-dose mode (XDS/XSCALE) selected')
return
def _read_norefine(self):
if '-norefine' in self._argv:
Flags.set_refine(False)
self._understood.append(self._argv.index('-norefine'))
# FIXME what does this do??? - switch off orientation refinement
# in integration
return
def _read_noremove(self):
if '-noremove' in self._argv:
self._understood.append(self._argv.index('-noremove'))
Flags.set_remove(False)
return
def _read_2d(self):
if '-2d' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=mosflm")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=mosflm")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=mosflmr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-2d'))
Debug.write('2DA pipeline selected')
return
def _read_2di(self):
if '-2di' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=mosflm")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=mosflm")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=mosflmr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-2di'))
Debug.write('2DA pipeline; mosflm indexing selected')
return
def _read_dials(self):
if '-dials' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=dials")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=dials")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=dials")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=ccp4a")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-dials'))
Debug.write('DIALS pipeline selected')
return
def _read_3d(self):
if '-3d' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xds")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3d'))
Debug.write('3DR pipeline selected')
return
def _read_3di(self):
if '-3di' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xds")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3di'))
Debug.write('3DR pipeline; XDS indexing selected')
return
def _read_3dii(self):
if '-3dii' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=xdsii")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3dii'))
Debug.write('3D II R pipeline (XDS IDXREF all images) selected')
return
def _read_3dd(self):
if '-3dd' in self._argv:
settings = PhilIndex.get_python_object().xia2.settings
if settings.indexer is None:
PhilIndex.update("xia2.settings.indexer=dials")
if settings.refiner is None:
PhilIndex.update("xia2.settings.refiner=xds")
if settings.integrater is None:
PhilIndex.update("xia2.settings.integrater=xdsr")
if settings.scaler is None:
PhilIndex.update("xia2.settings.scaler=xdsa")
PhilIndex.get_python_object()
self._understood.append(self._argv.index('-3dd'))
Debug.write('3DD pipeline (DIALS indexing) selected')
return
def _read_debug(self):
if '-debug' in self._argv:
# join the debug stream to the main output
Debug.join(Chatter)
self._understood.append(self._argv.index('-debug'))
Debug.write('Debugging output switched on')
return
def _read_interactive(self):
if '-interactive' in self._argv:
Flags.set_interactive(True)
self._understood.append(self._argv.index('-interactive'))
Debug.write('Interactive indexing ON')
return
def _read_ice(self):
if '-ice' in self._argv:
Flags.set_ice(True)
self._understood.append(self._argv.index('-ice'))
Debug.write('Ice ring exclusion ON')
return
def _read_egg(self):
if '-egg' in self._argv:
self._understood.append(self._argv.index('-egg'))
Flags.set_egg(True)
return
def _read_uniform_sd(self):
if '-no_uniform_sd' in self._argv:
Flags.set_uniform_sd(False)
self._understood.append(self._argv.index('-no_uniform_sd'))
Debug.write('Uniform SD OFF')
return
def _read_migrate_data(self):
if '-migrate_data' in self._argv:
Flags.set_migrate_data(True)
self._understood.append(self._argv.index('-migrate_data'))
Debug.write('Data migration switched on')
return
def _read_cell(self):
'''Read the cell constants from the command line.'''
index = -1
try:
index = self._argv.index('-cell')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
try:
cell = self._argv[index + 1].split(',')
except IndexError, e:
raise RuntimeError, \
'-cell correct use "-cell a,b,c,alpha,beta,gamma"'
if len(cell) != 6:
raise RuntimeError, \
'-cell correct use "-cell a,b,c,alpha,beta,gamma"'
_cell = tuple(map(float, cell))
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -cell option deprecated: please use unit_cell='%s' instead" %(
self._argv[index + 1]))
PhilIndex.update("xia2.settings.unit_cell=%s,%s,%s,%s,%s,%s" %_cell)
PhilIndex.get_python_object()
format = 6 * ' %7.2f'
self._understood.append(index)
self._understood.append(index + 1)
Debug.write('Cell read from command line:' + \
format % _cell)
return
def _help_cell(self):
'''Return a help string for the read cell method.'''
return '-cell a,b,c,alpha,beta,gamma'
def _read_free_fraction(self):
try:
index = self._argv.index('-free_fraction')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_free_fraction(float(self._argv[index + 1]))
Debug.write('Free fraction set to %f' % Flags.get_free_fraction())
return
def _help_free_fraction(self):
return '-free_fraction N'
def _read_free_total(self):
try:
index = self._argv.index('-free_total')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_free_total(int(self._argv[index + 1]))
Debug.write('Free total set to %f' % Flags.get_free_total())
return
def _help_free_total(self):
return '-free_total N'
def _read_mask(self):
try:
index = self._argv.index('-mask')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
self._understood.append(index + 1)
Flags.set_mask(self._argv[index + 1])
return
def get_mask(self):
return self._mask
def _help_mask(self):
return '-mask mask.dat'
def get_mask(self):
return self._mask
def _read_fixed_628(self):
try:
index = self._argv.index('-fixed_628')
except ValueError, e:
return
if index < 0:
raise RuntimeError, 'negative index'
self._understood.append(index)
Flags.set_fixed_628()
return
def _help_fixed_628(self):
return '-fixed_628'
def _read_indexer(self):
try:
index = self._argv.index('-indexer')
except ValueError, e:
return
indexer = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -indexer option deprecated: please use indexer='%s' instead" %(
indexer))
PhilIndex.update("xia2.settings.indexer=%s" %indexer)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_integrater(self):
try:
index = self._argv.index('-integrater')
except ValueError, e:
return
integrater = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -integrater option deprecated: please use integrater='%s' instead" %(
integrater))
PhilIndex.update("xia2.settings.integrater=%s" %integrater)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_scaler(self):
try:
index = self._argv.index('-scaler')
except ValueError, e:
return
scaler = self._argv[index + 1]
# XXX Warning added 2014-11-10
Chatter.write(
"Warning: -scaler option deprecated: please use scaler='%s' instead" %(
scaler))
PhilIndex.update("xia2.settings.scaler=%s" %scaler)
PhilIndex.get_python_object()
self._understood.append(index)
self._understood.append(index + 1)
return
def _read_executables(self):
try:
index = self._argv.index('-executable')
except ValueError, e:
return
executable_string = self._argv[index + 1]
assert('=' in executable_string)
executable, path = executable_string.split('=')
Executables.add(executable, path)
self._understood.append(index)
self._understood.append(index + 1)
return
CommandLine = _CommandLine()
CommandLine.setup()
if __name__ == '__main__':
print CommandLine.get_beam()
print CommandLine.get_xinfo()
|
#SPDX-License-Identifier: MIT
import ghdata
import os
import sys
if (sys.version_info > (3, 0)):
import configparser as configparser
else:
import ConfigParser as configparser
from flask import Flask, request, Response, send_from_directory
from flask_cors import CORS
GHDATA_API_VERSION = 'unstable'
def serialize(func, **args):
"""
Serailizes a function that returns a dataframe
"""
data = func(**args)
if hasattr(data, 'to_json'):
return data.to_json(orient='records', date_format='iso', date_unit='ms')
else:
return data
def flaskify_ghtorrent(ghtorrent, func):
"""
Simplifies API endpoints that just accept owner and repo,
serializes them and spits them out
"""
def generated_function(owner, repo):
repoid = ghtorrent.repoid(owner=owner, repo=repo)
return Response(response=serialize(func, repoid=repoid),
status=200,
mimetype="application/json")
generated_function.__name__ = func.__name__
return generated_function
def flaskify(func):
"""
Simplifies API endpoints that just accept owner and repo,
serializes them and spits them out
"""
def generated_function(owner, repo):
return Response(response=serialize(func, owner=owner, repo=repo),
status=200,
mimetype="application/json")
generated_function.__name__ = func.__name__
return generated_function
def read_config(parser, section, name, environment_variable, default):
try:
value = os.getenv(environment_variable, parser.get(section, name))
return value
except:
if not parser.has_section(section):
parser.add_section(section)
parser.set(section, name, default)
with open('ghdata.cfg', 'w') as configfile:
parser.write(configfile)
return default
def run():
app = Flask(__name__, static_url_path=os.path.abspath('static/'))
CORS(app)
# Try to open the config file and parse it
parser = configparser.RawConfigParser()
parser.read('ghdata.cfg')
try:
dbstr = 'mysql+pymysql://{}:{}@{}:{}/{}'.format(
read_config(parser, 'Database', 'user', 'GHDATA_DB_USER', 'root'),
read_config(parser, 'Database', 'pass', 'GHDATA_DB_PASS', 'password'),
read_config(parser, 'Database', 'host', 'GHDATA_DB_HOST', '127.0.0.1'),
read_config(parser, 'Database', 'port', 'GHDATA_DB_PORT', '3306'),
read_config(parser, 'Database', 'name', 'GHDATA_DB_NAME', 'msr14')
)
print("Connecting with " + dbstr)
ghtorrent = ghdata.GHTorrent(dbstr=dbstr)
except Exception as e:
print("Failed to connect to database (" + str(e) + ")");
host = read_config(parser, 'Server', 'host', 'GHDATA_HOST', '0.0.0.0')
port = read_config(parser, 'Server', 'port', 'GHDATA_PORT', '5000')
publicwww = ghdata.PublicWWW(api_key=read_config(parser, 'PublicWWW', 'APIKey', 'GHDATA_PUBLIC_WWW_API_KEY', 'None'))
github = ghdata.GitHubAPI(api_key=read_config(parser, 'GitHub', 'APIKey', 'GHDATA_GITHUB_API_KEY', 'None'))
if (read_config(parser, 'Development', 'developer', 'GHDATA_DEBUG', '0') == '1'):
debugmode = True
else:
debugmode = False
"""
@api {get} / API Status
@apiName Status
@apiGroup Misc
"""
@app.route('/{}/'.format(GHDATA_API_VERSION))
def api_root():
"""API status"""
# @todo: When we support multiple data sources this should keep track of their status
# @todo: Add GHTorrent test to determine status
ghtorrent_status = "good"
# @todo: Add GitHub API status
# @todo: Add PublicWWW API status
return """{"status": "healthy", "ghtorrent": "{}"}""".format(ghtorrent_status)
#######################
# Timeseries #
#######################
# @todo: Link to LF Metrics
"""
@api {get} /:owner/:repo/commits Commits by Week
@apiName CommitsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"commits": 153
},
{
"date": "2015-01-08T00:00:00.000Z",
"commits": 192
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/commits'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.commits))
"""
@api {get} /:owner/:repo/forks Forks by Week
@apiName ForksByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"forks": 13
},
{
"date": "2015-01-08T00:00:00.000Z",
"forks": 12
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/forks'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.forks))
"""
@api {get} /:owner/:repo/issues Issues by Week
@apiName IssuesByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"issues":13
},
{
"date": "2015-01-08T00:00:00.000Z",
"issues":15
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/issues'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.issues))
"""
@api {get} /:owner/:repo/issues/response_time Issue Response Time
@apiName IssueResponseTime
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"created_at": "2013-09-16T17:00:54.000Z",
"responded_at": "2013-09-16T17:20:58.000Z"
},
{
"created_at": "2013-09-16T09:31:34.000Z",
"responded_at": "2013-09-16T09:43:03.000Z"
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/issues/response_time'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.issue_response_time))
"""
@api {get} /:owner/:repo/pulls Pull Requests by Week
@apiName PullRequestsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"pull_requests": 1
"comments": 11
},
{
"date": "2015-01-08T00:00:00.000Z",
"pull_requests": 2
"comments": 31
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/pulls'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.pulls))
"""
@api {get} /:owner/:repo/stargazers Stargazers by Week
@apiName StargazersByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"watchers": 133
},
{
"date": "2015-01-08T00:00:00.000Z",
"watchers": 54
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/stargazers'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.stargazers))
"""
@api {get} /:owner/:repo/pulls/acceptance_rate Pull Request Acceptance Rate by Week
@apiDescription For each week, the rate is calculated as (pull requests merged that week) / (pull requests opened that week)
@apiName Stargazers
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"rate": 0.5
},
{
"date": "2015-01-08T00:00:00.000Z",
"rate": 0.33
}
]
"""
app.route('/{}/<owner>/<repo>/pulls/acceptance_rate'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.pull_acceptance_rate))
# Contribution Trends
"""
@api {get} /:owner/:repo/contributors Total Contributions by User
@apiName TotalContributions
@apiGroup Users
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"login": "foo",
"location": "Springfield",
"commits": 1337.0,
"pull_requests": 60.0,
"issues": null,
"commit_comments": 158.0,
"pull_request_comments": 718.0,
"issue_comments": 1668.0
},
{
"login": "bar",
"location": null,
"commits": 3968.0,
"pull_requests": null,
"issues": 12.0,
"commit_comments": 158.0,
"pull_request_comments": 718.0,
"issue_comments": 1568.0
}
]
"""
app.route('/{}/<owner>/<repo>/contributors'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.contributors))
#######################
# Contribution Trends #
#######################
"""
@api {get} /:owner/:repo/contributions Contributions by Week
@apiName ContributionsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiParam (String) user Limit results to the given user's contributions
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"commits": 37.0,
"pull_requests": null,
"issues": null,
"commit_comments": 7.0,
"pull_request_comments": 8.0,
"issue_comments": 17.0
},
{
"date": "2015-01-08T00:00:00.000Z",
"commits": 68.0,
"pull_requests": null,
"issues": 12.0,
"commit_comments": 18.0,
"pull_request_comments": 13.0,
"issue_comments": 28.0
}
]
"""
@app.route('/{}/<owner>/<repo>/contributions'.format(GHDATA_API_VERSION))
def contributions(owner, repo):
repoid = ghtorrent.repoid(owner=owner, repo=repo)
user = request.args.get('user')
if (user):
userid = ghtorrent.userid(username=user)
contribs = ghtorrent.contributions(repoid=repoid, userid=userid)
else:
contribs = ghtorrent.contributions(repoid=repoid)
return Response(response=contribs,
status=200,
mimetype="application/json")
# Diversity
"""
@api {get} /:owner/:repo/commits/locations Commits and Location by User
@apiName Stargazers
@apiGroup Diversity
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"login": "bonnie",
"location": "Rowena, TX",
"commits": 12
},
{
"login":"clyde",
"location":"Ellis County, TX",
"commits": 12
}
]
"""
app.route('/{}/<owner>/<repo>/commits/locations'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.committer_locations))
# Popularity
"""
@api {get} /:owner/:repo/linking_websites Linking Websites
@apiDescription Returns an array of websites and their rank according to http://publicwww.com/
@apiName LinkingWebsites
@apiGroup Popularity
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"url": "missouri.edu",
"rank": "1"
},
{
"url": "unomaha.edu",
"rank": "2"
}
]
"""
app.route('/{}/<owner>/<repo>/linking_websites'.format(GHDATA_API_VERSION))(flaskify(publicwww.linking_websites))
if (debugmode):
print(" * Serving static routes")
# Serve the front-end files in debug mode to make it easier for developers to work on the interface
# @todo: Figure out why this isn't working.
@app.route('/')
def index():
root_dir = os.path.dirname(os.getcwd())
print(root_dir + '/ghdata/static')
return send_from_directory(root_dir + '/ghdata/ghdata/static', 'index.html')
@app.route('/scripts/<path>')
def send_scripts(path):
root_dir = os.path.dirname(os.getcwd())
return send_from_directory(root_dir + '/ghdata/ghdata/static/scripts', path)
@app.route('/styles/<path>')
def send_styles(path):
root_dir = os.path.dirname(os.getcwd())
return send_from_directory(root_dir+ '/ghdata/ghdata/static/styles', path)
app.debug = True
app.run(host=host, port=int(port), debug=debugmode)
if __name__ == '__main__':
run()
Fix Windows error
#SPDX-License-Identifier: MIT
import ghdata
import os
import sys
if (sys.version_info > (3, 0)):
import configparser as configparser
else:
import ConfigParser as configparser
from flask import Flask, request, Response, send_from_directory
from flask_cors import CORS
GHDATA_API_VERSION = 'unstable'
def serialize(func, **args):
"""
Serailizes a function that returns a dataframe
"""
data = func(**args)
if hasattr(data, 'to_json'):
return data.to_json(orient='records', date_format='iso', date_unit='ms')
else:
return data
def flaskify_ghtorrent(ghtorrent, func):
"""
Simplifies API endpoints that just accept owner and repo,
serializes them and spits them out
"""
def generated_function(owner, repo):
repoid = ghtorrent.repoid(owner=owner, repo=repo)
return Response(response=serialize(func, repoid=repoid),
status=200,
mimetype="application/json")
generated_function.__name__ = func.__name__
return generated_function
def flaskify(func):
"""
Simplifies API endpoints that just accept owner and repo,
serializes them and spits them out
"""
def generated_function(owner, repo):
return Response(response=serialize(func, owner=owner, repo=repo),
status=200,
mimetype="application/json")
generated_function.__name__ = func.__name__
return generated_function
def read_config(parser, section, name, environment_variable, default):
try:
value = os.getenv(environment_variable, parser.get(section, name))
return value
except:
if not parser.has_section(section):
parser.add_section(section)
parser.set(section, name, default)
with open('ghdata.cfg', 'w') as configfile:
parser.write(configfile)
return default
def run():
app = Flask(__name__)
CORS(app)
# Try to open the config file and parse it
parser = configparser.RawConfigParser()
parser.read('ghdata.cfg')
try:
dbstr = 'mysql+pymysql://{}:{}@{}:{}/{}'.format(
read_config(parser, 'Database', 'user', 'GHDATA_DB_USER', 'root'),
read_config(parser, 'Database', 'pass', 'GHDATA_DB_PASS', 'password'),
read_config(parser, 'Database', 'host', 'GHDATA_DB_HOST', '127.0.0.1'),
read_config(parser, 'Database', 'port', 'GHDATA_DB_PORT', '3306'),
read_config(parser, 'Database', 'name', 'GHDATA_DB_NAME', 'msr14')
)
print("Connecting with " + dbstr)
ghtorrent = ghdata.GHTorrent(dbstr=dbstr)
except Exception as e:
print("Failed to connect to database (" + str(e) + ")");
host = read_config(parser, 'Server', 'host', 'GHDATA_HOST', '0.0.0.0')
port = read_config(parser, 'Server', 'port', 'GHDATA_PORT', '5000')
publicwww = ghdata.PublicWWW(api_key=read_config(parser, 'PublicWWW', 'APIKey', 'GHDATA_PUBLIC_WWW_API_KEY', 'None'))
github = ghdata.GitHubAPI(api_key=read_config(parser, 'GitHub', 'APIKey', 'GHDATA_GITHUB_API_KEY', 'None'))
if (read_config(parser, 'Development', 'developer', 'GHDATA_DEBUG', '0') == '1'):
debugmode = True
else:
debugmode = False
"""
@api {get} / API Status
@apiName Status
@apiGroup Misc
"""
@app.route('/{}/'.format(GHDATA_API_VERSION))
def api_root():
"""API status"""
# @todo: When we support multiple data sources this should keep track of their status
# @todo: Add GHTorrent test to determine status
ghtorrent_status = "good"
# @todo: Add GitHub API status
# @todo: Add PublicWWW API status
return """{"status": "healthy", "ghtorrent": "{}"}""".format(ghtorrent_status)
#######################
# Timeseries #
#######################
# @todo: Link to LF Metrics
"""
@api {get} /:owner/:repo/commits Commits by Week
@apiName CommitsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"commits": 153
},
{
"date": "2015-01-08T00:00:00.000Z",
"commits": 192
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/commits'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.commits))
"""
@api {get} /:owner/:repo/forks Forks by Week
@apiName ForksByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"forks": 13
},
{
"date": "2015-01-08T00:00:00.000Z",
"forks": 12
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/forks'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.forks))
"""
@api {get} /:owner/:repo/issues Issues by Week
@apiName IssuesByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"issues":13
},
{
"date": "2015-01-08T00:00:00.000Z",
"issues":15
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/issues'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.issues))
"""
@api {get} /:owner/:repo/issues/response_time Issue Response Time
@apiName IssueResponseTime
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"created_at": "2013-09-16T17:00:54.000Z",
"responded_at": "2013-09-16T17:20:58.000Z"
},
{
"created_at": "2013-09-16T09:31:34.000Z",
"responded_at": "2013-09-16T09:43:03.000Z"
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/issues/response_time'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.issue_response_time))
"""
@api {get} /:owner/:repo/pulls Pull Requests by Week
@apiName PullRequestsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"pull_requests": 1
"comments": 11
},
{
"date": "2015-01-08T00:00:00.000Z",
"pull_requests": 2
"comments": 31
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/pulls'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.pulls))
"""
@api {get} /:owner/:repo/stargazers Stargazers by Week
@apiName StargazersByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"watchers": 133
},
{
"date": "2015-01-08T00:00:00.000Z",
"watchers": 54
}
]
"""
app.route('/{}/<owner>/<repo>/timeseries/stargazers'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.stargazers))
"""
@api {get} /:owner/:repo/pulls/acceptance_rate Pull Request Acceptance Rate by Week
@apiDescription For each week, the rate is calculated as (pull requests merged that week) / (pull requests opened that week)
@apiName Stargazers
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"rate": 0.5
},
{
"date": "2015-01-08T00:00:00.000Z",
"rate": 0.33
}
]
"""
app.route('/{}/<owner>/<repo>/pulls/acceptance_rate'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.pull_acceptance_rate))
# Contribution Trends
"""
@api {get} /:owner/:repo/contributors Total Contributions by User
@apiName TotalContributions
@apiGroup Users
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"login": "foo",
"location": "Springfield",
"commits": 1337.0,
"pull_requests": 60.0,
"issues": null,
"commit_comments": 158.0,
"pull_request_comments": 718.0,
"issue_comments": 1668.0
},
{
"login": "bar",
"location": null,
"commits": 3968.0,
"pull_requests": null,
"issues": 12.0,
"commit_comments": 158.0,
"pull_request_comments": 718.0,
"issue_comments": 1568.0
}
]
"""
app.route('/{}/<owner>/<repo>/contributors'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.contributors))
#######################
# Contribution Trends #
#######################
"""
@api {get} /:owner/:repo/contributions Contributions by Week
@apiName ContributionsByWeek
@apiGroup Timeseries
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiParam (String) user Limit results to the given user's contributions
@apiSuccessExample {json} Success-Response:
[
{
"date": "2015-01-01T00:00:00.000Z",
"commits": 37.0,
"pull_requests": null,
"issues": null,
"commit_comments": 7.0,
"pull_request_comments": 8.0,
"issue_comments": 17.0
},
{
"date": "2015-01-08T00:00:00.000Z",
"commits": 68.0,
"pull_requests": null,
"issues": 12.0,
"commit_comments": 18.0,
"pull_request_comments": 13.0,
"issue_comments": 28.0
}
]
"""
@app.route('/{}/<owner>/<repo>/contributions'.format(GHDATA_API_VERSION))
def contributions(owner, repo):
repoid = ghtorrent.repoid(owner=owner, repo=repo)
user = request.args.get('user')
if (user):
userid = ghtorrent.userid(username=user)
contribs = ghtorrent.contributions(repoid=repoid, userid=userid)
else:
contribs = ghtorrent.contributions(repoid=repoid)
return Response(response=contribs,
status=200,
mimetype="application/json")
# Diversity
"""
@api {get} /:owner/:repo/commits/locations Commits and Location by User
@apiName Stargazers
@apiGroup Diversity
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"login": "bonnie",
"location": "Rowena, TX",
"commits": 12
},
{
"login":"clyde",
"location":"Ellis County, TX",
"commits": 12
}
]
"""
app.route('/{}/<owner>/<repo>/commits/locations'.format(GHDATA_API_VERSION))(
flaskify_ghtorrent(ghtorrent, ghtorrent.committer_locations))
# Popularity
"""
@api {get} /:owner/:repo/linking_websites Linking Websites
@apiDescription Returns an array of websites and their rank according to http://publicwww.com/
@apiName LinkingWebsites
@apiGroup Popularity
@apiParam {String} owner Username of the owner of the GitHub repository
@apiParam {String} repo Name of the GitHub repository
@apiSuccessExample {json} Success-Response:
[
{
"url": "missouri.edu",
"rank": "1"
},
{
"url": "unomaha.edu",
"rank": "2"
}
]
"""
app.route('/{}/<owner>/<repo>/linking_websites'.format(GHDATA_API_VERSION))(flaskify(publicwww.linking_websites))
if (debugmode):
print(" * Serving static routes")
# Serve the front-end files in debug mode to make it easier for developers to work on the interface
# @todo: Figure out why this isn't working.
@app.route('/')
def index():
root_dir = os.path.dirname(os.getcwd())
print(root_dir + '/ghdata/static')
return send_from_directory(root_dir + '/ghdata/ghdata/static', 'index.html')
@app.route('/scripts/<path>')
def send_scripts(path):
root_dir = os.path.dirname(os.getcwd())
return send_from_directory(root_dir + '/ghdata/ghdata/static/scripts', path)
@app.route('/styles/<path>')
def send_styles(path):
root_dir = os.path.dirname(os.getcwd())
return send_from_directory(root_dir+ '/ghdata/ghdata/static/styles', path)
app.debug = True
app.run(host=host, port=int(port), debug=debugmode)
if __name__ == '__main__':
run() |
import openpathsampling as paths
import numpy as np
class SingleTrajectoryAnalysis(object):
"""Analyze a trajectory or set of trajectories for transition properties.
Attributes
----------
dt : float
time step between frames
continuous_frames : dict
dictionary mapping state to a list of number of frames continuously
in that state from the analyzed trajectories.
lifetime_frames : dict
dictionary mapping state to a list of the number of frames to the
trajectory lengths for calculating the lifetime. See Notes for more.
transition_frames : dict
dictionary mapping the transition tuple (initial_state, final_state)
to a list of the number of frames involves in the transition and not
in either state.
flux_frames : dict
TODO
continuous_times : dict
As with continuous frames, but durations multiplied by self.dt
lifetimes : dict
As with lifetime_frames, but durations multiplied by self.dt
transitions_durations : dict
As with transition_frames, but durations multiplied by self.dt
Notes
-----
LIFETIME: the
"""
def __init__(self, transition, dt=None):
self.transition = transition
self.dt = dt
self.stateA = transition.stateA
self.stateB = transition.stateB
self.reset_analysis()
def reset_analysis(self):
"""Reset the analysis by emptying all saved segments."""
self.continuous_segments = {self.stateA: [], self.stateB: []}
self.lifetime_segments = {self.stateA: [], self.stateB: []}
self.transition_segments = {(self.stateA, self.stateB): [],
(self.stateB, self.stateA): []}
self.flux_segments = {self.stateA: {'in': [], 'out': []},
self.stateB: {'in': [], 'out': []}}
@property
def continuous_frames(self):
return {k: np.array([len(seg) for seg in self.continuous_segments[k]])
for k in self.continuous_segments.keys()}
@property
def continuous_times(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn
continuous_frames = self.continuous_frames
return {k : continuous_frames[k]*self.dt
for k in continuous_frames.keys()}
@property
def lifetime_frames(self):
return {k: np.array([len(seg) for seg in self.lifetime_segments[k]])
for k in self.lifetime_segments.keys()}
@property
def lifetimes(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn; use dt=1 otherwise
lifetime_frames = self.lifetime_frames
return {k : lifetime_frames[k]*self.dt
for k in lifetime_frames.keys()}
@property
def transition_duration_frames(self):
return {k: np.array([len(seg) for seg in self.transition_segments[k]])
for k in self.transition_segments.keys()}
@property
def transition_duration(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn; use dt=1 otherwise
transition_duration_frames = self.transition_duration_frames
return {k : transition_duration_frames[k]*self.dt
for k in transition_duration_frames.keys()}
def analyze_continuous_time(self, trajectory, state):
ensemble = paths.AllInXEnsemble(state)
self.continuous_segments[state] += ensemble.split(trajectory,
overlap=0)
def analyze_lifetime(self, trajectory, state):
other_state = list(set([self.stateA, self.stateB]) - set([state]))[0]
ensemble_BAB = paths.SequentialEnsemble([
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1),
paths.PartInXEnsemble(state) & paths.AllOutXEnsemble(other_state),
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1)
])
ensemble_AB = paths.SequentialEnsemble([
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1),
paths.OptionalEnsemble(paths.AllOutXEnsemble(other_state)),
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1)
])
BAB_split = ensemble_BAB.split(trajectory)
AB_split = [ensemble_AB.split(part)[0] for part in BAB_split]
self.lifetime_segments[state] += [subtraj[0:-1]
for subtraj in AB_split]
def analyze_transition_duration(self, trajectory, stateA, stateB):
# we define the transitions ensemble just in case the transition is,
# e.g., fixed path length TPS. We want flexible path length ensemble
transition_ensemble = paths.SequentialEnsemble([
paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1),
paths.OptionalEnsemble( # optional to allow instantaneous hops
paths.AllOutXEnsemble(stateA) & paths.AllOutXEnsemble(stateB)
),
paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1)
])
self.transition_segments[(stateA, stateB)] += [
seg[1:-1] for seg in transition_ensemble.split(trajectory)
]
def analyze_flux(self, trajectory, state):
other = list(set([self.stateA, self.stateB]) - set([state]))[0]
counts_out = paths.SequentialEnsemble([
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1),
paths.AllOutXEnsemble(state | other),
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1)
])
counts_in = paths.SequentialEnsemble([
paths.AllOutXEnsemble(state | other) & paths.LengthEnsemble(1),
paths.AllInXEnsemble(state),
paths.AllOutXEnsemble(state | other) & paths.LengthEnsemble(1)
])
flux_out_segments = counts_out.split(trajectory)
flux_in_segments = counts_in.split(trajectory)
for seg in flux_in_segments:
self.flux_segments[state]['in'] += [seg[1:-1]]
for seg in flux_out_segments:
self.flux_segments[state]['out'] += [seg[1:-1]]
def analyze(self, trajectories):
# TODO: I hate using isinstance, but I don't see another way
if isinstance(trajectories, paths.Trajectory):
trajectories = [trajectories]
for traj in trajectories:
for state in [self.stateA, self.stateB]:
self.analyze_continuous_time(traj, state)
self.analyze_lifetime(traj, state)
self.analyze_flux(traj, state)
self.analyze_transition_duration(traj, self.stateA, self.stateB)
self.analyze_transition_duration(traj, self.stateB, self.stateA)
# return self so we can init and analyze in one line
return self
# TODO: add a `summary` function to output a nice pandas frame or
# something
docstrings
import openpathsampling as paths
import numpy as np
class SingleTrajectoryAnalysis(object):
"""Analyze a trajectory or set of trajectories for transition properties.
Attributes
----------
dt : float
time step between frames
continuous_frames : dict
dictionary mapping state to a list of number of frames continuously
in that state from the analyzed trajectories.
lifetime_frames : dict
dictionary mapping state to a list of the number of frames to the
trajectory lengths for calculating the lifetime. See Notes for more.
transition_frames : dict
dictionary mapping the transition tuple (initial_state, final_state)
to a list of the number of frames involves in the transition and not
in either state.
flux_frames : dict
dictionary mapping each state to a dictionary of with keys 'in' (for
frames in the state) and 'out' (for frames outside the state)
continuous_times : dict
As with continuous frames, but durations multiplied by self.dt
lifetimes : dict
As with lifetime_frames, but durations multiplied by self.dt
transitions_durations : dict
As with transition_frames, but durations multiplied by self.dt
"""
def __init__(self, transition, dt=None):
self.transition = transition
self.dt = dt
self.stateA = transition.stateA
self.stateB = transition.stateB
self.reset_analysis()
def reset_analysis(self):
"""Reset the analysis by emptying all saved segments."""
self.continuous_segments = {self.stateA: [], self.stateB: []}
self.lifetime_segments = {self.stateA: [], self.stateB: []}
self.transition_segments = {(self.stateA, self.stateB): [],
(self.stateB, self.stateA): []}
self.flux_segments = {self.stateA: {'in': [], 'out': []},
self.stateB: {'in': [], 'out': []}}
@property
def continuous_frames(self):
return {k: np.array([len(seg) for seg in self.continuous_segments[k]])
for k in self.continuous_segments.keys()}
@property
def continuous_times(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn
continuous_frames = self.continuous_frames
return {k : continuous_frames[k]*self.dt
for k in continuous_frames.keys()}
@property
def lifetime_frames(self):
return {k: np.array([len(seg) for seg in self.lifetime_segments[k]])
for k in self.lifetime_segments.keys()}
@property
def lifetimes(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn; use dt=1 otherwise
lifetime_frames = self.lifetime_frames
return {k : lifetime_frames[k]*self.dt
for k in lifetime_frames.keys()}
@property
def transition_duration_frames(self):
return {k: np.array([len(seg) for seg in self.transition_segments[k]])
for k in self.transition_segments.keys()}
@property
def transition_duration(self):
if self.dt is None: # pragma: no cover
raise RuntimeError("No time delta set")
# TODO: this might become a logger.warn; use dt=1 otherwise
transition_duration_frames = self.transition_duration_frames
return {k : transition_duration_frames[k]*self.dt
for k in transition_duration_frames.keys()}
def analyze_continuous_time(self, trajectory, state):
"""Analysis to obtain continuous times for given state.
Parameters
----------
trajectory : :class:`.Trajectory`
trajectory to analyze
state : :class:`.Volume`
state volume to characterize. Must be one of the states in the
transition
"""
ensemble = paths.AllInXEnsemble(state)
self.continuous_segments[state] += ensemble.split(trajectory,
overlap=0)
def analyze_lifetime(self, trajectory, state):
"""Analysis to obtain lifetimes for given state.
Parameters
----------
trajectory : :class:`.Trajectory`
trajectory to analyze
state : :class:`.Volume`
state volume to characterize. Must be one of the states in the
transition
"""
other_state = list(set([self.stateA, self.stateB]) - set([state]))[0]
ensemble_BAB = paths.SequentialEnsemble([
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1),
paths.PartInXEnsemble(state) & paths.AllOutXEnsemble(other_state),
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1)
])
ensemble_AB = paths.SequentialEnsemble([
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1),
paths.OptionalEnsemble(paths.AllOutXEnsemble(other_state)),
paths.AllInXEnsemble(other_state) & paths.LengthEnsemble(1)
])
BAB_split = ensemble_BAB.split(trajectory)
AB_split = [ensemble_AB.split(part)[0] for part in BAB_split]
self.lifetime_segments[state] += [subtraj[0:-1]
for subtraj in AB_split]
def analyze_transition_duration(self, trajectory, stateA, stateB):
"""Analysis to obtain transition durations for given state.
Parameters
----------
trajectory : :class:`.Trajectory`
trajectory to analyze
stateA : :class:`.Volume`
initial state volume for the transition
stateB : :class:`.Volume`
final state volume for the transition
"""
# we define the transitions ensemble just in case the transition is,
# e.g., fixed path length TPS. We want flexible path length ensemble
transition_ensemble = paths.SequentialEnsemble([
paths.AllInXEnsemble(stateA) & paths.LengthEnsemble(1),
paths.OptionalEnsemble( # optional to allow instantaneous hops
paths.AllOutXEnsemble(stateA) & paths.AllOutXEnsemble(stateB)
),
paths.AllInXEnsemble(stateB) & paths.LengthEnsemble(1)
])
self.transition_segments[(stateA, stateB)] += [
seg[1:-1] for seg in transition_ensemble.split(trajectory)
]
def analyze_flux(self, trajectory, state):
"""Analysis to obtain flux segments for given state.
Parameters
----------
trajectory : :class:`.Trajectory`
trajectory to analyze
state : :class:`.Volume`
state volume to characterize. Must be one of the states in the
transition
"""
other = list(set([self.stateA, self.stateB]) - set([state]))[0]
counts_out = paths.SequentialEnsemble([
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1),
paths.AllOutXEnsemble(state | other),
paths.AllInXEnsemble(state) & paths.LengthEnsemble(1)
])
counts_in = paths.SequentialEnsemble([
paths.AllOutXEnsemble(state | other) & paths.LengthEnsemble(1),
paths.AllInXEnsemble(state),
paths.AllOutXEnsemble(state | other) & paths.LengthEnsemble(1)
])
flux_out_segments = counts_out.split(trajectory)
flux_in_segments = counts_in.split(trajectory)
for seg in flux_in_segments:
self.flux_segments[state]['in'] += [seg[1:-1]]
for seg in flux_out_segments:
self.flux_segments[state]['out'] += [seg[1:-1]]
def analyze(self, trajectories):
"""Full analysis of a trajectory or trajectories.
Parameters
----------
trajectories : :class:`.Trajectory` or list of :class:`.Trajectory`
"""
# TODO: I hate using isinstance, but I don't see another way
if isinstance(trajectories, paths.Trajectory):
trajectories = [trajectories]
for traj in trajectories:
for state in [self.stateA, self.stateB]:
self.analyze_continuous_time(traj, state)
self.analyze_lifetime(traj, state)
self.analyze_flux(traj, state)
self.analyze_transition_duration(traj, self.stateA, self.stateB)
self.analyze_transition_duration(traj, self.stateB, self.stateA)
# return self so we can init and analyze in one line
return self
# TODO: add a `summary` function to output a nice pandas frame or
# something
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os, time
from threading import Thread
from PIL import Image
from flask import current_app
from app import db, imgs
from app.models import Image as Img
from app.models import Post
from helpers.text import get_all_imgs
_image_thread = None
def jpeg_convert(infile):
""" Try to convert and compress an image to jpeg"""
f, e = os.path.splitext(infile)
outfile = f + '.jpg'
try:
img = Image.open(infile)
base_width = 932
w, h = img.size
if w > base_width:
ratio = base_width/w
new_height = int(h*ratio)
img = img.resize((base_width, new_height), Image.ANTIALIAS)
img.save(outfile, dpi=[100,100], quality=80)
except IOError:
return os.path.basename(infile)
return os.path.basename(outfile)
def crop_image(infile):
""" Crop an image. Check width and height, and crop according to the lower
parameter from the center"""
f, e = os.path.splitext(infile)
outfile = f + '_crop' + e
original = Image.open(infile)
w, h = original.size
max_width = 152
if w < h:
l = 0
r = w
t = (h // 2) - (w // 2)
b = (h // 2) + (w // 2)
elif h < w:
l = (w // 2) - (h // 2)
r = (w // 2) + (h // 2)
t = 0
b = h
cropped = original.crop((l,t,r,b))
w, h = cropped.size
if w > max_width and h > max_width:
cropped = cropped.resize((max_width, max_width), Image.ANTIALIAS)
cropped.save(outfile)
def remove_images(app):
from datetime import datetime
while True:
time.sleep(10)
conf = app.config['IMAGE_DELETE']
with app.app_context():
if ( datetime.utcnow().hour in conf['TIME_OF_DAY'] and
datetime.utcnow().weekday() in conf['WEEKDAY'] ):
images = Img.get_all_imgs()
db_imgs = [img.location + img.filename for img in images]
posts = Post.get_all()
post_imgs = get_all_imgs((post.body_html for post in posts))
diff_imgs = set(db_imgs) - set(post_imgs)
if diff_imgs:
app.logger.debug('Images found in db: {}'.format(db_imgs))
app.logger.debug('Images found in posts: {}'.format(db_imgs))
app.logger.debug('Images to delete: {}'.format(db_imgs))
for i in images:
if i.location + i.filename in diff_imgs:
if os.path.isfile(imgs.path(i.filename)):
os.remove(imgs.path(i.filename))
f, e = os.path.splitext(i.filename)
if os.path.isfile(imgs.path(f + '_crop' + e)):
os.remove(imgs.path(f + '_crop' + e))
db.session.delete(i)
db.session.commit()
def start_image_deletion_thread():
if not current_app.config['TESTING']:
global _image_thread
if _image_thread is None:
_image_thread = Thread(target=remove_images,
args=[current_app._get_current_object()])
current_app.logger.debug('Starting image deletion thread')
_image_thread.start()
Add logging to the rest of the helper module
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os, time
from threading import Thread
from PIL import Image
from flask import current_app
from app import db, imgs
from app.models import Image as Img
from app.models import Post
from helpers.text import get_all_imgs
_image_thread = None
def jpeg_convert(infile):
""" Try to convert and compress an image to jpeg"""
f, e = os.path.splitext(infile)
outfile = f + '.jpg'
try:
img = Image.open(infile)
base_width = 932
w, h = img.size
if w > base_width:
ratio = base_width/w
new_height = int(h*ratio)
img = img.resize((base_width, new_height), Image.ANTIALIAS)
img.save(outfile, dpi=[100,100], quality=80)
except IOError:
current_app.logger.exception('Could not save file: ')
return os.path.basename(infile)
return os.path.basename(outfile)
def crop_image(infile):
""" Crop an image. Check width and height, and crop according to the lower
parameter from the center"""
f, e = os.path.splitext(infile)
outfile = f + '_crop' + e
original = Image.open(infile)
w, h = original.size
max_width = 152
if w < h:
l = 0
r = w
t = (h // 2) - (w // 2)
b = (h // 2) + (w // 2)
elif h < w:
l = (w // 2) - (h // 2)
r = (w // 2) + (h // 2)
t = 0
b = h
cropped = original.crop((l,t,r,b))
w, h = cropped.size
if w > max_width and h > max_width:
cropped = cropped.resize((max_width, max_width), Image.ANTIALIAS)
cropped.save(outfile)
def remove_images(app):
from datetime import datetime
while True:
time.sleep(10)
conf = app.config['IMAGE_DELETE']
with app.app_context():
if ( datetime.utcnow().hour in conf['TIME_OF_DAY'] and
datetime.utcnow().weekday() in conf['WEEKDAY'] ):
images = Img.get_all_imgs()
db_imgs = [img.location + img.filename for img in images]
posts = Post.get_all()
post_imgs = get_all_imgs((post.body_html for post in posts))
diff_imgs = set(db_imgs) - set(post_imgs)
if diff_imgs:
app.logger.debug('Images found in db: {}'.format(db_imgs))
app.logger.debug('Images found in posts: {}'.format(db_imgs))
app.logger.debug('Images to delete: {}'.format(db_imgs))
for i in images:
if i.location + i.filename in diff_imgs:
if os.path.isfile(imgs.path(i.filename)):
os.remove(imgs.path(i.filename))
f, e = os.path.splitext(i.filename)
if os.path.isfile(imgs.path(f + '_crop' + e)):
os.remove(imgs.path(f + '_crop' + e))
db.session.delete(i)
db.session.commit()
def start_image_deletion_thread():
if not current_app.config['TESTING']:
global _image_thread
if _image_thread is None:
_image_thread = Thread(target=remove_images,
args=[current_app._get_current_object()])
current_app.logger.debug('Starting image deletion thread')
_image_thread.start()
|
import logging
import asyncio
import time
import random
import collections
import discord
import markovify
from discord.ext import commands
from .common import Cog
log = logging.getLogger(__name__)
def make_textmodel(texter, data):
texter.model = markovify.NewlineText(data, texter.chain_length)
async def make_texter(chain_length, data, texter_id):
texter = Texter(texter_id, chain_length)
await texter.fill(data)
return texter
class Texter:
"""Texter - Main texter class.
This class holds information about a markov chain generator.
"""
__slots__ = ('loop', 'id', 'refcount', 'chain_length', 'model')
def __init__(self, texter_id, chain_length=1, loop=None):
if loop is None:
loop = asyncio.get_event_loop()
self.id = texter_id
self.refcount = 1
self.chain_length = chain_length
self.loop = loop
self.model = None
def __repr__(self):
return f'Texter(refcount={self.refcount})'
async def fill(self, data):
"""Fill a texter with its text model."""
t_start = time.monotonic()
future_textmodel = self.loop.run_in_executor(None, make_textmodel, self, data)
await future_textmodel
delta = round((time.monotonic() - t_start) * 1000, 2)
log.info(f"Texter.fill: {delta}ms")
def _sentence(self, char_limit):
"""Get a sentence from a initialized texter."""
text = 'None'
if char_limit is not None:
text = self.model.make_short_sentence(char_limit)
else:
text = self.model.make_sentence()
return text
async def sentence(self, char_limit=None):
"""Get a sentence from a initialized texter."""
if self.refcount <= 4:
# max value refcount can be is 5
self.refcount += 1
res = None
count = 0
while res is None:
if count > 3: break
future_sentence = self.loop.run_in_executor(None, self._sentence, char_limit)
res = await future_sentence
count += 1
return str(res)
def clear(self):
del self.model, self.id, self.refcount, self.chain_length, self.loop
class Speak(Cog):
def __init__(self, bot):
super().__init__(bot)
self.text_generators = {}
self.generating = {}
self.coll_task = self.bot.loop.create_task(self.coll_task_func())
async def coll_task_func(self):
try:
while True:
await self.texter_collection()
await asyncio.sleep(360)
except asyncio.CancelledError:
pass
async def texter_collection(self):
"""Free memory by collecting unused Texters."""
amount = len(self.text_generators)
if amount < 1:
return
t_start = time.monotonic()
cleaned = 0
for texter in list(self.text_generators.values()):
if texter.refcount < 1:
texter.clear()
cleaned += 1
del self.text_generators[texter.id]
else:
texter.refcount -= 1
t_end = time.monotonic()
if cleaned > 0:
delta = round((t_end - t_start) * 1000, 2)
log.info(f'{amount} -> {amount - cleaned} in {delta}ms')
async def get_messages(self, guild, amount=2000) -> list:
channel_id = await self.config.cfg_get(guild, 'speak_channel')
channel = guild.get_channel(channel_id)
if channel is None:
channel = guild.default_channel
self.generating[guild.id] = True
try:
messages = []
async for message in channel.history(limit=amount):
author = message.author
if author == self.bot.user:
continue
if author.bot:
continue
messages.append(message.clean_content)
self.generating[guild.id] = False
return messages
except discord.Forbidden:
log.info(f'got Forbidden from {guild.id} when making message history')
self.generating[guild.id] = False
return ['None']
async def get_messages_str(self, guild, amount=2000):
m = await self.get_messages(guild, amount)
return '\n'.join(m)
async def new_texter(self, guild):
guild_messages = await self.get_messages_str(guild)
new_texter = await make_texter(1, guild_messages, guild.id)
self.text_generators[guild.id] = new_texter
async def get_texter(self, guild):
if guild.id not in self.text_generators:
await self.new_texter(guild)
return self.text_generators[guild.id]
async def make_sentence(self, ctx, char_limit=None):
with ctx.typing():
texter = await self.get_texter(ctx.guild)
sentence = await texter.sentence(char_limit)
return sentence
async def on_message(self, message):
ctx = await self.bot.get_context(message)
if message.author.bot:
return
if not isinstance(ctx.channel, discord.TextChannel):
return
prob = await self.config.cfg_get(ctx.guild, 'autoreply_prob')
if prob is None:
log.warning('[autoreply] how can autoreply_prob be none??')
return
force = False
for prefix in self.bot.config.SPEAK_PREFIXES:
if message.content.startswith(prefix):
log.info('[autoreply] forcing from speak prefix')
force = True
if not force:
if random.random() > prob:
return
if self.generating.get(ctx.guild.id):
return
sentence = await self.make_sentence(ctx)
await ctx.send(sentence)
@commands.command()
@commands.is_owner()
async def texclean(self, ctx, amount: int = 1):
"""Clean texters."""
before = len(self.text_generators)
t_start = time.monotonic()
for i in range(amount):
await self.texter_collection()
after = len(self.text_generators)
t_end = time.monotonic()
delta = round((t_end - t_start) * 1000, 2)
await ctx.send(f"`{before} => {after}, cleaned {before-after}, took {delta}ms`")
@commands.command()
@commands.is_owner()
async def ntexter(self, ctx, guild_id: int = None):
"""Create a new texter for a guild, overwrites existing one"""
if guild_id is None:
guild_id = ctx.guild.id
guild = self.bot.get_guild(guild_id)
t1 = time.monotonic()
await self.new_texter(guild)
t2 = time.monotonic()
delta = round((t2 - t1), 2)
await ctx.send(f'Took {delta} seconds loading texter.')
@commands.command(aliases=['spt'])
@commands.guild_only()
async def speaktrigger(self, ctx):
"""Force your Texter to say a sentence.
If the texter is still being generated, this command
does nothing while it isn't completly generated.
"""
if self.generating.get(ctx.guild.id):
return
sentence = await self.make_sentence(ctx)
await ctx.send(sentence)
@commands.command(hidden=True)
async def covfefe(self, ctx):
"""covfefe."""
await ctx.send("Despite the constant negative press covfefe.")
@commands.command(aliases=['jw'])
@commands.guild_only()
async def jwormhole(self, ctx):
"""lul wormhole"""
res = await self.make_sentence(ctx)
await ctx.send(f'<@127296623779774464> wormhole send {res}')
@commands.command()
@commands.guild_only()
async def madlibs(self, ctx, *, inputstr: str):
"""Changes any "---" in the input to a 12-letter generated sentence"""
inputstr = inputstr.replace('@everyone', '@\u200beveryone')
inputstr = inputstr.replace('@here', '@\u200bhere')
splitted = inputstr.split()
if splitted.count('---') < 1:
await ctx.send(":no_entry_sign: you can't just make josé say whatever you want! :no_entry_sign:")
return
if splitted.count('---') > 5:
await ctx.send("thats a .......... lot")
return
res = []
for word in splitted:
if word == '---':
res.append(await self.make_sentence(ctx, 12))
else:
res.append(word)
await ctx.send(' '.join(res))
@commands.command()
@commands.is_owner()
async def txstress(self, ctx):
"""Stress test texters LUL"""
t1 = time.monotonic()
txs = [(await self.new_texter(guild)) for guild in self.bot.guilds]
t2 = time.monotonic()
delta = round((t2 - t1), 2)
await ctx.send(f'Generated {len(txs)} in {delta}')
@commands.command()
async def txstat(self, ctx):
"""Show statistics about all texters"""
tg = self.text_generators
refcounts = collections.Counter()
for gid, tx in tg.items():
refcounts[tx.refcount] += 1
res = ['refcount | texters']
res += [f'{r} | {txc}' for (r, txc) in refcounts.most_common()]
res = '\n'.join(res)
await ctx.send(f'```{res}```')
def setup(bot):
bot.add_cog(Speak(bot))
speak: do not delete texter.id
import logging
import asyncio
import time
import random
import collections
import discord
import markovify
from discord.ext import commands
from .common import Cog
log = logging.getLogger(__name__)
def make_textmodel(texter, data):
texter.model = markovify.NewlineText(data, texter.chain_length)
async def make_texter(chain_length, data, texter_id):
texter = Texter(texter_id, chain_length)
await texter.fill(data)
return texter
class Texter:
"""Texter - Main texter class.
This class holds information about a markov chain generator.
"""
__slots__ = ('loop', 'id', 'refcount', 'chain_length', 'model')
def __init__(self, texter_id, chain_length=1, loop=None):
if loop is None:
loop = asyncio.get_event_loop()
self.id = texter_id
self.refcount = 1
self.chain_length = chain_length
self.loop = loop
self.model = None
def __repr__(self):
return f'Texter(refcount={self.refcount})'
async def fill(self, data):
"""Fill a texter with its text model."""
t_start = time.monotonic()
future_textmodel = self.loop.run_in_executor(None, make_textmodel, self, data)
await future_textmodel
delta = round((time.monotonic() - t_start) * 1000, 2)
log.info(f"Texter.fill: {delta}ms")
def _sentence(self, char_limit):
"""Get a sentence from a initialized texter."""
text = 'None'
if char_limit is not None:
text = self.model.make_short_sentence(char_limit)
else:
text = self.model.make_sentence()
return text
async def sentence(self, char_limit=None):
"""Get a sentence from a initialized texter."""
if self.refcount <= 4:
# max value refcount can be is 5
self.refcount += 1
res = None
count = 0
while res is None:
if count > 3: break
future_sentence = self.loop.run_in_executor(None, self._sentence, char_limit)
res = await future_sentence
count += 1
return str(res)
def clear(self):
del self.model, self.refcount, self.chain_length, self.loop
class Speak(Cog):
def __init__(self, bot):
super().__init__(bot)
self.text_generators = {}
self.generating = {}
self.coll_task = self.bot.loop.create_task(self.coll_task_func())
async def coll_task_func(self):
try:
while True:
await self.texter_collection()
await asyncio.sleep(360)
except asyncio.CancelledError:
pass
async def texter_collection(self):
"""Free memory by collecting unused Texters."""
amount = len(self.text_generators)
if amount < 1:
return
t_start = time.monotonic()
cleaned = 0
for texter in list(self.text_generators.values()):
if texter.refcount < 1:
texter.clear()
cleaned += 1
del self.text_generators[texter.id]
else:
texter.refcount -= 1
t_end = time.monotonic()
if cleaned > 0:
delta = round((t_end - t_start) * 1000, 2)
log.info(f'{amount} -> {amount - cleaned} in {delta}ms')
async def get_messages(self, guild, amount=2000) -> list:
channel_id = await self.config.cfg_get(guild, 'speak_channel')
channel = guild.get_channel(channel_id)
if channel is None:
channel = guild.default_channel
self.generating[guild.id] = True
try:
messages = []
async for message in channel.history(limit=amount):
author = message.author
if author == self.bot.user:
continue
if author.bot:
continue
messages.append(message.clean_content)
self.generating[guild.id] = False
return messages
except discord.Forbidden:
log.info(f'got Forbidden from {guild.id} when making message history')
self.generating[guild.id] = False
return ['None']
async def get_messages_str(self, guild, amount=2000):
m = await self.get_messages(guild, amount)
return '\n'.join(m)
async def new_texter(self, guild):
guild_messages = await self.get_messages_str(guild)
new_texter = await make_texter(1, guild_messages, guild.id)
self.text_generators[guild.id] = new_texter
async def get_texter(self, guild):
if guild.id not in self.text_generators:
await self.new_texter(guild)
return self.text_generators[guild.id]
async def make_sentence(self, ctx, char_limit=None):
with ctx.typing():
texter = await self.get_texter(ctx.guild)
sentence = await texter.sentence(char_limit)
return sentence
async def on_message(self, message):
ctx = await self.bot.get_context(message)
if message.author.bot:
return
if not isinstance(ctx.channel, discord.TextChannel):
return
prob = await self.config.cfg_get(ctx.guild, 'autoreply_prob')
if prob is None:
log.warning('[autoreply] how can autoreply_prob be none??')
return
force = False
for prefix in self.bot.config.SPEAK_PREFIXES:
if message.content.startswith(prefix):
log.info('[autoreply] forcing from speak prefix')
force = True
if not force:
if random.random() > prob:
return
if self.generating.get(ctx.guild.id):
return
sentence = await self.make_sentence(ctx)
await ctx.send(sentence)
@commands.command()
@commands.is_owner()
async def texclean(self, ctx, amount: int = 1):
"""Clean texters."""
before = len(self.text_generators)
t_start = time.monotonic()
for i in range(amount):
await self.texter_collection()
after = len(self.text_generators)
t_end = time.monotonic()
delta = round((t_end - t_start) * 1000, 2)
await ctx.send(f"`{before} => {after}, cleaned {before-after}, took {delta}ms`")
@commands.command()
@commands.is_owner()
async def ntexter(self, ctx, guild_id: int = None):
"""Create a new texter for a guild, overwrites existing one"""
if guild_id is None:
guild_id = ctx.guild.id
guild = self.bot.get_guild(guild_id)
t1 = time.monotonic()
await self.new_texter(guild)
t2 = time.monotonic()
delta = round((t2 - t1), 2)
await ctx.send(f'Took {delta} seconds loading texter.')
@commands.command(aliases=['spt'])
@commands.guild_only()
async def speaktrigger(self, ctx):
"""Force your Texter to say a sentence.
If the texter is still being generated, this command
does nothing while it isn't completly generated.
"""
if self.generating.get(ctx.guild.id):
return
sentence = await self.make_sentence(ctx)
await ctx.send(sentence)
@commands.command(hidden=True)
async def covfefe(self, ctx):
"""covfefe."""
await ctx.send("Despite the constant negative press covfefe.")
@commands.command(aliases=['jw'])
@commands.guild_only()
async def jwormhole(self, ctx):
"""lul wormhole"""
res = await self.make_sentence(ctx)
await ctx.send(f'<@127296623779774464> wormhole send {res}')
@commands.command()
@commands.guild_only()
async def madlibs(self, ctx, *, inputstr: str):
"""Changes any "---" in the input to a 12-letter generated sentence"""
inputstr = inputstr.replace('@everyone', '@\u200beveryone')
inputstr = inputstr.replace('@here', '@\u200bhere')
splitted = inputstr.split()
if splitted.count('---') < 1:
await ctx.send(":no_entry_sign: you can't just make josé say whatever you want! :no_entry_sign:")
return
if splitted.count('---') > 5:
await ctx.send("thats a .......... lot")
return
res = []
for word in splitted:
if word == '---':
res.append(await self.make_sentence(ctx, 12))
else:
res.append(word)
await ctx.send(' '.join(res))
@commands.command()
@commands.is_owner()
async def txstress(self, ctx):
"""Stress test texters LUL"""
t1 = time.monotonic()
txs = [(await self.new_texter(guild)) for guild in self.bot.guilds]
t2 = time.monotonic()
delta = round((t2 - t1), 2)
await ctx.send(f'Generated {len(txs)} in {delta}')
@commands.command()
async def txstat(self, ctx):
"""Show statistics about all texters"""
tg = self.text_generators
refcounts = collections.Counter()
for gid, tx in tg.items():
refcounts[tx.refcount] += 1
res = ['refcount | texters']
res += [f'{r} | {txc}' for (r, txc) in refcounts.most_common()]
res = '\n'.join(res)
await ctx.send(f'```{res}```')
def setup(bot):
bot.add_cog(Speak(bot))
|
"""
controllers.py
Point module controllers.
"""
from flask import Blueprint, request, Response, abort, jsonify
from werkzeug.exceptions import BadRequest
from datetime import datetime
import logging
from app.decorators import ignore_exception
from app.mod_point.models import Point
from app.mod_auth import oauth
from app.decorators import crossdomain
mod_point = Blueprint('point', __name__, url_prefix='/api/v1/point')
sfloat = ignore_exception(TypeError)(float)
sint = ignore_exception(TypeError)(int)
sbool = ignore_exception(TypeError)(bool)
@mod_point.route('/<adventure_slug>/<point_type>', methods=['GET'])
@crossdomain(origin='*')
def list_point(adventure_slug, point_type):
points = Point.objects(adventure=adventure_slug, type=point_type)
return Response(points.to_json(), mimetype='application/json') # TODO: remove $oid references from json output
@mod_point.route('/<point_id>', methods=['GET'])
@crossdomain(origin='*')
def get_point(point_id):
point = Point.objects.get(id=point_id)
return jsonify(point.to_dict())
@mod_point.route('/<point_id>', methods=['PUT'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def update_point(point_id):
point = Point.objects.get(id=point_id)
try:
update = {
'set__title': request.values.get('title', None),
'set__latitude': sfloat(request.values.get('latitude', None)),
'set__longitude': sfloat(request.values.get('longitude', None)),
'set__desc': request.values.get('desc', None),
'set__resource': request.values.get('resource', None),
'set__thumb': request.values.get('thumb', None),
'set__photo': request.values.get('photo', None),
'set__video': request.values.get('video', None),
'set__timestamp': datetime.strptime(request.values.get('timestamp', datetime.now().strftime("%Y-%m-%dT%H:%M:%S.000Z")), "%Y-%m-%dT%H:%M:%S.%fZ"),
'set__hide': sbool(request.values.get('hide', None)),
'set__delorme_id': sint(request.values.get('delorme_id', None))
}
point.update(**update)
point.reload()
return jsonify(point.to_dict())
except TypeError as e:
logging.error(e)
abort(400)
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
@mod_point.route('/<adventure_slug>/<point_type>', methods=['POST'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def add_point(adventure_slug, point_type):
try:
point = Point(
title=request.values.get('title', None),
latitude=sfloat(request.values.get('latitude', None)),
longitude=sfloat(request.values.get('longitude', None)),
desc=request.values.get('desc', None),
resource=request.values.get('resource', None),
timestamp=datetime.strptime(request.values.get('timestamp', datetime.now().strftime("%Y-%m-%dT%H:%M:%S.000Z")), "%Y-%m-%dT%H:%M:%S.%fZ"),
thumb=request.values.get('thumb', None),
photo=request.values.get('photo', None),
video=request.values.get('video', None),
hide=sbool(request.values.get('hide', None)),
delorme_id=sint(request.values.get('delorme_id', None)),
type=point_type,
adventure=adventure_slug
)
point.save()
return jsonify(point.to_dict())
except ValueError as e:
logging.error(e)
abort(400)
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
@mod_point.route('/<point_id>', methods=['DELETE'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def delete_point(point_id):
point = Point.objects.get(id=point_id)
try:
point.delete()
return jsonify(point.to_dict())
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
Improving point list response.
"""
controllers.py
Point module controllers.
"""
from flask import Blueprint, request, abort, jsonify
from werkzeug.exceptions import BadRequest
from datetime import datetime
import logging
from app.decorators import ignore_exception
from app.mod_point.models import Point
from app.mod_auth import oauth
from app.decorators import crossdomain
mod_point = Blueprint('point', __name__, url_prefix='/api/v1/point')
sfloat = ignore_exception(TypeError)(float)
sint = ignore_exception(TypeError)(int)
sbool = ignore_exception(TypeError)(bool)
@mod_point.route('/<adventure_slug>/<point_type>', methods=['GET'])
@crossdomain(origin='*')
def list_point(adventure_slug, point_type):
points = Point.objects(adventure=adventure_slug, type=point_type)
points_dict = []
for point in points:
points_dict.append(point.to_dict())
return jsonify(points=points_dict)
@mod_point.route('/<point_id>', methods=['GET'])
@crossdomain(origin='*')
def get_point(point_id):
point = Point.objects.get(id=point_id)
return jsonify(point.to_dict())
@mod_point.route('/<point_id>', methods=['PUT'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def update_point(point_id):
point = Point.objects.get(id=point_id)
try:
update = {
'set__title': request.values.get('title', None),
'set__latitude': sfloat(request.values.get('latitude', None)),
'set__longitude': sfloat(request.values.get('longitude', None)),
'set__desc': request.values.get('desc', None),
'set__resource': request.values.get('resource', None),
'set__thumb': request.values.get('thumb', None),
'set__photo': request.values.get('photo', None),
'set__video': request.values.get('video', None),
'set__timestamp': datetime.strptime(request.values.get('timestamp', datetime.now().strftime("%Y-%m-%dT%H:%M:%S.000Z")), "%Y-%m-%dT%H:%M:%S.%fZ"),
'set__hide': sbool(request.values.get('hide', None)),
'set__delorme_id': sint(request.values.get('delorme_id', None))
}
point.update(**update)
point.reload()
return jsonify(point.to_dict())
except TypeError as e:
logging.error(e)
abort(400)
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
@mod_point.route('/<adventure_slug>/<point_type>', methods=['POST'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def add_point(adventure_slug, point_type):
try:
point = Point(
title=request.values.get('title', None),
latitude=sfloat(request.values.get('latitude', None)),
longitude=sfloat(request.values.get('longitude', None)),
desc=request.values.get('desc', None),
resource=request.values.get('resource', None),
timestamp=datetime.strptime(request.values.get('timestamp', datetime.now().strftime("%Y-%m-%dT%H:%M:%S.000Z")), "%Y-%m-%dT%H:%M:%S.%fZ"),
thumb=request.values.get('thumb', None),
photo=request.values.get('photo', None),
video=request.values.get('video', None),
hide=sbool(request.values.get('hide', None)),
delorme_id=sint(request.values.get('delorme_id', None)),
type=point_type,
adventure=adventure_slug
)
point.save()
return jsonify(point.to_dict())
except ValueError as e:
logging.error(e)
abort(400)
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
@mod_point.route('/<point_id>', methods=['DELETE'])
@crossdomain(origin='*')
@oauth.require_oauth('email')
def delete_point(point_id):
point = Point.objects.get(id=point_id)
try:
point.delete()
return jsonify(point.to_dict())
except BadRequest as e:
logging.error(e)
abort(400)
except Exception as e:
logging.error(e)
abort(500)
return
|
# Copyright (c) 2010-2013, GEM Foundation.
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake 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 Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Core calculator functionality for computing stochastic event sets and ground
motion fields using the 'event-based' method.
Stochastic events sets (which can be thought of as collections of ruptures) are
computed iven a set of seismic sources and investigation time span (in years).
For more information on computing stochastic event sets, see
:mod:`openquake.hazardlib.calc.stochastic`.
One can optionally compute a ground motion field (GMF) given a rupture, a site
collection (which is a collection of geographical points with associated soil
parameters), and a ground shaking intensity model (GSIM).
For more information on computing ground motion fields, see
:mod:`openquake.hazardlib.calc.gmf`.
"""
import time
import random
import collections
import numpy.random
from django.db import transaction
from openquake.hazardlib.calc import gmf
from openquake.hazardlib.imt import from_string
from openquake.engine import logs, writer
from openquake.engine.calculators.hazard import general
from openquake.engine.calculators.hazard.classical import (
post_processing as cls_post_proc)
from openquake.engine.calculators.hazard.event_based import post_processing
from openquake.engine.db import models
from openquake.engine.utils import tasks
from openquake.engine.performance import EnginePerformanceMonitor, LightMonitor
#: Always 1 for the computation of ground motion fields in the event-based
#: hazard calculator.
DEFAULT_GMF_REALIZATIONS = 1
# NB: beware of large caches
inserter = writer.CacheInserter(models.GmfData, 1000)
@tasks.oqtask
def compute_ses_and_gmfs(job_id, src_seeds, gsims_by_rlz, task_no):
"""
Celery task for the stochastic event set calculator.
Samples logic trees and calls the stochastic event set calculator.
Once stochastic event sets are calculated, results will be saved to the
database. See :class:`openquake.engine.db.models.SESCollection`.
Optionally (specified in the job configuration using the
`ground_motion_fields` parameter), GMFs can be computed from each rupture
in each stochastic event set. GMFs are also saved to the database.
:param int job_id:
ID of the currently running job.
:param src_seeds:
List of pairs (source, seed)
:params gsims_by_rlz:
dictionary of GSIM
:param task_no:
an ordinal so that GMV can be collected in a reproducible order
"""
rlz_ids = [r.id for r in gsims_by_rlz]
ses_coll = models.SESCollection.objects.get(lt_realization_ids=rlz_ids)
hc = models.HazardCalculation.objects.get(oqjob=job_id)
all_ses = models.SES.objects.filter(ses_collection=ses_coll)
imts = map(from_string, hc.intensity_measure_types)
params = dict(
correl_model=general.get_correl_model(hc),
truncation_level=hc.truncation_level,
maximum_distance=hc.maximum_distance,
num_sites=len(hc.site_collection))
collector = GmfCollector(
[s.id for s in hc.site_collection], params, imts, gsims_by_rlz)
mon1 = LightMonitor('filtering sites', job_id, compute_ses_and_gmfs)
mon2 = LightMonitor('generating ruptures', job_id, compute_ses_and_gmfs)
mon3 = LightMonitor('filtering ruptures', job_id, compute_ses_and_gmfs)
mon4 = LightMonitor('saving ses', job_id, compute_ses_and_gmfs)
mon5 = LightMonitor('computing gmfs', job_id, compute_ses_and_gmfs)
# Compute and save stochastic event sets
rnd = random.Random()
for src, seed in src_seeds:
t0 = time.time()
rnd.seed(seed)
with mon1: # filtering sources
s_sites = src.filter_sites_by_distance_to_source(
hc.maximum_distance, hc.site_collection
) if hc.maximum_distance else hc.site_collection
if s_sites is None:
continue
# NB: the number of occurrences is very low, << 1, so it is
# more efficient to filter only the ruptures that occur
# and not to compute the occurrencies of the filtered ruptures
# the dictionary `ses_num_occ` contains [(ses, num_occurrences)]
# for each occurring rupture for each ses in the ses collection
ses_num_occ = collections.defaultdict(list)
with mon2: # generating ruptures
for rup in src.iter_ruptures():
for ses in all_ses:
numpy.random.seed(rnd.randint(0, models.MAX_SINT_32))
num_occurrences = rup.sample_number_of_occurrences()
if num_occurrences:
ses_num_occ[rup].append((ses, num_occurrences))
for rup in ses_num_occ:
with mon3: # filtering ruptures
r_sites = rup.source_typology.\
filter_sites_by_distance_to_rupture(
rup, hc.maximum_distance, s_sites
) if hc.maximum_distance else s_sites
if r_sites is None:
continue
# saving ses and generating gmf
for ses, num_occurrences in ses_num_occ[rup]:
for occ in range(1, num_occurrences + 1):
with mon4: # saving ruptures
rup_id = models.SESRupture.objects.create(
ses=ses,
rupture=rup,
tag='smlt=%02d|ses=%04d|src=%s|occ=%02d'
% (ses_coll.ordinal, ses.ordinal,
src.source_id, occ),
hypocenter=rup.hypocenter.wkt2d,
magnitude=rup.mag).id
if hc.ground_motion_fields:
with mon5: # computing GMFs
rup_seed = rnd.randint(0, models.MAX_SINT_32)
collector.calc_gmf(r_sites, rup, rup_id, rup_seed)
num_ruptures = sum(occ for ses, occ in ses_num_occ[rup]
for rup in ses_num_occ)
logs.LOG.info('job=%d, src=%s:%s, num_ruptures=%d, calc_time=%fs',
job_id, src.source_id, src.__class__.__name__,
num_ruptures, time.time() - t0)
mon1.flush()
mon2.flush()
mon3.flush()
mon4.flush()
mon5.flush()
if hc.ground_motion_fields:
with EnginePerformanceMonitor(
'saving gmfs', job_id, compute_ses_and_gmfs):
collector.save_gmfs(task_no)
class GmfCollector(object):
"""
A class to compute and save ground motion fields.
"""
def __init__(self, site_ids, params, imts, gsims_by_rlz):
self.site_ids = site_ids
self.params = params
self.imts = imts
self.gsims_by_rlz = gsims_by_rlz
self.gmvs_per_site = collections.defaultdict(list)
self.ruptures_per_site = collections.defaultdict(list)
def calc_gmf(self, r_sites, rupture, rupture_id, rupture_seed):
"""
Compute the GMF generated by the given rupture on the given
sites and collect the values in the dictionaries
.gmvs_per_site and .ruptures_per_site.
"""
triples = [(rupture, rupture_id, rupture_seed)]
for rlz, gsims in self.gsims_by_rlz.items():
for imt, idx, gmv, rup_id in _compute_gmf(
self.params, self.imts, gsims, r_sites, triples):
if gmv:
site_id = self.site_ids[idx]
self.gmvs_per_site[rlz, imt, site_id].append(gmv)
self.ruptures_per_site[rlz, imt, site_id].append(rup_id)
@transaction.commit_on_success(using='job_init')
def save_gmfs(self, task_no):
"""
Helper method to save the computed GMF data to the database.
:param task_no:
The ordinal of the task which generated the current GMFs to save
"""
for rlz, imt, site_id in self.gmvs_per_site:
imt_name, sa_period, sa_damping = imt
inserter.add(models.GmfData(
gmf=models.Gmf.objects.get(lt_realization=rlz),
task_no=task_no,
imt=imt_name,
sa_period=sa_period,
sa_damping=sa_damping,
site_id=site_id,
gmvs=self.gmvs_per_site[rlz, imt, site_id],
rupture_ids=self.ruptures_per_site[rlz, imt, site_id]))
inserter.flush()
self.gmvs_per_site.clear()
self.ruptures_per_site.clear()
# NB: I tried to return a single dictionary {site_id: [(gmv, rupt_id),...]}
# but it takes a lot more memory (MS)
def _compute_gmf(params, imts, gsims, site_coll, rupture_id_seed_triples):
"""
Compute a ground motion field value for each rupture, for all the
points affected by that rupture, for the given IMT. Returns a
dictionary with the nonzero contributions to each site id, and a dictionary
with the ids of the contributing ruptures for each site id.
assert len(ruptures) == len(rupture_seeds)
:param params:
a dictionary containing the keys
correl_model, truncation_level, maximum_distance
:param imts:
a list of hazardlib intensity measure types
:param gsims:
a dictionary {tectonic region type -> GSIM instance}
:param site_coll:
a SiteCollection instance
:param rupture_id_seed_triple:
a list of triples with types
(:class:`openquake.hazardlib.source.rupture.Rupture`, int, int)
"""
# Compute and save ground motion fields
for rupture, rup_id, rup_seed in rupture_id_seed_triples:
gmf_calc_kwargs = {
'rupture': rupture,
'sites': site_coll,
'imts': imts,
'gsim': gsims[rupture.tectonic_region_type],
'truncation_level': params['truncation_level'],
'realizations': DEFAULT_GMF_REALIZATIONS,
'correlation_model': params['correl_model'],
'num_sites': params['num_sites'],
}
numpy.random.seed(rup_seed)
gmf_dict = gmf.ground_motion_fields(**gmf_calc_kwargs)
for imt, gmf_1_realiz in gmf_dict.iteritems():
# since DEFAULT_GMF_REALIZATIONS is 1, gmf_1_realiz is a matrix
# with n_sites rows and 1 column
for idx, gmv in enumerate(gmf_1_realiz):
# convert a 1x1 matrix into a float
yield imt, idx, float(gmv), rup_id
class EventBasedHazardCalculator(general.BaseHazardCalculator):
"""
Probabilistic Event-Based hazard calculator. Computes stochastic event sets
and (optionally) ground motion fields.
"""
core_calc_task = compute_ses_and_gmfs
def task_arg_gen(self, _block_size=None):
"""
Loop through realizations and sources to generate a sequence of
task arg tuples. Each tuple of args applies to a single task.
Yielded results are tuples of the form job_id, sources, ses, seeds
(seeds will be used to seed numpy for temporal occurence sampling).
"""
hc = self.hc
rnd = random.Random()
rnd.seed(hc.random_seed)
task_no = 0
for job_id, block, gsims_by_rlz in super(
EventBasedHazardCalculator, self).task_arg_gen():
ss = [(src, rnd.randint(0, models.MAX_SINT_32))
for src in block] # source, seed pairs
yield job_id, ss, gsims_by_rlz, task_no
task_no += 1
# now the source_blocks_per_ltpath dictionary can be cleared
self.source_blocks_per_ltpath.clear()
def initialize_ses_db_records(self, ordinal, rlzs):
"""
Create :class:`~openquake.engine.db.models.Output`,
:class:`~openquake.engine.db.models.SESCollection` and
:class:`~openquake.engine.db.models.SES` "container" records for
a single realization.
Stochastic event set ruptures computed for this realization will be
associated to these containers.
NOTE: Many tasks can contribute ruptures to the same SES.
"""
rlz_ids = [r.id for r in rlzs]
output = models.Output.objects.create(
oq_job=self.job,
display_name='SES Collection smlt-%d-rlz-%s' % (
ordinal, ','.join(map(str, rlz_ids))),
output_type='ses')
ses_coll = models.SESCollection.objects.create(
output=output, lt_realization_ids=rlz_ids, ordinal=ordinal)
for rlz in rlzs:
if self.job.hazard_calculation.ground_motion_fields:
output = models.Output.objects.create(
oq_job=self.job,
display_name='GMF rlz-%s' % rlz.id,
output_type='gmf')
models.Gmf.objects.create(output=output, lt_realization=rlz)
all_ses = []
for i in xrange(1, self.hc.ses_per_logic_tree_path + 1):
all_ses.append(
models.SES.objects.create(
ses_collection=ses_coll,
investigation_time=self.hc.investigation_time,
ordinal=i))
return all_ses
def pre_execute(self):
"""
Do pre-execution work. At the moment, this work entails:
parsing and initializing sources, parsing and initializing the
site model (if there is one), parsing vulnerability and
exposure files, and generating logic tree realizations. (The
latter piece basically defines the work to be done in the
`execute` phase.)
"""
super(EventBasedHazardCalculator, self).pre_execute()
for i, rlzs in enumerate(self.rlzs_per_ltpath.itervalues()):
self.initialize_ses_db_records(i, rlzs)
def post_process(self):
"""
If requested, perform additional processing of GMFs to produce hazard
curves.
"""
if self.hc.hazard_curves_from_gmfs:
with EnginePerformanceMonitor('generating hazard curves',
self.job.id):
self.parallelize(
post_processing.gmf_to_hazard_curve_task,
post_processing.gmf_to_hazard_curve_arg_gen(self.job),
self.log_percent)
# If `mean_hazard_curves` is True and/or `quantile_hazard_curves`
# has some value (not an empty list), do this additional
# post-processing.
if self.hc.mean_hazard_curves or self.hc.quantile_hazard_curves:
with EnginePerformanceMonitor(
'generating mean/quantile curves', self.job.id):
self.do_aggregate_post_proc()
if self.hc.hazard_maps:
with EnginePerformanceMonitor(
'generating hazard maps', self.job.id):
self.parallelize(
cls_post_proc.hazard_curves_to_hazard_map_task,
cls_post_proc.hazard_curves_to_hazard_map_task_arg_gen(
self.job),
self.log_percent)
Better logging
Former-commit-id: 2aab78a33f1201da1f1303d8ce962c3adad29f42 [formerly 2aab78a33f1201da1f1303d8ce962c3adad29f42 [formerly 134eb0fdbc2cbe930d1ef83e78f99bd17e4cf8d4]]
Former-commit-id: e6ae51800349022996b47051b440a266337eedfa
Former-commit-id: cccc55f92a0fbbbdff44f068f87bed12303b2366
# Copyright (c) 2010-2013, GEM Foundation.
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake 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 Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Core calculator functionality for computing stochastic event sets and ground
motion fields using the 'event-based' method.
Stochastic events sets (which can be thought of as collections of ruptures) are
computed iven a set of seismic sources and investigation time span (in years).
For more information on computing stochastic event sets, see
:mod:`openquake.hazardlib.calc.stochastic`.
One can optionally compute a ground motion field (GMF) given a rupture, a site
collection (which is a collection of geographical points with associated soil
parameters), and a ground shaking intensity model (GSIM).
For more information on computing ground motion fields, see
:mod:`openquake.hazardlib.calc.gmf`.
"""
import time
import random
import collections
import numpy.random
from django.db import transaction
from openquake.hazardlib.calc import gmf
from openquake.hazardlib.imt import from_string
from openquake.engine import logs, writer
from openquake.engine.calculators.hazard import general
from openquake.engine.calculators.hazard.classical import (
post_processing as cls_post_proc)
from openquake.engine.calculators.hazard.event_based import post_processing
from openquake.engine.db import models
from openquake.engine.utils import tasks
from openquake.engine.performance import EnginePerformanceMonitor, LightMonitor
#: Always 1 for the computation of ground motion fields in the event-based
#: hazard calculator.
DEFAULT_GMF_REALIZATIONS = 1
# NB: beware of large caches
inserter = writer.CacheInserter(models.GmfData, 1000)
@tasks.oqtask
def compute_ses_and_gmfs(job_id, src_seeds, gsims_by_rlz, task_no):
"""
Celery task for the stochastic event set calculator.
Samples logic trees and calls the stochastic event set calculator.
Once stochastic event sets are calculated, results will be saved to the
database. See :class:`openquake.engine.db.models.SESCollection`.
Optionally (specified in the job configuration using the
`ground_motion_fields` parameter), GMFs can be computed from each rupture
in each stochastic event set. GMFs are also saved to the database.
:param int job_id:
ID of the currently running job.
:param src_seeds:
List of pairs (source, seed)
:params gsims_by_rlz:
dictionary of GSIM
:param task_no:
an ordinal so that GMV can be collected in a reproducible order
"""
rlz_ids = [r.id for r in gsims_by_rlz]
ses_coll = models.SESCollection.objects.get(lt_realization_ids=rlz_ids)
hc = models.HazardCalculation.objects.get(oqjob=job_id)
all_ses = models.SES.objects.filter(ses_collection=ses_coll)
imts = map(from_string, hc.intensity_measure_types)
params = dict(
correl_model=general.get_correl_model(hc),
truncation_level=hc.truncation_level,
maximum_distance=hc.maximum_distance,
num_sites=len(hc.site_collection))
collector = GmfCollector(
[s.id for s in hc.site_collection], params, imts, gsims_by_rlz)
mon1 = LightMonitor('filtering sites', job_id, compute_ses_and_gmfs)
mon2 = LightMonitor('generating ruptures', job_id, compute_ses_and_gmfs)
mon3 = LightMonitor('filtering ruptures', job_id, compute_ses_and_gmfs)
mon4 = LightMonitor('saving ses', job_id, compute_ses_and_gmfs)
mon5 = LightMonitor('computing gmfs', job_id, compute_ses_and_gmfs)
# Compute and save stochastic event sets
rnd = random.Random()
for src, seed in src_seeds:
t0 = time.time()
rnd.seed(seed)
with mon1: # filtering sources
s_sites = src.filter_sites_by_distance_to_source(
hc.maximum_distance, hc.site_collection
) if hc.maximum_distance else hc.site_collection
if s_sites is None:
continue
# NB: the number of occurrences is very low, << 1, so it is
# more efficient to filter only the ruptures that occur
# and not to compute the occurrencies of the filtered ruptures
# the dictionary `ses_num_occ` contains [(ses, num_occurrences)]
# for each occurring rupture for each ses in the ses collection
ses_num_occ = collections.defaultdict(list)
with mon2: # generating ruptures
for rup in src.iter_ruptures():
for ses in all_ses:
numpy.random.seed(rnd.randint(0, models.MAX_SINT_32))
num_occurrences = rup.sample_number_of_occurrences()
if num_occurrences:
ses_num_occ[rup].append((ses, num_occurrences))
for rup in ses_num_occ:
with mon3: # filtering ruptures
r_sites = rup.source_typology.\
filter_sites_by_distance_to_rupture(
rup, hc.maximum_distance, s_sites
) if hc.maximum_distance else s_sites
if r_sites is None:
continue
# saving ses and generating gmf
for ses, num_occurrences in ses_num_occ[rup]:
for occ in range(1, num_occurrences + 1):
with mon4: # saving ruptures
rup_id = models.SESRupture.objects.create(
ses=ses,
rupture=rup,
tag='smlt=%02d|ses=%04d|src=%s|occ=%02d'
% (ses_coll.ordinal, ses.ordinal,
src.source_id, occ),
hypocenter=rup.hypocenter.wkt2d,
magnitude=rup.mag).id
if hc.ground_motion_fields:
with mon5: # computing GMFs
rup_seed = rnd.randint(0, models.MAX_SINT_32)
collector.calc_gmf(r_sites, rup, rup_id, rup_seed)
# log calc_time per rupture
if ses_num_occ:
num_ruptures = sum(occ for ses, occ in ses_num_occ[rup]
for rup in ses_num_occ)
logs.LOG.info(
'job=%d, src=%s:%s, num_ruptures=%d/%d, calc_time=%fs',
job_id, src.source_id, src.__class__.__name__,
len(ses_num_occ), num_ruptures, time.time() - t0)
mon1.flush()
mon2.flush()
mon3.flush()
mon4.flush()
mon5.flush()
if hc.ground_motion_fields:
with EnginePerformanceMonitor(
'saving gmfs', job_id, compute_ses_and_gmfs):
collector.save_gmfs(task_no)
class GmfCollector(object):
"""
A class to compute and save ground motion fields.
"""
def __init__(self, site_ids, params, imts, gsims_by_rlz):
self.site_ids = site_ids
self.params = params
self.imts = imts
self.gsims_by_rlz = gsims_by_rlz
self.gmvs_per_site = collections.defaultdict(list)
self.ruptures_per_site = collections.defaultdict(list)
def calc_gmf(self, r_sites, rupture, rupture_id, rupture_seed):
"""
Compute the GMF generated by the given rupture on the given
sites and collect the values in the dictionaries
.gmvs_per_site and .ruptures_per_site.
"""
triples = [(rupture, rupture_id, rupture_seed)]
for rlz, gsims in self.gsims_by_rlz.items():
for imt, idx, gmv, rup_id in _compute_gmf(
self.params, self.imts, gsims, r_sites, triples):
if gmv:
site_id = self.site_ids[idx]
self.gmvs_per_site[rlz, imt, site_id].append(gmv)
self.ruptures_per_site[rlz, imt, site_id].append(rup_id)
@transaction.commit_on_success(using='job_init')
def save_gmfs(self, task_no):
"""
Helper method to save the computed GMF data to the database.
:param task_no:
The ordinal of the task which generated the current GMFs to save
"""
for rlz, imt, site_id in self.gmvs_per_site:
imt_name, sa_period, sa_damping = imt
inserter.add(models.GmfData(
gmf=models.Gmf.objects.get(lt_realization=rlz),
task_no=task_no,
imt=imt_name,
sa_period=sa_period,
sa_damping=sa_damping,
site_id=site_id,
gmvs=self.gmvs_per_site[rlz, imt, site_id],
rupture_ids=self.ruptures_per_site[rlz, imt, site_id]))
inserter.flush()
self.gmvs_per_site.clear()
self.ruptures_per_site.clear()
# NB: I tried to return a single dictionary {site_id: [(gmv, rupt_id),...]}
# but it takes a lot more memory (MS)
def _compute_gmf(params, imts, gsims, site_coll, rupture_id_seed_triples):
"""
Compute a ground motion field value for each rupture, for all the
points affected by that rupture, for the given IMT. Returns a
dictionary with the nonzero contributions to each site id, and a dictionary
with the ids of the contributing ruptures for each site id.
assert len(ruptures) == len(rupture_seeds)
:param params:
a dictionary containing the keys
correl_model, truncation_level, maximum_distance
:param imts:
a list of hazardlib intensity measure types
:param gsims:
a dictionary {tectonic region type -> GSIM instance}
:param site_coll:
a SiteCollection instance
:param rupture_id_seed_triple:
a list of triples with types
(:class:`openquake.hazardlib.source.rupture.Rupture`, int, int)
"""
# Compute and save ground motion fields
for rupture, rup_id, rup_seed in rupture_id_seed_triples:
gmf_calc_kwargs = {
'rupture': rupture,
'sites': site_coll,
'imts': imts,
'gsim': gsims[rupture.tectonic_region_type],
'truncation_level': params['truncation_level'],
'realizations': DEFAULT_GMF_REALIZATIONS,
'correlation_model': params['correl_model'],
'num_sites': params['num_sites'],
}
numpy.random.seed(rup_seed)
gmf_dict = gmf.ground_motion_fields(**gmf_calc_kwargs)
for imt, gmf_1_realiz in gmf_dict.iteritems():
# since DEFAULT_GMF_REALIZATIONS is 1, gmf_1_realiz is a matrix
# with n_sites rows and 1 column
for idx, gmv in enumerate(gmf_1_realiz):
# convert a 1x1 matrix into a float
yield imt, idx, float(gmv), rup_id
class EventBasedHazardCalculator(general.BaseHazardCalculator):
"""
Probabilistic Event-Based hazard calculator. Computes stochastic event sets
and (optionally) ground motion fields.
"""
core_calc_task = compute_ses_and_gmfs
def task_arg_gen(self, _block_size=None):
"""
Loop through realizations and sources to generate a sequence of
task arg tuples. Each tuple of args applies to a single task.
Yielded results are tuples of the form job_id, sources, ses, seeds
(seeds will be used to seed numpy for temporal occurence sampling).
"""
hc = self.hc
rnd = random.Random()
rnd.seed(hc.random_seed)
task_no = 0
for job_id, block, gsims_by_rlz in super(
EventBasedHazardCalculator, self).task_arg_gen():
ss = [(src, rnd.randint(0, models.MAX_SINT_32))
for src in block] # source, seed pairs
yield job_id, ss, gsims_by_rlz, task_no
task_no += 1
# now the source_blocks_per_ltpath dictionary can be cleared
self.source_blocks_per_ltpath.clear()
def initialize_ses_db_records(self, ordinal, rlzs):
"""
Create :class:`~openquake.engine.db.models.Output`,
:class:`~openquake.engine.db.models.SESCollection` and
:class:`~openquake.engine.db.models.SES` "container" records for
a single realization.
Stochastic event set ruptures computed for this realization will be
associated to these containers.
NOTE: Many tasks can contribute ruptures to the same SES.
"""
rlz_ids = [r.id for r in rlzs]
output = models.Output.objects.create(
oq_job=self.job,
display_name='SES Collection smlt-%d-rlz-%s' % (
ordinal, ','.join(map(str, rlz_ids))),
output_type='ses')
ses_coll = models.SESCollection.objects.create(
output=output, lt_realization_ids=rlz_ids, ordinal=ordinal)
for rlz in rlzs:
if self.job.hazard_calculation.ground_motion_fields:
output = models.Output.objects.create(
oq_job=self.job,
display_name='GMF rlz-%s' % rlz.id,
output_type='gmf')
models.Gmf.objects.create(output=output, lt_realization=rlz)
all_ses = []
for i in xrange(1, self.hc.ses_per_logic_tree_path + 1):
all_ses.append(
models.SES.objects.create(
ses_collection=ses_coll,
investigation_time=self.hc.investigation_time,
ordinal=i))
return all_ses
def pre_execute(self):
"""
Do pre-execution work. At the moment, this work entails:
parsing and initializing sources, parsing and initializing the
site model (if there is one), parsing vulnerability and
exposure files, and generating logic tree realizations. (The
latter piece basically defines the work to be done in the
`execute` phase.)
"""
super(EventBasedHazardCalculator, self).pre_execute()
for i, rlzs in enumerate(self.rlzs_per_ltpath.itervalues()):
self.initialize_ses_db_records(i, rlzs)
def post_process(self):
"""
If requested, perform additional processing of GMFs to produce hazard
curves.
"""
if self.hc.hazard_curves_from_gmfs:
with EnginePerformanceMonitor('generating hazard curves',
self.job.id):
self.parallelize(
post_processing.gmf_to_hazard_curve_task,
post_processing.gmf_to_hazard_curve_arg_gen(self.job),
self.log_percent)
# If `mean_hazard_curves` is True and/or `quantile_hazard_curves`
# has some value (not an empty list), do this additional
# post-processing.
if self.hc.mean_hazard_curves or self.hc.quantile_hazard_curves:
with EnginePerformanceMonitor(
'generating mean/quantile curves', self.job.id):
self.do_aggregate_post_proc()
if self.hc.hazard_maps:
with EnginePerformanceMonitor(
'generating hazard maps', self.job.id):
self.parallelize(
cls_post_proc.hazard_curves_to_hazard_map_task,
cls_post_proc.hazard_curves_to_hazard_map_task_arg_gen(
self.job),
self.log_percent)
|
__copyright__ = "Copyright 2017 Birkbeck, University of London"
__author__ = "Martin Paul Eve & Andy Byers"
__license__ = "AGPL v3"
__maintainer__ = "Birkbeck Centre for Technology and Publishing"
from django.urls import reverse
from utils import (
notify_helpers,
models as util_models,
setting_handler,
render_template,
)
from core import models as core_models
from review import logic as review_logic
def send_reviewer_withdrawl_notice(**kwargs):
review_assignment = kwargs['review_assignment']
request = kwargs['request']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0}\'s review of "{1}" has been withdrawn by {2}'.format(review_assignment.reviewer.full_name(),
review_assignment.article.title,
request.user.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Review Withdrawl',
'target': review_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_review_withdrawl', review_assignment.reviewer.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_editor_unassigned_notice(request, message, assignment, skip=False):
description = "{a.editor} unassigned from {a.article} by {r.user}".format(
a=assignment,
r=request,
)
if not skip:
log_dict = {
'level': 'Info', 'action_text': description,
'types': 'Editor Unassigned',
'target': assignment.article
}
notify_helpers.send_email_with_body_from_user(
request,
'subject_review_withdrawl',
assignment.editor.email,
message,
log_dict=log_dict,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_editor_assigned_acknowledgements_mandatory(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py. It is different to the below function in that this is called when an editor is
assigned, whereas the below is only called when the user opts to send a message to the editor.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
editor_assignment = kwargs['editor_assignment']
article = editor_assignment.article
request = kwargs['request']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
acknowledgement = kwargs['acknowledgement']
description = '{0} was assigned as the editor for "{1}"'.format(editor_assignment.editor.full_name(),
article.title)
context = {
'article': article,
'request': request,
'editor_assignment': editor_assignment
}
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Editor Assignment',
'target': article}
# send to assigned editor
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_editor_assignment',
editor_assignment.editor.email,
user_message_content, log_dict=log_dict)
# send to editor
if not acknowledgement:
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request, 'editor_assignment',
'subject_editor_assignment',
request.user.email, context,
log_dict=log_dict)
def send_editor_assigned_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
kwargs['acknowledgement'] = True
send_editor_assigned_acknowledgements_mandatory(**kwargs)
def send_reviewer_requested_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
kwargs['acknowledgement'] = True
send_reviewer_requested_acknowledgements_mandatory(**kwargs)
def send_reviewer_requested_acknowledgements_mandatory(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has been requested.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, user_message_content, skip (boolean) and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
user_message_content = kwargs['user_message_content']
acknowledgement = kwargs['acknowledgement']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = 'A review request was added to "{0}" for user {1}'.format(article.title,
review_assignment.reviewer.full_name())
context = {
'article': article,
'request': request,
'review_assignment': review_assignment
}
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Review Request',
'target': article}
# send to requested reviewer
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_review_request_sent',
review_assignment.reviewer.email,
user_message_content, log_dict=log_dict)
if not acknowledgement:
# send slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_request_sent',
'subject_review_request_sent',
review_assignment.editor.email, context,
log_dict=log_dict)
def send_review_complete_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has completed his or her
review. It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
request.user = review_assignment.reviewer
description = '{0} completed the review of "{1}": {2}'.format(review_assignment.reviewer.full_name(),
article.title,
review_assignment.get_decision_display())
util_models.LogEntry.add_entry(types='Review Complete', description=description, level='Info', actor=request.user,
target=article,
request=request)
context = {
'article': article,
'request': request,
'review_assignment': review_assignment
}
# send slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to reviewer
notify_helpers.send_email_with_body_from_setting_template(request, 'review_complete_reviewer_acknowledgement',
'subject_review_complete_reviewer_acknowledgement',
review_assignment.reviewer.email,
context)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_complete_acknowledgement',
'subject_review_complete_reviewer_acknowledgement',
review_assignment.editor.email, context)
def send_reviewer_accepted_or_decline_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has either accepted or
declined to review. It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, accepted and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
accepted = kwargs['accepted']
description = '{0} {1} to review {2}'.format(review_assignment.reviewer.full_name(),
('accepted' if accepted else 'declined'),
article.title)
util_models.LogEntry.add_entry(types='Review request {0}'.format(('accepted' if accepted else 'declined')),
description=description, level='Info', actor=request.user, target=article,
request=request)
review_url = review_logic.get_review_url(
request,
review_assignment,
)
context = {
'article': article,
'request': request,
'review_assignment': review_assignment,
'review_url': review_url,
}
# send to slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to reviewer
if accepted:
notify_helpers.send_email_with_body_from_setting_template(
request,
'review_accept_acknowledgement',
'subject_review_accept_acknowledgement',
review_assignment.reviewer.email,
context,
)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_acknowledgement',
'subject_review_acknowledgement',
review_assignment.editor.email, context)
else:
notify_helpers.send_email_with_body_from_setting_template(request, 'review_decline_acknowledgement',
'subject_review_decline_acknowledgement',
review_assignment.reviewer.email, context)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_acknowledgement',
'subject_review_acknowledgement',
review_assignment.editor.email, context)
def send_submission_acknowledgement(**kwargs):
"""
This function is called via the event handling framework and it
notifies site operators of a submission. It is
wired up in core/urls.py.
:param kwargs: a list of kwargs that includes article and request
:return: None
"""
article = kwargs['article']
request = kwargs['request']
util_models.LogEntry.add_entry(
types='Submission Complete',
description='A new article {0} was submitted'.format(article.title),
level='Info',
actor=request.user,
target=article,
request=request,
)
log_dict = {
'level': 'Info',
'action_text': 'A new article {0} was submitted'.format(article.title),
'types': 'New Submission Acknowledgement',
'target': article,
}
# generate URL
editor_review_url = request.journal.site_url(
path=reverse(
'review_unassigned_article',
kwargs={'article_id': article.pk},
)
)
notify_helpers.send_slack(
request,
'New submission: {0} {1}'.format(article.title,
editor_review_url),
['slack_editors'])
# send to author
context = {
'article': article,
'request': request,
'editor_review_url': editor_review_url,
}
notify_helpers.send_email_with_body_from_setting_template(
request,
'submission_acknowledgement',
'subject_submission_acknowledgement',
article.correspondence_author.email,
context,
log_dict=log_dict,
)
# send to all authors
editors_to_email = setting_handler.get_setting(
'general', 'editors_for_notification', request.journal).processed_value
if editors_to_email:
editor_pks = [int(pk) for pk in editors_to_email]
editor_emails = {role.user.email for role in core_models.AccountRole.objects.filter(
role__slug='editor', user__id__in=editor_pks)}
else:
editor_emails = set(request.journal.editor_emails)
assigned_to_section = (
article.section.editors.all() | article.section.section_editors.all())
editor_emails |= {editor.email for editor in assigned_to_section}
notify_helpers.send_email_with_body_from_setting_template(request,
'editor_new_submission',
'subject_editor_new_submission',
editor_emails,
context, log_dict=log_dict)
def send_article_decision(**kwargs):
article = kwargs['article']
request = kwargs['request']
decision = kwargs['decision']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0}\'s article "{1}" has been {2}ed by {3}'.format(article.correspondence_author.full_name(),
article.title,
decision,
request.user.full_name())
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Article Decision',
'target': article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'Article Review Decision',
article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_revisions_request(**kwargs):
request = kwargs['request']
revision = kwargs['revision']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0} has requested revisions for {1} due on {2}'.format(request.user.full_name(),
revision.article.title,
revision.date_due)
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Revision Request',
'target': revision.article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_request_revisions',
revision.article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_revisions_complete(**kwargs):
request = kwargs['request']
revision = kwargs['revision']
action_text = ''
for action in revision.actions.all():
action_text = "{0}<br><br>{1} - {2}".format(action_text, action.logged, action.text)
description = '<p>{0} has completed revisions for {1}</p> Actions:<br>{2}'.format(request.user.full_name(),
revision.article.title,
action_text)
notify_helpers.send_email_with_body_from_user(request,
'Article Revisions Complete',
revision.editor.email,
description)
notify_helpers.send_slack(request, description, ['slack_editors'])
util_models.LogEntry.add_entry(types='Revisions Complete', description=action_text, level='Info',
request=request, target=revision.article)
def send_copyedit_assignment(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has requested copyediting for {1} due on {2}'.format(request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.due)
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Assignment',
'target': copyedit_assignment.article}
response = notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_assignment_notification',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_updated(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
skip = kwargs.get('skip', False)
if not skip:
# send to slack
notify_helpers.send_slack(request,
'Copyedit assignment {0} updated'.format(copyedit_assignment.pk),
['slack_editors'])
log_dict = {'level': 'Info',
'action_text': 'Copyedit assignment #{number} update.'.format(number=copyedit_assignment.pk),
'types': 'Revision Request',
'target': copyedit_assignment.article}
# send to author
notify_helpers.send_email_with_body_from_setting_template(request,
'copyedit_updated',
'subject_copyedit_updated',
copyedit_assignment.copyeditor.email,
context={'request': request,
'copyedit_assignment': copyedit_assignment},
log_dict=log_dict)
def send_copyedit_deleted(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
skip = kwargs.get('skip', False)
description = 'Copyedit task {0} for article {1} deleted.'.format(copyedit_assignment.pk,
copyedit_assignment.article.title)
if not skip:
# send to slack
notify_helpers.send_slack(request,
'Copyedit assignment {0} updated'.format(copyedit_assignment.pk),
['slack_editors'])
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Assignment Deleted',
'target': copyedit_assignment.article}
# send to copyeditor
notify_helpers.send_email_with_body_from_setting_template(request,
'copyedit_deleted',
'subject_copyedit_deleted',
copyedit_assignment.copyeditor.email,
context={'request': request,
'copyedit_assignment': copyedit_assignment},
log_dict=log_dict)
def send_copyedit_decision(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
description = '{0} has accepted copyediting task for {1} due on {2}.'.format(
copyedit_assignment.copyeditor.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.due)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyediting Decision',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Copyediting Decision',
copyedit_assignment.editor.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_author_review(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has requested copyedit review for {1} from {2}'.format(
request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.article.correspondence_author.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Author Review',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_notify_author',
copyedit_assignment.article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_complete(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
article = kwargs['article']
description = 'Copyediting requested by {0} from {1} for article {2} has been completed'.format(
request.user.full_name(),
copyedit_assignment.copyeditor.full_name(),
article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Complete',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_notify_editor',
copyedit_assignment.editor.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_ack(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has acknowledged copyediting for {1}'.format(request.user.full_name(),
copyedit_assignment.article.title, )
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Acknowledgement',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_ack',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_reopen(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has reopened copyediting for {1} from {2}'.format(request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.copyeditor.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Complete',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_reopen_task',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_assignment(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has been assigned as a typesetter for {1}'.format(typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title)
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Assignment',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_notification',
typeset_task.typesetter.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_decision(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
decision = kwargs['decision']
description = '{0} has {1}ed the typesetting task for {2}'.format(typeset_task.typesetter.full_name(),
decision,
typeset_task.assignment.article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetter Decision',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Typesetting Decision',
typeset_task.assignment.production_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_task_deleted(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset']
description = '{0} has deleted a typesetter task assigned to {1} for article {2}'.format(
request.user.full_name(),
typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetter Assignment Deleted',
'target': typeset_task.assignment.article}
# send to author
notify_helpers.send_email_with_body_from_setting_template(request,
'typeset_deleted',
'subject_typeset_deleted',
typeset_task.typesetter.email,
context={'request': request,
'typeset_task': typeset_task}, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_complete(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
description = '{0} has completed typesetting for article {1}. \n\nThe following note was supplied:\n\n{2}'.format(
typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title,
typeset_task.note_from_typesetter,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Assignment Complete',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_complete_notification',
typeset_task.assignment.production_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_production_complete(**kwargs):
request = kwargs['request']
article = kwargs['article']
user_content_message = kwargs['user_content_message']
assignment = kwargs['assignment']
description = 'Production has been completed for article {0}.'.format(article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Production Complete',
'target': article}
for task in assignment.typesettask_set.all():
notify_helpers.send_email_with_body_from_user(request, 'Article Production Complete', task.typesetter.email,
user_content_message)
notify_helpers.send_email_with_body_from_setting_template(request,
'production_complete',
'subject_production_complete',
article.editor_emails(),
{'article': article, 'assignment': assignment},
log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def fire_proofing_manager_assignment(**kwargs):
request = kwargs['request']
proofing_assignment = kwargs['proofing_assignment']
article = proofing_assignment.article
description = '{0} has been assigned as proofing manager for {1}'.format(
proofing_assignment.proofing_manager.full_name(),
article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Manager Assigned',
'target': article}
context = {'request': request, 'proofing_assignment': proofing_assignment, 'article': article}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofing_manager',
'subject_notify_proofing_manager',
proofing_assignment.proofing_manager.email,
context,
log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def cancel_proofing_task(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
user_content_message = kwargs.get('user_content_message', '')
description = 'Proofing request for article {0} from {1} has been cancelled by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Task Cancelled',
'target': article}
context = {'request': request, 'proofing_task': proofing_task, 'user_content_message': user_content_message}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_cancelled',
'subject_notify_proofreader_cancelled',
proofing_task.proofreader.email,
context, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def edit_proofing_task(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
description = 'Proofing request for article {0} from {1} has been edited by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
context = {'request': request, 'proofing_task': proofing_task}
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Task Edited',
'target': article}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_edited',
'subject_notify_proofreader_edited',
proofing_task.proofreader.email,
context, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def notify_proofreader(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
user_content_message = kwargs['user_content_message']
description = 'Proofing request for article {0} from {1} has been requested by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofreading Requested',
'target': article}
notify_helpers.send_email_with_body_from_user(request, 'subject_notify_proofreader_assignment',
proofing_task.proofreader.email,
user_content_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofreader_decision(**kwargs):
request = kwargs['request']
proofing_task = kwargs['proofing_task']
decision = kwargs['decision']
description = '{0} has made a decision for proofing task on {1}: {2}'.format(
proofing_task.proofreader.full_name(),
proofing_task.round.assignment.article.title,
decision
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofreading Update',
'target': proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Proofreading Update',
proofing_task.round.assignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofreader_complete_notification(**kwargs):
request = kwargs['request']
proofing_task = kwargs['proofing_task']
article = kwargs['article']
description = '{0} has completed a proofing task for {1}'.format(
proofing_task.proofreader.full_name(),
article.title,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_complete',
'subject_notify_proofreader_complete',
proofing_task.round.assignment.proofing_manager.email,
{'proofing_task': proofing_task})
def send_proofing_typeset_request(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
article = kwargs['article']
user_content_message = kwargs['user_content_message']
skip = kwargs['skip']
description = '{0} has requested typesetting updates from {1} for {2}'.format(
request.user.full_name(),
typeset_task.typesetter.full_name(),
article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Updates Requested',
'target': article}
if not skip:
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_user(
request, 'subject_notify_typesetter_proofing_changes',
typeset_task.typesetter.email,
user_content_message, log_dict=log_dict)
def send_proofing_typeset_decision(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
decision = kwargs['decision']
description = '{0} has made a decision for proofing task on {1}: {2}'.format(
typeset_task.typesetter.full_name(),
typeset_task.proofing_task.round.assignment.article.title,
decision
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Typesetting',
'target': typeset_task.proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Proofing Typesetting Changes',
typeset_task.proofing_task.round.assignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_corrections_complete(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
article = kwargs['article']
description = '{0} has completed corrections task for article {1} (proofing task {2}'.format(
request.user.full_name(),
article.title,
typeset_task.pk,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Typesetting Complete',
'target': typeset_task.proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_corrections_complete',
article.proofingassignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofing_ack(**kwargs):
request = kwargs['request']
user_message = kwargs['user_message']
article = kwargs['article']
model_object = kwargs['model_object']
model_name = kwargs['model_name']
skip = kwargs['skip']
description = "{0} has acknowledged a task , {1}, by {2} for article {3}".format(request.user,
model_name,
model_object.actor().full_name(),
article.title)
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'Proofing Acknowledgement',
model_object.actor().email,
user_message)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofing_complete(**kwargs):
request = kwargs['request']
user_message = kwargs['user_message']
article = kwargs['article']
skip = kwargs['skip']
description = "Proofing is now complete for {0}".format(article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Complete',
'target': article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_notify_editor_proofing_complete',
article.editor_emails(),
user_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_author_publication_notification(**kwargs):
request = kwargs['request']
article = kwargs['article']
user_message = kwargs['user_message']
section_editors = kwargs['section_editors']
peer_reviewers = kwargs['peer_reviewers']
description = "Article, {0}, set for publication on {1}, by {2}".format(article.title,
article.date_published,
request.user.full_name())
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Article Published',
'target': article}
notify_helpers.send_email_with_body_from_user(request,
'{0} Publication'.format(article.title),
article.correspondence_author.email,
user_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
# Check for SEs and PRs and notify them as well
if section_editors:
for editor in article.section_editors():
notify_helpers.send_email_with_body_from_setting_template(request,
'section_editor_pub_notification',
'Article set for publication',
editor.email,
{'article': article, 'editor': editor})
if peer_reviewers:
for reviewer in article.peer_reviewers():
notify_helpers.send_email_with_body_from_setting_template(request,
'peer_reviewer_pub_notification',
'Article set for publication',
reviewer.email,
{'article': article, 'reviewer': reviewer})
def review_sec_override_notification(**kwargs):
request = kwargs['request']
override = kwargs['override']
description = "{0} overrode their access to {1}".format(override.editor.full_name(), override.article.title)
log_dict = {'level': 'Warning', 'action_text': description, 'types': 'Security Override',
'target': override.article}
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_user(request, 'Review Security Override',
request.journal.editor_emails,
description, log_dict=log_dict)
def send_draft_decison(**kwargs):
request = kwargs['request']
draft = kwargs['draft']
article = kwargs['article']
emails = article.section.editor_emails()
if not emails:
emails = request.journal.editor_emails
description = "Section Editor {0} has drafted a decision for Article {1}".format(
draft.section_editor.full_name(), article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Draft Decision',
'target': article}
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'draft_editor_message',
'subject_draft_editor_message',
emails,
{'draft': draft, 'article': article},
log_dict=log_dict)
def send_author_copyedit_complete(**kwargs):
request = kwargs['request']
copyedit = kwargs['copyedit']
author_review = kwargs['author_review']
description = "Author {0} has completed their copyediting task for article {1}".format(
author_review.author.full_name(),
copyedit.article.title,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'author_copyedit_complete',
'subject_author_copyedit_complete',
copyedit.editor.email,
{'copyedit': copyedit, 'author_review': author_review})
def preprint_submission(**kwargs):
"""
Called by events.Event.ON_PRPINT_SUBMISSIONS, logs and emails the author and preprint editor.
:param kwargs: Dictionary containing article and request objects
:return: None
"""
request = kwargs.get('request')
article = kwargs.get('article')
description = '{author} has submitted a new preprint titled {title}.'.format(author=request.user.full_name(),
title=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Submission',
'target': article}
# Send an email to the user
context = {'article': article}
template = request.press.preprint_submission
email_text = render_template.get_message_content(request, context, template, template_is_setting=True)
notify_helpers.send_email_with_body_from_user(request, 'Preprint Submission', request.user.email, email_text,
log_dict=log_dict)
# Send an email to the preprint editor
url = request.press_base_url + reverse('preprints_manager_article', kwargs={'article_id': article.pk})
editor_email_text = 'A new preprint has been submitted to {press}: <a href="{url}">{title}</a>.'.format(
press=request.press.name,
url=url,
title=article.title
)
for editor in request.press.preprint_editors():
notify_helpers.send_email_with_body_from_user(request, 'Preprint Submission', editor.email,
editor_email_text, log_dict=log_dict)
def preprint_publication(**kwargs):
"""
Called by events.Event.ON_PREPRINT_PUBLICATIONS handles logging and emails.
:param kwargs: Dictionary containing article and request objects
:return: None
"""
request = kwargs.get('request')
article = kwargs.get('article')
description = '{editor} has published a preprint titled {title}.'.format(editor=request.user.full_name(),
title=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Preprint Publication',
'target': article}
util_models.LogEntry.add_entry('Publication', description, 'Info', request.user, request, article)
# Send an email to the article owner.
context = {'article': article}
template = request.press.preprint_publication
email_text = render_template.get_message_content(request, context, template, template_is_setting=True)
notify_helpers.send_email_with_body_from_user(request, ' Preprint Submission Decision', article.owner.email,
email_text, log_dict=log_dict)
# Stops this notification being sent multiple times.c
article.preprint_decision_notification = True
article.save()
def preprint_comment(**kwargs):
request = kwargs.get('request')
article = kwargs.get('article')
email_text = 'A comment has been made on your article {article}, you can moderate comments ' \
'<a href="{base_url}{url}">on the journal site</a>.'.format(
article=article.title, base_url=request.press_base_url, url=reverse('preprints_comments',
kwargs={'article_id': article.pk}))
description = '{author} commented on {article}'.format(author=request.user.full_name(), article=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Preprint Comment',
'target': article}
notify_helpers.send_email_with_body_from_user(request, ' Preprint Comment', article.owner.email,
email_text, log_dict=log_dict)
def send_cancel_corrections(**kwargs):
request = kwargs.get('request')
article = kwargs.get('article')
correction = kwargs.get('correction')
description = '{user} has cancelled correction task {task}'.format(
user=request.user,
task=correction,
)
log_dict = {
'level': 'Info',
'action_text': description,
'types': 'Correction Cancelled',
'target': article,
}
notify_helpers.send_email_with_body_from_setting_template(
request,
'notify_correction_cancelled',
'subject_notify_correction_cancelled',
correction.typesetter.email,
context=kwargs,
log_dict=log_dict,
)
#1952 completes implementation of review_unassigned_article_url
__copyright__ = "Copyright 2017 Birkbeck, University of London"
__author__ = "Martin Paul Eve & Andy Byers"
__license__ = "AGPL v3"
__maintainer__ = "Birkbeck Centre for Technology and Publishing"
from django.urls import reverse
from utils import (
notify_helpers,
models as util_models,
setting_handler,
render_template,
)
from core import models as core_models
from review import logic as review_logic
def send_reviewer_withdrawl_notice(**kwargs):
review_assignment = kwargs['review_assignment']
request = kwargs['request']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0}\'s review of "{1}" has been withdrawn by {2}'.format(review_assignment.reviewer.full_name(),
review_assignment.article.title,
request.user.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Review Withdrawl',
'target': review_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_review_withdrawl', review_assignment.reviewer.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_editor_unassigned_notice(request, message, assignment, skip=False):
description = "{a.editor} unassigned from {a.article} by {r.user}".format(
a=assignment,
r=request,
)
if not skip:
log_dict = {
'level': 'Info', 'action_text': description,
'types': 'Editor Unassigned',
'target': assignment.article
}
notify_helpers.send_email_with_body_from_user(
request,
'subject_review_withdrawl',
assignment.editor.email,
message,
log_dict=log_dict,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_editor_assigned_acknowledgements_mandatory(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py. It is different to the below function in that this is called when an editor is
assigned, whereas the below is only called when the user opts to send a message to the editor.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
editor_assignment = kwargs['editor_assignment']
article = editor_assignment.article
request = kwargs['request']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
acknowledgement = kwargs['acknowledgement']
description = '{0} was assigned as the editor for "{1}"'.format(editor_assignment.editor.full_name(),
article.title)
context = {
'article': article,
'request': request,
'editor_assignment': editor_assignment
}
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Editor Assignment',
'target': article}
# send to assigned editor
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_editor_assignment',
editor_assignment.editor.email,
user_message_content, log_dict=log_dict)
# send to editor
if not acknowledgement:
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request, 'editor_assignment',
'subject_editor_assignment',
request.user.email, context,
log_dict=log_dict)
def send_editor_assigned_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
kwargs['acknowledgement'] = True
send_editor_assigned_acknowledgements_mandatory(**kwargs)
def send_reviewer_requested_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that an editor has been assigned.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes editor_assignment, user_message_content, skip (boolean) and request
:return: None
"""
kwargs['acknowledgement'] = True
send_reviewer_requested_acknowledgements_mandatory(**kwargs)
def send_reviewer_requested_acknowledgements_mandatory(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has been requested.
It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, user_message_content, skip (boolean) and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
user_message_content = kwargs['user_message_content']
acknowledgement = kwargs['acknowledgement']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = 'A review request was added to "{0}" for user {1}'.format(article.title,
review_assignment.reviewer.full_name())
context = {
'article': article,
'request': request,
'review_assignment': review_assignment
}
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Review Request',
'target': article}
# send to requested reviewer
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_review_request_sent',
review_assignment.reviewer.email,
user_message_content, log_dict=log_dict)
if not acknowledgement:
# send slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_request_sent',
'subject_review_request_sent',
review_assignment.editor.email, context,
log_dict=log_dict)
def send_review_complete_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has completed his or her
review. It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
request.user = review_assignment.reviewer
description = '{0} completed the review of "{1}": {2}'.format(review_assignment.reviewer.full_name(),
article.title,
review_assignment.get_decision_display())
util_models.LogEntry.add_entry(types='Review Complete', description=description, level='Info', actor=request.user,
target=article,
request=request)
context = {
'article': article,
'request': request,
'review_assignment': review_assignment
}
# send slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to reviewer
notify_helpers.send_email_with_body_from_setting_template(request, 'review_complete_reviewer_acknowledgement',
'subject_review_complete_reviewer_acknowledgement',
review_assignment.reviewer.email,
context)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_complete_acknowledgement',
'subject_review_complete_reviewer_acknowledgement',
review_assignment.editor.email, context)
def send_reviewer_accepted_or_decline_acknowledgements(**kwargs):
"""
This function is called via the event handling framework and it notifies that a reviewer has either accepted or
declined to review. It is wired up in core/urls.py.
:param kwargs: a list of kwargs that includes review_assignment, accepted and request
:return: None
"""
review_assignment = kwargs['review_assignment']
article = review_assignment.article
request = kwargs['request']
accepted = kwargs['accepted']
description = '{0} {1} to review {2}'.format(review_assignment.reviewer.full_name(),
('accepted' if accepted else 'declined'),
article.title)
util_models.LogEntry.add_entry(types='Review request {0}'.format(('accepted' if accepted else 'declined')),
description=description, level='Info', actor=request.user, target=article,
request=request)
review_url = review_logic.get_review_url(
request,
review_assignment,
)
context = {
'article': article,
'request': request,
'review_assignment': review_assignment,
'review_url': review_url,
}
# send to slack
notify_helpers.send_slack(request, description, ['slack_editors'])
# send to reviewer
if accepted:
notify_helpers.send_email_with_body_from_setting_template(
request,
'review_accept_acknowledgement',
'subject_review_accept_acknowledgement',
review_assignment.reviewer.email,
context,
)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_acknowledgement',
'subject_review_acknowledgement',
review_assignment.editor.email, context)
else:
notify_helpers.send_email_with_body_from_setting_template(request, 'review_decline_acknowledgement',
'subject_review_decline_acknowledgement',
review_assignment.reviewer.email, context)
# send to editor
notify_helpers.send_email_with_body_from_setting_template(request, 'review_acknowledgement',
'subject_review_acknowledgement',
review_assignment.editor.email, context)
def send_submission_acknowledgement(**kwargs):
"""
This function is called via the event handling framework and it
notifies site operators of a submission. It is
wired up in core/urls.py.
:param kwargs: a list of kwargs that includes article and request
:return: None
"""
article = kwargs['article']
request = kwargs['request']
util_models.LogEntry.add_entry(
types='Submission Complete',
description='A new article {0} was submitted'.format(article.title),
level='Info',
actor=request.user,
target=article,
request=request,
)
log_dict = {
'level': 'Info',
'action_text': 'A new article {0} was submitted'.format(article.title),
'types': 'New Submission Acknowledgement',
'target': article,
}
# generate URL
review_unassigned_article_url = request.journal.site_url(
path=reverse(
'review_unassigned_article',
kwargs={'article_id': article.pk},
)
)
notify_helpers.send_slack(
request,
'New submission: {0} {1}'.format(
article.title,
review_unassigned_article_url,
),
['slack_editors'])
# send to author
context = {
'article': article,
'request': request,
'review_unassigned_article_url': review_unassigned_article_url,
}
notify_helpers.send_email_with_body_from_setting_template(
request,
'submission_acknowledgement',
'subject_submission_acknowledgement',
article.correspondence_author.email,
context,
log_dict=log_dict,
)
# send to all authors
editors_to_email = setting_handler.get_setting(
'general', 'editors_for_notification', request.journal).processed_value
if editors_to_email:
editor_pks = [int(pk) for pk in editors_to_email]
editor_emails = {
role.user.email for role in core_models.AccountRole.objects.filter(
role__slug='editor',
user__id__in=editor_pks,
)
}
else:
editor_emails = set(request.journal.editor_emails)
assigned_to_section = (
article.section.editors.all() | article.section.section_editors.all())
editor_emails |= {editor.email for editor in assigned_to_section}
notify_helpers.send_email_with_body_from_setting_template(
request,
'editor_new_submission',
'subject_editor_new_submission',
editor_emails,
context,
log_dict=log_dict,
)
def send_article_decision(**kwargs):
article = kwargs['article']
request = kwargs['request']
decision = kwargs['decision']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0}\'s article "{1}" has been {2}ed by {3}'.format(article.correspondence_author.full_name(),
article.title,
decision,
request.user.full_name())
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Article Decision',
'target': article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'Article Review Decision',
article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_revisions_request(**kwargs):
request = kwargs['request']
revision = kwargs['revision']
user_message_content = kwargs['user_message_content']
if 'skip' not in kwargs:
kwargs['skip'] = True
skip = kwargs['skip']
description = '{0} has requested revisions for {1} due on {2}'.format(request.user.full_name(),
revision.article.title,
revision.date_due)
log_dict = {'level': 'Info',
'action_text': description,
'types': 'Revision Request',
'target': revision.article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_request_revisions',
revision.article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_revisions_complete(**kwargs):
request = kwargs['request']
revision = kwargs['revision']
action_text = ''
for action in revision.actions.all():
action_text = "{0}<br><br>{1} - {2}".format(action_text, action.logged, action.text)
description = '<p>{0} has completed revisions for {1}</p> Actions:<br>{2}'.format(request.user.full_name(),
revision.article.title,
action_text)
notify_helpers.send_email_with_body_from_user(request,
'Article Revisions Complete',
revision.editor.email,
description)
notify_helpers.send_slack(request, description, ['slack_editors'])
util_models.LogEntry.add_entry(types='Revisions Complete', description=action_text, level='Info',
request=request, target=revision.article)
def send_copyedit_assignment(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has requested copyediting for {1} due on {2}'.format(request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.due)
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Assignment',
'target': copyedit_assignment.article}
response = notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_assignment_notification',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_updated(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
skip = kwargs.get('skip', False)
if not skip:
# send to slack
notify_helpers.send_slack(request,
'Copyedit assignment {0} updated'.format(copyedit_assignment.pk),
['slack_editors'])
log_dict = {'level': 'Info',
'action_text': 'Copyedit assignment #{number} update.'.format(number=copyedit_assignment.pk),
'types': 'Revision Request',
'target': copyedit_assignment.article}
# send to author
notify_helpers.send_email_with_body_from_setting_template(request,
'copyedit_updated',
'subject_copyedit_updated',
copyedit_assignment.copyeditor.email,
context={'request': request,
'copyedit_assignment': copyedit_assignment},
log_dict=log_dict)
def send_copyedit_deleted(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
skip = kwargs.get('skip', False)
description = 'Copyedit task {0} for article {1} deleted.'.format(copyedit_assignment.pk,
copyedit_assignment.article.title)
if not skip:
# send to slack
notify_helpers.send_slack(request,
'Copyedit assignment {0} updated'.format(copyedit_assignment.pk),
['slack_editors'])
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Assignment Deleted',
'target': copyedit_assignment.article}
# send to copyeditor
notify_helpers.send_email_with_body_from_setting_template(request,
'copyedit_deleted',
'subject_copyedit_deleted',
copyedit_assignment.copyeditor.email,
context={'request': request,
'copyedit_assignment': copyedit_assignment},
log_dict=log_dict)
def send_copyedit_decision(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
description = '{0} has accepted copyediting task for {1} due on {2}.'.format(
copyedit_assignment.copyeditor.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.due)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyediting Decision',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Copyediting Decision',
copyedit_assignment.editor.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_author_review(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has requested copyedit review for {1} from {2}'.format(
request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.article.correspondence_author.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Author Review',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_notify_author',
copyedit_assignment.article.correspondence_author.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_complete(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
article = kwargs['article']
description = 'Copyediting requested by {0} from {1} for article {2} has been completed'.format(
request.user.full_name(),
copyedit_assignment.copyeditor.full_name(),
article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Complete',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_notify_editor',
copyedit_assignment.editor.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_ack(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has acknowledged copyediting for {1}'.format(request.user.full_name(),
copyedit_assignment.article.title, )
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Acknowledgement',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_ack',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_copyedit_reopen(**kwargs):
request = kwargs['request']
copyedit_assignment = kwargs['copyedit_assignment']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has reopened copyediting for {1} from {2}'.format(request.user.full_name(),
copyedit_assignment.article.title,
copyedit_assignment.copyeditor.full_name())
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Copyedit Complete',
'target': copyedit_assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_copyeditor_reopen_task',
copyedit_assignment.copyeditor.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_assignment(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
user_message_content = kwargs['user_message_content']
skip = kwargs.get('skip', False)
description = '{0} has been assigned as a typesetter for {1}'.format(typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title)
if not skip:
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Assignment',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_notification',
typeset_task.typesetter.email,
user_message_content, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_decision(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
decision = kwargs['decision']
description = '{0} has {1}ed the typesetting task for {2}'.format(typeset_task.typesetter.full_name(),
decision,
typeset_task.assignment.article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetter Decision',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Typesetting Decision',
typeset_task.assignment.production_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_task_deleted(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset']
description = '{0} has deleted a typesetter task assigned to {1} for article {2}'.format(
request.user.full_name(),
typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetter Assignment Deleted',
'target': typeset_task.assignment.article}
# send to author
notify_helpers.send_email_with_body_from_setting_template(request,
'typeset_deleted',
'subject_typeset_deleted',
typeset_task.typesetter.email,
context={'request': request,
'typeset_task': typeset_task}, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_typeset_complete(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
description = '{0} has completed typesetting for article {1}. \n\nThe following note was supplied:\n\n{2}'.format(
typeset_task.typesetter.full_name(),
typeset_task.assignment.article.title,
typeset_task.note_from_typesetter,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Assignment Complete',
'target': typeset_task.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_complete_notification',
typeset_task.assignment.production_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_production_complete(**kwargs):
request = kwargs['request']
article = kwargs['article']
user_content_message = kwargs['user_content_message']
assignment = kwargs['assignment']
description = 'Production has been completed for article {0}.'.format(article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Production Complete',
'target': article}
for task in assignment.typesettask_set.all():
notify_helpers.send_email_with_body_from_user(request, 'Article Production Complete', task.typesetter.email,
user_content_message)
notify_helpers.send_email_with_body_from_setting_template(request,
'production_complete',
'subject_production_complete',
article.editor_emails(),
{'article': article, 'assignment': assignment},
log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def fire_proofing_manager_assignment(**kwargs):
request = kwargs['request']
proofing_assignment = kwargs['proofing_assignment']
article = proofing_assignment.article
description = '{0} has been assigned as proofing manager for {1}'.format(
proofing_assignment.proofing_manager.full_name(),
article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Manager Assigned',
'target': article}
context = {'request': request, 'proofing_assignment': proofing_assignment, 'article': article}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofing_manager',
'subject_notify_proofing_manager',
proofing_assignment.proofing_manager.email,
context,
log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def cancel_proofing_task(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
user_content_message = kwargs.get('user_content_message', '')
description = 'Proofing request for article {0} from {1} has been cancelled by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Task Cancelled',
'target': article}
context = {'request': request, 'proofing_task': proofing_task, 'user_content_message': user_content_message}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_cancelled',
'subject_notify_proofreader_cancelled',
proofing_task.proofreader.email,
context, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def edit_proofing_task(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
description = 'Proofing request for article {0} from {1} has been edited by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
context = {'request': request, 'proofing_task': proofing_task}
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Task Edited',
'target': article}
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_edited',
'subject_notify_proofreader_edited',
proofing_task.proofreader.email,
context, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def notify_proofreader(**kwargs):
request = kwargs['request']
article = kwargs['article']
proofing_task = kwargs['proofing_task']
user_content_message = kwargs['user_content_message']
description = 'Proofing request for article {0} from {1} has been requested by {2}'.format(
article.title,
proofing_task.proofreader.full_name(),
request.user.full_name()
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofreading Requested',
'target': article}
notify_helpers.send_email_with_body_from_user(request, 'subject_notify_proofreader_assignment',
proofing_task.proofreader.email,
user_content_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofreader_decision(**kwargs):
request = kwargs['request']
proofing_task = kwargs['proofing_task']
decision = kwargs['decision']
description = '{0} has made a decision for proofing task on {1}: {2}'.format(
proofing_task.proofreader.full_name(),
proofing_task.round.assignment.article.title,
decision
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofreading Update',
'target': proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Article Proofreading Update',
proofing_task.round.assignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofreader_complete_notification(**kwargs):
request = kwargs['request']
proofing_task = kwargs['proofing_task']
article = kwargs['article']
description = '{0} has completed a proofing task for {1}'.format(
proofing_task.proofreader.full_name(),
article.title,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'notify_proofreader_complete',
'subject_notify_proofreader_complete',
proofing_task.round.assignment.proofing_manager.email,
{'proofing_task': proofing_task})
def send_proofing_typeset_request(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
article = kwargs['article']
user_content_message = kwargs['user_content_message']
skip = kwargs['skip']
description = '{0} has requested typesetting updates from {1} for {2}'.format(
request.user.full_name(),
typeset_task.typesetter.full_name(),
article.title,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Typesetting Updates Requested',
'target': article}
if not skip:
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_user(
request, 'subject_notify_typesetter_proofing_changes',
typeset_task.typesetter.email,
user_content_message, log_dict=log_dict)
def send_proofing_typeset_decision(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
decision = kwargs['decision']
description = '{0} has made a decision for proofing task on {1}: {2}'.format(
typeset_task.typesetter.full_name(),
typeset_task.proofing_task.round.assignment.article.title,
decision
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Typesetting',
'target': typeset_task.proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'Proofing Typesetting Changes',
typeset_task.proofing_task.round.assignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_corrections_complete(**kwargs):
request = kwargs['request']
typeset_task = kwargs['typeset_task']
article = kwargs['article']
description = '{0} has completed corrections task for article {1} (proofing task {2}'.format(
request.user.full_name(),
article.title,
typeset_task.pk,
)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Typesetting Complete',
'target': typeset_task.proofing_task.round.assignment.article}
notify_helpers.send_email_with_body_from_user(request, 'subject_typesetter_corrections_complete',
article.proofingassignment.proofing_manager.email,
description, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofing_ack(**kwargs):
request = kwargs['request']
user_message = kwargs['user_message']
article = kwargs['article']
model_object = kwargs['model_object']
model_name = kwargs['model_name']
skip = kwargs['skip']
description = "{0} has acknowledged a task , {1}, by {2} for article {3}".format(request.user,
model_name,
model_object.actor().full_name(),
article.title)
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'Proofing Acknowledgement',
model_object.actor().email,
user_message)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_proofing_complete(**kwargs):
request = kwargs['request']
user_message = kwargs['user_message']
article = kwargs['article']
skip = kwargs['skip']
description = "Proofing is now complete for {0}".format(article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Proofing Complete',
'target': article}
if not skip:
notify_helpers.send_email_with_body_from_user(request, 'subject_notify_editor_proofing_complete',
article.editor_emails(),
user_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
def send_author_publication_notification(**kwargs):
request = kwargs['request']
article = kwargs['article']
user_message = kwargs['user_message']
section_editors = kwargs['section_editors']
peer_reviewers = kwargs['peer_reviewers']
description = "Article, {0}, set for publication on {1}, by {2}".format(article.title,
article.date_published,
request.user.full_name())
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Article Published',
'target': article}
notify_helpers.send_email_with_body_from_user(request,
'{0} Publication'.format(article.title),
article.correspondence_author.email,
user_message, log_dict=log_dict)
notify_helpers.send_slack(request, description, ['slack_editors'])
# Check for SEs and PRs and notify them as well
if section_editors:
for editor in article.section_editors():
notify_helpers.send_email_with_body_from_setting_template(request,
'section_editor_pub_notification',
'Article set for publication',
editor.email,
{'article': article, 'editor': editor})
if peer_reviewers:
for reviewer in article.peer_reviewers():
notify_helpers.send_email_with_body_from_setting_template(request,
'peer_reviewer_pub_notification',
'Article set for publication',
reviewer.email,
{'article': article, 'reviewer': reviewer})
def review_sec_override_notification(**kwargs):
request = kwargs['request']
override = kwargs['override']
description = "{0} overrode their access to {1}".format(override.editor.full_name(), override.article.title)
log_dict = {'level': 'Warning', 'action_text': description, 'types': 'Security Override',
'target': override.article}
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_user(request, 'Review Security Override',
request.journal.editor_emails,
description, log_dict=log_dict)
def send_draft_decison(**kwargs):
request = kwargs['request']
draft = kwargs['draft']
article = kwargs['article']
emails = article.section.editor_emails()
if not emails:
emails = request.journal.editor_emails
description = "Section Editor {0} has drafted a decision for Article {1}".format(
draft.section_editor.full_name(), article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Draft Decision',
'target': article}
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'draft_editor_message',
'subject_draft_editor_message',
emails,
{'draft': draft, 'article': article},
log_dict=log_dict)
def send_author_copyedit_complete(**kwargs):
request = kwargs['request']
copyedit = kwargs['copyedit']
author_review = kwargs['author_review']
description = "Author {0} has completed their copyediting task for article {1}".format(
author_review.author.full_name(),
copyedit.article.title,
)
notify_helpers.send_slack(request, description, ['slack_editors'])
notify_helpers.send_email_with_body_from_setting_template(request,
'author_copyedit_complete',
'subject_author_copyedit_complete',
copyedit.editor.email,
{'copyedit': copyedit, 'author_review': author_review})
def preprint_submission(**kwargs):
"""
Called by events.Event.ON_PRPINT_SUBMISSIONS, logs and emails the author and preprint editor.
:param kwargs: Dictionary containing article and request objects
:return: None
"""
request = kwargs.get('request')
article = kwargs.get('article')
description = '{author} has submitted a new preprint titled {title}.'.format(author=request.user.full_name(),
title=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Submission',
'target': article}
# Send an email to the user
context = {'article': article}
template = request.press.preprint_submission
email_text = render_template.get_message_content(request, context, template, template_is_setting=True)
notify_helpers.send_email_with_body_from_user(request, 'Preprint Submission', request.user.email, email_text,
log_dict=log_dict)
# Send an email to the preprint editor
url = request.press_base_url + reverse('preprints_manager_article', kwargs={'article_id': article.pk})
editor_email_text = 'A new preprint has been submitted to {press}: <a href="{url}">{title}</a>.'.format(
press=request.press.name,
url=url,
title=article.title
)
for editor in request.press.preprint_editors():
notify_helpers.send_email_with_body_from_user(request, 'Preprint Submission', editor.email,
editor_email_text, log_dict=log_dict)
def preprint_publication(**kwargs):
"""
Called by events.Event.ON_PREPRINT_PUBLICATIONS handles logging and emails.
:param kwargs: Dictionary containing article and request objects
:return: None
"""
request = kwargs.get('request')
article = kwargs.get('article')
description = '{editor} has published a preprint titled {title}.'.format(editor=request.user.full_name(),
title=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Preprint Publication',
'target': article}
util_models.LogEntry.add_entry('Publication', description, 'Info', request.user, request, article)
# Send an email to the article owner.
context = {'article': article}
template = request.press.preprint_publication
email_text = render_template.get_message_content(request, context, template, template_is_setting=True)
notify_helpers.send_email_with_body_from_user(request, ' Preprint Submission Decision', article.owner.email,
email_text, log_dict=log_dict)
# Stops this notification being sent multiple times.c
article.preprint_decision_notification = True
article.save()
def preprint_comment(**kwargs):
request = kwargs.get('request')
article = kwargs.get('article')
email_text = 'A comment has been made on your article {article}, you can moderate comments ' \
'<a href="{base_url}{url}">on the journal site</a>.'.format(
article=article.title, base_url=request.press_base_url, url=reverse('preprints_comments',
kwargs={'article_id': article.pk}))
description = '{author} commented on {article}'.format(author=request.user.full_name(), article=article.title)
log_dict = {'level': 'Info', 'action_text': description, 'types': 'Preprint Comment',
'target': article}
notify_helpers.send_email_with_body_from_user(request, ' Preprint Comment', article.owner.email,
email_text, log_dict=log_dict)
def send_cancel_corrections(**kwargs):
request = kwargs.get('request')
article = kwargs.get('article')
correction = kwargs.get('correction')
description = '{user} has cancelled correction task {task}'.format(
user=request.user,
task=correction,
)
log_dict = {
'level': 'Info',
'action_text': description,
'types': 'Correction Cancelled',
'target': article,
}
notify_helpers.send_email_with_body_from_setting_template(
request,
'notify_correction_cancelled',
'subject_notify_correction_cancelled',
correction.typesetter.email,
context=kwargs,
log_dict=log_dict,
)
|
Change set_var_ics so it doesn't fail if there are extra things in the passed in KeyedList.
|
""" Starting point for DEM retrieval utilities.
"""
from math import pi, sin, cos
from os import unlink, close
from itertools import product
from tempfile import mkstemp
from sys import modules
import NED10m, NED100m, NED1km, SRTM1, SRTM3, VFP, Worldwide
from ModestMaps.Core import Coordinate
from TileStache.Geography import SphericalMercator
from TileStache.Core import Layer, Metatile
from TileStache.Config import Configuration
from TileStache.Caches import Disk
from osgeo import gdal, osr
from PIL import Image
import numpy
from .. import save_slope_aspect
# used to prevent clobbering in /vsimem/, see:
# http://osgeo-org.1803224.n2.nabble.com/gdal-dev-Outputting-to-vsimem-td6221295.html
vsimem_counter = 1
#
# Set up some useful projections.
#
osr.UseExceptions() # <-- otherwise errors will be silent and useless.
webmerc_proj = SphericalMercator()
webmerc_sref = osr.SpatialReference()
webmerc_sref.ImportFromProj4(webmerc_proj.srs)
class SeedingLayer (Layer):
""" Tilestache-compatible seeding layer for preparing tiled data.
Intended for use in hillup-seed.py script for preparing a tile directory.
"""
def __init__(self, demdir, tiledir, tmpdir, source):
"""
"""
cache = Disk(tiledir, dirs='safe')
config = Configuration(cache, '.')
Layer.__init__(self, config, SphericalMercator(), Metatile())
self.provider = Provider(self, demdir, tmpdir, source)
def name(self):
return '.'
class Provider:
""" TileStache provider for generating tiles of DEM slope and aspect data.
Source parameter can be "srtm-ned" (default) or "ned-only".
See http://tilestache.org/doc/#custom-providers for information
on how the Provider object interacts with TileStache.
"""
def __init__(self, layer, demdir, tmpdir=None, source='srtm-ned'):
self.tmpdir = tmpdir
self.demdir = demdir
self.source = source
def getTypeByExtension(self, ext):
if ext.lower() != 'tiff':
raise Exception()
return 'image/tiff', 'TIFF'
def renderArea(self, width, height, srs, xmin, ymin, xmax, ymax, zoom):
""" Return an instance of SlopeAndAspect for requested area.
"""
assert srs == webmerc_proj.srs # <-- good enough for now
if self.source == 'srtm-ned':
providers = choose_providers_srtm(zoom)
elif self.source == 'ned-only':
providers = choose_providers_ned(zoom)
elif self.source == 'vfp':
providers = [(VFP, 1)]
elif self.source == 'worldwide':
providers = [(Worldwide, 1)]
else:
providers = load_func_path(self.source)(zoom)
#
# Prepare information for datasets of the desired extent and projection.
#
xres = (xmax - xmin) / width
yres = (ymin - ymax) / height
area_wkt = webmerc_sref.ExportToWkt()
buffered_xform = xmin - xres, xres, 0, ymax - yres, 0, yres
#
# Reproject and merge DEM datasources into destination datasets.
#
driver = gdal.GetDriverByName('GTiff')
elevation = numpy.zeros((width+2, height+2), numpy.float32)
for (module, proportion) in providers:
cs2cs = osr.CoordinateTransformation(webmerc_sref, module.sref)
minlon, minlat, z = cs2cs.TransformPoint(xmin, ymin)
maxlon, maxlat, z = cs2cs.TransformPoint(xmax, ymax)
try:
handle, filename = mkstemp(dir=self.tmpdir, prefix='render-area-provider-', suffix='.tif')
close(handle)
ds_area = driver.Create(filename, width+2, height+2, 1, gdal.GDT_Float32)
ds_area.SetGeoTransform(buffered_xform)
ds_area.SetProjection(area_wkt)
ds_args = minlon, minlat, maxlon, maxlat, self.demdir
for ds_dem in module.datasources(*ds_args):
# estimate the raster density across source DEM and output
dem_samples = (maxlon - minlon) / ds_dem.GetGeoTransform()[1]
area_pixels = (xmax - xmin) / ds_area.GetGeoTransform()[1]
if dem_samples > area_pixels:
# cubic looks better squeezing down
resample = gdal.GRA_Cubic
else:
# cubic spline looks better stretching out
resample = gdal.GRA_CubicSpline
gdal.ReprojectImage(ds_dem, ds_area, ds_dem.GetProjection(), ds_area.GetProjection(), resample)
ds_dem.FlushCache()
if proportion == 1:
elevation = ds_area.ReadAsArray()
else:
elevation += ds_area.ReadAsArray() * proportion
ds_area.FlushCache()
finally:
unlink(filename)
#
# Calculate and save slope and aspect.
#
slope, aspect = calculate_slope_aspect(elevation, xres, yres)
tile_xform = xmin, xres, 0, ymax, 0, yres
return SlopeAndAspect(self.tmpdir, slope, aspect, area_wkt, tile_xform)
class SlopeAndAspect:
""" TileStache response object with PIL-like save() and crop() methods.
This object knows only how to save two-band 8-bit GeoTIFFs.
See http://tilestache.org/doc/#custom-providers for information
on how the SlopeAndAspect object interacts with TileStache.
"""
def __init__(self, tmpdir, slope, aspect, wkt, xform):
""" Instantiate with array of slope and aspect, and minimal geographic information.
"""
self.tmpdir = tmpdir
self.slope = slope
self.aspect = aspect
self.w, self.h = self.slope.shape
self.wkt = wkt
self.xform = xform
def save(self, output, format):
""" Save a two-band GeoTIFF to output file-like object.
"""
if format != 'TIFF':
raise Exception('File format other than TIFF for slope and aspect: "%s"' % format)
save_slope_aspect(self.slope, self.aspect, self.wkt, self.xform, output, self.tmpdir)
def crop(self, box):
""" Returns a rectangular region from the current image.
Box is a 4-tuple with left, upper, right, and lower pixels.
Not yet implemented!
"""
raise NotImplementedError()
def choose_providers_srtm(zoom):
""" Return a list of data sources and proportions for given zoom level.
Each data source is a module such as SRTM1 or SRTM3, and the proportions
must all add up to one. Return list has either one or two items.
"""
if zoom <= SRTM3.ideal_zoom:
return [(SRTM3, 1)]
elif SRTM3.ideal_zoom < zoom and zoom < SRTM1.ideal_zoom:
#bottom, top = SRTM3, SRTM1 # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif zoom == SRTM1.ideal_zoom:
#return [(SRTM1, 1)] # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif SRTM1.ideal_zoom < zoom and zoom < NED10m.ideal_zoom:
#bottom, top = SRTM1, NED10m # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif zoom >= NED10m.ideal_zoom:
return [(NED10m, 1)]
difference = float(top.ideal_zoom) - float(bottom.ideal_zoom)
proportion = 1. - (zoom - float(bottom.ideal_zoom)) / difference
return [(bottom, proportion), (top, 1 - proportion)]
def choose_providers_ned(zoom):
""" Return a list of data sources and proportions for given zoom level.
Each data source is a module such as NED10m or NED1km, and the proportions
must all add up to one. Return list has either one or two items.
"""
if zoom <= NED1km.ideal_zoom:
return [(NED1km, 1)]
elif NED1km.ideal_zoom < zoom and zoom < NED100m.ideal_zoom:
#bottom, top = NED1km, NED100m
bottom, top = NED1km, NED100m
elif zoom == NED100m.ideal_zoom:
return [(NED100m, 1)]
elif NED100m.ideal_zoom < zoom and zoom < NED10m.ideal_zoom:
#bottom, top = NED100m, NED10m
bottom, top = NED100m, NED10m
elif zoom >= NED10m.ideal_zoom:
return [(NED10m, 1)]
difference = float(top.ideal_zoom) - float(bottom.ideal_zoom)
proportion = 1. - (zoom - float(bottom.ideal_zoom)) / difference
return [(bottom, proportion), (top, 1 - proportion)]
def calculate_slope_aspect(elevation, xres, yres, z=1.0):
""" Return a pair of arrays 2 pixels smaller than the input elevation array.
Slope is returned in radians, from 0 for sheer face to pi/2 for
flat ground. Aspect is returned in radians, counterclockwise from -pi
at north around to pi.
Logic here is borrowed from hillshade.cpp:
http://www.perrygeo.net/wordpress/?p=7
"""
width, height = elevation.shape[0] - 2, elevation.shape[1] - 2
window = [z * elevation[row:(row + height), col:(col + width)]
for (row, col)
in product(range(3), range(3))]
x = ((window[0] + window[3] + window[3] + window[6]) \
- (window[2] + window[5] + window[5] + window[8])) \
/ (8.0 * xres);
y = ((window[6] + window[7] + window[7] + window[8]) \
- (window[0] + window[1] + window[1] + window[2])) \
/ (8.0 * yres);
# in radians, from 0 to pi/2
slope = pi/2 - numpy.arctan(numpy.sqrt(x*x + y*y))
# in radians counterclockwise, from -pi at north back to pi
aspect = numpy.arctan2(x, y)
return slope, aspect
def load_func_path(funcpath):
""" Load external function based on a path.
Example funcpath: "Module.Submodule:Function".
"""
modname, objname = funcpath.split(':', 1)
__import__(modname)
module = modules[modname]
_func = eval(objname, module.__dict__)
if _func is None:
raise Exception('eval(%(objname)s) in %(modname)s came up None' % locals())
return _func
Cordoned off dataset creation with new ds_composite
""" Starting point for DEM retrieval utilities.
"""
from math import pi, sin, cos
from os import unlink, close
from itertools import product
from tempfile import mkstemp
from sys import modules
import NED10m, NED100m, NED1km, SRTM1, SRTM3, VFP, Worldwide
from ModestMaps.Core import Coordinate
from TileStache.Geography import SphericalMercator
from TileStache.Core import Layer, Metatile
from TileStache.Config import Configuration
from TileStache.Caches import Disk
from osgeo import gdal, osr
from PIL import Image
import numpy
from .. import save_slope_aspect
# used to prevent clobbering in /vsimem/, see:
# http://osgeo-org.1803224.n2.nabble.com/gdal-dev-Outputting-to-vsimem-td6221295.html
vsimem_counter = 1
#
# Set up some useful projections.
#
osr.UseExceptions() # <-- otherwise errors will be silent and useless.
webmerc_proj = SphericalMercator()
webmerc_sref = osr.SpatialReference()
webmerc_sref.ImportFromProj4(webmerc_proj.srs)
class SeedingLayer (Layer):
""" Tilestache-compatible seeding layer for preparing tiled data.
Intended for use in hillup-seed.py script for preparing a tile directory.
"""
def __init__(self, demdir, tiledir, tmpdir, source):
"""
"""
cache = Disk(tiledir, dirs='safe')
config = Configuration(cache, '.')
Layer.__init__(self, config, SphericalMercator(), Metatile())
self.provider = Provider(self, demdir, tmpdir, source)
def name(self):
return '.'
class Provider:
""" TileStache provider for generating tiles of DEM slope and aspect data.
Source parameter can be "srtm-ned" (default) or "ned-only".
See http://tilestache.org/doc/#custom-providers for information
on how the Provider object interacts with TileStache.
"""
def __init__(self, layer, demdir, tmpdir=None, source='srtm-ned'):
self.tmpdir = tmpdir
self.demdir = demdir
self.source = source
def getTypeByExtension(self, ext):
if ext.lower() != 'tiff':
raise Exception()
return 'image/tiff', 'TIFF'
def renderArea(self, width, height, srs, xmin, ymin, xmax, ymax, zoom):
""" Return an instance of SlopeAndAspect for requested area.
"""
assert srs == webmerc_proj.srs # <-- good enough for now
if self.source == 'srtm-ned':
providers = choose_providers_srtm(zoom)
elif self.source == 'ned-only':
providers = choose_providers_ned(zoom)
elif self.source == 'vfp':
providers = [(VFP, 1)]
elif self.source == 'worldwide':
providers = [(Worldwide, 1)]
else:
providers = load_func_path(self.source)(zoom)
#
# Prepare information for datasets of the desired extent and projection.
#
xres = (xmax - xmin) / width
yres = (ymin - ymax) / height
area_wkt = webmerc_sref.ExportToWkt()
buffered_xform = xmin - xres, xres, 0, ymax - yres, 0, yres
def make_dataset(width, height, xform, wkt, nodata, tmpdir):
'''
'''
driver = gdal.GetDriverByName('GTiff')
handle, filename = mkstemp(dir=tmpdir, prefix='dem-tools-hillup-data-render-', suffix='.tif')
area = driver.Create(filename, width, height, 1, gdal.GDT_Float32)
area.SetGeoTransform(xform)
area.SetProjection(wkt)
area.GetRasterBand(1).WriteArray(numpy.ones((width, height), numpy.float32) * nodata, 0, 0)
area.GetRasterBand(1).SetNoDataValue(nodata)
return area
#
# Reproject and merge DEM datasources into destination datasets.
#
driver = gdal.GetDriverByName('GTiff')
elevation = numpy.zeros((width+2, height+2), numpy.float32)
nodata = -9999
ds_composite = make_dataset(width+2, height+2, buffered_xform, area_wkt, nodata, self.tmpdir)
for (module, proportion) in providers:
cs2cs = osr.CoordinateTransformation(webmerc_sref, module.sref)
minlon, minlat, z = cs2cs.TransformPoint(xmin, ymin)
maxlon, maxlat, z = cs2cs.TransformPoint(xmax, ymax)
try:
ds_area = make_dataset(width+2, height+2, buffered_xform, area_wkt, nodata, self.tmpdir)
ds_args = minlon, minlat, maxlon, maxlat, self.demdir
for ds_dem in module.datasources(*ds_args):
print ds_dem.GetFileList(), ds_dem.RasterCount
# estimate the raster density across source DEM and output
dem_samples = (maxlon - minlon) / ds_dem.GetGeoTransform()[1]
area_pixels = (xmax - xmin) / ds_area.GetGeoTransform()[1]
if dem_samples > area_pixels:
# cubic looks better squeezing down
resample = gdal.GRA_Cubic
else:
# cubic spline looks better stretching out
resample = gdal.GRA_CubicSpline
gdal.ReprojectImage(ds_dem, ds_area, ds_dem.GetProjection(), ds_area.GetProjection(), resample)
ds_dem.FlushCache()
if proportion == 1:
elevation = ds_area.ReadAsArray()
else:
elevation += ds_area.ReadAsArray() * proportion
ds_area.FlushCache()
finally:
#print ds_area.GetMaskBand()
print ds_area.ReadAsArray()
unlink(ds_area.GetFileList()[0])
#
# Calculate and save slope and aspect.
#
slope, aspect = calculate_slope_aspect(elevation, xres, yres)
unlink(ds_composite.GetFileList()[0])
tile_xform = xmin, xres, 0, ymax, 0, yres
return SlopeAndAspect(self.tmpdir, slope, aspect, area_wkt, tile_xform)
class SlopeAndAspect:
""" TileStache response object with PIL-like save() and crop() methods.
This object knows only how to save two-band 8-bit GeoTIFFs.
See http://tilestache.org/doc/#custom-providers for information
on how the SlopeAndAspect object interacts with TileStache.
"""
def __init__(self, tmpdir, slope, aspect, wkt, xform):
""" Instantiate with array of slope and aspect, and minimal geographic information.
"""
self.tmpdir = tmpdir
self.slope = slope
self.aspect = aspect
self.w, self.h = self.slope.shape
self.wkt = wkt
self.xform = xform
def save(self, output, format):
""" Save a two-band GeoTIFF to output file-like object.
"""
if format != 'TIFF':
raise Exception('File format other than TIFF for slope and aspect: "%s"' % format)
save_slope_aspect(self.slope, self.aspect, self.wkt, self.xform, output, self.tmpdir)
def crop(self, box):
""" Returns a rectangular region from the current image.
Box is a 4-tuple with left, upper, right, and lower pixels.
Not yet implemented!
"""
raise NotImplementedError()
def choose_providers_srtm(zoom):
""" Return a list of data sources and proportions for given zoom level.
Each data source is a module such as SRTM1 or SRTM3, and the proportions
must all add up to one. Return list has either one or two items.
"""
if zoom <= SRTM3.ideal_zoom:
return [(SRTM3, 1)]
elif SRTM3.ideal_zoom < zoom and zoom < SRTM1.ideal_zoom:
#bottom, top = SRTM3, SRTM1 # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif zoom == SRTM1.ideal_zoom:
#return [(SRTM1, 1)] # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif SRTM1.ideal_zoom < zoom and zoom < NED10m.ideal_zoom:
#bottom, top = SRTM1, NED10m # SRTM1 looks terrible
bottom, top = SRTM3, NED10m
elif zoom >= NED10m.ideal_zoom:
return [(NED10m, 1)]
difference = float(top.ideal_zoom) - float(bottom.ideal_zoom)
proportion = 1. - (zoom - float(bottom.ideal_zoom)) / difference
return [(bottom, proportion), (top, 1 - proportion)]
def choose_providers_ned(zoom):
""" Return a list of data sources and proportions for given zoom level.
Each data source is a module such as NED10m or NED1km, and the proportions
must all add up to one. Return list has either one or two items.
"""
if zoom <= NED1km.ideal_zoom:
return [(NED1km, 1)]
elif NED1km.ideal_zoom < zoom and zoom < NED100m.ideal_zoom:
#bottom, top = NED1km, NED100m
bottom, top = NED1km, NED100m
elif zoom == NED100m.ideal_zoom:
return [(NED100m, 1)]
elif NED100m.ideal_zoom < zoom and zoom < NED10m.ideal_zoom:
#bottom, top = NED100m, NED10m
bottom, top = NED100m, NED10m
elif zoom >= NED10m.ideal_zoom:
return [(NED10m, 1)]
difference = float(top.ideal_zoom) - float(bottom.ideal_zoom)
proportion = 1. - (zoom - float(bottom.ideal_zoom)) / difference
return [(bottom, proportion), (top, 1 - proportion)]
def calculate_slope_aspect(elevation, xres, yres, z=1.0):
""" Return a pair of arrays 2 pixels smaller than the input elevation array.
Slope is returned in radians, from 0 for sheer face to pi/2 for
flat ground. Aspect is returned in radians, counterclockwise from -pi
at north around to pi.
Logic here is borrowed from hillshade.cpp:
http://www.perrygeo.net/wordpress/?p=7
"""
width, height = elevation.shape[0] - 2, elevation.shape[1] - 2
window = [z * elevation[row:(row + height), col:(col + width)]
for (row, col)
in product(range(3), range(3))]
x = ((window[0] + window[3] + window[3] + window[6]) \
- (window[2] + window[5] + window[5] + window[8])) \
/ (8.0 * xres);
y = ((window[6] + window[7] + window[7] + window[8]) \
- (window[0] + window[1] + window[1] + window[2])) \
/ (8.0 * yres);
# in radians, from 0 to pi/2
slope = pi/2 - numpy.arctan(numpy.sqrt(x*x + y*y))
# in radians counterclockwise, from -pi at north back to pi
aspect = numpy.arctan2(x, y)
return slope, aspect
def load_func_path(funcpath):
""" Load external function based on a path.
Example funcpath: "Module.Submodule:Function".
"""
modname, objname = funcpath.split(':', 1)
__import__(modname)
module = modules[modname]
_func = eval(objname, module.__dict__)
if _func is None:
raise Exception('eval(%(objname)s) in %(modname)s came up None' % locals())
return _func
|
# Giles: y.py
# Copyright 2012 Phil Bordelon
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from giles.state import State
from giles.games.game import Game
from giles.games.seat import Seat
# What are the minimum and maximum sizes for the board?
Y_MIN_SIZE = 2
Y_MAX_SIZE = 26
# . 0
# . . 1
# . . . 2
# . . . . 3
# 0 1 2 3
#
# (1, 2) is adjacent to (1, 1), (2, 2), (0, 2), (1, 3), (2, 3), and (0, 1).
Y_DELTAS = ((0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (-1, -1))
# Because we're lazy and use a square board despite the shape of the Y, we
# fill the rest of the square with invalid characters that match neither
# side. Define white and black here too.
INVALID = "invalid"
WHITE = "white"
BLACK = "black"
COL_CHARACTERS="abcdefghijklmnopqrstuvwxyz"
class Y(Game):
"""A Y game table implementation. Invented by Claude Shannon.
Adapted from my Volity implementation.
"""
def __init__(self, server, table_name):
super(Y, self).__init__(server, table_name)
self.game_display_name = "Y"
self.game_name = "y"
self.seats = [
Seat("White"),
Seat("Black"),
]
self.min_players = 2
self.max_players = 2
self.state = State("config")
self.prefix = "(^RY^~): "
self.log_prefix = "%s/%s " % (self.game_display_name, self.game_name)
self.debug = True
# Y-specific guff.
self.seats[0].color = WHITE
self.seats[0].color_code = "^W"
self.seats[1].color = BLACK
self.seats[1].color_code = "^K"
self.board = None
self.size = 19
self.turn = None
self.turn_number = 0
self.move_list = []
self.resigner = None
self.last_x = None
self.last_y = None
self.init_board()
def init_board(self):
self.board = []
# We're going to be lazy and build a square board, then fill the
# half that doesn't make the proper shape with invalid marks.
for x in range(self.size):
self.board.append([None] * self.size)
# Looking at the grid above, you can see that for a given column,
# all row values less than that value are invalid.
for y in range(x):
self.board[x][y] = INVALID
# That's it!
def set_size(self, player, size_str):
if not size_str.isdigit():
player.tell_cc(self.prefix + "You didn't even send a number!\n")
return False
new_size = int(size_str)
if new_size < Y_MIN_SIZE or new_size > Y_MAX_SIZE:
player.tell_cc(self.prefix + "Too small or large. Must be 2 to 52 inclusive.\n")
return False
# Got a valid size.
self.size = new_size
self.init_board()
self.channel.broadcast_cc(self.prefix + "^M%s^~ has changed the size of the board to ^C%s^~.\n" % (player.display_name, str(new_size)))
return True
def move_to_values(self, move_str):
# All valid moves are of the form g22, J15, etc. Ditch blatantly
# invalid moves.
if type(move_str) != str or len(move_str) < 2 or len(move_str) > 3:
return None
# First character must be in COL_CHARACTERS.
col_char = move_str[0].lower()
if col_char not in COL_CHARACTERS:
return None
else:
x = COL_CHARACTERS.index(col_char)
# Next one or two must be digits.
row_chars = move_str[1:]
if not row_chars.isdigit():
return None
else:
y = int(row_chars) - 1
# Now verify that these are even in range for this board. Remember
# that column values greater than the row value are invalid; that
# provides a bound on the column value, so we only then need to
# check the row against the upper bound.
if (x < 0 or x > y or y >= self.size):
return None
# Valid!
return (x, y)
def move(self, seat, move_str):
# Get the actual values of the move.
values = self.move_to_values(move_str)
if not values:
seat.player.tell_cc(self.prefix + "Invalid move.\n")
return None
x, y = values
if self.board[x][y]:
seat.player.tell_cc(self.prefix + "That space is already occupied.\n")
return None
# Okay, it's an unoccupied space! Let's make the move.
self.board[x][y] = seat.color
self.channel.broadcast_cc(self.prefix + seat.color_code + "%s^~ has moved to ^C%s^~.\n" % (seat.player.display_name, move_str))
self.last_x = x
self.last_y = y
return (x, y)
def swap(self):
# This is an easy one. Take the first move and change the piece
# on the board from white to black.
self.board[self.move_list[0][0]][self.move_list[0][1]] = BLACK
self.channel.broadcast_cc(self.prefix + "^Y%s^~ has swapped ^WWhite^~'s first move.\n" % self.seats[1].player.display_name)
def print_board(self, player):
slash_line = " "
char_line = ""
for x in range(self.size):
msg = " "
color_char = "^W"
if x % 2 == 0:
color_char = "^K"
slash_line += color_char + "/^~ "
char_line += "%s " % COL_CHARACTERS[x]
for spc in range(self.size - x):
msg += " "
for y in range(x + 1):
piece = self.board[y][x]
if y == self.last_x and x == self.last_y:
msg += "^I"
if piece == BLACK:
msg += "^Kx^~ "
elif piece == WHITE:
msg += "^Wo^~ "
elif y % 2 == 0:
msg += "^m,^~ "
else:
msg += "^M.^~ "
msg += str(x + 1) + "\n"
player.tell_cc(msg)
player.tell_cc(slash_line + "\n")
player.tell_cc(char_line + "\n")
def get_turn_str(self):
if self.state.get() == "playing":
if self.seats[0].color == self.turn:
color_word = "^WWhite^~"
name_word = "^R%s^~" % self.seats[0].player.display_name
else:
color_word = "^KBlack^~"
name_word = "^Y%s^~" % self.seats[1].player.display_name
return "It is %s's turn (%s).\n" % (name_word, color_word)
else:
return "The game is not currently active.\n"
def send_board(self):
for player in self.channel.listeners:
self.print_board(player)
def resign(self, seat):
# Okay, this person can resign; it's their turn, after all.
self.channel.broadcast_cc(self.prefix + "^R%s^~ is resigning from the game.\n" % seat.player.display_name)
self.resigner = seat.color
return True
def show(self, player):
self.print_board(player)
player.tell_cc(self.get_turn_str())
def show_help(self, player):
super(Y, self).show_help(player)
player.tell_cc("\nY SETUP PHASE:\n\n")
player.tell_cc(" ^!size^. <size>, ^!sz^. Set board to size <size>.\n")
player.tell_cc(" ^!ready^., ^!done^., ^!r^., ^!d^. End setup phase.\n")
player.tell_cc("\nY PLAY:\n\n")
player.tell_cc(" ^!move^. <ln>, ^!play^., ^!mv^., ^!pl^. Make move <ln> (letter number).\n")
player.tell_cc(" ^!swap^. Swap the first move (only Black, only their first).\n")
player.tell_cc(" ^!resign^. Resign.\n")
def handle(self, player, command_str):
# Handle common commands.
handled = self.handle_common_commands(player, command_str)
state = self.state.get()
command_bits = command_str.strip().split()
primary = command_str.split()[0].lower()
if state == "config":
if primary in ('size', 'sz'):
if len(command_bits) == 2:
self.set_size(player, command_bits[1])
else:
player.tell_cc(self.prefix + "Invalid size command.\n")
handled = True
elif primary in ('done', 'ready', 'd', 'r'):
self.channel.broadcast_cc(self.prefix + "The game is now ready for players.\n")
self.state.set("need_players")
handled = True
elif state == "need_players":
# If both seats are full and the game is active, time to
# play!
if self.seats[0].player and self.seats[1].player and self.active:
self.state.set("playing")
self.channel.broadcast_cc(self.prefix + "^WWhite^~: ^R%s^~; ^KBlack^~: ^Y%s^~\n" %
(self.seats[0].player.display_name, self.seats[1].player.display_name))
self.turn = WHITE
self.turn_number = 1
self.send_board()
self.channel.broadcast_cc(self.prefix + self.get_turn_str())
elif state == "playing":
made_move = False
# For all move types, don't bother if it's not this player's turn.
if primary in ('move', 'mv', 'play', 'pl', 'swap', 'resign'):
seat = self.get_seat_of_player(player)
if not seat:
player.tell_cc(self.prefix + "You can't move; you're not playing!\n")
return
elif seat.color != self.turn:
player.tell_cc(self.prefix + "You must wait for your turn to move.\n")
return
if primary in ('move', 'mv', 'play', 'pl'):
if len(command_bits) == 2:
success = self.move(seat, command_bits[1])
if success:
move = success
made_move = True
else:
player.tell_cc(self.prefix + "Unsuccessful move.\n")
else:
player.tell_cc(self.prefix + "Unsuccessful move.\n")
handled = True
elif primary in ('swap',):
if self.turn_number == 2 and seat.player == player:
self.swap()
move = "swap"
made_move = True
else:
player.tell_cc(self.prefix + "Unsuccessful swap.\n")
handled = True
elif primary in ('resign',):
if self.resign(seat):
move = "resign"
made_move = True
handled = True
if made_move:
self.send_board()
self.move_list.append(move)
self.turn_number += 1
winner = self.find_winner()
if winner:
self.resolve(winner)
self.finish()
else:
if self.turn == WHITE:
self.turn = BLACK
else:
self.turn = WHITE
self.channel.broadcast_cc(self.prefix + self.get_turn_str())
if not handled:
player.tell_cc(self.prefix + "Invalid command.\n")
def find_winner(self):
# First, check resignations; that's a fast bail.
if self.resigner:
if self.resigner == WHITE:
return self.seats[1].player
elif self.resigner == BLACK:
return self.seats[0].player
else:
self.server.log.log(self.log_prefix + "Weirdness; a resign that's not a player.")
return None
# Well, darn, we have to do actual work. Time for recursion!
# To calculate a winner:
# - Pick a side.
# - For each piece on that side, see if it's connected to
# both other sides. If so, that player is a winner.
# - If not, there is no winner (as winners must connect all
# three sides).
self.found_winner = None
self.adjacency = []
# Set up our adjacency checker.
for i in range(self.size):
self.adjacency.append([None] * self.size)
# For each piece on the left side of the board...
for i in range(self.size):
if self.board[0][i]:
# We're not touching the other two sides yet.
self.touch_bottom = False
self.touch_right = False
self.update_adjacency(0, i, self.board[0][i])
if self.found_winner == WHITE:
return self.seats[0].player
elif self.found_winner == BLACK:
return self.seats[1].player
# No winner yet.
return None
def update_adjacency(self, x, y, color):
# Skip work if a winner's already found.
if self.found_winner:
return
# Skip work if we're off the board.
if (x < 0 or x > y or y >= self.size):
return
# Skip work if we've been here already.
if self.adjacency[x][y]:
return
# Skip work if it's empty or for the other player.
this_cell = self.board[x][y]
if this_cell != color:
return
# All right, it's this player's cell. Mark it visited.
self.adjacency[x][y] = color
# If we're on either the bottom or right edges, mark that.
if (y == self.size - 1):
self.touch_bottom = True
if (x == y):
self.touch_right = True
# Bail if we've met both win conditions.
if self.touch_bottom and self.touch_right:
self.found_winner = color
# Okay, no winner yet. Recurse on the six adjacent cells.
for x_delta, y_delta in Y_DELTAS:
self.update_adjacency(x + x_delta, y + y_delta, color)
def resolve(self, winner):
self.channel.broadcast_cc(self.prefix + "^C%s^~ wins!\n" % (winner.display_name))
Y: Use right size limits.
They were hardcoded; use the constants.
# Giles: y.py
# Copyright 2012 Phil Bordelon
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from giles.state import State
from giles.games.game import Game
from giles.games.seat import Seat
# What are the minimum and maximum sizes for the board?
Y_MIN_SIZE = 2
Y_MAX_SIZE = 26
# . 0
# . . 1
# . . . 2
# . . . . 3
# 0 1 2 3
#
# (1, 2) is adjacent to (1, 1), (2, 2), (0, 2), (1, 3), (2, 3), and (0, 1).
Y_DELTAS = ((0, -1), (0, 1), (-1, 0), (1, 0), (1, 1), (-1, -1))
# Because we're lazy and use a square board despite the shape of the Y, we
# fill the rest of the square with invalid characters that match neither
# side. Define white and black here too.
INVALID = "invalid"
WHITE = "white"
BLACK = "black"
COL_CHARACTERS="abcdefghijklmnopqrstuvwxyz"
class Y(Game):
"""A Y game table implementation. Invented by Claude Shannon.
Adapted from my Volity implementation.
"""
def __init__(self, server, table_name):
super(Y, self).__init__(server, table_name)
self.game_display_name = "Y"
self.game_name = "y"
self.seats = [
Seat("White"),
Seat("Black"),
]
self.min_players = 2
self.max_players = 2
self.state = State("config")
self.prefix = "(^RY^~): "
self.log_prefix = "%s/%s " % (self.game_display_name, self.game_name)
self.debug = True
# Y-specific guff.
self.seats[0].color = WHITE
self.seats[0].color_code = "^W"
self.seats[1].color = BLACK
self.seats[1].color_code = "^K"
self.board = None
self.size = 19
self.turn = None
self.turn_number = 0
self.move_list = []
self.resigner = None
self.last_x = None
self.last_y = None
self.init_board()
def init_board(self):
self.board = []
# We're going to be lazy and build a square board, then fill the
# half that doesn't make the proper shape with invalid marks.
for x in range(self.size):
self.board.append([None] * self.size)
# Looking at the grid above, you can see that for a given column,
# all row values less than that value are invalid.
for y in range(x):
self.board[x][y] = INVALID
# That's it!
def set_size(self, player, size_str):
if not size_str.isdigit():
player.tell_cc(self.prefix + "You didn't even send a number!\n")
return False
new_size = int(size_str)
if new_size < Y_MIN_SIZE or new_size > Y_MAX_SIZE:
player.tell_cc(self.prefix + "Too small or large. Must be %s to %s inclusive.\n" % (Y_MIN_SIZE, Y_MAX_SIZE))
return False
# Got a valid size.
self.size = new_size
self.init_board()
self.channel.broadcast_cc(self.prefix + "^M%s^~ has changed the size of the board to ^C%s^~.\n" % (player.display_name, str(new_size)))
return True
def move_to_values(self, move_str):
# All valid moves are of the form g22, J15, etc. Ditch blatantly
# invalid moves.
if type(move_str) != str or len(move_str) < 2 or len(move_str) > 3:
return None
# First character must be in COL_CHARACTERS.
col_char = move_str[0].lower()
if col_char not in COL_CHARACTERS:
return None
else:
x = COL_CHARACTERS.index(col_char)
# Next one or two must be digits.
row_chars = move_str[1:]
if not row_chars.isdigit():
return None
else:
y = int(row_chars) - 1
# Now verify that these are even in range for this board. Remember
# that column values greater than the row value are invalid; that
# provides a bound on the column value, so we only then need to
# check the row against the upper bound.
if (x < 0 or x > y or y >= self.size):
return None
# Valid!
return (x, y)
def move(self, seat, move_str):
# Get the actual values of the move.
values = self.move_to_values(move_str)
if not values:
seat.player.tell_cc(self.prefix + "Invalid move.\n")
return None
x, y = values
if self.board[x][y]:
seat.player.tell_cc(self.prefix + "That space is already occupied.\n")
return None
# Okay, it's an unoccupied space! Let's make the move.
self.board[x][y] = seat.color
self.channel.broadcast_cc(self.prefix + seat.color_code + "%s^~ has moved to ^C%s^~.\n" % (seat.player.display_name, move_str))
self.last_x = x
self.last_y = y
return (x, y)
def swap(self):
# This is an easy one. Take the first move and change the piece
# on the board from white to black.
self.board[self.move_list[0][0]][self.move_list[0][1]] = BLACK
self.channel.broadcast_cc(self.prefix + "^Y%s^~ has swapped ^WWhite^~'s first move.\n" % self.seats[1].player.display_name)
def print_board(self, player):
slash_line = " "
char_line = ""
for x in range(self.size):
msg = " "
color_char = "^W"
if x % 2 == 0:
color_char = "^K"
slash_line += color_char + "/^~ "
char_line += "%s " % COL_CHARACTERS[x]
for spc in range(self.size - x):
msg += " "
for y in range(x + 1):
piece = self.board[y][x]
if y == self.last_x and x == self.last_y:
msg += "^I"
if piece == BLACK:
msg += "^Kx^~ "
elif piece == WHITE:
msg += "^Wo^~ "
elif y % 2 == 0:
msg += "^m,^~ "
else:
msg += "^M.^~ "
msg += str(x + 1) + "\n"
player.tell_cc(msg)
player.tell_cc(slash_line + "\n")
player.tell_cc(char_line + "\n")
def get_turn_str(self):
if self.state.get() == "playing":
if self.seats[0].color == self.turn:
color_word = "^WWhite^~"
name_word = "^R%s^~" % self.seats[0].player.display_name
else:
color_word = "^KBlack^~"
name_word = "^Y%s^~" % self.seats[1].player.display_name
return "It is %s's turn (%s).\n" % (name_word, color_word)
else:
return "The game is not currently active.\n"
def send_board(self):
for player in self.channel.listeners:
self.print_board(player)
def resign(self, seat):
# Okay, this person can resign; it's their turn, after all.
self.channel.broadcast_cc(self.prefix + "^R%s^~ is resigning from the game.\n" % seat.player.display_name)
self.resigner = seat.color
return True
def show(self, player):
self.print_board(player)
player.tell_cc(self.get_turn_str())
def show_help(self, player):
super(Y, self).show_help(player)
player.tell_cc("\nY SETUP PHASE:\n\n")
player.tell_cc(" ^!size^. <size>, ^!sz^. Set board to size <size>.\n")
player.tell_cc(" ^!ready^., ^!done^., ^!r^., ^!d^. End setup phase.\n")
player.tell_cc("\nY PLAY:\n\n")
player.tell_cc(" ^!move^. <ln>, ^!play^., ^!mv^., ^!pl^. Make move <ln> (letter number).\n")
player.tell_cc(" ^!swap^. Swap the first move (only Black, only their first).\n")
player.tell_cc(" ^!resign^. Resign.\n")
def handle(self, player, command_str):
# Handle common commands.
handled = self.handle_common_commands(player, command_str)
state = self.state.get()
command_bits = command_str.strip().split()
primary = command_str.split()[0].lower()
if state == "config":
if primary in ('size', 'sz'):
if len(command_bits) == 2:
self.set_size(player, command_bits[1])
else:
player.tell_cc(self.prefix + "Invalid size command.\n")
handled = True
elif primary in ('done', 'ready', 'd', 'r'):
self.channel.broadcast_cc(self.prefix + "The game is now ready for players.\n")
self.state.set("need_players")
handled = True
elif state == "need_players":
# If both seats are full and the game is active, time to
# play!
if self.seats[0].player and self.seats[1].player and self.active:
self.state.set("playing")
self.channel.broadcast_cc(self.prefix + "^WWhite^~: ^R%s^~; ^KBlack^~: ^Y%s^~\n" %
(self.seats[0].player.display_name, self.seats[1].player.display_name))
self.turn = WHITE
self.turn_number = 1
self.send_board()
self.channel.broadcast_cc(self.prefix + self.get_turn_str())
elif state == "playing":
made_move = False
# For all move types, don't bother if it's not this player's turn.
if primary in ('move', 'mv', 'play', 'pl', 'swap', 'resign'):
seat = self.get_seat_of_player(player)
if not seat:
player.tell_cc(self.prefix + "You can't move; you're not playing!\n")
return
elif seat.color != self.turn:
player.tell_cc(self.prefix + "You must wait for your turn to move.\n")
return
if primary in ('move', 'mv', 'play', 'pl'):
if len(command_bits) == 2:
success = self.move(seat, command_bits[1])
if success:
move = success
made_move = True
else:
player.tell_cc(self.prefix + "Unsuccessful move.\n")
else:
player.tell_cc(self.prefix + "Unsuccessful move.\n")
handled = True
elif primary in ('swap',):
if self.turn_number == 2 and seat.player == player:
self.swap()
move = "swap"
made_move = True
else:
player.tell_cc(self.prefix + "Unsuccessful swap.\n")
handled = True
elif primary in ('resign',):
if self.resign(seat):
move = "resign"
made_move = True
handled = True
if made_move:
self.send_board()
self.move_list.append(move)
self.turn_number += 1
winner = self.find_winner()
if winner:
self.resolve(winner)
self.finish()
else:
if self.turn == WHITE:
self.turn = BLACK
else:
self.turn = WHITE
self.channel.broadcast_cc(self.prefix + self.get_turn_str())
if not handled:
player.tell_cc(self.prefix + "Invalid command.\n")
def find_winner(self):
# First, check resignations; that's a fast bail.
if self.resigner:
if self.resigner == WHITE:
return self.seats[1].player
elif self.resigner == BLACK:
return self.seats[0].player
else:
self.server.log.log(self.log_prefix + "Weirdness; a resign that's not a player.")
return None
# Well, darn, we have to do actual work. Time for recursion!
# To calculate a winner:
# - Pick a side.
# - For each piece on that side, see if it's connected to
# both other sides. If so, that player is a winner.
# - If not, there is no winner (as winners must connect all
# three sides).
self.found_winner = None
self.adjacency = []
# Set up our adjacency checker.
for i in range(self.size):
self.adjacency.append([None] * self.size)
# For each piece on the left side of the board...
for i in range(self.size):
if self.board[0][i]:
# We're not touching the other two sides yet.
self.touch_bottom = False
self.touch_right = False
self.update_adjacency(0, i, self.board[0][i])
if self.found_winner == WHITE:
return self.seats[0].player
elif self.found_winner == BLACK:
return self.seats[1].player
# No winner yet.
return None
def update_adjacency(self, x, y, color):
# Skip work if a winner's already found.
if self.found_winner:
return
# Skip work if we're off the board.
if (x < 0 or x > y or y >= self.size):
return
# Skip work if we've been here already.
if self.adjacency[x][y]:
return
# Skip work if it's empty or for the other player.
this_cell = self.board[x][y]
if this_cell != color:
return
# All right, it's this player's cell. Mark it visited.
self.adjacency[x][y] = color
# If we're on either the bottom or right edges, mark that.
if (y == self.size - 1):
self.touch_bottom = True
if (x == y):
self.touch_right = True
# Bail if we've met both win conditions.
if self.touch_bottom and self.touch_right:
self.found_winner = color
# Okay, no winner yet. Recurse on the six adjacent cells.
for x_delta, y_delta in Y_DELTAS:
self.update_adjacency(x + x_delta, y + y_delta, color)
def resolve(self, winner):
self.channel.broadcast_cc(self.prefix + "^C%s^~ wins!\n" % (winner.display_name))
|
# The Hazard Library
# Copyright (C) 2016-2017 GEM Foundation
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import os
import unittest
import numpy
import numpy.testing as npt
from openquake.baselib.general import DictArray
from openquake.hazardlib.source import NonParametricSeismicSource
from openquake.hazardlib.source.rupture import BaseRupture
from openquake.hazardlib.sourceconverter import SourceConverter
from openquake.hazardlib.const import TRT
from openquake.hazardlib.geo.surface import PlanarSurface, SimpleFaultSurface
from openquake.hazardlib.geo import Point, Line
from openquake.hazardlib.geo.geodetic import point_at
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.calc.hazard_curve import calc_hazard_curves
from openquake.hazardlib.calc.hazard_curve import classical
from openquake.hazardlib.gsim.sadigh_1997 import SadighEtAl1997
from openquake.hazardlib.gsim.si_midorikawa_1999 import SiMidorikawa1999SInter
from openquake.hazardlib.gsim.campbell_2003 import Campbell2003
from openquake.hazardlib.site import Site, SiteCollection
from openquake.hazardlib.pmf import PMF
from openquake.hazardlib.sourceconverter import SourceGroup
from openquake.hazardlib import nrml
def _create_rupture(distance, magnitude,
tectonic_region_type=TRT.ACTIVE_SHALLOW_CRUST):
# Return a rupture with a fixed geometry located at a given r_jb distance
# from a site located at (0.0, 0.0).
# parameter float distance:
# Joyner and Boore rupture-site distance
# parameter float magnitude:
# Rupture magnitude
# Find the point at a given distance
lonp, latp = point_at(0.0, 0.0, 90., distance)
mag = magnitude
rake = 0.0
tectonic_region_type = tectonic_region_type
hypocenter = Point(lonp, latp, 2.5)
surface = PlanarSurface.from_corner_points(0.01,
Point(lonp, -1, 0.),
Point(lonp, +1, 0.),
Point(lonp, +1, 5.),
Point(lonp, -1, 5.))
surface = SimpleFaultSurface.from_fault_data(
fault_trace=Line([Point(lonp, -1), Point(lonp, 1)]),
upper_seismogenic_depth=0.0,
lower_seismogenic_depth=5.0,
dip=90.0,
mesh_spacing=1.0)
# check effective rupture-site distance
from openquake.hazardlib.geo.mesh import Mesh
mesh = Mesh(numpy.array([0.0]), numpy.array([0.0]))
assert abs(surface.get_joyner_boore_distance(mesh)-distance) < 1e-2
return BaseRupture(mag, rake, tectonic_region_type, hypocenter,
surface, NonParametricSeismicSource)
def _create_non_param_sourceA(rjb, magnitude, pmf,
tectonic_region_type=TRT.ACTIVE_SHALLOW_CRUST):
# Create a non-parametric source
rupture = _create_rupture(rjb, magnitude)
pmf = pmf
data = [(rupture, pmf)]
return NonParametricSeismicSource('0', 'test', tectonic_region_type, data)
class HazardCurvesTestCase01(unittest.TestCase):
def setUp(self):
self.src1 = _create_non_param_sourceA(15., 6.3,
PMF([(0.6, 0), (0.4, 1)]))
self.src2 = _create_non_param_sourceA(10., 6.0,
PMF([(0.7, 0), (0.3, 1)]))
self.src3 = _create_non_param_sourceA(10., 6.0,
PMF([(0.7, 0), (0.3, 1)]),
TRT.GEOTHERMAL)
site = Site(Point(0.0, 0.0), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
self.sites = s_filter
self.imtls = DictArray({'PGA': [0.01, 0.1, 0.3]})
gsim = SadighEtAl1997()
gsim.minimum_distance = 12 # test minimum_distance
self.gsim_by_trt = {TRT.ACTIVE_SHALLOW_CRUST: gsim}
def test_hazard_curve_X(self):
# Test the former calculator
curves = calc_hazard_curves([self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
self.assertAlmostEqual(0.3, crv[0])
def test_hazard_curve_A(self):
# Test back-compatibility
# Classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.30000, 0.2646, 0.0625]),
crv, decimal=4)
def test_hazard_curve_B(self):
# Test simple calculation
group = SourceGroup(
TRT.ACTIVE_SHALLOW_CRUST, [self.src2], 'test', 'indep', 'indep')
groups = [group]
curves = calc_hazard_curves(groups,
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
npt.assert_almost_equal(numpy.array([0.30000, 0.2646, 0.0625]),
curves[0][0], decimal=4)
class HazardCurvePerGroupTest(HazardCurvesTestCase01):
def test_mutually_exclusive_ruptures(self):
# Test the calculation of hazard curves using mutually exclusive
# ruptures for a single source
gsim_by_trt = [SadighEtAl1997()]
rupture = _create_rupture(10., 6.)
data = [(rupture, PMF([(0.7, 0), (0.3, 1)])),
(rupture, PMF([(0.6, 0), (0.4, 1)]))]
src = NonParametricSeismicSource('0', 'test', TRT.ACTIVE_SHALLOW_CRUST,
data)
src.src_group_id = [0]
group = SourceGroup(
src.tectonic_region_type, [src], 'test', 'indep', 'mutex')
param = dict(imtls=self.imtls)
crv = classical(group, self.sites, gsim_by_trt, param)[0]
npt.assert_almost_equal(numpy.array([0.35000, 0.32497, 0.10398]),
crv[0].array[:, 0], decimal=4)
def test_raise_error_non_uniform_group(self):
# Test that the uniformity of a group (in terms of tectonic region)
# is correctly checked
self.assertRaises(
AssertionError, SourceGroup, TRT.ACTIVE_SHALLOW_CRUST,
[self.src1, self.src3], 'test', 'indep', 'indep')
class HazardCurvesTestCase02(HazardCurvesTestCase01):
def test_hazard_curve_A(self):
# Test classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src1],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.40000, 0.36088, 0.07703]),
crv, decimal=4)
def test_hazard_curve_B(self):
# Test classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src1, self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.58000, 0.53, 0.1347]),
crv, decimal=4)
class NankaiTestCase(unittest.TestCase):
# use a source model for the Nankai region provided by M. Pagani
def test(self):
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(135.68, 35.68), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
imtls = DictArray({'PGV': [20, 40, 80]})
gsim_by_trt = {'Subduction Interface': SiMidorikawa1999SInter()}
hcurves = calc_hazard_curves(groups, s_filter, imtls, gsim_by_trt)
npt.assert_almost_equal(
[1.11315443e-01, 3.92180097e-03, 3.02064427e-05],
hcurves['PGV'][0])
class MultiPointTestCase(unittest.TestCase):
def test(self):
d = os.path.dirname(os.path.dirname(__file__))
source_model = os.path.join(d, 'source_model/multi-point-source.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(0.1, 0.1), 800, True, z1pt0=100., z2pt5=1.)
sitecol = SiteCollection([site])
imtls = DictArray({'PGA': [0.01, 0.02, 0.04, 0.08, 0.16]})
gsim_by_trt = {'Stable Continental Crust': Campbell2003()}
hcurves = calc_hazard_curves(groups, sitecol, imtls, gsim_by_trt)
expected = [0.99999778, 0.9084039, 0.148975348,
0.0036909656, 2.76326e-05]
npt.assert_almost_equal(hcurves['PGA'][0], expected)
# splitting in point sources
[[mps1, mps2]] = groups
psources = list(mps1) + list(mps2)
hcurves = calc_hazard_curves(psources, sitecol, imtls, gsim_by_trt)
npt.assert_almost_equal(hcurves['PGA'][0], expected)
Fixed hazard tests
Former-commit-id: e9cf892d1c7eca2b126f78367fb669f9f420d5a5
# The Hazard Library
# Copyright (C) 2016-2017 GEM Foundation
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import os
import unittest
import numpy
import numpy.testing as npt
from openquake.baselib.general import DictArray
from openquake.hazardlib.source import NonParametricSeismicSource
from openquake.hazardlib.source.rupture import BaseRupture
from openquake.hazardlib.sourceconverter import SourceConverter
from openquake.hazardlib.const import TRT
from openquake.hazardlib.geo.surface import PlanarSurface, SimpleFaultSurface
from openquake.hazardlib.geo import Point, Line
from openquake.hazardlib.geo.geodetic import point_at
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.calc.hazard_curve import calc_hazard_curves
from openquake.hazardlib.calc.hazard_curve import classical
from openquake.hazardlib.gsim.sadigh_1997 import SadighEtAl1997
from openquake.hazardlib.gsim.si_midorikawa_1999 import SiMidorikawa1999SInter
from openquake.hazardlib.gsim.campbell_2003 import Campbell2003
from openquake.hazardlib.site import Site, SiteCollection
from openquake.hazardlib.pmf import PMF
from openquake.hazardlib.sourceconverter import SourceGroup
from openquake.hazardlib import nrml
def _create_rupture(distance, magnitude,
tectonic_region_type=TRT.ACTIVE_SHALLOW_CRUST):
# Return a rupture with a fixed geometry located at a given r_jb distance
# from a site located at (0.0, 0.0).
# parameter float distance:
# Joyner and Boore rupture-site distance
# parameter float magnitude:
# Rupture magnitude
# Find the point at a given distance
lonp, latp = point_at(0.0, 0.0, 90., distance)
mag = magnitude
rake = 0.0
tectonic_region_type = tectonic_region_type
hypocenter = Point(lonp, latp, 2.5)
surface = PlanarSurface.from_corner_points(0.01,
Point(lonp, -1, 0.),
Point(lonp, +1, 0.),
Point(lonp, +1, 5.),
Point(lonp, -1, 5.))
surface = SimpleFaultSurface.from_fault_data(
fault_trace=Line([Point(lonp, -1), Point(lonp, 1)]),
upper_seismogenic_depth=0.0,
lower_seismogenic_depth=5.0,
dip=90.0,
mesh_spacing=1.0)
# check effective rupture-site distance
from openquake.hazardlib.geo.mesh import Mesh
mesh = Mesh(numpy.array([0.0]), numpy.array([0.0]))
assert abs(surface.get_joyner_boore_distance(mesh)-distance) < 1e-2
return BaseRupture(mag, rake, tectonic_region_type, hypocenter,
surface, NonParametricSeismicSource)
def _create_non_param_sourceA(rjb, magnitude, pmf,
tectonic_region_type=TRT.ACTIVE_SHALLOW_CRUST):
# Create a non-parametric source
rupture = _create_rupture(rjb, magnitude)
pmf = pmf
data = [(rupture, pmf)]
return NonParametricSeismicSource('0', 'test', tectonic_region_type, data)
class HazardCurvesTestCase01(unittest.TestCase):
def setUp(self):
self.src1 = _create_non_param_sourceA(15., 6.3,
PMF([(0.6, 0), (0.4, 1)]))
self.src2 = _create_non_param_sourceA(10., 6.0,
PMF([(0.7, 0), (0.3, 1)]))
self.src3 = _create_non_param_sourceA(10., 6.0,
PMF([(0.7, 0), (0.3, 1)]),
TRT.GEOTHERMAL)
site = Site(Point(0.0, 0.0), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
self.sites = s_filter
self.imtls = DictArray({'PGA': [0.01, 0.1, 0.3]})
gsim = SadighEtAl1997()
gsim.minimum_distance = 12 # test minimum_distance
self.gsim_by_trt = {TRT.ACTIVE_SHALLOW_CRUST: gsim}
def test_hazard_curve_X(self):
# Test the former calculator
curves = calc_hazard_curves([self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
self.assertAlmostEqual(0.3, crv[0])
def test_hazard_curve_A(self):
# Test back-compatibility
# Classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.30000, 0.2646, 0.0625]),
crv, decimal=4)
def test_hazard_curve_B(self):
# Test simple calculation
group = SourceGroup(
TRT.ACTIVE_SHALLOW_CRUST, [self.src2], 'test', 'indep', 'indep')
groups = [group]
curves = calc_hazard_curves(groups,
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
npt.assert_almost_equal(numpy.array([0.30000, 0.2646, 0.0625]),
curves[0][0], decimal=4)
class HazardCurvePerGroupTest(HazardCurvesTestCase01):
def test_mutually_exclusive_ruptures(self):
# Test the calculation of hazard curves using mutually exclusive
# ruptures for a single source
gsim_by_trt = [SadighEtAl1997()]
rupture = _create_rupture(10., 6.)
data = [(rupture, PMF([(0.7, 0), (0.3, 1)])),
(rupture, PMF([(0.6, 0), (0.4, 1)]))]
src = NonParametricSeismicSource('0', 'test', TRT.ACTIVE_SHALLOW_CRUST,
data)
src.src_group_id = [0]
group = SourceGroup(
src.tectonic_region_type, [src], 'test', 'mutex', 'mutex')
param = dict(imtls=self.imtls)
crv = classical(group, self.sites, gsim_by_trt, param)[0]
npt.assert_almost_equal(numpy.array([0.35000, 0.32497, 0.10398]),
crv[0].array[:, 0], decimal=4)
def test_raise_error_non_uniform_group(self):
# Test that the uniformity of a group (in terms of tectonic region)
# is correctly checked
self.assertRaises(
AssertionError, SourceGroup, TRT.ACTIVE_SHALLOW_CRUST,
[self.src1, self.src3], 'test', 'indep', 'indep')
class HazardCurvesTestCase02(HazardCurvesTestCase01):
def test_hazard_curve_A(self):
# Test classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src1],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.40000, 0.36088, 0.07703]),
crv, decimal=4)
def test_hazard_curve_B(self):
# Test classical case i.e. independent sources in a list instance
curves = calc_hazard_curves([self.src1, self.src2],
self.sites,
self.imtls,
self.gsim_by_trt,
truncation_level=None)
crv = curves[0][0]
npt.assert_almost_equal(numpy.array([0.58000, 0.53, 0.1347]),
crv, decimal=4)
class NankaiTestCase(unittest.TestCase):
# use a source model for the Nankai region provided by M. Pagani
def test(self):
source_model = os.path.join(os.path.dirname(__file__), 'nankai.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(135.68, 35.68), 800, True, z1pt0=100., z2pt5=1.)
s_filter = SourceFilter(SiteCollection([site]), {})
imtls = DictArray({'PGV': [20, 40, 80]})
gsim_by_trt = {'Subduction Interface': SiMidorikawa1999SInter()}
hcurves = calc_hazard_curves(groups, s_filter, imtls, gsim_by_trt)
npt.assert_almost_equal(
[1.11315443e-01, 3.92180097e-03, 3.02064427e-05],
hcurves['PGV'][0])
class MultiPointTestCase(unittest.TestCase):
def test(self):
d = os.path.dirname(os.path.dirname(__file__))
source_model = os.path.join(d, 'source_model/multi-point-source.xml')
groups = nrml.parse(source_model, SourceConverter(
investigation_time=50., rupture_mesh_spacing=2.))
site = Site(Point(0.1, 0.1), 800, True, z1pt0=100., z2pt5=1.)
sitecol = SiteCollection([site])
imtls = DictArray({'PGA': [0.01, 0.02, 0.04, 0.08, 0.16]})
gsim_by_trt = {'Stable Continental Crust': Campbell2003()}
hcurves = calc_hazard_curves(groups, sitecol, imtls, gsim_by_trt)
expected = [0.99999778, 0.9084039, 0.148975348,
0.0036909656, 2.76326e-05]
npt.assert_almost_equal(hcurves['PGA'][0], expected)
# splitting in point sources
[[mps1, mps2]] = groups
psources = list(mps1) + list(mps2)
hcurves = calc_hazard_curves(psources, sitecol, imtls, gsim_by_trt)
npt.assert_almost_equal(hcurves['PGA'][0], expected)
|
import signal
import sys
import socket
import struct
from tools.debug import Debug
UDP_IP = "127.0.0.1"
UDP_PORT = 20777
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind((UDP_IP, UDP_PORT))
def main():
index = 1
value = None
while True:
data, address = sock.recvfrom(512)
if not data:
continue
stats = struct.unpack('66f', data[0:264])
new_value = "%.14f;%.14f;%d" % (stats[64], stats[63], int(stats[65]))
if new_value != value:
value = new_value
print(index, value)
index += 1
# noinspection PyUnusedLocal
def exit_gracefully(signum, frame):
Debug.warn('Process killed (%s). Exiting gracefully' % signum)
sock.close()
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, exit_gracefully)
signal.signal(signal.SIGTERM, exit_gracefully)
signal.signal(signal.SIGABRT, exit_gracefully)
main()
fix car scanner dependencies
import signal
import sys
import socket
import struct
from debug import Debug
UDP_IP = "127.0.0.1"
UDP_PORT = 20777
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.bind((UDP_IP, UDP_PORT))
def main():
index = 1
value = None
while True:
data, address = sock.recvfrom(512)
if not data:
continue
stats = struct.unpack('66f', data[0:264])
new_value = "%.14f;%.14f;%d" % (stats[64], stats[63], int(stats[65]))
if new_value != value:
value = new_value
print(index, value)
index += 1
# noinspection PyUnusedLocal
def exit_gracefully(signum, frame):
Debug.warn('Process killed (%s). Exiting gracefully' % signum)
sock.close()
sys.exit(0)
if __name__ == '__main__':
signal.signal(signal.SIGINT, exit_gracefully)
signal.signal(signal.SIGTERM, exit_gracefully)
signal.signal(signal.SIGABRT, exit_gracefully)
main()
|
import os
import sys
import json
import requests
from ArgumentParser import parser
# Processing
# ----------
def process_files(args):
"""
:param args:
The arguments parsed by argparse
:returns:
A dict containing file_names as keys and a
dict containing a key `content` as the value
Example return:
{
"file_name": {
"content": {
# file contents
}
}
}
"""
files = [os.path.abspath(file) for file in args.files]
file_contents = {}
for file in files:
try:
f = open(file)
file_contents[os.path.split(file)[1]] = dict(content=f.read())
f.close()
except FileNotFoundError:
print('File "{}"\n\tdoes not exist'.format(file))
should_create = input('Create the gist without this file [Y/n]: ') or 'Y'
if not should_create == 'Y':
sys.exit("gistey: exiting ...")
return file_contents
def create_gist(data):
"""
:param data:
The JSON data to be posted to the API
:returns:
request object of the POST request made to create the gist
"""
end_point = 'https://api.github.com/gists'
rq = requests.post(end_point, json=data)
return rq
def construct_data(args):
"""
:param args:
The arguments parsed by argparse
:returns:
`data` dict to be passed to crete the POST request
"""
data = {
"public": not args.secret,
"description": args.description if args.description else '',
"files": process_files(args)
}
return data
# Execution
# ---------
def main():
args = parser.parse_args()
response = create_gist(construct_data(args))
print("URL of the gist created: ", response.json()["html_url"])
if __name__ == "__main__":
main()
gistey.py: Use `.` for relative imports because of packaging
import os
import sys
import json
import requests
from .ArgumentParser import parser
# Processing
# ----------
def process_files(args):
"""
:param args:
The arguments parsed by argparse
:returns:
A dict containing file_names as keys and a
dict containing a key `content` as the value
Example return:
{
"file_name": {
"content": {
# file contents
}
}
}
"""
files = [os.path.abspath(file) for file in args.files]
file_contents = {}
for file in files:
try:
f = open(file)
file_contents[os.path.split(file)[1]] = dict(content=f.read())
f.close()
except FileNotFoundError:
print('File "{}"\n\tdoes not exist'.format(file))
should_create = input('Create the gist without this file [Y/n]: ') or 'Y'
if not should_create == 'Y':
sys.exit("gistey: exiting ...")
return file_contents
def create_gist(data):
"""
:param data:
The JSON data to be posted to the API
:returns:
request object of the POST request made to create the gist
"""
end_point = 'https://api.github.com/gists'
rq = requests.post(end_point, json=data)
return rq
def construct_data(args):
"""
:param args:
The arguments parsed by argparse
:returns:
`data` dict to be passed to crete the POST request
"""
data = {
"public": not args.secret,
"description": args.description if args.description else '',
"files": process_files(args)
}
return data
# Execution
# ---------
def main():
args = parser.parse_args()
response = create_gist(construct_data(args))
print("URL of the gist created: ", response.json()["html_url"])
if __name__ == "__main__":
main()
|
# Copyright 2015 Intel Corporation.
# Copyright 2015 Isaku Yamahata <isaku.yamahata at intel com>
# <isaku.yamahata at gmail com>
# 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.
import mock
from neutron.agent.linux import bridge_lib
from neutron.tests import base
class BridgeLibTest(base.BaseTestCase):
"""A test suite to exercise the bridge libraries """
_NAMESPACE = 'test-namespace'
_BR_NAME = 'test-br'
_IF_NAME = 'test-if'
def setUp(self):
super(BridgeLibTest, self).setUp()
ip_wrapper = mock.patch('neutron.agent.linux.ip_lib.IPWrapper').start()
self.execute = ip_wrapper.return_value.netns.execute
def _verify_bridge_mock(self, cmd):
self.execute.assert_called_once_with(cmd, run_as_root=True)
self.execute.reset_mock()
def _verify_bridge_sysctl_mock(self, cmd):
self.execute.assert_called_once_with(cmd, run_as_root=True,
log_fail_as_error=True)
self.execute.reset_mock()
def test_is_bridged_interface(self):
exists = lambda path: path == "/sys/class/net/tapOK/brport"
with mock.patch('os.path.exists', side_effect=exists):
self.assertTrue(bridge_lib.is_bridged_interface("tapOK"))
self.assertFalse(bridge_lib.is_bridged_interface("tapKO"))
def test_get_interface_bridge(self):
with mock.patch('os.readlink', side_effect=["prefix/br0", OSError()]):
br = bridge_lib.BridgeDevice.get_interface_bridge('tap0')
self.assertIsInstance(br, bridge_lib.BridgeDevice)
self.assertEqual("br0", br.name)
br = bridge_lib.BridgeDevice.get_interface_bridge('tap0')
self.assertIsNone(br)
def _test_br(self, namespace=None):
br = bridge_lib.BridgeDevice.addbr(self._BR_NAME, namespace)
self.assertEqual(namespace, br.namespace)
self._verify_bridge_mock(['brctl', 'addbr', self._BR_NAME])
br.setfd(0)
self._verify_bridge_mock(['brctl', 'setfd', self._BR_NAME, '0'])
br.disable_stp()
self._verify_bridge_mock(['brctl', 'stp', self._BR_NAME, 'off'])
br.disable_ipv6()
cmd = 'net.ipv6.conf.%s.disable_ipv6=1' % self._BR_NAME
self._verify_bridge_sysctl_mock(['sysctl', '-w', cmd])
br.addif(self._IF_NAME)
self._verify_bridge_mock(
['brctl', 'addif', self._BR_NAME, self._IF_NAME])
br.delif(self._IF_NAME)
self._verify_bridge_mock(
['brctl', 'delif', self._BR_NAME, self._IF_NAME])
br.delbr()
self._verify_bridge_mock(['brctl', 'delbr', self._BR_NAME])
def test_addbr_with_namespace(self):
self._test_br(self._NAMESPACE)
def test_addbr_without_namespace(self):
self._test_br()
def test_addbr_exists(self):
self.execute.side_effect = RuntimeError()
with mock.patch.object(bridge_lib.BridgeDevice, 'exists',
return_value=True):
bridge_lib.BridgeDevice.addbr(self._BR_NAME)
bridge_lib.BridgeDevice.addbr(self._BR_NAME)
def test_owns_interface(self):
br = bridge_lib.BridgeDevice('br-int')
exists = lambda path: path == "/sys/class/net/br-int/brif/abc"
with mock.patch('os.path.exists', side_effect=exists):
self.assertTrue(br.owns_interface("abc"))
self.assertFalse(br.owns_interface("def"))
def test_get_interfaces(self):
br = bridge_lib.BridgeDevice('br-int')
interfaces = ["tap1", "tap2"]
with mock.patch('os.listdir', side_effect=[interfaces, OSError()]):
self.assertEqual(interfaces, br.get_interfaces())
self.assertEqual([], br.get_interfaces())
Fix UT BridgeLibTest when IPv6 is disabled
There was missing mock of
ipv6_utils.is_enabled_and_bind_by_default() in BridgeLibTest
unit tests and that cause failing some of tests from this module
when tests are running on host with disabled IPv6.
Now it's mocked and tests are running properly and are
testing what they should test.
Closes-Bug: #1773818
Change-Id: I9144450ce85e020c0e33c5214a2178acbbbf5f54
# Copyright 2015 Intel Corporation.
# Copyright 2015 Isaku Yamahata <isaku.yamahata at intel com>
# <isaku.yamahata at gmail com>
# 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.
import mock
from neutron.agent.linux import bridge_lib
from neutron.tests import base
class BridgeLibTest(base.BaseTestCase):
"""A test suite to exercise the bridge libraries """
_NAMESPACE = 'test-namespace'
_BR_NAME = 'test-br'
_IF_NAME = 'test-if'
def setUp(self):
super(BridgeLibTest, self).setUp()
mock.patch(
'neutron.common.ipv6_utils.is_enabled_and_bind_by_default',
return_value=True).start()
ip_wrapper = mock.patch('neutron.agent.linux.ip_lib.IPWrapper').start()
self.execute = ip_wrapper.return_value.netns.execute
def _verify_bridge_mock(self, cmd):
self.execute.assert_called_once_with(cmd, run_as_root=True)
self.execute.reset_mock()
def _verify_bridge_sysctl_mock(self, cmd):
self.execute.assert_called_once_with(cmd, run_as_root=True,
log_fail_as_error=True)
self.execute.reset_mock()
def test_is_bridged_interface(self):
exists = lambda path: path == "/sys/class/net/tapOK/brport"
with mock.patch('os.path.exists', side_effect=exists):
self.assertTrue(bridge_lib.is_bridged_interface("tapOK"))
self.assertFalse(bridge_lib.is_bridged_interface("tapKO"))
def test_get_interface_bridge(self):
with mock.patch('os.readlink', side_effect=["prefix/br0", OSError()]):
br = bridge_lib.BridgeDevice.get_interface_bridge('tap0')
self.assertIsInstance(br, bridge_lib.BridgeDevice)
self.assertEqual("br0", br.name)
br = bridge_lib.BridgeDevice.get_interface_bridge('tap0')
self.assertIsNone(br)
def _test_br(self, namespace=None):
br = bridge_lib.BridgeDevice.addbr(self._BR_NAME, namespace)
self.assertEqual(namespace, br.namespace)
self._verify_bridge_mock(['brctl', 'addbr', self._BR_NAME])
br.setfd(0)
self._verify_bridge_mock(['brctl', 'setfd', self._BR_NAME, '0'])
br.disable_stp()
self._verify_bridge_mock(['brctl', 'stp', self._BR_NAME, 'off'])
br.disable_ipv6()
cmd = 'net.ipv6.conf.%s.disable_ipv6=1' % self._BR_NAME
self._verify_bridge_sysctl_mock(['sysctl', '-w', cmd])
br.addif(self._IF_NAME)
self._verify_bridge_mock(
['brctl', 'addif', self._BR_NAME, self._IF_NAME])
br.delif(self._IF_NAME)
self._verify_bridge_mock(
['brctl', 'delif', self._BR_NAME, self._IF_NAME])
br.delbr()
self._verify_bridge_mock(['brctl', 'delbr', self._BR_NAME])
def test_addbr_with_namespace(self):
self._test_br(self._NAMESPACE)
def test_addbr_without_namespace(self):
self._test_br()
def test_addbr_exists(self):
self.execute.side_effect = RuntimeError()
with mock.patch.object(bridge_lib.BridgeDevice, 'exists',
return_value=True):
bridge_lib.BridgeDevice.addbr(self._BR_NAME)
bridge_lib.BridgeDevice.addbr(self._BR_NAME)
def test_owns_interface(self):
br = bridge_lib.BridgeDevice('br-int')
exists = lambda path: path == "/sys/class/net/br-int/brif/abc"
with mock.patch('os.path.exists', side_effect=exists):
self.assertTrue(br.owns_interface("abc"))
self.assertFalse(br.owns_interface("def"))
def test_get_interfaces(self):
br = bridge_lib.BridgeDevice('br-int')
interfaces = ["tap1", "tap2"]
with mock.patch('os.listdir', side_effect=[interfaces, OSError()]):
self.assertEqual(interfaces, br.get_interfaces())
self.assertEqual([], br.get_interfaces())
|
import sublime
import sublime_plugin
import subprocess
import os
import re
class GitManager:
def __init__(self, view):
self.view = view
s = sublime.load_settings("Git-StatusBar.sublime-settings")
self.git = s.get("git", "git")
self.prefix = s.get("prefix", "")
def run_git(self, cmd, cwd=None):
plat = sublime.platform()
if not cwd:
cwd = self.getcwd()
if cwd:
if type(cmd) == str:
cmd = [cmd]
cmd = [self.git] + cmd
if plat == "windows":
# make sure console does not come up
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
cwd=cwd, startupinfo=startupinfo)
else:
my_env = os.environ.copy()
my_env["PATH"] = "/usr/local/bin/:" + my_env["PATH"]
p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
cwd=cwd, env=my_env)
p.wait()
stdoutdata, _ = p.communicate()
return stdoutdata.decode('utf-8')
def getcwd(self):
f = self.view.file_name()
cwd = None
if f:
cwd = os.path.dirname(f)
if not cwd:
window = self.view.window()
if window:
pd = window.project_data()
if pd:
cwd = pd.get("folders")[0].get("path")
return cwd
def branch(self):
ret = self.run_git(["symbolic-ref", "HEAD", "--short"])
if ret:
ret = ret.strip()
else:
output = self.run_git("branch")
if output:
m = re.search(r"\* *\(detached from (.*?)\)", output, flags=re.MULTILINE)
ret = m.group(1)
return ret
def is_dirty(self):
output = self.run_git("status")
ret = "working directory clean" not in output
return ret
def unpushed_info(self):
branch = self.branch()
a, b = 0, 0
if branch:
output = self.run_git(["branch", "-v"])
if output:
m = re.search(r"\* .*?\[behind ([0-9])+\]", output, flags=re.MULTILINE)
if m:
a = int(m.group(1))
m = re.search(r"\* .*?\[ahead ([0-9])+\]", output, flags=re.MULTILINE)
if m:
b = int(m.group(1))
return (a, b)
def badge(self):
branch = self.branch()
if not branch:
return ""
ret = branch
if self.is_dirty():
ret = ret + "*"
a, b = self.unpushed_info()
if a:
ret = ret + "-%d" % a
if b:
ret = ret + "+%d" % b
return self.prefix + ret
class GitStatusBarHandler(sublime_plugin.EventListener):
def update_status_bar(self, view):
if view.is_scratch() or view.settings().get('is_widget'):
return
gm = GitManager(view)
badge = gm.badge()
if badge:
view.set_status("git-statusbar", badge)
else:
view.erase_status("git-statusbar")
def on_new(self, view):
self.update_status_bar(view)
def on_load(self, view):
self.update_status_bar(view)
def on_activated(self, view):
self.update_status_bar(view)
def on_deactivated(self, view):
self.update_status_bar(view)
def on_post_save(self, view):
self.update_status_bar(view)
def on_pre_close(self, view):
self.update_status_bar(view)
def on_window_command(self, window, command_name, args):
if command_name == "hide_panel":
self.update_status_bar(window.active_view())
check if re.search returns
import sublime
import sublime_plugin
import subprocess
import os
import re
class GitManager:
def __init__(self, view):
self.view = view
s = sublime.load_settings("Git-StatusBar.sublime-settings")
self.git = s.get("git", "git")
self.prefix = s.get("prefix", "")
def run_git(self, cmd, cwd=None):
plat = sublime.platform()
if not cwd:
cwd = self.getcwd()
if cwd:
if type(cmd) == str:
cmd = [cmd]
cmd = [self.git] + cmd
if plat == "windows":
# make sure console does not come up
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
cwd=cwd, startupinfo=startupinfo)
else:
my_env = os.environ.copy()
my_env["PATH"] = "/usr/local/bin/:" + my_env["PATH"]
p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
cwd=cwd, env=my_env)
p.wait()
stdoutdata, _ = p.communicate()
return stdoutdata.decode('utf-8')
def getcwd(self):
f = self.view.file_name()
cwd = None
if f:
cwd = os.path.dirname(f)
if not cwd:
window = self.view.window()
if window:
pd = window.project_data()
if pd:
cwd = pd.get("folders")[0].get("path")
return cwd
def branch(self):
ret = self.run_git(["symbolic-ref", "HEAD", "--short"])
if ret:
ret = ret.strip()
else:
output = self.run_git("branch")
if output:
m = re.search(r"\* *\(detached from (.*?)\)", output, flags=re.MULTILINE)
if m:
ret = m.group(1)
return ret
def is_dirty(self):
output = self.run_git("status")
ret = "working directory clean" not in output
return ret
def unpushed_info(self):
branch = self.branch()
a, b = 0, 0
if branch:
output = self.run_git(["branch", "-v"])
if output:
m = re.search(r"\* .*?\[behind ([0-9])+\]", output, flags=re.MULTILINE)
if m:
a = int(m.group(1))
m = re.search(r"\* .*?\[ahead ([0-9])+\]", output, flags=re.MULTILINE)
if m:
b = int(m.group(1))
return (a, b)
def badge(self):
branch = self.branch()
if not branch:
return ""
ret = branch
if self.is_dirty():
ret = ret + "*"
a, b = self.unpushed_info()
if a:
ret = ret + "-%d" % a
if b:
ret = ret + "+%d" % b
return self.prefix + ret
class GitStatusBarHandler(sublime_plugin.EventListener):
def update_status_bar(self, view):
if view.is_scratch() or view.settings().get('is_widget'):
return
gm = GitManager(view)
badge = gm.badge()
if badge:
view.set_status("git-statusbar", badge)
else:
view.erase_status("git-statusbar")
def on_new(self, view):
self.update_status_bar(view)
def on_load(self, view):
self.update_status_bar(view)
def on_activated(self, view):
self.update_status_bar(view)
def on_deactivated(self, view):
self.update_status_bar(view)
def on_post_save(self, view):
self.update_status_bar(view)
def on_pre_close(self, view):
self.update_status_bar(view)
def on_window_command(self, window, command_name, args):
if command_name == "hide_panel":
self.update_status_bar(window.active_view())
|
""" History related magics and functionality """
#-----------------------------------------------------------------------------
# Copyright (C) 2010 The IPython Development Team.
#
# Distributed under the terms of the BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import print_function
# Stdlib imports
import atexit
import datetime
import os
import re
import sqlite3
import threading
# Our own packages
from IPython.config.configurable import Configurable
from IPython.testing.skipdoctest import skip_doctest
from IPython.utils import io
from IPython.utils.traitlets import Bool, Dict, Instance, Int, List, Unicode
from IPython.utils.warn import warn
#-----------------------------------------------------------------------------
# Classes and functions
#-----------------------------------------------------------------------------
class HistoryManager(Configurable):
"""A class to organize all history-related functionality in one place.
"""
# Public interface
# An instance of the IPython shell we are attached to
shell = Instance('IPython.core.interactiveshell.InteractiveShellABC')
# Lists to hold processed and raw history. These start with a blank entry
# so that we can index them starting from 1
input_hist_parsed = List([""])
input_hist_raw = List([""])
# A list of directories visited during session
dir_hist = List()
def _dir_hist_default(self):
try:
return [os.getcwdu()]
except OSError:
return []
# A dict of output history, keyed with ints from the shell's
# execution count.
output_hist = Dict()
# The text/plain repr of outputs.
output_hist_reprs = Dict()
# String holding the path to the history file
hist_file = Unicode(config=True)
# The SQLite database
db = Instance(sqlite3.Connection)
# The number of the current session in the history database
session_number = Int()
# Should we log output to the database? (default no)
db_log_output = Bool(False, config=True)
# Write to database every x commands (higher values save disk access & power)
# Values of 1 or less effectively disable caching.
db_cache_size = Int(0, config=True)
# The input and output caches
db_input_cache = List()
db_output_cache = List()
# History saving in separate thread
save_thread = Instance('IPython.core.history.HistorySavingThread')
# N.B. Event is a function returning an instance of _Event.
save_flag = Instance(threading._Event)
# Private interface
# Variables used to store the three last inputs from the user. On each new
# history update, we populate the user's namespace with these, shifted as
# necessary.
_i00 = Unicode(u'')
_i = Unicode(u'')
_ii = Unicode(u'')
_iii = Unicode(u'')
# A regex matching all forms of the exit command, so that we don't store
# them in the history (it's annoying to rewind the first entry and land on
# an exit call).
_exit_re = re.compile(r"(exit|quit)(\s*\(.*\))?$")
def __init__(self, shell, config=None, **traits):
"""Create a new history manager associated with a shell instance.
"""
# We need a pointer back to the shell for various tasks.
super(HistoryManager, self).__init__(shell=shell, config=config,
**traits)
if self.hist_file == u'':
# No one has set the hist_file, yet.
histfname = 'history'
self.hist_file = os.path.join(shell.profile_dir.location, histfname + '.sqlite')
try:
self.init_db()
except sqlite3.DatabaseError:
if os.path.isfile(self.hist_file):
# Try to move the file out of the way.
newpath = os.path.join(self.shell.profile_dir.location, "hist-corrupt.sqlite")
os.rename(self.hist_file, newpath)
print("ERROR! History file wasn't a valid SQLite database.",
"It was moved to %s" % newpath, "and a new file created.")
self.init_db()
else:
# The hist_file is probably :memory: or something else.
raise
self.save_flag = threading.Event()
self.db_input_cache_lock = threading.Lock()
self.db_output_cache_lock = threading.Lock()
self.save_thread = HistorySavingThread(self)
self.save_thread.start()
self.new_session()
def init_db(self):
"""Connect to the database, and create tables if necessary."""
# use detect_types so that timestamps return datetime objects
self.db = sqlite3.connect(self.hist_file, detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
self.db.execute("""CREATE TABLE IF NOT EXISTS sessions (session integer
primary key autoincrement, start timestamp,
end timestamp, num_cmds integer, remark text)""")
self.db.execute("""CREATE TABLE IF NOT EXISTS history
(session integer, line integer, source text, source_raw text,
PRIMARY KEY (session, line))""")
# Output history is optional, but ensure the table's there so it can be
# enabled later.
self.db.execute("""CREATE TABLE IF NOT EXISTS output_history
(session integer, line integer, output text,
PRIMARY KEY (session, line))""")
self.db.commit()
def new_session(self, conn=None):
"""Get a new session number."""
if conn is None:
conn = self.db
with conn:
cur = conn.execute("""INSERT INTO sessions VALUES (NULL, ?, NULL,
NULL, "") """, (datetime.datetime.now(),))
self.session_number = cur.lastrowid
def end_session(self):
"""Close the database session, filling in the end time and line count."""
self.writeout_cache()
with self.db:
self.db.execute("""UPDATE sessions SET end=?, num_cmds=? WHERE
session==?""", (datetime.datetime.now(),
len(self.input_hist_parsed)-1, self.session_number))
self.session_number = 0
def name_session(self, name):
"""Give the current session a name in the history database."""
with self.db:
self.db.execute("UPDATE sessions SET remark=? WHERE session==?",
(name, self.session_number))
def reset(self, new_session=True):
"""Clear the session history, releasing all object references, and
optionally open a new session."""
self.output_hist.clear()
# The directory history can't be completely empty
self.dir_hist[:] = [os.getcwdu()]
if new_session:
if self.session_number:
self.end_session()
self.input_hist_parsed[:] = [""]
self.input_hist_raw[:] = [""]
self.new_session()
## -------------------------------
## Methods for retrieving history:
## -------------------------------
def _run_sql(self, sql, params, raw=True, output=False):
"""Prepares and runs an SQL query for the history database.
Parameters
----------
sql : str
Any filtering expressions to go after SELECT ... FROM ...
params : tuple
Parameters passed to the SQL query (to replace "?")
raw, output : bool
See :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
toget = 'source_raw' if raw else 'source'
sqlfrom = "history"
if output:
sqlfrom = "history LEFT JOIN output_history USING (session, line)"
toget = "history.%s, output_history.output" % toget
cur = self.db.execute("SELECT session, line, %s FROM %s " %\
(toget, sqlfrom) + sql, params)
if output: # Regroup into 3-tuples, and parse JSON
return ((ses, lin, (inp, out)) for ses, lin, inp, out in cur)
return cur
def get_session_info(self, session=0):
"""get info about a session
Parameters
----------
session : int
Session number to retrieve. The current session is 0, and negative
numbers count back from current session, so -1 is previous session.
Returns
-------
(session_id [int], start [datetime], end [datetime], num_cmds [int], remark [unicode])
Sessions that are running or did not exit cleanly will have `end=None`
and `num_cmds=None`.
"""
if session <= 0:
session += self.session_number
query = "SELECT * from sessions where session == ?"
return self.db.execute(query, (session,)).fetchone()
def get_tail(self, n=10, raw=True, output=False, include_latest=False):
"""Get the last n lines from the history database.
Parameters
----------
n : int
The number of lines to get
raw, output : bool
See :meth:`get_range`
include_latest : bool
If False (default), n+1 lines are fetched, and the latest one
is discarded. This is intended to be used where the function
is called by a user command, which it should not return.
Returns
-------
Tuples as :meth:`get_range`
"""
self.writeout_cache()
if not include_latest:
n += 1
cur = self._run_sql("ORDER BY session DESC, line DESC LIMIT ?",
(n,), raw=raw, output=output)
if not include_latest:
return reversed(list(cur)[1:])
return reversed(list(cur))
def search(self, pattern="*", raw=True, search_raw=True,
output=False):
"""Search the database using unix glob-style matching (wildcards
* and ?).
Parameters
----------
pattern : str
The wildcarded pattern to match when searching
search_raw : bool
If True, search the raw input, otherwise, the parsed input
raw, output : bool
See :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
tosearch = "source_raw" if search_raw else "source"
if output:
tosearch = "history." + tosearch
self.writeout_cache()
return self._run_sql("WHERE %s GLOB ?" % tosearch, (pattern,),
raw=raw, output=output)
def _get_range_session(self, start=1, stop=None, raw=True, output=False):
"""Get input and output history from the current session. Called by
get_range, and takes similar parameters."""
input_hist = self.input_hist_raw if raw else self.input_hist_parsed
n = len(input_hist)
if start < 0:
start += n
if not stop:
stop = n
elif stop < 0:
stop += n
for i in range(start, stop):
if output:
line = (input_hist[i], self.output_hist_reprs.get(i))
else:
line = input_hist[i]
yield (0, i, line)
def get_range(self, session=0, start=1, stop=None, raw=True,output=False):
"""Retrieve input by session.
Parameters
----------
session : int
Session number to retrieve. The current session is 0, and negative
numbers count back from current session, so -1 is previous session.
start : int
First line to retrieve.
stop : int
End of line range (excluded from output itself). If None, retrieve
to the end of the session.
raw : bool
If True, return untranslated input
output : bool
If True, attempt to include output. This will be 'real' Python
objects for the current session, or text reprs from previous
sessions if db_log_output was enabled at the time. Where no output
is found, None is used.
Returns
-------
An iterator over the desired lines. Each line is a 3-tuple, either
(session, line, input) if output is False, or
(session, line, (input, output)) if output is True.
"""
if session == 0 or session==self.session_number: # Current session
return self._get_range_session(start, stop, raw, output)
if session < 0:
session += self.session_number
if stop:
lineclause = "line >= ? AND line < ?"
params = (session, start, stop)
else:
lineclause = "line>=?"
params = (session, start)
return self._run_sql("WHERE session==? AND %s""" % lineclause,
params, raw=raw, output=output)
def get_range_by_str(self, rangestr, raw=True, output=False):
"""Get lines of history from a string of ranges, as used by magic
commands %hist, %save, %macro, etc.
Parameters
----------
rangestr : str
A string specifying ranges, e.g. "5 ~2/1-4". See
:func:`magic_history` for full details.
raw, output : bool
As :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
for sess, s, e in extract_hist_ranges(rangestr):
for line in self.get_range(sess, s, e, raw=raw, output=output):
yield line
## ----------------------------
## Methods for storing history:
## ----------------------------
def store_inputs(self, line_num, source, source_raw=None):
"""Store source and raw input in history and create input cache
variables _i*.
Parameters
----------
line_num : int
The prompt number of this input.
source : str
Python input.
source_raw : str, optional
If given, this is the raw input without any IPython transformations
applied to it. If not given, ``source`` is used.
"""
if source_raw is None:
source_raw = source
source = source.rstrip('\n')
source_raw = source_raw.rstrip('\n')
# do not store exit/quit commands
if self._exit_re.match(source_raw.strip()):
return
self.input_hist_parsed.append(source)
self.input_hist_raw.append(source_raw)
with self.db_input_cache_lock:
self.db_input_cache.append((line_num, source, source_raw))
# Trigger to flush cache and write to DB.
if len(self.db_input_cache) >= self.db_cache_size:
self.save_flag.set()
# update the auto _i variables
self._iii = self._ii
self._ii = self._i
self._i = self._i00
self._i00 = source_raw
# hackish access to user namespace to create _i1,_i2... dynamically
new_i = '_i%s' % line_num
to_main = {'_i': self._i,
'_ii': self._ii,
'_iii': self._iii,
new_i : self._i00 }
self.shell.user_ns.update(to_main)
def store_output(self, line_num):
"""If database output logging is enabled, this saves all the
outputs from the indicated prompt number to the database. It's
called by run_cell after code has been executed.
Parameters
----------
line_num : int
The line number from which to save outputs
"""
if (not self.db_log_output) or (line_num not in self.output_hist_reprs):
return
output = self.output_hist_reprs[line_num]
with self.db_output_cache_lock:
self.db_output_cache.append((line_num, output))
if self.db_cache_size <= 1:
self.save_flag.set()
def _writeout_input_cache(self, conn):
with conn:
for line in self.db_input_cache:
conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(self.session_number,)+line)
def _writeout_output_cache(self, conn):
with conn:
for line in self.db_output_cache:
conn.execute("INSERT INTO output_history VALUES (?, ?, ?)",
(self.session_number,)+line)
def writeout_cache(self, conn=None):
"""Write any entries in the cache to the database."""
if conn is None:
conn = self.db
with self.db_input_cache_lock:
try:
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
self.new_session(conn)
print("ERROR! Session/line number was not unique in",
"database. History logging moved to new session",
self.session_number)
try: # Try writing to the new session. If this fails, don't recurse
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
pass
finally:
self.db_input_cache = []
with self.db_output_cache_lock:
try:
self._writeout_output_cache(conn)
except sqlite3.IntegrityError:
print("!! Session/line number for output was not unique",
"in database. Output will not be stored.")
finally:
self.db_output_cache = []
class HistorySavingThread(threading.Thread):
"""This thread takes care of writing history to the database, so that
the UI isn't held up while that happens.
It waits for the HistoryManager's save_flag to be set, then writes out
the history cache. The main thread is responsible for setting the flag when
the cache size reaches a defined threshold."""
daemon = True
stop_now = False
def __init__(self, history_manager):
super(HistorySavingThread, self).__init__()
self.history_manager = history_manager
atexit.register(self.stop)
def run(self):
# We need a separate db connection per thread:
try:
self.db = sqlite3.connect(self.history_manager.hist_file)
while True:
self.history_manager.save_flag.wait()
if self.stop_now:
return
self.history_manager.save_flag.clear()
self.history_manager.writeout_cache(self.db)
except Exception as e:
print(("The history saving thread hit an unexpected error (%s)."
"History will not be written to the database.") % repr(e))
def stop(self):
"""This can be called from the main thread to safely stop this thread.
Note that it does not attempt to write out remaining history before
exiting. That should be done by calling the HistoryManager's
end_session method."""
self.stop_now = True
self.history_manager.save_flag.set()
self.join()
# To match, e.g. ~5/8-~2/3
range_re = re.compile(r"""
((?P<startsess>~?\d+)/)?
(?P<start>\d+) # Only the start line num is compulsory
((?P<sep>[\-:])
((?P<endsess>~?\d+)/)?
(?P<end>\d+))?
$""", re.VERBOSE)
def extract_hist_ranges(ranges_str):
"""Turn a string of history ranges into 3-tuples of (session, start, stop).
Examples
--------
list(extract_input_ranges("~8/5-~7/4 2"))
[(-8, 5, None), (-7, 1, 4), (0, 2, 3)]
"""
for range_str in ranges_str.split():
rmatch = range_re.match(range_str)
if not rmatch:
continue
start = int(rmatch.group("start"))
end = rmatch.group("end")
end = int(end) if end else start+1 # If no end specified, get (a, a+1)
if rmatch.group("sep") == "-": # 1-3 == 1:4 --> [1, 2, 3]
end += 1
startsess = rmatch.group("startsess") or "0"
endsess = rmatch.group("endsess") or startsess
startsess = int(startsess.replace("~","-"))
endsess = int(endsess.replace("~","-"))
assert endsess >= startsess
if endsess == startsess:
yield (startsess, start, end)
continue
# Multiple sessions in one range:
yield (startsess, start, None)
for sess in range(startsess+1, endsess):
yield (sess, 1, None)
yield (endsess, 1, end)
def _format_lineno(session, line):
"""Helper function to format line numbers properly."""
if session == 0:
return str(line)
return "%s#%s" % (session, line)
@skip_doctest
def magic_history(self, parameter_s = ''):
"""Print input history (_i<n> variables), with most recent last.
%history -> print at most 40 inputs (some may be multi-line)\\
%history n -> print at most n inputs\\
%history n1 n2 -> print inputs between n1 and n2 (n2 not included)\\
By default, input history is printed without line numbers so it can be
directly pasted into an editor. Use -n to show them.
Ranges of history can be indicated using the syntax:
4 : Line 4, current session
4-6 : Lines 4-6, current session
243/1-5: Lines 1-5, session 243
~2/7 : Line 7, session 2 before current
~8/1-~6/5 : From the first line of 8 sessions ago, to the fifth line
of 6 sessions ago.
Multiple ranges can be entered, separated by spaces
The same syntax is used by %macro, %save, %edit, %rerun
Options:
-n: print line numbers for each input.
This feature is only available if numbered prompts are in use.
-o: also print outputs for each input.
-p: print classic '>>>' python prompts before each input. This is useful
for making documentation, and in conjunction with -o, for producing
doctest-ready output.
-r: (default) print the 'raw' history, i.e. the actual commands you typed.
-t: print the 'translated' history, as IPython understands it. IPython
filters your input and converts it all into valid Python source before
executing it (things like magics or aliases are turned into function
calls, for example). With this option, you'll see the native history
instead of the user-entered version: '%cd /' will be seen as
'get_ipython().magic("%cd /")' instead of '%cd /'.
-g: treat the arg as a pattern to grep for in (full) history.
This includes the saved history (almost all commands ever written).
Use '%hist -g' to show full saved history (may be very long).
-l: get the last n lines from all sessions. Specify n as a single arg, or
the default is the last 10 lines.
-f FILENAME: instead of printing the output to the screen, redirect it to
the given file. The file is always overwritten, though IPython asks for
confirmation first if it already exists.
Examples
--------
::
In [6]: %hist -n 4 6
4:a = 12
5:print a**2
"""
if not self.shell.displayhook.do_full_cache:
print('This feature is only available if numbered prompts are in use.')
return
opts,args = self.parse_options(parameter_s,'noprtglf:',mode='string')
# For brevity
history_manager = self.shell.history_manager
def _format_lineno(session, line):
"""Helper function to format line numbers properly."""
if session in (0, history_manager.session_number):
return str(line)
return "%s/%s" % (session, line)
# Check if output to specific file was requested.
try:
outfname = opts['f']
except KeyError:
outfile = io.stdout # default
# We don't want to close stdout at the end!
close_at_end = False
else:
if os.path.exists(outfname):
if not io.ask_yes_no("File %r exists. Overwrite?" % outfname):
print('Aborting.')
return
outfile = open(outfname,'w')
close_at_end = True
print_nums = 'n' in opts
get_output = 'o' in opts
pyprompts = 'p' in opts
# Raw history is the default
raw = not('t' in opts)
default_length = 40
pattern = None
if 'g' in opts: # Glob search
pattern = "*" + args + "*" if args else "*"
hist = history_manager.search(pattern, raw=raw, output=get_output)
print_nums = True
elif 'l' in opts: # Get 'tail'
try:
n = int(args)
except ValueError, IndexError:
n = 10
hist = history_manager.get_tail(n, raw=raw, output=get_output)
else:
if args: # Get history by ranges
hist = history_manager.get_range_by_str(args, raw, get_output)
else: # Just get history for the current session
hist = history_manager.get_range(raw=raw, output=get_output)
# We could be displaying the entire history, so let's not try to pull it
# into a list in memory. Anything that needs more space will just misalign.
width = 4
for session, lineno, inline in hist:
# Print user history with tabs expanded to 4 spaces. The GUI clients
# use hard tabs for easier usability in auto-indented code, but we want
# to produce PEP-8 compliant history for safe pasting into an editor.
if get_output:
inline, output = inline
inline = inline.expandtabs(4).rstrip()
multiline = "\n" in inline
line_sep = '\n' if multiline else ' '
if print_nums:
print('%s:%s' % (_format_lineno(session, lineno).rjust(width),
line_sep), file=outfile, end='')
if pyprompts:
print(">>> ", end="", file=outfile)
if multiline:
inline = "\n... ".join(inline.splitlines()) + "\n..."
print(inline, file=outfile)
if get_output and output:
print(output, file=outfile)
if close_at_end:
outfile.close()
def magic_rep(self, arg):
r"""Repeat a command, or get command to input line for editing. %recall and
%rep are equivalent.
- %recall (no arguments):
Place a string version of last computation result (stored in the special '_'
variable) to the next input prompt. Allows you to create elaborate command
lines without using copy-paste::
In[1]: l = ["hei", "vaan"]
In[2]: "".join(l)
Out[2]: heivaan
In[3]: %rep
In[4]: heivaan_ <== cursor blinking
%recall 45
Place history line 45 on the next input prompt. Use %hist to find
out the number.
%recall 1-4
Combine the specified lines into one cell, and place it on the next
input prompt. See %history for the slice syntax.
%recall foo+bar
If foo+bar can be evaluated in the user namespace, the result is
placed at the next input prompt. Otherwise, the history is searched
for lines which contain that substring, and the most recent one is
placed at the next input prompt.
"""
if not arg: # Last output
self.set_next_input(str(self.shell.user_ns["_"]))
return
# Get history range
histlines = self.history_manager.get_range_by_str(arg)
cmd = "\n".join(x[2] for x in histlines)
if cmd:
self.set_next_input(cmd.rstrip())
return
try: # Variable in user namespace
cmd = str(eval(arg, self.shell.user_ns))
except Exception: # Search for term in history
histlines = self.history_manager.search("*"+arg+"*")
for h in reversed([x[2] for x in histlines]):
if 'rep' in h:
continue
self.set_next_input(h.rstrip())
return
else:
self.set_next_input(cmd.rstrip())
print("Couldn't evaluate or find in history:", arg)
def magic_rerun(self, parameter_s=''):
"""Re-run previous input
By default, you can specify ranges of input history to be repeated
(as with %history). With no arguments, it will repeat the last line.
Options:
-l <n> : Repeat the last n lines of input, not including the
current command.
-g foo : Repeat the most recent line which contains foo
"""
opts, args = self.parse_options(parameter_s, 'l:g:', mode='string')
if "l" in opts: # Last n lines
n = int(opts['l'])
hist = self.history_manager.get_tail(n)
elif "g" in opts: # Search
p = "*"+opts['g']+"*"
hist = list(self.history_manager.search(p))
for l in reversed(hist):
if "rerun" not in l[2]:
hist = [l] # The last match which isn't a %rerun
break
else:
hist = [] # No matches except %rerun
elif args: # Specify history ranges
hist = self.history_manager.get_range_by_str(args)
else: # Last line
hist = self.history_manager.get_tail(1)
hist = [x[2] for x in hist]
if not hist:
print("No lines in history match specification")
return
histlines = "\n".join(hist)
print("=== Executing: ===")
print(histlines)
print("=== Output: ===")
self.run_cell("\n".join(hist), store_history=False)
def init_ipython(ip):
ip.define_magic("rep", magic_rep)
ip.define_magic("recall", magic_rep)
ip.define_magic("rerun", magic_rerun)
ip.define_magic("hist",magic_history) # Alternative name
ip.define_magic("history",magic_history)
# XXX - ipy_completers are in quarantine, need to be updated to new apis
#import ipy_completers
#ipy_completers.quick_completer('%hist' ,'-g -t -r -n')
Use casting trait for session number in history, so IPython will start on PyPy.
Closes gh-719
""" History related magics and functionality """
#-----------------------------------------------------------------------------
# Copyright (C) 2010 The IPython Development Team.
#
# Distributed under the terms of the BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import print_function
# Stdlib imports
import atexit
import datetime
import os
import re
import sqlite3
import threading
# Our own packages
from IPython.config.configurable import Configurable
from IPython.testing.skipdoctest import skip_doctest
from IPython.utils import io
from IPython.utils.traitlets import Bool, Dict, Instance, Int, CInt, List, Unicode
from IPython.utils.warn import warn
#-----------------------------------------------------------------------------
# Classes and functions
#-----------------------------------------------------------------------------
class HistoryManager(Configurable):
"""A class to organize all history-related functionality in one place.
"""
# Public interface
# An instance of the IPython shell we are attached to
shell = Instance('IPython.core.interactiveshell.InteractiveShellABC')
# Lists to hold processed and raw history. These start with a blank entry
# so that we can index them starting from 1
input_hist_parsed = List([""])
input_hist_raw = List([""])
# A list of directories visited during session
dir_hist = List()
def _dir_hist_default(self):
try:
return [os.getcwdu()]
except OSError:
return []
# A dict of output history, keyed with ints from the shell's
# execution count.
output_hist = Dict()
# The text/plain repr of outputs.
output_hist_reprs = Dict()
# String holding the path to the history file
hist_file = Unicode(config=True)
# The SQLite database
db = Instance(sqlite3.Connection)
# The number of the current session in the history database
session_number = CInt()
# Should we log output to the database? (default no)
db_log_output = Bool(False, config=True)
# Write to database every x commands (higher values save disk access & power)
# Values of 1 or less effectively disable caching.
db_cache_size = Int(0, config=True)
# The input and output caches
db_input_cache = List()
db_output_cache = List()
# History saving in separate thread
save_thread = Instance('IPython.core.history.HistorySavingThread')
# N.B. Event is a function returning an instance of _Event.
save_flag = Instance(threading._Event)
# Private interface
# Variables used to store the three last inputs from the user. On each new
# history update, we populate the user's namespace with these, shifted as
# necessary.
_i00 = Unicode(u'')
_i = Unicode(u'')
_ii = Unicode(u'')
_iii = Unicode(u'')
# A regex matching all forms of the exit command, so that we don't store
# them in the history (it's annoying to rewind the first entry and land on
# an exit call).
_exit_re = re.compile(r"(exit|quit)(\s*\(.*\))?$")
def __init__(self, shell, config=None, **traits):
"""Create a new history manager associated with a shell instance.
"""
# We need a pointer back to the shell for various tasks.
super(HistoryManager, self).__init__(shell=shell, config=config,
**traits)
if self.hist_file == u'':
# No one has set the hist_file, yet.
histfname = 'history'
self.hist_file = os.path.join(shell.profile_dir.location, histfname + '.sqlite')
try:
self.init_db()
except sqlite3.DatabaseError:
if os.path.isfile(self.hist_file):
# Try to move the file out of the way.
newpath = os.path.join(self.shell.profile_dir.location, "hist-corrupt.sqlite")
os.rename(self.hist_file, newpath)
print("ERROR! History file wasn't a valid SQLite database.",
"It was moved to %s" % newpath, "and a new file created.")
self.init_db()
else:
# The hist_file is probably :memory: or something else.
raise
self.save_flag = threading.Event()
self.db_input_cache_lock = threading.Lock()
self.db_output_cache_lock = threading.Lock()
self.save_thread = HistorySavingThread(self)
self.save_thread.start()
self.new_session()
def init_db(self):
"""Connect to the database, and create tables if necessary."""
# use detect_types so that timestamps return datetime objects
self.db = sqlite3.connect(self.hist_file, detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
self.db.execute("""CREATE TABLE IF NOT EXISTS sessions (session integer
primary key autoincrement, start timestamp,
end timestamp, num_cmds integer, remark text)""")
self.db.execute("""CREATE TABLE IF NOT EXISTS history
(session integer, line integer, source text, source_raw text,
PRIMARY KEY (session, line))""")
# Output history is optional, but ensure the table's there so it can be
# enabled later.
self.db.execute("""CREATE TABLE IF NOT EXISTS output_history
(session integer, line integer, output text,
PRIMARY KEY (session, line))""")
self.db.commit()
def new_session(self, conn=None):
"""Get a new session number."""
if conn is None:
conn = self.db
with conn:
cur = conn.execute("""INSERT INTO sessions VALUES (NULL, ?, NULL,
NULL, "") """, (datetime.datetime.now(),))
self.session_number = cur.lastrowid
def end_session(self):
"""Close the database session, filling in the end time and line count."""
self.writeout_cache()
with self.db:
self.db.execute("""UPDATE sessions SET end=?, num_cmds=? WHERE
session==?""", (datetime.datetime.now(),
len(self.input_hist_parsed)-1, self.session_number))
self.session_number = 0
def name_session(self, name):
"""Give the current session a name in the history database."""
with self.db:
self.db.execute("UPDATE sessions SET remark=? WHERE session==?",
(name, self.session_number))
def reset(self, new_session=True):
"""Clear the session history, releasing all object references, and
optionally open a new session."""
self.output_hist.clear()
# The directory history can't be completely empty
self.dir_hist[:] = [os.getcwdu()]
if new_session:
if self.session_number:
self.end_session()
self.input_hist_parsed[:] = [""]
self.input_hist_raw[:] = [""]
self.new_session()
## -------------------------------
## Methods for retrieving history:
## -------------------------------
def _run_sql(self, sql, params, raw=True, output=False):
"""Prepares and runs an SQL query for the history database.
Parameters
----------
sql : str
Any filtering expressions to go after SELECT ... FROM ...
params : tuple
Parameters passed to the SQL query (to replace "?")
raw, output : bool
See :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
toget = 'source_raw' if raw else 'source'
sqlfrom = "history"
if output:
sqlfrom = "history LEFT JOIN output_history USING (session, line)"
toget = "history.%s, output_history.output" % toget
cur = self.db.execute("SELECT session, line, %s FROM %s " %\
(toget, sqlfrom) + sql, params)
if output: # Regroup into 3-tuples, and parse JSON
return ((ses, lin, (inp, out)) for ses, lin, inp, out in cur)
return cur
def get_session_info(self, session=0):
"""get info about a session
Parameters
----------
session : int
Session number to retrieve. The current session is 0, and negative
numbers count back from current session, so -1 is previous session.
Returns
-------
(session_id [int], start [datetime], end [datetime], num_cmds [int], remark [unicode])
Sessions that are running or did not exit cleanly will have `end=None`
and `num_cmds=None`.
"""
if session <= 0:
session += self.session_number
query = "SELECT * from sessions where session == ?"
return self.db.execute(query, (session,)).fetchone()
def get_tail(self, n=10, raw=True, output=False, include_latest=False):
"""Get the last n lines from the history database.
Parameters
----------
n : int
The number of lines to get
raw, output : bool
See :meth:`get_range`
include_latest : bool
If False (default), n+1 lines are fetched, and the latest one
is discarded. This is intended to be used where the function
is called by a user command, which it should not return.
Returns
-------
Tuples as :meth:`get_range`
"""
self.writeout_cache()
if not include_latest:
n += 1
cur = self._run_sql("ORDER BY session DESC, line DESC LIMIT ?",
(n,), raw=raw, output=output)
if not include_latest:
return reversed(list(cur)[1:])
return reversed(list(cur))
def search(self, pattern="*", raw=True, search_raw=True,
output=False):
"""Search the database using unix glob-style matching (wildcards
* and ?).
Parameters
----------
pattern : str
The wildcarded pattern to match when searching
search_raw : bool
If True, search the raw input, otherwise, the parsed input
raw, output : bool
See :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
tosearch = "source_raw" if search_raw else "source"
if output:
tosearch = "history." + tosearch
self.writeout_cache()
return self._run_sql("WHERE %s GLOB ?" % tosearch, (pattern,),
raw=raw, output=output)
def _get_range_session(self, start=1, stop=None, raw=True, output=False):
"""Get input and output history from the current session. Called by
get_range, and takes similar parameters."""
input_hist = self.input_hist_raw if raw else self.input_hist_parsed
n = len(input_hist)
if start < 0:
start += n
if not stop:
stop = n
elif stop < 0:
stop += n
for i in range(start, stop):
if output:
line = (input_hist[i], self.output_hist_reprs.get(i))
else:
line = input_hist[i]
yield (0, i, line)
def get_range(self, session=0, start=1, stop=None, raw=True,output=False):
"""Retrieve input by session.
Parameters
----------
session : int
Session number to retrieve. The current session is 0, and negative
numbers count back from current session, so -1 is previous session.
start : int
First line to retrieve.
stop : int
End of line range (excluded from output itself). If None, retrieve
to the end of the session.
raw : bool
If True, return untranslated input
output : bool
If True, attempt to include output. This will be 'real' Python
objects for the current session, or text reprs from previous
sessions if db_log_output was enabled at the time. Where no output
is found, None is used.
Returns
-------
An iterator over the desired lines. Each line is a 3-tuple, either
(session, line, input) if output is False, or
(session, line, (input, output)) if output is True.
"""
if session == 0 or session==self.session_number: # Current session
return self._get_range_session(start, stop, raw, output)
if session < 0:
session += self.session_number
if stop:
lineclause = "line >= ? AND line < ?"
params = (session, start, stop)
else:
lineclause = "line>=?"
params = (session, start)
return self._run_sql("WHERE session==? AND %s""" % lineclause,
params, raw=raw, output=output)
def get_range_by_str(self, rangestr, raw=True, output=False):
"""Get lines of history from a string of ranges, as used by magic
commands %hist, %save, %macro, etc.
Parameters
----------
rangestr : str
A string specifying ranges, e.g. "5 ~2/1-4". See
:func:`magic_history` for full details.
raw, output : bool
As :meth:`get_range`
Returns
-------
Tuples as :meth:`get_range`
"""
for sess, s, e in extract_hist_ranges(rangestr):
for line in self.get_range(sess, s, e, raw=raw, output=output):
yield line
## ----------------------------
## Methods for storing history:
## ----------------------------
def store_inputs(self, line_num, source, source_raw=None):
"""Store source and raw input in history and create input cache
variables _i*.
Parameters
----------
line_num : int
The prompt number of this input.
source : str
Python input.
source_raw : str, optional
If given, this is the raw input without any IPython transformations
applied to it. If not given, ``source`` is used.
"""
if source_raw is None:
source_raw = source
source = source.rstrip('\n')
source_raw = source_raw.rstrip('\n')
# do not store exit/quit commands
if self._exit_re.match(source_raw.strip()):
return
self.input_hist_parsed.append(source)
self.input_hist_raw.append(source_raw)
with self.db_input_cache_lock:
self.db_input_cache.append((line_num, source, source_raw))
# Trigger to flush cache and write to DB.
if len(self.db_input_cache) >= self.db_cache_size:
self.save_flag.set()
# update the auto _i variables
self._iii = self._ii
self._ii = self._i
self._i = self._i00
self._i00 = source_raw
# hackish access to user namespace to create _i1,_i2... dynamically
new_i = '_i%s' % line_num
to_main = {'_i': self._i,
'_ii': self._ii,
'_iii': self._iii,
new_i : self._i00 }
self.shell.user_ns.update(to_main)
def store_output(self, line_num):
"""If database output logging is enabled, this saves all the
outputs from the indicated prompt number to the database. It's
called by run_cell after code has been executed.
Parameters
----------
line_num : int
The line number from which to save outputs
"""
if (not self.db_log_output) or (line_num not in self.output_hist_reprs):
return
output = self.output_hist_reprs[line_num]
with self.db_output_cache_lock:
self.db_output_cache.append((line_num, output))
if self.db_cache_size <= 1:
self.save_flag.set()
def _writeout_input_cache(self, conn):
with conn:
for line in self.db_input_cache:
conn.execute("INSERT INTO history VALUES (?, ?, ?, ?)",
(self.session_number,)+line)
def _writeout_output_cache(self, conn):
with conn:
for line in self.db_output_cache:
conn.execute("INSERT INTO output_history VALUES (?, ?, ?)",
(self.session_number,)+line)
def writeout_cache(self, conn=None):
"""Write any entries in the cache to the database."""
if conn is None:
conn = self.db
with self.db_input_cache_lock:
try:
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
self.new_session(conn)
print("ERROR! Session/line number was not unique in",
"database. History logging moved to new session",
self.session_number)
try: # Try writing to the new session. If this fails, don't recurse
self._writeout_input_cache(conn)
except sqlite3.IntegrityError:
pass
finally:
self.db_input_cache = []
with self.db_output_cache_lock:
try:
self._writeout_output_cache(conn)
except sqlite3.IntegrityError:
print("!! Session/line number for output was not unique",
"in database. Output will not be stored.")
finally:
self.db_output_cache = []
class HistorySavingThread(threading.Thread):
"""This thread takes care of writing history to the database, so that
the UI isn't held up while that happens.
It waits for the HistoryManager's save_flag to be set, then writes out
the history cache. The main thread is responsible for setting the flag when
the cache size reaches a defined threshold."""
daemon = True
stop_now = False
def __init__(self, history_manager):
super(HistorySavingThread, self).__init__()
self.history_manager = history_manager
atexit.register(self.stop)
def run(self):
# We need a separate db connection per thread:
try:
self.db = sqlite3.connect(self.history_manager.hist_file)
while True:
self.history_manager.save_flag.wait()
if self.stop_now:
return
self.history_manager.save_flag.clear()
self.history_manager.writeout_cache(self.db)
except Exception as e:
print(("The history saving thread hit an unexpected error (%s)."
"History will not be written to the database.") % repr(e))
def stop(self):
"""This can be called from the main thread to safely stop this thread.
Note that it does not attempt to write out remaining history before
exiting. That should be done by calling the HistoryManager's
end_session method."""
self.stop_now = True
self.history_manager.save_flag.set()
self.join()
# To match, e.g. ~5/8-~2/3
range_re = re.compile(r"""
((?P<startsess>~?\d+)/)?
(?P<start>\d+) # Only the start line num is compulsory
((?P<sep>[\-:])
((?P<endsess>~?\d+)/)?
(?P<end>\d+))?
$""", re.VERBOSE)
def extract_hist_ranges(ranges_str):
"""Turn a string of history ranges into 3-tuples of (session, start, stop).
Examples
--------
list(extract_input_ranges("~8/5-~7/4 2"))
[(-8, 5, None), (-7, 1, 4), (0, 2, 3)]
"""
for range_str in ranges_str.split():
rmatch = range_re.match(range_str)
if not rmatch:
continue
start = int(rmatch.group("start"))
end = rmatch.group("end")
end = int(end) if end else start+1 # If no end specified, get (a, a+1)
if rmatch.group("sep") == "-": # 1-3 == 1:4 --> [1, 2, 3]
end += 1
startsess = rmatch.group("startsess") or "0"
endsess = rmatch.group("endsess") or startsess
startsess = int(startsess.replace("~","-"))
endsess = int(endsess.replace("~","-"))
assert endsess >= startsess
if endsess == startsess:
yield (startsess, start, end)
continue
# Multiple sessions in one range:
yield (startsess, start, None)
for sess in range(startsess+1, endsess):
yield (sess, 1, None)
yield (endsess, 1, end)
def _format_lineno(session, line):
"""Helper function to format line numbers properly."""
if session == 0:
return str(line)
return "%s#%s" % (session, line)
@skip_doctest
def magic_history(self, parameter_s = ''):
"""Print input history (_i<n> variables), with most recent last.
%history -> print at most 40 inputs (some may be multi-line)\\
%history n -> print at most n inputs\\
%history n1 n2 -> print inputs between n1 and n2 (n2 not included)\\
By default, input history is printed without line numbers so it can be
directly pasted into an editor. Use -n to show them.
Ranges of history can be indicated using the syntax:
4 : Line 4, current session
4-6 : Lines 4-6, current session
243/1-5: Lines 1-5, session 243
~2/7 : Line 7, session 2 before current
~8/1-~6/5 : From the first line of 8 sessions ago, to the fifth line
of 6 sessions ago.
Multiple ranges can be entered, separated by spaces
The same syntax is used by %macro, %save, %edit, %rerun
Options:
-n: print line numbers for each input.
This feature is only available if numbered prompts are in use.
-o: also print outputs for each input.
-p: print classic '>>>' python prompts before each input. This is useful
for making documentation, and in conjunction with -o, for producing
doctest-ready output.
-r: (default) print the 'raw' history, i.e. the actual commands you typed.
-t: print the 'translated' history, as IPython understands it. IPython
filters your input and converts it all into valid Python source before
executing it (things like magics or aliases are turned into function
calls, for example). With this option, you'll see the native history
instead of the user-entered version: '%cd /' will be seen as
'get_ipython().magic("%cd /")' instead of '%cd /'.
-g: treat the arg as a pattern to grep for in (full) history.
This includes the saved history (almost all commands ever written).
Use '%hist -g' to show full saved history (may be very long).
-l: get the last n lines from all sessions. Specify n as a single arg, or
the default is the last 10 lines.
-f FILENAME: instead of printing the output to the screen, redirect it to
the given file. The file is always overwritten, though IPython asks for
confirmation first if it already exists.
Examples
--------
::
In [6]: %hist -n 4 6
4:a = 12
5:print a**2
"""
if not self.shell.displayhook.do_full_cache:
print('This feature is only available if numbered prompts are in use.')
return
opts,args = self.parse_options(parameter_s,'noprtglf:',mode='string')
# For brevity
history_manager = self.shell.history_manager
def _format_lineno(session, line):
"""Helper function to format line numbers properly."""
if session in (0, history_manager.session_number):
return str(line)
return "%s/%s" % (session, line)
# Check if output to specific file was requested.
try:
outfname = opts['f']
except KeyError:
outfile = io.stdout # default
# We don't want to close stdout at the end!
close_at_end = False
else:
if os.path.exists(outfname):
if not io.ask_yes_no("File %r exists. Overwrite?" % outfname):
print('Aborting.')
return
outfile = open(outfname,'w')
close_at_end = True
print_nums = 'n' in opts
get_output = 'o' in opts
pyprompts = 'p' in opts
# Raw history is the default
raw = not('t' in opts)
default_length = 40
pattern = None
if 'g' in opts: # Glob search
pattern = "*" + args + "*" if args else "*"
hist = history_manager.search(pattern, raw=raw, output=get_output)
print_nums = True
elif 'l' in opts: # Get 'tail'
try:
n = int(args)
except ValueError, IndexError:
n = 10
hist = history_manager.get_tail(n, raw=raw, output=get_output)
else:
if args: # Get history by ranges
hist = history_manager.get_range_by_str(args, raw, get_output)
else: # Just get history for the current session
hist = history_manager.get_range(raw=raw, output=get_output)
# We could be displaying the entire history, so let's not try to pull it
# into a list in memory. Anything that needs more space will just misalign.
width = 4
for session, lineno, inline in hist:
# Print user history with tabs expanded to 4 spaces. The GUI clients
# use hard tabs for easier usability in auto-indented code, but we want
# to produce PEP-8 compliant history for safe pasting into an editor.
if get_output:
inline, output = inline
inline = inline.expandtabs(4).rstrip()
multiline = "\n" in inline
line_sep = '\n' if multiline else ' '
if print_nums:
print('%s:%s' % (_format_lineno(session, lineno).rjust(width),
line_sep), file=outfile, end='')
if pyprompts:
print(">>> ", end="", file=outfile)
if multiline:
inline = "\n... ".join(inline.splitlines()) + "\n..."
print(inline, file=outfile)
if get_output and output:
print(output, file=outfile)
if close_at_end:
outfile.close()
def magic_rep(self, arg):
r"""Repeat a command, or get command to input line for editing. %recall and
%rep are equivalent.
- %recall (no arguments):
Place a string version of last computation result (stored in the special '_'
variable) to the next input prompt. Allows you to create elaborate command
lines without using copy-paste::
In[1]: l = ["hei", "vaan"]
In[2]: "".join(l)
Out[2]: heivaan
In[3]: %rep
In[4]: heivaan_ <== cursor blinking
%recall 45
Place history line 45 on the next input prompt. Use %hist to find
out the number.
%recall 1-4
Combine the specified lines into one cell, and place it on the next
input prompt. See %history for the slice syntax.
%recall foo+bar
If foo+bar can be evaluated in the user namespace, the result is
placed at the next input prompt. Otherwise, the history is searched
for lines which contain that substring, and the most recent one is
placed at the next input prompt.
"""
if not arg: # Last output
self.set_next_input(str(self.shell.user_ns["_"]))
return
# Get history range
histlines = self.history_manager.get_range_by_str(arg)
cmd = "\n".join(x[2] for x in histlines)
if cmd:
self.set_next_input(cmd.rstrip())
return
try: # Variable in user namespace
cmd = str(eval(arg, self.shell.user_ns))
except Exception: # Search for term in history
histlines = self.history_manager.search("*"+arg+"*")
for h in reversed([x[2] for x in histlines]):
if 'rep' in h:
continue
self.set_next_input(h.rstrip())
return
else:
self.set_next_input(cmd.rstrip())
print("Couldn't evaluate or find in history:", arg)
def magic_rerun(self, parameter_s=''):
"""Re-run previous input
By default, you can specify ranges of input history to be repeated
(as with %history). With no arguments, it will repeat the last line.
Options:
-l <n> : Repeat the last n lines of input, not including the
current command.
-g foo : Repeat the most recent line which contains foo
"""
opts, args = self.parse_options(parameter_s, 'l:g:', mode='string')
if "l" in opts: # Last n lines
n = int(opts['l'])
hist = self.history_manager.get_tail(n)
elif "g" in opts: # Search
p = "*"+opts['g']+"*"
hist = list(self.history_manager.search(p))
for l in reversed(hist):
if "rerun" not in l[2]:
hist = [l] # The last match which isn't a %rerun
break
else:
hist = [] # No matches except %rerun
elif args: # Specify history ranges
hist = self.history_manager.get_range_by_str(args)
else: # Last line
hist = self.history_manager.get_tail(1)
hist = [x[2] for x in hist]
if not hist:
print("No lines in history match specification")
return
histlines = "\n".join(hist)
print("=== Executing: ===")
print(histlines)
print("=== Output: ===")
self.run_cell("\n".join(hist), store_history=False)
def init_ipython(ip):
ip.define_magic("rep", magic_rep)
ip.define_magic("recall", magic_rep)
ip.define_magic("rerun", magic_rerun)
ip.define_magic("hist",magic_history) # Alternative name
ip.define_magic("history",magic_history)
# XXX - ipy_completers are in quarantine, need to be updated to new apis
#import ipy_completers
#ipy_completers.quick_completer('%hist' ,'-g -t -r -n')
|
# -*- encoding: iso-8859-1 -*-
##############################################################################
#
# Copyright (c) 2004-2006 TINY SPRL. (http://tiny.be) All Rights Reserved.
#
# $Id$
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company
#
# 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 2
# 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, write to the Free Software
# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
#
##############################################################################
# . Fields:
# - simple
# - relations (one2many, many2one, many2many)
# - function
#
# Fields Attributes:
# _classic_read: is a classic sql fields
# _type : field type
# readonly
# required
# size
#
import string
import netsvc
import psycopg
import warnings
import tools
def _symbol_set(symb):
if symb==None or symb==False:
return None
elif isinstance(symb, unicode):
return symb.encode('utf-8')
return str(symb)
class _column(object):
_classic_read = True
_classic_write = True
_properties = False
_type = 'unknown'
_obj = None
_symbol_c = '%s'
_symbol_f = _symbol_set
_symbol_set = (_symbol_c, _symbol_f)
_symbol_get = None
def __init__(self, string='unknown', required=False, readonly=False, domain=None, context='', states=None, priority=0, change_default=False, size=None, ondelete="set null", translate=False, select=False, **args):
self.states = states or {}
self.string = string
self.readonly = readonly
self.required = required
self.size = size
self.help = args.get('help', '')
self.priority = priority
self.change_default = change_default
self.ondelete = ondelete
self.translate = translate
self._domain = domain or []
self.relate =False
self._context = context
self.group_name = False
self.view_load = 0
self.select=select
for a in args:
if args[a]:
setattr(self, a, args[a])
if self.relate:
warnings.warn("The relate attribute doesn't work anymore, use act_window tag instead", DeprecationWarning)
def restart(self):
pass
def set(self, cr, obj, id, name, value, user=None, context=None):
cr.execute('update '+obj._table+' set '+name+'='+self._symbol_set[0]+' where id=%d', (self._symbol_set[1](value),id) )
def get(self, cr, obj, ids, name, context=None, values=None):
raise Exception, 'undefined get method !'
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
ids = obj.search(cr, uid, args+self._domain+[(name,'ilike',value)], offset, limit)
res = obj.read(cr, uid, ids, [name])
return [x[name] for x in res]
# ---------------------------------------------------------
# Simple fields
# ---------------------------------------------------------
class boolean(_column):
_type = 'boolean'
_symbol_c = '%s'
_symbol_f = lambda x: x and 'True' or 'False'
_symbol_set = (_symbol_c, _symbol_f)
class integer(_column):
_type = 'integer'
_symbol_c = '%d'
_symbol_f = lambda x: int(x or 0)
_symbol_set = (_symbol_c, _symbol_f)
class reference(_column):
_type = 'reference'
def __init__(self, string, selection, size, **args):
_column.__init__(self, string=string, size=size, selection=selection, **args)
class char(_column):
_type = 'char'
def __init__(self, string, size, **args):
_column.__init__(self, string=string, size=size, **args)
self._symbol_set = (self._symbol_c, self._symbol_set_char)
# takes a string (encoded in utf8) and returns a string (encoded in utf8)
def _symbol_set_char(self, symb):
#TODO:
# * we need to remove the "symb==False" from the next line BUT
# for now too many things rely on this broken behavior
# * the symb==None test should be common to all data types
if symb==None or symb==False:
return None
# we need to convert the string to a unicode object to be able
# to evaluate its length (and possibly truncate it) reliably
if isinstance(symb, str):
u_symb = unicode(symb, 'utf8')
elif isinstance(symb, unicode):
u_symb = symb
else:
u_symb = unicode(symb)
if len(u_symb) > self.size:
return u_symb[:self.size-3].encode('utf8') + '...'
else:
return u_symb.encode('utf8')
class text(_column):
_type = 'text'
import __builtin__
class float(_column):
_type = 'float'
_symbol_c = '%f'
_symbol_f = lambda x: __builtin__.float(x or 0.0)
_symbol_set = (_symbol_c, _symbol_f)
def __init__(self, string='unknown', digits=None, **args):
_column.__init__(self, string=string, **args)
self.digits = digits
# We'll need to use decimal one day or another
#try:
# import decimal
#except ImportError:
# from tools import decimal
#
#class float(_column):
# _type = 'float'
# _symbol_c = '%f'
# def __init__(self, string='unknown', digits=None, **args):
# _column.__init__(self, string=string, **args)
# self._symbol_set = (self._symbol_c, self._symbol_set_decimal)
# self.digits = digits
# if not digits:
# scale = 4
# else:
# scale = digits[1]
# self._scale = decimal.Decimal(str(10**-scale))
# self._context = decimal.Context(prec=scale, rounding=decimal.ROUND_HALF_UP)
#
# def _symbol_set_decimal(self, symb):
# if isinstance(symb, __builtin__.float):
# return decimal.Decimal('%f' % symb)
# return decimal.Decimal(symb)
class date(_column):
_type = 'date'
class datetime(_column):
_type = 'datetime'
class time(_column):
_type = 'time'
class binary(_column):
_type = 'binary'
_symbol_c = '%s'
_symbol_f = lambda symb: symb and psycopg.Binary(symb) or None
_symbol_set = (_symbol_c, _symbol_f)
class selection(_column):
_type = 'selection'
def __init__(self, selection, string='unknown', **args):
_column.__init__(self, string=string, **args)
self.selection = selection
def set(self, cr, obj, id, name, value, user=None, context=None):
if not context:
context={}
#CHECKME: a priori, ceci n'est jamais appel puisque le test ci-dessous est mauvais
# la raison est que selection n'est pas en classic_write = false
# a noter qu'on pourrait fournir un _symbol_set specifique, et ca suffirait
if value in self.selection:
raise Exception, 'BAD VALUE'
_column.set(self, cr, obj, id, name, value, user=None, context=context)
# ---------------------------------------------------------
# Relationals fields
# ---------------------------------------------------------
#
# Values: (0, 0, { fields }) create
# (1, ID, { fields }) modification
# (2, ID) remove (delete)
# (3, ID) unlink one (target id or target of relation)
# (4, ID) link
# (5) unlink all (only valid for one2many)
#
#CHECKME: dans la pratique c'est quoi la syntaxe utilisee pour le 5? (5) ou (5, 0)?
class one2one(_column):
_classic_read = False
_classic_write = True
_type = 'one2one'
def __init__(self, obj, string='unknown', **args):
warnings.warn("The one2one field doesn't work anymore", DeprecationWarning)
_column.__init__(self, string=string, **args)
self._obj = obj
def set(self, cr, obj_src, id, field, act, user=None, context=None):
if not context:
context={}
obj = obj_src.pool.get(self._obj)
self._table = obj_src.pool.get(self._obj)._table
if act[0]==0:
id_new = obj.create(cr, user, act[1])
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (id_new,id))
else:
cr.execute('select '+field+' from '+obj_src._table+' where id=%d', (act[0],))
id =cr.fetchone()[0]
obj.write(cr, user, [id] , act[1], context=context)
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name','like',value)], offset, limit)
class many2one(_column):
_classic_read = False
_classic_write = True
_type = 'many2one'
def __init__(self, obj, string='unknown', **args):
_column.__init__(self, string=string, **args)
self._obj = obj
#
# TODO: speed improvement
#
# name is the name of the relation field
def get(self, cr, obj, ids, name, user=None, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
for r in values:
res[r['id']] = r[name]
for id in ids:
res.setdefault(id, '')
obj = obj.pool.get(self._obj)
# build a dictionary of the form {'id_of_distant_resource': name_of_distant_resource}
from orm import except_orm
try:
names = dict(obj.name_get(cr, user, filter(None, res.values()), context))
except except_orm:
names={}
for id in filter(None, res.values()):
try:
names[id] = dict(obj.name_get(cr, user, [id], context))[id]
except except_orm, e:
if e.name == 'AccessError':
names[id] = "== Access denied =="
else :
raise
for r in res.keys():
if res[r] and res[r] in names:
res[r] = (res[r], names[res[r]])
else:
res[r] = False
return res
def set(self, cr, obj_src, id, field, values, user=None, context=None):
if not context:
context={}
obj = obj_src.pool.get(self._obj)
self._table = obj_src.pool.get(self._obj)._table
if type(values)==type([]):
for act in values:
if act[0]==0:
id_new = obj.create(cr, act[2])
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (id_new,id))
elif act[0]==1:
obj.write(cr, [act[1]], act[2], context=context)
elif act[0]==2:
cr.execute('delete from '+self._table+' where id=%d', (act[1],))
elif act[0]==3 or act[0]==5:
cr.execute('update '+obj_src._table+' set '+field+'=null where id=%d', (id,))
elif act[0]==4:
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (act[1],id))
else:
if values:
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (values,id))
else:
cr.execute('update '+obj_src._table+' set '+field+'=null where id=%d', (id,))
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name','like',value)], offset, limit)
class one2many(_column):
_classic_read = False
_classic_write = False
_type = 'one2many'
def __init__(self, obj, fields_id, string='unknown', limit=None, **args):
_column.__init__(self, string=string, **args)
self._obj = obj
self._fields_id = fields_id
self._limit = limit
#one2many can't be used as condition for defaults
assert(self.change_default != True)
def get(self, cr, obj, ids, name, user=None, offset=0, context=None, values=None):
if not context:
context = {}
if not values:
values = {}
res = {}
for id in ids:
res[id] = []
ids2 = obj.pool.get(self._obj).search(cr, user, [(self._fields_id,'in',ids)], limit=self._limit)
for r in obj.pool.get(self._obj)._read_flat(cr, user, ids2, [self._fields_id], context=context, load='_classic_write'):
res[r[self._fields_id]].append( r['id'] )
return res
def set(self, cr, obj, id, field, values, user=None, context=None):
if not context:
context={}
if not values:
return
_table = obj.pool.get(self._obj)._table
obj = obj.pool.get(self._obj)
for act in values:
if act[0]==0:
act[2][self._fields_id] = id
obj.create(cr, user, act[2], context=context)
elif act[0]==1:
obj.write(cr, user, [act[1]] , act[2], context=context)
elif act[0]==2:
obj.unlink(cr, user, [act[1]], context=context)
elif act[0]==3:
cr.execute('update '+_table+' set '+self._fields_id+'=null where id=%d', (act[1],))
elif act[0]==4:
cr.execute('update '+_table+' set '+self._fields_id+'=%d where id=%d', (id,act[1]))
elif act[0]==5:
cr.execute('update '+_table+' set '+self._fields_id+'=null where '+self._fields_id+'=%d', (id,))
elif act[0]==6:
if not len(act[2]):
act[2] = [0]
cr.execute('update '+_table+' set '+self._fields_id+'=NULL where '+self._fields_id+'=%d and id not in ('+','.join(map(str, act[2]))+')', (id,))
if len(act[2]):
cr.execute('update '+_table+' set '+self._fields_id+'=%d where id in ('+','.join(map(str, act[2]))+')', (id,))
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None, operator='like'):
return obj.pool.get(self._obj).name_search(cr, uid, value, self._domain, offset, limit)
#
# Values: (0, 0, { fields }) create
# (1, ID, { fields }) modification
# (2, ID) remove
# (3, ID) unlink
# (4, ID) link
# (5, ID) unlink all
# (6, ?, ids) set a list of links
#
class many2many(_column):
_classic_read = False
_classic_write = False
_type = 'many2many'
def __init__(self, obj, rel, id1, id2, string='unknown', limit=None, **args):
_column.__init__(self, string=string, **args)
self._obj = obj
self._rel = rel
self._id1 = id1
self._id2 = id2
self._limit = limit
def get(self, cr, obj, ids, name, user=None, offset=0, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
if not ids:
return res
for id in ids:
res[id] = []
ids_s = ','.join(map(str,ids))
limit_str = self._limit is not None and ' limit %d' % self._limit or ''
obj = obj.pool.get(self._obj)
d1, d2 = obj.pool.get('ir.rule').domain_get(cr, user, obj._name)
if d1:
d1 = ' and '+d1
cr.execute('SELECT '+self._rel+'.'+self._id2+','+self._rel+'.'+self._id1+' \
FROM '+self._rel+' , '+obj._table+' \
WHERE '+self._rel+'.'+self._id1+' in ('+ids_s+') \
AND '+self._rel+'.'+self._id2+' = '+obj._table+'.id '+d1
+limit_str+' order by '+obj._table+'.'+obj._order+' offset %d',
d2+[offset])
for r in cr.fetchall():
res[r[1]].append(r[0])
return res
def set(self, cr, obj, id, name, values, user=None, context=None):
if not context:
context={}
if not values:
return
obj = obj.pool.get(self._obj)
for act in values:
if act[0]==0:
idnew = obj.create(cr, user, act[2])
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d,%d)', (id,idnew))
elif act[0]==1:
obj.write(cr, user, [act[1]] , act[2], context=context)
elif act[0]==2:
obj.unlink(cr, user, [act[1]], context=context)
elif act[0]==3:
cr.execute('delete from '+self._rel+' where ' + self._id1 + '=%d and '+ self._id2 + '=%d', (id,act[1]))
elif act[0]==4:
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d,%d)', (id,act[1]))
elif act[0]==5:
cr.execute('update '+self._rel+' set '+self._id2+'=null where '+self._id2+'=%d', (id,))
elif act[0]==6:
d1, d2 = obj.pool.get('ir.rule').domain_get(cr, user, obj._name)
if d1:
d1 = ' and '+d1
cr.execute('delete from '+self._rel+' where '+self._id1+'=%d AND '+self._id2+' IN (SELECT '+self._rel+'.'+self._id2+' FROM '+self._rel+', '+obj._table+' WHERE '+self._rel+'.'+self._id1+'=%d AND '+self._rel+'.'+self._id2+' = '+obj._table+'.id '+ d1 +')', [id, id]+d2 )
for act_nbr in act[2]:
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d, %d)', (id, act_nbr))
#
# TODO: use a name_search
#
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None, operator='like'):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name',operator,value)], offset, limit)
# ---------------------------------------------------------
# Function fields
# ---------------------------------------------------------
class function(_column):
_classic_read = False
_classic_write = False
_type = 'function'
_properties = True
def __init__(self, fnct, arg=None, fnct_inv=None, fnct_inv_arg=None, type='float', fnct_search=None, obj=None, method=False, store=False, **args):
_column.__init__(self, **args)
self._obj = obj
self._method = method
self._fnct = fnct
self._fnct_inv = fnct_inv
self._arg = arg
if 'relation' in args:
self._obj = args['relation']
self._fnct_inv_arg = fnct_inv_arg
if not fnct_inv:
self.readonly = 1
self._type = type
self._fnct_search = fnct_search
self.store = store
if type == 'float':
self._symbol_c = '%f'
self._symbol_f = lambda x: __builtin__.float(x or 0.0)
self._symbol_set = (self._symbol_c, self._symbol_f)
def search(self, cr, uid, obj, name, args):
if not self._fnct_search:
#CHECKME: should raise an exception
return []
return self._fnct_search(obj, cr, uid, obj, name, args)
def get(self, cr, obj, ids, name, user=None, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
table = obj._table
if self._method:
# TODO get HAS to receive uid for permissions !
return self._fnct(obj, cr, user, ids, name, self._arg, context)
else:
return self._fnct(cr, table, ids, name, self._arg, context)
def set(self, cr, obj, id, name, value, user=None, context=None):
if not context:
context={}
if self._fnct_inv:
self._fnct_inv(obj, cr, user, id, name, value, self._fnct_inv_arg, context)
# ---------------------------------------------------------
# Serialized fields
# ---------------------------------------------------------
class serialized(_column):
def __init__(self, string='unknown', serialize_func=repr, deserialize_func=eval, type='text', **args):
self._serialize_func = serialize_func
self._deserialize_func = deserialize_func
self._type = type
self._symbol_set = (self._symbol_c, self._serialize_func)
self._symbol_get = self._deserialize_func
super(serialized, self).__init__(string=string, **args)
class property(function):
def _fnct_write(self2, self, cr, uid, id, prop, id_val, val, context=None):
if not context:
context={}
(obj_dest,) = val
definition_id = self2._field_get(self, cr, uid, prop)
property = self.pool.get('ir.property')
nid = property.search(cr, uid, [('fields_id','=',definition_id),('res_id','=',self._name+','+str(id))])
while len(nid):
cr.execute('delete from ir_property where id=%d', (nid.pop(),))
nid = property.search(cr, uid, [('fields_id','=',definition_id),('res_id','=',False)])
default_val = False
if nid:
default_val = property.browse(cr, uid, nid[0], context).value
company_id = self.pool.get('res.users').company_get(cr, uid, uid)
res = False
newval = (id_val and obj_dest+','+str(id_val)) or False
if (newval != default_val) and newval:
propdef = self.pool.get('ir.model.fields').browse(cr, uid, definition_id, context=context)
res = property.create(cr, uid, {
'name': propdef.name,
'value': newval,
'res_id': self._name+','+str(id),
'company_id': company_id,
'fields_id': definition_id
}, context=context)
return res
def _fnct_read(self2, self, cr, uid, ids, prop, val, context=None):
if not context:
context={}
property = self.pool.get('ir.property')
definition_id = self2._field_get(self, cr, uid, prop)
nid = property.search(cr, uid, [('fields_id','=',definition_id),('res_id','=',False)])
default_val = False
if nid:
d = property.browse(cr, uid, nid[0], context).value
default_val = (d and int(d.split(',')[1])) or False
vids = map(lambda id: self._name+','+str(id), ids)
nids = property.search(cr, uid, [('fields_id','=',definition_id),('res_id','in', vids)])
res = {}
for id in ids:
res[id]= default_val
for prop in property.browse(cr, uid, nids):
res[int(prop.res_id.split(',')[1])] = (prop.value and int(prop.value.split(',')[1])) or False
obj = self.pool.get(self2._obj)
names = dict(obj.name_get(cr, uid, filter(None, res.values()), context))
for r in res.keys():
if res[r] and res[r] in names:
res[r] = (res[r], names[res[r]])
else:
res[r] = False
return res
def _field_get(self, self2, cr, uid, prop):
if not self.field_id.get(cr.dbname):
cr.execute('select id from ir_model_fields where name=%s and model=%s', (prop, self2._name))
res = cr.fetchone()
self.field_id[cr.dbname] = res and res[0]
return self.field_id[cr.dbname]
def __init__(self, obj_prop, **args):
self.field_id = {}
function.__init__(self, self._fnct_read, False, self._fnct_write, (obj_prop, ), **args)
def restart(self):
self.field_id = {}
Review property fields
Improve coding guide lines
bzr revid: ced-5871e8bfa7e312653fc4773de6649b53a68019f5
# -*- encoding: iso-8859-1 -*-
##############################################################################
#
# Copyright (c) 2004-2006 TINY SPRL. (http://tiny.be) All Rights Reserved.
#
# $Id$
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company
#
# 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 2
# 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, write to the Free Software
# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
#
##############################################################################
# . Fields:
# - simple
# - relations (one2many, many2one, many2many)
# - function
#
# Fields Attributes:
# _classic_read: is a classic sql fields
# _type : field type
# readonly
# required
# size
#
import string
import netsvc
import psycopg
import warnings
import tools
def _symbol_set(symb):
if symb==None or symb==False:
return None
elif isinstance(symb, unicode):
return symb.encode('utf-8')
return str(symb)
class _column(object):
_classic_read = True
_classic_write = True
_properties = False
_type = 'unknown'
_obj = None
_symbol_c = '%s'
_symbol_f = _symbol_set
_symbol_set = (_symbol_c, _symbol_f)
_symbol_get = None
def __init__(self, string='unknown', required=False, readonly=False, domain=None, context='', states=None, priority=0, change_default=False, size=None, ondelete="set null", translate=False, select=False, **args):
self.states = states or {}
self.string = string
self.readonly = readonly
self.required = required
self.size = size
self.help = args.get('help', '')
self.priority = priority
self.change_default = change_default
self.ondelete = ondelete
self.translate = translate
self._domain = domain or []
self.relate =False
self._context = context
self.group_name = False
self.view_load = 0
self.select=select
for a in args:
if args[a]:
setattr(self, a, args[a])
if self.relate:
warnings.warn("The relate attribute doesn't work anymore, use act_window tag instead", DeprecationWarning)
def restart(self):
pass
def set(self, cr, obj, id, name, value, user=None, context=None):
cr.execute('update '+obj._table+' set '+name+'='+self._symbol_set[0]+' where id=%d', (self._symbol_set[1](value),id) )
def get(self, cr, obj, ids, name, context=None, values=None):
raise Exception, 'undefined get method !'
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
ids = obj.search(cr, uid, args+self._domain+[(name,'ilike',value)], offset, limit)
res = obj.read(cr, uid, ids, [name])
return [x[name] for x in res]
# ---------------------------------------------------------
# Simple fields
# ---------------------------------------------------------
class boolean(_column):
_type = 'boolean'
_symbol_c = '%s'
_symbol_f = lambda x: x and 'True' or 'False'
_symbol_set = (_symbol_c, _symbol_f)
class integer(_column):
_type = 'integer'
_symbol_c = '%d'
_symbol_f = lambda x: int(x or 0)
_symbol_set = (_symbol_c, _symbol_f)
class reference(_column):
_type = 'reference'
def __init__(self, string, selection, size, **args):
_column.__init__(self, string=string, size=size, selection=selection, **args)
class char(_column):
_type = 'char'
def __init__(self, string, size, **args):
_column.__init__(self, string=string, size=size, **args)
self._symbol_set = (self._symbol_c, self._symbol_set_char)
# takes a string (encoded in utf8) and returns a string (encoded in utf8)
def _symbol_set_char(self, symb):
#TODO:
# * we need to remove the "symb==False" from the next line BUT
# for now too many things rely on this broken behavior
# * the symb==None test should be common to all data types
if symb==None or symb==False:
return None
# we need to convert the string to a unicode object to be able
# to evaluate its length (and possibly truncate it) reliably
if isinstance(symb, str):
u_symb = unicode(symb, 'utf8')
elif isinstance(symb, unicode):
u_symb = symb
else:
u_symb = unicode(symb)
if len(u_symb) > self.size:
return u_symb[:self.size-3].encode('utf8') + '...'
else:
return u_symb.encode('utf8')
class text(_column):
_type = 'text'
import __builtin__
class float(_column):
_type = 'float'
_symbol_c = '%f'
_symbol_f = lambda x: __builtin__.float(x or 0.0)
_symbol_set = (_symbol_c, _symbol_f)
def __init__(self, string='unknown', digits=None, **args):
_column.__init__(self, string=string, **args)
self.digits = digits
# We'll need to use decimal one day or another
#try:
# import decimal
#except ImportError:
# from tools import decimal
#
#class float(_column):
# _type = 'float'
# _symbol_c = '%f'
# def __init__(self, string='unknown', digits=None, **args):
# _column.__init__(self, string=string, **args)
# self._symbol_set = (self._symbol_c, self._symbol_set_decimal)
# self.digits = digits
# if not digits:
# scale = 4
# else:
# scale = digits[1]
# self._scale = decimal.Decimal(str(10**-scale))
# self._context = decimal.Context(prec=scale, rounding=decimal.ROUND_HALF_UP)
#
# def _symbol_set_decimal(self, symb):
# if isinstance(symb, __builtin__.float):
# return decimal.Decimal('%f' % symb)
# return decimal.Decimal(symb)
class date(_column):
_type = 'date'
class datetime(_column):
_type = 'datetime'
class time(_column):
_type = 'time'
class binary(_column):
_type = 'binary'
_symbol_c = '%s'
_symbol_f = lambda symb: symb and psycopg.Binary(symb) or None
_symbol_set = (_symbol_c, _symbol_f)
class selection(_column):
_type = 'selection'
def __init__(self, selection, string='unknown', **args):
_column.__init__(self, string=string, **args)
self.selection = selection
def set(self, cr, obj, id, name, value, user=None, context=None):
if not context:
context={}
#CHECKME: a priori, ceci n'est jamais appel puisque le test ci-dessous est mauvais
# la raison est que selection n'est pas en classic_write = false
# a noter qu'on pourrait fournir un _symbol_set specifique, et ca suffirait
if value in self.selection:
raise Exception, 'BAD VALUE'
_column.set(self, cr, obj, id, name, value, user=None, context=context)
# ---------------------------------------------------------
# Relationals fields
# ---------------------------------------------------------
#
# Values: (0, 0, { fields }) create
# (1, ID, { fields }) modification
# (2, ID) remove (delete)
# (3, ID) unlink one (target id or target of relation)
# (4, ID) link
# (5) unlink all (only valid for one2many)
#
#CHECKME: dans la pratique c'est quoi la syntaxe utilisee pour le 5? (5) ou (5, 0)?
class one2one(_column):
_classic_read = False
_classic_write = True
_type = 'one2one'
def __init__(self, obj, string='unknown', **args):
warnings.warn("The one2one field doesn't work anymore", DeprecationWarning)
_column.__init__(self, string=string, **args)
self._obj = obj
def set(self, cr, obj_src, id, field, act, user=None, context=None):
if not context:
context={}
obj = obj_src.pool.get(self._obj)
self._table = obj_src.pool.get(self._obj)._table
if act[0]==0:
id_new = obj.create(cr, user, act[1])
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (id_new,id))
else:
cr.execute('select '+field+' from '+obj_src._table+' where id=%d', (act[0],))
id =cr.fetchone()[0]
obj.write(cr, user, [id] , act[1], context=context)
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name','like',value)], offset, limit)
class many2one(_column):
_classic_read = False
_classic_write = True
_type = 'many2one'
def __init__(self, obj, string='unknown', **args):
_column.__init__(self, string=string, **args)
self._obj = obj
#
# TODO: speed improvement
#
# name is the name of the relation field
def get(self, cr, obj, ids, name, user=None, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
for r in values:
res[r['id']] = r[name]
for id in ids:
res.setdefault(id, '')
obj = obj.pool.get(self._obj)
# build a dictionary of the form {'id_of_distant_resource': name_of_distant_resource}
from orm import except_orm
try:
names = dict(obj.name_get(cr, user, filter(None, res.values()), context))
except except_orm:
names={}
for id in filter(None, res.values()):
try:
names[id] = dict(obj.name_get(cr, user, [id], context))[id]
except except_orm, e:
if e.name == 'AccessError':
names[id] = "== Access denied =="
else :
raise
for r in res.keys():
if res[r] and res[r] in names:
res[r] = (res[r], names[res[r]])
else:
res[r] = False
return res
def set(self, cr, obj_src, id, field, values, user=None, context=None):
if not context:
context={}
obj = obj_src.pool.get(self._obj)
self._table = obj_src.pool.get(self._obj)._table
if type(values)==type([]):
for act in values:
if act[0]==0:
id_new = obj.create(cr, act[2])
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (id_new,id))
elif act[0]==1:
obj.write(cr, [act[1]], act[2], context=context)
elif act[0]==2:
cr.execute('delete from '+self._table+' where id=%d', (act[1],))
elif act[0]==3 or act[0]==5:
cr.execute('update '+obj_src._table+' set '+field+'=null where id=%d', (id,))
elif act[0]==4:
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (act[1],id))
else:
if values:
cr.execute('update '+obj_src._table+' set '+field+'=%d where id=%d', (values,id))
else:
cr.execute('update '+obj_src._table+' set '+field+'=null where id=%d', (id,))
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name','like',value)], offset, limit)
class one2many(_column):
_classic_read = False
_classic_write = False
_type = 'one2many'
def __init__(self, obj, fields_id, string='unknown', limit=None, **args):
_column.__init__(self, string=string, **args)
self._obj = obj
self._fields_id = fields_id
self._limit = limit
#one2many can't be used as condition for defaults
assert(self.change_default != True)
def get(self, cr, obj, ids, name, user=None, offset=0, context=None, values=None):
if not context:
context = {}
if not values:
values = {}
res = {}
for id in ids:
res[id] = []
ids2 = obj.pool.get(self._obj).search(cr, user, [(self._fields_id,'in',ids)], limit=self._limit)
for r in obj.pool.get(self._obj)._read_flat(cr, user, ids2, [self._fields_id], context=context, load='_classic_write'):
res[r[self._fields_id]].append( r['id'] )
return res
def set(self, cr, obj, id, field, values, user=None, context=None):
if not context:
context={}
if not values:
return
_table = obj.pool.get(self._obj)._table
obj = obj.pool.get(self._obj)
for act in values:
if act[0]==0:
act[2][self._fields_id] = id
obj.create(cr, user, act[2], context=context)
elif act[0]==1:
obj.write(cr, user, [act[1]] , act[2], context=context)
elif act[0]==2:
obj.unlink(cr, user, [act[1]], context=context)
elif act[0]==3:
cr.execute('update '+_table+' set '+self._fields_id+'=null where id=%d', (act[1],))
elif act[0]==4:
cr.execute('update '+_table+' set '+self._fields_id+'=%d where id=%d', (id,act[1]))
elif act[0]==5:
cr.execute('update '+_table+' set '+self._fields_id+'=null where '+self._fields_id+'=%d', (id,))
elif act[0]==6:
if not len(act[2]):
act[2] = [0]
cr.execute('update '+_table+' set '+self._fields_id+'=NULL where '+self._fields_id+'=%d and id not in ('+','.join(map(str, act[2]))+')', (id,))
if len(act[2]):
cr.execute('update '+_table+' set '+self._fields_id+'=%d where id in ('+','.join(map(str, act[2]))+')', (id,))
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None, operator='like'):
return obj.pool.get(self._obj).name_search(cr, uid, value, self._domain, offset, limit)
#
# Values: (0, 0, { fields }) create
# (1, ID, { fields }) modification
# (2, ID) remove
# (3, ID) unlink
# (4, ID) link
# (5, ID) unlink all
# (6, ?, ids) set a list of links
#
class many2many(_column):
_classic_read = False
_classic_write = False
_type = 'many2many'
def __init__(self, obj, rel, id1, id2, string='unknown', limit=None, **args):
_column.__init__(self, string=string, **args)
self._obj = obj
self._rel = rel
self._id1 = id1
self._id2 = id2
self._limit = limit
def get(self, cr, obj, ids, name, user=None, offset=0, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
if not ids:
return res
for id in ids:
res[id] = []
ids_s = ','.join(map(str,ids))
limit_str = self._limit is not None and ' limit %d' % self._limit or ''
obj = obj.pool.get(self._obj)
d1, d2 = obj.pool.get('ir.rule').domain_get(cr, user, obj._name)
if d1:
d1 = ' and '+d1
cr.execute('SELECT '+self._rel+'.'+self._id2+','+self._rel+'.'+self._id1+' \
FROM '+self._rel+' , '+obj._table+' \
WHERE '+self._rel+'.'+self._id1+' in ('+ids_s+') \
AND '+self._rel+'.'+self._id2+' = '+obj._table+'.id '+d1
+limit_str+' order by '+obj._table+'.'+obj._order+' offset %d',
d2+[offset])
for r in cr.fetchall():
res[r[1]].append(r[0])
return res
def set(self, cr, obj, id, name, values, user=None, context=None):
if not context:
context={}
if not values:
return
obj = obj.pool.get(self._obj)
for act in values:
if act[0]==0:
idnew = obj.create(cr, user, act[2])
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d,%d)', (id,idnew))
elif act[0]==1:
obj.write(cr, user, [act[1]] , act[2], context=context)
elif act[0]==2:
obj.unlink(cr, user, [act[1]], context=context)
elif act[0]==3:
cr.execute('delete from '+self._rel+' where ' + self._id1 + '=%d and '+ self._id2 + '=%d', (id,act[1]))
elif act[0]==4:
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d,%d)', (id,act[1]))
elif act[0]==5:
cr.execute('update '+self._rel+' set '+self._id2+'=null where '+self._id2+'=%d', (id,))
elif act[0]==6:
d1, d2 = obj.pool.get('ir.rule').domain_get(cr, user, obj._name)
if d1:
d1 = ' and '+d1
cr.execute('delete from '+self._rel+' where '+self._id1+'=%d AND '+self._id2+' IN (SELECT '+self._rel+'.'+self._id2+' FROM '+self._rel+', '+obj._table+' WHERE '+self._rel+'.'+self._id1+'=%d AND '+self._rel+'.'+self._id2+' = '+obj._table+'.id '+ d1 +')', [id, id]+d2 )
for act_nbr in act[2]:
cr.execute('insert into '+self._rel+' ('+self._id1+','+self._id2+') values (%d, %d)', (id, act_nbr))
#
# TODO: use a name_search
#
def search(self, cr, obj, args, name, value, offset=0, limit=None, uid=None, operator='like'):
return obj.pool.get(self._obj).search(cr, uid, args+self._domain+[('name',operator,value)], offset, limit)
# ---------------------------------------------------------
# Function fields
# ---------------------------------------------------------
class function(_column):
_classic_read = False
_classic_write = False
_type = 'function'
_properties = True
def __init__(self, fnct, arg=None, fnct_inv=None, fnct_inv_arg=None, type='float', fnct_search=None, obj=None, method=False, store=False, **args):
_column.__init__(self, **args)
self._obj = obj
self._method = method
self._fnct = fnct
self._fnct_inv = fnct_inv
self._arg = arg
if 'relation' in args:
self._obj = args['relation']
self._fnct_inv_arg = fnct_inv_arg
if not fnct_inv:
self.readonly = 1
self._type = type
self._fnct_search = fnct_search
self.store = store
if type == 'float':
self._symbol_c = '%f'
self._symbol_f = lambda x: __builtin__.float(x or 0.0)
self._symbol_set = (self._symbol_c, self._symbol_f)
def search(self, cr, uid, obj, name, args):
if not self._fnct_search:
#CHECKME: should raise an exception
return []
return self._fnct_search(obj, cr, uid, obj, name, args)
def get(self, cr, obj, ids, name, user=None, context=None, values=None):
if not context:
context={}
if not values:
values={}
res = {}
table = obj._table
if self._method:
# TODO get HAS to receive uid for permissions !
return self._fnct(obj, cr, user, ids, name, self._arg, context)
else:
return self._fnct(cr, table, ids, name, self._arg, context)
def set(self, cr, obj, id, name, value, user=None, context=None):
if not context:
context={}
if self._fnct_inv:
self._fnct_inv(obj, cr, user, id, name, value, self._fnct_inv_arg, context)
# ---------------------------------------------------------
# Serialized fields
# ---------------------------------------------------------
class serialized(_column):
def __init__(self, string='unknown', serialize_func=repr, deserialize_func=eval, type='text', **args):
self._serialize_func = serialize_func
self._deserialize_func = deserialize_func
self._type = type
self._symbol_set = (self._symbol_c, self._serialize_func)
self._symbol_get = self._deserialize_func
super(serialized, self).__init__(string=string, **args)
class property(function):
def _fnct_write(self, obj, cr, uid, id, prop, id_val, val, context=None):
if not context:
context={}
(obj_dest,) = val
definition_id = self._field_get(cr, uid, obj._name, prop)
property = obj.pool.get('ir.property')
nid = property.search(cr, uid, [('fields_id', '=', definition_id),
('res_id', '=', obj._name+','+str(id))])
while len(nid):
cr.execute('DELETE FROM ir_property WHERE id=%d', (nid.pop(),))
nid = property.search(cr, uid, [('fields_id', '=', definition_id),
('res_id', '=', False)])
default_val = False
if nid:
default_val = property.browse(cr, uid, nid[0], context).value
company_id = obj.pool.get('res.users').company_get(cr, uid, uid)
res = False
newval = (id_val and obj_dest+','+str(id_val)) or False
if (newval != default_val) and newval:
propdef = obj.pool.get('ir.model.fields').browse(cr, uid,
definition_id, context=context)
res = property.create(cr, uid, {
'name': propdef.name,
'value': newval,
'res_id': obj._name+','+str(id),
'company_id': company_id,
'fields_id': definition_id
}, context=context)
return res
def _fnct_read(self, obj, cr, uid, ids, prop, val, context=None):
if not context:
context={}
property = obj.pool.get('ir.property')
definition_id = self._field_get(cr, uid, obj._name, prop)
nid = property.search(cr, uid, [('fields_id', '=', definition_id),
('res_id', '=', False)])
default_val = False
if nid:
d = property.browse(cr, uid, nid[0], context).value
default_val = (d and int(d.split(',')[1])) or False
vids = [obj._name + ',' + str(id) for id in ids]
nids = property.search(cr, uid, [('fields_id', '=', definition_id),
('res_id', 'in', vids)])
res = {}
for id in ids:
res[id]= default_val
for prop in property.browse(cr, uid, nids):
res[int(prop.res_id.split(',')[1])] = (prop.value and \
int(prop.value.split(',')[1])) or False
obj = obj.pool.get(self._obj)
names = dict(obj.name_get(cr, uid, filter(None, res.values()), context))
for r in res.keys():
if res[r] and res[r] in names:
res[r] = (res[r], names[res[r]])
else:
res[r] = False
return res
def _field_get(self, cr, uid, model_name, prop):
if not self.field_id.get(cr.dbname):
cr.execute('SELECT id \
FROM ir_model_fields \
WHERE name=%s AND model=%s', (prop, model_name))
res = cr.fetchone()
self.field_id[cr.dbname] = res and res[0]
return self.field_id[cr.dbname]
def __init__(self, obj_prop, **args):
self.field_id = {}
function.__init__(self, self._fnct_read, False, self._fnct_write,
(obj_prop, ), **args)
def restart(self):
self.field_id = {}
|
#!/usr/bin/env python
"""A simple interactive kernel that talks to a frontend over 0MQ.
Things to do:
* Implement `set_parent` logic. Right before doing exec, the Kernel should
call set_parent on all the PUB objects with the message about to be executed.
* Implement random port and security key logic.
* Implement control messages.
* Implement event loop and poll version.
"""
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import print_function
# Standard library imports
import __builtin__
import atexit
import sys
import time
import traceback
import logging
import uuid
from datetime import datetime
from signal import (
signal, getsignal, default_int_handler, SIGINT, SIG_IGN
)
# System library imports
import zmq
from zmq.eventloop import ioloop
from zmq.eventloop.zmqstream import ZMQStream
# Local imports
from IPython.core import pylabtools
from IPython.config.configurable import Configurable
from IPython.config.application import boolean_flag, catch_config_error, Application
from IPython.core.application import ProfileDir
from IPython.core.error import StdinNotImplementedError
from IPython.core.shellapp import (
InteractiveShellApp, shell_flags, shell_aliases
)
from IPython.utils import io
from IPython.utils import py3compat
from IPython.utils.frame import extract_module_locals
from IPython.utils.jsonutil import json_clean
from IPython.utils.traitlets import (
Any, Instance, Float, Dict, CaselessStrEnum, List, Set, Integer, Unicode
)
from entry_point import base_launch_kernel
from kernelapp import KernelApp, kernel_flags, kernel_aliases
from serialize import serialize_object, unpack_apply_message
from session import Session, Message
from zmqshell import ZMQInteractiveShell
#-----------------------------------------------------------------------------
# Main kernel class
#-----------------------------------------------------------------------------
class Kernel(Configurable):
#---------------------------------------------------------------------------
# Kernel interface
#---------------------------------------------------------------------------
# attribute to override with a GUI
eventloop = Any(None)
def _eventloop_changed(self, name, old, new):
"""schedule call to eventloop from IOLoop"""
loop = ioloop.IOLoop.instance()
loop.add_timeout(time.time()+0.1, self.enter_eventloop)
shell = Instance('IPython.core.interactiveshell.InteractiveShellABC')
session = Instance(Session)
profile_dir = Instance('IPython.core.profiledir.ProfileDir')
shell_streams = List()
control_stream = Instance(ZMQStream)
iopub_socket = Instance(zmq.Socket)
stdin_socket = Instance(zmq.Socket)
log = Instance(logging.Logger)
user_module = Any()
def _user_module_changed(self, name, old, new):
if self.shell is not None:
self.shell.user_module = new
user_ns = Dict(default_value=None)
def _user_ns_changed(self, name, old, new):
if self.shell is not None:
self.shell.user_ns = new
self.shell.init_user_ns()
# identities:
int_id = Integer(-1)
ident = Unicode()
def _ident_default(self):
return unicode(uuid.uuid4())
# Private interface
# Time to sleep after flushing the stdout/err buffers in each execute
# cycle. While this introduces a hard limit on the minimal latency of the
# execute cycle, it helps prevent output synchronization problems for
# clients.
# Units are in seconds. The minimum zmq latency on local host is probably
# ~150 microseconds, set this to 500us for now. We may need to increase it
# a little if it's not enough after more interactive testing.
_execute_sleep = Float(0.0005, config=True)
# Frequency of the kernel's event loop.
# Units are in seconds, kernel subclasses for GUI toolkits may need to
# adapt to milliseconds.
_poll_interval = Float(0.05, config=True)
# If the shutdown was requested over the network, we leave here the
# necessary reply message so it can be sent by our registered atexit
# handler. This ensures that the reply is only sent to clients truly at
# the end of our shutdown process (which happens after the underlying
# IPython shell's own shutdown).
_shutdown_message = None
# This is a dict of port number that the kernel is listening on. It is set
# by record_ports and used by connect_request.
_recorded_ports = Dict()
# set of aborted msg_ids
aborted = Set()
def __init__(self, **kwargs):
super(Kernel, self).__init__(**kwargs)
# Initialize the InteractiveShell subclass
self.shell = ZMQInteractiveShell.instance(config=self.config,
profile_dir = self.profile_dir,
user_module = self.user_module,
user_ns = self.user_ns,
)
self.shell.displayhook.session = self.session
self.shell.displayhook.pub_socket = self.iopub_socket
self.shell.displayhook.topic = self._topic('pyout')
self.shell.display_pub.session = self.session
self.shell.display_pub.pub_socket = self.iopub_socket
# TMP - hack while developing
self.shell._reply_content = None
# Build dict of handlers for message types
msg_types = [ 'execute_request', 'complete_request',
'object_info_request', 'history_request',
'connect_request', 'shutdown_request',
'apply_request',
]
self.shell_handlers = {}
for msg_type in msg_types:
self.shell_handlers[msg_type] = getattr(self, msg_type)
control_msg_types = msg_types + [ 'clear_request', 'abort_request' ]
self.control_handlers = {}
for msg_type in control_msg_types:
self.control_handlers[msg_type] = getattr(self, msg_type)
def dispatch_control(self, msg):
"""dispatch control requests"""
idents,msg = self.session.feed_identities(msg, copy=False)
try:
msg = self.session.unserialize(msg, content=True, copy=False)
except:
self.log.error("Invalid Control Message", exc_info=True)
return
self.log.debug("Control received: %s", msg)
header = msg['header']
msg_id = header['msg_id']
msg_type = header['msg_type']
handler = self.control_handlers.get(msg_type, None)
if handler is None:
self.log.error("UNKNOWN CONTROL MESSAGE TYPE: %r", msg_type)
else:
try:
handler(self.control_stream, idents, msg)
except Exception:
self.log.error("Exception in control handler:", exc_info=True)
def dispatch_shell(self, stream, msg):
"""dispatch shell requests"""
# flush control requests first
if self.control_stream:
self.control_stream.flush()
idents,msg = self.session.feed_identities(msg, copy=False)
try:
msg = self.session.unserialize(msg, content=True, copy=False)
except:
self.log.error("Invalid Message", exc_info=True)
return
header = msg['header']
msg_id = header['msg_id']
msg_type = msg['header']['msg_type']
# Print some info about this message and leave a '--->' marker, so it's
# easier to trace visually the message chain when debugging. Each
# handler prints its message at the end.
self.log.debug('\n*** MESSAGE TYPE:%s***', msg_type)
self.log.debug(' Content: %s\n --->\n ', msg['content'])
if msg_id in self.aborted:
self.aborted.remove(msg_id)
# is it safe to assume a msg_id will not be resubmitted?
reply_type = msg_type.split('_')[0] + '_reply'
status = {'status' : 'aborted'}
sub = {'engine' : self.ident}
sub.update(status)
reply_msg = self.session.send(stream, reply_type, subheader=sub,
content=status, parent=msg, ident=idents)
return
handler = self.shell_handlers.get(msg_type, None)
if handler is None:
self.log.error("UNKNOWN MESSAGE TYPE: %r", msg_type)
else:
# ensure default_int_handler during handler call
sig = signal(SIGINT, default_int_handler)
try:
handler(stream, idents, msg)
except Exception:
self.log.error("Exception in message handler:", exc_info=True)
finally:
signal(SIGINT, sig)
def enter_eventloop(self):
"""enter eventloop"""
self.log.info("entering eventloop")
# restore default_int_handler
signal(SIGINT, default_int_handler)
while self.eventloop is not None:
try:
self.eventloop(self)
except KeyboardInterrupt:
# Ctrl-C shouldn't crash the kernel
self.log.error("KeyboardInterrupt caught in kernel")
continue
else:
# eventloop exited cleanly, this means we should stop (right?)
self.eventloop = None
break
self.log.info("exiting eventloop")
# if eventloop exits, IOLoop should stop
ioloop.IOLoop.instance().stop()
def start(self):
"""register dispatchers for streams"""
self.shell.exit_now = False
if self.control_stream:
self.control_stream.on_recv(self.dispatch_control, copy=False)
def make_dispatcher(stream):
def dispatcher(msg):
return self.dispatch_shell(stream, msg)
return dispatcher
for s in self.shell_streams:
s.on_recv(make_dispatcher(s), copy=False)
def do_one_iteration(self):
"""step eventloop just once"""
if self.control_stream:
self.control_stream.flush()
for stream in self.shell_streams:
# handle at most one request per iteration
stream.flush(zmq.POLLIN, 1)
stream.flush(zmq.POLLOUT)
def record_ports(self, ports):
"""Record the ports that this kernel is using.
The creator of the Kernel instance must call this methods if they
want the :meth:`connect_request` method to return the port numbers.
"""
self._recorded_ports = ports
#---------------------------------------------------------------------------
# Kernel request handlers
#---------------------------------------------------------------------------
def _make_subheader(self):
"""init subheader dict, for execute/apply_reply"""
return {
'dependencies_met' : True,
'engine' : self.ident,
'started': datetime.now(),
}
def _publish_pyin(self, code, parent, execution_count):
"""Publish the code request on the pyin stream."""
self.session.send(self.iopub_socket, u'pyin',
{u'code':code, u'execution_count': execution_count},
parent=parent, ident=self._topic('pyin')
)
def execute_request(self, stream, ident, parent):
self.session.send(self.iopub_socket,
u'status',
{u'execution_state':u'busy'},
parent=parent,
ident=self._topic('status'),
)
try:
content = parent[u'content']
code = content[u'code']
silent = content[u'silent']
except:
self.log.error("Got bad msg: ")
self.log.error("%s", parent)
return
sub = self._make_subheader()
shell = self.shell # we'll need this a lot here
# Replace raw_input. Note that is not sufficient to replace
# raw_input in the user namespace.
if content.get('allow_stdin', False):
raw_input = lambda prompt='': self._raw_input(prompt, ident, parent)
else:
raw_input = lambda prompt='' : self._no_raw_input()
if py3compat.PY3:
__builtin__.input = raw_input
else:
__builtin__.raw_input = raw_input
# Set the parent message of the display hook and out streams.
shell.displayhook.set_parent(parent)
shell.display_pub.set_parent(parent)
sys.stdout.set_parent(parent)
sys.stderr.set_parent(parent)
# Re-broadcast our input for the benefit of listening clients, and
# start computing output
if not silent:
self._publish_pyin(code, parent, shell.execution_count)
reply_content = {}
try:
# FIXME: the shell calls the exception handler itself.
shell.run_cell(code, store_history=not silent, silent=silent)
except:
status = u'error'
# FIXME: this code right now isn't being used yet by default,
# because the run_cell() call above directly fires off exception
# reporting. This code, therefore, is only active in the scenario
# where runlines itself has an unhandled exception. We need to
# uniformize this, for all exception construction to come from a
# single location in the codbase.
etype, evalue, tb = sys.exc_info()
tb_list = traceback.format_exception(etype, evalue, tb)
reply_content.update(shell._showtraceback(etype, evalue, tb_list))
else:
status = u'ok'
reply_content[u'status'] = status
# Return the execution counter so clients can display prompts
reply_content['execution_count'] = shell.execution_count - 1
# FIXME - fish exception info out of shell, possibly left there by
# runlines. We'll need to clean up this logic later.
if shell._reply_content is not None:
reply_content.update(shell._reply_content)
# reset after use
shell._reply_content = None
# At this point, we can tell whether the main code execution succeeded
# or not. If it did, we proceed to evaluate user_variables/expressions
if reply_content['status'] == 'ok':
reply_content[u'user_variables'] = \
shell.user_variables(content.get(u'user_variables', []))
reply_content[u'user_expressions'] = \
shell.user_expressions(content.get(u'user_expressions', {}))
else:
# If there was an error, don't even try to compute variables or
# expressions
reply_content[u'user_variables'] = {}
reply_content[u'user_expressions'] = {}
# Payloads should be retrieved regardless of outcome, so we can both
# recover partial output (that could have been generated early in a
# block, before an error) and clear the payload system always.
reply_content[u'payload'] = shell.payload_manager.read_payload()
# Be agressive about clearing the payload because we don't want
# it to sit in memory until the next execute_request comes in.
shell.payload_manager.clear_payload()
# Flush output before sending the reply.
sys.stdout.flush()
sys.stderr.flush()
# FIXME: on rare occasions, the flush doesn't seem to make it to the
# clients... This seems to mitigate the problem, but we definitely need
# to better understand what's going on.
if self._execute_sleep:
time.sleep(self._execute_sleep)
# Send the reply.
reply_content = json_clean(reply_content)
sub['status'] = reply_content['status']
if reply_content['status'] == 'error' and \
reply_content['ename'] == 'UnmetDependency':
sub['dependencies_met'] = False
reply_msg = self.session.send(stream, u'execute_reply',
reply_content, parent, subheader=sub,
ident=ident)
self.log.debug("%s", reply_msg)
if not silent and reply_msg['content']['status'] == u'error':
self._abort_queues()
self.session.send(self.iopub_socket,
u'status',
{u'execution_state':u'idle'},
parent=parent,
ident=self._topic('status'))
def complete_request(self, stream, ident, parent):
txt, matches = self._complete(parent)
matches = {'matches' : matches,
'matched_text' : txt,
'status' : 'ok'}
matches = json_clean(matches)
completion_msg = self.session.send(stream, 'complete_reply',
matches, parent, ident)
self.log.debug("%s", completion_msg)
def object_info_request(self, stream, ident, parent):
content = parent['content']
object_info = self.shell.object_inspect(content['oname'],
detail_level = content.get('detail_level', 0)
)
# Before we send this object over, we scrub it for JSON usage
oinfo = json_clean(object_info)
msg = self.session.send(stream, 'object_info_reply',
oinfo, parent, ident)
self.log.debug("%s", msg)
def history_request(self, stream, ident, parent):
# We need to pull these out, as passing **kwargs doesn't work with
# unicode keys before Python 2.6.5.
hist_access_type = parent['content']['hist_access_type']
raw = parent['content']['raw']
output = parent['content']['output']
if hist_access_type == 'tail':
n = parent['content']['n']
hist = self.shell.history_manager.get_tail(n, raw=raw, output=output,
include_latest=True)
elif hist_access_type == 'range':
session = parent['content']['session']
start = parent['content']['start']
stop = parent['content']['stop']
hist = self.shell.history_manager.get_range(session, start, stop,
raw=raw, output=output)
elif hist_access_type == 'search':
pattern = parent['content']['pattern']
hist = self.shell.history_manager.search(pattern, raw=raw,
output=output)
else:
hist = []
hist = list(hist)
content = {'history' : hist}
content = json_clean(content)
msg = self.session.send(stream, 'history_reply',
content, parent, ident)
self.log.debug("Sending history reply with %i entries", len(hist))
def connect_request(self, stream, ident, parent):
if self._recorded_ports is not None:
content = self._recorded_ports.copy()
else:
content = {}
msg = self.session.send(stream, 'connect_reply',
content, parent, ident)
self.log.debug("%s", msg)
def shutdown_request(self, stream, ident, parent):
self.shell.exit_now = True
content = dict(status='ok')
content.update(parent['content'])
self.session.send(stream, u'shutdown_reply', content, parent, ident=ident)
# same content, but different msg_id for broadcasting on IOPub
self._shutdown_message = self.session.msg(u'shutdown_reply',
content, parent
)
self._at_shutdown()
# call sys.exit after a short delay
loop = ioloop.IOLoop.instance()
loop.add_timeout(time.time()+0.1, loop.stop)
#---------------------------------------------------------------------------
# Engine methods
#---------------------------------------------------------------------------
def apply_request(self, stream, ident, parent):
try:
content = parent[u'content']
bufs = parent[u'buffers']
msg_id = parent['header']['msg_id']
except:
self.log.error("Got bad msg: %s", parent, exc_info=True)
return
# Set the parent message of the display hook and out streams.
self.shell.displayhook.set_parent(parent)
self.shell.display_pub.set_parent(parent)
sys.stdout.set_parent(parent)
sys.stderr.set_parent(parent)
# pyin_msg = self.session.msg(u'pyin',{u'code':code}, parent=parent)
# self.iopub_socket.send(pyin_msg)
# self.session.send(self.iopub_socket, u'pyin', {u'code':code},parent=parent)
sub = self._make_subheader()
try:
working = self.shell.user_ns
prefix = "_"+str(msg_id).replace("-","")+"_"
f,args,kwargs = unpack_apply_message(bufs, working, copy=False)
fname = getattr(f, '__name__', 'f')
fname = prefix+"f"
argname = prefix+"args"
kwargname = prefix+"kwargs"
resultname = prefix+"result"
ns = { fname : f, argname : args, kwargname : kwargs , resultname : None }
# print ns
working.update(ns)
code = "%s = %s(*%s,**%s)" % (resultname, fname, argname, kwargname)
try:
exec code in self.shell.user_global_ns, self.shell.user_ns
result = working.get(resultname)
finally:
for key in ns.iterkeys():
working.pop(key)
packed_result,buf = serialize_object(result)
result_buf = [packed_result]+buf
except:
exc_content = self._wrap_exception('apply')
# exc_msg = self.session.msg(u'pyerr', exc_content, parent)
self.session.send(self.iopub_socket, u'pyerr', exc_content, parent=parent,
ident=self._topic('pyerr'))
reply_content = exc_content
result_buf = []
if exc_content['ename'] == 'UnmetDependency':
sub['dependencies_met'] = False
else:
reply_content = {'status' : 'ok'}
# put 'ok'/'error' status in header, for scheduler introspection:
sub['status'] = reply_content['status']
# flush i/o
sys.stdout.flush()
sys.stderr.flush()
reply_msg = self.session.send(stream, u'apply_reply', reply_content,
parent=parent, ident=ident,buffers=result_buf, subheader=sub)
#---------------------------------------------------------------------------
# Control messages
#---------------------------------------------------------------------------
def abort_request(self, stream, ident, parent):
"""abort a specifig msg by id"""
msg_ids = parent['content'].get('msg_ids', None)
if isinstance(msg_ids, basestring):
msg_ids = [msg_ids]
if not msg_ids:
self.abort_queues()
for mid in msg_ids:
self.aborted.add(str(mid))
content = dict(status='ok')
reply_msg = self.session.send(stream, 'abort_reply', content=content,
parent=parent, ident=ident)
self.log.debug("%s", reply_msg)
def clear_request(self, stream, idents, parent):
"""Clear our namespace."""
self.shell.reset(False)
msg = self.session.send(stream, 'clear_reply', ident=idents, parent=parent,
content = dict(status='ok'))
#---------------------------------------------------------------------------
# Protected interface
#---------------------------------------------------------------------------
def _wrap_exception(self, method=None):
# import here, because _wrap_exception is only used in parallel,
# and parallel has higher min pyzmq version
from IPython.parallel.error import wrap_exception
e_info = dict(engine_uuid=self.ident, engine_id=self.int_id, method=method)
content = wrap_exception(e_info)
return content
def _topic(self, topic):
"""prefixed topic for IOPub messages"""
if self.int_id >= 0:
base = "engine.%i" % self.int_id
else:
base = "kernel.%s" % self.ident
return py3compat.cast_bytes("%s.%s" % (base, topic))
def _abort_queues(self):
for stream in self.shell_streams:
if stream:
self._abort_queue(stream)
def _abort_queue(self, stream):
poller = zmq.Poller()
poller.register(stream.socket, zmq.POLLIN)
while True:
idents,msg = self.session.recv(stream, zmq.NOBLOCK, content=True)
if msg is None:
return
self.log.info("Aborting:")
self.log.info("%s", msg)
msg_type = msg['header']['msg_type']
reply_type = msg_type.split('_')[0] + '_reply'
status = {'status' : 'aborted'}
sub = {'engine' : self.ident}
sub.update(status)
reply_msg = self.session.send(stream, reply_type, subheader=sub,
content=status, parent=msg, ident=idents)
self.log.debug("%s", reply_msg)
# We need to wait a bit for requests to come in. This can probably
# be set shorter for true asynchronous clients.
poller.poll(50)
def _no_raw_input(self):
"""Raise StdinNotImplentedError if active frontend doesn't support
stdin."""
raise StdinNotImplementedError("raw_input was called, but this "
"frontend does not support stdin.")
def _raw_input(self, prompt, ident, parent):
# Flush output before making the request.
sys.stderr.flush()
sys.stdout.flush()
# Send the input request.
content = json_clean(dict(prompt=prompt))
self.session.send(self.stdin_socket, u'input_request', content, parent,
ident=ident)
# Await a response.
while True:
try:
ident, reply = self.session.recv(self.stdin_socket, 0)
except Exception:
self.log.warn("Invalid Message:", exc_info=True)
else:
break
try:
value = reply['content']['value']
except:
self.log.error("Got bad raw_input reply: ")
self.log.error("%s", parent)
value = ''
if value == '\x04':
# EOF
raise EOFError
return value
def _complete(self, msg):
c = msg['content']
try:
cpos = int(c['cursor_pos'])
except:
# If we don't get something that we can convert to an integer, at
# least attempt the completion guessing the cursor is at the end of
# the text, if there's any, and otherwise of the line
cpos = len(c['text'])
if cpos==0:
cpos = len(c['line'])
return self.shell.complete(c['text'], c['line'], cpos)
def _object_info(self, context):
symbol, leftover = self._symbol_from_context(context)
if symbol is not None and not leftover:
doc = getattr(symbol, '__doc__', '')
else:
doc = ''
object_info = dict(docstring = doc)
return object_info
def _symbol_from_context(self, context):
if not context:
return None, context
base_symbol_string = context[0]
symbol = self.shell.user_ns.get(base_symbol_string, None)
if symbol is None:
symbol = __builtin__.__dict__.get(base_symbol_string, None)
if symbol is None:
return None, context
context = context[1:]
for i, name in enumerate(context):
new_symbol = getattr(symbol, name, None)
if new_symbol is None:
return symbol, context[i:]
else:
symbol = new_symbol
return symbol, []
def _at_shutdown(self):
"""Actions taken at shutdown by the kernel, called by python's atexit.
"""
# io.rprint("Kernel at_shutdown") # dbg
if self._shutdown_message is not None:
self.session.send(self.iopub_socket, self._shutdown_message, ident=self._topic('shutdown'))
self.log.debug("%s", self._shutdown_message)
[ s.flush(zmq.POLLOUT) for s in self.shell_streams ]
#-----------------------------------------------------------------------------
# Aliases and Flags for the IPKernelApp
#-----------------------------------------------------------------------------
flags = dict(kernel_flags)
flags.update(shell_flags)
addflag = lambda *args: flags.update(boolean_flag(*args))
flags['pylab'] = (
{'IPKernelApp' : {'pylab' : 'auto'}},
"""Pre-load matplotlib and numpy for interactive use with
the default matplotlib backend."""
)
aliases = dict(kernel_aliases)
aliases.update(shell_aliases)
# it's possible we don't want short aliases for *all* of these:
aliases.update(dict(
pylab='IPKernelApp.pylab',
))
#-----------------------------------------------------------------------------
# The IPKernelApp class
#-----------------------------------------------------------------------------
class IPKernelApp(KernelApp, InteractiveShellApp):
name = 'ipkernel'
aliases = Dict(aliases)
flags = Dict(flags)
classes = [Kernel, ZMQInteractiveShell, ProfileDir, Session]
# configurables
pylab = CaselessStrEnum(['tk', 'qt', 'wx', 'gtk', 'osx', 'inline', 'auto'],
config=True,
help="""Pre-load matplotlib and numpy for interactive use,
selecting a particular matplotlib backend and loop integration.
"""
)
@catch_config_error
def initialize(self, argv=None):
super(IPKernelApp, self).initialize(argv)
self.init_path()
self.init_shell()
self.init_extensions()
self.init_code()
def init_kernel(self):
shell_stream = ZMQStream(self.shell_socket)
kernel = Kernel(config=self.config, session=self.session,
shell_streams=[shell_stream],
iopub_socket=self.iopub_socket,
stdin_socket=self.stdin_socket,
log=self.log,
profile_dir=self.profile_dir,
)
self.kernel = kernel
kernel.record_ports(self.ports)
shell = kernel.shell
if self.pylab:
try:
gui, backend = pylabtools.find_gui_and_backend(self.pylab)
shell.enable_pylab(gui, import_all=self.pylab_import_all)
except Exception:
self.log.error("Pylab initialization failed", exc_info=True)
# print exception straight to stdout, because normally
# _showtraceback associates the reply with an execution,
# which means frontends will never draw it, as this exception
# is not associated with any execute request.
# replace pyerr-sending traceback with stdout
_showtraceback = shell._showtraceback
def print_tb(etype, evalue, stb):
print ("Error initializing pylab, pylab mode will not "
"be active", file=io.stderr)
print (shell.InteractiveTB.stb2text(stb), file=io.stdout)
shell._showtraceback = print_tb
# send the traceback over stdout
shell.showtraceback(tb_offset=0)
# restore proper _showtraceback method
shell._showtraceback = _showtraceback
def init_shell(self):
self.shell = self.kernel.shell
self.shell.configurables.append(self)
#-----------------------------------------------------------------------------
# Kernel main and launch functions
#-----------------------------------------------------------------------------
def launch_kernel(*args, **kwargs):
"""Launches a localhost IPython kernel, binding to the specified ports.
This function simply calls entry_point.base_launch_kernel with the right
first command to start an ipkernel. See base_launch_kernel for arguments.
Returns
-------
A tuple of form:
(kernel_process, shell_port, iopub_port, stdin_port, hb_port)
where kernel_process is a Popen object and the ports are integers.
"""
return base_launch_kernel('from IPython.zmq.ipkernel import main; main()',
*args, **kwargs)
def embed_kernel(module=None, local_ns=None, **kwargs):
"""Embed and start an IPython kernel in a given scope.
Parameters
----------
module : ModuleType, optional
The module to load into IPython globals (default: caller)
local_ns : dict, optional
The namespace to load into IPython user namespace (default: caller)
kwargs : various, optional
Further keyword args are relayed to the KernelApp constructor,
allowing configuration of the Kernel. Will only have an effect
on the first embed_kernel call for a given process.
"""
try:
from IPython.parallel.apps.ipengineapp import IPEngineApp
except ImportError:
# IPython.parallel has higher zmq dependency that IPython.zmq
class IPEngineApp:
pass
# get the app if it exists, or set it up if it doesn't
if Application.initialized():
app = Application.instance()
if isinstance(app, IPEngineApp):
app.listen_kernel()
# return right away, because we embed in the Engine's
# namespace, not the calling one.
return
else:
app = IPKernelApp.instance(**kwargs)
app.initialize([])
# Undo unnecessary sys module mangling from init_sys_modules.
# This would not be necessary if we could prevent it
# in the first place by using a different InteractiveShell
# subclass, as in the regular embed case.
main = app.kernel.shell._orig_sys_modules_main_mod
if main is not None:
sys.modules[app.kernel.shell._orig_sys_modules_main_name] = main
# load the calling scope if not given
(caller_module, caller_locals) = extract_module_locals(1)
if module is None:
module = caller_module
if local_ns is None:
local_ns = caller_locals
app.kernel.user_module = module
app.kernel.user_ns = local_ns
app.start()
def main():
"""Run an IPKernel as an application"""
app = IPKernelApp.instance()
app.initialize()
app.start()
if __name__ == '__main__':
main()
revert embed_kernel changes that implied bind_kernel in engine
#!/usr/bin/env python
"""A simple interactive kernel that talks to a frontend over 0MQ.
Things to do:
* Implement `set_parent` logic. Right before doing exec, the Kernel should
call set_parent on all the PUB objects with the message about to be executed.
* Implement random port and security key logic.
* Implement control messages.
* Implement event loop and poll version.
"""
#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------
from __future__ import print_function
# Standard library imports
import __builtin__
import atexit
import sys
import time
import traceback
import logging
import uuid
from datetime import datetime
from signal import (
signal, getsignal, default_int_handler, SIGINT, SIG_IGN
)
# System library imports
import zmq
from zmq.eventloop import ioloop
from zmq.eventloop.zmqstream import ZMQStream
# Local imports
from IPython.core import pylabtools
from IPython.config.configurable import Configurable
from IPython.config.application import boolean_flag, catch_config_error
from IPython.core.application import ProfileDir
from IPython.core.error import StdinNotImplementedError
from IPython.core.shellapp import (
InteractiveShellApp, shell_flags, shell_aliases
)
from IPython.utils import io
from IPython.utils import py3compat
from IPython.utils.frame import extract_module_locals
from IPython.utils.jsonutil import json_clean
from IPython.utils.traitlets import (
Any, Instance, Float, Dict, CaselessStrEnum, List, Set, Integer, Unicode
)
from entry_point import base_launch_kernel
from kernelapp import KernelApp, kernel_flags, kernel_aliases
from serialize import serialize_object, unpack_apply_message
from session import Session, Message
from zmqshell import ZMQInteractiveShell
#-----------------------------------------------------------------------------
# Main kernel class
#-----------------------------------------------------------------------------
class Kernel(Configurable):
#---------------------------------------------------------------------------
# Kernel interface
#---------------------------------------------------------------------------
# attribute to override with a GUI
eventloop = Any(None)
def _eventloop_changed(self, name, old, new):
"""schedule call to eventloop from IOLoop"""
loop = ioloop.IOLoop.instance()
loop.add_timeout(time.time()+0.1, self.enter_eventloop)
shell = Instance('IPython.core.interactiveshell.InteractiveShellABC')
session = Instance(Session)
profile_dir = Instance('IPython.core.profiledir.ProfileDir')
shell_streams = List()
control_stream = Instance(ZMQStream)
iopub_socket = Instance(zmq.Socket)
stdin_socket = Instance(zmq.Socket)
log = Instance(logging.Logger)
user_module = Any()
def _user_module_changed(self, name, old, new):
if self.shell is not None:
self.shell.user_module = new
user_ns = Dict(default_value=None)
def _user_ns_changed(self, name, old, new):
if self.shell is not None:
self.shell.user_ns = new
self.shell.init_user_ns()
# identities:
int_id = Integer(-1)
ident = Unicode()
def _ident_default(self):
return unicode(uuid.uuid4())
# Private interface
# Time to sleep after flushing the stdout/err buffers in each execute
# cycle. While this introduces a hard limit on the minimal latency of the
# execute cycle, it helps prevent output synchronization problems for
# clients.
# Units are in seconds. The minimum zmq latency on local host is probably
# ~150 microseconds, set this to 500us for now. We may need to increase it
# a little if it's not enough after more interactive testing.
_execute_sleep = Float(0.0005, config=True)
# Frequency of the kernel's event loop.
# Units are in seconds, kernel subclasses for GUI toolkits may need to
# adapt to milliseconds.
_poll_interval = Float(0.05, config=True)
# If the shutdown was requested over the network, we leave here the
# necessary reply message so it can be sent by our registered atexit
# handler. This ensures that the reply is only sent to clients truly at
# the end of our shutdown process (which happens after the underlying
# IPython shell's own shutdown).
_shutdown_message = None
# This is a dict of port number that the kernel is listening on. It is set
# by record_ports and used by connect_request.
_recorded_ports = Dict()
# set of aborted msg_ids
aborted = Set()
def __init__(self, **kwargs):
super(Kernel, self).__init__(**kwargs)
# Initialize the InteractiveShell subclass
self.shell = ZMQInteractiveShell.instance(config=self.config,
profile_dir = self.profile_dir,
user_module = self.user_module,
user_ns = self.user_ns,
)
self.shell.displayhook.session = self.session
self.shell.displayhook.pub_socket = self.iopub_socket
self.shell.displayhook.topic = self._topic('pyout')
self.shell.display_pub.session = self.session
self.shell.display_pub.pub_socket = self.iopub_socket
# TMP - hack while developing
self.shell._reply_content = None
# Build dict of handlers for message types
msg_types = [ 'execute_request', 'complete_request',
'object_info_request', 'history_request',
'connect_request', 'shutdown_request',
'apply_request',
]
self.shell_handlers = {}
for msg_type in msg_types:
self.shell_handlers[msg_type] = getattr(self, msg_type)
control_msg_types = msg_types + [ 'clear_request', 'abort_request' ]
self.control_handlers = {}
for msg_type in control_msg_types:
self.control_handlers[msg_type] = getattr(self, msg_type)
def dispatch_control(self, msg):
"""dispatch control requests"""
idents,msg = self.session.feed_identities(msg, copy=False)
try:
msg = self.session.unserialize(msg, content=True, copy=False)
except:
self.log.error("Invalid Control Message", exc_info=True)
return
self.log.debug("Control received: %s", msg)
header = msg['header']
msg_id = header['msg_id']
msg_type = header['msg_type']
handler = self.control_handlers.get(msg_type, None)
if handler is None:
self.log.error("UNKNOWN CONTROL MESSAGE TYPE: %r", msg_type)
else:
try:
handler(self.control_stream, idents, msg)
except Exception:
self.log.error("Exception in control handler:", exc_info=True)
def dispatch_shell(self, stream, msg):
"""dispatch shell requests"""
# flush control requests first
if self.control_stream:
self.control_stream.flush()
idents,msg = self.session.feed_identities(msg, copy=False)
try:
msg = self.session.unserialize(msg, content=True, copy=False)
except:
self.log.error("Invalid Message", exc_info=True)
return
header = msg['header']
msg_id = header['msg_id']
msg_type = msg['header']['msg_type']
# Print some info about this message and leave a '--->' marker, so it's
# easier to trace visually the message chain when debugging. Each
# handler prints its message at the end.
self.log.debug('\n*** MESSAGE TYPE:%s***', msg_type)
self.log.debug(' Content: %s\n --->\n ', msg['content'])
if msg_id in self.aborted:
self.aborted.remove(msg_id)
# is it safe to assume a msg_id will not be resubmitted?
reply_type = msg_type.split('_')[0] + '_reply'
status = {'status' : 'aborted'}
sub = {'engine' : self.ident}
sub.update(status)
reply_msg = self.session.send(stream, reply_type, subheader=sub,
content=status, parent=msg, ident=idents)
return
handler = self.shell_handlers.get(msg_type, None)
if handler is None:
self.log.error("UNKNOWN MESSAGE TYPE: %r", msg_type)
else:
# ensure default_int_handler during handler call
sig = signal(SIGINT, default_int_handler)
try:
handler(stream, idents, msg)
except Exception:
self.log.error("Exception in message handler:", exc_info=True)
finally:
signal(SIGINT, sig)
def enter_eventloop(self):
"""enter eventloop"""
self.log.info("entering eventloop")
# restore default_int_handler
signal(SIGINT, default_int_handler)
while self.eventloop is not None:
try:
self.eventloop(self)
except KeyboardInterrupt:
# Ctrl-C shouldn't crash the kernel
self.log.error("KeyboardInterrupt caught in kernel")
continue
else:
# eventloop exited cleanly, this means we should stop (right?)
self.eventloop = None
break
self.log.info("exiting eventloop")
# if eventloop exits, IOLoop should stop
ioloop.IOLoop.instance().stop()
def start(self):
"""register dispatchers for streams"""
self.shell.exit_now = False
if self.control_stream:
self.control_stream.on_recv(self.dispatch_control, copy=False)
def make_dispatcher(stream):
def dispatcher(msg):
return self.dispatch_shell(stream, msg)
return dispatcher
for s in self.shell_streams:
s.on_recv(make_dispatcher(s), copy=False)
def do_one_iteration(self):
"""step eventloop just once"""
if self.control_stream:
self.control_stream.flush()
for stream in self.shell_streams:
# handle at most one request per iteration
stream.flush(zmq.POLLIN, 1)
stream.flush(zmq.POLLOUT)
def record_ports(self, ports):
"""Record the ports that this kernel is using.
The creator of the Kernel instance must call this methods if they
want the :meth:`connect_request` method to return the port numbers.
"""
self._recorded_ports = ports
#---------------------------------------------------------------------------
# Kernel request handlers
#---------------------------------------------------------------------------
def _make_subheader(self):
"""init subheader dict, for execute/apply_reply"""
return {
'dependencies_met' : True,
'engine' : self.ident,
'started': datetime.now(),
}
def _publish_pyin(self, code, parent, execution_count):
"""Publish the code request on the pyin stream."""
self.session.send(self.iopub_socket, u'pyin',
{u'code':code, u'execution_count': execution_count},
parent=parent, ident=self._topic('pyin')
)
def execute_request(self, stream, ident, parent):
self.session.send(self.iopub_socket,
u'status',
{u'execution_state':u'busy'},
parent=parent,
ident=self._topic('status'),
)
try:
content = parent[u'content']
code = content[u'code']
silent = content[u'silent']
except:
self.log.error("Got bad msg: ")
self.log.error("%s", parent)
return
sub = self._make_subheader()
shell = self.shell # we'll need this a lot here
# Replace raw_input. Note that is not sufficient to replace
# raw_input in the user namespace.
if content.get('allow_stdin', False):
raw_input = lambda prompt='': self._raw_input(prompt, ident, parent)
else:
raw_input = lambda prompt='' : self._no_raw_input()
if py3compat.PY3:
__builtin__.input = raw_input
else:
__builtin__.raw_input = raw_input
# Set the parent message of the display hook and out streams.
shell.displayhook.set_parent(parent)
shell.display_pub.set_parent(parent)
sys.stdout.set_parent(parent)
sys.stderr.set_parent(parent)
# Re-broadcast our input for the benefit of listening clients, and
# start computing output
if not silent:
self._publish_pyin(code, parent, shell.execution_count)
reply_content = {}
try:
# FIXME: the shell calls the exception handler itself.
shell.run_cell(code, store_history=not silent, silent=silent)
except:
status = u'error'
# FIXME: this code right now isn't being used yet by default,
# because the run_cell() call above directly fires off exception
# reporting. This code, therefore, is only active in the scenario
# where runlines itself has an unhandled exception. We need to
# uniformize this, for all exception construction to come from a
# single location in the codbase.
etype, evalue, tb = sys.exc_info()
tb_list = traceback.format_exception(etype, evalue, tb)
reply_content.update(shell._showtraceback(etype, evalue, tb_list))
else:
status = u'ok'
reply_content[u'status'] = status
# Return the execution counter so clients can display prompts
reply_content['execution_count'] = shell.execution_count - 1
# FIXME - fish exception info out of shell, possibly left there by
# runlines. We'll need to clean up this logic later.
if shell._reply_content is not None:
reply_content.update(shell._reply_content)
# reset after use
shell._reply_content = None
# At this point, we can tell whether the main code execution succeeded
# or not. If it did, we proceed to evaluate user_variables/expressions
if reply_content['status'] == 'ok':
reply_content[u'user_variables'] = \
shell.user_variables(content.get(u'user_variables', []))
reply_content[u'user_expressions'] = \
shell.user_expressions(content.get(u'user_expressions', {}))
else:
# If there was an error, don't even try to compute variables or
# expressions
reply_content[u'user_variables'] = {}
reply_content[u'user_expressions'] = {}
# Payloads should be retrieved regardless of outcome, so we can both
# recover partial output (that could have been generated early in a
# block, before an error) and clear the payload system always.
reply_content[u'payload'] = shell.payload_manager.read_payload()
# Be agressive about clearing the payload because we don't want
# it to sit in memory until the next execute_request comes in.
shell.payload_manager.clear_payload()
# Flush output before sending the reply.
sys.stdout.flush()
sys.stderr.flush()
# FIXME: on rare occasions, the flush doesn't seem to make it to the
# clients... This seems to mitigate the problem, but we definitely need
# to better understand what's going on.
if self._execute_sleep:
time.sleep(self._execute_sleep)
# Send the reply.
reply_content = json_clean(reply_content)
sub['status'] = reply_content['status']
if reply_content['status'] == 'error' and \
reply_content['ename'] == 'UnmetDependency':
sub['dependencies_met'] = False
reply_msg = self.session.send(stream, u'execute_reply',
reply_content, parent, subheader=sub,
ident=ident)
self.log.debug("%s", reply_msg)
if not silent and reply_msg['content']['status'] == u'error':
self._abort_queues()
self.session.send(self.iopub_socket,
u'status',
{u'execution_state':u'idle'},
parent=parent,
ident=self._topic('status'))
def complete_request(self, stream, ident, parent):
txt, matches = self._complete(parent)
matches = {'matches' : matches,
'matched_text' : txt,
'status' : 'ok'}
matches = json_clean(matches)
completion_msg = self.session.send(stream, 'complete_reply',
matches, parent, ident)
self.log.debug("%s", completion_msg)
def object_info_request(self, stream, ident, parent):
content = parent['content']
object_info = self.shell.object_inspect(content['oname'],
detail_level = content.get('detail_level', 0)
)
# Before we send this object over, we scrub it for JSON usage
oinfo = json_clean(object_info)
msg = self.session.send(stream, 'object_info_reply',
oinfo, parent, ident)
self.log.debug("%s", msg)
def history_request(self, stream, ident, parent):
# We need to pull these out, as passing **kwargs doesn't work with
# unicode keys before Python 2.6.5.
hist_access_type = parent['content']['hist_access_type']
raw = parent['content']['raw']
output = parent['content']['output']
if hist_access_type == 'tail':
n = parent['content']['n']
hist = self.shell.history_manager.get_tail(n, raw=raw, output=output,
include_latest=True)
elif hist_access_type == 'range':
session = parent['content']['session']
start = parent['content']['start']
stop = parent['content']['stop']
hist = self.shell.history_manager.get_range(session, start, stop,
raw=raw, output=output)
elif hist_access_type == 'search':
pattern = parent['content']['pattern']
hist = self.shell.history_manager.search(pattern, raw=raw,
output=output)
else:
hist = []
hist = list(hist)
content = {'history' : hist}
content = json_clean(content)
msg = self.session.send(stream, 'history_reply',
content, parent, ident)
self.log.debug("Sending history reply with %i entries", len(hist))
def connect_request(self, stream, ident, parent):
if self._recorded_ports is not None:
content = self._recorded_ports.copy()
else:
content = {}
msg = self.session.send(stream, 'connect_reply',
content, parent, ident)
self.log.debug("%s", msg)
def shutdown_request(self, stream, ident, parent):
self.shell.exit_now = True
content = dict(status='ok')
content.update(parent['content'])
self.session.send(stream, u'shutdown_reply', content, parent, ident=ident)
# same content, but different msg_id for broadcasting on IOPub
self._shutdown_message = self.session.msg(u'shutdown_reply',
content, parent
)
self._at_shutdown()
# call sys.exit after a short delay
loop = ioloop.IOLoop.instance()
loop.add_timeout(time.time()+0.1, loop.stop)
#---------------------------------------------------------------------------
# Engine methods
#---------------------------------------------------------------------------
def apply_request(self, stream, ident, parent):
try:
content = parent[u'content']
bufs = parent[u'buffers']
msg_id = parent['header']['msg_id']
except:
self.log.error("Got bad msg: %s", parent, exc_info=True)
return
# Set the parent message of the display hook and out streams.
self.shell.displayhook.set_parent(parent)
self.shell.display_pub.set_parent(parent)
sys.stdout.set_parent(parent)
sys.stderr.set_parent(parent)
# pyin_msg = self.session.msg(u'pyin',{u'code':code}, parent=parent)
# self.iopub_socket.send(pyin_msg)
# self.session.send(self.iopub_socket, u'pyin', {u'code':code},parent=parent)
sub = self._make_subheader()
try:
working = self.shell.user_ns
prefix = "_"+str(msg_id).replace("-","")+"_"
f,args,kwargs = unpack_apply_message(bufs, working, copy=False)
fname = getattr(f, '__name__', 'f')
fname = prefix+"f"
argname = prefix+"args"
kwargname = prefix+"kwargs"
resultname = prefix+"result"
ns = { fname : f, argname : args, kwargname : kwargs , resultname : None }
# print ns
working.update(ns)
code = "%s = %s(*%s,**%s)" % (resultname, fname, argname, kwargname)
try:
exec code in self.shell.user_global_ns, self.shell.user_ns
result = working.get(resultname)
finally:
for key in ns.iterkeys():
working.pop(key)
packed_result,buf = serialize_object(result)
result_buf = [packed_result]+buf
except:
exc_content = self._wrap_exception('apply')
# exc_msg = self.session.msg(u'pyerr', exc_content, parent)
self.session.send(self.iopub_socket, u'pyerr', exc_content, parent=parent,
ident=self._topic('pyerr'))
reply_content = exc_content
result_buf = []
if exc_content['ename'] == 'UnmetDependency':
sub['dependencies_met'] = False
else:
reply_content = {'status' : 'ok'}
# put 'ok'/'error' status in header, for scheduler introspection:
sub['status'] = reply_content['status']
# flush i/o
sys.stdout.flush()
sys.stderr.flush()
reply_msg = self.session.send(stream, u'apply_reply', reply_content,
parent=parent, ident=ident,buffers=result_buf, subheader=sub)
#---------------------------------------------------------------------------
# Control messages
#---------------------------------------------------------------------------
def abort_request(self, stream, ident, parent):
"""abort a specifig msg by id"""
msg_ids = parent['content'].get('msg_ids', None)
if isinstance(msg_ids, basestring):
msg_ids = [msg_ids]
if not msg_ids:
self.abort_queues()
for mid in msg_ids:
self.aborted.add(str(mid))
content = dict(status='ok')
reply_msg = self.session.send(stream, 'abort_reply', content=content,
parent=parent, ident=ident)
self.log.debug("%s", reply_msg)
def clear_request(self, stream, idents, parent):
"""Clear our namespace."""
self.shell.reset(False)
msg = self.session.send(stream, 'clear_reply', ident=idents, parent=parent,
content = dict(status='ok'))
#---------------------------------------------------------------------------
# Protected interface
#---------------------------------------------------------------------------
def _wrap_exception(self, method=None):
# import here, because _wrap_exception is only used in parallel,
# and parallel has higher min pyzmq version
from IPython.parallel.error import wrap_exception
e_info = dict(engine_uuid=self.ident, engine_id=self.int_id, method=method)
content = wrap_exception(e_info)
return content
def _topic(self, topic):
"""prefixed topic for IOPub messages"""
if self.int_id >= 0:
base = "engine.%i" % self.int_id
else:
base = "kernel.%s" % self.ident
return py3compat.cast_bytes("%s.%s" % (base, topic))
def _abort_queues(self):
for stream in self.shell_streams:
if stream:
self._abort_queue(stream)
def _abort_queue(self, stream):
poller = zmq.Poller()
poller.register(stream.socket, zmq.POLLIN)
while True:
idents,msg = self.session.recv(stream, zmq.NOBLOCK, content=True)
if msg is None:
return
self.log.info("Aborting:")
self.log.info("%s", msg)
msg_type = msg['header']['msg_type']
reply_type = msg_type.split('_')[0] + '_reply'
status = {'status' : 'aborted'}
sub = {'engine' : self.ident}
sub.update(status)
reply_msg = self.session.send(stream, reply_type, subheader=sub,
content=status, parent=msg, ident=idents)
self.log.debug("%s", reply_msg)
# We need to wait a bit for requests to come in. This can probably
# be set shorter for true asynchronous clients.
poller.poll(50)
def _no_raw_input(self):
"""Raise StdinNotImplentedError if active frontend doesn't support
stdin."""
raise StdinNotImplementedError("raw_input was called, but this "
"frontend does not support stdin.")
def _raw_input(self, prompt, ident, parent):
# Flush output before making the request.
sys.stderr.flush()
sys.stdout.flush()
# Send the input request.
content = json_clean(dict(prompt=prompt))
self.session.send(self.stdin_socket, u'input_request', content, parent,
ident=ident)
# Await a response.
while True:
try:
ident, reply = self.session.recv(self.stdin_socket, 0)
except Exception:
self.log.warn("Invalid Message:", exc_info=True)
else:
break
try:
value = reply['content']['value']
except:
self.log.error("Got bad raw_input reply: ")
self.log.error("%s", parent)
value = ''
if value == '\x04':
# EOF
raise EOFError
return value
def _complete(self, msg):
c = msg['content']
try:
cpos = int(c['cursor_pos'])
except:
# If we don't get something that we can convert to an integer, at
# least attempt the completion guessing the cursor is at the end of
# the text, if there's any, and otherwise of the line
cpos = len(c['text'])
if cpos==0:
cpos = len(c['line'])
return self.shell.complete(c['text'], c['line'], cpos)
def _object_info(self, context):
symbol, leftover = self._symbol_from_context(context)
if symbol is not None and not leftover:
doc = getattr(symbol, '__doc__', '')
else:
doc = ''
object_info = dict(docstring = doc)
return object_info
def _symbol_from_context(self, context):
if not context:
return None, context
base_symbol_string = context[0]
symbol = self.shell.user_ns.get(base_symbol_string, None)
if symbol is None:
symbol = __builtin__.__dict__.get(base_symbol_string, None)
if symbol is None:
return None, context
context = context[1:]
for i, name in enumerate(context):
new_symbol = getattr(symbol, name, None)
if new_symbol is None:
return symbol, context[i:]
else:
symbol = new_symbol
return symbol, []
def _at_shutdown(self):
"""Actions taken at shutdown by the kernel, called by python's atexit.
"""
# io.rprint("Kernel at_shutdown") # dbg
if self._shutdown_message is not None:
self.session.send(self.iopub_socket, self._shutdown_message, ident=self._topic('shutdown'))
self.log.debug("%s", self._shutdown_message)
[ s.flush(zmq.POLLOUT) for s in self.shell_streams ]
#-----------------------------------------------------------------------------
# Aliases and Flags for the IPKernelApp
#-----------------------------------------------------------------------------
flags = dict(kernel_flags)
flags.update(shell_flags)
addflag = lambda *args: flags.update(boolean_flag(*args))
flags['pylab'] = (
{'IPKernelApp' : {'pylab' : 'auto'}},
"""Pre-load matplotlib and numpy for interactive use with
the default matplotlib backend."""
)
aliases = dict(kernel_aliases)
aliases.update(shell_aliases)
# it's possible we don't want short aliases for *all* of these:
aliases.update(dict(
pylab='IPKernelApp.pylab',
))
#-----------------------------------------------------------------------------
# The IPKernelApp class
#-----------------------------------------------------------------------------
class IPKernelApp(KernelApp, InteractiveShellApp):
name = 'ipkernel'
aliases = Dict(aliases)
flags = Dict(flags)
classes = [Kernel, ZMQInteractiveShell, ProfileDir, Session]
# configurables
pylab = CaselessStrEnum(['tk', 'qt', 'wx', 'gtk', 'osx', 'inline', 'auto'],
config=True,
help="""Pre-load matplotlib and numpy for interactive use,
selecting a particular matplotlib backend and loop integration.
"""
)
@catch_config_error
def initialize(self, argv=None):
super(IPKernelApp, self).initialize(argv)
self.init_path()
self.init_shell()
self.init_extensions()
self.init_code()
def init_kernel(self):
shell_stream = ZMQStream(self.shell_socket)
kernel = Kernel(config=self.config, session=self.session,
shell_streams=[shell_stream],
iopub_socket=self.iopub_socket,
stdin_socket=self.stdin_socket,
log=self.log,
profile_dir=self.profile_dir,
)
self.kernel = kernel
kernel.record_ports(self.ports)
shell = kernel.shell
if self.pylab:
try:
gui, backend = pylabtools.find_gui_and_backend(self.pylab)
shell.enable_pylab(gui, import_all=self.pylab_import_all)
except Exception:
self.log.error("Pylab initialization failed", exc_info=True)
# print exception straight to stdout, because normally
# _showtraceback associates the reply with an execution,
# which means frontends will never draw it, as this exception
# is not associated with any execute request.
# replace pyerr-sending traceback with stdout
_showtraceback = shell._showtraceback
def print_tb(etype, evalue, stb):
print ("Error initializing pylab, pylab mode will not "
"be active", file=io.stderr)
print (shell.InteractiveTB.stb2text(stb), file=io.stdout)
shell._showtraceback = print_tb
# send the traceback over stdout
shell.showtraceback(tb_offset=0)
# restore proper _showtraceback method
shell._showtraceback = _showtraceback
def init_shell(self):
self.shell = self.kernel.shell
self.shell.configurables.append(self)
#-----------------------------------------------------------------------------
# Kernel main and launch functions
#-----------------------------------------------------------------------------
def launch_kernel(*args, **kwargs):
"""Launches a localhost IPython kernel, binding to the specified ports.
This function simply calls entry_point.base_launch_kernel with the right
first command to start an ipkernel. See base_launch_kernel for arguments.
Returns
-------
A tuple of form:
(kernel_process, shell_port, iopub_port, stdin_port, hb_port)
where kernel_process is a Popen object and the ports are integers.
"""
return base_launch_kernel('from IPython.zmq.ipkernel import main; main()',
*args, **kwargs)
def embed_kernel(module=None, local_ns=None, **kwargs):
"""Embed and start an IPython kernel in a given scope.
Parameters
----------
module : ModuleType, optional
The module to load into IPython globals (default: caller)
local_ns : dict, optional
The namespace to load into IPython user namespace (default: caller)
kwargs : various, optional
Further keyword args are relayed to the KernelApp constructor,
allowing configuration of the Kernel. Will only have an effect
on the first embed_kernel call for a given process.
"""
# get the app if it exists, or set it up if it doesn't
if IPKernelApp.initialized():
app = IPKernelApp.instance()
else:
app = IPKernelApp.instance(**kwargs)
app.initialize([])
# Undo unnecessary sys module mangling from init_sys_modules.
# This would not be necessary if we could prevent it
# in the first place by using a different InteractiveShell
# subclass, as in the regular embed case.
main = app.kernel.shell._orig_sys_modules_main_mod
if main is not None:
sys.modules[app.kernel.shell._orig_sys_modules_main_name] = main
# load the calling scope if not given
(caller_module, caller_locals) = extract_module_locals(1)
if module is None:
module = caller_module
if local_ns is None:
local_ns = caller_locals
app.kernel.user_module = module
app.kernel.user_ns = local_ns
app.start()
def main():
"""Run an IPKernel as an application"""
app = IPKernelApp.instance()
app.initialize()
app.start()
if __name__ == '__main__':
main()
|
# Testing for the whole ISgeneTK package
import mods.gel_visualizer as gv
import mods.SeqProp as sp
import mods.plasmid_builder as pb
import mods.MSM as msm
def setup_module(module):
print ("") # this is to get a newline after the dots
print ("Initializing GeneTK Test...")
def teardown_module(module):
print ("Finalizing GeneTK Test...")
def is_setup_function():
print ("Setting up data tests")
def is_teardown_function():
print ("Tearing down data tests")
@with_setup(is_setup_function, is_teardown_function)
def test_data():
print 'Checking test data'
# assert multiply(3,4) == 12 <--- Do some assertions with all test data
class TestGene:
def setup(self):
print ("Setting up next test")
def teardown(self):
print ("Tearing down test")
@classmethod
def setup(self):
print ("Setting up test")
@classmethod
def teardown(self):
print ("Tearing down test")
def test_GelViz(self):
print ("Testing Gel.Viz...")
# Testing that digestSeq() works
max_lengths, lengths_lists = gv.digestSeq() # Fill in with inputs
assert len(max_lengths) =< len(length_lists), "Please make sure that your inputs are structured correctly."
for length in max_length:
assert isinstance (length, int), "Please make sure input is formatted correctly"
for lengths in lengths_list:
for lenght in lengths:
assert isinstance (length, int), "Please make sure input is formatted correctly"
# Testing that bigDraw() works
# Testing that smallDraw() works
def test_SeqProp(self):
print ("Testing SeqProp")
# break up into smaller functions and then assert for each
# if no re sites, start, stop, or if tm range bad print warning
def test_BUILDR(self):
print ("Testing BUILDR")
# Wait until BUILDR is finished
def test_MSM(self):
print ("Testing MSM")
# Wait until MSM is finished
Added SeqProp test script
# Testing for the whole ISgeneTK package
import mods.gel_visualizer as gv
import mods.SeqProp as sp
import mods.plasmid_builder as pb
import mods.MSM as msm
def setup_module(module):
print ("") # this is to get a newline after the dots
print ("Initializing GeneTK Test...")
def teardown_module(module):
print ("Finalizing GeneTK Test...")
def is_setup_function():
print ("Setting up data tests")
def is_teardown_function():
print ("Tearing down data tests")
@with_setup(is_setup_function, is_teardown_function)
def test_data():
print 'Checking test data'
# assert multiply(3,4) == 12 <--- Do some assertions with all test data
class TestGene:
def setup(self):
print ("Setting up next test")
def teardown(self):
print ("Tearing down test")
@classmethod
def setup(self):
print ("Setting up test")
@classmethod
def teardown(self):
print ("Tearing down test")
def test_GelViz(self):
print ("Testing Gel.Viz...")
# Testing that digestSeq() works
max_lengths, lengths_lists = gv.digestSeq() # NEED SAMPLE SEQUENCE
assert len(max_lengths) ==< len(length_lists), "Please make sure that your inputs are structured correctly."
for length in max_length:
assert isinstance (length, int), "Please make sure input is formatted correctly."
for lengths in lengths_list:
for length in lengths:
assert isinstance (length, int), "Please make sure input is formatted correctly."
# TEST THAT LENGTHS CORRECT FOR TEST DATA
assert lengths_lists == [[val, val], [val, val], [val, val]], "Restriction lengths do match correct lengths."
# Testing that bigDraw() works
# Testing that smallDraw() works
def test_SeqProp(self):
print ("Testing SeqProp...")
# Testing GC, Tm outputs
# NEED SAMPLE SEQUENCES X 2 (one small < 14, one big > 15)
gcc_small, gcn_small = sp.getGC(small_seq) # SMALL SEQUENCE HERE
assert gcc_small == Ns, "Small oligomer GC content does not match." # KNOWN GCC
gcc_big, gcn_big = sp.getGC(big_seq) # BIG SEQ HERE
assert gcc_big == Nb, "Large oligomer GC content does not match." # KNOWN GCC
Tm = sp.getTm(big_seq, gcc_big)
assert Tm == val, "Melting temperature does not match."
# Reverse Complement
assert len(big_seq) == len(sp.getRevComp(big_seq)), "Reverse complement length does not match."
# Testing Start, Stop, Exon, and Re Site Indices and Warnings
starts, stops = sp.getStartStop(big_seq)
assert len(starts) == val, "Number of start codon indices do not match."
assert len(stops) == val, "Number of stop codon indices do not match."
exons = sp.getExons(big_seq, starts, stops)
assert len(exons) == min([len(starts), len(stops)]), "Error with exon detection; number of exons does not match min(codons)"
# READ RE SITES HERE
re_sites = sp.getRe(sequence, re_sites)
assert len(re_sites) == val, "Number of restriction sites do not match."
def test_BUILDR(self):
print ("Testing BUILDR...")
# Wait until BUILDR is finished
def test_MSM(self):
print ("Testing MSM...")
# Wait until MSM is finished
|
import hashlib
try:
from urllib.request import urlopen
except ImportError: # Python 2
from urllib2 import urlopen
from django.core.exceptions import ImproperlyConfigured
from django.core.files.base import ContentFile
from django.core.files.storage import FileSystemStorage
from django.contrib.staticfiles.finders import BaseFinder
from django.contrib.staticfiles.utils import matches_patterns
from django.conf import settings
hash_func_map = {
'md5': hashlib.md5,
'sha1': hashlib.sha1,
'sha224': hashlib.sha224,
'sha256': hashlib.sha256,
'sha384': hashlib.sha384,
'sha512': hashlib.sha512,
}
class RemoteFinder(BaseFinder):
def __init__(self):
self.cache_dir = settings.REMOTE_FINDER_CACHE_DIR
self.storage = FileSystemStorage(self.cache_dir)
resources_setting = settings.REMOTE_FINDER_RESOURCES
if not isinstance(resources_setting, (list, tuple)):
raise ImproperlyConfigured("settings.REMOTE_FINDER_RESOURCES must be a list or tuple")
resources = {}
for resource in resources_setting:
try:
path, url, cksm = resource
except ValueError:
raise ImproperlyConfigured("Each item in settings.REMOTE_FINDER_RESOURCES must be a tuple of three elements (path, url, cksm).")
try:
hash_type, expected_hexdigest = cksm.split(':')
except ValueError:
raise ImproperlyConfigured("RemoteFinder checksum `%s` is not in `hash_type:hexdigest` format." % cksm)
try:
hash_func = hash_func_map[hash_type]
except KeyError:
raise ImproperlyConfigured("RemoteFinder: hash type `%s` unknown" % hash_type)
try:
expected_digest = bytearray.fromhex(expected_hexdigest)
except ValueError:
raise ImproperlyConfigured("Cannot parse hex string in settings.REMOTE_FINDER_RESOURCES: `%s`" % expected_hexdigest)
if len(expected_digest) != hash_func().digest_size:
raise ImproperlyConfigured("settings.REMOTE_FINDER_RESOURCES: %s digest expected %d bytes but %d provided: `%s`" % (hash_type, hash_func().digest_size, len(expected_digest), expected_hexdigest))
resources[path] = (url, hash_func, expected_digest)
self.resources = resources
def find(self, path, all=False):
try:
fetch_info = self.resources[path]
except KeyError:
return []
self.fetch(path, fetch_info)
match = self.storage.path(path)
if all:
return [match]
else:
return match
def fetch(self, path, fetch_info):
if self.storage.exists(path):
return
url, hash_func, expected_digest = fetch_info
# download the file
f = urlopen(url)
try:
content = f.read()
finally:
f.close()
# check its hash
digest = hash_func(content).digest()
if digest != expected_digest:
raise Exception("Digest does not match!")
# save it
name = self.storage.save(path, ContentFile(content))
if name != path:
print("Warning: %r != %r" % (name, path))
def list(self, ignore_patterns):
for path, fetch_info in self.resources.items():
if matches_patterns(path, ignore_patterns):
continue
self.fetch(path, fetch_info)
yield path, self.storage
# fixme: make a way to verify all hashes are correct, either on the cmdline or
# all the time
additional errors if required settings are not defined
import hashlib
import logging
try:
from urllib.request import urlopen
except ImportError: # Python 2
from urllib2 import urlopen
from django.core.exceptions import ImproperlyConfigured
from django.core.files.base import ContentFile
from django.core.files.storage import FileSystemStorage
from django.contrib.staticfiles.finders import BaseFinder
from django.contrib.staticfiles.utils import matches_patterns
from django.conf import settings
logger = logging.getLogger(__name__)
hash_func_map = {
'md5': hashlib.md5,
'sha1': hashlib.sha1,
'sha224': hashlib.sha224,
'sha256': hashlib.sha256,
'sha384': hashlib.sha384,
'sha512': hashlib.sha512,
}
class RemoteFinder(BaseFinder):
def __init__(self):
self.cache_dir = getattr(settings, "REMOTE_FINDER_CACHE_DIR", None)
if not self.cache_dir:
raise ImproperlyConfigured("settings.REMOTE_FINDER_CACHE_DIR must point to a cache directory.")
self.storage = FileSystemStorage(self.cache_dir)
try:
resources_setting = settings.REMOTE_FINDER_RESOURCES
except AttributeError:
logger.warning("RemoteFinder is enabled, but settings.REMOTE_FINDER_RESOURCES is not defined.")
resources_setting = ()
if not isinstance(resources_setting, (list, tuple)):
raise ImproperlyConfigured("settings.REMOTE_FINDER_RESOURCES must be a list or tuple")
resources = {}
for resource in resources_setting:
try:
path, url, cksm = resource
except ValueError:
raise ImproperlyConfigured("Each item in settings.REMOTE_FINDER_RESOURCES must be a tuple of three elements (path, url, cksm).")
try:
hash_type, expected_hexdigest = cksm.split(':')
except ValueError:
raise ImproperlyConfigured("RemoteFinder checksum `%s` is not in `hash_type:hexdigest` format." % cksm)
try:
hash_func = hash_func_map[hash_type]
except KeyError:
raise ImproperlyConfigured("RemoteFinder: hash type `%s` unknown" % hash_type)
try:
expected_digest = bytearray.fromhex(expected_hexdigest)
except ValueError:
raise ImproperlyConfigured("Cannot parse hex string in settings.REMOTE_FINDER_RESOURCES: `%s`" % expected_hexdigest)
if len(expected_digest) != hash_func().digest_size:
raise ImproperlyConfigured("settings.REMOTE_FINDER_RESOURCES: %s digest expected %d bytes but %d provided: `%s`" % (hash_type, hash_func().digest_size, len(expected_digest), expected_hexdigest))
resources[path] = (url, hash_func, expected_digest)
self.resources = resources
def find(self, path, all=False):
try:
fetch_info = self.resources[path]
except KeyError:
return []
self.fetch(path, fetch_info)
match = self.storage.path(path)
if all:
return [match]
else:
return match
def fetch(self, path, fetch_info):
if self.storage.exists(path):
return
url, hash_func, expected_digest = fetch_info
# download the file
f = urlopen(url)
try:
content = f.read()
finally:
f.close()
# check its hash
digest = hash_func(content).digest()
if digest != expected_digest:
raise Exception("Digest does not match!")
# save it
name = self.storage.save(path, ContentFile(content))
if name != path:
print("Warning: %r != %r" % (name, path))
def list(self, ignore_patterns):
for path, fetch_info in self.resources.items():
if matches_patterns(path, ignore_patterns):
continue
self.fetch(path, fetch_info)
yield path, self.storage
# fixme: make a way to verify all hashes are correct, either on the cmdline or
# all the time
|
#
# Copyright (C) 2012 Ash (Tuxdude) <tuxdude.github@gmail.com>
#
# This file is part of repobuddy.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser 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 Lesser General Public
# License along with this program. If not, see
# <http://www.gnu.org/licenses/>.
#
import os as _os
import shlex as _shlex
import shutil as _shutil
import subprocess as _subprocess
import sys as _sys
import traceback as _traceback
if _sys.version_info < (2, 7):
# pylint: disable=F0401
import cStringIO as _io
import ordereddict as _collections
import unittest2 as _unittest
else:
# pylint: disable=F0401
if _sys.version_info < (3, 0):
import cStringIO as _io
else:
import io as _io
import collections as _collections
import unittest as _unittest
from repobuddy.utils import RepoBuddyBaseException, Logger
class ShellError(RepoBuddyBaseException):
def __init__(self, error_str):
super(ShellError, self).__init__(error_str)
return
class ShellHelper: # pylint: disable=W0232
@classmethod
def exec_command(cls, command, base_dir, debug_output=True):
Logger.msg('>> ' + ' '.join(command))
try:
kwargs = {}
return_code = None
if not debug_output:
kwargs['stdout'] = open(_os.devnull, 'w')
kwargs['stderr'] = _subprocess.STDOUT
proc = _subprocess.Popen(command, # pylint: disable=W0142
cwd=base_dir,
**kwargs)
try:
proc.communicate()
except:
proc.kill()
proc.wait()
raise
return_code = proc.poll()
if return_code != 0:
raise ShellError('Command \'%s\' failed!' % command)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def append_text_to_file(cls, text, filename, base_dir):
try:
with open(_os.path.join(base_dir, filename), 'a') as file_handle:
file_handle.write(text)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def remove_file(cls, filename):
try:
_os.unlink(filename)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def make_dir(cls,
dirname,
create_parent_dirs=False,
only_if_not_exists=False):
if not create_parent_dirs:
try:
if not only_if_not_exists or not _os.path.exists(dirname):
_os.mkdir(dirname)
except (OSError, IOError) as err:
raise ShellError(str(err))
else:
try:
if not only_if_not_exists or not _os.path.exists(dirname):
_os.makedirs(dirname)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def remove_dir(cls, dirname):
if _os.path.isdir(dirname):
try:
_shutil.rmtree(dirname, ignore_errors=False)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
class TestCommon: # pylint: disable=W0232
@classmethod
def _git_append_add_commit(cls, text, filename, commit_log, exec_dir):
ShellHelper.append_text_to_file(text, filename, exec_dir)
ShellHelper.exec_command(
_shlex.split('git add %s' % filename),
exec_dir)
ShellHelper.exec_command(
_shlex.split('git commit -m "%s"' % commit_log),
exec_dir)
return
@classmethod
def setup_test_repos(cls, base_dir):
# Cleanup and create an empty directory
ShellHelper.remove_dir(base_dir)
ShellHelper.make_dir(base_dir)
# Set up the origin and clone repo paths
origin_repo_url = _os.path.join(base_dir, 'repo-origin')
clone_repo1 = _os.path.join(base_dir, 'clone1')
clone_repo2 = _os.path.join(base_dir, 'clone2')
# Set up the origin as a bare repo
ShellHelper.make_dir(origin_repo_url)
ShellHelper.exec_command(
_shlex.split('git init --bare'),
origin_repo_url)
# Create Clone1 from the origin
ShellHelper.exec_command(
_shlex.split('git clone %s %s' % (origin_repo_url, clone_repo1)),
base_dir)
# Create some content in clone1
cls._git_append_add_commit(
'First content...\n',
'README',
'First commit.',
clone_repo1)
cls._git_append_add_commit(
'Hardly useful...\n',
'dummy',
'Here we go.',
clone_repo1)
cls._git_append_add_commit(
'More content...\n',
'README',
'Appending to README.',
clone_repo1)
# Push the changes to origin
ShellHelper.exec_command(
_shlex.split('git push origin master'),
clone_repo1)
# Make some more changes, but do not push yet
cls._git_append_add_commit(
'Another line...\n',
'README',
'One more to README.',
clone_repo1)
cls._git_append_add_commit(
'Dummy2 in place...\n',
'dummy2',
'Creating dummy2.',
clone_repo1)
# Create clone2 from the origin
ShellHelper.exec_command(
_shlex.split('git clone %s %s' % (origin_repo_url, clone_repo2)),
base_dir)
# Add and commit changes in clone2
cls._git_append_add_commit(
'Another line...\n',
'dummy',
'One more to dummy.',
clone_repo2)
cls._git_append_add_commit(
'More dummy...\n',
'dummy',
'More dummy.',
clone_repo2)
# Create a new branch in clone2
ShellHelper.exec_command(
_shlex.split('git branch new-branch'),
clone_repo2)
ShellHelper.exec_command(
_shlex.split('git checkout new-branch'),
clone_repo2)
# Add some more changes in clone2's new-branch
cls._git_append_add_commit(
'More lines...\n',
'dummy',
'Another line to dummy.',
clone_repo2)
cls._git_append_add_commit(
'Just keep it coming...\n',
'dummy',
'Again :D',
clone_repo2)
# Switch back to master in clone2
ShellHelper.exec_command(
_shlex.split('git checkout master'),
clone_repo2)
# Push all branches to origin
ShellHelper.exec_command(
_shlex.split('git push origin --all'),
clone_repo2)
# Pull changes from origin into clone1
ShellHelper.exec_command(
_shlex.split('git fetch origin'),
clone_repo1)
ShellHelper.exec_command(
_shlex.split(
'git merge --commit -m "Merge origin into clone1" ' +
'origin/master'),
clone_repo1)
# Now push the merges back to origin
ShellHelper.exec_command(
_shlex.split('git push origin master'),
clone_repo1)
# Get the changes from origin into clone2
ShellHelper.exec_command(
_shlex.split('git fetch origin'),
clone_repo2)
ShellHelper.exec_command(
_shlex.split(
'git merge --commit -m "Merge origin into clone2" ' +
'origin/master'),
clone_repo2)
# Now push the merges back to origin
ShellHelper.exec_command(
_shlex.split('git push origin --all'),
clone_repo2)
# Now get rid of the clone repos, we only need the origin
ShellHelper.remove_dir(clone_repo1)
ShellHelper.remove_dir(clone_repo2)
return
class TestCaseBase(_unittest.TestCase):
def _set_tear_down_cb(self, method, *args, **kwargs):
self._tear_down_cb = method
self._tear_down_cb_args = args
self._tear_down_cb_kwargs = kwargs
return
def _clear_tear_down_cb(self):
self._tear_down_cb = None
self._tear_down_cb_args = None
self._tear_down_cb_kwargs = None
return
def __init__(self, methodName='runTest'):
super(TestCaseBase, self).__init__(methodName)
self._tear_down_cb = None
self._tear_down_cb_args = None
self._tear_down_cb_kwargs = None
if _sys.version_info >= (3, 2):
# pylint: disable=E1101
self._count_equal = self.assertCountEqual
else:
# pylint: disable=E1101
self._count_equal = self.assertItemsEqual
return
def setUp(self):
return
def tearDown(self):
if not self._tear_down_cb is None:
self._tear_down_cb(*self._tear_down_cb_args,
**self._tear_down_cb_kwargs)
self._clear_tear_down_cb()
return
class TestResult(_unittest.TestResult):
PASSED = 0
ERROR = 1
FAILED = 2
SKIPPED = 3
EXPECTED_FAILURE = 4
UNEXPECTED_SUCCESS = 5
_result_str = {PASSED: 'PASSED',
ERROR: 'ERROR',
FAILED: 'FAILED',
SKIPPED: 'SKIPPED',
EXPECTED_FAILURE: 'EXPECTED FAILURE',
UNEXPECTED_SUCCESS: 'UNEXPECTED_SUCCESS'}
def _update_result(self, test, err, result_type):
module_test_results = []
test_id = test.id().split('.')
if test_id[-2] not in self.test_results:
self.test_results[test_id[-2]] = module_test_results
else:
module_test_results = self.test_results[test_id[-2]]
result = {}
result['test_case'] = test_id[-1]
result['description'] = str(test.shortDescription())
result['result'] = result_type
if not err is None:
result['formated_traceback'] = \
''.join(_traceback.format_exception(err[0], err[1], err[2]))
module_test_results.append(result)
return
def __init__(self):
super(TestResult, self).__init__()
self.test_results = _collections.OrderedDict()
self.has_errors = False
self.has_failures = False
self.has_unexpected_success = False
return
@classmethod
def get_result_str(cls, result):
return cls._result_str[result]
def addError(self, test, err):
self._update_result(test, err, type(self).ERROR)
self.has_errors = True
return
def addFailure(self, test, err):
self._update_result(test, err, type(self).FAILED)
self.has_failures = True
return
def addSuccess(self, test):
self._update_result(test, None, type(self).PASSED)
return
def addSkip(self, test, reason):
self._update_result(test, None, type(self).SKIPPED)
return
def addExpectedFailure(self, test, err):
self._update_result(test, err, type(self).EXPECTED_FAILURE)
return
def addUnexpectedSuccess(self, test):
self._update_result(test, None, type(self).UNEXPECTED_SUCCESS)
self.has_unexpected_success = True
return
class TestRunner(_unittest.TextTestRunner):
def __init__(self, stream=_sys.stderr, descriptions=True, verbosity=1):
super(TestRunner, self).__init__(stream, descriptions, verbosity)
self._test_result = None
return
def _makeResult(self):
self._test_result = TestResult()
return self._test_result
def get_test_result(self):
return self._test_result
class TestSuiteManager(object):
_base_dir = None
@classmethod
def get_base_dir(cls):
return cls._base_dir
def __init__(self, base_dir):
if not _os.path.isdir(base_dir):
ShellHelper.make_dir(base_dir)
type(self)._base_dir = base_dir
self._test_suite = None
self._output = _io.StringIO()
self._test_result = None
return
def add_test_suite(self, test_suite):
if self._test_suite is None:
self._test_suite = test_suite
else:
self._test_suite.addTest(test_suite)
return
def run(self):
runner = TestRunner(
stream=self._output,
verbosity=0)
runner.run(self._test_suite)
self._test_result = runner.get_test_result()
return
def show_results(self):
Logger.msg('\n')
Logger.msg('*' * 120)
Logger.msg('{0:^120}'.format('Test Summary'))
Logger.msg('*' * 120)
Logger.msg(self._output.getvalue())
Logger.msg('-' * 120 + '\n\n')
error_traces_str = ''
failure_traces_str = ''
for test_suite, results in self._test_result.test_results.items():
Logger.msg('TestSuite: %s' % test_suite)
Logger.msg('#' * 120)
Logger.msg('{0:48} {1:56} {2:16}'.format(
'TestCase',
'Description',
'Result'))
Logger.msg('#' * 120)
for result in results:
Logger.msg('{0:48} {1:56} {2:16}'.format(
result['test_case'],
result['description'],
TestResult.get_result_str(result['result'])))
if result['result'] == TestResult.ERROR:
error_traces_str += \
'%s::%s\n%s\n' % (test_suite,
result['test_case'],
result['formated_traceback'])
elif result['result'] == TestResult.FAILED:
failure_traces_str += \
'%s::%s\n%s\n' % (test_suite,
result['test_case'],
result['formated_traceback'])
Logger.msg('-' * 120 + '\n\n')
if self._test_result.has_errors:
Logger.msg('#' * 120)
Logger.msg('Errors')
Logger.msg('#' * 120 + '\n')
Logger.msg(error_traces_str + '-' * 120 + '\n')
if self._test_result.has_failures:
Logger.msg('#' * 120)
Logger.msg('Failures')
Logger.msg('#' * 120 + '\n')
Logger.msg(failure_traces_str + '-' * 120 + '\n')
Logger.msg('Tests Run: ' + str(self._test_result.testsRun))
return
def was_successful(self):
return (not self._test_result.has_errors) and \
(not self._test_result.has_failures) and \
(not self._test_result.has_unexpected_success)
Add a ShellHelper method to read a file as a string.
#
# Copyright (C) 2012 Ash (Tuxdude) <tuxdude.github@gmail.com>
#
# This file is part of repobuddy.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser 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 Lesser General Public
# License along with this program. If not, see
# <http://www.gnu.org/licenses/>.
#
import os as _os
import shlex as _shlex
import shutil as _shutil
import subprocess as _subprocess
import sys as _sys
import traceback as _traceback
if _sys.version_info < (2, 7):
# pylint: disable=F0401
import cStringIO as _io
import ordereddict as _collections
import unittest2 as _unittest
else:
# pylint: disable=F0401
if _sys.version_info < (3, 0):
import cStringIO as _io
else:
import io as _io
import collections as _collections
import unittest as _unittest
from repobuddy.utils import RepoBuddyBaseException, Logger
class ShellError(RepoBuddyBaseException):
def __init__(self, error_str):
super(ShellError, self).__init__(error_str)
return
class ShellHelper: # pylint: disable=W0232
@classmethod
def exec_command(cls, command, base_dir, debug_output=True):
Logger.msg('>> ' + ' '.join(command))
try:
kwargs = {}
return_code = None
if not debug_output:
kwargs['stdout'] = open(_os.devnull, 'w')
kwargs['stderr'] = _subprocess.STDOUT
proc = _subprocess.Popen(command, # pylint: disable=W0142
cwd=base_dir,
**kwargs)
try:
proc.communicate()
except:
proc.kill()
proc.wait()
raise
return_code = proc.poll()
if return_code != 0:
raise ShellError('Command \'%s\' failed!' % command)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def read_file_as_string(cls, filename):
try:
with open(filename, 'r') as file_handle:
data = file_handle.read()
except (OSError, IOError) as err:
raise ShellError(str(err))
return data
@classmethod
def append_text_to_file(cls, text, filename, base_dir):
try:
with open(_os.path.join(base_dir, filename), 'a') as file_handle:
file_handle.write(text)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def remove_file(cls, filename):
try:
_os.unlink(filename)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def make_dir(cls,
dirname,
create_parent_dirs=False,
only_if_not_exists=False):
if not create_parent_dirs:
try:
if not only_if_not_exists or not _os.path.exists(dirname):
_os.mkdir(dirname)
except (OSError, IOError) as err:
raise ShellError(str(err))
else:
try:
if not only_if_not_exists or not _os.path.exists(dirname):
_os.makedirs(dirname)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
@classmethod
def remove_dir(cls, dirname):
if _os.path.isdir(dirname):
try:
_shutil.rmtree(dirname, ignore_errors=False)
except (OSError, IOError) as err:
raise ShellError(str(err))
return
class TestCommon: # pylint: disable=W0232
@classmethod
def _git_append_add_commit(cls, text, filename, commit_log, exec_dir):
ShellHelper.append_text_to_file(text, filename, exec_dir)
ShellHelper.exec_command(
_shlex.split('git add %s' % filename),
exec_dir)
ShellHelper.exec_command(
_shlex.split('git commit -m "%s"' % commit_log),
exec_dir)
return
@classmethod
def setup_test_repos(cls, base_dir):
# Cleanup and create an empty directory
ShellHelper.remove_dir(base_dir)
ShellHelper.make_dir(base_dir)
# Set up the origin and clone repo paths
origin_repo_url = _os.path.join(base_dir, 'repo-origin')
clone_repo1 = _os.path.join(base_dir, 'clone1')
clone_repo2 = _os.path.join(base_dir, 'clone2')
# Set up the origin as a bare repo
ShellHelper.make_dir(origin_repo_url)
ShellHelper.exec_command(
_shlex.split('git init --bare'),
origin_repo_url)
# Create Clone1 from the origin
ShellHelper.exec_command(
_shlex.split('git clone %s %s' % (origin_repo_url, clone_repo1)),
base_dir)
# Create some content in clone1
cls._git_append_add_commit(
'First content...\n',
'README',
'First commit.',
clone_repo1)
cls._git_append_add_commit(
'Hardly useful...\n',
'dummy',
'Here we go.',
clone_repo1)
cls._git_append_add_commit(
'More content...\n',
'README',
'Appending to README.',
clone_repo1)
# Push the changes to origin
ShellHelper.exec_command(
_shlex.split('git push origin master'),
clone_repo1)
# Make some more changes, but do not push yet
cls._git_append_add_commit(
'Another line...\n',
'README',
'One more to README.',
clone_repo1)
cls._git_append_add_commit(
'Dummy2 in place...\n',
'dummy2',
'Creating dummy2.',
clone_repo1)
# Create clone2 from the origin
ShellHelper.exec_command(
_shlex.split('git clone %s %s' % (origin_repo_url, clone_repo2)),
base_dir)
# Add and commit changes in clone2
cls._git_append_add_commit(
'Another line...\n',
'dummy',
'One more to dummy.',
clone_repo2)
cls._git_append_add_commit(
'More dummy...\n',
'dummy',
'More dummy.',
clone_repo2)
# Create a new branch in clone2
ShellHelper.exec_command(
_shlex.split('git branch new-branch'),
clone_repo2)
ShellHelper.exec_command(
_shlex.split('git checkout new-branch'),
clone_repo2)
# Add some more changes in clone2's new-branch
cls._git_append_add_commit(
'More lines...\n',
'dummy',
'Another line to dummy.',
clone_repo2)
cls._git_append_add_commit(
'Just keep it coming...\n',
'dummy',
'Again :D',
clone_repo2)
# Switch back to master in clone2
ShellHelper.exec_command(
_shlex.split('git checkout master'),
clone_repo2)
# Push all branches to origin
ShellHelper.exec_command(
_shlex.split('git push origin --all'),
clone_repo2)
# Pull changes from origin into clone1
ShellHelper.exec_command(
_shlex.split('git fetch origin'),
clone_repo1)
ShellHelper.exec_command(
_shlex.split(
'git merge --commit -m "Merge origin into clone1" ' +
'origin/master'),
clone_repo1)
# Now push the merges back to origin
ShellHelper.exec_command(
_shlex.split('git push origin master'),
clone_repo1)
# Get the changes from origin into clone2
ShellHelper.exec_command(
_shlex.split('git fetch origin'),
clone_repo2)
ShellHelper.exec_command(
_shlex.split(
'git merge --commit -m "Merge origin into clone2" ' +
'origin/master'),
clone_repo2)
# Now push the merges back to origin
ShellHelper.exec_command(
_shlex.split('git push origin --all'),
clone_repo2)
# Now get rid of the clone repos, we only need the origin
ShellHelper.remove_dir(clone_repo1)
ShellHelper.remove_dir(clone_repo2)
return
class TestCaseBase(_unittest.TestCase):
def _set_tear_down_cb(self, method, *args, **kwargs):
self._tear_down_cb = method
self._tear_down_cb_args = args
self._tear_down_cb_kwargs = kwargs
return
def _clear_tear_down_cb(self):
self._tear_down_cb = None
self._tear_down_cb_args = None
self._tear_down_cb_kwargs = None
return
def __init__(self, methodName='runTest'):
super(TestCaseBase, self).__init__(methodName)
self._tear_down_cb = None
self._tear_down_cb_args = None
self._tear_down_cb_kwargs = None
if _sys.version_info >= (3, 2):
# pylint: disable=E1101
self._count_equal = self.assertCountEqual
else:
# pylint: disable=E1101
self._count_equal = self.assertItemsEqual
return
def setUp(self):
return
def tearDown(self):
if not self._tear_down_cb is None:
self._tear_down_cb(*self._tear_down_cb_args,
**self._tear_down_cb_kwargs)
self._clear_tear_down_cb()
return
class TestResult(_unittest.TestResult):
PASSED = 0
ERROR = 1
FAILED = 2
SKIPPED = 3
EXPECTED_FAILURE = 4
UNEXPECTED_SUCCESS = 5
_result_str = {PASSED: 'PASSED',
ERROR: 'ERROR',
FAILED: 'FAILED',
SKIPPED: 'SKIPPED',
EXPECTED_FAILURE: 'EXPECTED FAILURE',
UNEXPECTED_SUCCESS: 'UNEXPECTED_SUCCESS'}
def _update_result(self, test, err, result_type):
module_test_results = []
test_id = test.id().split('.')
if test_id[-2] not in self.test_results:
self.test_results[test_id[-2]] = module_test_results
else:
module_test_results = self.test_results[test_id[-2]]
result = {}
result['test_case'] = test_id[-1]
result['description'] = str(test.shortDescription())
result['result'] = result_type
if not err is None:
result['formated_traceback'] = \
''.join(_traceback.format_exception(err[0], err[1], err[2]))
module_test_results.append(result)
return
def __init__(self):
super(TestResult, self).__init__()
self.test_results = _collections.OrderedDict()
self.has_errors = False
self.has_failures = False
self.has_unexpected_success = False
return
@classmethod
def get_result_str(cls, result):
return cls._result_str[result]
def addError(self, test, err):
self._update_result(test, err, type(self).ERROR)
self.has_errors = True
return
def addFailure(self, test, err):
self._update_result(test, err, type(self).FAILED)
self.has_failures = True
return
def addSuccess(self, test):
self._update_result(test, None, type(self).PASSED)
return
def addSkip(self, test, reason):
self._update_result(test, None, type(self).SKIPPED)
return
def addExpectedFailure(self, test, err):
self._update_result(test, err, type(self).EXPECTED_FAILURE)
return
def addUnexpectedSuccess(self, test):
self._update_result(test, None, type(self).UNEXPECTED_SUCCESS)
self.has_unexpected_success = True
return
class TestRunner(_unittest.TextTestRunner):
def __init__(self, stream=_sys.stderr, descriptions=True, verbosity=1):
super(TestRunner, self).__init__(stream, descriptions, verbosity)
self._test_result = None
return
def _makeResult(self):
self._test_result = TestResult()
return self._test_result
def get_test_result(self):
return self._test_result
class TestSuiteManager(object):
_base_dir = None
@classmethod
def get_base_dir(cls):
return cls._base_dir
def __init__(self, base_dir):
if not _os.path.isdir(base_dir):
ShellHelper.make_dir(base_dir)
type(self)._base_dir = base_dir
self._test_suite = None
self._output = _io.StringIO()
self._test_result = None
return
def add_test_suite(self, test_suite):
if self._test_suite is None:
self._test_suite = test_suite
else:
self._test_suite.addTest(test_suite)
return
def run(self):
runner = TestRunner(
stream=self._output,
verbosity=0)
runner.run(self._test_suite)
self._test_result = runner.get_test_result()
return
def show_results(self):
Logger.msg('\n')
Logger.msg('*' * 120)
Logger.msg('{0:^120}'.format('Test Summary'))
Logger.msg('*' * 120)
Logger.msg(self._output.getvalue())
Logger.msg('-' * 120 + '\n\n')
error_traces_str = ''
failure_traces_str = ''
for test_suite, results in self._test_result.test_results.items():
Logger.msg('TestSuite: %s' % test_suite)
Logger.msg('#' * 120)
Logger.msg('{0:48} {1:56} {2:16}'.format(
'TestCase',
'Description',
'Result'))
Logger.msg('#' * 120)
for result in results:
Logger.msg('{0:48} {1:56} {2:16}'.format(
result['test_case'],
result['description'],
TestResult.get_result_str(result['result'])))
if result['result'] == TestResult.ERROR:
error_traces_str += \
'%s::%s\n%s\n' % (test_suite,
result['test_case'],
result['formated_traceback'])
elif result['result'] == TestResult.FAILED:
failure_traces_str += \
'%s::%s\n%s\n' % (test_suite,
result['test_case'],
result['formated_traceback'])
Logger.msg('-' * 120 + '\n\n')
if self._test_result.has_errors:
Logger.msg('#' * 120)
Logger.msg('Errors')
Logger.msg('#' * 120 + '\n')
Logger.msg(error_traces_str + '-' * 120 + '\n')
if self._test_result.has_failures:
Logger.msg('#' * 120)
Logger.msg('Failures')
Logger.msg('#' * 120 + '\n')
Logger.msg(failure_traces_str + '-' * 120 + '\n')
Logger.msg('Tests Run: ' + str(self._test_result.testsRun))
return
def was_successful(self):
return (not self._test_result.has_errors) and \
(not self._test_result.has_failures) and \
(not self._test_result.has_unexpected_success)
|
# Python
import StringIO
from collections import Counter, OrderedDict
# Django
from django.core import urlresolvers
from django_countries import countries
from django.db.models import Count, FieldDoesNotExist
from django.contrib.sites.shortcuts import get_current_site
# 3rd Party
import xlsxwriter
# Project
from forms.models import (
NewspaperSheet, NewspaperPerson, TelevisionJournalist,
person_models, sheet_models, journalist_models)
from forms.modelutils import (TOPICS, GENDER, SPACE, OCCUPATION, FUNCTION, SCOPE,
YESNO, AGES, SOURCE, VICTIM_OF, SURVIVOR_OF, IS_PHOTOGRAPH, AGREE_DISAGREE,
RETWEET, TV_ROLE, MEDIA_TYPES,
CountryRegion)
from report_details import WS_INFO, REGION_COUNTRY_MAP, MAJOR_TOPICS, TOPIC_GROUPS, GROUP_TOPICS_MAP, FORMATS
def has_field(model, fld):
try:
model._meta.get_field(fld)
return True
except FieldDoesNotExist:
return False
def p(n, d):
""" Helper to calculate the percentage of n / d,
returning 0 if d == 0.
"""
if d == 0:
return 0.0
return float(n) / d
def get_regions():
"""
Return a (id, region_name) list for all regions
"""
country_regions = CountryRegion.objects\
.values('region')\
.exclude(region='Unmapped')
regions = set(item['region'] for item in country_regions)
return [(i, region) for i, region in enumerate(regions)]
def get_countries(selected=None):
"""
Return a (code, country) list for countries captured.
"""
captured_country_codes = set()
for model in sheet_models.itervalues():
rows = model.objects.values('country')
captured_country_codes.update([r['country'] for r in rows])
return [(code, name) for code, name in list(countries) if code in captured_country_codes]
def get_region_countries(region):
"""
Return a (code, country) list for a region.
"""
if region == 'ALL':
return get_countries()
else:
country_codes = REGION_COUNTRY_MAP[region]
return [(code, name) for code, name in list(countries) if code in country_codes]
def get_country_region(country):
"""
Return a (id, region_name) list to which a country belongs.
"""
if country == 'ALL':
return get_regions()
else:
return [(0, [k for k, v in REGION_COUNTRY_MAP.items() if country in v][0])]
def clean_title(text):
"""
Return the string passed in stripped of its numbers and parentheses
"""
if text != "Congo (the Democratic Republic of the)":
return text[text.find(')')+1:].lstrip()
return text
class XLSXDataExportBuilder():
def __init__(self, request):
self.domain = "http://%s" % get_current_site(request).domain
self.sheet_exclude_fields = ['monitor', 'url_and_multimedia', 'time_accessed', 'country_region']
self.person_exclude_fields = []
self.journalist_exclude_fields =[]
self.sheet_fields_with_id = ['topic', 'scope', 'person_secondary', 'inequality_women', 'stereotypes']
self.person_fields_with_id = ['sex', 'age', 'occupation', 'function', 'survivor_of', 'victim_of']
self.journalist_fields_with_id = ['sex', 'age']
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
for model in sheet_models.itervalues():
self.create_sheet_export(model, workbook)
for model in person_models.itervalues():
self.create_person_export(model, workbook)
for model in journalist_models.itervalues():
self.create_journalist_export(model, workbook)
workbook.close()
output.seek(0)
return output.read()
def create_sheet_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all()
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.sheet_fields_with_id)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_sheet_row(obj, ws, row+y, col, fields, self.sheet_fields_with_id)
def create_person_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.person_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.person_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_person_row(obj, ws, row+y, col, fields, self.person_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def create_journalist_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.journalist_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.journalist_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_journalist_row(obj, ws, row+y, col, fields, self.journalist_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def write_ws_titles(self, ws, row, col, fields, fields_with_id, append_sheet=False):
"""
Writes the column titles to the worksheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
:param append_sheet: Boolean specifying whether the related sheet object
needs to be appended to the row.
"""
if not append_sheet:
for field in fields:
ws.write(row, col, unicode(field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode(field.name+"_id"))
col += 1
ws.write(row, col, unicode('edit_url'))
col += 1
else:
for field in fields:
ws.write(row, col, unicode("sheet_" + field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode("sheet_" + field.name + "_id"))
col += 1
ws.write(row, col, unicode('sheet_edit_url'))
col += 1
return ws, col
def write_sheet_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Sheet models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'country':
ws.write(row, col, getattr(obj, field.name).code)
elif field.name == 'topic':
ws.write(row, col, unicode(TOPICS[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, TOPICS[getattr(obj, field.name)-1][0])
elif field.name == 'scope':
ws.write(row, col, unicode(SCOPE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, SCOPE[getattr(obj, field.name)-1][0])
elif field.name == 'person_secondary':
ws.write(row, col, unicode(SOURCE[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SOURCE[getattr(obj, field.name)][0])
elif field.name == 'inequality_women':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'stereotypes':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
else:
try:
ws.write(row, col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row, col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
obj._meta.app_label,
obj._meta.model_name),
args=(obj.id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_person_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Person models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age':
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == 'occupation':
ws.write(row, col, unicode(OCCUPATION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, OCCUPATION[getattr(obj, field.name)][0])
elif field.name == 'function':
ws.write(row, col, unicode(FUNCTION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, FUNCTION[getattr(obj, field.name)][0])
elif field.name == 'victim_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(VICTIM_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, VICTIM_OF[getattr(obj, field.name)][0])
elif field.name == 'survivor_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(SURVIVOR_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SURVIVOR_OF[getattr(obj, field.name)][0])
elif field.name == 'is_photograph':
ws.write(row, col, unicode(IS_PHOTOGRAPH[getattr(obj, field.name)-1][1]))
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in self.person_fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_journalist_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Journalist models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet_fields_with_id
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet_fields_with_id
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
class XLSXReportBuilder:
def __init__(self, form):
from reports.views import CountryForm, RegionForm
self.form = form
if isinstance(form, CountryForm):
self.countries = form.filter_countries()
self.regions = get_country_region(form.cleaned_data['country'])
self.report_type = 'country'
elif isinstance(form, RegionForm):
region = [name for i, name in form.REGIONS if str(i) == form.cleaned_data['region']][0]
self.countries = get_region_countries(region)
self.regions = [(0, region)]
self.report_type = 'region'
else:
self.countries = get_countries()
self.regions = get_regions()
self.report_type = 'global'
self.country_list = [code for code, name in self.countries]
self.region_list = [name for id, name in self.regions]
# Various gender utilities
self.male_female = [(id, value) for id, value in GENDER if id in [1, 2]]
self.male_female_ids = [id for id, value in self.male_female]
self.female = [(id, value) for id, value in GENDER if id == 1]
self.yes = [(id, value) for id, value in YESNO if id == 'Y']
self.gmmp_year = '2015'
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
# setup formats
self.heading = workbook.add_format(FORMATS['heading'])
self.col_heading = workbook.add_format(FORMATS['col_heading'])
self.col_heading_def = workbook.add_format(FORMATS['col_heading_def'])
self.sec_col_heading = workbook.add_format(FORMATS['sec_col_heading'])
self.sec_col_heading_def = workbook.add_format(FORMATS['sec_col_heading_def'])
self.label = workbook.add_format(FORMATS['label'])
self.N = workbook.add_format(FORMATS['N'])
self.P = workbook.add_format(FORMATS['P'])
# Use the following for specifying which reports to create durin dev
# test_functions = [
# 'ws_01', 'ws_02', 'ws_04', 'ws_05', 'ws_06', 'ws_07', 'ws_08', 'ws_09', 'ws_10',
# 'ws_11', 'ws_12', 'ws_13', 'ws_14', 'ws_15', 'ws_16', 'ws_17', 'ws_18', 'ws_19', 'ws_20',
# 'ws_21', 'ws_23', 'ws_24', 'ws_25', 'ws_26', 'ws_27', 'ws_28', 'ws_29', 'ws_30',
# 'ws_31', 'ws_32', 'ws_34', 'ws_35', 'ws_36', 'ws_38', 'ws_39', 'ws_40',
# 'ws_41', 'ws_42', 'ws_43', 'ws_44', 'ws_45', 'ws_46', 'ws_47', 'ws_48',]
test_functions = ['ws_02']
sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
for function in test_functions:
if self.report_type in sheet_info[function]['reports']:
ws = workbook.add_worksheet(sheet_info[function]['name'])
self.write_headers(ws, sheet_info[function]['title'], sheet_info[function]['desc'])
getattr(self, function)(ws)
# -------------------------------------------------------------------
# To ensure ordered worksheets
# sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
# for ws_num, ws_info in sheet_info.iteritems():
# if self.report_type in ws_info['reports']:
# ws = workbook.add_worksheet(ws_info['name'])
# self.write_headers(ws, ws_info['title'], ws_info['desc'])
# getattr(self, ws_num)(ws)
workbook.close()
output.seek(0)
return output.read()
def ws_01(self, ws):
"""
Cols: Media Type
Rows: Region
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country_region__region')\
.filter(country_region__region__in=self.region_list)\
.annotate(n=Count('id'))
for row in rows:
if row['country_region__region'] is not None:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [region[0] for region in self.regions if region[1] == row['country_region__region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_02(self, ws):
"""
Cols: Media Type
Rows: Country
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country')\
.filter(country__in=self.country_list)\
.annotate(n=Count('country'))
for row in rows:
if row['country'] is not None:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['country']): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.countries, row_perc=True)
def ws_04(self, ws):
"""
Cols: Region, Media type
Rows: Major Topic
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('topic')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
major_topic = TOPIC_GROUPS[r['topic']]
counts.update({(media_id, major_topic): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, MEDIA_TYPES, MAJOR_TOPICS, row_perc=False, sec_cols=10)
def ws_05(self, ws):
"""
Cols: Subject sex
Rows: Major Topic
"""
counts = Counter()
for model in person_models.itervalues():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r[topic_field]]): r['n']})
self.tabulate(ws, counts, self.male_female, MAJOR_TOPICS, row_perc=True, display_cols=self.female)
def ws_06(self, ws):
"""
Cols: Region, Subject sex: female only
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in person_models.itervalues():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country_region__region':region})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r[topic_field]]): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, MAJOR_TOPICS, row_perc=True, sec_cols=2, display_cols=self.female)
def ws_07(self, ws):
"""
Cols: Media Type
Rows: Subject Sex
"""
counts = Counter()
for media_type, model in person_models.iteritems():
rows = model.objects\
.values('sex')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, r['sex']): r['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.male_female, row_perc=False)
def ws_08(self, ws):
"""
Cols: Subject Sex
Rows: Scope
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'scope' in model.sheet_field().rel.to._meta.get_all_field_names():
scope = '%s__scope' % model.sheet_name()
rows = model.objects\
.values('sex', scope)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[scope]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SCOPE, row_perc=True, display_cols=self.female)
def ws_09(self, ws):
"""
Cols: Subject Sex
Rows: Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[topic]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_10(self, ws):
"""
Cols: Space
Rows: Minor Topics
:: Newspaper Sheets only
"""
# Calculate row values for column
counts = Counter()
rows = NewspaperSheet.objects\
.values('space', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['space'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, SPACE, MAJOR_TOPICS, row_perc=False)
def ws_11(self, ws):
"""
Cols: Equality Rights
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_12(self, ws):
"""
Cols: Region, Equality Rights
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region_name in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
# Some models has no equality rights field
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country_region__region=region_name)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_13(self, ws):
"""
Cols: Journalist Sex, Equality Rights
Rows: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
topic = '%s__topic' % model.sheet_name()
equality_rights = '%s__equality_rights' % model.sheet_name()
rows = model.objects\
.values(equality_rights, topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex=gender_id)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r[equality_rights], TOPIC_GROUPS[r[topic]]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_14(self, ws):
"""
Cols: Sex
Rows: Occupation
"""
counts = Counter()
for model in person_models.itervalues():
# some Person models don't have an occupation field
if 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, OCCUPATION, row_perc=True, display_cols=self.female)
def ws_15(self, ws):
"""
Cols: Sex
Rows: Function
"""
counts = Counter()
for model in person_models.itervalues():
# some Person models don't have a function field
if 'function' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, FUNCTION, row_perc=True, display_cols=self.female)
def ws_16(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
for function_id, function in FUNCTION:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=function_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_17(self, ws):
"""
Cols: Age, Sex of Subject
Rows: Function
"""
secondary_counts = OrderedDict()
for age_id, age in AGES:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'age' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(age=age_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
secondary_counts[age] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, FUNCTION, row_perc=False, sec_cols=4)
def ws_18(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for print
"""
counts = Counter()
rows = NewspaperPerson.objects\
.values('sex', 'age')\
.filter(newspaper_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_19(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for broadcast
"""
counts = Counter()
broadcast = ['Television']
for media_type, model in person_models.iteritems():
if media_type in broadcast:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_20(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
functions_count = Counter()
# Get top 5 functions
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
functions_count.update({(r['function']): r['n'] for r in rows})
top_5_function_ids = [id for id, count in sorted(functions_count.items(), key=lambda x: -x[1])[:5]]
top_5_functions = [(id, func) for id, func in FUNCTION if id in top_5_function_ids]
for func_id, function in top_5_functions:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=func_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_21(self, ws):
"""
Cols: Subject Sex
Rows: Victim type
"""
counts = Counter()
for model in person_models.itervalues():
if 'victim_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'victim_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.exclude(victim_of=None)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['victim_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, VICTIM_OF, row_perc=True)
def ws_23(self, ws):
"""
Cols: Subject Sex
Rows: Survivor type
"""
counts = Counter()
for model in person_models.itervalues():
if 'survivor_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'survivor_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.exclude(survivor_of=None)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['survivor_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SURVIVOR_OF, row_perc=True)
def ws_24(self, ws):
"""
Cols: Subject Sex
Rows: Family Role
"""
counts = Counter()
for model in person_models.itervalues():
if 'family_role' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_25(self, ws):
"""
Cols: Journalist Sex, Subject Sex
Rows: Family Role
"""
secondary_counts = OrderedDict()
for sex_id, sex in self.male_female:
counts = Counter()
for model in person_models.itervalues():
if 'family_role' in model._meta.get_all_field_names():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(**{sheet_name + '__' + journo_name + '__sex':sex_id})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
secondary_counts[sex] = counts
secondary_counts['col_title_def'] = [
'Sexof reporter',
'Sex of news subject']
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, YESNO, row_perc=False, sec_cols=4)
def ws_26(self, ws):
"""
Cols: Subject Sex
Rows: Whether Quoted
"""
counts = Counter()
for model in person_models.itervalues():
if 'is_quoted' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['is_quoted']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_27(self, ws):
"""
Cols: Subject Sex
Rows: Photographed
"""
counts = Counter()
for model in person_models.itervalues():
if 'is_photograph' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['is_photograph']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, IS_PHOTOGRAPH, row_perc=False)
def ws_28(self, ws):
"""
Cols: Medium
Rows: Region
:: Female reporters only
"""
counts = Counter()
for media_type, model in person_models.iteritems():
region = model.sheet_name() + '__country_region__region'
rows = model.objects\
.values(region)\
.filter(sex=1)\
.filter(**{region + '__in': self.region_list})\
.annotate(n=Count('id'))
for row in rows:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_29(self, ws):
"""
Cols: Regions
Rows: Scope
:: Female reporters only
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
scope = sheet_name + '__scope'
if 'scope' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, scope)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
counts.update({(region_id, row[scope]): row['n']})
self.tabulate(ws, counts, self.regions, SCOPE, row_perc=False)
def ws_30(self, ws):
"""
Cols: Region
Rows: Major Topics
:: Female reporters only
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, topic)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
major_topic = TOPIC_GROUPS[row[topic]]
counts.update({(region_id, major_topic): row['n']})
self.tabulate(ws, counts, self.regions, MAJOR_TOPICS, row_perc=False)
def ws_31(self, ws):
"""
Cols: Sex of Reporter
Rows: Minor Topics
"""
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[topic]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_32(self, ws):
"""
Cols: Medium
Rows: Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row[topic]): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, TOPICS, row_perc=False)
def ws_34(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of subject'
self.tabulate(ws, counts, self.male_female, GENDER, row_perc=True, display_cols=self.female)
def ws_35(self, ws):
"""
Cols: Sex of reporter
Rows: Age of reporter
:: Only for television
"""
counts = Counter()
rows = TelevisionJournalist.objects\
.values('sex', 'age')\
.filter(television_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True, display_cols=self.female)
def ws_36(self, ws):
"""
Cols: Sex of Reporter
Rows: Focus: about women
"""
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
about_women = sheet_name + '__about_women'
if 'about_women' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', about_women)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[about_women]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=True)
def ws_38(self, ws):
"""
Cols: Focus: about women
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['about_women'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_39(self, ws):
"""
Cols: Focus: about women
Rows: Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=True)
def ws_40(self, ws):
"""
Cols: Region, Topics
Rows: Focus: about women
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'about_women')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=2, display_cols=self.yes)
def ws_41(self, ws):
"""
Cols: Equality rights raised
Rows: Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=False)
def ws_42(self, ws):
"""
Cols: Region, Equality rights raised
Rows: Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_43(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Cols: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic, equality_rights)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=gender_id)\
.annotate(n=Count('id'))
counts.update({(r[equality_rights], r[topic]): r['n'] for r in rows})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_44(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Rows: Region
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(equality_rights, region)\
.filter(sex=gender_id)\
.filter(**{region + '__in':self.region_list})\
.annotate(n=Count('id'))
for r in rows:
region_id = [id for id, name in self.regions if name == r[region]][0]
counts.update({(r[equality_rights], region_id): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.regions, row_perc=False, sec_cols=4)
def ws_45(self, ws):
"""
Cols: Sex of news subject
Rows: Region
:: Equality rights raised == Yes
"""
counts = Counter()
for model in person_models.itervalues():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
region = model.sheet_name() + '__country_region__region'
equality_rights = model.sheet_name() + '__equality_rights'
rows = model.objects\
.values('sex', region)\
.filter(**{region + '__in':self.region_list})\
.filter(**{equality_rights:'Y'})\
.annotate(n=Count('id'))
for r in rows:
region_id = [id for id, name in self.regions if name == r[region]][0]
counts.update({(r['sex'], region_id): r['n']})
self.tabulate(ws, counts, self.male_female, self.regions, row_perc=True)
def ws_46(self, ws):
"""
Cols: Region, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(TOPIC_GROUPS[r['topic']], r['stereotypes']): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True, sec_cols=8)
def ws_47(self, ws):
"""
Cols: Stereotypes
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True)
def ws_48(self, ws):
"""
Cols: Sex of reporter, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
stereotypes = sheet_name + '__stereotypes'
if 'stereotypes' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(stereotypes, topic)\
.filter(sex=gender_id)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r[stereotypes], TOPIC_GROUPS[r[topic]]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=False, sec_cols=8)
def ws_53(self, ws):
"""
Cols: Topic
Rows: Country
:: Internet media type only
:: Female reporters only
"""
display_cols = [(id, value) for id, value in GENDER if id==1]
secondary_counts = OrderedDict()
model = sheet_models.get('Internet News')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
journo_sex_field = '%s__sex' % model.journalist_field_name()
rows = model.objects\
.values(journo_sex_field, 'country')\
.filter(topic__in=topic_ids)\
.annotate(n=Count('id'))
counts.update({(r[journo_sex_field], r['country']): r['n'] for r in rows})
secondary_counts[major_topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, display_cols=display_cols, sec_cols=2)
def ws_54(self, ws):
"""
Cols: Major Topic, sex of subject
Rows: Country
:: Internet media type only
"""
secondary_counts = OrderedDict()
model = person_models.get('Internet News')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('sex', country_field)\
.filter(**{model.sheet_name() + '__topic__in':topic_ids})\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[country_field]): r['n'] for r in rows})
secondary_counts[major_topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, sec_cols=8)
def ws_55(self, ws):
"""
Cols: Occupation
Rows: Country
:: Show all countries
:: Only female subjects
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet News')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'occupation')\
.filter(sex=1)\
.annotate(n=Count('id'))
counts.update({(r['occupation'], r[country_field]): r['n'] for r in rows})
self.tabulate(ws, counts, OCCUPATION, self.countries, row_perc=True)
def ws_56(self, ws):
"""
Cols: Function
Rows: Country
:: Show all countries
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet News')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'function')\
.annotate(n=Count('id'))
counts.update({(r['function'], r[country_field]): r['n'] for r in rows})
self.tabulate(ws, counts, FUNCTION, self.countries, row_perc=True)
def ws_57(self, ws):
"""
Cols: Sex of subject
Rows: Country, Family role
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['family_role']): row['n'] for row in rows}
# If only captured countries should be displayed use
# if counts.keys():
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_58(self, ws):
"""
Cols: Sex of subject
Rows: Country, is photographed
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['is_photograph']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, IS_PHOTOGRAPH, row_perc=True, sec_row=True, r=r)
r += len(IS_PHOTOGRAPH)
def ws_59(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
:: Internet media only
"""
counts = Counter()
model = person_models.get('Internet News')
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
self.tabulate(ws, counts, GENDER, GENDER, row_perc=False)
def ws_60(self, ws):
"""
Cols: Sex of subject
Rows: Country, age
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['age']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, AGES, row_perc=True, sec_row=True, r=r)
r += len(AGES)
def ws_61(self, ws):
"""
Cols: Sex of subject
Rows: Country, is_quoted
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['is_quoted']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_62(self, ws):
"""
Cols: Topic
Rows: Country, equality raised
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['equality_rights']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_63(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes challenged
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_64(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_65(self, ws):
"""
Cols: Topic
Rows: Country, tweet or retweet
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'retweet')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['retweet']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, RETWEET, row_perc=False, sec_row=True, r=r)
r += len(RETWEET)
def ws_66(self, ws):
"""
Cols: Topic
Rows: Country, sex of news subject
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Twitter')
topic_field = '%s__topic' % model.sheet_name()
for code, country in self.countries:
rows = model.objects\
.values(topic_field, 'sex')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts.update({(row[topic_field], row['sex']): row['n'] for row in rows})
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, GENDER, row_perc=True, sec_row=True, r=r)
r += len(GENDER)
def ws_67(self, ws):
"""
Cols: Topic
Rows: Country
:: Only female journalists
:: Show all countries
:: Twitter media type only
"""
counts = Counter()
model = sheet_models.get('Twitter')
rows = model.objects\
.values('topic', 'country')\
.filter(**{model.journalist_field_name() + '__sex':1})\
.annotate(n=Count('id'))
counts.update({(row['topic'], row['country']): row['n'] for row in rows})
self.tabulate(ws, counts, TOPICS, self.countries, row_perc=True, sec_row=False)
def ws_68(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=False, sec_row=True, r=r)
r += len(YESNO)
def ws_69(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_76(self, ws):
"""
Cols: Topic, Stereotypes
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in sheet_models.itervalues():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'country')\
.filter(topic=topic_id)\
.annotate(n=Count('id'))
counts.update({(r['stereotypes'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, self.countries, row_perc=True, sec_cols=8)
def ws_77(self, ws):
"""
Cols: Topic, Reference to gender equality
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'country')\
.filter(topic=topic_id)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.countries, row_perc=True, sec_cols=4)
def ws_78(self, ws):
"""
Cols: Topic, victim_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in person_models.itervalues():
if 'victim_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('victim_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})\
.annotate(n=Count('id'))
counts.update({(r['victim_of'], r[country_field]): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, VICTIM_OF, self.countries, row_perc=True, sec_cols=18)
def ws_79(self, ws):
"""
Cols: Topic, survivor_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in person_models.itervalues():
if 'survivor_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('survivor_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})\
.annotate(n=Count('id'))
counts.update({(r['survivor_of'], r[country_field]): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, SURVIVOR_OF, self.countries, row_perc=True, sec_cols=18)
# -------------------------------------------------------------------------------
# Helper functions
#
def write_headers(self, ws, title, description):
"""
Write the headers to the worksheet
"""
ws.write(0, 0, title, self.heading)
ws.write(1, 0, description, self.heading)
ws.write(3, 2, self.gmmp_year, self.heading)
def write_col_headings(self, ws, cols, c=2, r=4):
"""
:param ws: worksheet to write to
:param cols: list of `(col_id, col_title)` tuples of column ids and titles
:param r, c: initial position where cursor should start writing to
"""
for col_id, col_title in cols:
ws.write(r, c, clean_title(col_title), self.col_heading)
ws.write(r + 1, c, "N")
ws.write(r + 1, c + 1, "%")
c += 2
def write_primary_row_heading(self, ws, heading, c=0, r=6):
"""
:param ws: worksheet to write to
:param heading: row heading to write
:param r, c: position where heading should be written to
"""
ws.write(r, c, clean_title(heading), self.heading)
def tabulate_secondary_cols(self, ws, secondary_counts, cols, rows, row_perc=False, display_cols=None, sec_cols=4):
"""
:param ws: worksheet to write to
:param secondary_counts: dict in following format:
{'Primary column heading': Count object, ...}
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_cols: amount of cols needed for secondary cols
"""
r, c = 7, 1
# row titles
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
if 'col_title_def' in secondary_counts:
# Write definitions of column heading titles
ws.write(r-3, c-1, secondary_counts['col_title_def'][0], self.sec_col_heading_def)
ws.write(r-2, c-1, secondary_counts['col_title_def'][1], self.col_heading_def)
secondary_counts.pop('col_title_def')
for field, counts in secondary_counts.iteritems():
ws.merge_range(r-3, c, r-3, c+sec_cols-1, clean_title(field), self.sec_col_heading)
self.tabulate(ws, counts, cols, rows, row_perc=row_perc, sec_col=True, display_cols=display_cols, r=7, c=c)
c += sec_cols
def tabulate(self, ws, counts, cols, rows, row_perc=False, sec_col=False, sec_row=False, display_cols=None, c=1, r=6):
""" Emit a table.
:param ws: worksheet to write to
:param dict counts: dict from `(col_id, row_id)` tuples to count for that combination.
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_col: Are wecreating a secondary column title(default: False)
:param sec_row: Are we creating a secondary row title(default: False)
:param display_cols: Optional if only a subset of columns should be displayed e.g. only female
:param r, c: initial position where cursor should start writing to
"""
if row_perc:
# we'll need percentage by rows
row_totals = {}
for row_id, row_title in rows:
row_totals[row_id] = sum(counts.get((col_id, row_id), 0) for col_id, _ in cols) # noqa
# row titles
if not sec_col:
# Else already written
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
# if only filtered results should be shown
# e.g. only print female columns
if display_cols:
cols = display_cols
if 'col_title_def' in counts and not sec_row:
ws.write(r - 2, c-1, counts['col_title_def'], self.col_heading_def)
counts.pop('col_title_def')
# values, written by column
for col_id, col_heading in cols:
# column title
if not sec_row:
ws.merge_range(r-2, c, r-2, c+1, clean_title(col_heading), self.col_heading)
ws.write(r - 1, c, "N", self.label)
ws.write(r - 1, c + 1, "%", self.label)
if not row_perc:
# column totals
# Confirm: Perc of col total or matrix total?
# total = sum(counts.itervalues())
total = sum(counts.get((col_id, row_id), 0) for row_id, _ in rows)
# row values for this column
for i, row in enumerate(rows):
row_id, row_title = row
if row_perc:
# row totals
total = row_totals[row_id]
n = counts.get((col_id, row_id), 0)
ws.write(r + i, c, n, self.N)
ws.write(r + i, c + 1, p(n, total), self.P)
c += 2
Group ws-02 countries by region
# Python
import StringIO
from collections import Counter, OrderedDict
# Django
from django.core import urlresolvers
from django_countries import countries
from django.db.models import Count, FieldDoesNotExist
from django.contrib.sites.shortcuts import get_current_site
# 3rd Party
import xlsxwriter
# Project
from forms.models import (
NewspaperSheet, NewspaperPerson, TelevisionJournalist,
person_models, sheet_models, journalist_models)
from forms.modelutils import (TOPICS, GENDER, SPACE, OCCUPATION, FUNCTION, SCOPE,
YESNO, AGES, SOURCE, VICTIM_OF, SURVIVOR_OF, IS_PHOTOGRAPH, AGREE_DISAGREE,
RETWEET, TV_ROLE, MEDIA_TYPES,
CountryRegion)
from report_details import WS_INFO, REGION_COUNTRY_MAP, MAJOR_TOPICS, TOPIC_GROUPS, GROUP_TOPICS_MAP, FORMATS
def has_field(model, fld):
try:
model._meta.get_field(fld)
return True
except FieldDoesNotExist:
return False
def p(n, d):
""" Helper to calculate the percentage of n / d,
returning 0 if d == 0.
"""
if d == 0:
return 0.0
return float(n) / d
def get_regions():
"""
Return a (id, region_name) list for all regions
"""
country_regions = CountryRegion.objects\
.values('region')\
.exclude(region='Unmapped')
regions = set(item['region'] for item in country_regions)
return [(i, region) for i, region in enumerate(regions)]
def get_countries(selected=None):
"""
Return a (code, country) list for countries captured.
"""
captured_country_codes = set()
for model in sheet_models.itervalues():
rows = model.objects.values('country')
captured_country_codes.update([r['country'] for r in rows])
return [(code, name) for code, name in list(countries) if code in captured_country_codes]
def get_region_countries(region):
"""
Return a (code, country) list for a region.
"""
if region == 'ALL':
return get_countries()
else:
country_codes = REGION_COUNTRY_MAP[region]
return [(code, name) for code, name in list(countries) if code in country_codes]
def get_country_region(country):
"""
Return a (id, region_name) list to which a country belongs.
"""
if country == 'ALL':
return get_regions()
else:
return [(0, [k for k, v in REGION_COUNTRY_MAP.items() if country in v][0])]
def clean_title(text):
"""
Return the string passed in stripped of its numbers and parentheses
"""
if text != "Congo (the Democratic Republic of the)":
return text[text.find(')')+1:].lstrip()
return text
class XLSXDataExportBuilder():
def __init__(self, request):
self.domain = "http://%s" % get_current_site(request).domain
self.sheet_exclude_fields = ['monitor', 'url_and_multimedia', 'time_accessed', 'country_region']
self.person_exclude_fields = []
self.journalist_exclude_fields =[]
self.sheet_fields_with_id = ['topic', 'scope', 'person_secondary', 'inequality_women', 'stereotypes']
self.person_fields_with_id = ['sex', 'age', 'occupation', 'function', 'survivor_of', 'victim_of']
self.journalist_fields_with_id = ['sex', 'age']
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
for model in sheet_models.itervalues():
self.create_sheet_export(model, workbook)
for model in person_models.itervalues():
self.create_person_export(model, workbook)
for model in journalist_models.itervalues():
self.create_journalist_export(model, workbook)
workbook.close()
output.seek(0)
return output.read()
def create_sheet_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all()
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.sheet_fields_with_id)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_sheet_row(obj, ws, row+y, col, fields, self.sheet_fields_with_id)
def create_person_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.person_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.person_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_person_row(obj, ws, row+y, col, fields, self.person_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def create_journalist_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.journalist_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.journalist_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_journalist_row(obj, ws, row+y, col, fields, self.journalist_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def write_ws_titles(self, ws, row, col, fields, fields_with_id, append_sheet=False):
"""
Writes the column titles to the worksheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
:param append_sheet: Boolean specifying whether the related sheet object
needs to be appended to the row.
"""
if not append_sheet:
for field in fields:
ws.write(row, col, unicode(field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode(field.name+"_id"))
col += 1
ws.write(row, col, unicode('edit_url'))
col += 1
else:
for field in fields:
ws.write(row, col, unicode("sheet_" + field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode("sheet_" + field.name + "_id"))
col += 1
ws.write(row, col, unicode('sheet_edit_url'))
col += 1
return ws, col
def write_sheet_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Sheet models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'country':
ws.write(row, col, getattr(obj, field.name).code)
elif field.name == 'topic':
ws.write(row, col, unicode(TOPICS[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, TOPICS[getattr(obj, field.name)-1][0])
elif field.name == 'scope':
ws.write(row, col, unicode(SCOPE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, SCOPE[getattr(obj, field.name)-1][0])
elif field.name == 'person_secondary':
ws.write(row, col, unicode(SOURCE[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SOURCE[getattr(obj, field.name)][0])
elif field.name == 'inequality_women':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'stereotypes':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
else:
try:
ws.write(row, col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row, col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
obj._meta.app_label,
obj._meta.model_name),
args=(obj.id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_person_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Person models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age':
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == 'occupation':
ws.write(row, col, unicode(OCCUPATION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, OCCUPATION[getattr(obj, field.name)][0])
elif field.name == 'function':
ws.write(row, col, unicode(FUNCTION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, FUNCTION[getattr(obj, field.name)][0])
elif field.name == 'victim_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(VICTIM_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, VICTIM_OF[getattr(obj, field.name)][0])
elif field.name == 'survivor_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(SURVIVOR_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SURVIVOR_OF[getattr(obj, field.name)][0])
elif field.name == 'is_photograph':
ws.write(row, col, unicode(IS_PHOTOGRAPH[getattr(obj, field.name)-1][1]))
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in self.person_fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_journalist_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Journalist models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet_fields_with_id
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet_fields_with_id
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
class XLSXReportBuilder:
def __init__(self, form):
from reports.views import CountryForm, RegionForm
self.form = form
if isinstance(form, CountryForm):
self.countries = form.filter_countries()
self.regions = get_country_region(form.cleaned_data['country'])
self.report_type = 'country'
elif isinstance(form, RegionForm):
region = [name for i, name in form.REGIONS if str(i) == form.cleaned_data['region']][0]
self.countries = get_region_countries(region)
self.regions = [(0, region)]
self.report_type = 'region'
else:
self.countries = get_countries()
self.regions = get_regions()
self.report_type = 'global'
self.country_list = [code for code, name in self.countries]
self.region_list = [name for id, name in self.regions]
# Various gender utilities
self.male_female = [(id, value) for id, value in GENDER if id in [1, 2]]
self.male_female_ids = [id for id, value in self.male_female]
self.female = [(id, value) for id, value in GENDER if id == 1]
self.yes = [(id, value) for id, value in YESNO if id == 'Y']
self.gmmp_year = '2015'
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
# setup formats
self.heading = workbook.add_format(FORMATS['heading'])
self.col_heading = workbook.add_format(FORMATS['col_heading'])
self.col_heading_def = workbook.add_format(FORMATS['col_heading_def'])
self.sec_col_heading = workbook.add_format(FORMATS['sec_col_heading'])
self.sec_col_heading_def = workbook.add_format(FORMATS['sec_col_heading_def'])
self.label = workbook.add_format(FORMATS['label'])
self.N = workbook.add_format(FORMATS['N'])
self.P = workbook.add_format(FORMATS['P'])
# Use the following for specifying which reports to create durin dev
# test_functions = [
# 'ws_01', 'ws_02', 'ws_04', 'ws_05', 'ws_06', 'ws_07', 'ws_08', 'ws_09', 'ws_10',
# 'ws_11', 'ws_12', 'ws_13', 'ws_14', 'ws_15', 'ws_16', 'ws_17', 'ws_18', 'ws_19', 'ws_20',
# 'ws_21', 'ws_23', 'ws_24', 'ws_25', 'ws_26', 'ws_27', 'ws_28', 'ws_29', 'ws_30',
# 'ws_31', 'ws_32', 'ws_34', 'ws_35', 'ws_36', 'ws_38', 'ws_39', 'ws_40',
# 'ws_41', 'ws_42', 'ws_43', 'ws_44', 'ws_45', 'ws_46', 'ws_47', 'ws_48',]
test_functions = ['ws_02']
sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
for function in test_functions:
if self.report_type in sheet_info[function]['reports']:
ws = workbook.add_worksheet(sheet_info[function]['name'])
self.write_headers(ws, sheet_info[function]['title'], sheet_info[function]['desc'])
getattr(self, function)(ws)
# -------------------------------------------------------------------
# To ensure ordered worksheets
# sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
# for ws_num, ws_info in sheet_info.iteritems():
# if self.report_type in ws_info['reports']:
# ws = workbook.add_worksheet(ws_info['name'])
# self.write_headers(ws, ws_info['title'], ws_info['desc'])
# getattr(self, ws_num)(ws)
workbook.close()
output.seek(0)
return output.read()
def ws_01(self, ws):
"""
Cols: Media Type
Rows: Region
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country_region__region')\
.filter(country_region__region__in=self.region_list)\
.annotate(n=Count('id'))
for row in rows:
if row['country_region__region'] is not None:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [region[0] for region in self.regions if region[1] == row['country_region__region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_02(self, ws):
"""
Cols: Media Type
Rows: Region, Country
"""
r = 6
self.write_col_headings(ws, MEDIA_TYPES)
counts = Counter()
for region_id, region in self.regions:
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country')\
.filter(country__in=self.country_list)\
.annotate(n=Count('country'))
for row in rows:
if row['country'] is not None:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['country']): row['n']})
self.write_primary_row_heading(ws, region, r=r)
region_countries = [(code, country) for code, country in self.countries if code in REGION_COUNTRY_MAP[region]]
self.tabulate(ws, counts, MEDIA_TYPES, region_countries, row_perc=True, sec_row=True, r=r)
r += len(region_countries)
def ws_04(self, ws):
"""
Cols: Region, Media type
Rows: Major Topic
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('topic')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
major_topic = TOPIC_GROUPS[r['topic']]
counts.update({(media_id, major_topic): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, MEDIA_TYPES, MAJOR_TOPICS, row_perc=False, sec_cols=10)
def ws_05(self, ws):
"""
Cols: Subject sex
Rows: Major Topic
"""
counts = Counter()
for model in person_models.itervalues():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r[topic_field]]): r['n']})
self.tabulate(ws, counts, self.male_female, MAJOR_TOPICS, row_perc=True, display_cols=self.female)
def ws_06(self, ws):
"""
Cols: Region, Subject sex: female only
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in person_models.itervalues():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country_region__region':region})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r[topic_field]]): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, MAJOR_TOPICS, row_perc=True, sec_cols=2, display_cols=self.female)
def ws_07(self, ws):
"""
Cols: Media Type
Rows: Subject Sex
"""
counts = Counter()
for media_type, model in person_models.iteritems():
rows = model.objects\
.values('sex')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, r['sex']): r['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.male_female, row_perc=False)
def ws_08(self, ws):
"""
Cols: Subject Sex
Rows: Scope
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'scope' in model.sheet_field().rel.to._meta.get_all_field_names():
scope = '%s__scope' % model.sheet_name()
rows = model.objects\
.values('sex', scope)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[scope]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SCOPE, row_perc=True, display_cols=self.female)
def ws_09(self, ws):
"""
Cols: Subject Sex
Rows: Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[topic]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_10(self, ws):
"""
Cols: Space
Rows: Minor Topics
:: Newspaper Sheets only
"""
# Calculate row values for column
counts = Counter()
rows = NewspaperSheet.objects\
.values('space', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['space'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, SPACE, MAJOR_TOPICS, row_perc=False)
def ws_11(self, ws):
"""
Cols: Equality Rights
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_12(self, ws):
"""
Cols: Region, Equality Rights
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region_name in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
# Some models has no equality rights field
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country_region__region=region_name)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_13(self, ws):
"""
Cols: Journalist Sex, Equality Rights
Rows: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
topic = '%s__topic' % model.sheet_name()
equality_rights = '%s__equality_rights' % model.sheet_name()
rows = model.objects\
.values(equality_rights, topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex=gender_id)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r[equality_rights], TOPIC_GROUPS[r[topic]]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_14(self, ws):
"""
Cols: Sex
Rows: Occupation
"""
counts = Counter()
for model in person_models.itervalues():
# some Person models don't have an occupation field
if 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, OCCUPATION, row_perc=True, display_cols=self.female)
def ws_15(self, ws):
"""
Cols: Sex
Rows: Function
"""
counts = Counter()
for model in person_models.itervalues():
# some Person models don't have a function field
if 'function' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, FUNCTION, row_perc=True, display_cols=self.female)
def ws_16(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
for function_id, function in FUNCTION:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=function_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_17(self, ws):
"""
Cols: Age, Sex of Subject
Rows: Function
"""
secondary_counts = OrderedDict()
for age_id, age in AGES:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'age' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(age=age_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
secondary_counts[age] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, FUNCTION, row_perc=False, sec_cols=4)
def ws_18(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for print
"""
counts = Counter()
rows = NewspaperPerson.objects\
.values('sex', 'age')\
.filter(newspaper_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_19(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for broadcast
"""
counts = Counter()
broadcast = ['Television']
for media_type, model in person_models.iteritems():
if media_type in broadcast:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_20(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
functions_count = Counter()
# Get top 5 functions
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
functions_count.update({(r['function']): r['n'] for r in rows})
top_5_function_ids = [id for id, count in sorted(functions_count.items(), key=lambda x: -x[1])[:5]]
top_5_functions = [(id, func) for id, func in FUNCTION if id in top_5_function_ids]
for func_id, function in top_5_functions:
counts = Counter()
for model in person_models.itervalues():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=func_id)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_21(self, ws):
"""
Cols: Subject Sex
Rows: Victim type
"""
counts = Counter()
for model in person_models.itervalues():
if 'victim_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'victim_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.exclude(victim_of=None)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['victim_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, VICTIM_OF, row_perc=True)
def ws_23(self, ws):
"""
Cols: Subject Sex
Rows: Survivor type
"""
counts = Counter()
for model in person_models.itervalues():
if 'survivor_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'survivor_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.exclude(survivor_of=None)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['survivor_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SURVIVOR_OF, row_perc=True)
def ws_24(self, ws):
"""
Cols: Subject Sex
Rows: Family Role
"""
counts = Counter()
for model in person_models.itervalues():
if 'family_role' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_25(self, ws):
"""
Cols: Journalist Sex, Subject Sex
Rows: Family Role
"""
secondary_counts = OrderedDict()
for sex_id, sex in self.male_female:
counts = Counter()
for model in person_models.itervalues():
if 'family_role' in model._meta.get_all_field_names():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(**{sheet_name + '__' + journo_name + '__sex':sex_id})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
secondary_counts[sex] = counts
secondary_counts['col_title_def'] = [
'Sexof reporter',
'Sex of news subject']
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, YESNO, row_perc=False, sec_cols=4)
def ws_26(self, ws):
"""
Cols: Subject Sex
Rows: Whether Quoted
"""
counts = Counter()
for model in person_models.itervalues():
if 'is_quoted' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['is_quoted']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_27(self, ws):
"""
Cols: Subject Sex
Rows: Photographed
"""
counts = Counter()
for model in person_models.itervalues():
if 'is_photograph' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['is_photograph']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, IS_PHOTOGRAPH, row_perc=False)
def ws_28(self, ws):
"""
Cols: Medium
Rows: Region
:: Female reporters only
"""
counts = Counter()
for media_type, model in person_models.iteritems():
region = model.sheet_name() + '__country_region__region'
rows = model.objects\
.values(region)\
.filter(sex=1)\
.filter(**{region + '__in': self.region_list})\
.annotate(n=Count('id'))
for row in rows:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_29(self, ws):
"""
Cols: Regions
Rows: Scope
:: Female reporters only
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
scope = sheet_name + '__scope'
if 'scope' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, scope)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
counts.update({(region_id, row[scope]): row['n']})
self.tabulate(ws, counts, self.regions, SCOPE, row_perc=False)
def ws_30(self, ws):
"""
Cols: Region
Rows: Major Topics
:: Female reporters only
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, topic)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row[region]][0]
major_topic = TOPIC_GROUPS[row[topic]]
counts.update({(region_id, major_topic): row['n']})
self.tabulate(ws, counts, self.regions, MAJOR_TOPICS, row_perc=False)
def ws_31(self, ws):
"""
Cols: Sex of Reporter
Rows: Minor Topics
"""
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[topic]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_32(self, ws):
"""
Cols: Medium
Rows: Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=1)\
.annotate(n=Count('id'))
for row in rows:
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row[topic]): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, TOPICS, row_perc=False)
def ws_34(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
"""
counts = Counter()
for model in person_models.itervalues():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of subject'
self.tabulate(ws, counts, self.male_female, GENDER, row_perc=True, display_cols=self.female)
def ws_35(self, ws):
"""
Cols: Sex of reporter
Rows: Age of reporter
:: Only for television
"""
counts = Counter()
rows = TelevisionJournalist.objects\
.values('sex', 'age')\
.filter(television_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True, display_cols=self.female)
def ws_36(self, ws):
"""
Cols: Sex of Reporter
Rows: Focus: about women
"""
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
about_women = sheet_name + '__about_women'
if 'about_women' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', about_women)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[about_women]): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=True)
def ws_38(self, ws):
"""
Cols: Focus: about women
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['about_women'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_39(self, ws):
"""
Cols: Focus: about women
Rows: Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=True)
def ws_40(self, ws):
"""
Cols: Region, Topics
Rows: Focus: about women
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'about_women')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=2, display_cols=self.yes)
def ws_41(self, ws):
"""
Cols: Equality rights raised
Rows: Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=False)
def ws_42(self, ws):
"""
Cols: Region, Equality rights raised
Rows: Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_43(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Cols: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic, equality_rights)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=gender_id)\
.annotate(n=Count('id'))
counts.update({(r[equality_rights], r[topic]): r['n'] for r in rows})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_44(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Rows: Region
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(equality_rights, region)\
.filter(sex=gender_id)\
.filter(**{region + '__in':self.region_list})\
.annotate(n=Count('id'))
for r in rows:
region_id = [id for id, name in self.regions if name == r[region]][0]
counts.update({(r[equality_rights], region_id): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.regions, row_perc=False, sec_cols=4)
def ws_45(self, ws):
"""
Cols: Sex of news subject
Rows: Region
:: Equality rights raised == Yes
"""
counts = Counter()
for model in person_models.itervalues():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
region = model.sheet_name() + '__country_region__region'
equality_rights = model.sheet_name() + '__equality_rights'
rows = model.objects\
.values('sex', region)\
.filter(**{region + '__in':self.region_list})\
.filter(**{equality_rights:'Y'})\
.annotate(n=Count('id'))
for r in rows:
region_id = [id for id, name in self.regions if name == r[region]][0]
counts.update({(r['sex'], region_id): r['n']})
self.tabulate(ws, counts, self.male_female, self.regions, row_perc=True)
def ws_46(self, ws):
"""
Cols: Region, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for model in sheet_models.itervalues():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country_region__region=region)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(TOPIC_GROUPS[r['topic']], r['stereotypes']): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True, sec_cols=8)
def ws_47(self, ws):
"""
Cols: Stereotypes
Rows: Major Topics
"""
counts = Counter()
for model in sheet_models.itervalues():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country__in=self.country_list)\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True)
def ws_48(self, ws):
"""
Cols: Sex of reporter, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for model in journalist_models.itervalues():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
stereotypes = sheet_name + '__stereotypes'
if 'stereotypes' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(stereotypes, topic)\
.filter(sex=gender_id)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
for r in rows:
counts.update({(r[stereotypes], TOPIC_GROUPS[r[topic]]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=False, sec_cols=8)
def ws_53(self, ws):
"""
Cols: Topic
Rows: Country
:: Internet media type only
:: Female reporters only
"""
display_cols = [(id, value) for id, value in GENDER if id==1]
secondary_counts = OrderedDict()
model = sheet_models.get('Internet News')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
journo_sex_field = '%s__sex' % model.journalist_field_name()
rows = model.objects\
.values(journo_sex_field, 'country')\
.filter(topic__in=topic_ids)\
.annotate(n=Count('id'))
counts.update({(r[journo_sex_field], r['country']): r['n'] for r in rows})
secondary_counts[major_topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, display_cols=display_cols, sec_cols=2)
def ws_54(self, ws):
"""
Cols: Major Topic, sex of subject
Rows: Country
:: Internet media type only
"""
secondary_counts = OrderedDict()
model = person_models.get('Internet News')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('sex', country_field)\
.filter(**{model.sheet_name() + '__topic__in':topic_ids})\
.annotate(n=Count('id'))
counts.update({(r['sex'], r[country_field]): r['n'] for r in rows})
secondary_counts[major_topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, sec_cols=8)
def ws_55(self, ws):
"""
Cols: Occupation
Rows: Country
:: Show all countries
:: Only female subjects
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet News')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'occupation')\
.filter(sex=1)\
.annotate(n=Count('id'))
counts.update({(r['occupation'], r[country_field]): r['n'] for r in rows})
self.tabulate(ws, counts, OCCUPATION, self.countries, row_perc=True)
def ws_56(self, ws):
"""
Cols: Function
Rows: Country
:: Show all countries
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet News')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'function')\
.annotate(n=Count('id'))
counts.update({(r['function'], r[country_field]): r['n'] for r in rows})
self.tabulate(ws, counts, FUNCTION, self.countries, row_perc=True)
def ws_57(self, ws):
"""
Cols: Sex of subject
Rows: Country, Family role
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['family_role']): row['n'] for row in rows}
# If only captured countries should be displayed use
# if counts.keys():
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_58(self, ws):
"""
Cols: Sex of subject
Rows: Country, is photographed
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['is_photograph']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, IS_PHOTOGRAPH, row_perc=True, sec_row=True, r=r)
r += len(IS_PHOTOGRAPH)
def ws_59(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
:: Internet media only
"""
counts = Counter()
model = person_models.get('Internet News')
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.annotate(n=Count('id'))
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
self.tabulate(ws, counts, GENDER, GENDER, row_perc=False)
def ws_60(self, ws):
"""
Cols: Sex of subject
Rows: Country, age
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['age']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, AGES, row_perc=True, sec_row=True, r=r)
r += len(AGES)
def ws_61(self, ws):
"""
Cols: Sex of subject
Rows: Country, is_quoted
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts = {(row['sex'], row['is_quoted']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_62(self, ws):
"""
Cols: Topic
Rows: Country, equality raised
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['equality_rights']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_63(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes challenged
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_64(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet News')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_65(self, ws):
"""
Cols: Topic
Rows: Country, tweet or retweet
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'retweet')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['retweet']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, RETWEET, row_perc=False, sec_row=True, r=r)
r += len(RETWEET)
def ws_66(self, ws):
"""
Cols: Topic
Rows: Country, sex of news subject
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Twitter')
topic_field = '%s__topic' % model.sheet_name()
for code, country in self.countries:
rows = model.objects\
.values(topic_field, 'sex')\
.filter(**{model.sheet_name() + '__country':code})\
.annotate(n=Count('id'))
counts.update({(row[topic_field], row['sex']): row['n'] for row in rows})
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, GENDER, row_perc=True, sec_row=True, r=r)
r += len(GENDER)
def ws_67(self, ws):
"""
Cols: Topic
Rows: Country
:: Only female journalists
:: Show all countries
:: Twitter media type only
"""
counts = Counter()
model = sheet_models.get('Twitter')
rows = model.objects\
.values('topic', 'country')\
.filter(**{model.journalist_field_name() + '__sex':1})\
.annotate(n=Count('id'))
counts.update({(row['topic'], row['country']): row['n'] for row in rows})
self.tabulate(ws, counts, TOPICS, self.countries, row_perc=True, sec_row=False)
def ws_68(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=False, sec_row=True, r=r)
r += len(YESNO)
def ws_69(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)\
.annotate(n=Count('id'))
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_76(self, ws):
"""
Cols: Topic, Stereotypes
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in sheet_models.itervalues():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'country')\
.filter(topic=topic_id)\
.annotate(n=Count('id'))
counts.update({(r['stereotypes'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, self.countries, row_perc=True, sec_cols=8)
def ws_77(self, ws):
"""
Cols: Topic, Reference to gender equality
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in sheet_models.itervalues():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'country')\
.filter(topic=topic_id)\
.annotate(n=Count('id'))
counts.update({(r['equality_rights'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.countries, row_perc=True, sec_cols=4)
def ws_78(self, ws):
"""
Cols: Topic, victim_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in person_models.itervalues():
if 'victim_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('victim_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})\
.annotate(n=Count('id'))
counts.update({(r['victim_of'], r[country_field]): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, VICTIM_OF, self.countries, row_perc=True, sec_cols=18)
def ws_79(self, ws):
"""
Cols: Topic, survivor_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for model in person_models.itervalues():
if 'survivor_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('survivor_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})\
.annotate(n=Count('id'))
counts.update({(r['survivor_of'], r[country_field]): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, SURVIVOR_OF, self.countries, row_perc=True, sec_cols=18)
# -------------------------------------------------------------------------------
# Helper functions
#
def write_headers(self, ws, title, description):
"""
Write the headers to the worksheet
"""
ws.write(0, 0, title, self.heading)
ws.write(1, 0, description, self.heading)
ws.write(3, 2, self.gmmp_year, self.heading)
def write_col_headings(self, ws, cols, c=2, r=4):
"""
:param ws: worksheet to write to
:param cols: list of `(col_id, col_title)` tuples of column ids and titles
:param r, c: initial position where cursor should start writing to
"""
for col_id, col_title in cols:
ws.write(r, c, clean_title(col_title), self.col_heading)
ws.write(r + 1, c, "N")
ws.write(r + 1, c + 1, "%")
c += 2
def write_primary_row_heading(self, ws, heading, c=0, r=6):
"""
:param ws: worksheet to write to
:param heading: row heading to write
:param r, c: position where heading should be written to
"""
ws.write(r, c, clean_title(heading), self.heading)
def tabulate_secondary_cols(self, ws, secondary_counts, cols, rows, row_perc=False, display_cols=None, sec_cols=4):
"""
:param ws: worksheet to write to
:param secondary_counts: dict in following format:
{'Primary column heading': Count object, ...}
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_cols: amount of cols needed for secondary cols
"""
r, c = 7, 1
# row titles
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
if 'col_title_def' in secondary_counts:
# Write definitions of column heading titles
ws.write(r-3, c-1, secondary_counts['col_title_def'][0], self.sec_col_heading_def)
ws.write(r-2, c-1, secondary_counts['col_title_def'][1], self.col_heading_def)
secondary_counts.pop('col_title_def')
for field, counts in secondary_counts.iteritems():
ws.merge_range(r-3, c, r-3, c+sec_cols-1, clean_title(field), self.sec_col_heading)
self.tabulate(ws, counts, cols, rows, row_perc=row_perc, sec_col=True, display_cols=display_cols, r=7, c=c)
c += sec_cols
def tabulate(self, ws, counts, cols, rows, row_perc=False, sec_col=False, sec_row=False, display_cols=None, c=1, r=6):
""" Emit a table.
:param ws: worksheet to write to
:param dict counts: dict from `(col_id, row_id)` tuples to count for that combination.
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_col: Are wecreating a secondary column title(default: False)
:param sec_row: Are we creating a secondary row title(default: False)
:param display_cols: Optional if only a subset of columns should be displayed e.g. only female
:param r, c: initial position where cursor should start writing to
"""
if row_perc:
# we'll need percentage by rows
row_totals = {}
for row_id, row_title in rows:
row_totals[row_id] = sum(counts.get((col_id, row_id), 0) for col_id, _ in cols) # noqa
# row titles
if not sec_col:
# Else already written
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
# if only filtered results should be shown
# e.g. only print female columns
if display_cols:
cols = display_cols
if 'col_title_def' in counts and not sec_row:
ws.write(r - 2, c-1, counts['col_title_def'], self.col_heading_def)
counts.pop('col_title_def')
# values, written by column
for col_id, col_heading in cols:
# column title
if not sec_row:
ws.merge_range(r-2, c, r-2, c+1, clean_title(col_heading), self.col_heading)
ws.write(r - 1, c, "N", self.label)
ws.write(r - 1, c + 1, "%", self.label)
if not row_perc:
# column totals
# Confirm: Perc of col total or matrix total?
# total = sum(counts.itervalues())
total = sum(counts.get((col_id, row_id), 0) for row_id, _ in rows)
# row values for this column
for i, row in enumerate(rows):
row_id, row_title = row
if row_perc:
# row totals
total = row_totals[row_id]
n = counts.get((col_id, row_id), 0)
ws.write(r + i, c, n, self.N)
ws.write(r + i, c + 1, p(n, total), self.P)
c += 2
|
__script__.title = 'KKB Measurement Script'
__script__.version = '2.1'
from gumpy.commons import sics
from org.gumtree.gumnix.sics.control import ServerStatus
from pickle import Pickler, Unpickler
import time
from math import log as ln
from math import exp, isnan, isinf, sin
from __builtin__ import max as builtin_max
from __builtin__ import min as builtin_min
from org.eclipse.swt.widgets import FileDialog
from org.eclipse.swt import SWT
from org.eclipse.swt.widgets import Display
from java.io import File
import time
from Internal import sample_stage
'''
Disable dataset caching
'''
DatasetFactory.__cache_enabled__ = False
SINGLE_TYPE = SWT.SINGLE
SAVE_TYPE = SWT.SAVE
MULTI_TYPE = SWT.MULTI
class __Display_Runnable__(Runnable):
def __init__(self, type=SINGLE_TYPE, ext=['*.*']):
self.filename = None
self.filenames = None
self.path = None
self.type = type
self.ext = ext
def run(self):
global __UI__
dialog = FileDialog(__UI__.getShell(), self.type);
dialog.setFilterExtensions(self.ext)
dialog.open()
self.filename = dialog.getFilterPath() + File.separator + dialog.getFileName()
self.filenames = dialog.getFileNames()
self.path = dialog.getFilterPath()
def open_file_dialog(type=SWT.SINGLE, ext=['*.*']):
__display_run__ = __Display_Runnable__(type, ext)
Display.getDefault().asyncExec(__display_run__)
while __display_run__.filename is None:
time.sleep(0.5)
if type == SWT.MULTI:
fns = []
for fn in __display_run__.filenames:
fns.append(__display_run__.path + '/' + fn)
return fns
return __display_run__.filename
# # templates
reference_templates_dict = {}
reference_templates_dict['Si111'] = 180.3565
reference_templates_dict['Si311'] = -0.4100
steps_templates_list = []
# steps_templates['Background'] = [
# 'time', 'logscale',
# [20, 6.0e-5, 1200, 1200]]
# steps_templates['----------'] = [
# 'time', 'logscale',
# [0, 0, 0, 0]]
steps_templates_list.append([
'Si111: Logarithmic Overview Scan',
'time',
'logscale',
[17, 1.20e-4, 1, 1200],
[30, 22.0, 20, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (few features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[34, 20.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (fine features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[65, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Taiki Scan (15 points)',
'ba',
'logscale',
[2, 6.0e-5, 1000, 60],
[1, 10000, 1000, 60],
[10, 25, 1000, 60]])
'''
steps_templates_list.append([
'Si111: Kinetic Scan 4 points',
'time',
'logscale',
[ 0, 6.0e-5, 1, 1200],
[1, 5.0e-3, 180, 1200],
[3, 1.5e-2, 180, 1200]])
'''
steps_templates_list.append([
'----------',
'time',
'logscale',
[0, 0, 0, 0]])
steps_templates_list.append([
'Si311: Logarithmic Overview Scan',
'time',
'logscale',
[17, 2.0e-5, 1, 1200],
[30, 23.0, 20, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 80+29)',
'ba',
'logscale',
[80, 2e-5, 1000, 1200],
[29, 15.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 40+33)',
'ba',
'logscale',
[40, 2e-5, 1000, 1200],
[33, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, Taiki)',
'ba',
'logscale',
[33, 2e-5, 1000, 1200],
[25, 20.0, 1000, 1200]])
ret = sample_stage.check_declarations()
if not ret[0] :
open_warning(ret[1])
reload(sample_stage)
SAMPLE_STAGES = sample_stage.StagePool()
# # export path
__EXPORT_PATH__ = 'V:/shared/KKB Logbook/Temp Plot Data Repository/'
if not os.path.exists(__EXPORT_PATH__):
os.makedirs(__EXPORT_PATH__)
# # User Details
user_name = Par('string', 'Christine', options=['Christine', 'Lela', 'Jitendra'])
user_name.title = 'Name'
user_email = Par('string', 'cre@ansto.gov.au', options=['cre@ansto.gov.au', 'liliana.decampo@ansto.gov.au', 'jtm@ansto.gov.au'])
user_email.title = 'EMail'
g0 = Group('User Details')
g0.numColumns = 2
g0.add(user_name, user_email)
# # Sample Details
sample_name = Par('string', 'UNKNOWN', options=['Empty Cell', 'Empty Beam'], command="sample_thickness.enabled = sample_name.value not in ['Empty Cell', 'Empty Beam']")
sample_name.title = 'Name'
sample_description = Par('string', 'UNKNOWN')
sample_description.title = 'Description'
sample_thickness = Par('string', '1', options=['0.01', '0.1', '1.0', '10.0'])
sample_thickness.title = 'Thickness (mm)'
g0 = Group('Sample Details')
g0.numColumns = 2
g0.add(sample_name, sample_thickness, sample_description)
# Group('Sample Details').add(sample_name, sample_description, sample_thickness)
# # Crystal
crystal_name = Par('string', 'UNKNOWN')
crystal_name.title = 'Name'
crystal_name.enabled = False
try:
m2om = sics.getValue('/instrument/crystal/m2om').getFloatData()
if m2om > 90:
crystal_name.value = 'Si111 (4.74 Angstroms)'
else:
crystal_name.value = 'Si311 (2.37 Angstroms)'
except:
pass
g0 = Group('Crystal Info')
g0.numColumns = 2
g0.add(crystal_name)
# CRYSTAL END #############################################
# SLIT 1 #######################################################################
def updateOffset(gapBox, offsetBox):
offsetBox.enabled = 'fully' not in gapBox.value
def getSlitGapAndOffset(aPath, a0, bPath, b0):
try:
a = sics.getValue(aPath).getFloatData()
b = sics.getValue(bPath).getFloatData()
gap = (a - a0 - (b - b0)) / 1.0
offset = (a - a0 + (b - b0)) / 2.0
return (gap, offset)
except:
return (float('nan'), float('nan'))
crystal = str(crystal_name.value)
if 'Si111' in crystal:
ss1r0 = 28.35
ss1l0 = 27.75
elif 'Si311' in crystal:
ss1r0 = -9.16
ss1l0 = -9.76
else:
ss1r0 = float('nan')
ss1l0 = float('nan')
ss1u0 = -8.04
ss1d0 = -7.30
(ss1vg, ss1vo) = getSlitGapAndOffset('/instrument/slits/ss1u', ss1u0, '/instrument/slits/ss1d', ss1d0)
(ss1hg, ss1ho) = getSlitGapAndOffset('/instrument/slits/ss1r', ss1r0, '/instrument/slits/ss1l', ss1l0)
pss_ss1vg = Par('string', '%.1f' % ss1vg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1vg, pss_ss1vo)')
pss_ss1vg.title = 'Vertical Gap (mm)'
# pss_ss1vg.colspan = 50
pss_ss1vo = Par('float', ss1vo)
pss_ss1vo.title = 'Vertical Offset (mm)'
# pss_ss1vo.colspan = 50
pss_ss1hg = Par('string', '%.1f' % ss1hg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1hg, pss_ss1ho)')
pss_ss1hg.title = 'Horizontal Gap (mm)'
# pss_ss1hg.colspan = 50
pss_ss1ho = Par('float', ss1ho)
pss_ss1ho.title = 'Horizontal Offset (mm)'
# pss_ss1ho.colspan = 50
g0 = Group('Sample Slit Settings')
g0.numColumns = 2
g0.add(pss_ss1vg, pss_ss1vo, pss_ss1hg, pss_ss1ho)
# SLIT 1 END #######################################################################
## Scan parameters ##########################################################################################################
scan_variable = Par('string', 'm2om [deg]', options=[
#'pmom [deg]', 'pmchi [deg]',
'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'], command="scan_variable_plot.value = scan_variable.value")
scan_variable.title = 'Scan Variable'
scan_variable.colspan = 25
scan_reference = Par('float', '0.0')
scan_reference.title = 'Zero Angle'
scan_reference.colspan = 25
for key in reference_templates_dict.keys():
if key in crystal_name.value:
scan_reference.value = reference_templates_dict[key]
scan_mode = Par('string', 'ba', options=['ba', 'time'], command='setScanMode()')
scan_mode.title = 'Acquisition Mode'
scan_mode.colspan = 25
scan_min_time = Par('int', '5')
scan_min_time.title = 'Min Time (sec)'
scan_min_time.colspan = 25
empty_label = Par('label', '')
empty_label.colspan = 25
scan_sample_stage = Par('string', '', command = 'sample_stage_changed()')
scan_sample_stage.colspan = 25
scan_sample_stage.title = 'Sample Stage'
scan_sample_stage.options = SAMPLE_STAGES.get_stage_names()
current_stage = SAMPLE_STAGES.get_stage_in_service()
if not current_stage is None:
scan_sample_stage.value = current_stage.get_name()
scan_sample_position = Par('string', 'fixed')
scan_sample_position.title = 'Sample Position'
scan_sample_position.colspan = 25
scan_sample_position.options = ['fixed', '----------']
if not current_stage is None:
scan_sample_position.options += current_stage.get_sample_indexes()
logscale_position = Par('bool', False, command='setStepTitles()')
logscale_position.title = 'Logarithmic Steps'
logscale_position.colspan = 25
negative_steps = Par('bool', False)
negative_steps.title = 'Negative Steps'
negative_steps.colspan = 25
steps_label = Par('label', 'Please choose scan template or adjust steps manually: ')
steps_label.colspan = 200
steps_templates = Par('string', '', options=[item[0] for item in steps_templates_list], command='setTemplate()')
steps_templates.title = 'Scan Template'
steps_templates.colspan = 100
early_exit_enabled = Par('bool', True, command = "set_early_exit_enabled()")
early_exit_enabled.title = "Enable Early Exit"
early_exit_enabled.colspan = 25
background_frames = Par('int', 3)
background_frames.title = 'Background Frames'
background_frames.colspan = 25
background_threshold = Par('float', 0.26)
background_threshold.title = 'Background Threshold'
background_threshold.colspan = 25
# steps_space = Par('space', '')
# steps_space.colspan = 10
g0 = Group('Scan Parameters')
g0.numColumns = 100 # 9
g0.add(scan_variable, scan_mode, scan_reference, early_exit_enabled, \
logscale_position, scan_min_time, scan_sample_stage, background_frames, \
negative_steps, empty_label, scan_sample_position, background_threshold, \
steps_label, steps_templates)
def sample_stage_changed():
stage = SAMPLE_STAGES.get_stage_by_name(str(scan_sample_stage.value))
# scan_sample_position.value = 'fixed'
if not stage is None:
scan_sample_position.options = ['fixed', '----------'] + stage.get_sample_indexes()
else:
scan_sample_position.options = ['fixed', '----------']
def set_early_exit_enabled():
if early_exit_enabled.value:
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
stepInfo = []
for i in xrange(4):
steps_e = Par('bool', True, command='setEnabled(%i)' % i)
steps_e.title = '(%i)' % (i + 1)
steps_e.colspan = 10
steps_m = Par('int', 0, command='clearScanTemplateSelection()')
steps_m.title = 'Number of points'
steps_m.colspan = 20
steps_s = Par('float', 0, command='clearScanTemplateSelection()')
steps_s.title = 'Step Size [deg]'
steps_s.colspan = 20
steps_p = Par('int', 0, command='clearScanTemplateSelection()')
steps_p.title = 'Mode Preset'
steps_p.colspan = 25
steps_t = Par('int', 1200, command='clearScanTemplateSelection()')
steps_t.title = 'Max Time'
steps_t.colspan = 25
stepInfo.append({'enabled': steps_e, 'dataPoints':steps_m, 'stepSize':steps_s, 'preset':steps_p, 'maxTime':steps_t})
g0.add(steps_e, steps_m, steps_s, steps_p, steps_t)
def clearScanTemplateSelection():
steps_templates.value = None
btnPlotSteps = Act('btnPlotSteps_clicked()', 'Plot Measurement Steps') # 'compare measurement steps with previous scan')
btnPlotSteps.colspan = 50
cnfg_save_btn = Act('saveConfiguration()', 'Save Single Scan Parameters')
cnfg_save_btn.colspan = 50
btnTimeEstimation = Act('runTimeEstimation()', 'Time Estimation with selected Data Set')
btnTimeEstimation.colspan = 50
txtTimeEstimation = Par('int', '0')
txtTimeEstimation.title = 'Time Estimation (min)'
txtTimeEstimation.enabled = False
txtTimeEstimation.colspan = 50
g0.add(btnPlotSteps, cnfg_save_btn, btnTimeEstimation, txtTimeEstimation)
def runTimeEstimation():
if str(scan_mode.value) == 'time':
scan = getScan()
times = scan['presets']
txtTimeEstimation.value = int((sum(times) + len(times) * 25) / 60.0) # 25 seconds for each move
return
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
ds = openDataset(fns[0])
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
data[:] = [data[item[0]] for item in info] # sorting
# angle and count rate
a0 = [float(angle) for angle in scanVariable]
r0 = [float(rate) for rate in data[:]]
# angle, counts, max time and min time
model = ConfigurationModel()
scan = model.scan
tMin = model.min_time
a1 = scan['angles']
c1 = scan['presets']
t1 = scan['maxTimes']
total = 0.0
for i in xrange(len(a1)):
try:
rate = sample(a0, r0, a1[i])
time = c1[i] / rate
if time < tMin:
total += tMin
elif time > t1[i]:
total += t1[i]
else:
total += time
except ValueError as e:
if e.message == "OutOfRange":
total += t1[i] # add max time
else:
raise
txtTimeEstimation.value = int(total / 60.0)
def sample(x0, y0, x1):
from __builtin__ import max, min
if len(x0) != len(y0):
raise Exception("len(x0) != len(y0)")
x0_min = min(x0)
x0_max = max(x0)
if len(x0) < 2:
raise Exception("len(x0) < 2")
if x0_min >= x0_max:
raise Exception("x0_min >= x0_max")
if x1 < x0_min:
raise ValueError("OutOfRange")
if x0_max < x1:
raise ValueError("OutOfRange")
i0 = 0
i1 = 1
x0i0 = x0[i0]
y0i0 = y0[i0]
x0i1 = x0[i1]
y0i1 = y0[i1]
# in case first x values are equal
while x0i0 == x0i1:
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
# not iterable
while x0i1 < x1:
x0i0 = x0i1
y0i0 = y0i1
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
return y0i0 + (x1 - x0i0) * (y0i1 - y0i0) / (x0i1 - x0i0)
## Scan parameters END #########################################################################
## RUN ##############################################
cnfg_load_btn = Act('loadConfigurations()', 'Load Multiple Scan Parameters')
cnfg_lookup = dict()
cnfg_options = Par('string', '', options=[''], command="applyConfiguration()")
cnfg_options.title = 'Read'
start_scan = Act('startScan(ConfigurationModel())', '############# Run Single Scan #############')
cnfg_run_btn = Act('runConfigurations()', '############# Run Multiple Scans #############')
g0 = Group('Execute Scans')
g0.numColumns = 1
g0.add(start_scan, cnfg_load_btn, cnfg_options, cnfg_run_btn)
## Save/Load Configuration END############################################################################
def saveConfiguration():
file = open_file_dialog(type=SAVE_TYPE, ext=['*.kkb'])
try:
fh = open(file, 'w')
except:
print 'not saved'
return
try:
p = Pickler(fh)
# header
p.dump('KKB')
# content
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
p.dump(att)
p.dump(att_value)
print 'saved'
finally:
fh.close()
def loadConfigurations():
fileList = open_file_dialog(type=MULTI_TYPE, ext=['*.kkb'])
if not fileList:
return
finalDict = dict()
finalNames = []
for path in fileList:
fh = open(path, 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', os.path.basename(path)
else:
model = ConfigurationModel()
# set defaults
model.negative = False # old models may not have this attribute
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', os.path.basename(path)
break
setattr(model, att, p.load())
else:
name = os.path.basename(path)
finalDict[name] = path
finalNames.append(name)
finally:
fh.close()
cnfg_lookup.clear()
cnfg_lookup.update(finalDict)
cnfg_options.value = finalNames[0] if finalNames else ''
cnfg_options.options = finalNames
# time.sleep(0.5)
def applyConfiguration():
file = str(cnfg_options.value)
if not file:
return
fh = open(cnfg_lookup[file], 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
# print 'read:', file
model.apply()
finally:
fh.close()
def runConfigurations():
for file in cnfg_options.options:
fh = open(cnfg_lookup[file], 'r')
try:
cnfg_options.command = ''
cnfg_options.value = file
applyConfiguration()
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
print 'run:', file
startScan(model)
finally:
cnfg_options.command = 'applyConfiguration()'
fh.close()
# # Plot
tubes_label = Par('label', 'Main Detector:')
tubes_label.colspan = 1
combine_tube0 = Par('bool', True)
combine_tube0.title = ' Tube 0'
combine_tube0.colspan = 1
combine_tube1 = Par('bool', True)
combine_tube1.title = ' Tube 1'
combine_tube1.colspan = 1
combine_tube2 = Par('bool', True)
combine_tube2.title = ' Tube 2'
combine_tube2.colspan = 1
combine_tube3 = Par('bool', True)
combine_tube3.title = ' Tube 3'
combine_tube3.colspan = 1
combine_tube4 = Par('bool', True)
combine_tube4.title = ' Tube 4'
combine_tube4.colspan = 1
combine_tube6 = Par('bool', False)
combine_tube6.title = ' Tube 6'
combine_tube6.colspan = 1
combine_mode = Par('string', 'combined', options=['individual', 'combined'])
combine_mode.title = ' Mode'
combine_mode.colspan = 1
trans_tube_label = Par('label', 'Trans Detector: ')
trans_tube_label.colspan = 2
check_tube9 = Par('bool', True)
check_tube9.title = ' Tube 9: Si (311)'
check_tube9.colspan = 2
check_tube10 = Par('bool', False)
check_tube10.title = ' Tube 10: Si (111)'
check_tube10.colspan = 2
# steps_space = Par('space', '')
# steps_space.colspan = 12
scan_variable_plot = Par('string', 'm2om [deg]', options=[
'pmom [deg]', 'pmchi [deg]', 'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'])
scan_variable_plot.title = 'Scan Variable'
scan_variable_plot.colspan = 1
scan_variable_sorting = Par('bool', True)
scan_variable_sorting.title = 'Sorting'
scan_variable_sorting.colspan = 1
btnPlot = Act('btnPlot_clicked()', 'Plot Selected Data Set')
btnPlot.colspan = 8
g0 = Group('Plotting')
g0.numColumns = 7
g0.add(tubes_label, combine_tube0, combine_tube1, combine_tube2, combine_tube3, combine_tube4, combine_tube6, combine_mode, trans_tube_label, check_tube9, check_tube10, scan_variable_plot, scan_variable_sorting, btnPlot)
# export to csv
# btnExport = Act('export_clicked()', 'Export to CSV')
################################# SLIT 2 ##########################################################
ss2u0 = 0 # 2.00
ss2d0 = 0 # 1.40
ss2l0 = 0 # 5 0.50
ss2r0 = 0 # -2 -1.00
(ss2vg, ss2vo) = getSlitGapAndOffset('/instrument/slits/ss2u', ss2u0, '/instrument/slits/ss2d', ss2d0)
(ss2hg, ss2ho) = getSlitGapAndOffset('/instrument/slits/ss2r', ss2r0, '/instrument/slits/ss2l', ss2l0)
pss_ss2vg = Par('string', '%.1f' % ss2vg, options=pss_ss1vg.options, command='updateOffset(pss_ss2vg, pss_ss2vo)')
pss_ss2vg.title = 'Vertical Opening (mm)'
pss_ss2vo = Par('float', ss2vo)
pss_ss2vo.title = 'Vertical Offset (mm)'
pss_ss2hg = Par('string', '%.1f' % ss2hg, options=pss_ss1hg.options, command='updateOffset(pss_ss2hg, pss_ss2ho)')
pss_ss2hg.title = 'Horizontal Opening (mm)'
pss_ss2ho = Par('float', ss2ho)
pss_ss2ho.title = 'Horizontal Offset (mm)'
g0 = Group('Post-Sample Slit')
g0.numColumns = 2
g0.add(pss_ss2vg, pss_ss2vo, pss_ss2hg, pss_ss2ho)
################################# SLIT 2 END ##########################################################
def setStepTitles():
if logscale_position.value:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Factor [%]"
else:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Size [deg]"
__UI__.updateUI()
def setTemplate():
try:
matches = [item for item in steps_templates_list if item[0] == steps_templates.value]
if len(matches) != 1:
steps_templates.value = None
return
template = matches[0]
# ignore '----'
if template[0][0] == '-':
steps_templates.value = None
return
scan_mode.value = template[1]
if template[2] == 'logscale':
logscale_position.value = True
elif template[2] == 'linear':
logscale_position.value = False
setStepTitles()
# by default templates measure in positive direction
negative_steps.value = False
setScanMode()
headers = 3
for i in xrange(len(template) - headers):
templateItem = template[i + headers]
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = True
stepInfoItem['dataPoints'].enabled = True
stepInfoItem['dataPoints'].value = templateItem[0]
stepInfoItem['stepSize' ].enabled = True
stepInfoItem['stepSize' ].value = templateItem[1]
stepInfoItem['preset' ].enabled = True
stepInfoItem['preset' ].value = templateItem[2]
stepInfoItem['maxTime' ].enabled = scan_min_time.enabled
stepInfoItem['maxTime' ].value = templateItem[3]
for i in xrange(len(template) - headers, len(stepInfo)):
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = False
stepInfoItem['dataPoints'].enabled = False
stepInfoItem['stepSize' ].enabled = False
stepInfoItem['preset' ].enabled = False
stepInfoItem['maxTime' ].enabled = False
except:
pass
def setScanMode():
if scan_mode.value == 'time':
scan_min_time.enabled = False
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = False
else:
scan_min_time.enabled = True
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = stepInfoItem['enabled'].value
def setEnabled(index):
steps_templates.value = None
stepItem = stepInfo[index]
value = stepItem['enabled'].value
stepItem['dataPoints'].enabled = value
stepItem['stepSize' ].enabled = value
stepItem['preset' ].enabled = value
stepItem['maxTime' ].enabled = value and scan_min_time.enabled
setTemplate()
def getScan():
scan = { 'angles': [], 'presets': [], 'maxTimes': [], 'groups': [] }
first = True
angle_ref = scan_reference.value
angle = angle_ref
logscale = False # first data points are always on a linear scale
negative = bool(negative_steps.value)
for stepInfoItem in stepInfo:
if stepInfoItem['enabled'].value:
dataPoints = stepInfoItem['dataPoints'].value
stepSize = stepInfoItem['stepSize' ].value
preset = stepInfoItem['preset' ].value
maxTime = stepInfoItem['maxTime' ].value
if (dataPoints > 0) and (stepSize <= 0.0):
raise Exception('step sizes have to be positive')
for i in xrange(dataPoints):
if first and (i == 0):
angle -= ((dataPoints - 1) / 2.0) * stepSize;
elif logscale:
# for logscale stepSize is a stepFactor
angle = angle_ref + (angle - angle_ref) * (1.0 + 0.01 * stepSize)
else:
angle += stepSize
#print angle
scan['angles' ].append(angle)
scan['presets' ].append(preset)
scan['maxTimes'].append(maxTime)
if i == 0:
scan['groups'].append(angle)
first = False
logscale = bool(logscale_position.value)
if negative:
# negate angles with reference to zero angle
scan['angles'] = [angle_ref - (angle - angle_ref) for angle in scan['angles']]
return scan
def wait_for_idle():
c_time = time.time()
while not sics.getSicsController().getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
time.sleep(0.1)
if time.time() - c_time > 5:
serverStatus = sics.get_status()
c_time = time.time()
def startScan(configModel):
''' check instrument ready '''
is_ready = False
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
except:
pass
if not is_ready:
is_confirmed = open_question('The instrument is not ready '\
+ 'according to the SIS status. Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?')
if not is_confirmed:
slog('Instrument is not ready. Quit the scan.')
return
else:
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
except:
pass
if not is_ready:
slog('scan continued without instrument ready')
''' setup '''
scanVariable = configModel.scanVariable
crystal = configModel.crystal
mode = configModel.mode
MainDeadTime = 1.08E-6
TransDeadTime = 1.08E-6
if 'Si111' in crystal:
empLevel = 0.3
bkgLevel = 0.21
dOmega = 2.3E-6
gDQv = 0.0586
gDQh = 0
wavelength = 4.74
TransmissionTube = 10
TransBackground = 0 # counts per second
elif 'Si311' in crystal:
empLevel = 0.34
bkgLevel = 0.21
dOmega = 4.6E-7
gDQv = 0.117
gDQh = 0
wavelength = 2.37
TransmissionTube = 9
TransBackground = 0 # counts per second
else:
print 'selected crystal is invalid'
return
''' angles '''
scan = configModel.scan
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if ('m1om' in scanVariable) or ('m2om' in scanVariable):
tolerance = 6
approved = False
if 'Si111' in crystal:
if (180 - tolerance <= scan_angleMin) and (scan_angleMax <= 180 + tolerance):
approved = True
elif 'Si311' in crystal:
if (0 - tolerance <= scan_angleMin) and (scan_angleMax <= 0 + tolerance):
approved = True
if not approved:
print 'angle out of range'
return
''' execution '''
sics.execute('hset user/name ' + configModel.user_name)
sics.execute('hset user/email ' + configModel.user_email)
sics.execute('hset sample/name ' + configModel.sample_name)
sics.execute('hset sample/description ' + configModel.sample_description)
sics.execute('hset sample/thickness %g' % configModel.sample_thickness)
sics.execute('hset experiment/bkgLevel %g' % bkgLevel)
sics.execute('hset experiment/empLevel %g' % empLevel)
sics.execute('hset instrument/detector/MainDeadTime %g' % MainDeadTime)
sics.execute('hset instrument/detector/TransDeadTime %g' % TransDeadTime)
sics.execute('hset instrument/detector/TransBackground %g' % TransBackground)
sics.execute('hset instrument/detector/TransmissionTube %i' % TransmissionTube)
sics.execute('hset instrument/crystal/dOmega %g' % dOmega)
sics.execute('hset instrument/crystal/gDQv %g' % gDQv)
sics.execute('hset instrument/crystal/gDQh %g' % gDQh)
sics.execute('hset instrument/crystal/wavelength %g' % wavelength)
sics.execute('hset instrument/crystal/scan_variable ' + scanVariable);
sicsController = sics.getSicsController()
# slits
def getSlitValues(gap, offset, a0, b0, aOpen, bOpen):
if gap == 'fully opened':
return (aOpen, bOpen)
if gap == 'fully closed':
gap = -5.0
offset = 0.0
a = a0 + 0.5 * float(gap) + float(offset)
b = b0 - 0.5 * float(gap) + float(offset)
return (a, b)
ss1vg = configModel.ss1vg
ss1vo = configModel.ss1vo
ss1hg = configModel.ss1hg
ss1ho = configModel.ss1ho
ss2vg = configModel.ss2vg
ss2vo = configModel.ss2vo
ss2hg = configModel.ss2hg
ss2ho = configModel.ss2ho
(ss1u, ss1d) = getSlitValues(ss1vg, ss1vo, ss1u0, ss1d0, 35.8, -38.8)
(ss1r, ss1l) = getSlitValues(ss1hg, ss1ho, ss1r0, ss1l0, 57.0, -58.0)
(ss2u, ss2d) = getSlitValues(ss2vg, ss2vo, ss2u0, ss2d0, 37.0, -39.5)
(ss2r, ss2l) = getSlitValues(ss2hg, ss2ho, ss2r0, ss2l0, 35.0, -35.0)
# apply slits
run = {}
run['ss1u'] = ss1u
run['ss1d'] = ss1d
run['ss1r'] = ss1r
run['ss1l'] = ss1l
run['ss2u'] = ss2u
run['ss2d'] = ss2d
run['ss2r'] = ss2r
run['ss2l'] = ss2l
# sics.multiDrive(run)
dc = 'drive'
for key in run:
dc += ' ' + key + ' ' + str(run[key])
sics.execute(dc)
time.sleep(5)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
'''
sics.execute('run ss1u %.2f' % ss1u)
sics.execute('run ss1d %.2f' % ss1d)
sics.execute('run ss1r %.2f' % ss1r)
sics.execute('run ss1l %.2f' % ss1l)
sics.execute('run ss2u %.2f' % ss2u)
sics.execute('run ss2d %.2f' % ss2d)
sics.execute('run ss2r %.2f' % ss2r)
sics.execute('run ss2l %.2f' % ss2l)
'''
# load sample positions
sample_stage_name = configModel.sample_stage
sample_positions = str(configModel.sample_position)
if (len(sample_positions) == 0) or (sample_positions == 'fixed'):
samz_list = [None]
else:
samz_list = []
stage = SAMPLE_STAGES.get_stage_by_name(sample_stage_name)
if stage is None:
raise 'Invalid stage name ' + str(sample_stage_name)
samz_value = stage.get_samz(sample_positions)
samz_list.append(samz_value)
print samz_list
for samz in samz_list:
sics.execute('histmem stop')
time.sleep(3)
if mode == 'ba':
sics.execute('histmem mode unlimited')
sics.execute('histmem ba enable')
else:
sics.execute('histmem mode time')
sics.execute('histmem ba disable')
if samz is not None:
print 'run samz %.2f' % samz
sics.execute('run samz %.2f' % samz)
# sics.execute('prun samz 2' % samz) !!!
time.sleep(1)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
sics.execute('newfile HISTOGRAM_XYT')
# sics.execute('autosave 60') # 60 seconds
time.sleep(1)
# start/stop hmm
if mode == 'count_roi':
sics.execute('histmem preset 1')
time.sleep(1)
sics.execute('histmem start')
time.sleep(5)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
sics.execute('histmem stop')
print 'frames:', len(scan['angles'])
count_rate_history = []
for frame_index in xrange(len(scan['angles'])):
angle = scan['angles' ][frame_index]
preset = scan['presets' ][frame_index]
maxTime = scan['maxTimes'][frame_index]
print 'drive %s %.6f' % (scanVariable, angle)
# sics.drive(scanVariable, float(angle))
sics.execute('drive %s %.6f' % (scanVariable, angle))
time.sleep(10)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
print 'drive done'
time.sleep(1)
if mode == 'ba':
sics.execute('histmem ba roi roi')
sics.execute('histmem ba monitor %i' % 1)
sics.execute('histmem ba mintime %i' % configModel.min_time)
sics.execute('histmem ba maxtime %i' % maxTime)
sics.execute('histmem ba maxdetcount %i' % preset)
sics.execute('histmem ba maxbmcount -1')
sics.execute('histmem ba undermintime ba_maxdetcount')
print 'histmem start'
sics.execute('histmem start')
time0 = time.time()
while sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
if time.time() - time0 > 15.0:
print 'WARNING: HM may not have started counting. Gumtree will save anyway.'
break
else:
time.sleep(0.5)
time0 = time.time()
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
print 'time counted (estimate):', float(time.time() - time0)
else:
print 'histmem start'
while True:
if mode == 'count_roi':
sics.execute('histmem preset %i' % maxTime)
else:
sics.execute('histmem preset %i' % preset)
time.sleep(5)
sics.execute('histmem start')
time.sleep(5)
if mode == 'count_roi':
print 'count_roi'
time.sleep(configModel.min_time)
count_roi = 0
t0 = time.time()
while not sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
try:
count_roi = int(sicsext.runCommand('hmm configure num_events_filled_to_count_roi'))
# print count_roi
if count_roi > preset:
print count_roi
print 'reached desired count_roi'
sics.execute('histmem pause')
time.sleep(1)
break
except:
pass
time.sleep(0.5)
if time.time() - t0 > 5:
serverStatus = sics.get_status()
t0 = time.time()
break
else:
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
valid = False
for i in xrange(10):
time.sleep(1)
detector_time = sics.getValue('/instrument/detector/time').getFloatData()
valid = (detector_time >= preset - 1) or (detector_time >= preset * 0.90)
if valid:
break
print 'detector_time:', detector_time
if valid:
break
else:
print 'scan was invalid and needs to be repeated'
# sics.execute('histmem stop')
sics.execute('save %i' % frame_index)
frame_index += 1
print 'histmem done'
#check if in background
if early_exit_enabled.value :
try:
roi_counts = float(sics.get_raw_value('hmm configure num_events_filled_to_count_roi'))
roi_time = sics.getValue('/instrument/detector/time').getFloatData()
roi_rate = roi_counts / roi_time
print 'measured count rate:', roi_rate
count_rate_history.append(roi_rate)
bkg_frames = background_frames.value
bkg_range = background_threshold.value
if (len(count_rate_history) >= bkg_frames) and (builtin_max(count_rate_history[-bkg_frames:]) < bkg_range):
print 'background reached'
print 'scan completed (early exit)'
break
except:
pass
sics.execute('newfile clear')
# sics.execute('autosave 0') # disable autosave
# Get output filename
filenameController = sicsController.findDeviceController('datafilename')
savedFilename = filenameController.getValue().getStringData()
print 'saved:', savedFilename
sics.execute('histmem ba disable')
print 'done'
print
def btnPlotSteps_clicked():
scan = getScan()
# print 'zero angle:'
# print scan_reference.value
print ''
print 'scan variable range [%f, %f]' % (scan['angles'][0], scan['angles'][-1])
print ''
#Plot1.clear()
#Plot2.clear()
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if scan_angleMin == 0 and scan_angleMax == 0:
print 'please select a scan template'
return
if scan_angleMin == scan_angleMax:
print 'the min angle and max angle can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
#print [scan_angleMin, scan_angleMax]
if Plot1.ds != None:
Plot1.clear_masks()
Plot1.add_dataset(dummy)
Plot1.title = 'Preview'
Plot1.x_label = 'm2om'
Plot1.y_label = 'counts per sec'
# Plot1.x_range = [scan_angleMin,scan_angleMax]
inclusive = True
angles = scan['angles']
for i in xrange(1, len(angles)):
xL = angles[i - 1]
xH = angles[i ]
Plot1.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot1.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# convert to q PLOT 2
crystal = str(crystal_name.value)
if 'Si111' in crystal:
wavelength = 4.74
elif 'Si311' in crystal:
wavelength = 2.37
else:
wavelength = float('nan')
q = convert2q(angles, scan_reference.value, wavelength)
scan_angleMin = builtin_min(q)
scan_angleMax = builtin_max(q)
if isnan(scan_angleMin) or isnan(scan_angleMax):
print 'please check the wavelength'
return
if scan_angleMin == scan_angleMax:
print 'the min q and max q can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
if Plot2.ds != None:
Plot2.clear_masks()
Plot2.add_dataset(dummy)
Plot2.title = 'Preview'
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.x_range = [1e-6, q[-1]]
for i in xrange(1, len(q)):
xL = q[i - 1]
xH = q[i ]
Plot2.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot2.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# print "angles"
# print angles
# print q
print ''
print 'scan q-range [%f, %f]' % (q[0], q[-1])
print ''
def openDataset(path):
ds = df[str(path)]
ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm')
# ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm_xy')
ds.__iDictionary__.addEntry('time', 'entry1/instrument/detector/time')
ds.__iDictionary__.addEntry('m1om', 'entry1/instrument/crystal/m1om')
ds.__iDictionary__.addEntry('m1chi', 'entry1/instrument/crystal/m1chi')
ds.__iDictionary__.addEntry('m1x', 'entry1/instrument/crystal/m1x')
ds.__iDictionary__.addEntry('m2om', 'entry1/instrument/crystal/m2om')
ds.__iDictionary__.addEntry('m2chi', 'entry1/instrument/crystal/m2chi')
ds.__iDictionary__.addEntry('m2x', 'entry1/instrument/crystal/m2x')
ds.__iDictionary__.addEntry('m2y', 'entry1/instrument/crystal/m2y')
ds.__iDictionary__.addEntry('mdet', 'entry1/instrument/crystal/mdet')
ds.__iDictionary__.addEntry('pmom', 'entry1/instrument/crystal/pmom')
ds.__iDictionary__.addEntry('pmchi', 'entry1/instrument/crystal/pmchi')
ds.__iDictionary__.addEntry('ss1u', 'entry1/instrument/slits/ss1u')
ds.__iDictionary__.addEntry('ss1d', 'entry1/instrument/slits/ss1d')
ds.__iDictionary__.addEntry('ss1r', 'entry1/instrument/slits/ss1r')
ds.__iDictionary__.addEntry('ss1l', 'entry1/instrument/slits/ss1l')
ds.__iDictionary__.addEntry('ss2u', 'entry1/instrument/slits/ss2u')
ds.__iDictionary__.addEntry('ss2d', 'entry1/instrument/slits/ss2d')
ds.__iDictionary__.addEntry('ss2r', 'entry1/instrument/slits/ss2r')
ds.__iDictionary__.addEntry('ss2l', 'entry1/instrument/slits/ss2l')
ds.__iDictionary__.addEntry('ss1vo', 'entry1/instrument/slits/ss1vo')
ds.__iDictionary__.addEntry('ss1vg', 'entry1/instrument/slits/ss1vg')
ds.__iDictionary__.addEntry('ss1ho', 'entry1/instrument/slits/ss1ho')
ds.__iDictionary__.addEntry('ss1hg', 'entry1/instrument/slits/ss1hg')
ds.__iDictionary__.addEntry('ss2vo', 'entry1/instrument/slits/ss2vo')
ds.__iDictionary__.addEntry('ss2vg', 'entry1/instrument/slits/ss2vg')
ds.__iDictionary__.addEntry('ss2ho', 'entry1/instrument/slits/ss2ho')
ds.__iDictionary__.addEntry('ss2hg', 'entry1/instrument/slits/ss2hg')
ds.__iDictionary__.addEntry('samplename', 'entry1/sample/name')
ds.__iDictionary__.addEntry('wavelength', 'entry1/instrument/crystal/wavelength')
ds.__iDictionary__.addEntry('TimeStamp', 'entry1/time_stamp')
return ds
def btnPlot_clicked():
#Plot1.clear()
#Plot2.clear()
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
path = fns[0]
basename = os.path.basename(str(path))
basename = basename[:basename.find('.nx.hdf')]
ds = openDataset(path)
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
samplename = str(ds.samplename)
sorting = scan_variable_sorting.value
if sorting:
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
Plot1.clear()
if str(combine_mode.value) == 'individual':
for tid in tids:
if ds.hmm.ndim == 4:
data[:] = ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] = ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
# dataF = data.float_copy()
# dataF.title = 'Tube %i' % tid
# Plot1.add_dataset(dataF)
Plot1.title = 'Count Rate (individual)'
else:
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
data.title = 'Tubes ' + str(tids)
Plot1.set_dataset(data)
Plot1.set_mouse_follower_precision(6, 2, 2)
Plot1.title = basename + ' (combined): ' + samplename
# Plot1.title = Plot1.title + ' ' + basename
if Plot1.ds is not None:
Plot1.x_label = str(scan_variable_plot.value)
Plot1.y_label = 'counts per sec'
Plot2.clear()
time.sleep(0.3)
ds0 = Plot1.ds[0] # # don't understand how this works
xMax = 0
yMax = 0
for i in xrange(len(ds0)):
if yMax < ds0[i]:
xMax = ds0.axes[0][i]
yMax = ds0[i]
peakangle = xMax
q = convert2q(scanVariable, peakangle, ds.wavelength)
data.axes[0] = q[:]
Plot2.set_dataset(data)
Plot2.set_mouse_follower_precision(6, 2, 2)
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
# Plot1.title = 'Main Detector ' + basename + ': ' + samplename
# Plot2.title = 'Sample: ' + samplename + '; ' + sampledescription
Plot2.title = basename + ' (combined): ' + samplename
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.set_marker_on(True)
# plotXMax = Par('float', q[-1])
# Plot2.x_range = [1e-6, plotXMax.value]
if q[-1] > 1e-6 :
Plot2.x_range = [1e-6, q[-1]]
def convert2q(angles, reference, wavelength):
if wavelength is list:
wavelength = wavelength[0]
wavelength = float(wavelength)
deg2rad = 3.14159265359 / 180
f = 4 * 3.14159265359 / wavelength
if bool(negative_steps.value):
f *= -1.0
q = [(f * sin(deg2rad * (angle - reference) / 2)) for angle in angles]
return q
def __run_script__(fns):
# Use the provided resources, please don't remove.
global Plot1
global Plot2
global Plot3
print 'please press "Run Single Scan" or "Run Multiple Scans"'
btnPlot_clicked()
def __dispose__():
global Plot1
global Plot2
global Plot3
Plot1.clear()
Plot2.clear()
Plot3.clear()
# # model
class ConfigurationModel:
def __init__(self):
self.scanVariable = str(scan_variable.value)
self.scanVariable = self.scanVariable[:self.scanVariable.find(' ')]
self.crystal = str(crystal_name.value)
self.mode = str(scan_mode.value)
self.scan = getScan()
self.scan_reference = scan_reference.value
self.logscale = bool(logscale_position.value)
self.negative = bool(negative_steps.value)
self.stepInfo = []
for step in stepInfo:
d = dict()
for key in step.keys():
d[key] = step[key].value
self.stepInfo.append(d);
self.user_name = str(user_name.value)
self.user_email = str(user_email.value)
self.sample_name = str(sample_name.value)
self.sample_description = str(sample_description.value)
self.sample_thickness = float(sample_thickness.value)
# vertical/horizontal pre-slit
self.ss1vg = float(pss_ss1vg.value)
self.ss1vo = float(pss_ss1vo.value)
self.ss1hg = float(pss_ss1hg.value)
self.ss1ho = float(pss_ss1ho.value)
# vertical/horizontal post-slit
self.ss2vg = float(pss_ss2vg.value)
self.ss2vo = float(pss_ss2vo.value)
self.ss2hg = float(pss_ss2hg.value)
self.ss2ho = float(pss_ss2ho.value)
# load sample positions
self.sample_stage = str(scan_sample_stage.value)
self.sample_position = str(scan_sample_position.value)
self.min_time = int(scan_min_time.value)
# load early exit
self.early_exit_enabled = bool(early_exit_enabled.value)
self.bkg_frames = int(background_frames.value)
self.bkg_threshold = float(background_threshold.value)
def apply(self):
for option in scan_variable.options:
if self.scanVariable == option[:option.find(' ')]:
scan_variable.value = option
crystal_name.value = self.crystal
scan_mode.value = self.mode
logscale_position.value = self.logscale
negative_steps.value = self.negative
scan_reference.value = self.scan_reference
i = 0
for step in self.stepInfo:
for key in step.keys():
stepInfo[i][key].value = step[key]
setEnabled(i)
i += 1
setScanMode()
user_name.value = self.user_name
user_email.value = self.user_email
sample_name.value = self.sample_name
sample_description.value = self.sample_description
sample_thickness.value = self.sample_thickness
# vertical/horizontal pre-slit
pss_ss1vg.value = self.ss1vg
pss_ss1vo.value = self.ss1vo
pss_ss1hg.value = self.ss1hg
pss_ss1ho.value = self.ss1ho
# vertical/horizontal post-slit
pss_ss2vg.value = self.ss2vg
pss_ss2vo.value = self.ss2vo
pss_ss2hg.value = self.ss2hg
pss_ss2ho.value = self.ss2ho
# load sample positions
scan_sample_position.value = self.sample_position
scan_sample_stage.value = self.sample_stage
scan_min_time.value = self.min_time
# load early exit
early_exit_enabled.value = self.early_exit_enabled
background_frames.value = self.bkg_frames
background_threshold.value = self.bkg_threshold
if early_exit_enabled.value :
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
improve time estimation
__script__.title = 'KKB Measurement Script'
__script__.version = '2.1 2017-07-11 saved as KKB_scan'
from gumpy.commons import sics
from org.gumtree.gumnix.sics.control import ServerStatus
from pickle import Pickler, Unpickler
import time
from math import log as ln
from math import exp, isnan, isinf, sin
from __builtin__ import max as builtin_max
from __builtin__ import min as builtin_min
from org.eclipse.swt.widgets import FileDialog
from org.eclipse.swt import SWT
from org.eclipse.swt.widgets import Display
from java.io import File
import time
from Internal import sample_stage
'''
Disable dataset caching
'''
DatasetFactory.__cache_enabled__ = False
SINGLE_TYPE = SWT.SINGLE
SAVE_TYPE = SWT.SAVE
MULTI_TYPE = SWT.MULTI
class __Display_Runnable__(Runnable):
def __init__(self, type=SINGLE_TYPE, ext=['*.*']):
self.filename = None
self.filenames = None
self.path = None
self.type = type
self.ext = ext
def run(self):
global __UI__
dialog = FileDialog(__UI__.getShell(), self.type);
dialog.setFilterExtensions(self.ext)
dialog.open()
self.filename = dialog.getFilterPath() + File.separator + dialog.getFileName()
self.filenames = dialog.getFileNames()
self.path = dialog.getFilterPath()
def open_file_dialog(type=SWT.SINGLE, ext=['*.*']):
__display_run__ = __Display_Runnable__(type, ext)
Display.getDefault().asyncExec(__display_run__)
while __display_run__.filename is None:
time.sleep(0.5)
if type == SWT.MULTI:
fns = []
for fn in __display_run__.filenames:
fns.append(__display_run__.path + '/' + fn)
return fns
return __display_run__.filename
# # templates
reference_templates_dict = {}
reference_templates_dict['Si111'] = 180.3565
reference_templates_dict['Si311'] = -0.4100
steps_templates_list = []
# steps_templates['Background'] = [
# 'time', 'logscale',
# [20, 6.0e-5, 1200, 1200]]
# steps_templates['----------'] = [
# 'time', 'logscale',
# [0, 0, 0, 0]]
steps_templates_list.append([
'Si111: Logarithmic Overview Scan',
'time',
'logscale',
[17, 1.20e-4, 1, 1200],
[30, 22.0, 20, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (few features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[34, 20.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (fine features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[65, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Taiki Scan (15 points)',
'ba',
'logscale',
[2, 6.0e-5, 1000, 60],
[1, 10000, 1000, 60],
[10, 25, 1000, 60]])
'''
steps_templates_list.append([
'Si111: Kinetic Scan 4 points',
'time',
'logscale',
[ 0, 6.0e-5, 1, 1200],
[1, 5.0e-3, 180, 1200],
[3, 1.5e-2, 180, 1200]])
'''
steps_templates_list.append([
'----------',
'time',
'logscale',
[0, 0, 0, 0]])
steps_templates_list.append([
'Si311: Logarithmic Overview Scan',
'time',
'logscale',
[17, 2.0e-5, 1, 1200],
[30, 23.0, 20, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 80+29)',
'ba',
'logscale',
[80, 2e-5, 1000, 1200],
[29, 15.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 40+33)',
'ba',
'logscale',
[40, 2e-5, 1000, 1200],
[33, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, Taiki)',
'ba',
'logscale',
[33, 2e-5, 1000, 1200],
[25, 20.0, 1000, 1200]])
ret = sample_stage.check_declarations()
if not ret[0] :
open_warning(ret[1])
reload(sample_stage)
SAMPLE_STAGES = sample_stage.StagePool()
# # export path
__EXPORT_PATH__ = 'V:/shared/KKB Logbook/Temp Plot Data Repository/'
if not os.path.exists(__EXPORT_PATH__):
os.makedirs(__EXPORT_PATH__)
# # User Details
user_name = Par('string', 'Christine', options=['Christine', 'Lela', 'Jitendra'])
user_name.title = 'Name'
user_email = Par('string', 'cre@ansto.gov.au', options=['cre@ansto.gov.au', 'liliana.decampo@ansto.gov.au', 'jtm@ansto.gov.au'])
user_email.title = 'EMail'
g0 = Group('User Details')
g0.numColumns = 2
g0.add(user_name, user_email)
# # Sample Details
sample_name = Par('string', 'UNKNOWN', options=['Empty Cell', 'Empty Beam'], command="sample_thickness.enabled = sample_name.value not in ['Empty Cell', 'Empty Beam']")
sample_name.title = 'Name'
sample_description = Par('string', 'UNKNOWN')
sample_description.title = 'Description'
sample_thickness = Par('string', '1', options=['0.01', '0.1', '1.0', '10.0'])
sample_thickness.title = 'Thickness (mm)'
g0 = Group('Sample Details')
g0.numColumns = 2
g0.add(sample_name, sample_thickness, sample_description)
# Group('Sample Details').add(sample_name, sample_description, sample_thickness)
# # Crystal
crystal_name = Par('string', 'UNKNOWN')
crystal_name.title = 'Name'
crystal_name.enabled = False
try:
m2om = sics.getValue('/instrument/crystal/m2om').getFloatData()
if m2om > 90:
crystal_name.value = 'Si111 (4.74 Angstroms)'
else:
crystal_name.value = 'Si311 (2.37 Angstroms)'
except:
pass
g0 = Group('Crystal Info')
g0.numColumns = 2
g0.add(crystal_name)
# CRYSTAL END #############################################
# SLIT 1 #######################################################################
def updateOffset(gapBox, offsetBox):
offsetBox.enabled = 'fully' not in gapBox.value
def getSlitGapAndOffset(aPath, a0, bPath, b0):
try:
a = sics.getValue(aPath).getFloatData()
b = sics.getValue(bPath).getFloatData()
gap = (a - a0 - (b - b0)) / 1.0
offset = (a - a0 + (b - b0)) / 2.0
return (gap, offset)
except:
return (float('nan'), float('nan'))
crystal = str(crystal_name.value)
if 'Si111' in crystal:
ss1r0 = 28.35
ss1l0 = 27.75
elif 'Si311' in crystal:
ss1r0 = -9.16
ss1l0 = -9.76
else:
ss1r0 = float('nan')
ss1l0 = float('nan')
ss1u0 = -8.04
ss1d0 = -7.30
(ss1vg, ss1vo) = getSlitGapAndOffset('/instrument/slits/ss1u', ss1u0, '/instrument/slits/ss1d', ss1d0)
(ss1hg, ss1ho) = getSlitGapAndOffset('/instrument/slits/ss1r', ss1r0, '/instrument/slits/ss1l', ss1l0)
pss_ss1vg = Par('string', '%.1f' % ss1vg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1vg, pss_ss1vo)')
pss_ss1vg.title = 'Vertical Gap (mm)'
# pss_ss1vg.colspan = 50
pss_ss1vo = Par('float', ss1vo)
pss_ss1vo.title = 'Vertical Offset (mm)'
# pss_ss1vo.colspan = 50
pss_ss1hg = Par('string', '%.1f' % ss1hg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1hg, pss_ss1ho)')
pss_ss1hg.title = 'Horizontal Gap (mm)'
# pss_ss1hg.colspan = 50
pss_ss1ho = Par('float', ss1ho)
pss_ss1ho.title = 'Horizontal Offset (mm)'
# pss_ss1ho.colspan = 50
g0 = Group('Sample Slit Settings')
g0.numColumns = 2
g0.add(pss_ss1vg, pss_ss1vo, pss_ss1hg, pss_ss1ho)
# SLIT 1 END #######################################################################
## Scan parameters ##########################################################################################################
scan_variable = Par('string', 'm2om [deg]', options=[
#'pmom [deg]', 'pmchi [deg]',
'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'], command="scan_variable_plot.value = scan_variable.value")
scan_variable.title = 'Scan Variable'
scan_variable.colspan = 25
scan_reference = Par('float', '0.0')
scan_reference.title = 'Zero Angle'
scan_reference.colspan = 25
for key in reference_templates_dict.keys():
if key in crystal_name.value:
scan_reference.value = reference_templates_dict[key]
scan_mode = Par('string', 'ba', options=['ba', 'time'], command='setScanMode()')
scan_mode.title = 'Acquisition Mode'
scan_mode.colspan = 25
scan_min_time = Par('int', '5')
scan_min_time.title = 'Min Time (sec)'
scan_min_time.colspan = 25
empty_label = Par('label', '')
empty_label.colspan = 25
scan_sample_stage = Par('string', '', command = 'sample_stage_changed()')
scan_sample_stage.colspan = 25
scan_sample_stage.title = 'Sample Stage'
scan_sample_stage.options = SAMPLE_STAGES.get_stage_names()
current_stage = SAMPLE_STAGES.get_stage_in_service()
if not current_stage is None:
scan_sample_stage.value = current_stage.get_name()
scan_sample_position = Par('string', 'fixed')
scan_sample_position.title = 'Sample Position'
scan_sample_position.colspan = 25
scan_sample_position.options = ['fixed', '----------']
if not current_stage is None:
scan_sample_position.options += current_stage.get_sample_indexes()
logscale_position = Par('bool', False, command='setStepTitles()')
logscale_position.title = 'Logarithmic Steps'
logscale_position.colspan = 25
negative_steps = Par('bool', False)
negative_steps.title = 'Negative Steps'
negative_steps.colspan = 25
steps_label = Par('label', 'Please choose scan template or adjust steps manually: ')
steps_label.colspan = 200
steps_templates = Par('string', '', options=[item[0] for item in steps_templates_list], command='setTemplate()')
steps_templates.title = 'Scan Template'
steps_templates.colspan = 100
early_exit_enabled = Par('bool', True, command = "set_early_exit_enabled()")
early_exit_enabled.title = "Enable Early Exit"
early_exit_enabled.colspan = 25
background_frames = Par('int', 3)
background_frames.title = 'Background Frames'
background_frames.colspan = 25
background_threshold = Par('float', 0.26)
background_threshold.title = 'Background Threshold'
background_threshold.colspan = 25
# steps_space = Par('space', '')
# steps_space.colspan = 10
g0 = Group('Scan Parameters')
g0.numColumns = 100 # 9
g0.add(scan_variable, scan_mode, scan_reference, early_exit_enabled, \
logscale_position, scan_min_time, scan_sample_stage, background_frames, \
negative_steps, empty_label, scan_sample_position, background_threshold, \
steps_label, steps_templates)
def sample_stage_changed():
stage = SAMPLE_STAGES.get_stage_by_name(str(scan_sample_stage.value))
# scan_sample_position.value = 'fixed'
if not stage is None:
scan_sample_position.options = ['fixed', '----------'] + stage.get_sample_indexes()
else:
scan_sample_position.options = ['fixed', '----------']
def set_early_exit_enabled():
if early_exit_enabled.value:
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
stepInfo = []
for i in xrange(4):
steps_e = Par('bool', True, command='setEnabled(%i)' % i)
steps_e.title = '(%i)' % (i + 1)
steps_e.colspan = 10
steps_m = Par('int', 0, command='clearScanTemplateSelection()')
steps_m.title = 'Number of points'
steps_m.colspan = 20
steps_s = Par('float', 0, command='clearScanTemplateSelection()')
steps_s.title = 'Step Size [deg]'
steps_s.colspan = 20
steps_p = Par('int', 0, command='clearScanTemplateSelection()')
steps_p.title = 'Mode Preset'
steps_p.colspan = 25
steps_t = Par('int', 1200, command='clearScanTemplateSelection()')
steps_t.title = 'Max Time'
steps_t.colspan = 25
stepInfo.append({'enabled': steps_e, 'dataPoints':steps_m, 'stepSize':steps_s, 'preset':steps_p, 'maxTime':steps_t})
g0.add(steps_e, steps_m, steps_s, steps_p, steps_t)
def clearScanTemplateSelection():
steps_templates.value = None
btnPlotSteps = Act('btnPlotSteps_clicked()', 'Plot Measurement Steps') # 'compare measurement steps with previous scan')
btnPlotSteps.colspan = 50
cnfg_save_btn = Act('saveConfiguration()', 'Save Single Scan Parameters')
cnfg_save_btn.colspan = 50
btnTimeEstimation = Act('runTimeEstimation()', 'Time Estimation with selected Data Set')
btnTimeEstimation.colspan = 50
txtTimeEstimation = Par('int', '0')
txtTimeEstimation.title = 'Time Estimation (min)'
txtTimeEstimation.enabled = False
txtTimeEstimation.colspan = 50
g0.add(btnPlotSteps, cnfg_save_btn, btnTimeEstimation, txtTimeEstimation)
def runTimeEstimation():
if str(scan_mode.value) == 'time':
scan = getScan()
times = scan['presets']
txtTimeEstimation.value = int((sum(times) + len(times) * 25) / 60.0) # 25 seconds for each move
return
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
ds = openDataset(fns[0])
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
data[:] = [data[item[0]] for item in info] # sorting
# angle and count rate
a0 = [float(angle) for angle in scanVariable]
r0 = [float(rate) for rate in data[:]]
# angle, counts, max time and min time
model = ConfigurationModel()
scan = model.scan
tMin = model.min_time
a1 = scan['angles']
c1 = scan['presets']
t1 = scan['maxTimes']
total = 0.0
for i in xrange(len(a1)):
try:
rate = sample(a0, r0, a1[i])
time = c1[i] / rate
if time < tMin:
total += tMin
elif time > t1[i]:
total += t1[i]
else:
total += time
#print ("angle: " + str(a1[i])
# + " expected counts: " + str(c1[i])
# + " rate:" + str(rate)
# + " time:" + str(time)
# + " total:" + str(total))
except ValueError as e:
if e.message == "OutOfRange":
total += t1[i] # add max time
else:
raise
total += int(len(a1) * 25) # 25 seconds for each move
txtTimeEstimation.value = int(total / 60.0)
def sample(x0, y0, x1):
from __builtin__ import max, min
if len(x0) != len(y0):
raise Exception("len(x0) != len(y0)")
x0_min = min(x0)
x0_max = max(x0)
if len(x0) < 2:
raise Exception("len(x0) < 2")
if x0_min >= x0_max:
raise Exception("x0_min >= x0_max")
if x1 < x0_min:
raise ValueError("OutOfRange")
if x0_max < x1:
raise ValueError("OutOfRange")
i0 = 0
i1 = 1
x0i0 = x0[i0]
y0i0 = y0[i0]
x0i1 = x0[i1]
y0i1 = y0[i1]
# in case first x values are equal
while x0i0 == x0i1:
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
# not iterable
while x0i1 < x1:
x0i0 = x0i1
y0i0 = y0i1
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
return y0i0 + (x1 - x0i0) * (y0i1 - y0i0) / (x0i1 - x0i0)
## Scan parameters END #########################################################################
## RUN ##############################################
cnfg_load_btn = Act('loadConfigurations()', 'Load Multiple Scan Parameters')
cnfg_lookup = dict()
cnfg_options = Par('string', '', options=[''], command="applyConfiguration()")
cnfg_options.title = 'Read'
start_scan = Act('startScan(ConfigurationModel())', '############# Run Single Scan #############')
cnfg_run_btn = Act('runConfigurations()', '############# Run Multiple Scans #############')
g0 = Group('Execute Scans')
g0.numColumns = 1
g0.add(start_scan, cnfg_load_btn, cnfg_options, cnfg_run_btn)
## Save/Load Configuration END############################################################################
def saveConfiguration():
file = open_file_dialog(type=SAVE_TYPE, ext=['*.kkb'])
try:
fh = open(file, 'w')
except:
print 'not saved'
return
try:
p = Pickler(fh)
# header
p.dump('KKB')
# content
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
p.dump(att)
p.dump(att_value)
print 'saved'
finally:
fh.close()
def loadConfigurations():
fileList = open_file_dialog(type=MULTI_TYPE, ext=['*.kkb'])
if not fileList:
return
finalDict = dict()
finalNames = []
for path in fileList:
fh = open(path, 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', os.path.basename(path)
else:
model = ConfigurationModel()
# set defaults
model.negative = False # old models may not have this attribute
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', os.path.basename(path)
break
setattr(model, att, p.load())
else:
name = os.path.basename(path)
finalDict[name] = path
finalNames.append(name)
finally:
fh.close()
cnfg_lookup.clear()
cnfg_lookup.update(finalDict)
cnfg_options.value = finalNames[0] if finalNames else ''
cnfg_options.options = finalNames
# time.sleep(0.5)
def applyConfiguration():
file = str(cnfg_options.value)
if not file:
return
fh = open(cnfg_lookup[file], 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
# print 'read:', file
model.apply()
finally:
fh.close()
def runConfigurations():
for file in cnfg_options.options:
fh = open(cnfg_lookup[file], 'r')
try:
cnfg_options.command = ''
cnfg_options.value = file
applyConfiguration()
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
print 'run:', file
startScan(model)
finally:
cnfg_options.command = 'applyConfiguration()'
fh.close()
# # Plot
tubes_label = Par('label', 'Main Detector:')
tubes_label.colspan = 1
combine_tube0 = Par('bool', True)
combine_tube0.title = ' Tube 0'
combine_tube0.colspan = 1
combine_tube1 = Par('bool', True)
combine_tube1.title = ' Tube 1'
combine_tube1.colspan = 1
combine_tube2 = Par('bool', True)
combine_tube2.title = ' Tube 2'
combine_tube2.colspan = 1
combine_tube3 = Par('bool', True)
combine_tube3.title = ' Tube 3'
combine_tube3.colspan = 1
combine_tube4 = Par('bool', True)
combine_tube4.title = ' Tube 4'
combine_tube4.colspan = 1
combine_tube6 = Par('bool', False)
combine_tube6.title = ' Tube 6'
combine_tube6.colspan = 1
combine_mode = Par('string', 'combined', options=['individual', 'combined'])
combine_mode.title = ' Mode'
combine_mode.colspan = 1
trans_tube_label = Par('label', 'Trans Detector: ')
trans_tube_label.colspan = 2
check_tube9 = Par('bool', True)
check_tube9.title = ' Tube 9: Si (311)'
check_tube9.colspan = 2
check_tube10 = Par('bool', False)
check_tube10.title = ' Tube 10: Si (111)'
check_tube10.colspan = 2
# steps_space = Par('space', '')
# steps_space.colspan = 12
scan_variable_plot = Par('string', 'm2om [deg]', options=[
'pmom [deg]', 'pmchi [deg]', 'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'])
scan_variable_plot.title = 'Scan Variable'
scan_variable_plot.colspan = 1
scan_variable_sorting = Par('bool', True)
scan_variable_sorting.title = 'Sorting'
scan_variable_sorting.colspan = 1
btnPlot = Act('btnPlot_clicked()', 'Plot Selected Data Set')
btnPlot.colspan = 8
g0 = Group('Plotting')
g0.numColumns = 7
g0.add(tubes_label, combine_tube0, combine_tube1, combine_tube2, combine_tube3, combine_tube4, combine_tube6, combine_mode, trans_tube_label, check_tube9, check_tube10, scan_variable_plot, scan_variable_sorting, btnPlot)
# export to csv
# btnExport = Act('export_clicked()', 'Export to CSV')
################################# SLIT 2 ##########################################################
ss2u0 = 0 # 2.00
ss2d0 = 0 # 1.40
ss2l0 = 0 # 5 0.50
ss2r0 = 0 # -2 -1.00
(ss2vg, ss2vo) = getSlitGapAndOffset('/instrument/slits/ss2u', ss2u0, '/instrument/slits/ss2d', ss2d0)
(ss2hg, ss2ho) = getSlitGapAndOffset('/instrument/slits/ss2r', ss2r0, '/instrument/slits/ss2l', ss2l0)
pss_ss2vg = Par('string', '%.1f' % ss2vg, options=pss_ss1vg.options, command='updateOffset(pss_ss2vg, pss_ss2vo)')
pss_ss2vg.title = 'Vertical Opening (mm)'
pss_ss2vo = Par('float', ss2vo)
pss_ss2vo.title = 'Vertical Offset (mm)'
pss_ss2hg = Par('string', '%.1f' % ss2hg, options=pss_ss1hg.options, command='updateOffset(pss_ss2hg, pss_ss2ho)')
pss_ss2hg.title = 'Horizontal Opening (mm)'
pss_ss2ho = Par('float', ss2ho)
pss_ss2ho.title = 'Horizontal Offset (mm)'
g0 = Group('Post-Sample Slit')
g0.numColumns = 2
g0.add(pss_ss2vg, pss_ss2vo, pss_ss2hg, pss_ss2ho)
################################# SLIT 2 END ##########################################################
def setStepTitles():
if logscale_position.value:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Factor [%]"
else:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Size [deg]"
__UI__.updateUI()
def setTemplate():
try:
matches = [item for item in steps_templates_list if item[0] == steps_templates.value]
if len(matches) != 1:
steps_templates.value = None
return
template = matches[0]
# ignore '----'
if template[0][0] == '-':
steps_templates.value = None
return
scan_mode.value = template[1]
if template[2] == 'logscale':
logscale_position.value = True
elif template[2] == 'linear':
logscale_position.value = False
setStepTitles()
# by default templates measure in positive direction
negative_steps.value = False
setScanMode()
headers = 3
for i in xrange(len(template) - headers):
templateItem = template[i + headers]
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = True
stepInfoItem['dataPoints'].enabled = True
stepInfoItem['dataPoints'].value = templateItem[0]
stepInfoItem['stepSize' ].enabled = True
stepInfoItem['stepSize' ].value = templateItem[1]
stepInfoItem['preset' ].enabled = True
stepInfoItem['preset' ].value = templateItem[2]
stepInfoItem['maxTime' ].enabled = scan_min_time.enabled
stepInfoItem['maxTime' ].value = templateItem[3]
for i in xrange(len(template) - headers, len(stepInfo)):
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = False
stepInfoItem['dataPoints'].enabled = False
stepInfoItem['stepSize' ].enabled = False
stepInfoItem['preset' ].enabled = False
stepInfoItem['maxTime' ].enabled = False
except:
pass
def setScanMode():
if scan_mode.value == 'time':
scan_min_time.enabled = False
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = False
else:
scan_min_time.enabled = True
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = stepInfoItem['enabled'].value
def setEnabled(index):
steps_templates.value = None
stepItem = stepInfo[index]
value = stepItem['enabled'].value
stepItem['dataPoints'].enabled = value
stepItem['stepSize' ].enabled = value
stepItem['preset' ].enabled = value
stepItem['maxTime' ].enabled = value and scan_min_time.enabled
setTemplate()
def getScan():
scan = { 'angles': [], 'presets': [], 'maxTimes': [], 'groups': [] }
first = True
angle_ref = scan_reference.value
angle = angle_ref
logscale = False # first data points are always on a linear scale
negative = bool(negative_steps.value)
for stepInfoItem in stepInfo:
if stepInfoItem['enabled'].value:
dataPoints = stepInfoItem['dataPoints'].value
stepSize = stepInfoItem['stepSize' ].value
preset = stepInfoItem['preset' ].value
maxTime = stepInfoItem['maxTime' ].value
if (dataPoints > 0) and (stepSize <= 0.0):
raise Exception('step sizes have to be positive')
for i in xrange(dataPoints):
if first and (i == 0):
angle -= ((dataPoints - 1) / 2.0) * stepSize;
elif logscale:
# for logscale stepSize is a stepFactor
angle = angle_ref + (angle - angle_ref) * (1.0 + 0.01 * stepSize)
else:
angle += stepSize
#print angle
scan['angles' ].append(angle)
scan['presets' ].append(preset)
scan['maxTimes'].append(maxTime)
if i == 0:
scan['groups'].append(angle)
first = False
logscale = bool(logscale_position.value)
if negative:
# negate angles with reference to zero angle
scan['angles'] = [angle_ref - (angle - angle_ref) for angle in scan['angles']]
return scan
def wait_for_idle():
c_time = time.time()
while not sics.getSicsController().getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
time.sleep(0.1)
if time.time() - c_time > 5:
serverStatus = sics.get_status()
c_time = time.time()
def startScan(configModel):
''' check instrument ready '''
is_ready = False
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
except:
pass
if not is_ready:
is_confirmed = open_question('The instrument is not ready '\
+ 'according to the SIS status. Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?')
if not is_confirmed:
slog('Instrument is not ready. Quit the scan.')
return
else:
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
except:
pass
if not is_ready:
slog('scan continued without instrument ready')
''' setup '''
scanVariable = configModel.scanVariable
crystal = configModel.crystal
mode = configModel.mode
MainDeadTime = 1.08E-6
TransDeadTime = 1.08E-6
if 'Si111' in crystal:
empLevel = 0.3
bkgLevel = 0.21
dOmega = 2.3E-6
gDQv = 0.0586
gDQh = 0
wavelength = 4.74
TransmissionTube = 10
TransBackground = 0 # counts per second
elif 'Si311' in crystal:
empLevel = 0.34
bkgLevel = 0.21
dOmega = 4.6E-7
gDQv = 0.117
gDQh = 0
wavelength = 2.37
TransmissionTube = 9
TransBackground = 0 # counts per second
else:
print 'selected crystal is invalid'
return
''' angles '''
scan = configModel.scan
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if ('m1om' in scanVariable) or ('m2om' in scanVariable):
tolerance = 6
approved = False
if 'Si111' in crystal:
if (180 - tolerance <= scan_angleMin) and (scan_angleMax <= 180 + tolerance):
approved = True
elif 'Si311' in crystal:
if (0 - tolerance <= scan_angleMin) and (scan_angleMax <= 0 + tolerance):
approved = True
if not approved:
print 'angle out of range'
return
''' execution '''
sics.execute('hset user/name ' + configModel.user_name)
sics.execute('hset user/email ' + configModel.user_email)
sics.execute('hset sample/name ' + configModel.sample_name)
sics.execute('hset sample/description ' + configModel.sample_description)
sics.execute('hset sample/thickness %g' % configModel.sample_thickness)
sics.execute('hset experiment/bkgLevel %g' % bkgLevel)
sics.execute('hset experiment/empLevel %g' % empLevel)
sics.execute('hset instrument/detector/MainDeadTime %g' % MainDeadTime)
sics.execute('hset instrument/detector/TransDeadTime %g' % TransDeadTime)
sics.execute('hset instrument/detector/TransBackground %g' % TransBackground)
sics.execute('hset instrument/detector/TransmissionTube %i' % TransmissionTube)
sics.execute('hset instrument/crystal/dOmega %g' % dOmega)
sics.execute('hset instrument/crystal/gDQv %g' % gDQv)
sics.execute('hset instrument/crystal/gDQh %g' % gDQh)
sics.execute('hset instrument/crystal/wavelength %g' % wavelength)
sics.execute('hset instrument/crystal/scan_variable ' + scanVariable);
sicsController = sics.getSicsController()
# slits
def getSlitValues(gap, offset, a0, b0, aOpen, bOpen):
if gap == 'fully opened':
return (aOpen, bOpen)
if gap == 'fully closed':
gap = -5.0
offset = 0.0
a = a0 + 0.5 * float(gap) + float(offset)
b = b0 - 0.5 * float(gap) + float(offset)
return (a, b)
ss1vg = configModel.ss1vg
ss1vo = configModel.ss1vo
ss1hg = configModel.ss1hg
ss1ho = configModel.ss1ho
ss2vg = configModel.ss2vg
ss2vo = configModel.ss2vo
ss2hg = configModel.ss2hg
ss2ho = configModel.ss2ho
(ss1u, ss1d) = getSlitValues(ss1vg, ss1vo, ss1u0, ss1d0, 35.8, -38.8)
(ss1r, ss1l) = getSlitValues(ss1hg, ss1ho, ss1r0, ss1l0, 57.0, -58.0)
(ss2u, ss2d) = getSlitValues(ss2vg, ss2vo, ss2u0, ss2d0, 37.0, -39.5)
(ss2r, ss2l) = getSlitValues(ss2hg, ss2ho, ss2r0, ss2l0, 35.0, -35.0)
# apply slits
run = {}
run['ss1u'] = ss1u
run['ss1d'] = ss1d
run['ss1r'] = ss1r
run['ss1l'] = ss1l
run['ss2u'] = ss2u
run['ss2d'] = ss2d
run['ss2r'] = ss2r
run['ss2l'] = ss2l
# sics.multiDrive(run)
dc = 'drive'
for key in run:
dc += ' ' + key + ' ' + str(run[key])
sics.execute(dc)
time.sleep(5)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
'''
sics.execute('run ss1u %.2f' % ss1u)
sics.execute('run ss1d %.2f' % ss1d)
sics.execute('run ss1r %.2f' % ss1r)
sics.execute('run ss1l %.2f' % ss1l)
sics.execute('run ss2u %.2f' % ss2u)
sics.execute('run ss2d %.2f' % ss2d)
sics.execute('run ss2r %.2f' % ss2r)
sics.execute('run ss2l %.2f' % ss2l)
'''
# load sample positions
sample_stage_name = configModel.sample_stage
sample_positions = str(configModel.sample_position)
if (len(sample_positions) == 0) or (sample_positions == 'fixed'):
samz_list = [None]
else:
samz_list = []
stage = SAMPLE_STAGES.get_stage_by_name(sample_stage_name)
if stage is None:
raise 'Invalid stage name ' + str(sample_stage_name)
samz_value = stage.get_samz(sample_positions)
samz_list.append(samz_value)
print samz_list
for samz in samz_list:
sics.execute('histmem stop')
time.sleep(3)
if mode == 'ba':
sics.execute('histmem mode unlimited')
sics.execute('histmem ba enable')
else:
sics.execute('histmem mode time')
sics.execute('histmem ba disable')
if samz is not None:
print 'run samz %.2f' % samz
sics.execute('run samz %.2f' % samz)
# sics.execute('prun samz 2' % samz) !!!
time.sleep(1)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
sics.execute('newfile HISTOGRAM_XYT')
# sics.execute('autosave 60') # 60 seconds
time.sleep(1)
# start/stop hmm
if mode == 'count_roi':
sics.execute('histmem preset 1')
time.sleep(1)
sics.execute('histmem start')
time.sleep(5)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
sics.execute('histmem stop')
print 'frames:', len(scan['angles'])
count_rate_history = []
for frame_index in xrange(len(scan['angles'])):
angle = scan['angles' ][frame_index]
preset = scan['presets' ][frame_index]
maxTime = scan['maxTimes'][frame_index]
print 'drive %s %.6f' % (scanVariable, angle)
# sics.drive(scanVariable, float(angle))
sics.execute('drive %s %.6f' % (scanVariable, angle))
time.sleep(10)
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
print 'drive done'
time.sleep(1)
if mode == 'ba':
sics.execute('histmem ba roi roi')
sics.execute('histmem ba monitor %i' % 1)
sics.execute('histmem ba mintime %i' % configModel.min_time)
sics.execute('histmem ba maxtime %i' % maxTime)
sics.execute('histmem ba maxdetcount %i' % preset)
sics.execute('histmem ba maxbmcount -1')
sics.execute('histmem ba undermintime ba_maxdetcount')
print 'histmem start'
sics.execute('histmem start')
time0 = time.time()
while sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
if time.time() - time0 > 15.0:
print 'WARNING: HM may not have started counting. Gumtree will save anyway.'
break
else:
time.sleep(0.5)
time0 = time.time()
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
print 'time counted (estimate):', float(time.time() - time0)
else:
print 'histmem start'
while True:
if mode == 'count_roi':
sics.execute('histmem preset %i' % maxTime)
else:
sics.execute('histmem preset %i' % preset)
time.sleep(5)
sics.execute('histmem start')
time.sleep(5)
if mode == 'count_roi':
print 'count_roi'
time.sleep(configModel.min_time)
count_roi = 0
t0 = time.time()
while not sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
try:
count_roi = int(sicsext.runCommand('hmm configure num_events_filled_to_count_roi'))
# print count_roi
if count_roi > preset:
print count_roi
print 'reached desired count_roi'
sics.execute('histmem pause')
time.sleep(1)
break
except:
pass
time.sleep(0.5)
if time.time() - t0 > 5:
serverStatus = sics.get_status()
t0 = time.time()
break
else:
wait_for_idle()
# while not sics.get_status().equals(ServerStatus.EAGER_TO_EXECUTE):
# time.sleep(0.5)
valid = False
for i in xrange(10):
time.sleep(1)
detector_time = sics.getValue('/instrument/detector/time').getFloatData()
valid = (detector_time >= preset - 1) or (detector_time >= preset * 0.90)
if valid:
break
print 'detector_time:', detector_time
if valid:
break
else:
print 'scan was invalid and needs to be repeated'
# sics.execute('histmem stop')
sics.execute('save %i' % frame_index)
frame_index += 1
print 'histmem done'
#check if in background
if early_exit_enabled.value :
try:
roi_counts = float(sics.get_raw_value('hmm configure num_events_filled_to_count_roi'))
roi_time = sics.getValue('/instrument/detector/time').getFloatData()
roi_rate = roi_counts / roi_time
print 'measured count rate:', roi_rate
count_rate_history.append(roi_rate)
bkg_frames = background_frames.value
bkg_range = background_threshold.value
if (len(count_rate_history) >= bkg_frames) and (builtin_max(count_rate_history[-bkg_frames:]) < bkg_range):
print 'background reached'
print 'scan completed (early exit)'
break
except:
pass
sics.execute('newfile clear')
# sics.execute('autosave 0') # disable autosave
# Get output filename
filenameController = sicsController.findDeviceController('datafilename')
savedFilename = filenameController.getValue().getStringData()
print 'saved:', savedFilename
sics.execute('histmem ba disable')
print 'done'
print
def btnPlotSteps_clicked():
scan = getScan()
# print 'zero angle:'
# print scan_reference.value
print ''
print 'scan variable range [%f, %f]' % (scan['angles'][0], scan['angles'][-1])
print ''
#Plot1.clear()
#Plot2.clear()
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if scan_angleMin == 0 and scan_angleMax == 0:
print 'please select a scan template'
return
if scan_angleMin == scan_angleMax:
print 'the min angle and max angle can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
#print [scan_angleMin, scan_angleMax]
if Plot1.ds != None:
Plot1.clear_masks()
Plot1.add_dataset(dummy)
Plot1.title = 'Preview'
Plot1.x_label = 'm2om'
Plot1.y_label = 'counts per sec'
# Plot1.x_range = [scan_angleMin,scan_angleMax]
inclusive = True
angles = scan['angles']
for i in xrange(1, len(angles)):
xL = angles[i - 1]
xH = angles[i ]
Plot1.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot1.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# convert to q PLOT 2
crystal = str(crystal_name.value)
if 'Si111' in crystal:
wavelength = 4.74
elif 'Si311' in crystal:
wavelength = 2.37
else:
wavelength = float('nan')
q = convert2q(angles, scan_reference.value, wavelength)
scan_angleMin = builtin_min(q)
scan_angleMax = builtin_max(q)
if isnan(scan_angleMin) or isnan(scan_angleMax):
print 'please check the wavelength'
return
if scan_angleMin == scan_angleMax:
print 'the min q and max q can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
if Plot2.ds != None:
Plot2.clear_masks()
Plot2.add_dataset(dummy)
Plot2.title = 'Preview'
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.x_range = [1e-6, q[-1]]
for i in xrange(1, len(q)):
xL = q[i - 1]
xH = q[i ]
Plot2.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot2.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# print "angles"
# print angles
# print q
print ''
print 'scan q-range [%f, %f]' % (q[0], q[-1])
print ''
def openDataset(path):
ds = df[str(path)]
ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm')
# ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm_xy')
ds.__iDictionary__.addEntry('time', 'entry1/instrument/detector/time')
ds.__iDictionary__.addEntry('m1om', 'entry1/instrument/crystal/m1om')
ds.__iDictionary__.addEntry('m1chi', 'entry1/instrument/crystal/m1chi')
ds.__iDictionary__.addEntry('m1x', 'entry1/instrument/crystal/m1x')
ds.__iDictionary__.addEntry('m2om', 'entry1/instrument/crystal/m2om')
ds.__iDictionary__.addEntry('m2chi', 'entry1/instrument/crystal/m2chi')
ds.__iDictionary__.addEntry('m2x', 'entry1/instrument/crystal/m2x')
ds.__iDictionary__.addEntry('m2y', 'entry1/instrument/crystal/m2y')
ds.__iDictionary__.addEntry('mdet', 'entry1/instrument/crystal/mdet')
ds.__iDictionary__.addEntry('pmom', 'entry1/instrument/crystal/pmom')
ds.__iDictionary__.addEntry('pmchi', 'entry1/instrument/crystal/pmchi')
ds.__iDictionary__.addEntry('ss1u', 'entry1/instrument/slits/ss1u')
ds.__iDictionary__.addEntry('ss1d', 'entry1/instrument/slits/ss1d')
ds.__iDictionary__.addEntry('ss1r', 'entry1/instrument/slits/ss1r')
ds.__iDictionary__.addEntry('ss1l', 'entry1/instrument/slits/ss1l')
ds.__iDictionary__.addEntry('ss2u', 'entry1/instrument/slits/ss2u')
ds.__iDictionary__.addEntry('ss2d', 'entry1/instrument/slits/ss2d')
ds.__iDictionary__.addEntry('ss2r', 'entry1/instrument/slits/ss2r')
ds.__iDictionary__.addEntry('ss2l', 'entry1/instrument/slits/ss2l')
ds.__iDictionary__.addEntry('ss1vo', 'entry1/instrument/slits/ss1vo')
ds.__iDictionary__.addEntry('ss1vg', 'entry1/instrument/slits/ss1vg')
ds.__iDictionary__.addEntry('ss1ho', 'entry1/instrument/slits/ss1ho')
ds.__iDictionary__.addEntry('ss1hg', 'entry1/instrument/slits/ss1hg')
ds.__iDictionary__.addEntry('ss2vo', 'entry1/instrument/slits/ss2vo')
ds.__iDictionary__.addEntry('ss2vg', 'entry1/instrument/slits/ss2vg')
ds.__iDictionary__.addEntry('ss2ho', 'entry1/instrument/slits/ss2ho')
ds.__iDictionary__.addEntry('ss2hg', 'entry1/instrument/slits/ss2hg')
ds.__iDictionary__.addEntry('samplename', 'entry1/sample/name')
ds.__iDictionary__.addEntry('wavelength', 'entry1/instrument/crystal/wavelength')
ds.__iDictionary__.addEntry('TimeStamp', 'entry1/time_stamp')
return ds
def btnPlot_clicked():
#Plot1.clear()
#Plot2.clear()
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
path = fns[0]
basename = os.path.basename(str(path))
basename = basename[:basename.find('.nx.hdf')]
ds = openDataset(path)
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
samplename = str(ds.samplename)
sorting = scan_variable_sorting.value
if sorting:
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
Plot1.clear()
if str(combine_mode.value) == 'individual':
for tid in tids:
if ds.hmm.ndim == 4:
data[:] = ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] = ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
# dataF = data.float_copy()
# dataF.title = 'Tube %i' % tid
# Plot1.add_dataset(dataF)
Plot1.title = 'Count Rate (individual)'
else:
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
data.title = 'Tubes ' + str(tids)
Plot1.set_dataset(data)
Plot1.set_mouse_follower_precision(6, 2, 2)
Plot1.title = basename + ' (combined): ' + samplename
# Plot1.title = Plot1.title + ' ' + basename
if Plot1.ds is not None:
Plot1.x_label = str(scan_variable_plot.value)
Plot1.y_label = 'counts per sec'
Plot2.clear()
time.sleep(0.3)
ds0 = Plot1.ds[0] # # don't understand how this works
xMax = 0
yMax = 0
for i in xrange(len(ds0)):
if yMax < ds0[i]:
xMax = ds0.axes[0][i]
yMax = ds0[i]
peakangle = xMax
q = convert2q(scanVariable, peakangle, ds.wavelength)
data.axes[0] = q[:]
Plot2.set_dataset(data)
Plot2.set_mouse_follower_precision(6, 2, 2)
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
# Plot1.title = 'Main Detector ' + basename + ': ' + samplename
# Plot2.title = 'Sample: ' + samplename + '; ' + sampledescription
Plot2.title = basename + ' (combined): ' + samplename
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.set_marker_on(True)
# plotXMax = Par('float', q[-1])
# Plot2.x_range = [1e-6, plotXMax.value]
if q[-1] > 1e-6 :
Plot2.x_range = [1e-6, q[-1]]
def convert2q(angles, reference, wavelength):
if wavelength is list:
wavelength = wavelength[0]
wavelength = float(wavelength)
deg2rad = 3.14159265359 / 180
f = 4 * 3.14159265359 / wavelength
if bool(negative_steps.value):
f *= -1.0
q = [(f * sin(deg2rad * (angle - reference) / 2)) for angle in angles]
return q
def __run_script__(fns):
# Use the provided resources, please don't remove.
global Plot1
global Plot2
global Plot3
print 'please press "Run Single Scan" or "Run Multiple Scans"'
btnPlot_clicked()
def __dispose__():
global Plot1
global Plot2
global Plot3
Plot1.clear()
Plot2.clear()
Plot3.clear()
# # model
class ConfigurationModel:
def __init__(self):
self.scanVariable = str(scan_variable.value)
self.scanVariable = self.scanVariable[:self.scanVariable.find(' ')]
self.crystal = str(crystal_name.value)
self.mode = str(scan_mode.value)
self.scan = getScan()
self.scan_reference = scan_reference.value
self.logscale = bool(logscale_position.value)
self.negative = bool(negative_steps.value)
self.stepInfo = []
for step in stepInfo:
d = dict()
for key in step.keys():
d[key] = step[key].value
self.stepInfo.append(d);
self.user_name = str(user_name.value)
self.user_email = str(user_email.value)
self.sample_name = str(sample_name.value)
self.sample_description = str(sample_description.value)
self.sample_thickness = float(sample_thickness.value)
# vertical/horizontal pre-slit
self.ss1vg = float(pss_ss1vg.value)
self.ss1vo = float(pss_ss1vo.value)
self.ss1hg = float(pss_ss1hg.value)
self.ss1ho = float(pss_ss1ho.value)
# vertical/horizontal post-slit
self.ss2vg = float(pss_ss2vg.value)
self.ss2vo = float(pss_ss2vo.value)
self.ss2hg = float(pss_ss2hg.value)
self.ss2ho = float(pss_ss2ho.value)
# load sample positions
self.sample_stage = str(scan_sample_stage.value)
self.sample_position = str(scan_sample_position.value)
self.min_time = int(scan_min_time.value)
# load early exit
self.early_exit_enabled = bool(early_exit_enabled.value)
self.bkg_frames = int(background_frames.value)
self.bkg_threshold = float(background_threshold.value)
def apply(self):
for option in scan_variable.options:
if self.scanVariable == option[:option.find(' ')]:
scan_variable.value = option
crystal_name.value = self.crystal
scan_mode.value = self.mode
logscale_position.value = self.logscale
negative_steps.value = self.negative
scan_reference.value = self.scan_reference
i = 0
for step in self.stepInfo:
for key in step.keys():
stepInfo[i][key].value = step[key]
setEnabled(i)
i += 1
setScanMode()
user_name.value = self.user_name
user_email.value = self.user_email
sample_name.value = self.sample_name
sample_description.value = self.sample_description
sample_thickness.value = self.sample_thickness
# vertical/horizontal pre-slit
pss_ss1vg.value = self.ss1vg
pss_ss1vo.value = self.ss1vo
pss_ss1hg.value = self.ss1hg
pss_ss1ho.value = self.ss1ho
# vertical/horizontal post-slit
pss_ss2vg.value = self.ss2vg
pss_ss2vo.value = self.ss2vo
pss_ss2hg.value = self.ss2hg
pss_ss2ho.value = self.ss2ho
# load sample positions
scan_sample_position.value = self.sample_position
scan_sample_stage.value = self.sample_stage
scan_min_time.value = self.min_time
# load early exit
early_exit_enabled.value = self.early_exit_enabled
background_frames.value = self.bkg_frames
background_threshold.value = self.bkg_threshold
if early_exit_enabled.value :
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
|
# Python
import StringIO
from collections import Counter, OrderedDict
# Django
from django.core import urlresolvers
from django_countries import countries
from django.db import connection
from django.db.models import F, Count, Sum, FieldDoesNotExist
from django.contrib.sites.shortcuts import get_current_site
# 3rd Party
import xlsxwriter
# Project
from forms.models import (
NewspaperSheet, NewspaperPerson, TelevisionJournalist,
person_models, sheet_models, journalist_models)
from forms.modelutils import (TOPICS, GENDER, SPACE, OCCUPATION, FUNCTION, SCOPE,
YESNO, AGES, SOURCE, VICTIM_OF, SURVIVOR_OF, IS_PHOTOGRAPH, AGREE_DISAGREE,
RETWEET, TV_ROLE, MEDIA_TYPES,
CountryRegion)
from report_details import WS_INFO, REGION_COUNTRY_MAP, MAJOR_TOPICS, TOPIC_GROUPS, GROUP_TOPICS_MAP, FORMATS
def has_field(model, fld):
try:
model._meta.get_field(fld)
return True
except FieldDoesNotExist:
return False
def p(n, d):
""" Helper to calculate the percentage of n / d,
returning 0 if d == 0.
"""
if d == 0:
return 0.0
return float(n) / d
def get_regions():
"""
Return a (id, region_name) list for all regions
"""
country_regions = CountryRegion.objects\
.values('region')\
.exclude(region='Unmapped')
regions = set(item['region'] for item in country_regions)
return [(i, region) for i, region in enumerate(regions)]
def get_countries(selected=None):
"""
Return a (code, country) list for countries captured.
"""
captured_country_codes = set()
for model in sheet_models.itervalues():
rows = model.objects.values('country')
captured_country_codes.update([r['country'] for r in rows])
return [(code, name) for code, name in list(countries) if code in captured_country_codes]
def get_region_countries(region):
"""
Return a (code, country) list for a region.
"""
if region == 'ALL':
return get_countries()
else:
country_codes = REGION_COUNTRY_MAP[region]
return [(code, name) for code, name in list(countries) if code in country_codes]
def get_country_region(country):
"""
Return a (id, region_name) list to which a country belongs.
"""
if country == 'ALL':
return get_regions()
else:
return [(0, [k for k, v in REGION_COUNTRY_MAP.items() if country in v][0])]
def clean_title(text):
"""
Return the string passed in stripped of its numbers and parentheses
"""
if text != "Congo (the Democratic Republic of the)":
return text[text.find(')')+1:].lstrip()
return text
class XLSXDataExportBuilder():
def __init__(self, request):
self.domain = "http://%s" % get_current_site(request).domain
self.sheet_exclude_fields = ['monitor', 'url_and_multimedia', 'time_accessed', 'country_region']
self.person_exclude_fields = []
self.journalist_exclude_fields =[]
self.sheet_fields_with_id = ['topic', 'scope', 'person_secondary', 'inequality_women', 'stereotypes']
self.person_fields_with_id = ['sex', 'age', 'occupation', 'function', 'survivor_of', 'victim_of']
self.journalist_fields_with_id = ['sex', 'age']
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
for model in sheet_models.itervalues():
self.create_sheet_export(model, workbook)
for model in person_models.itervalues():
self.create_person_export(model, workbook)
for model in journalist_models.itervalues():
self.create_journalist_export(model, workbook)
workbook.close()
output.seek(0)
return output.read()
def create_sheet_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all()
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.sheet_fields_with_id)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_sheet_row(obj, ws, row+y, col, fields, self.sheet_fields_with_id)
def create_person_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.person_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.person_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_person_row(obj, ws, row+y, col, fields, self.person_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def create_journalist_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.journalist_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.journalist_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_journalist_row(obj, ws, row+y, col, fields, self.journalist_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def write_ws_titles(self, ws, row, col, fields, fields_with_id, append_sheet=False):
"""
Writes the column titles to the worksheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
:param append_sheet: Boolean specifying whether the related sheet object
needs to be appended to the row.
"""
if not append_sheet:
for field in fields:
ws.write(row, col, unicode(field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode(field.name+"_id"))
col += 1
ws.write(row, col, unicode('edit_url'))
col += 1
else:
for field in fields:
ws.write(row, col, unicode("sheet_" + field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode("sheet_" + field.name + "_id"))
col += 1
ws.write(row, col, unicode('sheet_edit_url'))
col += 1
return ws, col
def write_sheet_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Sheet models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'country':
ws.write(row, col, getattr(obj, field.name).code)
elif field.name == 'topic':
ws.write(row, col, unicode(TOPICS[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, TOPICS[getattr(obj, field.name)-1][0])
elif field.name == 'scope':
ws.write(row, col, unicode(SCOPE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, SCOPE[getattr(obj, field.name)-1][0])
elif field.name == 'person_secondary':
ws.write(row, col, unicode(SOURCE[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SOURCE[getattr(obj, field.name)][0])
elif field.name == 'inequality_women':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'stereotypes':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
else:
try:
ws.write(row, col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row, col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
obj._meta.app_label,
obj._meta.model_name),
args=(obj.id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_person_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Person models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age':
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == 'occupation':
ws.write(row, col, unicode(OCCUPATION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, OCCUPATION[getattr(obj, field.name)][0])
elif field.name == 'function':
ws.write(row, col, unicode(FUNCTION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, FUNCTION[getattr(obj, field.name)][0])
elif field.name == 'victim_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(VICTIM_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, VICTIM_OF[getattr(obj, field.name)][0])
elif field.name == 'survivor_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(SURVIVOR_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SURVIVOR_OF[getattr(obj, field.name)][0])
elif field.name == 'is_photograph':
ws.write(row, col, unicode(IS_PHOTOGRAPH[getattr(obj, field.name)-1][1]))
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in self.person_fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_journalist_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Journalist models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet_fields_with_id
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet_fields_with_id
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
class XLSXReportBuilder:
def __init__(self, form):
from reports.views import CountryForm, RegionForm
self.form = form
if isinstance(form, CountryForm):
self.countries = form.filter_countries()
self.regions = get_country_region(form.cleaned_data['country'])
self.report_type = 'country'
elif isinstance(form, RegionForm):
region = [name for i, name in form.REGIONS if str(i) == form.cleaned_data['region']][0]
self.countries = get_region_countries(region)
self.regions = [(0, region)]
self.report_type = 'region'
else:
self.countries = get_countries()
self.regions = get_regions()
self.report_type = 'global'
self.country_list = [code for code, name in self.countries]
self.region_list = [name for id, name in self.regions]
# Various gender utilities
self.male_female = [(id, value) for id, value in GENDER if id in [1, 2]]
self.male_female_ids = [id for id, value in self.male_female]
self.female = [(id, value) for id, value in GENDER if id == 1]
self.yes = [(id, value) for id, value in YESNO if id == 'Y']
self.gmmp_year = '2015'
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
# setup formats
self.heading = workbook.add_format(FORMATS['heading'])
self.col_heading = workbook.add_format(FORMATS['col_heading'])
self.col_heading_def = workbook.add_format(FORMATS['col_heading_def'])
self.sec_col_heading = workbook.add_format(FORMATS['sec_col_heading'])
self.sec_col_heading_def = workbook.add_format(FORMATS['sec_col_heading_def'])
self.label = workbook.add_format(FORMATS['label'])
self.N = workbook.add_format(FORMATS['N'])
self.P = workbook.add_format(FORMATS['P'])
# Use the following for specifying which reports to create during dev
# test_functions = [
# 'ws_01', 'ws_02', 'ws_04', 'ws_05', 'ws_06', 'ws_07', 'ws_08', 'ws_09', 'ws_10',
# 'ws_11', 'ws_12', 'ws_13', 'ws_14', 'ws_15', 'ws_16', 'ws_17', 'ws_18', 'ws_19', 'ws_20',
# 'ws_21', 'ws_23', 'ws_24', 'ws_25', 'ws_26', 'ws_27', 'ws_28', 'ws_29', 'ws_30',
# 'ws_31', 'ws_32', 'ws_34', 'ws_35', 'ws_36', 'ws_38', 'ws_39', 'ws_40',
# 'ws_41', 'ws_42', 'ws_43', 'ws_44', 'ws_45', 'ws_46', 'ws_47', 'ws_48',
# 'ws_49', 'ws_50', 'ws_51', 'ws_52', 'ws_53', 'ws_54', 'ws_55', 'ws_56',, 'ws_57', 'ws_58', 'ws_59', 'ws_60',
# 'ws_61', 'ws_62', 'ws_63', 'ws_64', 'ws_65', 'ws_66', 'ws_67', 'ws_68', 'ws_69', 'ws_70',
# 'ws_76', 'ws_77', 'ws_78', 'ws_79']
test_functions = ['ws_66']
sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
for function in test_functions:
if self.report_type in sheet_info[function]['reports']:
ws = workbook.add_worksheet(sheet_info[function]['name'])
self.write_headers(ws, sheet_info[function]['title'], sheet_info[function]['desc'])
getattr(self, function)(ws)
# -------------------------------------------------------------------
# To ensure ordered worksheets
# sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
# for ws_num, ws_info in sheet_info.iteritems():
# if self.report_type in ws_info['reports']:
# ws = workbook.add_worksheet(ws_info['name'])
# self.write_headers(ws, ws_info['title'], ws_info['desc'])
# getattr(self, ws_num)(ws)
workbook.close()
output.seek(0)
return output.read()
def dictfetchall(self, cursor):
"""
Returns all rows from a cursor as a dict
"""
desc = cursor.description
return [
dict(zip([col[0] for col in desc], row))
for row in cursor.fetchall()
]
def apply_weights(self, rows, db_table, media_type):
"""
param rows: Queryset to apply the weights to
param db_table: name of relevant sheet table
param: media_type: media type to weigh by
"""
query = rows.extra(
tables=['reports_weights'],
where=[
'reports_weights.country = %s.country' % (db_table),
'reports_weights.media_type = \'%s\'' % (media_type),
]).annotate()
raw_query, params = query.query.sql_with_params()
raw_query = raw_query.replace('SELECT', 'SELECT cast(round(SUM(reports_weights.weight)) as int) AS "n", ')
cursor = connection.cursor()
cursor.execute(raw_query, params)
return self.dictfetchall(cursor)
def ws_01(self, ws):
"""
Cols: Media Type
Rows: Region
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country_region__region')\
.filter(country_region__region__in=self.region_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for row in rows:
if row['region'] is not None:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [region[0] for region in self.regions if region[1] == row['region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_02(self, ws):
"""
Cols: Media Type
Rows: Region, Country
"""
r = 6
self.write_col_headings(ws, MEDIA_TYPES)
counts = Counter()
for region_id, region in self.regions:
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for row in rows:
if row['country'] is not None:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['country']): row['n']})
self.write_primary_row_heading(ws, region, r=r)
region_countries = [(code, country) for code, country in self.countries if code in REGION_COUNTRY_MAP[region]]
self.tabulate(ws, counts, MEDIA_TYPES, region_countries, row_perc=True, sec_row=True, r=r)
r += len(region_countries)
def ws_04(self, ws):
"""
Cols: Region, Media type
Rows: Major Topic
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('topic')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
major_topic = TOPIC_GROUPS[r['topic']]
counts.update({(media_id, major_topic): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, MEDIA_TYPES, MAJOR_TOPICS, row_perc=False, sec_cols=10)
def ws_05(self, ws):
"""
Cols: Subject sex
Rows: Major Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, self.male_female, MAJOR_TOPICS, row_perc=True, display_cols=self.female)
def ws_06(self, ws):
"""
Cols: Region, Subject sex: female only
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in person_models.iteritems():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country_region__region':region})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, MAJOR_TOPICS, row_perc=True, sec_cols=2, display_cols=self.female)
def ws_07(self, ws):
"""
Cols: Media Type
Rows: Subject Sex
"""
counts = Counter()
for media_type, model in person_models.iteritems():
rows = model.objects\
.values('sex')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, r['sex']): r['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.male_female, row_perc=False)
def ws_08(self, ws):
"""
Cols: Subject Sex
Rows: Scope
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'scope' in model.sheet_field().rel.to._meta.get_all_field_names():
scope = '%s__scope' % model.sheet_name()
rows = model.objects\
.values('sex', scope)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['scope']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SCOPE, row_perc=True, display_cols=self.female)
def ws_09(self, ws):
"""
Cols: Subject Sex
Rows: Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_10(self, ws):
"""
Cols: Space
Rows: Minor Topics
:: Newspaper Sheets only
"""
# Calculate row values for column
counts = Counter()
for media_type, model in sheet_models.iteritems():
if media_type == 'Print':
rows = model.objects\
.values('space', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['space'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, SPACE, MAJOR_TOPICS, row_perc=False)
def ws_11(self, ws):
"""
Cols: Equality Rights
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_12(self, ws):
"""
Cols: Region, Equality Rights
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region_name in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
# Some models has no equality rights field
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country_region__region=region_name)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_13(self, ws):
"""
Cols: Journalist Sex, Equality Rights
Rows: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
topic = '%s__topic' % model.sheet_name()
equality_rights = '%s__equality_rights' % model.sheet_name()
rows = model.objects\
.values(equality_rights, topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex=gender_id)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_14(self, ws):
"""
Cols: Sex
Rows: Occupation
"""
counts = Counter()
for media_type, model in person_models.iteritems():
# some Person models don't have an occupation field
if 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, OCCUPATION, row_perc=True, display_cols=self.female)
def ws_15(self, ws):
"""
Cols: Sex
Rows: Function
"""
counts = Counter()
for media_type, model in person_models.iteritems():
# some Person models don't have a function field
if 'function' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, FUNCTION, row_perc=True, display_cols=self.female)
def ws_16(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
for function_id, function in FUNCTION:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=function_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_17(self, ws):
"""
Cols: Age, Sex of Subject
Rows: Function
"""
secondary_counts = OrderedDict()
for age_id, age in AGES:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'age' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(age=age_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
secondary_counts[age] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, FUNCTION, row_perc=False, sec_cols=4)
def ws_18(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for print
"""
counts = Counter()
rows = NewspaperPerson.objects\
.values('sex', 'age')\
.filter(newspaper_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, NewspaperPerson.sheet_db_table(), 'Print')
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_19(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for broadcast
"""
counts = Counter()
broadcast = ['Television']
for media_type, model in person_models.iteritems():
if media_type in broadcast:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_20(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
functions_count = Counter()
# Get top 5 functions
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
functions_count.update({(r['function']): r['n'] for r in rows})
top_5_function_ids = [id for id, count in sorted(functions_count.items(), key=lambda x: -x[1])[:5]]
top_5_functions = [(id, func) for id, func in FUNCTION if id in top_5_function_ids]
for func_id, function in top_5_functions:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=func_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_21(self, ws):
"""
Cols: Subject Sex
Rows: Victim type
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'victim_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'victim_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.exclude(victim_of=None)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['victim_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, VICTIM_OF, row_perc=False)
def ws_23(self, ws):
"""
Cols: Subject Sex
Rows: Survivor type
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'survivor_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'survivor_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.exclude(survivor_of=None)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['survivor_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SURVIVOR_OF, row_perc=False)
def ws_24(self, ws):
"""
Cols: Subject Sex
Rows: Family Role
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'family_role' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_25(self, ws):
"""
Cols: Journalist Sex, Subject Sex
Rows: Family Role
"""
secondary_counts = OrderedDict()
for sex_id, sex in self.male_female:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'family_role' in model._meta.get_all_field_names():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(**{sheet_name + '__' + journo_name + '__sex':sex_id})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
secondary_counts[sex] = counts
secondary_counts['col_title_def'] = [
'Sex of reporter',
'Sex of news subject']
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, YESNO, row_perc=False, sec_cols=4)
def ws_26(self, ws):
"""
Cols: Subject Sex
Rows: Whether Quoted
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'is_quoted' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['is_quoted']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_27(self, ws):
"""
Cols: Subject Sex
Rows: Photographed
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'is_photograph' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['is_photograph']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, IS_PHOTOGRAPH, row_perc=False)
def ws_28(self, ws):
"""
Cols: Medium
Rows: Region
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
region = model.sheet_name() + '__country_region__region'
rows = model.objects\
.values(region)\
.filter(sex=1)\
.filter(**{region + '__in': self.region_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_29(self, ws):
"""
Cols: Regions
Rows: Scope
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
scope = sheet_name + '__scope'
if 'scope' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, scope)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
counts.update({(region_id, row['scope']): row['n']})
self.tabulate(ws, counts, self.regions, SCOPE, row_perc=False)
def ws_30(self, ws):
"""
Cols: Region
Rows: Major Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, topic)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(region_id, major_topic): row['n']})
self.tabulate(ws, counts, self.regions, MAJOR_TOPICS, row_perc=False)
def ws_31(self, ws):
"""
Cols: Sex of Reporter
Rows: Minor Topics
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_32(self, ws):
"""
Cols: Medium
Rows: Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['topic']): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, TOPICS, row_perc=False)
def ws_34(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
"""
counts = Counter()
for media_type, model in person_models.iteritems():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
# rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of reporter'
self.tabulate(ws, counts, self.male_female, GENDER, row_perc=True, display_cols=self.female)
def ws_35(self, ws):
"""
Cols: Sex of reporter
Rows: Age of reporter
:: Only for television
"""
counts = Counter()
rows = TelevisionJournalist.objects\
.values('sex', 'age')\
.filter(television_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, TelevisionJournalist.sheet_db_table(), 'Television')
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True, display_cols=self.female)
def ws_36(self, ws):
"""
Cols: Sex of Reporter
Rows: Focus: about women
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
about_women = sheet_name + '__about_women'
if 'about_women' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', about_women)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['about_women']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=True)
def ws_38(self, ws):
"""
Cols: Focus: about women
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['about_women'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_39(self, ws):
"""
Cols: Focus: about women
Rows: Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=True)
def ws_40(self, ws):
"""
Cols: Region, Topics
Rows: Focus: about women
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'about_women')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=2, display_cols=self.yes)
def ws_41(self, ws):
"""
Cols: Equality rights raised
Rows: Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=False)
def ws_42(self, ws):
"""
Cols: Region, Equality rights raised
Rows: Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_43(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Cols: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic, equality_rights)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=gender_id)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=4)
def ws_44(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Rows: Region
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(equality_rights, region)\
.filter(sex=gender_id)\
.filter(**{region + '__in':self.region_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
region_id = [id for id, name in self.regions if name == r['region']][0]
counts.update({(r['equality_rights'], region_id): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.regions, row_perc=False, sec_cols=4)
def ws_45(self, ws):
"""
Cols: Sex of news subject
Rows: Region
:: Equality rights raised == Yes
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
region = model.sheet_name() + '__country_region__region'
equality_rights = model.sheet_name() + '__equality_rights'
rows = model.objects\
.values('sex', region)\
.filter(**{region + '__in':self.region_list})\
.filter(**{equality_rights:'Y'})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
region_id = [id for id, name in self.regions if name == r['region']][0]
counts.update({(r['sex'], region_id): r['n']})
self.tabulate(ws, counts, self.male_female, self.regions, row_perc=True)
def ws_46(self, ws):
"""
Cols: Region, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(TOPIC_GROUPS[r['topic']], r['stereotypes']): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True, sec_cols=8)
def ws_47(self, ws):
"""
Cols: Stereotypes
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True)
def ws_48(self, ws):
"""
Cols: Sex of reporter, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
stereotypes = sheet_name + '__stereotypes'
if 'stereotypes' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(stereotypes, topic)\
.filter(sex=gender_id)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=False, sec_cols=8)
def ws_49(self, ws):
"""
Cols: Major Topics
Rows: Region
:: Internet media type only
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country_region__region')\
.filter(country_region__region__in=self.region_list)
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, region_id): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.regions, row_perc=True)
def ws_50(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories shared on Twitter
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(shared_via_twitter='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_51(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories shared on Facebook
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(shared_on_facebook='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_52(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories with reference to gener equality
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(equality_rights='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_53(self, ws):
"""
Cols: Topic
Rows: Country
:: Internet media type only
:: Female reporters only
"""
display_cols = [(id, value) for id, value in GENDER if id==1]
secondary_counts = OrderedDict()
model = sheet_models.get('Internet')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
journo_sex_field = '%s__sex' % model.journalist_field_name()
rows = model.objects\
.values(journo_sex_field, 'country')\
.filter(topic__in=topic_ids)
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
counts.update({(r['sex'], r['country']): r['n'] for r in rows})
major_topic_name = [mt[1] for mt in MAJOR_TOPICS if mt[0] == int(major_topic)][0]
secondary_counts[major_topic_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, display_cols=display_cols, sec_cols=2)
def ws_54(self, ws):
"""
Cols: Major Topic, sex of subject
Rows: Country
:: Internet media type only
"""
secondary_counts = OrderedDict()
model = person_models.get('Internet')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('sex', country_field)\
.filter(**{model.sheet_name() + '__topic__in':topic_ids})
rows = self.apply_weights(rows, model.sheet_db_table(), 'Internet')
counts.update({(r['sex'], r['country']): r['n'] for r in rows})
major_topic_name = [mt[1] for mt in MAJOR_TOPICS if mt[0] == int(major_topic)][0]
secondary_counts[major_topic_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, sec_cols=8)
def ws_55(self, ws):
"""
Cols: Occupation
Rows: Country
:: Show all countries
:: Only female subjects
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'occupation')\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r['occupation'], r['country']): r['n'] for r in rows})
self.tabulate(ws, counts, OCCUPATION, self.countries, row_perc=True)
def ws_56(self, ws):
"""
Cols: Function
Rows: Country
:: Show all countries
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'function')\
.annotate(n=Count('id'))
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r['function'], r['country']): r['n'] for r in rows})
self.tabulate(ws, counts, FUNCTION, self.countries, row_perc=True)
def ws_57(self, ws):
"""
Cols: Sex of subject
Rows: Country, Family role
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['family_role']): row['n'] for row in rows}
# If only captured countries should be displayed use
# if counts.keys():
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_58(self, ws):
"""
Cols: Sex of subject
Rows: Country, is photographed
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['is_photograph']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, IS_PHOTOGRAPH, row_perc=True, sec_row=True, r=r)
r += len(IS_PHOTOGRAPH)
def ws_59(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
:: Internet media only
"""
counts = Counter()
model = person_models.get('Internet')
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of reporter'
self.tabulate(ws, counts, GENDER, GENDER, row_perc=False)
def ws_60(self, ws):
"""
Cols: Sex of subject
Rows: Country, age
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['age']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, AGES, row_perc=True, sec_row=True, r=r)
r += len(AGES)
def ws_61(self, ws):
"""
Cols: Sex of subject
Rows: Country, is_quoted
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['is_quoted']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_62(self, ws):
"""
Cols: Topic
Rows: Country, equality raised
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['equality_rights']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_63(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes challenged
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_64(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_65(self, ws):
"""
Cols: Topic
Rows: Country, tweet or retweet
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'retweet')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['retweet']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, RETWEET, row_perc=False, sec_row=True, r=r)
r += len(RETWEET)
def ws_66(self, ws):
"""
Cols: Topic
Rows: Country, sex of news subject
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = person_models.get('Twitter')
topic_field = '%s__topic' % model.sheet_name()
for code, country in self.countries:
rows = model.objects\
.values(topic_field, 'sex')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Twitter")
counts.update({(row['topic'], row['sex']): row['n'] for row in rows})
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, GENDER, row_perc=True, sec_row=True, r=r)
r += len(GENDER)
def ws_67(self, ws):
"""
Cols: Topic
Rows: Country
:: Only female journalists
:: Show all countries
:: Twitter media type only
"""
counts = Counter()
model = sheet_models.get('Twitter')
rows = model.objects\
.values('topic', 'country')\
.filter(**{model.journalist_field_name() + '__sex':1})
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts.update({(row['topic'], row['country']): row['n'] for row in rows})
self.tabulate(ws, counts, TOPICS, self.countries, row_perc=True, sec_row=False)
def ws_68(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=False, sec_row=True, r=r)
r += len(YESNO)
def ws_69(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_76(self, ws):
"""
Cols: Topic, Stereotypes
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'country')\
.filter(topic=topic_id)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['stereotypes'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, self.countries, row_perc=True, sec_cols=8)
def ws_77(self, ws):
"""
Cols: Topic, Reference to gender equality
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'country')\
.filter(topic=topic_id)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.countries, row_perc=True, sec_cols=4)
def ws_78(self, ws):
"""
Cols: Topic, victim_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'victim_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('victim_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['victim_of'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, VICTIM_OF, self.countries, row_perc=True, sec_cols=18)
def ws_79(self, ws):
"""
Cols: Topic, survivor_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'survivor_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('survivor_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['survivor_of'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, SURVIVOR_OF, self.countries, row_perc=True, sec_cols=18)
# -------------------------------------------------------------------------------
# Helper functions
#
def write_headers(self, ws, title, description):
"""
Write the headers to the worksheet
"""
ws.write(0, 0, title, self.heading)
ws.write(1, 0, description, self.heading)
ws.write(3, 2, self.gmmp_year, self.heading)
def write_col_headings(self, ws, cols, c=2, r=4):
"""
:param ws: worksheet to write to
:param cols: list of `(col_id, col_title)` tuples of column ids and titles
:param r, c: initial position where cursor should start writing to
"""
for col_id, col_title in cols:
ws.write(r, c, clean_title(col_title), self.col_heading)
ws.write(r + 1, c, "N")
ws.write(r + 1, c + 1, "%")
c += 2
def write_primary_row_heading(self, ws, heading, c=0, r=6):
"""
:param ws: worksheet to write to
:param heading: row heading to write
:param r, c: position where heading should be written to
"""
ws.write(r, c, clean_title(heading), self.heading)
def tabulate_secondary_cols(self, ws, secondary_counts, cols, rows, row_perc=False, display_cols=None, sec_cols=4):
"""
:param ws: worksheet to write to
:param secondary_counts: dict in following format:
{'Primary column heading': Count object, ...}
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_cols: amount of cols needed for secondary cols
"""
r, c = 7, 1
# row titles
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
if 'col_title_def' in secondary_counts:
# Write definitions of column heading titles
ws.write(r-3, c-1, secondary_counts['col_title_def'][0], self.sec_col_heading_def)
ws.write(r-2, c-1, secondary_counts['col_title_def'][1], self.col_heading_def)
secondary_counts.pop('col_title_def')
for field, counts in secondary_counts.iteritems():
ws.merge_range(r-3, c, r-3, c+sec_cols-1, clean_title(field), self.sec_col_heading)
self.tabulate(ws, counts, cols, rows, row_perc=row_perc, sec_col=True, display_cols=display_cols, r=7, c=c)
c += sec_cols
def tabulate(self, ws, counts, cols, rows, row_perc=False, sec_col=False, sec_row=False, display_cols=None, c=1, r=6):
""" Emit a table.
:param ws: worksheet to write to
:param dict counts: dict from `(col_id, row_id)` tuples to count for that combination.
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_col: Are wecreating a secondary column title(default: False)
:param sec_row: Are we creating a secondary row title(default: False)
:param display_cols: Optional if only a subset of columns should be displayed e.g. only female
:param r, c: initial position where cursor should start writing to
"""
if row_perc:
# we'll need percentage by rows
row_totals = {}
for row_id, row_title in rows:
row_totals[row_id] = sum(counts.get((col_id, row_id), 0) for col_id, _ in cols) # noqa
# row titles
if not sec_col:
# Else already written
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
# if only filtered results should be shown
# e.g. only print female columns
if display_cols:
cols = display_cols
if 'col_title_def' in counts and not sec_row:
ws.write(r - 2, c-1, counts['col_title_def'], self.col_heading_def)
counts.pop('col_title_def')
# values, written by column
for col_id, col_heading in cols:
# column title
if not sec_row:
ws.merge_range(r-2, c, r-2, c+1, clean_title(col_heading), self.col_heading)
ws.write(r - 1, c, "N", self.label)
ws.write(r - 1, c + 1, "%", self.label)
if not row_perc:
# column totals
# Confirm: Perc of col total or matrix total?
# total = sum(counts.itervalues())
total = sum(counts.get((col_id, row_id), 0) for row_id, _ in rows)
# row values for this column
for i, row in enumerate(rows):
row_id, row_title = row
if row_perc:
# row totals
total = row_totals[row_id]
n = counts.get((col_id, row_id), 0)
ws.write(r + i, c, n, self.N)
ws.write(r + i, c + 1, p(n, total), self.P)
c += 2
Make small fixes
# Python
import StringIO
from collections import Counter, OrderedDict
# Django
from django.core import urlresolvers
from django_countries import countries
from django.db import connection
from django.db.models import F, Count, Sum, FieldDoesNotExist
from django.contrib.sites.shortcuts import get_current_site
# 3rd Party
import xlsxwriter
# Project
from forms.models import (
NewspaperSheet, NewspaperPerson, TelevisionJournalist,
person_models, sheet_models, journalist_models)
from forms.modelutils import (TOPICS, GENDER, SPACE, OCCUPATION, FUNCTION, SCOPE,
YESNO, AGES, SOURCE, VICTIM_OF, SURVIVOR_OF, IS_PHOTOGRAPH, AGREE_DISAGREE,
RETWEET, TV_ROLE, MEDIA_TYPES,
CountryRegion)
from report_details import WS_INFO, REGION_COUNTRY_MAP, MAJOR_TOPICS, TOPIC_GROUPS, GROUP_TOPICS_MAP, FORMATS
def has_field(model, fld):
try:
model._meta.get_field(fld)
return True
except FieldDoesNotExist:
return False
def p(n, d):
""" Helper to calculate the percentage of n / d,
returning 0 if d == 0.
"""
if d == 0:
return 0.0
return float(n) / d
def get_regions():
"""
Return a (id, region_name) list for all regions
"""
country_regions = CountryRegion.objects\
.values('region')\
.exclude(region='Unmapped')
regions = set(item['region'] for item in country_regions)
return [(i, region) for i, region in enumerate(regions)]
def get_countries(selected=None):
"""
Return a (code, country) list for countries captured.
"""
captured_country_codes = set()
for model in sheet_models.itervalues():
rows = model.objects.values('country')
captured_country_codes.update([r['country'] for r in rows])
return [(code, name) for code, name in list(countries) if code in captured_country_codes]
def get_region_countries(region):
"""
Return a (code, country) list for a region.
"""
if region == 'ALL':
return get_countries()
else:
country_codes = REGION_COUNTRY_MAP[region]
return [(code, name) for code, name in list(countries) if code in country_codes]
def get_country_region(country):
"""
Return a (id, region_name) list to which a country belongs.
"""
if country == 'ALL':
return get_regions()
else:
return [(0, [k for k, v in REGION_COUNTRY_MAP.items() if country in v][0])]
def clean_title(text):
"""
Return the string passed in stripped of its numbers and parentheses
"""
if text != "Congo (the Democratic Republic of the)":
return text[text.find(')')+1:].lstrip()
return text
class XLSXDataExportBuilder():
def __init__(self, request):
self.domain = "http://%s" % get_current_site(request).domain
self.sheet_exclude_fields = ['monitor', 'url_and_multimedia', 'time_accessed', 'country_region']
self.person_exclude_fields = []
self.journalist_exclude_fields =[]
self.sheet_fields_with_id = ['topic', 'scope', 'person_secondary', 'inequality_women', 'stereotypes']
self.person_fields_with_id = ['sex', 'age', 'occupation', 'function', 'survivor_of', 'victim_of']
self.journalist_fields_with_id = ['sex', 'age']
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
for model in sheet_models.itervalues():
self.create_sheet_export(model, workbook)
for model in person_models.itervalues():
self.create_person_export(model, workbook)
for model in journalist_models.itervalues():
self.create_journalist_export(model, workbook)
workbook.close()
output.seek(0)
return output.read()
def create_sheet_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all()
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.sheet_fields_with_id)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_sheet_row(obj, ws, row+y, col, fields, self.sheet_fields_with_id)
def create_person_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.person_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.person_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_person_row(obj, ws, row+y, col, fields, self.person_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def create_journalist_export(self, model, wb):
ws = wb.add_worksheet(model._meta.object_name)
obj_list = model.objects.all().prefetch_related(model.sheet_name())
row, col = 0, 0
fields = [field for field in model._meta.fields if not field.name in self.journalist_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, fields, self.journalist_fields_with_id)
sheet_model = model._meta.get_field(model.sheet_name()).rel.to
sheet_fields = [field for field in sheet_model._meta.fields if not field.name in self.sheet_exclude_fields]
ws, col = self.write_ws_titles(ws, row, col, sheet_fields, self.sheet_fields_with_id, append_sheet=True)
row += 1
col = 0
for y, obj in enumerate(obj_list):
col = 0
ws, col = self.write_journalist_row(obj, ws, row+y, col, fields, self.journalist_fields_with_id)
col += 1
sheet_obj = getattr(obj, model.sheet_name())
ws, col = self.write_sheet_row(sheet_obj, ws, row+y, col, sheet_fields, self.sheet_fields_with_id)
def write_ws_titles(self, ws, row, col, fields, fields_with_id, append_sheet=False):
"""
Writes the column titles to the worksheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
:param append_sheet: Boolean specifying whether the related sheet object
needs to be appended to the row.
"""
if not append_sheet:
for field in fields:
ws.write(row, col, unicode(field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode(field.name+"_id"))
col += 1
ws.write(row, col, unicode('edit_url'))
col += 1
else:
for field in fields:
ws.write(row, col, unicode("sheet_" + field.name))
col += 1
if field.name in fields_with_id:
ws.write(row, col, unicode("sheet_" + field.name + "_id"))
col += 1
ws.write(row, col, unicode('sheet_edit_url'))
col += 1
return ws, col
def write_sheet_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Sheet models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'country':
ws.write(row, col, getattr(obj, field.name).code)
elif field.name == 'topic':
ws.write(row, col, unicode(TOPICS[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, TOPICS[getattr(obj, field.name)-1][0])
elif field.name == 'scope':
ws.write(row, col, unicode(SCOPE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, SCOPE[getattr(obj, field.name)-1][0])
elif field.name == 'person_secondary':
ws.write(row, col, unicode(SOURCE[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SOURCE[getattr(obj, field.name)][0])
elif field.name == 'inequality_women':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'stereotypes':
ws.write(row, col, unicode(AGREE_DISAGREE[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, AGREE_DISAGREE[getattr(obj, field.name)-1][0])
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
else:
try:
ws.write(row, col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row, col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
obj._meta.app_label,
obj._meta.model_name),
args=(obj.id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_person_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Person models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
# Certain fields are 1-indexed
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age':
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == 'occupation':
ws.write(row, col, unicode(OCCUPATION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, OCCUPATION[getattr(obj, field.name)][0])
elif field.name == 'function':
ws.write(row, col, unicode(FUNCTION[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, FUNCTION[getattr(obj, field.name)][0])
elif field.name == 'victim_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(VICTIM_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, VICTIM_OF[getattr(obj, field.name)][0])
elif field.name == 'survivor_of' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(SURVIVOR_OF[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, SURVIVOR_OF[getattr(obj, field.name)][0])
elif field.name == 'is_photograph':
ws.write(row, col, unicode(IS_PHOTOGRAPH[getattr(obj, field.name)-1][1]))
elif field.name == 'space':
ws.write(row, col, unicode(SPACE[getattr(obj, field.name)-1][1]))
elif field.name == 'retweet':
ws.write(row, col, unicode(RETWEET[getattr(obj, field.name)-1][1]))
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in self.person_fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
def write_journalist_row(self, obj, ws, row, col, fields, fields_with_id):
"""
Writes a row of data of Journalist models to the worksheet
:param obj: Reference to the model instance which is being written to the sheet_fields_with_id
:param ws: Reference to the current worksheet
:param row, col: y,x postion of the cursor
:param fields: list of fields of the model which need to be written to the sheet_fields_with_id
:param fields_with_id: fields which need to be written over two columns: id + name
"""
for field in fields:
if field.name == 'sex':
ws.write(row, col, unicode(GENDER[getattr(obj, field.name)-1][1]))
col += 1
ws.write(row, col, GENDER[getattr(obj, field.name)-1][0])
elif field.name == 'age' and not getattr(obj, field.name) == None:
ws.write(row, col, unicode(AGES[getattr(obj, field.name)][1]))
col += 1
ws.write(row, col, AGES[getattr(obj, field.name)][0])
elif field.name == obj.sheet_name():
ws.write(row, col, getattr(obj, field.name).id)
# Get the parent model and id for building the edit link
parent_model = field.related.parent_model
parent_id = getattr(obj, field.name).id
else:
try:
ws.write(row,col, unicode(getattr(obj, field.name)))
if field.name in fields_with_id:
col += 1
except UnicodeEncodeError:
ws.write(row,col, unicode(getattr(obj, field.name).encode('ascii', 'replace')))
col += 1
# Write link to end of row
change_url = urlresolvers.reverse(
'admin:%s_%s_change' % (
parent_model._meta.app_label,
parent_model._meta.model_name),
args=(parent_id,))
ws.write_url(row, col, "%s%s" % (self.domain, change_url))
return ws, col
class XLSXReportBuilder:
def __init__(self, form):
from reports.views import CountryForm, RegionForm
self.form = form
if isinstance(form, CountryForm):
self.countries = form.filter_countries()
self.regions = get_country_region(form.cleaned_data['country'])
self.report_type = 'country'
elif isinstance(form, RegionForm):
region = [name for i, name in form.REGIONS if str(i) == form.cleaned_data['region']][0]
self.countries = get_region_countries(region)
self.regions = [(0, region)]
self.report_type = 'region'
else:
self.countries = get_countries()
self.regions = get_regions()
self.report_type = 'global'
self.country_list = [code for code, name in self.countries]
self.region_list = [name for id, name in self.regions]
# Various gender utilities
self.male_female = [(id, value) for id, value in GENDER if id in [1, 2]]
self.male_female_ids = [id for id, value in self.male_female]
self.female = [(id, value) for id, value in GENDER if id == 1]
self.yes = [(id, value) for id, value in YESNO if id == 'Y']
self.gmmp_year = '2015'
def build(self):
"""
Generate an Excel spreadsheet and return it as a string.
"""
output = StringIO.StringIO()
workbook = xlsxwriter.Workbook(output)
# setup formats
self.heading = workbook.add_format(FORMATS['heading'])
self.col_heading = workbook.add_format(FORMATS['col_heading'])
self.col_heading_def = workbook.add_format(FORMATS['col_heading_def'])
self.sec_col_heading = workbook.add_format(FORMATS['sec_col_heading'])
self.sec_col_heading_def = workbook.add_format(FORMATS['sec_col_heading_def'])
self.label = workbook.add_format(FORMATS['label'])
self.N = workbook.add_format(FORMATS['N'])
self.P = workbook.add_format(FORMATS['P'])
# Use the following for specifying which reports to create during dev
# test_functions = [
# 'ws_01', 'ws_02', 'ws_04', 'ws_05', 'ws_06', 'ws_07', 'ws_08', 'ws_09', 'ws_10',
# 'ws_11', 'ws_12', 'ws_13', 'ws_14', 'ws_15', 'ws_16', 'ws_17', 'ws_18', 'ws_19', 'ws_20',
# 'ws_21', 'ws_23', 'ws_24', 'ws_25', 'ws_26', 'ws_27', 'ws_28', 'ws_29', 'ws_30',
# 'ws_31', 'ws_32', 'ws_34', 'ws_35', 'ws_36', 'ws_38', 'ws_39', 'ws_40',
# 'ws_41', 'ws_42', 'ws_43', 'ws_44', 'ws_45', 'ws_46', 'ws_47', 'ws_48',
# 'ws_49', 'ws_50', 'ws_51', 'ws_52', 'ws_53', 'ws_54', 'ws_55', 'ws_56',, 'ws_57', 'ws_58', 'ws_59', 'ws_60',
# 'ws_61', 'ws_62', 'ws_63', 'ws_64', 'ws_65', 'ws_66', 'ws_67', 'ws_68', 'ws_69', 'ws_70',
# 'ws_76', 'ws_77', 'ws_78', 'ws_79']
test_functions = ['ws_05']
sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
for function in test_functions:
if self.report_type in sheet_info[function]['reports']:
ws = workbook.add_worksheet(sheet_info[function]['name'])
self.write_headers(ws, sheet_info[function]['title'], sheet_info[function]['desc'])
getattr(self, function)(ws)
# -------------------------------------------------------------------
# To ensure ordered worksheets
# sheet_info = OrderedDict(sorted(WS_INFO.items(), key=lambda t: t[0]))
# for ws_num, ws_info in sheet_info.iteritems():
# if self.report_type in ws_info['reports']:
# ws = workbook.add_worksheet(ws_info['name'])
# self.write_headers(ws, ws_info['title'], ws_info['desc'])
# getattr(self, ws_num)(ws)
workbook.close()
output.seek(0)
return output.read()
def dictfetchall(self, cursor):
"""
Returns all rows from a cursor as a dict
"""
desc = cursor.description
return [
dict(zip([col[0] for col in desc], row))
for row in cursor.fetchall()
]
def apply_weights(self, rows, db_table, media_type):
"""
param rows: Queryset to apply the weights to
param db_table: name of relevant sheet table
param: media_type: media type to weigh by
"""
query = rows.extra(
tables=['reports_weights'],
where=[
'reports_weights.country = %s.country' % (db_table),
'reports_weights.media_type = \'%s\'' % (media_type),
]).annotate()
raw_query, params = query.query.sql_with_params()
raw_query = raw_query.replace('SELECT', 'SELECT cast(round(SUM(reports_weights.weight)) as int) AS "n", ')
cursor = connection.cursor()
cursor.execute(raw_query, params)
return self.dictfetchall(cursor)
def ws_01(self, ws):
"""
Cols: Media Type
Rows: Region
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country_region__region')\
.filter(country_region__region__in=self.region_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for row in rows:
if row['region'] is not None:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [region[0] for region in self.regions if region[1] == row['region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_02(self, ws):
"""
Cols: Media Type
Rows: Region, Country
"""
r = 6
self.write_col_headings(ws, MEDIA_TYPES)
counts = Counter()
for region_id, region in self.regions:
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('country')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for row in rows:
if row['country'] is not None:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['country']): row['n']})
self.write_primary_row_heading(ws, region, r=r)
region_countries = [(code, country) for code, country in self.countries if code in REGION_COUNTRY_MAP[region]]
self.tabulate(ws, counts, MEDIA_TYPES, region_countries, row_perc=True, sec_row=True, r=r)
r += len(region_countries)
def ws_04(self, ws):
"""
Cols: Region, Media type
Rows: Major Topic
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('topic')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
major_topic = TOPIC_GROUPS[r['topic']]
counts.update({(media_id, major_topic): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, MEDIA_TYPES, MAJOR_TOPICS, row_perc=False, sec_cols=10)
def ws_05(self, ws):
"""
Cols: Subject sex
Rows: Major Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})
# .filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, GENDER, MAJOR_TOPICS, row_perc=True)
def ws_06(self, ws):
"""
Cols: Region, Subject sex: female only
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in person_models.iteritems():
topic_field = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic_field)\
.filter(**{model.sheet_name() + '__country_region__region':region})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['sex'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, MAJOR_TOPICS, row_perc=True, sec_cols=2, display_cols=self.female)
def ws_07(self, ws):
"""
Cols: Media Type
Rows: Subject Sex
"""
counts = Counter()
for media_type, model in person_models.iteritems():
rows = model.objects\
.values('sex')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
# Get media id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, r['sex']): r['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.male_female, row_perc=False)
def ws_08(self, ws):
"""
Cols: Subject Sex
Rows: Scope
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'scope' in model.sheet_field().rel.to._meta.get_all_field_names():
scope = '%s__scope' % model.sheet_name()
rows = model.objects\
.values('sex', scope)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['scope']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SCOPE, row_perc=True, display_cols=self.female)
def ws_09(self, ws):
"""
Cols: Subject Sex
Rows: Topic
"""
counts = Counter()
for media_type, model in person_models.iteritems():
topic = '%s__topic' % model.sheet_name()
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_10(self, ws):
"""
Cols: Space
Rows: Minor Topics
:: Newspaper Sheets only
"""
# Calculate row values for column
counts = Counter()
for media_type, model in sheet_models.iteritems():
if media_type == 'Print':
rows = model.objects\
.values('space', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['space'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, SPACE, MAJOR_TOPICS, row_perc=False)
def ws_11(self, ws):
"""
Cols: Equality Rights
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_12(self, ws):
"""
Cols: Region, Equality Rights
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region_name in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
# Some models has no equality rights field
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country_region__region=region_name)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[region_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_13(self, ws):
"""
Cols: Journalist Sex, Equality Rights
Rows: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
topic = '%s__topic' % model.sheet_name()
equality_rights = '%s__equality_rights' % model.sheet_name()
rows = model.objects\
.values(equality_rights, topic)\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex=gender_id)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['equality_rights'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, MAJOR_TOPICS, row_perc=True, sec_cols=4)
def ws_14(self, ws):
"""
Cols: Sex
Rows: Occupation
"""
counts = Counter()
for media_type, model in person_models.iteritems():
# some Person models don't have an occupation field
if 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, OCCUPATION, row_perc=True, display_cols=self.female)
def ws_15(self, ws):
"""
Cols: Sex
Rows: Function
"""
counts = Counter()
for media_type, model in person_models.iteritems():
# some Person models don't have a function field
if 'function' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in': self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, FUNCTION, row_perc=True, display_cols=self.female)
def ws_16(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
for function_id, function in FUNCTION:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=function_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_17(self, ws):
"""
Cols: Age, Sex of Subject
Rows: Function
"""
secondary_counts = OrderedDict()
for age_id, age in AGES:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'age' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(age=age_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['function']): r['n'] for r in rows})
secondary_counts[age] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, FUNCTION, row_perc=False, sec_cols=4)
def ws_18(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for print
"""
counts = Counter()
rows = NewspaperPerson.objects\
.values('sex', 'age')\
.filter(newspaper_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, NewspaperPerson.sheet_db_table(), 'Print')
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_19(self, ws):
"""
Cols: Sex
Rows: Age
:: Only for broadcast
"""
counts = Counter()
broadcast = ['Television']
for media_type, model in person_models.iteritems():
if media_type in broadcast:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True)
def ws_20(self, ws):
"""
Cols: Function, Sex
Rows: Occupation
"""
secondary_counts = OrderedDict()
functions_count = Counter()
# Get top 5 functions
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('function')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
functions_count.update({(r['function']): r['n'] for r in rows})
top_5_function_ids = [id for id, count in sorted(functions_count.items(), key=lambda x: -x[1])[:5]]
top_5_functions = [(id, func) for id, func in FUNCTION if id in top_5_function_ids]
for func_id, function in top_5_functions:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'function' in model._meta.get_all_field_names() and 'occupation' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'occupation')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(function=func_id)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['occupation']): r['n'] for r in rows})
secondary_counts[function] = counts
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, OCCUPATION, row_perc=False, sec_cols=4)
def ws_21(self, ws):
"""
Cols: Subject Sex
Rows: Victim type
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'victim_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'victim_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.exclude(victim_of=None)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['victim_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, VICTIM_OF, row_perc=False)
def ws_23(self, ws):
"""
Cols: Subject Sex
Rows: Survivor type
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'survivor_of' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'survivor_of')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.exclude(survivor_of=None)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['survivor_of']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, SURVIVOR_OF, row_perc=False)
def ws_24(self, ws):
"""
Cols: Subject Sex
Rows: Family Role
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'family_role' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_25(self, ws):
"""
Cols: Journalist Sex, Subject Sex
Rows: Family Role
"""
secondary_counts = OrderedDict()
for sex_id, sex in self.male_female:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'family_role' in model._meta.get_all_field_names():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(**{sheet_name + '__' + journo_name + '__sex':sex_id})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['family_role']): r['n'] for r in rows})
secondary_counts[sex] = counts
secondary_counts['col_title_def'] = [
'Sex of reporter',
'Sex of news subject']
self.tabulate_secondary_cols(ws, secondary_counts, self.male_female, YESNO, row_perc=False, sec_cols=4)
def ws_26(self, ws):
"""
Cols: Subject Sex
Rows: Whether Quoted
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'is_quoted' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['is_quoted']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_27(self, ws):
"""
Cols: Subject Sex
Rows: Photographed
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'is_photograph' in model._meta.get_all_field_names():
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['is_photograph']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, IS_PHOTOGRAPH, row_perc=False)
def ws_28(self, ws):
"""
Cols: Medium
Rows: Region
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
region = model.sheet_name() + '__country_region__region'
rows = model.objects\
.values(region)\
.filter(sex=1)\
.filter(**{region + '__in': self.region_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
# Get media and region id's to assign to counts
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
counts.update({(media_id, region_id): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, self.regions, row_perc=True)
def ws_29(self, ws):
"""
Cols: Regions
Rows: Scope
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
scope = sheet_name + '__scope'
if 'scope' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, scope)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
counts.update({(region_id, row['scope']): row['n']})
self.tabulate(ws, counts, self.regions, SCOPE, row_perc=False)
def ws_30(self, ws):
"""
Cols: Region
Rows: Major Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(region, topic)\
.filter(**{region + '__in': self.region_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(region_id, major_topic): row['n']})
self.tabulate(ws, counts, self.regions, MAJOR_TOPICS, row_perc=False)
def ws_31(self, ws):
"""
Cols: Sex of Reporter
Rows: Minor Topics
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, TOPICS, row_perc=True, display_cols=self.female)
def ws_32(self, ws):
"""
Cols: Medium
Rows: Topics
:: Female reporters only
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
if 'topic' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for row in rows:
media_id = [media[0] for media in MEDIA_TYPES if media[1] == media_type][0]
counts.update({(media_id, row['topic']): row['n']})
self.tabulate(ws, counts, MEDIA_TYPES, TOPICS, row_perc=False)
def ws_34(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
"""
counts = Counter()
for media_type, model in person_models.iteritems():
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)\
.annotate(n=Count('id'))
# rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of reporter'
self.tabulate(ws, counts, self.male_female, GENDER, row_perc=True, display_cols=self.female)
def ws_35(self, ws):
"""
Cols: Sex of reporter
Rows: Age of reporter
:: Only for television
"""
counts = Counter()
rows = TelevisionJournalist.objects\
.values('sex', 'age')\
.filter(television_sheet__country__in=self.country_list)\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, TelevisionJournalist.sheet_db_table(), 'Television')
counts.update({(r['sex'], r['age']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, AGES, row_perc=True, display_cols=self.female)
def ws_36(self, ws):
"""
Cols: Sex of Reporter
Rows: Focus: about women
"""
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
about_women = sheet_name + '__about_women'
if 'about_women' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values('sex', about_women)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex__in=self.male_female_ids)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['sex'], r['about_women']): r['n'] for r in rows})
self.tabulate(ws, counts, self.male_female, YESNO, row_perc=False)
def ws_38(self, ws):
"""
Cols: Focus: about women
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['about_women'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, YESNO, MAJOR_TOPICS, row_perc=True)
def ws_39(self, ws):
"""
Cols: Focus: about women
Rows: Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('about_women', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=True)
def ws_40(self, ws):
"""
Cols: Region, Topics
Rows: Focus: about women
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'about_women' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'about_women')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['about_women'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=False, sec_cols=2, display_cols=self.yes)
def ws_41(self, ws):
"""
Cols: Equality rights raised
Rows: Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
self.tabulate(ws, counts, YESNO, TOPICS, row_perc=False)
def ws_42(self, ws):
"""
Cols: Region, Equality rights raised
Rows: Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=True, sec_cols=4)
def ws_43(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Cols: Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(topic, equality_rights)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})\
.filter(sex=gender_id)
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['equality_rights'], r['topic']): r['n'] for r in rows})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, TOPICS, row_perc=True, sec_cols=4)
def ws_44(self, ws):
"""
Cols: Sex of reporter, Equality rights raised
Rows: Region
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
region = sheet_name + '__country_region__region'
equality_rights = sheet_name + '__equality_rights'
if 'equality_rights' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(equality_rights, region)\
.filter(sex=gender_id)\
.filter(**{region + '__in':self.region_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
region_id = [id for id, name in self.regions if name == r['region']][0]
counts.update({(r['equality_rights'], region_id): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.regions, row_perc=True, sec_cols=4)
def ws_45(self, ws):
"""
Cols: Sex of news subject
Rows: Region
:: Equality rights raised == Yes
"""
counts = Counter()
for media_type, model in person_models.iteritems():
if 'equality_rights' in model.sheet_field().rel.to._meta.get_all_field_names():
region = model.sheet_name() + '__country_region__region'
equality_rights = model.sheet_name() + '__equality_rights'
rows = model.objects\
.values('sex', region)\
.filter(**{region + '__in':self.region_list})\
.filter(**{equality_rights:'Y'})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
region_id = [id for id, name in self.regions if name == r['region']][0]
counts.update({(r['sex'], region_id): r['n']})
self.tabulate(ws, counts, self.male_female, self.regions, row_perc=True)
def ws_46(self, ws):
"""
Cols: Region, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for region_id, region in self.regions:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country_region__region=region)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(TOPIC_GROUPS[r['topic']], r['stereotypes']): r['n']})
secondary_counts[region] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True, sec_cols=8)
def ws_47(self, ws):
"""
Cols: Stereotypes
Rows: Major Topics
"""
counts = Counter()
for media_type, model in sheet_models.iteritems():
rows = model.objects\
.values('stereotypes', 'topic')\
.filter(country__in=self.country_list)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
self.tabulate(ws, counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=True)
def ws_48(self, ws):
"""
Cols: Sex of reporter, Stereotypes
Rows: Major Topics
"""
secondary_counts = OrderedDict()
for gender_id, gender in self.male_female:
counts = Counter()
for media_type, model in journalist_models.iteritems():
sheet_name = model.sheet_name()
topic = sheet_name + '__topic'
stereotypes = sheet_name + '__stereotypes'
if 'stereotypes' in model._meta.get_field(sheet_name).rel.to._meta.get_all_field_names():
rows = model.objects\
.values(stereotypes, topic)\
.filter(sex=gender_id)\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
for r in rows:
counts.update({(r['stereotypes'], TOPIC_GROUPS[r['topic']]): r['n']})
secondary_counts[gender] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, MAJOR_TOPICS, row_perc=False, sec_cols=8)
def ws_49(self, ws):
"""
Cols: Major Topics
Rows: Region
:: Internet media type only
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country_region__region')\
.filter(country_region__region__in=self.region_list)
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
region_id = [r[0] for r in self.regions if r[1] == row['region']][0]
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, region_id): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.regions, row_perc=True)
def ws_50(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories shared on Twitter
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(shared_via_twitter='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_51(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories shared on Facebook
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(shared_on_facebook='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_52(self, ws):
"""
Cols: Major Topics
Rows: Country
:: Internet media type only
:: Only stories with reference to gener equality
"""
counts = Counter()
model = sheet_models.get('Internet')
rows = model.objects\
.values('topic', 'country')\
.filter(country__in=self.country_list)\
.filter(equality_rights='Y')
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
for row in rows:
major_topic = TOPIC_GROUPS[row['topic']]
counts.update({(major_topic, row['country']): row['n']})
self.tabulate(ws, counts, MAJOR_TOPICS, self.countries, row_perc=True)
def ws_53(self, ws):
"""
Cols: Topic
Rows: Country
:: Internet media type only
:: Female reporters only
"""
display_cols = [(id, value) for id, value in GENDER if id==1]
secondary_counts = OrderedDict()
model = sheet_models.get('Internet')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
journo_sex_field = '%s__sex' % model.journalist_field_name()
rows = model.objects\
.values(journo_sex_field, 'country')\
.filter(topic__in=topic_ids)
rows = self.apply_weights(rows, model._meta.db_table, 'Internet')
counts.update({(r['sex'], r['country']): r['n'] for r in rows})
major_topic_name = [mt[1] for mt in MAJOR_TOPICS if mt[0] == int(major_topic)][0]
secondary_counts[major_topic_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, display_cols=display_cols, sec_cols=2)
def ws_54(self, ws):
"""
Cols: Major Topic, sex of subject
Rows: Country
:: Internet media type only
"""
secondary_counts = OrderedDict()
model = person_models.get('Internet')
for major_topic, topic_ids in GROUP_TOPICS_MAP.iteritems():
counts = Counter()
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('sex', country_field)\
.filter(**{model.sheet_name() + '__topic__in':topic_ids})
rows = self.apply_weights(rows, model.sheet_db_table(), 'Internet')
counts.update({(r['sex'], r['country']): r['n'] for r in rows})
major_topic_name = [mt[1] for mt in MAJOR_TOPICS if mt[0] == int(major_topic)][0]
secondary_counts[major_topic_name] = counts
self.tabulate_secondary_cols(ws, secondary_counts, GENDER, self.countries, row_perc=True, sec_cols=8)
def ws_55(self, ws):
"""
Cols: Occupation
Rows: Country
:: Show all countries
:: Only female subjects
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'occupation')\
.filter(sex=1)
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r['occupation'], r['country']): r['n'] for r in rows})
self.tabulate(ws, counts, OCCUPATION, self.countries, row_perc=True)
def ws_56(self, ws):
"""
Cols: Function
Rows: Country
:: Show all countries
:: Internet media type only
"""
counts = Counter()
model = person_models.get('Internet')
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values(country_field, 'function')\
.annotate(n=Count('id'))
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r['function'], r['country']): r['n'] for r in rows})
self.tabulate(ws, counts, FUNCTION, self.countries, row_perc=True)
def ws_57(self, ws):
"""
Cols: Sex of subject
Rows: Country, Family role
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'family_role')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['family_role']): row['n'] for row in rows}
# If only captured countries should be displayed use
# if counts.keys():
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_58(self, ws):
"""
Cols: Sex of subject
Rows: Country, is photographed
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_photograph')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['is_photograph']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, IS_PHOTOGRAPH, row_perc=True, sec_row=True, r=r)
r += len(IS_PHOTOGRAPH)
def ws_59(self, ws):
"""
Cols: Sex of reporter
Rows: Sex of subject
:: Internet media only
"""
counts = Counter()
model = person_models.get('Internet')
sheet_name = model.sheet_name()
journo_name = model._meta.get_field(model.sheet_name()).rel.to.journalist_field_name()
journo_sex = sheet_name + '__' + journo_name + '__sex'
rows = model.objects\
.values(journo_sex, 'sex')\
.filter(**{model.sheet_name() + '__country__in':self.country_list})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts.update({(r[journo_sex], r['sex']): r['n'] for r in rows})
counts['col_title_def'] = 'Sex of reporter'
self.tabulate(ws, counts, GENDER, GENDER, row_perc=False)
def ws_60(self, ws):
"""
Cols: Sex of subject
Rows: Country, age
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'age')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['age']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, AGES, row_perc=True, sec_row=True, r=r)
r += len(AGES)
def ws_61(self, ws):
"""
Cols: Sex of subject
Rows: Country, is_quoted
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, GENDER)
counts = Counter()
model = person_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('sex', 'is_quoted')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Internet")
counts = {(row['sex'], row['is_quoted']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, GENDER, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_62(self, ws):
"""
Cols: Topic
Rows: Country, equality raised
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'equality_rights')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['equality_rights']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_63(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes challenged
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_64(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Internet media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Internet')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Internet")
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=True, sec_row=True, r=r)
r += len(YESNO)
def ws_65(self, ws):
"""
Cols: Topic
Rows: Country, tweet or retweet
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'retweet')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['retweet']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, RETWEET, row_perc=False, sec_row=True, r=r)
r += len(RETWEET)
def ws_66(self, ws):
"""
Cols: Topic
Rows: Country, sex of news subject
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = person_models.get('Twitter')
topic_field = '%s__topic' % model.sheet_name()
for code, country in self.countries:
rows = model.objects\
.values(topic_field, 'sex')\
.filter(**{model.sheet_name() + '__country':code})
rows = self.apply_weights(rows, model.sheet_db_table(), "Twitter")
counts.update({(row['topic'], row['sex']): row['n'] for row in rows})
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, GENDER, row_perc=True, sec_row=True, r=r)
r += len(GENDER)
def ws_67(self, ws):
"""
Cols: Topic
Rows: Country
:: Only female journalists
:: Show all countries
:: Twitter media type only
"""
counts = Counter()
model = sheet_models.get('Twitter')
rows = model.objects\
.values('topic', 'country')\
.filter(**{model.journalist_field_name() + '__sex':1})
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts.update({(row['topic'], row['country']): row['n'] for row in rows})
self.tabulate(ws, counts, TOPICS, self.countries, row_perc=True, sec_row=False)
def ws_68(self, ws):
"""
Cols: Topic
Rows: Country, about women
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'about_women')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['about_women']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, YESNO, row_perc=False, sec_row=True, r=r)
r += len(YESNO)
def ws_69(self, ws):
"""
Cols: Topic
Rows: Country, stereotypes
:: Show all countries
:: Twitter media type only
"""
r = 6
self.write_col_headings(ws, TOPICS)
counts = Counter()
model = sheet_models.get('Twitter')
for code, country in self.countries:
rows = model.objects\
.values('topic', 'stereotypes')\
.filter(country=code)
rows = self.apply_weights(rows, model._meta.db_table, "Twitter")
counts = {(row['topic'], row['stereotypes']): row['n'] for row in rows}
self.write_primary_row_heading(ws, country, r=r)
self.tabulate(ws, counts, TOPICS, AGREE_DISAGREE, row_perc=True, sec_row=True, r=r)
r += len(AGREE_DISAGREE)
def ws_76(self, ws):
"""
Cols: Topic, Stereotypes
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'stereotypes' in model._meta.get_all_field_names():
rows = model.objects\
.values('stereotypes', 'country')\
.filter(topic=topic_id)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['stereotypes'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, AGREE_DISAGREE, self.countries, row_perc=True, sec_cols=8)
def ws_77(self, ws):
"""
Cols: Topic, Reference to gender equality
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in sheet_models.iteritems():
if 'equality_rights' in model._meta.get_all_field_names():
rows = model.objects\
.values('equality_rights', 'country')\
.filter(topic=topic_id)
rows = self.apply_weights(rows, model._meta.db_table, media_type)
counts.update({(r['equality_rights'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, YESNO, self.countries, row_perc=True, sec_cols=4)
def ws_78(self, ws):
"""
Cols: Topic, victim_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'victim_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('victim_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['victim_of'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, VICTIM_OF, self.countries, row_perc=True, sec_cols=18)
def ws_79(self, ws):
"""
Cols: Topic, survivor_of
Rows: Country
"""
secondary_counts = OrderedDict()
for topic_id, topic in TOPICS:
counts = Counter()
for media_type, model in person_models.iteritems():
if 'survivor_of' in model._meta.get_all_field_names():
country_field = '%s__country' % model.sheet_name()
rows = model.objects\
.values('survivor_of', country_field)\
.filter(**{model.sheet_name() + '__topic':topic_id})
rows = self.apply_weights(rows, model.sheet_db_table(), media_type)
counts.update({(r['survivor_of'], r['country']): r['n'] for r in rows})
secondary_counts[topic] = counts
self.tabulate_secondary_cols(ws, secondary_counts, SURVIVOR_OF, self.countries, row_perc=True, sec_cols=18)
# -------------------------------------------------------------------------------
# Helper functions
#
def write_headers(self, ws, title, description):
"""
Write the headers to the worksheet
"""
ws.write(0, 0, title, self.heading)
ws.write(1, 0, description, self.heading)
ws.write(3, 2, self.gmmp_year, self.heading)
def write_col_headings(self, ws, cols, c=2, r=4):
"""
:param ws: worksheet to write to
:param cols: list of `(col_id, col_title)` tuples of column ids and titles
:param r, c: initial position where cursor should start writing to
"""
for col_id, col_title in cols:
ws.write(r, c, clean_title(col_title), self.col_heading)
ws.write(r + 1, c, "N")
ws.write(r + 1, c + 1, "%")
c += 2
def write_primary_row_heading(self, ws, heading, c=0, r=6):
"""
:param ws: worksheet to write to
:param heading: row heading to write
:param r, c: position where heading should be written to
"""
ws.write(r, c, clean_title(heading), self.heading)
def tabulate_secondary_cols(self, ws, secondary_counts, cols, rows, row_perc=False, display_cols=None, sec_cols=4):
"""
:param ws: worksheet to write to
:param secondary_counts: dict in following format:
{'Primary column heading': Count object, ...}
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_cols: amount of cols needed for secondary cols
"""
r, c = 7, 1
# row titles
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
if 'col_title_def' in secondary_counts:
# Write definitions of column heading titles
ws.write(r-3, c-1, secondary_counts['col_title_def'][0], self.sec_col_heading_def)
ws.write(r-2, c-1, secondary_counts['col_title_def'][1], self.col_heading_def)
secondary_counts.pop('col_title_def')
for field, counts in secondary_counts.iteritems():
ws.merge_range(r-3, c, r-3, c+sec_cols-1, clean_title(field), self.sec_col_heading)
self.tabulate(ws, counts, cols, rows, row_perc=row_perc, sec_col=True, display_cols=display_cols, r=7, c=c)
c += sec_cols
def tabulate(self, ws, counts, cols, rows, row_perc=False, sec_col=False, sec_row=False, display_cols=None, c=1, r=6):
""" Emit a table.
:param ws: worksheet to write to
:param dict counts: dict from `(col_id, row_id)` tuples to count for that combination.
:param list cols: list of `(col_id, col_title)` tuples of column ids and titles
:param list rows: list of `(row_id, row_title)` tuples of row ids and titles
:param bool row_perc: should percentages by calculated by row instead of column (default: False)
:param sec_col: Are wecreating a secondary column title(default: False)
:param sec_row: Are we creating a secondary row title(default: False)
:param display_cols: Optional if only a subset of columns should be displayed e.g. only female
:param r, c: initial position where cursor should start writing to
"""
if row_perc:
# we'll need percentage by rows
row_totals = {}
for row_id, row_title in rows:
row_totals[row_id] = sum(counts.get((col_id, row_id), 0) for col_id, _ in cols) # noqa
# row titles
if not sec_col:
# Else already written
for i, row in enumerate(rows):
row_id, row_title = row
ws.write(r + i, c, clean_title(row_title), self.label)
c += 1
# if only filtered results should be shown
# e.g. only print female columns
if display_cols:
cols = display_cols
if 'col_title_def' in counts and not sec_row:
ws.write(r - 2, c-1, counts['col_title_def'], self.col_heading_def)
counts.pop('col_title_def')
# values, written by column
for col_id, col_heading in cols:
# column title
if not sec_row:
ws.merge_range(r-2, c, r-2, c+1, clean_title(col_heading), self.col_heading)
ws.write(r - 1, c, "N", self.label)
ws.write(r - 1, c + 1, "%", self.label)
if not row_perc:
# column totals
# Confirm: Perc of col total or matrix total?
# total = sum(counts.itervalues())
total = sum(counts.get((col_id, row_id), 0) for row_id, _ in rows)
# row values for this column
for i, row in enumerate(rows):
row_id, row_title = row
if row_perc:
# row totals
total = row_totals[row_id]
n = counts.get((col_id, row_id), 0)
ws.write(r + i, c, n, self.N)
ws.write(r + i, c + 1, p(n, total), self.P)
c += 2
|
import sys, os, glob
import json
from astropy.io import fits
from astropy.table import Table, join
import numpy as np
import time, datetime
from collections import OrderedDict
import subprocess
from copy import deepcopy
from desispec.scripts.tile_redshifts import generate_tile_redshift_scripts, get_tile_redshift_script_pathname, \
get_tile_redshift_relpath, get_tile_redshift_script_suffix
from desispec.workflow.queue import get_resubmission_states, update_from_queue
from desispec.workflow.timing import what_night_is_it
from desispec.workflow.desi_proc_funcs import get_desi_proc_batch_file_pathname, create_desi_proc_batch_script, \
get_desi_proc_batch_file_path
from desispec.workflow.utils import pathjoin, sleep_and_report
from desispec.workflow.tableio import write_table
from desispec.workflow.proctable import table_row_to_dict
from desiutil.log import get_logger
from desispec.io import findfile, specprod_root
from desispec.io.util import decode_camword, create_camword, difference_camwords, camword_to_spectros
#################################################
############## Misc Functions ###################
#################################################
def night_to_starting_iid(night=None):
"""
Creates an internal ID for a given night. The resulting integer is an 8 digit number.
The digits are YYMMDDxxx where YY is the years since 2000, MM and DD are the month and day. xxx are 000,
and are incremented for up to 1000 unique job ID's for a given night.
Args:
night, str or int. YYYYMMDD of the night to get the starting internal ID for.
Returns:
internal_id, int. 9 digit number consisting of YYMMDD000. YY is years after 2000, MMDD is month and day.
000 being the starting job number (0).
"""
if night is None:
night = what_night_is_it()
night = int(night)
internal_id = (night - 20000000) * 1000
return internal_id
#################################################
############ Script Functions ###################
#################################################
def batch_script_name(prow):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, JOBDESC, PROCCAMWORD defined)
and determines the script file pathname as defined by desi_proc's helper functions.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
Returns:
scriptfile, str. The complete pathname to the script file, as it is defined within the desi_proc ecosystem.
"""
expids = prow['EXPID']
if len(expids) == 0:
expids = None
pathname = get_desi_proc_batch_file_pathname(night = prow['NIGHT'], exp=expids, \
jobdesc=prow['JOBDESC'], cameras=prow['PROCCAMWORD'])
scriptfile = pathname + '.slurm'
return scriptfile
def check_for_outputs_on_disk(prow, resubmit_partial_complete=True):
"""
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated to reflect
the change in job status after creating and submitting the job for processing.
"""
prow['STATUS'] = 'UNKNOWN'
log = get_logger()
job_to_file_map = {
'prestdstar': 'sframe',
'stdstarfit': 'stdstars',
'poststdstar': 'cframe',
'nightlybias': 'biasnight',
'ccdcalib': 'badcolumns',
'arc': 'fitpsf',
'flat': 'fiberflat',
'psfnight': 'psfnight',
'nightlyflat': 'fiberflatnight',
'spectra': 'spectra_tile',
'coadds': 'coadds_tile',
'redshift': 'redrock_tile',
}
night = prow['NIGHT']
if prow['JOBDESC'] in ['cumulative','pernight-v0','pernight','perexp']:
filetype = 'redrock_tile'
else:
filetype = job_to_file_map[prow['JOBDESC']]
orig_camword = prow['PROCCAMWORD']
## if spectro based, look for spectros, else look for cameras
if prow['JOBDESC'] in ['stdstarfit','spectra','coadds','redshift']:
## Spectrograph based
spectros = camword_to_spectros(prow['PROCCAMWORD'])
n_desired = len(spectros)
## Suppress outputs about using tile based files in findfile if only looking for stdstarfits
if prow['JOBDESC'] == 'stdstarfit':
tileid = None
else:
tileid = prow['TILEID']
expid = prow['EXPID'][0]
existing_spectros = []
for spectro in spectros:
if os.path.exists(findfile(filetype=filetype, night=night, expid=expid, spectrograph=spectro, tile=tileid)):
existing_spectros.append(spectro)
completed = (len(existing_spectros) == n_desired)
if not completed and resubmit_partial_complete and len(existing_spectros) > 0:
existing_camword = 'a' + ''.join([str(spec) for spec in sorted(existing_spectros)])
prow['PROCCAMWORD'] = difference_camwords(prow['PROCCAMWORD'],existing_camword)
elif prow['JOBDESC'] in ['cumulative','pernight-v0','pernight','perexp']:
## Spectrograph based
spectros = camword_to_spectros(prow['PROCCAMWORD'])
n_desired = len(spectros)
## Suppress outputs about using tile based files in findfile if only looking for stdstarfits
tileid = prow['TILEID']
expid = prow['EXPID'][0]
redux_dir = specprod_root()
outdir = os.path.join(redux_dir,get_tile_redshift_relpath(tileid,group=prow['JOBDESC'],night=night,expid=expid))
suffix = get_tile_redshift_script_suffix(tileid, group=prow['JOBDESC'], night=night, expid=expid)
existing_spectros = []
for spectro in spectros:
if os.path.exists(os.path.join(outdir, f"redrock-{spectro}-{suffix}.fits")):
existing_spectros.append(spectro)
completed = (len(existing_spectros) == n_desired)
if not completed and resubmit_partial_complete and len(existing_spectros) > 0:
existing_camword = 'a' + ''.join([str(spec) for spec in sorted(existing_spectros)])
prow['PROCCAMWORD'] = difference_camwords(prow['PROCCAMWORD'],existing_camword)
else:
## Otheriwse camera based
cameras = decode_camword(prow['PROCCAMWORD'])
n_desired = len(cameras)
if len(prow['EXPID']) > 0:
expid = prow['EXPID'][0]
else:
expid = None
if len(prow['EXPID']) > 1 and prow['JOBDESC'] not in ['psfnight','nightlyflat']:
log.warning(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']}. This job type only makes " +
f"sense with a single exposure. Proceeding with {expid}.")
missing_cameras = []
for cam in cameras:
if not os.path.exists(findfile(filetype=filetype, night=night, expid=expid, camera=cam)):
missing_cameras.append(cam)
completed = (len(missing_cameras) == 0)
if not completed and resubmit_partial_complete and len(missing_cameras) < n_desired:
prow['PROCCAMWORD'] = create_camword(missing_cameras)
if completed:
prow['STATUS'] = 'COMPLETED'
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} already has " +
f"the desired {n_desired} {filetype}'s. Not submitting this job.")
elif resubmit_partial_complete and orig_camword != prow['PROCCAMWORD']:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} already has " +
f"some {filetype}'s. Submitting smaller camword={prow['PROCCAMWORD']}.")
elif not resubmit_partial_complete:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} doesn't have all " +
f"{filetype}'s and resubmit_partial_complete=False. "+
f"Submitting full camword={prow['PROCCAMWORD']}.")
else:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} has no " +
f"existing {filetype}'s. Submitting full camword={prow['PROCCAMWORD']}.")
return prow
def create_and_submit(prow, queue='realtime', reservation=None, dry_run=0, joint=False,
strictly_successful=False, check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper script that takes a processing table row and three modifier keywords, creates a submission script for the
compute nodes, and then submits that script to the Slurm scheduler with appropriate dependencies.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
queue, str. The name of the NERSC Slurm queue to submit to. Default is the realtime queue.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
joint, bool. Whether this is a joint fitting job (the job involves multiple exposures) and therefore needs to be
run with desi_proc_joint_fit. Default is False.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated to reflect
the change in job status after creating and submitting the job for processing.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
orig_prow = prow.copy()
if check_for_outputs:
prow = check_for_outputs_on_disk(prow, resubmit_partial_complete)
if prow['STATUS'].upper() == 'COMPLETED':
return prow
prow = create_batch_script(prow, queue=queue, dry_run=dry_run, joint=joint, system_name=system_name)
prow = submit_batch_script(prow, reservation=reservation, dry_run=dry_run, strictly_successful=strictly_successful)
## If resubmitted partial, the PROCCAMWORD and SCRIPTNAME will correspond to the pruned values. But we want to
## retain the full job's value, so get those from the old job.
if resubmit_partial_complete:
prow['PROCCAMWORD'] = orig_prow['PROCCAMWORD']
prow['SCRIPTNAME'] = orig_prow['SCRIPTNAME']
return prow
def desi_proc_command(prow, queue=None):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, OBSTYPE, JOBDESC, PROCCAMWORD defined)
and determines the proper command line call to process the data defined by the input row/dict.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
queue, str. The name of the NERSC Slurm queue to submit to. Default is None (which leaves it to the desi_proc default).
Returns:
cmd, str. The proper command to be submitted to desi_proc to process the job defined by the prow values.
"""
cmd = 'desi_proc'
cmd += ' --batch'
cmd += ' --nosubmit'
cmd += ' --traceshift'
if queue is not None:
cmd += f' -q {queue}'
if prow['OBSTYPE'].lower() == 'science':
if prow['JOBDESC'] == 'prestdstar':
cmd += ' --nostdstarfit --nofluxcalib'
elif prow['JOBDESC'] == 'poststdstar':
cmd += ' --noprestdstarfit --nostdstarfit'
elif prow['JOBDESC'] in ['nightlybias', 'ccdcalib']:
cmd += ' --nightlybias'
pcamw = str(prow['PROCCAMWORD'])
cmd += f" --cameras={pcamw} -n {prow['NIGHT']}"
if len(prow['EXPID']) > 0:
cmd += f" -e {prow['EXPID'][0]}"
if prow['BADAMPS'] != '':
cmd += ' --badamps={}'.format(prow['BADAMPS'])
return cmd
def desi_proc_joint_fit_command(prow, queue=None):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, OBSTYPE, PROCCAMWORD defined)
and determines the proper command line call to process the data defined by the input row/dict.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
queue, str. The name of the NERSC Slurm queue to submit to. Default is None (which leaves it to the desi_proc default).
Returns:
cmd, str. The proper command to be submitted to desi_proc_joint_fit to process the job defined by the prow values.
"""
cmd = 'desi_proc_joint_fit'
cmd += ' --batch'
cmd += ' --nosubmit'
cmd += ' --traceshift'
if queue is not None:
cmd += f' -q {queue}'
descriptor = prow['OBSTYPE'].lower()
night = prow['NIGHT']
specs = str(prow['PROCCAMWORD'])
expid_str = ','.join([str(eid) for eid in prow['EXPID']])
cmd += f' --obstype {descriptor}'
cmd += f' --cameras={specs} -n {night}'
if len(expid_str) > 0:
cmd += f' -e {expid_str}'
return cmd
def create_batch_script(prow, queue='realtime', dry_run=0, joint=False, system_name=None):
"""
Wrapper script that takes a processing table row and three modifier keywords and creates a submission script for the
compute nodes.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
queue, str. The name of the NERSC Slurm queue to submit to. Default is the realtime queue.
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written but not submitted.
If dry_run=2, the scripts will not be written nor submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
joint, bool. Whether this is a joint fitting job (the job involves multiple exposures) and therefore needs to be
run with desi_proc_joint_fit. Default is False.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
scriptname.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
log = get_logger()
if prow['JOBDESC'] in ['perexp','pernight','pernight-v0','cumulative']:
if dry_run > 1:
scriptpathname = get_tile_redshift_script_pathname(tileid=prow['TILEID'],group=prow['JOBDESC'],
night=prow['NIGHT'], expid=prow['EXPID'][0])
log.info("Output file would have been: {}".format(scriptpathname))
else:
#- run zmtl for cumulative redshifts but not others
run_zmtl = (prow['JOBDESC'] == 'cumulative')
scripts, failed_scripts = generate_tile_redshift_scripts(tileid=prow['TILEID'], group=prow['JOBDESC'],
night=[prow['NIGHT']], expid=prow['EXPID'],
run_zmtl=run_zmtl,
batch_queue=queue, system_name=system_name,
nosubmit=True)
if len(failed_scripts) > 0:
log.error(f"Redshifts failed for group={prow['JOBDESC']}, night={prow['NIGHT']}, "+
f"tileid={prow['TILEID']}, expid={prow['EXPID']}.")
log.info(f"Returned failed scriptname is {failed_scripts}")
elif len(scripts) > 1:
log.error(f"More than one redshifts returned for group={prow['JOBDESC']}, night={prow['NIGHT']}, "+
f"tileid={prow['TILEID']}, expid={prow['EXPID']}.")
log.info(f"Returned scriptnames were {scripts}")
else:
scriptpathname = scripts[0]
else:
if joint:
cmd = desi_proc_joint_fit_command(prow, queue=queue)
else:
cmd = desi_proc_command(prow, queue=queue)
scriptpathname = batch_script_name(prow)
if dry_run > 1:
log.info("Output file would have been: {}".format(scriptpathname))
log.info("Command to be run: {}".format(cmd.split()))
else:
log.info("Running: {}".format(cmd.split()))
expids = prow['EXPID']
if len(expids) == 0:
expids = None
scriptpathname = create_desi_proc_batch_script(night=prow['NIGHT'], exp=expids,
cameras=prow['PROCCAMWORD'],
jobdesc=prow['JOBDESC'],
queue=queue, cmdline=cmd,
system_name=system_name)
log.info("Outfile is: {}".format(scriptpathname))
prow['SCRIPTNAME'] = os.path.basename(scriptpathname)
return prow
def submit_batch_script(prow, dry_run=0, reservation=None, strictly_successful=False):
"""
Wrapper script that takes a processing table row and three modifier keywords and submits the scripts to the Slurm
scheduler.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
scriptname.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
log = get_logger()
dep_qids = prow['LATEST_DEP_QID']
dep_list, dep_str = '', ''
if len(dep_qids) > 0:
jobtype = prow['JOBDESC']
if strictly_successful:
depcond = 'afterok'
elif jobtype in ['arc', 'psfnight', 'prestdstar', 'stdstarfit']:
## (though psfnight and stdstarfit will require some inputs otherwise they'll go up in flames)
depcond = 'afterany'
else:
## if 'flat','nightlyflat','poststdstar', or any type of redshift, require strict success of inputs
depcond = 'afterok'
dep_str = f'--dependency={depcond}:'
if np.isscalar(dep_qids):
dep_list = str(dep_qids).strip(' \t')
if dep_list == '':
dep_str = ''
else:
dep_str += dep_list
else:
if len(dep_qids)>1:
dep_list = ':'.join(np.array(dep_qids).astype(str))
dep_str += dep_list
elif len(dep_qids) == 1 and dep_qids[0] not in [None, 0]:
dep_str += str(dep_qids[0])
else:
dep_str = ''
# script = f'{jobname}.slurm'
# script_path = pathjoin(batchdir, script)
if prow['JOBDESC'] in ['pernight-v0','pernight','perexp','cumulative']:
script_path = get_tile_redshift_script_pathname(tileid=prow['TILEID'],group=prow['JOBDESC'],
night=prow['NIGHT'], expid=np.min(prow['EXPID']))
jobname = os.path.split(script_path)[-1]
else:
batchdir = get_desi_proc_batch_file_path(night=prow['NIGHT'])
jobname = batch_script_name(prow)
script_path = pathjoin(batchdir, jobname)
batch_params = ['sbatch', '--parsable']
if dep_str != '':
batch_params.append(f'{dep_str}')
if reservation is not None:
batch_params.append(f'--reservation={reservation}')
batch_params.append(f'{script_path}')
if dry_run:
## in dry_run, mock Slurm ID's are generated using CPU seconds. Wait one second so we have unique ID's
current_qid = int(time.time() - 1.6e9)
time.sleep(1)
else:
current_qid = subprocess.check_output(batch_params, stderr=subprocess.STDOUT, text=True)
current_qid = int(current_qid.strip(' \t\n'))
log.info(batch_params)
log.info(f'Submitted {jobname} with dependencies {dep_str} and reservation={reservation}. Returned qid: {current_qid}')
prow['LATEST_QID'] = current_qid
prow['ALL_QIDS'] = np.append(prow['ALL_QIDS'],current_qid)
prow['STATUS'] = 'SUBMITTED'
prow['SUBMIT_DATE'] = int(time.time())
return prow
#############################################
########## Row Manipulations ############
#############################################
def define_and_assign_dependency(prow, calibjobs):
"""
Given input processing row and possible calibjobs, this defines the
JOBDESC keyword and assigns the dependency appropriate for the job type of
prow.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for
'OBSTYPE'. A row must have column names for
'JOBDESC', 'INT_DEP_IDS', and 'LATEST_DEP_ID'.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job. Each value that isn't None must contain
'INTID', and 'LATEST_QID'. If None, it assumes the
dependency doesn't exist and no dependency is assigned.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except
with modified values updated values for
'JOBDESC', 'INT_DEP_IDS'. and 'LATEST_DEP_ID'.
Note:
This modifies the input. Though Table.Row objects are generally copied
on modification, so the change to the input object in memory may or may
not be changed. As of writing, a row from a table given to this function
will not change during the execution of this function (but can be
overwritten explicitly with the returned row if desired).
"""
if prow['OBSTYPE'] in ['science', 'twiflat']:
if calibjobs['nightlyflat'] is not None:
dependency = calibjobs['nightlyflat']
elif calibjobs['psfnight'] is not None:
dependency = calibjobs['psfnight']
elif calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
prow['JOBDESC'] = 'prestdstar'
elif prow['OBSTYPE'] == 'flat':
if calibjobs['psfnight'] is not None:
dependency = calibjobs['psfnight']
elif calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
elif prow['OBSTYPE'] == 'arc':
if calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
else:
dependency = None
prow = assign_dependency(prow, dependency)
return prow
def assign_dependency(prow, dependency):
"""
Given input processing row and possible arcjob (processing row for psfnight) and flatjob (processing row for
nightlyflat), this defines the JOBDESC keyword and assigns the dependency appropriate for the job type of prow.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'OBSTYPE'. A row must have column names for
'JOBDESC', 'INT_DEP_IDS', and 'LATEST_DEP_ID'.
dependency, NoneType or scalar/list/array of Table.Row, dict. Processing row corresponding to the required input
for the job in prow. This must contain keyword
accessible values for 'INTID', and 'LATEST_QID'.
If None, it assumes the dependency doesn't exist
and no dependency is assigned.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
'JOBDESC', 'INT_DEP_IDS'. and 'LATEST_DEP_ID'.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
prow['INT_DEP_IDS'] = np.ndarray(shape=0).astype(int)
prow['LATEST_DEP_QID'] = np.ndarray(shape=0).astype(int)
if dependency is not None:
if type(dependency) in [list, np.array]:
ids, qids = [], []
for curdep in dependency:
if still_a_dependency(curdep):
ids.append(curdep['INTID'])
qids.append(curdep['LATEST_QID'])
prow['INT_DEP_IDS'] = np.array(ids, dtype=int)
prow['LATEST_DEP_QID'] = np.array(qids, dtype=int)
elif type(dependency) in [dict, OrderedDict, Table.Row] and still_a_dependency(dependency):
prow['INT_DEP_IDS'] = np.array([dependency['INTID']], dtype=int)
prow['LATEST_DEP_QID'] = np.array([dependency['LATEST_QID']], dtype=int)
return prow
def still_a_dependency(dependency):
"""
Defines the criteria for which a dependency is deemed complete (and therefore no longer a dependency).
Args:
dependency, Table.Row or dict. Processing row corresponding to the required input for the job in prow.
This must contain keyword accessible values for 'STATUS', and 'LATEST_QID'.
Returns:
bool. False if the criteria indicate that the dependency is completed and no longer a blocking factor (ie no longer
a genuine dependency). Returns True if the dependency is still a blocking factor such that the slurm
scheduler needs to be aware of the pending job.
"""
return dependency['LATEST_QID'] > 0 and dependency['STATUS'] != 'COMPLETED'
def get_type_and_tile(erow):
"""
Trivial function to return the OBSTYPE and the TILEID from an exposure table row
Args:
erow, Table.Row or dict. Must contain 'OBSTYPE' and 'TILEID' as keywords.
Returns:
tuple (str, str), corresponding to the OBSTYPE and TILEID values of the input erow.
"""
return str(erow['OBSTYPE']).lower(), erow['TILEID']
#############################################
######### Table manipulators ############
#############################################
def parse_previous_tables(etable, ptable, night):
"""
This takes in the exposure and processing tables and regenerates all the working memory variables needed for the
daily processing script.
Used by the daily processing to define most of its state-ful variables into working memory.
If the processing table is empty, these are simply declared and returned for use.
If the code had previously run and exited (or crashed), however, this will all the code to
re-establish itself by redefining these values.
Args:
etable, Table, Exposure table of all exposures that have been dealt with thus far.
ptable, Table, Processing table of all exposures that have been processed.
night, str or int, the night the data was taken.
Returns:
arcs, list of dicts, list of the individual arc jobs used for the psfnight (NOT all
the arcs, if multiple sets existed)
flats, list of dicts, list of the individual flat jobs used for the nightlyflat (NOT
all the flats, if multiple sets existed)
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile)
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
curtype, None, the obstype of the current job being run. Always None as first new job will define this.
lasttype, str or None, the obstype of the last individual exposure row to be processed.
curtile, None, the tileid of the current job (if science). Otherwise None. Always None as first
new job will define this.
lasttile, str or None, the tileid of the last job (if science). Otherwise None.
internal_id, int, an internal identifier unique to each job. Increments with each new job. This
is the latest unassigned value.
"""
log = get_logger()
arcs, flats, sciences = [], [], []
calibjobs = {'nightlybias': None, 'ccdcalib': None, 'psfnight': None,
'nightlyflat': None}
curtype,lasttype = None,None
curtile,lasttile = None,None
if len(ptable) > 0:
prow = ptable[-1]
internal_id = int(prow['INTID'])+1
lasttype,lasttile = get_type_and_tile(ptable[-1])
jobtypes = ptable['JOBDESC']
if 'nightlybias' in jobtypes:
calibjobs['nightlybias'] = table_row_to_dict(ptable[jobtypes=='nightlybias'][0])
log.info("Located nightlybias job in exposure table: {}".format(calibjobs['nightlybias']))
if 'ccdcalib' in jobtypes:
calibjobs['ccdcalib'] = table_row_to_dict(ptable[jobtypes=='ccdcalib'][0])
log.info("Located ccdcalib job in exposure table: {}".format(calibjobs['ccdcalib']))
if 'psfnight' in jobtypes:
calibjobs['psfnight'] = table_row_to_dict(ptable[jobtypes=='psfnight'][0])
log.info("Located joint fit psfnight job in exposure table: {}".format(calibjobs['psfnight']))
elif lasttype == 'arc':
seqnum = 10
for row in ptable[::-1]:
erow = etable[etable['EXPID']==row['EXPID'][0]]
if row['OBSTYPE'].lower() == 'arc' and int(erow['SEQNUM'])<seqnum:
arcs.append(table_row_to_dict(row))
seqnum = int(erow['SEQNUM'])
else:
break
## Because we work backword to fill in, we need to reverse them to get chronological order back
arcs = arcs[::-1]
if 'nightlyflat' in jobtypes:
calibjobs['nightlyflat'] = table_row_to_dict(ptable[jobtypes=='nightlyflat'][0])
log.info("Located joint fit nightlyflat job in exposure table: {}".format(calibjobs['nightlyflat']))
elif lasttype == 'flat':
for row in ptable[::-1]:
erow = etable[etable['EXPID']==row['EXPID'][0]]
if row['OBSTYPE'].lower() == 'flat' and int(erow['SEQTOT']) < 5:
if float(erow['EXPTIME']) > 100.:
flats.append(table_row_to_dict(row))
else:
break
flats = flats[::-1]
if lasttype.lower() == 'science':
for row in ptable[::-1]:
if row['OBSTYPE'].lower() == 'science' and row['TILEID'] == lasttile and \
row['JOBDESC'] == 'prestdstar' and row['LASTSTEP'] != 'skysub':
sciences.append(table_row_to_dict(row))
else:
break
sciences = sciences[::-1]
else:
internal_id = night_to_starting_iid(night)
return arcs,flats,sciences, \
calibjobs, \
curtype, lasttype, \
curtile, lasttile,\
internal_id
def update_and_recurvsively_submit(proc_table, submits=0, resubmission_states=None,
ptab_name=None, dry_run=0,reservation=None):
"""
Given an processing table, this loops over job rows and resubmits failed jobs (as defined by resubmission_states).
Before submitting a job, it checks the dependencies for failures. If a dependency needs to be resubmitted, it recursively
follows dependencies until it finds the first job without a failed dependency and resubmits that. Then resubmits the
other jobs with the new Slurm jobID's for proper dependency coordination within Slurm.
Args:
proc_table, Table, the processing table with a row per job.
submits, int, the number of submissions made to the queue. Used for saving files and in not overloading the scheduler.
resubmission_states, list or array of strings, each element should be a capitalized string corresponding to a
possible Slurm scheduler state, where you wish for jobs with that
outcome to be resubmitted
ptab_name, str, the full pathname where the processing table should be saved.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
Returns:
proc_table: Table, a table with the same rows as the input except that Slurm and jobid relevant columns have
been updated for those jobs that needed to be resubmitted.
submits: int, the number of submissions made to the queue. This is incremented from the input submits, so it is
the number of submissions made from this function call plus the input submits value.
Note:
This modifies the inputs of both proc_table and submits and returns them.
"""
log = get_logger()
if resubmission_states is None:
resubmission_states = get_resubmission_states()
log.info(f"Resubmitting jobs with current states in the following: {resubmission_states}")
proc_table = update_from_queue(proc_table, dry_run=False)
log.info("Updated processing table queue information:")
cols = ['INTID','EXPID','OBSTYPE','JOBDESC','TILEID','LATEST_QID','STATUS']
print(np.array(cols))
for row in proc_table:
print(np.array(row[cols]))
print("\n")
id_to_row_map = {row['INTID']: rown for rown, row in enumerate(proc_table)}
for rown in range(len(proc_table)):
if proc_table['STATUS'][rown] in resubmission_states:
proc_table, submits = recursive_submit_failed(rown, proc_table, submits,
id_to_row_map, ptab_name,
resubmission_states,
reservation, dry_run)
return proc_table, submits
def recursive_submit_failed(rown, proc_table, submits, id_to_row_map, ptab_name=None,
resubmission_states=None, reservation=None, dry_run=0):
"""
Given a row of a processing table and the full processing table, this resubmits the given job.
Before submitting a job, it checks the dependencies for failures in the processing table. If a dependency needs to
be resubmitted, it recursively follows dependencies until it finds the first job without a failed dependency and
resubmits that. Then resubmits the other jobs with the new Slurm jobID's for proper dependency coordination within Slurm.
Args:
rown, Table.Row, the row of the processing table that you want to resubmit.
proc_table, Table, the processing table with a row per job.
submits, int, the number of submissions made to the queue. Used for saving files and in not overloading the scheduler.
id_to_row_map, dict, lookup dictionary where the keys are internal ids (INTID's) and the values are the row position
in the processing table.
ptab_name, str, the full pathname where the processing table should be saved.
resubmission_states, list or array of strings, each element should be a capitalized string corresponding to a
possible Slurm scheduler state, where you wish for jobs with that
outcome to be resubmitted
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
Returns:
proc_table: Table, a table with the same rows as the input except that Slurm and jobid relevant columns have
been updated for those jobs that needed to be resubmitted.
submits: int, the number of submissions made to the queue. This is incremented from the input submits, so it is
the number of submissions made from this function call plus the input submits value.
Note:
This modifies the inputs of both proc_table and submits and returns them.
"""
log = get_logger()
row = proc_table[rown]
log.info(f"Identified row {row['INTID']} as needing resubmission.")
log.info(f"{row['INTID']}: Expid(s): {row['EXPID']} Job: {row['JOBDESC']}")
if resubmission_states is None:
resubmission_states = get_resubmission_states()
ideps = proc_table['INT_DEP_IDS'][rown]
if ideps is None:
proc_table['LATEST_DEP_QID'][rown] = np.ndarray(shape=0).astype(int)
else:
all_valid_states = list(resubmission_states.copy())
all_valid_states.extend(['RUNNING','PENDING','SUBMITTED','PROCESSING'])
for idep in np.sort(np.atleast_1d(ideps)):
if proc_table['STATUS'][id_to_row_map[idep]] not in all_valid_states:
log.warning(f"Proc INTID: {proc_table['INTID'][rown]} depended on" +
f" INTID {proc_table['INTID'][id_to_row_map[idep]]}" +
f" but that exposure has state" +
f" {proc_table['STATUS'][id_to_row_map[idep]]} that" +
f" isn't in the list of resubmission states." +
f" Exiting this job's resubmission attempt.")
return proc_table, submits
qdeps = []
for idep in np.sort(np.atleast_1d(ideps)):
if proc_table['STATUS'][id_to_row_map[idep]] in resubmission_states:
proc_table, submits = recursive_submit_failed(id_to_row_map[idep],
proc_table, submits,
id_to_row_map,
reservation=reservation,
dry_run=dry_run)
qdeps.append(proc_table['LATEST_QID'][id_to_row_map[idep]])
qdeps = np.atleast_1d(qdeps)
if len(qdeps) > 0:
proc_table['LATEST_DEP_QID'][rown] = qdeps
else:
log.error(f"number of qdeps should be 1 or more: Rown {rown}, ideps {ideps}")
proc_table[rown] = submit_batch_script(proc_table[rown], reservation=reservation,
strictly_successful=True, dry_run=dry_run)
submits += 1
if not dry_run:
sleep_and_report(1, message_suffix=f"after submitting job to queue")
if submits % 10 == 0:
if ptab_name is None:
write_table(proc_table, tabletype='processing', overwrite=True)
else:
write_table(proc_table, tablename=ptab_name, overwrite=True)
sleep_and_report(2, message_suffix=f"after writing to disk")
if submits % 100 == 0:
proc_table = update_from_queue(proc_table)
if ptab_name is None:
write_table(proc_table, tabletype='processing', overwrite=True)
else:
write_table(proc_table, tablename=ptab_name, overwrite=True)
sleep_and_report(10, message_suffix=f"after updating queue and writing to disk")
return proc_table, submits
#########################################
######## Joint fit ##############
#########################################
def joint_fit(ptable, prows, internal_id, queue, reservation, descriptor, z_submit_types=None,
dry_run=0, strictly_successful=False, check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Given a set of prows, this generates a processing table row, creates a batch script, and submits the appropriate
joint fitting job given by descriptor. If the joint fitting job is standard star fitting, the post standard star fits
for all the individual exposures also created and submitted. The returned ptable has all of these rows added to the
table given as input.
Args:
ptable, Table. The processing table where each row is a processed job.
prows, list or array of dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
queue, str. The name of the queue to submit the jobs to. If None is given the current desi_proc default is used.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
descriptor, str. Description of the joint fitting job. Can either be 'science' or 'stdstarfit', 'arc' or 'psfnight',
or 'flat' or 'nightlyflat'.
z_submit_types: list of str's. The "group" types of redshifts that should be submitted with each
exposure. If not specified or None, then no redshifts are submitted.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
ptable, Table. The same processing table as input except with added rows for the joint fit job and, in the case
of a stdstarfit, the poststdstar science exposure jobs.
joint_prow, dict. Row of a processing table corresponding to the joint fit job.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
"""
log = get_logger()
if len(prows) < 1:
return ptable, None, internal_id
if descriptor is None:
return ptable, None
elif descriptor == 'arc':
descriptor = 'psfnight'
elif descriptor == 'flat':
descriptor = 'nightlyflat'
elif descriptor == 'science':
if z_submit_types is None or len(z_submit_types) == 0:
descriptor = 'stdstarfit'
if descriptor not in ['psfnight', 'nightlyflat', 'science','stdstarfit']:
return ptable, None, internal_id
log.info(" ")
log.info(f"Joint fit criteria found. Running {descriptor}.\n")
if descriptor == 'science':
joint_prow = make_joint_prow(prows, descriptor='stdstarfit', internal_id=internal_id)
else:
joint_prow = make_joint_prow(prows, descriptor=descriptor, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
if descriptor in ['science','stdstarfit']:
if descriptor == 'science':
zprows = []
log.info(" ")
log.info(f"Submitting individual science exposures now that joint fitting of standard stars is submitted.\n")
for row in prows:
if row['LASTSTEP'] == 'stdstarfit':
continue
row['JOBDESC'] = 'poststdstar'
row['INTID'] = internal_id
internal_id += 1
row['ALL_QIDS'] = np.ndarray(shape=0).astype(int)
row = assign_dependency(row, joint_prow)
row = create_and_submit(row, queue=queue, reservation=reservation, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(row)
if descriptor == 'science' and row['LASTSTEP'] == 'all':
zprows.append(row)
## Now run redshifts
if descriptor == 'science' and len(zprows) > 0:
log.info(" ")
for zsubtype in z_submit_types:
if zsubtype == 'perexp':
for zprow in zprows:
log.info(f"Submitting redshift fit of type {zsubtype} for TILEID {zprow['TILEID']} and EXPID {zprow['EXPID']}.\n")
joint_prow = make_joint_prow([zprow], descriptor=zsubtype, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
else:
log.info(f"Submitting joint redshift fits of type {zsubtype} for TILEID {zprows[0]['TILEID']}.\n")
joint_prow = make_joint_prow(zprows, descriptor=zsubtype, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
if descriptor in ['psfnight', 'nightlyflat']:
log.info(f"Setting the calibration exposures as calibrators in the processing table.\n")
ptable = set_calibrator_flag(prows, ptable)
return ptable, joint_prow, internal_id
## wrapper functions for joint fitting
def science_joint_fit(ptable, sciences, internal_id, queue='realtime', reservation=None,
z_submit_types=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the stdstarfit joint fit and redshift fitting.
All variables are the same except:
Arg 'sciences' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'science'.
"""
return joint_fit(ptable=ptable, prows=sciences, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='science', z_submit_types=z_submit_types, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
def flat_joint_fit(ptable, flats, internal_id, queue='realtime',
reservation=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the nightlyflat joint fit.
All variables are the same except:
Arg 'flats' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'nightlyflat'.
"""
return joint_fit(ptable=ptable, prows=flats, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='nightlyflat', dry_run=dry_run, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs, resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name)
def arc_joint_fit(ptable, arcs, internal_id, queue='realtime',
reservation=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the psfnight joint fit.
All variables are the same except:
Arg 'arcs' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'psfnight'.
"""
return joint_fit(ptable=ptable, prows=arcs, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='psfnight', dry_run=dry_run, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs, resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name)
def make_joint_prow(prows, descriptor, internal_id):
"""
Given an input list or array of processing table rows and a descriptor, this creates a joint fit processing job row.
It starts by copying the first input row, overwrites relevant columns, and defines the new dependencies (based on the
input prows).
Args:
prows, list or array of dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
descriptor, str. Description of the joint fitting job. Can either be 'stdstarfit', 'psfnight', or 'nightlyflat'.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
Returns:
joint_prow, dict. Row of a processing table corresponding to the joint fit job.
"""
first_row = prows[0]
joint_prow = first_row.copy()
joint_prow['INTID'] = internal_id
joint_prow['JOBDESC'] = descriptor
joint_prow['LATEST_QID'] = -99
joint_prow['ALL_QIDS'] = np.ndarray(shape=0).astype(int)
joint_prow['SUBMIT_DATE'] = -99
joint_prow['STATUS'] = 'U'
joint_prow['SCRIPTNAME'] = ''
joint_prow['EXPID'] = np.array([ currow['EXPID'][0] for currow in prows ], dtype=int)
joint_prow = assign_dependency(joint_prow,dependency=prows)
return joint_prow
def checkfor_and_submit_joint_job(ptable, arcs, flats, sciences, calibjobs,
lasttype, internal_id, z_submit_types=None, dry_run=0,
queue='realtime', reservation=None, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Takes all the state-ful data from daily processing and determines whether a joint fit needs to be submitted. Places
the decision criteria into a single function for easier maintainability over time. These are separate from the
new standard manifest*.json method of indicating a calibration sequence is complete. That is checked independently
elsewhere and doesn't interact with this.
Args:
ptable, Table, Processing table of all exposures that have been processed.
arcs, list of dicts, list of the individual arc jobs to be used for the psfnight (NOT all
the arcs, if multiple sets existed). May be empty if none identified yet.
flats, list of dicts, list of the individual flat jobs to be used for the nightlyflat (NOT
all the flats, if multiple sets existed). May be empty if none identified yet.
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile). May be empty if none identified yet.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
lasttype, str or None, the obstype of the last individual exposure row to be processed.
internal_id, int, an internal identifier unique to each job. Increments with each new job. This
is the smallest unassigned value.
z_submit_types: list of str's. The "group" types of redshifts that should be submitted with each
exposure. If not specified or None, then no redshifts are submitted.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
queue, str. The name of the queue to submit the jobs to. If None is given the current desi_proc default is used.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell, cori-knl, permutter-gpu
Returns:
ptable, Table, Processing table of all exposures that have been processed.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile). May be empty if none identified yet or
we just submitted them for processing.
internal_id, int, if no job is submitted, this is the same as the input, otherwise it is incremented upward from
from the input such that it represents the smallest unused ID.
"""
if lasttype == 'science' and len(sciences) > 0:
log = get_logger()
skysubonly = np.array([sci['LASTSTEP'] == 'skysub' for sci in sciences])
if np.all(skysubonly):
log.error("Identified all exposures in joint fitting request as skysub-only. Not submitting")
sciences = []
return ptable, calibjobs, sciences, internal_id
if np.any(skysubonly):
log.error("Identified skysub-only exposures in joint fitting request")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("LASTSTEP's: {}".format([row['LASTSTEP'] for row in sciences]))
sciences = (np.array(sciences,dtype=object)[~skysubonly]).tolist()
log.info("Removed skysub only exposures in joint fitting:")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("LASTSTEP's: {}".format([row['LASTSTEP'] for row in sciences]))
from collections import Counter
tiles = np.array([sci['TILEID'] for sci in sciences])
counts = Counter(tiles)
if len(counts.most_common()) > 1:
log.error("Identified more than one tile in a joint fitting request")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("Tileid's: {}".format(tiles))
log.info("Returning without joint fitting any of these exposures.")
# most_common, nmost_common = counts.most_common()[0]
# if most_common == -99:
# most_common, nmost_common = counts.most_common()[1]
# log.warning(f"Given multiple tiles to jointly fit: {counts}. "+
# "Only processing the most common non-default " +
# f"tile: {most_common} with {nmost_common} exposures")
# sciences = (np.array(sciences,dtype=object)[tiles == most_common]).tolist()
# log.info("Tiles and exposure id's being submitted for joint fitting:")
# log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
# log.info("Tileid's: {}".format([row['TILEID'] for row in sciences]))
sciences = []
return ptable, calibjobs, sciences, internal_id
ptable, tilejob, internal_id = science_joint_fit(ptable, sciences, internal_id, z_submit_types=z_submit_types,
dry_run=dry_run, queue=queue, reservation=reservation,
strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
if tilejob is not None:
sciences = []
elif lasttype == 'flat' and calibjobs['nightlyflat'] is None and len(flats) == 12:
## Note here we have an assumption about the number of expected flats being greater than 11
ptable, calibjobs['nightlyflat'], internal_id \
= flat_joint_fit(ptable, flats, internal_id, dry_run=dry_run, queue=queue,
reservation=reservation, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
elif lasttype == 'arc' and calibjobs['psfnight'] is None and len(arcs) == 5:
## Note here we have an assumption about the number of expected arcs being greater than 4
ptable, calibjobs['psfnight'], internal_id \
= arc_joint_fit(ptable, arcs, internal_id, dry_run=dry_run, queue=queue,
reservation=reservation, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
return ptable, calibjobs, sciences, internal_id
def set_calibrator_flag(prows, ptable):
"""
Sets the "CALIBRATOR" column of a procesing table row to 1 (integer representation of True)
for all input rows. Used within joint fitting code to flag the exposures that were input
to the psfnight or nightlyflat for later reference.
Args:
prows, list or array of Table.Rows or dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
ptable, Table. The processing table where each row is a processed job.
Returns:
ptable, Table. The same processing table as input except with added rows for the joint fit job and, in the case
of a stdstarfit, the poststdstar science exposure jobs.
"""
for prow in prows:
ptable['CALIBRATOR'][ptable['INTID'] == prow['INTID']] = 1
return ptable
for resub need COMPLETED and don't need PROCESSING
import sys, os, glob
import json
from astropy.io import fits
from astropy.table import Table, join
import numpy as np
import time, datetime
from collections import OrderedDict
import subprocess
from copy import deepcopy
from desispec.scripts.tile_redshifts import generate_tile_redshift_scripts, get_tile_redshift_script_pathname, \
get_tile_redshift_relpath, get_tile_redshift_script_suffix
from desispec.workflow.queue import get_resubmission_states, update_from_queue
from desispec.workflow.timing import what_night_is_it
from desispec.workflow.desi_proc_funcs import get_desi_proc_batch_file_pathname, create_desi_proc_batch_script, \
get_desi_proc_batch_file_path
from desispec.workflow.utils import pathjoin, sleep_and_report
from desispec.workflow.tableio import write_table
from desispec.workflow.proctable import table_row_to_dict
from desiutil.log import get_logger
from desispec.io import findfile, specprod_root
from desispec.io.util import decode_camword, create_camword, difference_camwords, camword_to_spectros
#################################################
############## Misc Functions ###################
#################################################
def night_to_starting_iid(night=None):
"""
Creates an internal ID for a given night. The resulting integer is an 8 digit number.
The digits are YYMMDDxxx where YY is the years since 2000, MM and DD are the month and day. xxx are 000,
and are incremented for up to 1000 unique job ID's for a given night.
Args:
night, str or int. YYYYMMDD of the night to get the starting internal ID for.
Returns:
internal_id, int. 9 digit number consisting of YYMMDD000. YY is years after 2000, MMDD is month and day.
000 being the starting job number (0).
"""
if night is None:
night = what_night_is_it()
night = int(night)
internal_id = (night - 20000000) * 1000
return internal_id
#################################################
############ Script Functions ###################
#################################################
def batch_script_name(prow):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, JOBDESC, PROCCAMWORD defined)
and determines the script file pathname as defined by desi_proc's helper functions.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
Returns:
scriptfile, str. The complete pathname to the script file, as it is defined within the desi_proc ecosystem.
"""
expids = prow['EXPID']
if len(expids) == 0:
expids = None
pathname = get_desi_proc_batch_file_pathname(night = prow['NIGHT'], exp=expids, \
jobdesc=prow['JOBDESC'], cameras=prow['PROCCAMWORD'])
scriptfile = pathname + '.slurm'
return scriptfile
def check_for_outputs_on_disk(prow, resubmit_partial_complete=True):
"""
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated to reflect
the change in job status after creating and submitting the job for processing.
"""
prow['STATUS'] = 'UNKNOWN'
log = get_logger()
job_to_file_map = {
'prestdstar': 'sframe',
'stdstarfit': 'stdstars',
'poststdstar': 'cframe',
'nightlybias': 'biasnight',
'ccdcalib': 'badcolumns',
'arc': 'fitpsf',
'flat': 'fiberflat',
'psfnight': 'psfnight',
'nightlyflat': 'fiberflatnight',
'spectra': 'spectra_tile',
'coadds': 'coadds_tile',
'redshift': 'redrock_tile',
}
night = prow['NIGHT']
if prow['JOBDESC'] in ['cumulative','pernight-v0','pernight','perexp']:
filetype = 'redrock_tile'
else:
filetype = job_to_file_map[prow['JOBDESC']]
orig_camword = prow['PROCCAMWORD']
## if spectro based, look for spectros, else look for cameras
if prow['JOBDESC'] in ['stdstarfit','spectra','coadds','redshift']:
## Spectrograph based
spectros = camword_to_spectros(prow['PROCCAMWORD'])
n_desired = len(spectros)
## Suppress outputs about using tile based files in findfile if only looking for stdstarfits
if prow['JOBDESC'] == 'stdstarfit':
tileid = None
else:
tileid = prow['TILEID']
expid = prow['EXPID'][0]
existing_spectros = []
for spectro in spectros:
if os.path.exists(findfile(filetype=filetype, night=night, expid=expid, spectrograph=spectro, tile=tileid)):
existing_spectros.append(spectro)
completed = (len(existing_spectros) == n_desired)
if not completed and resubmit_partial_complete and len(existing_spectros) > 0:
existing_camword = 'a' + ''.join([str(spec) for spec in sorted(existing_spectros)])
prow['PROCCAMWORD'] = difference_camwords(prow['PROCCAMWORD'],existing_camword)
elif prow['JOBDESC'] in ['cumulative','pernight-v0','pernight','perexp']:
## Spectrograph based
spectros = camword_to_spectros(prow['PROCCAMWORD'])
n_desired = len(spectros)
## Suppress outputs about using tile based files in findfile if only looking for stdstarfits
tileid = prow['TILEID']
expid = prow['EXPID'][0]
redux_dir = specprod_root()
outdir = os.path.join(redux_dir,get_tile_redshift_relpath(tileid,group=prow['JOBDESC'],night=night,expid=expid))
suffix = get_tile_redshift_script_suffix(tileid, group=prow['JOBDESC'], night=night, expid=expid)
existing_spectros = []
for spectro in spectros:
if os.path.exists(os.path.join(outdir, f"redrock-{spectro}-{suffix}.fits")):
existing_spectros.append(spectro)
completed = (len(existing_spectros) == n_desired)
if not completed and resubmit_partial_complete and len(existing_spectros) > 0:
existing_camword = 'a' + ''.join([str(spec) for spec in sorted(existing_spectros)])
prow['PROCCAMWORD'] = difference_camwords(prow['PROCCAMWORD'],existing_camword)
else:
## Otheriwse camera based
cameras = decode_camword(prow['PROCCAMWORD'])
n_desired = len(cameras)
if len(prow['EXPID']) > 0:
expid = prow['EXPID'][0]
else:
expid = None
if len(prow['EXPID']) > 1 and prow['JOBDESC'] not in ['psfnight','nightlyflat']:
log.warning(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']}. This job type only makes " +
f"sense with a single exposure. Proceeding with {expid}.")
missing_cameras = []
for cam in cameras:
if not os.path.exists(findfile(filetype=filetype, night=night, expid=expid, camera=cam)):
missing_cameras.append(cam)
completed = (len(missing_cameras) == 0)
if not completed and resubmit_partial_complete and len(missing_cameras) < n_desired:
prow['PROCCAMWORD'] = create_camword(missing_cameras)
if completed:
prow['STATUS'] = 'COMPLETED'
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} already has " +
f"the desired {n_desired} {filetype}'s. Not submitting this job.")
elif resubmit_partial_complete and orig_camword != prow['PROCCAMWORD']:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} already has " +
f"some {filetype}'s. Submitting smaller camword={prow['PROCCAMWORD']}.")
elif not resubmit_partial_complete:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} doesn't have all " +
f"{filetype}'s and resubmit_partial_complete=False. "+
f"Submitting full camword={prow['PROCCAMWORD']}.")
else:
log.info(f"{prow['JOBDESC']} job with exposure(s) {prow['EXPID']} has no " +
f"existing {filetype}'s. Submitting full camword={prow['PROCCAMWORD']}.")
return prow
def create_and_submit(prow, queue='realtime', reservation=None, dry_run=0, joint=False,
strictly_successful=False, check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper script that takes a processing table row and three modifier keywords, creates a submission script for the
compute nodes, and then submits that script to the Slurm scheduler with appropriate dependencies.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
queue, str. The name of the NERSC Slurm queue to submit to. Default is the realtime queue.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
joint, bool. Whether this is a joint fitting job (the job involves multiple exposures) and therefore needs to be
run with desi_proc_joint_fit. Default is False.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated to reflect
the change in job status after creating and submitting the job for processing.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
orig_prow = prow.copy()
if check_for_outputs:
prow = check_for_outputs_on_disk(prow, resubmit_partial_complete)
if prow['STATUS'].upper() == 'COMPLETED':
return prow
prow = create_batch_script(prow, queue=queue, dry_run=dry_run, joint=joint, system_name=system_name)
prow = submit_batch_script(prow, reservation=reservation, dry_run=dry_run, strictly_successful=strictly_successful)
## If resubmitted partial, the PROCCAMWORD and SCRIPTNAME will correspond to the pruned values. But we want to
## retain the full job's value, so get those from the old job.
if resubmit_partial_complete:
prow['PROCCAMWORD'] = orig_prow['PROCCAMWORD']
prow['SCRIPTNAME'] = orig_prow['SCRIPTNAME']
return prow
def desi_proc_command(prow, queue=None):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, OBSTYPE, JOBDESC, PROCCAMWORD defined)
and determines the proper command line call to process the data defined by the input row/dict.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
queue, str. The name of the NERSC Slurm queue to submit to. Default is None (which leaves it to the desi_proc default).
Returns:
cmd, str. The proper command to be submitted to desi_proc to process the job defined by the prow values.
"""
cmd = 'desi_proc'
cmd += ' --batch'
cmd += ' --nosubmit'
cmd += ' --traceshift'
if queue is not None:
cmd += f' -q {queue}'
if prow['OBSTYPE'].lower() == 'science':
if prow['JOBDESC'] == 'prestdstar':
cmd += ' --nostdstarfit --nofluxcalib'
elif prow['JOBDESC'] == 'poststdstar':
cmd += ' --noprestdstarfit --nostdstarfit'
elif prow['JOBDESC'] in ['nightlybias', 'ccdcalib']:
cmd += ' --nightlybias'
pcamw = str(prow['PROCCAMWORD'])
cmd += f" --cameras={pcamw} -n {prow['NIGHT']}"
if len(prow['EXPID']) > 0:
cmd += f" -e {prow['EXPID'][0]}"
if prow['BADAMPS'] != '':
cmd += ' --badamps={}'.format(prow['BADAMPS'])
return cmd
def desi_proc_joint_fit_command(prow, queue=None):
"""
Wrapper script that takes a processing table row (or dictionary with NIGHT, EXPID, OBSTYPE, PROCCAMWORD defined)
and determines the proper command line call to process the data defined by the input row/dict.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'NIGHT', 'EXPID', 'JOBDESC', and 'PROCCAMWORD'.
queue, str. The name of the NERSC Slurm queue to submit to. Default is None (which leaves it to the desi_proc default).
Returns:
cmd, str. The proper command to be submitted to desi_proc_joint_fit to process the job defined by the prow values.
"""
cmd = 'desi_proc_joint_fit'
cmd += ' --batch'
cmd += ' --nosubmit'
cmd += ' --traceshift'
if queue is not None:
cmd += f' -q {queue}'
descriptor = prow['OBSTYPE'].lower()
night = prow['NIGHT']
specs = str(prow['PROCCAMWORD'])
expid_str = ','.join([str(eid) for eid in prow['EXPID']])
cmd += f' --obstype {descriptor}'
cmd += f' --cameras={specs} -n {night}'
if len(expid_str) > 0:
cmd += f' -e {expid_str}'
return cmd
def create_batch_script(prow, queue='realtime', dry_run=0, joint=False, system_name=None):
"""
Wrapper script that takes a processing table row and three modifier keywords and creates a submission script for the
compute nodes.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
queue, str. The name of the NERSC Slurm queue to submit to. Default is the realtime queue.
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written but not submitted.
If dry_run=2, the scripts will not be written nor submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
joint, bool. Whether this is a joint fitting job (the job involves multiple exposures) and therefore needs to be
run with desi_proc_joint_fit. Default is False.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
scriptname.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
log = get_logger()
if prow['JOBDESC'] in ['perexp','pernight','pernight-v0','cumulative']:
if dry_run > 1:
scriptpathname = get_tile_redshift_script_pathname(tileid=prow['TILEID'],group=prow['JOBDESC'],
night=prow['NIGHT'], expid=prow['EXPID'][0])
log.info("Output file would have been: {}".format(scriptpathname))
else:
#- run zmtl for cumulative redshifts but not others
run_zmtl = (prow['JOBDESC'] == 'cumulative')
scripts, failed_scripts = generate_tile_redshift_scripts(tileid=prow['TILEID'], group=prow['JOBDESC'],
night=[prow['NIGHT']], expid=prow['EXPID'],
run_zmtl=run_zmtl,
batch_queue=queue, system_name=system_name,
nosubmit=True)
if len(failed_scripts) > 0:
log.error(f"Redshifts failed for group={prow['JOBDESC']}, night={prow['NIGHT']}, "+
f"tileid={prow['TILEID']}, expid={prow['EXPID']}.")
log.info(f"Returned failed scriptname is {failed_scripts}")
elif len(scripts) > 1:
log.error(f"More than one redshifts returned for group={prow['JOBDESC']}, night={prow['NIGHT']}, "+
f"tileid={prow['TILEID']}, expid={prow['EXPID']}.")
log.info(f"Returned scriptnames were {scripts}")
else:
scriptpathname = scripts[0]
else:
if joint:
cmd = desi_proc_joint_fit_command(prow, queue=queue)
else:
cmd = desi_proc_command(prow, queue=queue)
scriptpathname = batch_script_name(prow)
if dry_run > 1:
log.info("Output file would have been: {}".format(scriptpathname))
log.info("Command to be run: {}".format(cmd.split()))
else:
log.info("Running: {}".format(cmd.split()))
expids = prow['EXPID']
if len(expids) == 0:
expids = None
scriptpathname = create_desi_proc_batch_script(night=prow['NIGHT'], exp=expids,
cameras=prow['PROCCAMWORD'],
jobdesc=prow['JOBDESC'],
queue=queue, cmdline=cmd,
system_name=system_name)
log.info("Outfile is: {}".format(scriptpathname))
prow['SCRIPTNAME'] = os.path.basename(scriptpathname)
return prow
def submit_batch_script(prow, dry_run=0, reservation=None, strictly_successful=False):
"""
Wrapper script that takes a processing table row and three modifier keywords and submits the scripts to the Slurm
scheduler.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for processing_table columns found in
desispect.workflow.proctable.get_processing_table_column_defs()
dry_run, int. If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
scriptname.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
log = get_logger()
dep_qids = prow['LATEST_DEP_QID']
dep_list, dep_str = '', ''
if len(dep_qids) > 0:
jobtype = prow['JOBDESC']
if strictly_successful:
depcond = 'afterok'
elif jobtype in ['arc', 'psfnight', 'prestdstar', 'stdstarfit']:
## (though psfnight and stdstarfit will require some inputs otherwise they'll go up in flames)
depcond = 'afterany'
else:
## if 'flat','nightlyflat','poststdstar', or any type of redshift, require strict success of inputs
depcond = 'afterok'
dep_str = f'--dependency={depcond}:'
if np.isscalar(dep_qids):
dep_list = str(dep_qids).strip(' \t')
if dep_list == '':
dep_str = ''
else:
dep_str += dep_list
else:
if len(dep_qids)>1:
dep_list = ':'.join(np.array(dep_qids).astype(str))
dep_str += dep_list
elif len(dep_qids) == 1 and dep_qids[0] not in [None, 0]:
dep_str += str(dep_qids[0])
else:
dep_str = ''
# script = f'{jobname}.slurm'
# script_path = pathjoin(batchdir, script)
if prow['JOBDESC'] in ['pernight-v0','pernight','perexp','cumulative']:
script_path = get_tile_redshift_script_pathname(tileid=prow['TILEID'],group=prow['JOBDESC'],
night=prow['NIGHT'], expid=np.min(prow['EXPID']))
jobname = os.path.split(script_path)[-1]
else:
batchdir = get_desi_proc_batch_file_path(night=prow['NIGHT'])
jobname = batch_script_name(prow)
script_path = pathjoin(batchdir, jobname)
batch_params = ['sbatch', '--parsable']
if dep_str != '':
batch_params.append(f'{dep_str}')
if reservation is not None:
batch_params.append(f'--reservation={reservation}')
batch_params.append(f'{script_path}')
if dry_run:
## in dry_run, mock Slurm ID's are generated using CPU seconds. Wait one second so we have unique ID's
current_qid = int(time.time() - 1.6e9)
time.sleep(1)
else:
current_qid = subprocess.check_output(batch_params, stderr=subprocess.STDOUT, text=True)
current_qid = int(current_qid.strip(' \t\n'))
log.info(batch_params)
log.info(f'Submitted {jobname} with dependencies {dep_str} and reservation={reservation}. Returned qid: {current_qid}')
prow['LATEST_QID'] = current_qid
prow['ALL_QIDS'] = np.append(prow['ALL_QIDS'],current_qid)
prow['STATUS'] = 'SUBMITTED'
prow['SUBMIT_DATE'] = int(time.time())
return prow
#############################################
########## Row Manipulations ############
#############################################
def define_and_assign_dependency(prow, calibjobs):
"""
Given input processing row and possible calibjobs, this defines the
JOBDESC keyword and assigns the dependency appropriate for the job type of
prow.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for
'OBSTYPE'. A row must have column names for
'JOBDESC', 'INT_DEP_IDS', and 'LATEST_DEP_ID'.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job. Each value that isn't None must contain
'INTID', and 'LATEST_QID'. If None, it assumes the
dependency doesn't exist and no dependency is assigned.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except
with modified values updated values for
'JOBDESC', 'INT_DEP_IDS'. and 'LATEST_DEP_ID'.
Note:
This modifies the input. Though Table.Row objects are generally copied
on modification, so the change to the input object in memory may or may
not be changed. As of writing, a row from a table given to this function
will not change during the execution of this function (but can be
overwritten explicitly with the returned row if desired).
"""
if prow['OBSTYPE'] in ['science', 'twiflat']:
if calibjobs['nightlyflat'] is not None:
dependency = calibjobs['nightlyflat']
elif calibjobs['psfnight'] is not None:
dependency = calibjobs['psfnight']
elif calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
prow['JOBDESC'] = 'prestdstar'
elif prow['OBSTYPE'] == 'flat':
if calibjobs['psfnight'] is not None:
dependency = calibjobs['psfnight']
elif calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
elif prow['OBSTYPE'] == 'arc':
if calibjobs['ccdcalib'] is not None:
dependency = calibjobs['ccdcalib']
else:
dependency = calibjobs['nightlybias']
else:
dependency = None
prow = assign_dependency(prow, dependency)
return prow
def assign_dependency(prow, dependency):
"""
Given input processing row and possible arcjob (processing row for psfnight) and flatjob (processing row for
nightlyflat), this defines the JOBDESC keyword and assigns the dependency appropriate for the job type of prow.
Args:
prow, Table.Row or dict. Must include keyword accessible definitions for 'OBSTYPE'. A row must have column names for
'JOBDESC', 'INT_DEP_IDS', and 'LATEST_DEP_ID'.
dependency, NoneType or scalar/list/array of Table.Row, dict. Processing row corresponding to the required input
for the job in prow. This must contain keyword
accessible values for 'INTID', and 'LATEST_QID'.
If None, it assumes the dependency doesn't exist
and no dependency is assigned.
Returns:
prow, Table.Row or dict. The same prow type and keywords as input except with modified values updated values for
'JOBDESC', 'INT_DEP_IDS'. and 'LATEST_DEP_ID'.
Note:
This modifies the input. Though Table.Row objects are generally copied on modification, so the change to the
input object in memory may or may not be changed. As of writing, a row from a table given to this function will
not change during the execution of this function (but can be overwritten explicitly with the returned row if desired).
"""
prow['INT_DEP_IDS'] = np.ndarray(shape=0).astype(int)
prow['LATEST_DEP_QID'] = np.ndarray(shape=0).astype(int)
if dependency is not None:
if type(dependency) in [list, np.array]:
ids, qids = [], []
for curdep in dependency:
if still_a_dependency(curdep):
ids.append(curdep['INTID'])
qids.append(curdep['LATEST_QID'])
prow['INT_DEP_IDS'] = np.array(ids, dtype=int)
prow['LATEST_DEP_QID'] = np.array(qids, dtype=int)
elif type(dependency) in [dict, OrderedDict, Table.Row] and still_a_dependency(dependency):
prow['INT_DEP_IDS'] = np.array([dependency['INTID']], dtype=int)
prow['LATEST_DEP_QID'] = np.array([dependency['LATEST_QID']], dtype=int)
return prow
def still_a_dependency(dependency):
"""
Defines the criteria for which a dependency is deemed complete (and therefore no longer a dependency).
Args:
dependency, Table.Row or dict. Processing row corresponding to the required input for the job in prow.
This must contain keyword accessible values for 'STATUS', and 'LATEST_QID'.
Returns:
bool. False if the criteria indicate that the dependency is completed and no longer a blocking factor (ie no longer
a genuine dependency). Returns True if the dependency is still a blocking factor such that the slurm
scheduler needs to be aware of the pending job.
"""
return dependency['LATEST_QID'] > 0 and dependency['STATUS'] != 'COMPLETED'
def get_type_and_tile(erow):
"""
Trivial function to return the OBSTYPE and the TILEID from an exposure table row
Args:
erow, Table.Row or dict. Must contain 'OBSTYPE' and 'TILEID' as keywords.
Returns:
tuple (str, str), corresponding to the OBSTYPE and TILEID values of the input erow.
"""
return str(erow['OBSTYPE']).lower(), erow['TILEID']
#############################################
######### Table manipulators ############
#############################################
def parse_previous_tables(etable, ptable, night):
"""
This takes in the exposure and processing tables and regenerates all the working memory variables needed for the
daily processing script.
Used by the daily processing to define most of its state-ful variables into working memory.
If the processing table is empty, these are simply declared and returned for use.
If the code had previously run and exited (or crashed), however, this will all the code to
re-establish itself by redefining these values.
Args:
etable, Table, Exposure table of all exposures that have been dealt with thus far.
ptable, Table, Processing table of all exposures that have been processed.
night, str or int, the night the data was taken.
Returns:
arcs, list of dicts, list of the individual arc jobs used for the psfnight (NOT all
the arcs, if multiple sets existed)
flats, list of dicts, list of the individual flat jobs used for the nightlyflat (NOT
all the flats, if multiple sets existed)
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile)
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
curtype, None, the obstype of the current job being run. Always None as first new job will define this.
lasttype, str or None, the obstype of the last individual exposure row to be processed.
curtile, None, the tileid of the current job (if science). Otherwise None. Always None as first
new job will define this.
lasttile, str or None, the tileid of the last job (if science). Otherwise None.
internal_id, int, an internal identifier unique to each job. Increments with each new job. This
is the latest unassigned value.
"""
log = get_logger()
arcs, flats, sciences = [], [], []
calibjobs = {'nightlybias': None, 'ccdcalib': None, 'psfnight': None,
'nightlyflat': None}
curtype,lasttype = None,None
curtile,lasttile = None,None
if len(ptable) > 0:
prow = ptable[-1]
internal_id = int(prow['INTID'])+1
lasttype,lasttile = get_type_and_tile(ptable[-1])
jobtypes = ptable['JOBDESC']
if 'nightlybias' in jobtypes:
calibjobs['nightlybias'] = table_row_to_dict(ptable[jobtypes=='nightlybias'][0])
log.info("Located nightlybias job in exposure table: {}".format(calibjobs['nightlybias']))
if 'ccdcalib' in jobtypes:
calibjobs['ccdcalib'] = table_row_to_dict(ptable[jobtypes=='ccdcalib'][0])
log.info("Located ccdcalib job in exposure table: {}".format(calibjobs['ccdcalib']))
if 'psfnight' in jobtypes:
calibjobs['psfnight'] = table_row_to_dict(ptable[jobtypes=='psfnight'][0])
log.info("Located joint fit psfnight job in exposure table: {}".format(calibjobs['psfnight']))
elif lasttype == 'arc':
seqnum = 10
for row in ptable[::-1]:
erow = etable[etable['EXPID']==row['EXPID'][0]]
if row['OBSTYPE'].lower() == 'arc' and int(erow['SEQNUM'])<seqnum:
arcs.append(table_row_to_dict(row))
seqnum = int(erow['SEQNUM'])
else:
break
## Because we work backword to fill in, we need to reverse them to get chronological order back
arcs = arcs[::-1]
if 'nightlyflat' in jobtypes:
calibjobs['nightlyflat'] = table_row_to_dict(ptable[jobtypes=='nightlyflat'][0])
log.info("Located joint fit nightlyflat job in exposure table: {}".format(calibjobs['nightlyflat']))
elif lasttype == 'flat':
for row in ptable[::-1]:
erow = etable[etable['EXPID']==row['EXPID'][0]]
if row['OBSTYPE'].lower() == 'flat' and int(erow['SEQTOT']) < 5:
if float(erow['EXPTIME']) > 100.:
flats.append(table_row_to_dict(row))
else:
break
flats = flats[::-1]
if lasttype.lower() == 'science':
for row in ptable[::-1]:
if row['OBSTYPE'].lower() == 'science' and row['TILEID'] == lasttile and \
row['JOBDESC'] == 'prestdstar' and row['LASTSTEP'] != 'skysub':
sciences.append(table_row_to_dict(row))
else:
break
sciences = sciences[::-1]
else:
internal_id = night_to_starting_iid(night)
return arcs,flats,sciences, \
calibjobs, \
curtype, lasttype, \
curtile, lasttile,\
internal_id
def update_and_recurvsively_submit(proc_table, submits=0, resubmission_states=None,
ptab_name=None, dry_run=0,reservation=None):
"""
Given an processing table, this loops over job rows and resubmits failed jobs (as defined by resubmission_states).
Before submitting a job, it checks the dependencies for failures. If a dependency needs to be resubmitted, it recursively
follows dependencies until it finds the first job without a failed dependency and resubmits that. Then resubmits the
other jobs with the new Slurm jobID's for proper dependency coordination within Slurm.
Args:
proc_table, Table, the processing table with a row per job.
submits, int, the number of submissions made to the queue. Used for saving files and in not overloading the scheduler.
resubmission_states, list or array of strings, each element should be a capitalized string corresponding to a
possible Slurm scheduler state, where you wish for jobs with that
outcome to be resubmitted
ptab_name, str, the full pathname where the processing table should be saved.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
Returns:
proc_table: Table, a table with the same rows as the input except that Slurm and jobid relevant columns have
been updated for those jobs that needed to be resubmitted.
submits: int, the number of submissions made to the queue. This is incremented from the input submits, so it is
the number of submissions made from this function call plus the input submits value.
Note:
This modifies the inputs of both proc_table and submits and returns them.
"""
log = get_logger()
if resubmission_states is None:
resubmission_states = get_resubmission_states()
log.info(f"Resubmitting jobs with current states in the following: {resubmission_states}")
proc_table = update_from_queue(proc_table, dry_run=False)
log.info("Updated processing table queue information:")
cols = ['INTID','EXPID','OBSTYPE','JOBDESC','TILEID','LATEST_QID','STATUS']
print(np.array(cols))
for row in proc_table:
print(np.array(row[cols]))
print("\n")
id_to_row_map = {row['INTID']: rown for rown, row in enumerate(proc_table)}
for rown in range(len(proc_table)):
if proc_table['STATUS'][rown] in resubmission_states:
proc_table, submits = recursive_submit_failed(rown, proc_table, submits,
id_to_row_map, ptab_name,
resubmission_states,
reservation, dry_run)
return proc_table, submits
def recursive_submit_failed(rown, proc_table, submits, id_to_row_map, ptab_name=None,
resubmission_states=None, reservation=None, dry_run=0):
"""
Given a row of a processing table and the full processing table, this resubmits the given job.
Before submitting a job, it checks the dependencies for failures in the processing table. If a dependency needs to
be resubmitted, it recursively follows dependencies until it finds the first job without a failed dependency and
resubmits that. Then resubmits the other jobs with the new Slurm jobID's for proper dependency coordination within Slurm.
Args:
rown, Table.Row, the row of the processing table that you want to resubmit.
proc_table, Table, the processing table with a row per job.
submits, int, the number of submissions made to the queue. Used for saving files and in not overloading the scheduler.
id_to_row_map, dict, lookup dictionary where the keys are internal ids (INTID's) and the values are the row position
in the processing table.
ptab_name, str, the full pathname where the processing table should be saved.
resubmission_states, list or array of strings, each element should be a capitalized string corresponding to a
possible Slurm scheduler state, where you wish for jobs with that
outcome to be resubmitted
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
Returns:
proc_table: Table, a table with the same rows as the input except that Slurm and jobid relevant columns have
been updated for those jobs that needed to be resubmitted.
submits: int, the number of submissions made to the queue. This is incremented from the input submits, so it is
the number of submissions made from this function call plus the input submits value.
Note:
This modifies the inputs of both proc_table and submits and returns them.
"""
log = get_logger()
row = proc_table[rown]
log.info(f"Identified row {row['INTID']} as needing resubmission.")
log.info(f"{row['INTID']}: Expid(s): {row['EXPID']} Job: {row['JOBDESC']}")
if resubmission_states is None:
resubmission_states = get_resubmission_states()
ideps = proc_table['INT_DEP_IDS'][rown]
if ideps is None:
proc_table['LATEST_DEP_QID'][rown] = np.ndarray(shape=0).astype(int)
else:
all_valid_states = list(resubmission_states.copy())
all_valid_states.extend(['RUNNING','PENDING','SUBMITTED','COMPLETED'])
for idep in np.sort(np.atleast_1d(ideps)):
if proc_table['STATUS'][id_to_row_map[idep]] not in all_valid_states:
log.warning(f"Proc INTID: {proc_table['INTID'][rown]} depended on" +
f" INTID {proc_table['INTID'][id_to_row_map[idep]]}" +
f" but that exposure has state" +
f" {proc_table['STATUS'][id_to_row_map[idep]]} that" +
f" isn't in the list of resubmission states." +
f" Exiting this job's resubmission attempt.")
return proc_table, submits
qdeps = []
for idep in np.sort(np.atleast_1d(ideps)):
if proc_table['STATUS'][id_to_row_map[idep]] in resubmission_states:
proc_table, submits = recursive_submit_failed(id_to_row_map[idep],
proc_table, submits,
id_to_row_map,
reservation=reservation,
dry_run=dry_run)
qdeps.append(proc_table['LATEST_QID'][id_to_row_map[idep]])
qdeps = np.atleast_1d(qdeps)
if len(qdeps) > 0:
proc_table['LATEST_DEP_QID'][rown] = qdeps
else:
log.error(f"number of qdeps should be 1 or more: Rown {rown}, ideps {ideps}")
proc_table[rown] = submit_batch_script(proc_table[rown], reservation=reservation,
strictly_successful=True, dry_run=dry_run)
submits += 1
if not dry_run:
sleep_and_report(1, message_suffix=f"after submitting job to queue")
if submits % 10 == 0:
if ptab_name is None:
write_table(proc_table, tabletype='processing', overwrite=True)
else:
write_table(proc_table, tablename=ptab_name, overwrite=True)
sleep_and_report(2, message_suffix=f"after writing to disk")
if submits % 100 == 0:
proc_table = update_from_queue(proc_table)
if ptab_name is None:
write_table(proc_table, tabletype='processing', overwrite=True)
else:
write_table(proc_table, tablename=ptab_name, overwrite=True)
sleep_and_report(10, message_suffix=f"after updating queue and writing to disk")
return proc_table, submits
#########################################
######## Joint fit ##############
#########################################
def joint_fit(ptable, prows, internal_id, queue, reservation, descriptor, z_submit_types=None,
dry_run=0, strictly_successful=False, check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Given a set of prows, this generates a processing table row, creates a batch script, and submits the appropriate
joint fitting job given by descriptor. If the joint fitting job is standard star fitting, the post standard star fits
for all the individual exposures also created and submitted. The returned ptable has all of these rows added to the
table given as input.
Args:
ptable, Table. The processing table where each row is a processed job.
prows, list or array of dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
queue, str. The name of the queue to submit the jobs to. If None is given the current desi_proc default is used.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
descriptor, str. Description of the joint fitting job. Can either be 'science' or 'stdstarfit', 'arc' or 'psfnight',
or 'flat' or 'nightlyflat'.
z_submit_types: list of str's. The "group" types of redshifts that should be submitted with each
exposure. If not specified or None, then no redshifts are submitted.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell or perlmutter-gpu
Returns:
ptable, Table. The same processing table as input except with added rows for the joint fit job and, in the case
of a stdstarfit, the poststdstar science exposure jobs.
joint_prow, dict. Row of a processing table corresponding to the joint fit job.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
"""
log = get_logger()
if len(prows) < 1:
return ptable, None, internal_id
if descriptor is None:
return ptable, None
elif descriptor == 'arc':
descriptor = 'psfnight'
elif descriptor == 'flat':
descriptor = 'nightlyflat'
elif descriptor == 'science':
if z_submit_types is None or len(z_submit_types) == 0:
descriptor = 'stdstarfit'
if descriptor not in ['psfnight', 'nightlyflat', 'science','stdstarfit']:
return ptable, None, internal_id
log.info(" ")
log.info(f"Joint fit criteria found. Running {descriptor}.\n")
if descriptor == 'science':
joint_prow = make_joint_prow(prows, descriptor='stdstarfit', internal_id=internal_id)
else:
joint_prow = make_joint_prow(prows, descriptor=descriptor, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
if descriptor in ['science','stdstarfit']:
if descriptor == 'science':
zprows = []
log.info(" ")
log.info(f"Submitting individual science exposures now that joint fitting of standard stars is submitted.\n")
for row in prows:
if row['LASTSTEP'] == 'stdstarfit':
continue
row['JOBDESC'] = 'poststdstar'
row['INTID'] = internal_id
internal_id += 1
row['ALL_QIDS'] = np.ndarray(shape=0).astype(int)
row = assign_dependency(row, joint_prow)
row = create_and_submit(row, queue=queue, reservation=reservation, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(row)
if descriptor == 'science' and row['LASTSTEP'] == 'all':
zprows.append(row)
## Now run redshifts
if descriptor == 'science' and len(zprows) > 0:
log.info(" ")
for zsubtype in z_submit_types:
if zsubtype == 'perexp':
for zprow in zprows:
log.info(f"Submitting redshift fit of type {zsubtype} for TILEID {zprow['TILEID']} and EXPID {zprow['EXPID']}.\n")
joint_prow = make_joint_prow([zprow], descriptor=zsubtype, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
else:
log.info(f"Submitting joint redshift fits of type {zsubtype} for TILEID {zprows[0]['TILEID']}.\n")
joint_prow = make_joint_prow(zprows, descriptor=zsubtype, internal_id=internal_id)
internal_id += 1
joint_prow = create_and_submit(joint_prow, queue=queue, reservation=reservation, joint=True, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
ptable.add_row(joint_prow)
if descriptor in ['psfnight', 'nightlyflat']:
log.info(f"Setting the calibration exposures as calibrators in the processing table.\n")
ptable = set_calibrator_flag(prows, ptable)
return ptable, joint_prow, internal_id
## wrapper functions for joint fitting
def science_joint_fit(ptable, sciences, internal_id, queue='realtime', reservation=None,
z_submit_types=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the stdstarfit joint fit and redshift fitting.
All variables are the same except:
Arg 'sciences' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'science'.
"""
return joint_fit(ptable=ptable, prows=sciences, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='science', z_submit_types=z_submit_types, dry_run=dry_run,
strictly_successful=strictly_successful, check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete, system_name=system_name)
def flat_joint_fit(ptable, flats, internal_id, queue='realtime',
reservation=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the nightlyflat joint fit.
All variables are the same except:
Arg 'flats' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'nightlyflat'.
"""
return joint_fit(ptable=ptable, prows=flats, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='nightlyflat', dry_run=dry_run, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs, resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name)
def arc_joint_fit(ptable, arcs, internal_id, queue='realtime',
reservation=None, dry_run=0, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Wrapper function for desiproc.workflow.procfuns.joint_fit specific to the psfnight joint fit.
All variables are the same except:
Arg 'arcs' is mapped to the prows argument of joint_fit.
The joint_fit argument descriptor is pre-defined as 'psfnight'.
"""
return joint_fit(ptable=ptable, prows=arcs, internal_id=internal_id, queue=queue, reservation=reservation,
descriptor='psfnight', dry_run=dry_run, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs, resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name)
def make_joint_prow(prows, descriptor, internal_id):
"""
Given an input list or array of processing table rows and a descriptor, this creates a joint fit processing job row.
It starts by copying the first input row, overwrites relevant columns, and defines the new dependencies (based on the
input prows).
Args:
prows, list or array of dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
descriptor, str. Description of the joint fitting job. Can either be 'stdstarfit', 'psfnight', or 'nightlyflat'.
internal_id, int, the next internal id to be used for assignment (already incremented up from the last used id number used).
Returns:
joint_prow, dict. Row of a processing table corresponding to the joint fit job.
"""
first_row = prows[0]
joint_prow = first_row.copy()
joint_prow['INTID'] = internal_id
joint_prow['JOBDESC'] = descriptor
joint_prow['LATEST_QID'] = -99
joint_prow['ALL_QIDS'] = np.ndarray(shape=0).astype(int)
joint_prow['SUBMIT_DATE'] = -99
joint_prow['STATUS'] = 'U'
joint_prow['SCRIPTNAME'] = ''
joint_prow['EXPID'] = np.array([ currow['EXPID'][0] for currow in prows ], dtype=int)
joint_prow = assign_dependency(joint_prow,dependency=prows)
return joint_prow
def checkfor_and_submit_joint_job(ptable, arcs, flats, sciences, calibjobs,
lasttype, internal_id, z_submit_types=None, dry_run=0,
queue='realtime', reservation=None, strictly_successful=False,
check_for_outputs=True, resubmit_partial_complete=True,
system_name=None):
"""
Takes all the state-ful data from daily processing and determines whether a joint fit needs to be submitted. Places
the decision criteria into a single function for easier maintainability over time. These are separate from the
new standard manifest*.json method of indicating a calibration sequence is complete. That is checked independently
elsewhere and doesn't interact with this.
Args:
ptable, Table, Processing table of all exposures that have been processed.
arcs, list of dicts, list of the individual arc jobs to be used for the psfnight (NOT all
the arcs, if multiple sets existed). May be empty if none identified yet.
flats, list of dicts, list of the individual flat jobs to be used for the nightlyflat (NOT
all the flats, if multiple sets existed). May be empty if none identified yet.
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile). May be empty if none identified yet.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
lasttype, str or None, the obstype of the last individual exposure row to be processed.
internal_id, int, an internal identifier unique to each job. Increments with each new job. This
is the smallest unassigned value.
z_submit_types: list of str's. The "group" types of redshifts that should be submitted with each
exposure. If not specified or None, then no redshifts are submitted.
dry_run, int, If nonzero, this is a simulated run. If dry_run=1 the scripts will be written or submitted. If
dry_run=2, the scripts will not be writter or submitted. Logging will remain the same
for testing as though scripts are being submitted. Default is 0 (false).
queue, str. The name of the queue to submit the jobs to. If None is given the current desi_proc default is used.
reservation: str. The reservation to submit jobs to. If None, it is not submitted to a reservation.
strictly_successful, bool. Whether all jobs require all inputs to have succeeded. For daily processing, this is
less desirable because e.g. the sciences can run with SVN default calibrations rather
than failing completely from failed calibrations. Default is False.
check_for_outputs, bool. Default is True. If True, the code checks for the existence of the expected final
data products for the script being submitted. If all files exist and this is True,
then the script will not be submitted. If some files exist and this is True, only the
subset of the cameras without the final data products will be generated and submitted.
resubmit_partial_complete, bool. Default is True. Must be used with check_for_outputs=True. If this flag is True,
jobs with some prior data are pruned using PROCCAMWORD to only process the
remaining cameras not found to exist.
system_name (str): batch system name, e.g. cori-haswell, cori-knl, permutter-gpu
Returns:
ptable, Table, Processing table of all exposures that have been processed.
calibjobs, dict. Dictionary containing 'nightlybias', 'ccdcalib', 'psfnight'
and 'nightlyflat'. Each key corresponds to a Table.Row or
None. The table.Row() values are for the corresponding
calibration job.
sciences, list of dicts, list of the most recent individual prestdstar science exposures
(if currently processing that tile). May be empty if none identified yet or
we just submitted them for processing.
internal_id, int, if no job is submitted, this is the same as the input, otherwise it is incremented upward from
from the input such that it represents the smallest unused ID.
"""
if lasttype == 'science' and len(sciences) > 0:
log = get_logger()
skysubonly = np.array([sci['LASTSTEP'] == 'skysub' for sci in sciences])
if np.all(skysubonly):
log.error("Identified all exposures in joint fitting request as skysub-only. Not submitting")
sciences = []
return ptable, calibjobs, sciences, internal_id
if np.any(skysubonly):
log.error("Identified skysub-only exposures in joint fitting request")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("LASTSTEP's: {}".format([row['LASTSTEP'] for row in sciences]))
sciences = (np.array(sciences,dtype=object)[~skysubonly]).tolist()
log.info("Removed skysub only exposures in joint fitting:")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("LASTSTEP's: {}".format([row['LASTSTEP'] for row in sciences]))
from collections import Counter
tiles = np.array([sci['TILEID'] for sci in sciences])
counts = Counter(tiles)
if len(counts.most_common()) > 1:
log.error("Identified more than one tile in a joint fitting request")
log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
log.info("Tileid's: {}".format(tiles))
log.info("Returning without joint fitting any of these exposures.")
# most_common, nmost_common = counts.most_common()[0]
# if most_common == -99:
# most_common, nmost_common = counts.most_common()[1]
# log.warning(f"Given multiple tiles to jointly fit: {counts}. "+
# "Only processing the most common non-default " +
# f"tile: {most_common} with {nmost_common} exposures")
# sciences = (np.array(sciences,dtype=object)[tiles == most_common]).tolist()
# log.info("Tiles and exposure id's being submitted for joint fitting:")
# log.info("Expid's: {}".format([row['EXPID'] for row in sciences]))
# log.info("Tileid's: {}".format([row['TILEID'] for row in sciences]))
sciences = []
return ptable, calibjobs, sciences, internal_id
ptable, tilejob, internal_id = science_joint_fit(ptable, sciences, internal_id, z_submit_types=z_submit_types,
dry_run=dry_run, queue=queue, reservation=reservation,
strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
if tilejob is not None:
sciences = []
elif lasttype == 'flat' and calibjobs['nightlyflat'] is None and len(flats) == 12:
## Note here we have an assumption about the number of expected flats being greater than 11
ptable, calibjobs['nightlyflat'], internal_id \
= flat_joint_fit(ptable, flats, internal_id, dry_run=dry_run, queue=queue,
reservation=reservation, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
elif lasttype == 'arc' and calibjobs['psfnight'] is None and len(arcs) == 5:
## Note here we have an assumption about the number of expected arcs being greater than 4
ptable, calibjobs['psfnight'], internal_id \
= arc_joint_fit(ptable, arcs, internal_id, dry_run=dry_run, queue=queue,
reservation=reservation, strictly_successful=strictly_successful,
check_for_outputs=check_for_outputs,
resubmit_partial_complete=resubmit_partial_complete,
system_name=system_name
)
return ptable, calibjobs, sciences, internal_id
def set_calibrator_flag(prows, ptable):
"""
Sets the "CALIBRATOR" column of a procesing table row to 1 (integer representation of True)
for all input rows. Used within joint fitting code to flag the exposures that were input
to the psfnight or nightlyflat for later reference.
Args:
prows, list or array of Table.Rows or dicts. The rows corresponding to the individual exposure jobs that are
inputs to the joint fit.
ptable, Table. The processing table where each row is a processed job.
Returns:
ptable, Table. The same processing table as input except with added rows for the joint fit job and, in the case
of a stdstarfit, the poststdstar science exposure jobs.
"""
for prow in prows:
ptable['CALIBRATOR'][ptable['INTID'] == prow['INTID']] = 1
return ptable
|
__script__.title = 'KKB Measurement Script'
__script__.version = '3.1'
from gumpy.commons import sics
from org.gumtree.gumnix.sics.control import ServerStatus
from pickle import Pickler, Unpickler
import time
from math import log as ln
from math import exp, isnan, isinf, sin
from __builtin__ import max as builtin_max
from __builtin__ import min as builtin_min
from org.eclipse.swt.widgets import FileDialog
from org.eclipse.swt import SWT
from org.eclipse.swt.widgets import Display
from java.io import File
from gumpy.nexus.fitting import Fitting, GAUSSIAN_FITTING
import math
from Internal import sample_stage
'''
Disable dataset caching
'''
DatasetFactory.__cache_enabled__ = False
SINGLE_TYPE = SWT.SINGLE
SAVE_TYPE = SWT.SAVE
MULTI_TYPE = SWT.MULTI
class __Display_Runnable__(Runnable):
def __init__(self, type=SINGLE_TYPE, ext=['*.*']):
self.filename = None
self.filenames = None
self.path = None
self.type = type
self.ext = ext
def run(self):
global __UI__
dialog = FileDialog(__UI__.getShell(), self.type);
dialog.setFilterExtensions(self.ext)
dialog.open()
self.filename = dialog.getFilterPath() + File.separator + dialog.getFileName()
self.filenames = dialog.getFileNames()
self.path = dialog.getFilterPath()
def open_file_dialog(type=SWT.SINGLE, ext=['*.*']):
__display_run__ = __Display_Runnable__(type, ext)
Display.getDefault().asyncExec(__display_run__)
while __display_run__.filename is None:
time.sleep(0.5)
if type == SWT.MULTI:
fns = []
for fn in __display_run__.filenames:
fns.append(__display_run__.path + '/' + fn)
return fns
return __display_run__.filename
# # templates
reference_templates_dict = {}
reference_templates_dict['Si111'] = 180.3565
reference_templates_dict['Si311'] = -0.4100
steps_templates_list = []
# steps_templates['Background'] = [
# 'time', 'logscale',
# [20, 6.0e-5, 1200, 1200]]
# steps_templates['----------'] = [
# 'time', 'logscale',
# [0, 0, 0, 0]]
steps_templates_list.append([
'Si111: Logarithmic Overview Scan',
'time',
'logscale',
[17, 1.20e-4, 1, 1200],
[30, 22.0, 20, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (few features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[34, 20.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (fine features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[65, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Taiki Scan (15 points)',
'ba',
'logscale',
[2, 6.0e-5, 1000, 60],
[1, 10000, 1000, 60],
[10, 25, 1000, 60]])
'''
steps_templates_list.append([
'Si111: Kinetic Scan 4 points',
'time',
'logscale',
[ 0, 6.0e-5, 1, 1200],
[1, 5.0e-3, 180, 1200],
[3, 1.5e-2, 180, 1200]])
'''
steps_templates_list.append([
'----------',
'time',
'logscale',
[0, 0, 0, 0]])
steps_templates_list.append([
'Si311: Logarithmic Overview Scan',
'time',
'logscale',
[17, 2.0e-5, 1, 1200],
[30, 23.0, 20, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 80+29)',
'ba',
'logscale',
[80, 2e-5, 1000, 1200],
[29, 15.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 40+33)',
'ba',
'logscale',
[40, 2e-5, 1000, 1200],
[33, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, Taiki)',
'ba',
'logscale',
[33, 2e-5, 1000, 1200],
[25, 20.0, 1000, 1200]])
ret = sample_stage.check_declarations()
if not ret[0] :
open_warning(ret[1])
reload(sample_stage)
SAMPLE_STAGES = sample_stage.StagePool()
# # export path
__EXPORT_PATH__ = 'V:/shared/KKB Logbook/Temp Plot Data Repository/'
if not os.path.exists(__EXPORT_PATH__):
os.makedirs(__EXPORT_PATH__)
# # User Details
user_name = Par('string', 'Christine', options=['Christine', 'Lela', 'Jitendra'])
user_name.title = 'Name'
user_email = Par('string', 'cre@ansto.gov.au', options=['cre@ansto.gov.au', 'liliana.decampo@ansto.gov.au', 'jtm@ansto.gov.au'])
user_email.title = 'EMail'
g0 = Group('User Details')
g0.numColumns = 2
g0.add(user_name, user_email)
# # Sample Details
sample_name = Par('string', 'UNKNOWN', options=['Empty Cell', 'Empty Beam'], command="sample_thickness.enabled = sample_name.value not in ['Empty Cell', 'Empty Beam']")
sample_name.title = 'Name'
sample_description = Par('string', 'UNKNOWN')
sample_description.title = 'Description'
sample_thickness = Par('string', '1', options=['0.01', '0.1', '1.0', '10.0'])
sample_thickness.title = 'Thickness (mm)'
g0 = Group('Sample Details')
g0.numColumns = 2
g0.add(sample_name, sample_thickness, sample_description)
# Group('Sample Details').add(sample_name, sample_description, sample_thickness)
# # Crystal
crystal_name = Par('string', 'UNKNOWN')
crystal_name.title = 'Name'
crystal_name.enabled = False
try:
m2om = sics.getValue('/instrument/crystal/m2om').getFloatData()
if m2om > 90:
crystal_name.value = 'Si111 (4.74 Angstroms)'
else:
crystal_name.value = 'Si311 (2.37 Angstroms)'
except:
pass
g0 = Group('Crystal Info')
g0.numColumns = 2
g0.add(crystal_name)
# CRYSTAL END #############################################
# SLIT 1 #######################################################################
def updateOffset(gapBox, offsetBox):
offsetBox.enabled = 'fully' not in gapBox.value
def getSlitGapAndOffset(aPath, a0, bPath, b0):
try:
a = sics.getValue(aPath).getFloatData()
b = sics.getValue(bPath).getFloatData()
gap = (a - a0 - (b - b0)) / 1.0
offset = (a - a0 + (b - b0)) / 2.0
return (gap, offset)
except:
return (float('nan'), float('nan'))
crystal = str(crystal_name.value)
if 'Si111' in crystal:
ss1r0 = 28.35
ss1l0 = 27.75
elif 'Si311' in crystal:
ss1r0 = -9.16
ss1l0 = -9.76
else:
ss1r0 = float('nan')
ss1l0 = float('nan')
ss1u0 = -8.04
ss1d0 = -7.30
(ss1vg, ss1vo) = getSlitGapAndOffset('/instrument/slits/ss1u', ss1u0, '/instrument/slits/ss1d', ss1d0)
(ss1hg, ss1ho) = getSlitGapAndOffset('/instrument/slits/ss1r', ss1r0, '/instrument/slits/ss1l', ss1l0)
pss_ss1vg = Par('string', '%.1f' % ss1vg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1vg, pss_ss1vo)')
pss_ss1vg.title = 'Vertical Gap (mm)'
# pss_ss1vg.colspan = 50
pss_ss1vo = Par('float', ss1vo)
pss_ss1vo.title = 'Vertical Offset (mm)'
# pss_ss1vo.colspan = 50
pss_ss1hg = Par('string', '%.1f' % ss1hg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1hg, pss_ss1ho)')
pss_ss1hg.title = 'Horizontal Gap (mm)'
# pss_ss1hg.colspan = 50
pss_ss1ho = Par('float', ss1ho)
pss_ss1ho.title = 'Horizontal Offset (mm)'
# pss_ss1ho.colspan = 50
g0 = Group('Sample Slit Settings')
g0.numColumns = 2
g0.add(pss_ss1vg, pss_ss1vo, pss_ss1hg, pss_ss1ho)
# SLIT 1 END #######################################################################
# SAMPLE ENVIRONMENT BLOCK #########################################################
gse = Group('Sample Environment')
gse.numColumns = 10
se_enabled1 = Par('bool', False, command = 'toggle_se(1)')
se_enabled1.title = 'Controller 1'
se_enabled1.colspan = 1
se_ctr1 = Par('string', '', options = [])
se_ctr1.title = 'name'
se_ctr1.colspan = 2
se_ctr1.enabled = False
se_pos1 = Par('float', 0.)
se_pos1 .title = 'Values'
se_pos1.colspan = 6
se_pos1.enabled = False
se_wait1 = Par('int', 0)
se_wait1 .title = 'Wait'
se_wait1.colspan = 1
se_wait1.enabled = False
se_enabled2 = Par('bool', False, command = 'toggle_se(2)')
se_enabled2.title = 'Controller 2'
se_enabled2.colspan = 1
se_ctr2 = Par('string', '', options = [])
se_ctr2.title = 'name'
se_ctr2.colspan = 2
se_ctr2.enabled = False
se_pos2 = Par('float', 0.)
se_pos2 .title = 'Values'
se_pos2.colspan = 6
se_pos2.enabled = False
se_wait2 = Par('int', 0)
se_wait2 .title = 'Wait'
se_wait2.colspan = 1
se_wait2.enabled = False
se_enabled3 = Par('bool', False, command = 'toggle_se(3)')
se_enabled3.title = 'Controller 3'
se_enabled3.colspan = 1
se_ctr3 = Par('string', '', options = [])
se_ctr3.title = 'name'
se_ctr3.colspan = 2
se_ctr3.enabled = False
se_pos3 = Par('float', 0.)
se_pos3 .title = 'Values'
se_pos3.colspan = 6
se_pos3.enabled = False
se_wait3 = Par('int', 0)
se_wait3 .title = 'Wait'
se_wait3.colspan = 1
se_wait3.enabled = False
gse.add(se_enabled1, se_ctr1, se_pos1, se_wait1,
se_enabled2, se_ctr2, se_pos2, se_wait2,
se_enabled3, se_ctr3, se_pos3, se_wait3,)
devices = sicsext.getDrivables()
se_ctr1.options = devices
se_ctr2.options = devices
se_ctr3.options = devices
def toggle_se(id):
id = int(id)
if id == 1:
flag = se_enabled1.value
se_ctr1.enabled = flag
se_pos1.enabled = flag
se_wait1.enabled = flag
elif id == 2:
flag = se_enabled2.value
se_ctr2.enabled = flag
se_pos2.enabled = flag
se_wait2.enabled = flag
elif id == 3:
flag = se_enabled3.value
se_ctr3.enabled = flag
se_pos3.enabled = flag
se_wait3.enabled = flag
else:
raise 'illegal index for sample environment'
# SAMPLE ENVIRONMENT BLOCK END #####################################################
## Scan parameters ##########################################################################################################
scan_variable = Par('string', 'm2om [deg]', options=[
#'pmom [deg]', 'pmchi [deg]',
'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'], command="scan_variable_plot.value = scan_variable.value")
scan_variable.title = 'Scan Variable'
scan_variable.colspan = 25
scan_reference = Par('float', '0.0')
scan_reference.title = 'Zero Angle'
scan_reference.colspan = 25
for key in reference_templates_dict.keys():
if key in crystal_name.value:
scan_reference.value = reference_templates_dict[key]
scan_mode = Par('string', 'ba', options=['ba', 'time'], command='setScanMode()')
scan_mode.title = 'Acquisition Mode'
scan_mode.colspan = 25
scan_min_time = Par('int', '5')
scan_min_time.title = 'Min Time (sec)'
scan_min_time.colspan = 25
empty_label = Par('label', '')
empty_label.colspan = 25
scan_sample_stage = Par('string', '', command = 'sample_stage_changed()')
scan_sample_stage.colspan = 25
scan_sample_stage.title = 'Sample Stage'
scan_sample_stage.options = SAMPLE_STAGES.get_stage_names()
current_stage = SAMPLE_STAGES.get_stage_in_service()
if not current_stage is None:
scan_sample_stage.value = current_stage.get_name()
scan_sample_position = Par('string', 'fixed')
scan_sample_position.title = 'Sample Position'
scan_sample_position.colspan = 25
scan_sample_position.options = ['fixed', '----------']
if not current_stage is None:
scan_sample_position.options += current_stage.get_sample_indexes()
logscale_position = Par('bool', False, command='setStepTitles()')
logscale_position.title = 'Logarithmic Steps'
logscale_position.colspan = 25
negative_steps = Par('bool', False)
negative_steps.title = 'Negative Steps'
negative_steps.colspan = 25
steps_label = Par('label', 'Please choose scan template or adjust steps manually: ')
steps_label.colspan = 200
steps_templates = Par('string', '', options=[item[0] for item in steps_templates_list], command='setTemplate()')
steps_templates.title = 'Scan Template'
steps_templates.colspan = 100
early_exit_enabled = Par('bool', True, command = "set_early_exit_enabled()")
early_exit_enabled.title = "Enable Early Exit"
early_exit_enabled.colspan = 25
background_frames = Par('int', 3)
background_frames.title = 'Background Frames'
background_frames.colspan = 25
background_threshold = Par('float', 0.26)
background_threshold.title = 'Background Threshold'
background_threshold.colspan = 25
# steps_space = Par('space', '')
# steps_space.colspan = 10
g0 = Group('Scan Parameters')
g0.numColumns = 100 # 9
g0.add(scan_variable, scan_mode, scan_reference, early_exit_enabled, \
logscale_position, scan_min_time, scan_sample_stage, background_frames, \
negative_steps, empty_label, scan_sample_position, background_threshold, \
steps_label, steps_templates)
def sample_stage_changed():
stage = SAMPLE_STAGES.get_stage_by_name(str(scan_sample_stage.value))
# scan_sample_position.value = 'fixed'
if not stage is None:
scan_sample_position.options = ['fixed', '----------'] + stage.get_sample_indexes()
else:
scan_sample_position.options = ['fixed', '----------']
def set_early_exit_enabled():
if early_exit_enabled.value:
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
stepInfo = []
for i in xrange(4):
steps_e = Par('bool', True, command='setEnabled(%i)' % i)
steps_e.title = '(%i)' % (i + 1)
steps_e.colspan = 10
steps_m = Par('int', 0, command='clearScanTemplateSelection()')
steps_m.title = 'Number of points'
steps_m.colspan = 20
steps_s = Par('float', 0, command='clearScanTemplateSelection()')
steps_s.title = 'Step Size [deg]'
steps_s.colspan = 20
steps_p = Par('int', 0, command='clearScanTemplateSelection()')
steps_p.title = 'Mode Preset'
steps_p.colspan = 25
steps_t = Par('int', 1200, command='clearScanTemplateSelection()')
steps_t.title = 'Max Time'
steps_t.colspan = 25
stepInfo.append({'enabled': steps_e, 'dataPoints':steps_m, 'stepSize':steps_s, 'preset':steps_p, 'maxTime':steps_t})
g0.add(steps_e, steps_m, steps_s, steps_p, steps_t)
def clearScanTemplateSelection():
steps_templates.value = None
btnPlotSteps = Act('btnPlotSteps_clicked()', 'Plot Measurement Steps') # 'compare measurement steps with previous scan')
btnPlotSteps.colspan = 50
cnfg_save_btn = Act('saveConfiguration()', 'Save Single Scan Parameters')
cnfg_save_btn.colspan = 50
btnTimeEstimation = Act('runTimeEstimation()', 'Time Estimation with selected Data Set')
btnTimeEstimation.colspan = 50
txtTimeEstimation = Par('int', '0')
txtTimeEstimation.title = 'Time Estimation (min)'
txtTimeEstimation.enabled = False
txtTimeEstimation.colspan = 50
g0.add(btnPlotSteps, cnfg_save_btn, btnTimeEstimation, txtTimeEstimation)
def runTimeEstimation():
if str(scan_mode.value) == 'time':
scan = getScan()
times = scan['presets']
txtTimeEstimation.value = int((sum(times) + len(times) * 25) / 60.0) # 25 seconds for each move
return
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
ds = openDataset(fns[0])
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
data[:] = [data[item[0]] for item in info] # sorting
# angle and count rate
a0 = [float(angle) for angle in scanVariable]
r0 = [float(rate) for rate in data[:]]
# angle, counts, max time and min time
model = ConfigurationModel()
scan = model.scan
tMin = model.min_time
a1 = scan['angles']
c1 = scan['presets']
t1 = scan['maxTimes']
total = 0.0
for i in xrange(len(a1)):
try:
rate = sample(a0, r0, a1[i])
time = c1[i] / rate
if time < tMin:
total += tMin
elif time > t1[i]:
total += t1[i]
else:
total += time
#print ("angle: " + str(a1[i])
# + " expected counts: " + str(c1[i])
# + " rate:" + str(rate)
# + " time:" + str(time)
# + " total:" + str(total))
except ValueError as e:
if e.message == "OutOfRange":
total += t1[i] # add max time
else:
raise
total += int(len(a1) * 25) # 25 seconds for each move
txtTimeEstimation.value = int(total / 60.0)
def sample(x0, y0, x1):
from __builtin__ import max, min
if len(x0) != len(y0):
raise Exception("len(x0) != len(y0)")
x0_min = min(x0)
x0_max = max(x0)
if len(x0) < 2:
raise Exception("len(x0) < 2")
if x0_min >= x0_max:
raise Exception("x0_min >= x0_max")
if x1 < x0_min:
raise ValueError("OutOfRange")
if x0_max < x1:
raise ValueError("OutOfRange")
i0 = 0
i1 = 1
x0i0 = x0[i0]
y0i0 = y0[i0]
x0i1 = x0[i1]
y0i1 = y0[i1]
# in case first x values are equal
while x0i0 == x0i1:
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
# not iterable
while x0i1 < x1:
x0i0 = x0i1
y0i0 = y0i1
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
return y0i0 + (x1 - x0i0) * (y0i1 - y0i0) / (x0i1 - x0i0)
## Scan parameters END #########################################################################
## RUN ##############################################
cnfg_load_btn = Act('loadConfigurations()', 'Load Multiple Scan Parameters')
cnfg_lookup = dict()
cnfg_options = Par('string', '', options=[''], command="applyConfiguration()")
cnfg_options.title = 'Read'
start_scan = Act('startScan(ConfigurationModel())', '############# Run Single Scan #############')
cnfg_run_btn = Act('runConfigurations()', '############# Run Multiple Scans #############')
g0 = Group('Execute Scans')
g0.numColumns = 1
g0.add(start_scan, cnfg_load_btn, cnfg_options, cnfg_run_btn)
## Save/Load Configuration END############################################################################
def saveConfiguration():
file = open_file_dialog(type=SAVE_TYPE, ext=['*.kkb'])
try:
fh = open(file, 'w')
except:
print 'not saved'
return
try:
p = Pickler(fh)
# header
p.dump('KKB')
# content
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
p.dump(att)
p.dump(att_value)
print 'saved'
finally:
fh.close()
def loadConfigurations():
fileList = open_file_dialog(type=MULTI_TYPE, ext=['*.kkb'])
if not fileList:
return
finalDict = dict()
finalNames = []
for path in fileList:
fh = open(path, 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', os.path.basename(path)
else:
model = ConfigurationModel()
# set defaults
model.negative = False # old models may not have this attribute
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', os.path.basename(path)
break
setattr(model, att, p.load())
else:
name = os.path.basename(path)
finalDict[name] = path
finalNames.append(name)
finally:
fh.close()
cnfg_lookup.clear()
cnfg_lookup.update(finalDict)
cnfg_options.options = finalNames
cnfg_options.value = finalNames[0] if finalNames else ''
# time.sleep(0.5)
def applyConfiguration():
file = str(cnfg_options.value)
if file is None or file == 'None' or file.strip() == '':
return
fh = open(cnfg_lookup[file], 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
# print 'read:', file
model.apply()
finally:
fh.close()
def runConfigurations():
for file in cnfg_options.options:
fh = open(cnfg_lookup[file], 'r')
try:
cnfg_options.command = ''
cnfg_options.value = file
applyConfiguration()
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
print 'run:', file
startScan(model)
finally:
cnfg_options.command = 'applyConfiguration()'
fh.close()
# # Plot
tubes_label = Par('label', 'Main Detector:')
tubes_label.colspan = 1
combine_tube0 = Par('bool', True)
combine_tube0.title = ' Tube 0'
combine_tube0.colspan = 1
combine_tube1 = Par('bool', True)
combine_tube1.title = ' Tube 1'
combine_tube1.colspan = 1
combine_tube2 = Par('bool', True)
combine_tube2.title = ' Tube 2'
combine_tube2.colspan = 1
combine_tube3 = Par('bool', True)
combine_tube3.title = ' Tube 3'
combine_tube3.colspan = 1
combine_tube4 = Par('bool', True)
combine_tube4.title = ' Tube 4'
combine_tube4.colspan = 1
combine_tube6 = Par('bool', False)
combine_tube6.title = ' Tube 6'
combine_tube6.colspan = 1
combine_mode = Par('string', 'combined', options=['individual', 'combined'])
combine_mode.title = ' Mode'
combine_mode.colspan = 1
trans_tube_label = Par('label', 'Trans Detector: ')
trans_tube_label.colspan = 2
check_tube9 = Par('bool', True)
check_tube9.title = ' Tube 9: Si (311)'
check_tube9.colspan = 2
check_tube10 = Par('bool', False)
check_tube10.title = ' Tube 10: Si (111)'
check_tube10.colspan = 2
# steps_space = Par('space', '')
# steps_space.colspan = 12
scan_variable_plot = Par('string', 'm2om [deg]', options=[
'pmom [deg]', 'pmchi [deg]', 'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'])
scan_variable_plot.title = 'Scan Variable'
scan_variable_plot.colspan = 1
scan_variable_sorting = Par('bool', True)
scan_variable_sorting.title = 'Sorting'
scan_variable_sorting.colspan = 1
btnPlot = Act('btnPlot_clicked()', 'Plot Selected Data Set')
btnPlot.colspan = 8
g0 = Group('Plotting')
g0.numColumns = 7
g0.add(tubes_label, combine_tube0, combine_tube1, combine_tube2, combine_tube3, combine_tube4, combine_tube6, combine_mode, trans_tube_label, check_tube9, check_tube10, scan_variable_plot, scan_variable_sorting, btnPlot)
# export to csv
# btnExport = Act('export_clicked()', 'Export to CSV')
################################# SLIT 2 ##########################################################
ss2u0 = 0 # 2.00
ss2d0 = 0 # 1.40
ss2l0 = 0 # 5 0.50
ss2r0 = 0 # -2 -1.00
(ss2vg, ss2vo) = getSlitGapAndOffset('/instrument/slits/ss2u', ss2u0, '/instrument/slits/ss2d', ss2d0)
(ss2hg, ss2ho) = getSlitGapAndOffset('/instrument/slits/ss2r', ss2r0, '/instrument/slits/ss2l', ss2l0)
pss_ss2vg = Par('string', '%.1f' % ss2vg, options=pss_ss1vg.options, command='updateOffset(pss_ss2vg, pss_ss2vo)')
pss_ss2vg.title = 'Vertical Opening (mm)'
pss_ss2vo = Par('float', ss2vo)
pss_ss2vo.title = 'Vertical Offset (mm)'
pss_ss2hg = Par('string', '%.1f' % ss2hg, options=pss_ss1hg.options, command='updateOffset(pss_ss2hg, pss_ss2ho)')
pss_ss2hg.title = 'Horizontal Opening (mm)'
pss_ss2ho = Par('float', ss2ho)
pss_ss2ho.title = 'Horizontal Offset (mm)'
g0 = Group('Post-Sample Slit')
g0.numColumns = 2
g0.add(pss_ss2vg, pss_ss2vo, pss_ss2hg, pss_ss2ho)
################################# SLIT 2 END ##########################################################
################################# CURVE FITTING START ##########################################################
g_fit = Group('Fitting')
g_fit.numColumns = 2
#data_name = Par('string', 'total_counts', \
# options = ['total_counts', 'bm1_counts', 'bm2_counts'])
#normalise = Par('bool', True)
#axis_name = Par('string', '')
#axis_name.enabled = True
#auto_fit = Par('bool', False)
#fit_min = Par('float', 'NaN')
#fit_min.title = 'min x'
#fit_max = Par('float', 'NaN')
#fit_max.title = 'max x'
peak_pos = Par('float', 'NaN')
peak_pos.title = 'fitting peak position'
FWHM = Par('float', 'NaN')
FWHM.title = 'fitting FWHM'
fact = Act('fit_curve()', 'Fit Again')
fact.colspan = 2
#offset_done = Par('bool', False)
#act3 = Act('offset_s2()', 'Set Device Zero Offset')
g_fit.add(peak_pos, FWHM, fact)
def fit_curve():
global Plot1
ds = Plot1.ds
if len(ds) == 0:
log('Error: no curve to fit in Plot1.\n')
return
for d in ds:
if d.title == 'fitting':
Plot1.remove_dataset(d)
d0 = ds[0]
fitting = Fitting(GAUSSIAN_FITTING)
try:
fitting.set_histogram(d0)
fitting.fitter.setResolutionMultiple(50)
val = peak_pos.value
if val == val:
fitting.set_param('mean', val)
val = FWHM.value
if val == val:
fitting.set_param('sigma', math.fabs(val / 2.35482))
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot1.add_dataset(res)
Plot1.pv.getPlot().setCurveMarkerVisible(Plot1.__get_NXseries__(res), False)
mean = fitting.params['mean']
mean_err = fitting.errors['mean']
FWHM.value = 2.35482 * math.fabs(fitting.params['sigma'])
FWHM_err = 5.54518 * math.fabs(fitting.errors['sigma'])
log('POS_OF_PEAK=' + str(mean) + ' +/- ' + str(mean_err))
log('FWHM=' + str(FWHM.value) + ' +/- ' + str(FWHM_err))
log('Chi2 = ' + str(fitting.fitter.getQuality()))
peak_pos.value = fitting.mean
# print fitting.params
except:
# traceback.print_exc(file = sys.stdout)
log('can not fit\n')
################################# CURVE FITTING END ##########################################################
def waitUntilSicsIs(status, dt=0.2):
controller = sics.getSicsController()
timeout = 5
while True:
sics.handleInterrupt()
count = 0
while not controller.getServerStatus().equals(status) and count < timeout:
time.sleep(dt)
count += dt
if controller.getServerStatus().equals(status):
break
else:
controller.refreshServerStatus()
sics.handleInterrupt()
def setStepTitles():
if logscale_position.value:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Factor [%]"
else:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Size [deg]"
__UI__.updateUI()
def setTemplate():
try:
matches = [item for item in steps_templates_list if item[0] == steps_templates.value]
if len(matches) != 1:
steps_templates.value = None
return
template = matches[0]
# ignore '----'
if template[0][0] == '-':
steps_templates.value = None
return
scan_mode.value = template[1]
if template[2] == 'logscale':
logscale_position.value = True
elif template[2] == 'linear':
logscale_position.value = False
setStepTitles()
# by default templates measure in positive direction
negative_steps.value = False
setScanMode()
headers = 3
for i in xrange(len(template) - headers):
templateItem = template[i + headers]
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = True
stepInfoItem['dataPoints'].enabled = True
stepInfoItem['dataPoints'].value = templateItem[0]
stepInfoItem['stepSize' ].enabled = True
stepInfoItem['stepSize' ].value = templateItem[1]
stepInfoItem['preset' ].enabled = True
stepInfoItem['preset' ].value = templateItem[2]
stepInfoItem['maxTime' ].enabled = scan_min_time.enabled
stepInfoItem['maxTime' ].value = templateItem[3]
for i in xrange(len(template) - headers, len(stepInfo)):
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = False
stepInfoItem['dataPoints'].enabled = False
stepInfoItem['stepSize' ].enabled = False
stepInfoItem['preset' ].enabled = False
stepInfoItem['maxTime' ].enabled = False
except:
pass
def setScanMode():
if scan_mode.value == 'time':
scan_min_time.enabled = False
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = False
else:
scan_min_time.enabled = True
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = stepInfoItem['enabled'].value
def setEnabled(index):
steps_templates.value = None
stepItem = stepInfo[index]
value = stepItem['enabled'].value
stepItem['dataPoints'].enabled = value
stepItem['stepSize' ].enabled = value
stepItem['preset' ].enabled = value
stepItem['maxTime' ].enabled = value and scan_min_time.enabled
setTemplate()
def getScan():
scan = { 'angles': [], 'presets': [], 'maxTimes': [], 'groups': [] }
first = True
angle_ref = scan_reference.value
angle = angle_ref
logscale = False # first data points are always on a linear scale
negative = bool(negative_steps.value)
for stepInfoItem in stepInfo:
if stepInfoItem['enabled'].value:
dataPoints = stepInfoItem['dataPoints'].value
stepSize = stepInfoItem['stepSize' ].value
preset = stepInfoItem['preset' ].value
maxTime = stepInfoItem['maxTime' ].value
if (dataPoints > 0) and (stepSize <= 0.0):
raise Exception('step sizes have to be positive')
for i in xrange(dataPoints):
if first and (i == 0):
angle -= ((dataPoints - 1) / 2.0) * stepSize;
elif logscale:
# for logscale stepSize is a stepFactor
angle = angle_ref + (angle - angle_ref) * (1.0 + 0.01 * stepSize)
else:
angle += stepSize
#print angle
scan['angles' ].append(angle)
scan['presets' ].append(preset)
scan['maxTimes'].append(maxTime)
if i == 0:
scan['groups'].append(angle)
first = False
logscale = bool(logscale_position.value)
if negative:
# negate angles with reference to zero angle
scan['angles'] = [angle_ref - (angle - angle_ref) for angle in scan['angles']]
return scan
def wait_for_idle():
c_time = time.time()
while not sics.getSicsController().getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
time.sleep(0.1)
if time.time() - c_time > 5:
serverStatus = sics.get_status()
c_time = time.time()
def startScan(configModel):
''' check instrument ready '''
all_ready = False
is_ready = False
is_shielded = False
msg = None
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
is_shielded = sics.getValue('/instrument/GreenPolyShield/greenpolyshield').getStringData().lower() == 'in'
if not is_ready:
if not is_shielded:
msg = 'The instrument is not ready and the green polyshield is not applied. ' \
+ 'Please get the '\
+ 'instrument ready and apply the polyshield. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
else:
msg = 'The instrument is not ready according to the SIS status. ' \
+ 'Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
else:
if not is_shielded:
msg = 'The green polyshield is not applied. ' \
+ 'Please apply the polyshield. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
all_ready = is_ready and is_shielded
except:
pass
if not all_ready:
if not msg:
msg = 'The instrument is not ready according to the SIS status. ' \
+ 'Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
is_confirmed = open_question(msg)
if not is_confirmed:
slog('Instrument is not ready. Quit the scan.')
return
else:
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
is_shielded = sics.getValue('/instrument/GreenPolyShield/greenpolyshield').getStringData().lower() == 'in'
except:
pass
if not is_ready:
slog('scan continued without instrument ready')
if not is_shielded:
slog('scan continued without green polysheild')
''' setup '''
scanVariable = configModel.scanVariable
crystal = configModel.crystal
mode = configModel.mode
MainDeadTime = 1.08E-6
TransDeadTime = 1.08E-6
if 'Si111' in crystal:
empLevel = 0.3
bkgLevel = 0.21
dOmega = 2.3E-6
gDQv = 0.0586
gDQh = 0
wavelength = 4.74
TransmissionTube = 10
TransBackground = 0 # counts per second
elif 'Si311' in crystal:
empLevel = 0.34
bkgLevel = 0.21
dOmega = 4.6E-7
gDQv = 0.117
gDQh = 0
wavelength = 2.37
TransmissionTube = 9
TransBackground = 0 # counts per second
else:
print 'selected crystal is invalid'
return
''' angles '''
scan = configModel.scan
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if ('m1om' in scanVariable) or ('m2om' in scanVariable):
tolerance = 6
approved = False
if 'Si111' in crystal:
if (180 - tolerance <= scan_angleMin) and (scan_angleMax <= 180 + tolerance):
approved = True
elif 'Si311' in crystal:
if (0 - tolerance <= scan_angleMin) and (scan_angleMax <= 0 + tolerance):
approved = True
if not approved:
print 'angle out of range'
return
''' execution '''
sics.execute('hset user/name ' + configModel.user_name)
sics.execute('hset user/email ' + configModel.user_email)
sics.execute('hset sample/name ' + configModel.sample_name)
sics.execute('hset sample/description ' + configModel.sample_description)
sics.execute('hset sample/thickness %g' % configModel.sample_thickness)
sics.execute('hset experiment/bkgLevel %g' % bkgLevel)
sics.execute('hset experiment/empLevel %g' % empLevel)
sics.execute('hset instrument/detector/MainDeadTime %g' % MainDeadTime)
sics.execute('hset instrument/detector/TransDeadTime %g' % TransDeadTime)
sics.execute('hset instrument/detector/TransBackground %g' % TransBackground)
sics.execute('hset instrument/detector/TransmissionTube %i' % TransmissionTube)
sics.execute('hset instrument/crystal/dOmega %g' % dOmega)
sics.execute('hset instrument/crystal/gDQv %g' % gDQv)
sics.execute('hset instrument/crystal/gDQh %g' % gDQh)
sics.execute('hset instrument/crystal/wavelength %g' % wavelength)
sics.execute('hset instrument/crystal/scan_variable ' + scanVariable);
sicsController = sics.getSicsController()
# slits
def getSlitValues(gap, offset, a0, b0, aOpen, bOpen):
if gap == 'fully opened':
return (aOpen, bOpen)
if gap == 'fully closed':
gap = -5.0
offset = 0.0
a = a0 + 0.5 * float(gap) + float(offset)
b = b0 - 0.5 * float(gap) + float(offset)
return (a, b)
ss1vg = configModel.ss1vg
ss1vo = configModel.ss1vo
ss1hg = configModel.ss1hg
ss1ho = configModel.ss1ho
ss2vg = configModel.ss2vg
ss2vo = configModel.ss2vo
ss2hg = configModel.ss2hg
ss2ho = configModel.ss2ho
(ss1u, ss1d) = getSlitValues(ss1vg, ss1vo, ss1u0, ss1d0, 35.8, -38.8)
(ss1r, ss1l) = getSlitValues(ss1hg, ss1ho, ss1r0, ss1l0, 57.0, -58.0)
(ss2u, ss2d) = getSlitValues(ss2vg, ss2vo, ss2u0, ss2d0, 37.0, -39.5)
(ss2r, ss2l) = getSlitValues(ss2hg, ss2ho, ss2r0, ss2l0, 35.0, -35.0)
# apply slits
run = {}
run['ss1u'] = ss1u
run['ss1d'] = ss1d
run['ss1r'] = ss1r
run['ss1l'] = ss1l
run['ss2u'] = ss2u
run['ss2d'] = ss2d
run['ss2r'] = ss2r
run['ss2l'] = ss2l
# sics.multiDrive(run)
dc = 'drive'
for key in run:
dc += ' ' + key + ' ' + str(run[key])
sics.execute(dc)
time.sleep(5)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
'''
sics.execute('run ss1u %.2f' % ss1u)
sics.execute('run ss1d %.2f' % ss1d)
sics.execute('run ss1r %.2f' % ss1r)
sics.execute('run ss1l %.2f' % ss1l)
sics.execute('run ss2u %.2f' % ss2u)
sics.execute('run ss2d %.2f' % ss2d)
sics.execute('run ss2r %.2f' % ss2r)
sics.execute('run ss2l %.2f' % ss2l)
'''
# drive sample environment devices
slog('check sample envirment setup')
multiDev = {}
se_wait = 0
if configModel.se_enabled1:
slog('sample controller 1 is enabled')
multiDev[configModel.se_ctr1] = configModel.se_pos1
if configModel.se_wait1 > se_wait:
se_wait = configModel.se_wait1
if configModel.se_enabled2:
slog('sample controller 2 is enabled')
multiDev[configModel.se_ctr2] = configModel.se_pos2
if configModel.se_wait2 > se_wait:
se_wait = configModel.se_wait2
if configModel.se_enabled3:
slog('sample controller 3 is enabled')
multiDev[configModel.se_ctr3] = configModel.se_pos3
if configModel.se_wait3 > se_wait:
se_wait = configModel.se_wait3
if len(multiDev) > 0:
slog('drive sample environment ' + str(multiDev))
sics.multiDrive(multiDev)
if se_wait > 0:
slog('wait for ' + str(se_wait) + ' seconds')
time.sleep(se_wait)
# load sample positions
sample_stage_name = configModel.sample_stage
sample_positions = str(configModel.sample_position)
if (len(sample_positions) == 0) or (sample_positions == 'fixed'):
samz_list = [None]
else:
samz_list = []
stage = SAMPLE_STAGES.get_stage_by_name(sample_stage_name)
if stage is None:
raise 'Invalid stage name ' + str(sample_stage_name)
samz_value = stage.get_samz(sample_positions)
samz_list.append(samz_value)
print samz_list
for samz in samz_list:
sics.execute('histmem stop')
time.sleep(3)
if mode == 'ba':
sics.execute('histmem mode unlimited')
sics.execute('histmem ba enable')
else:
sics.execute('histmem mode time')
sics.execute('histmem ba disable')
if samz is not None:
print 'run samz %.2f' % samz
sics.execute('run samz %.2f' % samz)
# sics.execute('prun samz 2' % samz) !!!
time.sleep(1)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
sics.execute('newfile HISTOGRAM_XYT')
# sics.execute('autosave 60') # 60 seconds
time.sleep(1)
# start/stop hmm
if mode == 'count_roi':
sics.execute('histmem preset 1')
time.sleep(1)
sics.execute('histmem start')
time.sleep(5)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
sics.execute('histmem stop')
print 'frames:', len(scan['angles'])
count_rate_history = []
for frame_index in xrange(len(scan['angles'])):
angle = scan['angles' ][frame_index]
preset = scan['presets' ][frame_index]
maxTime = scan['maxTimes'][frame_index]
print 'drive %s %.6f' % (scanVariable, angle)
# sics.drive(scanVariable, float(angle))
sics.execute('drive %s %.6f' % (scanVariable, angle))
time.sleep(10)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
print 'drive done'
time.sleep(1)
if mode == 'ba':
sics.execute('histmem ba roi roi')
sics.execute('histmem ba monitor %i' % 1)
sics.execute('histmem ba mintime %i' % configModel.min_time)
sics.execute('histmem ba maxtime %i' % maxTime)
sics.execute('histmem ba maxdetcount %i' % preset)
sics.execute('histmem ba maxbmcount -1')
sics.execute('histmem ba undermintime ba_maxdetcount')
print 'histmem start'
sics.execute('histmem start block')
time0 = time.time()
while sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
if time.time() - time0 > 15.0:
print 'WARNING: HM may not have started counting. Gumtree will save anyway.'
break
else:
time.sleep(0.1)
time0 = time.time()
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
print 'time counted (estimate):', float(time.time() - time0)
else:
print 'histmem start'
while True:
if mode == 'count_roi':
sics.execute('histmem preset %i' % maxTime)
else:
sics.execute('histmem preset %i' % preset)
time.sleep(5)
sics.execute('histmem start')
time.sleep(5)
if mode == 'count_roi':
print 'count_roi'
time.sleep(configModel.min_time)
count_roi = 0
while not sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
try:
count_roi = int(sicsext.runCommand('hmm configure num_events_filled_to_count_roi'))
# print count_roi
if count_roi > preset:
print count_roi
print 'reached desired count_roi'
sics.execute('histmem pause')
time.sleep(1)
break
except:
pass
time.sleep(0.5)
break
else:
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
valid = False
for i in xrange(10):
time.sleep(1)
detector_time = sics.getValue('/instrument/detector/time').getFloatData()
valid = (detector_time >= preset - 1) or (detector_time >= preset * 0.90)
if valid:
break
print 'detector_time:', detector_time
if valid:
break
else:
print 'scan was invalid and needs to be repeated'
# sics.execute('histmem stop')
sics.execute('save %i' % frame_index)
frame_index += 1
print 'histmem done'
#check if in background
if early_exit_enabled.value :
try:
roi_counts = float(sics.get_raw_value('hmm configure num_events_filled_to_count_roi'))
roi_time = sics.getValue('/instrument/detector/time').getFloatData()
roi_rate = roi_counts / roi_time
print 'measured count rate:', roi_rate
count_rate_history.append(roi_rate)
bkg_frames = background_frames.value
bkg_range = background_threshold.value
if (len(count_rate_history) >= bkg_frames) and (builtin_max(count_rate_history[-bkg_frames:]) < bkg_range):
print 'background reached'
print 'scan completed (early exit)'
break
except:
pass
sics.execute('newfile clear')
# sics.execute('autosave 0') # disable autosave
# Get output filename
filenameController = sicsController.findDeviceController('datafilename')
savedFilename = filenameController.getValue().getStringData()
print 'saved:', savedFilename
sics.execute('histmem ba disable')
# print 'fit the curve'
# fit_curve()
print 'done'
print
def btnPlotSteps_clicked():
scan = getScan()
# print 'zero angle:'
# print scan_reference.value
print ''
print 'scan variable range [%f, %f]' % (scan['angles'][0], scan['angles'][-1])
print ''
#Plot1.clear()
#Plot2.clear()
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if scan_angleMin == 0 and scan_angleMax == 0:
print 'please select a scan template'
return
if scan_angleMin == scan_angleMax:
print 'the min angle and max angle can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
#print [scan_angleMin, scan_angleMax]
if Plot1.ds != None:
Plot1.clear_masks()
Plot1.add_dataset(dummy)
Plot1.title = 'Preview'
Plot1.x_label = 'm2om'
Plot1.y_label = 'counts per sec'
# Plot1.x_range = [scan_angleMin,scan_angleMax]
inclusive = True
angles = scan['angles']
for i in xrange(1, len(angles)):
xL = angles[i - 1]
xH = angles[i ]
Plot1.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot1.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# convert to q PLOT 2
crystal = str(crystal_name.value)
if 'Si111' in crystal:
wavelength = 4.74
elif 'Si311' in crystal:
wavelength = 2.37
else:
wavelength = float('nan')
q = convert2q(angles, scan_reference.value, wavelength)
scan_angleMin = builtin_min(q)
scan_angleMax = builtin_max(q)
if isnan(scan_angleMin) or isnan(scan_angleMax):
print 'please check the wavelength'
return
if scan_angleMin == scan_angleMax:
print 'the min q and max q can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
if Plot2.ds != None:
Plot2.clear_masks()
Plot2.add_dataset(dummy)
Plot2.title = 'Preview'
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.x_range = [1e-6, q[-1]]
for i in xrange(1, len(q)):
xL = q[i - 1]
xH = q[i ]
Plot2.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot2.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# print "angles"
# print angles
# print q
print ''
print 'scan q-range [%f, %f]' % (q[0], q[-1])
print ''
def openDataset(path):
ds = df[str(path)]
ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm')
# ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm_xy')
ds.__iDictionary__.addEntry('time', 'entry1/instrument/detector/time')
ds.__iDictionary__.addEntry('m1om', 'entry1/instrument/crystal/m1om')
ds.__iDictionary__.addEntry('m1chi', 'entry1/instrument/crystal/m1chi')
ds.__iDictionary__.addEntry('m1x', 'entry1/instrument/crystal/m1x')
ds.__iDictionary__.addEntry('m2om', 'entry1/instrument/crystal/m2om')
ds.__iDictionary__.addEntry('m2chi', 'entry1/instrument/crystal/m2chi')
ds.__iDictionary__.addEntry('m2x', 'entry1/instrument/crystal/m2x')
ds.__iDictionary__.addEntry('m2y', 'entry1/instrument/crystal/m2y')
ds.__iDictionary__.addEntry('mdet', 'entry1/instrument/crystal/mdet')
ds.__iDictionary__.addEntry('pmom', 'entry1/instrument/crystal/pmom')
ds.__iDictionary__.addEntry('pmchi', 'entry1/instrument/crystal/pmchi')
ds.__iDictionary__.addEntry('ss1u', 'entry1/instrument/slits/ss1u')
ds.__iDictionary__.addEntry('ss1d', 'entry1/instrument/slits/ss1d')
ds.__iDictionary__.addEntry('ss1r', 'entry1/instrument/slits/ss1r')
ds.__iDictionary__.addEntry('ss1l', 'entry1/instrument/slits/ss1l')
ds.__iDictionary__.addEntry('ss2u', 'entry1/instrument/slits/ss2u')
ds.__iDictionary__.addEntry('ss2d', 'entry1/instrument/slits/ss2d')
ds.__iDictionary__.addEntry('ss2r', 'entry1/instrument/slits/ss2r')
ds.__iDictionary__.addEntry('ss2l', 'entry1/instrument/slits/ss2l')
ds.__iDictionary__.addEntry('ss1vo', 'entry1/instrument/slits/ss1vo')
ds.__iDictionary__.addEntry('ss1vg', 'entry1/instrument/slits/ss1vg')
ds.__iDictionary__.addEntry('ss1ho', 'entry1/instrument/slits/ss1ho')
ds.__iDictionary__.addEntry('ss1hg', 'entry1/instrument/slits/ss1hg')
ds.__iDictionary__.addEntry('ss2vo', 'entry1/instrument/slits/ss2vo')
ds.__iDictionary__.addEntry('ss2vg', 'entry1/instrument/slits/ss2vg')
ds.__iDictionary__.addEntry('ss2ho', 'entry1/instrument/slits/ss2ho')
ds.__iDictionary__.addEntry('ss2hg', 'entry1/instrument/slits/ss2hg')
ds.__iDictionary__.addEntry('samplename', 'entry1/sample/name')
ds.__iDictionary__.addEntry('wavelength', 'entry1/instrument/crystal/wavelength')
ds.__iDictionary__.addEntry('TimeStamp', 'entry1/time_stamp')
return ds
def btnPlot_clicked():
#Plot1.clear()
#Plot2.clear()
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
path = fns[0]
basename = os.path.basename(str(path))
basename = basename[:basename.find('.nx.hdf')]
ds = openDataset(path)
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
samplename = str(ds.samplename)
sorting = scan_variable_sorting.value
if sorting:
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
Plot1.clear()
if str(combine_mode.value) == 'individual':
for tid in tids:
if ds.hmm.ndim == 4:
data[:] = ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] = ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
# dataF = data.float_copy()
# dataF.title = 'Tube %i' % tid
# Plot1.add_dataset(dataF)
Plot1.title = 'Count Rate (individual)'
else:
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
data.title = 'Tubes ' + str(tids)
Plot1.set_dataset(data)
Plot1.set_mouse_follower_precision(6, 2, 2)
Plot1.title = basename + ' (combined): ' + samplename
# Plot1.title = Plot1.title + ' ' + basename
if Plot1.ds is not None:
Plot1.x_label = str(scan_variable_plot.value)
Plot1.y_label = 'counts per sec'
Plot2.clear()
time.sleep(0.3)
ds0 = Plot1.ds[0] # # don't understand how this works
xMax = 0
yMax = 0
for i in xrange(len(ds0)):
if yMax < ds0[i]:
xMax = ds0.axes[0][i]
yMax = ds0[i]
peakangle = xMax
q = convert2q(scanVariable, peakangle, ds.wavelength)
data = Dataset(data, axes=[q[:]])
# data.axes[0] = q[:]
Plot2.set_dataset(data)
Plot2.set_mouse_follower_precision(6, 2, 2)
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
# Plot1.title = 'Main Detector ' + basename + ': ' + samplename
# Plot2.title = 'Sample: ' + samplename + '; ' + sampledescription
Plot2.title = basename + ' (combined): ' + samplename
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.set_marker_on(True)
# plotXMax = Par('float', q[-1])
# Plot2.x_range = [1e-6, plotXMax.value]
if q[-1] > 1e-6 :
Plot2.x_range = [1e-6, q[-1]]
fit_curve()
def convert2q(angles, reference, wavelength):
if wavelength is list:
wavelength = wavelength[0]
wavelength = float(wavelength)
deg2rad = 3.14159265359 / 180
f = 4 * 3.14159265359 / wavelength
if bool(negative_steps.value):
f *= -1.0
q = [(f * sin(deg2rad * (angle - reference) / 2)) for angle in angles]
return q
def __run_script__(fns):
# Use the provided resources, please don't remove.
global Plot1
global Plot2
global Plot3
print 'please press "Run Single Scan" or "Run Multiple Scans"'
btnPlot_clicked()
def __dispose__():
global Plot1
global Plot2
global Plot3
Plot1.clear()
Plot2.clear()
Plot3.clear()
# # model
class ConfigurationModel:
def __init__(self):
self.scanVariable = str(scan_variable.value)
self.scanVariable = self.scanVariable[:self.scanVariable.find(' ')]
self.crystal = str(crystal_name.value)
self.mode = str(scan_mode.value)
self.scan = getScan()
self.scan_reference = scan_reference.value
self.logscale = bool(logscale_position.value)
self.negative = bool(negative_steps.value)
self.stepInfo = []
for step in stepInfo:
d = dict()
for key in step.keys():
d[key] = step[key].value
self.stepInfo.append(d);
self.user_name = str(user_name.value)
self.user_email = str(user_email.value)
self.sample_name = str(sample_name.value)
self.sample_description = str(sample_description.value)
self.sample_thickness = float(sample_thickness.value)
# vertical/horizontal pre-slit
self.ss1vg = float(pss_ss1vg.value)
self.ss1vo = float(pss_ss1vo.value)
self.ss1hg = float(pss_ss1hg.value)
self.ss1ho = float(pss_ss1ho.value)
# vertical/horizontal post-slit
self.ss2vg = float(pss_ss2vg.value)
self.ss2vo = float(pss_ss2vo.value)
self.ss2hg = float(pss_ss2hg.value)
self.ss2ho = float(pss_ss2ho.value)
self.se_enabled1 = bool(se_enabled1.value)
self.se_ctr1 = str(se_ctr1.value)
self.se_pos1 = float(se_pos1.value)
self.se_wait1 = int(se_wait1.value)
self.se_enabled2 = bool(se_enabled2.value)
self.se_ctr2 = str(se_ctr2.value)
self.se_pos2 = float(se_pos2.value)
self.se_wait2 = int(se_wait2.value)
self.se_enabled3 = bool(se_enabled3.value)
self.se_ctr3 = str(se_ctr3.value)
self.se_pos3 = float(se_pos3.value)
self.se_wait3 = int(se_wait3.value)
# load sample positions
self.sample_stage = str(scan_sample_stage.value)
self.sample_position = str(scan_sample_position.value)
self.min_time = int(scan_min_time.value)
# load early exit
self.early_exit_enabled = bool(early_exit_enabled.value)
self.bkg_frames = int(background_frames.value)
self.bkg_threshold = float(background_threshold.value)
def apply(self):
for option in scan_variable.options:
if self.scanVariable == option[:option.find(' ')]:
scan_variable.value = option
crystal_name.value = self.crystal
scan_mode.value = self.mode
logscale_position.value = self.logscale
negative_steps.value = self.negative
scan_reference.value = self.scan_reference
i = 0
for step in self.stepInfo:
for key in step.keys():
stepInfo[i][key].value = step[key]
setEnabled(i)
i += 1
setScanMode()
user_name.value = self.user_name
user_email.value = self.user_email
sample_name.value = self.sample_name
sample_description.value = self.sample_description
sample_thickness.value = self.sample_thickness
# vertical/horizontal pre-slit
pss_ss1vg.value = self.ss1vg
pss_ss1vo.value = self.ss1vo
pss_ss1hg.value = self.ss1hg
pss_ss1ho.value = self.ss1ho
# vertical/horizontal post-slit
pss_ss2vg.value = self.ss2vg
pss_ss2vo.value = self.ss2vo
pss_ss2hg.value = self.ss2hg
pss_ss2ho.value = self.ss2ho
se_enabled1.value = self.se_enabled1
se_ctr1.value = self.se_ctr1
se_pos1.value = self.se_pos1
se_wait1.value = self.se_wait1
toggle_se(1)
se_enabled2.value = self.se_enabled2
se_ctr2.value = self.se_ctr2
se_pos2.value = self.se_pos2
se_wait2.value = self.se_wait2
toggle_se(2)
se_enabled3.value = self.se_enabled3
se_ctr3.value = self.se_ctr3
se_pos3.value = self.se_pos3
se_wait3.value = self.se_wait3
toggle_se(3)
# load sample positions
scan_sample_position.value = self.sample_position
scan_sample_stage.value = self.sample_stage
scan_min_time.value = self.min_time
# load early exit
early_exit_enabled.value = self.early_exit_enabled
background_frames.value = self.bkg_frames
background_threshold.value = self.bkg_threshold
if early_exit_enabled.value :
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
check instrument ready for just once
__script__.title = 'KKB Measurement Script'
__script__.version = '3.1'
from gumpy.commons import sics
from org.gumtree.gumnix.sics.control import ServerStatus
from pickle import Pickler, Unpickler
import time
from math import log as ln
from math import exp, isnan, isinf, sin
from __builtin__ import max as builtin_max
from __builtin__ import min as builtin_min
from org.eclipse.swt.widgets import FileDialog
from org.eclipse.swt import SWT
from org.eclipse.swt.widgets import Display
from java.io import File
from gumpy.nexus.fitting import Fitting, GAUSSIAN_FITTING
import math
from Internal import sample_stage
'''
Disable dataset caching
'''
DatasetFactory.__cache_enabled__ = False
SINGLE_TYPE = SWT.SINGLE
SAVE_TYPE = SWT.SAVE
MULTI_TYPE = SWT.MULTI
class __Display_Runnable__(Runnable):
def __init__(self, type=SINGLE_TYPE, ext=['*.*']):
self.filename = None
self.filenames = None
self.path = None
self.type = type
self.ext = ext
def run(self):
global __UI__
dialog = FileDialog(__UI__.getShell(), self.type);
dialog.setFilterExtensions(self.ext)
dialog.open()
self.filename = dialog.getFilterPath() + File.separator + dialog.getFileName()
self.filenames = dialog.getFileNames()
self.path = dialog.getFilterPath()
def open_file_dialog(type=SWT.SINGLE, ext=['*.*']):
__display_run__ = __Display_Runnable__(type, ext)
Display.getDefault().asyncExec(__display_run__)
while __display_run__.filename is None:
time.sleep(0.5)
if type == SWT.MULTI:
fns = []
for fn in __display_run__.filenames:
fns.append(__display_run__.path + '/' + fn)
return fns
return __display_run__.filename
# # templates
reference_templates_dict = {}
reference_templates_dict['Si111'] = 180.3565
reference_templates_dict['Si311'] = -0.4100
steps_templates_list = []
# steps_templates['Background'] = [
# 'time', 'logscale',
# [20, 6.0e-5, 1200, 1200]]
# steps_templates['----------'] = [
# 'time', 'logscale',
# [0, 0, 0, 0]]
steps_templates_list.append([
'Si111: Logarithmic Overview Scan',
'time',
'logscale',
[17, 1.20e-4, 1, 1200],
[30, 22.0, 20, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (few features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[34, 20.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Scan (fine features)',
'ba',
'logscale',
[33, 6.0e-5, 1000, 1200],
[65, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si111: Logarithmic Taiki Scan (15 points)',
'ba',
'logscale',
[2, 6.0e-5, 1000, 60],
[1, 10000, 1000, 60],
[10, 25, 1000, 60]])
'''
steps_templates_list.append([
'Si111: Kinetic Scan 4 points',
'time',
'logscale',
[ 0, 6.0e-5, 1, 1200],
[1, 5.0e-3, 180, 1200],
[3, 1.5e-2, 180, 1200]])
'''
steps_templates_list.append([
'----------',
'time',
'logscale',
[0, 0, 0, 0]])
steps_templates_list.append([
'Si311: Logarithmic Overview Scan',
'time',
'logscale',
[17, 2.0e-5, 1, 1200],
[30, 23.0, 20, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 80+29)',
'ba',
'logscale',
[80, 2e-5, 1000, 1200],
[29, 15.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, broadened peak, 40+33)',
'ba',
'logscale',
[40, 2e-5, 1000, 1200],
[33, 10.0, 1000, 1200]])
steps_templates_list.append([
'Si311: Logarithmic Scan (few features, Taiki)',
'ba',
'logscale',
[33, 2e-5, 1000, 1200],
[25, 20.0, 1000, 1200]])
ret = sample_stage.check_declarations()
if not ret[0] :
open_warning(ret[1])
reload(sample_stage)
SAMPLE_STAGES = sample_stage.StagePool()
# # export path
__EXPORT_PATH__ = 'V:/shared/KKB Logbook/Temp Plot Data Repository/'
if not os.path.exists(__EXPORT_PATH__):
os.makedirs(__EXPORT_PATH__)
# # User Details
user_name = Par('string', 'Christine', options=['Christine', 'Lela', 'Jitendra'])
user_name.title = 'Name'
user_email = Par('string', 'cre@ansto.gov.au', options=['cre@ansto.gov.au', 'liliana.decampo@ansto.gov.au', 'jtm@ansto.gov.au'])
user_email.title = 'EMail'
g0 = Group('User Details')
g0.numColumns = 2
g0.add(user_name, user_email)
# # Sample Details
sample_name = Par('string', 'UNKNOWN', options=['Empty Cell', 'Empty Beam'], command="sample_thickness.enabled = sample_name.value not in ['Empty Cell', 'Empty Beam']")
sample_name.title = 'Name'
sample_description = Par('string', 'UNKNOWN')
sample_description.title = 'Description'
sample_thickness = Par('string', '1', options=['0.01', '0.1', '1.0', '10.0'])
sample_thickness.title = 'Thickness (mm)'
g0 = Group('Sample Details')
g0.numColumns = 2
g0.add(sample_name, sample_thickness, sample_description)
# Group('Sample Details').add(sample_name, sample_description, sample_thickness)
# # Crystal
crystal_name = Par('string', 'UNKNOWN')
crystal_name.title = 'Name'
crystal_name.enabled = False
try:
m2om = sics.getValue('/instrument/crystal/m2om').getFloatData()
if m2om > 90:
crystal_name.value = 'Si111 (4.74 Angstroms)'
else:
crystal_name.value = 'Si311 (2.37 Angstroms)'
except:
pass
g0 = Group('Crystal Info')
g0.numColumns = 2
g0.add(crystal_name)
# CRYSTAL END #############################################
# SLIT 1 #######################################################################
def updateOffset(gapBox, offsetBox):
offsetBox.enabled = 'fully' not in gapBox.value
def getSlitGapAndOffset(aPath, a0, bPath, b0):
try:
a = sics.getValue(aPath).getFloatData()
b = sics.getValue(bPath).getFloatData()
gap = (a - a0 - (b - b0)) / 1.0
offset = (a - a0 + (b - b0)) / 2.0
return (gap, offset)
except:
return (float('nan'), float('nan'))
crystal = str(crystal_name.value)
if 'Si111' in crystal:
ss1r0 = 28.35
ss1l0 = 27.75
elif 'Si311' in crystal:
ss1r0 = -9.16
ss1l0 = -9.76
else:
ss1r0 = float('nan')
ss1l0 = float('nan')
ss1u0 = -8.04
ss1d0 = -7.30
(ss1vg, ss1vo) = getSlitGapAndOffset('/instrument/slits/ss1u', ss1u0, '/instrument/slits/ss1d', ss1d0)
(ss1hg, ss1ho) = getSlitGapAndOffset('/instrument/slits/ss1r', ss1r0, '/instrument/slits/ss1l', ss1l0)
pss_ss1vg = Par('string', '%.1f' % ss1vg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1vg, pss_ss1vo)')
pss_ss1vg.title = 'Vertical Gap (mm)'
# pss_ss1vg.colspan = 50
pss_ss1vo = Par('float', ss1vo)
pss_ss1vo.title = 'Vertical Offset (mm)'
# pss_ss1vo.colspan = 50
pss_ss1hg = Par('string', '%.1f' % ss1hg, options=['fully closed', '5', '10', '15', '20', '25', '30', '40', '50', 'fully opened'], command='updateOffset(pss_ss1hg, pss_ss1ho)')
pss_ss1hg.title = 'Horizontal Gap (mm)'
# pss_ss1hg.colspan = 50
pss_ss1ho = Par('float', ss1ho)
pss_ss1ho.title = 'Horizontal Offset (mm)'
# pss_ss1ho.colspan = 50
g0 = Group('Sample Slit Settings')
g0.numColumns = 2
g0.add(pss_ss1vg, pss_ss1vo, pss_ss1hg, pss_ss1ho)
# SLIT 1 END #######################################################################
# SAMPLE ENVIRONMENT BLOCK #########################################################
gse = Group('Sample Environment')
gse.numColumns = 10
se_enabled1 = Par('bool', False, command = 'toggle_se(1)')
se_enabled1.title = 'Controller 1'
se_enabled1.colspan = 1
se_ctr1 = Par('string', '', options = [])
se_ctr1.title = 'name'
se_ctr1.colspan = 2
se_ctr1.enabled = False
se_pos1 = Par('float', 0.)
se_pos1 .title = 'Values'
se_pos1.colspan = 6
se_pos1.enabled = False
se_wait1 = Par('int', 0)
se_wait1 .title = 'Wait'
se_wait1.colspan = 1
se_wait1.enabled = False
se_enabled2 = Par('bool', False, command = 'toggle_se(2)')
se_enabled2.title = 'Controller 2'
se_enabled2.colspan = 1
se_ctr2 = Par('string', '', options = [])
se_ctr2.title = 'name'
se_ctr2.colspan = 2
se_ctr2.enabled = False
se_pos2 = Par('float', 0.)
se_pos2 .title = 'Values'
se_pos2.colspan = 6
se_pos2.enabled = False
se_wait2 = Par('int', 0)
se_wait2 .title = 'Wait'
se_wait2.colspan = 1
se_wait2.enabled = False
se_enabled3 = Par('bool', False, command = 'toggle_se(3)')
se_enabled3.title = 'Controller 3'
se_enabled3.colspan = 1
se_ctr3 = Par('string', '', options = [])
se_ctr3.title = 'name'
se_ctr3.colspan = 2
se_ctr3.enabled = False
se_pos3 = Par('float', 0.)
se_pos3 .title = 'Values'
se_pos3.colspan = 6
se_pos3.enabled = False
se_wait3 = Par('int', 0)
se_wait3 .title = 'Wait'
se_wait3.colspan = 1
se_wait3.enabled = False
gse.add(se_enabled1, se_ctr1, se_pos1, se_wait1,
se_enabled2, se_ctr2, se_pos2, se_wait2,
se_enabled3, se_ctr3, se_pos3, se_wait3,)
devices = sicsext.getDrivables()
se_ctr1.options = devices
se_ctr2.options = devices
se_ctr3.options = devices
def toggle_se(id):
id = int(id)
if id == 1:
flag = se_enabled1.value
se_ctr1.enabled = flag
se_pos1.enabled = flag
se_wait1.enabled = flag
elif id == 2:
flag = se_enabled2.value
se_ctr2.enabled = flag
se_pos2.enabled = flag
se_wait2.enabled = flag
elif id == 3:
flag = se_enabled3.value
se_ctr3.enabled = flag
se_pos3.enabled = flag
se_wait3.enabled = flag
else:
raise 'illegal index for sample environment'
# SAMPLE ENVIRONMENT BLOCK END #####################################################
## Scan parameters ##########################################################################################################
scan_variable = Par('string', 'm2om [deg]', options=[
#'pmom [deg]', 'pmchi [deg]',
'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'], command="scan_variable_plot.value = scan_variable.value")
scan_variable.title = 'Scan Variable'
scan_variable.colspan = 25
scan_reference = Par('float', '0.0')
scan_reference.title = 'Zero Angle'
scan_reference.colspan = 25
for key in reference_templates_dict.keys():
if key in crystal_name.value:
scan_reference.value = reference_templates_dict[key]
scan_mode = Par('string', 'ba', options=['ba', 'time'], command='setScanMode()')
scan_mode.title = 'Acquisition Mode'
scan_mode.colspan = 25
scan_min_time = Par('int', '5')
scan_min_time.title = 'Min Time (sec)'
scan_min_time.colspan = 25
empty_label = Par('label', '')
empty_label.colspan = 25
scan_sample_stage = Par('string', '', command = 'sample_stage_changed()')
scan_sample_stage.colspan = 25
scan_sample_stage.title = 'Sample Stage'
scan_sample_stage.options = SAMPLE_STAGES.get_stage_names()
current_stage = SAMPLE_STAGES.get_stage_in_service()
if not current_stage is None:
scan_sample_stage.value = current_stage.get_name()
scan_sample_position = Par('string', 'fixed')
scan_sample_position.title = 'Sample Position'
scan_sample_position.colspan = 25
scan_sample_position.options = ['fixed', '----------']
if not current_stage is None:
scan_sample_position.options += current_stage.get_sample_indexes()
logscale_position = Par('bool', False, command='setStepTitles()')
logscale_position.title = 'Logarithmic Steps'
logscale_position.colspan = 25
negative_steps = Par('bool', False)
negative_steps.title = 'Negative Steps'
negative_steps.colspan = 25
steps_label = Par('label', 'Please choose scan template or adjust steps manually: ')
steps_label.colspan = 200
steps_templates = Par('string', '', options=[item[0] for item in steps_templates_list], command='setTemplate()')
steps_templates.title = 'Scan Template'
steps_templates.colspan = 100
early_exit_enabled = Par('bool', True, command = "set_early_exit_enabled()")
early_exit_enabled.title = "Enable Early Exit"
early_exit_enabled.colspan = 25
background_frames = Par('int', 3)
background_frames.title = 'Background Frames'
background_frames.colspan = 25
background_threshold = Par('float', 0.26)
background_threshold.title = 'Background Threshold'
background_threshold.colspan = 25
# steps_space = Par('space', '')
# steps_space.colspan = 10
g0 = Group('Scan Parameters')
g0.numColumns = 100 # 9
g0.add(scan_variable, scan_mode, scan_reference, early_exit_enabled, \
logscale_position, scan_min_time, scan_sample_stage, background_frames, \
negative_steps, empty_label, scan_sample_position, background_threshold, \
steps_label, steps_templates)
def sample_stage_changed():
stage = SAMPLE_STAGES.get_stage_by_name(str(scan_sample_stage.value))
# scan_sample_position.value = 'fixed'
if not stage is None:
scan_sample_position.options = ['fixed', '----------'] + stage.get_sample_indexes()
else:
scan_sample_position.options = ['fixed', '----------']
def set_early_exit_enabled():
if early_exit_enabled.value:
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
stepInfo = []
for i in xrange(4):
steps_e = Par('bool', True, command='setEnabled(%i)' % i)
steps_e.title = '(%i)' % (i + 1)
steps_e.colspan = 10
steps_m = Par('int', 0, command='clearScanTemplateSelection()')
steps_m.title = 'Number of points'
steps_m.colspan = 20
steps_s = Par('float', 0, command='clearScanTemplateSelection()')
steps_s.title = 'Step Size [deg]'
steps_s.colspan = 20
steps_p = Par('int', 0, command='clearScanTemplateSelection()')
steps_p.title = 'Mode Preset'
steps_p.colspan = 25
steps_t = Par('int', 1200, command='clearScanTemplateSelection()')
steps_t.title = 'Max Time'
steps_t.colspan = 25
stepInfo.append({'enabled': steps_e, 'dataPoints':steps_m, 'stepSize':steps_s, 'preset':steps_p, 'maxTime':steps_t})
g0.add(steps_e, steps_m, steps_s, steps_p, steps_t)
def clearScanTemplateSelection():
steps_templates.value = None
btnPlotSteps = Act('btnPlotSteps_clicked()', 'Plot Measurement Steps') # 'compare measurement steps with previous scan')
btnPlotSteps.colspan = 50
cnfg_save_btn = Act('saveConfiguration()', 'Save Single Scan Parameters')
cnfg_save_btn.colspan = 50
btnTimeEstimation = Act('runTimeEstimation()', 'Time Estimation with selected Data Set')
btnTimeEstimation.colspan = 50
txtTimeEstimation = Par('int', '0')
txtTimeEstimation.title = 'Time Estimation (min)'
txtTimeEstimation.enabled = False
txtTimeEstimation.colspan = 50
g0.add(btnPlotSteps, cnfg_save_btn, btnTimeEstimation, txtTimeEstimation)
def runTimeEstimation():
if str(scan_mode.value) == 'time':
scan = getScan()
times = scan['presets']
txtTimeEstimation.value = int((sum(times) + len(times) * 25) / 60.0) # 25 seconds for each move
return
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
ds = openDataset(fns[0])
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
data[:] = [data[item[0]] for item in info] # sorting
# angle and count rate
a0 = [float(angle) for angle in scanVariable]
r0 = [float(rate) for rate in data[:]]
# angle, counts, max time and min time
model = ConfigurationModel()
scan = model.scan
tMin = model.min_time
a1 = scan['angles']
c1 = scan['presets']
t1 = scan['maxTimes']
total = 0.0
for i in xrange(len(a1)):
try:
rate = sample(a0, r0, a1[i])
time = c1[i] / rate
if time < tMin:
total += tMin
elif time > t1[i]:
total += t1[i]
else:
total += time
#print ("angle: " + str(a1[i])
# + " expected counts: " + str(c1[i])
# + " rate:" + str(rate)
# + " time:" + str(time)
# + " total:" + str(total))
except ValueError as e:
if e.message == "OutOfRange":
total += t1[i] # add max time
else:
raise
total += int(len(a1) * 25) # 25 seconds for each move
txtTimeEstimation.value = int(total / 60.0)
def sample(x0, y0, x1):
from __builtin__ import max, min
if len(x0) != len(y0):
raise Exception("len(x0) != len(y0)")
x0_min = min(x0)
x0_max = max(x0)
if len(x0) < 2:
raise Exception("len(x0) < 2")
if x0_min >= x0_max:
raise Exception("x0_min >= x0_max")
if x1 < x0_min:
raise ValueError("OutOfRange")
if x0_max < x1:
raise ValueError("OutOfRange")
i0 = 0
i1 = 1
x0i0 = x0[i0]
y0i0 = y0[i0]
x0i1 = x0[i1]
y0i1 = y0[i1]
# in case first x values are equal
while x0i0 == x0i1:
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
# not iterable
while x0i1 < x1:
x0i0 = x0i1
y0i0 = y0i1
i1 += 1
x0i1 = x0[i1]
y0i1 = y0[i1]
return y0i0 + (x1 - x0i0) * (y0i1 - y0i0) / (x0i1 - x0i0)
## Scan parameters END #########################################################################
## RUN ##############################################
cnfg_load_btn = Act('loadConfigurations()', 'Load Multiple Scan Parameters')
cnfg_lookup = dict()
cnfg_options = Par('string', '', options=[''], command="applyConfiguration()")
cnfg_options.title = 'Read'
start_scan = Act('runSingleScan()', '############# Run Single Scan #############')
cnfg_run_btn = Act('runConfigurations()', '############# Run Multiple Scans #############')
g0 = Group('Execute Scans')
g0.numColumns = 1
g0.add(start_scan, cnfg_load_btn, cnfg_options, cnfg_run_btn)
## Save/Load Configuration END############################################################################
def saveConfiguration():
file = open_file_dialog(type=SAVE_TYPE, ext=['*.kkb'])
try:
fh = open(file, 'w')
except:
print 'not saved'
return
try:
p = Pickler(fh)
# header
p.dump('KKB')
# content
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
p.dump(att)
p.dump(att_value)
print 'saved'
finally:
fh.close()
def loadConfigurations():
fileList = open_file_dialog(type=MULTI_TYPE, ext=['*.kkb'])
if not fileList:
return
finalDict = dict()
finalNames = []
for path in fileList:
fh = open(path, 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', os.path.basename(path)
else:
model = ConfigurationModel()
# set defaults
model.negative = False # old models may not have this attribute
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', os.path.basename(path)
break
setattr(model, att, p.load())
else:
name = os.path.basename(path)
finalDict[name] = path
finalNames.append(name)
finally:
fh.close()
cnfg_lookup.clear()
cnfg_lookup.update(finalDict)
cnfg_options.options = finalNames
cnfg_options.value = finalNames[0] if finalNames else ''
# time.sleep(0.5)
def applyConfiguration():
file = str(cnfg_options.value)
if file is None or file == 'None' or file.strip() == '':
return
fh = open(cnfg_lookup[file], 'r')
try:
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
# print 'read:', file
model.apply()
finally:
fh.close()
def runConfigurations():
checkInstrumentReady()
for file in cnfg_options.options:
fh = open(cnfg_lookup[file], 'r')
try:
cnfg_options.command = ''
cnfg_options.value = file
applyConfiguration()
p = Unpickler(fh)
if p.load() != 'KKB':
print 'ERROR:', file
else:
model = ConfigurationModel()
for att in dir(model):
att_value = getattr(model, att)
if (att.find('_') != 0) and ('instancemethod' not in str(type(att_value))):
if p.load() != att:
print 'FORMAT ERROR:', file
break
setattr(model, att, p.load())
else:
print 'run:', file
startScan(model)
finally:
cnfg_options.command = 'applyConfiguration()'
fh.close()
# # Plot
tubes_label = Par('label', 'Main Detector:')
tubes_label.colspan = 1
combine_tube0 = Par('bool', True)
combine_tube0.title = ' Tube 0'
combine_tube0.colspan = 1
combine_tube1 = Par('bool', True)
combine_tube1.title = ' Tube 1'
combine_tube1.colspan = 1
combine_tube2 = Par('bool', True)
combine_tube2.title = ' Tube 2'
combine_tube2.colspan = 1
combine_tube3 = Par('bool', True)
combine_tube3.title = ' Tube 3'
combine_tube3.colspan = 1
combine_tube4 = Par('bool', True)
combine_tube4.title = ' Tube 4'
combine_tube4.colspan = 1
combine_tube6 = Par('bool', False)
combine_tube6.title = ' Tube 6'
combine_tube6.colspan = 1
combine_mode = Par('string', 'combined', options=['individual', 'combined'])
combine_mode.title = ' Mode'
combine_mode.colspan = 1
trans_tube_label = Par('label', 'Trans Detector: ')
trans_tube_label.colspan = 2
check_tube9 = Par('bool', True)
check_tube9.title = ' Tube 9: Si (311)'
check_tube9.colspan = 2
check_tube10 = Par('bool', False)
check_tube10.title = ' Tube 10: Si (111)'
check_tube10.colspan = 2
# steps_space = Par('space', '')
# steps_space.colspan = 12
scan_variable_plot = Par('string', 'm2om [deg]', options=[
'pmom [deg]', 'pmchi [deg]', 'm1om [deg]', 'm1chi [deg]', 'm1x [mm]', 'm2om [deg]', 'm2chi [deg]', 'm2x [mm]', 'm2y [mm]', 'mdet [mm]',
'ss1u [mm]', 'ss1d [mm]', 'ss1l [mm]', 'ss1r [mm]',
'ss2u [mm]', 'ss2d [mm]', 'ss2l [mm]', 'ss2r [mm]',
'ss1vg [mm]', 'ss1vo [mm]', 'ss1hg [mm]', 'ss1ho [mm]',
'ss2vg [mm]', 'ss2vo [mm]', 'ss2hg [mm]', 'ss2ho [mm]'])
scan_variable_plot.title = 'Scan Variable'
scan_variable_plot.colspan = 1
scan_variable_sorting = Par('bool', True)
scan_variable_sorting.title = 'Sorting'
scan_variable_sorting.colspan = 1
btnPlot = Act('btnPlot_clicked()', 'Plot Selected Data Set')
btnPlot.colspan = 8
g0 = Group('Plotting')
g0.numColumns = 7
g0.add(tubes_label, combine_tube0, combine_tube1, combine_tube2, combine_tube3, combine_tube4, combine_tube6, combine_mode, trans_tube_label, check_tube9, check_tube10, scan_variable_plot, scan_variable_sorting, btnPlot)
# export to csv
# btnExport = Act('export_clicked()', 'Export to CSV')
################################# SLIT 2 ##########################################################
ss2u0 = 0 # 2.00
ss2d0 = 0 # 1.40
ss2l0 = 0 # 5 0.50
ss2r0 = 0 # -2 -1.00
(ss2vg, ss2vo) = getSlitGapAndOffset('/instrument/slits/ss2u', ss2u0, '/instrument/slits/ss2d', ss2d0)
(ss2hg, ss2ho) = getSlitGapAndOffset('/instrument/slits/ss2r', ss2r0, '/instrument/slits/ss2l', ss2l0)
pss_ss2vg = Par('string', '%.1f' % ss2vg, options=pss_ss1vg.options, command='updateOffset(pss_ss2vg, pss_ss2vo)')
pss_ss2vg.title = 'Vertical Opening (mm)'
pss_ss2vo = Par('float', ss2vo)
pss_ss2vo.title = 'Vertical Offset (mm)'
pss_ss2hg = Par('string', '%.1f' % ss2hg, options=pss_ss1hg.options, command='updateOffset(pss_ss2hg, pss_ss2ho)')
pss_ss2hg.title = 'Horizontal Opening (mm)'
pss_ss2ho = Par('float', ss2ho)
pss_ss2ho.title = 'Horizontal Offset (mm)'
g0 = Group('Post-Sample Slit')
g0.numColumns = 2
g0.add(pss_ss2vg, pss_ss2vo, pss_ss2hg, pss_ss2ho)
################################# SLIT 2 END ##########################################################
################################# CURVE FITTING START ##########################################################
g_fit = Group('Fitting')
g_fit.numColumns = 2
#data_name = Par('string', 'total_counts', \
# options = ['total_counts', 'bm1_counts', 'bm2_counts'])
#normalise = Par('bool', True)
#axis_name = Par('string', '')
#axis_name.enabled = True
#auto_fit = Par('bool', False)
#fit_min = Par('float', 'NaN')
#fit_min.title = 'min x'
#fit_max = Par('float', 'NaN')
#fit_max.title = 'max x'
peak_pos = Par('float', 'NaN')
peak_pos.title = 'fitting peak position'
FWHM = Par('float', 'NaN')
FWHM.title = 'fitting FWHM'
fact = Act('fit_curve()', 'Fit Again')
fact.colspan = 2
#offset_done = Par('bool', False)
#act3 = Act('offset_s2()', 'Set Device Zero Offset')
g_fit.add(peak_pos, FWHM, fact)
def fit_curve():
global Plot1
ds = Plot1.ds
if len(ds) == 0:
log('Error: no curve to fit in Plot1.\n')
return
for d in ds:
if d.title == 'fitting':
Plot1.remove_dataset(d)
d0 = ds[0]
fitting = Fitting(GAUSSIAN_FITTING)
try:
fitting.set_histogram(d0)
fitting.fitter.setResolutionMultiple(50)
val = peak_pos.value
if val == val:
fitting.set_param('mean', val)
val = FWHM.value
if val == val:
fitting.set_param('sigma', math.fabs(val / 2.35482))
res = fitting.fit()
res.var[:] = 0
res.title = 'fitting'
Plot1.add_dataset(res)
Plot1.pv.getPlot().setCurveMarkerVisible(Plot1.__get_NXseries__(res), False)
mean = fitting.params['mean']
mean_err = fitting.errors['mean']
FWHM.value = 2.35482 * math.fabs(fitting.params['sigma'])
FWHM_err = 5.54518 * math.fabs(fitting.errors['sigma'])
log('POS_OF_PEAK=' + str(mean) + ' +/- ' + str(mean_err))
log('FWHM=' + str(FWHM.value) + ' +/- ' + str(FWHM_err))
log('Chi2 = ' + str(fitting.fitter.getQuality()))
peak_pos.value = fitting.mean
# print fitting.params
except:
# traceback.print_exc(file = sys.stdout)
log('can not fit\n')
################################# CURVE FITTING END ##########################################################
def waitUntilSicsIs(status, dt=0.2):
controller = sics.getSicsController()
timeout = 5
while True:
sics.handleInterrupt()
count = 0
while not controller.getServerStatus().equals(status) and count < timeout:
time.sleep(dt)
count += dt
if controller.getServerStatus().equals(status):
break
else:
controller.refreshServerStatus()
sics.handleInterrupt()
def setStepTitles():
if logscale_position.value:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Factor [%]"
else:
for stepInfoItem in stepInfo[1:]:
stepInfoItem['stepSize'].title = "Step Size [deg]"
__UI__.updateUI()
def setTemplate():
try:
matches = [item for item in steps_templates_list if item[0] == steps_templates.value]
if len(matches) != 1:
steps_templates.value = None
return
template = matches[0]
# ignore '----'
if template[0][0] == '-':
steps_templates.value = None
return
scan_mode.value = template[1]
if template[2] == 'logscale':
logscale_position.value = True
elif template[2] == 'linear':
logscale_position.value = False
setStepTitles()
# by default templates measure in positive direction
negative_steps.value = False
setScanMode()
headers = 3
for i in xrange(len(template) - headers):
templateItem = template[i + headers]
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = True
stepInfoItem['dataPoints'].enabled = True
stepInfoItem['dataPoints'].value = templateItem[0]
stepInfoItem['stepSize' ].enabled = True
stepInfoItem['stepSize' ].value = templateItem[1]
stepInfoItem['preset' ].enabled = True
stepInfoItem['preset' ].value = templateItem[2]
stepInfoItem['maxTime' ].enabled = scan_min_time.enabled
stepInfoItem['maxTime' ].value = templateItem[3]
for i in xrange(len(template) - headers, len(stepInfo)):
stepInfoItem = stepInfo[i]
stepInfoItem['enabled' ].value = False
stepInfoItem['dataPoints'].enabled = False
stepInfoItem['stepSize' ].enabled = False
stepInfoItem['preset' ].enabled = False
stepInfoItem['maxTime' ].enabled = False
except:
pass
def setScanMode():
if scan_mode.value == 'time':
scan_min_time.enabled = False
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = False
else:
scan_min_time.enabled = True
for stepInfoItem in stepInfo:
stepInfoItem['maxTime'].enabled = stepInfoItem['enabled'].value
def setEnabled(index):
steps_templates.value = None
stepItem = stepInfo[index]
value = stepItem['enabled'].value
stepItem['dataPoints'].enabled = value
stepItem['stepSize' ].enabled = value
stepItem['preset' ].enabled = value
stepItem['maxTime' ].enabled = value and scan_min_time.enabled
setTemplate()
def getScan():
scan = { 'angles': [], 'presets': [], 'maxTimes': [], 'groups': [] }
first = True
angle_ref = scan_reference.value
angle = angle_ref
logscale = False # first data points are always on a linear scale
negative = bool(negative_steps.value)
for stepInfoItem in stepInfo:
if stepInfoItem['enabled'].value:
dataPoints = stepInfoItem['dataPoints'].value
stepSize = stepInfoItem['stepSize' ].value
preset = stepInfoItem['preset' ].value
maxTime = stepInfoItem['maxTime' ].value
if (dataPoints > 0) and (stepSize <= 0.0):
raise Exception('step sizes have to be positive')
for i in xrange(dataPoints):
if first and (i == 0):
angle -= ((dataPoints - 1) / 2.0) * stepSize;
elif logscale:
# for logscale stepSize is a stepFactor
angle = angle_ref + (angle - angle_ref) * (1.0 + 0.01 * stepSize)
else:
angle += stepSize
#print angle
scan['angles' ].append(angle)
scan['presets' ].append(preset)
scan['maxTimes'].append(maxTime)
if i == 0:
scan['groups'].append(angle)
first = False
logscale = bool(logscale_position.value)
if negative:
# negate angles with reference to zero angle
scan['angles'] = [angle_ref - (angle - angle_ref) for angle in scan['angles']]
return scan
def wait_for_idle():
c_time = time.time()
while not sics.getSicsController().getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
time.sleep(0.1)
if time.time() - c_time > 5:
serverStatus = sics.get_status()
c_time = time.time()
def checkInstrumentReady():
''' check instrument ready '''
all_ready = False
is_ready = False
is_shielded = False
msg = None
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
is_shielded = sics.getValue('/instrument/GreenPolyShield/greenpolyshield').getStringData().lower() == 'in'
if not is_ready:
if not is_shielded:
msg = 'The instrument is not ready and the green polyshield is not applied. ' \
+ 'Please get the '\
+ 'instrument ready and apply the polyshield. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
else:
msg = 'The instrument is not ready according to the SIS status. ' \
+ 'Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
else:
if not is_shielded:
msg = 'The green polyshield is not applied. ' \
+ 'Please apply the polyshield. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
all_ready = is_ready and is_shielded
except:
pass
if not all_ready:
if not msg:
msg = 'The instrument is not ready according to the SIS status. ' \
+ 'Please get the '\
+ 'instrument ready. Then click on "Yes" to continue. \n'\
+ 'Do you want to continue?'
is_confirmed = open_question(msg)
if not is_confirmed:
slog('Instrument is not ready. Quit the scan.')
return
else:
try:
is_ready = sics.getValue('/instrument/status/ready').getStringData() == 'TRUE'
is_shielded = sics.getValue('/instrument/GreenPolyShield/greenpolyshield').getStringData().lower() == 'in'
except:
pass
if not is_ready:
slog('scan continued without instrument ready')
if not is_shielded:
slog('scan continued without green polysheild')
def runSingleScan():
checkInstrumentReady()
startScan(ConfigurationModel())
def startScan(configModel):
''' setup '''
scanVariable = configModel.scanVariable
crystal = configModel.crystal
mode = configModel.mode
MainDeadTime = 1.08E-6
TransDeadTime = 1.08E-6
if 'Si111' in crystal:
empLevel = 0.3
bkgLevel = 0.21
dOmega = 2.3E-6
gDQv = 0.0586
gDQh = 0
wavelength = 4.74
TransmissionTube = 10
TransBackground = 0 # counts per second
elif 'Si311' in crystal:
empLevel = 0.34
bkgLevel = 0.21
dOmega = 4.6E-7
gDQv = 0.117
gDQh = 0
wavelength = 2.37
TransmissionTube = 9
TransBackground = 0 # counts per second
else:
print 'selected crystal is invalid'
return
''' angles '''
scan = configModel.scan
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if ('m1om' in scanVariable) or ('m2om' in scanVariable):
tolerance = 6
approved = False
if 'Si111' in crystal:
if (180 - tolerance <= scan_angleMin) and (scan_angleMax <= 180 + tolerance):
approved = True
elif 'Si311' in crystal:
if (0 - tolerance <= scan_angleMin) and (scan_angleMax <= 0 + tolerance):
approved = True
if not approved:
print 'angle out of range'
return
''' execution '''
sics.execute('hset user/name ' + configModel.user_name)
sics.execute('hset user/email ' + configModel.user_email)
sics.execute('hset sample/name ' + configModel.sample_name)
sics.execute('hset sample/description ' + configModel.sample_description)
sics.execute('hset sample/thickness %g' % configModel.sample_thickness)
sics.execute('hset experiment/bkgLevel %g' % bkgLevel)
sics.execute('hset experiment/empLevel %g' % empLevel)
sics.execute('hset instrument/detector/MainDeadTime %g' % MainDeadTime)
sics.execute('hset instrument/detector/TransDeadTime %g' % TransDeadTime)
sics.execute('hset instrument/detector/TransBackground %g' % TransBackground)
sics.execute('hset instrument/detector/TransmissionTube %i' % TransmissionTube)
sics.execute('hset instrument/crystal/dOmega %g' % dOmega)
sics.execute('hset instrument/crystal/gDQv %g' % gDQv)
sics.execute('hset instrument/crystal/gDQh %g' % gDQh)
sics.execute('hset instrument/crystal/wavelength %g' % wavelength)
sics.execute('hset instrument/crystal/scan_variable ' + scanVariable);
sicsController = sics.getSicsController()
# slits
def getSlitValues(gap, offset, a0, b0, aOpen, bOpen):
if gap == 'fully opened':
return (aOpen, bOpen)
if gap == 'fully closed':
gap = -5.0
offset = 0.0
a = a0 + 0.5 * float(gap) + float(offset)
b = b0 - 0.5 * float(gap) + float(offset)
return (a, b)
ss1vg = configModel.ss1vg
ss1vo = configModel.ss1vo
ss1hg = configModel.ss1hg
ss1ho = configModel.ss1ho
ss2vg = configModel.ss2vg
ss2vo = configModel.ss2vo
ss2hg = configModel.ss2hg
ss2ho = configModel.ss2ho
(ss1u, ss1d) = getSlitValues(ss1vg, ss1vo, ss1u0, ss1d0, 35.8, -38.8)
(ss1r, ss1l) = getSlitValues(ss1hg, ss1ho, ss1r0, ss1l0, 57.0, -58.0)
(ss2u, ss2d) = getSlitValues(ss2vg, ss2vo, ss2u0, ss2d0, 37.0, -39.5)
(ss2r, ss2l) = getSlitValues(ss2hg, ss2ho, ss2r0, ss2l0, 35.0, -35.0)
# apply slits
run = {}
run['ss1u'] = ss1u
run['ss1d'] = ss1d
run['ss1r'] = ss1r
run['ss1l'] = ss1l
run['ss2u'] = ss2u
run['ss2d'] = ss2d
run['ss2r'] = ss2r
run['ss2l'] = ss2l
# sics.multiDrive(run)
dc = 'drive'
for key in run:
dc += ' ' + key + ' ' + str(run[key])
sics.execute(dc)
time.sleep(5)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
'''
sics.execute('run ss1u %.2f' % ss1u)
sics.execute('run ss1d %.2f' % ss1d)
sics.execute('run ss1r %.2f' % ss1r)
sics.execute('run ss1l %.2f' % ss1l)
sics.execute('run ss2u %.2f' % ss2u)
sics.execute('run ss2d %.2f' % ss2d)
sics.execute('run ss2r %.2f' % ss2r)
sics.execute('run ss2l %.2f' % ss2l)
'''
# drive sample environment devices
slog('check sample envirment setup')
multiDev = {}
se_wait = 0
if configModel.se_enabled1:
slog('sample controller 1 is enabled')
multiDev[configModel.se_ctr1] = configModel.se_pos1
if configModel.se_wait1 > se_wait:
se_wait = configModel.se_wait1
if configModel.se_enabled2:
slog('sample controller 2 is enabled')
multiDev[configModel.se_ctr2] = configModel.se_pos2
if configModel.se_wait2 > se_wait:
se_wait = configModel.se_wait2
if configModel.se_enabled3:
slog('sample controller 3 is enabled')
multiDev[configModel.se_ctr3] = configModel.se_pos3
if configModel.se_wait3 > se_wait:
se_wait = configModel.se_wait3
if len(multiDev) > 0:
slog('drive sample environment ' + str(multiDev))
sics.multiDrive(multiDev)
if se_wait > 0:
slog('wait for ' + str(se_wait) + ' seconds')
time.sleep(se_wait)
# load sample positions
sample_stage_name = configModel.sample_stage
sample_positions = str(configModel.sample_position)
if (len(sample_positions) == 0) or (sample_positions == 'fixed'):
samz_list = [None]
else:
samz_list = []
stage = SAMPLE_STAGES.get_stage_by_name(sample_stage_name)
if stage is None:
raise 'Invalid stage name ' + str(sample_stage_name)
samz_value = stage.get_samz(sample_positions)
samz_list.append(samz_value)
print samz_list
for samz in samz_list:
sics.execute('histmem stop')
time.sleep(3)
if mode == 'ba':
sics.execute('histmem mode unlimited')
sics.execute('histmem ba enable')
else:
sics.execute('histmem mode time')
sics.execute('histmem ba disable')
if samz is not None:
print 'run samz %.2f' % samz
sics.execute('run samz %.2f' % samz)
# sics.execute('prun samz 2' % samz) !!!
time.sleep(1)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
sics.execute('newfile HISTOGRAM_XYT')
# sics.execute('autosave 60') # 60 seconds
time.sleep(1)
# start/stop hmm
if mode == 'count_roi':
sics.execute('histmem preset 1')
time.sleep(1)
sics.execute('histmem start')
time.sleep(5)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
sics.execute('histmem stop')
print 'frames:', len(scan['angles'])
count_rate_history = []
for frame_index in xrange(len(scan['angles'])):
angle = scan['angles' ][frame_index]
preset = scan['presets' ][frame_index]
maxTime = scan['maxTimes'][frame_index]
print 'drive %s %.6f' % (scanVariable, angle)
# sics.drive(scanVariable, float(angle))
sics.execute('drive %s %.6f' % (scanVariable, angle))
time.sleep(10)
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
print 'drive done'
time.sleep(1)
if mode == 'ba':
sics.execute('histmem ba roi roi')
sics.execute('histmem ba monitor %i' % 1)
sics.execute('histmem ba mintime %i' % configModel.min_time)
sics.execute('histmem ba maxtime %i' % maxTime)
sics.execute('histmem ba maxdetcount %i' % preset)
sics.execute('histmem ba maxbmcount -1')
sics.execute('histmem ba undermintime ba_maxdetcount')
print 'histmem start'
sics.execute('histmem start block')
time0 = time.time()
while sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
if time.time() - time0 > 15.0:
print 'WARNING: HM may not have started counting. Gumtree will save anyway.'
break
else:
time.sleep(0.1)
time0 = time.time()
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
print 'time counted (estimate):', float(time.time() - time0)
else:
print 'histmem start'
while True:
if mode == 'count_roi':
sics.execute('histmem preset %i' % maxTime)
else:
sics.execute('histmem preset %i' % preset)
time.sleep(5)
sics.execute('histmem start')
time.sleep(5)
if mode == 'count_roi':
print 'count_roi'
time.sleep(configModel.min_time)
count_roi = 0
while not sicsController.getServerStatus().equals(ServerStatus.EAGER_TO_EXECUTE):
try:
count_roi = int(sicsext.runCommand('hmm configure num_events_filled_to_count_roi'))
# print count_roi
if count_roi > preset:
print count_roi
print 'reached desired count_roi'
sics.execute('histmem pause')
time.sleep(1)
break
except:
pass
time.sleep(0.5)
break
else:
waitUntilSicsIs(ServerStatus.EAGER_TO_EXECUTE)
valid = False
for i in xrange(10):
time.sleep(1)
detector_time = sics.getValue('/instrument/detector/time').getFloatData()
valid = (detector_time >= preset - 1) or (detector_time >= preset * 0.90)
if valid:
break
print 'detector_time:', detector_time
if valid:
break
else:
print 'scan was invalid and needs to be repeated'
# sics.execute('histmem stop')
sics.execute('save %i' % frame_index)
frame_index += 1
print 'histmem done'
#check if in background
if early_exit_enabled.value :
try:
roi_counts = float(sics.get_raw_value('hmm configure num_events_filled_to_count_roi'))
roi_time = sics.getValue('/instrument/detector/time').getFloatData()
roi_rate = roi_counts / roi_time
print 'measured count rate:', roi_rate
count_rate_history.append(roi_rate)
bkg_frames = background_frames.value
bkg_range = background_threshold.value
if (len(count_rate_history) >= bkg_frames) and (builtin_max(count_rate_history[-bkg_frames:]) < bkg_range):
print 'background reached'
print 'scan completed (early exit)'
break
except:
pass
sics.execute('newfile clear')
# sics.execute('autosave 0') # disable autosave
# Get output filename
filenameController = sicsController.findDeviceController('datafilename')
savedFilename = filenameController.getValue().getStringData()
print 'saved:', savedFilename
sics.execute('histmem ba disable')
# print 'fit the curve'
# fit_curve()
print 'done'
print
def btnPlotSteps_clicked():
scan = getScan()
# print 'zero angle:'
# print scan_reference.value
print ''
print 'scan variable range [%f, %f]' % (scan['angles'][0], scan['angles'][-1])
print ''
#Plot1.clear()
#Plot2.clear()
scan_angleMin = builtin_min(scan['angles'])
scan_angleMax = builtin_max(scan['angles'])
if scan_angleMin == 0 and scan_angleMax == 0:
print 'please select a scan template'
return
if scan_angleMin == scan_angleMax:
print 'the min angle and max angle can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
#print [scan_angleMin, scan_angleMax]
if Plot1.ds != None:
Plot1.clear_masks()
Plot1.add_dataset(dummy)
Plot1.title = 'Preview'
Plot1.x_label = 'm2om'
Plot1.y_label = 'counts per sec'
# Plot1.x_range = [scan_angleMin,scan_angleMax]
inclusive = True
angles = scan['angles']
for i in xrange(1, len(angles)):
xL = angles[i - 1]
xH = angles[i ]
Plot1.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot1.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# convert to q PLOT 2
crystal = str(crystal_name.value)
if 'Si111' in crystal:
wavelength = 4.74
elif 'Si311' in crystal:
wavelength = 2.37
else:
wavelength = float('nan')
q = convert2q(angles, scan_reference.value, wavelength)
scan_angleMin = builtin_min(q)
scan_angleMax = builtin_max(q)
if isnan(scan_angleMin) or isnan(scan_angleMax):
print 'please check the wavelength'
return
if scan_angleMin == scan_angleMax:
print 'the min q and max q can not be the same'
return
dummy = zeros(2)
dummy.axes[0] = [scan_angleMin, scan_angleMax]
if Plot2.ds != None:
Plot2.clear_masks()
Plot2.add_dataset(dummy)
Plot2.title = 'Preview'
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.x_range = [1e-6, q[-1]]
for i in xrange(1, len(q)):
xL = q[i - 1]
xH = q[i ]
Plot2.add_mask_1d(xL, xH, '', inclusive)
inclusive = not inclusive
groups = scan['groups']
for i in xrange(len(groups)):
Plot2.add_mask_1d(groups[i], groups[i] + 1e-12, str(i + 1), True)
# print "angles"
# print angles
# print q
print ''
print 'scan q-range [%f, %f]' % (q[0], q[-1])
print ''
def openDataset(path):
ds = df[str(path)]
ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm')
# ds.__iDictionary__.addEntry('hmm', 'entry1/data/hmm_xy')
ds.__iDictionary__.addEntry('time', 'entry1/instrument/detector/time')
ds.__iDictionary__.addEntry('m1om', 'entry1/instrument/crystal/m1om')
ds.__iDictionary__.addEntry('m1chi', 'entry1/instrument/crystal/m1chi')
ds.__iDictionary__.addEntry('m1x', 'entry1/instrument/crystal/m1x')
ds.__iDictionary__.addEntry('m2om', 'entry1/instrument/crystal/m2om')
ds.__iDictionary__.addEntry('m2chi', 'entry1/instrument/crystal/m2chi')
ds.__iDictionary__.addEntry('m2x', 'entry1/instrument/crystal/m2x')
ds.__iDictionary__.addEntry('m2y', 'entry1/instrument/crystal/m2y')
ds.__iDictionary__.addEntry('mdet', 'entry1/instrument/crystal/mdet')
ds.__iDictionary__.addEntry('pmom', 'entry1/instrument/crystal/pmom')
ds.__iDictionary__.addEntry('pmchi', 'entry1/instrument/crystal/pmchi')
ds.__iDictionary__.addEntry('ss1u', 'entry1/instrument/slits/ss1u')
ds.__iDictionary__.addEntry('ss1d', 'entry1/instrument/slits/ss1d')
ds.__iDictionary__.addEntry('ss1r', 'entry1/instrument/slits/ss1r')
ds.__iDictionary__.addEntry('ss1l', 'entry1/instrument/slits/ss1l')
ds.__iDictionary__.addEntry('ss2u', 'entry1/instrument/slits/ss2u')
ds.__iDictionary__.addEntry('ss2d', 'entry1/instrument/slits/ss2d')
ds.__iDictionary__.addEntry('ss2r', 'entry1/instrument/slits/ss2r')
ds.__iDictionary__.addEntry('ss2l', 'entry1/instrument/slits/ss2l')
ds.__iDictionary__.addEntry('ss1vo', 'entry1/instrument/slits/ss1vo')
ds.__iDictionary__.addEntry('ss1vg', 'entry1/instrument/slits/ss1vg')
ds.__iDictionary__.addEntry('ss1ho', 'entry1/instrument/slits/ss1ho')
ds.__iDictionary__.addEntry('ss1hg', 'entry1/instrument/slits/ss1hg')
ds.__iDictionary__.addEntry('ss2vo', 'entry1/instrument/slits/ss2vo')
ds.__iDictionary__.addEntry('ss2vg', 'entry1/instrument/slits/ss2vg')
ds.__iDictionary__.addEntry('ss2ho', 'entry1/instrument/slits/ss2ho')
ds.__iDictionary__.addEntry('ss2hg', 'entry1/instrument/slits/ss2hg')
ds.__iDictionary__.addEntry('samplename', 'entry1/sample/name')
ds.__iDictionary__.addEntry('wavelength', 'entry1/instrument/crystal/wavelength')
ds.__iDictionary__.addEntry('TimeStamp', 'entry1/time_stamp')
return ds
def btnPlot_clicked():
#Plot1.clear()
#Plot2.clear()
fns = []
for sds in __DATASOURCE__.getSelectedDatasets():
fns.append(sds.getLocation())
if len(fns) != 1:
print 'select one dataset'
return
path = fns[0]
basename = os.path.basename(str(path))
basename = basename[:basename.find('.nx.hdf')]
ds = openDataset(path)
scanVariable = str(scan_variable.value)
scanVariable = scanVariable[:scanVariable.find(' ')]
scanVariable = ds[scanVariable]
samplename = str(ds.samplename)
sorting = scan_variable_sorting.value
if sorting:
info = sorted(enumerate(scanVariable), key=lambda item:item[1])
scanVariable = [item[1] for item in info]
shape = ds.shape
if shape[0] <= 1:
print 'Must have at least 2 scan positions'
return
n = shape[0]
# tubes
data = zeros(n)
tids = []
if combine_tube0.value:
tids.append(0)
if combine_tube1.value:
tids.append(1)
if combine_tube2.value:
tids.append(2)
if combine_tube3.value:
tids.append(3)
if combine_tube4.value:
tids.append(4)
if combine_tube6.value:
tids.append(6)
Plot1.clear()
if str(combine_mode.value) == 'individual':
for tid in tids:
if ds.hmm.ndim == 4:
data[:] = ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] = ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
# dataF = data.float_copy()
# dataF.title = 'Tube %i' % tid
# Plot1.add_dataset(dataF)
Plot1.title = 'Count Rate (individual)'
else:
for tid in tids:
if ds.hmm.ndim == 4:
data[:] += ds.hmm[:, 0, :, tid].sum(0) # hmm
else:
data[:] += ds.hmm[:, :, tid].sum(0) # hmm_xy
if data.size == 1:
data[0] = data[0] * 1.0 / ds.time
else:
data[:] = data[:] * 1.0 / ds.time
if sorting:
data[:] = [data[item[0]] for item in info] # sorting
data.var[:] = 0 # total_counts / (ds.time * ds.time)
axis0 = data.axes[0]
axis0[:] = scanVariable[:]
data.title = 'Tubes ' + str(tids)
Plot1.set_dataset(data)
Plot1.set_mouse_follower_precision(6, 2, 2)
Plot1.title = basename + ' (combined): ' + samplename
# Plot1.title = Plot1.title + ' ' + basename
if Plot1.ds is not None:
Plot1.x_label = str(scan_variable_plot.value)
Plot1.y_label = 'counts per sec'
Plot2.clear()
time.sleep(0.3)
ds0 = Plot1.ds[0] # # don't understand how this works
xMax = 0
yMax = 0
for i in xrange(len(ds0)):
if yMax < ds0[i]:
xMax = ds0.axes[0][i]
yMax = ds0[i]
peakangle = xMax
q = convert2q(scanVariable, peakangle, ds.wavelength)
data = Dataset(data, axes=[q[:]])
# data.axes[0] = q[:]
Plot2.set_dataset(data)
Plot2.set_mouse_follower_precision(6, 2, 2)
Plot2.x_label = 'q [1/A]'
Plot2.y_label = 'counts per sec'
# Plot1.title = 'Main Detector ' + basename + ': ' + samplename
# Plot2.title = 'Sample: ' + samplename + '; ' + sampledescription
Plot2.title = basename + ' (combined): ' + samplename
Plot2.set_log_x_on(True)
Plot2.set_log_y_on(True)
Plot2.set_marker_on(True)
# plotXMax = Par('float', q[-1])
# Plot2.x_range = [1e-6, plotXMax.value]
if q[-1] > 1e-6 :
Plot2.x_range = [1e-6, q[-1]]
fit_curve()
def convert2q(angles, reference, wavelength):
if wavelength is list:
wavelength = wavelength[0]
wavelength = float(wavelength)
deg2rad = 3.14159265359 / 180
f = 4 * 3.14159265359 / wavelength
if bool(negative_steps.value):
f *= -1.0
q = [(f * sin(deg2rad * (angle - reference) / 2)) for angle in angles]
return q
def __run_script__(fns):
# Use the provided resources, please don't remove.
global Plot1
global Plot2
global Plot3
print 'please press "Run Single Scan" or "Run Multiple Scans"'
btnPlot_clicked()
def __dispose__():
global Plot1
global Plot2
global Plot3
Plot1.clear()
Plot2.clear()
Plot3.clear()
# # model
class ConfigurationModel:
def __init__(self):
self.scanVariable = str(scan_variable.value)
self.scanVariable = self.scanVariable[:self.scanVariable.find(' ')]
self.crystal = str(crystal_name.value)
self.mode = str(scan_mode.value)
self.scan = getScan()
self.scan_reference = scan_reference.value
self.logscale = bool(logscale_position.value)
self.negative = bool(negative_steps.value)
self.stepInfo = []
for step in stepInfo:
d = dict()
for key in step.keys():
d[key] = step[key].value
self.stepInfo.append(d);
self.user_name = str(user_name.value)
self.user_email = str(user_email.value)
self.sample_name = str(sample_name.value)
self.sample_description = str(sample_description.value)
self.sample_thickness = float(sample_thickness.value)
# vertical/horizontal pre-slit
self.ss1vg = float(pss_ss1vg.value)
self.ss1vo = float(pss_ss1vo.value)
self.ss1hg = float(pss_ss1hg.value)
self.ss1ho = float(pss_ss1ho.value)
# vertical/horizontal post-slit
self.ss2vg = float(pss_ss2vg.value)
self.ss2vo = float(pss_ss2vo.value)
self.ss2hg = float(pss_ss2hg.value)
self.ss2ho = float(pss_ss2ho.value)
self.se_enabled1 = bool(se_enabled1.value)
self.se_ctr1 = str(se_ctr1.value)
self.se_pos1 = float(se_pos1.value)
self.se_wait1 = int(se_wait1.value)
self.se_enabled2 = bool(se_enabled2.value)
self.se_ctr2 = str(se_ctr2.value)
self.se_pos2 = float(se_pos2.value)
self.se_wait2 = int(se_wait2.value)
self.se_enabled3 = bool(se_enabled3.value)
self.se_ctr3 = str(se_ctr3.value)
self.se_pos3 = float(se_pos3.value)
self.se_wait3 = int(se_wait3.value)
# load sample positions
self.sample_stage = str(scan_sample_stage.value)
self.sample_position = str(scan_sample_position.value)
self.min_time = int(scan_min_time.value)
# load early exit
self.early_exit_enabled = bool(early_exit_enabled.value)
self.bkg_frames = int(background_frames.value)
self.bkg_threshold = float(background_threshold.value)
def apply(self):
for option in scan_variable.options:
if self.scanVariable == option[:option.find(' ')]:
scan_variable.value = option
crystal_name.value = self.crystal
scan_mode.value = self.mode
logscale_position.value = self.logscale
negative_steps.value = self.negative
scan_reference.value = self.scan_reference
i = 0
for step in self.stepInfo:
for key in step.keys():
stepInfo[i][key].value = step[key]
setEnabled(i)
i += 1
setScanMode()
user_name.value = self.user_name
user_email.value = self.user_email
sample_name.value = self.sample_name
sample_description.value = self.sample_description
sample_thickness.value = self.sample_thickness
# vertical/horizontal pre-slit
pss_ss1vg.value = self.ss1vg
pss_ss1vo.value = self.ss1vo
pss_ss1hg.value = self.ss1hg
pss_ss1ho.value = self.ss1ho
# vertical/horizontal post-slit
pss_ss2vg.value = self.ss2vg
pss_ss2vo.value = self.ss2vo
pss_ss2hg.value = self.ss2hg
pss_ss2ho.value = self.ss2ho
se_enabled1.value = self.se_enabled1
se_ctr1.value = self.se_ctr1
se_pos1.value = self.se_pos1
se_wait1.value = self.se_wait1
toggle_se(1)
se_enabled2.value = self.se_enabled2
se_ctr2.value = self.se_ctr2
se_pos2.value = self.se_pos2
se_wait2.value = self.se_wait2
toggle_se(2)
se_enabled3.value = self.se_enabled3
se_ctr3.value = self.se_ctr3
se_pos3.value = self.se_pos3
se_wait3.value = self.se_wait3
toggle_se(3)
# load sample positions
scan_sample_position.value = self.sample_position
scan_sample_stage.value = self.sample_stage
scan_min_time.value = self.min_time
# load early exit
early_exit_enabled.value = self.early_exit_enabled
background_frames.value = self.bkg_frames
background_threshold.value = self.bkg_threshold
if early_exit_enabled.value :
background_frames.enabled = True
background_threshold.enabled = True
else:
background_frames.enabled = False
background_threshold.enabled = False
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Copyright (c) 2014 windpro
Author : windpro
E-mail : windprog@gmail.com
Date : 14/12/26
Desc : 接口调用规范。
"""
from abc import ABCMeta, abstractmethod
class BaseSpeed(object):
__metaclass__ = ABCMeta
def add_one(self):
pass
class BaseCheckCallback(object):
__metaclass__ = ABCMeta
@abstractmethod
def __call__(self, method, url, req_query_string, req_headers, req_data, status_code, res_headers, res_data):
# 根据http请求结果判断
pass
class BaseUpdate(object):
__metaclass__ = ABCMeta
def __init__(self, expired, is_sync, save_check_callback=None, retry_limit=1, retry_check_callback=None):
# 缓存过期时间，单位秒
self.expired = expired
# 过期时获取的动作，True为等待最新数据完成才返回
self.is_sync = is_sync
# 检测是否需要储存缓存
self.save_check_callback = save_check_callback
# 检测是否需要重新尝试下载
self.retry_check_callback = retry_check_callback
# 重试次数，默认为1
self.retry_limit = 1
for callback in [save_check_callback, retry_check_callback]:
if callback and not isinstance(callback, BaseCheckCallback):
raise ValueError('%s must be BaseCheckCallback subclass instance' % callback.__name__)
@abstractmethod
def backend_call(self, method, url, req_query_string, req_headers, req_data):
# 后台运行
pass
@abstractmethod
def check_sync(self):
# 检测是否阻塞执行
pass
@abstractmethod
def is_expired_incache(self, method, url, req_query_string, req_headers, req_data, **kwargs):
# 返回元组() 第一个为是否过期,第二个为是否在缓存中
pass
class BaseRoute(object):
__metaclass__ = ABCMeta
@abstractmethod
def match(self, url):
# 检测url是否满足本路由条件
pass
@abstractmethod
def dumps(self):
# 将自身导出字符串
pass
class BaseHttpCache(object):
__metaclass__ = ABCMeta
@abstractmethod
def find(self, method, url, req_query_string, req_headers, req_data):
# 获取换成你
pass
@abstractmethod
def get_update_time(self, method, url, req_query_string, req_headers, req_data):
# 获取缓存上一次更新时间
pass
@abstractmethod
def save(self, method, url, req_query_string, req_headers, req_data, status_code, res_headers, res_data):
# 保存缓存
pass
@abstractmethod
def delete(self, method, url, req_query_string, req_headers, req_data):
# 删除缓存
pass
删除多余接口
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Copyright (c) 2014 windpro
Author : windpro
E-mail : windprog@gmail.com
Date : 14/12/26
Desc : 接口调用规范。
"""
from abc import ABCMeta, abstractmethod
class BaseSpeed(object):
__metaclass__ = ABCMeta
def add_one(self):
pass
class BaseCheckCallback(object):
__metaclass__ = ABCMeta
@abstractmethod
def __call__(self, method, url, req_query_string, req_headers, req_data, status_code, res_headers, res_data):
# 根据http请求结果判断
pass
class BaseUpdate(object):
__metaclass__ = ABCMeta
def __init__(self, expired, is_sync, save_check_callback=None, retry_limit=1, retry_check_callback=None):
# 缓存过期时间，单位秒
self.expired = expired
# 过期时获取的动作，True为等待最新数据完成才返回
self.is_sync = is_sync
# 检测是否需要储存缓存
self.save_check_callback = save_check_callback
# 检测是否需要重新尝试下载
self.retry_check_callback = retry_check_callback
# 重试次数，默认为1
self.retry_limit = 1
for callback in [save_check_callback, retry_check_callback]:
if callback and not isinstance(callback, BaseCheckCallback):
raise ValueError('%s must be BaseCheckCallback subclass instance' % callback.__name__)
@abstractmethod
def backend_call(self, method, url, req_query_string, req_headers, req_data):
# 后台运行
pass
@abstractmethod
def check_sync(self):
# 检测是否阻塞执行
pass
@abstractmethod
def is_expired_incache(self, method, url, req_query_string, req_headers, req_data, **kwargs):
# 返回元组() 第一个为是否过期,第二个为是否在缓存中
pass
class BaseRoute(object):
__metaclass__ = ABCMeta
@abstractmethod
def match(self, url):
# 检测url是否满足本路由条件
pass
class BaseHttpCache(object):
__metaclass__ = ABCMeta
@abstractmethod
def find(self, method, url, req_query_string, req_headers, req_data):
# 获取换成你
pass
@abstractmethod
def get_update_time(self, method, url, req_query_string, req_headers, req_data):
# 获取缓存上一次更新时间
pass
@abstractmethod
def save(self, method, url, req_query_string, req_headers, req_data, status_code, res_headers, res_data):
# 保存缓存
pass
@abstractmethod
def delete(self, method, url, req_query_string, req_headers, req_data):
# 删除缓存
pass
|
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class EtsfIo(Package):
"""ETSF_IO is a library implementing the Nanoquanta/ETSF file
format specifications.
ETSF_IO enables an architecture-independent exchange of crystallographic
data, electronic wavefunctions, densities and potentials, as well as
spectroscopic data. It is meant to be used by quantum-physical and
quantum-chemical applications relying upon Density Functional Theory (DFT).
"""
homepage = "http://www.etsf.eu/resources/software/libraries_and_tools"
url = "https://launchpad.net/etsf-io/1.0/1.0.4/+download/etsf_io-1.0.4.tar.gz"
version('1.0.4', sha256='3140c2cde17f578a0e6b63acb27a5f6e9352257a1371a17b9c15c3d0ef078fa4')
depends_on("netcdf-fortran")
depends_on("hdf5+mpi~cxx", when='+mpi') # required for NetCDF-4 support
def install(self, spec, prefix):
options = ['--prefix=%s' % prefix]
oapp = options.append
# Specify installation directory for Fortran module files
# Default is [INCLUDEDIR/FC_TYPE]
oapp("--with-moduledir=%s" % prefix.include)
# Netcdf4/HDF
hdf_libs = "-L%s -lhdf5_hl -lhdf5" % spec["hdf5"].prefix.lib
options.extend([
"--with-netcdf-incs=-I%s" % spec["netcdf-fortran"].prefix.include,
"--with-netcdf-libs=-L%s -lnetcdff -lnetcdf %s" % (
spec["netcdf-fortran"].prefix.lib, hdf_libs),
])
configure(*options)
make()
make("check")
make("install")
etsf-io: added missing `mpi` variant (#23083)
The variant was mentioned in a depends_on directive,
but never declared
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class EtsfIo(Package):
"""ETSF_IO is a library implementing the Nanoquanta/ETSF file
format specifications.
ETSF_IO enables an architecture-independent exchange of crystallographic
data, electronic wavefunctions, densities and potentials, as well as
spectroscopic data. It is meant to be used by quantum-physical and
quantum-chemical applications relying upon Density Functional Theory (DFT).
"""
homepage = "http://www.etsf.eu/resources/software/libraries_and_tools"
url = "https://launchpad.net/etsf-io/1.0/1.0.4/+download/etsf_io-1.0.4.tar.gz"
version('1.0.4', sha256='3140c2cde17f578a0e6b63acb27a5f6e9352257a1371a17b9c15c3d0ef078fa4')
variant('mpi', default=True, description='Add MPI support')
depends_on("netcdf-fortran")
depends_on("hdf5+mpi~cxx", when='+mpi') # required for NetCDF-4 support
def install(self, spec, prefix):
options = ['--prefix=%s' % prefix]
oapp = options.append
# Specify installation directory for Fortran module files
# Default is [INCLUDEDIR/FC_TYPE]
oapp("--with-moduledir=%s" % prefix.include)
# Netcdf4/HDF
hdf_libs = "-L%s -lhdf5_hl -lhdf5" % spec["hdf5"].prefix.lib
options.extend([
"--with-netcdf-incs=-I%s" % spec["netcdf-fortran"].prefix.include,
"--with-netcdf-libs=-L%s -lnetcdff -lnetcdf %s" % (
spec["netcdf-fortran"].prefix.lib, hdf_libs),
])
configure(*options)
make()
make("check")
make("install")
|
import os
import pytz
import numpy as np
import desisurvey.NTS
import desisurvey.svstats
import desiutil.log
from desisurvey.scripts import collect_etc
from astropy.io import fits
from astropy.time import Time
from astropy.coordinates import EarthLocation
from astropy import units as u
def run_plan(obsplan=None):
utc = pytz.timezone('utc')
tz = pytz.timezone('US/Arizona')
kpno = EarthLocation.of_site('kpno')
nts = desisurvey.NTS.NTS(obsplan=obsplan)
t0 = nts.scheduler.night_ephem['brightdusk']
nts_dir, _ = os.path.split(nts.obsplan)
etcfn = os.path.join(nts_dir, 'etc-stats-{}.fits'.format(nts_dir))
exps = fits.getdata(etcfn, 'EXPS')
nincond = collect_etc.number_in_conditions(exps)
donecond = desisurvey.svstats.donefrac_in_conditions(nincond)
desiutil.log.get_logger().setLevel(desiutil.log.WARNING)
previoustiles = []
print('local lst cond tile ra dec program fac tot split '
'b/g/d')
while t0 < nts.scheduler.night_ephem['brightdawn']:
expdict = dict(mjd=t0, previoustiles=previoustiles)
res = nts.next_tile(exposure=expdict, speculative=True)
if not res['foundtile']:
print('no tiles!')
t0 += 60
continue
previoustiles.append(res['fiberassign'])
lst = Time(t0, format='mjd', location=kpno).sidereal_time('apparent')
lst = lst.to(u.deg).value
tt = Time(t0, format='mjd').to_datetime(timezone=utc)
nsofar = (donecond[donecond['TILEID'] == res['fiberassign']])
ind = np.flatnonzero(
nts.scheduler.tiles.tileID == res['fiberassign'])[0]
ra = nts.scheduler.tiles.tileRA[ind]
dec = nts.scheduler.tiles.tileDEC[ind]
if len(nsofar) > 0:
nsofar = [nsofar['NNIGHT_DARK'], nsofar['NNIGHT_GRAY'],
nsofar['NNIGHT_BRIGHT']]
else:
nsofar = [0, 0, 0]
print('%s %5.1f %6s %d %5.1f %5.1f %10s %3.1f %4d %6s %d/%d/%d' % (
tt.astimezone(tz).strftime('%H:%M'), lst, res['conditions'],
res['fiberassign'], ra, dec, res['program'],
res['exposure_factor'], res['esttime'].astype('i4'),
('%dx%d' % (res['count'], res['exptime'])), *nsofar))
t0 += (res['exptime']+180)*res['count']/60/60/24
def parse(options=None):
import argparse
parser = argparse.ArgumentParser(
description='run an example night plan',
epilog='EXAMPLE: %(prog)s [YYYYMMDD/config.yaml]')
parser.add_argument('obsplan', nargs='?', default=None, type=str,
help='obsplan to use; default YYYYMMDD/config.yaml')
if options is None:
args = parser.parse_args()
else:
args = parser.parse_args(options)
return args
def main(args):
run_plan(obsplan=args.obsplan)
Fix typo in run_plan output.
import os
import pytz
import numpy as np
import desisurvey.NTS
import desisurvey.svstats
import desiutil.log
from desisurvey.scripts import collect_etc
from astropy.io import fits
from astropy.time import Time
from astropy.coordinates import EarthLocation
from astropy import units as u
def run_plan(obsplan=None):
utc = pytz.timezone('utc')
tz = pytz.timezone('US/Arizona')
kpno = EarthLocation.of_site('kpno')
nts = desisurvey.NTS.NTS(obsplan=obsplan)
t0 = nts.scheduler.night_ephem['brightdusk']
nts_dir, _ = os.path.split(nts.obsplan)
etcfn = os.path.join(nts_dir, 'etc-stats-{}.fits'.format(nts_dir))
exps = fits.getdata(etcfn, 'EXPS')
nincond = collect_etc.number_in_conditions(exps)
donecond = desisurvey.svstats.donefrac_in_conditions(nincond)
desiutil.log.get_logger().setLevel(desiutil.log.WARNING)
previoustiles = []
print('local lst cond tile ra dec program fac tot split '
'd/g/b')
while t0 < nts.scheduler.night_ephem['brightdawn']:
expdict = dict(mjd=t0, previoustiles=previoustiles)
res = nts.next_tile(exposure=expdict, speculative=True)
if not res['foundtile']:
print('no tiles!')
t0 += 60
continue
previoustiles.append(res['fiberassign'])
lst = Time(t0, format='mjd', location=kpno).sidereal_time('apparent')
lst = lst.to(u.deg).value
tt = Time(t0, format='mjd').to_datetime(timezone=utc)
nsofar = (donecond[donecond['TILEID'] == res['fiberassign']])
ind = np.flatnonzero(
nts.scheduler.tiles.tileID == res['fiberassign'])[0]
ra = nts.scheduler.tiles.tileRA[ind]
dec = nts.scheduler.tiles.tileDEC[ind]
if len(nsofar) > 0:
nsofar = [nsofar['NNIGHT_DARK'], nsofar['NNIGHT_GRAY'],
nsofar['NNIGHT_BRIGHT']]
else:
nsofar = [0, 0, 0]
print('%s %5.1f %6s %d %5.1f %5.1f %10s %3.1f %4d %6s %d/%d/%d' % (
tt.astimezone(tz).strftime('%H:%M'), lst, res['conditions'],
res['fiberassign'], ra, dec, res['program'],
res['exposure_factor'], res['esttime'].astype('i4'),
('%dx%d' % (res['count'], res['exptime'])), *nsofar))
t0 += (res['exptime']+180)*res['count']/60/60/24
def parse(options=None):
import argparse
parser = argparse.ArgumentParser(
description='run an example night plan',
epilog='EXAMPLE: %(prog)s [YYYYMMDD/config.yaml]')
parser.add_argument('obsplan', nargs='?', default=None, type=str,
help='obsplan to use; default YYYYMMDD/config.yaml')
if options is None:
args = parser.parse_args()
else:
args = parser.parse_args(options)
return args
def main(args):
run_plan(obsplan=args.obsplan)
|
#!/usr/bin/python
"""
This module is the central part of MaxiNet and is intended to be the only
part of MaxiNet which needs to be used by the user or third-party applications
"""
import re
import sys
import logging
from functools import partial
import time
import subprocess
import random
import atexit
from mininet.node import RemoteController, UserSwitch
import Pyro4
from MaxiNet.Frontend.tools import Tools
import tools
from client import Frontend, log_and_reraise_remote_exception
from partitioner import Partitioner
from MaxiNet.Frontend.cli import CLI
# the following block is to support deprecation warnings. this is really not
# solved nicely and should probably be somewhere else
import warnings
import functools
logger = logging.getLogger(__name__)
def deprecated(func):
'''This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.'''
@functools.wraps(func)
def new_func(*args, **kwargs):
logger.warn("Call to deprecated function {}.".format(func.__name__))
warnings.warn_explicit(
"Call to deprecated function {}.".format(func.__name__),
category=DeprecationWarning,
filename=func.func_code.co_filename,
lineno=func.func_code.co_firstlineno + 1
)
return func(*args, **kwargs)
return new_func
def run_cmd(cmd):
"""
run cmd on frontend machine
"""
return subprocess.check_output(cmd,shell=False)
def run_cmd_shell(cmd):
"""
run cmd on frontend machine with the shell
"""
return subprocess.check_output(cmd,shell=True)
class Worker:
"""
represents a worker machine in an running experiment
"""
def __init__(self,frontend,wid,switch=UserSwitch):
self.creator = frontend.getObjectProxy(wid+".mnCreator")
self.cmd = frontend.getObjectProxy(wid+".cmd")
self.config = frontend.getObjectProxy("config")
if(not self.config.runWith1500MTU()):
self._fix_mtus()
self.switch=switch
self.x11tunnels=[]
self.wid=int(wid[6:])
@log_and_reraise_remote_exception
def hn(self):
"""
return hostname of worker machine
"""
return self.cmd.get_hostname()
def set_switch(self,switch):
"""
set default switch class
"""
self.switch=switch
@log_and_reraise_remote_exception
def configLinkStatus(self, src, dst, status):
"""
wrapper for configLinkStatus method on remote mininet"""
self.creator.configLinkStatus(src,dst,status)
@log_and_reraise_remote_exception
def ip(self):
"""
return public ip adress of worker machine
"""
return self.config.getIP(self.hn())
@log_and_reraise_remote_exception
def start(self,topo,tunnels, controller=None):
"""
start mininet instance on worker machine emulating the in topo specified
topology.
if controller is not specified mininet will start an own controller for
this net
"""
if controller:
self.creator.create_mininet(topo=topo, tunnels=tunnels, controller = controller, switch=self.switch)
else:
self.creator.create_mininet(topo=topo, tunnels=tunnels, switch=self.switch)
@log_and_reraise_remote_exception
def daemonize(self,cmd):
"""
run command in background and terminate when MaxiNet is shut down
"""
self.cmd.daemonize(cmd)
@log_and_reraise_remote_exception
def tunnelX11(self,node):
"""
create X11 tunnel on worker to make x-forwarding work
"""
if(not node in self.x11tunnels):
try:
display = subprocess.check_output("ssh -Y "+self.hn()+ " env | grep DISPLAY",shell=True)[8:]
self.creator.tunnelX11(node,display)
self.x11tunnels.append(node)
except subprocess.CalledProcessError:
return False
return True
@log_and_reraise_remote_exception
def run_cmd_on_host(self,host,cmd):
"""
run cmd in context of host and return output, where host is the name of an host in
the mininet instance running on the worker machine
"""
return self.creator.runCmdOnHost(host,cmd)
@log_and_reraise_remote_exception
def run_cmd(self,cmd):
"""
run cmd on worker machine and return output
"""
return self.cmd.check_output(cmd)
@log_and_reraise_remote_exception
def run_script(self,cmd):
"""
run cmd on worker machine from the Worker/bin directory return output
"""
return self.cmd.script_check_output(cmd)
@log_and_reraise_remote_exception
def rpc(self, host, cmd, *params1, **params2):
"""
internal function to do rpc calls
"""
return self.creator.rpc(host, cmd, *params1, **params2)
@log_and_reraise_remote_exception
def rattr(self, host,name):
"""
internal function to get attributes of objects
"""
return self.creator.attr(host, name)
@log_and_reraise_remote_exception
def _fix_mtus(self):
if self.ip() is None :
logger.warn("no ip configured - can not fix MTU ")
return 0
intf = self.run_cmd("ip addr show to "+self.ip()+"/24 | head -n1 | cut -d' ' -f2 | tr -d :").strip()
if intf == "" :
logger.warn("could not find eth device - can not fix MTU")
return 0
mtu = int(self.run_cmd("ip li show dev "+intf+" | head -n1 | cut -d ' ' -f5"))
if(mtu<1600):
self.run_cmd("ip li se dev "+intf+" mtu 1600")
@log_and_reraise_remote_exception
def stop(self):
"""
stop mininet instance on this worker
"""
return self.creator.stop()
def get_file(self,src,dst):
"""
transfer file specified by src on worker to dst on frontend.
uses scp command to transfer file
"""
cmd_get = ["scp",self.hn()+":\""+src+"\"",dst]
subprocess.call(cmd_get)
def put_file(self,src,dst):
"""
transfer file specified by src on frontend to dst on worker.
uses scp command to transfer file
"""
cmd_put = ["scp",src,self.hn()+":"+dst]
subprocess.call(cmd_put)
@log_and_reraise_remote_exception
def addHost(self,name, cls=None, **params):
"""
add host at runtime. you probably want to use Experiment.addHost
"""
return self.creator.addHost(name,cls,**params)
@log_and_reraise_remote_exception
def addSwitch(self,name, cls=None,**params):
"""
add switch at runtime. you probably want to use Experiment.addSwitch
"""
return self.creator.addSwitch(name,cls,**params)
@log_and_reraise_remote_exception
def addController(self,name="c0", controller=None, **params):
"""
add controller at runtime. you probably want to use Experiment.addController
"""
return self.creator.addHost(name,controller,**params)
@log_and_reraise_remote_exception
def addTunnel(self,name, switch, port, cls, **params):
"""
add tunnel at runtime. you probably want to use Experiment.addLink
"""
return self.creator.addTunnel(name, switch, port, cls,**params)
@log_and_reraise_remote_exception
def addLink(self, node1, node2, port1 = None, port2 = None, cls = None, **params):
"""
add link at runtime. you probably want to use Experiment.addLink
"""
return self.creator.addLink(node1, node2, port1, port2, cls, **params)
class TunHelper:
"""
internal class to manage tunnel interface names
"""
def __init__(self):
self.tunnr = 0
self.keynr = 0
def get_tun_nr(self):
self.tunnr = self.tunnr +1
return self.tunnr -1
def get_key_nr(self):
self.keynr = self.keynr+1
return self.keynr -1
def get_last_tun_nr(self):
return self.tunnr - 1
def get_last_key_nr(self):
return self.keynr - 1
class Cluster:
"""
manage a set of workers via this class.
to create several different topologys do not destroy/recreate this class but
define several Experiment instances running sequential
"""
def __init__(self,*hosts):
"""
create Cluster object. Starting with MaxiNet 0.2 the hosts parameter is
optional. If None is given all configured hosts will be used
"""
self.running=False
self.logger = logging.getLogger(__name__)
self.tunhelper = TunHelper()
self.config = tools.Config("","",register = False)
logging.basicConfig(level=self.config.getLoggingLevel())
if hosts:
self.hosts = hosts
else:
self.hosts = self.config.getHosts()
self.worker=[]
if(self.hosts[0]!=subprocess.check_output(["hostname"]).strip()):
rhost = self.config.getIP(self.hosts[0])
else:
if(len(self.hosts)>1):
rhost = self.config.getIP(self.hosts[1])
else:
rhost = self.config.getIP(self.hosts[0])
if sys.platform == "darwin":
route = subprocess.check_output(["route", "-vn", "get", rhost]).splitlines()[-1]
m = re.search(r' (\d+\.\d+\.\d+\.\d+)$', route)
else:
route = subprocess.check_output("ip route get "+rhost+" | head -n1", shell=True).strip()
m = re.search(r'src (\d+\.\d+\.\d+\.\d+)', route)
if m is not None:
self.localIP = m.group(1)
else:
self.localIP="127.0.0.1"
self.nsport=9090
self.frontend = Frontend(self.localIP, self.nsport)
self.config = tools.Config(self.localIP,self.nsport)
atexit.register(run_cmd, self.getWorkerMangerCMD("--stop"))
atexit.register(self._stop)
def getWorkerMangerCMD(self,cmd):
cmdline = [self.config.getWorkerScript("worker_manager.py"), "--ns",
self.localIP+":" + str(self.nsport),
cmd]
if self.config.debugPyroOnWorker():
cmdline.append("--debugPyro")
if self.config.keepScreenOpenOnError():
cmdline.append("--keepScreenOpenOnError")
cmdline.extend(self.hosts)
return cmdline
def is_running(self):
return self.running
def get_worker_shares(self):
"""
returns list of workload shares per worker
"""
shares=None
if(self.running):
shares=[]
for w in self.worker:
shares.append(self.config.getShare(w.hn()))
wsum=0
for w in shares:
wsum+=w
shares = map(lambda x: x/float(wsum),shares)
return shares
def check_reachability(self,ip):
"""
check whether ip is reachable for a packet with MTU > 1500
"""
cmd = ["ping","-c 2","-s 1520",ip]
if(subprocess.call(cmd,stdout=open("/dev/null"),) == 1):
return False
else:
return True
def start(self):
"""
start MaxiNet on assigned worker machines and establish communication. Returns True in case of successful startup.
"""
self.logger.info("starting worker processes")
cmd = self.getWorkerMangerCMD("--start")
if self.frontend.hmac_key():
cmd.extend(["--hmac",self.frontend.hmac_key()])
self.logger.debug(run_cmd(cmd))
timeout = 10
for i in range(0,len(self.hosts)):
self.logger.info("waiting for Worker "+str(i+1)+" to register on nameserver...")
started=False
end = time.time()+timeout
while(not started):
try:
self.frontend.lookup("worker"+str(i+1)+".mnCreator")
started=True
except Pyro4.errors.NamingError:
if(time.time()>end):
raise RuntimeError("Timed out waiting for worker "+str(i+1)+".mnCreator to register.")
time.sleep(0.1)
started=False
end=time.time()+timeout
while(not started):
try:
self.frontend.lookup("worker"+str(i+1)+".cmd")
started=True
except Pyro4.errors.NamingError:
time.sleep(0.1)
if(time.time()>end):
raise RuntimeError("Timed out waiting for worker "+str(i+1)+".cmd to register.")
self.worker.append(Worker(self.frontend,"worker"+str(i+1)))
if(not self.config.runWith1500MTU()):
for host in self.hosts:
if(not self.check_reachability(self.config.getIP(host))):
self.logger.error("Host "+host+" is not reachable with an MTU > 1500.")
raise RuntimeError("Host "+host+" is not reachable with an MTU > 1500.")
for worker in self.worker:
worker.run_script("load_tunneling.sh")
self.logger.info("worker processes started")
self.running = True
return True
def _stop(self):
if(self.running):
self.logger.info("removing tunnels...")
self.remove_all_tunnels()
#self.logger.info("shutting cluster down...")
self.logger.info("removing nameserver entries...")
for i in range(0,self.num_workers()):
self.frontend.remove("worker"+str(i)+".mnCreator")
self.frontend.remove("worker"+str(i)+".cmd")
self.logger.info("shutting down frontend...")
self.logger.info("Goodbye.")
self.running=False
def stop(self):
"""
stop cluster and shut it down
"""
self._stop()
def num_workers(self):
"""
return number of worker nodes in this cluster
"""
return len(self.workers())
def workers(self):
"""
return list of worker instances for this cluster, ordered by worker id
"""
if(self.is_running()):
return self.worker
else:
return []
def get_worker(self, wid):
"""
return worker with id wid
"""
return self.workers()[wid]
def create_tunnel(self,w1,w2):
"""
create gre tunnel connecting worker machine w1 and w2 and return name of
created network interface
"""
tid = self.tunhelper.get_tun_nr()
tkey = self.tunhelper.get_key_nr()
intf = "mn_tun"+str(tid)
ip1=w1.ip()
ip2=w2.ip()
self.logger.debug("invoking tunnel create commands on "+ip1+" and "+ip2)
w1.run_script("create_tunnel.sh "+ip1+" "+ip2+" "+intf+" "+str(tkey))
w2.run_script("create_tunnel.sh "+ip2+" "+ip1+" "+intf+" "+str(tkey))
self.logger.debug("done")
return intf
def remove_all_tunnels(self):
"""
shut down all tunnels created in this cluster
"""
for worker in self.workers():
worker.run_script("delete_tunnels.sh")
self.tunhelper = TunHelper()
class Experiment:
"""
use this class to specify experiment. Experiments are created for
one-time-usage and have to be stopped in the end. One cluster instance can
run several experiments in sequence
"""
def __init__(self,cluster, topology,controller=None, is_partitioned=False, switch=UserSwitch):
self.cluster = cluster
self.logger = logging.getLogger(__name__)
self.topology=None
self.config = tools.Config(register = False)
self.starttime = time.localtime()
self.printed_log_info = False
self.isMonitoring = False
if is_partitioned:
self.topology = topology
else:
self.origtopology = topology
self.node_to_workerid = {}
self.node_to_wrapper = {}
self.nodes = []
self.hosts = []
self.tunnellookup = {}
self.switches = []
self.switch=switch
if controller:
contr = controller
else:
contr = self.config.getController()
if contr.find(":")>=0:
(host, port) = contr.split(":")
else:
host = contr
port = "6633"
self.controller = partial(RemoteController, ip=host, port=int(port))
def configLinkStatus(self, src, dst, status):
"""Change status of src <-> dst links.
src: node name
dst: node name
status: string {up, down}"""
ws = self.get_worker(src)
wd = self.get_worker(dst)
if(ws==wd): # src and dst are on same worker. let mininet handle this
ws.configLinkStatus(src,dst,status)
else:
src=self.get(src)
dst=self.get(dst)
intf = self.tunnellookup[(src.name,dst.name)]
src.cmd("ifconfig "+intf+" "+status)
dst.cmd("ifconfig "+intf+" "+status)
@deprecated
def find_worker(self,node):
"""
return worker which emulates the specified node.
Replaced by get_worker
"""
return self.get_worker(node)
def get_worker(self,node):
"""
return worker which emulates the specified node.
"""
if(isinstance(node,NodeWrapper)):
return node.worker
return self.cluster.get_worker(self.node_to_workerid[node])
def get_log_folder(self):
"""
returns folder to which log files will be saved
"""
return "/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"
def terminate_logging(self):
for worker in self.cluster.workers():
worker.run_cmd("killall getRxTx.sh getMemoryUsage.sh")
self.isMonitoring = False
def log_cpu(self):
"""
log cpu useage of workers and place log files in /tmp/maxinet_logs/
"""
for worker in self.cluster.workers():
self.log_cpu_of_worker(worker)
def log_cpu_of_worker(self,worker):
"""
log cpu usage of worker and place log file in /tmp/maxinet_logs/
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
atexit.register(worker.get_file,"/tmp/maxinet_cpu_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
worker.daemonize("LANG=en_EN.UTF-8 mpstat 1 | while read l; do echo -n \"`date +%s` \" ; echo \"$l \" ; done > \"/tmp/maxinet_cpu_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def log_free_memory(self):
"""
log memory usage of workers and place log files in /tmp/maxinet_logs
Format is:
timestamp,FreeMemory,Buffers,Cached
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
for worker in self.cluster.workers():
atexit.register(worker.get_file,"/tmp/maxinet_mem_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
memmon = worker.config.getWorkerScript("getMemoryUsage.sh")
worker.daemonize(memmon + " > \"/tmp/maxinet_mem_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def log_interfaces_of_node(self,node):
"""
logs statistics of interfaces of node and places them in /tmp/maxinet_logs
Format is:
timestamp,received bytes,sent bytes,received packets,sent packets
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
node = self.get(node)
worker = self.get_worker(node)
for intf in node.intfNames():
self.log_interface(worker,intf)
def log_interface(self,worker,intf):
"""
logs statistics of interface of worker and places them in /tmp/maxinet_logs
Format is:
timestamp,received bytes,sent bytes,received packets,sent packets
"""
atexit.register(worker.get_file,"/tmp/maxinet_intf_"+intf+"_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
ethmon = worker.config.getWorkerScript("getRxTx.sh")
worker.daemonize(ethmon + " "+intf+" > \"/tmp/maxinet_intf_"+intf+"_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def monitor(self):
"""
logs statistics of worker interfaces and memory usage and places them in /tmp/maxinet_logs
"""
self.isMonitoring = True
atexit.register(self._print_monitor_info)
self.log_free_memory()
self.log_cpu()
for worker in self.cluster.workers():
intf = worker.run_cmd("ip addr show to "+worker.ip()+"/24 | head -n1 | cut -d' ' -f2 | tr -d :").strip()
if(intf == ""):
self.logger.warn("could not find main interface for "+worker.hn()+". no logging possible.")
else:
self.log_interface(worker,intf)
def _print_log_info(self):
if(not self.printed_log_info):
self.printed_log_info=True
self.logger.info("Log files will be placed in /tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/. You might want to save them somewhere else.")
def _print_monitor_info(self):
self.logger.info("You monitored this experiment. To generate a graph from your logs call \"/usr/local/share/MaxiNet/maxinet_plot.py /tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/ plot.png\" ")
def CLI(self,plocals,pglobals):
"""
open interactive command line interface
"""
CLI(self,plocals,pglobals)
def addNode(self, name,**params):
"""
add node at runtime.
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
wid = random.randint(0,self.cluster.num_workers()-1)
if "pos" in params.keys():
wid = self.node_to_workerid[params["pos"]]
del params["pos"]
if "wid" in params.keys():
wid = int(params["wid"])-1
del params["wid"]
worker = self.cluster.get_worker(wid)
self.node_to_workerid[name]=wid
self.node_to_wrapper[name]=NodeWrapper(name,self.get_worker(name))
self.nodes.append(self.node_to_wrapper[name])
def addHost(self, name, cls = None, **params):
"""
add host at runtime.
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name,**params)
self.get_worker(name).addHost(name,cls, **params)
self.hosts.append(self.get(name))
return self.get(name)
def addSwitch(self, name, cls = None, **params):
"""
add switch at runtime
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name, **params)
self.get_worker(name).addSwitch(name,cls, **params)
self.switches.append(self.get(name))
return self.get(name)
def addController(self, name="c0",controller = None, **params):
"""
add controller at runtime
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name, **params)
self.get_worker(name).addController(name, controller, **params)
return self.get(name)
def name(self, node):
"""
return name assigned to specified network node.
"""
if(isinstance(node,NodeWrapper)):
return node.nn
return node
def addLink(self, node1, node2, port1 = None, port2 = None, cls = None, autoconf=False, **params):
"""
add links at runtime. will create tunnels between workers if necessary,
but can not create tunnels between hosts and switches. (links will work fine)
autoconf parameter handles attach() and config calls on switches and hosts
"""
w1 = self.get_worker(node1)
w2 = self.get_worker(node2)
node1 = self.get(node1)
node2 = self.get(node2)
if(w1==w2):
self.logger.debug("no tunneling needed")
l=w1.addLink(self.name(node1),self.name(node2), port1, port2, cls, **params)
else:
self.logger.debug("tunneling needed")
if(not ((node1 in self.switches) and (node2 in self.switches))):
self.logger.error("We cannot create tunnels between switches and hosts. Sorry.")
raise RuntimeError("Can't create tunnel between switch and host")
intf = self.cluster.create_tunnel(w1,w2)
w1.addTunnel(intf,self.name(node1), port1, cls, **params)
w2.addTunnel(intf,self.name(node2), port2, cls, **params)
l=((self.name(node1),intf),(self.name(node2),intf))
if(autoconf):
if(node1 in self.switches):
node1.attach(l[0][1])
else:
node1.configDefault()
if(node2 in self.switches):
node2.attach(l[1][1])
else:
node2.configDefault()
if(self.config.runWith1500MTU()):
self.setMTU(node1,1450)
self.setMTU(node2,1450)
def _find_topo_of_node(self,node, topos):
for topo in topos:
if node in topo.g.nodes():
return topo
def get_node(self, nodename):
"""
return node that is specified by nodename
:rtype : NodeWrapper
"""
if(self.node_to_wrapper.has_key(nodename)):
return self.node_to_wrapper[nodename]
else:
return None
def get(self,nodename):
"""
alias for get_node
"""
return self.get_node(nodename)
def setup(self):
"""
start cluster if not yet started, assign topology parts to workers and
start workers
"""
if(not self.cluster.is_running()):
self.cluster.start()
if(not self.cluster.is_running()):
raise RuntimeError("Cluster won't start")
self.logger.info("Clustering topology...")
if(not self.topology):
parti = Partitioner()
parti.loadtopo(self.origtopology)
self.topology = parti.partition(self.cluster.num_workers(),self.cluster.get_worker_shares()) # assigning shares to workers requires that the workers are already startet. elsewise we don't have a way to determine the workerid of the worker. topologies are assigned to workers in ascending workerid order
self.logger.debug("Tunnels: "+str(self.topology.getTunnels()))
subtopos = self.topology.getTopos()
if(len(subtopos) > self.cluster.num_workers()):
raise RuntimeError("Cluster does not have enough workers for given topology")
for subtopo in subtopos:
for node in subtopo.nodes():
self.node_to_workerid[node]=subtopos.index(subtopo)
self.nodes.append(NodeWrapper(node, self.get_worker(node)))
self.node_to_wrapper[node]=self.nodes[-1]
if (not subtopo.isSwitch(node)):
self.hosts.append(self.nodes[-1])
else:
self.switches.append(self.nodes[-1])
self.logger.debug("Nodemapping: %s",self.node_to_workerid)
tunnels = [[] for x in range(len(subtopos))]
for tunnel in self.topology.getTunnels():
w1 = self.get_worker(tunnel[0])
w2 = self.get_worker(tunnel[1])
intf = self.cluster.create_tunnel(w1,w2)
self.tunnellookup[(tunnel[0],tunnel[1])]=intf
self.tunnellookup[(tunnel[1],tunnel[0])]=intf
for i in range(0,2):
tunnels[self.node_to_workerid[tunnel[i]]].append([intf, tunnel[i], tunnel[2]]) # Assumes that workerid = subtopoid
for topo in subtopos:
self.cluster.workers()[subtopos.index(topo)].set_switch(self.switch)
if(self.controller):
self.cluster.workers()[subtopos.index(topo)].start(topo=topo, tunnels=tunnels[subtopos.index(topo)], controller=self.controller)
else:
self.cluster.workers()[subtopos.index(topo)].start(topo=topo, tunnels=tunnels[subtopos.index(topo)])
if (self.config.runWith1500MTU()):
for topo in subtopos:
for host in topo.nodes():
self.setMTU(host,1450)
def setMTU(self,host,mtu):
for intf in self.get(host).intfNames():
self.get(host).cmd("ifconfig "+intf+" mtu "+str(mtu))
@deprecated
def run_cmd_on_host(self,host, cmd):
"""
run cmd on emulated host specified by host name and return output
This function is deprecated and will be removed in a future version of
MaxiNet. Use experiment.get(node).cmd() instead
"""
return self.get_worker(host).run_cmd_on_host(host,cmd)
def stop(self):
"""
stop experiment and shut down emulation on workers
"""
if self.isMonitoring:
self.terminate_logging()
for worker in self.cluster.workers():
worker.stop()
self.cluster.remove_all_tunnels()
class NodeWrapper:
"""
wrapper that allows most commands that can be used in mininet to be used
in MaxiNet
"""
# this feels like doing rpc via rpc...
def __init__(self,nodename,worker):
self.nn = nodename
self.worker = worker
def _call(self,cmd, *params1, **params2):
return self.worker.rpc(self.nn, cmd, *params1, **params2)
def _get(self,name):
return self.worker.rattr(self.nn, name)
def __getattr__(self,name):
def method(*params1,**params2):
return self._call(name,*params1,**params2)
# the following commands SHOULD work. no guarantee given
if name in [ "cleanup", "read", "readline", "write", "terminate", "stop",
"waitReadable", "sendCmd", "sendInt", "monitor", "waitOutput",
"cmd", "cmdPrint", "pexec", "newPort", "addIntf", "defaultIntf",
"intf", "connectionsTo", "deleteIntfs", "setARP", "setIP", "IP",
"MAC", "intfIsUp", "config", "configDefault",
"intfNames", "cgroupSet", "cgroupGet", "cgroupDel", "chrt",
"rtInfo", "cfsInfo", "setCPUFrac", "setCPUs", "defaultDpid",
"defaultIntf", "connected", "setup", "dpctl", "start", "stop",
"attach", "detach", "controllerUUIDs", "checkListening"]:
return method
elif name in [ "name","inNamespace","params","nameToIntf","waiting"]:
return self._get(name)
else:
raise AttributeError(name)
def __repr__(self):
return "NodeWrapper ("+self.nn+" at "+str(self.worker)+")"
Fix MaxiNet setup with small MTU
#!/usr/bin/python
"""
This module is the central part of MaxiNet and is intended to be the only
part of MaxiNet which needs to be used by the user or third-party applications
"""
import re
import sys
import logging
from functools import partial
import time
import subprocess
import random
import atexit
from mininet.node import RemoteController, UserSwitch
import Pyro4
from MaxiNet.Frontend.tools import Tools
import tools
from client import Frontend, log_and_reraise_remote_exception
from partitioner import Partitioner
from MaxiNet.Frontend.cli import CLI
# the following block is to support deprecation warnings. this is really not
# solved nicely and should probably be somewhere else
import warnings
import functools
logger = logging.getLogger(__name__)
def deprecated(func):
'''This is a decorator which can be used to mark functions
as deprecated. It will result in a warning being emitted
when the function is used.'''
@functools.wraps(func)
def new_func(*args, **kwargs):
logger.warn("Call to deprecated function {}.".format(func.__name__))
warnings.warn_explicit(
"Call to deprecated function {}.".format(func.__name__),
category=DeprecationWarning,
filename=func.func_code.co_filename,
lineno=func.func_code.co_firstlineno + 1
)
return func(*args, **kwargs)
return new_func
def run_cmd(cmd):
"""
run cmd on frontend machine
"""
return subprocess.check_output(cmd,shell=False)
def run_cmd_shell(cmd):
"""
run cmd on frontend machine with the shell
"""
return subprocess.check_output(cmd,shell=True)
class Worker:
"""
represents a worker machine in an running experiment
"""
def __init__(self,frontend,wid,switch=UserSwitch):
self.creator = frontend.getObjectProxy(wid+".mnCreator")
self.cmd = frontend.getObjectProxy(wid+".cmd")
self.config = frontend.getObjectProxy("config")
if(not self.config.runWith1500MTU()):
self._fix_mtus()
self.switch=switch
self.x11tunnels=[]
self.wid=int(wid[6:])
@log_and_reraise_remote_exception
def hn(self):
"""
return hostname of worker machine
"""
return self.cmd.get_hostname()
def set_switch(self,switch):
"""
set default switch class
"""
self.switch=switch
@log_and_reraise_remote_exception
def configLinkStatus(self, src, dst, status):
"""
wrapper for configLinkStatus method on remote mininet"""
self.creator.configLinkStatus(src,dst,status)
@log_and_reraise_remote_exception
def ip(self):
"""
return public ip adress of worker machine
"""
return self.config.getIP(self.hn())
@log_and_reraise_remote_exception
def start(self,topo,tunnels, controller=None):
"""
start mininet instance on worker machine emulating the in topo specified
topology.
if controller is not specified mininet will start an own controller for
this net
"""
if controller:
self.creator.create_mininet(topo=topo, tunnels=tunnels, controller = controller, switch=self.switch)
else:
self.creator.create_mininet(topo=topo, tunnels=tunnels, switch=self.switch)
@log_and_reraise_remote_exception
def daemonize(self,cmd):
"""
run command in background and terminate when MaxiNet is shut down
"""
self.cmd.daemonize(cmd)
@log_and_reraise_remote_exception
def tunnelX11(self,node):
"""
create X11 tunnel on worker to make x-forwarding work
"""
if(not node in self.x11tunnels):
try:
display = subprocess.check_output("ssh -Y "+self.hn()+ " env | grep DISPLAY",shell=True)[8:]
self.creator.tunnelX11(node,display)
self.x11tunnels.append(node)
except subprocess.CalledProcessError:
return False
return True
@log_and_reraise_remote_exception
def run_cmd_on_host(self,host,cmd):
"""
run cmd in context of host and return output, where host is the name of an host in
the mininet instance running on the worker machine
"""
return self.creator.runCmdOnHost(host,cmd)
@log_and_reraise_remote_exception
def run_cmd(self,cmd):
"""
run cmd on worker machine and return output
"""
return self.cmd.check_output(cmd)
@log_and_reraise_remote_exception
def run_script(self,cmd):
"""
run cmd on worker machine from the Worker/bin directory return output
"""
return self.cmd.script_check_output(cmd)
@log_and_reraise_remote_exception
def rpc(self, host, cmd, *params1, **params2):
"""
internal function to do rpc calls
"""
return self.creator.rpc(host, cmd, *params1, **params2)
@log_and_reraise_remote_exception
def rattr(self, host,name):
"""
internal function to get attributes of objects
"""
return self.creator.attr(host, name)
@log_and_reraise_remote_exception
def _fix_mtus(self):
if self.ip() is None :
logger.warn("no ip configured - can not fix MTU ")
return 0
intf = self.run_cmd("ip addr show to "+self.ip()+"/24 | head -n1 | cut -d' ' -f2 | tr -d :").strip()
if intf == "" :
logger.warn("could not find eth device - can not fix MTU")
return 0
mtu = int(self.run_cmd("ip li show dev "+intf+" | head -n1 | cut -d ' ' -f5"))
if(mtu<1600):
self.run_cmd("ip li se dev "+intf+" mtu 1600")
@log_and_reraise_remote_exception
def stop(self):
"""
stop mininet instance on this worker
"""
return self.creator.stop()
def get_file(self,src,dst):
"""
transfer file specified by src on worker to dst on frontend.
uses scp command to transfer file
"""
cmd_get = ["scp",self.hn()+":\""+src+"\"",dst]
subprocess.call(cmd_get)
def put_file(self,src,dst):
"""
transfer file specified by src on frontend to dst on worker.
uses scp command to transfer file
"""
cmd_put = ["scp",src,self.hn()+":"+dst]
subprocess.call(cmd_put)
@log_and_reraise_remote_exception
def addHost(self,name, cls=None, **params):
"""
add host at runtime. you probably want to use Experiment.addHost
"""
return self.creator.addHost(name,cls,**params)
@log_and_reraise_remote_exception
def addSwitch(self,name, cls=None,**params):
"""
add switch at runtime. you probably want to use Experiment.addSwitch
"""
return self.creator.addSwitch(name,cls,**params)
@log_and_reraise_remote_exception
def addController(self,name="c0", controller=None, **params):
"""
add controller at runtime. you probably want to use Experiment.addController
"""
return self.creator.addHost(name,controller,**params)
@log_and_reraise_remote_exception
def addTunnel(self,name, switch, port, cls, **params):
"""
add tunnel at runtime. you probably want to use Experiment.addLink
"""
return self.creator.addTunnel(name, switch, port, cls,**params)
@log_and_reraise_remote_exception
def addLink(self, node1, node2, port1 = None, port2 = None, cls = None, **params):
"""
add link at runtime. you probably want to use Experiment.addLink
"""
return self.creator.addLink(node1, node2, port1, port2, cls, **params)
class TunHelper:
"""
internal class to manage tunnel interface names
"""
def __init__(self):
self.tunnr = 0
self.keynr = 0
def get_tun_nr(self):
self.tunnr = self.tunnr +1
return self.tunnr -1
def get_key_nr(self):
self.keynr = self.keynr+1
return self.keynr -1
def get_last_tun_nr(self):
return self.tunnr - 1
def get_last_key_nr(self):
return self.keynr - 1
class Cluster:
"""
manage a set of workers via this class.
to create several different topologys do not destroy/recreate this class but
define several Experiment instances running sequential
"""
def __init__(self,*hosts):
"""
create Cluster object. Starting with MaxiNet 0.2 the hosts parameter is
optional. If None is given all configured hosts will be used
"""
self.running=False
self.logger = logging.getLogger(__name__)
self.tunhelper = TunHelper()
self.config = tools.Config("","",register = False)
logging.basicConfig(level=self.config.getLoggingLevel())
if hosts:
self.hosts = hosts
else:
self.hosts = self.config.getHosts()
self.worker=[]
if(self.hosts[0]!=subprocess.check_output(["hostname"]).strip()):
rhost = self.config.getIP(self.hosts[0])
else:
if(len(self.hosts)>1):
rhost = self.config.getIP(self.hosts[1])
else:
rhost = self.config.getIP(self.hosts[0])
if sys.platform == "darwin":
route = subprocess.check_output(["route", "-vn", "get", rhost]).splitlines()[-1]
m = re.search(r' (\d+\.\d+\.\d+\.\d+)$', route)
else:
route = subprocess.check_output("ip route get "+rhost+" | head -n1", shell=True).strip()
m = re.search(r'src (\d+\.\d+\.\d+\.\d+)', route)
if m is not None:
self.localIP = m.group(1)
else:
self.localIP="127.0.0.1"
self.nsport=9090
self.frontend = Frontend(self.localIP, self.nsport)
self.config = tools.Config(self.localIP,self.nsport)
atexit.register(run_cmd, self.getWorkerMangerCMD("--stop"))
atexit.register(self._stop)
def getWorkerMangerCMD(self,cmd):
cmdline = [self.config.getWorkerScript("worker_manager.py"), "--ns",
self.localIP+":" + str(self.nsport),
cmd]
if self.config.debugPyroOnWorker():
cmdline.append("--debugPyro")
if self.config.keepScreenOpenOnError():
cmdline.append("--keepScreenOpenOnError")
cmdline.extend(self.hosts)
return cmdline
def is_running(self):
return self.running
def get_worker_shares(self):
"""
returns list of workload shares per worker
"""
shares=None
if(self.running):
shares=[]
for w in self.worker:
shares.append(self.config.getShare(w.hn()))
wsum=0
for w in shares:
wsum+=w
shares = map(lambda x: x/float(wsum),shares)
return shares
def check_reachability(self,ip):
"""
check whether ip is reachable for a packet with MTU > 1500
"""
cmd = ["ping","-c 2","-s 1520",ip]
if(subprocess.call(cmd,stdout=open("/dev/null"),) == 1):
return False
else:
return True
def start(self):
"""
start MaxiNet on assigned worker machines and establish communication. Returns True in case of successful startup.
"""
self.logger.info("starting worker processes")
cmd = self.getWorkerMangerCMD("--start")
if self.frontend.hmac_key():
cmd.extend(["--hmac",self.frontend.hmac_key()])
self.logger.debug(run_cmd(cmd))
timeout = 10
for i in range(0,len(self.hosts)):
self.logger.info("waiting for Worker "+str(i+1)+" to register on nameserver...")
started=False
end = time.time()+timeout
while(not started):
try:
self.frontend.lookup("worker"+str(i+1)+".mnCreator")
started=True
except Pyro4.errors.NamingError:
if(time.time()>end):
raise RuntimeError("Timed out waiting for worker "+str(i+1)+".mnCreator to register.")
time.sleep(0.1)
started=False
end=time.time()+timeout
while(not started):
try:
self.frontend.lookup("worker"+str(i+1)+".cmd")
started=True
except Pyro4.errors.NamingError:
time.sleep(0.1)
if(time.time()>end):
raise RuntimeError("Timed out waiting for worker "+str(i+1)+".cmd to register.")
self.worker.append(Worker(self.frontend,"worker"+str(i+1)))
if(not self.config.runWith1500MTU()):
for host in self.hosts:
if(not self.check_reachability(self.config.getIP(host))):
self.logger.error("Host "+host+" is not reachable with an MTU > 1500.")
raise RuntimeError("Host "+host+" is not reachable with an MTU > 1500.")
for worker in self.worker:
worker.run_script("load_tunneling.sh")
self.logger.info("worker processes started")
self.running = True
return True
def _stop(self):
if(self.running):
self.logger.info("removing tunnels...")
self.remove_all_tunnels()
#self.logger.info("shutting cluster down...")
self.logger.info("removing nameserver entries...")
for i in range(0,self.num_workers()):
self.frontend.remove("worker"+str(i)+".mnCreator")
self.frontend.remove("worker"+str(i)+".cmd")
self.logger.info("shutting down frontend...")
self.logger.info("Goodbye.")
self.running=False
def stop(self):
"""
stop cluster and shut it down
"""
self._stop()
def num_workers(self):
"""
return number of worker nodes in this cluster
"""
return len(self.workers())
def workers(self):
"""
return list of worker instances for this cluster, ordered by worker id
"""
if(self.is_running()):
return self.worker
else:
return []
def get_worker(self, wid):
"""
return worker with id wid
"""
return self.workers()[wid]
def create_tunnel(self,w1,w2):
"""
create gre tunnel connecting worker machine w1 and w2 and return name of
created network interface
"""
tid = self.tunhelper.get_tun_nr()
tkey = self.tunhelper.get_key_nr()
intf = "mn_tun"+str(tid)
ip1=w1.ip()
ip2=w2.ip()
self.logger.debug("invoking tunnel create commands on "+ip1+" and "+ip2)
w1.run_script("create_tunnel.sh "+ip1+" "+ip2+" "+intf+" "+str(tkey))
w2.run_script("create_tunnel.sh "+ip2+" "+ip1+" "+intf+" "+str(tkey))
self.logger.debug("done")
return intf
def remove_all_tunnels(self):
"""
shut down all tunnels created in this cluster
"""
for worker in self.workers():
worker.run_script("delete_tunnels.sh")
self.tunhelper = TunHelper()
class Experiment:
"""
use this class to specify experiment. Experiments are created for
one-time-usage and have to be stopped in the end. One cluster instance can
run several experiments in sequence
"""
def __init__(self,cluster, topology,controller=None, is_partitioned=False, switch=UserSwitch):
self.cluster = cluster
self.logger = logging.getLogger(__name__)
self.topology=None
self.config = tools.Config(register = False)
self.starttime = time.localtime()
self.printed_log_info = False
self.isMonitoring = False
if is_partitioned:
self.topology = topology
else:
self.origtopology = topology
self.node_to_workerid = {}
self.node_to_wrapper = {}
self.nodes = []
self.hosts = []
self.tunnellookup = {}
self.switches = []
self.switch=switch
if controller:
contr = controller
else:
contr = self.config.getController()
if contr.find(":")>=0:
(host, port) = contr.split(":")
else:
host = contr
port = "6633"
self.controller = partial(RemoteController, ip=host, port=int(port))
def configLinkStatus(self, src, dst, status):
"""Change status of src <-> dst links.
src: node name
dst: node name
status: string {up, down}"""
ws = self.get_worker(src)
wd = self.get_worker(dst)
if(ws==wd): # src and dst are on same worker. let mininet handle this
ws.configLinkStatus(src,dst,status)
else:
src=self.get(src)
dst=self.get(dst)
intf = self.tunnellookup[(src.name,dst.name)]
src.cmd("ifconfig "+intf+" "+status)
dst.cmd("ifconfig "+intf+" "+status)
@deprecated
def find_worker(self,node):
"""
return worker which emulates the specified node.
Replaced by get_worker
"""
return self.get_worker(node)
def get_worker(self,node):
"""
return worker which emulates the specified node.
"""
if(isinstance(node,NodeWrapper)):
return node.worker
return self.cluster.get_worker(self.node_to_workerid[node])
def get_log_folder(self):
"""
returns folder to which log files will be saved
"""
return "/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"
def terminate_logging(self):
for worker in self.cluster.workers():
worker.run_cmd("killall getRxTx.sh getMemoryUsage.sh")
self.isMonitoring = False
def log_cpu(self):
"""
log cpu useage of workers and place log files in /tmp/maxinet_logs/
"""
for worker in self.cluster.workers():
self.log_cpu_of_worker(worker)
def log_cpu_of_worker(self,worker):
"""
log cpu usage of worker and place log file in /tmp/maxinet_logs/
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
atexit.register(worker.get_file,"/tmp/maxinet_cpu_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
worker.daemonize("LANG=en_EN.UTF-8 mpstat 1 | while read l; do echo -n \"`date +%s` \" ; echo \"$l \" ; done > \"/tmp/maxinet_cpu_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def log_free_memory(self):
"""
log memory usage of workers and place log files in /tmp/maxinet_logs
Format is:
timestamp,FreeMemory,Buffers,Cached
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
for worker in self.cluster.workers():
atexit.register(worker.get_file,"/tmp/maxinet_mem_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
memmon = worker.config.getWorkerScript("getMemoryUsage.sh")
worker.daemonize(memmon + " > \"/tmp/maxinet_mem_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def log_interfaces_of_node(self,node):
"""
logs statistics of interfaces of node and places them in /tmp/maxinet_logs
Format is:
timestamp,received bytes,sent bytes,received packets,sent packets
"""
subprocess.call(["mkdir","-p","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/"])
node = self.get(node)
worker = self.get_worker(node)
for intf in node.intfNames():
self.log_interface(worker,intf)
def log_interface(self,worker,intf):
"""
logs statistics of interface of worker and places them in /tmp/maxinet_logs
Format is:
timestamp,received bytes,sent bytes,received packets,sent packets
"""
atexit.register(worker.get_file,"/tmp/maxinet_intf_"+intf+"_"+str(worker.wid)+"_("+worker.hn()+").log","/tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/")
ethmon = worker.config.getWorkerScript("getRxTx.sh")
worker.daemonize(ethmon + " "+intf+" > \"/tmp/maxinet_intf_"+intf+"_"+str(worker.wid)+"_("+worker.hn()+").log\"")
atexit.register(self._print_log_info)
def monitor(self):
"""
logs statistics of worker interfaces and memory usage and places them in /tmp/maxinet_logs
"""
self.isMonitoring = True
atexit.register(self._print_monitor_info)
self.log_free_memory()
self.log_cpu()
for worker in self.cluster.workers():
intf = worker.run_cmd("ip addr show to "+worker.ip()+"/24 | head -n1 | cut -d' ' -f2 | tr -d :").strip()
if(intf == ""):
self.logger.warn("could not find main interface for "+worker.hn()+". no logging possible.")
else:
self.log_interface(worker,intf)
def _print_log_info(self):
if(not self.printed_log_info):
self.printed_log_info=True
self.logger.info("Log files will be placed in /tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/. You might want to save them somewhere else.")
def _print_monitor_info(self):
self.logger.info("You monitored this experiment. To generate a graph from your logs call \"/usr/local/share/MaxiNet/maxinet_plot.py /tmp/maxinet_logs/"+Tools.time_to_string(self.starttime)+"/ plot.png\" ")
def CLI(self,plocals,pglobals):
"""
open interactive command line interface
"""
CLI(self,plocals,pglobals)
def addNode(self, name,**params):
"""
add node at runtime.
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
wid = random.randint(0,self.cluster.num_workers()-1)
if "pos" in params.keys():
wid = self.node_to_workerid[params["pos"]]
del params["pos"]
if "wid" in params.keys():
wid = int(params["wid"])-1
del params["wid"]
worker = self.cluster.get_worker(wid)
self.node_to_workerid[name]=wid
self.node_to_wrapper[name]=NodeWrapper(name,self.get_worker(name))
self.nodes.append(self.node_to_wrapper[name])
def addHost(self, name, cls = None, **params):
"""
add host at runtime.
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name,**params)
self.get_worker(name).addHost(name,cls, **params)
self.hosts.append(self.get(name))
return self.get(name)
def addSwitch(self, name, cls = None, **params):
"""
add switch at runtime
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name, **params)
self.get_worker(name).addSwitch(name,cls, **params)
self.switches.append(self.get(name))
return self.get(name)
def addController(self, name="c0",controller = None, **params):
"""
add controller at runtime
use parameter wid to specifiy worker id or pos to specify worker of
existing node. otherwise random worker is chosen
"""
self.addNode(name, **params)
self.get_worker(name).addController(name, controller, **params)
return self.get(name)
def name(self, node):
"""
return name assigned to specified network node.
"""
if(isinstance(node,NodeWrapper)):
return node.nn
return node
def addLink(self, node1, node2, port1 = None, port2 = None, cls = None, autoconf=False, **params):
"""
add links at runtime. will create tunnels between workers if necessary,
but can not create tunnels between hosts and switches. (links will work fine)
autoconf parameter handles attach() and config calls on switches and hosts
"""
w1 = self.get_worker(node1)
w2 = self.get_worker(node2)
node1 = self.get(node1)
node2 = self.get(node2)
if(w1==w2):
self.logger.debug("no tunneling needed")
l=w1.addLink(self.name(node1),self.name(node2), port1, port2, cls, **params)
else:
self.logger.debug("tunneling needed")
if(not ((node1 in self.switches) and (node2 in self.switches))):
self.logger.error("We cannot create tunnels between switches and hosts. Sorry.")
raise RuntimeError("Can't create tunnel between switch and host")
intf = self.cluster.create_tunnel(w1,w2)
w1.addTunnel(intf,self.name(node1), port1, cls, **params)
w2.addTunnel(intf,self.name(node2), port2, cls, **params)
l=((self.name(node1),intf),(self.name(node2),intf))
if(autoconf):
if(node1 in self.switches):
node1.attach(l[0][1])
else:
node1.configDefault()
if(node2 in self.switches):
node2.attach(l[1][1])
else:
node2.configDefault()
if(self.config.runWith1500MTU()):
self.setMTU(node1,1450)
self.setMTU(node2,1450)
def _find_topo_of_node(self,node, topos):
for topo in topos:
if node in topo.g.nodes():
return topo
def get_node(self, nodename):
"""
return node that is specified by nodename
:rtype : NodeWrapper
"""
if(self.node_to_wrapper.has_key(nodename)):
return self.node_to_wrapper[nodename]
else:
return None
def get(self,nodename):
"""
alias for get_node
"""
return self.get_node(nodename)
def setup(self):
"""
start cluster if not yet started, assign topology parts to workers and
start workers
"""
if(not self.cluster.is_running()):
self.cluster.start()
if(not self.cluster.is_running()):
raise RuntimeError("Cluster won't start")
self.logger.info("Clustering topology...")
if(not self.topology):
parti = Partitioner()
parti.loadtopo(self.origtopology)
self.topology = parti.partition(self.cluster.num_workers(),self.cluster.get_worker_shares()) # assigning shares to workers requires that the workers are already startet. elsewise we don't have a way to determine the workerid of the worker. topologies are assigned to workers in ascending workerid order
self.logger.debug("Tunnels: "+str(self.topology.getTunnels()))
subtopos = self.topology.getTopos()
if(len(subtopos) > self.cluster.num_workers()):
raise RuntimeError("Cluster does not have enough workers for given topology")
for subtopo in subtopos:
for node in subtopo.nodes():
self.node_to_workerid[node]=subtopos.index(subtopo)
self.nodes.append(NodeWrapper(node, self.get_worker(node)))
self.node_to_wrapper[node]=self.nodes[-1]
if (not subtopo.isSwitch(node)):
self.hosts.append(self.nodes[-1])
else:
self.switches.append(self.nodes[-1])
self.logger.debug("Nodemapping: %s",self.node_to_workerid)
tunnels = [[] for x in range(len(subtopos))]
for tunnel in self.topology.getTunnels():
w1 = self.get_worker(tunnel[0])
w2 = self.get_worker(tunnel[1])
intf = self.cluster.create_tunnel(w1,w2)
self.tunnellookup[(tunnel[0],tunnel[1])]=intf
self.tunnellookup[(tunnel[1],tunnel[0])]=intf
for i in range(0,2):
tunnels[self.node_to_workerid[tunnel[i]]].append([intf, tunnel[i], tunnel[2]]) # Assumes that workerid = subtopoid
for topo in subtopos:
self.cluster.workers()[subtopos.index(topo)].set_switch(self.switch)
if(self.controller):
self.cluster.workers()[subtopos.index(topo)].start(topo=topo, tunnels=tunnels[subtopos.index(topo)], controller=self.controller)
else:
self.cluster.workers()[subtopos.index(topo)].start(topo=topo, tunnels=tunnels[subtopos.index(topo)])
if (self.config.runWith1500MTU()):
for topo in subtopos:
for host in topo.nodes():
self.setMTU(host,1450)
def setMTU(self,host,mtu):
for intf in host.intfNames():
host.cmd("ifconfig "+intf+" mtu "+str(mtu))
@deprecated
def run_cmd_on_host(self,host, cmd):
"""
run cmd on emulated host specified by host name and return output
This function is deprecated and will be removed in a future version of
MaxiNet. Use experiment.get(node).cmd() instead
"""
return self.get_worker(host).run_cmd_on_host(host,cmd)
def stop(self):
"""
stop experiment and shut down emulation on workers
"""
if self.isMonitoring:
self.terminate_logging()
for worker in self.cluster.workers():
worker.stop()
self.cluster.remove_all_tunnels()
class NodeWrapper:
"""
wrapper that allows most commands that can be used in mininet to be used
in MaxiNet
"""
# this feels like doing rpc via rpc...
def __init__(self,nodename,worker):
self.nn = nodename
self.worker = worker
def _call(self,cmd, *params1, **params2):
return self.worker.rpc(self.nn, cmd, *params1, **params2)
def _get(self,name):
return self.worker.rattr(self.nn, name)
def __getattr__(self,name):
def method(*params1,**params2):
return self._call(name,*params1,**params2)
# the following commands SHOULD work. no guarantee given
if name in [ "cleanup", "read", "readline", "write", "terminate", "stop",
"waitReadable", "sendCmd", "sendInt", "monitor", "waitOutput",
"cmd", "cmdPrint", "pexec", "newPort", "addIntf", "defaultIntf",
"intf", "connectionsTo", "deleteIntfs", "setARP", "setIP", "IP",
"MAC", "intfIsUp", "config", "configDefault",
"intfNames", "cgroupSet", "cgroupGet", "cgroupDel", "chrt",
"rtInfo", "cfsInfo", "setCPUFrac", "setCPUs", "defaultDpid",
"defaultIntf", "connected", "setup", "dpctl", "start", "stop",
"attach", "detach", "controllerUUIDs", "checkListening"]:
return method
elif name in [ "name","inNamespace","params","nameToIntf","waiting"]:
return self._get(name)
else:
raise AttributeError(name)
def __repr__(self):
return "NodeWrapper ("+self.nn+" at "+str(self.worker)+")"
|
"""
Provides a JSON API for common components.
"""
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import json
from django.http.response import HttpResponse
from django.utils.decorators import method_decorator
from django.urls import path
from django.views.decorators.csrf import csrf_exempt
from django.urls import include, re_path
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.exceptions import NotAcceptable, NotFound
from django_filters.rest_framework import DjangoFilterBackend
from rest_framework import filters, generics, permissions
from rest_framework import serializers
from django_q.tasks import async_task
import common.models
import common.serializers
from InvenTree.helpers import inheritors
from plugin.models import NotificationUserSetting
from plugin.serializers import NotificationUserSettingSerializer
class CsrfExemptMixin(object):
"""
Exempts the view from CSRF requirements.
"""
@method_decorator(csrf_exempt)
def dispatch(self, *args, **kwargs):
return super(CsrfExemptMixin, self).dispatch(*args, **kwargs)
class WebhookView(CsrfExemptMixin, APIView):
"""
Endpoint for receiving webhooks.
"""
authentication_classes = []
permission_classes = []
model_class = common.models.WebhookEndpoint
run_async = False
def post(self, request, endpoint, *args, **kwargs):
# get webhook definition
self._get_webhook(endpoint, request, *args, **kwargs)
# check headers
headers = request.headers
try:
payload = json.loads(request.body)
except json.decoder.JSONDecodeError as error:
raise NotAcceptable(error.msg)
# validate
self.webhook.validate_token(payload, headers, request)
# process data
message = self.webhook.save_data(payload, headers, request)
if self.run_async:
async_task(self._process_payload, message.id)
else:
self._process_result(
self.webhook.process_payload(message, payload, headers),
message,
)
data = self.webhook.get_return(payload, headers, request)
return HttpResponse(data)
def _process_payload(self, message_id):
message = common.models.WebhookMessage.objects.get(message_id=message_id)
self._process_result(
self.webhook.process_payload(message, message.body, message.header),
message,
)
def _process_result(self, result, message):
if result:
message.worked_on = result
message.save()
else:
message.delete()
def _escalate_object(self, obj):
classes = inheritors(obj.__class__)
for cls in classes:
mdl_name = cls._meta.model_name
if hasattr(obj, mdl_name):
return getattr(obj, mdl_name)
return obj
def _get_webhook(self, endpoint, request, *args, **kwargs):
try:
webhook = self.model_class.objects.get(endpoint_id=endpoint)
self.webhook = self._escalate_object(webhook)
self.webhook.init(request, *args, **kwargs)
return self.webhook.process_webhook()
except self.model_class.DoesNotExist:
raise NotFound()
class SettingsList(generics.ListAPIView):
filter_backends = [
DjangoFilterBackend,
filters.SearchFilter,
filters.OrderingFilter,
]
ordering_fields = [
'pk',
'key',
'name',
]
search_fields = [
'key',
]
class GlobalSettingsList(SettingsList):
"""
API endpoint for accessing a list of global settings objects
"""
queryset = common.models.InvenTreeSetting.objects.all()
serializer_class = common.serializers.GlobalSettingsSerializer
class GlobalSettingsPermissions(permissions.BasePermission):
"""
Special permission class to determine if the user is "staff"
"""
def has_permission(self, request, view):
"""
Check that the requesting user is 'admin'
"""
try:
user = request.user
return user.is_staff
except AttributeError:
return False
class GlobalSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "global setting" object.
- User must have 'staff' status to view / edit
"""
queryset = common.models.InvenTreeSetting.objects.all()
serializer_class = common.serializers.GlobalSettingsSerializer
permission_classes = [
GlobalSettingsPermissions,
]
class UserSettingsList(SettingsList):
"""
API endpoint for accessing a list of user settings objects
"""
queryset = common.models.InvenTreeUserSetting.objects.all()
serializer_class = common.serializers.UserSettingsSerializer
def filter_queryset(self, queryset):
"""
Only list settings which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return common.models.InvenTreeUserSetting.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class UserSettingsPermissions(permissions.BasePermission):
"""
Special permission class to determine if the user can view / edit a particular setting
"""
def has_object_permission(self, request, view, obj):
try:
user = request.user
except AttributeError:
return False
return user == obj.user
class UserSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "user setting" object
- User can only view / edit settings their own settings objects
"""
queryset = common.models.InvenTreeUserSetting.objects.all()
serializer_class = common.serializers.UserSettingsSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationUserSettingsList(SettingsList):
"""
API endpoint for accessing a list of notification user settings objects
"""
queryset = NotificationUserSetting.objects.all()
serializer_class = NotificationUserSettingSerializer
def filter_queryset(self, queryset):
"""
Only list settings which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return NotificationUserSetting.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class NotificationUserSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "notification user setting" object
- User can only view / edit settings their own settings objects
"""
queryset = NotificationUserSetting.objects.all()
serializer_class = NotificationUserSettingSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationList(generics.ListAPIView):
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationMessageSerializer
filter_backends = [
DjangoFilterBackend,
filters.SearchFilter,
filters.OrderingFilter,
]
ordering_fields = [
'category',
'name',
'read',
]
search_fields = [
'name',
'message',
]
filter_fields = [
'category',
'read',
]
def filter_queryset(self, queryset):
"""
Only list notifications which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return common.models.NotificationMessage.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class NotificationDetail(generics.RetrieveUpdateDestroyAPIView):
"""
Detail view for an individual notification object
- User can only view / delete their own notification objects
"""
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationMessageSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationReadEdit(generics.CreateAPIView):
"""
general API endpoint to manipulate read state of a notification
"""
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationReadSerializer
permission_classes = [
UserSettingsPermissions,
]
def get_serializer_context(self):
context = super().get_serializer_context()
if self.request:
context['instance'] = self.get_object()
return context
def perform_create(self, serializer):
message = self.get_object()
try:
message.read = self.target
message.save()
except Exception as exc:
raise serializers.ValidationError(detail=serializers.as_serializer_error(exc))
class NotificationRead(NotificationReadEdit):
"""
API endpoint to mark a notification as read.
"""
target = True
class NotificationUnread(NotificationReadEdit):
"""
API endpoint to mark a notification as unread.
"""
target = False
class NotificationReadAll(generics.RetrieveAPIView):
"""
API endpoint to mark all notifications as read.
"""
queryset = common.models.NotificationMessage.objects.all()
permission_classes = [
UserSettingsPermissions,
]
def get(self, request, *args, **kwargs):
try:
self.queryset.filter(user=request.user, read=False).update(read=True)
return Response({'status': 'ok'})
except Exception as exc:
raise serializers.ValidationError(detail=serializers.as_serializer_error(exc))
settings_api_urls = [
# User settings
re_path(r'^user/', include([
# User Settings Detail
re_path(r'^(?P<pk>\d+)/', UserSettingsDetail.as_view(), name='api-user-setting-detail'),
# User Settings List
re_path(r'^.*$', UserSettingsList.as_view(), name='api-user-setting-list'),
])),
# Notification settings
re_path(r'^notification/', include([
# Notification Settings Detail
re_path(r'^(?P<pk>\d+)/', NotificationUserSettingsDetail.as_view(), name='api-notification-setting-detail'),
# Notification Settings List
re_path(r'^.*$', NotificationUserSettingsList.as_view(), name='api-notifcation-setting-list'),
])),
# Global settings
re_path(r'^global/', include([
# Global Settings Detail
re_path(r'^(?P<pk>\d+)/', GlobalSettingsDetail.as_view(), name='api-global-setting-detail'),
# Global Settings List
re_path(r'^.*$', GlobalSettingsList.as_view(), name='api-global-setting-list'),
])),
]
common_api_urls = [
# Webhooks
path('webhook/<slug:endpoint>/', WebhookView.as_view(), name='api-webhook'),
# Notifications
re_path(r'^notifications/', include([
# Individual purchase order detail URLs
re_path(r'^(?P<pk>\d+)/', include([
re_path(r'^read/', NotificationRead.as_view(), name='api-notifications-read'),
re_path(r'^unread/', NotificationUnread.as_view(), name='api-notifications-unread'),
re_path(r'.*$', NotificationDetail.as_view(), name='api-notifications-detail'),
])),
# Read all
re_path(r'^readall/', NotificationReadAll.as_view(), name='api-notifications-readall'),
# Notification messages list
re_path(r'^.*$', NotificationList.as_view(), name='api-notifications-list'),
])),
]
only safety test - api is not callable as non-user
"""
Provides a JSON API for common components.
"""
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import json
from django.http.response import HttpResponse
from django.utils.decorators import method_decorator
from django.urls import path
from django.views.decorators.csrf import csrf_exempt
from django.urls import include, re_path
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework.exceptions import NotAcceptable, NotFound
from django_filters.rest_framework import DjangoFilterBackend
from rest_framework import filters, generics, permissions
from rest_framework import serializers
from django_q.tasks import async_task
import common.models
import common.serializers
from InvenTree.helpers import inheritors
from plugin.models import NotificationUserSetting
from plugin.serializers import NotificationUserSettingSerializer
class CsrfExemptMixin(object):
"""
Exempts the view from CSRF requirements.
"""
@method_decorator(csrf_exempt)
def dispatch(self, *args, **kwargs):
return super(CsrfExemptMixin, self).dispatch(*args, **kwargs)
class WebhookView(CsrfExemptMixin, APIView):
"""
Endpoint for receiving webhooks.
"""
authentication_classes = []
permission_classes = []
model_class = common.models.WebhookEndpoint
run_async = False
def post(self, request, endpoint, *args, **kwargs):
# get webhook definition
self._get_webhook(endpoint, request, *args, **kwargs)
# check headers
headers = request.headers
try:
payload = json.loads(request.body)
except json.decoder.JSONDecodeError as error:
raise NotAcceptable(error.msg)
# validate
self.webhook.validate_token(payload, headers, request)
# process data
message = self.webhook.save_data(payload, headers, request)
if self.run_async:
async_task(self._process_payload, message.id)
else:
self._process_result(
self.webhook.process_payload(message, payload, headers),
message,
)
data = self.webhook.get_return(payload, headers, request)
return HttpResponse(data)
def _process_payload(self, message_id):
message = common.models.WebhookMessage.objects.get(message_id=message_id)
self._process_result(
self.webhook.process_payload(message, message.body, message.header),
message,
)
def _process_result(self, result, message):
if result:
message.worked_on = result
message.save()
else:
message.delete()
def _escalate_object(self, obj):
classes = inheritors(obj.__class__)
for cls in classes:
mdl_name = cls._meta.model_name
if hasattr(obj, mdl_name):
return getattr(obj, mdl_name)
return obj
def _get_webhook(self, endpoint, request, *args, **kwargs):
try:
webhook = self.model_class.objects.get(endpoint_id=endpoint)
self.webhook = self._escalate_object(webhook)
self.webhook.init(request, *args, **kwargs)
return self.webhook.process_webhook()
except self.model_class.DoesNotExist:
raise NotFound()
class SettingsList(generics.ListAPIView):
filter_backends = [
DjangoFilterBackend,
filters.SearchFilter,
filters.OrderingFilter,
]
ordering_fields = [
'pk',
'key',
'name',
]
search_fields = [
'key',
]
class GlobalSettingsList(SettingsList):
"""
API endpoint for accessing a list of global settings objects
"""
queryset = common.models.InvenTreeSetting.objects.all()
serializer_class = common.serializers.GlobalSettingsSerializer
class GlobalSettingsPermissions(permissions.BasePermission):
"""
Special permission class to determine if the user is "staff"
"""
def has_permission(self, request, view):
"""
Check that the requesting user is 'admin'
"""
try:
user = request.user
return user.is_staff
except AttributeError:
return False
class GlobalSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "global setting" object.
- User must have 'staff' status to view / edit
"""
queryset = common.models.InvenTreeSetting.objects.all()
serializer_class = common.serializers.GlobalSettingsSerializer
permission_classes = [
GlobalSettingsPermissions,
]
class UserSettingsList(SettingsList):
"""
API endpoint for accessing a list of user settings objects
"""
queryset = common.models.InvenTreeUserSetting.objects.all()
serializer_class = common.serializers.UserSettingsSerializer
def filter_queryset(self, queryset):
"""
Only list settings which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return common.models.InvenTreeUserSetting.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class UserSettingsPermissions(permissions.BasePermission):
"""
Special permission class to determine if the user can view / edit a particular setting
"""
def has_object_permission(self, request, view, obj):
try:
user = request.user
except AttributeError: # pragma: no cover
return False
return user == obj.user
class UserSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "user setting" object
- User can only view / edit settings their own settings objects
"""
queryset = common.models.InvenTreeUserSetting.objects.all()
serializer_class = common.serializers.UserSettingsSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationUserSettingsList(SettingsList):
"""
API endpoint for accessing a list of notification user settings objects
"""
queryset = NotificationUserSetting.objects.all()
serializer_class = NotificationUserSettingSerializer
def filter_queryset(self, queryset):
"""
Only list settings which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return NotificationUserSetting.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class NotificationUserSettingsDetail(generics.RetrieveUpdateAPIView):
"""
Detail view for an individual "notification user setting" object
- User can only view / edit settings their own settings objects
"""
queryset = NotificationUserSetting.objects.all()
serializer_class = NotificationUserSettingSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationList(generics.ListAPIView):
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationMessageSerializer
filter_backends = [
DjangoFilterBackend,
filters.SearchFilter,
filters.OrderingFilter,
]
ordering_fields = [
'category',
'name',
'read',
]
search_fields = [
'name',
'message',
]
filter_fields = [
'category',
'read',
]
def filter_queryset(self, queryset):
"""
Only list notifications which apply to the current user
"""
try:
user = self.request.user
except AttributeError:
return common.models.NotificationMessage.objects.none()
queryset = super().filter_queryset(queryset)
queryset = queryset.filter(user=user)
return queryset
class NotificationDetail(generics.RetrieveUpdateDestroyAPIView):
"""
Detail view for an individual notification object
- User can only view / delete their own notification objects
"""
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationMessageSerializer
permission_classes = [
UserSettingsPermissions,
]
class NotificationReadEdit(generics.CreateAPIView):
"""
general API endpoint to manipulate read state of a notification
"""
queryset = common.models.NotificationMessage.objects.all()
serializer_class = common.serializers.NotificationReadSerializer
permission_classes = [
UserSettingsPermissions,
]
def get_serializer_context(self):
context = super().get_serializer_context()
if self.request:
context['instance'] = self.get_object()
return context
def perform_create(self, serializer):
message = self.get_object()
try:
message.read = self.target
message.save()
except Exception as exc:
raise serializers.ValidationError(detail=serializers.as_serializer_error(exc))
class NotificationRead(NotificationReadEdit):
"""
API endpoint to mark a notification as read.
"""
target = True
class NotificationUnread(NotificationReadEdit):
"""
API endpoint to mark a notification as unread.
"""
target = False
class NotificationReadAll(generics.RetrieveAPIView):
"""
API endpoint to mark all notifications as read.
"""
queryset = common.models.NotificationMessage.objects.all()
permission_classes = [
UserSettingsPermissions,
]
def get(self, request, *args, **kwargs):
try:
self.queryset.filter(user=request.user, read=False).update(read=True)
return Response({'status': 'ok'})
except Exception as exc:
raise serializers.ValidationError(detail=serializers.as_serializer_error(exc))
settings_api_urls = [
# User settings
re_path(r'^user/', include([
# User Settings Detail
re_path(r'^(?P<pk>\d+)/', UserSettingsDetail.as_view(), name='api-user-setting-detail'),
# User Settings List
re_path(r'^.*$', UserSettingsList.as_view(), name='api-user-setting-list'),
])),
# Notification settings
re_path(r'^notification/', include([
# Notification Settings Detail
re_path(r'^(?P<pk>\d+)/', NotificationUserSettingsDetail.as_view(), name='api-notification-setting-detail'),
# Notification Settings List
re_path(r'^.*$', NotificationUserSettingsList.as_view(), name='api-notifcation-setting-list'),
])),
# Global settings
re_path(r'^global/', include([
# Global Settings Detail
re_path(r'^(?P<pk>\d+)/', GlobalSettingsDetail.as_view(), name='api-global-setting-detail'),
# Global Settings List
re_path(r'^.*$', GlobalSettingsList.as_view(), name='api-global-setting-list'),
])),
]
common_api_urls = [
# Webhooks
path('webhook/<slug:endpoint>/', WebhookView.as_view(), name='api-webhook'),
# Notifications
re_path(r'^notifications/', include([
# Individual purchase order detail URLs
re_path(r'^(?P<pk>\d+)/', include([
re_path(r'^read/', NotificationRead.as_view(), name='api-notifications-read'),
re_path(r'^unread/', NotificationUnread.as_view(), name='api-notifications-unread'),
re_path(r'.*$', NotificationDetail.as_view(), name='api-notifications-detail'),
])),
# Read all
re_path(r'^readall/', NotificationReadAll.as_view(), name='api-notifications-readall'),
# Notification messages list
re_path(r'^.*$', NotificationList.as_view(), name='api-notifications-list'),
])),
]
|
##############################################################################
# Copyright (c) 2013-2016, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-647188
#
# For details, see https://github.com/llnl/spack
# Please also see the LICENSE file for our notice and the LGPL.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License (as
# published by the Free Software Foundation) version 2.1, February 1999.
#
# 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 terms and
# conditions of the GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
##############################################################################
from spack import *
class Libtiff(AutotoolsPackage):
"""libtiff graphics format library"""
homepage = "http://www.simplesystems.org/libtiff/"
url = "http://download.osgeo.org/libtiff/tiff-4.0.3.tar.gz"
version('4.0.6', 'd1d2e940dea0b5ad435f21f03d96dd72')
version('4.0.3', '051c1068e6a0627f461948c365290410')
depends_on('jpeg')
depends_on('zlib')
depends_on('xz')
Add latest version of libtiff (#4067)
##############################################################################
# Copyright (c) 2013-2016, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-647188
#
# For details, see https://github.com/llnl/spack
# Please also see the LICENSE file for our notice and the LGPL.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License (as
# published by the Free Software Foundation) version 2.1, February 1999.
#
# 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 terms and
# conditions of the GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
##############################################################################
from spack import *
class Libtiff(AutotoolsPackage):
"""libtiff graphics format library"""
homepage = "http://www.simplesystems.org/libtiff/"
url = "http://download.osgeo.org/libtiff/tiff-4.0.7.tar.gz"
version('4.0.7', '77ae928d2c6b7fb46a21c3a29325157b')
version('4.0.6', 'd1d2e940dea0b5ad435f21f03d96dd72')
version('4.0.3', '051c1068e6a0627f461948c365290410')
depends_on('jpeg')
depends_on('zlib')
depends_on('xz')
|
#!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# 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.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
import sys
import os
import numpy
import numpy.random
import random
import itertools
import time
import math
import copy
import cPickle
import struct
from nupic.bindings.math import SM32, SM_01_32_32, count_gte, GetNTAReal
from nupic.bindings.algorithms import Inhibition2, cpp_overlap, cpp_overlap_sbm
from nupic.bindings.algorithms import adjustMasterValidPermanence
from nupic.bindings.math import Random as NupicRandom
from nupic.math.cross import cross
from operator import itemgetter
import nupic.research.fdrutilities as fdru
realDType = GetNTAReal()
gPylabInitialized = False
# kDutyCycleFactor add dutyCycleAfterInh to overlap in Inhibition step to be a
# tie breaker
kDutyCycleFactor = 0.01
################################################################################
def _extractCallingMethodArgs():
"""
Returns args dictionary from the calling method
"""
import inspect
import copy
callingFrame = inspect.stack()[1][0]
argNames, _, _, frameLocalVarDict = inspect.getargvalues(callingFrame)
argNames.remove("self")
args = copy.copy(frameLocalVarDict)
for varName in frameLocalVarDict:
if varName not in argNames:
args.pop(varName)
return args
"""
Class for spatial pooling based on fixed random distributed
representation (FDR)
"""
class FDRCSpatial2(object):
"""
This version of FDRCSpatial inlcudes adaptive receptive fields, no-dupe rules
and gradual boosting. It supports 1-D and 2-D topologies with cloning
"""
def __init__(self,
inputShape = (32, 32),
inputBorder = 8,
inputDensity = 1.0,
coincidencesShape = (48, 48),
coincInputRadius = 16,
coincInputPoolPct = 1.0,
gaussianDist = False,
commonDistributions = False,
localAreaDensity = -1.0,
numActivePerInhArea = 10.0,
stimulusThreshold = 0,
synPermInactiveDec = 0.01,
synPermActiveInc = 0.1,
synPermActiveSharedDec = 0.0,
synPermOrphanDec = 0.0,
synPermConnected = 0.10,
minPctDutyCycleBeforeInh = 0.001,
minPctDutyCycleAfterInh = 0.001,
dutyCyclePeriod = 1000,
maxFiringBoost = 10.0,
maxSSFiringBoost = 2.0,
maxSynPermBoost = 10.0,
minDistance = 0.0,
cloneMap = None,
numCloneMasters = -1,
seed = -1,
spVerbosity = 0,
printPeriodicStats = 0,
testMode = False,
globalInhibition = False,
spReconstructionParam = "unweighted_mean",
useHighTier = True,
randomSP = False,
):
"""
Parameters:
----------------------------
inputShape: The dimensions of the input vector. Format is (height,
width) e.g. (24, 72). If the input is from a sensor,
it is interpreted as having a 2-D topology of 24
pixels high and 72 wide.
inputBorder: The first column from an edge will be centered
over an input which is 'inputBorder' inputs from the
edge.
inputDensity: The density of the input. This is only to aid in
figuring out the initial number of connected synapses
to place on each column. The lower the inputDensity,
the more initial connections will be assigned to each
column.
coincidencesShape: The dimensions of column layout. Format is (height,
width) e.g. (80,100) means a total of 80*100 = 800 are
arranged in a 2-D topology with 80 rows and
100 columns.
coincInputRadius: This defines the max radius of the receptive field of
each column. This is used to limit memory requirements
and processing time. It could be set large enough to
encompass the entire input field and the SP would
still work fine, but require more memory and
processing time.
This parameter defines a square area: a column will
have a max square RF with sides of length
2 * coincInputRadius + 1.
coincInputPoolPct What percent of the columns's receptive field is
available for potential synapses. At initialization
time, we will choose
coincInputPoolPct * (2*coincInputRadius + 1)^2
potential synapses from the receptive field.
gaussianDist: If true, the initial permanences assigned to each
column will have a gaussian distribution to them,
making the column favor inputs directly below it over
inputs farther away. If false, the initial permanences
will have a random distribution across the column's
entire potential receptive field.
commonDistributions: If set to True (the default, faster startup time),
each column will be given the same initial permanence
values.
This is normally OK when you will be training, but if
you will be sticking with the untrained network,
you will want to set this to False (which makes
startup take longer).
localAreaDensity: The desired density of active columns within a local
inhibition area (the size of which is set by the
internally calculated inhibitionRadius, which is in
turn determined from the average size of the connected
receptive fields of all columns). The inhibition logic
will insure that at most N columns remain ON within a
local inhibition area, where N = localAreaDensity *
(total number of columns in inhibition area).
numActivePerInhArea: An alternate way to control the density of the active
columns. If numActivePerInhArea is specified then
localAreaDensity must be -1, and vice versa. When
using numActivePerInhArea, the inhibition logic will
insure that at most 'numActivePerInhArea' columns
remain ON within a local inhibition area (the size of
which is set by the internally calculated
inhibitionRadius, which is in turn determined from the
average size of the connected receptive fields of all
columns). When using this method, as columns learn and
grow their effective receptive fields, the
inhibitionRadius will grow, and hence the net density
of the active columns will *decrease*. This is in
contrast to the localAreaDensity method, which keeps
the density of active columns the same regardless of
the size of their receptive fields.
stimulusThreshold: This is a number specifying the minimum number of
synapses that must be on in order for a columns to
turn ON.
The purpose of this is to prevent noise input from
activating columns.
synPermInactiveDec: How much an inactive synapse is decremented, specified
as a percent of a fully grown synapse.
synPermActiveInc: How much to increase the permanence of an active
synapse, specified as a percent of a fully grown
synapse.
synPermActiveSharedDec: How much to decrease the permanence of an active
synapse which is connected to another column that is
active at the same time. Specified as a percent of a
fully grown synapse.
synPermOrphanDec: How much to decrease the permanence of an active
synapse on a column which has high overlap with the
input, but was inhibited (an "orphan" column).
synPermConnected: The default connected threshold. Any synapse whose
permanence value is above the connected threshold is
a "connected synapse", meaning it can contribute to
the cell's firing. Typical value is 0.10. Cells whose
activity level before inhibition falls below
minDutyCycleBeforeInh will have their own internal
synPermConnectedCell threshold set below this default
value.
(This concept applies to both SP and TP and so 'cells'
is correct here as opposed to 'columns')
minPctDutyCycleBeforeInh: A number between 0 and 1.0, used to set a floor on
how often a column should have at least
stimulusThreshold active inputs. Periodically, each
column looks at the duty cycle before inhibition of
all other column within its inhibition radius and sets
its own internal minimal acceptable duty cycle to:
minPctDutyCycleBeforeInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle before
inhibition falls below this computed value will get
all of its permanence values boosted up by
synPermActiveInc. Raising all permanences in response
to a sub-par duty cycle before inhibition allows a
cell to search for new inputs when either its
previously learned inputs are no longer ever active,
or when the vast majority of them have been "hijacked"
by other columns due to the no-dupe rule.
minPctDutyCycleAfterInh: A number between 0 and 1.0, used to set a floor on
how often a column should turn ON after inhibition.
Periodically, each column looks at the duty cycle
after inhibition of all other columns within its
inhibition radius and sets its own internal minimal
acceptable duty cycle to:
minPctDutyCycleAfterInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle after
inhibition falls below this computed value will get
its internal boost factor increased.
dutyCyclePeriod: The period used to calculate duty cycles. Higher
values make it take longer to respond to changes in
boost or synPerConnectedCell. Shorter values make it
more unstable and likely to oscillate.
maxFiringBoost: The maximum firing level boost factor. Each column's
raw firing strength gets multiplied by a boost factor
before it gets considered for inhibition.
The actual boost factor for a column is number between
1.0 and maxFiringBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxFiringBoost
is used if the duty cycle is 0, and any duty cycle in
between is linearly extrapolated from these 2
endpoints.
maxSSFiringBoost: Once a column turns ON, it's boost will immediately
fall down to maxSSFiringBoost if it is above it. This
is accomplished by internally raising it's computed
duty cycle accordingly. This prevents a cell which has
had it's boost raised extremely high from turning ON
for too many diverse inputs in a row within a short
period of time.
maxSynPermBoost: The maximum synPermActiveInc boost factor. Each
column's synPermActiveInc gets multiplied by a boost
factor to make the column more or less likely to form
new connections.
The actual boost factor used is a number between
1.0 and maxSynPermBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxSynPermBoost
is used if the duty cycle is 0, and any duty cycle
in between is linearly extrapolated from these 2
endpoints.
minDistance: This parameter impacts how finely the input space is
quantized. It is a value between 0 and 1.0. If set
to 0, then every unique input presentation will
generate a unique output representation, within the
limits of the total number of columns available.
Higher values will tend to group similar inputs
together into the same output representation. Only
column which overlap with the input less than
100*(1.0-minDistance) percent will
have a possibility of losing the inhibition
competition against a boosted, 'bored' cell.
cloneMap: An array (numColumnsHigh, numColumnsWide) that
contains the clone index to use for each column.
numCloneMasters: The number of distinct clones in the map. This
is just outputCloningWidth*outputCloningHeight.
seed: Seed for our own pseudo-random number generator.
spVerbosity: spVerbosity level: 0, 1, 2, or 3
printPeriodicStats: If > 0, then every 'printPeriodicStats' iterations,
the SP will print to stdout some statistics related to
learning, such as the average pct under and
over-coverage, average number of active columns, etc.
in the last 'showLearningStats' iterations.
testMode: If True, run the SP in test mode. This runs both the
C++ and python implementations on all internal
functions that support both and insures that both
produce the same result.
globalInhibition: If true, enforce the
localAreaDensity/numActivePerInhArea
globally over the entire region, ignoring any
dynamically calculated inhibitionRadius. In effect,
this is the same as setting the inhibition radius to
include the entire region.
spReconstructionParam:Specifies which SP reconstruction optimization to be
used. Each column's firing strength is weighted by the
percent Overlap, permanence or duty Cycle if this
parameter is set to 'pctOverlap', 'permanence', or
'dutycycle' respectively. If parameter is set to
'maximum_firingstrength', the maximum of the firing
strengths (weighted by permanence) is used instead of
the weighted sum.
useHighTier: The "high tier" feature is to deal with sparse input
spaces. If over (1-minDistance) percent of a column's
connected synapses are active, it will automatically
become one of the winning columns.
If False, columns are activated based on their absolute
overlap with the input. Also, boosting will be
disabled to prevent pattern oscillation
randomSP: If True, the SP will not update its permanences and
will instead use it's initial configuration for all
inferences.
"""
#--------------------------------------------------------------------------
# Save our __init__ args for debugging
self._initArgsDict = _extractCallingMethodArgs()
# Handle people instantiating us directly that don't pass in a cloneMap...
# This creates a clone map without any cloning
if cloneMap is None:
cloneMap, numCloneMasters = fdru.makeCloneMap(
columnsShape=coincidencesShape,
outputCloningWidth=coincidencesShape[1],
outputCloningHeight=coincidencesShape[0]
)
self.numCloneMasters = numCloneMasters
self._cloneMapFlat = cloneMap.reshape((-1,))
# Save creation parameters
self.inputShape = (int(inputShape[0]), int(inputShape[1]))
self.inputBorder = inputBorder
self.inputDensity = inputDensity
self.coincidencesShape = coincidencesShape
self.coincInputRadius = coincInputRadius
self.coincInputPoolPct = coincInputPoolPct
self.gaussianDist = gaussianDist
self.commonDistributions = commonDistributions
self.localAreaDensity = localAreaDensity
self.numActivePerInhArea = numActivePerInhArea
self.stimulusThreshold = stimulusThreshold
self.synPermInactiveDec = synPermInactiveDec
self.synPermActiveInc = synPermActiveInc
self.synPermActiveSharedDec = synPermActiveSharedDec
self.synPermOrphanDec = synPermOrphanDec
self.synPermConnected = synPermConnected
self.minPctDutyCycleBeforeInh = minPctDutyCycleBeforeInh
self.minPctDutyCycleAfterInh = minPctDutyCycleAfterInh
self.dutyCyclePeriod = dutyCyclePeriod
self.maxFiringBoost = maxFiringBoost
self.maxSSFiringBoost = maxSSFiringBoost
self.maxSynPermBoost = maxSynPermBoost
self.minDistance = minDistance
self.spVerbosity = spVerbosity
self.printPeriodicStats = printPeriodicStats
self.testMode = testMode
self.globalInhibition = globalInhibition
self.spReconstructionParam = spReconstructionParam
self.useHighTier= useHighTier != 0
self.randomSP = randomSP != 0
if not self.useHighTier:
self.minPctDutyCycleAfterInh = 0
self.fileCount = 0
self._runIter = 0
# Start at iteration #0
self._iterNum = 0 # Number of learning iterations
self._inferenceIterNum = 0 # Number of inference iterations
# Print creation parameters
if spVerbosity >= 3:
self.printParams()
print "seed =", seed
# Check for errors
assert (self.numActivePerInhArea == -1 or self.localAreaDensity == -1)
assert (self.inputShape[1] > 2*self.inputBorder)
# 1D layouts have inputShape[0] == 1
if self.inputShape[0] > 1:
assert (self.inputShape[0] > 2*self.inputBorder)
# Calculate other member variables
self._coincCount = int(self.coincidencesShape[0] * \
self.coincidencesShape[1])
self._inputCount = int(self.inputShape[0] * self.inputShape[1])
self._synPermMin = 0.0
self._synPermMax = 1.0
self._pylabInitialized = False
# The rate at which we bump up all synapses in response to not passing
# stimulusThreshold
self._synPermBelowStimulusInc = self.synPermConnected / 10.0
self._hasTopology = True
if self.inputShape[0] == 1: # 1-D layout
self._coincRFShape = (1, (2*coincInputRadius + 1))
# If we only have 1 column of coincidences, then assume the user wants
# each coincidence to cover the entire input
if self.coincidencesShape[1] == 1:
assert self.inputBorder >= (self.inputShape[1] - 1) // 2
assert coincInputRadius >= (self.inputShape[1] - 1) // 2
self._coincRFShape = (1, self.inputShape[1])
self._hasTopology = False
else: # 2-D layout
self._coincRFShape = ((2*coincInputRadius + 1), (2*coincInputRadius + 1))
# This gets set to True in finishLearning. Once set, we don't allow
# learning anymore and delete all member variables needed only for
# learning
self._doneLearning = False
# Init random seed
self._seed(seed)
# Hard-coded in the current case
self.randomTieBreakingFraction = 0.5
# The permanence values used to initialize the master coincs are from
# this initial permanence array
# The initial permanence is gaussian shaped with mean at center and variance
# carefully chosen to have connected synapses
initialPermanence = self._initialPermanence()
# masterPotentialM, masterPermanenceM and masterConnectedM are numpy arrays
# of dimensions (coincCount, coincRfShape[0], coincRFShape[1])
#
# masterPotentialM: Keeps track of the potential synapses of each
# master. Potential synapses are marked as True
# masterPermanenceM: Holds the permanence values of the potential synapses.
# The values can range from 0.0 to 1.0
# masterConnectedM: Keeps track of the connected synapses of each
# master. Connected synapses are the potential synapses
# with permanence values greater than synPermConnected.
self._masterPotentialM, self._masterPermanenceM = \
self._makeMasterCoincidences(self.numCloneMasters, self._coincRFShape,
self.coincInputPoolPct, initialPermanence,
self.random)
# Update connected coincidences, the connected synapses have permanence
# values greater than synPermConnected
self._masterConnectedM = []
dense = numpy.zeros(self._coincRFShape)
for i in xrange(self.numCloneMasters):
self._masterConnectedM.append(SM_01_32_32(dense))
# coinc sizes are used in normalizing the raw overlaps
self._masterConnectedCoincSizes = numpy.empty(self.numCloneMasters,
'uint32')
# Make one mondo coincidence matrix for all cells at once. It has one row
# per cell. The width of each row is the entire input width. There will be
# ones in each row where that cell has connections. When we have cloning,
# and we modify the connections for a clone master, we will update all
# cells that share that clone master with the new connections.
self._allConnectedM = SM_01_32_32(self._inputCount)
self._allConnectedM.resize(self._coincCount, self._inputCount)
# =========================================================================
# Initialize the dutyCycles and boost factors per clone master
self._dutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._dutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# TODO: We don't need to store _boostFactors, can be calculated from duty
# cycle
self._firingBoostFactors = numpy.ones(self.numCloneMasters,
dtype=realDType)
if self.useHighTier:
self._firingBoostFactors *= maxFiringBoost
# Selectively turn on/off C++ for various methods
if self.testMode:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
else:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
# This is used to hold our learning stats (via getLearningStats())
self._learningStats = dict()
# These will hold our random state, which we return from __getstate__ and
# reseed our random number generators from in __setstate__ so that
# a saved/restored SP produces the exact same behavior as one that
# continues. This behavior allows us to write unit tests that verify that
# the behavior of an SP does not change due to saving/loading from a
# checkpoint
self._randomState = None
self._numpyRandomState = None
self._nupicRandomState = None
# =========================================================================
# Init ephemeral members
# This also calculates the slices and global inhibitionRadius and allocates
# the inhibitionObj
self._initEphemerals()
# =========================================================================
# If we have no cloning, make sure no column has potential or connected
# synapses outside the input area
if self.numCloneMasters == self._coincCount:
validMask = numpy.zeros(self._coincRFShape, dtype=realDType)
for masterNum in xrange(self._coincCount):
coincSlice = self._coincSlices[masterNum]
validMask.fill(0)
validMask[coincSlice] = 1
self._masterPotentialM[masterNum].logicalAnd(SM_01_32_32(validMask))
self._masterPermanenceM[masterNum].elementMultiply(validMask)
# Raise all permanences up until the number of connected is above
# our desired target,
self._raiseAllPermanences(masterNum,
minConnections = self.stimulusThreshold / self.inputDensity)
# =========================================================================
#self._cellMappings = []
#for cell in xrange(self._coincCount):
# inputSlice = self._inputSlices[cell]
# mapping = self._inputLayout[inputSlice]
# self._cellMappings.append(mapping)
# =========================================================================
# Calculate the number of connected synapses in each master coincidence now
self._updateConnectedCoincidences()
#############################################################################
def _getEphemeralMembers(self):
"""
List of our member variables that we don't need to be saved
"""
return [
'_inputLayout',
'_cellsForMaster',
'_columnCenters',
#'_cellRFClipped',
'_inputSlices',
'_coincSlices',
'_activeInput',
'_permChanges',
'_dupeInput',
'_onCells',
'_masterOnCells',
'_onCellIndices',
'_inhibitionObj',
'_denseOutput',
'_overlaps',
'_anomalyScores',
'_inputUse',
'_updatePermanenceGivenInputFP',
'_computeOverlapsFP',
'_stats',
'_rfRadiusAvg',
'_rfRadiusMin',
'_rfRadiusMax',
'_topDownOut',
'_topDownParentCounts',
]
#############################################################################
def _initEphemerals(self):
"""
Initialize all ephemeral members after being restored to a pickled state.
"""
# Used by functions which refers to inputs in absolute space
# getLearnedCM, cm,....
self._inputLayout = numpy.arange(self._inputCount,
dtype=numpy.uint32).reshape(self.inputShape)
# This array returns the list of cell indices that correspond to each master
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
self._cellsForMaster = []
for masterNum in xrange(self.numCloneMasters):
self._cellsForMaster.append(
numpy.where(self._cloneMapFlat == masterNum)[0])
else:
self._cellsForMaster = None
# TODO: slices are not required for the C++ helper functions
# Figure out the slices of shaped input that each column sees...
# Figure out the valid region of each column
# The reason these slices are in initEphemerals is because numpy slices
# can't be pickled
self._setSlices()
# This holds the output of the inhibition computation - which cells are
# on after inhibition
self._onCells = numpy.zeros(self._coincCount, dtype=realDType)
self._masterOnCells = numpy.zeros(self.numCloneMasters, dtype=realDType)
self._onCellIndices = numpy.zeros(self._coincCount, dtype='uint32')
# The inhibition object gets allocated by _updateInhibitionObj() during
# the first compute and re-allocated periodically during learning
self._inhibitionObj = None
self._rfRadiusAvg = 0 # Also calculated by _updateInhibitionObj
self._rfRadiusMin = 0
self._rfRadiusMax = 0
# Used by the caller to optionally cache the dense output
self._denseOutput = None
# This holds the overlaps (in absolute number of connected synapses) of each
# coinc with input
self._overlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the percent overlaps (number of active inputs / number of
# connected synapses) of each coinc with input
self._pctOverlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This is the value of the anomaly score for each column (after inhibition)
self._anomalyScores = numpy.zeros_like(self._overlaps)
# This holds the overlaps before stimulus threshold - used for verbose
# messages only
self._overlapsBST = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the number of coincs connected to an input
if not self._doneLearning:
self._inputUse = numpy.zeros(self.inputShape, dtype=realDType)
# These are boolean matrices, the same shape as the input
if not self._doneLearning:
self._activeInput = numpy.zeros(self.inputShape, dtype='bool')
self._dupeInput = numpy.zeros(self.inputShape, dtype='bool')
# This is used to hold self.synPermActiveInc where the input is on
# and -self.synPermInctiveDec where the input is off
if not self._doneLearning:
self._permChanges = numpy.zeros(self.inputShape, dtype=realDType)
# These are used to compute and hold the output from topDownCompute
# self._topDownOut = numpy.zeros(self.inputShape, dtype=realDType)
# self._topDownParentCounts = numpy.zeros(self.inputShape, dtype='int')
# Fill in the updatePermanenceGivenInput method pointer, which depends on
# chosen language
if self._updatePermanenceGivenInputImp == "py":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputPy
elif self._updatePermanenceGivenInputImp == "cpp":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputCPP
elif self._updatePermanenceGivenInputImp == "test":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputTest
else:
assert (False)
# Fill in the computeOverlaps method pointer, which depends on
# chosen language
if self._computeOverlapsImp == "py":
self._computeOverlapsFP = self._computeOverlapsPy
elif self._computeOverlapsImp == "cpp":
self._computeOverlapsFP = self._computeOverlapsCPP
elif self._computeOverlapsImp == "test":
self._computeOverlapsFP = self._computeOverlapsTest
else:
assert (False)
# ----------------------------------------------------------------------
# These variables are used for keeping track of learning statistics (when
# self.printPeriodicStats is used).
self._periodicStatsCreate()
#############################################################################
def compute(self, flatInput, learn=False, infer=True, computeAnomaly=False):
""" Compute with the current input vector.
Parameters:
----------------------------
input : the input vector (numpy array)
learn : if True, adapt the input histogram based on this input
infer : whether to do inference or not
"""
# If we are using a random SP, ignore the learn parameter
if self.randomSP:
learn = False
# If finishLearning has been called, don't allow learning anymore
if learn and self._doneLearning:
raise RuntimeError("Learning can not be performed once finishLearning"
" has been called.")
assert (learn or infer)
assert (flatInput.ndim == 1) and (flatInput.shape[0] == self._inputCount)
assert (flatInput.dtype == realDType)
input = flatInput.reshape(self.inputShape)
# Make sure we've allocated the inhibition object lazily
if self._inhibitionObj is None:
self._updateInhibitionObj()
# Reset first timer
if self.printPeriodicStats > 0 and self._iterNum == 0:
self._periodicStatsReset()
# Using cloning?
cloningOn = (self.numCloneMasters != self._coincCount)
# If we have high verbosity, save the overlaps before stimulus threshold
# so we can print them out at the end
if self.spVerbosity >= 2:
print "==============================================================="
print "Iter:%d" % self._iterNum, "inferenceIter:%d" % \
self._inferenceIterNum
self._computeOverlapsFP(input, stimulusThreshold=0)
self._overlapsBST[:] = self._overlaps
connectedCountsOnEntry = self._masterConnectedCoincSizes.copy()
if self.spVerbosity >= 3:
inputNZ = flatInput.nonzero()[0]
print "active inputs: (%d)" % len(inputNZ), inputNZ
# ----------------------------------------------------------------------
# TODO: Port to C++, arguments may be different - t1YXArr,
# coincInputRadius,...
# Calculate the raw overlap of each cell
# Overlaps less than stimulus threshold are set to zero in
# _calculateOverlaps
# This places the result into self._overlaps
self._computeOverlapsFP(input, stimulusThreshold=self.stimulusThreshold)
# Save the original overlap values, before boosting, for the purpose of
# anomaly detection
if computeAnomaly:
self._anomalyScores[:] = self._overlaps[:]
if learn:
# ----------------------------------------------------------------------
# Update each cell's duty cycle before inhibition
# Only cells with overlaps greater stimulus threshold are considered as
# active.
# Stimulus threshold has already been applied
# TODO: Port to C++? Loops over all coincs
# Only updating is carried out here, bump up happens later
onCellIndices = numpy.where(self._overlaps > 0)
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
self._onCells.fill(0)
self._onCells[onCellIndices] = 1
denseOn = self._onCells
# dutyCyclePeriod = self._iterNum + 1 let _dutyCycleBeforeInh
# and _dutyCycleAfterInh represent real firing percentage at the
# beginning of learning. This will effect boosting and let unlearned
# coincidences have high boostFactor at beginning.
self.dutyCyclePeriod = min(self._iterNum + 1, 1000)
self._dutyCycleBeforeInh = ((self.dutyCyclePeriod-1) \
* self._dutyCycleBeforeInh + denseOn) / self.dutyCyclePeriod
# ----------------------------------------------------------------------
# Compute firing levels based on boost factor and raw overlap. Update
# self._overlaps in place, replacing it with the boosted overlap. We also
# computes percent overlap of each column and store that into
# self._pctOverlaps
# With cloning
if cloningOn:
self._pctOverlaps[:] = self._overlaps
self._pctOverlaps /= self._masterConnectedCoincSizes[self._cloneMapFlat]
boostFactors = self._firingBoostFactors[self._cloneMapFlat]
else:
self._pctOverlaps[:] = self._overlaps
potentials = self._masterConnectedCoincSizes
self._pctOverlaps /= numpy.maximum(1, potentials)
boostFactors = self._firingBoostFactors
# To process minDistance, we do the following:
# 1.) All cells which do not overlap the input "highly" (less than
# minDistance), are considered to be in the "low tier" and get their
# overlap multiplied by their respective boost factor.
# 2.) All other cells, which DO overlap the input highly, get a "high tier
# offset" added to their overlaps, and boost is not applied. The
# "high tier offset" is computed as the max of all the boosted
# overlaps from step #1. This insures that a cell in this high tier
# will never lose to a cell from the low tier.
# if self.useHighTier \
# and len(numpy.where(self._dutyCycleAfterInh == 0)[0]) == 0:
# self.useHighTier = False
if self.useHighTier:
highTier = numpy.where(self._pctOverlaps >= (1.0 - self.minDistance))[0]
else:
highTier = []
someInHighTier = len(highTier) > 0
if someInHighTier:
boostFactors = numpy.array(boostFactors)
boostFactors[highTier] = 1.0
# apply boostFactors only in learning phase not in inference phase.
if learn:
self._overlaps *= boostFactors
if someInHighTier:
highTierOffset = self._overlaps.max() + 1.0
self._overlaps[highTier] += highTierOffset
# Cache the dense output for debugging
if self._denseOutput is not None:
self._denseOutput = self._overlaps.copy()
# ----------------------------------------------------------------------
# Incorporate inhibition and see who is firing after inhibition.
# We don't need this method to process stimulusThreshold because we
# already processed it.
# Also, we pass in a small 'addToWinners' amount which gets added to the
# winning elements as we go along. This prevents us from choosing more than
# topN winners per inhibition region when more than topN elements all have
# the same max high score.
learnedCellsOverlaps = numpy.array(self._overlaps)
if infer and not learn:
# Cells that have never learnt are not allowed to win during inhibition
if not self.randomSP:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = 0
else:
#Boost the unlearned cells to 1000 so that the winning columns are picked randomly
#From the set of unlearned columns
#Boost columns that havent been learned with uniformly to 1000 so that inhibition picks
#randomly from them.
if self.useHighTier:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = learnedCellsOverlaps.max() + 1
# #Boost columns that are in highTier (ie. they match the input very well
learnedCellsOverlaps[highTier] += learnedCellsOverlaps.max() + 1
# Small random tiebreaker for columns with equal overlap
tieBreaker = numpy.random.rand(*learnedCellsOverlaps.shape).astype(realDType)
learnedCellsOverlaps += 0.1 * tieBreaker
numOn = self._inhibitionObj.compute(
learnedCellsOverlaps,
self._onCellIndices,
0.0, # stimulusThreshold
max(learnedCellsOverlaps)/1000.0, # addToWinners
)
self._onCells.fill(0)
if numOn > 0:
onCellIndices = self._onCellIndices[0:numOn]
self._onCells[onCellIndices] = 1
else:
onCellIndices = []
# Compute the anomaly scores only for the winning columns
if computeAnomaly:
self._anomalyScores *= self._onCells
self._anomalyScores *= self._dutyCycleAfterInh
if self.spVerbosity >= 2:
print "inhRadius", self._inhibitionObj.getInhibitionRadius()
print "inhLocalAreaDensity", self._inhibitionObj.getLocalAreaDensity()
print "numFiring", numOn
# ----------------------------------------------------------------------
# Capturing learning stats? If so, capture the cell overlap statistics
if self.printPeriodicStats > 0:
activePctOverlaps = self._pctOverlaps[onCellIndices]
self._stats['cellPctOverlapSums'] += activePctOverlaps.sum()
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
else:
onMasterIndices = onCellIndices
self._stats['cellOverlapSums'] += \
(activePctOverlaps * \
self._masterConnectedCoincSizes[onMasterIndices]).sum()
# ----------------------------------------------------------------------
# Compute which cells had very high overlap, but were still
# inhibited. These we are calling our "orphan cells", because they are
# representing an input which is already better represented by another
# cell.
if self.synPermOrphanDec > 0:
orphanCellIndices = set(numpy.where(self._pctOverlaps >= 1.0)[0])
orphanCellIndices.difference_update(onCellIndices)
else:
orphanCellIndices = []
if learn:
# ----------------------------------------------------------------------
# Update the number of coinc connections per input
# During learning (adapting permanence values), we need to be able to
# recognize dupe inputs - inputs that go two 2 or more active cells
if self.synPermActiveSharedDec != 0:
self._updateInputUse(onCellIndices)
# ----------------------------------------------------------------------
# For the firing cells, update permanence values
onMasterIndices = self._adaptSynapses(onCellIndices, orphanCellIndices,
input)
# ----------------------------------------------------------------------
# Increase the permanence values of columns which haven't passed
# stimulus threshold of overlap with at least a minimum frequency
self._bumpUpWeakCoincidences()
# ----------------------------------------------------------------------
# Update each cell's after-inhibition duty cycle
# TODO: As the on-cells are sparse after inhibition, we can have
# a different updateDutyCycles function taking advantage of the sparsity
if cloningOn:
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
denseOn = self._onCells
self._dutyCycleAfterInh = ((self.dutyCyclePeriod - 1) \
* self._dutyCycleAfterInh + denseOn) / self.dutyCyclePeriod
# For the cell's that just fired with a very high boost, bring their
# boost back down to 1.0. This prevents a cell that was trying very
# hard to grab input, and was just successful, from grabbing every
# other input that comes around in the near future.
# for masterNum in onMasterIndices:
# if self._firingBoostFactors[masterNum] > self.maxSSFiringBoost:
# self._dutyCycleAfterInh[masterNum] = self._dutyCycleAfterInh.max()/5
#Set the duty cycle to a safe margin over the boosting duty cycle but
#avoid artificially boosting it to a reconstruction significant level.
# self._dutyCycleAfterInh[masterNum] = self.minPctDutyCycleAfterInh*10
# ----------------------------------------------------------------------
# Update the boost factors based on firings rate after inhibition
self._updateBoostFactors()
# =======================================================================
# Increment iteration number and perform our periodic tasks if it's time
if ((self._iterNum + 1) % 50) == 0:
self._updateInhibitionObj()
self._updateMinDutyCycles(self._dutyCycleBeforeInh,
self.minPctDutyCycleBeforeInh, self._minDutyCycleBeforeInh)
self._updateMinDutyCycles(self._dutyCycleAfterInh,
self.minPctDutyCycleAfterInh, self._minDutyCycleAfterInh)
# Next iteration
if learn:
self._iterNum += 1
if infer:
self._inferenceIterNum += 1
if learn:
# =======================================================================
# Capture and possibly print the periodic stats
if self.printPeriodicStats > 0:
self._periodicStatsComputeEnd(onCellIndices, flatInput.nonzero()[0])
# Verbose print other stats
if self.spVerbosity >= 2:
cloning = (self.numCloneMasters != self._coincCount)
print " #connected on entry: ", fdru.numpyStr(connectedCountsOnEntry,
'%d ', includeIndices=True)
print " #connected on exit: ", fdru.numpyStr(
self._masterConnectedCoincSizes,
'%d ', includeIndices=True)
if self.spVerbosity >= 3 or not cloning:
print " overlaps: ", fdru.numpyStr(self._overlapsBST, '%d ',
includeIndices=True, includeZeros=False)
print " firing levels: ", fdru.numpyStr(self._overlaps, '%.4f ',
includeIndices=True, includeZeros=False)
print " on after inhibition: ", onCellIndices
if not self._doneLearning:
print " minDutyCycleBeforeInh:", fdru.numpyStr(
self._minDutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " dutyCycleBeforeInh: ", fdru.numpyStr(self._dutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " belowMinBeforeInh: " % numpy.nonzero(
self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh)[0]
print " minDutyCycleAfterInh: ", fdru.numpyStr(
self._minDutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " dutyCycleAfterInh: ", fdru.numpyStr(self._dutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " belowMinAfterInh: " % numpy.nonzero(
self._dutyCycleAfterInh \
< self._minDutyCycleAfterInh)[0]
print " firingBoosts: ", fdru.numpyStr(self._firingBoostFactors,
'%.4f ', includeIndices=True)
print
elif self.spVerbosity >= 1:
print "SP: learn: ", learn
print "SP: active outputs(%d): " % (len(onCellIndices)), onCellIndices
self._runIter += 1
# Return inference result
return self._onCells
#############################################################################
# def topDownCompute(self, topDownIn):
# """ Top-down compute - generate expected input given output of the SP
# Parameters:
# ----------------------------
# topDownIn: top down input, from the level above us
# retval: best estimate of the SP input that would have generated
# topDownIn.
# """
# #If topDownIn is not defined, generate the outputs based on the current state and overlaps of the sp
# #This is meant for spatial prediction tasks.
# if(topDownIn==None):
# #Get highest overlaps
# sortedOverlaps = numpy.sort(self._overlapsNoBoost)
# #Threshold overlaps to .5 of the max overlap, this will cut out most columns that do not
# #encode the input, but keep most of the columns that have been encoded with the same predicted field
# halfOnThresh = numpy.where(self._overlapsNoBoost>sortedOverlaps[-1]*.5)
# #keep at least the top 120 by overlap
# overlapThresh = sortedOverlaps[-120]
# pastOverlapThresh = numpy.where(self._overlapsNoBoost>=overlapThresh)
# pastOverlapThresh = numpy.union1d(pastOverlapThresh[0], halfOnThresh[0])
# zippedOLplusDC = zip(pastOverlapThresh,self._dutyCycleAfterInh[pastOverlapThresh])
# zippedOLplusDC.sort(key=itemgetter(1), reverse=True)
# selectedCols = zippedOLplusDC[0:min(40,len(zippedOLplusDC))]
# pastThresh,dutyCycles = zip(*selectedCols)
# topDownIn = numpy.zeros(self._overlaps.shape)
# topDownIn[list(pastThresh)] = 1
# # Init topdown out. This is shaped to the input
# topDownOut = self._topDownOut
# self._topDownOut.fill(0)
# self._topDownParentCounts.fill(0)
# # =========================================================================
# # Get the contributions from each of the active cells to the inputs.
# # We compute the average contribution to each input from each of the
# # active cells that connects to it.
# # If topDownIn is not flat, flatten it
# if len(topDownIn.shape) > 1:
# topDownIn = topDownIn.reshape(-1)
# activeCells = topDownIn.nonzero()[0]
# if self.spReconstructionParam == "dutycycle":
# maxDutyCycle = max(1-self._dutyCycleAfterInh)
# if self.spReconstructionParam == "pctoverlap":
# maxPctOverlap = max(self._pctOverlaps)
# # From each output, get the expected input that generated it
# cloningOn = (self.numCloneMasters != self._coincCount)
# for cell in activeCells:
# if cloningOn:
# masterNum = self._cloneMapFlat[cell]
# else:
# masterNum = cell
# # Get the permanences for this master
# activeInputs = self._masterConnectedM[masterNum].toDense()
# # Add the connected inputs to the topDownOut
# inputSlice = self._inputSlices[cell]
# coincSlice = self._coincSlices[cell]
# # Weight each connected input by the cell's firing strength
# if self.spReconstructionParam == "unweighted_mean":
# topDownOut[inputSlice] += topDownIn[cell] * activeInputs[coincSlice]
# elif self.spReconstructionParam == "permanence":
# maxPermanence = max(self._masterPermanenceM[cell].getRow(0))
# topDownOut[inputSlice] += topDownIn[cell] * activeInputs[coincSlice] * \
# (0.5 + self._masterPermanenceM[cell].getRow(0)/maxPermanence*0.5)
# elif self.spReconstructionParam == "pctoverlap":
# topDownOut[inputSlice] += topDownIn[cell] * activeInputs[coincSlice] * \
# (0.5 + self._pctOverlaps[cell]/maxPctOverlap*0.5)
# elif self.spReconstructionParam == "dutycycle":
# topDownOut[inputSlice] += topDownIn[cell] * activeInputs[coincSlice] * \
# (0.5 + (1-(self._dutyCycleAfterInh[cell]/maxDutyCycle))*0.5)
# elif self.spReconstructionParam == "maximum_firingstrength":
# maxPermanence = max(self._masterPermanenceM[cell].getRow(0))
# strength = topDownIn[cell]*activeInputs[coincSlice] * \
# (0.5 + self._masterPermanenceM[cell].getRow(0)/maxPermanence*0.5)
# topDownOut[inputSlice] = numpy.maximum(topDownOut[inputSlice],
# strength)
# # Bump up the parent counts for these inputs
# self._topDownParentCounts[inputSlice] += activeInputs[coincSlice]
# # Old method of normalizing
# # Divide each input's accumulated weight by it's number of parents
# # numpy.clip(self._topDownParentCounts, 1.0, numpy.inf,
# # self._topDownParentCounts)
# # topDownOut /= self._topDownParentCounts
# if "maximum_firingstrength" not in self.spReconstructionParam:
# topDownInTotal = topDownIn.sum()
# if topDownInTotal:
# topDownOut /= topDownInTotal
# return topDownOut.reshape(-1)
# return topDownIn.copy()
#############################################################################
def __getstate__(self):
# Update our random states
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
state = self.__dict__.copy()
# Delete ephemeral members that we don't want pickled
for ephemeralMemberName in self._getEphemeralMembers():
if ephemeralMemberName in state:
del state[ephemeralMemberName]
return state
#############################################################################
def __setstate__(self, state):
self.__dict__.update(state)
# ----------------------------------------------------------------------
# Support older checkpoints
# These fields were added on 2010-10-05 and _iterNum was preserved
if not hasattr(self, '_randomState'):
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
self._iterNum = 0
# ------------------------------------------------------------------------
# Init our random number generators
random.setstate(self._randomState)
numpy.random.set_state(self._numpyRandomState)
self.random.setState(self._nupicRandomState)
# Load things that couldn't be pickled...
self._initEphemerals()
############################################################################
def getAnomalyScore(self):
""" Get the aggregate anomaly score for this input pattern
Returns: A single scalar value for the anomaly score
"""
numNonzero = len(numpy.nonzero(self._anomalyScores)[0])
return 1.0 / (numpy.sum(self._anomalyScores) + 1)
#############################################################################
def getLearningStats(self):
""" Return a dictionary containing a set of statistics related to learning.
Here is a list of what is returned:
'activeCountAvg':
The average number of active columns seen over the last
N training iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'underCoveragePct':
The average under-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'overCoveragePct':
The average over-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesAvg':
The overall average number of connection changes made per active
column per iteration, over the last N training iterations, where N
is set by the constructor parameter printPeriodicStats. This gives an
indication as to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMin':
The minimum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMax':
The maximum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'rfSize':
The average receptive field size of the columns.
'inhibitionRadius':
The average inihbition radius of the columns.
'targetDensityPct':
The most recent target local area density used, as a percent (0 -> 100)
'coincidenceSizeAvg':
The average learned coincidence size
'coincidenceSizeMin':
The minimum learned coincidence size
'coincidenceSizeMax':
The maximum learned coincidence size
'dcBeforeInhibitionAvg':
The average of duty cycle before inhbition of all coincidences
'dcBeforeInhibitionMin':
The minimum duty cycle before inhbition of all coincidences
'dcBeforeInhibitionAvg':
The maximum duty cycle before inhbition of all coincidences
'dcAfterInhibitionAvg':
The average of duty cycle after inhbition of all coincidences
'dcAfterInhibitionMin':
The minimum duty cycle after inhbition of all coincidences
'dcAfterInhibitionAvg':
The maximum duty cycle after inhbition of all coincidences
'firingBoostAvg':
The average firing boost
'firingBoostMin':
The minimum firing boost
'firingBoostMax':
The maximum firing boost
"""
# Fill in the stats that can be computed on the fly. The transient stats
# that depend on printPeriodicStats being on, have already been stored
self._learningStats['rfRadiusAvg'] = self._rfRadiusAvg
self._learningStats['rfRadiusMin'] = self._rfRadiusMin
self._learningStats['rfRadiusMax'] = self._rfRadiusMax
if self._inhibitionObj is not None:
self._learningStats['inhibitionRadius'] = \
self._inhibitionObj.getInhibitionRadius()
self._learningStats['targetDensityPct'] = \
100.0 * self._inhibitionObj.getLocalAreaDensity()
else:
print "Warning: No inhibitionObj found for getLearningStats"
self._learningStats['inhibitionRadius'] = 0.0
self._learningStats['targetDensityPct'] = 0.0
self._learningStats['coincidenceSizeAvg'] = \
self._masterConnectedCoincSizes.mean()
self._learningStats['coincidenceSizeMin'] = \
self._masterConnectedCoincSizes.min()
self._learningStats['coincidenceSizeMax'] = \
self._masterConnectedCoincSizes.max()
if not self._doneLearning:
self._learningStats['dcBeforeInhibitionAvg'] = \
self._dutyCycleBeforeInh.mean()
self._learningStats['dcBeforeInhibitionMin'] = \
self._dutyCycleBeforeInh.min()
self._learningStats['dcBeforeInhibitionMax'] = \
self._dutyCycleBeforeInh.max()
self._learningStats['dcAfterInhibitionAvg'] = \
self._dutyCycleAfterInh.mean()
self._learningStats['dcAfterInhibitionMin'] = \
self._dutyCycleAfterInh.min()
self._learningStats['dcAfterInhibitionMax'] = \
self._dutyCycleAfterInh.max()
self._learningStats['firingBoostAvg'] = \
self._firingBoostFactors.mean()
self._learningStats['firingBoostMin'] = \
self._firingBoostFactors.min()
self._learningStats['firingBoostMax'] = \
self._firingBoostFactors.max()
return self._learningStats
#############################################################################
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
random.seed(seed)
numpy.random.seed(seed)
else:
self.random = NupicRandom()
#############################################################################
def _initialPermanence(self):
""" Create and return a 2D matrix filled with initial permanence values.
The returned matrix will be of shape:
(2*coincInputRadius + 1, 2*coincInputRadius + 1).
The initial permanence values are set between 0 and 1.0, with enough chosen
above synPermConnected to make it highly likely that a cell will pass
stimulusThreshold, given the size of the potential RF, the input pool
sampling percentage, and the expected density of the active inputs.
If gaussianDist is True, the center of the matrix will contain the highest
permanence values and lower values will be farther from the center.
If gaussianDist is False, the highest permanence values will be evenly
distributed throughout the potential RF.
"""
# Figure out the target number of connected synapses. We want about 2X
# stimulusThreshold
minOn = 2 * max(self.stimulusThreshold, 10) / self.coincInputPoolPct \
/ self.inputDensity
# ========================================================================
# Get the gaussian distribution, with max magnitude just slightly above
# synPermConnected. Try to find a sigma that gives us about 2X
# stimulusThreshold connected synapses after sub-sampling for
# coincInputPoolPct. We will assume everything within +/- sigma will be
# connected. This logic uses the fact that an x value of sigma generates a
# magnitude of 0.6.
if self.gaussianDist:
# Only supported when we have 2D layouts
if self._coincRFShape[0] != self._coincRFShape[1]:
raise RuntimeError("Gaussian distibuted permanences are currently only"
"supported for 2-D layouts")
# The width and height of the center "blob" in inputs is the square root
# of the area
onAreaDim = numpy.sqrt(minOn)
# Sigma is at the edge of the center blob
sigma = onAreaDim/2
# Create the gaussian with a value of 1.0 at the center
perms = self._gaussianMatrix(dim=max(self._coincRFShape), sigma=sigma)
# The distance between the min and max values within the gaussian will
# be given by 'grange'. In a gaussian, the value at sigma away from the
# center is 0.6 * the value at the center. We want the values at sigma
# to be synPermConnected
maxValue = 1.0 / 0.6 * self.synPermConnected
perms *= maxValue
perms.shape = (-1,)
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# farther away than 2 sigma to 0. The value of a gaussing at 2 sigma
# is 0.135 * the value at the center
perms[perms < (0.135 * maxValue)] = 0
# ========================================================================
# Evenly distribute the permanences through the RF
else:
# Create a random distribution from 0 to 1.
perms = numpy.random.random(self._coincRFShape)
perms = perms.astype(realDType)
# Set the range of values to be between 0 and
# synPermConnected+synPermInctiveDec. This ensures that a pattern
# will always be learned in 1 iteration
maxValue = min(1.0, self.synPermConnected + self.synPermInactiveDec)
# What percentage do we want to be connected?
connectPct = 0.50
# What value from the 0 to 1 distribution will map to synPermConnected?
threshold = 1.0 - connectPct
# Which will be the connected and unconnected synapses?
connectedSyns = perms >= threshold
unconnectedSyns = numpy.logical_not(connectedSyns)
# Squeeze all values between threshold and 1.0 to be between
# synPermConnected and synPermConnected + synPermActiveInc / 4
# This makes sure the firing coincidence perms matching input bit get
# greater than synPermConnected and other unconnectedSyns get deconnected
# in one firing learning iteration.
srcOffset = threshold
srcRange = 1.0 - threshold
dstOffset = self.synPermConnected
dstRange = maxValue - self.synPermConnected
perms[connectedSyns] = (perms[connectedSyns] - srcOffset)/srcRange \
* dstRange / 4.0 + dstOffset
# Squeeze all values between 0 and threshold to be between 0 and
# synPermConnected
srcRange = threshold - 0.0
dstRange = self.synPermConnected - 0.0
perms[unconnectedSyns] = perms[unconnectedSyns]/srcRange \
* dstRange
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# below synPermActiveInc/2 to 0
perms[perms < (self.synPermActiveInc / 2.0)] = 0
perms.shape = (-1,)
return perms
#############################################################################
def _gaussianMatrix(self, dim, sigma):
"""
Create and return a 2D matrix filled with a gaussian distribution. The
returned matrix will be of shape (dim, dim). The mean of the gaussian
will be in the center of the matrix and have a value of 1.0.
"""
gaussian = lambda x,sigma: numpy.exp(-(x**2)/(2*(sigma**2)))
# Allocate the matrix
m = numpy.empty((dim, dim), dtype=realDType)
# Find the center
center = (dim - 1) / 2.0
# TODO: Simplify using numpy.meshgrid
# Fill it in
for y in xrange(dim):
for x in xrange(dim):
dist = numpy.sqrt((x-center)**2 + (y-center)**2)
m[y,x] = gaussian(dist, sigma)
return m
#############################################################################
def _makeMasterCoincidences(self, numCloneMasters, coincRFShape,
coincInputPoolPct, initialPermanence=None,
nupicRandom=None):
"""Make the master coincidence matrices and mater input histograms.
# TODO: Update this example
>>> FDRCSpatial._makeMasterCoincidences(1, 2, 0.33)
(array([[[ True, True, False, False, False],
[False, True, False, False, True],
[False, True, False, False, False],
[False, False, False, True, False],
[ True, False, False, False, False]]], dtype=bool), array([[[ 0.26982325, 0.19995725, 0. , 0. , 0. ],
[ 0. , 0.94128972, 0. , 0. , 0.36316112],
[ 0. , 0.06312726, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0.29740077, 0. ],
[ 0.81071907, 0. , 0. , 0. , 0. ]]], dtype=float32))
"""
if nupicRandom is None:
nupicRandom = NupicRandom(42)
if initialPermanence is None:
initialPermanence = self._initialPermanence()
coincRfArea = (coincRFShape[0] * coincRFShape[1])
coincInputPool = coincInputPoolPct * coincRfArea
# We will generate a list of sparse matrices
masterPotentialM = []
masterPermanenceM = []
toSample = numpy.arange(coincRfArea, dtype='uint32')
toUse = numpy.empty(coincInputPool, dtype='uint32')
#denseM = numpy.zeros(coincRfArea, dtype='uint32')
denseM = numpy.zeros(coincRfArea, dtype=realDType)
for i in xrange(numCloneMasters):
nupicRandom.getUInt32Sample(toSample, toUse)
# Put in 1's into the potential locations
denseM.fill(0)
denseM[toUse] = 1
masterPotentialM.append(SM_01_32_32(denseM.reshape(coincRFShape)))
# Put in the initial permanences
denseM *= initialPermanence
masterPermanenceM.append(SM32(denseM.reshape(coincRFShape)))
# If we are not using common initial permanences, create another
# unique one for the next cell
if not self.commonDistributions:
initialPermanence = self._initialPermanence()
return masterPotentialM, masterPermanenceM
#############################################################################
def _updateConnectedCoincidences(self, masters=None):
"""Update 'connected' version of the given coincidence.
Each 'connected' coincidence is effectively a binary matrix (AKA boolean)
matrix that is the same size as the input histogram matrices. They have
a 1 wherever the inputHistogram is "above synPermConnected".
"""
# If no masterNum given, update all of them
if masters is None:
masters = xrange(self.numCloneMasters)
(nCellRows, nCellCols) = self._coincRFShape
cloningOn = (self.numCloneMasters != self._coincCount)
for masterNum in masters:
# Where are we connected?
masterConnectedNZ = \
self._masterPermanenceM[masterNum].whereGreaterEqual(0,
nCellRows, 0, nCellCols, self.synPermConnected)
rowIdxs = masterConnectedNZ[:,0]
colIdxs = masterConnectedNZ[:,1]
self._masterConnectedM[masterNum].setAllNonZeros(nCellRows, nCellCols,
rowIdxs, colIdxs)
self._masterConnectedCoincSizes[masterNum] = len(rowIdxs)
# Update the corresponding rows in the super, mondo connected matrix that
# come from this master
masterConnected = \
self._masterConnectedM[masterNum].toDense().astype('bool') # 0.2s
if cloningOn:
cells = self._cellsForMaster[masterNum]
else:
cells = [masterNum]
for cell in cells:
inputSlice = self._inputSlices[cell]
coincSlice = self._coincSlices[cell]
masterSubset = masterConnected[coincSlice]
sparseCols = self._inputLayout[inputSlice][masterSubset]
self._allConnectedM.replaceSparseRow(cell, sparseCols) # 4s.
#############################################################################
def _setSlices(self):
"""Compute self._columnSlices and self._inputSlices
self._inputSlices are used to index into the input (assuming it's been
shaped to a 2D array) to get the receptive field of each column. There
is one item in the list for each column.
self._coincSlices are used to index into the coinc (assuming it's been
shaped to a 2D array) to get the valid area of the column. There
is one item in the list for each column.
This function is called upon unpickling, since we can't pickle slices.
"""
self._columnCenters = numpy.array(self._computeCoincCenters(self.inputShape,
self.coincidencesShape, self.inputBorder))
coincInputRadius = self.coincInputRadius
(coincHeight, coincWidth) = self._coincRFShape
inputShape = self.inputShape
inputBorder = self.inputBorder
# ---------------------------------------------------------------------
# Compute the input slices for each cell. This is the slice of the entire
# input which intersects with the cell's permanence matrix.
if self._hasTopology:
self._inputSlices = [numpy.s_[max(0, cy-coincInputRadius): \
min(inputShape[0], cy+coincInputRadius + 1),
max(0, cx-coincInputRadius): \
min(inputShape[1], cx+coincInputRadius + 1)]
for (cy, cx) in self._columnCenters]
else:
self._inputSlices = [numpy.s_[0:inputShape[0], 0:inputShape[1]]
for (cy, cx) in self._columnCenters]
self._inputSlices2 = numpy.zeros((4*len(self._inputSlices)), dtype="uint32")
k = 0
for i in range(len(self._inputSlices)):
self._inputSlices2[k] = self._inputSlices[i][0].start
self._inputSlices2[k + 1] = self._inputSlices[i][0].stop
self._inputSlices2[k + 2] = self._inputSlices[i][1].start
self._inputSlices2[k + 3] = self._inputSlices[i][1].stop
k = k + 4
# ---------------------------------------------------------------------
# Compute the coinc slices for each cell. This is which portion of the
# cell's permanence matrix intersects with the input.
if self._hasTopology:
if self.inputShape[0] > 1:
self._coincSlices = [numpy.s_[max(0, coincInputRadius - cy): \
min(coincHeight, coincInputRadius + inputShape[0] - cy),
max(0, coincInputRadius-cx): \
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:1, max(0, coincInputRadius-cx): \
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:coincHeight, 0:coincWidth]
for (cy, cx) in self._columnCenters]
self._coincSlices2 = numpy.zeros((4*len(self._coincSlices)), dtype="uint32")
k = 0
for i in range(len(self._coincSlices)):
self._coincSlices2[k] = self._coincSlices[i][0].start
self._coincSlices2[k + 1] = self._coincSlices[i][0].stop
self._coincSlices2[k + 2] = self._coincSlices[i][1].start
self._coincSlices2[k + 3] = self._coincSlices[i][1].stop
k = k + 4
#############################################################################
@staticmethod
def _computeCoincCenters(inputShape, coincidencesShape, inputBorder):
"""Compute the centers of all coincidences, given parameters.
This function is semi-public: tools may use it to generate good
visualizations of what the FDRCSpatial node is doing.
NOTE: It must be static or global function so that it can be called by
the ColumnActivityTab inspector *before* the first compute (before the
SP has been constructed).
If the input shape is (7,20), shown below with * for each input
********************
********************
********************
********************
********************
********************
********************
if inputBorder is 1, we distribute the coincidences evenly over the
the area after removing the edges, @ shows the allowed input area below
********************
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
********************
each coincidence is centered at the closest @ and looks at a area with
coincInputRadius below it
This function call returns an iterator over the coincidence centers. Each
element in iterator is a tuple: (y, x). The iterator returns elements in a
fixed order.
"""
# Determine Y centers
if inputShape[0] > 1: # 2-D layout
startHeight = inputBorder
stopHeight = inputShape[0] - inputBorder
else:
startHeight = stopHeight = 0
heightCenters = numpy.linspace(startHeight,
stopHeight,
coincidencesShape[0],
endpoint=False).astype('int32')
# Determine X centers
startWidth = inputBorder
stopWidth = inputShape[1] - inputBorder
widthCenters = numpy.linspace(startWidth,
stopWidth,
coincidencesShape[1],
endpoint=False).astype('int32')
return list(cross(heightCenters, widthCenters))
#############################################################################
def _updateInhibitionObj(self):
"""
Calculate the average inhibitionRadius to use and update the inhibition
object accordingly. This looks at the size of the average connected
receptive field and uses that to determine the inhibition radius.
"""
# ========================================================================
# Compute the inhibition radius.
# If using global inhibition, just set it to include the entire region
if self.globalInhibition:
avgRadius = max(self.coincidencesShape)
# Else, set it based on the average size of the connected synapses area in
# each cell.
else:
totalDim = 0
# Get the dimensions of the connected receptive fields of each cell to
# compute the average
minDim = numpy.inf
maxDim = 0
for masterNum in xrange(self.numCloneMasters):
masterConnected = self._masterConnectedM[masterNum]
nzs = masterConnected.getAllNonZeros()
(rows, cols) = zip(*nzs)
rows = numpy.array(rows)
cols = numpy.array(cols)
if len(rows) >= 2:
height = rows.max() - rows.min() + 1
else:
height = 1
if len(cols) >= 2:
width = cols.max() - cols.min() + 1
else:
width = 1
avgDim = (height + width) / 2.0
minDim = min(minDim, avgDim)
maxDim = max(maxDim, avgDim)
totalDim += avgDim
# Get average width/height in input space
avgDim = totalDim / self.numCloneMasters
self._rfRadiusAvg = (avgDim - 1.0) / 2.0
self._rfRadiusMin = (minDim - 1.0) / 2.0
self._rfRadiusMax = (maxDim - 1.0) / 2.0
# How many columns in cell space does it correspond to?
if self.inputShape[0] > 1: # 2-D layout
coincsPerInputX = float(self.coincidencesShape[1]) \
/ (self.inputShape[1] - 2*self.inputBorder)
coincsPerInputY = float(self.coincidencesShape[0]) \
/ (self.inputShape[0] - 2*self.inputBorder)
else:
coincsPerInputX = coincsPerInputY = \
float(self.coincidencesShape[1] * self.coincidencesShape[0]) \
/ (self.inputShape[1] - 2*self.inputBorder)
avgDim *= (coincsPerInputX + coincsPerInputY) / 2
avgRadius = (avgDim - 1.0) / 2.0
avgRadius = max(1.0, avgRadius)
# Can't be greater than the overall width or height of the level
maxDim = max(self.coincidencesShape)
avgRadius = min(avgRadius, maxDim)
avgRadius = int(round(avgRadius))
# ========================================================================
# Is there a need to re-instantiate the inhibition object?
if self._inhibitionObj is None \
or self._inhibitionObj.getInhibitionRadius() != avgRadius:
# What is our target density?
if self.localAreaDensity > 0:
localAreaDensity = self.localAreaDensity
else:
numCellsPerInhArea = (avgRadius * 2.0 + 1.0) ** 2
totalCells = self.coincidencesShape[0] * self.coincidencesShape[1]
numCellsPerInhArea = min(numCellsPerInhArea, totalCells)
localAreaDensity = float(self.numActivePerInhArea) / numCellsPerInhArea
# Don't let it be greater than 0.50
localAreaDensity = min(localAreaDensity, 0.50)
if self.spVerbosity >= 2:
print "Updating inhibition object:"
print " avg. rfRadius:", self._rfRadiusAvg
print " avg. inhRadius:", avgRadius
print " Setting density to:", localAreaDensity
self._inhibitionObj = Inhibition2(self.coincidencesShape[0], # height
self.coincidencesShape[1], # width
avgRadius, # inhRadius
localAreaDensity) # density
#############################################################################
def _updateMinDutyCycles(self, actDutyCycles, minPctDutyCycle, minDutyCycles):
"""
Calculate and update the minimum acceptable duty cycle for each cell based
on the duty cycles of the cells within its inhibition radius and the
minPctDutyCycle.
Parameters:
-----------------------------------------------------------------------
actDutyCycles: The actual duty cycles of all cells
minPctDutyCycle: Each cell's minimum duty cycle will be set to
minPctDutyCycle times the duty cycle of the most active
cell within its inhibition radius
minDutyCycles: This array will be updated in place with the new minimum
acceptable duty cycles
"""
# What is the inhibition radius?
inhRadius = self._inhibitionObj.getInhibitionRadius()
# Reshape the actDutyCycles to match the topology of the level
cloningOn = (self.numCloneMasters != self._coincCount)
if not cloningOn:
actDutyCycles = actDutyCycles.reshape(self.coincidencesShape)
minDutyCycles = minDutyCycles.reshape(self.coincidencesShape)
# Special, faster handling when inhibition radius includes the entire
# set of cells
if cloningOn or inhRadius >= max(self.coincidencesShape):
minDutyCycle = minPctDutyCycle * actDutyCycles.max()
minDutyCycles.fill(minPctDutyCycle * actDutyCycles.max())
# Else, process each cell
else:
(numRows, numCols) = self.coincidencesShape
for row in xrange(numRows):
top = max(0, row - inhRadius)
bottom = min(row + inhRadius + 1, numRows)
for col in xrange(numCols):
left = max(0, col - inhRadius)
right = min(col + inhRadius + 1, numCols)
maxDutyCycle = actDutyCycles[top:bottom, left:right].max()
minDutyCycles[row, col] = maxDutyCycle * minPctDutyCycle
if self.spVerbosity >= 2:
print "Actual duty cycles:"
print fdru.numpyStr(actDutyCycles, '%.4f')
print "Recomputed min duty cycles, using inhRadius of", inhRadius
print fdru.numpyStr(minDutyCycles, '%.4f')
#############################################################################
def _computeOverlapsPy(self, inputShaped, stimulusThreshold):
"""
Computes overlaps for every column for the current input in place. The
overlaps less than stimulus threshold are set to zero here.
For columns with input RF going off the edge of input field, only regions
within the input field are considered. This is equivalent to padding the
input field with zeros.
Parameters:
------------------------------------------------------------------------
inputShaped: input at the current time step, shaped to the input
topology
stimulusThreshold: stimulusThreshold to use
Member variables used/updated:
------------------------------------------------------------------------
_inputSlices: Index into the input (assuming it's been shaped to a 2D
array) to get the receptive field of each column.
_coincSlices: Index into the coinc (assuming it's been shaped to a 2D
array) to get the valid region of each column.
_overlaps: Result is placed into this array which holds the overlaps of
each column with the input
"""
flatInput = inputShaped.reshape(-1)
self._allConnectedM.rightVecSumAtNZ_fast(flatInput, self._overlaps)
# Apply stimulusThreshold
# TODO: Is there a faster numpy operation for this?
self._overlaps[self._overlaps < stimulusThreshold] = 0
self._overlapsNoBoost = self._overlaps.copy()
#############################################################################
def _computeOverlapsCPP(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but using a C++ implementation.
"""
cpp_overlap(self._cloneMapFlat,
self._inputSlices2, self._coincSlices2,
inputShaped, self._masterConnectedM,
stimulusThreshold,
self._overlaps);
#############################################################################
def _computeOverlapsTest(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but compares the python and C++
implementations.
"""
# Py version
self._computeOverlapsPy(inputShaped, stimulusThreshold)
overlaps2 = copy.deepcopy(self._overlaps)
# C++ version
self._computeOverlapsCPP(inputShaped, stimulusThreshold)
if (abs(self._overlaps - overlaps2) > 1e-6).any():
print self._overlaps, overlaps2, abs(self._overlaps - overlaps2)
import pdb; pdb.set_trace()
sys.exit(0)
#############################################################################
def _raiseAllPermanences(self, masterNum, minConnections=None,
densePerm=None, densePotential=None):
"""
Raise all permanences of the given master. If minConnections is given, the
permanences will be raised until at least minConnections of them are
connected strength.
If minConnections is left at None, all permanences will be raised by
self._synPermBelowStimulusInc.
After raising all permanences, we also "sparsify" the permanence matrix
and set to 0 any permanences which are already very close to 0, this
keeps the memory requirements of the sparse matrices used to store
the permanences lower.
Parameters:
----------------------------------------------------------------------------
masterNum: Which master to bump up
minConnections: Desired number of connected synapses to have
If None, then all permanences are simply bumped up
by self._synPermBelowStimulusInc
densePerm: The dense representation of the master's permanence
matrix, if available. If not specified, we will
create this from the stored sparse representation.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix. The stored sparse
matrix will ALWAYS be updated from the densePerm if
the densePerm is provided.
densePotential: The dense representation of the master's potential
synapses matrix, if available. If not specified, we
will create this from the stored sparse potential
matrix.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix.
retval: (modified, numConnections)
modified: True if any permanences were raised
numConnections: Number of actual connected synapses
(not computed if minConnections was
None, so None is returned in that
case.)
"""
# It's faster to perform this operation on the dense matrices and
# then convert to sparse once we're done since we will be potentially
# introducing and then later removing a bunch of non-zeros.
# -------------------------------------------------------------------
# Get references to the sparse perms and potential syns for this master
sparsePerm = self._masterPermanenceM[masterNum]
sparsePotential = self._masterPotentialM[masterNum]
# We will trim off all synapse permanences below this value to 0 in order
# to keep the memory requirements of the SparseMatrix lower
trimThreshold = self.synPermActiveInc / 2.0
# -------------------------------------------------------------------
# See if we already have the required number of connections. If we don't,
# get the dense form of the permanences if we don't have them already
if densePerm is None:
# See if we already have enough connections, if so, we can avoid the
# overhead of converting to dense
if minConnections is not None:
numConnected = sparsePerm.countWhereGreaterEqual(
0, self._coincRFShape[0], 0, self._coincRFShape[1],
self.synPermConnected)
if numConnected >= minConnections:
return (False, numConnected)
densePerm = self._masterPermanenceM[masterNum].toDense()
elif minConnections is not None:
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (False, numConnected)
# Get the dense form of the potential synapse locations
if densePotential is None:
densePotential = self._masterPotentialM[masterNum].toDense()
# -------------------------------------------------------------------
# Form the array with the increments
incrementM = densePotential.astype(realDType)
incrementM *= self._synPermBelowStimulusInc
# -------------------------------------------------------------------
# Increment until we reach our target number of connections
assert (densePerm.dtype == realDType)
while True:
densePerm += incrementM
if minConnections is None:
numConnected = None
break
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
break
# -------------------------------------------------------------------
# Convert back to sparse form and trim any values that are already
# close to zero
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (True, numConnected)
#############################################################################
def _bumpUpWeakCoincidences(self):
"""
This bump-up ensures every coincidence have non-zero connections. We find
all coincidences which have overlaps less than stimulus threshold.
We add synPermActiveInc to all the synapses. This step when repeated over
time leads to synapses crossing synPermConnected threshold.
"""
# Update each cell's connected threshold based on the duty cycle before
# inhibition. The connected threshold is linearly interpolated
# between the points (dutyCycle:0, thresh:0) and (dutyCycle:minDuty,
# thresh:synPermConnected). This is a line defined as: y = mx + b
# thresh = synPermConnected/minDuty * dutyCycle
bumpUpList = (self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh).nonzero()[0]
for master in bumpUpList:
self._raiseAllPermanences(master)
# Update the connected synapses for each master we touched
self._updateConnectedCoincidences(bumpUpList)
if self.spVerbosity >= 2 and len(bumpUpList) > 0:
print "Bumping up permanences in following cells due to falling below" \
"minDutyCycleBeforeInh:", bumpUpList
#############################################################################
def _updateBoostFactors(self):
"""
Update the boost factors. The boost factors is linearly interpolated
between the points (dutyCycle:0, boost:maxFiringBoost) and
(dutyCycle:minDuty, boost:1.0). This is a line defined as: y = mx + b
boost = (1-maxFiringBoost)/minDuty * dutyCycle + maxFiringBoost
Parameters:
------------------------------------------------------------------------
boostFactors: numpy array of boost factors, defined per master
"""
if self._minDutyCycleAfterInh.sum() > 0:
self._firingBoostFactors = (1 - self.maxFiringBoost)\
/self._minDutyCycleAfterInh * self._dutyCycleAfterInh \
+ self.maxFiringBoost
self._firingBoostFactors[self._dutyCycleAfterInh \
> self._minDutyCycleAfterInh] = 1.0
# if self._dutyCycleAfterInh.min() == 0: # where there are unlearned coincs
# self._firingBoostFactors[self._dutyCycleAfterInh == 0] = \
# self.maxFiringBoost
#############################################################################
def _updateInputUse(self, onCellIndices):
"""
During learning (adapting permanence values), we need to be able to tell
which inputs are going to 2 or more active cells at once.
We step through each coinc and mark all the inputs it is connected to. The
inputUse array acts as a counter for the number of connections to the coincs
from each input.
Parameters:
------------------------------------------------------------------------
inputUse: numpy array of number of coincs connected to each input
"""
allConnected = SM32(self._allConnectedM)
# TODO: avoid this copy
self._inputUse[:] = allConnected.addListOfRows(
onCellIndices).reshape(self.inputShape)
#############################################################################
def _adaptSynapses(self, onCellIndices, orphanCellIndices, input):
"""
This is the main function in learning of SP. The permanence values are
changed based on the learning rules.
Parameters:
------------------------------------------------------------------------
onCellIndices: columns which are turned on after inhibition. The
permanence values of these coincs are adapted based on the
input.
orphanCellIndices: columns which had very high overlap with the input, but
ended up being inhibited
input: Input, shaped to the input topology
retval: list of masterCellIndices that were actually updated, or
None if cloning is off
"""
# Capturing learning stats?
if self.printPeriodicStats > 0:
self._stats['explainedInputsCurIteration'] = set()
# Precompute the active, inactive, and dupe inputs up front for speed
# TODO: put these into pre-allocated arrays for speed
self._activeInput[:] = input
# Create a matrix containing the default permanence deltas for each input
self._permChanges.fill(-1 * self.synPermInactiveDec)
self._permChanges[self._activeInput] = self.synPermActiveInc
if self.synPermActiveSharedDec != 0:
numpy.logical_and(self._activeInput, self._inputUse>1, self._dupeInput)
self._permChanges[self._dupeInput] -= self.synPermActiveSharedDec
# Cloning? If so, scramble the onCells so that we pick a random one to
# update for each master. We only update a master cell at most one time
# per input presentation.
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
# Scramble the onCellIndices so that we pick a random one to update
onCellIndices = list(onCellIndices)
random.shuffle(onCellIndices)
visitedMasters = set()
# For the firing cells, update permanence values
for columnNum in itertools.chain(onCellIndices, orphanCellIndices):
# Get the master number
masterNum = self._cloneMapFlat[columnNum]
# If cloning, only visit each master once
if cloningOn:
if masterNum in visitedMasters:
continue
visitedMasters.add(masterNum)
# Get the slices of input that overlap with the valid area of this master
inputSlice = self._inputSlices[columnNum]
rfActiveInput = self._activeInput[inputSlice]
rfPermChanges = self._permChanges[inputSlice]
# Get the potential synapses, permanence values, and connected synapses
# for this master
masterPotential = self._masterPotentialM[masterNum].toDense()
masterPermanence = self._masterPermanenceM[masterNum].toDense()
masterConnected = \
self._masterConnectedM[masterNum].toDense().astype('bool')
# Make changes only over the areas that overlap the input level. For
# coincidences near the edge of the level for example, this excludes the
# synapses outside the edge.
coincSlice = self._coincSlices[columnNum]
masterValidPermanence= masterPermanence[coincSlice]
# Capturing learning stats?
if self.printPeriodicStats > 0:
masterValidConnected = masterConnected[coincSlice]
explainedInputs = self._inputLayout[inputSlice][masterValidConnected]
self._stats['explainedInputsCurIteration'].update(explainedInputs)
if self.spVerbosity >= 3:
print " adapting cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " initialConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
print " firingLevel: %d" % (self._overlaps[columnNum])
print " firingBoostFactor: %f" % (self._firingBoostFactors[masterNum])
print " input slice: \n"
self._printInputSlice(rfActiveInput, prefix=' ')
# Update permanences given the active input (NOTE: The "FP" in this
# function name stands for "Function Pointer").
if columnNum in orphanCellIndices:
# Decrease permanence of active inputs
masterValidPermanence[rfActiveInput] -= self.synPermOrphanDec
else:
self._updatePermanenceGivenInputFP(columnNum, masterNum, input,
self._inputUse, masterPermanence, masterValidPermanence,
rfActiveInput, rfPermChanges)
# Clip to absolute min and max permanence values
numpy.clip(masterPermanence, self._synPermMin, self._synPermMax,
out=masterPermanence)
# Keep only the potential syns for this cell
numpy.multiply(masterPermanence, masterPotential, masterPermanence)
# If we are tracking learning stats, prepare to see how many changes
# were made to the cell connections
if self.printPeriodicStats > 0:
masterConnectedOrig = SM_01_32_32(self._masterConnectedM[masterNum])
# ---------------------------------------------------------------------
# If the number of connected synapses happens to fall below
# stimulusThreshold, bump up all permanences a bit.
# We could also just wait for the "duty cycle falls below
# minDutyCycleBeforeInb" logic to catch it, but doing it here is
# pre-emptive and much faster.
#
# The "duty cycle falls below minDutyCycleBeforeInb" logic will still
# catch other possible situations, like:
# * if the set of inputs a cell learned suddenly stop firing due to
# input statistic changes
# * damage to the level below
# * input is very sparse and we still don't pass stimulusThreshold even
# with stimulusThreshold conneted synapses.
self._raiseAllPermanences(masterNum,
minConnections=self.stimulusThreshold,
densePerm=masterPermanence,
densePotential=masterPotential)
# Update the matrices that contain the connected syns for this cell.
self._updateConnectedCoincidences([masterNum])
# If we are tracking learning stats, see how many changes were made to
# this cell's connections
if self.printPeriodicStats > 0:
origNumConnections = masterConnectedOrig.nNonZeros()
masterConnectedOrig.logicalAnd(self._masterConnectedM[masterNum])
numUnchanged = masterConnectedOrig.nNonZeros()
numChanges = origNumConnections - numUnchanged
numChanges += self._masterConnectedM[masterNum].nNonZeros() \
- numUnchanged
self._stats['numChangedConnectionsSum'][masterNum] += numChanges
self._stats['numLearns'][masterNum] += 1
# Verbose?
if self.spVerbosity >= 3:
print " done cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " newConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
self._printSyns(columnNum, prefix=' ',
showValues=(self.spVerbosity >= 4))
print
# Return list of updated masters
if cloningOn:
return list(visitedMasters)
else:
return onCellIndices
#############################################################################
def _updatePermanenceGivenInputPy(self, columnNum, masterNum, input, inputUse,
permanence, permanenceSlice, activeInputSlice, permChangesSlice):
"""
Given the input to a master coincidence, update it's permanence values
based on our learning rules.
On Entry, we are given the slice of the permanence matrix that corresponds
only to the area of the coincidence master that is within the borders of
the entire input field.
Parameters:
------------------------------------------------------------------------
columnNum: The column number of this cell
masterNum: The master coincidence that corresponds to this column
input: The entire input, shaped appropriately
inputUse: The same shape as input. Each entry is a count of the
number of *currently active cells* that are connected
to that input.
permanence: The entire masterPermanence matrix for this master
permanenceSlice: The slice of the masterPermanence matrix for this master
that intersects the input field, i.e. does not overhang
the outside edges of the input.
activeInputSlice: The portion of 'input' that intersects permanenceSlice,
set to True where input != 0
permChangesSlice: The portion of 'input' that intersects permanenceSlice,
set to self.synPermActiveInc where input != 0 and
self.synPermInactiveDec where the input == 0. This is
used to optimally apply self.synPermActiveInc and
self.synPermInactiveDec at the same time and can be
used for any cell whose _synPermBoostFactor is set
to 1.0.
"""
# Apply the baseline increment/decrements
permanenceSlice += permChangesSlice
# If this cell has permanence boost, apply the incremental
#############################################################################
def _updatePermanenceGivenInputCPP(self, columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but using a C++ implementation.
"""
inputNCols = self.inputShape[1]
masterNCols = self._masterPotentialM[masterNum].shape[1]
#TODO: synPermBoostFactors has been removed. CPP implementation has not been updated for this.
adjustMasterValidPermanence(columnNum,
masterNum,
inputNCols,
masterNCols,
self.synPermActiveInc,
self.synPermInactiveDec,
self.synPermActiveSharedDec,
input,
inputUse,
self._inputSlices2,
self._coincSlices2,
self._synPermBoostFactors,
permanence)
#############################################################################
def _updatePermanenceGivenInputTest(self, columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but compares the python and C++
implementations.
"""
mp2 = copy.deepcopy(permanence)
mvp2 = copy.deepcopy(permanenceSlice)
# Py version
import pdb; pdb.set_trace()
self._updatePermanenceGivenInputPy(columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice)
# C++ version
self._updatePermanenceGivenInputCPP(columnNum, masterNum, input,
inputUse, mp2, mvp2, activeInputSlice, permChangesSlice)
if abs(mp2 - permanence).max() > 1e-6:
print abs(mp2 - permanence).max()
import pdb; pdb.set_trace()
sys.exit(0)
#############################################################################
def _periodicStatsCreate(self):
"""
Allocate the periodic stats structure
Parameters:
------------------------------------------------------------------
"""
self._stats = dict()
self._stats['numChangedConnectionsSum'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['numLearns'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['maxBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# This dict maintains mappings of specific input patterns to specific
# output patterns. It is used to detect "thrashing" of cells. We measure
# how similar the output presentation of a specific input is to the
# last time we saw it.
self._stats['inputPatterns'] = dict()
self._stats['inputPatternsLimit'] = 5000
self._periodicStatsReset()
#############################################################################
def _periodicStatsReset(self):
"""
Reset the periodic stats this is done every N iterations before capturing
a new set of stats
Parameters:
------------------------------------------------------------------
"""
self._stats['numSamples'] = 0
self._stats['numOnSum'] = 0
self._stats['underCoveragePctSum'] = 0
self._stats['overCoveragePctSum'] = 0
self._stats['cellOverlapSums'] = 0
self._stats['cellPctOverlapSums'] = 0
self._stats['explainedInputsCurIteration'] = set()
self._stats['startTime'] = time.time()
# These keep a count of the # of changed connections per update
# for each master
self._stats['numChangedConnectionsSum'].fill(0)
self._stats['numLearns'].fill(0)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'].fill(self.maxFiringBoost)
self._stats['maxBoostFactor'].fill(0)
# This keeps track of the average distance between the SP output of
# a specific input pattern now and the last time we saw it.
self._stats['outputPatternDistanceSum'] = 0
self._stats['outputPatternSamples'] = 0
#############################################################################
def _periodicStatsComputeEnd(self, activeCells, activeInputs):
"""
Called at the end of compute. This increments the number of computes
and also summarizes the under and over coverage and whatever other
periodic stats we need.
If the period is up, it then prints the accumuated stats and resets them
for the next period
Parameters:
------------------------------------------------------------------
activeCells: list of the active cells
activeInputs: list of the active inputs
"""
# Update number of samples
self._stats['numSamples'] += 1
# Compute under and over coverage
numOn = len(activeCells)
self._stats['numOnSum'] += numOn
expInput = self._stats['explainedInputsCurIteration']
inputLen = len(activeInputs)
underCoverage = len(set(activeInputs).difference(expInput))
self._stats['underCoveragePctSum'] += float(underCoverage) / inputLen
expInput.difference_update(activeInputs)
overCoverage = len(expInput)
self._stats['overCoveragePctSum'] += float(overCoverage) / inputLen
# Keep track of the min and max boost factor seen for each column
numpy.minimum(self._firingBoostFactors, self._stats['minBoostFactor'],
self._stats['minBoostFactor'])
numpy.maximum(self._firingBoostFactors, self._stats['maxBoostFactor'],
self._stats['maxBoostFactor'])
# Calculate the distance in the SP output between this input now
# and the last time we saw it.
inputPattern = str(sorted(activeInputs))
(outputNZ, sampleIdx) = \
self._stats['inputPatterns'].get(inputPattern, (None, None))
activeCellSet = set(activeCells)
if outputNZ is not None:
distance = len(activeCellSet.difference(outputNZ)) \
+ len(outputNZ.difference(activeCellSet))
#print "DISTANCE: ", distance
#if len(self._stats['inputPatterns']) == 100 and distance > 0:
# print "input pattern (%d):" % (sampleIdx), inputPattern
# print "prior output:", sorted(outputNZ)
# print "new output:", sorted(activeCellSet)
# print "distance: ", distance
self._stats['inputPatterns'][inputPattern] = (activeCellSet, sampleIdx)
self._stats['outputPatternDistanceSum'] += distance
self._stats['outputPatternSamples'] += 1
# Add this sample to our dict, if it's not too large already
elif len(self._stats['inputPatterns']) < self._stats['inputPatternsLimit']:
self._stats['inputPatterns'][inputPattern] = \
(activeCellSet, self._iterNum)
# -----------------------------------------------------------------------
# If it's not time to print them out, return now
if (self._iterNum % self.printPeriodicStats) != 0:
return
numSamples = float(self._stats['numSamples'])
# Calculate number of changes made per master
masterTouched = numpy.where(self._stats['numLearns'] > 0)
if len(masterTouched[0]) == 0:
numMasterChanges = numpy.zeros(1)
else:
numMasterChanges = self._stats['numChangedConnectionsSum'][masterTouched]
numMasterChanges /= self._stats['numLearns'][masterTouched]
# This fills in the static learning stats into self._learningStats
self.getLearningStats()
# Calculate and copy the transient learning stats into the
# self._learningStats dict, for possible retrieval later by
# the getLearningStats() method
self._learningStats['elapsedTime'] = time.time() - self._stats['startTime']
self._learningStats['activeCountAvg'] = self._stats['numOnSum'] / numSamples
self._learningStats['underCoveragePct'] = \
100.0*self._stats['underCoveragePctSum'] / numSamples
self._learningStats['overCoveragePct'] = \
100.0*self._stats['overCoveragePctSum'] / numSamples
self._learningStats['numConnectionChangesAvg'] = numMasterChanges.mean()
self._learningStats['numConnectionChangesMin'] = numMasterChanges.min()
self._learningStats['numConnectionChangesMax'] = numMasterChanges.max()
self._learningStats['avgCellOverlap'] = \
float(self._stats['cellOverlapSums']) / max(1, self._stats['numOnSum'])
self._learningStats['avgCellPctOverlap'] = \
100.0*self._stats['cellPctOverlapSums'] / max(1, self._stats['numOnSum'])
self._learningStats['firingBoostMaxChangePct'] = 100.0 \
* (self._stats['maxBoostFactor'] / self._stats['minBoostFactor']).max() \
- 100.0
self._learningStats['outputRepresentationChangeAvg'] = \
float(self._stats['outputPatternDistanceSum']) / \
max(1, self._stats['outputPatternSamples'])
self._learningStats['outputRepresentationChangePctAvg'] = \
100.0 * self._learningStats['outputRepresentationChangeAvg'] / \
max(1,self._learningStats['activeCountAvg'])
self._learningStats['numUniqueInputsSeen'] = \
len(self._stats['inputPatterns'])
if self._learningStats['numUniqueInputsSeen'] >= \
self._stats['inputPatternsLimit']:
self._learningStats['numUniqueInputsSeen'] = -1
# -------------------------------------------------------------------
# Print all stats captured
print "Learning stats for the last %d iterations:" % (numSamples)
print " iteration #: %d" % (self._iterNum)
print " inference iteration #: %d" % (self._inferenceIterNum)
print " elapsed time: %.2f" \
% (self._learningStats['elapsedTime'])
print " avg activeCount: %.1f" \
% (self._learningStats['activeCountAvg'])
print " avg under/overCoverage: %-6.1f / %-6.1f %%" \
% (self._learningStats['underCoveragePct'],
self._learningStats['overCoveragePct'])
print " avg cell overlap: %-6.1f / %-6.1f %%" \
% (self._learningStats['avgCellOverlap'],
self._learningStats['avgCellPctOverlap'])
print " avg/min/max RF radius: %-6.1f / %-6.1f / %-6.1f" \
% (self._learningStats['rfRadiusAvg'],
self._learningStats['rfRadiusMin'],
self._learningStats['rfRadiusMax'])
print " inhibition radius: %d" \
% (self._learningStats['inhibitionRadius'])
print " target density: %.5f %%" \
% (self._learningStats['targetDensityPct'])
print " avg/min/max coinc. size: %-6.1f / %-6d / %-6d" \
% (self._learningStats['coincidenceSizeAvg'],
self._learningStats['coincidenceSizeMin'],
self._learningStats['coincidenceSizeMax'])
print " avg/min/max DC before inh: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['dcBeforeInhibitionAvg'],
self._learningStats['dcBeforeInhibitionMin'],
self._learningStats['dcBeforeInhibitionMax'])
print " avg/min/max DC after inh: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['dcAfterInhibitionAvg'],
self._learningStats['dcAfterInhibitionMin'],
self._learningStats['dcAfterInhibitionMax'])
print " avg/min/max boost: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['firingBoostAvg'],
self._learningStats['firingBoostMin'],
self._learningStats['firingBoostMax'])
print " avg/min/max # conn. changes: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['numConnectionChangesAvg'],
self._learningStats['numConnectionChangesMin'],
self._learningStats['numConnectionChangesMax'])
print " max change in boost: %.1f %%" \
% (self._learningStats['firingBoostMaxChangePct'])
print " avg change in output repr.: %-6.1f / %-6.1f %%" \
% (self._learningStats['outputRepresentationChangeAvg'],
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
print " # of unique input pats seen: %d" \
% (self._learningStats['numUniqueInputsSeen'])
#self._printMemberSizes()
# Reset the stats for the next period
self._periodicStatsReset()
##############################################################################
def _printInputSlice(self, inputSlice, prefix=''):
""" Print the given input slice in a nice human readable format.
Parameters:
---------------------------------------------------------------------
cell: The slice of input to print
prefix: This is printed at the start of each row of the
coincidence
"""
# Shape of each coincidence
(rfHeight, rfWidth) = inputSlice.shape
syns = inputSlice != 0
def _synStr(x):
if not x:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
print prefix, ''.join(items)
##############################################################################
def _printSyns(self, cell, prefix='', showValues=False):
""" Print the synapse permanence values for the given cell in a nice,
human, readable format.
Parameters:
---------------------------------------------------------------------
cell: which cell to print
prefix: This is printed at the start of each row of the
coincidence
showValues: If True, print the values of each permanence.
If False, just print a ' ' if not connected and a '*'
if connected
"""
# Shape of each coincidence
(rfHeight, rfWidth) = self.inputShape
# Get the synapse permanences.
masterNum = self._cloneMapFlat[cell]
syns = self._masterPermanenceM[masterNum].toDense()
if showValues:
def _synStr(x):
if x == 0:
return ' -- '
elif x < 0.001:
return ' 0 '
elif x >= self.synPermConnected:
return '#%3.2f' % x
else:
return ' %3.2f' % x
else:
def _synStr(x):
if x < self.synPermConnected:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
if showValues:
print prefix, ' '.join(items)
else:
print prefix, ''.join(items)
##############################################################################
def _printMemberSizes(self, over=100):
""" Print the size of each member
"""
members = self.__dict__.keys()
sizeNamePairs = []
totalSize = 0
for member in members:
item = self.__dict__[member]
if hasattr(item, '__func__'):
continue
try:
if hasattr(item, '__len__'):
size = 0
for i in xrange(len(item)):
size += len(cPickle.dumps(item[i]))
else:
size = len(cPickle.dumps(item))
except:
print "WARNING: Can't pickle %s" % (member)
size = 0
sizeNamePairs.append((size, member))
totalSize += size
# Print them out from highest to lowest
sizeNamePairs.sort(reverse=True)
for (size, name) in sizeNamePairs:
if size > over:
print "%10d (%10.3fMb) %s" % (size, size/1000000.0, name)
print "\nTOTAL: %10d (%10.3fMB) " % (totalSize, totalSize/1000000.0)
##############################################################################
def printParams(self):
""" Print the main creation parameters associated with this instance.
"""
print "FDRCSpatial2 creation parameters: "
print "inputShape =", self.inputShape
print "inputBorder =", self.inputBorder
print "inputDensity =", self.inputDensity
print "coincidencesShape =", self.coincidencesShape
print "coincInputRadius =", self.coincInputRadius
print "coincInputPoolPct =", self.coincInputPoolPct
print "gaussianDist =", self.gaussianDist
print "commonDistributions =", self.commonDistributions
print "localAreaDensity =", self.localAreaDensity
print "numActivePerInhArea =", self.numActivePerInhArea
print "stimulusThreshold =", self.stimulusThreshold
print "synPermInactiveDec =", self.synPermInactiveDec
print "synPermActiveInc =", self.synPermActiveInc
print "synPermActiveSharedDec =", self.synPermActiveSharedDec
print "synPermOrphanDec =", self.synPermOrphanDec
print "synPermConnected =", self.synPermConnected
print "minPctDutyCycleBeforeInh =", self.minPctDutyCycleBeforeInh
print "minPctDutyCycleAfterInh =", self.minPctDutyCycleAfterInh
print "dutyCyclePeriod =", self.dutyCyclePeriod
print "maxFiringBoost =", self.maxFiringBoost
print "maxSSFiringBoost =", self.maxSSFiringBoost
print "maxSynPermBoost =", self.maxSynPermBoost
print "minDistance =", self.minDistance
print "spVerbosity =", self.spVerbosity
print "printPeriodicStats =", self.printPeriodicStats
print "testMode =", self.testMode
print "numCloneMasters =", self.numCloneMasters
removed topDownCompute from FDRCSpatial2
#!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# 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.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
import sys
import os
import numpy
import numpy.random
import random
import itertools
import time
import math
import copy
import cPickle
import struct
from nupic.bindings.math import SM32, SM_01_32_32, count_gte, GetNTAReal
from nupic.bindings.algorithms import Inhibition2, cpp_overlap, cpp_overlap_sbm
from nupic.bindings.algorithms import adjustMasterValidPermanence
from nupic.bindings.math import Random as NupicRandom
from nupic.math.cross import cross
from operator import itemgetter
import nupic.research.fdrutilities as fdru
realDType = GetNTAReal()
gPylabInitialized = False
# kDutyCycleFactor add dutyCycleAfterInh to overlap in Inhibition step to be a
# tie breaker
kDutyCycleFactor = 0.01
################################################################################
def _extractCallingMethodArgs():
"""
Returns args dictionary from the calling method
"""
import inspect
import copy
callingFrame = inspect.stack()[1][0]
argNames, _, _, frameLocalVarDict = inspect.getargvalues(callingFrame)
argNames.remove("self")
args = copy.copy(frameLocalVarDict)
for varName in frameLocalVarDict:
if varName not in argNames:
args.pop(varName)
return args
"""
Class for spatial pooling based on fixed random distributed
representation (FDR)
"""
class FDRCSpatial2(object):
"""
This version of FDRCSpatial inlcudes adaptive receptive fields, no-dupe rules
and gradual boosting. It supports 1-D and 2-D topologies with cloning
"""
def __init__(self,
inputShape = (32, 32),
inputBorder = 8,
inputDensity = 1.0,
coincidencesShape = (48, 48),
coincInputRadius = 16,
coincInputPoolPct = 1.0,
gaussianDist = False,
commonDistributions = False,
localAreaDensity = -1.0,
numActivePerInhArea = 10.0,
stimulusThreshold = 0,
synPermInactiveDec = 0.01,
synPermActiveInc = 0.1,
synPermActiveSharedDec = 0.0,
synPermOrphanDec = 0.0,
synPermConnected = 0.10,
minPctDutyCycleBeforeInh = 0.001,
minPctDutyCycleAfterInh = 0.001,
dutyCyclePeriod = 1000,
maxFiringBoost = 10.0,
maxSSFiringBoost = 2.0,
maxSynPermBoost = 10.0,
minDistance = 0.0,
cloneMap = None,
numCloneMasters = -1,
seed = -1,
spVerbosity = 0,
printPeriodicStats = 0,
testMode = False,
globalInhibition = False,
spReconstructionParam = "unweighted_mean",
useHighTier = True,
randomSP = False,
):
"""
Parameters:
----------------------------
inputShape: The dimensions of the input vector. Format is (height,
width) e.g. (24, 72). If the input is from a sensor,
it is interpreted as having a 2-D topology of 24
pixels high and 72 wide.
inputBorder: The first column from an edge will be centered
over an input which is 'inputBorder' inputs from the
edge.
inputDensity: The density of the input. This is only to aid in
figuring out the initial number of connected synapses
to place on each column. The lower the inputDensity,
the more initial connections will be assigned to each
column.
coincidencesShape: The dimensions of column layout. Format is (height,
width) e.g. (80,100) means a total of 80*100 = 800 are
arranged in a 2-D topology with 80 rows and
100 columns.
coincInputRadius: This defines the max radius of the receptive field of
each column. This is used to limit memory requirements
and processing time. It could be set large enough to
encompass the entire input field and the SP would
still work fine, but require more memory and
processing time.
This parameter defines a square area: a column will
have a max square RF with sides of length
2 * coincInputRadius + 1.
coincInputPoolPct What percent of the columns's receptive field is
available for potential synapses. At initialization
time, we will choose
coincInputPoolPct * (2*coincInputRadius + 1)^2
potential synapses from the receptive field.
gaussianDist: If true, the initial permanences assigned to each
column will have a gaussian distribution to them,
making the column favor inputs directly below it over
inputs farther away. If false, the initial permanences
will have a random distribution across the column's
entire potential receptive field.
commonDistributions: If set to True (the default, faster startup time),
each column will be given the same initial permanence
values.
This is normally OK when you will be training, but if
you will be sticking with the untrained network,
you will want to set this to False (which makes
startup take longer).
localAreaDensity: The desired density of active columns within a local
inhibition area (the size of which is set by the
internally calculated inhibitionRadius, which is in
turn determined from the average size of the connected
receptive fields of all columns). The inhibition logic
will insure that at most N columns remain ON within a
local inhibition area, where N = localAreaDensity *
(total number of columns in inhibition area).
numActivePerInhArea: An alternate way to control the density of the active
columns. If numActivePerInhArea is specified then
localAreaDensity must be -1, and vice versa. When
using numActivePerInhArea, the inhibition logic will
insure that at most 'numActivePerInhArea' columns
remain ON within a local inhibition area (the size of
which is set by the internally calculated
inhibitionRadius, which is in turn determined from the
average size of the connected receptive fields of all
columns). When using this method, as columns learn and
grow their effective receptive fields, the
inhibitionRadius will grow, and hence the net density
of the active columns will *decrease*. This is in
contrast to the localAreaDensity method, which keeps
the density of active columns the same regardless of
the size of their receptive fields.
stimulusThreshold: This is a number specifying the minimum number of
synapses that must be on in order for a columns to
turn ON.
The purpose of this is to prevent noise input from
activating columns.
synPermInactiveDec: How much an inactive synapse is decremented, specified
as a percent of a fully grown synapse.
synPermActiveInc: How much to increase the permanence of an active
synapse, specified as a percent of a fully grown
synapse.
synPermActiveSharedDec: How much to decrease the permanence of an active
synapse which is connected to another column that is
active at the same time. Specified as a percent of a
fully grown synapse.
synPermOrphanDec: How much to decrease the permanence of an active
synapse on a column which has high overlap with the
input, but was inhibited (an "orphan" column).
synPermConnected: The default connected threshold. Any synapse whose
permanence value is above the connected threshold is
a "connected synapse", meaning it can contribute to
the cell's firing. Typical value is 0.10. Cells whose
activity level before inhibition falls below
minDutyCycleBeforeInh will have their own internal
synPermConnectedCell threshold set below this default
value.
(This concept applies to both SP and TP and so 'cells'
is correct here as opposed to 'columns')
minPctDutyCycleBeforeInh: A number between 0 and 1.0, used to set a floor on
how often a column should have at least
stimulusThreshold active inputs. Periodically, each
column looks at the duty cycle before inhibition of
all other column within its inhibition radius and sets
its own internal minimal acceptable duty cycle to:
minPctDutyCycleBeforeInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle before
inhibition falls below this computed value will get
all of its permanence values boosted up by
synPermActiveInc. Raising all permanences in response
to a sub-par duty cycle before inhibition allows a
cell to search for new inputs when either its
previously learned inputs are no longer ever active,
or when the vast majority of them have been "hijacked"
by other columns due to the no-dupe rule.
minPctDutyCycleAfterInh: A number between 0 and 1.0, used to set a floor on
how often a column should turn ON after inhibition.
Periodically, each column looks at the duty cycle
after inhibition of all other columns within its
inhibition radius and sets its own internal minimal
acceptable duty cycle to:
minPctDutyCycleAfterInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle after
inhibition falls below this computed value will get
its internal boost factor increased.
dutyCyclePeriod: The period used to calculate duty cycles. Higher
values make it take longer to respond to changes in
boost or synPerConnectedCell. Shorter values make it
more unstable and likely to oscillate.
maxFiringBoost: The maximum firing level boost factor. Each column's
raw firing strength gets multiplied by a boost factor
before it gets considered for inhibition.
The actual boost factor for a column is number between
1.0 and maxFiringBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxFiringBoost
is used if the duty cycle is 0, and any duty cycle in
between is linearly extrapolated from these 2
endpoints.
maxSSFiringBoost: Once a column turns ON, it's boost will immediately
fall down to maxSSFiringBoost if it is above it. This
is accomplished by internally raising it's computed
duty cycle accordingly. This prevents a cell which has
had it's boost raised extremely high from turning ON
for too many diverse inputs in a row within a short
period of time.
maxSynPermBoost: The maximum synPermActiveInc boost factor. Each
column's synPermActiveInc gets multiplied by a boost
factor to make the column more or less likely to form
new connections.
The actual boost factor used is a number between
1.0 and maxSynPermBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxSynPermBoost
is used if the duty cycle is 0, and any duty cycle
in between is linearly extrapolated from these 2
endpoints.
minDistance: This parameter impacts how finely the input space is
quantized. It is a value between 0 and 1.0. If set
to 0, then every unique input presentation will
generate a unique output representation, within the
limits of the total number of columns available.
Higher values will tend to group similar inputs
together into the same output representation. Only
column which overlap with the input less than
100*(1.0-minDistance) percent will
have a possibility of losing the inhibition
competition against a boosted, 'bored' cell.
cloneMap: An array (numColumnsHigh, numColumnsWide) that
contains the clone index to use for each column.
numCloneMasters: The number of distinct clones in the map. This
is just outputCloningWidth*outputCloningHeight.
seed: Seed for our own pseudo-random number generator.
spVerbosity: spVerbosity level: 0, 1, 2, or 3
printPeriodicStats: If > 0, then every 'printPeriodicStats' iterations,
the SP will print to stdout some statistics related to
learning, such as the average pct under and
over-coverage, average number of active columns, etc.
in the last 'showLearningStats' iterations.
testMode: If True, run the SP in test mode. This runs both the
C++ and python implementations on all internal
functions that support both and insures that both
produce the same result.
globalInhibition: If true, enforce the
localAreaDensity/numActivePerInhArea
globally over the entire region, ignoring any
dynamically calculated inhibitionRadius. In effect,
this is the same as setting the inhibition radius to
include the entire region.
spReconstructionParam:Specifies which SP reconstruction optimization to be
used. Each column's firing strength is weighted by the
percent Overlap, permanence or duty Cycle if this
parameter is set to 'pctOverlap', 'permanence', or
'dutycycle' respectively. If parameter is set to
'maximum_firingstrength', the maximum of the firing
strengths (weighted by permanence) is used instead of
the weighted sum.
useHighTier: The "high tier" feature is to deal with sparse input
spaces. If over (1-minDistance) percent of a column's
connected synapses are active, it will automatically
become one of the winning columns.
If False, columns are activated based on their absolute
overlap with the input. Also, boosting will be
disabled to prevent pattern oscillation
randomSP: If True, the SP will not update its permanences and
will instead use it's initial configuration for all
inferences.
"""
#--------------------------------------------------------------------------
# Save our __init__ args for debugging
self._initArgsDict = _extractCallingMethodArgs()
# Handle people instantiating us directly that don't pass in a cloneMap...
# This creates a clone map without any cloning
if cloneMap is None:
cloneMap, numCloneMasters = fdru.makeCloneMap(
columnsShape=coincidencesShape,
outputCloningWidth=coincidencesShape[1],
outputCloningHeight=coincidencesShape[0]
)
self.numCloneMasters = numCloneMasters
self._cloneMapFlat = cloneMap.reshape((-1,))
# Save creation parameters
self.inputShape = (int(inputShape[0]), int(inputShape[1]))
self.inputBorder = inputBorder
self.inputDensity = inputDensity
self.coincidencesShape = coincidencesShape
self.coincInputRadius = coincInputRadius
self.coincInputPoolPct = coincInputPoolPct
self.gaussianDist = gaussianDist
self.commonDistributions = commonDistributions
self.localAreaDensity = localAreaDensity
self.numActivePerInhArea = numActivePerInhArea
self.stimulusThreshold = stimulusThreshold
self.synPermInactiveDec = synPermInactiveDec
self.synPermActiveInc = synPermActiveInc
self.synPermActiveSharedDec = synPermActiveSharedDec
self.synPermOrphanDec = synPermOrphanDec
self.synPermConnected = synPermConnected
self.minPctDutyCycleBeforeInh = minPctDutyCycleBeforeInh
self.minPctDutyCycleAfterInh = minPctDutyCycleAfterInh
self.dutyCyclePeriod = dutyCyclePeriod
self.maxFiringBoost = maxFiringBoost
self.maxSSFiringBoost = maxSSFiringBoost
self.maxSynPermBoost = maxSynPermBoost
self.minDistance = minDistance
self.spVerbosity = spVerbosity
self.printPeriodicStats = printPeriodicStats
self.testMode = testMode
self.globalInhibition = globalInhibition
self.spReconstructionParam = spReconstructionParam
self.useHighTier= useHighTier != 0
self.randomSP = randomSP != 0
if not self.useHighTier:
self.minPctDutyCycleAfterInh = 0
self.fileCount = 0
self._runIter = 0
# Start at iteration #0
self._iterNum = 0 # Number of learning iterations
self._inferenceIterNum = 0 # Number of inference iterations
# Print creation parameters
if spVerbosity >= 3:
self.printParams()
print "seed =", seed
# Check for errors
assert (self.numActivePerInhArea == -1 or self.localAreaDensity == -1)
assert (self.inputShape[1] > 2*self.inputBorder)
# 1D layouts have inputShape[0] == 1
if self.inputShape[0] > 1:
assert (self.inputShape[0] > 2*self.inputBorder)
# Calculate other member variables
self._coincCount = int(self.coincidencesShape[0] * \
self.coincidencesShape[1])
self._inputCount = int(self.inputShape[0] * self.inputShape[1])
self._synPermMin = 0.0
self._synPermMax = 1.0
self._pylabInitialized = False
# The rate at which we bump up all synapses in response to not passing
# stimulusThreshold
self._synPermBelowStimulusInc = self.synPermConnected / 10.0
self._hasTopology = True
if self.inputShape[0] == 1: # 1-D layout
self._coincRFShape = (1, (2*coincInputRadius + 1))
# If we only have 1 column of coincidences, then assume the user wants
# each coincidence to cover the entire input
if self.coincidencesShape[1] == 1:
assert self.inputBorder >= (self.inputShape[1] - 1) // 2
assert coincInputRadius >= (self.inputShape[1] - 1) // 2
self._coincRFShape = (1, self.inputShape[1])
self._hasTopology = False
else: # 2-D layout
self._coincRFShape = ((2*coincInputRadius + 1), (2*coincInputRadius + 1))
# This gets set to True in finishLearning. Once set, we don't allow
# learning anymore and delete all member variables needed only for
# learning
self._doneLearning = False
# Init random seed
self._seed(seed)
# Hard-coded in the current case
self.randomTieBreakingFraction = 0.5
# The permanence values used to initialize the master coincs are from
# this initial permanence array
# The initial permanence is gaussian shaped with mean at center and variance
# carefully chosen to have connected synapses
initialPermanence = self._initialPermanence()
# masterPotentialM, masterPermanenceM and masterConnectedM are numpy arrays
# of dimensions (coincCount, coincRfShape[0], coincRFShape[1])
#
# masterPotentialM: Keeps track of the potential synapses of each
# master. Potential synapses are marked as True
# masterPermanenceM: Holds the permanence values of the potential synapses.
# The values can range from 0.0 to 1.0
# masterConnectedM: Keeps track of the connected synapses of each
# master. Connected synapses are the potential synapses
# with permanence values greater than synPermConnected.
self._masterPotentialM, self._masterPermanenceM = \
self._makeMasterCoincidences(self.numCloneMasters, self._coincRFShape,
self.coincInputPoolPct, initialPermanence,
self.random)
# Update connected coincidences, the connected synapses have permanence
# values greater than synPermConnected
self._masterConnectedM = []
dense = numpy.zeros(self._coincRFShape)
for i in xrange(self.numCloneMasters):
self._masterConnectedM.append(SM_01_32_32(dense))
# coinc sizes are used in normalizing the raw overlaps
self._masterConnectedCoincSizes = numpy.empty(self.numCloneMasters,
'uint32')
# Make one mondo coincidence matrix for all cells at once. It has one row
# per cell. The width of each row is the entire input width. There will be
# ones in each row where that cell has connections. When we have cloning,
# and we modify the connections for a clone master, we will update all
# cells that share that clone master with the new connections.
self._allConnectedM = SM_01_32_32(self._inputCount)
self._allConnectedM.resize(self._coincCount, self._inputCount)
# =========================================================================
# Initialize the dutyCycles and boost factors per clone master
self._dutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._dutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# TODO: We don't need to store _boostFactors, can be calculated from duty
# cycle
self._firingBoostFactors = numpy.ones(self.numCloneMasters,
dtype=realDType)
if self.useHighTier:
self._firingBoostFactors *= maxFiringBoost
# Selectively turn on/off C++ for various methods
if self.testMode:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
else:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
# This is used to hold our learning stats (via getLearningStats())
self._learningStats = dict()
# These will hold our random state, which we return from __getstate__ and
# reseed our random number generators from in __setstate__ so that
# a saved/restored SP produces the exact same behavior as one that
# continues. This behavior allows us to write unit tests that verify that
# the behavior of an SP does not change due to saving/loading from a
# checkpoint
self._randomState = None
self._numpyRandomState = None
self._nupicRandomState = None
# =========================================================================
# Init ephemeral members
# This also calculates the slices and global inhibitionRadius and allocates
# the inhibitionObj
self._initEphemerals()
# =========================================================================
# If we have no cloning, make sure no column has potential or connected
# synapses outside the input area
if self.numCloneMasters == self._coincCount:
validMask = numpy.zeros(self._coincRFShape, dtype=realDType)
for masterNum in xrange(self._coincCount):
coincSlice = self._coincSlices[masterNum]
validMask.fill(0)
validMask[coincSlice] = 1
self._masterPotentialM[masterNum].logicalAnd(SM_01_32_32(validMask))
self._masterPermanenceM[masterNum].elementMultiply(validMask)
# Raise all permanences up until the number of connected is above
# our desired target,
self._raiseAllPermanences(masterNum,
minConnections = self.stimulusThreshold / self.inputDensity)
# =========================================================================
#self._cellMappings = []
#for cell in xrange(self._coincCount):
# inputSlice = self._inputSlices[cell]
# mapping = self._inputLayout[inputSlice]
# self._cellMappings.append(mapping)
# =========================================================================
# Calculate the number of connected synapses in each master coincidence now
self._updateConnectedCoincidences()
#############################################################################
def _getEphemeralMembers(self):
"""
List of our member variables that we don't need to be saved
"""
return [
'_inputLayout',
'_cellsForMaster',
'_columnCenters',
#'_cellRFClipped',
'_inputSlices',
'_coincSlices',
'_activeInput',
'_permChanges',
'_dupeInput',
'_onCells',
'_masterOnCells',
'_onCellIndices',
'_inhibitionObj',
'_denseOutput',
'_overlaps',
'_anomalyScores',
'_inputUse',
'_updatePermanenceGivenInputFP',
'_computeOverlapsFP',
'_stats',
'_rfRadiusAvg',
'_rfRadiusMin',
'_rfRadiusMax',
'_topDownOut',
'_topDownParentCounts',
]
#############################################################################
def _initEphemerals(self):
"""
Initialize all ephemeral members after being restored to a pickled state.
"""
# Used by functions which refers to inputs in absolute space
# getLearnedCM, cm,....
self._inputLayout = numpy.arange(self._inputCount,
dtype=numpy.uint32).reshape(self.inputShape)
# This array returns the list of cell indices that correspond to each master
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
self._cellsForMaster = []
for masterNum in xrange(self.numCloneMasters):
self._cellsForMaster.append(
numpy.where(self._cloneMapFlat == masterNum)[0])
else:
self._cellsForMaster = None
# TODO: slices are not required for the C++ helper functions
# Figure out the slices of shaped input that each column sees...
# Figure out the valid region of each column
# The reason these slices are in initEphemerals is because numpy slices
# can't be pickled
self._setSlices()
# This holds the output of the inhibition computation - which cells are
# on after inhibition
self._onCells = numpy.zeros(self._coincCount, dtype=realDType)
self._masterOnCells = numpy.zeros(self.numCloneMasters, dtype=realDType)
self._onCellIndices = numpy.zeros(self._coincCount, dtype='uint32')
# The inhibition object gets allocated by _updateInhibitionObj() during
# the first compute and re-allocated periodically during learning
self._inhibitionObj = None
self._rfRadiusAvg = 0 # Also calculated by _updateInhibitionObj
self._rfRadiusMin = 0
self._rfRadiusMax = 0
# Used by the caller to optionally cache the dense output
self._denseOutput = None
# This holds the overlaps (in absolute number of connected synapses) of each
# coinc with input
self._overlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the percent overlaps (number of active inputs / number of
# connected synapses) of each coinc with input
self._pctOverlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This is the value of the anomaly score for each column (after inhibition)
self._anomalyScores = numpy.zeros_like(self._overlaps)
# This holds the overlaps before stimulus threshold - used for verbose
# messages only
self._overlapsBST = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the number of coincs connected to an input
if not self._doneLearning:
self._inputUse = numpy.zeros(self.inputShape, dtype=realDType)
# These are boolean matrices, the same shape as the input
if not self._doneLearning:
self._activeInput = numpy.zeros(self.inputShape, dtype='bool')
self._dupeInput = numpy.zeros(self.inputShape, dtype='bool')
# This is used to hold self.synPermActiveInc where the input is on
# and -self.synPermInctiveDec where the input is off
if not self._doneLearning:
self._permChanges = numpy.zeros(self.inputShape, dtype=realDType)
# These are used to compute and hold the output from topDownCompute
# self._topDownOut = numpy.zeros(self.inputShape, dtype=realDType)
# self._topDownParentCounts = numpy.zeros(self.inputShape, dtype='int')
# Fill in the updatePermanenceGivenInput method pointer, which depends on
# chosen language
if self._updatePermanenceGivenInputImp == "py":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputPy
elif self._updatePermanenceGivenInputImp == "cpp":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputCPP
elif self._updatePermanenceGivenInputImp == "test":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputTest
else:
assert (False)
# Fill in the computeOverlaps method pointer, which depends on
# chosen language
if self._computeOverlapsImp == "py":
self._computeOverlapsFP = self._computeOverlapsPy
elif self._computeOverlapsImp == "cpp":
self._computeOverlapsFP = self._computeOverlapsCPP
elif self._computeOverlapsImp == "test":
self._computeOverlapsFP = self._computeOverlapsTest
else:
assert (False)
# ----------------------------------------------------------------------
# These variables are used for keeping track of learning statistics (when
# self.printPeriodicStats is used).
self._periodicStatsCreate()
#############################################################################
def compute(self, flatInput, learn=False, infer=True, computeAnomaly=False):
""" Compute with the current input vector.
Parameters:
----------------------------
input : the input vector (numpy array)
learn : if True, adapt the input histogram based on this input
infer : whether to do inference or not
"""
# If we are using a random SP, ignore the learn parameter
if self.randomSP:
learn = False
# If finishLearning has been called, don't allow learning anymore
if learn and self._doneLearning:
raise RuntimeError("Learning can not be performed once finishLearning"
" has been called.")
assert (learn or infer)
assert (flatInput.ndim == 1) and (flatInput.shape[0] == self._inputCount)
assert (flatInput.dtype == realDType)
input = flatInput.reshape(self.inputShape)
# Make sure we've allocated the inhibition object lazily
if self._inhibitionObj is None:
self._updateInhibitionObj()
# Reset first timer
if self.printPeriodicStats > 0 and self._iterNum == 0:
self._periodicStatsReset()
# Using cloning?
cloningOn = (self.numCloneMasters != self._coincCount)
# If we have high verbosity, save the overlaps before stimulus threshold
# so we can print them out at the end
if self.spVerbosity >= 2:
print "==============================================================="
print "Iter:%d" % self._iterNum, "inferenceIter:%d" % \
self._inferenceIterNum
self._computeOverlapsFP(input, stimulusThreshold=0)
self._overlapsBST[:] = self._overlaps
connectedCountsOnEntry = self._masterConnectedCoincSizes.copy()
if self.spVerbosity >= 3:
inputNZ = flatInput.nonzero()[0]
print "active inputs: (%d)" % len(inputNZ), inputNZ
# ----------------------------------------------------------------------
# TODO: Port to C++, arguments may be different - t1YXArr,
# coincInputRadius,...
# Calculate the raw overlap of each cell
# Overlaps less than stimulus threshold are set to zero in
# _calculateOverlaps
# This places the result into self._overlaps
self._computeOverlapsFP(input, stimulusThreshold=self.stimulusThreshold)
# Save the original overlap values, before boosting, for the purpose of
# anomaly detection
if computeAnomaly:
self._anomalyScores[:] = self._overlaps[:]
if learn:
# ----------------------------------------------------------------------
# Update each cell's duty cycle before inhibition
# Only cells with overlaps greater stimulus threshold are considered as
# active.
# Stimulus threshold has already been applied
# TODO: Port to C++? Loops over all coincs
# Only updating is carried out here, bump up happens later
onCellIndices = numpy.where(self._overlaps > 0)
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
self._onCells.fill(0)
self._onCells[onCellIndices] = 1
denseOn = self._onCells
# dutyCyclePeriod = self._iterNum + 1 let _dutyCycleBeforeInh
# and _dutyCycleAfterInh represent real firing percentage at the
# beginning of learning. This will effect boosting and let unlearned
# coincidences have high boostFactor at beginning.
self.dutyCyclePeriod = min(self._iterNum + 1, 1000)
self._dutyCycleBeforeInh = ((self.dutyCyclePeriod-1) \
* self._dutyCycleBeforeInh + denseOn) / self.dutyCyclePeriod
# ----------------------------------------------------------------------
# Compute firing levels based on boost factor and raw overlap. Update
# self._overlaps in place, replacing it with the boosted overlap. We also
# computes percent overlap of each column and store that into
# self._pctOverlaps
# With cloning
if cloningOn:
self._pctOverlaps[:] = self._overlaps
self._pctOverlaps /= self._masterConnectedCoincSizes[self._cloneMapFlat]
boostFactors = self._firingBoostFactors[self._cloneMapFlat]
else:
self._pctOverlaps[:] = self._overlaps
potentials = self._masterConnectedCoincSizes
self._pctOverlaps /= numpy.maximum(1, potentials)
boostFactors = self._firingBoostFactors
# To process minDistance, we do the following:
# 1.) All cells which do not overlap the input "highly" (less than
# minDistance), are considered to be in the "low tier" and get their
# overlap multiplied by their respective boost factor.
# 2.) All other cells, which DO overlap the input highly, get a "high tier
# offset" added to their overlaps, and boost is not applied. The
# "high tier offset" is computed as the max of all the boosted
# overlaps from step #1. This insures that a cell in this high tier
# will never lose to a cell from the low tier.
# if self.useHighTier \
# and len(numpy.where(self._dutyCycleAfterInh == 0)[0]) == 0:
# self.useHighTier = False
if self.useHighTier:
highTier = numpy.where(self._pctOverlaps >= (1.0 - self.minDistance))[0]
else:
highTier = []
someInHighTier = len(highTier) > 0
if someInHighTier:
boostFactors = numpy.array(boostFactors)
boostFactors[highTier] = 1.0
# apply boostFactors only in learning phase not in inference phase.
if learn:
self._overlaps *= boostFactors
if someInHighTier:
highTierOffset = self._overlaps.max() + 1.0
self._overlaps[highTier] += highTierOffset
# Cache the dense output for debugging
if self._denseOutput is not None:
self._denseOutput = self._overlaps.copy()
# ----------------------------------------------------------------------
# Incorporate inhibition and see who is firing after inhibition.
# We don't need this method to process stimulusThreshold because we
# already processed it.
# Also, we pass in a small 'addToWinners' amount which gets added to the
# winning elements as we go along. This prevents us from choosing more than
# topN winners per inhibition region when more than topN elements all have
# the same max high score.
learnedCellsOverlaps = numpy.array(self._overlaps)
if infer and not learn:
# Cells that have never learnt are not allowed to win during inhibition
if not self.randomSP:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = 0
else:
#Boost the unlearned cells to 1000 so that the winning columns are picked randomly
#From the set of unlearned columns
#Boost columns that havent been learned with uniformly to 1000 so that inhibition picks
#randomly from them.
if self.useHighTier:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = learnedCellsOverlaps.max() + 1
# #Boost columns that are in highTier (ie. they match the input very well
learnedCellsOverlaps[highTier] += learnedCellsOverlaps.max() + 1
# Small random tiebreaker for columns with equal overlap
tieBreaker = numpy.random.rand(*learnedCellsOverlaps.shape).astype(realDType)
learnedCellsOverlaps += 0.1 * tieBreaker
numOn = self._inhibitionObj.compute(
learnedCellsOverlaps,
self._onCellIndices,
0.0, # stimulusThreshold
max(learnedCellsOverlaps)/1000.0, # addToWinners
)
self._onCells.fill(0)
if numOn > 0:
onCellIndices = self._onCellIndices[0:numOn]
self._onCells[onCellIndices] = 1
else:
onCellIndices = []
# Compute the anomaly scores only for the winning columns
if computeAnomaly:
self._anomalyScores *= self._onCells
self._anomalyScores *= self._dutyCycleAfterInh
if self.spVerbosity >= 2:
print "inhRadius", self._inhibitionObj.getInhibitionRadius()
print "inhLocalAreaDensity", self._inhibitionObj.getLocalAreaDensity()
print "numFiring", numOn
# ----------------------------------------------------------------------
# Capturing learning stats? If so, capture the cell overlap statistics
if self.printPeriodicStats > 0:
activePctOverlaps = self._pctOverlaps[onCellIndices]
self._stats['cellPctOverlapSums'] += activePctOverlaps.sum()
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
else:
onMasterIndices = onCellIndices
self._stats['cellOverlapSums'] += \
(activePctOverlaps * \
self._masterConnectedCoincSizes[onMasterIndices]).sum()
# ----------------------------------------------------------------------
# Compute which cells had very high overlap, but were still
# inhibited. These we are calling our "orphan cells", because they are
# representing an input which is already better represented by another
# cell.
if self.synPermOrphanDec > 0:
orphanCellIndices = set(numpy.where(self._pctOverlaps >= 1.0)[0])
orphanCellIndices.difference_update(onCellIndices)
else:
orphanCellIndices = []
if learn:
# ----------------------------------------------------------------------
# Update the number of coinc connections per input
# During learning (adapting permanence values), we need to be able to
# recognize dupe inputs - inputs that go two 2 or more active cells
if self.synPermActiveSharedDec != 0:
self._updateInputUse(onCellIndices)
# ----------------------------------------------------------------------
# For the firing cells, update permanence values
onMasterIndices = self._adaptSynapses(onCellIndices, orphanCellIndices,
input)
# ----------------------------------------------------------------------
# Increase the permanence values of columns which haven't passed
# stimulus threshold of overlap with at least a minimum frequency
self._bumpUpWeakCoincidences()
# ----------------------------------------------------------------------
# Update each cell's after-inhibition duty cycle
# TODO: As the on-cells are sparse after inhibition, we can have
# a different updateDutyCycles function taking advantage of the sparsity
if cloningOn:
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
denseOn = self._onCells
self._dutyCycleAfterInh = ((self.dutyCyclePeriod - 1) \
* self._dutyCycleAfterInh + denseOn) / self.dutyCyclePeriod
# For the cell's that just fired with a very high boost, bring their
# boost back down to 1.0. This prevents a cell that was trying very
# hard to grab input, and was just successful, from grabbing every
# other input that comes around in the near future.
# for masterNum in onMasterIndices:
# if self._firingBoostFactors[masterNum] > self.maxSSFiringBoost:
# self._dutyCycleAfterInh[masterNum] = self._dutyCycleAfterInh.max()/5
#Set the duty cycle to a safe margin over the boosting duty cycle but
#avoid artificially boosting it to a reconstruction significant level.
# self._dutyCycleAfterInh[masterNum] = self.minPctDutyCycleAfterInh*10
# ----------------------------------------------------------------------
# Update the boost factors based on firings rate after inhibition
self._updateBoostFactors()
# =======================================================================
# Increment iteration number and perform our periodic tasks if it's time
if ((self._iterNum + 1) % 50) == 0:
self._updateInhibitionObj()
self._updateMinDutyCycles(self._dutyCycleBeforeInh,
self.minPctDutyCycleBeforeInh, self._minDutyCycleBeforeInh)
self._updateMinDutyCycles(self._dutyCycleAfterInh,
self.minPctDutyCycleAfterInh, self._minDutyCycleAfterInh)
# Next iteration
if learn:
self._iterNum += 1
if infer:
self._inferenceIterNum += 1
if learn:
# =======================================================================
# Capture and possibly print the periodic stats
if self.printPeriodicStats > 0:
self._periodicStatsComputeEnd(onCellIndices, flatInput.nonzero()[0])
# Verbose print other stats
if self.spVerbosity >= 2:
cloning = (self.numCloneMasters != self._coincCount)
print " #connected on entry: ", fdru.numpyStr(connectedCountsOnEntry,
'%d ', includeIndices=True)
print " #connected on exit: ", fdru.numpyStr(
self._masterConnectedCoincSizes,
'%d ', includeIndices=True)
if self.spVerbosity >= 3 or not cloning:
print " overlaps: ", fdru.numpyStr(self._overlapsBST, '%d ',
includeIndices=True, includeZeros=False)
print " firing levels: ", fdru.numpyStr(self._overlaps, '%.4f ',
includeIndices=True, includeZeros=False)
print " on after inhibition: ", onCellIndices
if not self._doneLearning:
print " minDutyCycleBeforeInh:", fdru.numpyStr(
self._minDutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " dutyCycleBeforeInh: ", fdru.numpyStr(self._dutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " belowMinBeforeInh: " % numpy.nonzero(
self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh)[0]
print " minDutyCycleAfterInh: ", fdru.numpyStr(
self._minDutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " dutyCycleAfterInh: ", fdru.numpyStr(self._dutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " belowMinAfterInh: " % numpy.nonzero(
self._dutyCycleAfterInh \
< self._minDutyCycleAfterInh)[0]
print " firingBoosts: ", fdru.numpyStr(self._firingBoostFactors,
'%.4f ', includeIndices=True)
print
elif self.spVerbosity >= 1:
print "SP: learn: ", learn
print "SP: active outputs(%d): " % (len(onCellIndices)), onCellIndices
self._runIter += 1
# Return inference result
return self._onCells
#############################################################################
def __getstate__(self):
# Update our random states
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
state = self.__dict__.copy()
# Delete ephemeral members that we don't want pickled
for ephemeralMemberName in self._getEphemeralMembers():
if ephemeralMemberName in state:
del state[ephemeralMemberName]
return state
#############################################################################
def __setstate__(self, state):
self.__dict__.update(state)
# ----------------------------------------------------------------------
# Support older checkpoints
# These fields were added on 2010-10-05 and _iterNum was preserved
if not hasattr(self, '_randomState'):
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
self._iterNum = 0
# ------------------------------------------------------------------------
# Init our random number generators
random.setstate(self._randomState)
numpy.random.set_state(self._numpyRandomState)
self.random.setState(self._nupicRandomState)
# Load things that couldn't be pickled...
self._initEphemerals()
############################################################################
def getAnomalyScore(self):
""" Get the aggregate anomaly score for this input pattern
Returns: A single scalar value for the anomaly score
"""
numNonzero = len(numpy.nonzero(self._anomalyScores)[0])
return 1.0 / (numpy.sum(self._anomalyScores) + 1)
#############################################################################
def getLearningStats(self):
""" Return a dictionary containing a set of statistics related to learning.
Here is a list of what is returned:
'activeCountAvg':
The average number of active columns seen over the last
N training iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'underCoveragePct':
The average under-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'overCoveragePct':
The average over-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesAvg':
The overall average number of connection changes made per active
column per iteration, over the last N training iterations, where N
is set by the constructor parameter printPeriodicStats. This gives an
indication as to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMin':
The minimum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMax':
The maximum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'rfSize':
The average receptive field size of the columns.
'inhibitionRadius':
The average inihbition radius of the columns.
'targetDensityPct':
The most recent target local area density used, as a percent (0 -> 100)
'coincidenceSizeAvg':
The average learned coincidence size
'coincidenceSizeMin':
The minimum learned coincidence size
'coincidenceSizeMax':
The maximum learned coincidence size
'dcBeforeInhibitionAvg':
The average of duty cycle before inhbition of all coincidences
'dcBeforeInhibitionMin':
The minimum duty cycle before inhbition of all coincidences
'dcBeforeInhibitionAvg':
The maximum duty cycle before inhbition of all coincidences
'dcAfterInhibitionAvg':
The average of duty cycle after inhbition of all coincidences
'dcAfterInhibitionMin':
The minimum duty cycle after inhbition of all coincidences
'dcAfterInhibitionAvg':
The maximum duty cycle after inhbition of all coincidences
'firingBoostAvg':
The average firing boost
'firingBoostMin':
The minimum firing boost
'firingBoostMax':
The maximum firing boost
"""
# Fill in the stats that can be computed on the fly. The transient stats
# that depend on printPeriodicStats being on, have already been stored
self._learningStats['rfRadiusAvg'] = self._rfRadiusAvg
self._learningStats['rfRadiusMin'] = self._rfRadiusMin
self._learningStats['rfRadiusMax'] = self._rfRadiusMax
if self._inhibitionObj is not None:
self._learningStats['inhibitionRadius'] = \
self._inhibitionObj.getInhibitionRadius()
self._learningStats['targetDensityPct'] = \
100.0 * self._inhibitionObj.getLocalAreaDensity()
else:
print "Warning: No inhibitionObj found for getLearningStats"
self._learningStats['inhibitionRadius'] = 0.0
self._learningStats['targetDensityPct'] = 0.0
self._learningStats['coincidenceSizeAvg'] = \
self._masterConnectedCoincSizes.mean()
self._learningStats['coincidenceSizeMin'] = \
self._masterConnectedCoincSizes.min()
self._learningStats['coincidenceSizeMax'] = \
self._masterConnectedCoincSizes.max()
if not self._doneLearning:
self._learningStats['dcBeforeInhibitionAvg'] = \
self._dutyCycleBeforeInh.mean()
self._learningStats['dcBeforeInhibitionMin'] = \
self._dutyCycleBeforeInh.min()
self._learningStats['dcBeforeInhibitionMax'] = \
self._dutyCycleBeforeInh.max()
self._learningStats['dcAfterInhibitionAvg'] = \
self._dutyCycleAfterInh.mean()
self._learningStats['dcAfterInhibitionMin'] = \
self._dutyCycleAfterInh.min()
self._learningStats['dcAfterInhibitionMax'] = \
self._dutyCycleAfterInh.max()
self._learningStats['firingBoostAvg'] = \
self._firingBoostFactors.mean()
self._learningStats['firingBoostMin'] = \
self._firingBoostFactors.min()
self._learningStats['firingBoostMax'] = \
self._firingBoostFactors.max()
return self._learningStats
#############################################################################
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
random.seed(seed)
numpy.random.seed(seed)
else:
self.random = NupicRandom()
#############################################################################
def _initialPermanence(self):
""" Create and return a 2D matrix filled with initial permanence values.
The returned matrix will be of shape:
(2*coincInputRadius + 1, 2*coincInputRadius + 1).
The initial permanence values are set between 0 and 1.0, with enough chosen
above synPermConnected to make it highly likely that a cell will pass
stimulusThreshold, given the size of the potential RF, the input pool
sampling percentage, and the expected density of the active inputs.
If gaussianDist is True, the center of the matrix will contain the highest
permanence values and lower values will be farther from the center.
If gaussianDist is False, the highest permanence values will be evenly
distributed throughout the potential RF.
"""
# Figure out the target number of connected synapses. We want about 2X
# stimulusThreshold
minOn = 2 * max(self.stimulusThreshold, 10) / self.coincInputPoolPct \
/ self.inputDensity
# ========================================================================
# Get the gaussian distribution, with max magnitude just slightly above
# synPermConnected. Try to find a sigma that gives us about 2X
# stimulusThreshold connected synapses after sub-sampling for
# coincInputPoolPct. We will assume everything within +/- sigma will be
# connected. This logic uses the fact that an x value of sigma generates a
# magnitude of 0.6.
if self.gaussianDist:
# Only supported when we have 2D layouts
if self._coincRFShape[0] != self._coincRFShape[1]:
raise RuntimeError("Gaussian distibuted permanences are currently only"
"supported for 2-D layouts")
# The width and height of the center "blob" in inputs is the square root
# of the area
onAreaDim = numpy.sqrt(minOn)
# Sigma is at the edge of the center blob
sigma = onAreaDim/2
# Create the gaussian with a value of 1.0 at the center
perms = self._gaussianMatrix(dim=max(self._coincRFShape), sigma=sigma)
# The distance between the min and max values within the gaussian will
# be given by 'grange'. In a gaussian, the value at sigma away from the
# center is 0.6 * the value at the center. We want the values at sigma
# to be synPermConnected
maxValue = 1.0 / 0.6 * self.synPermConnected
perms *= maxValue
perms.shape = (-1,)
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# farther away than 2 sigma to 0. The value of a gaussing at 2 sigma
# is 0.135 * the value at the center
perms[perms < (0.135 * maxValue)] = 0
# ========================================================================
# Evenly distribute the permanences through the RF
else:
# Create a random distribution from 0 to 1.
perms = numpy.random.random(self._coincRFShape)
perms = perms.astype(realDType)
# Set the range of values to be between 0 and
# synPermConnected+synPermInctiveDec. This ensures that a pattern
# will always be learned in 1 iteration
maxValue = min(1.0, self.synPermConnected + self.synPermInactiveDec)
# What percentage do we want to be connected?
connectPct = 0.50
# What value from the 0 to 1 distribution will map to synPermConnected?
threshold = 1.0 - connectPct
# Which will be the connected and unconnected synapses?
connectedSyns = perms >= threshold
unconnectedSyns = numpy.logical_not(connectedSyns)
# Squeeze all values between threshold and 1.0 to be between
# synPermConnected and synPermConnected + synPermActiveInc / 4
# This makes sure the firing coincidence perms matching input bit get
# greater than synPermConnected and other unconnectedSyns get deconnected
# in one firing learning iteration.
srcOffset = threshold
srcRange = 1.0 - threshold
dstOffset = self.synPermConnected
dstRange = maxValue - self.synPermConnected
perms[connectedSyns] = (perms[connectedSyns] - srcOffset)/srcRange \
* dstRange / 4.0 + dstOffset
# Squeeze all values between 0 and threshold to be between 0 and
# synPermConnected
srcRange = threshold - 0.0
dstRange = self.synPermConnected - 0.0
perms[unconnectedSyns] = perms[unconnectedSyns]/srcRange \
* dstRange
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# below synPermActiveInc/2 to 0
perms[perms < (self.synPermActiveInc / 2.0)] = 0
perms.shape = (-1,)
return perms
#############################################################################
def _gaussianMatrix(self, dim, sigma):
"""
Create and return a 2D matrix filled with a gaussian distribution. The
returned matrix will be of shape (dim, dim). The mean of the gaussian
will be in the center of the matrix and have a value of 1.0.
"""
gaussian = lambda x,sigma: numpy.exp(-(x**2)/(2*(sigma**2)))
# Allocate the matrix
m = numpy.empty((dim, dim), dtype=realDType)
# Find the center
center = (dim - 1) / 2.0
# TODO: Simplify using numpy.meshgrid
# Fill it in
for y in xrange(dim):
for x in xrange(dim):
dist = numpy.sqrt((x-center)**2 + (y-center)**2)
m[y,x] = gaussian(dist, sigma)
return m
#############################################################################
def _makeMasterCoincidences(self, numCloneMasters, coincRFShape,
coincInputPoolPct, initialPermanence=None,
nupicRandom=None):
"""Make the master coincidence matrices and mater input histograms.
# TODO: Update this example
>>> FDRCSpatial._makeMasterCoincidences(1, 2, 0.33)
(array([[[ True, True, False, False, False],
[False, True, False, False, True],
[False, True, False, False, False],
[False, False, False, True, False],
[ True, False, False, False, False]]], dtype=bool), array([[[ 0.26982325, 0.19995725, 0. , 0. , 0. ],
[ 0. , 0.94128972, 0. , 0. , 0.36316112],
[ 0. , 0.06312726, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0.29740077, 0. ],
[ 0.81071907, 0. , 0. , 0. , 0. ]]], dtype=float32))
"""
if nupicRandom is None:
nupicRandom = NupicRandom(42)
if initialPermanence is None:
initialPermanence = self._initialPermanence()
coincRfArea = (coincRFShape[0] * coincRFShape[1])
coincInputPool = coincInputPoolPct * coincRfArea
# We will generate a list of sparse matrices
masterPotentialM = []
masterPermanenceM = []
toSample = numpy.arange(coincRfArea, dtype='uint32')
toUse = numpy.empty(coincInputPool, dtype='uint32')
#denseM = numpy.zeros(coincRfArea, dtype='uint32')
denseM = numpy.zeros(coincRfArea, dtype=realDType)
for i in xrange(numCloneMasters):
nupicRandom.getUInt32Sample(toSample, toUse)
# Put in 1's into the potential locations
denseM.fill(0)
denseM[toUse] = 1
masterPotentialM.append(SM_01_32_32(denseM.reshape(coincRFShape)))
# Put in the initial permanences
denseM *= initialPermanence
masterPermanenceM.append(SM32(denseM.reshape(coincRFShape)))
# If we are not using common initial permanences, create another
# unique one for the next cell
if not self.commonDistributions:
initialPermanence = self._initialPermanence()
return masterPotentialM, masterPermanenceM
#############################################################################
def _updateConnectedCoincidences(self, masters=None):
"""Update 'connected' version of the given coincidence.
Each 'connected' coincidence is effectively a binary matrix (AKA boolean)
matrix that is the same size as the input histogram matrices. They have
a 1 wherever the inputHistogram is "above synPermConnected".
"""
# If no masterNum given, update all of them
if masters is None:
masters = xrange(self.numCloneMasters)
(nCellRows, nCellCols) = self._coincRFShape
cloningOn = (self.numCloneMasters != self._coincCount)
for masterNum in masters:
# Where are we connected?
masterConnectedNZ = \
self._masterPermanenceM[masterNum].whereGreaterEqual(0,
nCellRows, 0, nCellCols, self.synPermConnected)
rowIdxs = masterConnectedNZ[:,0]
colIdxs = masterConnectedNZ[:,1]
self._masterConnectedM[masterNum].setAllNonZeros(nCellRows, nCellCols,
rowIdxs, colIdxs)
self._masterConnectedCoincSizes[masterNum] = len(rowIdxs)
# Update the corresponding rows in the super, mondo connected matrix that
# come from this master
masterConnected = \
self._masterConnectedM[masterNum].toDense().astype('bool') # 0.2s
if cloningOn:
cells = self._cellsForMaster[masterNum]
else:
cells = [masterNum]
for cell in cells:
inputSlice = self._inputSlices[cell]
coincSlice = self._coincSlices[cell]
masterSubset = masterConnected[coincSlice]
sparseCols = self._inputLayout[inputSlice][masterSubset]
self._allConnectedM.replaceSparseRow(cell, sparseCols) # 4s.
#############################################################################
def _setSlices(self):
"""Compute self._columnSlices and self._inputSlices
self._inputSlices are used to index into the input (assuming it's been
shaped to a 2D array) to get the receptive field of each column. There
is one item in the list for each column.
self._coincSlices are used to index into the coinc (assuming it's been
shaped to a 2D array) to get the valid area of the column. There
is one item in the list for each column.
This function is called upon unpickling, since we can't pickle slices.
"""
self._columnCenters = numpy.array(self._computeCoincCenters(self.inputShape,
self.coincidencesShape, self.inputBorder))
coincInputRadius = self.coincInputRadius
(coincHeight, coincWidth) = self._coincRFShape
inputShape = self.inputShape
inputBorder = self.inputBorder
# ---------------------------------------------------------------------
# Compute the input slices for each cell. This is the slice of the entire
# input which intersects with the cell's permanence matrix.
if self._hasTopology:
self._inputSlices = [numpy.s_[max(0, cy-coincInputRadius): \
min(inputShape[0], cy+coincInputRadius + 1),
max(0, cx-coincInputRadius): \
min(inputShape[1], cx+coincInputRadius + 1)]
for (cy, cx) in self._columnCenters]
else:
self._inputSlices = [numpy.s_[0:inputShape[0], 0:inputShape[1]]
for (cy, cx) in self._columnCenters]
self._inputSlices2 = numpy.zeros((4*len(self._inputSlices)), dtype="uint32")
k = 0
for i in range(len(self._inputSlices)):
self._inputSlices2[k] = self._inputSlices[i][0].start
self._inputSlices2[k + 1] = self._inputSlices[i][0].stop
self._inputSlices2[k + 2] = self._inputSlices[i][1].start
self._inputSlices2[k + 3] = self._inputSlices[i][1].stop
k = k + 4
# ---------------------------------------------------------------------
# Compute the coinc slices for each cell. This is which portion of the
# cell's permanence matrix intersects with the input.
if self._hasTopology:
if self.inputShape[0] > 1:
self._coincSlices = [numpy.s_[max(0, coincInputRadius - cy): \
min(coincHeight, coincInputRadius + inputShape[0] - cy),
max(0, coincInputRadius-cx): \
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:1, max(0, coincInputRadius-cx): \
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:coincHeight, 0:coincWidth]
for (cy, cx) in self._columnCenters]
self._coincSlices2 = numpy.zeros((4*len(self._coincSlices)), dtype="uint32")
k = 0
for i in range(len(self._coincSlices)):
self._coincSlices2[k] = self._coincSlices[i][0].start
self._coincSlices2[k + 1] = self._coincSlices[i][0].stop
self._coincSlices2[k + 2] = self._coincSlices[i][1].start
self._coincSlices2[k + 3] = self._coincSlices[i][1].stop
k = k + 4
#############################################################################
@staticmethod
def _computeCoincCenters(inputShape, coincidencesShape, inputBorder):
"""Compute the centers of all coincidences, given parameters.
This function is semi-public: tools may use it to generate good
visualizations of what the FDRCSpatial node is doing.
NOTE: It must be static or global function so that it can be called by
the ColumnActivityTab inspector *before* the first compute (before the
SP has been constructed).
If the input shape is (7,20), shown below with * for each input
********************
********************
********************
********************
********************
********************
********************
if inputBorder is 1, we distribute the coincidences evenly over the
the area after removing the edges, @ shows the allowed input area below
********************
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
********************
each coincidence is centered at the closest @ and looks at a area with
coincInputRadius below it
This function call returns an iterator over the coincidence centers. Each
element in iterator is a tuple: (y, x). The iterator returns elements in a
fixed order.
"""
# Determine Y centers
if inputShape[0] > 1: # 2-D layout
startHeight = inputBorder
stopHeight = inputShape[0] - inputBorder
else:
startHeight = stopHeight = 0
heightCenters = numpy.linspace(startHeight,
stopHeight,
coincidencesShape[0],
endpoint=False).astype('int32')
# Determine X centers
startWidth = inputBorder
stopWidth = inputShape[1] - inputBorder
widthCenters = numpy.linspace(startWidth,
stopWidth,
coincidencesShape[1],
endpoint=False).astype('int32')
return list(cross(heightCenters, widthCenters))
#############################################################################
def _updateInhibitionObj(self):
"""
Calculate the average inhibitionRadius to use and update the inhibition
object accordingly. This looks at the size of the average connected
receptive field and uses that to determine the inhibition radius.
"""
# ========================================================================
# Compute the inhibition radius.
# If using global inhibition, just set it to include the entire region
if self.globalInhibition:
avgRadius = max(self.coincidencesShape)
# Else, set it based on the average size of the connected synapses area in
# each cell.
else:
totalDim = 0
# Get the dimensions of the connected receptive fields of each cell to
# compute the average
minDim = numpy.inf
maxDim = 0
for masterNum in xrange(self.numCloneMasters):
masterConnected = self._masterConnectedM[masterNum]
nzs = masterConnected.getAllNonZeros()
(rows, cols) = zip(*nzs)
rows = numpy.array(rows)
cols = numpy.array(cols)
if len(rows) >= 2:
height = rows.max() - rows.min() + 1
else:
height = 1
if len(cols) >= 2:
width = cols.max() - cols.min() + 1
else:
width = 1
avgDim = (height + width) / 2.0
minDim = min(minDim, avgDim)
maxDim = max(maxDim, avgDim)
totalDim += avgDim
# Get average width/height in input space
avgDim = totalDim / self.numCloneMasters
self._rfRadiusAvg = (avgDim - 1.0) / 2.0
self._rfRadiusMin = (minDim - 1.0) / 2.0
self._rfRadiusMax = (maxDim - 1.0) / 2.0
# How many columns in cell space does it correspond to?
if self.inputShape[0] > 1: # 2-D layout
coincsPerInputX = float(self.coincidencesShape[1]) \
/ (self.inputShape[1] - 2*self.inputBorder)
coincsPerInputY = float(self.coincidencesShape[0]) \
/ (self.inputShape[0] - 2*self.inputBorder)
else:
coincsPerInputX = coincsPerInputY = \
float(self.coincidencesShape[1] * self.coincidencesShape[0]) \
/ (self.inputShape[1] - 2*self.inputBorder)
avgDim *= (coincsPerInputX + coincsPerInputY) / 2
avgRadius = (avgDim - 1.0) / 2.0
avgRadius = max(1.0, avgRadius)
# Can't be greater than the overall width or height of the level
maxDim = max(self.coincidencesShape)
avgRadius = min(avgRadius, maxDim)
avgRadius = int(round(avgRadius))
# ========================================================================
# Is there a need to re-instantiate the inhibition object?
if self._inhibitionObj is None \
or self._inhibitionObj.getInhibitionRadius() != avgRadius:
# What is our target density?
if self.localAreaDensity > 0:
localAreaDensity = self.localAreaDensity
else:
numCellsPerInhArea = (avgRadius * 2.0 + 1.0) ** 2
totalCells = self.coincidencesShape[0] * self.coincidencesShape[1]
numCellsPerInhArea = min(numCellsPerInhArea, totalCells)
localAreaDensity = float(self.numActivePerInhArea) / numCellsPerInhArea
# Don't let it be greater than 0.50
localAreaDensity = min(localAreaDensity, 0.50)
if self.spVerbosity >= 2:
print "Updating inhibition object:"
print " avg. rfRadius:", self._rfRadiusAvg
print " avg. inhRadius:", avgRadius
print " Setting density to:", localAreaDensity
self._inhibitionObj = Inhibition2(self.coincidencesShape[0], # height
self.coincidencesShape[1], # width
avgRadius, # inhRadius
localAreaDensity) # density
#############################################################################
def _updateMinDutyCycles(self, actDutyCycles, minPctDutyCycle, minDutyCycles):
"""
Calculate and update the minimum acceptable duty cycle for each cell based
on the duty cycles of the cells within its inhibition radius and the
minPctDutyCycle.
Parameters:
-----------------------------------------------------------------------
actDutyCycles: The actual duty cycles of all cells
minPctDutyCycle: Each cell's minimum duty cycle will be set to
minPctDutyCycle times the duty cycle of the most active
cell within its inhibition radius
minDutyCycles: This array will be updated in place with the new minimum
acceptable duty cycles
"""
# What is the inhibition radius?
inhRadius = self._inhibitionObj.getInhibitionRadius()
# Reshape the actDutyCycles to match the topology of the level
cloningOn = (self.numCloneMasters != self._coincCount)
if not cloningOn:
actDutyCycles = actDutyCycles.reshape(self.coincidencesShape)
minDutyCycles = minDutyCycles.reshape(self.coincidencesShape)
# Special, faster handling when inhibition radius includes the entire
# set of cells
if cloningOn or inhRadius >= max(self.coincidencesShape):
minDutyCycle = minPctDutyCycle * actDutyCycles.max()
minDutyCycles.fill(minPctDutyCycle * actDutyCycles.max())
# Else, process each cell
else:
(numRows, numCols) = self.coincidencesShape
for row in xrange(numRows):
top = max(0, row - inhRadius)
bottom = min(row + inhRadius + 1, numRows)
for col in xrange(numCols):
left = max(0, col - inhRadius)
right = min(col + inhRadius + 1, numCols)
maxDutyCycle = actDutyCycles[top:bottom, left:right].max()
minDutyCycles[row, col] = maxDutyCycle * minPctDutyCycle
if self.spVerbosity >= 2:
print "Actual duty cycles:"
print fdru.numpyStr(actDutyCycles, '%.4f')
print "Recomputed min duty cycles, using inhRadius of", inhRadius
print fdru.numpyStr(minDutyCycles, '%.4f')
#############################################################################
def _computeOverlapsPy(self, inputShaped, stimulusThreshold):
"""
Computes overlaps for every column for the current input in place. The
overlaps less than stimulus threshold are set to zero here.
For columns with input RF going off the edge of input field, only regions
within the input field are considered. This is equivalent to padding the
input field with zeros.
Parameters:
------------------------------------------------------------------------
inputShaped: input at the current time step, shaped to the input
topology
stimulusThreshold: stimulusThreshold to use
Member variables used/updated:
------------------------------------------------------------------------
_inputSlices: Index into the input (assuming it's been shaped to a 2D
array) to get the receptive field of each column.
_coincSlices: Index into the coinc (assuming it's been shaped to a 2D
array) to get the valid region of each column.
_overlaps: Result is placed into this array which holds the overlaps of
each column with the input
"""
flatInput = inputShaped.reshape(-1)
self._allConnectedM.rightVecSumAtNZ_fast(flatInput, self._overlaps)
# Apply stimulusThreshold
# TODO: Is there a faster numpy operation for this?
self._overlaps[self._overlaps < stimulusThreshold] = 0
self._overlapsNoBoost = self._overlaps.copy()
#############################################################################
def _computeOverlapsCPP(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but using a C++ implementation.
"""
cpp_overlap(self._cloneMapFlat,
self._inputSlices2, self._coincSlices2,
inputShaped, self._masterConnectedM,
stimulusThreshold,
self._overlaps);
#############################################################################
def _computeOverlapsTest(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but compares the python and C++
implementations.
"""
# Py version
self._computeOverlapsPy(inputShaped, stimulusThreshold)
overlaps2 = copy.deepcopy(self._overlaps)
# C++ version
self._computeOverlapsCPP(inputShaped, stimulusThreshold)
if (abs(self._overlaps - overlaps2) > 1e-6).any():
print self._overlaps, overlaps2, abs(self._overlaps - overlaps2)
import pdb; pdb.set_trace()
sys.exit(0)
#############################################################################
def _raiseAllPermanences(self, masterNum, minConnections=None,
densePerm=None, densePotential=None):
"""
Raise all permanences of the given master. If minConnections is given, the
permanences will be raised until at least minConnections of them are
connected strength.
If minConnections is left at None, all permanences will be raised by
self._synPermBelowStimulusInc.
After raising all permanences, we also "sparsify" the permanence matrix
and set to 0 any permanences which are already very close to 0, this
keeps the memory requirements of the sparse matrices used to store
the permanences lower.
Parameters:
----------------------------------------------------------------------------
masterNum: Which master to bump up
minConnections: Desired number of connected synapses to have
If None, then all permanences are simply bumped up
by self._synPermBelowStimulusInc
densePerm: The dense representation of the master's permanence
matrix, if available. If not specified, we will
create this from the stored sparse representation.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix. The stored sparse
matrix will ALWAYS be updated from the densePerm if
the densePerm is provided.
densePotential: The dense representation of the master's potential
synapses matrix, if available. If not specified, we
will create this from the stored sparse potential
matrix.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix.
retval: (modified, numConnections)
modified: True if any permanences were raised
numConnections: Number of actual connected synapses
(not computed if minConnections was
None, so None is returned in that
case.)
"""
# It's faster to perform this operation on the dense matrices and
# then convert to sparse once we're done since we will be potentially
# introducing and then later removing a bunch of non-zeros.
# -------------------------------------------------------------------
# Get references to the sparse perms and potential syns for this master
sparsePerm = self._masterPermanenceM[masterNum]
sparsePotential = self._masterPotentialM[masterNum]
# We will trim off all synapse permanences below this value to 0 in order
# to keep the memory requirements of the SparseMatrix lower
trimThreshold = self.synPermActiveInc / 2.0
# -------------------------------------------------------------------
# See if we already have the required number of connections. If we don't,
# get the dense form of the permanences if we don't have them already
if densePerm is None:
# See if we already have enough connections, if so, we can avoid the
# overhead of converting to dense
if minConnections is not None:
numConnected = sparsePerm.countWhereGreaterEqual(
0, self._coincRFShape[0], 0, self._coincRFShape[1],
self.synPermConnected)
if numConnected >= minConnections:
return (False, numConnected)
densePerm = self._masterPermanenceM[masterNum].toDense()
elif minConnections is not None:
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (False, numConnected)
# Get the dense form of the potential synapse locations
if densePotential is None:
densePotential = self._masterPotentialM[masterNum].toDense()
# -------------------------------------------------------------------
# Form the array with the increments
incrementM = densePotential.astype(realDType)
incrementM *= self._synPermBelowStimulusInc
# -------------------------------------------------------------------
# Increment until we reach our target number of connections
assert (densePerm.dtype == realDType)
while True:
densePerm += incrementM
if minConnections is None:
numConnected = None
break
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
break
# -------------------------------------------------------------------
# Convert back to sparse form and trim any values that are already
# close to zero
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (True, numConnected)
#############################################################################
def _bumpUpWeakCoincidences(self):
"""
This bump-up ensures every coincidence have non-zero connections. We find
all coincidences which have overlaps less than stimulus threshold.
We add synPermActiveInc to all the synapses. This step when repeated over
time leads to synapses crossing synPermConnected threshold.
"""
# Update each cell's connected threshold based on the duty cycle before
# inhibition. The connected threshold is linearly interpolated
# between the points (dutyCycle:0, thresh:0) and (dutyCycle:minDuty,
# thresh:synPermConnected). This is a line defined as: y = mx + b
# thresh = synPermConnected/minDuty * dutyCycle
bumpUpList = (self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh).nonzero()[0]
for master in bumpUpList:
self._raiseAllPermanences(master)
# Update the connected synapses for each master we touched
self._updateConnectedCoincidences(bumpUpList)
if self.spVerbosity >= 2 and len(bumpUpList) > 0:
print "Bumping up permanences in following cells due to falling below" \
"minDutyCycleBeforeInh:", bumpUpList
#############################################################################
def _updateBoostFactors(self):
"""
Update the boost factors. The boost factors is linearly interpolated
between the points (dutyCycle:0, boost:maxFiringBoost) and
(dutyCycle:minDuty, boost:1.0). This is a line defined as: y = mx + b
boost = (1-maxFiringBoost)/minDuty * dutyCycle + maxFiringBoost
Parameters:
------------------------------------------------------------------------
boostFactors: numpy array of boost factors, defined per master
"""
if self._minDutyCycleAfterInh.sum() > 0:
self._firingBoostFactors = (1 - self.maxFiringBoost)\
/self._minDutyCycleAfterInh * self._dutyCycleAfterInh \
+ self.maxFiringBoost
self._firingBoostFactors[self._dutyCycleAfterInh \
> self._minDutyCycleAfterInh] = 1.0
# if self._dutyCycleAfterInh.min() == 0: # where there are unlearned coincs
# self._firingBoostFactors[self._dutyCycleAfterInh == 0] = \
# self.maxFiringBoost
#############################################################################
def _updateInputUse(self, onCellIndices):
"""
During learning (adapting permanence values), we need to be able to tell
which inputs are going to 2 or more active cells at once.
We step through each coinc and mark all the inputs it is connected to. The
inputUse array acts as a counter for the number of connections to the coincs
from each input.
Parameters:
------------------------------------------------------------------------
inputUse: numpy array of number of coincs connected to each input
"""
allConnected = SM32(self._allConnectedM)
# TODO: avoid this copy
self._inputUse[:] = allConnected.addListOfRows(
onCellIndices).reshape(self.inputShape)
#############################################################################
def _adaptSynapses(self, onCellIndices, orphanCellIndices, input):
"""
This is the main function in learning of SP. The permanence values are
changed based on the learning rules.
Parameters:
------------------------------------------------------------------------
onCellIndices: columns which are turned on after inhibition. The
permanence values of these coincs are adapted based on the
input.
orphanCellIndices: columns which had very high overlap with the input, but
ended up being inhibited
input: Input, shaped to the input topology
retval: list of masterCellIndices that were actually updated, or
None if cloning is off
"""
# Capturing learning stats?
if self.printPeriodicStats > 0:
self._stats['explainedInputsCurIteration'] = set()
# Precompute the active, inactive, and dupe inputs up front for speed
# TODO: put these into pre-allocated arrays for speed
self._activeInput[:] = input
# Create a matrix containing the default permanence deltas for each input
self._permChanges.fill(-1 * self.synPermInactiveDec)
self._permChanges[self._activeInput] = self.synPermActiveInc
if self.synPermActiveSharedDec != 0:
numpy.logical_and(self._activeInput, self._inputUse>1, self._dupeInput)
self._permChanges[self._dupeInput] -= self.synPermActiveSharedDec
# Cloning? If so, scramble the onCells so that we pick a random one to
# update for each master. We only update a master cell at most one time
# per input presentation.
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
# Scramble the onCellIndices so that we pick a random one to update
onCellIndices = list(onCellIndices)
random.shuffle(onCellIndices)
visitedMasters = set()
# For the firing cells, update permanence values
for columnNum in itertools.chain(onCellIndices, orphanCellIndices):
# Get the master number
masterNum = self._cloneMapFlat[columnNum]
# If cloning, only visit each master once
if cloningOn:
if masterNum in visitedMasters:
continue
visitedMasters.add(masterNum)
# Get the slices of input that overlap with the valid area of this master
inputSlice = self._inputSlices[columnNum]
rfActiveInput = self._activeInput[inputSlice]
rfPermChanges = self._permChanges[inputSlice]
# Get the potential synapses, permanence values, and connected synapses
# for this master
masterPotential = self._masterPotentialM[masterNum].toDense()
masterPermanence = self._masterPermanenceM[masterNum].toDense()
masterConnected = \
self._masterConnectedM[masterNum].toDense().astype('bool')
# Make changes only over the areas that overlap the input level. For
# coincidences near the edge of the level for example, this excludes the
# synapses outside the edge.
coincSlice = self._coincSlices[columnNum]
masterValidPermanence= masterPermanence[coincSlice]
# Capturing learning stats?
if self.printPeriodicStats > 0:
masterValidConnected = masterConnected[coincSlice]
explainedInputs = self._inputLayout[inputSlice][masterValidConnected]
self._stats['explainedInputsCurIteration'].update(explainedInputs)
if self.spVerbosity >= 3:
print " adapting cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " initialConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
print " firingLevel: %d" % (self._overlaps[columnNum])
print " firingBoostFactor: %f" % (self._firingBoostFactors[masterNum])
print " input slice: \n"
self._printInputSlice(rfActiveInput, prefix=' ')
# Update permanences given the active input (NOTE: The "FP" in this
# function name stands for "Function Pointer").
if columnNum in orphanCellIndices:
# Decrease permanence of active inputs
masterValidPermanence[rfActiveInput] -= self.synPermOrphanDec
else:
self._updatePermanenceGivenInputFP(columnNum, masterNum, input,
self._inputUse, masterPermanence, masterValidPermanence,
rfActiveInput, rfPermChanges)
# Clip to absolute min and max permanence values
numpy.clip(masterPermanence, self._synPermMin, self._synPermMax,
out=masterPermanence)
# Keep only the potential syns for this cell
numpy.multiply(masterPermanence, masterPotential, masterPermanence)
# If we are tracking learning stats, prepare to see how many changes
# were made to the cell connections
if self.printPeriodicStats > 0:
masterConnectedOrig = SM_01_32_32(self._masterConnectedM[masterNum])
# ---------------------------------------------------------------------
# If the number of connected synapses happens to fall below
# stimulusThreshold, bump up all permanences a bit.
# We could also just wait for the "duty cycle falls below
# minDutyCycleBeforeInb" logic to catch it, but doing it here is
# pre-emptive and much faster.
#
# The "duty cycle falls below minDutyCycleBeforeInb" logic will still
# catch other possible situations, like:
# * if the set of inputs a cell learned suddenly stop firing due to
# input statistic changes
# * damage to the level below
# * input is very sparse and we still don't pass stimulusThreshold even
# with stimulusThreshold conneted synapses.
self._raiseAllPermanences(masterNum,
minConnections=self.stimulusThreshold,
densePerm=masterPermanence,
densePotential=masterPotential)
# Update the matrices that contain the connected syns for this cell.
self._updateConnectedCoincidences([masterNum])
# If we are tracking learning stats, see how many changes were made to
# this cell's connections
if self.printPeriodicStats > 0:
origNumConnections = masterConnectedOrig.nNonZeros()
masterConnectedOrig.logicalAnd(self._masterConnectedM[masterNum])
numUnchanged = masterConnectedOrig.nNonZeros()
numChanges = origNumConnections - numUnchanged
numChanges += self._masterConnectedM[masterNum].nNonZeros() \
- numUnchanged
self._stats['numChangedConnectionsSum'][masterNum] += numChanges
self._stats['numLearns'][masterNum] += 1
# Verbose?
if self.spVerbosity >= 3:
print " done cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " newConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
self._printSyns(columnNum, prefix=' ',
showValues=(self.spVerbosity >= 4))
print
# Return list of updated masters
if cloningOn:
return list(visitedMasters)
else:
return onCellIndices
#############################################################################
def _updatePermanenceGivenInputPy(self, columnNum, masterNum, input, inputUse,
permanence, permanenceSlice, activeInputSlice, permChangesSlice):
"""
Given the input to a master coincidence, update it's permanence values
based on our learning rules.
On Entry, we are given the slice of the permanence matrix that corresponds
only to the area of the coincidence master that is within the borders of
the entire input field.
Parameters:
------------------------------------------------------------------------
columnNum: The column number of this cell
masterNum: The master coincidence that corresponds to this column
input: The entire input, shaped appropriately
inputUse: The same shape as input. Each entry is a count of the
number of *currently active cells* that are connected
to that input.
permanence: The entire masterPermanence matrix for this master
permanenceSlice: The slice of the masterPermanence matrix for this master
that intersects the input field, i.e. does not overhang
the outside edges of the input.
activeInputSlice: The portion of 'input' that intersects permanenceSlice,
set to True where input != 0
permChangesSlice: The portion of 'input' that intersects permanenceSlice,
set to self.synPermActiveInc where input != 0 and
self.synPermInactiveDec where the input == 0. This is
used to optimally apply self.synPermActiveInc and
self.synPermInactiveDec at the same time and can be
used for any cell whose _synPermBoostFactor is set
to 1.0.
"""
# Apply the baseline increment/decrements
permanenceSlice += permChangesSlice
# If this cell has permanence boost, apply the incremental
#############################################################################
def _updatePermanenceGivenInputCPP(self, columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but using a C++ implementation.
"""
inputNCols = self.inputShape[1]
masterNCols = self._masterPotentialM[masterNum].shape[1]
#TODO: synPermBoostFactors has been removed. CPP implementation has not been updated for this.
adjustMasterValidPermanence(columnNum,
masterNum,
inputNCols,
masterNCols,
self.synPermActiveInc,
self.synPermInactiveDec,
self.synPermActiveSharedDec,
input,
inputUse,
self._inputSlices2,
self._coincSlices2,
self._synPermBoostFactors,
permanence)
#############################################################################
def _updatePermanenceGivenInputTest(self, columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but compares the python and C++
implementations.
"""
mp2 = copy.deepcopy(permanence)
mvp2 = copy.deepcopy(permanenceSlice)
# Py version
import pdb; pdb.set_trace()
self._updatePermanenceGivenInputPy(columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice)
# C++ version
self._updatePermanenceGivenInputCPP(columnNum, masterNum, input,
inputUse, mp2, mvp2, activeInputSlice, permChangesSlice)
if abs(mp2 - permanence).max() > 1e-6:
print abs(mp2 - permanence).max()
import pdb; pdb.set_trace()
sys.exit(0)
#############################################################################
def _periodicStatsCreate(self):
"""
Allocate the periodic stats structure
Parameters:
------------------------------------------------------------------
"""
self._stats = dict()
self._stats['numChangedConnectionsSum'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['numLearns'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['maxBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# This dict maintains mappings of specific input patterns to specific
# output patterns. It is used to detect "thrashing" of cells. We measure
# how similar the output presentation of a specific input is to the
# last time we saw it.
self._stats['inputPatterns'] = dict()
self._stats['inputPatternsLimit'] = 5000
self._periodicStatsReset()
#############################################################################
def _periodicStatsReset(self):
"""
Reset the periodic stats this is done every N iterations before capturing
a new set of stats
Parameters:
------------------------------------------------------------------
"""
self._stats['numSamples'] = 0
self._stats['numOnSum'] = 0
self._stats['underCoveragePctSum'] = 0
self._stats['overCoveragePctSum'] = 0
self._stats['cellOverlapSums'] = 0
self._stats['cellPctOverlapSums'] = 0
self._stats['explainedInputsCurIteration'] = set()
self._stats['startTime'] = time.time()
# These keep a count of the # of changed connections per update
# for each master
self._stats['numChangedConnectionsSum'].fill(0)
self._stats['numLearns'].fill(0)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'].fill(self.maxFiringBoost)
self._stats['maxBoostFactor'].fill(0)
# This keeps track of the average distance between the SP output of
# a specific input pattern now and the last time we saw it.
self._stats['outputPatternDistanceSum'] = 0
self._stats['outputPatternSamples'] = 0
#############################################################################
def _periodicStatsComputeEnd(self, activeCells, activeInputs):
"""
Called at the end of compute. This increments the number of computes
and also summarizes the under and over coverage and whatever other
periodic stats we need.
If the period is up, it then prints the accumuated stats and resets them
for the next period
Parameters:
------------------------------------------------------------------
activeCells: list of the active cells
activeInputs: list of the active inputs
"""
# Update number of samples
self._stats['numSamples'] += 1
# Compute under and over coverage
numOn = len(activeCells)
self._stats['numOnSum'] += numOn
expInput = self._stats['explainedInputsCurIteration']
inputLen = len(activeInputs)
underCoverage = len(set(activeInputs).difference(expInput))
self._stats['underCoveragePctSum'] += float(underCoverage) / inputLen
expInput.difference_update(activeInputs)
overCoverage = len(expInput)
self._stats['overCoveragePctSum'] += float(overCoverage) / inputLen
# Keep track of the min and max boost factor seen for each column
numpy.minimum(self._firingBoostFactors, self._stats['minBoostFactor'],
self._stats['minBoostFactor'])
numpy.maximum(self._firingBoostFactors, self._stats['maxBoostFactor'],
self._stats['maxBoostFactor'])
# Calculate the distance in the SP output between this input now
# and the last time we saw it.
inputPattern = str(sorted(activeInputs))
(outputNZ, sampleIdx) = \
self._stats['inputPatterns'].get(inputPattern, (None, None))
activeCellSet = set(activeCells)
if outputNZ is not None:
distance = len(activeCellSet.difference(outputNZ)) \
+ len(outputNZ.difference(activeCellSet))
#print "DISTANCE: ", distance
#if len(self._stats['inputPatterns']) == 100 and distance > 0:
# print "input pattern (%d):" % (sampleIdx), inputPattern
# print "prior output:", sorted(outputNZ)
# print "new output:", sorted(activeCellSet)
# print "distance: ", distance
self._stats['inputPatterns'][inputPattern] = (activeCellSet, sampleIdx)
self._stats['outputPatternDistanceSum'] += distance
self._stats['outputPatternSamples'] += 1
# Add this sample to our dict, if it's not too large already
elif len(self._stats['inputPatterns']) < self._stats['inputPatternsLimit']:
self._stats['inputPatterns'][inputPattern] = \
(activeCellSet, self._iterNum)
# -----------------------------------------------------------------------
# If it's not time to print them out, return now
if (self._iterNum % self.printPeriodicStats) != 0:
return
numSamples = float(self._stats['numSamples'])
# Calculate number of changes made per master
masterTouched = numpy.where(self._stats['numLearns'] > 0)
if len(masterTouched[0]) == 0:
numMasterChanges = numpy.zeros(1)
else:
numMasterChanges = self._stats['numChangedConnectionsSum'][masterTouched]
numMasterChanges /= self._stats['numLearns'][masterTouched]
# This fills in the static learning stats into self._learningStats
self.getLearningStats()
# Calculate and copy the transient learning stats into the
# self._learningStats dict, for possible retrieval later by
# the getLearningStats() method
self._learningStats['elapsedTime'] = time.time() - self._stats['startTime']
self._learningStats['activeCountAvg'] = self._stats['numOnSum'] / numSamples
self._learningStats['underCoveragePct'] = \
100.0*self._stats['underCoveragePctSum'] / numSamples
self._learningStats['overCoveragePct'] = \
100.0*self._stats['overCoveragePctSum'] / numSamples
self._learningStats['numConnectionChangesAvg'] = numMasterChanges.mean()
self._learningStats['numConnectionChangesMin'] = numMasterChanges.min()
self._learningStats['numConnectionChangesMax'] = numMasterChanges.max()
self._learningStats['avgCellOverlap'] = \
float(self._stats['cellOverlapSums']) / max(1, self._stats['numOnSum'])
self._learningStats['avgCellPctOverlap'] = \
100.0*self._stats['cellPctOverlapSums'] / max(1, self._stats['numOnSum'])
self._learningStats['firingBoostMaxChangePct'] = 100.0 \
* (self._stats['maxBoostFactor'] / self._stats['minBoostFactor']).max() \
- 100.0
self._learningStats['outputRepresentationChangeAvg'] = \
float(self._stats['outputPatternDistanceSum']) / \
max(1, self._stats['outputPatternSamples'])
self._learningStats['outputRepresentationChangePctAvg'] = \
100.0 * self._learningStats['outputRepresentationChangeAvg'] / \
max(1,self._learningStats['activeCountAvg'])
self._learningStats['numUniqueInputsSeen'] = \
len(self._stats['inputPatterns'])
if self._learningStats['numUniqueInputsSeen'] >= \
self._stats['inputPatternsLimit']:
self._learningStats['numUniqueInputsSeen'] = -1
# -------------------------------------------------------------------
# Print all stats captured
print "Learning stats for the last %d iterations:" % (numSamples)
print " iteration #: %d" % (self._iterNum)
print " inference iteration #: %d" % (self._inferenceIterNum)
print " elapsed time: %.2f" \
% (self._learningStats['elapsedTime'])
print " avg activeCount: %.1f" \
% (self._learningStats['activeCountAvg'])
print " avg under/overCoverage: %-6.1f / %-6.1f %%" \
% (self._learningStats['underCoveragePct'],
self._learningStats['overCoveragePct'])
print " avg cell overlap: %-6.1f / %-6.1f %%" \
% (self._learningStats['avgCellOverlap'],
self._learningStats['avgCellPctOverlap'])
print " avg/min/max RF radius: %-6.1f / %-6.1f / %-6.1f" \
% (self._learningStats['rfRadiusAvg'],
self._learningStats['rfRadiusMin'],
self._learningStats['rfRadiusMax'])
print " inhibition radius: %d" \
% (self._learningStats['inhibitionRadius'])
print " target density: %.5f %%" \
% (self._learningStats['targetDensityPct'])
print " avg/min/max coinc. size: %-6.1f / %-6d / %-6d" \
% (self._learningStats['coincidenceSizeAvg'],
self._learningStats['coincidenceSizeMin'],
self._learningStats['coincidenceSizeMax'])
print " avg/min/max DC before inh: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['dcBeforeInhibitionAvg'],
self._learningStats['dcBeforeInhibitionMin'],
self._learningStats['dcBeforeInhibitionMax'])
print " avg/min/max DC after inh: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['dcAfterInhibitionAvg'],
self._learningStats['dcAfterInhibitionMin'],
self._learningStats['dcAfterInhibitionMax'])
print " avg/min/max boost: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['firingBoostAvg'],
self._learningStats['firingBoostMin'],
self._learningStats['firingBoostMax'])
print " avg/min/max # conn. changes: %-6.4f / %-6.4f / %-6.4f" \
% (self._learningStats['numConnectionChangesAvg'],
self._learningStats['numConnectionChangesMin'],
self._learningStats['numConnectionChangesMax'])
print " max change in boost: %.1f %%" \
% (self._learningStats['firingBoostMaxChangePct'])
print " avg change in output repr.: %-6.1f / %-6.1f %%" \
% (self._learningStats['outputRepresentationChangeAvg'],
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
print " # of unique input pats seen: %d" \
% (self._learningStats['numUniqueInputsSeen'])
#self._printMemberSizes()
# Reset the stats for the next period
self._periodicStatsReset()
##############################################################################
def _printInputSlice(self, inputSlice, prefix=''):
""" Print the given input slice in a nice human readable format.
Parameters:
---------------------------------------------------------------------
cell: The slice of input to print
prefix: This is printed at the start of each row of the
coincidence
"""
# Shape of each coincidence
(rfHeight, rfWidth) = inputSlice.shape
syns = inputSlice != 0
def _synStr(x):
if not x:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
print prefix, ''.join(items)
##############################################################################
def _printSyns(self, cell, prefix='', showValues=False):
""" Print the synapse permanence values for the given cell in a nice,
human, readable format.
Parameters:
---------------------------------------------------------------------
cell: which cell to print
prefix: This is printed at the start of each row of the
coincidence
showValues: If True, print the values of each permanence.
If False, just print a ' ' if not connected and a '*'
if connected
"""
# Shape of each coincidence
(rfHeight, rfWidth) = self.inputShape
# Get the synapse permanences.
masterNum = self._cloneMapFlat[cell]
syns = self._masterPermanenceM[masterNum].toDense()
if showValues:
def _synStr(x):
if x == 0:
return ' -- '
elif x < 0.001:
return ' 0 '
elif x >= self.synPermConnected:
return '#%3.2f' % x
else:
return ' %3.2f' % x
else:
def _synStr(x):
if x < self.synPermConnected:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
if showValues:
print prefix, ' '.join(items)
else:
print prefix, ''.join(items)
##############################################################################
def _printMemberSizes(self, over=100):
""" Print the size of each member
"""
members = self.__dict__.keys()
sizeNamePairs = []
totalSize = 0
for member in members:
item = self.__dict__[member]
if hasattr(item, '__func__'):
continue
try:
if hasattr(item, '__len__'):
size = 0
for i in xrange(len(item)):
size += len(cPickle.dumps(item[i]))
else:
size = len(cPickle.dumps(item))
except:
print "WARNING: Can't pickle %s" % (member)
size = 0
sizeNamePairs.append((size, member))
totalSize += size
# Print them out from highest to lowest
sizeNamePairs.sort(reverse=True)
for (size, name) in sizeNamePairs:
if size > over:
print "%10d (%10.3fMb) %s" % (size, size/1000000.0, name)
print "\nTOTAL: %10d (%10.3fMB) " % (totalSize, totalSize/1000000.0)
##############################################################################
def printParams(self):
""" Print the main creation parameters associated with this instance.
"""
print "FDRCSpatial2 creation parameters: "
print "inputShape =", self.inputShape
print "inputBorder =", self.inputBorder
print "inputDensity =", self.inputDensity
print "coincidencesShape =", self.coincidencesShape
print "coincInputRadius =", self.coincInputRadius
print "coincInputPoolPct =", self.coincInputPoolPct
print "gaussianDist =", self.gaussianDist
print "commonDistributions =", self.commonDistributions
print "localAreaDensity =", self.localAreaDensity
print "numActivePerInhArea =", self.numActivePerInhArea
print "stimulusThreshold =", self.stimulusThreshold
print "synPermInactiveDec =", self.synPermInactiveDec
print "synPermActiveInc =", self.synPermActiveInc
print "synPermActiveSharedDec =", self.synPermActiveSharedDec
print "synPermOrphanDec =", self.synPermOrphanDec
print "synPermConnected =", self.synPermConnected
print "minPctDutyCycleBeforeInh =", self.minPctDutyCycleBeforeInh
print "minPctDutyCycleAfterInh =", self.minPctDutyCycleAfterInh
print "dutyCyclePeriod =", self.dutyCyclePeriod
print "maxFiringBoost =", self.maxFiringBoost
print "maxSSFiringBoost =", self.maxSSFiringBoost
print "maxSynPermBoost =", self.maxSynPermBoost
print "minDistance =", self.minDistance
print "spVerbosity =", self.spVerbosity
print "printPeriodicStats =", self.printPeriodicStats
print "testMode =", self.testMode
print "numCloneMasters =", self.numCloneMasters
|
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
url = "https://github.com/haampie/libtree/releases/download/v1.0.3/sources.tar.gz"
maintainers = ['haampie']
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
libtree package: add version 1.2.0, 1.1.4, and 1.1.3 (#17035)
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
url = "https://github.com/haampie/libtree/releases/download/v1.0.3/sources.tar.gz"
maintainers = ['haampie']
version('1.2.0', sha256='4316a52aed7c8d2f7d2736c935bbda952204be92e56948110a143283764c427c')
version('1.1.4', sha256='cfafb24c8f5e0d356c82777c338d58730ca6f3cb76dfe8a6857ee3ad65bf8be7')
version('1.1.3', sha256='7baf5aaecd3a076bf1e7a1aa86979e7b841ab3f678ca8ac0e2a22bbbccf0dd06')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
|
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# 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.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""Spatial pooler implementation.
TODO: Change print statements to use the logging module.
"""
import copy
import cPickle
import inspect
import itertools
import math
import numpy
import numpy.random
from operator import itemgetter
import os
import random
import struct
import sys
import time
from nupic.bindings.algorithms import (adjustMasterValidPermanence, cpp_overlap,
cpp_overlap_sbm, Inhibition2)
from nupic.bindings.math import (count_gte, GetNTAReal, Random as NupicRandom,
SM_01_32_32, SM32)
from nupic.math.cross import cross
from nupic.research import fdrutilities as fdru
realDType = GetNTAReal()
gPylabInitialized = False
# kDutyCycleFactor add dutyCycleAfterInh to overlap in Inhibition step to be a
# tie breaker
kDutyCycleFactor = 0.01
def _extractCallingMethodArgs():
"""
Returns args dictionary from the calling method
"""
callingFrame = inspect.stack()[1][0]
argNames, _, _, frameLocalVarDict = inspect.getargvalues(callingFrame)
argNames.remove("self")
args = copy.copy(frameLocalVarDict)
for varName in frameLocalVarDict:
if varName not in argNames:
args.pop(varName)
return args
class FDRCSpatial2(object):
"""
Class for spatial pooling based on fixed random distributed
representation (FDR).
This version of FDRCSpatial inlcudes adaptive receptive fields, no-dupe rules
and gradual boosting. It supports 1-D and 2-D topologies with cloning.
"""
def __init__(self,
inputShape=(32, 32),
inputBorder=8,
inputDensity=1.0,
coincidencesShape=(48, 48),
coincInputRadius=16,
coincInputPoolPct=1.0,
gaussianDist=False,
commonDistributions=False,
localAreaDensity=-1.0,
numActivePerInhArea=10.0,
stimulusThreshold=0,
synPermInactiveDec=0.01,
synPermActiveInc=0.1,
synPermActiveSharedDec=0.0,
synPermOrphanDec=0.0,
synPermConnected=0.10,
minPctDutyCycleBeforeInh=0.001,
minPctDutyCycleAfterInh=0.001,
dutyCyclePeriod=1000,
maxFiringBoost=10.0,
maxSSFiringBoost=2.0,
maxSynPermBoost=10.0,
minDistance=0.0,
cloneMap=None,
numCloneMasters=-1,
seed=-1,
spVerbosity=0,
printPeriodicStats=0,
testMode=False,
globalInhibition=False,
spReconstructionParam="unweighted_mean",
useHighTier=True,
randomSP=False,
):
"""
Parameters:
----------------------------
inputShape: The dimensions of the input vector. Format is (height,
width) e.g. (24, 72). If the input is from a sensor,
it is interpreted as having a 2-D topology of 24
pixels high and 72 wide.
inputBorder: The first column from an edge will be centered
over an input which is 'inputBorder' inputs from the
edge.
inputDensity: The density of the input. This is only to aid in
figuring out the initial number of connected synapses
to place on each column. The lower the inputDensity,
the more initial connections will be assigned to each
column.
coincidencesShape: The dimensions of column layout. Format is (height,
width) e.g. (80,100) means a total of 80*100 = 800 are
arranged in a 2-D topology with 80 rows and
100 columns.
coincInputRadius: This defines the max radius of the receptive field of
each column. This is used to limit memory requirements
and processing time. It could be set large enough to
encompass the entire input field and the SP would
still work fine, but require more memory and
processing time.
This parameter defines a square area: a column will
have a max square RF with sides of length
2 * coincInputRadius + 1.
coincInputPoolPct What percent of the columns's receptive field is
available for potential synapses. At initialization
time, we will choose
coincInputPoolPct * (2*coincInputRadius + 1)^2
potential synapses from the receptive field.
gaussianDist: If true, the initial permanences assigned to each
column will have a gaussian distribution to them,
making the column favor inputs directly below it over
inputs farther away. If false, the initial permanences
will have a random distribution across the column's
entire potential receptive field.
commonDistributions: If set to True (the default, faster startup time),
each column will be given the same initial permanence
values.
This is normally OK when you will be training, but if
you will be sticking with the untrained network,
you will want to set this to False (which makes
startup take longer).
localAreaDensity: The desired density of active columns within a local
inhibition area (the size of which is set by the
internally calculated inhibitionRadius, which is in
turn determined from the average size of the connected
receptive fields of all columns). The inhibition logic
will insure that at most N columns remain ON within a
local inhibition area, where N = localAreaDensity *
(total number of columns in inhibition area).
numActivePerInhArea: An alternate way to control the density of the active
columns. If numActivePerInhArea is specified then
localAreaDensity must be -1, and vice versa. When
using numActivePerInhArea, the inhibition logic will
insure that at most 'numActivePerInhArea' columns
remain ON within a local inhibition area (the size of
which is set by the internally calculated
inhibitionRadius, which is in turn determined from the
average size of the connected receptive fields of all
columns). When using this method, as columns learn and
grow their effective receptive fields, the
inhibitionRadius will grow, and hence the net density
of the active columns will *decrease*. This is in
contrast to the localAreaDensity method, which keeps
the density of active columns the same regardless of
the size of their receptive fields.
stimulusThreshold: This is a number specifying the minimum number of
synapses that must be on in order for a columns to
turn ON.
The purpose of this is to prevent noise input from
activating columns.
synPermInactiveDec: How much an inactive synapse is decremented, specified
as a percent of a fully grown synapse.
synPermActiveInc: How much to increase the permanence of an active
synapse, specified as a percent of a fully grown
synapse.
synPermActiveSharedDec: How much to decrease the permanence of an active
synapse which is connected to another column that is
active at the same time. Specified as a percent of a
fully grown synapse.
synPermOrphanDec: How much to decrease the permanence of an active
synapse on a column which has high overlap with the
input, but was inhibited (an "orphan" column).
synPermConnected: The default connected threshold. Any synapse whose
permanence value is above the connected threshold is
a "connected synapse", meaning it can contribute to
the cell's firing. Typical value is 0.10. Cells whose
activity level before inhibition falls below
minDutyCycleBeforeInh will have their own internal
synPermConnectedCell threshold set below this default
value.
(This concept applies to both SP and TP and so 'cells'
is correct here as opposed to 'columns')
minPctDutyCycleBeforeInh: A number between 0 and 1.0, used to set a floor on
how often a column should have at least
stimulusThreshold active inputs. Periodically, each
column looks at the duty cycle before inhibition of
all other column within its inhibition radius and sets
its own internal minimal acceptable duty cycle to:
minPctDutyCycleBeforeInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle before
inhibition falls below this computed value will get
all of its permanence values boosted up by
synPermActiveInc. Raising all permanences in response
to a sub-par duty cycle before inhibition allows a
cell to search for new inputs when either its
previously learned inputs are no longer ever active,
or when the vast majority of them have been "hijacked"
by other columns due to the no-dupe rule.
minPctDutyCycleAfterInh: A number between 0 and 1.0, used to set a floor on
how often a column should turn ON after inhibition.
Periodically, each column looks at the duty cycle
after inhibition of all other columns within its
inhibition radius and sets its own internal minimal
acceptable duty cycle to:
minPctDutyCycleAfterInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle after
inhibition falls below this computed value will get
its internal boost factor increased.
dutyCyclePeriod: The period used to calculate duty cycles. Higher
values make it take longer to respond to changes in
boost or synPerConnectedCell. Shorter values make it
more unstable and likely to oscillate.
maxFiringBoost: The maximum firing level boost factor. Each column's
raw firing strength gets multiplied by a boost factor
before it gets considered for inhibition.
The actual boost factor for a column is number between
1.0 and maxFiringBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxFiringBoost
is used if the duty cycle is 0, and any duty cycle in
between is linearly extrapolated from these 2
endpoints.
maxSSFiringBoost: Once a column turns ON, it's boost will immediately
fall down to maxSSFiringBoost if it is above it. This
is accomplished by internally raising it's computed
duty cycle accordingly. This prevents a cell which has
had it's boost raised extremely high from turning ON
for too many diverse inputs in a row within a short
period of time.
maxSynPermBoost: The maximum synPermActiveInc boost factor. Each
column's synPermActiveInc gets multiplied by a boost
factor to make the column more or less likely to form
new connections.
The actual boost factor used is a number between
1.0 and maxSynPermBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxSynPermBoost
is used if the duty cycle is 0, and any duty cycle
in between is linearly extrapolated from these 2
endpoints.
minDistance: This parameter impacts how finely the input space is
quantized. It is a value between 0 and 1.0. If set
to 0, then every unique input presentation will
generate a unique output representation, within the
limits of the total number of columns available.
Higher values will tend to group similar inputs
together into the same output representation. Only
column which overlap with the input less than
100*(1.0-minDistance) percent will
have a possibility of losing the inhibition
competition against a boosted, 'bored' cell.
cloneMap: An array (numColumnsHigh, numColumnsWide) that
contains the clone index to use for each column.
numCloneMasters: The number of distinct clones in the map. This
is just outputCloningWidth*outputCloningHeight.
seed: Seed for our own pseudo-random number generator.
spVerbosity: spVerbosity level: 0, 1, 2, or 3
printPeriodicStats: If > 0, then every 'printPeriodicStats' iterations,
the SP will print to stdout some statistics related to
learning, such as the average pct under and
over-coverage, average number of active columns, etc.
in the last 'showLearningStats' iterations.
testMode: If True, run the SP in test mode. This runs both the
C++ and python implementations on all internal
functions that support both and insures that both
produce the same result.
globalInhibition: If true, enforce the
localAreaDensity/numActivePerInhArea
globally over the entire region, ignoring any
dynamically calculated inhibitionRadius. In effect,
this is the same as setting the inhibition radius to
include the entire region.
spReconstructionParam:Specifies which SP reconstruction optimization to be
used. Each column's firing strength is weighted by the
percent Overlap, permanence or duty Cycle if this
parameter is set to 'pctOverlap', 'permanence', or
'dutycycle' respectively. If parameter is set to
'maximum_firingstrength', the maximum of the firing
strengths (weighted by permanence) is used instead of
the weighted sum.
useHighTier: The "high tier" feature is to deal with sparse input
spaces. If over (1-minDistance) percent of a column's
connected synapses are active, it will automatically
become one of the winning columns.
If False, columns are activated based on their absolute
overlap with the input. Also, boosting will be
disabled to prevent pattern oscillation
randomSP: If True, the SP will not update its permanences and
will instead use it's initial configuration for all
inferences.
"""
# Save our __init__ args for debugging
self._initArgsDict = _extractCallingMethodArgs()
# Handle people instantiating us directly that don't pass in a cloneMap...
# This creates a clone map without any cloning
if cloneMap is None:
cloneMap, numCloneMasters = fdru.makeCloneMap(
columnsShape=coincidencesShape,
outputCloningWidth=coincidencesShape[1],
outputCloningHeight=coincidencesShape[0]
)
self.numCloneMasters = numCloneMasters
self._cloneMapFlat = cloneMap.reshape((-1,))
# Save creation parameters
self.inputShape = int(inputShape[0]), int(inputShape[1])
self.inputBorder = inputBorder
self.inputDensity = inputDensity
self.coincidencesShape = coincidencesShape
self.coincInputRadius = coincInputRadius
self.coincInputPoolPct = coincInputPoolPct
self.gaussianDist = gaussianDist
self.commonDistributions = commonDistributions
self.localAreaDensity = localAreaDensity
self.numActivePerInhArea = numActivePerInhArea
self.stimulusThreshold = stimulusThreshold
self.synPermInactiveDec = synPermInactiveDec
self.synPermActiveInc = synPermActiveInc
self.synPermActiveSharedDec = synPermActiveSharedDec
self.synPermOrphanDec = synPermOrphanDec
self.synPermConnected = synPermConnected
self.minPctDutyCycleBeforeInh = minPctDutyCycleBeforeInh
self.minPctDutyCycleAfterInh = minPctDutyCycleAfterInh
self.dutyCyclePeriod = dutyCyclePeriod
self.maxFiringBoost = maxFiringBoost
self.maxSSFiringBoost = maxSSFiringBoost
self.maxSynPermBoost = maxSynPermBoost
self.minDistance = minDistance
self.spVerbosity = spVerbosity
self.printPeriodicStats = printPeriodicStats
self.testMode = testMode
self.globalInhibition = globalInhibition
self.spReconstructionParam = spReconstructionParam
self.useHighTier= useHighTier != 0
self.randomSP = randomSP != 0
if not self.useHighTier:
self.minPctDutyCycleAfterInh = 0
self.fileCount = 0
self._runIter = 0
# Start at iteration #0
self._iterNum = 0 # Number of learning iterations
self._inferenceIterNum = 0 # Number of inference iterations
# Print creation parameters
if spVerbosity >= 3:
self.printParams()
print "seed =", seed
# Check for errors
assert (self.numActivePerInhArea == -1 or self.localAreaDensity == -1)
assert (self.inputShape[1] > 2 * self.inputBorder)
# 1D layouts have inputShape[0] == 1
if self.inputShape[0] > 1:
assert self.inputShape[0] > 2 * self.inputBorder
# Calculate other member variables
self._coincCount = int(self.coincidencesShape[0] *
self.coincidencesShape[1])
self._inputCount = int(self.inputShape[0] * self.inputShape[1])
self._synPermMin = 0.0
self._synPermMax = 1.0
self._pylabInitialized = False
# The rate at which we bump up all synapses in response to not passing
# stimulusThreshold
self._synPermBelowStimulusInc = self.synPermConnected / 10.0
self._hasTopology = True
if self.inputShape[0] == 1: # 1-D layout
self._coincRFShape = (1, (2 * coincInputRadius + 1))
# If we only have 1 column of coincidences, then assume the user wants
# each coincidence to cover the entire input
if self.coincidencesShape[1] == 1:
assert self.inputBorder >= (self.inputShape[1] - 1) // 2
assert coincInputRadius >= (self.inputShape[1] - 1) // 2
self._coincRFShape = (1, self.inputShape[1])
self._hasTopology = False
else: # 2-D layout
self._coincRFShape = ((2*coincInputRadius + 1), (2*coincInputRadius + 1))
# This gets set to True in finishLearning. Once set, we don't allow
# learning anymore and delete all member variables needed only for
# learning.
self._doneLearning = False
# Init random seed
self._seed(seed)
# Hard-coded in the current case
self.randomTieBreakingFraction = 0.5
# The permanence values used to initialize the master coincs are from
# this initial permanence array
# The initial permanence is gaussian shaped with mean at center and variance
# carefully chosen to have connected synapses
initialPermanence = self._initialPermanence()
# masterPotentialM, masterPermanenceM and masterConnectedM are numpy arrays
# of dimensions (coincCount, coincRfShape[0], coincRFShape[1])
#
# masterPotentialM: Keeps track of the potential synapses of each
# master. Potential synapses are marked as True
# masterPermanenceM: Holds the permanence values of the potential synapses.
# The values can range from 0.0 to 1.0
# masterConnectedM: Keeps track of the connected synapses of each
# master. Connected synapses are the potential synapses
# with permanence values greater than synPermConnected.
self._masterPotentialM, self._masterPermanenceM = (
self._makeMasterCoincidences(self.numCloneMasters, self._coincRFShape,
self.coincInputPoolPct, initialPermanence,
self.random))
# Update connected coincidences, the connected synapses have permanence
# values greater than synPermConnected.
self._masterConnectedM = []
dense = numpy.zeros(self._coincRFShape)
for i in xrange(self.numCloneMasters):
self._masterConnectedM.append(SM_01_32_32(dense))
# coinc sizes are used in normalizing the raw overlaps
self._masterConnectedCoincSizes = numpy.empty(self.numCloneMasters,
'uint32')
# Make one mondo coincidence matrix for all cells at once. It has one row
# per cell. The width of each row is the entire input width. There will be
# ones in each row where that cell has connections. When we have cloning,
# and we modify the connections for a clone master, we will update all
# cells that share that clone master with the new connections.
self._allConnectedM = SM_01_32_32(self._inputCount)
self._allConnectedM.resize(self._coincCount, self._inputCount)
# Initialize the dutyCycles and boost factors per clone master
self._dutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._dutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# TODO: We don't need to store _boostFactors, can be calculated from duty
# cycle
self._firingBoostFactors = numpy.ones(self.numCloneMasters,
dtype=realDType)
if self.useHighTier:
self._firingBoostFactors *= maxFiringBoost
# Selectively turn on/off C++ for various methods
# TODO: Can we remove the conditional?
if self.testMode:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
else:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
# This is used to hold our learning stats (via getLearningStats())
self._learningStats = dict()
# These will hold our random state, which we return from __getstate__ and
# reseed our random number generators from in __setstate__ so that
# a saved/restored SP produces the exact same behavior as one that
# continues. This behavior allows us to write unit tests that verify that
# the behavior of an SP does not change due to saving/loading from a
# checkpoint
self._randomState = None
self._numpyRandomState = None
self._nupicRandomState = None
# Init ephemeral members
# This also calculates the slices and global inhibitionRadius and allocates
# the inhibitionObj
self._initEphemerals()
# If we have no cloning, make sure no column has potential or connected
# synapses outside the input area
if self.numCloneMasters == self._coincCount:
validMask = numpy.zeros(self._coincRFShape, dtype=realDType)
for masterNum in xrange(self._coincCount):
coincSlice = self._coincSlices[masterNum]
validMask.fill(0)
validMask[coincSlice] = 1
self._masterPotentialM[masterNum].logicalAnd(SM_01_32_32(validMask))
self._masterPermanenceM[masterNum].elementMultiply(validMask)
# Raise all permanences up until the number of connected is above
# our desired target,
self._raiseAllPermanences(masterNum,
minConnections = self.stimulusThreshold / self.inputDensity)
# Calculate the number of connected synapses in each master coincidence now
self._updateConnectedCoincidences()
def _getEphemeralMembers(self):
"""
List of our member variables that we don't need to be saved
"""
return ['_inputLayout',
'_cellsForMaster',
'_columnCenters',
#'_cellRFClipped',
'_inputSlices',
'_coincSlices',
'_activeInput',
'_permChanges',
'_dupeInput',
'_onCells',
'_masterOnCells',
'_onCellIndices',
'_inhibitionObj',
'_denseOutput',
'_overlaps',
'_anomalyScores',
'_inputUse',
'_updatePermanenceGivenInputFP',
'_computeOverlapsFP',
'_stats',
'_rfRadiusAvg',
'_rfRadiusMin',
'_rfRadiusMax',
'_topDownOut',
'_topDownParentCounts',
]
def _initEphemerals(self):
"""
Initialize all ephemeral members after being restored to a pickled state.
"""
# Used by functions which refers to inputs in absolute space
# getLearnedCM, cm,....
self._inputLayout = numpy.arange(self._inputCount,
dtype=numpy.uint32).reshape(self.inputShape)
# This array returns the list of cell indices that correspond to each master
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
self._cellsForMaster = []
for masterNum in xrange(self.numCloneMasters):
self._cellsForMaster.append(
numpy.where(self._cloneMapFlat == masterNum)[0])
else:
self._cellsForMaster = None
# TODO: slices are not required for the C++ helper functions
# Figure out the slices of shaped input that each column sees...
# Figure out the valid region of each column
# The reason these slices are in initEphemerals is because numpy slices
# can't be pickled
self._setSlices()
# This holds the output of the inhibition computation - which cells are
# on after inhibition
self._onCells = numpy.zeros(self._coincCount, dtype=realDType)
self._masterOnCells = numpy.zeros(self.numCloneMasters, dtype=realDType)
self._onCellIndices = numpy.zeros(self._coincCount, dtype='uint32')
# The inhibition object gets allocated by _updateInhibitionObj() during
# the first compute and re-allocated periodically during learning
self._inhibitionObj = None
self._rfRadiusAvg = 0 # Also calculated by _updateInhibitionObj
self._rfRadiusMin = 0
self._rfRadiusMax = 0
# Used by the caller to optionally cache the dense output
self._denseOutput = None
# This holds the overlaps (in absolute number of connected synapses) of each
# coinc with input.
self._overlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the percent overlaps (number of active inputs / number of
# connected synapses) of each coinc with input.
self._pctOverlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This is the value of the anomaly score for each column (after inhibition).
self._anomalyScores = numpy.zeros_like(self._overlaps)
# This holds the overlaps before stimulus threshold - used for verbose
# messages only.
self._overlapsBST = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the number of coincs connected to an input.
if not self._doneLearning:
self._inputUse = numpy.zeros(self.inputShape, dtype=realDType)
# These are boolean matrices, the same shape as the input.
if not self._doneLearning:
self._activeInput = numpy.zeros(self.inputShape, dtype='bool')
self._dupeInput = numpy.zeros(self.inputShape, dtype='bool')
# This is used to hold self.synPermActiveInc where the input is on
# and -self.synPermInctiveDec where the input is off
if not self._doneLearning:
self._permChanges = numpy.zeros(self.inputShape, dtype=realDType)
# These are used to compute and hold the output from topDownCompute
# self._topDownOut = numpy.zeros(self.inputShape, dtype=realDType)
# self._topDownParentCounts = numpy.zeros(self.inputShape, dtype='int')
# Fill in the updatePermanenceGivenInput method pointer, which depends on
# chosen language.
if self._updatePermanenceGivenInputImp == "py":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputPy
elif self._updatePermanenceGivenInputImp == "cpp":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputCPP
elif self._updatePermanenceGivenInputImp == "test":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputTest
else:
assert False
# Fill in the computeOverlaps method pointer, which depends on
# chosen language.
if self._computeOverlapsImp == "py":
self._computeOverlapsFP = self._computeOverlapsPy
elif self._computeOverlapsImp == "cpp":
self._computeOverlapsFP = self._computeOverlapsCPP
elif self._computeOverlapsImp == "test":
self._computeOverlapsFP = self._computeOverlapsTest
else:
assert False
# These variables are used for keeping track of learning statistics (when
# self.printPeriodicStats is used).
self._periodicStatsCreate()
def compute(self, flatInput, learn=False, infer=True, computeAnomaly=False):
"""Compute with the current input vector.
Parameters:
----------------------------
input : the input vector (numpy array)
learn : if True, adapt the input histogram based on this input
infer : whether to do inference or not
"""
# If we are using a random SP, ignore the learn parameter
if self.randomSP:
learn = False
# If finishLearning has been called, don't allow learning anymore
if learn and self._doneLearning:
raise RuntimeError("Learning can not be performed once finishLearning"
" has been called.")
assert (learn or infer)
assert (flatInput.ndim == 1) and (flatInput.shape[0] == self._inputCount)
assert (flatInput.dtype == realDType)
input = flatInput.reshape(self.inputShape)
# Make sure we've allocated the inhibition object lazily
if self._inhibitionObj is None:
self._updateInhibitionObj()
# Reset first timer
if self.printPeriodicStats > 0 and self._iterNum == 0:
self._periodicStatsReset()
# Using cloning?
cloningOn = (self.numCloneMasters != self._coincCount)
# If we have high verbosity, save the overlaps before stimulus threshold
# so we can print them out at the end
if self.spVerbosity >= 2:
print "==============================================================="
print "Iter:%d" % self._iterNum, "inferenceIter:%d" % \
self._inferenceIterNum
self._computeOverlapsFP(input, stimulusThreshold=0)
self._overlapsBST[:] = self._overlaps
connectedCountsOnEntry = self._masterConnectedCoincSizes.copy()
if self.spVerbosity >= 3:
inputNZ = flatInput.nonzero()[0]
print "active inputs: (%d)" % len(inputNZ), inputNZ
# TODO: Port to C++, arguments may be different - t1YXArr,
# coincInputRadius,...
# Calculate the raw overlap of each cell
# Overlaps less than stimulus threshold are set to zero in
# _calculateOverlaps
# This places the result into self._overlaps
self._computeOverlapsFP(input, stimulusThreshold=self.stimulusThreshold)
# Save the original overlap values, before boosting, for the purpose of
# anomaly detection
if computeAnomaly:
self._anomalyScores[:] = self._overlaps[:]
if learn:
# Update each cell's duty cycle before inhibition
# Only cells with overlaps greater stimulus threshold are considered as
# active.
# Stimulus threshold has already been applied
# TODO: Port to C++? Loops over all coincs
# Only updating is carried out here, bump up happens later
onCellIndices = numpy.where(self._overlaps > 0)
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
self._onCells.fill(0)
self._onCells[onCellIndices] = 1
denseOn = self._onCells
# dutyCyclePeriod = self._iterNum + 1 let _dutyCycleBeforeInh
# and _dutyCycleAfterInh represent real firing percentage at the
# beginning of learning. This will effect boosting and let unlearned
# coincidences have high boostFactor at beginning.
self.dutyCyclePeriod = min(self._iterNum + 1, 1000)
self._dutyCycleBeforeInh = (
((self.dutyCyclePeriod - 1) * self._dutyCycleBeforeInh + denseOn) /
self.dutyCyclePeriod)
# Compute firing levels based on boost factor and raw overlap. Update
# self._overlaps in place, replacing it with the boosted overlap. We also
# computes percent overlap of each column and store that into
# self._pctOverlaps
if cloningOn:
self._pctOverlaps[:] = self._overlaps
self._pctOverlaps /= self._masterConnectedCoincSizes[self._cloneMapFlat]
boostFactors = self._firingBoostFactors[self._cloneMapFlat]
else:
self._pctOverlaps[:] = self._overlaps
potentials = self._masterConnectedCoincSizes
self._pctOverlaps /= numpy.maximum(1, potentials)
boostFactors = self._firingBoostFactors
# To process minDistance, we do the following:
# 1.) All cells which do not overlap the input "highly" (less than
# minDistance), are considered to be in the "low tier" and get their
# overlap multiplied by their respective boost factor.
# 2.) All other cells, which DO overlap the input highly, get a "high tier
# offset" added to their overlaps, and boost is not applied. The
# "high tier offset" is computed as the max of all the boosted
# overlaps from step #1. This insures that a cell in this high tier
# will never lose to a cell from the low tier.
if self.useHighTier:
highTier = numpy.where(self._pctOverlaps >= (1.0 - self.minDistance))[0]
else:
highTier = []
someInHighTier = len(highTier) > 0
if someInHighTier:
boostFactors = numpy.array(boostFactors)
boostFactors[highTier] = 1.0
# Apply boostFactors only in learning phase not in inference phase.
if learn:
self._overlaps *= boostFactors
if someInHighTier:
highTierOffset = self._overlaps.max() + 1.0
self._overlaps[highTier] += highTierOffset
# Cache the dense output for debugging.
if self._denseOutput is not None:
self._denseOutput = self._overlaps.copy()
# Incorporate inhibition and see who is firing after inhibition.
# We don't need this method to process stimulusThreshold because we
# already processed it.
# Also, we pass in a small 'addToWinners' amount which gets added to the
# winning elements as we go along. This prevents us from choosing more than
# topN winners per inhibition region when more than topN elements all have
# the same max high score.
learnedCellsOverlaps = numpy.array(self._overlaps)
if infer and not learn:
# Cells that have never learnt are not allowed to win during inhibition
if not self.randomSP:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = 0
else:
# Boost the unlearned cells to 1000 so that the winning columns are
# picked randomly. From the set of unlearned columns. Boost columns that
# havent been learned with uniformly to 1000 so that inhibition picks
# randomly from them.
if self.useHighTier:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = (
learnedCellsOverlaps.max() + 1)
# Boost columns that are in highTier (ie. they match the input very
# well).
learnedCellsOverlaps[highTier] += learnedCellsOverlaps.max() + 1
# Small random tiebreaker for columns with equal overlap
tieBreaker = numpy.random.rand(*learnedCellsOverlaps.shape).astype(
realDType)
learnedCellsOverlaps += 0.1 * tieBreaker
numOn = self._inhibitionObj.compute(
learnedCellsOverlaps,
self._onCellIndices,
0.0, # stimulusThreshold
max(learnedCellsOverlaps)/1000.0, # addToWinners
)
self._onCells.fill(0)
if numOn > 0:
onCellIndices = self._onCellIndices[0:numOn]
self._onCells[onCellIndices] = 1
else:
onCellIndices = []
# Compute the anomaly scores only for the winning columns.
if computeAnomaly:
self._anomalyScores *= self._onCells
self._anomalyScores *= self._dutyCycleAfterInh
if self.spVerbosity >= 2:
print "inhRadius", self._inhibitionObj.getInhibitionRadius()
print "inhLocalAreaDensity", self._inhibitionObj.getLocalAreaDensity()
print "numFiring", numOn
# Capturing learning stats? If so, capture the cell overlap statistics
if self.printPeriodicStats > 0:
activePctOverlaps = self._pctOverlaps[onCellIndices]
self._stats['cellPctOverlapSums'] += activePctOverlaps.sum()
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
else:
onMasterIndices = onCellIndices
self._stats['cellOverlapSums'] += (
activePctOverlaps *
self._masterConnectedCoincSizes[onMasterIndices]).sum()
# Compute which cells had very high overlap, but were still
# inhibited. These we are calling our "orphan cells", because they are
# representing an input which is already better represented by another
# cell.
if self.synPermOrphanDec > 0:
orphanCellIndices = set(numpy.where(self._pctOverlaps >= 1.0)[0])
orphanCellIndices.difference_update(onCellIndices)
else:
orphanCellIndices = []
if learn:
# Update the number of coinc connections per input
# During learning (adapting permanence values), we need to be able to
# recognize dupe inputs - inputs that go two 2 or more active cells
if self.synPermActiveSharedDec != 0:
self._updateInputUse(onCellIndices)
# For the firing cells, update permanence values.
onMasterIndices = self._adaptSynapses(onCellIndices, orphanCellIndices,
input)
# Increase the permanence values of columns which haven't passed
# stimulus threshold of overlap with at least a minimum frequency
self._bumpUpWeakCoincidences()
# Update each cell's after-inhibition duty cycle
# TODO: As the on-cells are sparse after inhibition, we can have
# a different updateDutyCycles function taking advantage of the sparsity
if cloningOn:
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
denseOn = self._onCells
self._dutyCycleAfterInh = ((
(self.dutyCyclePeriod - 1) * self._dutyCycleAfterInh + denseOn) /
self.dutyCyclePeriod)
# Update the boost factors based on firings rate after inhibition.
self._updateBoostFactors()
# Increment iteration number and perform our periodic tasks if it's time.
if (self._iterNum + 1) % 50 == 0:
self._updateInhibitionObj()
self._updateMinDutyCycles(
self._dutyCycleBeforeInh, self.minPctDutyCycleBeforeInh,
self._minDutyCycleBeforeInh)
self._updateMinDutyCycles(
self._dutyCycleAfterInh, self.minPctDutyCycleAfterInh,
self._minDutyCycleAfterInh)
# Next iteration
if learn:
self._iterNum += 1
if infer:
self._inferenceIterNum += 1
if learn:
# Capture and possibly print the periodic stats
if self.printPeriodicStats > 0:
self._periodicStatsComputeEnd(onCellIndices, flatInput.nonzero()[0])
# Verbose print other stats
if self.spVerbosity >= 2:
cloning = (self.numCloneMasters != self._coincCount)
print " #connected on entry: ", fdru.numpyStr(
connectedCountsOnEntry, '%d ', includeIndices=True)
print " #connected on exit: ", fdru.numpyStr(
self._masterConnectedCoincSizes, '%d ', includeIndices=True)
if self.spVerbosity >= 3 or not cloning:
print " overlaps: ", fdru.numpyStr(self._overlapsBST, '%d ',
includeIndices=True, includeZeros=False)
print " firing levels: ", fdru.numpyStr(self._overlaps, '%.4f ',
includeIndices=True, includeZeros=False)
print " on after inhibition: ", onCellIndices
if not self._doneLearning:
print " minDutyCycleBeforeInh:", fdru.numpyStr(
self._minDutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " dutyCycleBeforeInh: ", fdru.numpyStr(self._dutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " belowMinBeforeInh: " % numpy.nonzero(
self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh)[0]
print " minDutyCycleAfterInh: ", fdru.numpyStr(
self._minDutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " dutyCycleAfterInh: ", fdru.numpyStr(self._dutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " belowMinAfterInh: " % numpy.nonzero(
self._dutyCycleAfterInh \
< self._minDutyCycleAfterInh)[0]
print " firingBoosts: ", fdru.numpyStr(self._firingBoostFactors,
'%.4f ', includeIndices=True)
print
elif self.spVerbosity >= 1:
print "SP: learn: ", learn
print "SP: active outputs(%d): " % (len(onCellIndices)), onCellIndices
self._runIter += 1
# Return inference result
return self._onCells
def __getstate__(self):
# Update our random states
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
state = self.__dict__.copy()
# Delete ephemeral members that we don't want pickled
for ephemeralMemberName in self._getEphemeralMembers():
if ephemeralMemberName in state:
del state[ephemeralMemberName]
return state
def __setstate__(self, state):
self.__dict__.update(state)
# Support older checkpoints
# These fields were added on 2010-10-05 and _iterNum was preserved
if not hasattr(self, '_randomState'):
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
self._iterNum = 0
# Init our random number generators
random.setstate(self._randomState)
numpy.random.set_state(self._numpyRandomState)
self.random.setState(self._nupicRandomState)
# Load things that couldn't be pickled...
self._initEphemerals()
def getAnomalyScore(self):
"""Get the aggregate anomaly score for this input pattern
Returns: A single scalar value for the anomaly score
"""
numNonzero = len(numpy.nonzero(self._anomalyScores)[0])
return 1.0 / (numpy.sum(self._anomalyScores) + 1)
def getLearningStats(self):
"""Return a dictionary containing a set of statistics related to learning.
Here is a list of what is returned:
'activeCountAvg':
The average number of active columns seen over the last
N training iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'underCoveragePct':
The average under-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'overCoveragePct':
The average over-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesAvg':
The overall average number of connection changes made per active
column per iteration, over the last N training iterations, where N
is set by the constructor parameter printPeriodicStats. This gives an
indication as to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMin':
The minimum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMax':
The maximum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'rfSize':
The average receptive field size of the columns.
'inhibitionRadius':
The average inihbition radius of the columns.
'targetDensityPct':
The most recent target local area density used, as a percent (0 -> 100)
'coincidenceSizeAvg':
The average learned coincidence size
'coincidenceSizeMin':
The minimum learned coincidence size
'coincidenceSizeMax':
The maximum learned coincidence size
'dcBeforeInhibitionAvg':
The average of duty cycle before inhbition of all coincidences
'dcBeforeInhibitionMin':
The minimum duty cycle before inhbition of all coincidences
'dcBeforeInhibitionAvg':
The maximum duty cycle before inhbition of all coincidences
'dcAfterInhibitionAvg':
The average of duty cycle after inhbition of all coincidences
'dcAfterInhibitionMin':
The minimum duty cycle after inhbition of all coincidences
'dcAfterInhibitionAvg':
The maximum duty cycle after inhbition of all coincidences
'firingBoostAvg':
The average firing boost
'firingBoostMin':
The minimum firing boost
'firingBoostMax':
The maximum firing boost
"""
# Fill in the stats that can be computed on the fly. The transient stats
# that depend on printPeriodicStats being on, have already been stored
self._learningStats['rfRadiusAvg'] = self._rfRadiusAvg
self._learningStats['rfRadiusMin'] = self._rfRadiusMin
self._learningStats['rfRadiusMax'] = self._rfRadiusMax
if self._inhibitionObj is not None:
self._learningStats['inhibitionRadius'] = (
self._inhibitionObj.getInhibitionRadius())
self._learningStats['targetDensityPct'] = (
100.0 * self._inhibitionObj.getLocalAreaDensity())
else:
print "Warning: No inhibitionObj found for getLearningStats"
self._learningStats['inhibitionRadius'] = 0.0
self._learningStats['targetDensityPct'] = 0.0
self._learningStats['coincidenceSizeAvg'] = (
self._masterConnectedCoincSizes.mean())
self._learningStats['coincidenceSizeMin'] = (
self._masterConnectedCoincSizes.min())
self._learningStats['coincidenceSizeMax'] = (
self._masterConnectedCoincSizes.max())
if not self._doneLearning:
self._learningStats['dcBeforeInhibitionAvg'] = (
self._dutyCycleBeforeInh.mean())
self._learningStats['dcBeforeInhibitionMin'] = (
self._dutyCycleBeforeInh.min())
self._learningStats['dcBeforeInhibitionMax'] = (
self._dutyCycleBeforeInh.max())
self._learningStats['dcAfterInhibitionAvg'] = (
self._dutyCycleAfterInh.mean())
self._learningStats['dcAfterInhibitionMin'] = (
self._dutyCycleAfterInh.min())
self._learningStats['dcAfterInhibitionMax'] = (
self._dutyCycleAfterInh.max())
self._learningStats['firingBoostAvg'] = self._firingBoostFactors.mean()
self._learningStats['firingBoostMin'] = self._firingBoostFactors.min()
self._learningStats['firingBoostMax'] = self._firingBoostFactors.max()
return self._learningStats
def resetStats(self):
"""Reset the stats (periodic, ???). This will usually be called by
user code at the start of each inference run (for a particular data set).
TODO: which other stats need to be reset? Learning stats?
"""
self._periodicStatsReset()
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
random.seed(seed)
numpy.random.seed(seed)
else:
self.random = NupicRandom()
def _initialPermanence(self):
"""Create and return a 2D matrix filled with initial permanence values.
The returned matrix will be of shape:
(2*coincInputRadius + 1, 2*coincInputRadius + 1).
The initial permanence values are set between 0 and 1.0, with enough chosen
above synPermConnected to make it highly likely that a cell will pass
stimulusThreshold, given the size of the potential RF, the input pool
sampling percentage, and the expected density of the active inputs.
If gaussianDist is True, the center of the matrix will contain the highest
permanence values and lower values will be farther from the center.
If gaussianDist is False, the highest permanence values will be evenly
distributed throughout the potential RF.
"""
# Figure out the target number of connected synapses. We want about 2X
# stimulusThreshold
minOn = 2 * max(self.stimulusThreshold, 10) / self.coincInputPoolPct \
/ self.inputDensity
# Get the gaussian distribution, with max magnitude just slightly above
# synPermConnected. Try to find a sigma that gives us about 2X
# stimulusThreshold connected synapses after sub-sampling for
# coincInputPoolPct. We will assume everything within +/- sigma will be
# connected. This logic uses the fact that an x value of sigma generates a
# magnitude of 0.6.
if self.gaussianDist:
# Only supported when we have 2D layouts
if self._coincRFShape[0] != self._coincRFShape[1]:
raise RuntimeError("Gaussian distibuted permanences are currently only"
"supported for 2-D layouts")
# The width and height of the center "blob" in inputs is the square root
# of the area
onAreaDim = numpy.sqrt(minOn)
# Sigma is at the edge of the center blob
sigma = onAreaDim/2
# Create the gaussian with a value of 1.0 at the center
perms = self._gaussianMatrix(dim=max(self._coincRFShape), sigma=sigma)
# The distance between the min and max values within the gaussian will
# be given by 'grange'. In a gaussian, the value at sigma away from the
# center is 0.6 * the value at the center. We want the values at sigma
# to be synPermConnected
maxValue = 1.0 / 0.6 * self.synPermConnected
perms *= maxValue
perms.shape = (-1,)
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# farther away than 2 sigma to 0. The value of a gaussing at 2 sigma
# is 0.135 * the value at the center
perms[perms < (0.135 * maxValue)] = 0
# Evenly distribute the permanences through the RF
else:
# Create a random distribution from 0 to 1.
perms = numpy.random.random(self._coincRFShape)
perms = perms.astype(realDType)
# Set the range of values to be between 0 and
# synPermConnected+synPermInctiveDec. This ensures that a pattern
# will always be learned in 1 iteration
maxValue = min(1.0, self.synPermConnected + self.synPermInactiveDec)
# What percentage do we want to be connected?
connectPct = 0.50
# What value from the 0 to 1 distribution will map to synPermConnected?
threshold = 1.0 - connectPct
# Which will be the connected and unconnected synapses?
connectedSyns = perms >= threshold
unconnectedSyns = numpy.logical_not(connectedSyns)
# Squeeze all values between threshold and 1.0 to be between
# synPermConnected and synPermConnected + synPermActiveInc / 4
# This makes sure the firing coincidence perms matching input bit get
# greater than synPermConnected and other unconnectedSyns get deconnected
# in one firing learning iteration.
srcOffset = threshold
srcRange = 1.0 - threshold
dstOffset = self.synPermConnected
dstRange = maxValue - self.synPermConnected
perms[connectedSyns] = (perms[connectedSyns] - srcOffset)/srcRange \
* dstRange / 4.0 + dstOffset
# Squeeze all values between 0 and threshold to be between 0 and
# synPermConnected
srcRange = threshold - 0.0
dstRange = self.synPermConnected - 0.0
perms[unconnectedSyns] = perms[unconnectedSyns]/srcRange \
* dstRange
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# below synPermActiveInc/2 to 0
perms[perms < (self.synPermActiveInc / 2.0)] = 0
perms.shape = (-1,)
return perms
def _gaussianMatrix(self, dim, sigma):
"""
Create and return a 2D matrix filled with a gaussian distribution. The
returned matrix will be of shape (dim, dim). The mean of the gaussian
will be in the center of the matrix and have a value of 1.0.
"""
gaussian = lambda x, sigma: numpy.exp(-(x**2) / (2*(sigma**2)))
# Allocate the matrix
m = numpy.empty((dim, dim), dtype=realDType)
# Find the center
center = (dim - 1) / 2.0
# TODO: Simplify using numpy.meshgrid
# Fill it in
for y in xrange(dim):
for x in xrange(dim):
dist = numpy.sqrt((x-center)**2 + (y-center)**2)
m[y,x] = gaussian(dist, sigma)
return m
def _makeMasterCoincidences(self, numCloneMasters, coincRFShape,
coincInputPoolPct, initialPermanence=None,
nupicRandom=None):
"""Make the master coincidence matrices and mater input histograms.
# TODO: Update this example
>>> FDRCSpatial._makeMasterCoincidences(1, 2, 0.33)
(array([[[ True, True, False, False, False],
[False, True, False, False, True],
[False, True, False, False, False],
[False, False, False, True, False],
[ True, False, False, False, False]]], dtype=bool), array([[[ 0.26982325, 0.19995725, 0. , 0. , 0. ],
[ 0. , 0.94128972, 0. , 0. , 0.36316112],
[ 0. , 0.06312726, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0.29740077, 0. ],
[ 0.81071907, 0. , 0. , 0. , 0. ]]], dtype=float32))
"""
if nupicRandom is None:
nupicRandom = NupicRandom(42)
if initialPermanence is None:
initialPermanence = self._initialPermanence()
coincRfArea = (coincRFShape[0] * coincRFShape[1])
coincInputPool = coincInputPoolPct * coincRfArea
# We will generate a list of sparse matrices
masterPotentialM = []
masterPermanenceM = []
toSample = numpy.arange(coincRfArea, dtype='uint32')
toUse = numpy.empty(coincInputPool, dtype='uint32')
denseM = numpy.zeros(coincRfArea, dtype=realDType)
for i in xrange(numCloneMasters):
nupicRandom.getUInt32Sample(toSample, toUse)
# Put in 1's into the potential locations
denseM.fill(0)
denseM[toUse] = 1
masterPotentialM.append(SM_01_32_32(denseM.reshape(coincRFShape)))
# Put in the initial permanences
denseM *= initialPermanence
masterPermanenceM.append(SM32(denseM.reshape(coincRFShape)))
# If we are not using common initial permanences, create another
# unique one for the next cell
if not self.commonDistributions:
initialPermanence = self._initialPermanence()
return masterPotentialM, masterPermanenceM
def _updateConnectedCoincidences(self, masters=None):
"""Update 'connected' version of the given coincidence.
Each 'connected' coincidence is effectively a binary matrix (AKA boolean)
matrix that is the same size as the input histogram matrices. They have
a 1 wherever the inputHistogram is "above synPermConnected".
"""
# If no masterNum given, update all of them
if masters is None:
masters = xrange(self.numCloneMasters)
nCellRows, nCellCols = self._coincRFShape
cloningOn = (self.numCloneMasters != self._coincCount)
for masterNum in masters:
# Where are we connected?
masterConnectedNZ = (
self._masterPermanenceM[masterNum].whereGreaterEqual(
0, nCellRows, 0, nCellCols, self.synPermConnected))
rowIdxs = masterConnectedNZ[:,0]
colIdxs = masterConnectedNZ[:,1]
self._masterConnectedM[masterNum].setAllNonZeros(
nCellRows, nCellCols, rowIdxs, colIdxs)
self._masterConnectedCoincSizes[masterNum] = len(rowIdxs)
# Update the corresponding rows in the super, mondo connected matrix that
# come from this master
masterConnected = (
self._masterConnectedM[masterNum].toDense().astype('bool')) # 0.2s
if cloningOn:
cells = self._cellsForMaster[masterNum]
else:
cells = [masterNum]
for cell in cells:
inputSlice = self._inputSlices[cell]
coincSlice = self._coincSlices[cell]
masterSubset = masterConnected[coincSlice]
sparseCols = self._inputLayout[inputSlice][masterSubset]
self._allConnectedM.replaceSparseRow(cell, sparseCols) # 4s.
def _setSlices(self):
"""Compute self._columnSlices and self._inputSlices
self._inputSlices are used to index into the input (assuming it's been
shaped to a 2D array) to get the receptive field of each column. There
is one item in the list for each column.
self._coincSlices are used to index into the coinc (assuming it's been
shaped to a 2D array) to get the valid area of the column. There
is one item in the list for each column.
This function is called upon unpickling, since we can't pickle slices.
"""
self._columnCenters = numpy.array(self._computeCoincCenters(
self.inputShape, self.coincidencesShape, self.inputBorder))
coincInputRadius = self.coincInputRadius
coincHeight, coincWidth = self._coincRFShape
inputShape = self.inputShape
inputBorder = self.inputBorder
# Compute the input slices for each cell. This is the slice of the entire
# input which intersects with the cell's permanence matrix.
if self._hasTopology:
self._inputSlices = [
numpy.s_[max(0, cy-coincInputRadius):
min(inputShape[0], cy+coincInputRadius + 1),
max(0, cx-coincInputRadius):
min(inputShape[1], cx+coincInputRadius + 1)]
for (cy, cx) in self._columnCenters]
else:
self._inputSlices = [numpy.s_[0:inputShape[0], 0:inputShape[1]]
for (cy, cx) in self._columnCenters]
self._inputSlices2 = numpy.zeros(4 * len(self._inputSlices),
dtype="uint32")
k = 0
for i in range(len(self._inputSlices)):
self._inputSlices2[k] = self._inputSlices[i][0].start
self._inputSlices2[k + 1] = self._inputSlices[i][0].stop
self._inputSlices2[k + 2] = self._inputSlices[i][1].start
self._inputSlices2[k + 3] = self._inputSlices[i][1].stop
k = k + 4
# Compute the coinc slices for each cell. This is which portion of the
# cell's permanence matrix intersects with the input.
if self._hasTopology:
if self.inputShape[0] > 1:
self._coincSlices = [
numpy.s_[max(0, coincInputRadius - cy):
min(coincHeight, coincInputRadius + inputShape[0] - cy),
max(0, coincInputRadius-cx):
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [
numpy.s_[0:1,
max(0, coincInputRadius-cx):
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:coincHeight, 0:coincWidth]
for (cy, cx) in self._columnCenters]
self._coincSlices2 = numpy.zeros((4*len(self._coincSlices)), dtype="uint32")
k = 0
for i in range(len(self._coincSlices)):
self._coincSlices2[k] = self._coincSlices[i][0].start
self._coincSlices2[k + 1] = self._coincSlices[i][0].stop
self._coincSlices2[k + 2] = self._coincSlices[i][1].start
self._coincSlices2[k + 3] = self._coincSlices[i][1].stop
k = k + 4
@staticmethod
def _computeCoincCenters(inputShape, coincidencesShape, inputBorder):
"""Compute the centers of all coincidences, given parameters.
This function is semi-public: tools may use it to generate good
visualizations of what the FDRCSpatial node is doing.
NOTE: It must be static or global function so that it can be called by
the ColumnActivityTab inspector *before* the first compute (before the
SP has been constructed).
If the input shape is (7,20), shown below with * for each input.
********************
********************
********************
********************
********************
********************
********************
If inputBorder is 1, we distribute the coincidences evenly over the
the area after removing the edges, @ shows the allowed input area below.
********************
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
********************
Each coincidence is centered at the closest @ and looks at a area with
coincInputRadius below it.
This function call returns an iterator over the coincidence centers. Each
element in iterator is a tuple: (y, x). The iterator returns elements in a
fixed order.
"""
# Determine Y centers
if inputShape[0] > 1: # 2-D layout
startHeight = inputBorder
stopHeight = inputShape[0] - inputBorder
else:
startHeight = stopHeight = 0
heightCenters = numpy.linspace(startHeight,
stopHeight,
coincidencesShape[0],
endpoint=False).astype('int32')
# Determine X centers
startWidth = inputBorder
stopWidth = inputShape[1] - inputBorder
widthCenters = numpy.linspace(startWidth,
stopWidth,
coincidencesShape[1],
endpoint=False).astype('int32')
return list(cross(heightCenters, widthCenters))
def _updateInhibitionObj(self):
"""
Calculate the average inhibitionRadius to use and update the inhibition
object accordingly. This looks at the size of the average connected
receptive field and uses that to determine the inhibition radius.
"""
# Compute the inhibition radius.
# If using global inhibition, just set it to include the entire region
if self.globalInhibition:
avgRadius = max(self.coincidencesShape)
# Else, set it based on the average size of the connected synapses area in
# each cell.
else:
totalDim = 0
# Get the dimensions of the connected receptive fields of each cell to
# compute the average
minDim = numpy.inf
maxDim = 0
for masterNum in xrange(self.numCloneMasters):
masterConnected = self._masterConnectedM[masterNum]
nzs = masterConnected.getAllNonZeros()
rows, cols = zip(*nzs)
rows = numpy.array(rows)
cols = numpy.array(cols)
if len(rows) >= 2:
height = rows.max() - rows.min() + 1
else:
height = 1
if len(cols) >= 2:
width = cols.max() - cols.min() + 1
else:
width = 1
avgDim = (height + width) / 2.0
minDim = min(minDim, avgDim)
maxDim = max(maxDim, avgDim)
totalDim += avgDim
# Get average width/height in input space
avgDim = totalDim / self.numCloneMasters
self._rfRadiusAvg = (avgDim - 1.0) / 2.0
self._rfRadiusMin = (minDim - 1.0) / 2.0
self._rfRadiusMax = (maxDim - 1.0) / 2.0
# How many columns in cell space does it correspond to?
if self.inputShape[0] > 1: # 2-D layout
coincsPerInputX = (float(self.coincidencesShape[1]) /
(self.inputShape[1] - 2 * self.inputBorder))
coincsPerInputY = (float(self.coincidencesShape[0]) /
(self.inputShape[0] - 2 * self.inputBorder))
else:
coincsPerInputX = coincsPerInputY = (
float(self.coincidencesShape[1] * self.coincidencesShape[0]) /
(self.inputShape[1] - 2 * self.inputBorder))
avgDim *= (coincsPerInputX + coincsPerInputY) / 2
avgRadius = (avgDim - 1.0) / 2.0
avgRadius = max(1.0, avgRadius)
# Can't be greater than the overall width or height of the level
maxDim = max(self.coincidencesShape)
avgRadius = min(avgRadius, maxDim)
avgRadius = int(round(avgRadius))
# Is there a need to re-instantiate the inhibition object?
if (self._inhibitionObj is None or
self._inhibitionObj.getInhibitionRadius() != avgRadius):
# What is our target density?
if self.localAreaDensity > 0:
localAreaDensity = self.localAreaDensity
else:
numCellsPerInhArea = (avgRadius * 2.0 + 1.0) ** 2
totalCells = self.coincidencesShape[0] * self.coincidencesShape[1]
numCellsPerInhArea = min(numCellsPerInhArea, totalCells)
localAreaDensity = float(self.numActivePerInhArea) / numCellsPerInhArea
# Don't let it be greater than 0.50
localAreaDensity = min(localAreaDensity, 0.50)
if self.spVerbosity >= 2:
print "Updating inhibition object:"
print " avg. rfRadius:", self._rfRadiusAvg
print " avg. inhRadius:", avgRadius
print " Setting density to:", localAreaDensity
self._inhibitionObj = Inhibition2(self.coincidencesShape[0], # height
self.coincidencesShape[1], # width
avgRadius, # inhRadius
localAreaDensity) # density
def _updateMinDutyCycles(self, actDutyCycles, minPctDutyCycle, minDutyCycles):
"""
Calculate and update the minimum acceptable duty cycle for each cell based
on the duty cycles of the cells within its inhibition radius and the
minPctDutyCycle.
Parameters:
-----------------------------------------------------------------------
actDutyCycles: The actual duty cycles of all cells
minPctDutyCycle: Each cell's minimum duty cycle will be set to
minPctDutyCycle times the duty cycle of the most active
cell within its inhibition radius
minDutyCycles: This array will be updated in place with the new minimum
acceptable duty cycles
"""
# What is the inhibition radius?
inhRadius = self._inhibitionObj.getInhibitionRadius()
# Reshape the actDutyCycles to match the topology of the level
cloningOn = (self.numCloneMasters != self._coincCount)
if not cloningOn:
actDutyCycles = actDutyCycles.reshape(self.coincidencesShape)
minDutyCycles = minDutyCycles.reshape(self.coincidencesShape)
# Special, faster handling when inhibition radius includes the entire
# set of cells.
if cloningOn or inhRadius >= max(self.coincidencesShape):
minDutyCycle = minPctDutyCycle * actDutyCycles.max()
minDutyCycles.fill(minPctDutyCycle * actDutyCycles.max())
# Else, process each cell
else:
(numRows, numCols) = self.coincidencesShape
for row in xrange(numRows):
top = max(0, row - inhRadius)
bottom = min(row + inhRadius + 1, numRows)
for col in xrange(numCols):
left = max(0, col - inhRadius)
right = min(col + inhRadius + 1, numCols)
maxDutyCycle = actDutyCycles[top:bottom, left:right].max()
minDutyCycles[row, col] = maxDutyCycle * minPctDutyCycle
if self.spVerbosity >= 2:
print "Actual duty cycles:"
print fdru.numpyStr(actDutyCycles, '%.4f')
print "Recomputed min duty cycles, using inhRadius of", inhRadius
print fdru.numpyStr(minDutyCycles, '%.4f')
def _computeOverlapsPy(self, inputShaped, stimulusThreshold):
"""
Computes overlaps for every column for the current input in place. The
overlaps less than stimulus threshold are set to zero here.
For columns with input RF going off the edge of input field, only regions
within the input field are considered. This is equivalent to padding the
input field with zeros.
Parameters:
------------------------------------------------------------------------
inputShaped: input at the current time step, shaped to the input
topology
stimulusThreshold: stimulusThreshold to use
Member variables used/updated:
------------------------------------------------------------------------
_inputSlices: Index into the input (assuming it's been shaped to a 2D
array) to get the receptive field of each column.
_coincSlices: Index into the coinc (assuming it's been shaped to a 2D
array) to get the valid region of each column.
_overlaps: Result is placed into this array which holds the overlaps of
each column with the input
"""
flatInput = inputShaped.reshape(-1)
self._allConnectedM.rightVecSumAtNZ_fast(flatInput, self._overlaps)
# Apply stimulusThreshold
# TODO: Is there a faster numpy operation for this?
self._overlaps[self._overlaps < stimulusThreshold] = 0
self._overlapsNoBoost = self._overlaps.copy()
def _computeOverlapsCPP(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but using a C++ implementation.
"""
cpp_overlap(self._cloneMapFlat,
self._inputSlices2, self._coincSlices2,
inputShaped, self._masterConnectedM,
stimulusThreshold,
self._overlaps)
def _computeOverlapsTest(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but compares the python and C++
implementations.
"""
# Py version
self._computeOverlapsPy(inputShaped, stimulusThreshold)
overlaps2 = copy.deepcopy(self._overlaps)
# C++ version
self._computeOverlapsCPP(inputShaped, stimulusThreshold)
if (abs(self._overlaps - overlaps2) > 1e-6).any():
print self._overlaps, overlaps2, abs(self._overlaps - overlaps2)
import pdb; pdb.set_trace()
sys.exit(0)
def _raiseAllPermanences(self, masterNum, minConnections=None,
densePerm=None, densePotential=None):
"""
Raise all permanences of the given master. If minConnections is given, the
permanences will be raised until at least minConnections of them are
connected strength.
If minConnections is left at None, all permanences will be raised by
self._synPermBelowStimulusInc.
After raising all permanences, we also "sparsify" the permanence matrix
and set to 0 any permanences which are already very close to 0, this
keeps the memory requirements of the sparse matrices used to store
the permanences lower.
Parameters:
----------------------------------------------------------------------------
masterNum: Which master to bump up
minConnections: Desired number of connected synapses to have
If None, then all permanences are simply bumped up
by self._synPermBelowStimulusInc
densePerm: The dense representation of the master's permanence
matrix, if available. If not specified, we will
create this from the stored sparse representation.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix. The stored sparse
matrix will ALWAYS be updated from the densePerm if
the densePerm is provided.
densePotential: The dense representation of the master's potential
synapses matrix, if available. If not specified, we
will create this from the stored sparse potential
matrix.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix.
retval: (modified, numConnections)
modified: True if any permanences were raised
numConnections: Number of actual connected synapses
(not computed if minConnections was
None, so None is returned in that
case.)
"""
# It's faster to perform this operation on the dense matrices and
# then convert to sparse once we're done since we will be potentially
# introducing and then later removing a bunch of non-zeros.
# Get references to the sparse perms and potential syns for this master
sparsePerm = self._masterPermanenceM[masterNum]
sparsePotential = self._masterPotentialM[masterNum]
# We will trim off all synapse permanences below this value to 0 in order
# to keep the memory requirements of the SparseMatrix lower
trimThreshold = self.synPermActiveInc / 2.0
# See if we already have the required number of connections. If we don't,
# get the dense form of the permanences if we don't have them already
if densePerm is None:
# See if we already have enough connections, if so, we can avoid the
# overhead of converting to dense
if minConnections is not None:
numConnected = sparsePerm.countWhereGreaterEqual(
0, self._coincRFShape[0], 0, self._coincRFShape[1],
self.synPermConnected)
if numConnected >= minConnections:
return (False, numConnected)
densePerm = self._masterPermanenceM[masterNum].toDense()
elif minConnections is not None:
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (False, numConnected)
# Get the dense form of the potential synapse locations
if densePotential is None:
densePotential = self._masterPotentialM[masterNum].toDense()
# Form the array with the increments
incrementM = densePotential.astype(realDType)
incrementM *= self._synPermBelowStimulusInc
# Increment until we reach our target number of connections
assert (densePerm.dtype == realDType)
while True:
densePerm += incrementM
if minConnections is None:
numConnected = None
break
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
break
# Convert back to sparse form and trim any values that are already
# close to zero
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (True, numConnected)
def _bumpUpWeakCoincidences(self):
"""
This bump-up ensures every coincidence have non-zero connections. We find
all coincidences which have overlaps less than stimulus threshold.
We add synPermActiveInc to all the synapses. This step when repeated over
time leads to synapses crossing synPermConnected threshold.
"""
# Update each cell's connected threshold based on the duty cycle before
# inhibition. The connected threshold is linearly interpolated
# between the points (dutyCycle:0, thresh:0) and (dutyCycle:minDuty,
# thresh:synPermConnected). This is a line defined as: y = mx + b
# thresh = synPermConnected/minDuty * dutyCycle
bumpUpList = (
self._dutyCycleBeforeInh < self._minDutyCycleBeforeInh).nonzero()[0]
for master in bumpUpList:
self._raiseAllPermanences(master)
# Update the connected synapses for each master we touched.
self._updateConnectedCoincidences(bumpUpList)
if self.spVerbosity >= 2 and len(bumpUpList) > 0:
print ("Bumping up permanences in following cells due to falling below"
"minDutyCycleBeforeInh:"), bumpUpList
def _updateBoostFactors(self):
"""
Update the boost factors. The boost factors is linearly interpolated
between the points (dutyCycle:0, boost:maxFiringBoost) and
(dutyCycle:minDuty, boost:1.0). This is a line defined as: y = mx + b
boost = (1-maxFiringBoost)/minDuty * dutyCycle + maxFiringBoost
Parameters:
------------------------------------------------------------------------
boostFactors: numpy array of boost factors, defined per master
"""
if self._minDutyCycleAfterInh.sum() > 0:
self._firingBoostFactors = (
(1 - self.maxFiringBoost) /
self._minDutyCycleAfterInh * self._dutyCycleAfterInh +
self.maxFiringBoost)
self._firingBoostFactors[self._dutyCycleAfterInh >
self._minDutyCycleAfterInh] = 1.0
def _updateInputUse(self, onCellIndices):
"""
During learning (adapting permanence values), we need to be able to tell
which inputs are going to 2 or more active cells at once.
We step through each coinc and mark all the inputs it is connected to. The
inputUse array acts as a counter for the number of connections to the coincs
from each input.
Parameters:
------------------------------------------------------------------------
inputUse: numpy array of number of coincs connected to each input
"""
allConnected = SM32(self._allConnectedM)
# TODO: avoid this copy
self._inputUse[:] = allConnected.addListOfRows(
onCellIndices).reshape(self.inputShape)
def _adaptSynapses(self, onCellIndices, orphanCellIndices, input):
"""
This is the main function in learning of SP. The permanence values are
changed based on the learning rules.
Parameters:
------------------------------------------------------------------------
onCellIndices: columns which are turned on after inhibition. The
permanence values of these coincs are adapted based on the
input.
orphanCellIndices: columns which had very high overlap with the input, but
ended up being inhibited
input: Input, shaped to the input topology
retval: list of masterCellIndices that were actually updated, or
None if cloning is off
"""
# Capturing learning stats?
if self.printPeriodicStats > 0:
self._stats['explainedInputsCurIteration'] = set()
# Precompute the active, inactive, and dupe inputs up front for speed
# TODO: put these into pre-allocated arrays for speed
self._activeInput[:] = input
# Create a matrix containing the default permanence deltas for each input
self._permChanges.fill(-1 * self.synPermInactiveDec)
self._permChanges[self._activeInput] = self.synPermActiveInc
if self.synPermActiveSharedDec != 0:
numpy.logical_and(self._activeInput, self._inputUse>1, self._dupeInput)
self._permChanges[self._dupeInput] -= self.synPermActiveSharedDec
# Cloning? If so, scramble the onCells so that we pick a random one to
# update for each master. We only update a master cell at most one time
# per input presentation.
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
# Scramble the onCellIndices so that we pick a random one to update
onCellIndices = list(onCellIndices)
random.shuffle(onCellIndices)
visitedMasters = set()
# For the firing cells, update permanence values
for columnNum in itertools.chain(onCellIndices, orphanCellIndices):
# Get the master number
masterNum = self._cloneMapFlat[columnNum]
# If cloning, only visit each master once
if cloningOn:
if masterNum in visitedMasters:
continue
visitedMasters.add(masterNum)
# Get the slices of input that overlap with the valid area of this master
inputSlice = self._inputSlices[columnNum]
rfActiveInput = self._activeInput[inputSlice]
rfPermChanges = self._permChanges[inputSlice]
# Get the potential synapses, permanence values, and connected synapses
# for this master
masterPotential = self._masterPotentialM[masterNum].toDense()
masterPermanence = self._masterPermanenceM[masterNum].toDense()
masterConnected = (
self._masterConnectedM[masterNum].toDense().astype('bool'))
# Make changes only over the areas that overlap the input level. For
# coincidences near the edge of the level for example, this excludes the
# synapses outside the edge.
coincSlice = self._coincSlices[columnNum]
masterValidPermanence= masterPermanence[coincSlice]
# Capturing learning stats?
if self.printPeriodicStats > 0:
masterValidConnected = masterConnected[coincSlice]
explainedInputs = self._inputLayout[inputSlice][masterValidConnected]
self._stats['explainedInputsCurIteration'].update(explainedInputs)
if self.spVerbosity >= 3:
print " adapting cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " initialConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
print " firingLevel: %d" % (self._overlaps[columnNum])
print " firingBoostFactor: %f" % (self._firingBoostFactors[masterNum])
print " input slice: \n"
self._printInputSlice(rfActiveInput, prefix=' ')
# Update permanences given the active input (NOTE: The "FP" in this
# function name stands for "Function Pointer").
if columnNum in orphanCellIndices:
# Decrease permanence of active inputs
masterValidPermanence[rfActiveInput] -= self.synPermOrphanDec
else:
self._updatePermanenceGivenInputFP(columnNum, masterNum, input,
self._inputUse, masterPermanence, masterValidPermanence,
rfActiveInput, rfPermChanges)
# Clip to absolute min and max permanence values
numpy.clip(masterPermanence, self._synPermMin, self._synPermMax,
out=masterPermanence)
# Keep only the potential syns for this cell
numpy.multiply(masterPermanence, masterPotential, masterPermanence)
# If we are tracking learning stats, prepare to see how many changes
# were made to the cell connections
if self.printPeriodicStats > 0:
masterConnectedOrig = SM_01_32_32(self._masterConnectedM[masterNum])
# If the number of connected synapses happens to fall below
# stimulusThreshold, bump up all permanences a bit.
# We could also just wait for the "duty cycle falls below
# minDutyCycleBeforeInb" logic to catch it, but doing it here is
# pre-emptive and much faster.
#
# The "duty cycle falls below minDutyCycleBeforeInb" logic will still
# catch other possible situations, like:
# * if the set of inputs a cell learned suddenly stop firing due to
# input statistic changes
# * damage to the level below
# * input is very sparse and we still don't pass stimulusThreshold even
# with stimulusThreshold conneted synapses.
self._raiseAllPermanences(masterNum,
minConnections=self.stimulusThreshold,
densePerm=masterPermanence,
densePotential=masterPotential)
# Update the matrices that contain the connected syns for this cell.
self._updateConnectedCoincidences([masterNum])
# If we are tracking learning stats, see how many changes were made to
# this cell's connections
if self.printPeriodicStats > 0:
origNumConnections = masterConnectedOrig.nNonZeros()
masterConnectedOrig.logicalAnd(self._masterConnectedM[masterNum])
numUnchanged = masterConnectedOrig.nNonZeros()
numChanges = origNumConnections - numUnchanged
numChanges += (self._masterConnectedM[masterNum].nNonZeros() -
numUnchanged)
self._stats['numChangedConnectionsSum'][masterNum] += numChanges
self._stats['numLearns'][masterNum] += 1
# Verbose?
if self.spVerbosity >= 3:
print " done cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " newConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
self._printSyns(columnNum, prefix=' ',
showValues=(self.spVerbosity >= 4))
print
# Return list of updated masters
if cloningOn:
return list(visitedMasters)
else:
return onCellIndices
def _updatePermanenceGivenInputPy(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Given the input to a master coincidence, update it's permanence values
based on our learning rules.
On Entry, we are given the slice of the permanence matrix that corresponds
only to the area of the coincidence master that is within the borders of
the entire input field.
Parameters:
------------------------------------------------------------------------
columnNum: The column number of this cell
masterNum: The master coincidence that corresponds to this column
input: The entire input, shaped appropriately
inputUse: The same shape as input. Each entry is a count of the
number of *currently active cells* that are connected
to that input.
permanence: The entire masterPermanence matrix for this master
permanenceSlice: The slice of the masterPermanence matrix for this master
that intersects the input field, i.e. does not overhang
the outside edges of the input.
activeInputSlice: The portion of 'input' that intersects permanenceSlice,
set to True where input != 0
permChangesSlice: The portion of 'input' that intersects permanenceSlice,
set to self.synPermActiveInc where input != 0 and
self.synPermInactiveDec where the input == 0. This is
used to optimally apply self.synPermActiveInc and
self.synPermInactiveDec at the same time and can be
used for any cell whose _synPermBoostFactor is set
to 1.0.
"""
# TODO: This function does nothing.
# Apply the baseline increment/decrements
permanenceSlice += permChangesSlice
# If this cell has permanence boost, apply the incremental
def _updatePermanenceGivenInputCPP(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but using a C++ implementation.
"""
inputNCols = self.inputShape[1]
masterNCols = self._masterPotentialM[masterNum].shape[1]
# TODO: synPermBoostFactors has been removed. CPP implementation has not
# been updated for this.
adjustMasterValidPermanence(columnNum,
masterNum,
inputNCols,
masterNCols,
self.synPermActiveInc,
self.synPermInactiveDec,
self.synPermActiveSharedDec,
input,
inputUse,
self._inputSlices2,
self._coincSlices2,
self._synPermBoostFactors,
permanence)
def _updatePermanenceGivenInputTest(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but compares the python and C++
implementations.
"""
mp2 = copy.deepcopy(permanence)
mvp2 = copy.deepcopy(permanenceSlice)
# Py version
import pdb; pdb.set_trace()
self._updatePermanenceGivenInputPy(columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice)
# C++ version
self._updatePermanenceGivenInputCPP(columnNum, masterNum, input,
inputUse, mp2, mvp2, activeInputSlice, permChangesSlice)
if abs(mp2 - permanence).max() > 1e-6:
print abs(mp2 - permanence).max()
import pdb; pdb.set_trace()
sys.exit(0)
def _periodicStatsCreate(self):
"""
Allocate the periodic stats structure
"""
self._stats = dict()
self._stats['numChangedConnectionsSum'] = numpy.zeros(
self.numCloneMasters, dtype=realDType)
self._stats['numLearns'] = numpy.zeros(
self.numCloneMasters, dtype=realDType)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['maxBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# This dict maintains mappings of specific input patterns to specific
# output patterns. It is used to detect "thrashing" of cells. We measure
# how similar the output presentation of a specific input is to the
# last time we saw it.
self._stats['inputPatterns'] = dict()
self._stats['inputPatternsLimit'] = 5000
self._periodicStatsReset()
def _periodicStatsReset(self):
"""
Reset the periodic stats this is done every N iterations before capturing
a new set of stats.
"""
self._stats['numSamples'] = 0
self._stats['numOnSum'] = 0
self._stats['underCoveragePctSum'] = 0
self._stats['overCoveragePctSum'] = 0
self._stats['cellOverlapSums'] = 0
self._stats['cellPctOverlapSums'] = 0
self._stats['explainedInputsCurIteration'] = set()
self._stats['startTime'] = time.time()
# These keep a count of the # of changed connections per update
# for each master
self._stats['numChangedConnectionsSum'].fill(0)
self._stats['numLearns'].fill(0)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'].fill(self.maxFiringBoost)
self._stats['maxBoostFactor'].fill(0)
# This keeps track of the average distance between the SP output of
# a specific input pattern now and the last time we saw it.
self._stats['outputPatternDistanceSum'] = 0
self._stats['outputPatternSamples'] = 0
def _periodicStatsComputeEnd(self, activeCells, activeInputs):
"""
Called at the end of compute. This increments the number of computes
and also summarizes the under and over coverage and whatever other
periodic stats we need.
If the period is up, it then prints the accumuated stats and resets them
for the next period
Parameters:
------------------------------------------------------------------
activeCells: list of the active cells
activeInputs: list of the active inputs
"""
# Update number of samples
self._stats['numSamples'] += 1
# Compute under and over coverage
numOn = len(activeCells)
self._stats['numOnSum'] += numOn
expInput = self._stats['explainedInputsCurIteration']
inputLen = len(activeInputs)
underCoverage = len(set(activeInputs).difference(expInput))
self._stats['underCoveragePctSum'] += float(underCoverage) / inputLen
expInput.difference_update(activeInputs)
overCoverage = len(expInput)
self._stats['overCoveragePctSum'] += float(overCoverage) / inputLen
# Keep track of the min and max boost factor seen for each column
numpy.minimum(self._firingBoostFactors, self._stats['minBoostFactor'],
self._stats['minBoostFactor'])
numpy.maximum(self._firingBoostFactors, self._stats['maxBoostFactor'],
self._stats['maxBoostFactor'])
# Calculate the distance in the SP output between this input now
# and the last time we saw it.
inputPattern = str(sorted(activeInputs))
outputNZ, sampleIdx = self._stats['inputPatterns'].get(inputPattern,
(None, None))
activeCellSet = set(activeCells)
if outputNZ is not None:
distance = (len(activeCellSet.difference(outputNZ)) +
len(outputNZ.difference(activeCellSet)))
self._stats['inputPatterns'][inputPattern] = (activeCellSet, sampleIdx)
self._stats['outputPatternDistanceSum'] += distance
self._stats['outputPatternSamples'] += 1
# Add this sample to our dict, if it's not too large already
elif len(self._stats['inputPatterns']) < self._stats['inputPatternsLimit']:
self._stats['inputPatterns'][inputPattern] = (activeCellSet,
self._iterNum)
# If it's not time to print them out, return now.
if (self._iterNum % self.printPeriodicStats) != 0:
return
numSamples = float(self._stats['numSamples'])
# Calculate number of changes made per master
masterTouched = numpy.where(self._stats['numLearns'] > 0)
if len(masterTouched[0]) == 0:
numMasterChanges = numpy.zeros(1)
else:
numMasterChanges = self._stats['numChangedConnectionsSum'][masterTouched]
numMasterChanges /= self._stats['numLearns'][masterTouched]
# This fills in the static learning stats into self._learningStats
self.getLearningStats()
# Calculate and copy the transient learning stats into the
# self._learningStats dict, for possible retrieval later by
# the getLearningStats() method.
self._learningStats['elapsedTime'] = time.time() - self._stats['startTime']
self._learningStats['activeCountAvg'] = (self._stats['numOnSum'] /
numSamples)
self._learningStats['underCoveragePct'] = (
100.0 * self._stats['underCoveragePctSum'] / numSamples)
self._learningStats['overCoveragePct'] = (
(100.0 * self._stats['overCoveragePctSum'] / numSamples))
self._learningStats['numConnectionChangesAvg'] = numMasterChanges.mean()
self._learningStats['numConnectionChangesMin'] = numMasterChanges.min()
self._learningStats['numConnectionChangesMax'] = numMasterChanges.max()
self._learningStats['avgCellOverlap'] = (
(float(self._stats['cellOverlapSums']) /
max(1, self._stats['numOnSum'])))
self._learningStats['avgCellPctOverlap'] = (
(100.0 * self._stats['cellPctOverlapSums'] /
max(1, self._stats['numOnSum'])))
self._learningStats['firingBoostMaxChangePct'] = (
100.0 * (self._stats['maxBoostFactor'] /
self._stats['minBoostFactor']).max() - 100.0)
self._learningStats['outputRepresentationChangeAvg'] = (
float(self._stats['outputPatternDistanceSum']) /
max(1, self._stats['outputPatternSamples']))
self._learningStats['outputRepresentationChangePctAvg'] = (
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
self._learningStats['numUniqueInputsSeen'] = (
len(self._stats['inputPatterns']))
if (self._learningStats['numUniqueInputsSeen'] >=
self._stats['inputPatternsLimit']):
self._learningStats['numUniqueInputsSeen'] = -1
# Print all stats captured
print "Learning stats for the last %d iterations:" % (numSamples)
print " iteration #: %d" % (self._iterNum)
print " inference iteration #: %d" % (self._inferenceIterNum)
print " elapsed time: %.2f" % (
self._learningStats['elapsedTime'])
print " avg activeCount: %.1f" % (
self._learningStats['activeCountAvg'])
print " avg under/overCoverage: %-6.1f / %-6.1f %%" % (
self._learningStats['underCoveragePct'],
self._learningStats['overCoveragePct'])
print " avg cell overlap: %-6.1f / %-6.1f %%" % (
self._learningStats['avgCellOverlap'],
self._learningStats['avgCellPctOverlap'])
print " avg/min/max RF radius: %-6.1f / %-6.1f / %-6.1f" % (
self._learningStats['rfRadiusAvg'],
self._learningStats['rfRadiusMin'],
self._learningStats['rfRadiusMax'])
print " inhibition radius: %d" % (
self._learningStats['inhibitionRadius'])
print " target density: %.5f %%" % (
self._learningStats['targetDensityPct'])
print " avg/min/max coinc. size: %-6.1f / %-6d / %-6d" % (
self._learningStats['coincidenceSizeAvg'],
self._learningStats['coincidenceSizeMin'],
self._learningStats['coincidenceSizeMax'])
print " avg/min/max DC before inh: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['dcBeforeInhibitionAvg'],
self._learningStats['dcBeforeInhibitionMin'],
self._learningStats['dcBeforeInhibitionMax'])
print " avg/min/max DC after inh: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['dcAfterInhibitionAvg'],
self._learningStats['dcAfterInhibitionMin'],
self._learningStats['dcAfterInhibitionMax'])
print " avg/min/max boost: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['firingBoostAvg'],
self._learningStats['firingBoostMin'],
self._learningStats['firingBoostMax'])
print " avg/min/max # conn. changes: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['numConnectionChangesAvg'],
self._learningStats['numConnectionChangesMin'],
self._learningStats['numConnectionChangesMax'])
print " max change in boost: %.1f %%" % (
self._learningStats['firingBoostMaxChangePct'])
print " avg change in output repr.: %-6.1f / %-6.1f %%" % (
self._learningStats['outputRepresentationChangeAvg'],
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
print " # of unique input pats seen: %d" % (
self._learningStats['numUniqueInputsSeen'])
# Reset the stats for the next period.
self._periodicStatsReset()
def _printInputSlice(self, inputSlice, prefix=''):
"""Print the given input slice in a nice human readable format.
Parameters:
---------------------------------------------------------------------
cell: The slice of input to print
prefix: This is printed at the start of each row of the
coincidence
"""
# Shape of each coincidence
rfHeight, rfWidth = inputSlice.shape
syns = inputSlice != 0
def _synStr(x):
if not x:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
print prefix, ''.join(items)
def _printSyns(self, cell, prefix='', showValues=False):
"""Print the synapse permanence values for the given cell in a nice,
human, readable format.
Parameters:
---------------------------------------------------------------------
cell: which cell to print
prefix: This is printed at the start of each row of the
coincidence
showValues: If True, print the values of each permanence.
If False, just print a ' ' if not connected and a '*'
if connected
"""
# Shape of each coincidence
(rfHeight, rfWidth) = self.inputShape
# Get the synapse permanences.
masterNum = self._cloneMapFlat[cell]
syns = self._masterPermanenceM[masterNum].toDense()
if showValues:
def _synStr(x):
if x == 0:
return ' -- '
elif x < 0.001:
return ' 0 '
elif x >= self.synPermConnected:
return '#%3.2f' % x
else:
return ' %3.2f' % x
else:
def _synStr(x):
if x < self.synPermConnected:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
if showValues:
print prefix, ' '.join(items)
else:
print prefix, ''.join(items)
def _printMemberSizes(self, over=100):
"""Print the size of each member."""
members = self.__dict__.keys()
sizeNamePairs = []
totalSize = 0
for member in members:
item = self.__dict__[member]
if hasattr(item, '__func__'):
continue
try:
if hasattr(item, '__len__'):
size = 0
for i in xrange(len(item)):
size += len(cPickle.dumps(item[i]))
else:
size = len(cPickle.dumps(item))
except:
print "WARNING: Can't pickle %s" % (member)
size = 0
sizeNamePairs.append((size, member))
totalSize += size
# Print them out from highest to lowest
sizeNamePairs.sort(reverse=True)
for (size, name) in sizeNamePairs:
if size > over:
print "%10d (%10.3fMb) %s" % (size, size/1000000.0, name)
print "\nTOTAL: %10d (%10.3fMB) " % (totalSize, totalSize/1000000.0)
def printParams(self):
"""Print the main creation parameters associated with this instance."""
print "FDRCSpatial2 creation parameters: "
print "inputShape =", self.inputShape
print "inputBorder =", self.inputBorder
print "inputDensity =", self.inputDensity
print "coincidencesShape =", self.coincidencesShape
print "coincInputRadius =", self.coincInputRadius
print "coincInputPoolPct =", self.coincInputPoolPct
print "gaussianDist =", self.gaussianDist
print "commonDistributions =", self.commonDistributions
print "localAreaDensity =", self.localAreaDensity
print "numActivePerInhArea =", self.numActivePerInhArea
print "stimulusThreshold =", self.stimulusThreshold
print "synPermInactiveDec =", self.synPermInactiveDec
print "synPermActiveInc =", self.synPermActiveInc
print "synPermActiveSharedDec =", self.synPermActiveSharedDec
print "synPermOrphanDec =", self.synPermOrphanDec
print "synPermConnected =", self.synPermConnected
print "minPctDutyCycleBeforeInh =", self.minPctDutyCycleBeforeInh
print "minPctDutyCycleAfterInh =", self.minPctDutyCycleAfterInh
print "dutyCyclePeriod =", self.dutyCyclePeriod
print "maxFiringBoost =", self.maxFiringBoost
print "maxSSFiringBoost =", self.maxSSFiringBoost
print "maxSynPermBoost =", self.maxSynPermBoost
print "minDistance =", self.minDistance
print "spVerbosity =", self.spVerbosity
print "printPeriodicStats =", self.printPeriodicStats
print "testMode =", self.testMode
print "numCloneMasters =", self.numCloneMasters
Update printParams in old SP
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have purchased from
# Numenta, Inc. a separate commercial license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 3 as
# published by the Free Software Foundation.
#
# 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.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""Spatial pooler implementation.
TODO: Change print statements to use the logging module.
"""
import copy
import cPickle
import inspect
import itertools
import math
import numpy
import numpy.random
from operator import itemgetter
import os
import random
import struct
import sys
import time
from nupic.bindings.algorithms import (adjustMasterValidPermanence, cpp_overlap,
cpp_overlap_sbm, Inhibition2)
from nupic.bindings.math import (count_gte, GetNTAReal, Random as NupicRandom,
SM_01_32_32, SM32)
from nupic.math.cross import cross
from nupic.research import fdrutilities as fdru
realDType = GetNTAReal()
gPylabInitialized = False
# kDutyCycleFactor add dutyCycleAfterInh to overlap in Inhibition step to be a
# tie breaker
kDutyCycleFactor = 0.01
def _extractCallingMethodArgs():
"""
Returns args dictionary from the calling method
"""
callingFrame = inspect.stack()[1][0]
argNames, _, _, frameLocalVarDict = inspect.getargvalues(callingFrame)
argNames.remove("self")
args = copy.copy(frameLocalVarDict)
for varName in frameLocalVarDict:
if varName not in argNames:
args.pop(varName)
return args
class FDRCSpatial2(object):
"""
Class for spatial pooling based on fixed random distributed
representation (FDR).
This version of FDRCSpatial inlcudes adaptive receptive fields, no-dupe rules
and gradual boosting. It supports 1-D and 2-D topologies with cloning.
"""
def __init__(self,
inputShape=(32, 32),
inputBorder=8,
inputDensity=1.0,
coincidencesShape=(48, 48),
coincInputRadius=16,
coincInputPoolPct=1.0,
gaussianDist=False,
commonDistributions=False,
localAreaDensity=-1.0,
numActivePerInhArea=10.0,
stimulusThreshold=0,
synPermInactiveDec=0.01,
synPermActiveInc=0.1,
synPermActiveSharedDec=0.0,
synPermOrphanDec=0.0,
synPermConnected=0.10,
minPctDutyCycleBeforeInh=0.001,
minPctDutyCycleAfterInh=0.001,
dutyCyclePeriod=1000,
maxFiringBoost=10.0,
maxSSFiringBoost=2.0,
maxSynPermBoost=10.0,
minDistance=0.0,
cloneMap=None,
numCloneMasters=-1,
seed=-1,
spVerbosity=0,
printPeriodicStats=0,
testMode=False,
globalInhibition=False,
spReconstructionParam="unweighted_mean",
useHighTier=True,
randomSP=False,
):
"""
Parameters:
----------------------------
inputShape: The dimensions of the input vector. Format is (height,
width) e.g. (24, 72). If the input is from a sensor,
it is interpreted as having a 2-D topology of 24
pixels high and 72 wide.
inputBorder: The first column from an edge will be centered
over an input which is 'inputBorder' inputs from the
edge.
inputDensity: The density of the input. This is only to aid in
figuring out the initial number of connected synapses
to place on each column. The lower the inputDensity,
the more initial connections will be assigned to each
column.
coincidencesShape: The dimensions of column layout. Format is (height,
width) e.g. (80,100) means a total of 80*100 = 800 are
arranged in a 2-D topology with 80 rows and
100 columns.
coincInputRadius: This defines the max radius of the receptive field of
each column. This is used to limit memory requirements
and processing time. It could be set large enough to
encompass the entire input field and the SP would
still work fine, but require more memory and
processing time.
This parameter defines a square area: a column will
have a max square RF with sides of length
2 * coincInputRadius + 1.
coincInputPoolPct What percent of the columns's receptive field is
available for potential synapses. At initialization
time, we will choose
coincInputPoolPct * (2*coincInputRadius + 1)^2
potential synapses from the receptive field.
gaussianDist: If true, the initial permanences assigned to each
column will have a gaussian distribution to them,
making the column favor inputs directly below it over
inputs farther away. If false, the initial permanences
will have a random distribution across the column's
entire potential receptive field.
commonDistributions: If set to True (the default, faster startup time),
each column will be given the same initial permanence
values.
This is normally OK when you will be training, but if
you will be sticking with the untrained network,
you will want to set this to False (which makes
startup take longer).
localAreaDensity: The desired density of active columns within a local
inhibition area (the size of which is set by the
internally calculated inhibitionRadius, which is in
turn determined from the average size of the connected
receptive fields of all columns). The inhibition logic
will insure that at most N columns remain ON within a
local inhibition area, where N = localAreaDensity *
(total number of columns in inhibition area).
numActivePerInhArea: An alternate way to control the density of the active
columns. If numActivePerInhArea is specified then
localAreaDensity must be -1, and vice versa. When
using numActivePerInhArea, the inhibition logic will
insure that at most 'numActivePerInhArea' columns
remain ON within a local inhibition area (the size of
which is set by the internally calculated
inhibitionRadius, which is in turn determined from the
average size of the connected receptive fields of all
columns). When using this method, as columns learn and
grow their effective receptive fields, the
inhibitionRadius will grow, and hence the net density
of the active columns will *decrease*. This is in
contrast to the localAreaDensity method, which keeps
the density of active columns the same regardless of
the size of their receptive fields.
stimulusThreshold: This is a number specifying the minimum number of
synapses that must be on in order for a columns to
turn ON.
The purpose of this is to prevent noise input from
activating columns.
synPermInactiveDec: How much an inactive synapse is decremented, specified
as a percent of a fully grown synapse.
synPermActiveInc: How much to increase the permanence of an active
synapse, specified as a percent of a fully grown
synapse.
synPermActiveSharedDec: How much to decrease the permanence of an active
synapse which is connected to another column that is
active at the same time. Specified as a percent of a
fully grown synapse.
synPermOrphanDec: How much to decrease the permanence of an active
synapse on a column which has high overlap with the
input, but was inhibited (an "orphan" column).
synPermConnected: The default connected threshold. Any synapse whose
permanence value is above the connected threshold is
a "connected synapse", meaning it can contribute to
the cell's firing. Typical value is 0.10. Cells whose
activity level before inhibition falls below
minDutyCycleBeforeInh will have their own internal
synPermConnectedCell threshold set below this default
value.
(This concept applies to both SP and TP and so 'cells'
is correct here as opposed to 'columns')
minPctDutyCycleBeforeInh: A number between 0 and 1.0, used to set a floor on
how often a column should have at least
stimulusThreshold active inputs. Periodically, each
column looks at the duty cycle before inhibition of
all other column within its inhibition radius and sets
its own internal minimal acceptable duty cycle to:
minPctDutyCycleBeforeInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle before
inhibition falls below this computed value will get
all of its permanence values boosted up by
synPermActiveInc. Raising all permanences in response
to a sub-par duty cycle before inhibition allows a
cell to search for new inputs when either its
previously learned inputs are no longer ever active,
or when the vast majority of them have been "hijacked"
by other columns due to the no-dupe rule.
minPctDutyCycleAfterInh: A number between 0 and 1.0, used to set a floor on
how often a column should turn ON after inhibition.
Periodically, each column looks at the duty cycle
after inhibition of all other columns within its
inhibition radius and sets its own internal minimal
acceptable duty cycle to:
minPctDutyCycleAfterInh *
max(other columns' duty cycles).
On each iteration, any column whose duty cycle after
inhibition falls below this computed value will get
its internal boost factor increased.
dutyCyclePeriod: The period used to calculate duty cycles. Higher
values make it take longer to respond to changes in
boost or synPerConnectedCell. Shorter values make it
more unstable and likely to oscillate.
maxFiringBoost: The maximum firing level boost factor. Each column's
raw firing strength gets multiplied by a boost factor
before it gets considered for inhibition.
The actual boost factor for a column is number between
1.0 and maxFiringBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxFiringBoost
is used if the duty cycle is 0, and any duty cycle in
between is linearly extrapolated from these 2
endpoints.
maxSSFiringBoost: Once a column turns ON, it's boost will immediately
fall down to maxSSFiringBoost if it is above it. This
is accomplished by internally raising it's computed
duty cycle accordingly. This prevents a cell which has
had it's boost raised extremely high from turning ON
for too many diverse inputs in a row within a short
period of time.
maxSynPermBoost: The maximum synPermActiveInc boost factor. Each
column's synPermActiveInc gets multiplied by a boost
factor to make the column more or less likely to form
new connections.
The actual boost factor used is a number between
1.0 and maxSynPermBoost. A boost factor of 1.0 is used
if the duty cycle is >= minDutyCycle, maxSynPermBoost
is used if the duty cycle is 0, and any duty cycle
in between is linearly extrapolated from these 2
endpoints.
minDistance: This parameter impacts how finely the input space is
quantized. It is a value between 0 and 1.0. If set
to 0, then every unique input presentation will
generate a unique output representation, within the
limits of the total number of columns available.
Higher values will tend to group similar inputs
together into the same output representation. Only
column which overlap with the input less than
100*(1.0-minDistance) percent will
have a possibility of losing the inhibition
competition against a boosted, 'bored' cell.
cloneMap: An array (numColumnsHigh, numColumnsWide) that
contains the clone index to use for each column.
numCloneMasters: The number of distinct clones in the map. This
is just outputCloningWidth*outputCloningHeight.
seed: Seed for our own pseudo-random number generator.
spVerbosity: spVerbosity level: 0, 1, 2, or 3
printPeriodicStats: If > 0, then every 'printPeriodicStats' iterations,
the SP will print to stdout some statistics related to
learning, such as the average pct under and
over-coverage, average number of active columns, etc.
in the last 'showLearningStats' iterations.
testMode: If True, run the SP in test mode. This runs both the
C++ and python implementations on all internal
functions that support both and insures that both
produce the same result.
globalInhibition: If true, enforce the
localAreaDensity/numActivePerInhArea
globally over the entire region, ignoring any
dynamically calculated inhibitionRadius. In effect,
this is the same as setting the inhibition radius to
include the entire region.
spReconstructionParam:Specifies which SP reconstruction optimization to be
used. Each column's firing strength is weighted by the
percent Overlap, permanence or duty Cycle if this
parameter is set to 'pctOverlap', 'permanence', or
'dutycycle' respectively. If parameter is set to
'maximum_firingstrength', the maximum of the firing
strengths (weighted by permanence) is used instead of
the weighted sum.
useHighTier: The "high tier" feature is to deal with sparse input
spaces. If over (1-minDistance) percent of a column's
connected synapses are active, it will automatically
become one of the winning columns.
If False, columns are activated based on their absolute
overlap with the input. Also, boosting will be
disabled to prevent pattern oscillation
randomSP: If True, the SP will not update its permanences and
will instead use it's initial configuration for all
inferences.
"""
# Save our __init__ args for debugging
self._initArgsDict = _extractCallingMethodArgs()
# Handle people instantiating us directly that don't pass in a cloneMap...
# This creates a clone map without any cloning
if cloneMap is None:
cloneMap, numCloneMasters = fdru.makeCloneMap(
columnsShape=coincidencesShape,
outputCloningWidth=coincidencesShape[1],
outputCloningHeight=coincidencesShape[0]
)
self.numCloneMasters = numCloneMasters
self._cloneMapFlat = cloneMap.reshape((-1,))
# Save creation parameters
self.inputShape = int(inputShape[0]), int(inputShape[1])
self.inputBorder = inputBorder
self.inputDensity = inputDensity
self.coincidencesShape = coincidencesShape
self.coincInputRadius = coincInputRadius
self.coincInputPoolPct = coincInputPoolPct
self.gaussianDist = gaussianDist
self.commonDistributions = commonDistributions
self.localAreaDensity = localAreaDensity
self.numActivePerInhArea = numActivePerInhArea
self.stimulusThreshold = stimulusThreshold
self.synPermInactiveDec = synPermInactiveDec
self.synPermActiveInc = synPermActiveInc
self.synPermActiveSharedDec = synPermActiveSharedDec
self.synPermOrphanDec = synPermOrphanDec
self.synPermConnected = synPermConnected
self.minPctDutyCycleBeforeInh = minPctDutyCycleBeforeInh
self.minPctDutyCycleAfterInh = minPctDutyCycleAfterInh
self.dutyCyclePeriod = dutyCyclePeriod
self.maxFiringBoost = maxFiringBoost
self.maxSSFiringBoost = maxSSFiringBoost
self.maxSynPermBoost = maxSynPermBoost
self.minDistance = minDistance
self.spVerbosity = spVerbosity
self.printPeriodicStats = printPeriodicStats
self.testMode = testMode
self.globalInhibition = globalInhibition
self.spReconstructionParam = spReconstructionParam
self.useHighTier= useHighTier != 0
self.randomSP = randomSP != 0
if not self.useHighTier:
self.minPctDutyCycleAfterInh = 0
self.fileCount = 0
self._runIter = 0
# Start at iteration #0
self._iterNum = 0 # Number of learning iterations
self._inferenceIterNum = 0 # Number of inference iterations
# Print creation parameters
if spVerbosity >= 2:
self.printParams()
print "seed =", seed
# Check for errors
assert (self.numActivePerInhArea == -1 or self.localAreaDensity == -1)
assert (self.inputShape[1] > 2 * self.inputBorder)
# 1D layouts have inputShape[0] == 1
if self.inputShape[0] > 1:
assert self.inputShape[0] > 2 * self.inputBorder
# Calculate other member variables
self._coincCount = int(self.coincidencesShape[0] *
self.coincidencesShape[1])
self._inputCount = int(self.inputShape[0] * self.inputShape[1])
self._synPermMin = 0.0
self._synPermMax = 1.0
self._pylabInitialized = False
# The rate at which we bump up all synapses in response to not passing
# stimulusThreshold
self._synPermBelowStimulusInc = self.synPermConnected / 10.0
self._hasTopology = True
if self.inputShape[0] == 1: # 1-D layout
self._coincRFShape = (1, (2 * coincInputRadius + 1))
# If we only have 1 column of coincidences, then assume the user wants
# each coincidence to cover the entire input
if self.coincidencesShape[1] == 1:
assert self.inputBorder >= (self.inputShape[1] - 1) // 2
assert coincInputRadius >= (self.inputShape[1] - 1) // 2
self._coincRFShape = (1, self.inputShape[1])
self._hasTopology = False
else: # 2-D layout
self._coincRFShape = ((2*coincInputRadius + 1), (2*coincInputRadius + 1))
# This gets set to True in finishLearning. Once set, we don't allow
# learning anymore and delete all member variables needed only for
# learning.
self._doneLearning = False
# Init random seed
self._seed(seed)
# Hard-coded in the current case
self.randomTieBreakingFraction = 0.5
# The permanence values used to initialize the master coincs are from
# this initial permanence array
# The initial permanence is gaussian shaped with mean at center and variance
# carefully chosen to have connected synapses
initialPermanence = self._initialPermanence()
# masterPotentialM, masterPermanenceM and masterConnectedM are numpy arrays
# of dimensions (coincCount, coincRfShape[0], coincRFShape[1])
#
# masterPotentialM: Keeps track of the potential synapses of each
# master. Potential synapses are marked as True
# masterPermanenceM: Holds the permanence values of the potential synapses.
# The values can range from 0.0 to 1.0
# masterConnectedM: Keeps track of the connected synapses of each
# master. Connected synapses are the potential synapses
# with permanence values greater than synPermConnected.
self._masterPotentialM, self._masterPermanenceM = (
self._makeMasterCoincidences(self.numCloneMasters, self._coincRFShape,
self.coincInputPoolPct, initialPermanence,
self.random))
# Update connected coincidences, the connected synapses have permanence
# values greater than synPermConnected.
self._masterConnectedM = []
dense = numpy.zeros(self._coincRFShape)
for i in xrange(self.numCloneMasters):
self._masterConnectedM.append(SM_01_32_32(dense))
# coinc sizes are used in normalizing the raw overlaps
self._masterConnectedCoincSizes = numpy.empty(self.numCloneMasters,
'uint32')
# Make one mondo coincidence matrix for all cells at once. It has one row
# per cell. The width of each row is the entire input width. There will be
# ones in each row where that cell has connections. When we have cloning,
# and we modify the connections for a clone master, we will update all
# cells that share that clone master with the new connections.
self._allConnectedM = SM_01_32_32(self._inputCount)
self._allConnectedM.resize(self._coincCount, self._inputCount)
# Initialize the dutyCycles and boost factors per clone master
self._dutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleBeforeInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._dutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._minDutyCycleAfterInh = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# TODO: We don't need to store _boostFactors, can be calculated from duty
# cycle
self._firingBoostFactors = numpy.ones(self.numCloneMasters,
dtype=realDType)
if self.useHighTier:
self._firingBoostFactors *= maxFiringBoost
# Selectively turn on/off C++ for various methods
# TODO: Can we remove the conditional?
if self.testMode:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
else:
self._computeOverlapsImp = "py" # "py or "cpp" or "test"
self._updatePermanenceGivenInputImp = "py" # "py" or "cpp or "test"
# This is used to hold our learning stats (via getLearningStats())
self._learningStats = dict()
# These will hold our random state, which we return from __getstate__ and
# reseed our random number generators from in __setstate__ so that
# a saved/restored SP produces the exact same behavior as one that
# continues. This behavior allows us to write unit tests that verify that
# the behavior of an SP does not change due to saving/loading from a
# checkpoint
self._randomState = None
self._numpyRandomState = None
self._nupicRandomState = None
# Init ephemeral members
# This also calculates the slices and global inhibitionRadius and allocates
# the inhibitionObj
self._initEphemerals()
# If we have no cloning, make sure no column has potential or connected
# synapses outside the input area
if self.numCloneMasters == self._coincCount:
validMask = numpy.zeros(self._coincRFShape, dtype=realDType)
for masterNum in xrange(self._coincCount):
coincSlice = self._coincSlices[masterNum]
validMask.fill(0)
validMask[coincSlice] = 1
self._masterPotentialM[masterNum].logicalAnd(SM_01_32_32(validMask))
self._masterPermanenceM[masterNum].elementMultiply(validMask)
# Raise all permanences up until the number of connected is above
# our desired target,
self._raiseAllPermanences(masterNum,
minConnections = self.stimulusThreshold / self.inputDensity)
# Calculate the number of connected synapses in each master coincidence now
self._updateConnectedCoincidences()
def _getEphemeralMembers(self):
"""
List of our member variables that we don't need to be saved
"""
return ['_inputLayout',
'_cellsForMaster',
'_columnCenters',
#'_cellRFClipped',
'_inputSlices',
'_coincSlices',
'_activeInput',
'_permChanges',
'_dupeInput',
'_onCells',
'_masterOnCells',
'_onCellIndices',
'_inhibitionObj',
'_denseOutput',
'_overlaps',
'_anomalyScores',
'_inputUse',
'_updatePermanenceGivenInputFP',
'_computeOverlapsFP',
'_stats',
'_rfRadiusAvg',
'_rfRadiusMin',
'_rfRadiusMax',
'_topDownOut',
'_topDownParentCounts',
]
def _initEphemerals(self):
"""
Initialize all ephemeral members after being restored to a pickled state.
"""
# Used by functions which refers to inputs in absolute space
# getLearnedCM, cm,....
self._inputLayout = numpy.arange(self._inputCount,
dtype=numpy.uint32).reshape(self.inputShape)
# This array returns the list of cell indices that correspond to each master
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
self._cellsForMaster = []
for masterNum in xrange(self.numCloneMasters):
self._cellsForMaster.append(
numpy.where(self._cloneMapFlat == masterNum)[0])
else:
self._cellsForMaster = None
# TODO: slices are not required for the C++ helper functions
# Figure out the slices of shaped input that each column sees...
# Figure out the valid region of each column
# The reason these slices are in initEphemerals is because numpy slices
# can't be pickled
self._setSlices()
# This holds the output of the inhibition computation - which cells are
# on after inhibition
self._onCells = numpy.zeros(self._coincCount, dtype=realDType)
self._masterOnCells = numpy.zeros(self.numCloneMasters, dtype=realDType)
self._onCellIndices = numpy.zeros(self._coincCount, dtype='uint32')
# The inhibition object gets allocated by _updateInhibitionObj() during
# the first compute and re-allocated periodically during learning
self._inhibitionObj = None
self._rfRadiusAvg = 0 # Also calculated by _updateInhibitionObj
self._rfRadiusMin = 0
self._rfRadiusMax = 0
# Used by the caller to optionally cache the dense output
self._denseOutput = None
# This holds the overlaps (in absolute number of connected synapses) of each
# coinc with input.
self._overlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the percent overlaps (number of active inputs / number of
# connected synapses) of each coinc with input.
self._pctOverlaps = numpy.zeros(self._coincCount, dtype=realDType)
# This is the value of the anomaly score for each column (after inhibition).
self._anomalyScores = numpy.zeros_like(self._overlaps)
# This holds the overlaps before stimulus threshold - used for verbose
# messages only.
self._overlapsBST = numpy.zeros(self._coincCount, dtype=realDType)
# This holds the number of coincs connected to an input.
if not self._doneLearning:
self._inputUse = numpy.zeros(self.inputShape, dtype=realDType)
# These are boolean matrices, the same shape as the input.
if not self._doneLearning:
self._activeInput = numpy.zeros(self.inputShape, dtype='bool')
self._dupeInput = numpy.zeros(self.inputShape, dtype='bool')
# This is used to hold self.synPermActiveInc where the input is on
# and -self.synPermInctiveDec where the input is off
if not self._doneLearning:
self._permChanges = numpy.zeros(self.inputShape, dtype=realDType)
# These are used to compute and hold the output from topDownCompute
# self._topDownOut = numpy.zeros(self.inputShape, dtype=realDType)
# self._topDownParentCounts = numpy.zeros(self.inputShape, dtype='int')
# Fill in the updatePermanenceGivenInput method pointer, which depends on
# chosen language.
if self._updatePermanenceGivenInputImp == "py":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputPy
elif self._updatePermanenceGivenInputImp == "cpp":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputCPP
elif self._updatePermanenceGivenInputImp == "test":
self._updatePermanenceGivenInputFP = self._updatePermanenceGivenInputTest
else:
assert False
# Fill in the computeOverlaps method pointer, which depends on
# chosen language.
if self._computeOverlapsImp == "py":
self._computeOverlapsFP = self._computeOverlapsPy
elif self._computeOverlapsImp == "cpp":
self._computeOverlapsFP = self._computeOverlapsCPP
elif self._computeOverlapsImp == "test":
self._computeOverlapsFP = self._computeOverlapsTest
else:
assert False
# These variables are used for keeping track of learning statistics (when
# self.printPeriodicStats is used).
self._periodicStatsCreate()
def compute(self, flatInput, learn=False, infer=True, computeAnomaly=False):
"""Compute with the current input vector.
Parameters:
----------------------------
input : the input vector (numpy array)
learn : if True, adapt the input histogram based on this input
infer : whether to do inference or not
"""
# If we are using a random SP, ignore the learn parameter
if self.randomSP:
learn = False
# If finishLearning has been called, don't allow learning anymore
if learn and self._doneLearning:
raise RuntimeError("Learning can not be performed once finishLearning"
" has been called.")
assert (learn or infer)
assert (flatInput.ndim == 1) and (flatInput.shape[0] == self._inputCount)
assert (flatInput.dtype == realDType)
input = flatInput.reshape(self.inputShape)
# Make sure we've allocated the inhibition object lazily
if self._inhibitionObj is None:
self._updateInhibitionObj()
# Reset first timer
if self.printPeriodicStats > 0 and self._iterNum == 0:
self._periodicStatsReset()
# Using cloning?
cloningOn = (self.numCloneMasters != self._coincCount)
# If we have high verbosity, save the overlaps before stimulus threshold
# so we can print them out at the end
if self.spVerbosity >= 2:
print "==============================================================="
print "Iter:%d" % self._iterNum, "inferenceIter:%d" % \
self._inferenceIterNum
self._computeOverlapsFP(input, stimulusThreshold=0)
self._overlapsBST[:] = self._overlaps
connectedCountsOnEntry = self._masterConnectedCoincSizes.copy()
if self.spVerbosity >= 3:
inputNZ = flatInput.nonzero()[0]
print "active inputs: (%d)" % len(inputNZ), inputNZ
# TODO: Port to C++, arguments may be different - t1YXArr,
# coincInputRadius,...
# Calculate the raw overlap of each cell
# Overlaps less than stimulus threshold are set to zero in
# _calculateOverlaps
# This places the result into self._overlaps
self._computeOverlapsFP(input, stimulusThreshold=self.stimulusThreshold)
# Save the original overlap values, before boosting, for the purpose of
# anomaly detection
if computeAnomaly:
self._anomalyScores[:] = self._overlaps[:]
if learn:
# Update each cell's duty cycle before inhibition
# Only cells with overlaps greater stimulus threshold are considered as
# active.
# Stimulus threshold has already been applied
# TODO: Port to C++? Loops over all coincs
# Only updating is carried out here, bump up happens later
onCellIndices = numpy.where(self._overlaps > 0)
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
self._onCells.fill(0)
self._onCells[onCellIndices] = 1
denseOn = self._onCells
# dutyCyclePeriod = self._iterNum + 1 let _dutyCycleBeforeInh
# and _dutyCycleAfterInh represent real firing percentage at the
# beginning of learning. This will effect boosting and let unlearned
# coincidences have high boostFactor at beginning.
self.dutyCyclePeriod = min(self._iterNum + 1, 1000)
self._dutyCycleBeforeInh = (
((self.dutyCyclePeriod - 1) * self._dutyCycleBeforeInh + denseOn) /
self.dutyCyclePeriod)
# Compute firing levels based on boost factor and raw overlap. Update
# self._overlaps in place, replacing it with the boosted overlap. We also
# computes percent overlap of each column and store that into
# self._pctOverlaps
if cloningOn:
self._pctOverlaps[:] = self._overlaps
self._pctOverlaps /= self._masterConnectedCoincSizes[self._cloneMapFlat]
boostFactors = self._firingBoostFactors[self._cloneMapFlat]
else:
self._pctOverlaps[:] = self._overlaps
potentials = self._masterConnectedCoincSizes
self._pctOverlaps /= numpy.maximum(1, potentials)
boostFactors = self._firingBoostFactors
# To process minDistance, we do the following:
# 1.) All cells which do not overlap the input "highly" (less than
# minDistance), are considered to be in the "low tier" and get their
# overlap multiplied by their respective boost factor.
# 2.) All other cells, which DO overlap the input highly, get a "high tier
# offset" added to their overlaps, and boost is not applied. The
# "high tier offset" is computed as the max of all the boosted
# overlaps from step #1. This insures that a cell in this high tier
# will never lose to a cell from the low tier.
if self.useHighTier:
highTier = numpy.where(self._pctOverlaps >= (1.0 - self.minDistance))[0]
else:
highTier = []
someInHighTier = len(highTier) > 0
if someInHighTier:
boostFactors = numpy.array(boostFactors)
boostFactors[highTier] = 1.0
# Apply boostFactors only in learning phase not in inference phase.
if learn:
self._overlaps *= boostFactors
if someInHighTier:
highTierOffset = self._overlaps.max() + 1.0
self._overlaps[highTier] += highTierOffset
# Cache the dense output for debugging.
if self._denseOutput is not None:
self._denseOutput = self._overlaps.copy()
# Incorporate inhibition and see who is firing after inhibition.
# We don't need this method to process stimulusThreshold because we
# already processed it.
# Also, we pass in a small 'addToWinners' amount which gets added to the
# winning elements as we go along. This prevents us from choosing more than
# topN winners per inhibition region when more than topN elements all have
# the same max high score.
learnedCellsOverlaps = numpy.array(self._overlaps)
if infer and not learn:
# Cells that have never learnt are not allowed to win during inhibition
if not self.randomSP:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = 0
else:
# Boost the unlearned cells to 1000 so that the winning columns are
# picked randomly. From the set of unlearned columns. Boost columns that
# havent been learned with uniformly to 1000 so that inhibition picks
# randomly from them.
if self.useHighTier:
learnedCellsOverlaps[numpy.where(self._dutyCycleAfterInh == 0)[0]] = (
learnedCellsOverlaps.max() + 1)
# Boost columns that are in highTier (ie. they match the input very
# well).
learnedCellsOverlaps[highTier] += learnedCellsOverlaps.max() + 1
# Small random tiebreaker for columns with equal overlap
tieBreaker = numpy.random.rand(*learnedCellsOverlaps.shape).astype(
realDType)
learnedCellsOverlaps += 0.1 * tieBreaker
numOn = self._inhibitionObj.compute(
learnedCellsOverlaps,
self._onCellIndices,
0.0, # stimulusThreshold
max(learnedCellsOverlaps)/1000.0, # addToWinners
)
self._onCells.fill(0)
if numOn > 0:
onCellIndices = self._onCellIndices[0:numOn]
self._onCells[onCellIndices] = 1
else:
onCellIndices = []
# Compute the anomaly scores only for the winning columns.
if computeAnomaly:
self._anomalyScores *= self._onCells
self._anomalyScores *= self._dutyCycleAfterInh
if self.spVerbosity >= 2:
print "inhRadius", self._inhibitionObj.getInhibitionRadius()
print "inhLocalAreaDensity", self._inhibitionObj.getLocalAreaDensity()
print "numFiring", numOn
# Capturing learning stats? If so, capture the cell overlap statistics
if self.printPeriodicStats > 0:
activePctOverlaps = self._pctOverlaps[onCellIndices]
self._stats['cellPctOverlapSums'] += activePctOverlaps.sum()
if cloningOn:
onMasterIndices = self._cloneMapFlat[onCellIndices]
else:
onMasterIndices = onCellIndices
self._stats['cellOverlapSums'] += (
activePctOverlaps *
self._masterConnectedCoincSizes[onMasterIndices]).sum()
# Compute which cells had very high overlap, but were still
# inhibited. These we are calling our "orphan cells", because they are
# representing an input which is already better represented by another
# cell.
if self.synPermOrphanDec > 0:
orphanCellIndices = set(numpy.where(self._pctOverlaps >= 1.0)[0])
orphanCellIndices.difference_update(onCellIndices)
else:
orphanCellIndices = []
if learn:
# Update the number of coinc connections per input
# During learning (adapting permanence values), we need to be able to
# recognize dupe inputs - inputs that go two 2 or more active cells
if self.synPermActiveSharedDec != 0:
self._updateInputUse(onCellIndices)
# For the firing cells, update permanence values.
onMasterIndices = self._adaptSynapses(onCellIndices, orphanCellIndices,
input)
# Increase the permanence values of columns which haven't passed
# stimulus threshold of overlap with at least a minimum frequency
self._bumpUpWeakCoincidences()
# Update each cell's after-inhibition duty cycle
# TODO: As the on-cells are sparse after inhibition, we can have
# a different updateDutyCycles function taking advantage of the sparsity
if cloningOn:
self._masterOnCells.fill(0)
self._masterOnCells[onMasterIndices] = 1
denseOn = self._masterOnCells
else:
denseOn = self._onCells
self._dutyCycleAfterInh = ((
(self.dutyCyclePeriod - 1) * self._dutyCycleAfterInh + denseOn) /
self.dutyCyclePeriod)
# Update the boost factors based on firings rate after inhibition.
self._updateBoostFactors()
# Increment iteration number and perform our periodic tasks if it's time.
if (self._iterNum + 1) % 50 == 0:
self._updateInhibitionObj()
self._updateMinDutyCycles(
self._dutyCycleBeforeInh, self.minPctDutyCycleBeforeInh,
self._minDutyCycleBeforeInh)
self._updateMinDutyCycles(
self._dutyCycleAfterInh, self.minPctDutyCycleAfterInh,
self._minDutyCycleAfterInh)
# Next iteration
if learn:
self._iterNum += 1
if infer:
self._inferenceIterNum += 1
if learn:
# Capture and possibly print the periodic stats
if self.printPeriodicStats > 0:
self._periodicStatsComputeEnd(onCellIndices, flatInput.nonzero()[0])
# Verbose print other stats
if self.spVerbosity >= 2:
cloning = (self.numCloneMasters != self._coincCount)
print " #connected on entry: ", fdru.numpyStr(
connectedCountsOnEntry, '%d ', includeIndices=True)
print " #connected on exit: ", fdru.numpyStr(
self._masterConnectedCoincSizes, '%d ', includeIndices=True)
if self.spVerbosity >= 3 or not cloning:
print " overlaps: ", fdru.numpyStr(self._overlapsBST, '%d ',
includeIndices=True, includeZeros=False)
print " firing levels: ", fdru.numpyStr(self._overlaps, '%.4f ',
includeIndices=True, includeZeros=False)
print " on after inhibition: ", onCellIndices
if not self._doneLearning:
print " minDutyCycleBeforeInh:", fdru.numpyStr(
self._minDutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " dutyCycleBeforeInh: ", fdru.numpyStr(self._dutyCycleBeforeInh,
'%.4f ', includeIndices=True)
print " belowMinBeforeInh: " % numpy.nonzero(
self._dutyCycleBeforeInh \
< self._minDutyCycleBeforeInh)[0]
print " minDutyCycleAfterInh: ", fdru.numpyStr(
self._minDutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " dutyCycleAfterInh: ", fdru.numpyStr(self._dutyCycleAfterInh,
'%.4f ', includeIndices=True)
print " belowMinAfterInh: " % numpy.nonzero(
self._dutyCycleAfterInh \
< self._minDutyCycleAfterInh)[0]
print " firingBoosts: ", fdru.numpyStr(self._firingBoostFactors,
'%.4f ', includeIndices=True)
print
elif self.spVerbosity >= 1:
print "SP: learn: ", learn
print "SP: active outputs(%d): " % (len(onCellIndices)), onCellIndices
self._runIter += 1
# Return inference result
return self._onCells
def __getstate__(self):
# Update our random states
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
state = self.__dict__.copy()
# Delete ephemeral members that we don't want pickled
for ephemeralMemberName in self._getEphemeralMembers():
if ephemeralMemberName in state:
del state[ephemeralMemberName]
return state
def __setstate__(self, state):
self.__dict__.update(state)
# Support older checkpoints
# These fields were added on 2010-10-05 and _iterNum was preserved
if not hasattr(self, '_randomState'):
self._randomState = random.getstate()
self._numpyRandomState = numpy.random.get_state()
self._nupicRandomState = self.random.getState()
self._iterNum = 0
# Init our random number generators
random.setstate(self._randomState)
numpy.random.set_state(self._numpyRandomState)
self.random.setState(self._nupicRandomState)
# Load things that couldn't be pickled...
self._initEphemerals()
def getAnomalyScore(self):
"""Get the aggregate anomaly score for this input pattern
Returns: A single scalar value for the anomaly score
"""
numNonzero = len(numpy.nonzero(self._anomalyScores)[0])
return 1.0 / (numpy.sum(self._anomalyScores) + 1)
def getLearningStats(self):
"""Return a dictionary containing a set of statistics related to learning.
Here is a list of what is returned:
'activeCountAvg':
The average number of active columns seen over the last
N training iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'underCoveragePct':
The average under-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'overCoveragePct':
The average over-coverage of the input as seen over the last N training
iterations, where N is set by the constructor parameter
printPeriodicStats.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesAvg':
The overall average number of connection changes made per active
column per iteration, over the last N training iterations, where N
is set by the constructor parameter printPeriodicStats. This gives an
indication as to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMin':
The minimum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'numConnectionChangesMax':
The maximum number of connection changes made to an active column per
iteration, over the last N training iterations, where N is set by the
constructor parameter printPeriodicStats. This gives an indication as
to how much learning is still occuring.
If printPeriodicStats is not turned on (== 0), then this is -1
'rfSize':
The average receptive field size of the columns.
'inhibitionRadius':
The average inihbition radius of the columns.
'targetDensityPct':
The most recent target local area density used, as a percent (0 -> 100)
'coincidenceSizeAvg':
The average learned coincidence size
'coincidenceSizeMin':
The minimum learned coincidence size
'coincidenceSizeMax':
The maximum learned coincidence size
'dcBeforeInhibitionAvg':
The average of duty cycle before inhbition of all coincidences
'dcBeforeInhibitionMin':
The minimum duty cycle before inhbition of all coincidences
'dcBeforeInhibitionAvg':
The maximum duty cycle before inhbition of all coincidences
'dcAfterInhibitionAvg':
The average of duty cycle after inhbition of all coincidences
'dcAfterInhibitionMin':
The minimum duty cycle after inhbition of all coincidences
'dcAfterInhibitionAvg':
The maximum duty cycle after inhbition of all coincidences
'firingBoostAvg':
The average firing boost
'firingBoostMin':
The minimum firing boost
'firingBoostMax':
The maximum firing boost
"""
# Fill in the stats that can be computed on the fly. The transient stats
# that depend on printPeriodicStats being on, have already been stored
self._learningStats['rfRadiusAvg'] = self._rfRadiusAvg
self._learningStats['rfRadiusMin'] = self._rfRadiusMin
self._learningStats['rfRadiusMax'] = self._rfRadiusMax
if self._inhibitionObj is not None:
self._learningStats['inhibitionRadius'] = (
self._inhibitionObj.getInhibitionRadius())
self._learningStats['targetDensityPct'] = (
100.0 * self._inhibitionObj.getLocalAreaDensity())
else:
print "Warning: No inhibitionObj found for getLearningStats"
self._learningStats['inhibitionRadius'] = 0.0
self._learningStats['targetDensityPct'] = 0.0
self._learningStats['coincidenceSizeAvg'] = (
self._masterConnectedCoincSizes.mean())
self._learningStats['coincidenceSizeMin'] = (
self._masterConnectedCoincSizes.min())
self._learningStats['coincidenceSizeMax'] = (
self._masterConnectedCoincSizes.max())
if not self._doneLearning:
self._learningStats['dcBeforeInhibitionAvg'] = (
self._dutyCycleBeforeInh.mean())
self._learningStats['dcBeforeInhibitionMin'] = (
self._dutyCycleBeforeInh.min())
self._learningStats['dcBeforeInhibitionMax'] = (
self._dutyCycleBeforeInh.max())
self._learningStats['dcAfterInhibitionAvg'] = (
self._dutyCycleAfterInh.mean())
self._learningStats['dcAfterInhibitionMin'] = (
self._dutyCycleAfterInh.min())
self._learningStats['dcAfterInhibitionMax'] = (
self._dutyCycleAfterInh.max())
self._learningStats['firingBoostAvg'] = self._firingBoostFactors.mean()
self._learningStats['firingBoostMin'] = self._firingBoostFactors.min()
self._learningStats['firingBoostMax'] = self._firingBoostFactors.max()
return self._learningStats
def resetStats(self):
"""Reset the stats (periodic, ???). This will usually be called by
user code at the start of each inference run (for a particular data set).
TODO: which other stats need to be reset? Learning stats?
"""
self._periodicStatsReset()
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
random.seed(seed)
numpy.random.seed(seed)
else:
self.random = NupicRandom()
def _initialPermanence(self):
"""Create and return a 2D matrix filled with initial permanence values.
The returned matrix will be of shape:
(2*coincInputRadius + 1, 2*coincInputRadius + 1).
The initial permanence values are set between 0 and 1.0, with enough chosen
above synPermConnected to make it highly likely that a cell will pass
stimulusThreshold, given the size of the potential RF, the input pool
sampling percentage, and the expected density of the active inputs.
If gaussianDist is True, the center of the matrix will contain the highest
permanence values and lower values will be farther from the center.
If gaussianDist is False, the highest permanence values will be evenly
distributed throughout the potential RF.
"""
# Figure out the target number of connected synapses. We want about 2X
# stimulusThreshold
minOn = 2 * max(self.stimulusThreshold, 10) / self.coincInputPoolPct \
/ self.inputDensity
# Get the gaussian distribution, with max magnitude just slightly above
# synPermConnected. Try to find a sigma that gives us about 2X
# stimulusThreshold connected synapses after sub-sampling for
# coincInputPoolPct. We will assume everything within +/- sigma will be
# connected. This logic uses the fact that an x value of sigma generates a
# magnitude of 0.6.
if self.gaussianDist:
# Only supported when we have 2D layouts
if self._coincRFShape[0] != self._coincRFShape[1]:
raise RuntimeError("Gaussian distibuted permanences are currently only"
"supported for 2-D layouts")
# The width and height of the center "blob" in inputs is the square root
# of the area
onAreaDim = numpy.sqrt(minOn)
# Sigma is at the edge of the center blob
sigma = onAreaDim/2
# Create the gaussian with a value of 1.0 at the center
perms = self._gaussianMatrix(dim=max(self._coincRFShape), sigma=sigma)
# The distance between the min and max values within the gaussian will
# be given by 'grange'. In a gaussian, the value at sigma away from the
# center is 0.6 * the value at the center. We want the values at sigma
# to be synPermConnected
maxValue = 1.0 / 0.6 * self.synPermConnected
perms *= maxValue
perms.shape = (-1,)
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# farther away than 2 sigma to 0. The value of a gaussing at 2 sigma
# is 0.135 * the value at the center
perms[perms < (0.135 * maxValue)] = 0
# Evenly distribute the permanences through the RF
else:
# Create a random distribution from 0 to 1.
perms = numpy.random.random(self._coincRFShape)
perms = perms.astype(realDType)
# Set the range of values to be between 0 and
# synPermConnected+synPermInctiveDec. This ensures that a pattern
# will always be learned in 1 iteration
maxValue = min(1.0, self.synPermConnected + self.synPermInactiveDec)
# What percentage do we want to be connected?
connectPct = 0.50
# What value from the 0 to 1 distribution will map to synPermConnected?
threshold = 1.0 - connectPct
# Which will be the connected and unconnected synapses?
connectedSyns = perms >= threshold
unconnectedSyns = numpy.logical_not(connectedSyns)
# Squeeze all values between threshold and 1.0 to be between
# synPermConnected and synPermConnected + synPermActiveInc / 4
# This makes sure the firing coincidence perms matching input bit get
# greater than synPermConnected and other unconnectedSyns get deconnected
# in one firing learning iteration.
srcOffset = threshold
srcRange = 1.0 - threshold
dstOffset = self.synPermConnected
dstRange = maxValue - self.synPermConnected
perms[connectedSyns] = (perms[connectedSyns] - srcOffset)/srcRange \
* dstRange / 4.0 + dstOffset
# Squeeze all values between 0 and threshold to be between 0 and
# synPermConnected
srcRange = threshold - 0.0
dstRange = self.synPermConnected - 0.0
perms[unconnectedSyns] = perms[unconnectedSyns]/srcRange \
* dstRange
# Now, let's clip off the low values to reduce the number of non-zeros
# we have and reduce our memory requirements. We'll clip everything
# below synPermActiveInc/2 to 0
perms[perms < (self.synPermActiveInc / 2.0)] = 0
perms.shape = (-1,)
return perms
def _gaussianMatrix(self, dim, sigma):
"""
Create and return a 2D matrix filled with a gaussian distribution. The
returned matrix will be of shape (dim, dim). The mean of the gaussian
will be in the center of the matrix and have a value of 1.0.
"""
gaussian = lambda x, sigma: numpy.exp(-(x**2) / (2*(sigma**2)))
# Allocate the matrix
m = numpy.empty((dim, dim), dtype=realDType)
# Find the center
center = (dim - 1) / 2.0
# TODO: Simplify using numpy.meshgrid
# Fill it in
for y in xrange(dim):
for x in xrange(dim):
dist = numpy.sqrt((x-center)**2 + (y-center)**2)
m[y,x] = gaussian(dist, sigma)
return m
def _makeMasterCoincidences(self, numCloneMasters, coincRFShape,
coincInputPoolPct, initialPermanence=None,
nupicRandom=None):
"""Make the master coincidence matrices and mater input histograms.
# TODO: Update this example
>>> FDRCSpatial._makeMasterCoincidences(1, 2, 0.33)
(array([[[ True, True, False, False, False],
[False, True, False, False, True],
[False, True, False, False, False],
[False, False, False, True, False],
[ True, False, False, False, False]]], dtype=bool), array([[[ 0.26982325, 0.19995725, 0. , 0. , 0. ],
[ 0. , 0.94128972, 0. , 0. , 0.36316112],
[ 0. , 0.06312726, 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0.29740077, 0. ],
[ 0.81071907, 0. , 0. , 0. , 0. ]]], dtype=float32))
"""
if nupicRandom is None:
nupicRandom = NupicRandom(42)
if initialPermanence is None:
initialPermanence = self._initialPermanence()
coincRfArea = (coincRFShape[0] * coincRFShape[1])
coincInputPool = coincInputPoolPct * coincRfArea
# We will generate a list of sparse matrices
masterPotentialM = []
masterPermanenceM = []
toSample = numpy.arange(coincRfArea, dtype='uint32')
toUse = numpy.empty(coincInputPool, dtype='uint32')
denseM = numpy.zeros(coincRfArea, dtype=realDType)
for i in xrange(numCloneMasters):
nupicRandom.getUInt32Sample(toSample, toUse)
# Put in 1's into the potential locations
denseM.fill(0)
denseM[toUse] = 1
masterPotentialM.append(SM_01_32_32(denseM.reshape(coincRFShape)))
# Put in the initial permanences
denseM *= initialPermanence
masterPermanenceM.append(SM32(denseM.reshape(coincRFShape)))
# If we are not using common initial permanences, create another
# unique one for the next cell
if not self.commonDistributions:
initialPermanence = self._initialPermanence()
return masterPotentialM, masterPermanenceM
def _updateConnectedCoincidences(self, masters=None):
"""Update 'connected' version of the given coincidence.
Each 'connected' coincidence is effectively a binary matrix (AKA boolean)
matrix that is the same size as the input histogram matrices. They have
a 1 wherever the inputHistogram is "above synPermConnected".
"""
# If no masterNum given, update all of them
if masters is None:
masters = xrange(self.numCloneMasters)
nCellRows, nCellCols = self._coincRFShape
cloningOn = (self.numCloneMasters != self._coincCount)
for masterNum in masters:
# Where are we connected?
masterConnectedNZ = (
self._masterPermanenceM[masterNum].whereGreaterEqual(
0, nCellRows, 0, nCellCols, self.synPermConnected))
rowIdxs = masterConnectedNZ[:,0]
colIdxs = masterConnectedNZ[:,1]
self._masterConnectedM[masterNum].setAllNonZeros(
nCellRows, nCellCols, rowIdxs, colIdxs)
self._masterConnectedCoincSizes[masterNum] = len(rowIdxs)
# Update the corresponding rows in the super, mondo connected matrix that
# come from this master
masterConnected = (
self._masterConnectedM[masterNum].toDense().astype('bool')) # 0.2s
if cloningOn:
cells = self._cellsForMaster[masterNum]
else:
cells = [masterNum]
for cell in cells:
inputSlice = self._inputSlices[cell]
coincSlice = self._coincSlices[cell]
masterSubset = masterConnected[coincSlice]
sparseCols = self._inputLayout[inputSlice][masterSubset]
self._allConnectedM.replaceSparseRow(cell, sparseCols) # 4s.
def _setSlices(self):
"""Compute self._columnSlices and self._inputSlices
self._inputSlices are used to index into the input (assuming it's been
shaped to a 2D array) to get the receptive field of each column. There
is one item in the list for each column.
self._coincSlices are used to index into the coinc (assuming it's been
shaped to a 2D array) to get the valid area of the column. There
is one item in the list for each column.
This function is called upon unpickling, since we can't pickle slices.
"""
self._columnCenters = numpy.array(self._computeCoincCenters(
self.inputShape, self.coincidencesShape, self.inputBorder))
coincInputRadius = self.coincInputRadius
coincHeight, coincWidth = self._coincRFShape
inputShape = self.inputShape
inputBorder = self.inputBorder
# Compute the input slices for each cell. This is the slice of the entire
# input which intersects with the cell's permanence matrix.
if self._hasTopology:
self._inputSlices = [
numpy.s_[max(0, cy-coincInputRadius):
min(inputShape[0], cy+coincInputRadius + 1),
max(0, cx-coincInputRadius):
min(inputShape[1], cx+coincInputRadius + 1)]
for (cy, cx) in self._columnCenters]
else:
self._inputSlices = [numpy.s_[0:inputShape[0], 0:inputShape[1]]
for (cy, cx) in self._columnCenters]
self._inputSlices2 = numpy.zeros(4 * len(self._inputSlices),
dtype="uint32")
k = 0
for i in range(len(self._inputSlices)):
self._inputSlices2[k] = self._inputSlices[i][0].start
self._inputSlices2[k + 1] = self._inputSlices[i][0].stop
self._inputSlices2[k + 2] = self._inputSlices[i][1].start
self._inputSlices2[k + 3] = self._inputSlices[i][1].stop
k = k + 4
# Compute the coinc slices for each cell. This is which portion of the
# cell's permanence matrix intersects with the input.
if self._hasTopology:
if self.inputShape[0] > 1:
self._coincSlices = [
numpy.s_[max(0, coincInputRadius - cy):
min(coincHeight, coincInputRadius + inputShape[0] - cy),
max(0, coincInputRadius-cx):
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [
numpy.s_[0:1,
max(0, coincInputRadius-cx):
min(coincWidth, coincInputRadius + inputShape[1] - cx)]
for (cy, cx) in self._columnCenters]
else:
self._coincSlices = [numpy.s_[0:coincHeight, 0:coincWidth]
for (cy, cx) in self._columnCenters]
self._coincSlices2 = numpy.zeros((4*len(self._coincSlices)), dtype="uint32")
k = 0
for i in range(len(self._coincSlices)):
self._coincSlices2[k] = self._coincSlices[i][0].start
self._coincSlices2[k + 1] = self._coincSlices[i][0].stop
self._coincSlices2[k + 2] = self._coincSlices[i][1].start
self._coincSlices2[k + 3] = self._coincSlices[i][1].stop
k = k + 4
@staticmethod
def _computeCoincCenters(inputShape, coincidencesShape, inputBorder):
"""Compute the centers of all coincidences, given parameters.
This function is semi-public: tools may use it to generate good
visualizations of what the FDRCSpatial node is doing.
NOTE: It must be static or global function so that it can be called by
the ColumnActivityTab inspector *before* the first compute (before the
SP has been constructed).
If the input shape is (7,20), shown below with * for each input.
********************
********************
********************
********************
********************
********************
********************
If inputBorder is 1, we distribute the coincidences evenly over the
the area after removing the edges, @ shows the allowed input area below.
********************
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
*@@@@@@@@@@@@@@@@@@*
********************
Each coincidence is centered at the closest @ and looks at a area with
coincInputRadius below it.
This function call returns an iterator over the coincidence centers. Each
element in iterator is a tuple: (y, x). The iterator returns elements in a
fixed order.
"""
# Determine Y centers
if inputShape[0] > 1: # 2-D layout
startHeight = inputBorder
stopHeight = inputShape[0] - inputBorder
else:
startHeight = stopHeight = 0
heightCenters = numpy.linspace(startHeight,
stopHeight,
coincidencesShape[0],
endpoint=False).astype('int32')
# Determine X centers
startWidth = inputBorder
stopWidth = inputShape[1] - inputBorder
widthCenters = numpy.linspace(startWidth,
stopWidth,
coincidencesShape[1],
endpoint=False).astype('int32')
return list(cross(heightCenters, widthCenters))
def _updateInhibitionObj(self):
"""
Calculate the average inhibitionRadius to use and update the inhibition
object accordingly. This looks at the size of the average connected
receptive field and uses that to determine the inhibition radius.
"""
# Compute the inhibition radius.
# If using global inhibition, just set it to include the entire region
if self.globalInhibition:
avgRadius = max(self.coincidencesShape)
# Else, set it based on the average size of the connected synapses area in
# each cell.
else:
totalDim = 0
# Get the dimensions of the connected receptive fields of each cell to
# compute the average
minDim = numpy.inf
maxDim = 0
for masterNum in xrange(self.numCloneMasters):
masterConnected = self._masterConnectedM[masterNum]
nzs = masterConnected.getAllNonZeros()
rows, cols = zip(*nzs)
rows = numpy.array(rows)
cols = numpy.array(cols)
if len(rows) >= 2:
height = rows.max() - rows.min() + 1
else:
height = 1
if len(cols) >= 2:
width = cols.max() - cols.min() + 1
else:
width = 1
avgDim = (height + width) / 2.0
minDim = min(minDim, avgDim)
maxDim = max(maxDim, avgDim)
totalDim += avgDim
# Get average width/height in input space
avgDim = totalDim / self.numCloneMasters
self._rfRadiusAvg = (avgDim - 1.0) / 2.0
self._rfRadiusMin = (minDim - 1.0) / 2.0
self._rfRadiusMax = (maxDim - 1.0) / 2.0
# How many columns in cell space does it correspond to?
if self.inputShape[0] > 1: # 2-D layout
coincsPerInputX = (float(self.coincidencesShape[1]) /
(self.inputShape[1] - 2 * self.inputBorder))
coincsPerInputY = (float(self.coincidencesShape[0]) /
(self.inputShape[0] - 2 * self.inputBorder))
else:
coincsPerInputX = coincsPerInputY = (
float(self.coincidencesShape[1] * self.coincidencesShape[0]) /
(self.inputShape[1] - 2 * self.inputBorder))
avgDim *= (coincsPerInputX + coincsPerInputY) / 2
avgRadius = (avgDim - 1.0) / 2.0
avgRadius = max(1.0, avgRadius)
# Can't be greater than the overall width or height of the level
maxDim = max(self.coincidencesShape)
avgRadius = min(avgRadius, maxDim)
avgRadius = int(round(avgRadius))
# Is there a need to re-instantiate the inhibition object?
if (self._inhibitionObj is None or
self._inhibitionObj.getInhibitionRadius() != avgRadius):
# What is our target density?
if self.localAreaDensity > 0:
localAreaDensity = self.localAreaDensity
else:
numCellsPerInhArea = (avgRadius * 2.0 + 1.0) ** 2
totalCells = self.coincidencesShape[0] * self.coincidencesShape[1]
numCellsPerInhArea = min(numCellsPerInhArea, totalCells)
localAreaDensity = float(self.numActivePerInhArea) / numCellsPerInhArea
# Don't let it be greater than 0.50
localAreaDensity = min(localAreaDensity, 0.50)
if self.spVerbosity >= 2:
print "Updating inhibition object:"
print " avg. rfRadius:", self._rfRadiusAvg
print " avg. inhRadius:", avgRadius
print " Setting density to:", localAreaDensity
self._inhibitionObj = Inhibition2(self.coincidencesShape[0], # height
self.coincidencesShape[1], # width
avgRadius, # inhRadius
localAreaDensity) # density
def _updateMinDutyCycles(self, actDutyCycles, minPctDutyCycle, minDutyCycles):
"""
Calculate and update the minimum acceptable duty cycle for each cell based
on the duty cycles of the cells within its inhibition radius and the
minPctDutyCycle.
Parameters:
-----------------------------------------------------------------------
actDutyCycles: The actual duty cycles of all cells
minPctDutyCycle: Each cell's minimum duty cycle will be set to
minPctDutyCycle times the duty cycle of the most active
cell within its inhibition radius
minDutyCycles: This array will be updated in place with the new minimum
acceptable duty cycles
"""
# What is the inhibition radius?
inhRadius = self._inhibitionObj.getInhibitionRadius()
# Reshape the actDutyCycles to match the topology of the level
cloningOn = (self.numCloneMasters != self._coincCount)
if not cloningOn:
actDutyCycles = actDutyCycles.reshape(self.coincidencesShape)
minDutyCycles = minDutyCycles.reshape(self.coincidencesShape)
# Special, faster handling when inhibition radius includes the entire
# set of cells.
if cloningOn or inhRadius >= max(self.coincidencesShape):
minDutyCycle = minPctDutyCycle * actDutyCycles.max()
minDutyCycles.fill(minPctDutyCycle * actDutyCycles.max())
# Else, process each cell
else:
(numRows, numCols) = self.coincidencesShape
for row in xrange(numRows):
top = max(0, row - inhRadius)
bottom = min(row + inhRadius + 1, numRows)
for col in xrange(numCols):
left = max(0, col - inhRadius)
right = min(col + inhRadius + 1, numCols)
maxDutyCycle = actDutyCycles[top:bottom, left:right].max()
minDutyCycles[row, col] = maxDutyCycle * minPctDutyCycle
if self.spVerbosity >= 2:
print "Actual duty cycles:"
print fdru.numpyStr(actDutyCycles, '%.4f')
print "Recomputed min duty cycles, using inhRadius of", inhRadius
print fdru.numpyStr(minDutyCycles, '%.4f')
def _computeOverlapsPy(self, inputShaped, stimulusThreshold):
"""
Computes overlaps for every column for the current input in place. The
overlaps less than stimulus threshold are set to zero here.
For columns with input RF going off the edge of input field, only regions
within the input field are considered. This is equivalent to padding the
input field with zeros.
Parameters:
------------------------------------------------------------------------
inputShaped: input at the current time step, shaped to the input
topology
stimulusThreshold: stimulusThreshold to use
Member variables used/updated:
------------------------------------------------------------------------
_inputSlices: Index into the input (assuming it's been shaped to a 2D
array) to get the receptive field of each column.
_coincSlices: Index into the coinc (assuming it's been shaped to a 2D
array) to get the valid region of each column.
_overlaps: Result is placed into this array which holds the overlaps of
each column with the input
"""
flatInput = inputShaped.reshape(-1)
self._allConnectedM.rightVecSumAtNZ_fast(flatInput, self._overlaps)
# Apply stimulusThreshold
# TODO: Is there a faster numpy operation for this?
self._overlaps[self._overlaps < stimulusThreshold] = 0
self._overlapsNoBoost = self._overlaps.copy()
def _computeOverlapsCPP(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but using a C++ implementation.
"""
cpp_overlap(self._cloneMapFlat,
self._inputSlices2, self._coincSlices2,
inputShaped, self._masterConnectedM,
stimulusThreshold,
self._overlaps)
def _computeOverlapsTest(self, inputShaped, stimulusThreshold):
"""
Same as _computeOverlapsPy, but compares the python and C++
implementations.
"""
# Py version
self._computeOverlapsPy(inputShaped, stimulusThreshold)
overlaps2 = copy.deepcopy(self._overlaps)
# C++ version
self._computeOverlapsCPP(inputShaped, stimulusThreshold)
if (abs(self._overlaps - overlaps2) > 1e-6).any():
print self._overlaps, overlaps2, abs(self._overlaps - overlaps2)
import pdb; pdb.set_trace()
sys.exit(0)
def _raiseAllPermanences(self, masterNum, minConnections=None,
densePerm=None, densePotential=None):
"""
Raise all permanences of the given master. If minConnections is given, the
permanences will be raised until at least minConnections of them are
connected strength.
If minConnections is left at None, all permanences will be raised by
self._synPermBelowStimulusInc.
After raising all permanences, we also "sparsify" the permanence matrix
and set to 0 any permanences which are already very close to 0, this
keeps the memory requirements of the sparse matrices used to store
the permanences lower.
Parameters:
----------------------------------------------------------------------------
masterNum: Which master to bump up
minConnections: Desired number of connected synapses to have
If None, then all permanences are simply bumped up
by self._synPermBelowStimulusInc
densePerm: The dense representation of the master's permanence
matrix, if available. If not specified, we will
create this from the stored sparse representation.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix. The stored sparse
matrix will ALWAYS be updated from the densePerm if
the densePerm is provided.
densePotential: The dense representation of the master's potential
synapses matrix, if available. If not specified, we
will create this from the stored sparse potential
matrix.
Providing this will avoid some compute overhead.
If provided, it is assumed that it is more recent
than the stored sparse matrix.
retval: (modified, numConnections)
modified: True if any permanences were raised
numConnections: Number of actual connected synapses
(not computed if minConnections was
None, so None is returned in that
case.)
"""
# It's faster to perform this operation on the dense matrices and
# then convert to sparse once we're done since we will be potentially
# introducing and then later removing a bunch of non-zeros.
# Get references to the sparse perms and potential syns for this master
sparsePerm = self._masterPermanenceM[masterNum]
sparsePotential = self._masterPotentialM[masterNum]
# We will trim off all synapse permanences below this value to 0 in order
# to keep the memory requirements of the SparseMatrix lower
trimThreshold = self.synPermActiveInc / 2.0
# See if we already have the required number of connections. If we don't,
# get the dense form of the permanences if we don't have them already
if densePerm is None:
# See if we already have enough connections, if so, we can avoid the
# overhead of converting to dense
if minConnections is not None:
numConnected = sparsePerm.countWhereGreaterEqual(
0, self._coincRFShape[0], 0, self._coincRFShape[1],
self.synPermConnected)
if numConnected >= minConnections:
return (False, numConnected)
densePerm = self._masterPermanenceM[masterNum].toDense()
elif minConnections is not None:
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (False, numConnected)
# Get the dense form of the potential synapse locations
if densePotential is None:
densePotential = self._masterPotentialM[masterNum].toDense()
# Form the array with the increments
incrementM = densePotential.astype(realDType)
incrementM *= self._synPermBelowStimulusInc
# Increment until we reach our target number of connections
assert (densePerm.dtype == realDType)
while True:
densePerm += incrementM
if minConnections is None:
numConnected = None
break
numConnected = count_gte(densePerm.reshape(-1), self.synPermConnected)
if numConnected >= minConnections:
break
# Convert back to sparse form and trim any values that are already
# close to zero
sparsePerm.fromDense(densePerm)
sparsePerm.threshold(trimThreshold)
return (True, numConnected)
def _bumpUpWeakCoincidences(self):
"""
This bump-up ensures every coincidence have non-zero connections. We find
all coincidences which have overlaps less than stimulus threshold.
We add synPermActiveInc to all the synapses. This step when repeated over
time leads to synapses crossing synPermConnected threshold.
"""
# Update each cell's connected threshold based on the duty cycle before
# inhibition. The connected threshold is linearly interpolated
# between the points (dutyCycle:0, thresh:0) and (dutyCycle:minDuty,
# thresh:synPermConnected). This is a line defined as: y = mx + b
# thresh = synPermConnected/minDuty * dutyCycle
bumpUpList = (
self._dutyCycleBeforeInh < self._minDutyCycleBeforeInh).nonzero()[0]
for master in bumpUpList:
self._raiseAllPermanences(master)
# Update the connected synapses for each master we touched.
self._updateConnectedCoincidences(bumpUpList)
if self.spVerbosity >= 2 and len(bumpUpList) > 0:
print ("Bumping up permanences in following cells due to falling below"
"minDutyCycleBeforeInh:"), bumpUpList
def _updateBoostFactors(self):
"""
Update the boost factors. The boost factors is linearly interpolated
between the points (dutyCycle:0, boost:maxFiringBoost) and
(dutyCycle:minDuty, boost:1.0). This is a line defined as: y = mx + b
boost = (1-maxFiringBoost)/minDuty * dutyCycle + maxFiringBoost
Parameters:
------------------------------------------------------------------------
boostFactors: numpy array of boost factors, defined per master
"""
if self._minDutyCycleAfterInh.sum() > 0:
self._firingBoostFactors = (
(1 - self.maxFiringBoost) /
self._minDutyCycleAfterInh * self._dutyCycleAfterInh +
self.maxFiringBoost)
self._firingBoostFactors[self._dutyCycleAfterInh >
self._minDutyCycleAfterInh] = 1.0
def _updateInputUse(self, onCellIndices):
"""
During learning (adapting permanence values), we need to be able to tell
which inputs are going to 2 or more active cells at once.
We step through each coinc and mark all the inputs it is connected to. The
inputUse array acts as a counter for the number of connections to the coincs
from each input.
Parameters:
------------------------------------------------------------------------
inputUse: numpy array of number of coincs connected to each input
"""
allConnected = SM32(self._allConnectedM)
# TODO: avoid this copy
self._inputUse[:] = allConnected.addListOfRows(
onCellIndices).reshape(self.inputShape)
def _adaptSynapses(self, onCellIndices, orphanCellIndices, input):
"""
This is the main function in learning of SP. The permanence values are
changed based on the learning rules.
Parameters:
------------------------------------------------------------------------
onCellIndices: columns which are turned on after inhibition. The
permanence values of these coincs are adapted based on the
input.
orphanCellIndices: columns which had very high overlap with the input, but
ended up being inhibited
input: Input, shaped to the input topology
retval: list of masterCellIndices that were actually updated, or
None if cloning is off
"""
# Capturing learning stats?
if self.printPeriodicStats > 0:
self._stats['explainedInputsCurIteration'] = set()
# Precompute the active, inactive, and dupe inputs up front for speed
# TODO: put these into pre-allocated arrays for speed
self._activeInput[:] = input
# Create a matrix containing the default permanence deltas for each input
self._permChanges.fill(-1 * self.synPermInactiveDec)
self._permChanges[self._activeInput] = self.synPermActiveInc
if self.synPermActiveSharedDec != 0:
numpy.logical_and(self._activeInput, self._inputUse>1, self._dupeInput)
self._permChanges[self._dupeInput] -= self.synPermActiveSharedDec
# Cloning? If so, scramble the onCells so that we pick a random one to
# update for each master. We only update a master cell at most one time
# per input presentation.
cloningOn = (self.numCloneMasters != self._coincCount)
if cloningOn:
# Scramble the onCellIndices so that we pick a random one to update
onCellIndices = list(onCellIndices)
random.shuffle(onCellIndices)
visitedMasters = set()
# For the firing cells, update permanence values
for columnNum in itertools.chain(onCellIndices, orphanCellIndices):
# Get the master number
masterNum = self._cloneMapFlat[columnNum]
# If cloning, only visit each master once
if cloningOn:
if masterNum in visitedMasters:
continue
visitedMasters.add(masterNum)
# Get the slices of input that overlap with the valid area of this master
inputSlice = self._inputSlices[columnNum]
rfActiveInput = self._activeInput[inputSlice]
rfPermChanges = self._permChanges[inputSlice]
# Get the potential synapses, permanence values, and connected synapses
# for this master
masterPotential = self._masterPotentialM[masterNum].toDense()
masterPermanence = self._masterPermanenceM[masterNum].toDense()
masterConnected = (
self._masterConnectedM[masterNum].toDense().astype('bool'))
# Make changes only over the areas that overlap the input level. For
# coincidences near the edge of the level for example, this excludes the
# synapses outside the edge.
coincSlice = self._coincSlices[columnNum]
masterValidPermanence= masterPermanence[coincSlice]
# Capturing learning stats?
if self.printPeriodicStats > 0:
masterValidConnected = masterConnected[coincSlice]
explainedInputs = self._inputLayout[inputSlice][masterValidConnected]
self._stats['explainedInputsCurIteration'].update(explainedInputs)
if self.spVerbosity >= 3:
print " adapting cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " initialConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
print " firingLevel: %d" % (self._overlaps[columnNum])
print " firingBoostFactor: %f" % (self._firingBoostFactors[masterNum])
print " input slice: \n"
self._printInputSlice(rfActiveInput, prefix=' ')
# Update permanences given the active input (NOTE: The "FP" in this
# function name stands for "Function Pointer").
if columnNum in orphanCellIndices:
# Decrease permanence of active inputs
masterValidPermanence[rfActiveInput] -= self.synPermOrphanDec
else:
self._updatePermanenceGivenInputFP(columnNum, masterNum, input,
self._inputUse, masterPermanence, masterValidPermanence,
rfActiveInput, rfPermChanges)
# Clip to absolute min and max permanence values
numpy.clip(masterPermanence, self._synPermMin, self._synPermMax,
out=masterPermanence)
# Keep only the potential syns for this cell
numpy.multiply(masterPermanence, masterPotential, masterPermanence)
# If we are tracking learning stats, prepare to see how many changes
# were made to the cell connections
if self.printPeriodicStats > 0:
masterConnectedOrig = SM_01_32_32(self._masterConnectedM[masterNum])
# If the number of connected synapses happens to fall below
# stimulusThreshold, bump up all permanences a bit.
# We could also just wait for the "duty cycle falls below
# minDutyCycleBeforeInb" logic to catch it, but doing it here is
# pre-emptive and much faster.
#
# The "duty cycle falls below minDutyCycleBeforeInb" logic will still
# catch other possible situations, like:
# * if the set of inputs a cell learned suddenly stop firing due to
# input statistic changes
# * damage to the level below
# * input is very sparse and we still don't pass stimulusThreshold even
# with stimulusThreshold conneted synapses.
self._raiseAllPermanences(masterNum,
minConnections=self.stimulusThreshold,
densePerm=masterPermanence,
densePotential=masterPotential)
# Update the matrices that contain the connected syns for this cell.
self._updateConnectedCoincidences([masterNum])
# If we are tracking learning stats, see how many changes were made to
# this cell's connections
if self.printPeriodicStats > 0:
origNumConnections = masterConnectedOrig.nNonZeros()
masterConnectedOrig.logicalAnd(self._masterConnectedM[masterNum])
numUnchanged = masterConnectedOrig.nNonZeros()
numChanges = origNumConnections - numUnchanged
numChanges += (self._masterConnectedM[masterNum].nNonZeros() -
numUnchanged)
self._stats['numChangedConnectionsSum'][masterNum] += numChanges
self._stats['numLearns'][masterNum] += 1
# Verbose?
if self.spVerbosity >= 3:
print " done cell:%d [%d:%d] (master:%d)" % (columnNum,
columnNum // self.coincidencesShape[1],
columnNum % self.coincidencesShape[1],
masterNum)
print " newConnected: %d" % \
(self._masterConnectedM[masterNum].nNonZeros())
self._printSyns(columnNum, prefix=' ',
showValues=(self.spVerbosity >= 4))
print
# Return list of updated masters
if cloningOn:
return list(visitedMasters)
else:
return onCellIndices
def _updatePermanenceGivenInputPy(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Given the input to a master coincidence, update it's permanence values
based on our learning rules.
On Entry, we are given the slice of the permanence matrix that corresponds
only to the area of the coincidence master that is within the borders of
the entire input field.
Parameters:
------------------------------------------------------------------------
columnNum: The column number of this cell
masterNum: The master coincidence that corresponds to this column
input: The entire input, shaped appropriately
inputUse: The same shape as input. Each entry is a count of the
number of *currently active cells* that are connected
to that input.
permanence: The entire masterPermanence matrix for this master
permanenceSlice: The slice of the masterPermanence matrix for this master
that intersects the input field, i.e. does not overhang
the outside edges of the input.
activeInputSlice: The portion of 'input' that intersects permanenceSlice,
set to True where input != 0
permChangesSlice: The portion of 'input' that intersects permanenceSlice,
set to self.synPermActiveInc where input != 0 and
self.synPermInactiveDec where the input == 0. This is
used to optimally apply self.synPermActiveInc and
self.synPermInactiveDec at the same time and can be
used for any cell whose _synPermBoostFactor is set
to 1.0.
"""
# TODO: This function does nothing.
# Apply the baseline increment/decrements
permanenceSlice += permChangesSlice
# If this cell has permanence boost, apply the incremental
def _updatePermanenceGivenInputCPP(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but using a C++ implementation.
"""
inputNCols = self.inputShape[1]
masterNCols = self._masterPotentialM[masterNum].shape[1]
# TODO: synPermBoostFactors has been removed. CPP implementation has not
# been updated for this.
adjustMasterValidPermanence(columnNum,
masterNum,
inputNCols,
masterNCols,
self.synPermActiveInc,
self.synPermInactiveDec,
self.synPermActiveSharedDec,
input,
inputUse,
self._inputSlices2,
self._coincSlices2,
self._synPermBoostFactors,
permanence)
def _updatePermanenceGivenInputTest(
self, columnNum, masterNum, input, inputUse, permanence, permanenceSlice,
activeInputSlice, permChangesSlice):
"""
Same as _updatePermanenceGivenInputPy, but compares the python and C++
implementations.
"""
mp2 = copy.deepcopy(permanence)
mvp2 = copy.deepcopy(permanenceSlice)
# Py version
import pdb; pdb.set_trace()
self._updatePermanenceGivenInputPy(columnNum, masterNum, input,
inputUse, permanence, permanenceSlice, activeInputSlice,
permChangesSlice)
# C++ version
self._updatePermanenceGivenInputCPP(columnNum, masterNum, input,
inputUse, mp2, mvp2, activeInputSlice, permChangesSlice)
if abs(mp2 - permanence).max() > 1e-6:
print abs(mp2 - permanence).max()
import pdb; pdb.set_trace()
sys.exit(0)
def _periodicStatsCreate(self):
"""
Allocate the periodic stats structure
"""
self._stats = dict()
self._stats['numChangedConnectionsSum'] = numpy.zeros(
self.numCloneMasters, dtype=realDType)
self._stats['numLearns'] = numpy.zeros(
self.numCloneMasters, dtype=realDType)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
self._stats['maxBoostFactor'] = numpy.zeros(self.numCloneMasters,
dtype=realDType)
# This dict maintains mappings of specific input patterns to specific
# output patterns. It is used to detect "thrashing" of cells. We measure
# how similar the output presentation of a specific input is to the
# last time we saw it.
self._stats['inputPatterns'] = dict()
self._stats['inputPatternsLimit'] = 5000
self._periodicStatsReset()
def _periodicStatsReset(self):
"""
Reset the periodic stats this is done every N iterations before capturing
a new set of stats.
"""
self._stats['numSamples'] = 0
self._stats['numOnSum'] = 0
self._stats['underCoveragePctSum'] = 0
self._stats['overCoveragePctSum'] = 0
self._stats['cellOverlapSums'] = 0
self._stats['cellPctOverlapSums'] = 0
self._stats['explainedInputsCurIteration'] = set()
self._stats['startTime'] = time.time()
# These keep a count of the # of changed connections per update
# for each master
self._stats['numChangedConnectionsSum'].fill(0)
self._stats['numLearns'].fill(0)
# These keep track of the min and max boost factor seen for each
# column during each training period
self._stats['minBoostFactor'].fill(self.maxFiringBoost)
self._stats['maxBoostFactor'].fill(0)
# This keeps track of the average distance between the SP output of
# a specific input pattern now and the last time we saw it.
self._stats['outputPatternDistanceSum'] = 0
self._stats['outputPatternSamples'] = 0
def _periodicStatsComputeEnd(self, activeCells, activeInputs):
"""
Called at the end of compute. This increments the number of computes
and also summarizes the under and over coverage and whatever other
periodic stats we need.
If the period is up, it then prints the accumuated stats and resets them
for the next period
Parameters:
------------------------------------------------------------------
activeCells: list of the active cells
activeInputs: list of the active inputs
"""
# Update number of samples
self._stats['numSamples'] += 1
# Compute under and over coverage
numOn = len(activeCells)
self._stats['numOnSum'] += numOn
expInput = self._stats['explainedInputsCurIteration']
inputLen = len(activeInputs)
underCoverage = len(set(activeInputs).difference(expInput))
self._stats['underCoveragePctSum'] += float(underCoverage) / inputLen
expInput.difference_update(activeInputs)
overCoverage = len(expInput)
self._stats['overCoveragePctSum'] += float(overCoverage) / inputLen
# Keep track of the min and max boost factor seen for each column
numpy.minimum(self._firingBoostFactors, self._stats['minBoostFactor'],
self._stats['minBoostFactor'])
numpy.maximum(self._firingBoostFactors, self._stats['maxBoostFactor'],
self._stats['maxBoostFactor'])
# Calculate the distance in the SP output between this input now
# and the last time we saw it.
inputPattern = str(sorted(activeInputs))
outputNZ, sampleIdx = self._stats['inputPatterns'].get(inputPattern,
(None, None))
activeCellSet = set(activeCells)
if outputNZ is not None:
distance = (len(activeCellSet.difference(outputNZ)) +
len(outputNZ.difference(activeCellSet)))
self._stats['inputPatterns'][inputPattern] = (activeCellSet, sampleIdx)
self._stats['outputPatternDistanceSum'] += distance
self._stats['outputPatternSamples'] += 1
# Add this sample to our dict, if it's not too large already
elif len(self._stats['inputPatterns']) < self._stats['inputPatternsLimit']:
self._stats['inputPatterns'][inputPattern] = (activeCellSet,
self._iterNum)
# If it's not time to print them out, return now.
if (self._iterNum % self.printPeriodicStats) != 0:
return
numSamples = float(self._stats['numSamples'])
# Calculate number of changes made per master
masterTouched = numpy.where(self._stats['numLearns'] > 0)
if len(masterTouched[0]) == 0:
numMasterChanges = numpy.zeros(1)
else:
numMasterChanges = self._stats['numChangedConnectionsSum'][masterTouched]
numMasterChanges /= self._stats['numLearns'][masterTouched]
# This fills in the static learning stats into self._learningStats
self.getLearningStats()
# Calculate and copy the transient learning stats into the
# self._learningStats dict, for possible retrieval later by
# the getLearningStats() method.
self._learningStats['elapsedTime'] = time.time() - self._stats['startTime']
self._learningStats['activeCountAvg'] = (self._stats['numOnSum'] /
numSamples)
self._learningStats['underCoveragePct'] = (
100.0 * self._stats['underCoveragePctSum'] / numSamples)
self._learningStats['overCoveragePct'] = (
(100.0 * self._stats['overCoveragePctSum'] / numSamples))
self._learningStats['numConnectionChangesAvg'] = numMasterChanges.mean()
self._learningStats['numConnectionChangesMin'] = numMasterChanges.min()
self._learningStats['numConnectionChangesMax'] = numMasterChanges.max()
self._learningStats['avgCellOverlap'] = (
(float(self._stats['cellOverlapSums']) /
max(1, self._stats['numOnSum'])))
self._learningStats['avgCellPctOverlap'] = (
(100.0 * self._stats['cellPctOverlapSums'] /
max(1, self._stats['numOnSum'])))
self._learningStats['firingBoostMaxChangePct'] = (
100.0 * (self._stats['maxBoostFactor'] /
self._stats['minBoostFactor']).max() - 100.0)
self._learningStats['outputRepresentationChangeAvg'] = (
float(self._stats['outputPatternDistanceSum']) /
max(1, self._stats['outputPatternSamples']))
self._learningStats['outputRepresentationChangePctAvg'] = (
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
self._learningStats['numUniqueInputsSeen'] = (
len(self._stats['inputPatterns']))
if (self._learningStats['numUniqueInputsSeen'] >=
self._stats['inputPatternsLimit']):
self._learningStats['numUniqueInputsSeen'] = -1
# Print all stats captured
print "Learning stats for the last %d iterations:" % (numSamples)
print " iteration #: %d" % (self._iterNum)
print " inference iteration #: %d" % (self._inferenceIterNum)
print " elapsed time: %.2f" % (
self._learningStats['elapsedTime'])
print " avg activeCount: %.1f" % (
self._learningStats['activeCountAvg'])
print " avg under/overCoverage: %-6.1f / %-6.1f %%" % (
self._learningStats['underCoveragePct'],
self._learningStats['overCoveragePct'])
print " avg cell overlap: %-6.1f / %-6.1f %%" % (
self._learningStats['avgCellOverlap'],
self._learningStats['avgCellPctOverlap'])
print " avg/min/max RF radius: %-6.1f / %-6.1f / %-6.1f" % (
self._learningStats['rfRadiusAvg'],
self._learningStats['rfRadiusMin'],
self._learningStats['rfRadiusMax'])
print " inhibition radius: %d" % (
self._learningStats['inhibitionRadius'])
print " target density: %.5f %%" % (
self._learningStats['targetDensityPct'])
print " avg/min/max coinc. size: %-6.1f / %-6d / %-6d" % (
self._learningStats['coincidenceSizeAvg'],
self._learningStats['coincidenceSizeMin'],
self._learningStats['coincidenceSizeMax'])
print " avg/min/max DC before inh: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['dcBeforeInhibitionAvg'],
self._learningStats['dcBeforeInhibitionMin'],
self._learningStats['dcBeforeInhibitionMax'])
print " avg/min/max DC after inh: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['dcAfterInhibitionAvg'],
self._learningStats['dcAfterInhibitionMin'],
self._learningStats['dcAfterInhibitionMax'])
print " avg/min/max boost: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['firingBoostAvg'],
self._learningStats['firingBoostMin'],
self._learningStats['firingBoostMax'])
print " avg/min/max # conn. changes: %-6.4f / %-6.4f / %-6.4f" % (
self._learningStats['numConnectionChangesAvg'],
self._learningStats['numConnectionChangesMin'],
self._learningStats['numConnectionChangesMax'])
print " max change in boost: %.1f %%" % (
self._learningStats['firingBoostMaxChangePct'])
print " avg change in output repr.: %-6.1f / %-6.1f %%" % (
self._learningStats['outputRepresentationChangeAvg'],
100.0 * self._learningStats['outputRepresentationChangeAvg'] /
max(1,self._learningStats['activeCountAvg']))
print " # of unique input pats seen: %d" % (
self._learningStats['numUniqueInputsSeen'])
# Reset the stats for the next period.
self._periodicStatsReset()
def _printInputSlice(self, inputSlice, prefix=''):
"""Print the given input slice in a nice human readable format.
Parameters:
---------------------------------------------------------------------
cell: The slice of input to print
prefix: This is printed at the start of each row of the
coincidence
"""
# Shape of each coincidence
rfHeight, rfWidth = inputSlice.shape
syns = inputSlice != 0
def _synStr(x):
if not x:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
print prefix, ''.join(items)
def _printSyns(self, cell, prefix='', showValues=False):
"""Print the synapse permanence values for the given cell in a nice,
human, readable format.
Parameters:
---------------------------------------------------------------------
cell: which cell to print
prefix: This is printed at the start of each row of the
coincidence
showValues: If True, print the values of each permanence.
If False, just print a ' ' if not connected and a '*'
if connected
"""
# Shape of each coincidence
(rfHeight, rfWidth) = self.inputShape
# Get the synapse permanences.
masterNum = self._cloneMapFlat[cell]
syns = self._masterPermanenceM[masterNum].toDense()
if showValues:
def _synStr(x):
if x == 0:
return ' -- '
elif x < 0.001:
return ' 0 '
elif x >= self.synPermConnected:
return '#%3.2f' % x
else:
return ' %3.2f' % x
else:
def _synStr(x):
if x < self.synPermConnected:
return ' '
else:
return '*'
# Print them out
for row in xrange(syns.shape[0]):
items = map(_synStr, syns[row])
if showValues:
print prefix, ' '.join(items)
else:
print prefix, ''.join(items)
def _printMemberSizes(self, over=100):
"""Print the size of each member."""
members = self.__dict__.keys()
sizeNamePairs = []
totalSize = 0
for member in members:
item = self.__dict__[member]
if hasattr(item, '__func__'):
continue
try:
if hasattr(item, '__len__'):
size = 0
for i in xrange(len(item)):
size += len(cPickle.dumps(item[i]))
else:
size = len(cPickle.dumps(item))
except:
print "WARNING: Can't pickle %s" % (member)
size = 0
sizeNamePairs.append((size, member))
totalSize += size
# Print them out from highest to lowest
sizeNamePairs.sort(reverse=True)
for (size, name) in sizeNamePairs:
if size > over:
print "%10d (%10.3fMb) %s" % (size, size/1000000.0, name)
print "\nTOTAL: %10d (%10.3fMB) " % (totalSize, totalSize/1000000.0)
def printParams(self):
"""Print the main creation parameters associated with this instance."""
print "FDRCSpatial2 creation parameters: "
print "inputShape =", self.inputShape
print "inputBorder =", self.inputBorder
print "inputDensity =", self.inputDensity
print "coincidencesShape =", self.coincidencesShape
print "coincInputRadius =", self.coincInputRadius
print "coincInputPoolPct =", self.coincInputPoolPct
print "gaussianDist =", self.gaussianDist
print "commonDistributions =", self.commonDistributions
print "localAreaDensity =", self.localAreaDensity
print "numActivePerInhArea =", self.numActivePerInhArea
print "stimulusThreshold =", self.stimulusThreshold
print "synPermInactiveDec =", self.synPermInactiveDec
print "synPermActiveInc =", self.synPermActiveInc
print "synPermActiveSharedDec =", self.synPermActiveSharedDec
print "synPermOrphanDec =", self.synPermOrphanDec
print "synPermConnected =", self.synPermConnected
print "minPctDutyCycleBeforeInh =", self.minPctDutyCycleBeforeInh
print "minPctDutyCycleAfterInh =", self.minPctDutyCycleAfterInh
print "dutyCyclePeriod =", self.dutyCyclePeriod
print "maxFiringBoost =", self.maxFiringBoost
print "maxSSFiringBoost =", self.maxSSFiringBoost
print "maxSynPermBoost =", self.maxSynPermBoost
print "useHighTier =",self.useHighTier
print "minDistance =", self.minDistance
print "spVerbosity =", self.spVerbosity
print "printPeriodicStats =", self.printPeriodicStats
print "testMode =", self.testMode
print "numCloneMasters =", self.numCloneMasters
|
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
url = "https://github.com/haampie/libtree/releases/download/v1.0.3/sources.tar.gz"
maintainers = ['haampie']
version('1.2.2', sha256='4ccf09227609869b85a170550b636defcf0b0674ecb0785063b81785b1c29bdd')
version('1.2.1', sha256='26791c0f418b93d502879db0e1fd2fd3081b885ad87326611d992a5f8977a9b0')
version('1.2.0', sha256='3e74655f22b1dcc19e8a1b9e7796b8ad44bc37f29e9a99134119e8521e28be97')
version('1.1.4', sha256='38648f67c8fa72c3a4a3af2bb254b5fd6989c0f1362387ab298176db5cbbcc4e')
version('1.1.3', sha256='4c681d7b67ef3d62f95450fb7eb84e33ff10a3b9db1f7e195b965b2c3c58226b')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
libtree: add v1.2.3 (#25270)
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
url = "https://github.com/haampie/libtree/releases/download/v1.0.3/sources.tar.gz"
maintainers = ['haampie']
version('1.2.3', sha256='4a912cf97109219fe931942a30579336b6ab9865395447bd157bbfa74bf4e8cf')
version('1.2.2', sha256='4ccf09227609869b85a170550b636defcf0b0674ecb0785063b81785b1c29bdd')
version('1.2.1', sha256='26791c0f418b93d502879db0e1fd2fd3081b885ad87326611d992a5f8977a9b0')
version('1.2.0', sha256='3e74655f22b1dcc19e8a1b9e7796b8ad44bc37f29e9a99134119e8521e28be97')
version('1.1.4', sha256='38648f67c8fa72c3a4a3af2bb254b5fd6989c0f1362387ab298176db5cbbcc4e')
version('1.1.3', sha256='4c681d7b67ef3d62f95450fb7eb84e33ff10a3b9db1f7e195b965b2c3c58226b')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
|
from unittest import TestCase
from os.path import (expanduser, join, isdir, isfile)
from os import (remove, makedirs)
from shutil import rmtree
from glob import glob
from niftynet.utilities.niftynet_global_config import NiftyNetGlobalConfig
class NiftyNetGlobalConfigTest(TestCase):
"""For reliably testing the global config file, the tests are grouped
and ordered by including a number.
https://docs.python.org/2/library/unittest.html says: "Note that the
order in which the various test cases will be run is determined by
sorting the test function names with respect to the built-in ordering
for strings."
"""
@classmethod
def typify(cls, file_path):
"""Append file type extension to passed file path."""
return '.'.join([file_path, cls.file_type])
@classmethod
def remove_path(cls, path):
"""Remove passed item, whether it's a file or directory."""
if isdir(path):
rmtree(path)
elif isfile(path):
remove(path)
@classmethod
def setUpClass(cls):
cls.config_home = join(expanduser('~'), '.niftynet')
cls.file_type = 'ini'
cls.config_file = join(cls.config_home, cls.typify('config'))
cls.header = '[global]'
cls.default_config_opts = {
'home': '~/niftynet'
}
@classmethod
def tearDownClass(cls):
# TODO
pass
def setUp(self):
NiftyNetGlobalConfigTest.remove_path(NiftyNetGlobalConfigTest.config_home)
NiftyNetGlobalConfigTest.remove_path(
NiftyNetGlobalConfigTest.default_config_opts['home']
)
def tearDown(self):
self.setUp()
def test_000_global_config_singleton(self):
global_config_1 = NiftyNetGlobalConfig()
global_config_2 = NiftyNetGlobalConfig()
self.assertEqual(global_config_1, global_config_2)
self.assertTrue(global_config_1 is global_config_2)
def test_010_non_existing_config_file_created(self):
self.assertFalse(isfile(NiftyNetGlobalConfigTest.config_file))
global_config = NiftyNetGlobalConfig()
self.assertTrue(isfile(NiftyNetGlobalConfigTest.config_file))
self.assertEqual(global_config.get_niftynet_config_folder(),
NiftyNetGlobalConfigTest.config_home)
def test_011_existing_config_file_loaded(self):
# create a config file with a custom NiftyNet home
makedirs(NiftyNetGlobalConfigTest.config_home)
custom_niftynet_home = '~/customniftynethome'
custom_niftynet_home_abs = expanduser(custom_niftynet_home)
config = ''.join(['home = ', custom_niftynet_home])
with open(NiftyNetGlobalConfigTest.config_file, 'w') as config_file:
config_file.write('\n'.join(
[NiftyNetGlobalConfigTest.header, config]))
global_config = NiftyNetGlobalConfig()
self.assertEqual(global_config.get_niftynet_home_folder(),
custom_niftynet_home_abs)
def test_012_incorrect_config_file_backed_up(self):
# create an incorrect config file at the correct location
makedirs(NiftyNetGlobalConfigTest.config_home)
incorrect_config = 'invalid_home_tag = ~/niftynet'
with open(NiftyNetGlobalConfigTest.config_file, 'w') as config_file:
config_file.write('\n'.join(
[NiftyNetGlobalConfigTest.header, incorrect_config]))
# the following should back it up and replace it with default config
global_config = NiftyNetGlobalConfig()
self.assertTrue(isfile(NiftyNetGlobalConfigTest.config_file))
self.assertEqual(global_config.get_niftynet_config_folder(),
NiftyNetGlobalConfigTest.config_home)
# check if incorrect file was backed up
found_files = glob(join(NiftyNetGlobalConfigTest.config_home,
NiftyNetGlobalConfigTest.typify('config-backup-*')))
self.assertTrue(len(found_files) == 1)
with open(found_files[0], 'r') as backup_file:
self.assertEqual(backup_file.read(), incorrect_config)
# cleanup: remove backup file
NiftyNetGlobalConfigTest.remove_path(backup_file)
Issue #182: using expanduser instead of path literal when removing created home dir in tests
from unittest import TestCase
from os.path import (expanduser, join, isdir, isfile)
from os import (remove, makedirs)
from shutil import rmtree
from glob import glob
from niftynet.utilities.niftynet_global_config import NiftyNetGlobalConfig
class NiftyNetGlobalConfigTest(TestCase):
"""For reliably testing the global config file, the tests are grouped
and ordered by including a number.
https://docs.python.org/2/library/unittest.html says: "Note that the
order in which the various test cases will be run is determined by
sorting the test function names with respect to the built-in ordering
for strings."
"""
@classmethod
def typify(cls, file_path):
"""Append file type extension to passed file path."""
return '.'.join([file_path, cls.file_type])
@classmethod
def remove_path(cls, path):
"""Remove passed item, whether it's a file or directory."""
if isdir(path):
rmtree(path)
elif isfile(path):
remove(path)
@classmethod
def setUpClass(cls):
cls.config_home = join(expanduser('~'), '.niftynet')
cls.file_type = 'ini'
cls.config_file = join(cls.config_home, cls.typify('config'))
cls.header = '[global]'
cls.default_config_opts = {
'home': '~/niftynet'
}
@classmethod
def tearDownClass(cls):
# TODO
pass
def setUp(self):
NiftyNetGlobalConfigTest.remove_path(NiftyNetGlobalConfigTest.config_home)
NiftyNetGlobalConfigTest.remove_path(
expanduser(NiftyNetGlobalConfigTest.default_config_opts['home'])
)
def tearDown(self):
self.setUp()
def test_000_global_config_singleton(self):
global_config_1 = NiftyNetGlobalConfig()
global_config_2 = NiftyNetGlobalConfig()
self.assertEqual(global_config_1, global_config_2)
self.assertTrue(global_config_1 is global_config_2)
def test_010_non_existing_config_file_created(self):
self.assertFalse(isfile(NiftyNetGlobalConfigTest.config_file))
global_config = NiftyNetGlobalConfig()
self.assertTrue(isfile(NiftyNetGlobalConfigTest.config_file))
self.assertEqual(global_config.get_niftynet_config_folder(),
NiftyNetGlobalConfigTest.config_home)
def test_011_existing_config_file_loaded(self):
# create a config file with a custom NiftyNet home
makedirs(NiftyNetGlobalConfigTest.config_home)
custom_niftynet_home = '~/customniftynethome'
custom_niftynet_home_abs = expanduser(custom_niftynet_home)
config = ''.join(['home = ', custom_niftynet_home])
with open(NiftyNetGlobalConfigTest.config_file, 'w') as config_file:
config_file.write('\n'.join(
[NiftyNetGlobalConfigTest.header, config]))
global_config = NiftyNetGlobalConfig()
self.assertEqual(global_config.get_niftynet_home_folder(),
custom_niftynet_home_abs)
def test_012_incorrect_config_file_backed_up(self):
# create an incorrect config file at the correct location
makedirs(NiftyNetGlobalConfigTest.config_home)
incorrect_config = 'invalid_home_tag = ~/niftynet'
with open(NiftyNetGlobalConfigTest.config_file, 'w') as config_file:
config_file.write('\n'.join(
[NiftyNetGlobalConfigTest.header, incorrect_config]))
# the following should back it up and replace it with default config
global_config = NiftyNetGlobalConfig()
self.assertTrue(isfile(NiftyNetGlobalConfigTest.config_file))
self.assertEqual(global_config.get_niftynet_config_folder(),
NiftyNetGlobalConfigTest.config_home)
# check if incorrect file was backed up
found_files = glob(join(NiftyNetGlobalConfigTest.config_home,
NiftyNetGlobalConfigTest.typify('config-backup-*')))
self.assertTrue(len(found_files) == 1)
with open(found_files[0], 'r') as backup_file:
self.assertEqual(backup_file.read(), incorrect_config)
# cleanup: remove backup file
NiftyNetGlobalConfigTest.remove_path(backup_file)
|
#!/usr/bin/env python
'''
Usage:
git-bin [-v] [--debug] <command> [--] <file>...
git-bin init
git-bin (-h|--help|--version)
Commands:
add store file in binstore and add it's link to the index
edit retrieve a file from the binstore for local edit
checkout restore the link to the last added version of the file
init
Options:
--help -h print this help
--version print version and exit
--verbose -v enable verbose printing
--debug debug mode
'''
# '''
# Usage:
# git-bin (add|edit|reset|checkout --) <file>...
# git-bin (-h|--help|--version)
# '''
import os.path
import stat
import filecmp
import pkg_resources
from docopt import docopt
import utils
import commands as cmd
import git
class Binstore(object):
def __init__(self):
pass
def init(self):
""" Initialize git-bin for this git repository."""
raise NotImplementedError
def add_file(self, filename):
""" Add the specified file to the binstore. """
raise NotImplementedError
def edit_file(self, filename):
""" Retrieve the specified file for editing. """
raise NotImplementedError
def reset_file(self, filename):
""" Reset the specified file. """
def __contains__(self, item):
""" Test whether a given item is in this binstore. The item may be a hash or a symlink in the repo """
raise NotImplementedError
def available(self):
""" Test to see whether the binstore can be reached. """
raise NotImplementedError
class SSHFSBinstore(Binstore):
pass
class BinstoreException(Exception):
pass
class FilesystemBinstore(Binstore):
def __init__(self, gitrepo):
Binstore.__init__(self)
self.gitrepo = gitrepo
# retrieve the binstore path from the .git/config
# first look for the binstore base in the git config tree.
binstore_base = self.gitrepo.config.get("git-bin", "binstorebase", None)
# if that fails, try the environment variable
binstore_base = binstore_base or os.environ.get("BINSTORE_BASE", binstore_base)
if not binstore_base:
raise BinstoreException("No git-bin.binstorebase is specified. You probably want to add this to your ~/.gitconfig")
self.init(binstore_base)
def init(self, binstore_base):
self.localpath = os.path.join(self.gitrepo.path, ".git", "binstore")
#self.path = self.gitrepo.config.get("binstore", "path", None)
self.path = os.path.join(binstore_base, self.gitrepo.reponame)
if not os.path.exists(self.localpath):
commands = cmd.CompoundCommand(
cmd.MakeDirectoryCommand(self.path),
cmd.LinkToFileCommand(self.localpath, self.path),
)
commands.execute()
#self.gitrepo.config.set("binstore", "path", self.path)
if not os.path.exists(self.path):
raise BinstoreException("A binstore.path is set (%s), but it doesn't exist. Weird." % self.path)
def get_binstore_filename(self, filename):
""" get the real filename of a given file in the binstore. """
# Note: this function assumes that the filename is in the binstore. You
# probably want to check that first.
if os.path.islink(filename):
#return os.readlink(filename)
return os.path.realpath(filename)
digest = utils.md5_file(filename)
return os.path.join(self.localpath, digest)
def has(self, filename):
""" check whether a particular file is in the binstore or not. """
if os.path.islink(filename):
link_target = os.path.realpath(filename)
if os.path.dirname(link_target) != os.path.realpath(self.localpath):
return False
return os.path.exists(self.get_binstore_filename(filename))
def add_file(self, filename):
binstore_filename = self.get_binstore_filename(filename)
# TODO: make hash algorithm configurable
# relative link is needed, here, so it points from the file directly to
# the .git directory
relative_link = os.path.relpath(binstore_filename, os.path.dirname(filename))
# create only a link if file already exists in binstore
if os.path.exists(binstore_filename):
print('WARNING: File with that hash already exists in binstore.')
if filecmp.cmp(filename, binstore_filename):
print(' Creating a link to existing file')
commands = cmd.CompoundCommand(
cmd.SafeRemoveCommand(filename),
cmd.LinkToFileCommand(filename, relative_link),
cmd.GitAddCommand(self.gitrepo, filename),
)
else:
raise ValueError('hash collision found between %s and %s',
filename, binstore_filename)
else:
commands = cmd.CompoundCommand(
cmd.SafeMoveFileCommand(filename, binstore_filename),
cmd.LinkToFileCommand(filename, relative_link),
cmd.ChmodCommand(stat.S_IRUSR | stat.S_IRGRP | stat.S_IROTH,
binstore_filename),
cmd.GitAddCommand(self.gitrepo, filename),
)
commands.execute()
def edit_file(self, filename):
print "edit_file(%s)" % filename
print "binstore_filename: %s" % self.get_binstore_filename(filename)
temp_filename = os.path.join(os.path.dirname(filename),
".tmp_%s" % os.path.basename(filename))
print "temp_filename: %s" % temp_filename
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(self.get_binstore_filename(filename),
temp_filename),
cmd.SafeMoveFileCommand(temp_filename, filename),
)
commands.execute()
def is_binstore_link(self, filename):
if not os.path.islink(filename):
return False
print os.readlink(filename)
print self.localpath
if (os.readlink(filename).startswith(self.localpath) and
self.has(os.readlink(filename))):
return True
return False
class CompatabilityFilesystemBinstore(FilesystemBinstore):
def __init__(self, gitrepo):
FilesystemBinstore.__init__(self, gitrepo)
def init(self, binstore_base):
self.path = os.path.join(binstore_base, self.gitrepo.reponame)
self.localpath = self.path
if not os.path.exists(self.path):
raise BinstoreException("In compatibility mode, but binstore doesn't exist. What exactly are you trying to pull?")
class UnknownCommandException(Exception):
pass
class GitBin(object):
def __init__(self, gitrepo, binstore):
self.gitrepo = gitrepo
self.binstore = binstore
def dispatch_command(self, name, arguments):
if not hasattr(self, name):
raise UnknownCommandException(
"The command '%s' is not known to git-bin" % name)
filenames = utils.expand_filenames(arguments['<file>'])
getattr(self, name)(filenames)
def add(self, filenames):
""" Add a list of files, specified by their full paths, to the binstore. """
print "GitBin.add(%s)" % filenames
for filename in filenames:
print "\t%s" % filename
# we want to add broken symlinks as well
if not os.path.lexists(filename):
print "'%s' did not match any files" % filename
continue
# if the file is a link, but the target is not in the binstore (i.e.
# this was a real symlink originally), we can just add it. This
# check is before the check for dirs so that we don't traverse
# symlinked dirs.
if os.path.islink(filename):
if not self.binstore.is_binstore_link(filename):
# a symlink, but not into the binstore. Just add the link
# itself:
self.gitrepo.add(filename)
# whether it's a binstore link or not, we can just continue
continue
if not utils.is_file_binary(filename):
self.gitrepo.add(filename)
continue
# TODO: maybe create an empty file with some marking
# now we just skip it
if utils.is_file_pipe(filename):
continue
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first add all directories recursively
len(dirs) and self.add([os.path.join(root, dn) for dn in dirs])
# now add all the files
len(files) and self.add([os.path.join(root, fn) for fn in files])
continue
# at this point, we're only dealing with a file, so let's add it to
# the binstore
self.binstore.add_file(filename)
def init(self, args):
pass
# normal git reset works like this:
# 1. if the file is staged, it is unstaged. The file itself is untouched.
# 2. if the file is unstaged, nothing happens.
# To revert local changes in a modified file, you need to perform a
# `checkout --`.
# 1. if the file is staged, nothing happens.
# 2. if the file is tracked and unstaged, it's contents are reset to the
# value at head.
# 3. if the file is untracked, an error occurs.
# (see: http://git-scm.com/book/en/Git-Basics-Undoing-Things)
#
# legacy git-bin implemented the following logic:
# 1. if the file is not binary (note that staged/unstaged is not
# differentiated):
# 1.1 if the file is added, a `git reset HEAD` is performed.
# 1.2 if the file is modified, a `git checkout --` is performed.
# 2. if the file is a binary file:
# 2.1 if the file is added, the file is copied back from the binstore and
# a `git reset HEAD` is performed.
# 2.2 if the file is modified
# 2.2.1 and its hash is in the binstore: a `git checkout --` is performed.
# 2.2.1 but its hash is not in the binstore and there is a typechange, a
# copy of the file is saved in /tmp and then the `git checkout --` is
# performed.
#
# essentially we need two distinct operations:
# - unstage: just get it out of the index, but don't touch the file
# itself.o
# For a binary file that has just been git-bin-add-ed, but was not
# previously tracked, we will want to revert to the original file contents.
# This more closely resembles the intention of the regular unstage operation
# - restore: change back to the contents at HEAD.
# For a binstore file this would mean switching back to the
# symlink. If there was actually a modification, we also want to save a
# 'just-in-case' file.
# if we use the standard git nomenclature:
# - unstage -> reset
# - restore -> checkout --
# let's implement these operations separately. We might implement a
# compatibility mode.
def reset(self, filenames):
""" Unstage a list of files """
print "GitBin.reset(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first reset all directories recursively
len(dirs) and self.reset([os.path.join(root, dn) for dn in dirs])
# now reset all the files
len(files) and self.reset([os.path.join(root, fn) for fn in files])
continue
status = self.gitrepo.status(filename)
if not status & git.STATUS_STAGED_MASK == git.STATUS_STAGED:
# not staged, skip it.
print "you probably meant to do: git bin checkout -- %s" % filename
continue
# unstage the file:
self.gitrepo.unstage(filename)
# key: F=real file; S=symlink; T=typechange; M=modified; s=staged
# {1} ([F] -> GBAdded[Ss]) -> Untracked[S]
# {2} ([S] -> GBEdit[TF] -> Modified[TF] -> GBAdded[MSs])
# -> Modified[MS]
new_status = self.gitrepo.status(filename)
if self.binstore.has(filename) and (
new_status & git.STATUS_UNTRACKED or
new_status & git.STATUS_MODIFIED):
# TODO: in case {1} it's possible that we might be leaving an
# orphan unreferenced file in the binstore. We might want to
# deal with this.
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(
self.binstore.get_binstore_filename(filename),
filename),
)
commands.execute()
def checkout(self, filenames):
""" Revert local modifications to a list of files """
print "GitBin.checkout(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first checkout_dashdash all directories recursively
len(dirs) and self.checkout([os.path.join(root, dn) for dn in dirs])
# now checkout_dashdash all the files
len(files) and self.checkout([os.path.join(root, fn) for fn in files])
continue
status = self.gitrepo.status(filename)
if (status & git.STATUS_STAGED_MASK) == git.STATUS_STAGED:
# staged, skip it.
print "you probably meant to do: git bin reset %s" % filename
continue
if not (status & git.STATUS_CHANGED_MASK):
# the file hasn't changed, skip it.
continue
# The first two cases can just be passed through to regular git
# checkout --.
# {1} (GBAdded[MSs] -> Reset[MS])
# {2} (GBEdit[TF])
# In the third case, there is some local modification that we should
# save 'just in case' first.
# {3} (GBEdit[TF] -> Modified[TF]) (*)
if (status & git.STATUS_TYPECHANGED) and not self.binstore.has(filename):
justincase_filename = os.path.join(
"/tmp",
"%s.%s.justincase" % (filename,
utils.md5_file(filename)))
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(
self.binstore.get_binstore_filename(filename),
justincase_filename),
)
commands.execute()
self.gitrepo.restore(filename)
def edit(self, filenames):
""" Retrieve file contents for editing """
print "GitBin.edit(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first edit all directories recursively
len(dirs) and self.edit([os.path.join(root, dn) for dn in dirs])
# now edit all the files
len(files) and self.edit([os.path.join(root, fn) for fn in files])
continue
if os.path.islink(filename) and self.binstore.has(filename):
self.binstore.edit_file(filename)
# TODO:
# - implement git operations
# - impelement binstore
# - use symlink in .git/ folder
# - reverse lookups
# - implement offline/online commands
# - use a .gitbin file to store parameters
# - init command?
# - if file doesn't exist, suggest creating it on first use
# - this file should be committed
# - detect online binstore available. if so, and was offline, suggest going online.
def print_exception(prefix, exception, verbose=False):
print "%s: %s" % (prefix, exception)
if verbose:
import traceback
traceback.print_exc()
def get_binstore(repo):
return FilesystemBinstore(repo)
def _main(args):
try:
gitrepo = git.GitRepo()
binstore = get_binstore(gitrepo)
gitbin = GitBin(gitrepo, binstore)
cmd = args['<command>']
if args['init']:
gitbin.dispatch_command('init', args)
elif cmd is not None:
gitbin.dispatch_command(cmd, args)
except git.GitException, e:
print_exception("git", e, args['--debug'])
print(__doc__)
exit(1)
except BinstoreException, e:
print_exception("binstore", e, args['--debug'])
exit(1)
except UnknownCommandException, e:
print(__doc__)
exit(1)
def main():
#args = build_options_parser().parse_args()
import sys
version = pkg_resources.require("gitbin")[0].version
args = docopt(__doc__, version=version, options_first=True)
if args:
_main(args)
if __name__ == '__main__':
main()
Fixed checkout to copy the modified file to /tmp
#!/usr/bin/env python
'''
Usage:
git-bin [-v] [--debug] <command> [--] <file>...
git-bin init
git-bin (-h|--help|--version)
Commands:
add store file in binstore and add it's link to the index
edit retrieve a file from the binstore for local edit
checkout restore the link to the last added version of the file
init
Options:
--help -h print this help
--version print version and exit
--verbose -v enable verbose printing
--debug debug mode
'''
# '''
# Usage:
# git-bin (add|edit|reset|checkout --) <file>...
# git-bin (-h|--help|--version)
# '''
import os.path
import stat
import filecmp
import pkg_resources
from docopt import docopt
import utils
import commands as cmd
import git
class Binstore(object):
def __init__(self):
pass
def init(self):
""" Initialize git-bin for this git repository."""
raise NotImplementedError
def add_file(self, filename):
""" Add the specified file to the binstore. """
raise NotImplementedError
def edit_file(self, filename):
""" Retrieve the specified file for editing. """
raise NotImplementedError
def reset_file(self, filename):
""" Reset the specified file. """
def __contains__(self, item):
""" Test whether a given item is in this binstore. The item may be a hash or a symlink in the repo """
raise NotImplementedError
def available(self):
""" Test to see whether the binstore can be reached. """
raise NotImplementedError
class SSHFSBinstore(Binstore):
pass
class BinstoreException(Exception):
pass
class FilesystemBinstore(Binstore):
def __init__(self, gitrepo):
Binstore.__init__(self)
self.gitrepo = gitrepo
# retrieve the binstore path from the .git/config
# first look for the binstore base in the git config tree.
binstore_base = self.gitrepo.config.get("git-bin", "binstorebase", None)
# if that fails, try the environment variable
binstore_base = binstore_base or os.environ.get("BINSTORE_BASE", binstore_base)
if not binstore_base:
raise BinstoreException("No git-bin.binstorebase is specified. You probably want to add this to your ~/.gitconfig")
self.init(binstore_base)
def init(self, binstore_base):
self.localpath = os.path.join(self.gitrepo.path, ".git", "binstore")
#self.path = self.gitrepo.config.get("binstore", "path", None)
self.path = os.path.join(binstore_base, self.gitrepo.reponame)
if not os.path.exists(self.localpath):
commands = cmd.CompoundCommand(
cmd.MakeDirectoryCommand(self.path),
cmd.LinkToFileCommand(self.localpath, self.path),
)
commands.execute()
#self.gitrepo.config.set("binstore", "path", self.path)
if not os.path.exists(self.path):
raise BinstoreException("A binstore.path is set (%s), but it doesn't exist. Weird." % self.path)
def get_binstore_filename(self, filename):
""" get the real filename of a given file in the binstore. """
# Note: this function assumes that the filename is in the binstore. You
# probably want to check that first.
if os.path.islink(filename):
#return os.readlink(filename)
return os.path.realpath(filename)
digest = utils.md5_file(filename)
return os.path.join(self.localpath, digest)
def has(self, filename):
""" check whether a particular file is in the binstore or not. """
if os.path.islink(filename):
link_target = os.path.realpath(filename)
if os.path.dirname(link_target) != os.path.realpath(self.localpath):
return False
return os.path.exists(self.get_binstore_filename(filename))
def add_file(self, filename):
binstore_filename = self.get_binstore_filename(filename)
# TODO: make hash algorithm configurable
# relative link is needed, here, so it points from the file directly to
# the .git directory
relative_link = os.path.relpath(binstore_filename, os.path.dirname(filename))
# create only a link if file already exists in binstore
if os.path.exists(binstore_filename):
print('WARNING: File with that hash already exists in binstore.')
if filecmp.cmp(filename, binstore_filename):
print(' Creating a link to existing file')
commands = cmd.CompoundCommand(
cmd.SafeRemoveCommand(filename),
cmd.LinkToFileCommand(filename, relative_link),
cmd.GitAddCommand(self.gitrepo, filename),
)
else:
raise ValueError('hash collision found between %s and %s',
filename, binstore_filename)
else:
commands = cmd.CompoundCommand(
cmd.SafeMoveFileCommand(filename, binstore_filename),
cmd.LinkToFileCommand(filename, relative_link),
cmd.ChmodCommand(stat.S_IRUSR | stat.S_IRGRP | stat.S_IROTH,
binstore_filename),
cmd.GitAddCommand(self.gitrepo, filename),
)
commands.execute()
def edit_file(self, filename):
print "edit_file(%s)" % filename
print "binstore_filename: %s" % self.get_binstore_filename(filename)
temp_filename = os.path.join(os.path.dirname(filename),
".tmp_%s" % os.path.basename(filename))
print "temp_filename: %s" % temp_filename
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(self.get_binstore_filename(filename),
temp_filename),
cmd.SafeMoveFileCommand(temp_filename, filename),
)
commands.execute()
def is_binstore_link(self, filename):
if not os.path.islink(filename):
return False
print os.readlink(filename)
print self.localpath
if (os.readlink(filename).startswith(self.localpath) and
self.has(os.readlink(filename))):
return True
return False
class CompatabilityFilesystemBinstore(FilesystemBinstore):
def __init__(self, gitrepo):
FilesystemBinstore.__init__(self, gitrepo)
def init(self, binstore_base):
self.path = os.path.join(binstore_base, self.gitrepo.reponame)
self.localpath = self.path
if not os.path.exists(self.path):
raise BinstoreException("In compatibility mode, but binstore doesn't exist. What exactly are you trying to pull?")
class UnknownCommandException(Exception):
pass
class GitBin(object):
def __init__(self, gitrepo, binstore):
self.gitrepo = gitrepo
self.binstore = binstore
def dispatch_command(self, name, arguments):
if not hasattr(self, name):
raise UnknownCommandException(
"The command '%s' is not known to git-bin" % name)
filenames = utils.expand_filenames(arguments['<file>'])
getattr(self, name)(filenames)
def add(self, filenames):
""" Add a list of files, specified by their full paths, to the binstore. """
print "GitBin.add(%s)" % filenames
for filename in filenames:
print "\t%s" % filename
# we want to add broken symlinks as well
if not os.path.lexists(filename):
print "'%s' did not match any files" % filename
continue
# if the file is a link, but the target is not in the binstore (i.e.
# this was a real symlink originally), we can just add it. This
# check is before the check for dirs so that we don't traverse
# symlinked dirs.
if os.path.islink(filename):
if not self.binstore.is_binstore_link(filename):
# a symlink, but not into the binstore. Just add the link
# itself:
self.gitrepo.add(filename)
# whether it's a binstore link or not, we can just continue
continue
if not utils.is_file_binary(filename):
self.gitrepo.add(filename)
continue
# TODO: maybe create an empty file with some marking
# now we just skip it
if utils.is_file_pipe(filename):
continue
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first add all directories recursively
len(dirs) and self.add([os.path.join(root, dn) for dn in dirs])
# now add all the files
len(files) and self.add([os.path.join(root, fn) for fn in files])
continue
# at this point, we're only dealing with a file, so let's add it to
# the binstore
self.binstore.add_file(filename)
def init(self, args):
pass
# normal git reset works like this:
# 1. if the file is staged, it is unstaged. The file itself is untouched.
# 2. if the file is unstaged, nothing happens.
# To revert local changes in a modified file, you need to perform a
# `checkout --`.
# 1. if the file is staged, nothing happens.
# 2. if the file is tracked and unstaged, it's contents are reset to the
# value at head.
# 3. if the file is untracked, an error occurs.
# (see: http://git-scm.com/book/en/Git-Basics-Undoing-Things)
#
# legacy git-bin implemented the following logic:
# 1. if the file is not binary (note that staged/unstaged is not
# differentiated):
# 1.1 if the file is added, a `git reset HEAD` is performed.
# 1.2 if the file is modified, a `git checkout --` is performed.
# 2. if the file is a binary file:
# 2.1 if the file is added, the file is copied back from the binstore and
# a `git reset HEAD` is performed.
# 2.2 if the file is modified
# 2.2.1 and its hash is in the binstore: a `git checkout --` is performed.
# 2.2.1 but its hash is not in the binstore and there is a typechange, a
# copy of the file is saved in /tmp and then the `git checkout --` is
# performed.
#
# essentially we need two distinct operations:
# - unstage: just get it out of the index, but don't touch the file
# itself.o
# For a binary file that has just been git-bin-add-ed, but was not
# previously tracked, we will want to revert to the original file contents.
# This more closely resembles the intention of the regular unstage operation
# - restore: change back to the contents at HEAD.
# For a binstore file this would mean switching back to the
# symlink. If there was actually a modification, we also want to save a
# 'just-in-case' file.
# if we use the standard git nomenclature:
# - unstage -> reset
# - restore -> checkout --
# let's implement these operations separately. We might implement a
# compatibility mode.
def reset(self, filenames):
""" Unstage a list of files """
print "GitBin.reset(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first reset all directories recursively
len(dirs) and self.reset([os.path.join(root, dn) for dn in dirs])
# now reset all the files
len(files) and self.reset([os.path.join(root, fn) for fn in files])
continue
status = self.gitrepo.status(filename)
if not status & git.STATUS_STAGED_MASK == git.STATUS_STAGED:
# not staged, skip it.
print "you probably meant to do: git bin checkout -- %s" % filename
continue
# unstage the file:
self.gitrepo.unstage(filename)
# key: F=real file; S=symlink; T=typechange; M=modified; s=staged
# {1} ([F] -> GBAdded[Ss]) -> Untracked[S]
# {2} ([S] -> GBEdit[TF] -> Modified[TF] -> GBAdded[MSs])
# -> Modified[MS]
new_status = self.gitrepo.status(filename)
if self.binstore.has(filename) and (
new_status & git.STATUS_UNTRACKED or
new_status & git.STATUS_MODIFIED):
# TODO: in case {1} it's possible that we might be leaving an
# orphan unreferenced file in the binstore. We might want to
# deal with this.
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(
self.binstore.get_binstore_filename(filename),
filename),
)
commands.execute()
def checkout(self, filenames):
""" Revert local modifications to a list of files """
print "GitBin.checkout(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first checkout_dashdash all directories recursively
len(dirs) and self.checkout([os.path.join(root, dn) for dn in dirs])
# now checkout_dashdash all the files
len(files) and self.checkout([os.path.join(root, fn) for fn in files])
continue
status = self.gitrepo.status(filename)
if (status & git.STATUS_STAGED_MASK) == git.STATUS_STAGED:
# staged, skip it.
print "you probably meant to do: git bin reset %s" % filename
continue
if not (status & git.STATUS_CHANGED_MASK):
# the file hasn't changed, skip it.
continue
# The first two cases can just be passed through to regular git
# checkout --.
# {1} (GBAdded[MSs] -> Reset[MS])
# {2} (GBEdit[TF])
# In the third case, there is some local modification that we should
# save 'just in case' first.
# {3} (GBEdit[TF] -> Modified[TF]) (*)
if (status & git.STATUS_TYPECHANGED) and not self.binstore.has(filename):
justincase_filename = os.path.join(
"/tmp",
"%s.%s.justincase" % (filename,
utils.md5_file(filename)))
commands = cmd.CompoundCommand(
cmd.CopyFileCommand(
# self.binstore.get_binstore_filename(filename),
filename,
justincase_filename),
)
commands.execute()
self.gitrepo.restore(filename)
def edit(self, filenames):
""" Retrieve file contents for editing """
print "GitBin.edit(%s)" % filenames
for filename in filenames:
# if the filename is a directory, recurse into it.
# TODO: maybe make recursive directory crawls optional/configurable
if os.path.isdir(filename):
print "\trecursing into %s" % filename
for root, dirs, files in os.walk(filename):
# first edit all directories recursively
len(dirs) and self.edit([os.path.join(root, dn) for dn in dirs])
# now edit all the files
len(files) and self.edit([os.path.join(root, fn) for fn in files])
continue
if os.path.islink(filename) and self.binstore.has(filename):
self.binstore.edit_file(filename)
# TODO:
# - implement git operations
# - impelement binstore
# - use symlink in .git/ folder
# - reverse lookups
# - implement offline/online commands
# - use a .gitbin file to store parameters
# - init command?
# - if file doesn't exist, suggest creating it on first use
# - this file should be committed
# - detect online binstore available. if so, and was offline, suggest going online.
def print_exception(prefix, exception, verbose=False):
print "%s: %s" % (prefix, exception)
if verbose:
import traceback
traceback.print_exc()
def get_binstore(repo):
return FilesystemBinstore(repo)
def _main(args):
try:
gitrepo = git.GitRepo()
binstore = get_binstore(gitrepo)
gitbin = GitBin(gitrepo, binstore)
cmd = args['<command>']
if args['init']:
gitbin.dispatch_command('init', args)
elif cmd is not None:
gitbin.dispatch_command(cmd, args)
except git.GitException, e:
print_exception("git", e, args['--debug'])
print(__doc__)
exit(1)
except BinstoreException, e:
print_exception("binstore", e, args['--debug'])
exit(1)
except UnknownCommandException, e:
print(__doc__)
exit(1)
def main():
#args = build_options_parser().parse_args()
import sys
version = pkg_resources.require("gitbin")[0].version
args = docopt(__doc__, version=version, options_first=True)
if args:
_main(args)
if __name__ == '__main__':
main()
|
"""
**hep_ml.losses** contains different loss functions to use in gradient boosting.
Apart from standard classification losses, **hep_ml** contains losses for uniform classification
(see :class:`BinFlatnessLossFunction`, :class:`KnnFlatnessLossFunction`, :class:`KnnAdaLossFunction`)
and for ranking (see :class:`RankBoostLossFunction`)
**Interface**
Loss functions inside **hep_ml** are stateful estimators and require initial fitting,
which is done automatically inside gradient boosting.
All loss function should be derived from AbstractLossFunction and implement this interface.
Examples
________
Training gradient boosting, optimizing LogLoss and using all features
>>> from hep_ml.gradientboosting import UGradientBoostingClassifier, LogLossFunction
>>> classifier = UGradientBoostingClassifier(loss=LogLossFunction(), n_estimators=100)
>>> classifier.fit(X, y, sample_weight=sample_weight)
Using composite loss function and subsampling:
>>> loss = CompositeLossFunction()
>>> classifier = UGradientBoostingClassifier(loss=loss, subsample=0.5)
To get uniform predictions in mass in background (note that mass should not present in features):
>>> loss = BinFlatnessLossFunction(uniform_features=['mass'], uniform_label=0, train_features=['pt', 'flight_time'])
>>> classifier = UGradientBoostingClassifier(loss=loss)
To get uniform predictions in both signal and background:
>>> loss = BinFlatnessLossFunction(uniform_features=['mass'], uniform_label=[0, 1], train_features=['pt', 'flight_time'])
>>> classifier = UGradientBoostingClassifier(loss=loss)
"""
from __future__ import division, print_function, absolute_import
import numbers
import warnings
import numpy
import pandas
from scipy import sparse
from scipy.special import expit
from sklearn.utils.validation import check_random_state
from sklearn.base import BaseEstimator
from .commonutils import compute_knn_indices_of_signal, check_sample_weight, check_uniform_label, weighted_quantile
from .metrics_utils import bin_to_group_indices, compute_bin_indices, compute_group_weights, \
group_indices_to_groups_matrix
__author__ = 'Alex Rogozhnikov'
__all__ = [
'AbstractLossFunction',
'MSELossFunction',
'MAELossFunction',
'LogLossFunction',
'AdaLossFunction',
'CompositeLossFunction',
'BinFlatnessLossFunction',
'KnnFlatnessLossFunction',
'KnnAdaLossFunction',
'RankBoostLossFunction'
]
def _compute_positions(y_pred, sample_weight):
"""
For each event computes it position among other events by prediction.
position = (weighted) part of elements with lower predictions => position belongs to [0, 1]
This function is very close to `scipy.stats.rankdata`, but supports weights.
"""
order = numpy.argsort(y_pred)
ordered_weights = sample_weight[order]
ordered_weights /= float(numpy.sum(ordered_weights))
efficiencies = (numpy.cumsum(ordered_weights) - 0.5 * ordered_weights)
return efficiencies[numpy.argsort(order)]
class AbstractLossFunction(BaseEstimator):
"""
This is base class for loss functions used in `hep_ml`.
Main differences compared to `scikit-learn` loss functions:
1. losses are stateful, and may require fitting of training data before usage.
2. thus, when computing gradient, hessian, one shall provide predictions of all events.
3. losses are object that shall be passed as estimators to gradient boosting (see examples).
4. only two-class case is supported, and different classes may have different role and meaning.
"""
def fit(self, X, y, sample_weight):
""" This method is optional, it is called before all the others."""
return self
def negative_gradient(self, y_pred):
"""The y_pred should contain all the events passed to `fit` method,
moreover, the order should be the same"""
raise NotImplementedError()
def __call__(self, y_pred):
"""The y_pred should contain all the events passed to `fit` method,
moreover, the order should be the same"""
raise NotImplementedError()
def prepare_tree_params(self, y_pred):
"""Prepares parameters for regression tree that minimizes MSE
:param y_pred: contains predictions for all the events passed to `fit` method,
moreover, the order should be the same
:return: tuple (tree_target, tree_weight) with target and weight to be used in decision tree
"""
return self.negative_gradient(y_pred), numpy.ones(len(y_pred))
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
"""
Method for pruning. Loss function can prepare better values for leaves
:param terminal_regions: indices of terminal regions of each event.
:param leaf_values: numpy.array, current mapping of leaf indices to prediction values.
:param y_pred: predictions before adding new tree.
:return: numpy.array with new prediction values for all leaves.
"""
return leaf_values
def compute_optimal_step(self, y_pred):
"""
Compute optimal global step. This method is typically used to make optimal step
before fitting trees to reduce variance.
:param y_pred: initial predictions, numpy.array of shape [n_samples]
:return: float
"""
return 0.
class HessianLossFunction(AbstractLossFunction):
"""Loss function with diagonal hessian, provides uses Newton-Raphson step to update trees. """
def __init__(self, regularization=5.):
"""
:param regularization: float, penalty for leaves with few events,
corresponds roughly to the number of added events of both classes to each leaf.
"""
self.regularization = regularization
def fit(self, X, y, sample_weight):
self.regularization_ = self.regularization * numpy.mean(sample_weight)
return self
def hessian(self, y_pred):
""" Returns diagonal of hessian matrix.
:param y_pred: numpy.array of shape [n_samples] with events passed in the same order as in `fit`.
:return: numpy.array of shape [n_sampels] with second derivatives with respect to each prediction.
"""
raise NotImplementedError('Override this method in loss function.')
def prepare_tree_params(self, y_pred):
grad = self.negative_gradient(y_pred)
hess = self.hessian(y_pred) + 0.01
return grad / hess, hess
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
""" This expression comes from optimization of second-order approximation of loss function."""
min_length = len(leaf_values)
nominators = numpy.bincount(terminal_regions, weights=self.negative_gradient(y_pred), minlength=min_length)
denominators = numpy.bincount(terminal_regions, weights=self.hessian(y_pred), minlength=min_length)
return nominators / (denominators + self.regularization_)
def compute_optimal_step(self, y_pred):
"""
Optimal step is computed using Newton-Raphson algorithm (10 iterations).
:param y_pred: predictions (usually, zeros)
:return: float
"""
terminal_regions = numpy.zeros(len(y_pred), dtype='int')
leaf_values = numpy.zeros(shape=1)
step = 0.
for _ in range(10):
step_ = self.prepare_new_leaves_values(terminal_regions, leaf_values=leaf_values, y_pred=y_pred + step)[0]
step += 0.5 * step_
return step
# region Classification losses
class AdaLossFunction(HessianLossFunction):
""" AdaLossFunction is the same as Exponential Loss Function (aka exploss) """
def fit(self, X, y, sample_weight):
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
return numpy.sum(self.sample_weight * numpy.exp(- self.y_signed * y_pred))
def negative_gradient(self, y_pred):
return self.y_signed * self.sample_weight * numpy.exp(- self.y_signed * y_pred)
def hessian(self, y_pred):
return self.sample_weight * numpy.exp(- self.y_signed * y_pred)
def prepare_tree_params(self, y_pred):
return self.y_signed, self.hessian(y_pred)
class LogLossFunction(HessianLossFunction):
"""Logistic loss function (logloss), aka binomial deviance, aka cross-entropy,
aka log-likelihood loss.
"""
def fit(self, X, y, sample_weight):
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
return numpy.sum(self.sample_weight * numpy.logaddexp(0, - self.y_signed * y_pred))
def negative_gradient(self, y_pred):
return self.y_signed * self.sample_weight * expit(- self.y_signed * y_pred)
def hessian(self, y_pred):
expits = expit(self.y_signed * y_pred)
return self.sample_weight * expits * (1 - expits)
def prepare_tree_params(self, y_pred):
return self.y_signed * expit(- self.y_signed * y_pred), self.sample_weight
class CompositeLossFunction(HessianLossFunction):
"""
Composite loss function is defined as exploss for backgorund events and logloss for signal with proper constants.
Such kind of loss functions is very useful to optimize AMS or in situations where very clean signal is expected.
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
self.sig_w = (y == 1) * self.sample_weight
self.bck_w = (y == 0) * self.sample_weight
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
result = numpy.sum(self.sig_w * numpy.logaddexp(0, -y_pred))
result += numpy.sum(self.bck_w * numpy.exp(0.5 * y_pred))
return result
def negative_gradient(self, y_pred):
result = self.sig_w * expit(- y_pred)
result -= 0.5 * self.bck_w * numpy.exp(0.5 * y_pred)
return result
def hessian(self, y_pred):
expits = expit(- y_pred)
return self.sig_w * expits * (1 - expits) + self.bck_w * 0.25 * numpy.exp(0.5 * y_pred)
# endregion
# region Regression Losses
class MSELossFunction(HessianLossFunction):
r""" Mean squared error loss function, used for regression.
:math:`\text{loss} = \sum_i (y_i - \hat{y}_i)^2`
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight, normalize=True)
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
return self
def __call__(self, y_pred):
return 0.5 * numpy.sum(self.sample_weight * (self.y - y_pred) ** 2)
def negative_gradient(self, y_pred):
return self.sample_weight * (self.y - y_pred)
def hessian(self, y_pred):
return self.sample_weight
def prepare_tree_params(self, y_pred):
return self.y - y_pred, self.sample_weight
def compute_optimal_step(self, y_pred):
return numpy.average(self.y - y_pred, weights=self.sample_weight)
class MAELossFunction(AbstractLossFunction):
r""" Mean absolute error loss function, used for regression.
:math:`\text{loss} = \sum_i |y_i - \hat{y}_i|`
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight, normalize=True)
return self
def __call__(self, y_pred):
return 0.5 * numpy.sum(self.sample_weight * numpy.abs(self.y - y_pred))
def negative_gradient(self, y_pred):
return self.sample_weight * numpy.sign(self.y - y_pred)
def prepare_tree_params(self, y_pred):
return numpy.sign(self.y - y_pred), self.sample_weight
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
# TODO use weighted median
new_leaf_values = numpy.zeros(len(leaf_values), dtype='float')
target = (self.y - y_pred)
for terminal_region in range(len(leaf_values)):
values = target[terminal_regions == terminal_region]
values = numpy.insert(values, [0], [0])
new_leaf_values[terminal_region] = numpy.median(values)
return new_leaf_values
def compute_optimal_step(self, y_pred):
return weighted_quantile(self.y - y_pred, quantiles=[0.5], sample_weight=self.sample_weight)[0]
# endregion RegressionLosses
class RankBoostLossFunction(HessianLossFunction):
def __init__(self, request_column, penalty_power=1., update_iterations=1):
r"""RankBoostLossFunction is target of optimization in RankBoost [RB]_ algorithm,
which was developed for ranking and introduces penalties for wrong order of predictions.
However, this implementation goes further and there is selection of optimal leaf values based
on iterative procedure. This implementation also uses matrix decomposition of loss function,
which is very effective, when labels are from some very limited set (usually it is 0, 1, 2, 3, 4)
:math:`\text{loss} = \sum_{ij} w_{ij} exp(pred_i - pred_j)`,
:math:`w_{ij} = ( \alpha + \beta * [query_i = query_j]) R_{label_i, label_j}`, where
:math:`R_{ij} = 0` if :math:`i \leq j`, else :math:`R_{ij} = (i - j)^{p}`
:param str request_column: name of column with search query ids. The higher attention is payed
to samples with same query.
:param float penalty_power: describes dependence of penalty on the difference between target labels.
:param int update_iterations: number of minimization steps to provide optimal values.
.. [RB] Y. Freund et al. An Efficient Boosting Algorithm for Combining Preferences
"""
self.update_terations = update_iterations
self.penalty_power = penalty_power
self.request_column = request_column
HessianLossFunction.__init__(self, regularization=0.1)
def fit(self, X, y, sample_weight):
self.queries = X[self.request_column]
self.y = y
self.possible_queries, normed_queries = numpy.unique(self.queries, return_inverse=True)
self.possible_ranks, normed_ranks = numpy.unique(self.y, return_inverse=True)
self.lookups = [normed_ranks, normed_queries * len(self.possible_ranks) + normed_ranks]
self.minlengths = [len(self.possible_ranks), len(self.possible_ranks) * len(self.possible_queries)]
self.rank_penalties = numpy.zeros([len(self.possible_ranks), len(self.possible_ranks)], dtype=float)
for r1 in self.possible_ranks:
for r2 in self.possible_ranks:
if r1 < r2:
self.rank_penalties[r1, r2] = (r2 - r1) ** self.penalty_power
self.penalty_matrices = []
self.penalty_matrices.append(self.rank_penalties / numpy.sqrt(1 + len(y)))
n_queries = numpy.bincount(normed_queries)
assert len(n_queries) == len(self.possible_queries)
self.penalty_matrices.append(
sparse.block_diag([self.rank_penalties * 1. / numpy.sqrt(1 + nq) for nq in n_queries]))
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
def __call__(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
result = 0.
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
result += pos_stats.T.dot(penalty_matrix.dot(neg_stats))
return result
def negative_gradient(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
gradient = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
gradient += pos_exponent * penalty_matrix.dot(neg_stats)[lookup]
gradient -= neg_exponent * penalty_matrix.T.dot(pos_stats)[lookup]
return - gradient
def hessian(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
result = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
result += pos_exponent * penalty_matrix.dot(neg_stats)[lookup]
result += neg_exponent * penalty_matrix.T.dot(pos_stats)[lookup]
return result
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
leaves_values = numpy.zeros(len(leaf_values))
for _ in range(self.update_terations):
y_test = y_pred + leaves_values[terminal_regions]
new_leaves_values = self._prepare_new_leaves_values(terminal_regions, leaves_values, y_test)
leaves_values = 0.5 * new_leaves_values + leaves_values
return leaves_values
def _prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
"""
For each event we shall represent loss as w_plus * e^{pred} + w_minus * e^{-pred},
then we are able to construct optimal step.
Pay attention: this is not an optimal, since we are ignoring,
that some events belong to the same leaf
"""
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
w_plus = numpy.zeros(len(y_pred), dtype=float)
w_minus = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
w_plus += penalty_matrix.dot(neg_stats)[lookup]
w_minus += penalty_matrix.T.dot(pos_stats)[lookup]
w_plus_leaf = numpy.bincount(terminal_regions, weights=w_plus * pos_exponent) + self.regularization
w_minus_leaf = numpy.bincount(terminal_regions, weights=w_minus * neg_exponent) + self.regularization
return 0.5 * numpy.log(w_minus_leaf / w_plus_leaf)
# region MatrixLossFunction
class AbstractMatrixLossFunction(HessianLossFunction):
def __init__(self, uniform_features, regularization=5.):
r"""AbstractMatrixLossFunction is a base class to be inherited by other loss functions,
which choose the particular A matrix and w vector. The formula of loss is:
\text{loss} = \sum_i w_i * exp(- \sum_j a_ij y_j score_j)
"""
self.uniform_features = uniform_features
# real matrix and vector will be computed during fitting
self.A = None
self.A_t = None
self.w = None
HessianLossFunction.__init__(self, regularization=regularization)
def fit(self, X, y, sample_weight):
"""This method is used to compute A matrix and w based on train dataset"""
assert len(X) == len(y), "different size of arrays"
A, w = self.compute_parameters(X, y, sample_weight)
self.A = sparse.csr_matrix(A)
self.A_t = sparse.csr_matrix(self.A.transpose())
self.A_t_sq = self.A_t.multiply(self.A_t)
self.w = numpy.array(w)
assert A.shape[0] == len(w), "inconsistent sizes"
assert A.shape[1] == len(X), "wrong size of matrix"
self.y_signed = numpy.array(2 * y - 1)
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
return self
def __call__(self, y_pred):
"""Computing the loss itself"""
assert len(y_pred) == self.A.shape[1], "something is wrong with sizes"
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
return numpy.sum(self.w * exponents)
def negative_gradient(self, y_pred):
"""Computing negative gradient"""
assert len(y_pred) == self.A.shape[1], "something is wrong with sizes"
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
result = self.A_t.dot(self.w * exponents) * self.y_signed
return result
def hessian(self, y_pred):
assert len(y_pred) == self.A.shape[1], 'something wrong with sizes'
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
result = self.A_t_sq.dot(self.w * exponents)
return result
def compute_parameters(self, trainX, trainY, trainW):
"""This method should be overloaded in descendant, and should return A, w (matrix and vector)"""
raise NotImplementedError()
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
# current approach uses Newton-Raphson step
# TODO compare with iterative suboptimal choice of value, based on exp(a x) ~ a exp(x)
regions_matrix = sparse.csc_matrix((self.y_signed, [numpy.arange(len(self.y_signed)), terminal_regions]))
# Z is matrix of shape [n_exponents, n_terminal_regions]
# with contributions of each terminal region to each exponent
Z = self.A.dot(regions_matrix)
Z = Z.T
nominator = Z.dot(self.w * exponents)
denominator = Z.multiply(Z).dot(self.w * exponents)
return nominator / (denominator + 1e-5)
class KnnAdaLossFunction(AbstractMatrixLossFunction):
def __init__(self, uniform_features, uniform_label, knn=10, row_norm=1.):
r"""Modification of AdaLoss to achieve uniformity of predictions
:math:`\text{loss} = \sum_i w_i * exp(- \sum_j a_{ij} y_j score_j)`
`A` matrix is square, each row corresponds to a single event in train dataset, in each row we put ones
to the closest neighbours if this event from uniform class.
See [BU]_ for details.
:param list[str] uniform_features: the features, along which uniformity is desired
:param int|list[int] uniform_label: the label (labels) of 'uniform classes'
:param int knn: the number of nonzero elements in the row, corresponding to event in 'uniform class'
.. [BU] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.knn = knn
self.row_norm = row_norm
self.uniform_label = check_uniform_label(uniform_label)
AbstractMatrixLossFunction.__init__(self, uniform_features)
def compute_parameters(self, trainX, trainY, trainW):
A_parts = []
w_parts = []
for label in self.uniform_label:
label_mask = numpy.array(trainY == label)
n_label = numpy.sum(label_mask)
knn_indices = compute_knn_indices_of_signal(trainX[self.uniform_features], label_mask, self.knn)
knn_indices = knn_indices[label_mask, :]
ind_ptr = numpy.arange(0, n_label * self.knn + 1, self.knn)
column_indices = knn_indices.flatten()
data = numpy.ones(n_label * self.knn, dtype=float) * self.row_norm / self.knn
A_part = sparse.csr_matrix((data, column_indices, ind_ptr), shape=[n_label, len(trainX)])
w_part = numpy.mean(numpy.take(trainW, knn_indices), axis=1)
assert A_part.shape[0] == len(w_part)
A_parts.append(A_part)
w_parts.append(w_part)
for label in set(trainY) - set(self.uniform_label):
label_mask = trainY == label
n_label = numpy.sum(label_mask)
ind_ptr = numpy.arange(0, n_label + 1)
column_indices = numpy.where(label_mask)[0].flatten()
data = numpy.ones(n_label, dtype=float) * self.row_norm
A_part = sparse.csr_matrix((data, column_indices, ind_ptr), shape=[n_label, len(trainX)])
w_part = trainW[label_mask]
A_parts.append(A_part)
w_parts.append(w_part)
A = sparse.vstack(A_parts, format='csr', dtype=float)
w = numpy.concatenate(w_parts)
assert A.shape == (len(trainX), len(trainX))
return A, w
# endregion
# region ReweightLossFunction
# Mathematically at each stage we
# 0. recompute weights
# 1. normalize ratio between distributions (negatives are in opposite distribution)
# 2. chi2 - changing only sign, weights are the same
# 3. optimal value: simply log as usual (negatives are in the same distribution with sign -)
class ReweightLossFunction(AbstractLossFunction):
def __init__(self, regularization=5.):
"""
Loss function used to reweight events. Conventions:
y=0 - target distribution, y=1 - original distribution.
Weights after look like:
w = w_0 for target distribution
w = w_0 * exp(pred) for events from original distribution
(so pred for target distribution is ignored)
:param regularization: roughly, it's number of events added in each leaf to prevent overfitting.
"""
self.regularization = regularization
def fit(self, X, y, sample_weight):
assert numpy.all(numpy.in1d(y, [0, 1]))
if sample_weight is None:
self.sample_weight = numpy.ones(len(X), dtype=float)
else:
self.sample_weight = numpy.array(sample_weight, dtype=float)
self.y = y
# signs encounter transfer to opposite distribution
self.signs = (2 * y - 1) * numpy.sign(sample_weight)
self.mask_original = numpy.array(self.y)
self.mask_target = numpy.array(1 - self.y)
return self
def _compute_weights(self, y_pred):
"""We need renormalization at eac step"""
weights = self.sample_weight * numpy.exp(self.y * y_pred)
return check_sample_weight(self.y, weights, normalize=True, normalize_by_class=True)
def __call__(self, *args, **kwargs):
""" Loss function doesn't have precise expression """
return 0
def negative_gradient(self, y_pred):
return 0.
def prepare_tree_params(self, y_pred):
return self.signs, numpy.abs(self._compute_weights(y_pred))
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
weights = self._compute_weights(y_pred)
w_target = numpy.bincount(terminal_regions, weights=self.mask_target * weights)
w_original = numpy.bincount(terminal_regions, weights=self.mask_original * weights)
# suppressing possibly negative samples
w_target = w_target.clip(0)
w_original = w_original.clip(0)
return numpy.log(w_target + self.regularization) - numpy.log(w_original + self.regularization)
# endregion
# region FlatnessLossFunction
def _exp_margin(margin):
""" margin = - y_signed * y_pred """
return numpy.exp(numpy.clip(margin, -1e5, 2))
class AbstractFlatnessLossFunction(AbstractLossFunction):
"""Base class for FlatnessLosses"""
def __init__(self, uniform_features, uniform_label, power=2., fl_coefficient=3.,
allow_wrong_signs=True):
self.uniform_features = uniform_features
if isinstance(uniform_label, numbers.Number):
self.uniform_label = numpy.array([uniform_label])
else:
self.uniform_label = numpy.array(uniform_label)
self.power = power
self.fl_coefficient = fl_coefficient
self.allow_wrong_signs = allow_wrong_signs
def fit(self, X, y, sample_weight=None):
sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
assert len(X) == len(y), 'lengths are different'
X = pandas.DataFrame(X)
self.group_indices = dict()
self.group_matrices = dict()
self.group_weights = dict()
occurences = numpy.zeros(len(X))
for label in self.uniform_label:
self.group_indices[label] = self._compute_groups_indices(X, y, label=label)
self.group_matrices[label] = group_indices_to_groups_matrix(self.group_indices[label], len(X))
self.group_weights[label] = compute_group_weights(self.group_matrices[label], sample_weight=sample_weight)
for group in self.group_indices[label]:
occurences[group] += 1
out_of_bins = (occurences == 0) & numpy.in1d(y, self.uniform_label)
if numpy.mean(out_of_bins) > 0.01:
warnings.warn("%i events out of all bins " % numpy.sum(out_of_bins), UserWarning)
self.y = y
self.y_signed = 2 * y - 1
self.sample_weight = numpy.copy(sample_weight)
self.divided_weight = sample_weight / numpy.maximum(occurences, 1)
return self
def _compute_groups_indices(self, X, y, label):
raise NotImplementedError('To be overriden in descendants.')
def __call__(self, pred):
# the actual value does not play any role in boosting
# optimizing here
return 0
def _compute_fl_derivatives(self, y_pred):
y_pred = numpy.ravel(y_pred)
neg_gradient = numpy.zeros(len(self.y), dtype=numpy.float)
for label in self.uniform_label:
label_mask = self.y == label
global_positions = numpy.zeros(len(y_pred), dtype=float)
global_positions[label_mask] = \
_compute_positions(y_pred[label_mask], sample_weight=self.sample_weight[label_mask])
for indices_in_bin in self.group_indices[label]:
local_pos = _compute_positions(y_pred[indices_in_bin],
sample_weight=self.sample_weight[indices_in_bin])
global_pos = global_positions[indices_in_bin]
bin_gradient = self.power * numpy.sign(local_pos - global_pos) * \
numpy.abs(local_pos - global_pos) ** (self.power - 1)
neg_gradient[indices_in_bin] += bin_gradient
neg_gradient *= self.divided_weight
# check that events outside uniform uniform classes are not touched
assert numpy.all(neg_gradient[~numpy.in1d(self.y, self.uniform_label)] == 0)
return neg_gradient
def negative_gradient(self, y_pred):
y_signed = self.y_signed
neg_gradient = self._compute_fl_derivatives(y_pred) * self.fl_coefficient
# adding ExpLoss
neg_gradient += y_signed * self.sample_weight * _exp_margin(-y_signed * y_pred)
if not self.allow_wrong_signs:
neg_gradient = y_signed * numpy.clip(y_signed * neg_gradient, 0, 1e5)
return neg_gradient
class BinFlatnessLossFunction(AbstractFlatnessLossFunction):
def __init__(self, uniform_features, uniform_label, n_bins=10, power=2., fl_coefficient=3.,
allow_wrong_signs=True):
r"""
This loss function contains separately penalty for non-flatness and for bad prediction quality.
See [FL]_ for details.
:math:`\text{loss} =\text{ExpLoss} + c \times \text{FlatnessLoss}`
FlatnessLoss computed using binning of uniform variables
:param list[str] uniform_features: names of features, along which we want to obtain uniformity of predictions
:param int|list[int] uniform_label: the label(s) of classes for which uniformity is desired
:param int n_bins: number of bins along each variable
:param float power: the loss contains the difference :math:`| F - F_bin |^p`, where p is power
:param float fl_coefficient: multiplier for flatness_loss. Controls the tradeoff of quality vs uniformity.
:param bool allow_wrong_signs: defines whether gradient may different sign from the "sign of class"
(i.e. may have negative gradient on signal). If False, values will be clipped to zero.
.. [FL] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.n_bins = n_bins
AbstractFlatnessLossFunction.__init__(self, uniform_features,
uniform_label=uniform_label, power=power,
fl_coefficient=fl_coefficient,
allow_wrong_signs=allow_wrong_signs)
def _compute_groups_indices(self, X, y, label):
"""Returns a list, each element is events' indices in some group."""
label_mask = y == label
extended_bin_limits = []
for var in self.uniform_features:
f_min, f_max = numpy.min(X[var][label_mask]), numpy.max(X[var][label_mask])
extended_bin_limits.append(numpy.linspace(f_min, f_max, 2 * self.n_bins + 1))
groups_indices = list()
for shift in [0, 1]:
bin_limits = []
for axis_limits in extended_bin_limits:
bin_limits.append(axis_limits[1 + shift:-1:2])
bin_indices = compute_bin_indices(X.ix[:, self.uniform_features].values, bin_limits=bin_limits)
groups_indices += list(bin_to_group_indices(bin_indices, mask=label_mask))
return groups_indices
class KnnFlatnessLossFunction(AbstractFlatnessLossFunction):
def __init__(self, uniform_features, uniform_label, n_neighbours=100, power=2., fl_coefficient=3.,
max_groups=5000, allow_wrong_signs=True, random_state=42):
r"""
This loss function contains separately penalty for non-flatness and for bad prediction quality.
See [FL]_ for details.
:math:`\text{loss} = \text{ExpLoss} + c \times \text{FlatnessLoss}`
FlatnessLoss computed using nearest neighbors in space of uniform features
:param list[str] uniform_features: names of features, along which we want to obtain uniformity of predictions
:param int|list[int] uniform_label: the label(s) of classes for which uniformity is desired
:param int n_neighbours: number of neighbors used in flatness loss
:param float power: the loss contains the difference :math:`| F - F_bin |^p`, where p is power
:param float fl_coefficient: multiplier for flatness_loss. Controls the tradeoff of quality vs uniformity.
:param bool allow_wrong_signs: defines whether gradient may different sign from the "sign of class"
(i.e. may have negative gradient on signal). If False, values will be clipped to zero.
:param int max_groups: to limit memory consumption when training sample is large,
we randomly pick this number of points with their members.
.. [FL] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.n_neighbours = n_neighbours
self.max_groups = max_groups
self.random_state = random_state
AbstractFlatnessLossFunction.__init__(self, uniform_features,
uniform_label=uniform_label, power=power,
fl_coefficient=fl_coefficient,
allow_wrong_signs=allow_wrong_signs)
def _compute_groups_indices(self, X, y, label):
mask = y == label
self.random_state = check_random_state(self.random_state)
knn_indices = compute_knn_indices_of_signal(X[self.uniform_features], mask,
n_neighbours=self.n_neighbours)[mask, :]
if len(knn_indices) > self.max_groups:
selected_group = self.random_state.choice(len(knn_indices), size=self.max_groups, replace=False)
return knn_indices[selected_group, :]
else:
return knn_indices
# endregion
added reweight loss function to documentation
"""
**hep_ml.losses** contains different loss functions to use in gradient boosting.
Apart from standard classification losses, **hep_ml** contains losses for uniform classification
(see :class:`BinFlatnessLossFunction`, :class:`KnnFlatnessLossFunction`, :class:`KnnAdaLossFunction`)
and for ranking (see :class:`RankBoostLossFunction`)
**Interface**
Loss functions inside **hep_ml** are stateful estimators and require initial fitting,
which is done automatically inside gradient boosting.
All loss function should be derived from AbstractLossFunction and implement this interface.
Examples
________
Training gradient boosting, optimizing LogLoss and using all features
>>> from hep_ml.gradientboosting import UGradientBoostingClassifier, LogLossFunction
>>> classifier = UGradientBoostingClassifier(loss=LogLossFunction(), n_estimators=100)
>>> classifier.fit(X, y, sample_weight=sample_weight)
Using composite loss function and subsampling:
>>> loss = CompositeLossFunction()
>>> classifier = UGradientBoostingClassifier(loss=loss, subsample=0.5)
To get uniform predictions in mass in background (note that mass should not present in features):
>>> loss = BinFlatnessLossFunction(uniform_features=['mass'], uniform_label=0, train_features=['pt', 'flight_time'])
>>> classifier = UGradientBoostingClassifier(loss=loss)
To get uniform predictions in both signal and background:
>>> loss = BinFlatnessLossFunction(uniform_features=['mass'], uniform_label=[0, 1], train_features=['pt', 'flight_time'])
>>> classifier = UGradientBoostingClassifier(loss=loss)
"""
from __future__ import division, print_function, absolute_import
import numbers
import warnings
import numpy
import pandas
from scipy import sparse
from scipy.special import expit
from sklearn.utils.validation import check_random_state
from sklearn.base import BaseEstimator
from .commonutils import compute_knn_indices_of_signal, check_sample_weight, check_uniform_label, weighted_quantile
from .metrics_utils import bin_to_group_indices, compute_bin_indices, compute_group_weights, \
group_indices_to_groups_matrix
__author__ = 'Alex Rogozhnikov'
__all__ = [
'AbstractLossFunction',
'MSELossFunction',
'MAELossFunction',
'LogLossFunction',
'AdaLossFunction',
'CompositeLossFunction',
'BinFlatnessLossFunction',
'KnnFlatnessLossFunction',
'KnnAdaLossFunction',
'RankBoostLossFunction',
'ReweightLossFunction'
]
def _compute_positions(y_pred, sample_weight):
"""
For each event computes it position among other events by prediction.
position = (weighted) part of elements with lower predictions => position belongs to [0, 1]
This function is very close to `scipy.stats.rankdata`, but supports weights.
"""
order = numpy.argsort(y_pred)
ordered_weights = sample_weight[order]
ordered_weights /= float(numpy.sum(ordered_weights))
efficiencies = (numpy.cumsum(ordered_weights) - 0.5 * ordered_weights)
return efficiencies[numpy.argsort(order)]
class AbstractLossFunction(BaseEstimator):
"""
This is base class for loss functions used in `hep_ml`.
Main differences compared to `scikit-learn` loss functions:
1. losses are stateful, and may require fitting of training data before usage.
2. thus, when computing gradient, hessian, one shall provide predictions of all events.
3. losses are object that shall be passed as estimators to gradient boosting (see examples).
4. only two-class case is supported, and different classes may have different role and meaning.
"""
def fit(self, X, y, sample_weight):
""" This method is optional, it is called before all the others."""
return self
def negative_gradient(self, y_pred):
"""The y_pred should contain all the events passed to `fit` method,
moreover, the order should be the same"""
raise NotImplementedError()
def __call__(self, y_pred):
"""The y_pred should contain all the events passed to `fit` method,
moreover, the order should be the same"""
raise NotImplementedError()
def prepare_tree_params(self, y_pred):
"""Prepares parameters for regression tree that minimizes MSE
:param y_pred: contains predictions for all the events passed to `fit` method,
moreover, the order should be the same
:return: tuple (tree_target, tree_weight) with target and weight to be used in decision tree
"""
return self.negative_gradient(y_pred), numpy.ones(len(y_pred))
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
"""
Method for pruning. Loss function can prepare better values for leaves
:param terminal_regions: indices of terminal regions of each event.
:param leaf_values: numpy.array, current mapping of leaf indices to prediction values.
:param y_pred: predictions before adding new tree.
:return: numpy.array with new prediction values for all leaves.
"""
return leaf_values
def compute_optimal_step(self, y_pred):
"""
Compute optimal global step. This method is typically used to make optimal step
before fitting trees to reduce variance.
:param y_pred: initial predictions, numpy.array of shape [n_samples]
:return: float
"""
return 0.
class HessianLossFunction(AbstractLossFunction):
"""Loss function with diagonal hessian, provides uses Newton-Raphson step to update trees. """
def __init__(self, regularization=5.):
"""
:param regularization: float, penalty for leaves with few events,
corresponds roughly to the number of added events of both classes to each leaf.
"""
self.regularization = regularization
def fit(self, X, y, sample_weight):
self.regularization_ = self.regularization * numpy.mean(sample_weight)
return self
def hessian(self, y_pred):
""" Returns diagonal of hessian matrix.
:param y_pred: numpy.array of shape [n_samples] with events passed in the same order as in `fit`.
:return: numpy.array of shape [n_sampels] with second derivatives with respect to each prediction.
"""
raise NotImplementedError('Override this method in loss function.')
def prepare_tree_params(self, y_pred):
grad = self.negative_gradient(y_pred)
hess = self.hessian(y_pred) + 0.01
return grad / hess, hess
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
""" This expression comes from optimization of second-order approximation of loss function."""
min_length = len(leaf_values)
nominators = numpy.bincount(terminal_regions, weights=self.negative_gradient(y_pred), minlength=min_length)
denominators = numpy.bincount(terminal_regions, weights=self.hessian(y_pred), minlength=min_length)
return nominators / (denominators + self.regularization_)
def compute_optimal_step(self, y_pred):
"""
Optimal step is computed using Newton-Raphson algorithm (10 iterations).
:param y_pred: predictions (usually, zeros)
:return: float
"""
terminal_regions = numpy.zeros(len(y_pred), dtype='int')
leaf_values = numpy.zeros(shape=1)
step = 0.
for _ in range(10):
step_ = self.prepare_new_leaves_values(terminal_regions, leaf_values=leaf_values, y_pred=y_pred + step)[0]
step += 0.5 * step_
return step
# region Classification losses
class AdaLossFunction(HessianLossFunction):
""" AdaLossFunction is the same as Exponential Loss Function (aka exploss) """
def fit(self, X, y, sample_weight):
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
return numpy.sum(self.sample_weight * numpy.exp(- self.y_signed * y_pred))
def negative_gradient(self, y_pred):
return self.y_signed * self.sample_weight * numpy.exp(- self.y_signed * y_pred)
def hessian(self, y_pred):
return self.sample_weight * numpy.exp(- self.y_signed * y_pred)
def prepare_tree_params(self, y_pred):
return self.y_signed, self.hessian(y_pred)
class LogLossFunction(HessianLossFunction):
"""Logistic loss function (logloss), aka binomial deviance, aka cross-entropy,
aka log-likelihood loss.
"""
def fit(self, X, y, sample_weight):
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
return numpy.sum(self.sample_weight * numpy.logaddexp(0, - self.y_signed * y_pred))
def negative_gradient(self, y_pred):
return self.y_signed * self.sample_weight * expit(- self.y_signed * y_pred)
def hessian(self, y_pred):
expits = expit(self.y_signed * y_pred)
return self.sample_weight * expits * (1 - expits)
def prepare_tree_params(self, y_pred):
return self.y_signed * expit(- self.y_signed * y_pred), self.sample_weight
class CompositeLossFunction(HessianLossFunction):
"""
Composite loss function is defined as exploss for backgorund events and logloss for signal with proper constants.
Such kind of loss functions is very useful to optimize AMS or in situations where very clean signal is expected.
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
self.y_signed = 2 * y - 1
self.sig_w = (y == 1) * self.sample_weight
self.bck_w = (y == 0) * self.sample_weight
HessianLossFunction.fit(self, X, y, sample_weight=self.sample_weight)
return self
def __call__(self, y_pred):
result = numpy.sum(self.sig_w * numpy.logaddexp(0, -y_pred))
result += numpy.sum(self.bck_w * numpy.exp(0.5 * y_pred))
return result
def negative_gradient(self, y_pred):
result = self.sig_w * expit(- y_pred)
result -= 0.5 * self.bck_w * numpy.exp(0.5 * y_pred)
return result
def hessian(self, y_pred):
expits = expit(- y_pred)
return self.sig_w * expits * (1 - expits) + self.bck_w * 0.25 * numpy.exp(0.5 * y_pred)
# endregion
# region Regression Losses
class MSELossFunction(HessianLossFunction):
r""" Mean squared error loss function, used for regression.
:math:`\text{loss} = \sum_i (y_i - \hat{y}_i)^2`
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight, normalize=True)
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
return self
def __call__(self, y_pred):
return 0.5 * numpy.sum(self.sample_weight * (self.y - y_pred) ** 2)
def negative_gradient(self, y_pred):
return self.sample_weight * (self.y - y_pred)
def hessian(self, y_pred):
return self.sample_weight
def prepare_tree_params(self, y_pred):
return self.y - y_pred, self.sample_weight
def compute_optimal_step(self, y_pred):
return numpy.average(self.y - y_pred, weights=self.sample_weight)
class MAELossFunction(AbstractLossFunction):
r""" Mean absolute error loss function, used for regression.
:math:`\text{loss} = \sum_i |y_i - \hat{y}_i|`
"""
def fit(self, X, y, sample_weight):
self.y = y
self.sample_weight = check_sample_weight(y, sample_weight=sample_weight, normalize=True)
return self
def __call__(self, y_pred):
return 0.5 * numpy.sum(self.sample_weight * numpy.abs(self.y - y_pred))
def negative_gradient(self, y_pred):
return self.sample_weight * numpy.sign(self.y - y_pred)
def prepare_tree_params(self, y_pred):
return numpy.sign(self.y - y_pred), self.sample_weight
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
# TODO use weighted median
new_leaf_values = numpy.zeros(len(leaf_values), dtype='float')
target = (self.y - y_pred)
for terminal_region in range(len(leaf_values)):
values = target[terminal_regions == terminal_region]
values = numpy.insert(values, [0], [0])
new_leaf_values[terminal_region] = numpy.median(values)
return new_leaf_values
def compute_optimal_step(self, y_pred):
return weighted_quantile(self.y - y_pred, quantiles=[0.5], sample_weight=self.sample_weight)[0]
# endregion RegressionLosses
class RankBoostLossFunction(HessianLossFunction):
def __init__(self, request_column, penalty_power=1., update_iterations=1):
r"""RankBoostLossFunction is target of optimization in RankBoost [RB]_ algorithm,
which was developed for ranking and introduces penalties for wrong order of predictions.
However, this implementation goes further and there is selection of optimal leaf values based
on iterative procedure. This implementation also uses matrix decomposition of loss function,
which is very effective, when labels are from some very limited set (usually it is 0, 1, 2, 3, 4)
:math:`\text{loss} = \sum_{ij} w_{ij} exp(pred_i - pred_j)`,
:math:`w_{ij} = ( \alpha + \beta * [query_i = query_j]) R_{label_i, label_j}`, where
:math:`R_{ij} = 0` if :math:`i \leq j`, else :math:`R_{ij} = (i - j)^{p}`
:param str request_column: name of column with search query ids. The higher attention is payed
to samples with same query.
:param float penalty_power: describes dependence of penalty on the difference between target labels.
:param int update_iterations: number of minimization steps to provide optimal values.
.. [RB] Y. Freund et al. An Efficient Boosting Algorithm for Combining Preferences
"""
self.update_terations = update_iterations
self.penalty_power = penalty_power
self.request_column = request_column
HessianLossFunction.__init__(self, regularization=0.1)
def fit(self, X, y, sample_weight):
self.queries = X[self.request_column]
self.y = y
self.possible_queries, normed_queries = numpy.unique(self.queries, return_inverse=True)
self.possible_ranks, normed_ranks = numpy.unique(self.y, return_inverse=True)
self.lookups = [normed_ranks, normed_queries * len(self.possible_ranks) + normed_ranks]
self.minlengths = [len(self.possible_ranks), len(self.possible_ranks) * len(self.possible_queries)]
self.rank_penalties = numpy.zeros([len(self.possible_ranks), len(self.possible_ranks)], dtype=float)
for r1 in self.possible_ranks:
for r2 in self.possible_ranks:
if r1 < r2:
self.rank_penalties[r1, r2] = (r2 - r1) ** self.penalty_power
self.penalty_matrices = []
self.penalty_matrices.append(self.rank_penalties / numpy.sqrt(1 + len(y)))
n_queries = numpy.bincount(normed_queries)
assert len(n_queries) == len(self.possible_queries)
self.penalty_matrices.append(
sparse.block_diag([self.rank_penalties * 1. / numpy.sqrt(1 + nq) for nq in n_queries]))
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
def __call__(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
result = 0.
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
result += pos_stats.T.dot(penalty_matrix.dot(neg_stats))
return result
def negative_gradient(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
gradient = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
gradient += pos_exponent * penalty_matrix.dot(neg_stats)[lookup]
gradient -= neg_exponent * penalty_matrix.T.dot(pos_stats)[lookup]
return - gradient
def hessian(self, y_pred):
y_pred -= y_pred.mean()
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
result = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
result += pos_exponent * penalty_matrix.dot(neg_stats)[lookup]
result += neg_exponent * penalty_matrix.T.dot(pos_stats)[lookup]
return result
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
leaves_values = numpy.zeros(len(leaf_values))
for _ in range(self.update_terations):
y_test = y_pred + leaves_values[terminal_regions]
new_leaves_values = self._prepare_new_leaves_values(terminal_regions, leaves_values, y_test)
leaves_values = 0.5 * new_leaves_values + leaves_values
return leaves_values
def _prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
"""
For each event we shall represent loss as w_plus * e^{pred} + w_minus * e^{-pred},
then we are able to construct optimal step.
Pay attention: this is not an optimal, since we are ignoring,
that some events belong to the same leaf
"""
pos_exponent = numpy.exp(y_pred)
neg_exponent = numpy.exp(-y_pred)
w_plus = numpy.zeros(len(y_pred), dtype=float)
w_minus = numpy.zeros(len(y_pred), dtype=float)
for lookup, length, penalty_matrix in zip(self.lookups, self.minlengths, self.penalty_matrices):
pos_stats = numpy.bincount(lookup, weights=pos_exponent, minlength=length)
neg_stats = numpy.bincount(lookup, weights=neg_exponent, minlength=length)
w_plus += penalty_matrix.dot(neg_stats)[lookup]
w_minus += penalty_matrix.T.dot(pos_stats)[lookup]
w_plus_leaf = numpy.bincount(terminal_regions, weights=w_plus * pos_exponent) + self.regularization
w_minus_leaf = numpy.bincount(terminal_regions, weights=w_minus * neg_exponent) + self.regularization
return 0.5 * numpy.log(w_minus_leaf / w_plus_leaf)
# region MatrixLossFunction
class AbstractMatrixLossFunction(HessianLossFunction):
def __init__(self, uniform_features, regularization=5.):
r"""AbstractMatrixLossFunction is a base class to be inherited by other loss functions,
which choose the particular A matrix and w vector. The formula of loss is:
\text{loss} = \sum_i w_i * exp(- \sum_j a_ij y_j score_j)
"""
self.uniform_features = uniform_features
# real matrix and vector will be computed during fitting
self.A = None
self.A_t = None
self.w = None
HessianLossFunction.__init__(self, regularization=regularization)
def fit(self, X, y, sample_weight):
"""This method is used to compute A matrix and w based on train dataset"""
assert len(X) == len(y), "different size of arrays"
A, w = self.compute_parameters(X, y, sample_weight)
self.A = sparse.csr_matrix(A)
self.A_t = sparse.csr_matrix(self.A.transpose())
self.A_t_sq = self.A_t.multiply(self.A_t)
self.w = numpy.array(w)
assert A.shape[0] == len(w), "inconsistent sizes"
assert A.shape[1] == len(X), "wrong size of matrix"
self.y_signed = numpy.array(2 * y - 1)
HessianLossFunction.fit(self, X, y, sample_weight=sample_weight)
return self
def __call__(self, y_pred):
"""Computing the loss itself"""
assert len(y_pred) == self.A.shape[1], "something is wrong with sizes"
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
return numpy.sum(self.w * exponents)
def negative_gradient(self, y_pred):
"""Computing negative gradient"""
assert len(y_pred) == self.A.shape[1], "something is wrong with sizes"
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
result = self.A_t.dot(self.w * exponents) * self.y_signed
return result
def hessian(self, y_pred):
assert len(y_pred) == self.A.shape[1], 'something wrong with sizes'
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
result = self.A_t_sq.dot(self.w * exponents)
return result
def compute_parameters(self, trainX, trainY, trainW):
"""This method should be overloaded in descendant, and should return A, w (matrix and vector)"""
raise NotImplementedError()
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
exponents = numpy.exp(- self.A.dot(self.y_signed * y_pred))
# current approach uses Newton-Raphson step
# TODO compare with iterative suboptimal choice of value, based on exp(a x) ~ a exp(x)
regions_matrix = sparse.csc_matrix((self.y_signed, [numpy.arange(len(self.y_signed)), terminal_regions]))
# Z is matrix of shape [n_exponents, n_terminal_regions]
# with contributions of each terminal region to each exponent
Z = self.A.dot(regions_matrix)
Z = Z.T
nominator = Z.dot(self.w * exponents)
denominator = Z.multiply(Z).dot(self.w * exponents)
return nominator / (denominator + 1e-5)
class KnnAdaLossFunction(AbstractMatrixLossFunction):
def __init__(self, uniform_features, uniform_label, knn=10, row_norm=1.):
r"""Modification of AdaLoss to achieve uniformity of predictions
:math:`\text{loss} = \sum_i w_i * exp(- \sum_j a_{ij} y_j score_j)`
`A` matrix is square, each row corresponds to a single event in train dataset, in each row we put ones
to the closest neighbours if this event from uniform class.
See [BU]_ for details.
:param list[str] uniform_features: the features, along which uniformity is desired
:param int|list[int] uniform_label: the label (labels) of 'uniform classes'
:param int knn: the number of nonzero elements in the row, corresponding to event in 'uniform class'
.. [BU] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.knn = knn
self.row_norm = row_norm
self.uniform_label = check_uniform_label(uniform_label)
AbstractMatrixLossFunction.__init__(self, uniform_features)
def compute_parameters(self, trainX, trainY, trainW):
A_parts = []
w_parts = []
for label in self.uniform_label:
label_mask = numpy.array(trainY == label)
n_label = numpy.sum(label_mask)
knn_indices = compute_knn_indices_of_signal(trainX[self.uniform_features], label_mask, self.knn)
knn_indices = knn_indices[label_mask, :]
ind_ptr = numpy.arange(0, n_label * self.knn + 1, self.knn)
column_indices = knn_indices.flatten()
data = numpy.ones(n_label * self.knn, dtype=float) * self.row_norm / self.knn
A_part = sparse.csr_matrix((data, column_indices, ind_ptr), shape=[n_label, len(trainX)])
w_part = numpy.mean(numpy.take(trainW, knn_indices), axis=1)
assert A_part.shape[0] == len(w_part)
A_parts.append(A_part)
w_parts.append(w_part)
for label in set(trainY) - set(self.uniform_label):
label_mask = trainY == label
n_label = numpy.sum(label_mask)
ind_ptr = numpy.arange(0, n_label + 1)
column_indices = numpy.where(label_mask)[0].flatten()
data = numpy.ones(n_label, dtype=float) * self.row_norm
A_part = sparse.csr_matrix((data, column_indices, ind_ptr), shape=[n_label, len(trainX)])
w_part = trainW[label_mask]
A_parts.append(A_part)
w_parts.append(w_part)
A = sparse.vstack(A_parts, format='csr', dtype=float)
w = numpy.concatenate(w_parts)
assert A.shape == (len(trainX), len(trainX))
return A, w
# endregion
# region ReweightLossFunction
# Mathematically at each stage we
# 0. recompute weights
# 1. normalize ratio between distributions (negatives are in opposite distribution)
# 2. chi2 - changing only sign, weights are the same
# 3. optimal value: simply log (negatives are in the same distribution with sign -)
class ReweightLossFunction(AbstractLossFunction):
def __init__(self, regularization=5.):
"""
Loss function used to reweight destributions. Works inside :class:`hep_ml.reweight.GBReweighter`
Conventions:
y=0 - target distribution, y=1 - original distribution.
Weights after look like:
w = w_0 for target distribution
w = w_0 * exp(pred) for events from original distribution
(so pred for target distribution is ignored)
:param regularization: roughly, it's number of events added in each leaf to prevent overfitting.
"""
self.regularization = regularization
def fit(self, X, y, sample_weight):
assert numpy.all(numpy.in1d(y, [0, 1]))
if sample_weight is None:
self.sample_weight = numpy.ones(len(X), dtype=float)
else:
self.sample_weight = numpy.array(sample_weight, dtype=float)
self.y = y
# signs encounter transfer to opposite distribution
self.signs = (2 * y - 1) * numpy.sign(sample_weight)
self.mask_original = numpy.array(self.y)
self.mask_target = numpy.array(1 - self.y)
return self
def _compute_weights(self, y_pred):
"""We need renormalization at eac step"""
weights = self.sample_weight * numpy.exp(self.y * y_pred)
return check_sample_weight(self.y, weights, normalize=True, normalize_by_class=True)
def __call__(self, *args, **kwargs):
""" Loss function doesn't have precise expression """
return 0
def negative_gradient(self, y_pred):
return 0.
def prepare_tree_params(self, y_pred):
return self.signs, numpy.abs(self._compute_weights(y_pred))
def prepare_new_leaves_values(self, terminal_regions, leaf_values, y_pred):
weights = self._compute_weights(y_pred)
w_target = numpy.bincount(terminal_regions, weights=self.mask_target * weights)
w_original = numpy.bincount(terminal_regions, weights=self.mask_original * weights)
# suppressing possibly negative samples
w_target = w_target.clip(0)
w_original = w_original.clip(0)
return numpy.log(w_target + self.regularization) - numpy.log(w_original + self.regularization)
# endregion
# region FlatnessLossFunction
def _exp_margin(margin):
""" margin = - y_signed * y_pred """
return numpy.exp(numpy.clip(margin, -1e5, 2))
class AbstractFlatnessLossFunction(AbstractLossFunction):
"""Base class for FlatnessLosses"""
def __init__(self, uniform_features, uniform_label, power=2., fl_coefficient=3.,
allow_wrong_signs=True):
self.uniform_features = uniform_features
if isinstance(uniform_label, numbers.Number):
self.uniform_label = numpy.array([uniform_label])
else:
self.uniform_label = numpy.array(uniform_label)
self.power = power
self.fl_coefficient = fl_coefficient
self.allow_wrong_signs = allow_wrong_signs
def fit(self, X, y, sample_weight=None):
sample_weight = check_sample_weight(y, sample_weight=sample_weight,
normalize=True, normalize_by_class=True)
assert len(X) == len(y), 'lengths are different'
X = pandas.DataFrame(X)
self.group_indices = dict()
self.group_matrices = dict()
self.group_weights = dict()
occurences = numpy.zeros(len(X))
for label in self.uniform_label:
self.group_indices[label] = self._compute_groups_indices(X, y, label=label)
self.group_matrices[label] = group_indices_to_groups_matrix(self.group_indices[label], len(X))
self.group_weights[label] = compute_group_weights(self.group_matrices[label], sample_weight=sample_weight)
for group in self.group_indices[label]:
occurences[group] += 1
out_of_bins = (occurences == 0) & numpy.in1d(y, self.uniform_label)
if numpy.mean(out_of_bins) > 0.01:
warnings.warn("%i events out of all bins " % numpy.sum(out_of_bins), UserWarning)
self.y = y
self.y_signed = 2 * y - 1
self.sample_weight = numpy.copy(sample_weight)
self.divided_weight = sample_weight / numpy.maximum(occurences, 1)
return self
def _compute_groups_indices(self, X, y, label):
raise NotImplementedError('To be overriden in descendants.')
def __call__(self, pred):
# the actual value does not play any role in boosting
# optimizing here
return 0
def _compute_fl_derivatives(self, y_pred):
y_pred = numpy.ravel(y_pred)
neg_gradient = numpy.zeros(len(self.y), dtype=numpy.float)
for label in self.uniform_label:
label_mask = self.y == label
global_positions = numpy.zeros(len(y_pred), dtype=float)
global_positions[label_mask] = \
_compute_positions(y_pred[label_mask], sample_weight=self.sample_weight[label_mask])
for indices_in_bin in self.group_indices[label]:
local_pos = _compute_positions(y_pred[indices_in_bin],
sample_weight=self.sample_weight[indices_in_bin])
global_pos = global_positions[indices_in_bin]
bin_gradient = self.power * numpy.sign(local_pos - global_pos) * \
numpy.abs(local_pos - global_pos) ** (self.power - 1)
neg_gradient[indices_in_bin] += bin_gradient
neg_gradient *= self.divided_weight
# check that events outside uniform uniform classes are not touched
assert numpy.all(neg_gradient[~numpy.in1d(self.y, self.uniform_label)] == 0)
return neg_gradient
def negative_gradient(self, y_pred):
y_signed = self.y_signed
neg_gradient = self._compute_fl_derivatives(y_pred) * self.fl_coefficient
# adding ExpLoss
neg_gradient += y_signed * self.sample_weight * _exp_margin(-y_signed * y_pred)
if not self.allow_wrong_signs:
neg_gradient = y_signed * numpy.clip(y_signed * neg_gradient, 0, 1e5)
return neg_gradient
class BinFlatnessLossFunction(AbstractFlatnessLossFunction):
def __init__(self, uniform_features, uniform_label, n_bins=10, power=2., fl_coefficient=3.,
allow_wrong_signs=True):
r"""
This loss function contains separately penalty for non-flatness and for bad prediction quality.
See [FL]_ for details.
:math:`\text{loss} =\text{ExpLoss} + c \times \text{FlatnessLoss}`
FlatnessLoss computed using binning of uniform variables
:param list[str] uniform_features: names of features, along which we want to obtain uniformity of predictions
:param int|list[int] uniform_label: the label(s) of classes for which uniformity is desired
:param int n_bins: number of bins along each variable
:param float power: the loss contains the difference :math:`| F - F_bin |^p`, where p is power
:param float fl_coefficient: multiplier for flatness_loss. Controls the tradeoff of quality vs uniformity.
:param bool allow_wrong_signs: defines whether gradient may different sign from the "sign of class"
(i.e. may have negative gradient on signal). If False, values will be clipped to zero.
.. [FL] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.n_bins = n_bins
AbstractFlatnessLossFunction.__init__(self, uniform_features,
uniform_label=uniform_label, power=power,
fl_coefficient=fl_coefficient,
allow_wrong_signs=allow_wrong_signs)
def _compute_groups_indices(self, X, y, label):
"""Returns a list, each element is events' indices in some group."""
label_mask = y == label
extended_bin_limits = []
for var in self.uniform_features:
f_min, f_max = numpy.min(X[var][label_mask]), numpy.max(X[var][label_mask])
extended_bin_limits.append(numpy.linspace(f_min, f_max, 2 * self.n_bins + 1))
groups_indices = list()
for shift in [0, 1]:
bin_limits = []
for axis_limits in extended_bin_limits:
bin_limits.append(axis_limits[1 + shift:-1:2])
bin_indices = compute_bin_indices(X.ix[:, self.uniform_features].values, bin_limits=bin_limits)
groups_indices += list(bin_to_group_indices(bin_indices, mask=label_mask))
return groups_indices
class KnnFlatnessLossFunction(AbstractFlatnessLossFunction):
def __init__(self, uniform_features, uniform_label, n_neighbours=100, power=2., fl_coefficient=3.,
max_groups=5000, allow_wrong_signs=True, random_state=42):
r"""
This loss function contains separately penalty for non-flatness and for bad prediction quality.
See [FL]_ for details.
:math:`\text{loss} = \text{ExpLoss} + c \times \text{FlatnessLoss}`
FlatnessLoss computed using nearest neighbors in space of uniform features
:param list[str] uniform_features: names of features, along which we want to obtain uniformity of predictions
:param int|list[int] uniform_label: the label(s) of classes for which uniformity is desired
:param int n_neighbours: number of neighbors used in flatness loss
:param float power: the loss contains the difference :math:`| F - F_bin |^p`, where p is power
:param float fl_coefficient: multiplier for flatness_loss. Controls the tradeoff of quality vs uniformity.
:param bool allow_wrong_signs: defines whether gradient may different sign from the "sign of class"
(i.e. may have negative gradient on signal). If False, values will be clipped to zero.
:param int max_groups: to limit memory consumption when training sample is large,
we randomly pick this number of points with their members.
.. [FL] A. Rogozhnikov et al, New approaches for boosting to uniformity
http://arxiv.org/abs/1410.4140
"""
self.n_neighbours = n_neighbours
self.max_groups = max_groups
self.random_state = random_state
AbstractFlatnessLossFunction.__init__(self, uniform_features,
uniform_label=uniform_label, power=power,
fl_coefficient=fl_coefficient,
allow_wrong_signs=allow_wrong_signs)
def _compute_groups_indices(self, X, y, label):
mask = y == label
self.random_state = check_random_state(self.random_state)
knn_indices = compute_knn_indices_of_signal(X[self.uniform_features], mask,
n_neighbours=self.n_neighbours)[mask, :]
if len(knn_indices) > self.max_groups:
selected_group = self.random_state.choice(len(knn_indices), size=self.max_groups, replace=False)
return knn_indices[selected_group, :]
else:
return knn_indices
# endregion
|
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Libtree(CMakePackage):
"""ldd as a tree with an option to bundle dependencies into a
single folder"""
homepage = "https://github.com/haampie/libtree"
git = "https://github.com/haampie/libtree.git"
url = "https://github.com/haampie/libtree/archive/refs/tags/v2.0.0.tar.gz"
maintainers = ['haampie']
version('master', branch='master')
version('2.0.0', sha256='099e85d8ba3c3d849ce05b8ba2791dd25cd042a813be947fb321b0676ef71883')
version('1.2.3', sha256='4a912cf97109219fe931942a30579336b6ab9865395447bd157bbfa74bf4e8cf')
version('1.2.2', sha256='4ccf09227609869b85a170550b636defcf0b0674ecb0785063b81785b1c29bdd')
version('1.2.1', sha256='26791c0f418b93d502879db0e1fd2fd3081b885ad87326611d992a5f8977a9b0')
version('1.2.0', sha256='3e74655f22b1dcc19e8a1b9e7796b8ad44bc37f29e9a99134119e8521e28be97')
version('1.1.4', sha256='38648f67c8fa72c3a4a3af2bb254b5fd6989c0f1362387ab298176db5cbbcc4e')
version('1.1.3', sha256='4c681d7b67ef3d62f95450fb7eb84e33ff10a3b9db1f7e195b965b2c3c58226b')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
def url_for_version(self, version):
if version < Version("2.0.0"):
return "https://github.com/haampie/libtree/releases/download/v{0}/sources.tar.gz".format(version)
return "https://github.com/haampie/libtree/archive/refs/tags/v{0}.tar.gz".format(version)
variant('chrpath', default=False, description='Use chrpath for deployment')
variant('strip', default=False, description='Use binutils strip for deployment')
# header only dependencies
depends_on('cpp-termcolor', when='@2.0:', type='build')
depends_on('cxxopts', when='@2.0:', type='build')
depends_on('elfio', when='@2.0:', type='build')
# runtime deps
depends_on('chrpath', when='+chrpath', type='run')
depends_on('binutils', when='+strip', type='run')
# testing
depends_on('googletest', type='test')
def cmake_args(self):
tests_enabled = 'ON' if self.run_tests else 'OFF'
if self.spec.satisfies('@2.0:'):
tests_define = 'LIBTREE_BUILD_TESTS'
else:
tests_define = 'BUILD_TESTING'
return [
self.define(tests_define, tests_enabled)
]
def check(self):
with working_dir(self.build_directory):
ctest('--output-on-failure')
Add libtree 3.0.0-rc9 (#27990)
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack.build_systems.cmake import CMakePackage
class Libtree(MakefilePackage):
"""ldd as a tree"""
homepage = "https://github.com/haampie/libtree"
git = "https://github.com/haampie/libtree.git"
url = "https://github.com/haampie/libtree/archive/refs/tags/v2.0.0.tar.gz"
maintainers = ['haampie']
version('master', branch='master')
version('3.0.0-rc9', sha256='6956c51d58a4f43a23fea0fe7fc5141034829058bfc434e089c9ef1d1848229a')
version('2.0.0', sha256='099e85d8ba3c3d849ce05b8ba2791dd25cd042a813be947fb321b0676ef71883')
version('1.2.3', sha256='4a912cf97109219fe931942a30579336b6ab9865395447bd157bbfa74bf4e8cf')
version('1.2.2', sha256='4ccf09227609869b85a170550b636defcf0b0674ecb0785063b81785b1c29bdd')
version('1.2.1', sha256='26791c0f418b93d502879db0e1fd2fd3081b885ad87326611d992a5f8977a9b0')
version('1.2.0', sha256='3e74655f22b1dcc19e8a1b9e7796b8ad44bc37f29e9a99134119e8521e28be97')
version('1.1.4', sha256='38648f67c8fa72c3a4a3af2bb254b5fd6989c0f1362387ab298176db5cbbcc4e')
version('1.1.3', sha256='4c681d7b67ef3d62f95450fb7eb84e33ff10a3b9db1f7e195b965b2c3c58226b')
version('1.1.2', sha256='31641c6bf6c2980ffa7b4c57392460434f97ba66fe51fe6346867430b33a0374')
version('1.1.1', sha256='3e8543145a40a94e9e2ce9fed003d2bf68294e1fce9607028a286bc132e17dc4')
version('1.1.0', sha256='6cf36fb9a4c8c3af01855527d4931110732bb2d1c19be9334c689f1fd1c78536')
version('1.0.4', sha256='b15a54b6f388b8bd8636e288fcb581029f1e65353660387b0096a554ad8e9e45')
version('1.0.3', sha256='67ce886c191d50959a5727246cdb04af38872cd811c9ed4e3822f77a8f40b20b')
def url_for_version(self, version):
if version < Version("2.0.0"):
return "https://github.com/haampie/libtree/releases/download/v{0}/sources.tar.gz".format(version)
return "https://github.com/haampie/libtree/archive/refs/tags/v{0}.tar.gz".format(version)
# Version 3.x (Makefile)
@when('@3:')
def install(self, spec, prefix):
make('install', 'PREFIX=' + prefix)
# Version 2.x and earlier (CMake)
with when('@:2'):
variant('chrpath', default=False, description='Use chrpath for deployment')
variant('strip', default=False, description='Use binutils strip for deployment')
variant('build_type', default='RelWithDebInfo',
description='CMake build type',
values=('Debug', 'Release', 'RelWithDebInfo', 'MinSizeRel'))
depends_on('googletest', type='test')
depends_on('cmake@3:', type='build')
depends_on('chrpath', when='+chrpath', type='run')
depends_on('binutils', when='+strip', type='run')
# header only dependencies
depends_on('cpp-termcolor', when='@2.0.0:2', type='build')
depends_on('cxxopts', when='@2.0.0:2', type='build')
depends_on('elfio', when='@2.0.0:2', type='build')
def cmake_args(self):
tests_enabled = 'ON' if self.run_tests else 'OFF'
if self.spec.satisfies('@2.0:'):
tests_define = 'LIBTREE_BUILD_TESTS'
else:
tests_define = 'BUILD_TESTING'
return [
CMakePackage.define(tests_define, tests_enabled)
]
@when('@:2')
def edit(self, spec, prefix):
options = CMakePackage._std_args(self) + self.cmake_args()
options.append(self.stage.source_path)
with working_dir(self.build_directory):
cmake(*options)
@when('@:2')
def check(self):
with working_dir(self.build_directory):
ctest('--output-on-failure')
|
from __future__ import print_function
import sys
import numpy as np
import os
import glob
import pickle as cPickle
import csv
import ntpath
from scipy import linalg as la
from scipy.spatial import distance
import sklearn.svm
import sklearn.decomposition
import sklearn.ensemble
import plotly
import plotly.subplots
import plotly.graph_objs as go
import sklearn.metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
sys.path.insert(0, os.path.join(
os.path.dirname(os.path.realpath(__file__)), "../"))
from pyAudioAnalysis import MidTermFeatures as aF
from pyAudioAnalysis import audioBasicIO
from sklearn.model_selection import GroupShuffleSplit
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
shortTermWindow = 0.050
shortTermStep = 0.050
eps = 0.00000001
class Knn:
def __init__(self, features, labels, neighbors):
self.features = features
self.labels = labels
self.neighbors = neighbors
def classify(self, test_sample):
n_classes = np.unique(self.labels).shape[0]
y_dist = (distance.cdist(self.features,
test_sample.reshape(1, test_sample.shape[0]),
'euclidean')).T
i_sort = np.argsort(y_dist)
P = np.zeros((n_classes,))
for i in range(n_classes):
P[i] = np.nonzero(self.labels[i_sort[0]
[0:self.neighbors]] == i)[0].shape[0] / float(self.neighbors)
return np.argmax(P), P
def classifier_wrapper(classifier, classifier_type, test_sample):
"""
This function is used as a wrapper to pattern classification.
ARGUMENTS:
- classifier: a classifier object of type sklearn.svm.SVC or
kNN (defined in this library) or sklearn.ensemble.
RandomForestClassifier or sklearn.ensemble.
GradientBoostingClassifier or
sklearn.ensemble.ExtraTreesClassifier
- classifier_type: "svm" or "knn" or "randomforests" or
"gradientboosting" or "extratrees"
- test_sample: a feature vector (np array)
RETURNS:
- R: class ID
- P: probability estimate
EXAMPLE (for some audio signal stored in array x):
import audioFeatureExtraction as aF
import audioTrainTest as aT
# load the classifier (here SVM, for kNN use load_model_knn instead):
[classifier, MEAN, STD, classNames, mt_win, mt_step, st_win, st_step] =
aT.load_model(model_name)
# mid-term feature extraction:
[mt_features, _, _] = aF.mid_feature_extraction(x, Fs, mt_win * Fs,
mt_step * Fs, round(Fs*st_win), round(Fs*st_step));
# feature normalization:
curFV = (mt_features[:, i] - MEAN) / STD;
# classification
[Result, P] = classifierWrapper(classifier, model_type, curFV)
"""
class_id = -1
probability = -1
if classifier_type == "knn":
class_id, probability = classifier.classify(test_sample)
elif classifier_type == "svm" or \
classifier_type == "randomforest" or \
classifier_type == "gradientboosting" or \
classifier_type == "extratrees" or \
classifier_type == "svm_rbf":
class_id = classifier.predict(test_sample.reshape(1, -1))[0]
probability = classifier.predict_proba(test_sample.reshape(1, -1))[0]
return class_id, probability
def regression_wrapper(model, model_type, test_sample):
"""
This function is used as a wrapper to pattern classification.
ARGUMENTS:
- model: regression model
- model_type: "svm" or "knn" (TODO)
- test_sample: a feature vector (np array)
RETURNS:
- R: regression result (estimated value)
EXAMPLE (for some audio signal stored in array x):
TODO
"""
if model_type == "svm" or model_type == "randomforest" or \
model_type == "svm_rbf":
return model.predict(test_sample.reshape(1,-1))[0]
# elif classifier_type == "knn":
# TODO
def train_knn(features, labels, neighbors):
"""
Train a kNN classifier.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- neighbors: parameter K
RETURNS:
- kNN: the trained kNN variable
"""
knn = Knn(features, labels, neighbors)
return knn
def train_svm(features, labels, c_param, kernel='linear'):
"""
Train a multi-class probabilitistic SVM classifier.
Note: This function is simply a wrapper to the sklearn functionality
for SVM training
See function trainSVM_feature() to use a wrapper on both the
feature extraction and the SVM training
(and parameter tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- c_param: SVM parameter C (cost of constraints violation)
RETURNS:
- svm: the trained SVM variable
NOTE:
This function trains a linear-kernel SVM for a given C value.
For a different kernel, other types of parameters should be provided.
"""
svm = sklearn.svm.SVC(C=c_param, kernel=kernel, probability=True,
gamma='auto')
svm.fit(features, labels)
return svm
def train_random_forest(features, labels, n_estimators):
"""
Train a multi-class random forest classifier.
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- n_estimators: number of trees in the forest
RETURNS:
- rf: the trained random forest
"""
rf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators)
rf.fit(features, labels)
return rf
def train_gradient_boosting(features, labels, n_estimators):
"""
Train a gradient boosting classifier
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- n_estimators: number of trees in the forest
RETURNS:
- rf: the trained model
"""
rf = sklearn.ensemble.GradientBoostingClassifier(n_estimators=n_estimators)
rf.fit(features, labels)
return rf
def train_extra_trees(features, labels, n_estimators):
"""
Train an extra tree
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
RETURNS:
- et: the trained model
"""
et = sklearn.ensemble.ExtraTreesClassifier(n_estimators=n_estimators)
et.fit(features, labels)
return et
def train_svm_regression(features, labels, c_param, kernel='linear'):
svm = sklearn.svm.SVR(C=c_param, kernel=kernel)
svm.fit(features, labels)
train_err = np.mean(np.abs(svm.predict(features) - labels))
return svm, train_err
def train_random_forest_regression(features, labels, n_estimators):
rf = sklearn.ensemble.RandomForestRegressor(n_estimators=n_estimators)
rf.fit(features, labels)
train_err = np.mean(np.abs(rf.predict(features) - labels))
return rf, train_err
def extract_features_and_train(paths, mid_window, mid_step, short_window,
short_step, classifier_type, model_name,
compute_beat=False, train_percentage=0.90,
dict_of_ids=None,
use_smote=False):
"""
This function is used as a wrapper to segment-based audio feature extraction
and classifier training.
ARGUMENTS:
paths: list of paths of directories. Each directory
contains a signle audio class whose samples
are stored in seperate WAV files.
mid_window, mid_step: mid-term window length and step
short_window, short_step: short-term window and step
classifier_type: "svm" or "knn" or "randomforest" or
"gradientboosting" or "extratrees"
model_name: name of the model to be saved
dict_of_ids: a dictionary which has as keys the full path of audio files and as values the respective group ids
RETURNS:
None. Resulting classifier along with the respective model
parameters are saved on files.
"""
# STEP A: Feature Extraction:
features, class_names, file_names = \
aF.multiple_directory_feature_extraction(paths, mid_window, mid_step,
short_window, short_step,
compute_beat=compute_beat)
file_names = [item for sublist in file_names for item in sublist]
if dict_of_ids:
list_of_ids = [dict_of_ids[file] for file in file_names]
else:
list_of_ids = None
if len(features) == 0:
print("trainSVM_feature ERROR: No data found in any input folder!")
return
n_feats = features[0].shape[1]
feature_names = ["features" + str(d + 1) for d in range(n_feats)]
for i, feat in enumerate(features):
if len(feat) == 0:
print("trainSVM_feature ERROR: " + paths[i] +
" folder is empty or non-existing!")
return
# STEP B: classifier Evaluation and Parameter Selection:
if classifier_type == "svm" or classifier_type == "svm_rbf":
classifier_par = np.array([0.001, 0.01, 0.5, 1.0, 5.0, 10.0, 20.0])
elif classifier_type == "randomforest":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
elif classifier_type == "knn":
classifier_par = np.array([1, 3, 5, 7, 9, 11, 13, 15])
elif classifier_type == "gradientboosting":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
elif classifier_type == "extratrees":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
# get optimal classifier parameter:
temp_features = []
for feat in features:
temp = []
for i in range(feat.shape[0]):
temp_fv = feat[i, :]
if (not np.isnan(temp_fv).any()) and (not np.isinf(temp_fv).any()):
temp.append(temp_fv.tolist())
else:
print("NaN Found! Feature vector not used for training")
temp_features.append(np.array(temp))
features = temp_features
best_param = evaluate_classifier(features, class_names, classifier_type,
classifier_par, 0, list_of_ids, n_exp=-1,
train_percentage=train_percentage,
smote=use_smote)
print("Selected params: {0:.5f}".format(best_param))
# STEP C: Train and Save the classifier to file
# First Use mean/std standard feature scaling:
features, labels = features_to_matrix(features)
scaler = StandardScaler()
features = scaler.fit_transform(features)
mean = scaler.mean_.tolist()
std = scaler.scale_.tolist()
# Then train the final classifier
if classifier_type == "svm":
classifier = train_svm(features, labels, best_param)
elif classifier_type == "svm_rbf":
classifier = train_svm(features, labels, best_param, kernel='rbf')
elif classifier_type == "randomforest":
classifier = train_random_forest(features, labels, best_param)
elif classifier_type == "gradientboosting":
classifier = train_gradient_boosting(features, labels, best_param)
elif classifier_type == "extratrees":
classifier = train_extra_trees(features, labels, best_param)
# And save the model to a file, along with
# - the scaling -mean/std- vectors)
# - the feature extraction parameters
if classifier_type == "knn":
feature_matrix = features.tolist()
labels = labels.tolist()
save_path = model_name
save_parameters(save_path, feature_matrix, labels, mean, std,
class_names, best_param, mid_window, mid_step,
short_window, short_step, compute_beat)
elif classifier_type == "svm" or classifier_type == "svm_rbf" or \
classifier_type == "randomforest" or \
classifier_type == "gradientboosting" or \
classifier_type == "extratrees":
with open(model_name, 'wb') as fid:
cPickle.dump(classifier, fid)
save_path = model_name + "MEANS"
save_parameters(save_path, mean, std, class_names, mid_window, mid_step,
short_window, short_step, compute_beat)
def save_parameters(path, *parameters):
with open(path, 'wb') as file_handle:
for param in parameters:
cPickle.dump(param, file_handle, protocol=cPickle.HIGHEST_PROTOCOL)
def feature_extraction_train_regression(folder_name, mid_window, mid_step,
short_window, short_step, model_type,
model_name, compute_beat=False):
"""
This function is used as a wrapper to segment-based audio
feature extraction and classifier training.
ARGUMENTS:
folder_name: path of directory containing the WAV files
and Regression CSVs
mt_win, mt_step: mid-term window length and step
st_win, st_step: short-term window and step
model_type: "svm" or "knn" or "randomforest"
model_name: name of the model to be saved
RETURNS:
None. Resulting regression model along with the respective
model parameters are saved on files.
"""
# STEP A: Feature Extraction:
features, _, filenames = \
aF.multiple_directory_feature_extraction([folder_name], mid_window,
mid_step, short_window,
short_step,
compute_beat=compute_beat)
features = features[0]
filenames = [ntpath.basename(f) for f in filenames[0]]
f_final = []
# Read CSVs:
csv_files = glob.glob(folder_name + os.sep + "*.csv")
regression_labels = []
regression_names = []
f_final = []
for c in csv_files:
cur_regression_labels = []
f_temp = []
# open the csv file that contains the current target value's annotations
with open(c, 'rt') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in csv_reader:
if len(row) == 2:
# ... and if the current filename exists
# in the list of filenames
if row[0] in filenames:
index = filenames.index(row[0])
cur_regression_labels.append(float(row[1]))
f_temp.append(features[index, :])
else:
print("Warning: {} not found "
"in list of files.".format(row[0]))
else:
print("Warning: Row with unknown format in regression file")
f_final.append(np.array(f_temp))
# cur_regression_labels is the list of values
# for the current regression problem
regression_labels.append(np.array(cur_regression_labels))
# regression task name
regression_names.append(ntpath.basename(c).replace(".csv", ""))
if len(features) == 0:
print("ERROR: No data found in any input folder!")
return
# STEP B: classifier Evaluation and Parameter Selection:
if model_type == "svm" or model_type == "svm_rbf":
model_params = np.array([0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5,
1.0, 5.0, 10.0])
elif model_type == "randomforest":
model_params = np.array([5, 10, 25, 50, 100])
errors = []
errors_base = []
best_params = []
for iRegression, r in enumerate(regression_names):
# get optimal classifeir parameter:
print("Regression task " + r)
bestParam, error, berror = evaluate_regression(f_final[iRegression],
regression_labels[
iRegression],
100, model_type,
model_params)
errors.append(error)
errors_base.append(berror)
best_params.append(bestParam)
print("Selected params: {0:.5f}".format(bestParam))
# scale the features (mean-std) and keep the mean/std parameters
# to be saved with the model
scaler = StandardScaler()
features_norm = scaler.fit_transform(f_final[iRegression])
mean = scaler.mean_.tolist()
std = scaler.scale_.tolist()
# STEP C: Save the model to file
if model_type == "svm":
classifier, _ = train_svm_regression(features_norm,
regression_labels[iRegression],
bestParam)
if model_type == "svm_rbf":
classifier, _ = train_svm_regression(features_norm,
regression_labels[iRegression],
bestParam, kernel='rbf')
if model_type == "randomforest":
classifier, _ = train_random_forest_regression(features_norm,
regression_labels[
iRegression],
bestParam)
# Save the model to a file, along with
# - the scaling -mean/std- vectors)
# - the feature extraction parameters
if model_type == "svm" or model_type == "svm_rbf" \
or model_type == "randomforest":
with open(model_name + "_" + r, 'wb') as fid:
cPickle.dump(classifier, fid)
save_path = model_name + "_" + r + "MEANS"
save_parameters(save_path, mean, std, mid_window, mid_step,
short_window, short_step, compute_beat)
return errors, errors_base, best_params
def load_model_knn(knn_model_name, is_regression=False):
with open(knn_model_name, "rb") as fo:
features = cPickle.load(fo)
labels = cPickle.load(fo)
mean = cPickle.load(fo)
std = cPickle.load(fo)
if not is_regression:
classes = cPickle.load(fo)
neighbors = cPickle.load(fo)
mid_window = cPickle.load(fo)
mid_step = cPickle.load(fo)
short_window = cPickle.load(fo)
short_step = cPickle.load(fo)
compute_beat = cPickle.load(fo)
features = np.array(features)
labels = np.array(labels)
mean = np.array(mean)
std = np.array(std)
classifier = Knn(features, labels, neighbors)
# Note: a direct call to the kNN constructor is used here
if is_regression:
return classifier, mean, std, mid_window, mid_step, short_window, \
short_step, compute_beat
else:
return classifier, mean, std, classes, mid_window, mid_step, \
short_window, short_step, compute_beat
def load_model(model_name, is_regression=False):
"""
This function loads an SVM model either for classification or training.
ARGMUMENTS:
- SVMmodel_name: the path of the model to be loaded
- is_regression: a flag indigating whereas this model
is regression or not
"""
with open(model_name + "MEANS", "rb") as fo:
mean = cPickle.load(fo)
std = cPickle.load(fo)
if not is_regression:
classNames = cPickle.load(fo)
mid_window = cPickle.load(fo)
mid_step = cPickle.load(fo)
short_window = cPickle.load(fo)
short_step = cPickle.load(fo)
compute_beat = cPickle.load(fo)
mean = np.array(mean)
std = np.array(std)
with open(model_name, 'rb') as fid:
svm_model = cPickle.load(fid)
if is_regression:
return svm_model, mean, std, mid_window, mid_step, short_window, \
short_step, compute_beat
else:
return svm_model, mean, std, classNames, mid_window, mid_step, \
short_window, short_step, compute_beat
def group_split(X, y, train_indeces, test_indeces, split_id):
"""
This function splits the data in train and test set according to train/test indeces based on LeaveOneGroupOut
ARGUMENTS:
X: array-like of shape (n_samples, n_features)
y: array-like of shape (n_samples,)
train_indeces: The training set indices
test_indeces: The testing set indices
split_id: the split number
RETURNS:
List containing train-test split of inputs.
"""
train_index = train_indeces[split_id]
test_index = test_indeces[split_id]
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
return X_train, X_test, y_train, y_test
def evaluate_classifier(features, class_names, classifier_name, params,
parameter_mode, list_of_ids=None, n_exp=-1,
train_percentage=0.90,
smote=False):
"""
ARGUMENTS:
features: a list ([numOfClasses x 1]) whose elements containt
np matrices of features. Each matrix features[i] of
class i is [n_samples x numOfDimensions]
class_names: list of class names (strings)
classifier_name: svm or knn or randomforest
params: list of classifier parameters (for parameter
tuning during cross-validation)
parameter_mode: 0: choose parameters that lead to maximum overall
classification ACCURACY
1: choose parameters that lead to maximum overall
f1 MEASURE
n_exp: number of cross-validation experiments
(use -1 for auto calculation based on the num of samples)
train_percentage: percentage of training (vs validation) data
default 0.90
RETURNS:
bestParam: the value of the input parameter that optimizes the
selected performance measure
"""
# transcode list of feature matrices to X, y (sklearn)
X, y = features_to_matrix(features)
# features_norm = features;
n_classes = len(features)
ac_all = []
f1_all = []
pre_class_all = []
rec_classes_all = []
f1_classes_all = []
cms_all = []
# dynamically compute total number of samples:
# (so that if number of samples is >10K only one train-val repetition
# is performed)
n_samples_total = X.shape[0]
if n_exp == -1:
n_exp = int(50000 / n_samples_total) + 1
if list_of_ids:
train_indeces, test_indeces = [], []
gss = GroupShuffleSplit(n_splits=n_exp, train_size=.8)
for train_index, test_index in gss.split(X, y, list_of_ids):
train_indeces.append(train_index)
test_indeces.append(test_index)
for Ci, C in enumerate(params):
# for each param value
cm = np.zeros((n_classes, n_classes))
f1_per_exp = []
r1t_all = []
p1t_all = []
y_pred_all = []
y_test_all = []
for e in range(n_exp):
y_pred, y_real = [], []
# for each cross-validation iteration:
print("Param = {0:.5f} - classifier Evaluation "
"Experiment {1:d} of {2:d}".format(C, e+1, n_exp))
# split features:
if list_of_ids:
X_train, X_test, y_train, y_test = group_split(X, y, train_indeces, test_indeces, e)
else:
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=1-train_percentage)
# mean/std scale the features:
scaler = StandardScaler()
if smote:
sm = SMOTE(random_state = 2)
#sm = RandomUnderSampler(random_state=0)
X_train, y_train = sm.fit_resample(X_train, y_train)
scaler.fit(X_train)
X_train = scaler.transform(X_train)
# train multi-class svms:
if classifier_name == "svm":
classifier = train_svm(X_train, y_train, C)
elif classifier_name == "svm_rbf":
classifier = train_svm(X_train, y_train, C, kernel='rbf')
elif classifier_name == "knn":
classifier = train_knn(X_train, y_train, C)
elif classifier_name == "randomforest":
classifier = train_random_forest(X_train, y_train, C)
elif classifier_name == "gradientboosting":
classifier = train_gradient_boosting(X_train, y_train, C)
elif classifier_name == "extratrees":
classifier = train_extra_trees(X_train, y_train,C)
# get predictions and compute current comfusion matrix
cmt = np.zeros((n_classes, n_classes))
X_test = scaler.transform(X_test)
for i_test_sample in range(X_test.shape[0]):
y_pred.append(classifier_wrapper(classifier,
classifier_name,
X_test[i_test_sample, :])[0])
cmt = sklearn.metrics.confusion_matrix(y_test, y_pred)
f1t = sklearn.metrics.f1_score(y_test, y_pred, average='macro')
r1t_all.append(sklearn.metrics.recall_score(y_test, y_pred, average='macro'))
p1t_all.append(sklearn.metrics.precision_score(y_test, y_pred, average='macro'))
y_pred_all += y_pred
y_test_all += y_test.tolist()
f1_per_exp.append(f1t)
if cmt.size != cm.size:
all_classes = set(y)
split_classes = set(y_test)
missing_classes = all_classes.difference(split_classes)
missing_classes = list(missing_classes)
missing_classes = [int(x) for x in missing_classes]
cmt = np.insert(cmt, missing_classes, 0, axis=0)
cmt = np.insert(cmt, missing_classes, 0, axis=1)
cm = cm + cmt
cm = cm + 0.0000000010
rec = np.array([cm[ci, ci] / np.sum(cm[ci, :])
for ci in range(cm.shape[0])])
pre = np.array([cm[ci, ci] / np.sum(cm[:, ci])
for ci in range(cm.shape[0])])
pre_class_all.append(pre)
rec_classes_all.append(rec)
f1 = 2 * rec * pre / (rec + pre)
# this is just for debugging (it should be equal to f1)
f1_b = sklearn.metrics.f1_score(y_test_all, y_pred_all,
average='macro')
# Note: np.mean(f1_per_exp) will not be exacty equal to the
# overall f1 (i.e. f1 and f1_b because these are calculated on a
# per-sample basis)
f1_std = np.std(f1_per_exp)
print(np.mean(f1), f1_b, f1_std)
f1_classes_all.append(f1)
ac_all.append(np.sum(np.diagonal(cm)) / np.sum(cm))
cms_all.append(cm)
f1_all.append(np.mean(f1))
print("\t\t", end="")
for i, c in enumerate(class_names):
if i == len(class_names)-1:
print("{0:s}\t\t".format(c), end="")
else:
print("{0:s}\t\t\t".format(c), end="")
print("OVERALL")
print("\tC", end="")
for c in class_names:
print("\tPRE\tREC\tf1", end="")
print("\t{0:s}\t{1:s}".format("ACC", "f1"))
best_ac_ind = np.argmax(ac_all)
best_f1_ind = np.argmax(f1_all)
for i in range(len(pre_class_all)):
print("\t{0:.3f}".format(params[i]), end="")
for c in range(len(pre_class_all[i])):
print("\t{0:.1f}\t{1:.1f}\t{2:.1f}".format(100.0 *
pre_class_all[i][c],
100.0 *
rec_classes_all[i][c],
100.0 *
f1_classes_all[i][c]),
end="")
print("\t{0:.1f}\t{1:.1f}".format(100.0 * ac_all[i], 100.0 * f1_all[i]),
end="")
if i == best_f1_ind:
print("\t best f1", end="")
if i == best_ac_ind:
print("\t best Acc", end="")
print("")
if parameter_mode == 0:
# keep parameters that maximize overall classification accuracy:
print("Confusion Matrix:")
print_confusion_matrix(cms_all[best_ac_ind], class_names)
return params[best_ac_ind]
elif parameter_mode == 1:
# keep parameters that maximize overall f1 measure:
print("Confusion Matrix:")
print_confusion_matrix(cms_all[best_f1_ind], class_names)
return params[best_f1_ind]
def evaluate_regression(features, labels, n_exp, method_name, params):
"""
ARGUMENTS:
features: np matrices of features [n_samples x numOfDimensions]
labels: list of sample labels
n_exp: number of cross-validation experiments
method_name: "svm" or "randomforest"
params: list of classifier params to be evaluated
RETURNS:
bestParam: the value of the input parameter that optimizes
the selected performance measure
"""
# mean/std feature scaling:
scaler = StandardScaler()
features_norm = scaler.fit_transform(features)
n_samples = labels.shape[0]
per_train = 0.9
errors_all = []
er_train_all = []
er_base_all = []
for Ci, C in enumerate(params): # for each param value
errors = []
errors_train = []
errors_baseline = []
for e in range(n_exp): # for each cross-validation iteration:
# split features:
randperm = np.random.permutation(range(n_samples))
n_train = int(round(per_train * n_samples))
f_train = [features_norm[randperm[i]]
for i in range(n_train)]
f_test = [features_norm[randperm[i+n_train]]
for i in range(n_samples - n_train)]
l_train = [labels[randperm[i]] for i in range(n_train)]
l_test = [labels[randperm[i + n_train]]
for i in range(n_samples - n_train)]
# train multi-class svms:
f_train = np.matrix(f_train)
if method_name == "svm":
classifier, train_err = \
train_svm_regression(f_train, l_train, C)
elif method_name == "svm_rbf":
classifier, train_err = \
train_svm_regression(f_train, l_train, C,
kernel='rbf')
elif method_name == "randomforest":
classifier, train_err = \
train_random_forest_regression(f_train, l_train, C)
error_test = []
error_test_baseline = []
for itest, fTest in enumerate(f_test):
R = regression_wrapper(classifier, method_name, fTest)
Rbaseline = np.mean(l_train)
error_test.append((R - l_test[itest]) *
(R - l_test[itest]))
error_test_baseline.append((Rbaseline - l_test[itest]) *
(Rbaseline - l_test[itest]))
error = np.array(error_test).mean()
error_baseline = np.array(error_test_baseline).mean()
errors.append(error)
errors_train.append(train_err)
errors_baseline.append(error_baseline)
errors_all.append(np.array(errors).mean())
er_train_all.append(np.array(errors_train).mean())
er_base_all.append(np.array(errors_baseline).mean())
best_ind = np.argmin(errors_all)
print("{0:s}\t\t{1:s}\t\t{2:s}\t\t{3:s}".format("Param", "MSE",
"T-MSE", "R-MSE"))
for i in range(len(errors_all)):
print("{0:.4f}\t\t{1:.2f}\t\t{2:.2f}\t\t{3:.2f}".format(params[i],
errors_all[i],
er_train_all[i],
er_base_all[i]),
end="")
if i == best_ind:
print("\t\t best",end="")
print("")
return params[best_ind], errors_all[best_ind], er_base_all[best_ind]
def print_confusion_matrix(cm, class_names):
"""
This function prints a confusion matrix for a particular classification task.
ARGUMENTS:
cm: a 2-D np array of the confusion matrix
(cm[i,j] is the number of times a sample from class i
was classified in class j)
class_names: a list that contains the names of the classes
"""
if cm.shape[0] != len(class_names):
print("printConfusionMatrix: Wrong argument sizes\n")
return
for c in class_names:
if len(c) > 4:
c = c[0:3]
print("\t{0:s}".format(c), end="")
print("")
for i, c in enumerate(class_names):
if len(c) > 4:
c = c[0:3]
print("{0:s}".format(c), end="")
for j in range(len(class_names)):
print("\t{0:.2f}".format(100.0 * cm[i][j] / np.sum(cm)), end="")
print("")
def features_to_matrix(features):
"""
features_to_matrix(features)
This function takes a list of feature matrices as argument and returns
a single concatenated feature matrix and the respective class labels.
ARGUMENTS:
- features: a list of feature matrices
RETURNS:
- feature_matrix: a concatenated matrix of features
- labels: a vector of class indices
"""
labels = np.array([])
feature_matrix = np.array([])
for i, f in enumerate(features):
if i == 0:
feature_matrix = f
labels = i * np.ones((len(f), 1))
else:
feature_matrix = np.vstack((feature_matrix, f))
labels = np.append(labels, i * np.ones((len(f), 1)))
return feature_matrix, labels
def pca_wrapper(features, dimensions):
features, labels = features_to_matrix(features)
pca = sklearn.decomposition.PCA(n_components = dimensions)
pca.fit(features)
coeff = pca.components_
coeff = coeff[:, 0:dimensions]
features_transformed = []
for f in features:
ft = f.copy()
# ft = pca.transform(ft, k=nDims)
ft = np.dot(f, coeff)
features_transformed.append(ft)
return features_transformed, coeff
def compute_class_rec_pre_f1(c_mat):
"""
Gets recall, precision and f1 PER CLASS, given the confusion matrix
:param c_mat: the [n_class x n_class] confusion matrix
:return: rec, pre and f1 for each class
"""
n_class = c_mat.shape[0]
rec, pre, f1 = [], [], []
for i in range(n_class):
rec.append(float(c_mat[i, i]) / np.sum(c_mat[i, :]))
pre.append(float(c_mat[i, i]) / np.sum(c_mat[:, i]))
f1.append(2 * rec[-1] * pre[-1] / (rec[-1] + pre[-1]))
return rec, pre, f1
def evaluate_model_for_folders(input_test_folders, model_name, model_type,
positive_class, plot=True):
"""
evaluate_model_for_folders(input_test_folders, model_name, model_type)
This function evaluates a model by computing the confusion matrix, the
per class performance metrics and by generating a ROC and Precision / Recall
diagrams (for a particular class of interest), for a given test dataset.
The dataset needs to be organized in folders (one folder per audio class),
exactly like in extract_features_and_train()
:param input_test_folders: list of folders (each folder represents a
separate audio class)
:param model_name: path to the model to be tested
:param model_type: type of the model
:param positive_class name of the positive class
:param plot (True default) if to plot 2 diagrams on plotly
:return: thr_prre, pre, rec (thresholds, precision recall values)
thr_roc, fpr, tpr (thresholds, false positive , true positive rates)
Usage example:
from pyAudioAnalysis import audioTrainTest as aT
thr_prre, pre, rec, thr_roc, fpr, tpr =
aT.evaluate_model_for_folders(["4_classes_small/speech",
"4_classes_small/music"],
"data/models/svm_rbf_4class",
"svm_rbf", "speech")
"""
class_names = []
y_true_binary = []
y_true = []
y_pred = []
probs_positive = []
for i, d in enumerate(input_test_folders):
if d[-1] == os.sep:
class_names.append(d.split(os.sep)[-2])
else:
class_names.append(d.split(os.sep)[-1])
types = ('*.wav', '*.aif', '*.aiff', '*.mp3', '*.au', '*.ogg')
wav_file_list = []
for files in types:
wav_file_list.extend(glob.glob(os.path.join(d, files)))
# get list of audio files for current folder and run classifier
for w in wav_file_list:
c, p, probs_names = file_classification(w, model_name, model_type)
y_pred.append(c)
y_true.append(probs_names.index(class_names[i]))
if i==probs_names.index(positive_class):
y_true_binary.append(1)
else:
y_true_binary.append(0)
prob_positive = p[probs_names.index(positive_class)]
probs_positive.append(prob_positive)
pre, rec, thr_prre = sklearn.metrics.precision_recall_curve(y_true_binary,
probs_positive)
fpr, tpr, thr_roc = sklearn.metrics.roc_curve(y_true_binary, probs_positive)
cm = sklearn.metrics.confusion_matrix(y_true, y_pred)
rec_c, pre_c, f1_c = compute_class_rec_pre_f1(cm)
f1 = (sklearn.metrics.f1_score(y_true, y_pred, average='macro'))
acc = (sklearn.metrics.accuracy_score(y_true, y_pred))
print(cm)
print(rec_c, pre_c, f1_c, f1, acc)
if plot:
titles = ["Confusion matrix, acc = {0:.1f}%, "
" F1 (macro): {1:.1f}%".format(100 * acc, 100 * f1),
"Class-wise Performance measures",
"Pre vs Rec for " + positive_class,
"ROC for " + positive_class]
figs = plotly.subplots.make_subplots(rows=2, cols=2,
subplot_titles=titles)
heatmap = go.Heatmap(z=np.flip(cm, axis=0), x=class_names,
y=list(reversed(class_names)),
colorscale=[[0, '#4422ff'], [1, '#ff4422']],
name="confusin matrix", showscale=False)
mark_prop1 = dict(color='rgba(80, 220, 150, 0.5)',
line=dict(color='rgba(80, 220, 150, 1)', width=2))
mark_prop2 = dict(color='rgba(80, 150, 220, 0.5)',
line=dict(color='rgba(80, 150, 220, 1)', width=2))
mark_prop3 = dict(color='rgba(250, 150, 150, 0.5)',
line=dict(color='rgba(250, 150, 150, 1)', width=3))
b1 = go.Bar(x=class_names, y=rec_c, name="Recall", marker=mark_prop1)
b2 = go.Bar(x=class_names, y=pre_c, name="Precision", marker=mark_prop2)
b3 = go.Bar(x=class_names, y=f1_c, name="F1", marker=mark_prop3)
figs.append_trace(heatmap, 1, 1);
figs.append_trace(b1, 1, 2)
figs.append_trace(b2, 1, 2);
figs.append_trace(b3, 1, 2)
figs.append_trace(go.Scatter(x=thr_prre, y=pre, name="Precision",
marker=mark_prop1), 2, 1)
figs.append_trace(go.Scatter(x=thr_prre, y=rec, name="Recall",
marker=mark_prop2), 2, 1)
figs.append_trace(go.Scatter(x=fpr, y=tpr, showlegend=False), 2, 2)
figs.update_xaxes(title_text="threshold", row=2, col=1)
figs.update_xaxes(title_text="false positive rate", row=2, col=2)
figs.update_yaxes(title_text="true positive rate", row=2, col=2)
plotly.offline.plot(figs, filename="temp.html", auto_open=True)
return cm, thr_prre, pre, rec, thr_roc, fpr, tpr
def file_classification(input_file, model_name, model_type):
# Load classifier:
if not os.path.isfile(model_name):
print("fileClassification: input model_name not found!")
return -1, -1, -1
if isinstance(input_file, str) and not os.path.isfile(input_file):
print("fileClassification: wav file not found!")
return -1, -1, -1
if model_type == 'knn':
classifier, mean, std, classes, mid_window, mid_step, short_window, \
short_step, compute_beat = load_model_knn(model_name)
else:
classifier, mean, std, classes, mid_window, mid_step, short_window, \
short_step, compute_beat = load_model(model_name)
# read audio file and convert to mono
sampling_rate, signal = audioBasicIO.read_audio_file(input_file)
signal = audioBasicIO.stereo_to_mono(signal)
if sampling_rate == 0:
# audio file IO problem
return -1, -1, -1
if signal.shape[0] / float(sampling_rate) < mid_window:
mid_window = signal.shape[0] / float(sampling_rate)
# feature extraction:
mid_features, s, _ = \
aF.mid_feature_extraction(signal, sampling_rate,
mid_window * sampling_rate,
mid_step * sampling_rate,
round(sampling_rate * short_window),
round(sampling_rate * short_step))
# long term averaging of mid-term statistics
mid_features = mid_features.mean(axis=1)
if compute_beat:
beat, beat_conf = aF.beat_extraction(s, short_step)
mid_features = np.append(mid_features, beat)
mid_features = np.append(mid_features, beat_conf)
feature_vector = (mid_features - mean) / std # normalization
# classification
class_id, probability = classifier_wrapper(classifier, model_type,
feature_vector)
return class_id, probability, classes
def file_regression(input_file, model_name, model_type):
# Load classifier:
if not os.path.isfile(input_file):
print("fileClassification: wav file not found!")
return -1, -1, -1
regression_models = glob.glob(model_name + "_*")
regression_models2 = []
for r in regression_models:
if r[-5::] != "MEANS":
regression_models2.append(r)
regression_models = regression_models2
regression_names = []
for r in regression_models:
regression_names.append(r[r.rfind("_")+1::])
# FEATURE EXTRACTION
# LOAD ONLY THE FIRST MODEL (for mt_win, etc)
if model_type == 'svm' or model_type == "svm_rbf" or \
model_type == 'randomforest':
_, _, _, mid_window, mid_step, short_window, short_step, compute_beat \
= load_model(regression_models[0], True)
# read audio file and convert to mono
samping_rate, signal = audioBasicIO.read_audio_file(input_file)
signal = audioBasicIO.stereo_to_mono(signal)
# feature extraction:
mid_features, s, _ = \
aF.mid_feature_extraction(signal, samping_rate, mid_window * samping_rate,
mid_step * samping_rate,
round(samping_rate * short_window),
round(samping_rate * short_step))
# long term averaging of mid-term statistics
mid_features = mid_features.mean(axis=1)
if compute_beat:
beat, beat_conf = aF.beat_extraction(s, short_step)
mid_features = np.append(mid_features, beat)
mid_features = np.append(mid_features, beat_conf)
# REGRESSION
R = []
for ir, r in enumerate(regression_models):
if not os.path.isfile(r):
print("fileClassification: input model_name not found!")
return (-1, -1, -1)
if model_type == 'svm' or model_type == "svm_rbf" \
or model_type == 'randomforest':
model, mean, std, _, _, _, _, _ = load_model(r, True)
curFV = (mid_features - mean) / std # normalization
R.append(regression_wrapper(model, model_type, curFV)) # classification
return R, regression_names
def lda(data, labels, red_dim):
# Centre data
data -= data.mean(axis=0)
n_data = np.shape(data)[0]
n_dim = np.shape(data)[1]
Sw = np.zeros((n_dim, n_dim))
C = np.cov((data.T))
# Loop over classes
classes = np.unique(labels)
for i in range(len(classes)):
# Find relevant datapoints
indices = (np.where(labels == classes[i]))
d = np.squeeze(data[indices, :])
classcov = np.cov((d.T))
Sw += float(np.shape(indices)[0])/n_data * classcov
Sb = C - Sw
# Now solve for W
# Compute eigenvalues, eigenvectors and sort into order
evals, evecs = la.eig(Sw, Sb)
indices = np.argsort(evals)
indices = indices[::-1]
evecs = evecs[:, indices]
w = evecs[:, :red_dim]
new_data = np.dot(data, w)
return new_data, w
def train_speaker_models():
"""
This script is used to train the speaker-related models
(NOTE: data paths are hard-coded and NOT included in the library,
the models are, however included)
import audioTrainTest as aT
aT.trainSpeakerModelsScript()
"""
mt_win = 2.0
mt_step = 2.0
st_win = 0.020
st_step = 0.020
dir_name = "DIARIZATION_ALL/all"
list_of_dirs = [os.path.join(dir_name, name)
for name in os.listdir(dir_name)
if os.path.isdir(os.path.join(dir_name, name))]
extract_features_and_train(list_of_dirs, mt_win, mt_step, st_win, st_step,
"knn", "data/knnSpeakerAll",
compute_beat=False, train_percentage=0.50)
dir_name = "DIARIZATION_ALL/female_male"
list_of_dirs = [os.path.join(dir_name, name)
for name in os.listdir(dir_name)
if os.path.isdir(os.path.join(dir_name, name))]
extract_features_and_train(list_of_dirs, mt_win, mt_step, st_win, st_step,
"knn", "data/knnSpeakerFemaleMale",
compute_beat=False, train_percentage=0.50)
def main(argv):
return 0
if __name__ == '__main__':
main(sys.argv)
autopep8
from __future__ import print_function
from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import GroupShuffleSplit
from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import MidTermFeatures as aF
import sys
import numpy as np
import os
import glob
import pickle as cPickle
import csv
import ntpath
from scipy import linalg as la
from scipy.spatial import distance
import sklearn.svm
import sklearn.decomposition
import sklearn.ensemble
import plotly
import plotly.subplots
import plotly.graph_objs as go
import sklearn.metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
sys.path.insert(0, os.path.join(
os.path.dirname(os.path.realpath(__file__)), "../"))
shortTermWindow = 0.050
shortTermStep = 0.050
eps = 0.00000001
class Knn:
def __init__(self, features, labels, neighbors):
self.features = features
self.labels = labels
self.neighbors = neighbors
def classify(self, test_sample):
n_classes = np.unique(self.labels).shape[0]
y_dist = (distance.cdist(self.features,
test_sample.reshape(1, test_sample.shape[0]),
'euclidean')).T
i_sort = np.argsort(y_dist)
P = np.zeros((n_classes,))
for i in range(n_classes):
P[i] = np.nonzero(self.labels[i_sort[0]
[0:self.neighbors]] == i)[0].shape[0] / float(self.neighbors)
return np.argmax(P), P
def classifier_wrapper(classifier, classifier_type, test_sample):
"""
This function is used as a wrapper to pattern classification.
ARGUMENTS:
- classifier: a classifier object of type sklearn.svm.SVC or
kNN (defined in this library) or sklearn.ensemble.
RandomForestClassifier or sklearn.ensemble.
GradientBoostingClassifier or
sklearn.ensemble.ExtraTreesClassifier
- classifier_type: "svm" or "knn" or "randomforests" or
"gradientboosting" or "extratrees"
- test_sample: a feature vector (np array)
RETURNS:
- R: class ID
- P: probability estimate
EXAMPLE (for some audio signal stored in array x):
import audioFeatureExtraction as aF
import audioTrainTest as aT
# load the classifier (here SVM, for kNN use load_model_knn instead):
[classifier, MEAN, STD, classNames, mt_win, mt_step, st_win, st_step] =
aT.load_model(model_name)
# mid-term feature extraction:
[mt_features, _, _] = aF.mid_feature_extraction(x, Fs, mt_win * Fs,
mt_step * Fs, round(Fs*st_win), round(Fs*st_step));
# feature normalization:
curFV = (mt_features[:, i] - MEAN) / STD;
# classification
[Result, P] = classifierWrapper(classifier, model_type, curFV)
"""
class_id = -1
probability = -1
if classifier_type == "knn":
class_id, probability = classifier.classify(test_sample)
elif classifier_type == "svm" or \
classifier_type == "randomforest" or \
classifier_type == "gradientboosting" or \
classifier_type == "extratrees" or \
classifier_type == "svm_rbf":
class_id = classifier.predict(test_sample.reshape(1, -1))[0]
probability = classifier.predict_proba(test_sample.reshape(1, -1))[0]
return class_id, probability
def regression_wrapper(model, model_type, test_sample):
"""
This function is used as a wrapper to pattern classification.
ARGUMENTS:
- model: regression model
- model_type: "svm" or "knn" (TODO)
- test_sample: a feature vector (np array)
RETURNS:
- R: regression result (estimated value)
EXAMPLE (for some audio signal stored in array x):
TODO
"""
if model_type == "svm" or model_type == "randomforest" or \
model_type == "svm_rbf":
return model.predict(test_sample.reshape(1, -1))[0]
# elif classifier_type == "knn":
# TODO
def train_knn(features, labels, neighbors):
"""
Train a kNN classifier.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- neighbors: parameter K
RETURNS:
- kNN: the trained kNN variable
"""
knn = Knn(features, labels, neighbors)
return knn
def train_svm(features, labels, c_param, kernel='linear'):
"""
Train a multi-class probabilitistic SVM classifier.
Note: This function is simply a wrapper to the sklearn functionality
for SVM training
See function trainSVM_feature() to use a wrapper on both the
feature extraction and the SVM training
(and parameter tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- c_param: SVM parameter C (cost of constraints violation)
RETURNS:
- svm: the trained SVM variable
NOTE:
This function trains a linear-kernel SVM for a given C value.
For a different kernel, other types of parameters should be provided.
"""
svm = sklearn.svm.SVC(C=c_param, kernel=kernel, probability=True,
gamma='auto')
svm.fit(features, labels)
return svm
def train_random_forest(features, labels, n_estimators):
"""
Train a multi-class random forest classifier.
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- n_estimators: number of trees in the forest
RETURNS:
- rf: the trained random forest
"""
rf = sklearn.ensemble.RandomForestClassifier(n_estimators=n_estimators)
rf.fit(features, labels)
return rf
def train_gradient_boosting(features, labels, n_estimators):
"""
Train a gradient boosting classifier
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
- n_estimators: number of trees in the forest
RETURNS:
- rf: the trained model
"""
rf = sklearn.ensemble.GradientBoostingClassifier(n_estimators=n_estimators)
rf.fit(features, labels)
return rf
def train_extra_trees(features, labels, n_estimators):
"""
Train an extra tree
Note: This function is simply a wrapper to the sklearn functionality
for model training.
See function extract_features_and_train() to use a wrapper on both
the feature extraction and the model training (and parameter
tuning) processes.
ARGUMENTS:
- features: a feature matrix [n_samples x numOfDimensions]
- labels: a label matrix: [n_samples x 1]
- n_estimators: number of trees in the forest
RETURNS:
- et: the trained model
"""
et = sklearn.ensemble.ExtraTreesClassifier(n_estimators=n_estimators)
et.fit(features, labels)
return et
def train_svm_regression(features, labels, c_param, kernel='linear'):
svm = sklearn.svm.SVR(C=c_param, kernel=kernel)
svm.fit(features, labels)
train_err = np.mean(np.abs(svm.predict(features) - labels))
return svm, train_err
def train_random_forest_regression(features, labels, n_estimators):
rf = sklearn.ensemble.RandomForestRegressor(n_estimators=n_estimators)
rf.fit(features, labels)
train_err = np.mean(np.abs(rf.predict(features) - labels))
return rf, train_err
def extract_features_and_train(paths, mid_window, mid_step, short_window,
short_step, classifier_type, model_name,
compute_beat=False, train_percentage=0.90,
dict_of_ids=None,
use_smote=False):
"""
This function is used as a wrapper to segment-based audio feature extraction
and classifier training.
ARGUMENTS:
paths: list of paths of directories. Each directory
contains a signle audio class whose samples
are stored in seperate WAV files.
mid_window, mid_step: mid-term window length and step
short_window, short_step: short-term window and step
classifier_type: "svm" or "knn" or "randomforest" or
"gradientboosting" or "extratrees"
model_name: name of the model to be saved
dict_of_ids: a dictionary which has as keys the full path of audio files and as values the respective group ids
RETURNS:
None. Resulting classifier along with the respective model
parameters are saved on files.
"""
# STEP A: Feature Extraction:
features, class_names, file_names = \
aF.multiple_directory_feature_extraction(paths, mid_window, mid_step,
short_window, short_step,
compute_beat=compute_beat)
file_names = [item for sublist in file_names for item in sublist]
if dict_of_ids:
list_of_ids = [dict_of_ids[file] for file in file_names]
else:
list_of_ids = None
if len(features) == 0:
print("trainSVM_feature ERROR: No data found in any input folder!")
return
n_feats = features[0].shape[1]
feature_names = ["features" + str(d + 1) for d in range(n_feats)]
for i, feat in enumerate(features):
if len(feat) == 0:
print("trainSVM_feature ERROR: " + paths[i] +
" folder is empty or non-existing!")
return
# STEP B: classifier Evaluation and Parameter Selection:
if classifier_type == "svm" or classifier_type == "svm_rbf":
classifier_par = np.array([0.001, 0.01, 0.5, 1.0, 5.0, 10.0, 20.0])
elif classifier_type == "randomforest":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
elif classifier_type == "knn":
classifier_par = np.array([1, 3, 5, 7, 9, 11, 13, 15])
elif classifier_type == "gradientboosting":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
elif classifier_type == "extratrees":
classifier_par = np.array([10, 25, 50, 100, 200, 500])
# get optimal classifier parameter:
temp_features = []
for feat in features:
temp = []
for i in range(feat.shape[0]):
temp_fv = feat[i, :]
if (not np.isnan(temp_fv).any()) and (not np.isinf(temp_fv).any()):
temp.append(temp_fv.tolist())
else:
print("NaN Found! Feature vector not used for training")
temp_features.append(np.array(temp))
features = temp_features
best_param = evaluate_classifier(features, class_names, classifier_type,
classifier_par, 0, list_of_ids, n_exp=-1,
train_percentage=train_percentage,
smote=use_smote)
print("Selected params: {0:.5f}".format(best_param))
# STEP C: Train and Save the classifier to file
# First Use mean/std standard feature scaling:
features, labels = features_to_matrix(features)
scaler = StandardScaler()
features = scaler.fit_transform(features)
mean = scaler.mean_.tolist()
std = scaler.scale_.tolist()
# Then train the final classifier
if classifier_type == "svm":
classifier = train_svm(features, labels, best_param)
elif classifier_type == "svm_rbf":
classifier = train_svm(features, labels, best_param, kernel='rbf')
elif classifier_type == "randomforest":
classifier = train_random_forest(features, labels, best_param)
elif classifier_type == "gradientboosting":
classifier = train_gradient_boosting(features, labels, best_param)
elif classifier_type == "extratrees":
classifier = train_extra_trees(features, labels, best_param)
# And save the model to a file, along with
# - the scaling -mean/std- vectors)
# - the feature extraction parameters
if classifier_type == "knn":
feature_matrix = features.tolist()
labels = labels.tolist()
save_path = model_name
save_parameters(save_path, feature_matrix, labels, mean, std,
class_names, best_param, mid_window, mid_step,
short_window, short_step, compute_beat)
elif classifier_type == "svm" or classifier_type == "svm_rbf" or \
classifier_type == "randomforest" or \
classifier_type == "gradientboosting" or \
classifier_type == "extratrees":
with open(model_name, 'wb') as fid:
cPickle.dump(classifier, fid)
save_path = model_name + "MEANS"
save_parameters(save_path, mean, std, class_names, mid_window, mid_step,
short_window, short_step, compute_beat)
def save_parameters(path, *parameters):
with open(path, 'wb') as file_handle:
for param in parameters:
cPickle.dump(param, file_handle, protocol=cPickle.HIGHEST_PROTOCOL)
def feature_extraction_train_regression(folder_name, mid_window, mid_step,
short_window, short_step, model_type,
model_name, compute_beat=False):
"""
This function is used as a wrapper to segment-based audio
feature extraction and classifier training.
ARGUMENTS:
folder_name: path of directory containing the WAV files
and Regression CSVs
mt_win, mt_step: mid-term window length and step
st_win, st_step: short-term window and step
model_type: "svm" or "knn" or "randomforest"
model_name: name of the model to be saved
RETURNS:
None. Resulting regression model along with the respective
model parameters are saved on files.
"""
# STEP A: Feature Extraction:
features, _, filenames = \
aF.multiple_directory_feature_extraction([folder_name], mid_window,
mid_step, short_window,
short_step,
compute_beat=compute_beat)
features = features[0]
filenames = [ntpath.basename(f) for f in filenames[0]]
f_final = []
# Read CSVs:
csv_files = glob.glob(folder_name + os.sep + "*.csv")
regression_labels = []
regression_names = []
f_final = []
for c in csv_files:
cur_regression_labels = []
f_temp = []
# open the csv file that contains the current target value's annotations
with open(c, 'rt') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|')
for row in csv_reader:
if len(row) == 2:
# ... and if the current filename exists
# in the list of filenames
if row[0] in filenames:
index = filenames.index(row[0])
cur_regression_labels.append(float(row[1]))
f_temp.append(features[index, :])
else:
print("Warning: {} not found "
"in list of files.".format(row[0]))
else:
print("Warning: Row with unknown format in regression file")
f_final.append(np.array(f_temp))
# cur_regression_labels is the list of values
# for the current regression problem
regression_labels.append(np.array(cur_regression_labels))
# regression task name
regression_names.append(ntpath.basename(c).replace(".csv", ""))
if len(features) == 0:
print("ERROR: No data found in any input folder!")
return
# STEP B: classifier Evaluation and Parameter Selection:
if model_type == "svm" or model_type == "svm_rbf":
model_params = np.array([0.001, 0.005, 0.01, 0.05, 0.1, 0.25, 0.5,
1.0, 5.0, 10.0])
elif model_type == "randomforest":
model_params = np.array([5, 10, 25, 50, 100])
errors = []
errors_base = []
best_params = []
for iRegression, r in enumerate(regression_names):
# get optimal classifeir parameter:
print("Regression task " + r)
bestParam, error, berror = evaluate_regression(f_final[iRegression],
regression_labels[
iRegression],
100, model_type,
model_params)
errors.append(error)
errors_base.append(berror)
best_params.append(bestParam)
print("Selected params: {0:.5f}".format(bestParam))
# scale the features (mean-std) and keep the mean/std parameters
# to be saved with the model
scaler = StandardScaler()
features_norm = scaler.fit_transform(f_final[iRegression])
mean = scaler.mean_.tolist()
std = scaler.scale_.tolist()
# STEP C: Save the model to file
if model_type == "svm":
classifier, _ = train_svm_regression(features_norm,
regression_labels[iRegression],
bestParam)
if model_type == "svm_rbf":
classifier, _ = train_svm_regression(features_norm,
regression_labels[iRegression],
bestParam, kernel='rbf')
if model_type == "randomforest":
classifier, _ = train_random_forest_regression(features_norm,
regression_labels[
iRegression],
bestParam)
# Save the model to a file, along with
# - the scaling -mean/std- vectors)
# - the feature extraction parameters
if model_type == "svm" or model_type == "svm_rbf" \
or model_type == "randomforest":
with open(model_name + "_" + r, 'wb') as fid:
cPickle.dump(classifier, fid)
save_path = model_name + "_" + r + "MEANS"
save_parameters(save_path, mean, std, mid_window, mid_step,
short_window, short_step, compute_beat)
return errors, errors_base, best_params
def load_model_knn(knn_model_name, is_regression=False):
with open(knn_model_name, "rb") as fo:
features = cPickle.load(fo)
labels = cPickle.load(fo)
mean = cPickle.load(fo)
std = cPickle.load(fo)
if not is_regression:
classes = cPickle.load(fo)
neighbors = cPickle.load(fo)
mid_window = cPickle.load(fo)
mid_step = cPickle.load(fo)
short_window = cPickle.load(fo)
short_step = cPickle.load(fo)
compute_beat = cPickle.load(fo)
features = np.array(features)
labels = np.array(labels)
mean = np.array(mean)
std = np.array(std)
classifier = Knn(features, labels, neighbors)
# Note: a direct call to the kNN constructor is used here
if is_regression:
return classifier, mean, std, mid_window, mid_step, short_window, \
short_step, compute_beat
else:
return classifier, mean, std, classes, mid_window, mid_step, \
short_window, short_step, compute_beat
def load_model(model_name, is_regression=False):
"""
This function loads an SVM model either for classification or training.
ARGMUMENTS:
- SVMmodel_name: the path of the model to be loaded
- is_regression: a flag indigating whereas this model
is regression or not
"""
with open(model_name + "MEANS", "rb") as fo:
mean = cPickle.load(fo)
std = cPickle.load(fo)
if not is_regression:
classNames = cPickle.load(fo)
mid_window = cPickle.load(fo)
mid_step = cPickle.load(fo)
short_window = cPickle.load(fo)
short_step = cPickle.load(fo)
compute_beat = cPickle.load(fo)
mean = np.array(mean)
std = np.array(std)
with open(model_name, 'rb') as fid:
svm_model = cPickle.load(fid)
if is_regression:
return svm_model, mean, std, mid_window, mid_step, short_window, \
short_step, compute_beat
else:
return svm_model, mean, std, classNames, mid_window, mid_step, \
short_window, short_step, compute_beat
def group_split(X, y, train_indeces, test_indeces, split_id):
"""
This function splits the data in train and test set according to train/test indeces based on LeaveOneGroupOut
ARGUMENTS:
X: array-like of shape (n_samples, n_features)
y: array-like of shape (n_samples,)
train_indeces: The training set indices
test_indeces: The testing set indices
split_id: the split number
RETURNS:
List containing train-test split of inputs.
"""
train_index = train_indeces[split_id]
test_index = test_indeces[split_id]
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
return X_train, X_test, y_train, y_test
def evaluate_classifier(features, class_names, classifier_name, params,
parameter_mode, list_of_ids=None, n_exp=-1,
train_percentage=0.90,
smote=False):
"""
ARGUMENTS:
features: a list ([numOfClasses x 1]) whose elements containt
np matrices of features. Each matrix features[i] of
class i is [n_samples x numOfDimensions]
class_names: list of class names (strings)
classifier_name: svm or knn or randomforest
params: list of classifier parameters (for parameter
tuning during cross-validation)
parameter_mode: 0: choose parameters that lead to maximum overall
classification ACCURACY
1: choose parameters that lead to maximum overall
f1 MEASURE
n_exp: number of cross-validation experiments
(use -1 for auto calculation based on the num of samples)
train_percentage: percentage of training (vs validation) data
default 0.90
RETURNS:
bestParam: the value of the input parameter that optimizes the
selected performance measure
"""
# transcode list of feature matrices to X, y (sklearn)
X, y = features_to_matrix(features)
# features_norm = features;
n_classes = len(features)
ac_all = []
f1_all = []
pre_class_all = []
rec_classes_all = []
f1_classes_all = []
cms_all = []
# dynamically compute total number of samples:
# (so that if number of samples is >10K only one train-val repetition
# is performed)
n_samples_total = X.shape[0]
if n_exp == -1:
n_exp = int(50000 / n_samples_total) + 1
if list_of_ids:
train_indeces, test_indeces = [], []
gss = GroupShuffleSplit(n_splits=n_exp, train_size=.8)
for train_index, test_index in gss.split(X, y, list_of_ids):
train_indeces.append(train_index)
test_indeces.append(test_index)
for Ci, C in enumerate(params):
# for each param value
cm = np.zeros((n_classes, n_classes))
f1_per_exp = []
r1t_all = []
p1t_all = []
y_pred_all = []
y_test_all = []
for e in range(n_exp):
y_pred, y_real = [], []
# for each cross-validation iteration:
print("Param = {0:.5f} - classifier Evaluation "
"Experiment {1:d} of {2:d}".format(C, e+1, n_exp))
# split features:
if list_of_ids:
X_train, X_test, y_train, y_test = group_split(
X, y, train_indeces, test_indeces, e)
else:
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=1-train_percentage)
# mean/std scale the features:
scaler = StandardScaler()
if smote:
sm = SMOTE(random_state=2)
#sm = RandomUnderSampler(random_state=0)
X_train, y_train = sm.fit_resample(X_train, y_train)
scaler.fit(X_train)
X_train = scaler.transform(X_train)
# train multi-class svms:
if classifier_name == "svm":
classifier = train_svm(X_train, y_train, C)
elif classifier_name == "svm_rbf":
classifier = train_svm(X_train, y_train, C, kernel='rbf')
elif classifier_name == "knn":
classifier = train_knn(X_train, y_train, C)
elif classifier_name == "randomforest":
classifier = train_random_forest(X_train, y_train, C)
elif classifier_name == "gradientboosting":
classifier = train_gradient_boosting(X_train, y_train, C)
elif classifier_name == "extratrees":
classifier = train_extra_trees(X_train, y_train, C)
# get predictions and compute current comfusion matrix
cmt = np.zeros((n_classes, n_classes))
X_test = scaler.transform(X_test)
for i_test_sample in range(X_test.shape[0]):
y_pred.append(classifier_wrapper(classifier,
classifier_name,
X_test[i_test_sample, :])[0])
cmt = sklearn.metrics.confusion_matrix(y_test, y_pred)
f1t = sklearn.metrics.f1_score(y_test, y_pred, average='macro')
r1t_all.append(sklearn.metrics.recall_score(
y_test, y_pred, average='macro'))
p1t_all.append(sklearn.metrics.precision_score(
y_test, y_pred, average='macro'))
y_pred_all += y_pred
y_test_all += y_test.tolist()
f1_per_exp.append(f1t)
if cmt.size != cm.size:
all_classes = set(y)
split_classes = set(y_test)
missing_classes = all_classes.difference(split_classes)
missing_classes = list(missing_classes)
missing_classes = [int(x) for x in missing_classes]
cmt = np.insert(cmt, missing_classes, 0, axis=0)
cmt = np.insert(cmt, missing_classes, 0, axis=1)
cm = cm + cmt
cm = cm + 0.0000000010
rec = np.array([cm[ci, ci] / np.sum(cm[ci, :])
for ci in range(cm.shape[0])])
pre = np.array([cm[ci, ci] / np.sum(cm[:, ci])
for ci in range(cm.shape[0])])
pre_class_all.append(pre)
rec_classes_all.append(rec)
f1 = 2 * rec * pre / (rec + pre)
# this is just for debugging (it should be equal to f1)
f1_b = sklearn.metrics.f1_score(y_test_all, y_pred_all,
average='macro')
# Note: np.mean(f1_per_exp) will not be exacty equal to the
# overall f1 (i.e. f1 and f1_b because these are calculated on a
# per-sample basis)
f1_std = np.std(f1_per_exp)
print(np.mean(f1), f1_b, f1_std)
f1_classes_all.append(f1)
ac_all.append(np.sum(np.diagonal(cm)) / np.sum(cm))
cms_all.append(cm)
f1_all.append(np.mean(f1))
print("\t\t", end="")
for i, c in enumerate(class_names):
if i == len(class_names)-1:
print("{0:s}\t\t".format(c), end="")
else:
print("{0:s}\t\t\t".format(c), end="")
print("OVERALL")
print("\tC", end="")
for c in class_names:
print("\tPRE\tREC\tf1", end="")
print("\t{0:s}\t{1:s}".format("ACC", "f1"))
best_ac_ind = np.argmax(ac_all)
best_f1_ind = np.argmax(f1_all)
for i in range(len(pre_class_all)):
print("\t{0:.3f}".format(params[i]), end="")
for c in range(len(pre_class_all[i])):
print("\t{0:.1f}\t{1:.1f}\t{2:.1f}".format(100.0 *
pre_class_all[i][c],
100.0 *
rec_classes_all[i][c],
100.0 *
f1_classes_all[i][c]),
end="")
print("\t{0:.1f}\t{1:.1f}".format(100.0 * ac_all[i], 100.0 * f1_all[i]),
end="")
if i == best_f1_ind:
print("\t best f1", end="")
if i == best_ac_ind:
print("\t best Acc", end="")
print("")
if parameter_mode == 0:
# keep parameters that maximize overall classification accuracy:
print("Confusion Matrix:")
print_confusion_matrix(cms_all[best_ac_ind], class_names)
return params[best_ac_ind]
elif parameter_mode == 1:
# keep parameters that maximize overall f1 measure:
print("Confusion Matrix:")
print_confusion_matrix(cms_all[best_f1_ind], class_names)
return params[best_f1_ind]
def evaluate_regression(features, labels, n_exp, method_name, params):
"""
ARGUMENTS:
features: np matrices of features [n_samples x numOfDimensions]
labels: list of sample labels
n_exp: number of cross-validation experiments
method_name: "svm" or "randomforest"
params: list of classifier params to be evaluated
RETURNS:
bestParam: the value of the input parameter that optimizes
the selected performance measure
"""
# mean/std feature scaling:
scaler = StandardScaler()
features_norm = scaler.fit_transform(features)
n_samples = labels.shape[0]
per_train = 0.9
errors_all = []
er_train_all = []
er_base_all = []
for Ci, C in enumerate(params): # for each param value
errors = []
errors_train = []
errors_baseline = []
for e in range(n_exp): # for each cross-validation iteration:
# split features:
randperm = np.random.permutation(range(n_samples))
n_train = int(round(per_train * n_samples))
f_train = [features_norm[randperm[i]]
for i in range(n_train)]
f_test = [features_norm[randperm[i+n_train]]
for i in range(n_samples - n_train)]
l_train = [labels[randperm[i]] for i in range(n_train)]
l_test = [labels[randperm[i + n_train]]
for i in range(n_samples - n_train)]
# train multi-class svms:
f_train = np.matrix(f_train)
if method_name == "svm":
classifier, train_err = \
train_svm_regression(f_train, l_train, C)
elif method_name == "svm_rbf":
classifier, train_err = \
train_svm_regression(f_train, l_train, C,
kernel='rbf')
elif method_name == "randomforest":
classifier, train_err = \
train_random_forest_regression(f_train, l_train, C)
error_test = []
error_test_baseline = []
for itest, fTest in enumerate(f_test):
R = regression_wrapper(classifier, method_name, fTest)
Rbaseline = np.mean(l_train)
error_test.append((R - l_test[itest]) *
(R - l_test[itest]))
error_test_baseline.append((Rbaseline - l_test[itest]) *
(Rbaseline - l_test[itest]))
error = np.array(error_test).mean()
error_baseline = np.array(error_test_baseline).mean()
errors.append(error)
errors_train.append(train_err)
errors_baseline.append(error_baseline)
errors_all.append(np.array(errors).mean())
er_train_all.append(np.array(errors_train).mean())
er_base_all.append(np.array(errors_baseline).mean())
best_ind = np.argmin(errors_all)
print("{0:s}\t\t{1:s}\t\t{2:s}\t\t{3:s}".format("Param", "MSE",
"T-MSE", "R-MSE"))
for i in range(len(errors_all)):
print("{0:.4f}\t\t{1:.2f}\t\t{2:.2f}\t\t{3:.2f}".format(params[i],
errors_all[i],
er_train_all[i],
er_base_all[i]),
end="")
if i == best_ind:
print("\t\t best", end="")
print("")
return params[best_ind], errors_all[best_ind], er_base_all[best_ind]
def print_confusion_matrix(cm, class_names):
"""
This function prints a confusion matrix for a particular classification task.
ARGUMENTS:
cm: a 2-D np array of the confusion matrix
(cm[i,j] is the number of times a sample from class i
was classified in class j)
class_names: a list that contains the names of the classes
"""
if cm.shape[0] != len(class_names):
print("printConfusionMatrix: Wrong argument sizes\n")
return
for c in class_names:
if len(c) > 4:
c = c[0:3]
print("\t{0:s}".format(c), end="")
print("")
for i, c in enumerate(class_names):
if len(c) > 4:
c = c[0:3]
print("{0:s}".format(c), end="")
for j in range(len(class_names)):
print("\t{0:.2f}".format(100.0 * cm[i][j] / np.sum(cm)), end="")
print("")
def features_to_matrix(features):
"""
features_to_matrix(features)
This function takes a list of feature matrices as argument and returns
a single concatenated feature matrix and the respective class labels.
ARGUMENTS:
- features: a list of feature matrices
RETURNS:
- feature_matrix: a concatenated matrix of features
- labels: a vector of class indices
"""
labels = np.array([])
feature_matrix = np.array([])
for i, f in enumerate(features):
if i == 0:
feature_matrix = f
labels = i * np.ones((len(f), 1))
else:
feature_matrix = np.vstack((feature_matrix, f))
labels = np.append(labels, i * np.ones((len(f), 1)))
return feature_matrix, labels
def pca_wrapper(features, dimensions):
features, labels = features_to_matrix(features)
pca = sklearn.decomposition.PCA(n_components=dimensions)
pca.fit(features)
coeff = pca.components_
coeff = coeff[:, 0:dimensions]
features_transformed = []
for f in features:
ft = f.copy()
# ft = pca.transform(ft, k=nDims)
ft = np.dot(f, coeff)
features_transformed.append(ft)
return features_transformed, coeff
def compute_class_rec_pre_f1(c_mat):
"""
Gets recall, precision and f1 PER CLASS, given the confusion matrix
:param c_mat: the [n_class x n_class] confusion matrix
:return: rec, pre and f1 for each class
"""
n_class = c_mat.shape[0]
rec, pre, f1 = [], [], []
for i in range(n_class):
rec.append(float(c_mat[i, i]) / np.sum(c_mat[i, :]))
pre.append(float(c_mat[i, i]) / np.sum(c_mat[:, i]))
f1.append(2 * rec[-1] * pre[-1] / (rec[-1] + pre[-1]))
return rec, pre, f1
def evaluate_model_for_folders(input_test_folders, model_name, model_type,
positive_class, plot=True):
"""
evaluate_model_for_folders(input_test_folders, model_name, model_type)
This function evaluates a model by computing the confusion matrix, the
per class performance metrics and by generating a ROC and Precision / Recall
diagrams (for a particular class of interest), for a given test dataset.
The dataset needs to be organized in folders (one folder per audio class),
exactly like in extract_features_and_train()
:param input_test_folders: list of folders (each folder represents a
separate audio class)
:param model_name: path to the model to be tested
:param model_type: type of the model
:param positive_class name of the positive class
:param plot (True default) if to plot 2 diagrams on plotly
:return: thr_prre, pre, rec (thresholds, precision recall values)
thr_roc, fpr, tpr (thresholds, false positive , true positive rates)
Usage example:
from pyAudioAnalysis import audioTrainTest as aT
thr_prre, pre, rec, thr_roc, fpr, tpr =
aT.evaluate_model_for_folders(["4_classes_small/speech",
"4_classes_small/music"],
"data/models/svm_rbf_4class",
"svm_rbf", "speech")
"""
class_names = []
y_true_binary = []
y_true = []
y_pred = []
probs_positive = []
for i, d in enumerate(input_test_folders):
if d[-1] == os.sep:
class_names.append(d.split(os.sep)[-2])
else:
class_names.append(d.split(os.sep)[-1])
types = ('*.wav', '*.aif', '*.aiff', '*.mp3', '*.au', '*.ogg')
wav_file_list = []
for files in types:
wav_file_list.extend(glob.glob(os.path.join(d, files)))
# get list of audio files for current folder and run classifier
for w in wav_file_list:
c, p, probs_names = file_classification(w, model_name, model_type)
y_pred.append(c)
y_true.append(probs_names.index(class_names[i]))
if i == probs_names.index(positive_class):
y_true_binary.append(1)
else:
y_true_binary.append(0)
prob_positive = p[probs_names.index(positive_class)]
probs_positive.append(prob_positive)
pre, rec, thr_prre = sklearn.metrics.precision_recall_curve(y_true_binary,
probs_positive)
fpr, tpr, thr_roc = sklearn.metrics.roc_curve(
y_true_binary, probs_positive)
cm = sklearn.metrics.confusion_matrix(y_true, y_pred)
rec_c, pre_c, f1_c = compute_class_rec_pre_f1(cm)
f1 = (sklearn.metrics.f1_score(y_true, y_pred, average='macro'))
acc = (sklearn.metrics.accuracy_score(y_true, y_pred))
print(cm)
print(rec_c, pre_c, f1_c, f1, acc)
if plot:
titles = ["Confusion matrix, acc = {0:.1f}%, "
" F1 (macro): {1:.1f}%".format(100 * acc, 100 * f1),
"Class-wise Performance measures",
"Pre vs Rec for " + positive_class,
"ROC for " + positive_class]
figs = plotly.subplots.make_subplots(rows=2, cols=2,
subplot_titles=titles)
heatmap = go.Heatmap(z=np.flip(cm, axis=0), x=class_names,
y=list(reversed(class_names)),
colorscale=[[0, '#4422ff'], [1, '#ff4422']],
name="confusin matrix", showscale=False)
mark_prop1 = dict(color='rgba(80, 220, 150, 0.5)',
line=dict(color='rgba(80, 220, 150, 1)', width=2))
mark_prop2 = dict(color='rgba(80, 150, 220, 0.5)',
line=dict(color='rgba(80, 150, 220, 1)', width=2))
mark_prop3 = dict(color='rgba(250, 150, 150, 0.5)',
line=dict(color='rgba(250, 150, 150, 1)', width=3))
b1 = go.Bar(x=class_names, y=rec_c, name="Recall", marker=mark_prop1)
b2 = go.Bar(x=class_names, y=pre_c,
name="Precision", marker=mark_prop2)
b3 = go.Bar(x=class_names, y=f1_c, name="F1", marker=mark_prop3)
figs.append_trace(heatmap, 1, 1)
figs.append_trace(b1, 1, 2)
figs.append_trace(b2, 1, 2)
figs.append_trace(b3, 1, 2)
figs.append_trace(go.Scatter(x=thr_prre, y=pre, name="Precision",
marker=mark_prop1), 2, 1)
figs.append_trace(go.Scatter(x=thr_prre, y=rec, name="Recall",
marker=mark_prop2), 2, 1)
figs.append_trace(go.Scatter(x=fpr, y=tpr, showlegend=False), 2, 2)
figs.update_xaxes(title_text="threshold", row=2, col=1)
figs.update_xaxes(title_text="false positive rate", row=2, col=2)
figs.update_yaxes(title_text="true positive rate", row=2, col=2)
plotly.offline.plot(figs, filename="temp.html", auto_open=True)
return cm, thr_prre, pre, rec, thr_roc, fpr, tpr
def file_classification(input_file, model_name, model_type):
# Load classifier:
if not os.path.isfile(model_name):
print("fileClassification: input model_name not found!")
return -1, -1, -1
if isinstance(input_file, str) and not os.path.isfile(input_file):
print("fileClassification: wav file not found!")
return -1, -1, -1
if model_type == 'knn':
classifier, mean, std, classes, mid_window, mid_step, short_window, \
short_step, compute_beat = load_model_knn(model_name)
else:
classifier, mean, std, classes, mid_window, mid_step, short_window, \
short_step, compute_beat = load_model(model_name)
# read audio file and convert to mono
sampling_rate, signal = audioBasicIO.read_audio_file(input_file)
signal = audioBasicIO.stereo_to_mono(signal)
if sampling_rate == 0:
# audio file IO problem
return -1, -1, -1
if signal.shape[0] / float(sampling_rate) < mid_window:
mid_window = signal.shape[0] / float(sampling_rate)
# feature extraction:
mid_features, s, _ = \
aF.mid_feature_extraction(signal, sampling_rate,
mid_window * sampling_rate,
mid_step * sampling_rate,
round(sampling_rate * short_window),
round(sampling_rate * short_step))
# long term averaging of mid-term statistics
mid_features = mid_features.mean(axis=1)
if compute_beat:
beat, beat_conf = aF.beat_extraction(s, short_step)
mid_features = np.append(mid_features, beat)
mid_features = np.append(mid_features, beat_conf)
feature_vector = (mid_features - mean) / std # normalization
# classification
class_id, probability = classifier_wrapper(classifier, model_type,
feature_vector)
return class_id, probability, classes
def file_regression(input_file, model_name, model_type):
# Load classifier:
if not os.path.isfile(input_file):
print("fileClassification: wav file not found!")
return -1, -1, -1
regression_models = glob.glob(model_name + "_*")
regression_models2 = []
for r in regression_models:
if r[-5::] != "MEANS":
regression_models2.append(r)
regression_models = regression_models2
regression_names = []
for r in regression_models:
regression_names.append(r[r.rfind("_")+1::])
# FEATURE EXTRACTION
# LOAD ONLY THE FIRST MODEL (for mt_win, etc)
if model_type == 'svm' or model_type == "svm_rbf" or \
model_type == 'randomforest':
_, _, _, mid_window, mid_step, short_window, short_step, compute_beat \
= load_model(regression_models[0], True)
# read audio file and convert to mono
samping_rate, signal = audioBasicIO.read_audio_file(input_file)
signal = audioBasicIO.stereo_to_mono(signal)
# feature extraction:
mid_features, s, _ = \
aF.mid_feature_extraction(signal, samping_rate, mid_window * samping_rate,
mid_step * samping_rate,
round(samping_rate * short_window),
round(samping_rate * short_step))
# long term averaging of mid-term statistics
mid_features = mid_features.mean(axis=1)
if compute_beat:
beat, beat_conf = aF.beat_extraction(s, short_step)
mid_features = np.append(mid_features, beat)
mid_features = np.append(mid_features, beat_conf)
# REGRESSION
R = []
for ir, r in enumerate(regression_models):
if not os.path.isfile(r):
print("fileClassification: input model_name not found!")
return (-1, -1, -1)
if model_type == 'svm' or model_type == "svm_rbf" \
or model_type == 'randomforest':
model, mean, std, _, _, _, _, _ = load_model(r, True)
curFV = (mid_features - mean) / std # normalization
# classification
R.append(regression_wrapper(model, model_type, curFV))
return R, regression_names
def lda(data, labels, red_dim):
# Centre data
data -= data.mean(axis=0)
n_data = np.shape(data)[0]
n_dim = np.shape(data)[1]
Sw = np.zeros((n_dim, n_dim))
C = np.cov((data.T))
# Loop over classes
classes = np.unique(labels)
for i in range(len(classes)):
# Find relevant datapoints
indices = (np.where(labels == classes[i]))
d = np.squeeze(data[indices, :])
classcov = np.cov((d.T))
Sw += float(np.shape(indices)[0])/n_data * classcov
Sb = C - Sw
# Now solve for W
# Compute eigenvalues, eigenvectors and sort into order
evals, evecs = la.eig(Sw, Sb)
indices = np.argsort(evals)
indices = indices[::-1]
evecs = evecs[:, indices]
w = evecs[:, :red_dim]
new_data = np.dot(data, w)
return new_data, w
def train_speaker_models():
"""
This script is used to train the speaker-related models
(NOTE: data paths are hard-coded and NOT included in the library,
the models are, however included)
import audioTrainTest as aT
aT.trainSpeakerModelsScript()
"""
mt_win = 2.0
mt_step = 2.0
st_win = 0.020
st_step = 0.020
dir_name = "DIARIZATION_ALL/all"
list_of_dirs = [os.path.join(dir_name, name)
for name in os.listdir(dir_name)
if os.path.isdir(os.path.join(dir_name, name))]
extract_features_and_train(list_of_dirs, mt_win, mt_step, st_win, st_step,
"knn", "data/knnSpeakerAll",
compute_beat=False, train_percentage=0.50)
dir_name = "DIARIZATION_ALL/female_male"
list_of_dirs = [os.path.join(dir_name, name)
for name in os.listdir(dir_name)
if os.path.isdir(os.path.join(dir_name, name))]
extract_features_and_train(list_of_dirs, mt_win, mt_step, st_win, st_step,
"knn", "data/knnSpeakerFemaleMale",
compute_beat=False, train_percentage=0.50)
def main(argv):
return 0
if __name__ == '__main__':
main(sys.argv)
|
# -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2006, 2007, 2008, 2009, 2010, 2011 CERN.
##
## Invenio 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 2 of the
## License, or (at your option) any later version.
##
## Invenio 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 Invenio; if not, write to the Free Software Foundation, Inc.,
## 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
"""BibFormat element - Prints keywords
"""
__revision__ = "$Id$"
import cgi
import six
from urllib import quote
from invenio.base.globals import cfg
def format_element(bfo, keyword_prefix, keyword_suffix, separator=' ; ', link='yes'):
"""
Display keywords of the record.
@param keyword_prefix: a prefix before each keyword
@param keyword_suffix: a suffix after each keyword
@param separator: a separator between keywords
@param link: links the keywords if 'yes' (HTML links)
"""
CFG_SITE_URL = cfg['CFG_SITE_URL']
if isinstance(CFG_SITE_URL, six.text_type):
CFG_SITE_URL = CFG_SITE_URL.encode('utf8')
keywords = bfo.fields('6531_a')
if len(keywords) > 0:
if link == 'yes':
keywords = ['<a href="' + CFG_SITE_URL + '/search?f=keyword&p='+ \
quote('"' + keyword + '"') + \
'&ln='+ bfo.lang+ \
'">' + cgi.escape(keyword) + '</a>'
for keyword in keywords]
else:
keywords = [cgi.escape(keyword)
for keyword in keywords]
keywords = [keyword_prefix + keyword + keyword_suffix
for keyword in keywords]
return separator.join(keywords)
def escape_values(bfo):
"""
Called by BibFormat in order to check if output of this element
should be escaped.
"""
return 0
formatter: unicode decoding error fix
* Fixes unicode decoding error in bfe_keyword bibformat element.
Signed-off-by: Lars Holm Nielsen <7eaffca9f7cd98839b5821d0d23e09ff764b82cd@cern.ch>
# -*- coding: utf-8 -*-
##
## This file is part of Invenio.
## Copyright (C) 2006, 2007, 2008, 2009, 2010, 2011, 2014 CERN.
##
## Invenio 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 2 of the
## License, or (at your option) any later version.
##
## Invenio 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 Invenio; if not, write to the Free Software Foundation, Inc.,
## 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
"""BibFormat element - Prints keywords
"""
__revision__ = "$Id$"
import cgi
import six
from urllib import quote
from invenio.base.globals import cfg
def format_element(bfo, keyword_prefix, keyword_suffix, separator=' ; ',
link='yes'):
"""
Display keywords of the record.
@param keyword_prefix: a prefix before each keyword
@param keyword_suffix: a suffix after each keyword
@param separator: a separator between keywords
@param link: links the keywords if 'yes' (HTML links)
"""
CFG_SITE_URL = cfg['CFG_SITE_URL']
if isinstance(CFG_SITE_URL, six.text_type):
CFG_SITE_URL = CFG_SITE_URL.encode('utf8')
keywords = bfo.fields('6531_a')
if len(keywords) > 0:
if link == 'yes':
keywords = [
'<a href="' + CFG_SITE_URL + '/search?f=keyword&p=' +
quote('"' + keyword + '"') +
'&ln=' + str(bfo.lang) +
'">' + cgi.escape(keyword) + '</a>'
for keyword in keywords]
else:
keywords = [cgi.escape(keyword)
for keyword in keywords]
keywords = [keyword_prefix + keyword + keyword_suffix
for keyword in keywords]
return separator.join(keywords)
def escape_values(bfo):
"""
Called by BibFormat in order to check if output of this element
should be escaped.
"""
return 0
|
"""
puzzlesolver_compound_tests.py
:created on: 20160701
__author__ = 'Frederic Dupont'
:License: GPL3
"""
import unittest
from sudoku.puzzle import Puzzle, make_grid_from_string
# from sudoku.puzzleconstants import DIGITS, SQUARES
from sudoku.puzzlesolver import PuzzleSolver
class TestPuzzleSolver(unittest.TestCase):
def setUp(self):
# Very Hard --> require search
self.g1 = '4.....8.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......'
self.s1 = '4.....8.5.3..........7......2.....6.....8.4...4..1.......6.3.7.5.32.1...1.4......'
self.g2 = '1...895..5....7819........72.4..8.7.9.71.54.8.8.7..3.531.4..78.4682....3..985...1'
self.s2 = '172.895.454..2781989.5142.7254938176937165428681742395315496782468271953729853641'
# Not so hard - no search needed
self.g3 = '003020600900305001001806400008102900700000008006708200002609500800203009005010300'
self.s3 = '483921657967345821251876493548132976729564138136798245372689514814253769695417382'
self.g4 = '.82...59....8.1..3..52...78...37842...........27945...91...68..2..7.9....73...95.'
self.s4 = '382467591796851243145293678561378429439612785827945316914526837258739164673184952'
self.g5 = '437...189.6.183.......9.536.73..1...9..4.7..1...5..69.124.7.......645.1.356...974'
self.s5 = '437256189569183742812794536673921458985467321241538697124379865798645213356812974'
self.g6 = '.6..7.4..5......12...1....7..5.4.27..3.....4..19.6.8...4...1...79.6....8..1.3..2.'
self.s6 = '163275489578496312924183567685349271237518946419762853346821795792654138851937624'
self.g7 = '7.9..3.85812..6.375.478..1.3.196..5.426..789...52..6.1143.7.56....3..1.8......3..'
self.s7 = '769123485812456937534789216381964752426517893975238641143872569697345128258691374'
self.g8 = '31.....78526.9.13.8..6.359228.4.9..1..3.862..6..1.5..3...2.13467.1...8...6...891.'
self.s8 = '319542678526897134874613592285439761143786259697125483958271346731964825462358917'
self.g9 = '.1869.....5....71..7..3..62.21863...7..5...365...419.88...72.4..47.18.9.....563..'
self.s9 = '218697453356284719479135862921863574784529136563741928835972641647318295192456387'
self.g10 = '...12.4.5.4.6..7.8.26......47.5...31.........31...8..9.8..1.......2..5.4..24.79.6'
self.s10 = '837129465941653728526874193478592631269341857315768249684915372793286514152437986'
self.g11 = '.5...478...67.95..2.7.5.1.3.2.....6..981.543..3.....7.1.5..7..8..35.26.7.728...5.'
self.s11 = '359214786816739524247658193721483965698175432534926871165397248983542617472861359'
# hard puzzle (X wings and requires 5 full iterations to be solved, but no search)
self.g12 = '4.......1..6.35.24..984.6.7.3....4..9..68.3.5.1..7.2....542.7.32.....54.......1.2'
self.s12 = '423796851786135924159842637538219476972684315614573289865421793291367548347958162'
if __name__ == '__main__':
unittest.main()
Add easy puzzle test case for eliminate_propagate_fill()
"""
puzzlesolver_compound_tests.py
:created on: 20160701
__author__ = 'Frederic Dupont'
:License: GPL3
"""
import unittest
from sudoku.puzzle import Puzzle, make_grid_from_string
# from sudoku.puzzleconstants import DIGITS, SQUARES
from sudoku.puzzlesolver import PuzzleSolver
class TestPuzzleSolver(unittest.TestCase):
def setUp(self):
# Very Hard --> require search
self.g1 = '4.....8.5.3..........7......2.....6.....8.4......1.......6.3.7.5..2.....1.4......'
self.s1 = '4.....8.5.3..........7......2.....6.....8.4...4..1.......6.3.7.5.32.1...1.4......'
self.g2 = '1...895..5....7819........72.4..8.7.9.71.54.8.8.7..3.531.4..78.4682....3..985...1'
self.s2 = '172.895.454..2781989.5142.7254938176937165428681742395315496782468271953729853641'
# Not so hard - no search needed
self.g3 = '003020600900305001001806400008102900700000008006708200002609500800203009005010300'
self.s3 = '483921657967345821251876493548132976729564138136798245372689514814253769695417382'
self.g4 = '.82...59....8.1..3..52...78...37842...........27945...91...68..2..7.9....73...95.'
self.s4 = '382467591796851243145293678561378429439612785827945316914526837258739164673184952'
self.g5 = '437...189.6.183.......9.536.73..1...9..4.7..1...5..69.124.7.......645.1.356...974'
self.s5 = '437256189569183742812794536673921458985467321241538697124379865798645213356812974'
self.g6 = '.6..7.4..5......12...1....7..5.4.27..3.....4..19.6.8...4...1...79.6....8..1.3..2.'
self.s6 = '163275489578496312924183567685349271237518946419762853346821795792654138851937624'
self.g7 = '7.9..3.85812..6.375.478..1.3.196..5.426..789...52..6.1143.7.56....3..1.8......3..'
self.s7 = '769123485812456937534789216381964752426517893975238641143872569697345128258691374'
self.g8 = '31.....78526.9.13.8..6.359228.4.9..1..3.862..6..1.5..3...2.13467.1...8...6...891.'
self.s8 = '319542678526897134874613592285439761143786259697125483958271346731964825462358917'
self.g9 = '.1869.....5....71..7..3..62.21863...7..5...365...419.88...72.4..47.18.9.....563..'
self.s9 = '218697453356284719479135862921863574784529136563741928835972641647318295192456387'
self.g10 = '...12.4.5.4.6..7.8.26......47.5...31.........31...8..9.8..1.......2..5.4..24.79.6'
self.s10 = '837129465941653728526874193478592631269341857315768249684915372793286514152437986'
self.g11 = '.5...478...67.95..2.7.5.1.3.2.....6..981.543..3.....7.1.5..7..8..35.26.7.728...5.'
self.s11 = '359214786816739524247658193721483965698175432534926871165397248983542617472861359'
# hard puzzle (X wings and requires 5 full iterations to be solved, but no search)
self.g12 = '4.......1..6.35.24..984.6.7.3....4..9..68.3.5.1..7.2....542.7.32.....54.......1.2'
self.s12 = '423796851786135924159842637538219476972684315614573289865421793291367548347958162'
@staticmethod
def _apply_eliminate_propagate_fill(puzzle_string):
"""
:return: a tuple containing
the result of eliminate_propagate_fill() on the provided string,
and a repr of the "solved" puzzle
"""
grid = make_grid_from_string(puzzle_string)
grid.parse_grid_candidates()
solver = PuzzleSolver(grid.clone())
result = solver.eliminate_propagate_fill()
return result, repr(solver._puzzle)
def test_eliminate_propagate_fill_g3(self):
puzzle_string, expected_string = self.g3, self.s3
result, resulting_string = TestPuzzleSolver._apply_eliminate_propagate_fill(puzzle_string)
self.assertTrue(result)
self.assertEqual(expected_string, resulting_string)
if __name__ == '__main__':
unittest.main()
|
import json
import requests
from .fields import BaseField, CharField, ModelField
from . import settings
class APIModelCollection:
def __init__(self, model, api, list_url, get_url):
self._items = []
self._model = model
self._api = api
self._list_url = list_url
self._get_url = get_url
def list(self):
if self._list_url is not None:
items = API.authenticated_get_request(
request_url=self._list_url,
token=self._api.token
)
item_dicts = items.json()
self._items = []
for item_dict in item_dicts:
obj = self._model(api=self._api)
obj.set_data(data=item_dict)
self._items.append(obj)
return self._items
def get(self, **kwargs):
if self._get_url is not None:
keys = {key: value for key, value in kwargs.items()}
self._get_url = self._get_url.format(**keys)
item = API.authenticated_get_request(
request_url=self._get_url,
token=self._api.token
)
item_dict = item.json()
obj = self._model(api=self._api)
obj.set_data(data=item_dict)
return obj
else:
return None
def add(self, item):
if isinstance(item, self._model):
self._items.append(item)
else:
print("Item is not of type: {}".format(self._model))
class APICollaboratorCollection(APIModelCollection):
def __init__(self, model, api, list_url, get_url, add_url):
super().__init__(model, api, list_url, get_url)
self._add_url = add_url
def get(self, login):
if self._list_url is not None and self._get_url is not None:
self._get_url = self._get_url.format(login=login)
if len(self._items) is 0:
self._items = self.list()
if login in [collaborator.login for collaborator in self._items]:
item = API.authenticated_get_request(
request_url=self._get_url,
token=self._api.token
)
item_dict = item.json()
obj = self._model(api=self._api)
obj.set_data(data=item_dict)
return obj
return None
def add(self, item):
super().add(item)
if item in self._items:
if self._add_url is not None:
self._add_url = self._add_url.format(login=item.login)
API.authenticated_put_request(
request_url=self._add_url,
token=self._api.token
)
def save(self):
pass
class API:
def __init__(self, token):
self.token = token
@staticmethod
def auth_headers(token):
return {'access_token': token}
@staticmethod
def authenticated_get_request(request_url, token):
headers = API.auth_headers(token)
response = requests.get(request_url, params=headers)
return response
@staticmethod
def authenticated_put_request(request_url, token, data=None):
headers = API.auth_headers(token)
response = requests.put(url=request_url, data=data, params=headers)
return response
@staticmethod
def authenticated_patch_request(request_url, token, data=None):
headers = API.auth_headers(token)
response = requests.patch(url=request_url, data=data, params=headers)
return response
@property
def limit(self):
limit = API.authenticated_get_request(settings.RATE_LIMIT_URL,
self.token)
return limit.content
@property
def repos(self):
return APIModelCollection(
model=Repository,
api=self,
list_url=settings.CURRENT_USER_REPOSITORIES_URL,
get_url=settings.REPOSITORY_URL)
class BaseModel(type):
def __new__(cls, name, bases, attrs):
fields = {k: v for k, v in attrs.items() if isinstance(v, BaseField)}
attrs['_fields'] = fields
return type.__new__(cls, name, bases, attrs)
class Model(object, metaclass=BaseModel):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def __setattr__(self, key, value):
if key in self._fields:
field = self._fields[key]
field.set(value)
field._related_obj = self
super(Model, self).__setattr__(key, field.deserialize())
else:
super(Model, self).__setattr__(key, value)
def to_dict(self):
return dict((key, self._fields[key].serialize(getattr(self, key)))
for key in self._fields.keys() if hasattr(self, key))
def to_json(self):
return json.dumps(self.to_dict())
def set_data(self, data, is_json=False):
if is_json:
data = json.loads(data)
for key in self._fields:
if key in data:
setattr(self, key, data.get(key))
class APIModel(Model):
api = ModelField(API)
def _save(self, save_url):
response = API.authenticated_patch_request(save_url,
token=self.api.token,
data=self.to_json())
print(response.content)
class User(APIModel):
login = CharField()
def save(self):
self._save(settings.AUTHENTICATED_USER)
def __repr__(self):
return self.login
class Repository(APIModel):
name = CharField()
full_name = CharField()
description = CharField()
def save(self):
self._save(settings.REPOSITORY_URL.format(full_name=self.full_name))
@property
def collaborators(self):
return APICollaboratorCollection(
model=User,
api=self.api,
list_url=settings.COLLABORATORS_LIST_URL.format(
full_name=self.full_name),
get_url=settings.COLLABORATOR_URL,
add_url=settings.COLLABORATOR_ADD_URL.format(
full_name=self.full_name))
def __repr__(self):
return self.full_name
Restructure Model collections
import json
import requests
from .fields import BaseField, CharField, ModelField
from . import settings
class API:
def __init__(self, token):
self.token = token
@staticmethod
def auth_headers(token):
return {'access_token': token}
@staticmethod
def authenticated_get_request(request_url, token):
headers = API.auth_headers(token)
response = requests.get(request_url, params=headers)
return response
@staticmethod
def authenticated_put_request(request_url, token, data=None):
headers = API.auth_headers(token)
response = requests.put(url=request_url, data=data, params=headers)
return response
@staticmethod
def authenticated_patch_request(request_url, token, data=None):
headers = API.auth_headers(token)
response = requests.patch(url=request_url, data=data, params=headers)
return response
@property
def limit(self):
limit = API.authenticated_get_request(settings.RATE_LIMIT_URL,
self.token)
return limit.content
@property
def repos(self):
return APIRepositoryCollection(api=self, parent=self)
class APIModelCollection:
def __init__(self, api, parent=None):
self._items = []
self._api = api
self.parent = parent
class APIRepositoryCollection(APIModelCollection):
def list(self):
response = API.authenticated_get_request(
request_url=settings.CURRENT_USER_REPOSITORIES_URL,
token=self._api.token
)
item_dicts = response.json()
self._items = []
for item_dict in item_dicts:
obj = Repository(api=self._api)
obj.set_data(data=item_dict)
self._items.append(obj)
return self._items
def get(self, full_name):
get_url = settings.REPOSITORY_URL.format(full_name=full_name)
response = API.authenticated_get_request(
request_url=get_url,
token=self._api.token
)
item_dict = response.json()
obj = Repository(api=self._api)
obj.set_data(data=item_dict)
return obj
class APICollaboratorCollection(APIModelCollection):
def list(self):
items = API.authenticated_get_request(
request_url=settings.COLLABORATORS_LIST_URL.format(
full_name=self.parent.full_name),
token=self._api.token
)
item_dicts = items.json()
self._items = []
for item_dict in item_dicts:
obj = User(api=self._api)
obj.set_data(data=item_dict)
self._items.append(obj)
return self._items
def get(self, login):
get_url = settings.COLLABORATOR_URL.format(
full_name=self.parent.full_name,
login=login
)
if len(self._items) is 0:
self._items = self.list()
if login in [collaborator.login for collaborator in self._items]:
item = API.authenticated_get_request(
request_url=get_url,
token=self._api.token
)
item_dict = item.json()
obj = User(api=self._api)
obj.set_data(data=item_dict)
return obj
return None
def add(self, item):
if item in self._items:
add_url = settings.COLLABORATOR_ADD_URL.format(
full_name=self.parent.full_name,
login=item.login)
API.authenticated_put_request(
request_url=add_url,
token=self._api.token
)
class BaseModel(type):
def __new__(cls, name, bases, attrs):
fields = {k: v for k, v in attrs.items() if isinstance(v, BaseField)}
attrs['_fields'] = fields
return type.__new__(cls, name, bases, attrs)
class Model(object, metaclass=BaseModel):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def __setattr__(self, key, value):
if key in self._fields:
field = self._fields[key]
field.set(value)
field._related_obj = self
super(Model, self).__setattr__(key, field.deserialize())
else:
super(Model, self).__setattr__(key, value)
def to_dict(self):
return dict((key, self._fields[key].serialize(getattr(self, key)))
for key in self._fields.keys() if hasattr(self, key))
def to_json(self):
return json.dumps(self.to_dict())
def set_data(self, data, is_json=False):
if is_json:
data = json.loads(data)
for key in self._fields:
if key in data:
setattr(self, key, data.get(key))
class APIModel(Model):
api = ModelField(API)
def _save(self, save_url):
API.authenticated_patch_request(save_url,
token=self.api.token,
data=self.to_json())
class User(APIModel):
login = CharField()
def save(self):
self._save(settings.AUTHENTICATED_USER)
def __repr__(self):
return self.login
class Repository(APIModel):
name = CharField()
full_name = CharField()
description = CharField()
def save(self):
self._save(settings.REPOSITORY_URL.format(full_name=self.full_name))
@property
def collaborators(self):
return APICollaboratorCollection(api=self.api, parent=self)
def __repr__(self):
return self.full_name
|
from collections import OrderedDict
from django.http import Http404, HttpResponseForbidden
from django.shortcuts import render, redirect, get_object_or_404, get_list_or_404
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied
from django.db import IntegrityError
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import User
from django.contrib.auth.decorators import login_required
from django.template.context_processors import csrf
from django.views.decorators.cache import cache_page
from .forms import RegisterUserForm, AddMonsterInstanceForm, EditMonsterInstanceForm, AwakenMonsterInstanceForm, \
EditEssenceStorageForm, EditProfileForm
from .models import Monster, Summoner, MonsterInstance
@cache_page(60 * 10)
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
)
new_user.save()
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
def log_in(request):
context = {}
context.update(csrf(request))
if request.method == 'POST':
username = request.POST['username']
userpass = request.POST['userpass']
user = authenticate(username=username, password=userpass)
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
# If the above falls through then the login failed
context['login_failure'] = True
context['username'] = username
# No data POSTed or the above login/auth failed.
return render(request, 'herders/login.html', context)
def log_out(request):
logout(request)
return redirect('news:latest_news')
def profile(request, profile_name=None, view_mode='list', sort_method='grade'):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in. ')
summoner = get_object_or_404(Summoner, user__username=profile_name)
# Determine if the person logged in is the one requesting the view
is_owner = request.user.is_authenticated() and summoner.user == request.user
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': profile_name,
'is_owner': is_owner,
'view_mode': view_mode,
'sort_method': sort_method,
'return_path': request.path,
'view': 'profile',
}
if is_owner or summoner.public:
if view_mode.lower() == 'list':
context['monster_stable'] = MonsterInstance.objects.filter(owner=summoner)
return render(request, 'herders/profile/profile_view.html', context)
elif view_mode.lower() == 'box':
if sort_method == 'grade':
monster_stable = OrderedDict()
monster_stable['6*'] = MonsterInstance.objects.filter(owner=summoner, stars=6).order_by('-level', 'monster__name')
monster_stable['5*'] = MonsterInstance.objects.filter(owner=summoner, stars=5).order_by('-level', 'monster__name')
monster_stable['4*'] = MonsterInstance.objects.filter(owner=summoner, stars=4).order_by('-level', 'monster__name')
monster_stable['3*'] = MonsterInstance.objects.filter(owner=summoner, stars=3).order_by('-level', 'monster__name')
monster_stable['2*'] = MonsterInstance.objects.filter(owner=summoner, stars=2).order_by('-level', 'monster__name')
monster_stable['1*'] = MonsterInstance.objects.filter(owner=summoner, stars=1).order_by('-level', 'monster__name')
elif sort_method == 'level':
monster_stable = OrderedDict()
monster_stable['40-31'] = MonsterInstance.objects.filter(owner=summoner, level__gt=30).order_by('-level', '-stars', 'monster__name')
monster_stable['30-21'] = MonsterInstance.objects.filter(owner=summoner, level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__name')
monster_stable['20-11'] = MonsterInstance.objects.filter(owner=summoner, level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__name')
monster_stable['10-1'] = MonsterInstance.objects.filter(owner=summoner, level__lte=10).order_by('-level', '-stars', 'monster__name')
elif sort_method == 'attribute':
monster_stable = OrderedDict()
monster_stable['water'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
return render(request, 'herders/profile/profile_box.html', context)
else:
raise Http404('Unknown profile view mode')
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = EditProfileForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'is_owner': True, # Because of @login_required decorator
'profile_name': profile_name,
'return_path': return_path,
'profile_form': form,
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
@login_required
def profile_storage(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'is_owner': True,
'profile_name': request.user.username,
'storage_form': form,
'view': 'profile',
'profile_view': 'materials',
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_storage.html', context)
@login_required()
def monster_instance_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = AddMonsterInstanceForm(request.POST or None)
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
return redirect(return_path)
else:
# Re-show same page but with form filled in and errors shown
context = {
'profile_name': profile_name,
'add_monster_form': form,
'return_path': return_path,
'is_owner': True,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_add.html', context)
def monster_instance_view(request, profile_name, instance_id):
context = {
'view': 'profile',
}
return render(request, 'herders/unimplemented.html')
def monster_instance_edit(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = monster.owner == request.user.summoner
form = EditMonsterInstanceForm(request.POST or None, instance=monster)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'edit_monster_form': form,
'view': 'profile',
}
if request.method == 'POST':
if is_owner:
if form.is_valid():
form.save()
return redirect(return_path)
else:
# Redisplay form with validation error messages
context['validation_errors'] = form.non_field_errors()
return render(request, 'herders/profile/profile_monster_edit.html', context)
else:
raise PermissionDenied()
else:
return render(request, 'herders/profile/profile_monster_edit.html', context)
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
context = {
'view': 'profile',
}
return render(request, 'herders/unimplemented.html')
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = monster.owner == request.user.summoner
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': request.user.username,
'is_owner': is_owner, # Because of @login_required decorator
'monster': monster,
'awaken_monster_form': form,
}
if request.method == 'POST' and form.is_valid() and is_owner:
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
if monster.monster.awaken_magic_mats_high:
summoner.storage_magic_high -= monster.monster.awaken_magic_mats_high
if monster.monster.awaken_magic_mats_mid:
summoner.storage_magic_mid -= monster.monster.awaken_magic_mats_mid
if monster.monster.awaken_magic_mats_low:
summoner.storage_magic_low -= monster.monster.awaken_magic_mats_low
if monster.monster.element == Monster.ELEMENT_FIRE:
if monster.monster.awaken_ele_mats_high:
summoner.storage_fire_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_fire_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_fire_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WATER:
if monster.monster.awaken_ele_mats_high:
summoner.storage_water_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_water_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_water_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WIND:
if monster.monster.awaken_ele_mats_high:
summoner.storage_wind_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_wind_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_wind_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_DARK:
if monster.monster.awaken_ele_mats_high:
summoner.storage_dark_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_dark_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_dark_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_LIGHT:
if monster.monster.awaken_ele_mats_high:
summoner.storage_light_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_light_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_light_low -= monster.monster.awaken_ele_mats_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to()
monster.save()
return redirect(return_path)
else:
# Retreive list of awakening materials from summoner profile
summoner = Summoner.objects.get(user=request.user)
available_materials = {
'storage_magic_low': summoner.storage_magic_low,
'storage_magic_mid': summoner.storage_magic_mid,
'storage_magic_high': summoner.storage_magic_high
}
if monster.monster.element == Monster.ELEMENT_FIRE:
available_materials['storage_ele_low'] = summoner.storage_fire_low
available_materials['storage_ele_mid'] = summoner.storage_fire_mid
available_materials['storage_ele_high'] = summoner.storage_fire_high
elif monster.monster.element == Monster.ELEMENT_WATER:
available_materials['storage_ele_low'] = summoner.storage_water_low
available_materials['storage_ele_mid'] = summoner.storage_water_mid
available_materials['storage_ele_high'] = summoner.storage_water_high
elif monster.monster.element == Monster.ELEMENT_WIND:
available_materials['storage_ele_low'] = summoner.storage_wind_low
available_materials['storage_ele_mid'] = summoner.storage_wind_mid
available_materials['storage_ele_high'] = summoner.storage_wind_high
elif monster.monster.element == Monster.ELEMENT_DARK:
available_materials['storage_ele_low'] = summoner.storage_dark_low
available_materials['storage_ele_mid'] = summoner.storage_dark_mid
available_materials['storage_ele_high'] = summoner.storage_dark_high
elif monster.monster.element == Monster.ELEMENT_LIGHT:
available_materials['storage_ele_low'] = summoner.storage_light_low
available_materials['storage_ele_mid'] = summoner.storage_light_mid
available_materials['storage_ele_high'] = summoner.storage_light_high
context['available_materials'] = available_materials
return render(request, 'herders/profile/profile_awaken.html', context)
@login_required
def fusion(request, profile_name):
context = {
'view': 'fusion',
}
return render(request, 'herders/unimplemented.html', context)
@login_required
def teams(request, profile_name):
context = {
'view': 'teams',
}
return render(request, 'herders/unimplemented.html', context)
@cache_page(60 * 60)
def bestiary(request, monster_element=None):
print monster_element
context = {
'view': 'bestiary',
'monster_element': monster_element,
}
if monster_element is not None:
if monster_element == 'all':
context['monster_list'] = Monster.objects.all()
else:
context['monster_list'] = get_list_or_404(Monster, element=monster_element)
else:
context['no_filter'] = True
return render(request, 'herders/bestiary.html', context)
@cache_page(60 * 60)
def bestiary_detail(request, monster_id):
context = {
'view': 'bestiary',
}
return render(request, 'herders/unimplemented.html')
Removed bestiary caching until issue with appearing logged in is fixed
from collections import OrderedDict
from django.http import Http404, HttpResponseForbidden
from django.shortcuts import render, redirect, get_object_or_404, get_list_or_404
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied
from django.db import IntegrityError
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import User
from django.contrib.auth.decorators import login_required
from django.template.context_processors import csrf
from django.views.decorators.cache import cache_page
from .forms import RegisterUserForm, AddMonsterInstanceForm, EditMonsterInstanceForm, AwakenMonsterInstanceForm, \
EditEssenceStorageForm, EditProfileForm
from .models import Monster, Summoner, MonsterInstance
@cache_page(60 * 10)
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
)
new_user.save()
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
def log_in(request):
context = {}
context.update(csrf(request))
if request.method == 'POST':
username = request.POST['username']
userpass = request.POST['userpass']
user = authenticate(username=username, password=userpass)
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
# If the above falls through then the login failed
context['login_failure'] = True
context['username'] = username
# No data POSTed or the above login/auth failed.
return render(request, 'herders/login.html', context)
def log_out(request):
logout(request)
return redirect('news:latest_news')
def profile(request, profile_name=None, view_mode='list', sort_method='grade'):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in. ')
summoner = get_object_or_404(Summoner, user__username=profile_name)
# Determine if the person logged in is the one requesting the view
is_owner = request.user.is_authenticated() and summoner.user == request.user
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': profile_name,
'is_owner': is_owner,
'view_mode': view_mode,
'sort_method': sort_method,
'return_path': request.path,
'view': 'profile',
}
if is_owner or summoner.public:
if view_mode.lower() == 'list':
context['monster_stable'] = MonsterInstance.objects.filter(owner=summoner)
return render(request, 'herders/profile/profile_view.html', context)
elif view_mode.lower() == 'box':
if sort_method == 'grade':
monster_stable = OrderedDict()
monster_stable['6*'] = MonsterInstance.objects.filter(owner=summoner, stars=6).order_by('-level', 'monster__name')
monster_stable['5*'] = MonsterInstance.objects.filter(owner=summoner, stars=5).order_by('-level', 'monster__name')
monster_stable['4*'] = MonsterInstance.objects.filter(owner=summoner, stars=4).order_by('-level', 'monster__name')
monster_stable['3*'] = MonsterInstance.objects.filter(owner=summoner, stars=3).order_by('-level', 'monster__name')
monster_stable['2*'] = MonsterInstance.objects.filter(owner=summoner, stars=2).order_by('-level', 'monster__name')
monster_stable['1*'] = MonsterInstance.objects.filter(owner=summoner, stars=1).order_by('-level', 'monster__name')
elif sort_method == 'level':
monster_stable = OrderedDict()
monster_stable['40-31'] = MonsterInstance.objects.filter(owner=summoner, level__gt=30).order_by('-level', '-stars', 'monster__name')
monster_stable['30-21'] = MonsterInstance.objects.filter(owner=summoner, level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__name')
monster_stable['20-11'] = MonsterInstance.objects.filter(owner=summoner, level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__name')
monster_stable['10-1'] = MonsterInstance.objects.filter(owner=summoner, level__lte=10).order_by('-level', '-stars', 'monster__name')
elif sort_method == 'attribute':
monster_stable = OrderedDict()
monster_stable['water'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = MonsterInstance.objects.filter(owner=summoner, monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
return render(request, 'herders/profile/profile_box.html', context)
else:
raise Http404('Unknown profile view mode')
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = EditProfileForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'is_owner': True, # Because of @login_required decorator
'profile_name': profile_name,
'return_path': return_path,
'profile_form': form,
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
@login_required
def profile_storage(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'is_owner': True,
'profile_name': request.user.username,
'storage_form': form,
'view': 'profile',
'profile_view': 'materials',
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_storage.html', context)
@login_required()
def monster_instance_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
form = AddMonsterInstanceForm(request.POST or None)
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
return redirect(return_path)
else:
# Re-show same page but with form filled in and errors shown
context = {
'profile_name': profile_name,
'add_monster_form': form,
'return_path': return_path,
'is_owner': True,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_add.html', context)
def monster_instance_view(request, profile_name, instance_id):
context = {
'view': 'profile',
}
return render(request, 'herders/unimplemented.html')
def monster_instance_edit(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = monster.owner == request.user.summoner
form = EditMonsterInstanceForm(request.POST or None, instance=monster)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'edit_monster_form': form,
'view': 'profile',
}
if request.method == 'POST':
if is_owner:
if form.is_valid():
form.save()
return redirect(return_path)
else:
# Redisplay form with validation error messages
context['validation_errors'] = form.non_field_errors()
return render(request, 'herders/profile/profile_monster_edit.html', context)
else:
raise PermissionDenied()
else:
return render(request, 'herders/profile/profile_monster_edit.html', context)
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
context = {
'view': 'profile',
}
return render(request, 'herders/unimplemented.html')
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = monster.owner == request.user.summoner
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': request.user.username,
'is_owner': is_owner, # Because of @login_required decorator
'monster': monster,
'awaken_monster_form': form,
}
if request.method == 'POST' and form.is_valid() and is_owner:
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
if monster.monster.awaken_magic_mats_high:
summoner.storage_magic_high -= monster.monster.awaken_magic_mats_high
if monster.monster.awaken_magic_mats_mid:
summoner.storage_magic_mid -= monster.monster.awaken_magic_mats_mid
if monster.monster.awaken_magic_mats_low:
summoner.storage_magic_low -= monster.monster.awaken_magic_mats_low
if monster.monster.element == Monster.ELEMENT_FIRE:
if monster.monster.awaken_ele_mats_high:
summoner.storage_fire_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_fire_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_fire_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WATER:
if monster.monster.awaken_ele_mats_high:
summoner.storage_water_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_water_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_water_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WIND:
if monster.monster.awaken_ele_mats_high:
summoner.storage_wind_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_wind_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_wind_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_DARK:
if monster.monster.awaken_ele_mats_high:
summoner.storage_dark_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_dark_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_dark_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_LIGHT:
if monster.monster.awaken_ele_mats_high:
summoner.storage_light_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_light_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_light_low -= monster.monster.awaken_ele_mats_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to()
monster.save()
return redirect(return_path)
else:
# Retreive list of awakening materials from summoner profile
summoner = Summoner.objects.get(user=request.user)
available_materials = {
'storage_magic_low': summoner.storage_magic_low,
'storage_magic_mid': summoner.storage_magic_mid,
'storage_magic_high': summoner.storage_magic_high
}
if monster.monster.element == Monster.ELEMENT_FIRE:
available_materials['storage_ele_low'] = summoner.storage_fire_low
available_materials['storage_ele_mid'] = summoner.storage_fire_mid
available_materials['storage_ele_high'] = summoner.storage_fire_high
elif monster.monster.element == Monster.ELEMENT_WATER:
available_materials['storage_ele_low'] = summoner.storage_water_low
available_materials['storage_ele_mid'] = summoner.storage_water_mid
available_materials['storage_ele_high'] = summoner.storage_water_high
elif monster.monster.element == Monster.ELEMENT_WIND:
available_materials['storage_ele_low'] = summoner.storage_wind_low
available_materials['storage_ele_mid'] = summoner.storage_wind_mid
available_materials['storage_ele_high'] = summoner.storage_wind_high
elif monster.monster.element == Monster.ELEMENT_DARK:
available_materials['storage_ele_low'] = summoner.storage_dark_low
available_materials['storage_ele_mid'] = summoner.storage_dark_mid
available_materials['storage_ele_high'] = summoner.storage_dark_high
elif monster.monster.element == Monster.ELEMENT_LIGHT:
available_materials['storage_ele_low'] = summoner.storage_light_low
available_materials['storage_ele_mid'] = summoner.storage_light_mid
available_materials['storage_ele_high'] = summoner.storage_light_high
context['available_materials'] = available_materials
return render(request, 'herders/profile/profile_awaken.html', context)
@login_required
def fusion(request, profile_name):
context = {
'view': 'fusion',
}
return render(request, 'herders/unimplemented.html', context)
@login_required
def teams(request, profile_name):
context = {
'view': 'teams',
}
return render(request, 'herders/unimplemented.html', context)
def bestiary(request, monster_element=None):
print monster_element
context = {
'view': 'bestiary',
'monster_element': monster_element,
}
if monster_element is not None:
if monster_element == 'all':
context['monster_list'] = Monster.objects.all()
else:
context['monster_list'] = get_list_or_404(Monster, element=monster_element)
else:
context['no_filter'] = True
return render(request, 'herders/bestiary.html', context)
def bestiary_detail(request, monster_id):
context = {
'view': 'bestiary',
}
return render(request, 'herders/unimplemented.html')
|
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import sys
from spack import *
class Mpibind(AutotoolsPackage):
"""A memory-driven algorithm to map parallel codes
to heterogeneous architectures"""
homepage = "https://github.com/LLNL/mpibind"
url = "https://github.com/LLNL/mpibind/archive/refs/tags/v0.5.0.tar.gz"
git = "https://github.com/LLNL/mpibind.git"
maintainers = ['eleon']
# The build process uses 'git describe --tags' to get the
# package version, thus we need 'get_full_repo'
version('master', branch='master', get_full_repo=True)
version('0.5.0', sha256='51bb27341109aeef121a8630bd56f5551c70ebfd337a459fb70ef9015d97d2b7')
variant('cuda', default=False,
description='Build w/support for NVIDIA GPUs.')
variant('rocm', default=False,
description='Build w/support for AMD GPUs.')
depends_on('autoconf', type='build')
depends_on('automake', type='build')
depends_on('libtool', type='build')
depends_on('m4', type='build')
depends_on('pkgconf', type='build')
depends_on('hwloc@2:+libxml2', type='link')
depends_on('hwloc@2:+pci', when=(sys.platform != 'darwin'), type='link')
depends_on('hwloc@2:+cuda+nvml', when='+cuda', type='link')
depends_on('hwloc@2.4:+rocm+opencl', when='+rocm', type='link')
def autoreconf(self, spec, prefix):
autoreconf('--install', '--verbose', '--force')
# To build and run the tests, make sure 'libtap' is installed
# on the target system and is recognized by pkg-config.
# Unfortunately, libtap is not in Spack.
mpibind: add v0.7.0 and new flux variant (#26359)
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
import sys
from spack import *
class Mpibind(AutotoolsPackage):
"""A memory-driven algorithm to map parallel codes
to heterogeneous architectures"""
homepage = "https://github.com/LLNL/mpibind"
url = "https://github.com/LLNL/mpibind/archive/refs/tags/v0.5.0.tar.gz"
git = "https://github.com/LLNL/mpibind.git"
maintainers = ['eleon']
# The build process uses 'git describe --tags' to get the
# package version, thus we need 'get_full_repo'
version('master', branch='master', get_full_repo=True)
version('0.7.0', sha256='33077e7eb50322d2bcfe87bb3ea9159c2e49f6f045cbbcd2e69e763c3bec4330')
version('0.5.0', sha256='51bb27341109aeef121a8630bd56f5551c70ebfd337a459fb70ef9015d97d2b7')
variant('cuda', default=False,
description='Build w/support for NVIDIA GPUs.')
variant('rocm', default=False,
description='Build w/support for AMD GPUs.')
variant('flux', default=False,
description='Build the Flux plugin.')
depends_on('autoconf', type='build')
depends_on('automake', type='build')
depends_on('libtool', type='build')
depends_on('m4', type='build')
depends_on('pkgconf', type='build')
depends_on('hwloc@2:+libxml2', type='link')
depends_on('hwloc@2:+pci', when=(sys.platform != 'darwin'), type='link')
depends_on('hwloc@2:+cuda+nvml', when='+cuda', type='link')
depends_on('hwloc@2.4:+rocm+opencl', when='+rocm', type='link')
# Requiring @master temporarily while Flux adds
# FLUX_SHELL_RC_PATH to a stable version (>0.29.0).
# mpibind will require at least such version.
depends_on('flux-core@master', when='+flux', type='link')
def autoreconf(self, spec, prefix):
autoreconf('--install', '--verbose', '--force')
@when('+flux')
def setup_run_environment(self, env):
"""Load the mpibind plugin into Flux"""
env.prepend_path('FLUX_SHELL_RC_PATH',
join_path(self.prefix, 'share', 'mpibind'))
# To build and run the tests, make sure 'libtap' is installed
# on the target system and is recognized by pkg-config.
# Unfortunately, libtap is not in Spack.
|
#!/usr/bin/python2.5
#
# Copyright 2008 the Melange 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.
"""Views for Sponsor profiles.
"""
__authors__ = [
'"Sverre Rabbelier" <sverre@rabbelier.nl>',
'"Lennard de Rijk" <ljvderijk@gmail.com>',
'"Pawel Solyga" <pawel.solyga@gmail.com>',
]
from google.appengine.api import users
from django import forms
from django.utils.translation import ugettext_lazy
from soc.logic import dicts
from soc.logic import validate
from soc.views import helper
from soc.views.helper import widgets
from soc.views.models import base
import soc.models.document
import soc.logic.models.document
import soc.logic.dicts
import soc.views.helper
import soc.views.helper.widgets
class CreateForm(helper.forms.BaseForm):
"""Django form displayed when Developer creates a Document.
"""
content = forms.fields.CharField(widget=helper.widgets.TinyMCE(
attrs={'rows':10, 'cols':40}))
class Meta:
model = soc.models.document.Document
#: list of model fields which will *not* be gathered by the form
exclude = ['inheritance_line', 'author', 'created', 'modified']
def clean_partial_path(self):
partial_path = self.cleaned_data.get('partial_path')
# TODO(tlarsen): combine path and link_name and check for uniqueness
if not validate.isPartialPathFormatValid(partial_path):
raise forms.ValidationError("This partial path is in wrong format.")
return partial_path
def clean_link_name(self):
link_name = self.cleaned_data.get('link_name')
# TODO(tlarsen): combine path and link_name and check for uniqueness
if not validate.isLinkNameFormatValid(link_name):
raise forms.ValidationError("This link name is in wrong format.")
return link_name
class EditForm(CreateForm):
"""Django form displayed a Document is edited.
"""
doc_key_name = forms.fields.CharField(widget=forms.HiddenInput)
created_by = forms.fields.CharField(widget=helper.widgets.ReadOnlyInput(),
required=False)
class View(base.View):
"""View methods for the Docs model
"""
def __init__(self, original_params=None, original_rights=None):
"""Defines the fields and methods required for the base View class
to provide the user with list, public, create, edit and delete views.
Params:
original_params: a dict with params for this View
original_rights: a dict with right definitions for this View
"""
self._logic = soc.logic.models.document.logic
params = {}
rights = {}
params['name'] = "Document"
params['name_short'] = "Docs"
params['name_plural'] = "Documents"
params['edit_form'] = EditForm
params['create_form'] = CreateForm
# TODO(tlarsen) Add support for Django style template lookup
params['edit_template'] = 'soc/models/edit.html'
params['public_template'] = 'soc/docs/public.html'
params['list_template'] = 'soc/models/list.html'
params['lists_template'] = {
'list_main': 'soc/list/list_main.html',
'list_pagination': 'soc/list/list_pagination.html',
'list_row': 'soc/docs/list/docs_row.html',
'list_heading': 'soc/docs/list/docs_heading.html',
}
params['delete_redirect'] = '/docs/list'
params['create_redirect'] = 'soc/models/edit.html'
params['save_message'] = [ugettext_lazy('Profile saved.')]
params['edit_params'] = {
self.DEF_SUBMIT_MSG_PARAM_NAME: self.DEF_SUBMIT_MSG_PROFILE_SAVED,
}
rights['list'] = [helper.access.checkIsDeveloper]
rights['delete'] = [helper.access.checkIsDeveloper]
params = dicts.merge(original_params, params)
rights = dicts.merge(original_rights, rights)
base.View.__init__(self, rights=rights, params=params)
def _editPost(self, request, entity, fields):
"""See base.View._editPost().
"""
id = users.get_current_user()
user = soc.logic.models.user.logic.getForFields({'id': id}, unique=True)
fields['author'] = user
def _editGet(self, request, entity, form):
"""See base.View._editGet().
"""
form.fields['created_by'].initial = entity.author.link_name
form.fields['doc_key_name'].initial = entity.key().name(),
view = View()
create = view.create
edit = view.edit
delete = view.delete
list = view.list
public = view.public
Fixed typo in docstring
--HG--
extra : convert_revision : svn%3A32761e7d-7263-4528-b7be-7235b26367ec/trunk%401017
#!/usr/bin/python2.5
#
# Copyright 2008 the Melange 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.
"""Views for Documents.
"""
__authors__ = [
'"Sverre Rabbelier" <sverre@rabbelier.nl>',
'"Lennard de Rijk" <ljvderijk@gmail.com>',
'"Pawel Solyga" <pawel.solyga@gmail.com>',
]
from google.appengine.api import users
from django import forms
from django.utils.translation import ugettext_lazy
from soc.logic import dicts
from soc.logic import validate
from soc.views import helper
from soc.views.helper import widgets
from soc.views.models import base
import soc.models.document
import soc.logic.models.document
import soc.logic.dicts
import soc.views.helper
import soc.views.helper.widgets
class CreateForm(helper.forms.BaseForm):
"""Django form displayed when Developer creates a Document.
"""
content = forms.fields.CharField(widget=helper.widgets.TinyMCE(
attrs={'rows':10, 'cols':40}))
class Meta:
model = soc.models.document.Document
#: list of model fields which will *not* be gathered by the form
exclude = ['inheritance_line', 'author', 'created', 'modified']
def clean_partial_path(self):
partial_path = self.cleaned_data.get('partial_path')
# TODO(tlarsen): combine path and link_name and check for uniqueness
if not validate.isPartialPathFormatValid(partial_path):
raise forms.ValidationError("This partial path is in wrong format.")
return partial_path
def clean_link_name(self):
link_name = self.cleaned_data.get('link_name')
# TODO(tlarsen): combine path and link_name and check for uniqueness
if not validate.isLinkNameFormatValid(link_name):
raise forms.ValidationError("This link name is in wrong format.")
return link_name
class EditForm(CreateForm):
"""Django form displayed a Document is edited.
"""
doc_key_name = forms.fields.CharField(widget=forms.HiddenInput)
created_by = forms.fields.CharField(widget=helper.widgets.ReadOnlyInput(),
required=False)
class View(base.View):
"""View methods for the Docs model
"""
def __init__(self, original_params=None, original_rights=None):
"""Defines the fields and methods required for the base View class
to provide the user with list, public, create, edit and delete views.
Params:
original_params: a dict with params for this View
original_rights: a dict with right definitions for this View
"""
self._logic = soc.logic.models.document.logic
params = {}
rights = {}
params['name'] = "Document"
params['name_short'] = "Docs"
params['name_plural'] = "Documents"
params['edit_form'] = EditForm
params['create_form'] = CreateForm
# TODO(tlarsen) Add support for Django style template lookup
params['edit_template'] = 'soc/models/edit.html'
params['public_template'] = 'soc/docs/public.html'
params['list_template'] = 'soc/models/list.html'
params['lists_template'] = {
'list_main': 'soc/list/list_main.html',
'list_pagination': 'soc/list/list_pagination.html',
'list_row': 'soc/docs/list/docs_row.html',
'list_heading': 'soc/docs/list/docs_heading.html',
}
params['delete_redirect'] = '/docs/list'
params['create_redirect'] = 'soc/models/edit.html'
params['save_message'] = [ugettext_lazy('Profile saved.')]
params['edit_params'] = {
self.DEF_SUBMIT_MSG_PARAM_NAME: self.DEF_SUBMIT_MSG_PROFILE_SAVED,
}
rights['list'] = [helper.access.checkIsDeveloper]
rights['delete'] = [helper.access.checkIsDeveloper]
params = dicts.merge(original_params, params)
rights = dicts.merge(original_rights, rights)
base.View.__init__(self, rights=rights, params=params)
def _editPost(self, request, entity, fields):
"""See base.View._editPost().
"""
id = users.get_current_user()
user = soc.logic.models.user.logic.getForFields({'id': id}, unique=True)
fields['author'] = user
def _editGet(self, request, entity, form):
"""See base.View._editGet().
"""
form.fields['created_by'].initial = entity.author.link_name
form.fields['doc_key_name'].initial = entity.key().name(),
view = View()
create = view.create
edit = view.edit
delete = view.delete
list = view.list
public = view.public
|
import argparse
import importlib
import os
import pickle
import sys
from . import builtins
from . import find
from . import packages
from . import rules
from . import utils
from .build_inputs import BuildInputs
from .environment import Environment
from .version import __version__
bfgfile = 'build.bfg'
def is_srcdir(path):
return os.path.exists(os.path.join(path, bfgfile))
def samefile(path1, path2):
if hasattr(os.path, 'samefile'):
return os.path.samefile(path1, path2)
else:
# This isn't entirely accurate, but it's close enough, and should only
# be necessary for Windows with Python 2.x.
return os.path.realpath(path1) == os.path.realpath(path2)
def parse_args(parser, args=None, namespace=None):
def check_dir(path, check_exist=False):
if not os.path.exists(path):
raise ValueError('{} does not exist'.format(path))
if not os.path.isdir(path):
parser.error('{} is not a directory'.format(path))
args = parser.parse_args(args, namespace)
if not args.regenerate:
if not args.srcdir:
parser.error('at least one of srcdir or builddir must be defined')
if args.builddir:
check_dir(args.srcdir, check_exist=True)
else:
args.builddir = '.'
if is_srcdir(args.srcdir):
args.srcdir, args.builddir = args.builddir, args.srcdir
if os.path.exists(args.builddir):
check_dir(args.builddir)
if samefile(args.srcdir, args.builddir):
parser.error('source and build directories must be different')
if not is_srcdir(args.srcdir):
parser.error('source directory must contain a build.bfg file')
if is_srcdir(args.builddir):
parser.error('build directory must not contain a build.bfg file')
if not os.path.exists(args.builddir):
os.mkdir(args.builddir)
args.srcdir = os.path.abspath(args.srcdir)
args.builddir = os.path.abspath(args.builddir)
else:
args.srcdir, args.builddir = None, args.srcdir
if args.srcdir:
parser.error('source directory cannot be passed when regenerating')
if not args.builddir:
args.builddir = '.'
check_dir(args.builddir, check_exist=True)
args.builddir = os.path.abspath(args.builddir)
return args
def main():
parser = argparse.ArgumentParser()
parser.add_argument('srcdir', nargs='?', help='source directory')
parser.add_argument('builddir', nargs='?', help='build directory')
parser.add_argument('--version', action='version',
version='%(prog)s ' + __version__)
parser.add_argument('--backend', default='make', help='backend')
parser.add_argument('--prefix', default='/usr', help='installation prefix')
parser.add_argument('--regenerate', action='store_true',
help='regenerate build files')
args = parse_args(parser)
try:
if args.regenerate:
env = Environment.load(args.builddir)
else:
env = Environment(
bfgpath=os.path.realpath(sys.argv[0]),
srcdir=args.srcdir,
builddir=args.builddir,
backend=args.backend,
install_prefix=os.path.abspath(args.prefix),
variables=dict(os.environ),
)
env.save(args.builddir)
except Exception as e:
sys.stderr.write('{}: {}\n'.format(parser.prog, e))
return 1
build = BuildInputs()
os.chdir(env.srcdir)
execfile(os.path.join(env.srcdir, bfgfile), builtins.bind(build, env))
backend = importlib.import_module('.' + env.backend, 'bfg9000.backends')
backend.write(env, build)
Fix error handling in folder validation
import argparse
import importlib
import os
import pickle
import sys
from . import builtins
from . import find
from . import packages
from . import rules
from . import utils
from .build_inputs import BuildInputs
from .environment import Environment
from .version import __version__
bfgfile = 'build.bfg'
def is_srcdir(path):
return os.path.exists(os.path.join(path, bfgfile))
def samefile(path1, path2):
if hasattr(os.path, 'samefile'):
return os.path.samefile(path1, path2)
else:
# This isn't entirely accurate, but it's close enough, and should only
# be necessary for Windows with Python 2.x.
return os.path.realpath(path1) == os.path.realpath(path2)
def parse_args(parser, args=None, namespace=None):
def check_dir(path, check_exist=False):
if not os.path.exists(path):
parser.error('{!r} does not exist'.format(path))
if not os.path.isdir(path):
parser.error('{!r} is not a directory'.format(path))
args = parser.parse_args(args, namespace)
if not args.regenerate:
if not args.srcdir:
parser.error('at least one of srcdir or builddir must be defined')
if args.builddir:
check_dir(args.srcdir, check_exist=True)
else:
args.builddir = '.'
if is_srcdir(args.srcdir):
args.srcdir, args.builddir = args.builddir, args.srcdir
if os.path.exists(args.builddir):
check_dir(args.builddir)
if samefile(args.srcdir, args.builddir):
parser.error('source and build directories must be different')
if not is_srcdir(args.srcdir):
parser.error('source directory must contain a build.bfg file')
if is_srcdir(args.builddir):
parser.error('build directory must not contain a build.bfg file')
if not os.path.exists(args.builddir):
os.mkdir(args.builddir)
args.srcdir = os.path.abspath(args.srcdir)
args.builddir = os.path.abspath(args.builddir)
else:
args.srcdir, args.builddir = None, args.srcdir
if args.srcdir:
parser.error('source directory cannot be passed when regenerating')
if not args.builddir:
args.builddir = '.'
check_dir(args.builddir, check_exist=True)
args.builddir = os.path.abspath(args.builddir)
return args
def main():
parser = argparse.ArgumentParser()
parser.add_argument('srcdir', nargs='?', help='source directory')
parser.add_argument('builddir', nargs='?', help='build directory')
parser.add_argument('--version', action='version',
version='%(prog)s ' + __version__)
parser.add_argument('--backend', default='make', help='backend')
parser.add_argument('--prefix', default='/usr', help='installation prefix')
parser.add_argument('--regenerate', action='store_true',
help='regenerate build files')
args = parse_args(parser)
try:
if args.regenerate:
env = Environment.load(args.builddir)
else:
env = Environment(
bfgpath=os.path.realpath(sys.argv[0]),
srcdir=args.srcdir,
builddir=args.builddir,
backend=args.backend,
install_prefix=os.path.abspath(args.prefix),
variables=dict(os.environ),
)
env.save(args.builddir)
except Exception as e:
sys.stderr.write('{}: {}\n'.format(parser.prog, e))
return 1
build = BuildInputs()
os.chdir(env.srcdir)
execfile(os.path.join(env.srcdir, bfgfile), builtins.bind(build, env))
backend = importlib.import_module('.' + env.backend, 'bfg9000.backends')
backend.write(env, build)
|
import pyopencl as cl
import numpy
from pyPaSWAS.Core.SmithWaterman import SmithWaterman
from pyPaSWAS.Core import STOP_DIRECTION, LEFT_DIRECTION, NO_DIRECTION, UPPER_DIRECTION, UPPER_LEFT_DIRECTION
from pyPaSWAS.Core.PaSWAS import CPUcode
from pyPaSWAS.Core.PaSWAS import GPUcode
from pyPaSWAS.Core.StartingPoint import StartingPoint
class SmithWatermanOcl(SmithWaterman):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWaterman.__init__(self, logger, score, settings)
#self.oclcode = OCLcode(self.logger)
# platforms: A single ICD on a computer
self.platform = None
# device: device which will perform computation (for example a CPU or GPU)
self.device = None
# context: manages a command-queue, memory, program and kernel objects
self.ctx = None
# queue: stores instructions for the device
self.queue = None
# program: the compiled kernel program
self.program = None
# device_type: type of device to run computations on
self.device_type = 0
self._set_device_type(self.settings.device_type)
self._set_platform(self.settings.platform_name)
self._initialize_device(int(self.settings.device_number))
def _init_oclcode(self):
# Compiling part of the CUDA code in advance
self.oclcode.set_shared_xy_code(self.shared_x, self.shared_y)
self.oclcode.set_direction_code(NO_DIRECTION, UPPER_LEFT_DIRECTION,
UPPER_DIRECTION, LEFT_DIRECTION,
STOP_DIRECTION)
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
pass
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
pass
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
pass
def __del__(self):
'''Destructor. Removes the current running context'''
del self.program
del self.queue
del self.ctx
del self.device
del self.platform
self.device_type = 0
def _set_device_type(self, device_type):
'''Sets the device type'''
if device_type.upper() == 'ACCELERATOR':
self.device_type = cl.device_type.ACCELERATOR
elif device_type.upper() == 'GPU':
self.device_type = cl.device_type.GPU
elif device_type.upper() == 'CPU':
self.device_type = cl.device_type.CPU
else:
self.logger.debug("Warning: device type is set to default: CPU")
self.device_type = cl.device_type.CPU
def _set_platform(self, platform_name):
found_platform = False
for platform in cl.get_platforms():
for device in platform.get_devices():
if (platform_name.upper() in str(platform).upper()
and device.get_info(cl.device_info.TYPE) == self.device_type):
self.platform = platform
found_platform = True
break
if(found_platform):
self.logger.debug("Found platform {}".format(str(self.platform)))
break
if not (self.platform):
for platform in cl.get_platforms():
for device in platform.get_devices():
if (device.get_info(cl.device_info.TYPE) == self.device_type):
self.platform = platform
found_platform = True
break
if(found_platform):
self.logger.debug('Found platform {}, however this is not the platform indicated by the user'.format(str(self.platform)))
break
if not (self.platform):
raise RuntimeError('Failed to find platform')
def _initialize_device(self, device_number):
'''
Initalizes a device and verifies its computational abilities.
@param device_number: int value representing the device to use
'''
self.logger.debug('Initializing device {0}'.format(device_number))
self.device = self.platform.get_devices(device_type=self.device_type)[device_number]
self.ctx = cl.Context(devices=[self.device])
self.queue = cl.CommandQueue(self.ctx)
#self.logger.debug("context:{}".format(self.ctx) )
def _device_global_mem_size(self):
#return clCharacterize.usable_local_mem_size(self.device)
# GLOBAL_MEM_SIZE
return self.device.get_info(cl.device_info.MAX_MEM_ALLOC_SIZE)
def _clear_memory(self):
'''Clears the claimed memory on the device.'''
self.logger.debug('Clearing device memory.')
self._clear_normal_memory()
self._clear_zero_copy_memory()
def _clear_normal_memory(self):
self.logger.debug('Clearing normal device memory.')
if (self.d_sequences is not None):
self.d_sequences.release()
if (self.d_targets is not None):
self.d_targets.release()
if (self.d_matrix is not None):
self.d_matrix.release()
if (self.d_global_maxima is not None):
self.d_global_maxima.release()
def _clear_zero_copy_memory(self):
self.logger.debug('Clearing zero-copy device memory.')
if (self.d_starting_points_zero_copy is not None):
self.d_starting_points_zero_copy.release()
if (self.d_global_direction_zero_copy is not None):
self.d_global_direction_zero_copy.release()
if (self.d_max_possible_score_zero_copy is not None):
self.d_max_possible_score_zero_copy.release()
def _init_normal_memory(self):
'''
#_init_memory will initialize all required memory on the device based on the current settings.
Make sure to initialize these values!
'''
# Sequence device memory
self.logger.debug('Initializing normal device memory.')
memory = self.length_of_x_sequences * self.number_of_sequences
self.d_sequences = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
mem_size = memory
# Target device memory
memory = self.length_of_y_sequences * self.number_targets
self.d_targets = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
mem_size += memory
return mem_size
def _init_zero_copy_memory(self):
self.logger.debug('Initializing zero-copy memory.')
# Starting points host memory allocation and device copy
memory = (self.size_of_startingpoint * self.maximum_number_starting_points * self.number_of_sequences *
self.number_targets)
self.d_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size = memory
# Global directions host memory allocation and device copy
memory = (self.length_of_x_sequences * self.number_of_sequences * self.length_of_y_sequences * self.number_targets)
self.d_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size += memory
# Maximum zero copy memory allocation and device copy
memory = (self.number_of_sequences * self.number_of_targets * SmithWaterman.float_size)
self.d_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size += memory
return mem_size
def _init_memory(self):
mem_size = self._init_normal_memory()
mem_size += self._init_zero_copy_memory()
self.logger.debug('Allocated: {}MB of memory'.format(str(mem_size / 1024.0 / 1024.00)))
def _init_zero_copy(self):
''' Initializes the index used for the 'zero copy' of the found starting points '''
self.d_index_increment = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=SmithWaterman.int_size)
index = numpy.zeros((1), dtype=numpy.int32)
cl.enqueue_write_buffer(self.queue, self.d_index_increment, index).wait()
def _compile_code(self):
"""Compile the device code with current settings"""
self.logger.debug('Compiling OpenCL code.')
code = self.oclcode.get_code(self.score, self.number_of_sequences, self.number_targets, self.length_of_x_sequences, self.length_of_y_sequences)
#self.logger.debug('Code: \n{}'.format(code))
self.program = cl.Program(self.ctx, code).build()
def copy_sequences(self, h_sequences, h_targets):
'''
Copy the sequences and targets to the device
@param h_sequences: the sequences to be copied. Should be a single string containing all sequences
@param h_targets: the targets to be copied. Should be a single string containing all sequences
'''
cl.enqueue_copy(self.queue, self.d_sequences, h_sequences).wait()
cl.enqueue_copy(self.queue, self.d_targets, h_targets).wait()
def _get_number_of_starting_points(self):
''' Returns the number of startingpoints. '''
self.logger.debug('Getting number of starting points.')
self.index = numpy.zeros((1), dtype=numpy.int32)
cl.enqueue_copy(self.queue, self.index, self.d_index_increment)
return self.index[0]
def _fill_max_possible_score(self, target_index, targets, i, index, records_seqs):
for tI in range(self.number_of_targets):
if tI+target_index < len(targets) and i+index < len(records_seqs):
self.set_minimum_score(tI*self.max_sequences + i, float(self.score.highest_score) * (len(records_seqs[i+index])
if len(records_seqs[i+index]) < len(targets[tI+target_index])
else len(targets[tI+target_index])) * float(self.filter_factor))
def _set_max_possible_score(self, target_index, targets, i, index, records_seqs):
'''fills the max_possible_score datastructure on the host'''
self.h_max_possible_score_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_max_possible_score_zero_copy,
cl.map_flags.WRITE, 0,
self.number_of_sequences * self.number_targets ,
dtype=numpy.float32)[0]
self._fill_max_possible_score(target_index, targets, i, index, records_seqs)
#Unmap memory object
del self.h_max_possible_score_zero_copy
def _get_starting_point_byte_array(self):
'''
Get the resulting starting points
@return gives the resulting starting point array as byte array
'''
self.h_starting_points_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_starting_points_zero_copy, cl.map_flags.READ, 0,
(self.size_of_startingpoint *
self.maximum_number_starting_points *
self.number_of_sequences *
self.number_targets, 1), dtype=numpy.byte)[0]
return self.h_starting_points_zero_copy
def _print_alignments(self, sequences, targets, start_seq, start_target, hit_list=None):
SmithWaterman._print_alignments(self, sequences, targets, start_seq, start_target, hit_list)
#unmap memory objects
del self.h_global_direction_zero_copy
del self.h_starting_points_zero_copy
class SmithWatermanCPU(SmithWatermanOcl):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanOcl.__init__(self, logger, score, settings)
self.oclcode = CPUcode(self.logger)
self.workload_x = 4
self.workload_y = 4
self.workgroup_x = self.shared_x // self.workload_x
self.workgroup_y = self.shared_y // self.workload_y
self.d_semaphores = None
self._init_oclcode()
def _init_normal_memory(self):
mem_size = SmithWatermanOcl._init_normal_memory(self)
# Input matrix device memory
memory = (SmithWaterman.float_size * (self.length_of_x_sequences + 1) * self.number_of_sequences *
(self.length_of_y_sequences + 1) * self.number_targets)
self.d_matrix = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
pattern = numpy.zeros((1),dtype=numpy.float32)
cl.enqueue_fill_buffer(self.queue, self.d_matrix, pattern, 0, size = memory)
# Maximum global device memory
memory = (SmithWaterman.float_size * self.x_div_shared_x * self.number_of_sequences *
self.y_div_shared_y * self.number_targets * self.workload_x * self.workload_y)
self.d_global_maxima = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
memory = (SmithWaterman.int_size *
self.length_of_x_sequences *
self.number_of_sequences *
self.length_of_y_sequences *
self.number_targets)
self.d_semaphores = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
pattern = numpy.zeros((1),dtype=numpy.int32)
cl.enqueue_fill_buffer(self.queue, self.d_semaphores, pattern, 0, size=memory)
mem_size += memory
return mem_size
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.length_of_x_sequences,
self.length_of_y_sequences), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _get_direction(self, direction_array, sequence, target, block_x, block_y, value_x, value_y):
<<<<<<< HEAD
#return direction_array[sequence][target][starting_point.value_x][starting_point.value_y]
=======
#self.logger.debug("{}, {}, {}, {}".format(sequence,target,block_x*self.workload_x + value_x,block_y*self.workload_y + value_y))
#return direction_array[sequence][target][block_x*self.workload_x + value_x][block_y*self.workload_y + value_y]
>>>>>>> branch 'pyopencl' of https://github.com/swarris/pyPaSWAS.git
return direction_array[sequence][target][block_x*self.shared_x + value_x][block_y*self.shared_y + value_y]
def _set_direction(self, direction, direction_array, sequence, target, block_x, block_y, value_x, value_y):
<<<<<<< HEAD
#direction_array[sequence][target][starting_point.value_x][starting_point.value_y] = direction
direction_array[sequence][target][block_x*self.shared_x + value_x][block_y*self.shared_y + value_y] = direction
=======
#direction_array[sequence][target][block_x*self.workload_x + value_x][block_y*self.workload_y + value_y] = direction
direction_array[sequence][target][block_x*self.shared_x + value_x][block_y*self.shared_y + value_y] = direction
>>>>>>> branch 'pyopencl' of https://github.com/swarris/pyPaSWAS.git
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
dim_block = (self.workgroup_x, self.workgroup_y)
dim_grid_sw = (self.number_of_sequences * self.workgroup_x, self.number_targets * number_of_blocks * self.workgroup_y)
self.program.calculateScore(self.queue,
dim_grid_sw,
dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_sequences,
self.d_targets,
self.d_global_maxima,
self.d_global_direction_zero_copy).wait()
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
dim_block = (self.workgroup_x, self.workgroup_y)
dim_grid_sw = (self.number_of_sequences * self.workgroup_x, self.number_targets * number_of_blocks * self.workgroup_y)
self.program.traceback(self.queue, dim_grid_sw, dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_global_maxima,
self.d_global_direction_zero_copy,
self.d_index_increment,
self.d_starting_points_zero_copy,
self.d_max_possible_score_zero_copy,
self.d_semaphores).wait()
def _clear_memory(self):
SmithWatermanOcl._clear_memory(self)
if (self.d_semaphores is not None):
self.d_semaphores.release()
class SmithWatermanGPU(SmithWatermanOcl):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanOcl.__init__(self, logger, score, settings)
self.oclcode = GPUcode(self.logger)
self._init_oclcode()
def _init_normal_memory(self):
mem_size = SmithWatermanOcl._init_normal_memory(self)
# Input matrix device memory
memory = (SmithWaterman.float_size * self.length_of_x_sequences * self.number_of_sequences *
self.length_of_y_sequences * self.number_targets)
self.d_matrix = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
# Maximum global device memory
memory = (SmithWaterman.float_size * self.x_div_shared_x * self.number_of_sequences *
self.y_div_shared_y * self.number_targets)
self.d_global_maxima = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
return mem_size
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.x_div_shared_x,
self.y_div_shared_y,
self.shared_x,
self.shared_y), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
dim_block = (self.shared_x, self.shared_y)
dim_grid_sw = (self.number_of_sequences * self.shared_x, self.number_targets * number_of_blocks * self.shared_y)
self.program.calculateScore(self.queue,
dim_grid_sw,
dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_sequences,
self.d_targets,
self.d_global_maxima,
self.d_global_direction_zero_copy).wait()
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
dim_block = (self.shared_x, self.shared_y)
dim_grid_sw = (self.number_of_sequences * self.shared_x, self.number_targets * number_of_blocks * self.shared_y)
self.program.traceback(self.queue, dim_grid_sw, dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_global_maxima,
self.d_global_direction_zero_copy,
self.d_index_increment,
self.d_starting_points_zero_copy,
self.d_max_possible_score_zero_copy).wait()
class SmithWatermanNVIDIA(SmithWatermanGPU):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanGPU.__init__(self, logger, score, settings)
self.pinned_starting_points_zero_copy = None
self.pinned_max_possible_score_zero_copy = None
self.pinned_global_direction_zero_copy = None
self._init_oclcode()
def _init_zero_copy_memory(self):
self.logger.debug('Initializing NVIDIA zero-copy memory.')
# Starting points host memory allocation and device copy
memory = (self.size_of_startingpoint * self.maximum_number_starting_points * self.number_of_sequences *
self.number_targets)
self.pinned_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=memory)
self.h_starting_points_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_starting_points_zero_copy, cl.map_flags.READ, 0,
(memory, 1), dtype=numpy.byte)[0]
mem_size = memory
# Global directions host memory allocation and device copy
memory = (self.length_of_x_sequences * self.number_of_sequences * self.length_of_y_sequences *
self.number_targets)
self.pinned_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=memory)
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_global_direction_zero_copy, cl.map_flags.READ, 0,
(memory, 1), dtype=numpy.byte)[0]
mem_size += memory
# Maximum zero copy memory allocation and device copy
memory = (self.number_of_sequences * self.number_of_targets * SmithWaterman.float_size)
self.pinned_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
self.h_max_possible_score_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_max_possible_score_zero_copy, cl.map_flags.WRITE, 0,
(self.number_of_sequences * self.number_of_targets, 1), dtype=numpy.float32)[0]
mem_size += memory
# Zero copy buffers are allocated twice in NVIDIA
return 2*mem_size
def _set_max_possible_score(self, target_index, targets, i, index, records_seqs):
cl.enqueue_copy(self.queue, self.d_max_possible_score_zero_copy, self.h_max_possible_score_zero_copy).wait()
self._fill_max_possible_score(target_index, targets, i, index, records_seqs)
def _get_direction_byte_array(self):
# cl.enqueue_copy(self.queue, self.h_global_direction_zero_copy, self.d_global_direction_zero_copy).wait()
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.x_div_shared_x,
self.y_div_shared_y,
self.shared_x,
self.shared_y), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _get_starting_point_byte_array(self):
cl.enqueue_copy(self.queue, self.h_starting_points_zero_copy, self.d_starting_points_zero_copy).wait()
return self.h_starting_points_zero_copy
def _clear_zero_copy_memory(self):
SmithWatermanGPU._clear_zero_copy_memory(self)
if (self.pinned_starting_points_zero_copy is not None):
self.pinned_starting_points_zero_copy.release()
if (self.pinned_global_direction_zero_copy is not None):
self.pinned_global_direction_zero_copy.release()
if (self.pinned_max_possible_score_zero_copy is not None):
self.pinned_max_possible_score_zero_copy.release()
def _compile_ocl_code(self):
"""Compile the OpenCL code with current settings"""
self.logger.debug('Compiling NVIDIA OpenCL code.')
code = self.oclcode.get_code(self.score, self.number_of_sequences, self.number_targets, self.length_of_x_sequences, self.length_of_y_sequences)
self.program = cl.Program(self.ctx, code).build(options=['-D', 'NVIDIA'])
Fixed staging error
import pyopencl as cl
import numpy
from pyPaSWAS.Core.SmithWaterman import SmithWaterman
from pyPaSWAS.Core import STOP_DIRECTION, LEFT_DIRECTION, NO_DIRECTION, UPPER_DIRECTION, UPPER_LEFT_DIRECTION
from pyPaSWAS.Core.PaSWAS import CPUcode
from pyPaSWAS.Core.PaSWAS import GPUcode
from pyPaSWAS.Core.StartingPoint import StartingPoint
class SmithWatermanOcl(SmithWaterman):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWaterman.__init__(self, logger, score, settings)
#self.oclcode = OCLcode(self.logger)
# platforms: A single ICD on a computer
self.platform = None
# device: device which will perform computation (for example a CPU or GPU)
self.device = None
# context: manages a command-queue, memory, program and kernel objects
self.ctx = None
# queue: stores instructions for the device
self.queue = None
# program: the compiled kernel program
self.program = None
# device_type: type of device to run computations on
self.device_type = 0
self._set_device_type(self.settings.device_type)
self._set_platform(self.settings.platform_name)
self._initialize_device(int(self.settings.device_number))
def _init_oclcode(self):
# Compiling part of the CUDA code in advance
self.oclcode.set_shared_xy_code(self.shared_x, self.shared_y)
self.oclcode.set_direction_code(NO_DIRECTION, UPPER_LEFT_DIRECTION,
UPPER_DIRECTION, LEFT_DIRECTION,
STOP_DIRECTION)
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
pass
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
pass
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
pass
def __del__(self):
'''Destructor. Removes the current running context'''
del self.program
del self.queue
del self.ctx
del self.device
del self.platform
self.device_type = 0
def _set_device_type(self, device_type):
'''Sets the device type'''
if device_type.upper() == 'ACCELERATOR':
self.device_type = cl.device_type.ACCELERATOR
elif device_type.upper() == 'GPU':
self.device_type = cl.device_type.GPU
elif device_type.upper() == 'CPU':
self.device_type = cl.device_type.CPU
else:
self.logger.debug("Warning: device type is set to default: CPU")
self.device_type = cl.device_type.CPU
def _set_platform(self, platform_name):
found_platform = False
for platform in cl.get_platforms():
for device in platform.get_devices():
if (platform_name.upper() in str(platform).upper()
and device.get_info(cl.device_info.TYPE) == self.device_type):
self.platform = platform
found_platform = True
break
if(found_platform):
self.logger.debug("Found platform {}".format(str(self.platform)))
break
if not (self.platform):
for platform in cl.get_platforms():
for device in platform.get_devices():
if (device.get_info(cl.device_info.TYPE) == self.device_type):
self.platform = platform
found_platform = True
break
if(found_platform):
self.logger.debug('Found platform {}, however this is not the platform indicated by the user'.format(str(self.platform)))
break
if not (self.platform):
raise RuntimeError('Failed to find platform')
def _initialize_device(self, device_number):
'''
Initalizes a device and verifies its computational abilities.
@param device_number: int value representing the device to use
'''
self.logger.debug('Initializing device {0}'.format(device_number))
self.device = self.platform.get_devices(device_type=self.device_type)[device_number]
self.ctx = cl.Context(devices=[self.device])
self.queue = cl.CommandQueue(self.ctx)
#self.logger.debug("context:{}".format(self.ctx) )
def _device_global_mem_size(self):
#return clCharacterize.usable_local_mem_size(self.device)
# GLOBAL_MEM_SIZE
return self.device.get_info(cl.device_info.MAX_MEM_ALLOC_SIZE)
def _clear_memory(self):
'''Clears the claimed memory on the device.'''
self.logger.debug('Clearing device memory.')
self._clear_normal_memory()
self._clear_zero_copy_memory()
def _clear_normal_memory(self):
self.logger.debug('Clearing normal device memory.')
if (self.d_sequences is not None):
self.d_sequences.release()
if (self.d_targets is not None):
self.d_targets.release()
if (self.d_matrix is not None):
self.d_matrix.release()
if (self.d_global_maxima is not None):
self.d_global_maxima.release()
def _clear_zero_copy_memory(self):
self.logger.debug('Clearing zero-copy device memory.')
if (self.d_starting_points_zero_copy is not None):
self.d_starting_points_zero_copy.release()
if (self.d_global_direction_zero_copy is not None):
self.d_global_direction_zero_copy.release()
if (self.d_max_possible_score_zero_copy is not None):
self.d_max_possible_score_zero_copy.release()
def _init_normal_memory(self):
'''
#_init_memory will initialize all required memory on the device based on the current settings.
Make sure to initialize these values!
'''
# Sequence device memory
self.logger.debug('Initializing normal device memory.')
memory = self.length_of_x_sequences * self.number_of_sequences
self.d_sequences = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
mem_size = memory
# Target device memory
memory = self.length_of_y_sequences * self.number_targets
self.d_targets = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
mem_size += memory
return mem_size
def _init_zero_copy_memory(self):
self.logger.debug('Initializing zero-copy memory.')
# Starting points host memory allocation and device copy
memory = (self.size_of_startingpoint * self.maximum_number_starting_points * self.number_of_sequences *
self.number_targets)
self.d_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size = memory
# Global directions host memory allocation and device copy
memory = (self.length_of_x_sequences * self.number_of_sequences * self.length_of_y_sequences * self.number_targets)
self.d_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size += memory
# Maximum zero copy memory allocation and device copy
memory = (self.number_of_sequences * self.number_of_targets * SmithWaterman.float_size)
self.d_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.ALLOC_HOST_PTR, size=memory)
mem_size += memory
return mem_size
def _init_memory(self):
mem_size = self._init_normal_memory()
mem_size += self._init_zero_copy_memory()
self.logger.debug('Allocated: {}MB of memory'.format(str(mem_size / 1024.0 / 1024.00)))
def _init_zero_copy(self):
''' Initializes the index used for the 'zero copy' of the found starting points '''
self.d_index_increment = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=SmithWaterman.int_size)
index = numpy.zeros((1), dtype=numpy.int32)
cl.enqueue_write_buffer(self.queue, self.d_index_increment, index).wait()
def _compile_code(self):
"""Compile the device code with current settings"""
self.logger.debug('Compiling OpenCL code.')
code = self.oclcode.get_code(self.score, self.number_of_sequences, self.number_targets, self.length_of_x_sequences, self.length_of_y_sequences)
#self.logger.debug('Code: \n{}'.format(code))
self.program = cl.Program(self.ctx, code).build()
def copy_sequences(self, h_sequences, h_targets):
'''
Copy the sequences and targets to the device
@param h_sequences: the sequences to be copied. Should be a single string containing all sequences
@param h_targets: the targets to be copied. Should be a single string containing all sequences
'''
cl.enqueue_copy(self.queue, self.d_sequences, h_sequences).wait()
cl.enqueue_copy(self.queue, self.d_targets, h_targets).wait()
def _get_number_of_starting_points(self):
''' Returns the number of startingpoints. '''
self.logger.debug('Getting number of starting points.')
self.index = numpy.zeros((1), dtype=numpy.int32)
cl.enqueue_copy(self.queue, self.index, self.d_index_increment)
return self.index[0]
def _fill_max_possible_score(self, target_index, targets, i, index, records_seqs):
for tI in range(self.number_of_targets):
if tI+target_index < len(targets) and i+index < len(records_seqs):
self.set_minimum_score(tI*self.max_sequences + i, float(self.score.highest_score) * (len(records_seqs[i+index])
if len(records_seqs[i+index]) < len(targets[tI+target_index])
else len(targets[tI+target_index])) * float(self.filter_factor))
def _set_max_possible_score(self, target_index, targets, i, index, records_seqs):
'''fills the max_possible_score datastructure on the host'''
self.h_max_possible_score_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_max_possible_score_zero_copy,
cl.map_flags.WRITE, 0,
self.number_of_sequences * self.number_targets ,
dtype=numpy.float32)[0]
self._fill_max_possible_score(target_index, targets, i, index, records_seqs)
#Unmap memory object
del self.h_max_possible_score_zero_copy
def _get_starting_point_byte_array(self):
'''
Get the resulting starting points
@return gives the resulting starting point array as byte array
'''
self.h_starting_points_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_starting_points_zero_copy, cl.map_flags.READ, 0,
(self.size_of_startingpoint *
self.maximum_number_starting_points *
self.number_of_sequences *
self.number_targets, 1), dtype=numpy.byte)[0]
return self.h_starting_points_zero_copy
def _print_alignments(self, sequences, targets, start_seq, start_target, hit_list=None):
SmithWaterman._print_alignments(self, sequences, targets, start_seq, start_target, hit_list)
#unmap memory objects
del self.h_global_direction_zero_copy
del self.h_starting_points_zero_copy
class SmithWatermanCPU(SmithWatermanOcl):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanOcl.__init__(self, logger, score, settings)
self.oclcode = CPUcode(self.logger)
self.workload_x = 4
self.workload_y = 4
self.workgroup_x = self.shared_x // self.workload_x
self.workgroup_y = self.shared_y // self.workload_y
self.d_semaphores = None
self._init_oclcode()
def _init_normal_memory(self):
mem_size = SmithWatermanOcl._init_normal_memory(self)
# Input matrix device memory
memory = (SmithWaterman.float_size * (self.length_of_x_sequences + 1) * self.number_of_sequences *
(self.length_of_y_sequences + 1) * self.number_targets)
self.d_matrix = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
pattern = numpy.zeros((1),dtype=numpy.float32)
cl.enqueue_fill_buffer(self.queue, self.d_matrix, pattern, 0, size = memory)
# Maximum global device memory
memory = (SmithWaterman.float_size * self.x_div_shared_x * self.number_of_sequences *
self.y_div_shared_y * self.number_targets * self.workload_x * self.workload_y)
self.d_global_maxima = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
memory = (SmithWaterman.int_size *
self.length_of_x_sequences *
self.number_of_sequences *
self.length_of_y_sequences *
self.number_targets)
self.d_semaphores = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
pattern = numpy.zeros((1),dtype=numpy.int32)
cl.enqueue_fill_buffer(self.queue, self.d_semaphores, pattern, 0, size=memory)
mem_size += memory
return mem_size
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.length_of_x_sequences,
self.length_of_y_sequences), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _get_direction(self, direction_array, sequence, target, block_x, block_y, value_x, value_y):
return direction_array[sequence][target][block_x*self.shared_x + value_x][block_y*self.shared_y + value_y]
def _set_direction(self, direction, direction_array, sequence, target, block_x, block_y, value_x, value_y):
direction_array[sequence][target][block_x*self.shared_x + value_x][block_y*self.shared_y + value_y] = direction
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
dim_block = (self.workgroup_x, self.workgroup_y)
dim_grid_sw = (self.number_of_sequences * self.workgroup_x, self.number_targets * number_of_blocks * self.workgroup_y)
self.program.calculateScore(self.queue,
dim_grid_sw,
dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_sequences,
self.d_targets,
self.d_global_maxima,
self.d_global_direction_zero_copy).wait()
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
dim_block = (self.workgroup_x, self.workgroup_y)
dim_grid_sw = (self.number_of_sequences * self.workgroup_x, self.number_targets * number_of_blocks * self.workgroup_y)
self.program.traceback(self.queue, dim_grid_sw, dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_global_maxima,
self.d_global_direction_zero_copy,
self.d_index_increment,
self.d_starting_points_zero_copy,
self.d_max_possible_score_zero_copy,
self.d_semaphores).wait()
def _clear_memory(self):
SmithWatermanOcl._clear_memory(self)
if (self.d_semaphores is not None):
self.d_semaphores.release()
class SmithWatermanGPU(SmithWatermanOcl):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanOcl.__init__(self, logger, score, settings)
self.oclcode = GPUcode(self.logger)
self._init_oclcode()
def _init_normal_memory(self):
mem_size = SmithWatermanOcl._init_normal_memory(self)
# Input matrix device memory
memory = (SmithWaterman.float_size * self.length_of_x_sequences * self.number_of_sequences *
self.length_of_y_sequences * self.number_targets)
self.d_matrix = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
# Maximum global device memory
memory = (SmithWaterman.float_size * self.x_div_shared_x * self.number_of_sequences *
self.y_div_shared_y * self.number_targets)
self.d_global_maxima = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE, size=memory)
mem_size += memory
return mem_size
def _get_direction_byte_array(self):
'''
Get the resulting directions
@return gives the resulting direction array as byte array
'''
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.x_div_shared_x,
self.y_div_shared_y,
self.shared_x,
self.shared_y), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _execute_calculate_score_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the calculate score kernel'''
dim_block = (self.shared_x, self.shared_y)
dim_grid_sw = (self.number_of_sequences * self.shared_x, self.number_targets * number_of_blocks * self.shared_y)
self.program.calculateScore(self.queue,
dim_grid_sw,
dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_sequences,
self.d_targets,
self.d_global_maxima,
self.d_global_direction_zero_copy).wait()
def _execute_traceback_kernel(self, number_of_blocks, idx, idy):
''' Executes a single run of the traceback kernel'''
dim_block = (self.shared_x, self.shared_y)
dim_grid_sw = (self.number_of_sequences * self.shared_x, self.number_targets * number_of_blocks * self.shared_y)
self.program.traceback(self.queue, dim_grid_sw, dim_block,
self.d_matrix,
numpy.int32(idx),
numpy.int32(idy),
numpy.int32(number_of_blocks),
self.d_global_maxima,
self.d_global_direction_zero_copy,
self.d_index_increment,
self.d_starting_points_zero_copy,
self.d_max_possible_score_zero_copy).wait()
class SmithWatermanNVIDIA(SmithWatermanGPU):
'''
classdocs
'''
def __init__(self, logger, score, settings):
'''
Constructor
'''
SmithWatermanGPU.__init__(self, logger, score, settings)
self.pinned_starting_points_zero_copy = None
self.pinned_max_possible_score_zero_copy = None
self.pinned_global_direction_zero_copy = None
self._init_oclcode()
def _init_zero_copy_memory(self):
self.logger.debug('Initializing NVIDIA zero-copy memory.')
# Starting points host memory allocation and device copy
memory = (self.size_of_startingpoint * self.maximum_number_starting_points * self.number_of_sequences *
self.number_targets)
self.pinned_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_starting_points_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=memory)
self.h_starting_points_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_starting_points_zero_copy, cl.map_flags.READ, 0,
(memory, 1), dtype=numpy.byte)[0]
mem_size = memory
# Global directions host memory allocation and device copy
memory = (self.length_of_x_sequences * self.number_of_sequences * self.length_of_y_sequences *
self.number_targets)
self.pinned_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_global_direction_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.WRITE_ONLY, size=memory)
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_global_direction_zero_copy, cl.map_flags.READ, 0,
(memory, 1), dtype=numpy.byte)[0]
mem_size += memory
# Maximum zero copy memory allocation and device copy
memory = (self.number_of_sequences * self.number_of_targets * SmithWaterman.float_size)
self.pinned_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.ALLOC_HOST_PTR, size=memory)
self.d_max_possible_score_zero_copy = cl.Buffer(self.ctx, cl.mem_flags.READ_ONLY, size=memory)
self.h_max_possible_score_zero_copy = cl.enqueue_map_buffer(self.queue, self.pinned_max_possible_score_zero_copy, cl.map_flags.WRITE, 0,
(self.number_of_sequences * self.number_of_targets, 1), dtype=numpy.float32)[0]
mem_size += memory
# Zero copy buffers are allocated twice in NVIDIA
return 2*mem_size
def _set_max_possible_score(self, target_index, targets, i, index, records_seqs):
cl.enqueue_copy(self.queue, self.d_max_possible_score_zero_copy, self.h_max_possible_score_zero_copy).wait()
self._fill_max_possible_score(target_index, targets, i, index, records_seqs)
def _get_direction_byte_array(self):
# cl.enqueue_copy(self.queue, self.h_global_direction_zero_copy, self.d_global_direction_zero_copy).wait()
self.h_global_direction_zero_copy = cl.enqueue_map_buffer(self.queue, self.d_global_direction_zero_copy, cl.map_flags.READ, 0,
(self.number_of_sequences,
self.number_targets,
self.x_div_shared_x,
self.y_div_shared_y,
self.shared_x,
self.shared_y), dtype=numpy.byte)[0]
return self.h_global_direction_zero_copy
def _get_starting_point_byte_array(self):
cl.enqueue_copy(self.queue, self.h_starting_points_zero_copy, self.d_starting_points_zero_copy).wait()
return self.h_starting_points_zero_copy
def _clear_zero_copy_memory(self):
SmithWatermanGPU._clear_zero_copy_memory(self)
if (self.pinned_starting_points_zero_copy is not None):
self.pinned_starting_points_zero_copy.release()
if (self.pinned_global_direction_zero_copy is not None):
self.pinned_global_direction_zero_copy.release()
if (self.pinned_max_possible_score_zero_copy is not None):
self.pinned_max_possible_score_zero_copy.release()
def _compile_ocl_code(self):
"""Compile the OpenCL code with current settings"""
self.logger.debug('Compiling NVIDIA OpenCL code.')
code = self.oclcode.get_code(self.score, self.number_of_sequences, self.number_targets, self.length_of_x_sequences, self.length_of_y_sequences)
self.program = cl.Program(self.ctx, code).build(options=['-D', 'NVIDIA'])
|
# coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
import unittest
from collections import defaultdict
from contextlib import contextmanager
from tempfile import mkdtemp
from textwrap import dedent
from pants.backend.core.targets.dependencies import Dependencies
from pants.base.address import SyntheticAddress
from pants.base.build_configuration import BuildConfiguration
from pants.base.build_file import FilesystemBuildFile
from pants.base.build_file_address_mapper import BuildFileAddressMapper
from pants.base.build_file_aliases import BuildFileAliases
from pants.base.build_file_parser import BuildFileParser
from pants.base.build_graph import BuildGraph
from pants.base.build_root import BuildRoot
from pants.base.cmd_line_spec_parser import CmdLineSpecParser
from pants.base.config import Config
from pants.base.exceptions import TaskError
from pants.base.source_root import SourceRoot
from pants.base.target import Target
from pants.goal.goal import Goal
from pants.goal.products import MultipleRootedProducts, UnionProducts
from pants.option.options import Options
from pants.option.options_bootstrapper import OptionsBootstrapper, register_bootstrap_options
from pants.subsystem.subsystem import Subsystem
from pants.util.contextutil import pushd, temporary_dir
from pants.util.dirutil import safe_mkdir, safe_open, safe_rmtree, touch
from pants_test.base.context_utils import create_context
# TODO: Rename to 'TestBase', for uniformity, and also for logic: This is a baseclass
# for tests, not a test of a thing called 'Base'.
class BaseTest(unittest.TestCase):
"""A baseclass useful for tests requiring a temporary buildroot."""
def build_path(self, relpath):
"""Returns the canonical BUILD file path for the given relative build path."""
if os.path.basename(relpath).startswith('BUILD'):
return relpath
else:
return os.path.join(relpath, 'BUILD')
def create_dir(self, relpath):
"""Creates a directory under the buildroot.
relpath: The relative path to the directory from the build root.
"""
path = os.path.join(self.build_root, relpath)
safe_mkdir(path)
return path
def create_file(self, relpath, contents='', mode='wb'):
"""Writes to a file under the buildroot.
relpath: The relative path to the file from the build root.
contents: A string containing the contents of the file - '' by default..
mode: The mode to write to the file in - over-write by default.
"""
path = os.path.join(self.build_root, relpath)
with safe_open(path, mode=mode) as fp:
fp.write(contents)
return path
def add_to_build_file(self, relpath, target):
"""Adds the given target specification to the BUILD file at relpath.
relpath: The relative path to the BUILD file from the build root.
target: A string containing the target definition as it would appear in a BUILD file.
"""
self.create_file(self.build_path(relpath), target, mode='a')
cls = self.address_mapper._build_file_type
return cls(root_dir=self.build_root, relpath=self.build_path(relpath))
def make_target(self,
spec='',
target_type=Target,
dependencies=None,
resources = None,
derived_from=None,
**kwargs):
address = SyntheticAddress.parse(spec)
target = target_type(name=address.target_name,
address=address,
build_graph=self.build_graph,
**kwargs)
dependencies = dependencies or []
dependencies.extend(resources or [])
self.build_graph.inject_target(target,
dependencies=[dep.address for dep in dependencies],
derived_from=derived_from)
return target
@property
def alias_groups(self):
return BuildFileAliases.create(targets={'target': Dependencies})
def setUp(self):
super(BaseTest, self).setUp()
Goal.clear()
self.real_build_root = BuildRoot().path
self.build_root = os.path.realpath(mkdtemp(suffix='_BUILD_ROOT'))
self.pants_workdir = os.path.join(self.build_root, '.pants.d')
safe_mkdir(self.pants_workdir)
self.options = defaultdict(dict) # scope -> key-value mapping.
self.options[''] = {
'pants_workdir': self.pants_workdir,
'pants_supportdir': os.path.join(self.build_root, 'build-support'),
'pants_distdir': os.path.join(self.build_root, 'dist'),
'pants_configdir': os.path.join(self.build_root, 'config'),
'cache_key_gen_version': '0-test',
}
BuildRoot().path = self.build_root
Subsystem.reset()
self.create_file('pants.ini')
build_configuration = BuildConfiguration()
build_configuration.register_aliases(self.alias_groups)
self.build_file_parser = BuildFileParser(build_configuration, self.build_root)
self.address_mapper = BuildFileAddressMapper(self.build_file_parser, FilesystemBuildFile)
self.build_graph = BuildGraph(address_mapper=self.address_mapper)
self.bootstrap_option_values = OptionsBootstrapper().get_bootstrap_options().for_global_scope()
def reset_build_graph(self):
"""Start over with a fresh build graph with no targets in it."""
self.address_mapper = BuildFileAddressMapper(self.build_file_parser, FilesystemBuildFile)
self.build_graph = BuildGraph(address_mapper=self.address_mapper)
def set_options_for_scope(self, scope, **kwargs):
self.options[scope].update(kwargs)
def context(self, for_task_types=None, options=None, target_roots=None,
console_outstream=None, workspace=None):
for_task_types = for_task_types or []
options = options or {}
option_values = defaultdict(dict)
registered_global_subsystems = set()
# Get default values for all options registered by the tasks in for_task_types.
# TODO: This is clunky and somewhat repetitive of the real registration code.
for task_type in for_task_types:
scope = task_type.options_scope
if scope is None:
raise TaskError('You must set a scope on your task type before using it in tests.')
# We provide our own test-only registration implementation, bypassing argparse.
# When testing we set option values directly, so we don't care about cmd-line flags, config,
# env vars etc. In fact, for test isolation we explicitly don't want to look at those.
def register_func(on_scope):
def register(*rargs, **rkwargs):
scoped_options = option_values[on_scope]
default = rkwargs.get('default')
if default is None and rkwargs.get('action') == 'append':
default = []
for flag_name in rargs:
option_name = flag_name.lstrip('-').replace('-', '_')
scoped_options[option_name] = default
register.bootstrap = self.bootstrap_option_values
register.scope = on_scope
return register
register_bootstrap_options(register_func(Options.GLOBAL_SCOPE), self.build_root)
task_type.register_options(register_func(scope))
for subsystem in task_type.global_subsystems():
if subsystem not in registered_global_subsystems:
subsystem.register_options(register_func(subsystem.qualify_scope(Options.GLOBAL_SCOPE)))
registered_global_subsystems.add(subsystem)
for subsystem in task_type.task_subsystems():
subsystem.register_options(register_func(subsystem.qualify_scope(scope)))
# Now override with any caller-specified values.
# TODO(benjy): Get rid of the options arg, and require tests to call set_options.
for scope, opts in options.items():
for key, val in opts.items():
option_values[scope][key] = val
for scope, opts in self.options.items():
for key, val in opts.items():
option_values[scope][key] = val
# Make inner scopes inherit option values from their enclosing scopes.
# Iterating in sorted order guarantees that we see outer scopes before inner scopes,
# and therefore only have to inherit from our immediately enclosing scope.
for scope in sorted(option_values.keys()):
if scope != Options.GLOBAL_SCOPE:
enclosing_scope = scope.rpartition('.')[0]
opts = option_values[scope]
for key, val in option_values.get(enclosing_scope, {}).items():
if key not in opts: # Inner scope values override the inherited ones.
opts[key] = val
context = create_context(options=option_values,
target_roots=target_roots,
build_graph=self.build_graph,
build_file_parser=self.build_file_parser,
address_mapper=self.address_mapper,
console_outstream=console_outstream,
workspace=workspace)
Subsystem._options = context.options
return context
def tearDown(self):
BuildRoot().reset()
SourceRoot.reset()
safe_rmtree(self.build_root)
FilesystemBuildFile.clear_cache()
def target(self, spec):
"""Resolves the given target address to a Target object.
address: The BUILD target address to resolve.
Returns the corresponding Target or else None if the address does not point to a defined Target.
"""
address = SyntheticAddress.parse(spec)
self.build_graph.inject_address_closure(address)
return self.build_graph.get_target(address)
def targets(self, spec):
"""Resolves a target spec to one or more Target objects.
spec: Either BUILD target address or else a target glob using the siblings ':' or
descendants '::' suffixes.
Returns the set of all Targets found.
"""
spec_parser = CmdLineSpecParser(self.build_root, self.address_mapper)
addresses = list(spec_parser.parse_addresses(spec))
for address in addresses:
self.build_graph.inject_address_closure(address)
targets = [self.build_graph.get_target(address) for address in addresses]
return targets
def create_files(self, path, files):
"""Writes to a file under the buildroot with contents same as file name.
path: The relative path to the file from the build root.
files: List of file names.
"""
for f in files:
self.create_file(os.path.join(path, f), contents=f)
def create_library(self, path, target_type, name, sources=None, **kwargs):
"""Creates a library target of given type at the BUILD file at path with sources
path: The relative path to the BUILD file from the build root.
target_type: valid pants target type.
name: Name of the library target.
sources: List of source file at the path relative to path.
**kwargs: Optional attributes that can be set for any library target.
Currently it includes support for resources, java_sources, provides
and dependencies.
"""
if sources:
self.create_files(path, sources)
self.add_to_build_file(path, dedent('''
%(target_type)s(name='%(name)s',
%(sources)s
%(resources)s
%(java_sources)s
%(provides)s
%(dependencies)s
)
''' % dict(target_type=target_type,
name=name,
sources=('sources=%s,' % repr(sources)
if sources else ''),
resources=('resources=["%s"],' % kwargs.get('resources')
if 'resources' in kwargs else ''),
java_sources=('java_sources=[%s],'
% ','.join(map(lambda str_target: '"%s"' % str_target,
kwargs.get('java_sources')))
if 'java_sources' in kwargs else ''),
provides=('provides=%s,' % kwargs.get('provides')
if 'provides' in kwargs else ''),
dependencies=('dependencies=%s,' % kwargs.get('dependencies')
if 'dependencies' in kwargs else ''),
)))
return self.target('%s:%s' % (path, name))
def create_resources(self, path, name, *sources):
return self.create_library(path, 'resources', name, sources)
@contextmanager
def workspace(self, *buildfiles):
with temporary_dir() as root_dir:
with BuildRoot().temporary(root_dir):
with pushd(root_dir):
for buildfile in buildfiles:
touch(os.path.join(root_dir, buildfile))
yield os.path.realpath(root_dir)
def populate_compile_classpath(self, context, classpath=None):
"""
Helps actual test cases to populate the 'compile_classpath' products data mapping
in the context, which holds the classpath value for targets.
:param context: The execution context where the products data mapping lives.
:param classpath: a list of classpath strings. If not specified, ['none'] will be used.
"""
compile_classpaths = context.products.get_data('compile_classpath', lambda: UnionProducts())
compile_classpaths.add_for_targets(context.targets(), [('default', entry) for entry in classpath or ['none']])
@contextmanager
def add_data(self, context_products, data_type, target, *products):
make_products = lambda: defaultdict(MultipleRootedProducts)
data_by_target = context_products.get_data(data_type, make_products)
with temporary_dir() as outdir:
def create_product(product):
abspath = os.path.join(outdir, product)
with safe_open(abspath, mode='w') as fp:
fp.write(product)
return abspath
data_by_target[target].add_abs_paths(outdir, map(create_product, products))
yield temporary_dir
@contextmanager
def add_products(self, context_products, product_type, target, *products):
product_mapping = context_products.get(product_type)
with temporary_dir() as outdir:
def create_product(product):
with safe_open(os.path.join(outdir, product), mode='w') as fp:
fp.write(product)
return product
product_mapping.add(target, outdir, map(create_product, products))
yield temporary_dir
def set_bootstrap_options(self, **values):
"""Override some of the bootstrap option values."""
self.bootstrap_option_values.update(values)
self.set_options_for_scope(Options.GLOBAL_SCOPE, **values)
Restore Config caching in tests.
It turns out that some tests still use it, subtly.
Things were working before because of the order in which
some tests happened to be run :(
TODO: Figure out that test interdependency issue.
Testing Done:
CI passed: https://travis-ci.org/pantsbuild/pants/builds/61229496.
Bugs closed: 1487
Reviewed at https://rbcommons.com/s/twitter/r/2160/
# coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
import unittest
from collections import defaultdict
from contextlib import contextmanager
from tempfile import mkdtemp
from textwrap import dedent
from pants.backend.core.targets.dependencies import Dependencies
from pants.base.address import SyntheticAddress
from pants.base.build_configuration import BuildConfiguration
from pants.base.build_file import FilesystemBuildFile
from pants.base.build_file_address_mapper import BuildFileAddressMapper
from pants.base.build_file_aliases import BuildFileAliases
from pants.base.build_file_parser import BuildFileParser
from pants.base.build_graph import BuildGraph
from pants.base.build_root import BuildRoot
from pants.base.cmd_line_spec_parser import CmdLineSpecParser
from pants.base.config import Config
from pants.base.exceptions import TaskError
from pants.base.source_root import SourceRoot
from pants.base.target import Target
from pants.goal.goal import Goal
from pants.goal.products import MultipleRootedProducts, UnionProducts
from pants.option.options import Options
from pants.option.options_bootstrapper import OptionsBootstrapper, register_bootstrap_options
from pants.subsystem.subsystem import Subsystem
from pants.util.contextutil import pushd, temporary_dir
from pants.util.dirutil import safe_mkdir, safe_open, safe_rmtree, touch
from pants_test.base.context_utils import create_context
# TODO: Rename to 'TestBase', for uniformity, and also for logic: This is a baseclass
# for tests, not a test of a thing called 'Base'.
class BaseTest(unittest.TestCase):
"""A baseclass useful for tests requiring a temporary buildroot."""
@classmethod
def setUpClass(cls):
"""Ensure that all code has a config to read from the cache.
TODO: Yuck. Get rid of this after plumbing options through in the right places.
"""
super(BaseTest, cls).setUpClass()
Config.cache(Config.load())
def build_path(self, relpath):
"""Returns the canonical BUILD file path for the given relative build path."""
if os.path.basename(relpath).startswith('BUILD'):
return relpath
else:
return os.path.join(relpath, 'BUILD')
def create_dir(self, relpath):
"""Creates a directory under the buildroot.
relpath: The relative path to the directory from the build root.
"""
path = os.path.join(self.build_root, relpath)
safe_mkdir(path)
return path
def create_file(self, relpath, contents='', mode='wb'):
"""Writes to a file under the buildroot.
relpath: The relative path to the file from the build root.
contents: A string containing the contents of the file - '' by default..
mode: The mode to write to the file in - over-write by default.
"""
path = os.path.join(self.build_root, relpath)
with safe_open(path, mode=mode) as fp:
fp.write(contents)
return path
def add_to_build_file(self, relpath, target):
"""Adds the given target specification to the BUILD file at relpath.
relpath: The relative path to the BUILD file from the build root.
target: A string containing the target definition as it would appear in a BUILD file.
"""
self.create_file(self.build_path(relpath), target, mode='a')
cls = self.address_mapper._build_file_type
return cls(root_dir=self.build_root, relpath=self.build_path(relpath))
def make_target(self,
spec='',
target_type=Target,
dependencies=None,
resources = None,
derived_from=None,
**kwargs):
address = SyntheticAddress.parse(spec)
target = target_type(name=address.target_name,
address=address,
build_graph=self.build_graph,
**kwargs)
dependencies = dependencies or []
dependencies.extend(resources or [])
self.build_graph.inject_target(target,
dependencies=[dep.address for dep in dependencies],
derived_from=derived_from)
return target
@property
def alias_groups(self):
return BuildFileAliases.create(targets={'target': Dependencies})
def setUp(self):
super(BaseTest, self).setUp()
Goal.clear()
self.real_build_root = BuildRoot().path
self.build_root = os.path.realpath(mkdtemp(suffix='_BUILD_ROOT'))
self.pants_workdir = os.path.join(self.build_root, '.pants.d')
safe_mkdir(self.pants_workdir)
self.options = defaultdict(dict) # scope -> key-value mapping.
self.options[''] = {
'pants_workdir': self.pants_workdir,
'pants_supportdir': os.path.join(self.build_root, 'build-support'),
'pants_distdir': os.path.join(self.build_root, 'dist'),
'pants_configdir': os.path.join(self.build_root, 'config'),
'cache_key_gen_version': '0-test',
}
BuildRoot().path = self.build_root
Subsystem.reset()
self.create_file('pants.ini')
build_configuration = BuildConfiguration()
build_configuration.register_aliases(self.alias_groups)
self.build_file_parser = BuildFileParser(build_configuration, self.build_root)
self.address_mapper = BuildFileAddressMapper(self.build_file_parser, FilesystemBuildFile)
self.build_graph = BuildGraph(address_mapper=self.address_mapper)
self.bootstrap_option_values = OptionsBootstrapper().get_bootstrap_options().for_global_scope()
def reset_build_graph(self):
"""Start over with a fresh build graph with no targets in it."""
self.address_mapper = BuildFileAddressMapper(self.build_file_parser, FilesystemBuildFile)
self.build_graph = BuildGraph(address_mapper=self.address_mapper)
def set_options_for_scope(self, scope, **kwargs):
self.options[scope].update(kwargs)
def context(self, for_task_types=None, options=None, target_roots=None,
console_outstream=None, workspace=None):
for_task_types = for_task_types or []
options = options or {}
option_values = defaultdict(dict)
registered_global_subsystems = set()
# Get default values for all options registered by the tasks in for_task_types.
# TODO: This is clunky and somewhat repetitive of the real registration code.
for task_type in for_task_types:
scope = task_type.options_scope
if scope is None:
raise TaskError('You must set a scope on your task type before using it in tests.')
# We provide our own test-only registration implementation, bypassing argparse.
# When testing we set option values directly, so we don't care about cmd-line flags, config,
# env vars etc. In fact, for test isolation we explicitly don't want to look at those.
def register_func(on_scope):
def register(*rargs, **rkwargs):
scoped_options = option_values[on_scope]
default = rkwargs.get('default')
if default is None and rkwargs.get('action') == 'append':
default = []
for flag_name in rargs:
option_name = flag_name.lstrip('-').replace('-', '_')
scoped_options[option_name] = default
register.bootstrap = self.bootstrap_option_values
register.scope = on_scope
return register
register_bootstrap_options(register_func(Options.GLOBAL_SCOPE), self.build_root)
task_type.register_options(register_func(scope))
for subsystem in task_type.global_subsystems():
if subsystem not in registered_global_subsystems:
subsystem.register_options(register_func(subsystem.qualify_scope(Options.GLOBAL_SCOPE)))
registered_global_subsystems.add(subsystem)
for subsystem in task_type.task_subsystems():
subsystem.register_options(register_func(subsystem.qualify_scope(scope)))
# Now override with any caller-specified values.
# TODO(benjy): Get rid of the options arg, and require tests to call set_options.
for scope, opts in options.items():
for key, val in opts.items():
option_values[scope][key] = val
for scope, opts in self.options.items():
for key, val in opts.items():
option_values[scope][key] = val
# Make inner scopes inherit option values from their enclosing scopes.
# Iterating in sorted order guarantees that we see outer scopes before inner scopes,
# and therefore only have to inherit from our immediately enclosing scope.
for scope in sorted(option_values.keys()):
if scope != Options.GLOBAL_SCOPE:
enclosing_scope = scope.rpartition('.')[0]
opts = option_values[scope]
for key, val in option_values.get(enclosing_scope, {}).items():
if key not in opts: # Inner scope values override the inherited ones.
opts[key] = val
context = create_context(options=option_values,
target_roots=target_roots,
build_graph=self.build_graph,
build_file_parser=self.build_file_parser,
address_mapper=self.address_mapper,
console_outstream=console_outstream,
workspace=workspace)
Subsystem._options = context.options
return context
def tearDown(self):
BuildRoot().reset()
SourceRoot.reset()
safe_rmtree(self.build_root)
FilesystemBuildFile.clear_cache()
def target(self, spec):
"""Resolves the given target address to a Target object.
address: The BUILD target address to resolve.
Returns the corresponding Target or else None if the address does not point to a defined Target.
"""
address = SyntheticAddress.parse(spec)
self.build_graph.inject_address_closure(address)
return self.build_graph.get_target(address)
def targets(self, spec):
"""Resolves a target spec to one or more Target objects.
spec: Either BUILD target address or else a target glob using the siblings ':' or
descendants '::' suffixes.
Returns the set of all Targets found.
"""
spec_parser = CmdLineSpecParser(self.build_root, self.address_mapper)
addresses = list(spec_parser.parse_addresses(spec))
for address in addresses:
self.build_graph.inject_address_closure(address)
targets = [self.build_graph.get_target(address) for address in addresses]
return targets
def create_files(self, path, files):
"""Writes to a file under the buildroot with contents same as file name.
path: The relative path to the file from the build root.
files: List of file names.
"""
for f in files:
self.create_file(os.path.join(path, f), contents=f)
def create_library(self, path, target_type, name, sources=None, **kwargs):
"""Creates a library target of given type at the BUILD file at path with sources
path: The relative path to the BUILD file from the build root.
target_type: valid pants target type.
name: Name of the library target.
sources: List of source file at the path relative to path.
**kwargs: Optional attributes that can be set for any library target.
Currently it includes support for resources, java_sources, provides
and dependencies.
"""
if sources:
self.create_files(path, sources)
self.add_to_build_file(path, dedent('''
%(target_type)s(name='%(name)s',
%(sources)s
%(resources)s
%(java_sources)s
%(provides)s
%(dependencies)s
)
''' % dict(target_type=target_type,
name=name,
sources=('sources=%s,' % repr(sources)
if sources else ''),
resources=('resources=["%s"],' % kwargs.get('resources')
if 'resources' in kwargs else ''),
java_sources=('java_sources=[%s],'
% ','.join(map(lambda str_target: '"%s"' % str_target,
kwargs.get('java_sources')))
if 'java_sources' in kwargs else ''),
provides=('provides=%s,' % kwargs.get('provides')
if 'provides' in kwargs else ''),
dependencies=('dependencies=%s,' % kwargs.get('dependencies')
if 'dependencies' in kwargs else ''),
)))
return self.target('%s:%s' % (path, name))
def create_resources(self, path, name, *sources):
return self.create_library(path, 'resources', name, sources)
@contextmanager
def workspace(self, *buildfiles):
with temporary_dir() as root_dir:
with BuildRoot().temporary(root_dir):
with pushd(root_dir):
for buildfile in buildfiles:
touch(os.path.join(root_dir, buildfile))
yield os.path.realpath(root_dir)
def populate_compile_classpath(self, context, classpath=None):
"""
Helps actual test cases to populate the 'compile_classpath' products data mapping
in the context, which holds the classpath value for targets.
:param context: The execution context where the products data mapping lives.
:param classpath: a list of classpath strings. If not specified, ['none'] will be used.
"""
compile_classpaths = context.products.get_data('compile_classpath', lambda: UnionProducts())
compile_classpaths.add_for_targets(context.targets(), [('default', entry) for entry in classpath or ['none']])
@contextmanager
def add_data(self, context_products, data_type, target, *products):
make_products = lambda: defaultdict(MultipleRootedProducts)
data_by_target = context_products.get_data(data_type, make_products)
with temporary_dir() as outdir:
def create_product(product):
abspath = os.path.join(outdir, product)
with safe_open(abspath, mode='w') as fp:
fp.write(product)
return abspath
data_by_target[target].add_abs_paths(outdir, map(create_product, products))
yield temporary_dir
@contextmanager
def add_products(self, context_products, product_type, target, *products):
product_mapping = context_products.get(product_type)
with temporary_dir() as outdir:
def create_product(product):
with safe_open(os.path.join(outdir, product), mode='w') as fp:
fp.write(product)
return product
product_mapping.add(target, outdir, map(create_product, products))
yield temporary_dir
def set_bootstrap_options(self, **values):
"""Override some of the bootstrap option values."""
self.bootstrap_option_values.update(values)
self.set_options_for_scope(Options.GLOBAL_SCOPE, **values)
|
from collections import OrderedDict
from django.http import Http404, HttpResponseForbidden
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied
from django.contrib import messages
from django.contrib.auth import authenticate, login
from django.contrib.auth.models import User
from django.contrib.auth.decorators import login_required
from django.core.cache import cache
from django.db import IntegrityError
from django.db.models import Q
from django.forms.formsets import formset_factory
from django.shortcuts import render, redirect, get_object_or_404
from .forms import RegisterUserForm, AddMonsterInstanceForm, EditMonsterInstanceForm, AwakenMonsterInstanceForm, \
PowerUpMonsterInstanceForm, EditEssenceStorageForm, EditSummonerForm, EditUserForm, EditTeamForm, AddTeamGroupForm, \
DeleteTeamGroupForm
from .models import Monster, Summoner, MonsterInstance, Fusion, TeamGroup, Team
from .fusion import essences_missing, total_awakening_cost
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
def profile(request, profile_name=None, view_mode='list', sort_method='grade'):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in. ')
summoner = get_object_or_404(Summoner, user__username=profile_name)
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'view_mode': view_mode,
'sort_method': sort_method,
'return_path': request.path,
'view': 'profile',
}
if is_owner or summoner.public:
if view_mode.lower() == 'list':
context['monster_stable'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner)
return render(request, 'herders/profile/profile_view.html', context)
elif view_mode.lower() == 'box':
if sort_method == 'grade':
monster_stable = OrderedDict()
monster_stable['6*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=6).order_by('-level', 'monster__name')
monster_stable['5*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=5).order_by('-level', 'monster__name')
monster_stable['4*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=4).order_by('-level', 'monster__name')
monster_stable['3*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=3).order_by('-level', 'monster__name')
monster_stable['2*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=2).order_by('-level', 'monster__name')
monster_stable['1*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=1).order_by('-level', 'monster__name')
elif sort_method == 'level':
monster_stable = OrderedDict()
monster_stable['40'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level=40).order_by('-level', '-stars', 'monster__name')
monster_stable['39-31'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__name')
monster_stable['30-21'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__name')
monster_stable['20-11'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__name')
monster_stable['10-1'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__lte=10).order_by('-level', '-stars', 'monster__name')
elif sort_method == 'attribute':
monster_stable = OrderedDict()
monster_stable['water'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
return render(request, 'herders/profile/profile_box.html', context)
else:
raise Http404('Unknown profile view mode')
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.success(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def profile_storage(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'is_owner': is_owner,
'profile_name': request.user.username,
'summoner': summoner,
'storage_form': form,
'view': 'storage',
'profile_view': 'materials',
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/essence_storage.html', context)
@login_required()
def monster_instance_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddMonsterInstanceForm(request.POST or None)
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
# Re-show same page but with form filled in and errors shown
context = {
'profile_name': profile_name,
'summoner': summoner,
'add_monster_form': form,
'return_path': return_path,
'is_owner': is_owner,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_add.html', context)
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
MonsterInstance.objects.create(owner=summoner, monster=monster_to_add, stars=stars, level=level, fodder=True, notes='', priority=MonsterInstance.PRIORITY_DONE)
return redirect(return_path)
else:
return HttpResponseForbidden()
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
context = {
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_view.html', context)
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = EditMonsterInstanceForm(request.POST or None, instance=monster)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'summoner': summoner,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'edit_monster_form': form,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if form.is_valid():
monster = form.save(commit=False)
monster.save()
messages.success(request, 'Saved changes to %s.' % monster)
return redirect(return_path)
else:
# Redisplay form with validation error messages
context['validation_errors'] = form.non_field_errors()
else:
raise PermissionDenied()
return render(request, 'herders/profile/profile_monster_edit.html', context)
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
PowerUpFormset = formset_factory(PowerUpMonsterInstanceForm, extra=5, max_num=5)
if request.method == 'POST':
formset = PowerUpFormset(request.POST)
else:
formset = PowerUpFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'power_up_formset_action': request.path + '?next=' + return_path,
'power_up_formset': formset,
'view': 'profile',
}
food_monsters = []
validation_errors = {}
if is_owner:
if request.method == 'POST':
# return render(request, 'herders/view_post_data.html', {'post_data': request.POST})
if formset.is_valid():
# Create list of submitted food monsters
for instance in formset.cleaned_data:
# Some fields may be blank if user skipped a form input or didn't fill in all 5
if instance:
food_monsters.append(instance['monster'])
# Check that all food monsters are unique - This is done whether or not user bypassed evolution checks
if len(food_monsters) != len(set(food_monsters)):
validation_errors['food_monster_unique'] = "You submitted duplicate food monsters. Please select unique monsters for each slot."
# Check that monster is not being fed to itself
for food in food_monsters:
if food == monster:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
if not request.POST.get('ignore_errors', False):
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars." % monster.stars
else:
# Record state of ignore evolve rules for form redisplay
context['ignore_evolve_checked'] = True
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.success(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
return redirect(return_path)
else:
return redirect(
reverse('herders:monster_instance_edit', kwargs={'profile_name':profile_name, 'instance_id': instance_id}) +
'?next=' + return_path
)
else:
context['form_errors'] = formset.errors
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
return render(request, 'herders/profile/profile_power_up.html', context)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
if monster.monster.is_awakened:
return redirect(return_path)
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'summoner': summoner,
'is_owner': is_owner, # Because of @login_required decorator
'return_path': return_path,
'monster': monster,
'awaken_monster_form': form,
}
if request.method == 'POST' and form.is_valid() and is_owner:
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
if monster.monster.awaken_magic_mats_high:
summoner.storage_magic_high -= monster.monster.awaken_magic_mats_high
if monster.monster.awaken_magic_mats_mid:
summoner.storage_magic_mid -= monster.monster.awaken_magic_mats_mid
if monster.monster.awaken_magic_mats_low:
summoner.storage_magic_low -= monster.monster.awaken_magic_mats_low
if monster.monster.element == Monster.ELEMENT_FIRE:
if monster.monster.awaken_ele_mats_high:
summoner.storage_fire_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_fire_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_fire_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WATER:
if monster.monster.awaken_ele_mats_high:
summoner.storage_water_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_water_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_water_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WIND:
if monster.monster.awaken_ele_mats_high:
summoner.storage_wind_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_wind_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_wind_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_DARK:
if monster.monster.awaken_ele_mats_high:
summoner.storage_dark_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_dark_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_dark_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_LIGHT:
if monster.monster.awaken_ele_mats_high:
summoner.storage_light_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_light_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_light_low -= monster.monster.awaken_ele_mats_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
return redirect(return_path)
else:
storage = summoner.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().iteritems():
available_essences[element] = OrderedDict()
for size, cost in essences.iteritems():
available_essences[element][size] = dict()
available_essences[element][size]['qty'] = storage[element][size]
available_essences[element][size]['sufficient'] = storage[element][size] >= cost
print available_essences
context['available_essences'] = available_essences
return render(request, 'herders/profile/profile_monster_awaken.html', context)
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:fusion', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
}
fusions = Fusion.objects.all().select_related()
progress = OrderedDict()
if is_owner or summoner.public:
for fusion in fusions:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.objects.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).count() > 0
# Scan summoner's collection for instances each ingredient
for ingredient in fusion.ingredients.all():
owned_ingredients = MonsterInstance.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
sub_fusion_available = Fusion.objects.filter(product=ingredient.awakens_from).exists()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
ingredient_progress = {
'instance': ingredient,
'sub_fusion_available': sub_fusion_available,
'owned': owned_ingredients,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
}
ingredients.append(ingredient_progress)
fusion_ready = True
for i in ingredients:
if not i['complete']:
fusion_ready = False
total_cost = total_awakening_cost(ingredients)
total_missing = essences_missing(summoner.get_storage(), total_cost)
progress[fusion.product.name] = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_materials': {
'total_cost': total_cost,
'missing': total_missing,
},
'ready': fusion_ready,
}
# Iterate through again and find any sub-fusions that are possible. Add their missing essences together for a total count
from copy import deepcopy
for monster, fusion in progress.iteritems():
# print 'Checking sub-fusions for ' + monster
combined_total_cost = deepcopy(fusion['awakening_materials']['total_cost'])
sub_fusions_found = False
# Check if ingredients for this fusion are fuseable themselves
for ingredient in fusion['ingredients']:
if ingredient['sub_fusion_available'] and not ingredient['acquired']:
sub_fusions_found = True
# print ' Found sub-fusion for ' + str(ingredient['instance'])
# Get the totals for the sub-fusions and add to the current fusion cost
sub_fusion = progress.get(ingredient['instance'].awakens_from.name, None)
for element, sizes in fusion['awakening_materials']['total_cost'].iteritems():
# print ' element: ' + str(element)
if element not in combined_total_cost:
combined_total_cost[element] = OrderedDict()
# print sub_fusion['awakening_materials']['missing']
for size in set(sizes.keys() + sub_fusion['awakening_materials']['missing'][element].keys()):
# print ' size: ' + size
# print ' sub fusion: ' + str(sub_fusion['awakening_materials']['total_cost'][element].get(size, 0))
# print ' current combined_total_cost: ' + str(combined_total_cost[element].get(size, 0))
combined_total_cost[element][size] = combined_total_cost[element].get(size, 0) + sub_fusion['awakening_materials']['total_cost'][element].get(size, 0)
# print ' new combined_total_cost: ' + str(combined_total_cost[element][size])
if sub_fusions_found:
fusion['awakening_materials']['combined'] = essences_missing(summoner.get_storage(), combined_total_cost)
context['fusions'] = progress
return render(request, 'herders/profile/profile_fusion.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.success(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
return render(request, 'herders/profile/teams/team_detail.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.success(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def bestiary(request):
context = {
'view': 'bestiary',
}
monster_list = cache.get('bestiary_data')
if monster_list is None:
monster_list = Monster.objects.select_related('awakens_from', 'awakens_to').all()
cache.set('bestiary_data', monster_list, 900)
context['monster_list'] = monster_list
return render(request, 'herders/bestiary.html', context)
def bestiary_detail(request, monster_id):
monster = get_object_or_404(Monster, pk=monster_id)
context = {
'view': 'bestiary',
}
if monster.is_awakened and monster.awakens_from is not None:
base_monster = monster.awakens_from
awakened_monster = monster
else:
base_monster = monster
awakened_monster = monster.awakens_to
# Run some calcs to provide stat deltas between awakened and unawakened
base_stats = base_monster.get_stats()
context['base_monster'] = base_monster
context['base_monster_stats'] = base_stats
context['base_monster_leader_skill'] = base_monster.leader_skill
context['base_monster_skills'] = base_monster.skills.all().order_by('slot')
if base_monster.awakens_to:
awakened_stats = awakened_monster.get_stats()
# Calculate change in stats as monster undergoes awakening
if base_stats['6']['1']['HP'] is not None:
awakened_stats_deltas = dict()
for stat, value in base_stats['6']['40'].iteritems():
if awakened_stats['6']['40'][stat] != value:
awakened_stats_deltas[stat] = int(round((awakened_stats['6']['40'][stat] / float(value)) * 100 - 100))
if base_monster.speed != awakened_monster.speed:
awakened_stats_deltas['SPD'] = awakened_monster.speed - base_monster.speed
if base_monster.crit_rate != awakened_monster.crit_rate:
awakened_stats_deltas['CRIT_Rate'] = awakened_monster.crit_rate - base_monster.crit_rate
if base_monster.crit_damage != awakened_monster.crit_damage:
awakened_stats_deltas['CRIT_DMG'] = awakened_monster.crit_damage - base_monster.crit_damage
if base_monster.accuracy != awakened_monster.accuracy:
awakened_stats_deltas['Accuracy'] = awakened_monster.accuracy - base_monster.accuracy
if base_monster.resistance != awakened_monster.resistance:
awakened_stats_deltas['Resistance'] = awakened_monster.resistance - base_monster.resistance
context['awakened_monster_stats_deltas'] = awakened_stats_deltas
context['awakened_monster'] = awakened_monster
context['awakened_monster_stats'] = awakened_stats
context['awakened_monster_leader_skill'] = awakened_monster.leader_skill
context['awakened_monster_skills'] = awakened_monster.skills.all().order_by('slot')
return render(request, 'herders/bestiary_detail.html', context)
Fixed error if team has no leader.
from collections import OrderedDict
from django.http import Http404, HttpResponseForbidden
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied
from django.contrib import messages
from django.contrib.auth import authenticate, login
from django.contrib.auth.models import User
from django.contrib.auth.decorators import login_required
from django.core.cache import cache
from django.db import IntegrityError
from django.db.models import Q
from django.forms.formsets import formset_factory
from django.shortcuts import render, redirect, get_object_or_404
from .forms import RegisterUserForm, AddMonsterInstanceForm, EditMonsterInstanceForm, AwakenMonsterInstanceForm, \
PowerUpMonsterInstanceForm, EditEssenceStorageForm, EditSummonerForm, EditUserForm, EditTeamForm, AddTeamGroupForm, \
DeleteTeamGroupForm
from .models import Monster, Summoner, MonsterInstance, Fusion, TeamGroup, Team
from .fusion import essences_missing, total_awakening_cost
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile', profile_name=user.username, view_mode='list')
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
def profile(request, profile_name=None, view_mode='list', sort_method='grade'):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in. ')
summoner = get_object_or_404(Summoner, user__username=profile_name)
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'add_monster_form': AddMonsterInstanceForm(),
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'view_mode': view_mode,
'sort_method': sort_method,
'return_path': request.path,
'view': 'profile',
}
if is_owner or summoner.public:
if view_mode.lower() == 'list':
context['monster_stable'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner)
return render(request, 'herders/profile/profile_view.html', context)
elif view_mode.lower() == 'box':
if sort_method == 'grade':
monster_stable = OrderedDict()
monster_stable['6*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=6).order_by('-level', 'monster__name')
monster_stable['5*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=5).order_by('-level', 'monster__name')
monster_stable['4*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=4).order_by('-level', 'monster__name')
monster_stable['3*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=3).order_by('-level', 'monster__name')
monster_stable['2*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=2).order_by('-level', 'monster__name')
monster_stable['1*'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, stars=1).order_by('-level', 'monster__name')
elif sort_method == 'level':
monster_stable = OrderedDict()
monster_stable['40'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level=40).order_by('-level', '-stars', 'monster__name')
monster_stable['39-31'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__name')
monster_stable['30-21'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__name')
monster_stable['20-11'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__name')
monster_stable['10-1'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, level__lte=10).order_by('-level', '-stars', 'monster__name')
elif sort_method == 'attribute':
monster_stable = OrderedDict()
monster_stable['water'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = MonsterInstance.objects.select_related('monster').filter(owner=summoner, monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
return render(request, 'herders/profile/profile_box.html', context)
else:
raise Http404('Unknown profile view mode')
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.success(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def profile_storage(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'is_owner': is_owner,
'profile_name': request.user.username,
'summoner': summoner,
'storage_form': form,
'view': 'storage',
'profile_view': 'materials',
}
if request.method == 'POST' and form.is_valid():
form.save()
return redirect(return_path)
else:
return render(request, 'herders/essence_storage.html', context)
@login_required()
def monster_instance_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddMonsterInstanceForm(request.POST or None)
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
# Re-show same page but with form filled in and errors shown
context = {
'profile_name': profile_name,
'summoner': summoner,
'add_monster_form': form,
'return_path': return_path,
'is_owner': is_owner,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_add.html', context)
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
MonsterInstance.objects.create(owner=summoner, monster=monster_to_add, stars=stars, level=level, fodder=True, notes='', priority=MonsterInstance.PRIORITY_DONE)
return redirect(return_path)
else:
return HttpResponseForbidden()
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
context = {
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'view': 'profile',
}
return render(request, 'herders/profile/profile_monster_view.html', context)
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = EditMonsterInstanceForm(request.POST or None, instance=monster)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'summoner': summoner,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'edit_monster_form': form,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if form.is_valid():
monster = form.save(commit=False)
monster.save()
messages.success(request, 'Saved changes to %s.' % monster)
return redirect(return_path)
else:
# Redisplay form with validation error messages
context['validation_errors'] = form.non_field_errors()
else:
raise PermissionDenied()
return render(request, 'herders/profile/profile_monster_edit.html', context)
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
PowerUpFormset = formset_factory(PowerUpMonsterInstanceForm, extra=5, max_num=5)
if request.method == 'POST':
formset = PowerUpFormset(request.POST)
else:
formset = PowerUpFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'monster': monster,
'is_owner': is_owner,
'power_up_formset_action': request.path + '?next=' + return_path,
'power_up_formset': formset,
'view': 'profile',
}
food_monsters = []
validation_errors = {}
if is_owner:
if request.method == 'POST':
# return render(request, 'herders/view_post_data.html', {'post_data': request.POST})
if formset.is_valid():
# Create list of submitted food monsters
for instance in formset.cleaned_data:
# Some fields may be blank if user skipped a form input or didn't fill in all 5
if instance:
food_monsters.append(instance['monster'])
# Check that all food monsters are unique - This is done whether or not user bypassed evolution checks
if len(food_monsters) != len(set(food_monsters)):
validation_errors['food_monster_unique'] = "You submitted duplicate food monsters. Please select unique monsters for each slot."
# Check that monster is not being fed to itself
for food in food_monsters:
if food == monster:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
if not request.POST.get('ignore_errors', False):
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars." % monster.stars
else:
# Record state of ignore evolve rules for form redisplay
context['ignore_evolve_checked'] = True
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.success(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
return redirect(return_path)
else:
return redirect(
reverse('herders:monster_instance_edit', kwargs={'profile_name':profile_name, 'instance_id': instance_id}) +
'?next=' + return_path
)
else:
context['form_errors'] = formset.errors
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
return render(request, 'herders/profile/profile_power_up.html', context)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
if monster.monster.is_awakened:
return redirect(return_path)
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'summoner': summoner,
'is_owner': is_owner, # Because of @login_required decorator
'return_path': return_path,
'monster': monster,
'awaken_monster_form': form,
}
if request.method == 'POST' and form.is_valid() and is_owner:
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
if monster.monster.awaken_magic_mats_high:
summoner.storage_magic_high -= monster.monster.awaken_magic_mats_high
if monster.monster.awaken_magic_mats_mid:
summoner.storage_magic_mid -= monster.monster.awaken_magic_mats_mid
if monster.monster.awaken_magic_mats_low:
summoner.storage_magic_low -= monster.monster.awaken_magic_mats_low
if monster.monster.element == Monster.ELEMENT_FIRE:
if monster.monster.awaken_ele_mats_high:
summoner.storage_fire_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_fire_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_fire_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WATER:
if monster.monster.awaken_ele_mats_high:
summoner.storage_water_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_water_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_water_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_WIND:
if monster.monster.awaken_ele_mats_high:
summoner.storage_wind_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_wind_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_wind_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_DARK:
if monster.monster.awaken_ele_mats_high:
summoner.storage_dark_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_dark_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_dark_low -= monster.monster.awaken_ele_mats_low
elif monster.monster.element == Monster.ELEMENT_LIGHT:
if monster.monster.awaken_ele_mats_high:
summoner.storage_light_high -= monster.monster.awaken_ele_mats_high
if monster.monster.awaken_ele_mats_mid:
summoner.storage_light_mid -= monster.monster.awaken_ele_mats_mid
if monster.monster.awaken_ele_mats_low:
summoner.storage_light_low -= monster.monster.awaken_ele_mats_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
return redirect(return_path)
else:
storage = summoner.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().iteritems():
available_essences[element] = OrderedDict()
for size, cost in essences.iteritems():
available_essences[element][size] = dict()
available_essences[element][size]['qty'] = storage[element][size]
available_essences[element][size]['sufficient'] = storage[element][size] >= cost
context['available_essences'] = available_essences
return render(request, 'herders/profile/profile_monster_awaken.html', context)
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile', kwargs={'profile_name': profile_name, 'view_mode': 'list'})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:fusion', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
}
fusions = Fusion.objects.all().select_related()
progress = OrderedDict()
if is_owner or summoner.public:
for fusion in fusions:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.objects.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).count() > 0
# Scan summoner's collection for instances each ingredient
for ingredient in fusion.ingredients.all():
owned_ingredients = MonsterInstance.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
sub_fusion_available = Fusion.objects.filter(product=ingredient.awakens_from).exists()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
ingredient_progress = {
'instance': ingredient,
'sub_fusion_available': sub_fusion_available,
'owned': owned_ingredients,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
}
ingredients.append(ingredient_progress)
fusion_ready = True
for i in ingredients:
if not i['complete']:
fusion_ready = False
total_cost = total_awakening_cost(ingredients)
total_missing = essences_missing(summoner.get_storage(), total_cost)
progress[fusion.product.name] = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_materials': {
'total_cost': total_cost,
'missing': total_missing,
},
'ready': fusion_ready,
}
# Iterate through again and find any sub-fusions that are possible. Add their missing essences together for a total count
from copy import deepcopy
for monster, fusion in progress.iteritems():
# print 'Checking sub-fusions for ' + monster
combined_total_cost = deepcopy(fusion['awakening_materials']['total_cost'])
sub_fusions_found = False
# Check if ingredients for this fusion are fuseable themselves
for ingredient in fusion['ingredients']:
if ingredient['sub_fusion_available'] and not ingredient['acquired']:
sub_fusions_found = True
# print ' Found sub-fusion for ' + str(ingredient['instance'])
# Get the totals for the sub-fusions and add to the current fusion cost
sub_fusion = progress.get(ingredient['instance'].awakens_from.name, None)
for element, sizes in fusion['awakening_materials']['total_cost'].iteritems():
# print ' element: ' + str(element)
if element not in combined_total_cost:
combined_total_cost[element] = OrderedDict()
# print sub_fusion['awakening_materials']['missing']
for size in set(sizes.keys() + sub_fusion['awakening_materials']['missing'][element].keys()):
# print ' size: ' + size
# print ' sub fusion: ' + str(sub_fusion['awakening_materials']['total_cost'][element].get(size, 0))
# print ' current combined_total_cost: ' + str(combined_total_cost[element].get(size, 0))
combined_total_cost[element][size] = combined_total_cost[element].get(size, 0) + sub_fusion['awakening_materials']['total_cost'][element].get(size, 0)
# print ' new combined_total_cost: ' + str(combined_total_cost[element][size])
if sub_fusions_found:
fusion['awakening_materials']['combined'] = essences_missing(summoner.get_storage(), combined_total_cost)
context['fusions'] = progress
return render(request, 'herders/profile/profile_fusion.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.success(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader and team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
return render(request, 'herders/profile/teams/team_detail.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
summoner = get_object_or_404(Summoner, user__username=profile_name)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.success(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def bestiary(request):
context = {
'view': 'bestiary',
}
monster_list = cache.get('bestiary_data')
if monster_list is None:
monster_list = Monster.objects.select_related('awakens_from', 'awakens_to').all()
cache.set('bestiary_data', monster_list, 900)
context['monster_list'] = monster_list
return render(request, 'herders/bestiary.html', context)
def bestiary_detail(request, monster_id):
monster = get_object_or_404(Monster, pk=monster_id)
context = {
'view': 'bestiary',
}
if monster.is_awakened and monster.awakens_from is not None:
base_monster = monster.awakens_from
awakened_monster = monster
else:
base_monster = monster
awakened_monster = monster.awakens_to
# Run some calcs to provide stat deltas between awakened and unawakened
base_stats = base_monster.get_stats()
context['base_monster'] = base_monster
context['base_monster_stats'] = base_stats
context['base_monster_leader_skill'] = base_monster.leader_skill
context['base_monster_skills'] = base_monster.skills.all().order_by('slot')
if base_monster.awakens_to:
awakened_stats = awakened_monster.get_stats()
# Calculate change in stats as monster undergoes awakening
if base_stats['6']['1']['HP'] is not None:
awakened_stats_deltas = dict()
for stat, value in base_stats['6']['40'].iteritems():
if awakened_stats['6']['40'][stat] != value:
awakened_stats_deltas[stat] = int(round((awakened_stats['6']['40'][stat] / float(value)) * 100 - 100))
if base_monster.speed != awakened_monster.speed:
awakened_stats_deltas['SPD'] = awakened_monster.speed - base_monster.speed
if base_monster.crit_rate != awakened_monster.crit_rate:
awakened_stats_deltas['CRIT_Rate'] = awakened_monster.crit_rate - base_monster.crit_rate
if base_monster.crit_damage != awakened_monster.crit_damage:
awakened_stats_deltas['CRIT_DMG'] = awakened_monster.crit_damage - base_monster.crit_damage
if base_monster.accuracy != awakened_monster.accuracy:
awakened_stats_deltas['Accuracy'] = awakened_monster.accuracy - base_monster.accuracy
if base_monster.resistance != awakened_monster.resistance:
awakened_stats_deltas['Resistance'] = awakened_monster.resistance - base_monster.resistance
context['awakened_monster_stats_deltas'] = awakened_stats_deltas
context['awakened_monster'] = awakened_monster
context['awakened_monster_stats'] = awakened_stats
context['awakened_monster_leader_skill'] = awakened_monster.leader_skill
context['awakened_monster_skills'] = awakened_monster.skills.all().order_by('slot')
return render(request, 'herders/bestiary_detail.html', context)
|
# Copyright 2012, Nachi Ueno, NTT MCL, Inc.
# 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.
"""
Views for managing Neutron Routers.
"""
import logging
from django.core.urlresolvers import reverse
from django.core.urlresolvers import reverse_lazy
from django.utils.translation import ugettext_lazy as _
from horizon import exceptions
from horizon import forms
from horizon import messages
from openstack_dashboard import api
LOG = logging.getLogger(__name__)
class CreateForm(forms.SelfHandlingForm):
name = forms.CharField(max_length=255, label=_("Router Name"))
admin_state_up = forms.ChoiceField(label=_("Admin State"),
choices=[(True, _('UP')),
(False, _('DOWN'))],
required=False)
external_network = forms.ChoiceField(label=_("External Network"),
required=False)
mode = forms.ChoiceField(label=_("Router Type"))
ha = forms.ChoiceField(label=_("High Availability Mode"))
failure_url = 'horizon:project:routers:index'
def __init__(self, request, *args, **kwargs):
super(CreateForm, self).__init__(request, *args, **kwargs)
self.dvr_allowed = api.neutron.get_feature_permission(self.request,
"dvr", "create")
if self.dvr_allowed:
mode_choices = [('server_default', _('Use Server Default')),
('centralized', _('Centralized')),
('distributed', _('Distributed'))]
self.fields['mode'].choices = mode_choices
else:
del self.fields['mode']
self.ha_allowed = api.neutron.get_feature_permission(self.request,
"l3-ha", "create")
if self.ha_allowed:
ha_choices = [('server_default', _('Use Server Default')),
('enabled', _('Enable HA mode')),
('disabled', _('Disable HA mode'))]
self.fields['ha'].choices = ha_choices
else:
del self.fields['ha']
networks = self._get_network_list(request)
if networks:
self.fields['external_network'].choices = networks
else:
del self.fields['external_network']
def _get_network_list(self, request):
search_opts = {'router:external': True}
try:
networks = api.neutron.network_list(request, **search_opts)
except Exception:
msg = _('Failed to get network list.')
LOG.info(msg)
messages.warning(request, msg)
networks = []
choices = [(network.id, network.name or network.id)
for network in networks]
if choices:
choices.insert(0, ("", _("Select network")))
return choices
def handle(self, request, data):
try:
params = {'name': data['name']}
if 'admin_state_up' in data and data['admin_state_up']:
params['admin_state_up'] = data['admin_state_up']
if 'external_network' in data and data['external_network']:
params['external_gateway_info'] = {'network_id':
data['external_network']}
if (self.dvr_allowed and data['mode'] != 'server_default'):
params['distributed'] = (data['mode'] == 'distributed')
if (self.ha_allowed and data['ha'] != 'server_default'):
params['ha'] = (data['ha'] == 'enabled')
router = api.neutron.router_create(request, **params)
message = _('Router %s was successfully created.') % data['name']
messages.success(request, message)
return router
except Exception as exc:
if exc.status_code == 409:
msg = _('Quota exceeded for resource router.')
else:
msg = _('Failed to create router "%s".') % data['name']
LOG.info(msg)
redirect = reverse(self.failure_url)
exceptions.handle(request, msg, redirect=redirect)
return False
class UpdateForm(forms.SelfHandlingForm):
name = forms.CharField(label=_("Name"), required=False)
admin_state = forms.ChoiceField(choices=[(True, _('UP')),
(False, _('DOWN'))],
label=_("Admin State"))
router_id = forms.CharField(label=_("ID"),
widget=forms.TextInput(
attrs={'readonly': 'readonly'}))
mode = forms.ChoiceField(label=_("Router Type"))
ha = forms.BooleanField(label=_("High Availability Mode"), required=False)
redirect_url = reverse_lazy('horizon:project:routers:index')
def __init__(self, request, *args, **kwargs):
super(UpdateForm, self).__init__(request, *args, **kwargs)
self.dvr_allowed = api.neutron.get_feature_permission(self.request,
"dvr", "update")
if not self.dvr_allowed:
del self.fields['mode']
elif kwargs.get('initial', {}).get('mode') == 'distributed':
# Neutron supports only changing from centralized to
# distributed now.
mode_choices = [('distributed', _('Distributed'))]
self.fields['mode'].widget = forms.TextInput(attrs={'readonly':
'readonly'})
self.fields['mode'].choices = mode_choices
else:
mode_choices = [('centralized', _('Centralized')),
('distributed', _('Distributed'))]
self.fields['mode'].choices = mode_choices
# TODO(amotoki): Due to Neutron Bug 1378525, Neutron disables
# PUT operation. It will be fixed in Kilo cycle.
# self.ha_allowed = api.neutron.get_feature_permission(
# self.request, "l3-ha", "update")
self.ha_allowed = False
if not self.ha_allowed:
del self.fields['ha']
def handle(self, request, data):
try:
params = {'admin_state_up': (data['admin_state'] == 'True'),
'name': data['name']}
if self.dvr_allowed:
params['distributed'] = (data['mode'] == 'distributed')
if self.ha_allowed:
params['ha'] = data['ha']
router = api.neutron.router_update(request, data['router_id'],
**params)
msg = _('Router %s was successfully updated.') % data['name']
LOG.debug(msg)
messages.success(request, msg)
return router
except Exception:
msg = _('Failed to update router %s') % data['name']
LOG.info(msg)
exceptions.handle(request, msg, redirect=self.redirect_url)
Router namespace not created in controller
When creating a router with an external network attached
from the Create Router form a router namespace doesn't
get created on the controller.
By calling the correct APIs, the namespace get
created.
To check the namespace list run the below command
on controller node.
'sudo ip netns list'
Change-Id: I829c707353830ff808b6f6a0b29ccedeca588616
Closes-Bug: #1535707
# Copyright 2012, Nachi Ueno, NTT MCL, Inc.
# 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.
"""
Views for managing Neutron Routers.
"""
import logging
from django.core.urlresolvers import reverse
from django.core.urlresolvers import reverse_lazy
from django.utils.translation import ugettext_lazy as _
from horizon import exceptions
from horizon import forms
from horizon import messages
from openstack_dashboard import api
LOG = logging.getLogger(__name__)
class CreateForm(forms.SelfHandlingForm):
name = forms.CharField(max_length=255, label=_("Router Name"))
admin_state_up = forms.ChoiceField(label=_("Admin State"),
choices=[(True, _('UP')),
(False, _('DOWN'))],
required=False)
external_network = forms.ChoiceField(label=_("External Network"),
required=False)
mode = forms.ChoiceField(label=_("Router Type"))
ha = forms.ChoiceField(label=_("High Availability Mode"))
failure_url = 'horizon:project:routers:index'
def __init__(self, request, *args, **kwargs):
super(CreateForm, self).__init__(request, *args, **kwargs)
self.dvr_allowed = api.neutron.get_feature_permission(self.request,
"dvr", "create")
if self.dvr_allowed:
mode_choices = [('server_default', _('Use Server Default')),
('centralized', _('Centralized')),
('distributed', _('Distributed'))]
self.fields['mode'].choices = mode_choices
else:
del self.fields['mode']
self.ha_allowed = api.neutron.get_feature_permission(self.request,
"l3-ha", "create")
if self.ha_allowed:
ha_choices = [('server_default', _('Use Server Default')),
('enabled', _('Enable HA mode')),
('disabled', _('Disable HA mode'))]
self.fields['ha'].choices = ha_choices
else:
del self.fields['ha']
networks = self._get_network_list(request)
if networks:
self.fields['external_network'].choices = networks
else:
del self.fields['external_network']
def _get_network_list(self, request):
search_opts = {'router:external': True}
try:
networks = api.neutron.network_list(request, **search_opts)
except Exception:
msg = _('Failed to get network list.')
LOG.info(msg)
messages.warning(request, msg)
networks = []
choices = [(network.id, network.name or network.id)
for network in networks]
if choices:
choices.insert(0, ("", _("Select network")))
return choices
def handle(self, request, data):
try:
params = {'name': data['name']}
if 'admin_state_up' in data and data['admin_state_up']:
params['admin_state_up'] = data['admin_state_up']
if (self.dvr_allowed and data['mode'] != 'server_default'):
params['distributed'] = (data['mode'] == 'distributed')
if (self.ha_allowed and data['ha'] != 'server_default'):
params['ha'] = (data['ha'] == 'enabled')
router = api.neutron.router_create(request, **params)
except Exception as exc:
if exc.status_code == 409:
msg = _('Quota exceeded for resource router.')
else:
msg = _('Failed to create router "%s".') % data['name']
LOG.info(msg)
redirect = reverse(self.failure_url)
exceptions.handle(request, msg, redirect=redirect)
return False
# workaround for neutron bug #1535707
try:
if ('external_network' in data and
data['external_network']):
api.neutron.router_add_gateway(request,
router['id'],
data['external_network'])
message = _('Router %s was successfully created.') % data['name']
messages.success(request, message)
return router
except Exception:
try:
api.neutron.router_delete(request, router['id'])
message = _('Router %s was created but connecting to'
' an external network failed. The created'
' router has been deleted, as the overall'
' operation failed.') % data['name']
LOG.info(message)
redirect = reverse(self.failure_url)
exceptions.handle(request, message, redirect=redirect)
return False
except Exception:
message = _('Router %(name)s was created but connecting to'
' an external network failed. Attempts to'
' delete the new router also failed.'
' Router %(name)s still exists but is not connect'
' to the desired external network.') % {
'name': data['name']}
LOG.info(message)
redirect = reverse(self.failure_url)
exceptions.handle(request, message, redirect=redirect)
return False
class UpdateForm(forms.SelfHandlingForm):
name = forms.CharField(label=_("Name"), required=False)
admin_state = forms.ChoiceField(choices=[(True, _('UP')),
(False, _('DOWN'))],
label=_("Admin State"))
router_id = forms.CharField(label=_("ID"),
widget=forms.TextInput(
attrs={'readonly': 'readonly'}))
mode = forms.ChoiceField(label=_("Router Type"))
ha = forms.BooleanField(label=_("High Availability Mode"), required=False)
redirect_url = reverse_lazy('horizon:project:routers:index')
def __init__(self, request, *args, **kwargs):
super(UpdateForm, self).__init__(request, *args, **kwargs)
self.dvr_allowed = api.neutron.get_feature_permission(self.request,
"dvr", "update")
if not self.dvr_allowed:
del self.fields['mode']
elif kwargs.get('initial', {}).get('mode') == 'distributed':
# Neutron supports only changing from centralized to
# distributed now.
mode_choices = [('distributed', _('Distributed'))]
self.fields['mode'].widget = forms.TextInput(attrs={'readonly':
'readonly'})
self.fields['mode'].choices = mode_choices
else:
mode_choices = [('centralized', _('Centralized')),
('distributed', _('Distributed'))]
self.fields['mode'].choices = mode_choices
# TODO(amotoki): Due to Neutron Bug 1378525, Neutron disables
# PUT operation. It will be fixed in Kilo cycle.
# self.ha_allowed = api.neutron.get_feature_permission(
# self.request, "l3-ha", "update")
self.ha_allowed = False
if not self.ha_allowed:
del self.fields['ha']
def handle(self, request, data):
try:
params = {'admin_state_up': (data['admin_state'] == 'True'),
'name': data['name']}
if self.dvr_allowed:
params['distributed'] = (data['mode'] == 'distributed')
if self.ha_allowed:
params['ha'] = data['ha']
router = api.neutron.router_update(request, data['router_id'],
**params)
msg = _('Router %s was successfully updated.') % data['name']
LOG.debug(msg)
messages.success(request, msg)
return router
except Exception:
msg = _('Failed to update router %s') % data['name']
LOG.info(msg)
exceptions.handle(request, msg, redirect=self.redirect_url)
|
from __future__ import absolute_import, print_function
import os
import pytest
from tests import DATA_DIR
from mantid.api import AnalysisDataService
from addie.rietveld import event_handler
from addie.main import MainWindow
bragg_file_names = ['GSAS_NaNO3_230C.gsa', 'GSAS_NaNO3_275C.gsa']
bragg_file_list = [os.path.join(DATA_DIR, name) for name in bragg_file_names]
@pytest.fixture
def rietveld_event_handler(qtbot):
return event_handler
def test_load_bragg_files(qtbot, rietveld_event_handler):
"""Test load_bragg_files when no files to load"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_names)
def test_plot_bragg_bank_for_multi_bank(qtbot, rietveld_event_handler):
"""Test plot_bragg_bank for Multi-Bank mode"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_list)
main_window.rietveld_ui.graphicsView_bragg.set_to_single_gss(True)
main_window.rietveld_ui.radioButton_multiBank.setChecked(True)
main_window.rietveld_ui.radioButton_multiGSS.setChecked(False)
rietveld_event_handler.plot_bragg_bank(main_window)
def test_plot_bragg_bank_for_multi_gsas(qtbot, rietveld_event_handler):
"""Test plot_bragg_bank for Multi-GSAS mode"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_list)
main_window.rietveld_ui.graphicsView_bragg.set_to_single_gss(False)
main_window.rietveld_ui.radioButton_multiBank.setChecked(False)
main_window.rietveld_ui.radioButton_multiGSS.setChecked(True)
rietveld_event_handler.plot_bragg_bank(main_window)
def test_evt_change_gss_mode_exception(qtbot, rietveld_event_handler):
"""Test we raise exception for main_window==None to change_gss_mode"""
with pytest.raises(NotImplementedError):
rietveld_event_handler.evt_change_gss_mode(None)
def test_load_bragg_by_filename(qtbot, rietveld_event_handler):
"""Test that we can load Bragg *.gsa (GSAS) files"""
filename = 'NOM_127827.gsa'
files = [os.path.join(DATA_DIR, filename)]
for filename in files:
wksp, angles = rietveld_event_handler.load_bragg_by_filename(filename)
wksp == os.path.basename(filename).split('.')[0]
angles_exp = [15.1, 31., 65., 120.4, 150.1, 8.6] # copied from file by hand
for obs, expected in zip(angles, angles_exp):
assert obs == pytest.approx(expected, rel=0.1)
assert AnalysisDataService.doesExist(wksp)
Add real test for addie.rietveld.event_handler.load_bragg_files
from __future__ import absolute_import, print_function
import os
import pytest
from tests import DATA_DIR
from mantid.api import AnalysisDataService
from addie.rietveld import event_handler
from addie.main import MainWindow
bragg_file_names = ['GSAS_NaNO3_230C.gsa', 'GSAS_NaNO3_275C.gsa']
bragg_file_list = [os.path.join(DATA_DIR, name) for name in bragg_file_names]
@pytest.fixture
def rietveld_event_handler(qtbot):
return event_handler
def test_load_bragg_files_no_files(qtbot, rietveld_event_handler):
"""Test load_bragg_files when no files to load"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, None)
bragg_tree = main_window.rietveld_ui.treeWidget_braggWSList
assert bragg_tree.model().rowCount() == 1
def test_load_bragg_files(qtbot, rietveld_event_handler):
"""Test load_bragg_files when we load in 2 files
Will have a row count == 3 since there is the empty 'workspaces' row
"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_list)
bragg_tree = main_window.rietveld_ui.treeWidget_braggWSList
assert bragg_tree.model().rowCount() == 3
def test_plot_bragg_bank_for_multi_bank(qtbot, rietveld_event_handler):
"""Test plot_bragg_bank for Multi-Bank mode"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_list)
main_window.rietveld_ui.graphicsView_bragg.set_to_single_gss(True)
main_window.rietveld_ui.radioButton_multiBank.setChecked(True)
main_window.rietveld_ui.radioButton_multiGSS.setChecked(False)
rietveld_event_handler.plot_bragg_bank(main_window)
def test_plot_bragg_bank_for_multi_gsas(qtbot, rietveld_event_handler):
"""Test plot_bragg_bank for Multi-GSAS mode"""
main_window = MainWindow()
rietveld_event_handler.load_bragg_files(main_window, bragg_file_list)
main_window.rietveld_ui.graphicsView_bragg.set_to_single_gss(False)
main_window.rietveld_ui.radioButton_multiBank.setChecked(False)
main_window.rietveld_ui.radioButton_multiGSS.setChecked(True)
rietveld_event_handler.plot_bragg_bank(main_window)
def test_evt_change_gss_mode_exception(qtbot, rietveld_event_handler):
"""Test we raise exception for main_window==None to change_gss_mode"""
with pytest.raises(NotImplementedError):
rietveld_event_handler.evt_change_gss_mode(None)
def test_load_bragg_by_filename(qtbot, rietveld_event_handler):
"""Test that we can load Bragg *.gsa (GSAS) files"""
filename = 'NOM_127827.gsa'
files = [os.path.join(DATA_DIR, filename)]
for filename in files:
wksp, angles = rietveld_event_handler.load_bragg_by_filename(filename)
wksp == os.path.basename(filename).split('.')[0]
angles_exp = [15.1, 31., 65., 120.4, 150.1, 8.6] # copied from file by hand
for obs, expected in zip(angles, angles_exp):
assert obs == pytest.approx(expected, rel=0.1)
assert AnalysisDataService.doesExist(wksp)
|
import numpy as np
import matplotlib.pyplot as plt
import scipy
import lmfit
import itertools
import logging
from pycqed.simulations import transmon
from pycqed.utilities.timer import Timer
log = logging.getLogger(__name__)
#################################
# Fitting Functions Library #
#################################
def RandomizedBenchmarkingLeakage(numCliff, pu, pd, p0):
# from https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.116.020501
return pu/(pd+pu) * (1-np.exp(-(pd+pu)*numCliff)) + \
p0*np.exp(-(pd+pu)*numCliff)
def RandomizedBenchmarkingDecay(numCliff, Amplitude, p, offset):
val = Amplitude * (p ** numCliff) + offset
return val
def DoubleExpDampOscFunc(t, tau_1, tau_2,
freq_1, freq_2,
phase_1, phase_2,
amp_1, amp_2, osc_offset):
cos_1 = amp_1 * (np.cos(2 * np.pi * freq_1 * t + phase_1)) * \
np.exp(-(t / tau_1))
cos_2 = amp_2 * (np.cos(2 * np.pi * freq_2 * t + phase_2)) * \
np.exp(-(t / tau_2))
return cos_1 + cos_2 + osc_offset
def double_RandomizedBenchmarkingDecay(numCliff, p, offset,
invert=1):
"""
A variety of the RB-curve that allows fitting both the inverting and
non-inverting exponential.
The amplitude of the decay curve is constrained to start at 0 or 1.
The offset is the common point both curves converge to.
pick invert to be 1 or 0
"""
# Inverting clifford curve
val_inv = (1 - offset) * (p ** numCliff) + offset
# flipping clifford curve
val_flip = -offset * (p ** numCliff) + offset
# Using invert as a boolean but not using if statement to allow for
# arrays to be input in the function
val = (1 - invert) * val_flip + invert * val_inv
return val
def LorentzFunc(f, amplitude, center, sigma):
val = amplitude / np.pi * (sigma / ((f - center) ** 2 + sigma ** 2))
return val
def Lorentzian(f, A, offset, f0, kappa):
val = offset + A / np.pi * (kappa / ((f - f0) ** 2 + kappa ** 2))
return val
def TwinLorentzFunc(f, A_gf_over_2, A, f0_gf_over_2, f0,
kappa_gf_over_2, kappa, background=0):
"""
Twin lorentz with background.
Args:
f (float): frequency sweep points in Hz
A (float): amplitude of the tallest/deepest Lorentzian structure
in the data
A_gf_over_2 (float): amplitude of the other Lorentzian structure in the
data; since this function is used for high power
qubit spectroscopy, this parameter refers to the
Lorentzian structure corresponding to the gf/2
transition
f0 (float): frequency of the tallest/deepest Lorentzian structure
in the data
f0_gf_over_2 (float): frequency of the other Lorentzian structure in the
data; since this function is used for high power
qubit spectroscopy, this parameter refers to the
Lorentzian structure corresponding to the gf/2
transition
kappa (float): kappa (FWHM) of the tallest/deepest Lorentzian structure
in the data
kappa_gf_over_2 (float): kappa (FWHM) of the other Lorentzian structure in
the data; since this function is used for high
power qubit spectroscopy, this parameter refers to
the Lorentzian structure corresponding to the gf/2
transition
background (float): background offset
"""
val = (A_gf_over_2 / np.pi * (kappa_gf_over_2 / ((f - f0_gf_over_2) ** 2 + kappa_gf_over_2 ** 2)) +
A / np.pi * (kappa / ((f - f0) ** 2 + kappa ** 2)) + background)
return val
def Qubit_dac_to_freq(dac_voltage, f_max,
dac_sweet_spot=None, V_per_phi0=None, phi_park=None,
dac_flux_coefficient=None, E_c=0,
asymmetry=0.5):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
dac_sweet_spot (V): voltage at which the sweet-spot is found
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2)),
'''
# using E_c as fit parameter is numerically not stable (changing E_c does only
# slightly change the function. Set E_c to zero
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
qubit_freq = (f_max + E_c) * (
asymmetry ** 2 + (1 - asymmetry ** 2) *
np.cos(np.pi / V_per_phi0 *
(dac_voltage - dac_sweet_spot)) ** 2) ** 0.25 - E_c
return qubit_freq
def Qubit_dac_to_freq_precise(dac_voltage, Ej_max, E_c, asymmetry,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None
):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
Ejmax (Hz): Maximum Ej of qubit
Ec (Hz): charging energy of the qubit
d = abs((EJ1-EJ2)/(EJ1+EJ2)))
dac_sweet_spot (V): voltage at which the sweet-spot is found
V_per_phi0 (V): volt per phi0 (convert voltage to flux
'''
if np.ndim(dac_voltage) == 0:
dac_voltage = np.array([dac_voltage])
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
phi = np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot)
Ej = 2 * np.pi * Ej_max * np.cos(phi) * np.sqrt(1 + asymmetry ** 2 * np.tan(phi) ** 2)
E_c = 2 * np.pi * E_c
freqs = []
for ej in Ej:
freqs.append((transmon.transmon_levels(E_c, ej) / (2 * np.pi))[0])
qubit_freq = np.array(freqs)
return qubit_freq
def Qubit_dac_to_freq_res(dac_voltage, Ej_max, E_c, asymmetry, coupling, fr,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None, dim_charge=31,
):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
Ejmax (Hz): Maximum Ej of qubit
Ec (Hz): charging energy of the qubit
d = abs((EJ1-EJ2)/(EJ1+EJ2)))
dac_sweet_spot (V): voltage at which the sweet-spot is found
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
phi_park
'''
return_float = False
if np.ndim(dac_voltage) == 0:
dac_voltage = np.array([dac_voltage])
return_float = True
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
phi = np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot)
Ej = Ej_max * np.cos(phi) * np.sqrt(1 + asymmetry ** 2 * np.tan(phi) ** 2)
with Timer('fitmod.loop', verbose=False):
freqs = [(transmon.transmon_resonator_levels(E_c,
ej,
fr,
coupling,
states=[(1, 0), (2, 0)],
dim_charge=dim_charge
))[0]
for ej in Ej]
qubit_freq = np.array(freqs)
return qubit_freq[0] if return_float else qubit_freq
def Qubit_freq_to_dac_res(frequency, Ej_max, E_c, asymmetry, coupling, fr,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None,
branch='smallest'):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
This function implements the inverse of "Qubit_dac_to_freq"
frequency (Hz)
Ej_max (Hz): Maximum josephson energy
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
couping (Hz): coupling to resonator
fr (Hz): frequency of resonator
dac_sweet_spot (V): voltage at which the sweet-spot is found
branch (enum: 'positive' 'negative' or "smallest")
'''
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
return_float = False
if np.ndim(frequency) == 0:
frequency = [frequency]
return_float = True
E_j = [transmon.transmon_resonator_ej_anh_frg_chi(
f, ec=E_c, frb=fr, gb=coupling)[0]
for f in frequency]
E_j = np.array(E_j)
r = E_j / Ej_max
if np.any(r > 1):
log.warning(f'Ratio Ej/Ej_max is larger than 1 at '
f'indices {np.argwhere(r > 1)}.Truncating to 1.')
r[r>1] = 1
phi = np.arccos(np.sqrt((r**2 - asymmetry**2)/(1-asymmetry**2)))
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
dac_voltage_pos = phi * V_per_phi0 / np.pi + dac_sweet_spot
dac_voltage_neg = -phi * V_per_phi0 / np.pi + dac_sweet_spot
if branch == 'positive':
dac_voltage = dac_voltage_pos
elif branch == 'negative':
dac_voltage = dac_voltage_neg
elif branch == 'smallest':
if np.ndim(phi) != 0:
dac_voltage = np.array([dac_voltage_pos, dac_voltage_neg])
idxs0 = np.argmin(np.abs(dac_voltage), 0)
idxs1 = np.arange(len(dac_voltage_pos))
dac_voltage = dac_voltage[idxs0, idxs1]
else:
dac_voltage = dac_voltage_pos \
if abs(dac_voltage_pos) < abs(dac_voltage_neg) \
else dac_voltage_neg
else:
raise ValueError('branch {} not recognized'.format(branch))
return dac_voltage[0] if return_float else dac_voltage
def Resonator_dac_to_freq(dac_voltage, f_max_qubit, f_0_res,
E_c, dac_sweet_spot,
coupling, V_per_phi0=None,
dac_flux_coefficient=None,
asymmetry=0):
qubit_freq = Qubit_dac_to_freq(dac_voltage=dac_voltage, f_max=f_max_qubit, E_c=E_c,
dac_sweet_spot=dac_sweet_spot, V_per_phi0=V_per_phi0,
dac_flux_coefficient=dac_flux_coefficient,
asymmetry=asymmetry)
delta_qr = (qubit_freq - f_0_res)
lamb_shift = (coupling ** 2 / delta_qr)
resonator_freq = f_0_res - lamb_shift
return resonator_freq
def Qubit_dac_to_detun(dac_voltage, f_max, E_c, dac_sweet_spot, V_per_phi0,
asymmetry=0):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
dac_sweet_spot (V): voltage at which the sweet-spot is found
asymmetry (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
'''
return f_max - Qubit_dac_to_freq(dac_voltage,
f_max=f_max, E_c=E_c,
dac_sweet_spot=dac_sweet_spot,
V_per_phi0=V_per_phi0,
asymmetry=asymmetry)
def Qubit_freq_to_dac(frequency, f_max,
dac_sweet_spot=None, V_per_phi0=None,
dac_flux_coefficient=None, asymmetry=0, E_c=0,
phi_park=None,
branch='smallest'):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
This function implements the inverse of "Qubit_dac_to_freq"
frequency (Hz)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
dac_sweet_spot (V): voltage at which the sweet-spot is found
branch (enum: 'positive' 'negative')
'''
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
# asymm_term = (asymmetry**2 + (1-asymmetry**2))
# dac_term = np.arccos(((frequency+E_c)/((f_max+E_c) * asymm_term))**2)
dac_term = np.arccos(np.sqrt(
(((frequency + E_c) / (f_max + E_c)) ** 4 - asymmetry ** 2) /
(1 - asymmetry ** 2)))
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
dac_voltage_pos = dac_term * V_per_phi0 / np.pi + dac_sweet_spot
dac_voltage_neg = -dac_term * V_per_phi0 / np.pi + dac_sweet_spot
if branch == 'positive':
dac_voltage = dac_voltage_pos
elif branch == 'negative':
dac_voltage = dac_voltage_neg
elif branch == 'smallest':
if np.ndim(dac_term) != 0:
dac_voltage = np.array([dac_voltage_pos, dac_voltage_neg])
idxs0 = np.argmin(np.abs(dac_voltage), 0)
idxs1 = np.arange(len(dac_voltage_pos))
dac_voltage = dac_voltage[idxs0, idxs1]
else:
dac_voltage = dac_voltage_pos \
if abs(dac_voltage_pos) < abs(dac_voltage_neg) \
else dac_voltage_neg
else:
raise ValueError('branch {} not recognized'.format(branch))
return dac_voltage
def Qubit_dac_sensitivity(dac_voltage, f_max: float, E_c: float,
dac_sweet_spot: float, V_per_phi0: float,
asymmetry: float = 0):
'''
Derivative of the qubit detuning vs dac at dac_voltage.
The returned quantity is "dfreq/dPhi (dac_voltage)"
'''
cos_term = np.cos(np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot))
sin_term = np.sin(np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot))
return ((f_max + E_c) * (1 - asymmetry ** 2) * np.pi / (2 * V_per_phi0) *
cos_term * sin_term * (asymmetry ** 2 + (1 - asymmetry ** 2) *
cos_term ** 2) ** (-0.75))
def QubitFreqDac(dac_voltage, f_max, E_c,
dac_sweet_spot, dac_flux_coefficient, asymmetry=0):
log.warning('deprecated, replace QubitFreqDac with Qubit_dac_to_freq')
return Qubit_dac_to_freq(dac_voltage, f_max, E_c,
dac_sweet_spot, dac_flux_coefficient, asymmetry)
def QubitFreqFlux(flux, f_max, E_c,
flux_zero, dac_offset=0):
'The cosine Arc model for calibrated flux.'
calculated_frequency = (f_max + E_c) * np.sqrt(np.abs(
np.cos(np.pi * (flux - dac_offset) / flux_zero))) - E_c
return calculated_frequency
def CosFunc(t, amplitude, frequency, phase, offset):
'''
parameters:
t, time in s
amplitude a.u.
frequency in Hz (f, not omega!)
phase in rad
offset a.u.
'''
return amplitude * np.cos(2 * np.pi * frequency * t + phase) + offset
def ResidZZFuncJoint(t, amplitude, amplitude1, tau, alpha, t11, frequency,
phase, offset):
x = t11*alpha
without_pulse = amplitude * np.exp(-t/tau)*np.cos(
2*np.pi*frequency*t + phase) + offset
with_pulse = amplitude1 * np.exp(-t/tau)*(x*np.exp(-t*alpha/x)*np.cos(
2*np.pi*(frequency+alpha)*t + phase) - np.sin(
2*np.pi*frequency*t + phase))/np.sqrt(1+x**2) + offset
return without_pulse, with_pulse
# return amplitude * np.exp(-(t / tau) ** n) * (np.cos(
# 2 * np.pi * frequency * t + phase) + oscillation_offset) + \
# exponential_offset
def ResidZZFunc(t, amplitude, tau, alpha, x, frequency, phase, offset):
return amplitude * np.exp(-t/tau)*(x*np.exp(-t*alpha/x)*np.cos(
2*np.pi*(frequency+alpha)*t + phase) - np.sin(
2*np.pi*frequency*t + phase))/np.sqrt(1+x**2) + offset
def ExpDecayFunc(t, tau, amplitude, offset, n):
return amplitude * np.exp(-(t / tau) ** n) + offset
def ExpDecayPmod(t, T2echo, delta, n0, chi, kappa, phase, amplitude, offset):
"""
specific form of exponential decay used for residual resonator photon readout
"""
return amplitude * (1 - np.imag(np.exp(-t*(1/T2echo + 2j*np.pi*delta)+1j*(
phase-2*n0*chi*(1-np.exp(-t*(kappa + 2j*chi)))/(kappa + 2j*chi)))))+offset
def CombinedOszExpDecayFunc(t, tau, tau_gauss, phase, n0, chi, delta, amplitude,
oscillation_offset, offset):
"""
Combination of gaussian and exponential decay
"""
return amplitude * np.exp(-(t / tau)-(t / tau_gauss))*(np.cos(
2 * np.pi * (n0*2*chi/(2*np.pi)+delta) * t + phase) +
oscillation_offset) + offset
def idle_error_rate_exp_decay(N, N1, N2, A, offset):
"""
exponential decay consisting of two components
"""
return A * np.exp(-N / N1 - (N / N2) ** 2) + offset
def gain_corr_ExpDecayFunc(t, tau, amp, gc):
"""
Specific form of an exponential decay used for flux corrections.
Includes a "gain correction" parameter that is ignored when correcting
the distortions.
"""
y = gc * (1 + amp * np.exp(-t / tau))
return y
def gain_corr_double_ExpDecayFunc(t, tau_A, tau_B, amp_A, amp_B, gc):
"""
Specific form of an exponential decay used for flux corrections.
Includes a "gain correction" parameter that is ignored when correcting
the distortions.
"""
y = gc * (1 + amp_A * np.exp(-t / tau_A) + amp_B * np.exp(-t / tau_B))
return y
def ExpDampOscFunc(t, tau, n, frequency, phase, amplitude,
oscillation_offset, exponential_offset):
return amplitude * np.exp(-(t / tau) ** n) * (np.cos(
2 * np.pi * frequency * t + phase) + oscillation_offset) + \
exponential_offset
def GaussExpDampOscFunc(t, tau, tau_2, frequency, phase, amplitude,
oscillation_offset, exponential_offset):
return amplitude * np.exp(-(t / tau_2) ** 2 - (t / tau)) * (np.cos(
2 * np.pi * frequency * t + phase) + oscillation_offset) + exponential_offset
def ExpDampDblOscFunc(t, tau, n, freq_1, freq_2, phase_1, phase_2,
amp_1, amp_2,
osc_offset_1, osc_offset_2, exponential_offset):
'''
Exponential decay with double cosine modulation
'''
exp_decay = np.exp(-(t / tau) ** n)
cos_1 = (np.cos(
2 * np.pi * freq_1 * t + phase_1) + osc_offset_1)
cos_2 = (np.cos(
2 * np.pi * freq_2 * t + phase_2) + osc_offset_2)
return amp_1 * exp_decay * cos_1 + amp_2 * exp_decay * cos_2 + exponential_offset
def HangerFuncAmplitude(f, f0, Q, Qe, A, theta):
'''
This is the function for a hanger which does not take into account
a possible slope.
This function may be preferred over SlopedHangerFunc if the area around
the hanger is small.
In this case it may misjudge the slope
Theta is the asymmetry parameter
Note! units are inconsistent
f is in Hz
f0 is in GHz
'''
return abs(A * (1. - Q / Qe * np.exp(1.j * theta) / (1. + 2.j * Q * (f / 1.e9 - f0) / f0)))
def HangerFuncComplex(f, pars):
'''
This is the complex function for a hanger which DOES NOT
take into account a possible slope
Input:
f = frequency
pars = parameters dictionary
f0, Q, Qe, A, theta, phi_v, phi_0
Author: Stefano Poletto
'''
f0 = pars['f0']
Q = pars['Q']
Qe = pars['Qe']
A = pars['A']
theta = pars['theta']
phi_v = pars['phi_v']
phi_0 = pars['phi_0']
S21 = A * (1 - Q / Qe * np.exp(1j * theta) / (1 + 2.j * Q * (f / 1.e9 - f0) / f0)) * \
np.exp(1j * (phi_v * f + phi_0))
return S21
def PolyBgHangerFuncAmplitude(f, f0, Q, Qe, A, theta, poly_coeffs):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible polynomial background
# NOT DEBUGGED
return np.abs((1. + np.polyval(poly_coeffs, (f / 1.e9 - f0) / f0)) *
HangerFuncAmplitude(f, f0, Q, Qe, A, theta))
def SlopedHangerFuncAmplitude(f, f0, Q, Qe, A, theta, slope):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible slope df
return np.abs((1. + slope * (f / 1.e9 - f0) / f0) *
HangerFuncAmplitude(f, f0, Q, Qe, A, theta))
def SlopedHangerFuncComplex(f, f0, Q, Qe, A, theta, phi_v, phi_0, slope):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible slope df
return (1. + slope * (f / 1.e9 - f0) / f0) * np.exp(1.j * (phi_v * f + phi_0 - phi_v * f[0])) * \
HangerFuncComplex(f, f0, Q, Qe, A, theta)
def linear_with_offset(x, a, b):
'''
A linear signal with a fixed offset.
'''
return a * x + b
def linear_with_background(x, a, b):
'''
A linear signal with a fixed background.
'''
return np.sqrt((a * x) ** 2 + b ** 2)
def linear_with_background_and_offset(x, a, b, c):
'''
A linear signal with a fixed background.
'''
return np.sqrt((a * x) ** 2 + b ** 2) + c
def gaussianCDF(x, amplitude, mu, sigma):
"""
CDF of gaussian is P(X<=x) = .5 erfc((mu-x)/(sqrt(2)sig))
"""
return 0.5 * amplitude * scipy.special.erfc((mu - x) / (np.sqrt(2)*sigma))
def double_gaussianCDF(x, A_amplitude, A_mu, A_sigma,
B_amplitude, B_mu, B_sigma):
"""
CDF of two gaussians added on top of each other.
uses "gaussianCDF"
"""
CDF_A = gaussianCDF(x, amplitude=A_amplitude, mu=A_mu, sigma=A_sigma)
CDF_B = gaussianCDF(x, amplitude=B_amplitude, mu=B_mu, sigma=B_sigma)
return CDF_A + CDF_B
def TwoErrorFunc(x, amp, mu_A, mu_B, sigma, offset):
'''
parameters:
'''
return offset + double_gaussianCDF(x, amp, mu_A, sigma, -amp, mu_B, sigma)
def ro_gauss(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
'''
Two double-gaussians with sigma and mu/center of the residuals equal to the
according state.
'''
gauss = lmfit.lineshapes.gaussian
A_gauss = gauss(x=x[0], center=A_center, sigma=A_sigma, amplitude=A_amplitude)
B_gauss = gauss(x=x[1], center=B_center, sigma=B_sigma, amplitude=B_amplitude)
gauss0 = ((1-A_spurious)*A_gauss + A_spurious*B_gauss)
gauss1 = ((1-B_spurious)*B_gauss + B_spurious*A_gauss)
return [gauss0, gauss1]
def ro_CDF(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
cdf = gaussianCDF
A_gauss = cdf(x=x[0], mu=A_center, sigma=A_sigma, amplitude=A_amplitude)
B_gauss = cdf(x=x[1], mu=B_center, sigma=B_sigma, amplitude=B_amplitude)
gauss0 = ((1-A_spurious)*A_gauss + A_spurious*B_gauss)
gauss1 = ((1-B_spurious)*B_gauss + B_spurious*A_gauss)
return [gauss0, gauss1]
def ro_CDF_discr(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
#A_amplitude /= 1-A_spurious
#B_amplitude /= 1-B_spurious
return ro_CDF(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious=0, B_spurious=0)
def gaussian_2D(x, y, amplitude=1,
center_x=0, center_y=0,
sigma_x=1, sigma_y=1):
'''
A 2D gaussian function. if you want to use this for fitting you need to
flatten your data first.
'''
gauss = lmfit.lineshapes.gaussian
val = (gauss(x, amplitude, center_x, sigma_x) *
gauss(y, amplitude, center_y, sigma_y))
return val
def DoubleExpDecayFunc(t, tau1, tau2, amp1, amp2, offset, n):
return (offset +
amp1 * np.exp(-(t / tau1) ** n) +
amp2 * np.exp(-(t / tau2) ** n))
def TripleExpDecayFunc(t, tau1, tau2, tau3, amp1, amp2, amp3, offset, n):
return (offset +
amp1 * np.exp(-(t / tau1) ** n) +
amp2 * np.exp(-(t / tau2) ** n) +
amp3 * np.exp(-(t / tau3) ** n))
def avoided_crossing_mediated_coupling(flux, f_bus, f_center1, f_center2,
c1, c2, g, flux_state=0):
"""
Calculates the frequencies of an avoided crossing for the following model.
[f_b, g, g ]
[g, f_1, 0 ]
[g, 0, f_2]
f1 = c1*flux + f_center1
f2 = c2*flux + f_center2
f_b = constant
g: the coupling strength, beware to relabel your variable if using this
model to fit J1 or J2.
flux_state: this is a switch used for fitting. It determines which
transition to return
"""
if type(flux_state) == int:
flux_state = [flux_state] * len(flux)
frequencies = np.zeros([len(flux), 2])
for kk, dac in enumerate(flux):
f_1 = dac * c1 + f_center1
f_2 = dac * c2 + f_center2
matrix = [[f_bus, g, g],
[g, f_1, 0.],
[g, 0., f_2]]
frequencies[kk, :] = np.linalg.eigvalsh(matrix)[:2]
result = np.where(flux_state, frequencies[:, 0], frequencies[:, 1])
return result
def avoided_crossing_direct_coupling(flux, f_center1, f_center2,
c1, c2, g, flux_state=0):
"""
Calculates the frequencies of an avoided crossing for the following model.
[f_1, g ]
[g, f_2]
f1 = c1*flux + f_center1
f2 = c2*flux + f_center2
g: the coupling strength, beware to relabel your variable if using this
model to fit J1 or J2.
flux_state: this is a switch used for fitting. It determines which
transition to return
"""
if type(flux_state) == int:
flux_state = [flux_state] * len(flux)
frequencies = np.zeros([len(flux), 2])
for kk, dac in enumerate(flux):
f_1 = dac * c1 + f_center1
f_2 = dac * c2 + f_center2
matrix = [[f_1, g],
[g, f_2]]
frequencies[kk, :] = np.linalg.eigvalsh(matrix)[:2]
result = np.where(flux_state, frequencies[:, 0], frequencies[:, 1])
return result
def ErfWindow(t, t_start, t_end, t_rise, amplitude, offset):
'''
parameters:
t, time in s
t_start, start of window in s
t_end, end of window in s
amplitude a.u.
offset a.u.
t_rise in s (rise time)
'''
return offset + amplitude/2*(scipy.special.erf((t - t_start)/(t_rise/2.6))
- scipy.special.erf((t - t_end)/(t_rise/2.6)))
def hanger_with_pf(f, phi, J, kappa_pf, omega_ro, omega_pf, gamma_ro, A):
S12 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f-omega_ro))/
(4*J*J+(kappa_pf-2j*(f-omega_pf))*
(gamma_ro-2j*(f-omega_ro))) )
return S12
def simultan_hanger_with_pf(f, phi, J, kappa_pf, omega_ro_0, omega_ro_1,
omega_pf, gamma_ro, A):
f0 = f[:int(len(f)/2)]
f1 = f[int(len(f)/2):]
S12_0 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f0-omega_ro_0))/
(4*J*J+(kappa_pf-2j*(f0-omega_pf))*
(gamma_ro-2j*(f0-omega_ro_0))) )
S12_1 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f1-omega_ro_1))/
(4*J*J+(kappa_pf-2j*(f1-omega_pf))*
(gamma_ro-2j*(f1-omega_ro_1))) )
return np.hstack((S12_0, S12_1))
def fit_hanger_with_pf(model, data, simultan=False):
"""
model: a lmfit model of the pf_ro_S12 function
data: of the shape [[freq,|S12|],[freq,|S12|],...] where freq should be in GHz
return: a lmfit model.fit object
"""
def hanger_with_pf_guess(model, data):
A_guess = 1
midmax = (data[0, 1]+data[-1, 1])/2
J_guess = 0.006
omega_pf_guess = min(data, key = lambda t: t[1])[0]
pf_filter = [False]*len(data)
for x in range(0,len(data)):
if data[x,1] <= midmax:
pf_filter[x] = True
kappa_pf_guess = min(2*abs(list(itertools.compress(
data[:, 0],pf_filter))[0]- omega_pf_guess),
2*abs(list(itertools.compress(
data[:, 0],pf_filter))[-1]- omega_pf_guess), 0.05)
omega_ro_filter = [True]*len(data)
for x in range(0,len(data)):
if abs(data[x, 0]-omega_pf_guess) <= kappa_pf_guess/2:
omega_ro_filter[x] = False
omega_ro_guess = min(list(itertools.compress(
data,omega_ro_filter)), key = lambda t: t[1])[0]
for x in range(0,len(data)):
if min(omega_pf_guess, omega_ro_guess) <= data[x,0]:
if data[x,0] <= max(omega_pf_guess, omega_ro_guess):
if midmax <= data[x,1]: midmax = data[x,0]
model.set_param_hint('A', value=A_guess, min = 0, max = 1)
model.set_param_hint('phi', value=4 , min = 0, max = 2*np.pi)
model.set_param_hint('J', value=J_guess, min=0, max=0.015)
model.set_param_hint('kappa_pf', value=kappa_pf_guess, min=0, max=0.05)
model.set_param_hint('omega_pf', value=omega_pf_guess, min=0)
model.set_param_hint('gamma_ro', value=0.0001, vary = False)
model.set_param_hint('omega_ro', value=omega_ro_guess)
params = model.make_params()
return params
if simultan:
data_loc_0 = np.copy(data[0][:-2])
data_loc_1 = np.copy(data[1][:-2])
fit0 = fit_hanger_with_pf(HangerWithPfModel, np.transpose(
[data_loc_0[:,0], data_loc_0[:,1]]), simultan=False)
fit1 = fit_hanger_with_pf(HangerWithPfModel, np.transpose(
[data_loc_1[:,0], data_loc_1[:,1]]), simultan=False)
if not len(data_loc_1) == len(data_loc_0):
if len(data_loc_1) < len(data_loc_0):
data_loc_0 = data_loc_0[:len(data_loc_1)]
else:
data_loc_1 = data_loc_1[:len(data_loc_0)]
data_loc_0[:,0] *= 1e-9
data_loc_1[:,0] *= 1e-9
data_loc = np.vstack((data_loc_0, data_loc_1))
phi_guess = (fit0.params['phi'].value+fit1.params['phi'].value)/2
J_guess = (fit0.params['J'].value+fit1.params['J'].value)/2*1e-9
kap_guess = (fit0.params['kappa_pf'].value+
fit1.params['kappa_pf'].value)/2*1e-9
omega_pf_guess = (fit0.params['omega_pf'].value+
fit1.params['omega_pf'].value)/2*1e-9
model.set_param_hint('A', value=1, min = 0, max = 1)
model.set_param_hint('A', value=1, min = 0, max = 1)
model.set_param_hint('phi', value=phi_guess , min = 0, max = 2*np.pi)
model.set_param_hint('J', value=J_guess, min=0, max=0.015)
model.set_param_hint('kappa_pf', value=kap_guess, min=0, max=0.05)
model.set_param_hint('omega_pf', value=omega_pf_guess, min=0)
model.set_param_hint('gamma_ro', value=0.0001, vary = False)
model.set_param_hint('omega_ro_0', value=
fit0.params['omega_ro'].value*1e-9, min=0)
model.set_param_hint('omega_ro_1', value=
fit1.params['omega_ro'].value*1e-9, min=0)
guess = model.make_params()
fit_out = model.fit( data_loc[:,1]/max(data_loc[:,1]),
guess, f=data_loc[:,0])
tol = 0.4
else:
data_loc = np.copy(data)
data_loc[:,0] *= 1e-9
guess = hanger_with_pf_guess(model,np.transpose(
[data_loc[:,0],data_loc[:,1]/max(data_loc[:,1])]))
fit_out = model.fit( data_loc[:,1]/
max(data_loc[:,1]),guess,f=data_loc[:,0],)
tol = 0.1
if fit_out.chisqr > tol:
fit_lst = []
for shift_pf in np.linspace(-0.01, 0.01, 5):
for shift_ro in np.linspace(-0.01, 0.01, 5):
for phase in np.linspace(0, 2*np.pi, 6):
for kappa_shift in np.linspace(-0.015,0.015,4):
for J_shift in np.linspace(-0.002,0.004,4):
if simultan:
fit_lst.append(model.fit( data_loc[:,1]/
max(data_loc[:,1]),guess,
f=data_loc[:,0],
phi=phase,omega_pf=float(guess['omega_pf'])+shift_pf,
omega_ro_0=float(guess['omega_ro_0'])+shift_ro,
omega_ro_1=float(guess['omega_ro_1'])+shift_ro,
kappa_pf=float(guess['kappa_pf'])+kappa_shift,
J=float(guess['J'])+J_shift))
else:
fit_lst.append(model.fit( data_loc[:,1]/max(
data_loc[:,1]),guess,f=data_loc[:,0],phi=phase,
omega_pf=float(guess['omega_pf'])+shift_pf,
omega_ro=float(guess['omega_ro'])+shift_ro,
kappa_pf=float(guess['kappa_pf'])+kappa_shift,
J=float(guess['J'])+J_shift))
if fit_lst[-1].chisqr <= tol: break
chisqr_lst = [fit.chisqr for fit in fit_lst]
fit_out = fit_lst[np.argmin(chisqr_lst)]
fit_out.params['J'].max = 15e6
fit_out.params['kappa_pf'].max = 50e6
fit_out.params['omega_pf'].value *= 1e9
fit_out.params['kappa_pf'].value *= 1e9
fit_out.params['J'].value *= 1e9
fit_out.params['gamma_ro'].value *= 1e9
fit_out.params['A'].value *= max(data_loc[:,1])
if simultan:
fit_out.params['omega_ro_0'].value *= 1e9
fit_out.params['omega_ro_1'].value *= 1e9
else:
fit_out.params['omega_ro'].value *= 1e9
if fit_out.chisqr > tol:
log.warning('The fit did not converge properly: chi^2 = '
''+str(fit_out.chisqr))
#Unpack simultan fits
if simultan:
fit0.params['omega_pf'].value = fit_out.params['omega_pf'].value
fit0.params['kappa_pf'].value = fit_out.params['kappa_pf'].value
fit0.params['J'].max = fit_out.params['J'].max
fit0.params['J'].value = fit_out.params['J'].value
fit0.params['gamma_ro'].value = fit_out.params['gamma_ro'].value
fit0.params['omega_ro'].value = fit_out.params['omega_ro_0'].value
fit1.params['omega_pf'].value = fit_out.params['omega_pf'].value
fit1.params['kappa_pf'].value = fit_out.params['kappa_pf'].value
fit1.params['J'].max = fit_out.params['J'].max
fit1.params['J'].value = fit_out.params['J'].value
fit1.params['gamma_ro'].value = fit_out.params['gamma_ro'].value
fit1.params['omega_ro'].value = fit_out.params['omega_ro_1'].value
fit_out = [fit0, fit1]
return fit_out
######################
# Residual functions #
######################
def residual_complex_fcn(pars, cmp_fcn, x, y):
'''
Residual of a complex function with complex results 'y' and
real input values 'x'
For resonators 'x' is the the frequency, 'y' the complex transmission
Input:
pars = parameters dictionary (check the corresponding function 'cmp_fcn' for the parameters to pass)
cmp_fcn = complex function
x = input real values to 'cmp_fcn'
y = output complex values from 'cmp_fcn'
Author = Stefano Poletto
'''
cmp_values = cmp_fcn(x, pars)
res = cmp_values - y
res = np.append(res.real, res.imag)
return res
####################
# Guess functions #
####################
def exp_dec_guess(model, data, t):
'''
Assumes exponential decay in estimating the parameters
'''
offs_guess = data[np.argmax(t)]
amp_guess = data[np.argmin(t)] - offs_guess
# guess tau by looking for value closest to 1/e
tau_guess = t[np.argmin(abs((amp_guess * (1 / np.e) + offs_guess) - data))]
model.set_param_hint('amplitude', value=amp_guess)
model.set_param_hint('tau', value=tau_guess)
model.set_param_hint('n', value=1, vary=False)
model.set_param_hint('offset', value=offs_guess)
params = model.make_params()
return params
def group_consecutives(vals, step=1):
"""Return list of consecutive lists of numbers from vals (number list)."""
run = []
result = [run]
expect = None
for v in vals:
if (v == expect) or (expect is None):
run.append(v)
else:
run = [v]
result.append(run)
expect = v + step
return result
def arc_guess(freq, dac, dd=0.1):
'''
Expects the dac values to be sorted!
:param freq:
:param dac:
:param dd:
:return:
'''
p = round(max(dd * len(dac), 1))
f_small = np.average(np.sort(freq)[:p]) + np.std(np.sort(freq)[:p])
f_big = np.average(np.sort(freq)[-p:]) - np.std(np.sort(freq)[-p:])
#print(f_small * 1e-9, f_big * 1e-9)
fmax = np.max(freq)
fmin = np.min(freq)
dacs_ss = np.where(freq >= f_big)[0]
dacs_as = np.where(freq <= f_small)[0]
dacs_ss_groups = group_consecutives(vals=dacs_ss, step=1)
dacs_as_groups = group_consecutives(vals=dacs_as, step=1)
dacs_ss_single = []
for g in dacs_ss_groups:
ind = g[np.argmax(freq[g])]
# ind = int(round(np.average(g)))
dacs_ss_single.append(ind)
dac_ss_group_index = np.argmin(np.abs(dac[dacs_ss_single]))
dac_ss_index = dacs_ss_single[dac_ss_group_index]
min_left = 0
min_right = len(dac) - 1
dacs_as_single = []
for g in dacs_as_groups:
if 0 in g:
ind = 0
elif len(dac) - 1 in g:
ind = len(dac) - 1
else:
ind = int(round(np.average(g)))
if ind < dac_ss_index:
min_left = max(ind, min_left)
elif ind > dac_ss_index:
min_right = min(ind, min_right)
dacs_as_single.append(ind)
# print('maxs', dacs_ss_single)
# print('mins', dacs_as_single)
arc_len = (dac[min_right] - dac[min_left])
# print('%d to %d = %.5f' % (min_left, min_right, arc_len))
if min_left == 0 or min_right == len(dac) - 1:
arc_len *= 2
elif len(dacs_ss_groups) > 1:
arc_len = np.average(dac[dacs_ss_single[1:]] - dac[dacs_ss_single[:-1]])
return fmax, fmin, dac[dac_ss_index], arc_len
def Resonator_dac_arch_guess(model, freq, dac_voltage, f_max_qubit: float = None, E_c: float = None):
fmax, fmin, dac_ss, period = arc_guess(freq=freq, dac=dac_voltage)
coup_guess = 15e6
# todo make better f_res guess
f_res = np.mean(freq) # - (coup_guess ** 2 / (f_max_qubit - fmax))
f_max_qubit_vary = f_max_qubit is None
f_max_qubit = f_max_qubit or f_res - 500e6
model.set_param_hint('f_0_res', value=f_res, min=f_res / 2, max=2 * f_res)
model.set_param_hint('f_max_qubit', value=f_max_qubit, min=3e9, max=8.5e9, vary=f_max_qubit_vary)
model.set_param_hint('dac_sweet_spot', value=dac_ss, min=(dac_ss - 0.005) / 2, max=2 * (dac_ss + 0.005))
model.set_param_hint('V_per_phi0', value=period, min=(period - 0.005) / 3, max=5 * (period + 0.005))
model.set_param_hint('asymmetry', value=0, max=1, min=-1)
model.set_param_hint('coupling', value=coup_guess, min=1e6, max=80e6)
E_c = E_c or 260e6
model.set_param_hint('E_c', value=E_c, min=50e6, max=400e6)
params = model.make_params()
return params
def Qubit_dac_arch_guess(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('f_max', value=fixed_params.get('f_max', f_max),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def Qubit_dac_arch_guess_precise(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('Ej_max', value=fixed_params.get('Ej_max'),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def Qubit_dac_arch_guess_res(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('Ej_max', value=fixed_params.get('Ej_max'),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
model.set_param_hint('coupling', value=fixed_params.get('coupling', 100e6),
vary=not 'coupling' in fixed_params)
model.set_param_hint('fr', value=fixed_params.get('fr', 7e9),
vary=not 'fr' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def idle_err_rate_guess(model, data, N):
'''
Assumes exponential decay in estimating the parameters
'''
amp_guess = 0.5
offset = np.mean(data)
N1 = np.mean(N)
N2 = np.mean(N)
params = model.make_params(A=amp_guess,
N1=N1,
N2=N2,
offset=offset)
return params
def fft_freq_phase_guess(data, t, freq_guess=None):
'''
Guess for a cosine fit using FFT, only works for evenly spaced points
'''
# Freq guess ! only valid with uniform sampling
# Only first half of array is used, because the second half contains the
# negative frequecy components, and we want a positive frequency.
w = np.fft.fft(data)[:len(data) // 2]
f = np.fft.fftfreq(len(data), t[1] - t[0])[:len(w)]
if freq_guess is None:
w[0] = 0 # Removes DC component from fourier transform
# Use absolute value of complex valued spectrum
abs_w = np.abs(w)
freq_guess = abs(f[abs_w == max(abs_w)][0])
ph_guess = 2 * np.pi - (2 * np.pi * t[data == max(data)] * freq_guess)[0]
# the condition data == max(data) can have several solutions
# (for example when discretization is visible)
# to prevent errors we pick the first solution
return freq_guess, ph_guess
def Cos_guess(model, data, t, **kwargs):
"""
Tip: to use this assign this guess function as a method to a model use:
model.guess = Cos_guess.__get__(
model, model.__class__)
"""
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
offs_guess = np.mean(data)
freq_guess, ph_guess = fft_freq_phase_guess(data, t, **kwargs)
model.set_param_hint('period', expr='1/frequency')
params = model.make_params(amplitude=amp_guess,
frequency=freq_guess,
phase=ph_guess,
offset=offs_guess)
params['amplitude'].min = 0 # Ensures positive amp
params['frequency'].min = 0
return params
def exp_damp_osc_guess(model, data, t, n_guess=1):
"""
Makes a guess for an exponentially damped oscillation.
Uses the fft_freq_phase guess to guess the oscillation parameters.
The guess for the exponential is simpler as it sets the exponent (n) at 1
and the tau at 2/3 of the total range
"""
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
freq_guess, ph_guess = fft_freq_phase_guess(data, t)
osc_offs_guess = 0
tau_guess = 2 / 3 * max(t)
exp_offs_guess = np.mean(data)
params = model.make_params(amplitude=amp_guess,
frequency=freq_guess,
phase=ph_guess,
oscillation_offset=osc_offs_guess,
exponential_offset=exp_offs_guess,
n=n_guess,
tau=tau_guess)
return params
def Cos_amp_phase_guess(model, data, f, t):
'''
Guess for a cosine fit with fixed frequency f.
'''
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
offs_guess = np.mean(data)
ph_guess = (-2 * np.pi * t[data == max(data)] * f)[0]
# the condition data == max(data) can have several solutions
# (for example when discretization is visible)
# to prevent errors we pick the first solution
# model.set_param_hint('period', expr='1')
params = model.make_params(amplitude=amp_guess,
phase=ph_guess,
offset=offs_guess)
params['amplitude'].min = 0 # Ensures positive amp
return params
def gauss_2D_guess(model, data, x, y):
'''
takes the mean of every row/column and then uses the regular gauss guess
function to get a guess for the model parameters.
Assumptions on input data
* input is a flattened version of a 2D grid.
* total surface under the gaussians sums up to 1.
Potential improvements:
Make the input also accept a 2D grid of data to prevent reshaping.
Find a way to extract amplitude guess from data itself, note that
taking the sum of the data (which should correspond to all data under
the curve) does not do the trick.
Note: possibly not compatible if the model uses prefixes.
'''
dx = x[1:]-x[:-1]
dy = y[1:]-y[:-1]
sums = np.sum(((data[:-1,:-1]*dx).transpose()*dy))
amp = np.sqrt(sums)
data_grid = data.reshape(-1, len(np.unique(x)))
x_proj_data = np.mean(data_grid, axis=0)
y_proj_data = np.mean(data_grid, axis=1)
x.sort()
y.sort()
xm = lmfit.models.GaussianModel()
ym = lmfit.models.GaussianModel()
x_guess = xm.guess(data=x_proj_data, x=np.unique(x))
x_res = xm.fit(data=x_proj_data, x=np.unique(x), params=x_guess)
y_guess = ym.guess(data=y_proj_data, x=np.unique(y))
y_res = ym.fit(data=y_proj_data, x=np.unique(y), params=y_guess)
x_guess = x_res.params
y_guess = y_res.params
model.set_param_hint('amplitude', value=amp, min=0.9*amp, max=1.1*amp,
vary=True)
model.set_param_hint('sigma_x', value=x_guess['sigma'].value, min=0,
vary=True)
model.set_param_hint('sigma_y', value=y_guess['sigma'].value, min=0,
vary=True)
params = model.make_params(center_x=x_guess['center'].value,
center_y=y_guess['center'].value,)
return params
def double_gauss_2D_guess(model, data, x, y):
'''
takes the mean of every row/column and then uses the guess
function of the double gauss.
Assumptions on input data
* input is a flattened version of a 2D grid.
Note: possibly not compatible if the model uses prefixes.
Note 2: see also gauss_2D_guess() for some notes on how to improve this
function.
'''
data_grid = data.reshape(-1, len(np.unique(x)))
x_proj_data = np.mean(data_grid, axis=0)
y_proj_data = np.mean(data_grid, axis=1)
# The syntax here is slighly different than when calling a regular guess
# function because I do not overwrite the class attribute properly.
x_guess = double_gauss_guess(model=None, data=x_proj_data, x=np.unique(x))
y_guess = double_gauss_guess(model=None, data=y_proj_data, x=np.unique(y))
if model is not None:
pars = model.make_params(A_sigma_x=x_guess['A_sigma'],
A_sigma_y=y_guess['A_sigma'],
A_center_x=x_guess['A_center'],
A_center_y=y_guess['A_center'],
A_amplitude=1,
B_sigma_x=x_guess['B_sigma'],
B_sigma_y=y_guess['B_sigma'],
B_center_y=y_guess['B_center'],
B_center_x=x_guess['B_center'],
B_amplitude=1)
return pars
else:
return x_guess, y_guess
def double_gauss_guess(model, data, x=None, **kwargs):
'''
Finds a guess for the intial parametes of the double gauss model.
Guess is based on taking the cumulative sum of the data and
finding the points corresponding to 25% and 75%
it finds sigma by using the property that ~33% of the data is contained
in the range mu-sigma to mu+sigma.
Tip: to use this assign this guess function as a method to a model use:
model.guess = double_gauss_guess.__get__(
model, model.__class__)
'''
if x is None:
x = np.arange(len(data))
cdf = np.cumsum(data)
norm_cdf = cdf / cdf[-1]
par_dict = {'A_center': x[(np.abs(norm_cdf - 0.25)).argmin()],
'B_center': x[(np.abs(norm_cdf - 0.75)).argmin()],
'A_sigma': (x[(np.abs(norm_cdf - 0.25 - .33 / 2)).argmin()] -
x[(np.abs(norm_cdf - 0.25 + .33 / 2)).argmin()]),
'B_sigma': (x[(np.abs(norm_cdf - 0.75 - .33 / 2)).argmin()] -
x[(np.abs(norm_cdf - 0.75 + .33 / 2)).argmin()])}
amp = max(data) * (par_dict['A_sigma'] + par_dict['B_sigma']) / 2.
if model is not None:
# Specify explicitly because not all pars are set to those from the par
# dict
pars = model.make_params(A_center=par_dict['A_center'],
B_center=par_dict['B_center'],
A_sigma=par_dict['A_sigma'],
B_sigma=par_dict['B_sigma'],
A_amplitude=amp, B_amplitude=amp)
return pars
# The else clause is added explicitly to reuse this function for the
# 2D double gauss model
else:
return par_dict
def ro_double_gauss_guess(model, data, x, fixed_p01 = False, fixed_p10 = False):
# An initial guess is done on the binned data with single gaussians
# to constrain the fit params and avoid fitting noise if
# e.g., mmt. ind. rel. is very low
gmod0 = lmfit.models.GaussianModel()
guess0 = gmod0.guess(data=data[0], x=x[0])
gmod1 = lmfit.models.GaussianModel()
guess1 = gmod1.guess(data=data[1], x=x[1])
model.set_param_hint(
'A_center', vary=True, value=guess0['center'].value,
min=guess0['center'] - 2 * guess0['sigma'],
max=guess0['center'] + 2 * guess0['sigma'])
model.set_param_hint(
'B_center', vary=True, value=guess1['center'].value,
min=guess1['center'] - 2 * guess1['sigma'],
max=guess1['center'] + 2 * guess1['sigma'])
model.set_param_hint('A_sigma', value=guess0['sigma'].value, vary=True)
model.set_param_hint('B_sigma', value=guess1['sigma'].value, vary=True)
# Amplitudes
intarea0 = sum_int(x=x[0], y=data[0])[-1]
intarea1 = sum_int(x=x[1], y=data[1])[-1]
model.set_param_hint('A_amplitude', value=intarea0, vary=False)
model.set_param_hint('B_amplitude', value=intarea1, vary=False)
model.set_param_hint('SNR', expr='abs(A_center-B_center)*2/(A_sigma+B_sigma)',
vary=False)
# Spurious excitement
f = np.sqrt(2*np.pi)
amp0 = 0.99 * np.max(data[0]) * guess0['sigma'] * f
amp1 = 0.99 * np.max(data[1]) * guess1['sigma'] * f
spurious0 = max(1-(amp0/intarea0), 1e-3)
spurious1 = max(1-(amp1/intarea1), 1e-3)
p01 = fixed_p01 if fixed_p01 is not False else spurious0
p10 = fixed_p10 if fixed_p10 is not False else spurious1
model.set_param_hint('A_spurious', value=p01, min=0, max=1,
vary=fixed_p01 is False)
model.set_param_hint('B_spurious', value=p10, min=0, max=1,
vary=fixed_p10 is False)
return model.make_params()
def sum_int(x,y):
return np.cumsum(y[:-1]*(x[1:]-x[:-1]))
def DoubleGaussian(freq, sigma, mu, ampl, sigma0, mu0, ampl0, offset):
'''
Double Gaussian function
'''
return ampl/(sigma*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu)/sigma)**2) + \
ampl0/(sigma0*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu0)/sigma0)**2) + \
offset
def Gaussian(freq, sigma, mu, ampl, offset):
'''
Gaussian function
'''
return ampl/(sigma*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu)/sigma)**2) + offset
def Gaussian_guess(model, data, freq, **kwargs):
"""
Tip: to use this assign this guess function as a method to a model use:
model.guess = Gaussian_guess.__get__(
model, model.__class__)
"""
mu_guess = freq[np.argmax(data)]
offs_guess = np.median(data)
p = (data - offs_guess)**2
p /= p.sum()
sigma_guess = np.sqrt(((freq - mu_guess)**2 * p).sum())/10
amp_guess = max(data - offs_guess)*sigma_guess*np.sqrt(2*np.pi)
params = model.make_params(sigma=sigma_guess,
mu=mu_guess,
ampl=amp_guess,
offset=offs_guess)
params['mu'].min = np.min(freq)
params['mu'].max = np.max(freq)
return params
def half_feed_line_S12_J_func(omega, J, kappaPF, gammaPF, gammaRR, omegaPF, omegaRR, phi, A , B, alpha):
return abs( A+np.exp(-1j*phi)*2*B*((-1+np.exp(1j*alpha))*(4*J**2+(gammaPF-2*1j*(omegaPF-omega))*(gammaRR-2j*omegaRR+2j*omega)))/(16*J**2+(4*gammaPF+(3+np.exp(1j*alpha))*kappaPF-8j*(omegaPF-omega))*(gammaRR-2j*omegaRR+2j*omega)) )
def half_feed_line_S12_J_guess(model,data):
'''
data should have the frequencies in Hz in the first column and the transmission in the second column
'''
background_guess = max(data,key=lambda item:item[1])[1]/2
amp_guess = max(data,key=lambda item:item[1])[0]-min(data,key=lambda item:item[1])[0]
omegaPF_guess = min(data,key=lambda item:item[1])[0]
#Remove the PF dip to find the second smallest feature at the RR frequency
kappaPF_guess_data = np.extract( (data[:,1]<=(min(data,key=lambda item:item[1])[1]+ background_guess/2)),data[:,0])
kappaPF_guess = min(abs( kappaPF_guess_data[0]-omegaPF_guess),abs( kappaPF_guess_data[-1]-omegaPF_guess),5e10)
omegaRR_guess = min( np.transpose([np.extract(abs(data[:,0]- omegaPF_guess) >= kappaPF_guess/2,data[:,0]),np.extract(abs(data[:,0]- omegaPF_guess) >= kappaPF_guess/2,data[:,1])]),key=lambda item:item[1])[0]
J_guess = kappaPF_guess/4
model.set_param_hint('J',value=J_guess,min=0,max=2e7)
model.set_param_hint('kappaPF',value=kappaPF_guess,min=1e6,max=1e8)
model.set_param_hint('gammaPF',value=0.001,min=0,max=1e6)
model.set_param_hint('gammaRR',value=0.001,min=0,max=1e6)
model.set_param_hint('omegaPF',value=omegaPF_guess,min=omegaPF_guess-2e7,max=omegaPF_guess+2e7)
model.set_param_hint('omegaRR',value=omegaRR_guess,min=omegaRR_guess-2e7,max=omegaRR_guess+2e7)
model.set_param_hint('phi',value=(data[0,1]-data[-1,1])/background_guess)
model.set_param_hint('A',value=background_guess,min=(background_guess)-5,max=(2000*background_guess)+5)
model.set_param_hint('B',value=background_guess,min=(background_guess)-5,max=(2000*background_guess)+5)
model.set_param_hint('alpha',value=3,min=0,max=10)
params=model.make_params()
return params
def TwoErrorFunc_guess(model, delays, data):
offset_guess = data[1]
amp_guess = data[data.size//2] - data[1]
delay_interval = (delays[-1]-delays[1])
mu_A_guess = delays[1] + 0.1*delay_interval
mu_B_guess = delays[1] + 0.9*delay_interval
sigma_guess = 3e-9
params = model.make_params(amp=amp_guess,
mu_A=mu_A_guess,
mu_B=mu_B_guess,
sigma=sigma_guess,
offset = offset_guess)
return params
def mixer_lo_leakage(vi, vq, li=0.0, lq=0.0, theta_i=0, theta_q=0, offset=0.0):
"""Model for maximum amplitude of LO leakage of an IQ mixer.
Args:
vi (:obj:'float'): DC bias voltage applied on the I input of the mixer.
vq (:obj:'float'): DC bias voltage applied on the Q input of the mixer.
li (float, optional): [TODO:description]. Defaults to 0.0.
lq (float, optional): [TODO:description]. Defaults to 0.0.
theta_i (int, optional): [TODO:description]. Defaults to 0.0.
theta_q (int, optional): [TODO:description]. Defaults to 0.0.
offset (float, optional): Offset in dBV accounting for losses in the
signal chain. Defaults to 0.0 dBV.
Returns:
:obj:'float': maximum amplitude of the LO leakage for given parameters
Model Schematic:
I >-- +V_I ---------------- I R -- li*exp(i*theta_i) -------+
LO |
| |
LO >------------\-/-----------+ |
X +----> RF
--/-\-----------+ |
| |
LO |
Q >-- +V_Q ---------------- I R -- lq*exp(i*theta_q-pi/2) --+
"""
return 20*np.log10(np.abs(vi
+ li * np.exp(1j*theta_i)
- 1j * vq
+ lq * np.exp(1j*(theta_q-np.pi/2))
)) + offset
def mixer_lo_leakage_guess(model, **kwargs):
"""Prepare and return parameters of an :py:lmfit.model: for the model
mixer_lo_leakage.
Args:
model (:py:lmfit.model:): The model that the parameter hints will be added to
and that is used to generate the parameters. this model should have
the following parameters: 'li', 'lq', 'theta_i', 'theta_q', 'scale'
Returns:
:py:lmfit.parameters: Parameters
"""
pi_half = np.pi/2
model.set_param_hint('li', value=0.0, min=0, max=1)
model.set_param_hint('lq', value=0.0, min=0, max=1, vary=False)
model.set_param_hint('theta_i', value=0.0, min=-pi_half, max=pi_half)
model.set_param_hint('theta_q', value=0.0, min=-pi_half, max=pi_half, vary=False)
model.set_param_hint('offset', value=0.0, min=-4.0, max=+4.0)
return model.make_params()
#################################
# User defined Models #
#################################
# NOTE: it is actually better to instantiate the model within your analysis
# file, this prevents the model params having a memory.
# A valid reason to define it here would be exp_dec_guess if you want to add a guess function
CosModel = lmfit.Model(CosFunc)
CosModel.guess = Cos_guess
half_Feed_lineS12_J_Model = lmfit.Model(half_feed_line_S12_J_func)
half_Feed_lineS12_J_Model.guess = half_feed_line_S12_J_guess
ExpDecayModel = lmfit.Model(ExpDecayFunc)
TripleExpDecayModel = lmfit.Model(TripleExpDecayFunc)
ExpDecayModel.guess = exp_dec_guess # todo: fix
ExpDampOscModel = lmfit.Model(ExpDampOscFunc)
GaussExpDampOscModel = lmfit.Model(GaussExpDampOscFunc)
ExpDampDblOscModel = lmfit.Model(ExpDampDblOscFunc)
DoubleExpDampOscModel = lmfit.Model(DoubleExpDampOscFunc)
HangerAmplitudeModel = lmfit.Model(HangerFuncAmplitude)
SlopedHangerAmplitudeModel = lmfit.Model(SlopedHangerFuncAmplitude)
PolyBgHangerAmplitudeModel = lmfit.Model(PolyBgHangerFuncAmplitude)
HangerComplexModel = lmfit.Model(HangerFuncComplex)
SlopedHangerComplexModel = lmfit.Model(SlopedHangerFuncComplex)
QubitFreqDacModel = lmfit.Model(Qubit_dac_to_freq)
QubitFreqFluxModel = lmfit.Model(QubitFreqFlux)
TwinLorentzModel = lmfit.Model(TwinLorentzFunc)
LorentzianModel = lmfit.Model(Lorentzian)
RBModel = lmfit.Model(RandomizedBenchmarkingDecay)
LinOModel = lmfit.Model(linear_with_offset)
LinBGModel = lmfit.Model(linear_with_background)
LinBGOModel = lmfit.Model(linear_with_background_and_offset)
ErfWindowModel = lmfit.Model(ErfWindow)
GaussianModel_v2 = lmfit.models.GaussianModel
GaussianModel = lmfit.Model(Gaussian)
ExponentialModel = lmfit.models.ExponentialModel
HangerWithPfModel = lmfit.Model(hanger_with_pf)
SimHangerWithPfModel = lmfit.Model(simultan_hanger_with_pf,
independent_vars=['f'])
# 2D models
Gaus2D_model = lmfit.Model(gaussian_2D, independent_vars=['x', 'y'])
Gaus2D_model.guess = gauss_2D_guess # Note: not proper way to add guess func
DoubleGauss2D_model = (lmfit.Model(gaussian_2D, independent_vars=['x', 'y'],
prefix='A_') +
lmfit.Model(gaussian_2D, independent_vars=['x', 'y'],
prefix='B_'))
DoubleGauss2D_model.guess = double_gauss_2D_guess
###################################
# Models based on lmfit functions #
###################################
LorentzModel = lmfit.Model(lmfit.models.lorentzian)
Lorentz_w_background_Model = lmfit.models.LorentzianModel() + \
lmfit.models.LinearModel()
PolyBgHangerAmplitudeModel = (HangerAmplitudeModel *
lmfit.models.PolynomialModel(degree=7))
DoubleGaussModel = (lmfit.models.GaussianModel(prefix='A_') +
lmfit.models.GaussianModel(prefix='B_'))
DoubleGaussModel.guess = double_gauss_guess # defines a guess function
def plot_fitres2D_heatmap(fit_res, x, y, axs=None, cmap='viridis'):
'''
Convenience function for plotting results of flattened 2D fits.
It could be argued this does not belong in fitting models (it is not a
model) but I put it here as it is closely related to all the stuff we do
with lmfit. If anyone has a better location in mind, let me know (MAR).
'''
# fixing the data rotation with [::-1]
nr_cols = len(np.unique(x))
data_2D = fit_res.data.reshape(-1, nr_cols, order='C')[::-1]
fit_2D = fit_res.best_fit.reshape(-1, nr_cols, order='C')[::-1]
guess_2D = fit_res.init_fit.reshape(-1, nr_cols, order='C')[::-1]
if axs is None:
f, axs = plt.subplots(1, 3, figsize=(14, 6))
axs[0].imshow(data_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[1].imshow(fit_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[2].imshow(guess_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[0].set_title('data')
axs[1].set_title('fit-result')
axs[2].set_title('initial guess')
return axs
def mixer_imbalance_sideband(x, y, g=1.0, phi=0, scale=1.0):
return np.log10(np.abs((1 - x/g * np.exp(-1j*np.deg2rad(phi + y))
) * scale))
def mixer_imbalance_sideband_guess(model, **kwargs):
model.set_param_hint('g', value=1.0, min=0.5, max=1.5)
model.set_param_hint('phi', value=0, min=-20, max=20)
model.set_param_hint('scale', value=1, min=-1e3, max=1e3)
return model.make_params()
# Before defining a new model, take a look at the built in models in lmfit.
# From http://lmfit.github.io/lmfit-py/builtin_models.html
# Built-in Fitting Models in the models module
# Peak-like models
# GaussianModel
# LorentzianModel
# VoigtModel
# PseudoVoigtModel
# Pearson7Model
# StudentsTModel
# BreitWignerModel
# LognormalModel
# DampedOcsillatorModel
# ExponentialGaussianModel
# SkewedGaussianModel
# DonaichModel
# Linear and Polynomial Models
# ConstantModel
# LinearModel
# QuadraticModel
# ParabolicModel
# PolynomialModel
# Step-like models
# StepModel
# RectangleModel
# Exponential and Power law models
# ExponentialModel
# PowerLawModel
Mixer calib: improvements in model mixer_imbalance_sideband
added docstrings, corrected conversion between V and dBV, adopted parameter limits, ...
import numpy as np
import matplotlib.pyplot as plt
import scipy
import lmfit
import itertools
import logging
from pycqed.simulations import transmon
from pycqed.utilities.timer import Timer
log = logging.getLogger(__name__)
#################################
# Fitting Functions Library #
#################################
def RandomizedBenchmarkingLeakage(numCliff, pu, pd, p0):
# from https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.116.020501
return pu/(pd+pu) * (1-np.exp(-(pd+pu)*numCliff)) + \
p0*np.exp(-(pd+pu)*numCliff)
def RandomizedBenchmarkingDecay(numCliff, Amplitude, p, offset):
val = Amplitude * (p ** numCliff) + offset
return val
def DoubleExpDampOscFunc(t, tau_1, tau_2,
freq_1, freq_2,
phase_1, phase_2,
amp_1, amp_2, osc_offset):
cos_1 = amp_1 * (np.cos(2 * np.pi * freq_1 * t + phase_1)) * \
np.exp(-(t / tau_1))
cos_2 = amp_2 * (np.cos(2 * np.pi * freq_2 * t + phase_2)) * \
np.exp(-(t / tau_2))
return cos_1 + cos_2 + osc_offset
def double_RandomizedBenchmarkingDecay(numCliff, p, offset,
invert=1):
"""
A variety of the RB-curve that allows fitting both the inverting and
non-inverting exponential.
The amplitude of the decay curve is constrained to start at 0 or 1.
The offset is the common point both curves converge to.
pick invert to be 1 or 0
"""
# Inverting clifford curve
val_inv = (1 - offset) * (p ** numCliff) + offset
# flipping clifford curve
val_flip = -offset * (p ** numCliff) + offset
# Using invert as a boolean but not using if statement to allow for
# arrays to be input in the function
val = (1 - invert) * val_flip + invert * val_inv
return val
def LorentzFunc(f, amplitude, center, sigma):
val = amplitude / np.pi * (sigma / ((f - center) ** 2 + sigma ** 2))
return val
def Lorentzian(f, A, offset, f0, kappa):
val = offset + A / np.pi * (kappa / ((f - f0) ** 2 + kappa ** 2))
return val
def TwinLorentzFunc(f, A_gf_over_2, A, f0_gf_over_2, f0,
kappa_gf_over_2, kappa, background=0):
"""
Twin lorentz with background.
Args:
f (float): frequency sweep points in Hz
A (float): amplitude of the tallest/deepest Lorentzian structure
in the data
A_gf_over_2 (float): amplitude of the other Lorentzian structure in the
data; since this function is used for high power
qubit spectroscopy, this parameter refers to the
Lorentzian structure corresponding to the gf/2
transition
f0 (float): frequency of the tallest/deepest Lorentzian structure
in the data
f0_gf_over_2 (float): frequency of the other Lorentzian structure in the
data; since this function is used for high power
qubit spectroscopy, this parameter refers to the
Lorentzian structure corresponding to the gf/2
transition
kappa (float): kappa (FWHM) of the tallest/deepest Lorentzian structure
in the data
kappa_gf_over_2 (float): kappa (FWHM) of the other Lorentzian structure in
the data; since this function is used for high
power qubit spectroscopy, this parameter refers to
the Lorentzian structure corresponding to the gf/2
transition
background (float): background offset
"""
val = (A_gf_over_2 / np.pi * (kappa_gf_over_2 / ((f - f0_gf_over_2) ** 2 + kappa_gf_over_2 ** 2)) +
A / np.pi * (kappa / ((f - f0) ** 2 + kappa ** 2)) + background)
return val
def Qubit_dac_to_freq(dac_voltage, f_max,
dac_sweet_spot=None, V_per_phi0=None, phi_park=None,
dac_flux_coefficient=None, E_c=0,
asymmetry=0.5):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
dac_sweet_spot (V): voltage at which the sweet-spot is found
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2)),
'''
# using E_c as fit parameter is numerically not stable (changing E_c does only
# slightly change the function. Set E_c to zero
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
qubit_freq = (f_max + E_c) * (
asymmetry ** 2 + (1 - asymmetry ** 2) *
np.cos(np.pi / V_per_phi0 *
(dac_voltage - dac_sweet_spot)) ** 2) ** 0.25 - E_c
return qubit_freq
def Qubit_dac_to_freq_precise(dac_voltage, Ej_max, E_c, asymmetry,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None
):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
Ejmax (Hz): Maximum Ej of qubit
Ec (Hz): charging energy of the qubit
d = abs((EJ1-EJ2)/(EJ1+EJ2)))
dac_sweet_spot (V): voltage at which the sweet-spot is found
V_per_phi0 (V): volt per phi0 (convert voltage to flux
'''
if np.ndim(dac_voltage) == 0:
dac_voltage = np.array([dac_voltage])
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
phi = np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot)
Ej = 2 * np.pi * Ej_max * np.cos(phi) * np.sqrt(1 + asymmetry ** 2 * np.tan(phi) ** 2)
E_c = 2 * np.pi * E_c
freqs = []
for ej in Ej:
freqs.append((transmon.transmon_levels(E_c, ej) / (2 * np.pi))[0])
qubit_freq = np.array(freqs)
return qubit_freq
def Qubit_dac_to_freq_res(dac_voltage, Ej_max, E_c, asymmetry, coupling, fr,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None, dim_charge=31,
):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
Ejmax (Hz): Maximum Ej of qubit
Ec (Hz): charging energy of the qubit
d = abs((EJ1-EJ2)/(EJ1+EJ2)))
dac_sweet_spot (V): voltage at which the sweet-spot is found
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
phi_park
'''
return_float = False
if np.ndim(dac_voltage) == 0:
dac_voltage = np.array([dac_voltage])
return_float = True
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
phi = np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot)
Ej = Ej_max * np.cos(phi) * np.sqrt(1 + asymmetry ** 2 * np.tan(phi) ** 2)
with Timer('fitmod.loop', verbose=False):
freqs = [(transmon.transmon_resonator_levels(E_c,
ej,
fr,
coupling,
states=[(1, 0), (2, 0)],
dim_charge=dim_charge
))[0]
for ej in Ej]
qubit_freq = np.array(freqs)
return qubit_freq[0] if return_float else qubit_freq
def Qubit_freq_to_dac_res(frequency, Ej_max, E_c, asymmetry, coupling, fr,
dac_sweet_spot=0.0, V_per_phi0=None,
dac_flux_coefficient=None,
phi_park=None,
branch='smallest'):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
This function implements the inverse of "Qubit_dac_to_freq"
frequency (Hz)
Ej_max (Hz): Maximum josephson energy
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
couping (Hz): coupling to resonator
fr (Hz): frequency of resonator
dac_sweet_spot (V): voltage at which the sweet-spot is found
branch (enum: 'positive' 'negative' or "smallest")
'''
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
return_float = False
if np.ndim(frequency) == 0:
frequency = [frequency]
return_float = True
E_j = [transmon.transmon_resonator_ej_anh_frg_chi(
f, ec=E_c, frb=fr, gb=coupling)[0]
for f in frequency]
E_j = np.array(E_j)
r = E_j / Ej_max
if np.any(r > 1):
log.warning(f'Ratio Ej/Ej_max is larger than 1 at '
f'indices {np.argwhere(r > 1)}.Truncating to 1.')
r[r>1] = 1
phi = np.arccos(np.sqrt((r**2 - asymmetry**2)/(1-asymmetry**2)))
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
dac_voltage_pos = phi * V_per_phi0 / np.pi + dac_sweet_spot
dac_voltage_neg = -phi * V_per_phi0 / np.pi + dac_sweet_spot
if branch == 'positive':
dac_voltage = dac_voltage_pos
elif branch == 'negative':
dac_voltage = dac_voltage_neg
elif branch == 'smallest':
if np.ndim(phi) != 0:
dac_voltage = np.array([dac_voltage_pos, dac_voltage_neg])
idxs0 = np.argmin(np.abs(dac_voltage), 0)
idxs1 = np.arange(len(dac_voltage_pos))
dac_voltage = dac_voltage[idxs0, idxs1]
else:
dac_voltage = dac_voltage_pos \
if abs(dac_voltage_pos) < abs(dac_voltage_neg) \
else dac_voltage_neg
else:
raise ValueError('branch {} not recognized'.format(branch))
return dac_voltage[0] if return_float else dac_voltage
def Resonator_dac_to_freq(dac_voltage, f_max_qubit, f_0_res,
E_c, dac_sweet_spot,
coupling, V_per_phi0=None,
dac_flux_coefficient=None,
asymmetry=0):
qubit_freq = Qubit_dac_to_freq(dac_voltage=dac_voltage, f_max=f_max_qubit, E_c=E_c,
dac_sweet_spot=dac_sweet_spot, V_per_phi0=V_per_phi0,
dac_flux_coefficient=dac_flux_coefficient,
asymmetry=asymmetry)
delta_qr = (qubit_freq - f_0_res)
lamb_shift = (coupling ** 2 / delta_qr)
resonator_freq = f_0_res - lamb_shift
return resonator_freq
def Qubit_dac_to_detun(dac_voltage, f_max, E_c, dac_sweet_spot, V_per_phi0,
asymmetry=0):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
dac_voltage (V)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
dac_sweet_spot (V): voltage at which the sweet-spot is found
asymmetry (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
'''
return f_max - Qubit_dac_to_freq(dac_voltage,
f_max=f_max, E_c=E_c,
dac_sweet_spot=dac_sweet_spot,
V_per_phi0=V_per_phi0,
asymmetry=asymmetry)
def Qubit_freq_to_dac(frequency, f_max,
dac_sweet_spot=None, V_per_phi0=None,
dac_flux_coefficient=None, asymmetry=0, E_c=0,
phi_park=None,
branch='smallest'):
'''
The cosine Arc model for uncalibrated flux for asymmetric qubit.
This function implements the inverse of "Qubit_dac_to_freq"
frequency (Hz)
f_max (Hz): sweet-spot frequency of the qubit
E_c (Hz): charging energy of the qubit
V_per_phi0 (V): volt per phi0 (convert voltage to flux)
asym (dimensionless asymmetry param) = abs((EJ1-EJ2)/(EJ1+EJ2))
dac_sweet_spot (V): voltage at which the sweet-spot is found
branch (enum: 'positive' 'negative')
'''
if V_per_phi0 is None and dac_flux_coefficient is None:
raise ValueError('Please specify "V_per_phi0".')
if dac_sweet_spot is None and phi_park is None:
raise ValueError('Please specify "phi_park".')
elif dac_sweet_spot is not None and phi_park is not None:
raise ValueError('"phi_park" and "dac_sweet_spot" cannot '
'be used simultaneously.')
if phi_park is not None:
dac_sweet_spot = phi_park * V_per_phi0
# asymm_term = (asymmetry**2 + (1-asymmetry**2))
# dac_term = np.arccos(((frequency+E_c)/((f_max+E_c) * asymm_term))**2)
dac_term = np.arccos(np.sqrt(
(((frequency + E_c) / (f_max + E_c)) ** 4 - asymmetry ** 2) /
(1 - asymmetry ** 2)))
if dac_flux_coefficient is not None:
log.warning('"dac_flux_coefficient" deprecated. Please use the '
'physically meaningful "V_per_phi0" instead.')
V_per_phi0 = np.pi / dac_flux_coefficient
dac_voltage_pos = dac_term * V_per_phi0 / np.pi + dac_sweet_spot
dac_voltage_neg = -dac_term * V_per_phi0 / np.pi + dac_sweet_spot
if branch == 'positive':
dac_voltage = dac_voltage_pos
elif branch == 'negative':
dac_voltage = dac_voltage_neg
elif branch == 'smallest':
if np.ndim(dac_term) != 0:
dac_voltage = np.array([dac_voltage_pos, dac_voltage_neg])
idxs0 = np.argmin(np.abs(dac_voltage), 0)
idxs1 = np.arange(len(dac_voltage_pos))
dac_voltage = dac_voltage[idxs0, idxs1]
else:
dac_voltage = dac_voltage_pos \
if abs(dac_voltage_pos) < abs(dac_voltage_neg) \
else dac_voltage_neg
else:
raise ValueError('branch {} not recognized'.format(branch))
return dac_voltage
def Qubit_dac_sensitivity(dac_voltage, f_max: float, E_c: float,
dac_sweet_spot: float, V_per_phi0: float,
asymmetry: float = 0):
'''
Derivative of the qubit detuning vs dac at dac_voltage.
The returned quantity is "dfreq/dPhi (dac_voltage)"
'''
cos_term = np.cos(np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot))
sin_term = np.sin(np.pi / V_per_phi0 * (dac_voltage - dac_sweet_spot))
return ((f_max + E_c) * (1 - asymmetry ** 2) * np.pi / (2 * V_per_phi0) *
cos_term * sin_term * (asymmetry ** 2 + (1 - asymmetry ** 2) *
cos_term ** 2) ** (-0.75))
def QubitFreqDac(dac_voltage, f_max, E_c,
dac_sweet_spot, dac_flux_coefficient, asymmetry=0):
log.warning('deprecated, replace QubitFreqDac with Qubit_dac_to_freq')
return Qubit_dac_to_freq(dac_voltage, f_max, E_c,
dac_sweet_spot, dac_flux_coefficient, asymmetry)
def QubitFreqFlux(flux, f_max, E_c,
flux_zero, dac_offset=0):
'The cosine Arc model for calibrated flux.'
calculated_frequency = (f_max + E_c) * np.sqrt(np.abs(
np.cos(np.pi * (flux - dac_offset) / flux_zero))) - E_c
return calculated_frequency
def CosFunc(t, amplitude, frequency, phase, offset):
'''
parameters:
t, time in s
amplitude a.u.
frequency in Hz (f, not omega!)
phase in rad
offset a.u.
'''
return amplitude * np.cos(2 * np.pi * frequency * t + phase) + offset
def ResidZZFuncJoint(t, amplitude, amplitude1, tau, alpha, t11, frequency,
phase, offset):
x = t11*alpha
without_pulse = amplitude * np.exp(-t/tau)*np.cos(
2*np.pi*frequency*t + phase) + offset
with_pulse = amplitude1 * np.exp(-t/tau)*(x*np.exp(-t*alpha/x)*np.cos(
2*np.pi*(frequency+alpha)*t + phase) - np.sin(
2*np.pi*frequency*t + phase))/np.sqrt(1+x**2) + offset
return without_pulse, with_pulse
# return amplitude * np.exp(-(t / tau) ** n) * (np.cos(
# 2 * np.pi * frequency * t + phase) + oscillation_offset) + \
# exponential_offset
def ResidZZFunc(t, amplitude, tau, alpha, x, frequency, phase, offset):
return amplitude * np.exp(-t/tau)*(x*np.exp(-t*alpha/x)*np.cos(
2*np.pi*(frequency+alpha)*t + phase) - np.sin(
2*np.pi*frequency*t + phase))/np.sqrt(1+x**2) + offset
def ExpDecayFunc(t, tau, amplitude, offset, n):
return amplitude * np.exp(-(t / tau) ** n) + offset
def ExpDecayPmod(t, T2echo, delta, n0, chi, kappa, phase, amplitude, offset):
"""
specific form of exponential decay used for residual resonator photon readout
"""
return amplitude * (1 - np.imag(np.exp(-t*(1/T2echo + 2j*np.pi*delta)+1j*(
phase-2*n0*chi*(1-np.exp(-t*(kappa + 2j*chi)))/(kappa + 2j*chi)))))+offset
def CombinedOszExpDecayFunc(t, tau, tau_gauss, phase, n0, chi, delta, amplitude,
oscillation_offset, offset):
"""
Combination of gaussian and exponential decay
"""
return amplitude * np.exp(-(t / tau)-(t / tau_gauss))*(np.cos(
2 * np.pi * (n0*2*chi/(2*np.pi)+delta) * t + phase) +
oscillation_offset) + offset
def idle_error_rate_exp_decay(N, N1, N2, A, offset):
"""
exponential decay consisting of two components
"""
return A * np.exp(-N / N1 - (N / N2) ** 2) + offset
def gain_corr_ExpDecayFunc(t, tau, amp, gc):
"""
Specific form of an exponential decay used for flux corrections.
Includes a "gain correction" parameter that is ignored when correcting
the distortions.
"""
y = gc * (1 + amp * np.exp(-t / tau))
return y
def gain_corr_double_ExpDecayFunc(t, tau_A, tau_B, amp_A, amp_B, gc):
"""
Specific form of an exponential decay used for flux corrections.
Includes a "gain correction" parameter that is ignored when correcting
the distortions.
"""
y = gc * (1 + amp_A * np.exp(-t / tau_A) + amp_B * np.exp(-t / tau_B))
return y
def ExpDampOscFunc(t, tau, n, frequency, phase, amplitude,
oscillation_offset, exponential_offset):
return amplitude * np.exp(-(t / tau) ** n) * (np.cos(
2 * np.pi * frequency * t + phase) + oscillation_offset) + \
exponential_offset
def GaussExpDampOscFunc(t, tau, tau_2, frequency, phase, amplitude,
oscillation_offset, exponential_offset):
return amplitude * np.exp(-(t / tau_2) ** 2 - (t / tau)) * (np.cos(
2 * np.pi * frequency * t + phase) + oscillation_offset) + exponential_offset
def ExpDampDblOscFunc(t, tau, n, freq_1, freq_2, phase_1, phase_2,
amp_1, amp_2,
osc_offset_1, osc_offset_2, exponential_offset):
'''
Exponential decay with double cosine modulation
'''
exp_decay = np.exp(-(t / tau) ** n)
cos_1 = (np.cos(
2 * np.pi * freq_1 * t + phase_1) + osc_offset_1)
cos_2 = (np.cos(
2 * np.pi * freq_2 * t + phase_2) + osc_offset_2)
return amp_1 * exp_decay * cos_1 + amp_2 * exp_decay * cos_2 + exponential_offset
def HangerFuncAmplitude(f, f0, Q, Qe, A, theta):
'''
This is the function for a hanger which does not take into account
a possible slope.
This function may be preferred over SlopedHangerFunc if the area around
the hanger is small.
In this case it may misjudge the slope
Theta is the asymmetry parameter
Note! units are inconsistent
f is in Hz
f0 is in GHz
'''
return abs(A * (1. - Q / Qe * np.exp(1.j * theta) / (1. + 2.j * Q * (f / 1.e9 - f0) / f0)))
def HangerFuncComplex(f, pars):
'''
This is the complex function for a hanger which DOES NOT
take into account a possible slope
Input:
f = frequency
pars = parameters dictionary
f0, Q, Qe, A, theta, phi_v, phi_0
Author: Stefano Poletto
'''
f0 = pars['f0']
Q = pars['Q']
Qe = pars['Qe']
A = pars['A']
theta = pars['theta']
phi_v = pars['phi_v']
phi_0 = pars['phi_0']
S21 = A * (1 - Q / Qe * np.exp(1j * theta) / (1 + 2.j * Q * (f / 1.e9 - f0) / f0)) * \
np.exp(1j * (phi_v * f + phi_0))
return S21
def PolyBgHangerFuncAmplitude(f, f0, Q, Qe, A, theta, poly_coeffs):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible polynomial background
# NOT DEBUGGED
return np.abs((1. + np.polyval(poly_coeffs, (f / 1.e9 - f0) / f0)) *
HangerFuncAmplitude(f, f0, Q, Qe, A, theta))
def SlopedHangerFuncAmplitude(f, f0, Q, Qe, A, theta, slope):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible slope df
return np.abs((1. + slope * (f / 1.e9 - f0) / f0) *
HangerFuncAmplitude(f, f0, Q, Qe, A, theta))
def SlopedHangerFuncComplex(f, f0, Q, Qe, A, theta, phi_v, phi_0, slope):
# This is the function for a hanger (lambda/4 resonator) which takes into
# account a possible slope df
return (1. + slope * (f / 1.e9 - f0) / f0) * np.exp(1.j * (phi_v * f + phi_0 - phi_v * f[0])) * \
HangerFuncComplex(f, f0, Q, Qe, A, theta)
def linear_with_offset(x, a, b):
'''
A linear signal with a fixed offset.
'''
return a * x + b
def linear_with_background(x, a, b):
'''
A linear signal with a fixed background.
'''
return np.sqrt((a * x) ** 2 + b ** 2)
def linear_with_background_and_offset(x, a, b, c):
'''
A linear signal with a fixed background.
'''
return np.sqrt((a * x) ** 2 + b ** 2) + c
def gaussianCDF(x, amplitude, mu, sigma):
"""
CDF of gaussian is P(X<=x) = .5 erfc((mu-x)/(sqrt(2)sig))
"""
return 0.5 * amplitude * scipy.special.erfc((mu - x) / (np.sqrt(2)*sigma))
def double_gaussianCDF(x, A_amplitude, A_mu, A_sigma,
B_amplitude, B_mu, B_sigma):
"""
CDF of two gaussians added on top of each other.
uses "gaussianCDF"
"""
CDF_A = gaussianCDF(x, amplitude=A_amplitude, mu=A_mu, sigma=A_sigma)
CDF_B = gaussianCDF(x, amplitude=B_amplitude, mu=B_mu, sigma=B_sigma)
return CDF_A + CDF_B
def TwoErrorFunc(x, amp, mu_A, mu_B, sigma, offset):
'''
parameters:
'''
return offset + double_gaussianCDF(x, amp, mu_A, sigma, -amp, mu_B, sigma)
def ro_gauss(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
'''
Two double-gaussians with sigma and mu/center of the residuals equal to the
according state.
'''
gauss = lmfit.lineshapes.gaussian
A_gauss = gauss(x=x[0], center=A_center, sigma=A_sigma, amplitude=A_amplitude)
B_gauss = gauss(x=x[1], center=B_center, sigma=B_sigma, amplitude=B_amplitude)
gauss0 = ((1-A_spurious)*A_gauss + A_spurious*B_gauss)
gauss1 = ((1-B_spurious)*B_gauss + B_spurious*A_gauss)
return [gauss0, gauss1]
def ro_CDF(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
cdf = gaussianCDF
A_gauss = cdf(x=x[0], mu=A_center, sigma=A_sigma, amplitude=A_amplitude)
B_gauss = cdf(x=x[1], mu=B_center, sigma=B_sigma, amplitude=B_amplitude)
gauss0 = ((1-A_spurious)*A_gauss + A_spurious*B_gauss)
gauss1 = ((1-B_spurious)*B_gauss + B_spurious*A_gauss)
return [gauss0, gauss1]
def ro_CDF_discr(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious, B_spurious):
#A_amplitude /= 1-A_spurious
#B_amplitude /= 1-B_spurious
return ro_CDF(x, A_center, B_center, A_sigma, B_sigma, A_amplitude,
B_amplitude, A_spurious=0, B_spurious=0)
def gaussian_2D(x, y, amplitude=1,
center_x=0, center_y=0,
sigma_x=1, sigma_y=1):
'''
A 2D gaussian function. if you want to use this for fitting you need to
flatten your data first.
'''
gauss = lmfit.lineshapes.gaussian
val = (gauss(x, amplitude, center_x, sigma_x) *
gauss(y, amplitude, center_y, sigma_y))
return val
def DoubleExpDecayFunc(t, tau1, tau2, amp1, amp2, offset, n):
return (offset +
amp1 * np.exp(-(t / tau1) ** n) +
amp2 * np.exp(-(t / tau2) ** n))
def TripleExpDecayFunc(t, tau1, tau2, tau3, amp1, amp2, amp3, offset, n):
return (offset +
amp1 * np.exp(-(t / tau1) ** n) +
amp2 * np.exp(-(t / tau2) ** n) +
amp3 * np.exp(-(t / tau3) ** n))
def avoided_crossing_mediated_coupling(flux, f_bus, f_center1, f_center2,
c1, c2, g, flux_state=0):
"""
Calculates the frequencies of an avoided crossing for the following model.
[f_b, g, g ]
[g, f_1, 0 ]
[g, 0, f_2]
f1 = c1*flux + f_center1
f2 = c2*flux + f_center2
f_b = constant
g: the coupling strength, beware to relabel your variable if using this
model to fit J1 or J2.
flux_state: this is a switch used for fitting. It determines which
transition to return
"""
if type(flux_state) == int:
flux_state = [flux_state] * len(flux)
frequencies = np.zeros([len(flux), 2])
for kk, dac in enumerate(flux):
f_1 = dac * c1 + f_center1
f_2 = dac * c2 + f_center2
matrix = [[f_bus, g, g],
[g, f_1, 0.],
[g, 0., f_2]]
frequencies[kk, :] = np.linalg.eigvalsh(matrix)[:2]
result = np.where(flux_state, frequencies[:, 0], frequencies[:, 1])
return result
def avoided_crossing_direct_coupling(flux, f_center1, f_center2,
c1, c2, g, flux_state=0):
"""
Calculates the frequencies of an avoided crossing for the following model.
[f_1, g ]
[g, f_2]
f1 = c1*flux + f_center1
f2 = c2*flux + f_center2
g: the coupling strength, beware to relabel your variable if using this
model to fit J1 or J2.
flux_state: this is a switch used for fitting. It determines which
transition to return
"""
if type(flux_state) == int:
flux_state = [flux_state] * len(flux)
frequencies = np.zeros([len(flux), 2])
for kk, dac in enumerate(flux):
f_1 = dac * c1 + f_center1
f_2 = dac * c2 + f_center2
matrix = [[f_1, g],
[g, f_2]]
frequencies[kk, :] = np.linalg.eigvalsh(matrix)[:2]
result = np.where(flux_state, frequencies[:, 0], frequencies[:, 1])
return result
def ErfWindow(t, t_start, t_end, t_rise, amplitude, offset):
'''
parameters:
t, time in s
t_start, start of window in s
t_end, end of window in s
amplitude a.u.
offset a.u.
t_rise in s (rise time)
'''
return offset + amplitude/2*(scipy.special.erf((t - t_start)/(t_rise/2.6))
- scipy.special.erf((t - t_end)/(t_rise/2.6)))
def hanger_with_pf(f, phi, J, kappa_pf, omega_ro, omega_pf, gamma_ro, A):
S12 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f-omega_ro))/
(4*J*J+(kappa_pf-2j*(f-omega_pf))*
(gamma_ro-2j*(f-omega_ro))) )
return S12
def simultan_hanger_with_pf(f, phi, J, kappa_pf, omega_ro_0, omega_ro_1,
omega_pf, gamma_ro, A):
f0 = f[:int(len(f)/2)]
f1 = f[int(len(f)/2):]
S12_0 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f0-omega_ro_0))/
(4*J*J+(kappa_pf-2j*(f0-omega_pf))*
(gamma_ro-2j*(f0-omega_ro_0))) )
S12_1 = A * np.abs( np.cos(phi) - np.exp(1j*phi)*kappa_pf*
(gamma_ro-2j*(f1-omega_ro_1))/
(4*J*J+(kappa_pf-2j*(f1-omega_pf))*
(gamma_ro-2j*(f1-omega_ro_1))) )
return np.hstack((S12_0, S12_1))
def fit_hanger_with_pf(model, data, simultan=False):
"""
model: a lmfit model of the pf_ro_S12 function
data: of the shape [[freq,|S12|],[freq,|S12|],...] where freq should be in GHz
return: a lmfit model.fit object
"""
def hanger_with_pf_guess(model, data):
A_guess = 1
midmax = (data[0, 1]+data[-1, 1])/2
J_guess = 0.006
omega_pf_guess = min(data, key = lambda t: t[1])[0]
pf_filter = [False]*len(data)
for x in range(0,len(data)):
if data[x,1] <= midmax:
pf_filter[x] = True
kappa_pf_guess = min(2*abs(list(itertools.compress(
data[:, 0],pf_filter))[0]- omega_pf_guess),
2*abs(list(itertools.compress(
data[:, 0],pf_filter))[-1]- omega_pf_guess), 0.05)
omega_ro_filter = [True]*len(data)
for x in range(0,len(data)):
if abs(data[x, 0]-omega_pf_guess) <= kappa_pf_guess/2:
omega_ro_filter[x] = False
omega_ro_guess = min(list(itertools.compress(
data,omega_ro_filter)), key = lambda t: t[1])[0]
for x in range(0,len(data)):
if min(omega_pf_guess, omega_ro_guess) <= data[x,0]:
if data[x,0] <= max(omega_pf_guess, omega_ro_guess):
if midmax <= data[x,1]: midmax = data[x,0]
model.set_param_hint('A', value=A_guess, min = 0, max = 1)
model.set_param_hint('phi', value=4 , min = 0, max = 2*np.pi)
model.set_param_hint('J', value=J_guess, min=0, max=0.015)
model.set_param_hint('kappa_pf', value=kappa_pf_guess, min=0, max=0.05)
model.set_param_hint('omega_pf', value=omega_pf_guess, min=0)
model.set_param_hint('gamma_ro', value=0.0001, vary = False)
model.set_param_hint('omega_ro', value=omega_ro_guess)
params = model.make_params()
return params
if simultan:
data_loc_0 = np.copy(data[0][:-2])
data_loc_1 = np.copy(data[1][:-2])
fit0 = fit_hanger_with_pf(HangerWithPfModel, np.transpose(
[data_loc_0[:,0], data_loc_0[:,1]]), simultan=False)
fit1 = fit_hanger_with_pf(HangerWithPfModel, np.transpose(
[data_loc_1[:,0], data_loc_1[:,1]]), simultan=False)
if not len(data_loc_1) == len(data_loc_0):
if len(data_loc_1) < len(data_loc_0):
data_loc_0 = data_loc_0[:len(data_loc_1)]
else:
data_loc_1 = data_loc_1[:len(data_loc_0)]
data_loc_0[:,0] *= 1e-9
data_loc_1[:,0] *= 1e-9
data_loc = np.vstack((data_loc_0, data_loc_1))
phi_guess = (fit0.params['phi'].value+fit1.params['phi'].value)/2
J_guess = (fit0.params['J'].value+fit1.params['J'].value)/2*1e-9
kap_guess = (fit0.params['kappa_pf'].value+
fit1.params['kappa_pf'].value)/2*1e-9
omega_pf_guess = (fit0.params['omega_pf'].value+
fit1.params['omega_pf'].value)/2*1e-9
model.set_param_hint('A', value=1, min = 0, max = 1)
model.set_param_hint('A', value=1, min = 0, max = 1)
model.set_param_hint('phi', value=phi_guess , min = 0, max = 2*np.pi)
model.set_param_hint('J', value=J_guess, min=0, max=0.015)
model.set_param_hint('kappa_pf', value=kap_guess, min=0, max=0.05)
model.set_param_hint('omega_pf', value=omega_pf_guess, min=0)
model.set_param_hint('gamma_ro', value=0.0001, vary = False)
model.set_param_hint('omega_ro_0', value=
fit0.params['omega_ro'].value*1e-9, min=0)
model.set_param_hint('omega_ro_1', value=
fit1.params['omega_ro'].value*1e-9, min=0)
guess = model.make_params()
fit_out = model.fit( data_loc[:,1]/max(data_loc[:,1]),
guess, f=data_loc[:,0])
tol = 0.4
else:
data_loc = np.copy(data)
data_loc[:,0] *= 1e-9
guess = hanger_with_pf_guess(model,np.transpose(
[data_loc[:,0],data_loc[:,1]/max(data_loc[:,1])]))
fit_out = model.fit( data_loc[:,1]/
max(data_loc[:,1]),guess,f=data_loc[:,0],)
tol = 0.1
if fit_out.chisqr > tol:
fit_lst = []
for shift_pf in np.linspace(-0.01, 0.01, 5):
for shift_ro in np.linspace(-0.01, 0.01, 5):
for phase in np.linspace(0, 2*np.pi, 6):
for kappa_shift in np.linspace(-0.015,0.015,4):
for J_shift in np.linspace(-0.002,0.004,4):
if simultan:
fit_lst.append(model.fit( data_loc[:,1]/
max(data_loc[:,1]),guess,
f=data_loc[:,0],
phi=phase,omega_pf=float(guess['omega_pf'])+shift_pf,
omega_ro_0=float(guess['omega_ro_0'])+shift_ro,
omega_ro_1=float(guess['omega_ro_1'])+shift_ro,
kappa_pf=float(guess['kappa_pf'])+kappa_shift,
J=float(guess['J'])+J_shift))
else:
fit_lst.append(model.fit( data_loc[:,1]/max(
data_loc[:,1]),guess,f=data_loc[:,0],phi=phase,
omega_pf=float(guess['omega_pf'])+shift_pf,
omega_ro=float(guess['omega_ro'])+shift_ro,
kappa_pf=float(guess['kappa_pf'])+kappa_shift,
J=float(guess['J'])+J_shift))
if fit_lst[-1].chisqr <= tol: break
chisqr_lst = [fit.chisqr for fit in fit_lst]
fit_out = fit_lst[np.argmin(chisqr_lst)]
fit_out.params['J'].max = 15e6
fit_out.params['kappa_pf'].max = 50e6
fit_out.params['omega_pf'].value *= 1e9
fit_out.params['kappa_pf'].value *= 1e9
fit_out.params['J'].value *= 1e9
fit_out.params['gamma_ro'].value *= 1e9
fit_out.params['A'].value *= max(data_loc[:,1])
if simultan:
fit_out.params['omega_ro_0'].value *= 1e9
fit_out.params['omega_ro_1'].value *= 1e9
else:
fit_out.params['omega_ro'].value *= 1e9
if fit_out.chisqr > tol:
log.warning('The fit did not converge properly: chi^2 = '
''+str(fit_out.chisqr))
#Unpack simultan fits
if simultan:
fit0.params['omega_pf'].value = fit_out.params['omega_pf'].value
fit0.params['kappa_pf'].value = fit_out.params['kappa_pf'].value
fit0.params['J'].max = fit_out.params['J'].max
fit0.params['J'].value = fit_out.params['J'].value
fit0.params['gamma_ro'].value = fit_out.params['gamma_ro'].value
fit0.params['omega_ro'].value = fit_out.params['omega_ro_0'].value
fit1.params['omega_pf'].value = fit_out.params['omega_pf'].value
fit1.params['kappa_pf'].value = fit_out.params['kappa_pf'].value
fit1.params['J'].max = fit_out.params['J'].max
fit1.params['J'].value = fit_out.params['J'].value
fit1.params['gamma_ro'].value = fit_out.params['gamma_ro'].value
fit1.params['omega_ro'].value = fit_out.params['omega_ro_1'].value
fit_out = [fit0, fit1]
return fit_out
######################
# Residual functions #
######################
def residual_complex_fcn(pars, cmp_fcn, x, y):
'''
Residual of a complex function with complex results 'y' and
real input values 'x'
For resonators 'x' is the the frequency, 'y' the complex transmission
Input:
pars = parameters dictionary (check the corresponding function 'cmp_fcn' for the parameters to pass)
cmp_fcn = complex function
x = input real values to 'cmp_fcn'
y = output complex values from 'cmp_fcn'
Author = Stefano Poletto
'''
cmp_values = cmp_fcn(x, pars)
res = cmp_values - y
res = np.append(res.real, res.imag)
return res
####################
# Guess functions #
####################
def exp_dec_guess(model, data, t):
'''
Assumes exponential decay in estimating the parameters
'''
offs_guess = data[np.argmax(t)]
amp_guess = data[np.argmin(t)] - offs_guess
# guess tau by looking for value closest to 1/e
tau_guess = t[np.argmin(abs((amp_guess * (1 / np.e) + offs_guess) - data))]
model.set_param_hint('amplitude', value=amp_guess)
model.set_param_hint('tau', value=tau_guess)
model.set_param_hint('n', value=1, vary=False)
model.set_param_hint('offset', value=offs_guess)
params = model.make_params()
return params
def group_consecutives(vals, step=1):
"""Return list of consecutive lists of numbers from vals (number list)."""
run = []
result = [run]
expect = None
for v in vals:
if (v == expect) or (expect is None):
run.append(v)
else:
run = [v]
result.append(run)
expect = v + step
return result
def arc_guess(freq, dac, dd=0.1):
'''
Expects the dac values to be sorted!
:param freq:
:param dac:
:param dd:
:return:
'''
p = round(max(dd * len(dac), 1))
f_small = np.average(np.sort(freq)[:p]) + np.std(np.sort(freq)[:p])
f_big = np.average(np.sort(freq)[-p:]) - np.std(np.sort(freq)[-p:])
#print(f_small * 1e-9, f_big * 1e-9)
fmax = np.max(freq)
fmin = np.min(freq)
dacs_ss = np.where(freq >= f_big)[0]
dacs_as = np.where(freq <= f_small)[0]
dacs_ss_groups = group_consecutives(vals=dacs_ss, step=1)
dacs_as_groups = group_consecutives(vals=dacs_as, step=1)
dacs_ss_single = []
for g in dacs_ss_groups:
ind = g[np.argmax(freq[g])]
# ind = int(round(np.average(g)))
dacs_ss_single.append(ind)
dac_ss_group_index = np.argmin(np.abs(dac[dacs_ss_single]))
dac_ss_index = dacs_ss_single[dac_ss_group_index]
min_left = 0
min_right = len(dac) - 1
dacs_as_single = []
for g in dacs_as_groups:
if 0 in g:
ind = 0
elif len(dac) - 1 in g:
ind = len(dac) - 1
else:
ind = int(round(np.average(g)))
if ind < dac_ss_index:
min_left = max(ind, min_left)
elif ind > dac_ss_index:
min_right = min(ind, min_right)
dacs_as_single.append(ind)
# print('maxs', dacs_ss_single)
# print('mins', dacs_as_single)
arc_len = (dac[min_right] - dac[min_left])
# print('%d to %d = %.5f' % (min_left, min_right, arc_len))
if min_left == 0 or min_right == len(dac) - 1:
arc_len *= 2
elif len(dacs_ss_groups) > 1:
arc_len = np.average(dac[dacs_ss_single[1:]] - dac[dacs_ss_single[:-1]])
return fmax, fmin, dac[dac_ss_index], arc_len
def Resonator_dac_arch_guess(model, freq, dac_voltage, f_max_qubit: float = None, E_c: float = None):
fmax, fmin, dac_ss, period = arc_guess(freq=freq, dac=dac_voltage)
coup_guess = 15e6
# todo make better f_res guess
f_res = np.mean(freq) # - (coup_guess ** 2 / (f_max_qubit - fmax))
f_max_qubit_vary = f_max_qubit is None
f_max_qubit = f_max_qubit or f_res - 500e6
model.set_param_hint('f_0_res', value=f_res, min=f_res / 2, max=2 * f_res)
model.set_param_hint('f_max_qubit', value=f_max_qubit, min=3e9, max=8.5e9, vary=f_max_qubit_vary)
model.set_param_hint('dac_sweet_spot', value=dac_ss, min=(dac_ss - 0.005) / 2, max=2 * (dac_ss + 0.005))
model.set_param_hint('V_per_phi0', value=period, min=(period - 0.005) / 3, max=5 * (period + 0.005))
model.set_param_hint('asymmetry', value=0, max=1, min=-1)
model.set_param_hint('coupling', value=coup_guess, min=1e6, max=80e6)
E_c = E_c or 260e6
model.set_param_hint('E_c', value=E_c, min=50e6, max=400e6)
params = model.make_params()
return params
def Qubit_dac_arch_guess(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('f_max', value=fixed_params.get('f_max', f_max),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def Qubit_dac_arch_guess_precise(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('Ej_max', value=fixed_params.get('Ej_max'),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def Qubit_dac_arch_guess_res(model, data, dac_voltage, fixed_params=None):
f_max, dac_ss = np.max(data), dac_voltage[np.argmax(data)]
f_min, dac_lss = np.min(data), dac_voltage[np.argmin(data)]
V_per_phi0 = abs(2*(dac_ss-dac_lss))
d = (f_min)**2/(f_max)**2
if fixed_params is None:
fixed_params = {"E_c": 0}
model.set_param_hint('Ej_max', value=fixed_params.get('Ej_max'),
min=0, vary=not 'f_max' in fixed_params)
model.set_param_hint('V_per_phi0', value=fixed_params.get('V_per_phi0', V_per_phi0),
min=0, vary=not 'V_per_phi0' in fixed_params)
model.set_param_hint('asymmetry', value=fixed_params.get('asymmetry', d),
min=0, max=1, vary=not 'asymmetry' in fixed_params)
model.set_param_hint('E_c', value=fixed_params.get('E_c', 0),
vary=not 'E_c' in fixed_params)
model.set_param_hint('coupling', value=fixed_params.get('coupling', 100e6),
vary=not 'coupling' in fixed_params)
model.set_param_hint('fr', value=fixed_params.get('fr', 7e9),
vary=not 'fr' in fixed_params)
if "phi_park" in fixed_params:
model.set_param_hint('phi_park', value=fixed_params['phi_park'],
vary=False)
# dac_sweet_spot should be eligible for fitting only if phi_park is not fixed.
# We cannot specify both in the model as they refer to the same physical quantity.
# Note that current config does not allow to fit phi_park
else:
model.set_param_hint('dac_sweet_spot', value=fixed_params.get('dac_sweet_spot', dac_ss),
min=-3, max=3, vary=not 'dac_sweet_spot' in fixed_params)
params = model.make_params()
return params
def idle_err_rate_guess(model, data, N):
'''
Assumes exponential decay in estimating the parameters
'''
amp_guess = 0.5
offset = np.mean(data)
N1 = np.mean(N)
N2 = np.mean(N)
params = model.make_params(A=amp_guess,
N1=N1,
N2=N2,
offset=offset)
return params
def fft_freq_phase_guess(data, t, freq_guess=None):
'''
Guess for a cosine fit using FFT, only works for evenly spaced points
'''
# Freq guess ! only valid with uniform sampling
# Only first half of array is used, because the second half contains the
# negative frequecy components, and we want a positive frequency.
w = np.fft.fft(data)[:len(data) // 2]
f = np.fft.fftfreq(len(data), t[1] - t[0])[:len(w)]
if freq_guess is None:
w[0] = 0 # Removes DC component from fourier transform
# Use absolute value of complex valued spectrum
abs_w = np.abs(w)
freq_guess = abs(f[abs_w == max(abs_w)][0])
ph_guess = 2 * np.pi - (2 * np.pi * t[data == max(data)] * freq_guess)[0]
# the condition data == max(data) can have several solutions
# (for example when discretization is visible)
# to prevent errors we pick the first solution
return freq_guess, ph_guess
def Cos_guess(model, data, t, **kwargs):
"""
Tip: to use this assign this guess function as a method to a model use:
model.guess = Cos_guess.__get__(
model, model.__class__)
"""
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
offs_guess = np.mean(data)
freq_guess, ph_guess = fft_freq_phase_guess(data, t, **kwargs)
model.set_param_hint('period', expr='1/frequency')
params = model.make_params(amplitude=amp_guess,
frequency=freq_guess,
phase=ph_guess,
offset=offs_guess)
params['amplitude'].min = 0 # Ensures positive amp
params['frequency'].min = 0
return params
def exp_damp_osc_guess(model, data, t, n_guess=1):
"""
Makes a guess for an exponentially damped oscillation.
Uses the fft_freq_phase guess to guess the oscillation parameters.
The guess for the exponential is simpler as it sets the exponent (n) at 1
and the tau at 2/3 of the total range
"""
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
freq_guess, ph_guess = fft_freq_phase_guess(data, t)
osc_offs_guess = 0
tau_guess = 2 / 3 * max(t)
exp_offs_guess = np.mean(data)
params = model.make_params(amplitude=amp_guess,
frequency=freq_guess,
phase=ph_guess,
oscillation_offset=osc_offs_guess,
exponential_offset=exp_offs_guess,
n=n_guess,
tau=tau_guess)
return params
def Cos_amp_phase_guess(model, data, f, t):
'''
Guess for a cosine fit with fixed frequency f.
'''
amp_guess = abs(max(data) - min(data)) / 2 # amp is positive by convention
offs_guess = np.mean(data)
ph_guess = (-2 * np.pi * t[data == max(data)] * f)[0]
# the condition data == max(data) can have several solutions
# (for example when discretization is visible)
# to prevent errors we pick the first solution
# model.set_param_hint('period', expr='1')
params = model.make_params(amplitude=amp_guess,
phase=ph_guess,
offset=offs_guess)
params['amplitude'].min = 0 # Ensures positive amp
return params
def gauss_2D_guess(model, data, x, y):
'''
takes the mean of every row/column and then uses the regular gauss guess
function to get a guess for the model parameters.
Assumptions on input data
* input is a flattened version of a 2D grid.
* total surface under the gaussians sums up to 1.
Potential improvements:
Make the input also accept a 2D grid of data to prevent reshaping.
Find a way to extract amplitude guess from data itself, note that
taking the sum of the data (which should correspond to all data under
the curve) does not do the trick.
Note: possibly not compatible if the model uses prefixes.
'''
dx = x[1:]-x[:-1]
dy = y[1:]-y[:-1]
sums = np.sum(((data[:-1,:-1]*dx).transpose()*dy))
amp = np.sqrt(sums)
data_grid = data.reshape(-1, len(np.unique(x)))
x_proj_data = np.mean(data_grid, axis=0)
y_proj_data = np.mean(data_grid, axis=1)
x.sort()
y.sort()
xm = lmfit.models.GaussianModel()
ym = lmfit.models.GaussianModel()
x_guess = xm.guess(data=x_proj_data, x=np.unique(x))
x_res = xm.fit(data=x_proj_data, x=np.unique(x), params=x_guess)
y_guess = ym.guess(data=y_proj_data, x=np.unique(y))
y_res = ym.fit(data=y_proj_data, x=np.unique(y), params=y_guess)
x_guess = x_res.params
y_guess = y_res.params
model.set_param_hint('amplitude', value=amp, min=0.9*amp, max=1.1*amp,
vary=True)
model.set_param_hint('sigma_x', value=x_guess['sigma'].value, min=0,
vary=True)
model.set_param_hint('sigma_y', value=y_guess['sigma'].value, min=0,
vary=True)
params = model.make_params(center_x=x_guess['center'].value,
center_y=y_guess['center'].value,)
return params
def double_gauss_2D_guess(model, data, x, y):
'''
takes the mean of every row/column and then uses the guess
function of the double gauss.
Assumptions on input data
* input is a flattened version of a 2D grid.
Note: possibly not compatible if the model uses prefixes.
Note 2: see also gauss_2D_guess() for some notes on how to improve this
function.
'''
data_grid = data.reshape(-1, len(np.unique(x)))
x_proj_data = np.mean(data_grid, axis=0)
y_proj_data = np.mean(data_grid, axis=1)
# The syntax here is slighly different than when calling a regular guess
# function because I do not overwrite the class attribute properly.
x_guess = double_gauss_guess(model=None, data=x_proj_data, x=np.unique(x))
y_guess = double_gauss_guess(model=None, data=y_proj_data, x=np.unique(y))
if model is not None:
pars = model.make_params(A_sigma_x=x_guess['A_sigma'],
A_sigma_y=y_guess['A_sigma'],
A_center_x=x_guess['A_center'],
A_center_y=y_guess['A_center'],
A_amplitude=1,
B_sigma_x=x_guess['B_sigma'],
B_sigma_y=y_guess['B_sigma'],
B_center_y=y_guess['B_center'],
B_center_x=x_guess['B_center'],
B_amplitude=1)
return pars
else:
return x_guess, y_guess
def double_gauss_guess(model, data, x=None, **kwargs):
'''
Finds a guess for the intial parametes of the double gauss model.
Guess is based on taking the cumulative sum of the data and
finding the points corresponding to 25% and 75%
it finds sigma by using the property that ~33% of the data is contained
in the range mu-sigma to mu+sigma.
Tip: to use this assign this guess function as a method to a model use:
model.guess = double_gauss_guess.__get__(
model, model.__class__)
'''
if x is None:
x = np.arange(len(data))
cdf = np.cumsum(data)
norm_cdf = cdf / cdf[-1]
par_dict = {'A_center': x[(np.abs(norm_cdf - 0.25)).argmin()],
'B_center': x[(np.abs(norm_cdf - 0.75)).argmin()],
'A_sigma': (x[(np.abs(norm_cdf - 0.25 - .33 / 2)).argmin()] -
x[(np.abs(norm_cdf - 0.25 + .33 / 2)).argmin()]),
'B_sigma': (x[(np.abs(norm_cdf - 0.75 - .33 / 2)).argmin()] -
x[(np.abs(norm_cdf - 0.75 + .33 / 2)).argmin()])}
amp = max(data) * (par_dict['A_sigma'] + par_dict['B_sigma']) / 2.
if model is not None:
# Specify explicitly because not all pars are set to those from the par
# dict
pars = model.make_params(A_center=par_dict['A_center'],
B_center=par_dict['B_center'],
A_sigma=par_dict['A_sigma'],
B_sigma=par_dict['B_sigma'],
A_amplitude=amp, B_amplitude=amp)
return pars
# The else clause is added explicitly to reuse this function for the
# 2D double gauss model
else:
return par_dict
def ro_double_gauss_guess(model, data, x, fixed_p01 = False, fixed_p10 = False):
# An initial guess is done on the binned data with single gaussians
# to constrain the fit params and avoid fitting noise if
# e.g., mmt. ind. rel. is very low
gmod0 = lmfit.models.GaussianModel()
guess0 = gmod0.guess(data=data[0], x=x[0])
gmod1 = lmfit.models.GaussianModel()
guess1 = gmod1.guess(data=data[1], x=x[1])
model.set_param_hint(
'A_center', vary=True, value=guess0['center'].value,
min=guess0['center'] - 2 * guess0['sigma'],
max=guess0['center'] + 2 * guess0['sigma'])
model.set_param_hint(
'B_center', vary=True, value=guess1['center'].value,
min=guess1['center'] - 2 * guess1['sigma'],
max=guess1['center'] + 2 * guess1['sigma'])
model.set_param_hint('A_sigma', value=guess0['sigma'].value, vary=True)
model.set_param_hint('B_sigma', value=guess1['sigma'].value, vary=True)
# Amplitudes
intarea0 = sum_int(x=x[0], y=data[0])[-1]
intarea1 = sum_int(x=x[1], y=data[1])[-1]
model.set_param_hint('A_amplitude', value=intarea0, vary=False)
model.set_param_hint('B_amplitude', value=intarea1, vary=False)
model.set_param_hint('SNR', expr='abs(A_center-B_center)*2/(A_sigma+B_sigma)',
vary=False)
# Spurious excitement
f = np.sqrt(2*np.pi)
amp0 = 0.99 * np.max(data[0]) * guess0['sigma'] * f
amp1 = 0.99 * np.max(data[1]) * guess1['sigma'] * f
spurious0 = max(1-(amp0/intarea0), 1e-3)
spurious1 = max(1-(amp1/intarea1), 1e-3)
p01 = fixed_p01 if fixed_p01 is not False else spurious0
p10 = fixed_p10 if fixed_p10 is not False else spurious1
model.set_param_hint('A_spurious', value=p01, min=0, max=1,
vary=fixed_p01 is False)
model.set_param_hint('B_spurious', value=p10, min=0, max=1,
vary=fixed_p10 is False)
return model.make_params()
def sum_int(x,y):
return np.cumsum(y[:-1]*(x[1:]-x[:-1]))
def DoubleGaussian(freq, sigma, mu, ampl, sigma0, mu0, ampl0, offset):
'''
Double Gaussian function
'''
return ampl/(sigma*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu)/sigma)**2) + \
ampl0/(sigma0*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu0)/sigma0)**2) + \
offset
def Gaussian(freq, sigma, mu, ampl, offset):
'''
Gaussian function
'''
return ampl/(sigma*np.sqrt(2*np.pi))*np.exp(-0.5*((freq - mu)/sigma)**2) + offset
def Gaussian_guess(model, data, freq, **kwargs):
"""
Tip: to use this assign this guess function as a method to a model use:
model.guess = Gaussian_guess.__get__(
model, model.__class__)
"""
mu_guess = freq[np.argmax(data)]
offs_guess = np.median(data)
p = (data - offs_guess)**2
p /= p.sum()
sigma_guess = np.sqrt(((freq - mu_guess)**2 * p).sum())/10
amp_guess = max(data - offs_guess)*sigma_guess*np.sqrt(2*np.pi)
params = model.make_params(sigma=sigma_guess,
mu=mu_guess,
ampl=amp_guess,
offset=offs_guess)
params['mu'].min = np.min(freq)
params['mu'].max = np.max(freq)
return params
def half_feed_line_S12_J_func(omega, J, kappaPF, gammaPF, gammaRR, omegaPF, omegaRR, phi, A , B, alpha):
return abs( A+np.exp(-1j*phi)*2*B*((-1+np.exp(1j*alpha))*(4*J**2+(gammaPF-2*1j*(omegaPF-omega))*(gammaRR-2j*omegaRR+2j*omega)))/(16*J**2+(4*gammaPF+(3+np.exp(1j*alpha))*kappaPF-8j*(omegaPF-omega))*(gammaRR-2j*omegaRR+2j*omega)) )
def half_feed_line_S12_J_guess(model,data):
'''
data should have the frequencies in Hz in the first column and the transmission in the second column
'''
background_guess = max(data,key=lambda item:item[1])[1]/2
amp_guess = max(data,key=lambda item:item[1])[0]-min(data,key=lambda item:item[1])[0]
omegaPF_guess = min(data,key=lambda item:item[1])[0]
#Remove the PF dip to find the second smallest feature at the RR frequency
kappaPF_guess_data = np.extract( (data[:,1]<=(min(data,key=lambda item:item[1])[1]+ background_guess/2)),data[:,0])
kappaPF_guess = min(abs( kappaPF_guess_data[0]-omegaPF_guess),abs( kappaPF_guess_data[-1]-omegaPF_guess),5e10)
omegaRR_guess = min( np.transpose([np.extract(abs(data[:,0]- omegaPF_guess) >= kappaPF_guess/2,data[:,0]),np.extract(abs(data[:,0]- omegaPF_guess) >= kappaPF_guess/2,data[:,1])]),key=lambda item:item[1])[0]
J_guess = kappaPF_guess/4
model.set_param_hint('J',value=J_guess,min=0,max=2e7)
model.set_param_hint('kappaPF',value=kappaPF_guess,min=1e6,max=1e8)
model.set_param_hint('gammaPF',value=0.001,min=0,max=1e6)
model.set_param_hint('gammaRR',value=0.001,min=0,max=1e6)
model.set_param_hint('omegaPF',value=omegaPF_guess,min=omegaPF_guess-2e7,max=omegaPF_guess+2e7)
model.set_param_hint('omegaRR',value=omegaRR_guess,min=omegaRR_guess-2e7,max=omegaRR_guess+2e7)
model.set_param_hint('phi',value=(data[0,1]-data[-1,1])/background_guess)
model.set_param_hint('A',value=background_guess,min=(background_guess)-5,max=(2000*background_guess)+5)
model.set_param_hint('B',value=background_guess,min=(background_guess)-5,max=(2000*background_guess)+5)
model.set_param_hint('alpha',value=3,min=0,max=10)
params=model.make_params()
return params
def TwoErrorFunc_guess(model, delays, data):
offset_guess = data[1]
amp_guess = data[data.size//2] - data[1]
delay_interval = (delays[-1]-delays[1])
mu_A_guess = delays[1] + 0.1*delay_interval
mu_B_guess = delays[1] + 0.9*delay_interval
sigma_guess = 3e-9
params = model.make_params(amp=amp_guess,
mu_A=mu_A_guess,
mu_B=mu_B_guess,
sigma=sigma_guess,
offset = offset_guess)
return params
def mixer_imbalance_sideband(alpha, phi_skew, g=1.0, phi=0.0, offset=0.0):
"""Analytical model for the max. amplitude of the unwanted SB of an IQ mixer.
Args:
alpha (float): Correction factor that was applied to the amplitude of
the Q signal.
phi_skew (float): Phase correction of the Q signal relative to the I
signal.
g (float, optional): Power ratio between the LO power splitter outputs.
It is defined as power(LO_I)/power(LO_Q).
Defaults to 1.0.
phi (float, optional): Phase between the two ports of the LO power
splitter in degree. Is defined as phase(LO_Q)-phase(LO_I). Defaults
to 0 degree.
offset (float, optional): Offset in dBV accounting for losses in the
signal chain. Defaults to 0.0 dBV.
Returns:
float: maximum sideband amplitude for the specified parameters in dBV
Model Schematic:
I >------------------------------ I R ----+
LO |
| |
LO >------\-/-----------------------+ |
X (90° power splitter) +----> RF
--/-\--- 1/g*exp(i*phase) --+ |
| |
LO |
Q >--- alpha*exp(i*phi_skew) ---- I R ----+
"""
return 20*np.log10(np.abs(1 - alpha/g
* np.exp(-1j*np.deg2rad(phi + phi_skew)))
) + offset
def mixer_imbalance_sideband_guess(model, **kwargs):
"""Prepare and return parameters of an :py:lmfit.model: for the model
mixer_imbalance_sideband.
Args:
model (:py:lmfit.model:): The model that the parameter hints will be
added to and that is used to generate the parameters using the
:py:lmfit.model.make_params() method.
Returns:
:py:lmfit.parameters: Parameters
"""
model.set_param_hint('g', value=1.0, min=0.5, max=1.5)
model.set_param_hint('phi', value=0, min=-20, max=20)
model.set_param_hint('offset', value=0.0, min=-4.0, max=+4.0)
return model.make_params()
def mixer_lo_leakage(vi, vq, li=0.0, lq=0.0, theta_i=0, theta_q=0, offset=0.0):
"""Model for maximum amplitude of LO leakage of an IQ mixer.
Args:
vi (:obj:'float'): DC bias voltage applied on the I input of the mixer.
vq (:obj:'float'): DC bias voltage applied on the Q input of the mixer.
li (float, optional): [TODO:description]. Defaults to 0.0.
lq (float, optional): [TODO:description]. Defaults to 0.0.
theta_i (int, optional): [TODO:description]. Defaults to 0.0.
theta_q (int, optional): [TODO:description]. Defaults to 0.0.
offset (float, optional): Offset in dBV accounting for losses in the
signal chain. Defaults to 0.0 dBV.
Returns:
:obj:'float': maximum amplitude of the LO leakage for given parameters
Model Schematic:
I >-- +V_I ---------------- I R -- li*exp(i*theta_i) -------+
LO |
| |
LO >------------\-/-----------+ |
X +----> RF
--/-\-----------+ |
| |
LO |
Q >-- +V_Q ---------------- I R -- lq*exp(i*theta_q-pi/2) --+
"""
return 20*np.log10(np.abs(vi
+ li * np.exp(1j*theta_i)
- 1j * vq
+ lq * np.exp(1j*(theta_q-np.pi/2))
)) + offset
def mixer_lo_leakage_guess(model, **kwargs):
"""Prepare and return parameters of an :py:lmfit.model: for the model
mixer_lo_leakage.
Args:
model (:py:lmfit.model:): The model that the parameter hints will be added to
and that is used to generate the parameters. this model should have
the following parameters: 'li', 'lq', 'theta_i', 'theta_q', 'scale'
Returns:
:py:lmfit.parameters: Parameters
"""
pi_half = np.pi/2
model.set_param_hint('li', value=0.0, min=0, max=1)
model.set_param_hint('lq', value=0.0, min=0, max=1, vary=False)
model.set_param_hint('theta_i', value=0.0, min=-pi_half, max=pi_half)
model.set_param_hint('theta_q', value=0.0, min=-pi_half, max=pi_half, vary=False)
model.set_param_hint('offset', value=0.0, min=-4.0, max=+4.0)
return model.make_params()
#################################
# User defined Models #
#################################
# NOTE: it is actually better to instantiate the model within your analysis
# file, this prevents the model params having a memory.
# A valid reason to define it here would be exp_dec_guess if you want to add a guess function
CosModel = lmfit.Model(CosFunc)
CosModel.guess = Cos_guess
half_Feed_lineS12_J_Model = lmfit.Model(half_feed_line_S12_J_func)
half_Feed_lineS12_J_Model.guess = half_feed_line_S12_J_guess
ExpDecayModel = lmfit.Model(ExpDecayFunc)
TripleExpDecayModel = lmfit.Model(TripleExpDecayFunc)
ExpDecayModel.guess = exp_dec_guess # todo: fix
ExpDampOscModel = lmfit.Model(ExpDampOscFunc)
GaussExpDampOscModel = lmfit.Model(GaussExpDampOscFunc)
ExpDampDblOscModel = lmfit.Model(ExpDampDblOscFunc)
DoubleExpDampOscModel = lmfit.Model(DoubleExpDampOscFunc)
HangerAmplitudeModel = lmfit.Model(HangerFuncAmplitude)
SlopedHangerAmplitudeModel = lmfit.Model(SlopedHangerFuncAmplitude)
PolyBgHangerAmplitudeModel = lmfit.Model(PolyBgHangerFuncAmplitude)
HangerComplexModel = lmfit.Model(HangerFuncComplex)
SlopedHangerComplexModel = lmfit.Model(SlopedHangerFuncComplex)
QubitFreqDacModel = lmfit.Model(Qubit_dac_to_freq)
QubitFreqFluxModel = lmfit.Model(QubitFreqFlux)
TwinLorentzModel = lmfit.Model(TwinLorentzFunc)
LorentzianModel = lmfit.Model(Lorentzian)
RBModel = lmfit.Model(RandomizedBenchmarkingDecay)
LinOModel = lmfit.Model(linear_with_offset)
LinBGModel = lmfit.Model(linear_with_background)
LinBGOModel = lmfit.Model(linear_with_background_and_offset)
ErfWindowModel = lmfit.Model(ErfWindow)
GaussianModel_v2 = lmfit.models.GaussianModel
GaussianModel = lmfit.Model(Gaussian)
ExponentialModel = lmfit.models.ExponentialModel
HangerWithPfModel = lmfit.Model(hanger_with_pf)
SimHangerWithPfModel = lmfit.Model(simultan_hanger_with_pf,
independent_vars=['f'])
# 2D models
Gaus2D_model = lmfit.Model(gaussian_2D, independent_vars=['x', 'y'])
Gaus2D_model.guess = gauss_2D_guess # Note: not proper way to add guess func
DoubleGauss2D_model = (lmfit.Model(gaussian_2D, independent_vars=['x', 'y'],
prefix='A_') +
lmfit.Model(gaussian_2D, independent_vars=['x', 'y'],
prefix='B_'))
DoubleGauss2D_model.guess = double_gauss_2D_guess
###################################
# Models based on lmfit functions #
###################################
LorentzModel = lmfit.Model(lmfit.models.lorentzian)
Lorentz_w_background_Model = lmfit.models.LorentzianModel() + \
lmfit.models.LinearModel()
PolyBgHangerAmplitudeModel = (HangerAmplitudeModel *
lmfit.models.PolynomialModel(degree=7))
DoubleGaussModel = (lmfit.models.GaussianModel(prefix='A_') +
lmfit.models.GaussianModel(prefix='B_'))
DoubleGaussModel.guess = double_gauss_guess # defines a guess function
def plot_fitres2D_heatmap(fit_res, x, y, axs=None, cmap='viridis'):
'''
Convenience function for plotting results of flattened 2D fits.
It could be argued this does not belong in fitting models (it is not a
model) but I put it here as it is closely related to all the stuff we do
with lmfit. If anyone has a better location in mind, let me know (MAR).
'''
# fixing the data rotation with [::-1]
nr_cols = len(np.unique(x))
data_2D = fit_res.data.reshape(-1, nr_cols, order='C')[::-1]
fit_2D = fit_res.best_fit.reshape(-1, nr_cols, order='C')[::-1]
guess_2D = fit_res.init_fit.reshape(-1, nr_cols, order='C')[::-1]
if axs is None:
f, axs = plt.subplots(1, 3, figsize=(14, 6))
axs[0].imshow(data_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[1].imshow(fit_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[2].imshow(guess_2D, extent=[x[0], x[-1], y[0], y[-1]],
cmap=cmap, vmin=np.min(data_2D), vmax=np.max(data_2D))
axs[0].set_title('data')
axs[1].set_title('fit-result')
axs[2].set_title('initial guess')
return axs
# Before defining a new model, take a look at the built in models in lmfit.
# From http://lmfit.github.io/lmfit-py/builtin_models.html
# Built-in Fitting Models in the models module
# Peak-like models
# GaussianModel
# LorentzianModel
# VoigtModel
# PseudoVoigtModel
# Pearson7Model
# StudentsTModel
# BreitWignerModel
# LognormalModel
# DampedOcsillatorModel
# ExponentialGaussianModel
# SkewedGaussianModel
# DonaichModel
# Linear and Polynomial Models
# ConstantModel
# LinearModel
# QuadraticModel
# ParabolicModel
# PolynomialModel
# Step-like models
# StepModel
# RectangleModel
# Exponential and Power law models
# ExponentialModel
# PowerLawModel
|
from collections import OrderedDict
from copy import deepcopy
from django.http import Http404, HttpResponseForbidden, JsonResponse, HttpResponse
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied, ObjectDoesNotExist
from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import Group
from django.contrib.auth.decorators import login_required
from django.db import IntegrityError
from django.forms.models import modelformset_factory
from django.shortcuts import render, redirect, get_object_or_404
from django.template import loader, RequestContext
from bestiary.models import Monster, Fusion, Building
from .forms import *
from .filters import *
from .models import Summoner, BuildingInstance, MonsterInstance, MonsterPiece, TeamGroup, Team, RuneInstance, RuneCraftInstance
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_user.groups.add(Group.objects.get(name='Summoners'))
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile_default', profile_name=user.username)
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
@login_required
def change_username(request):
user = request.user
form = CrispyChangeUsernameForm(request.POST or None)
context = {
'form': form,
}
if request.method == 'POST' and form.is_valid():
try:
user.username = form.cleaned_data['username']
user.save()
return redirect('username_change_complete')
except IntegrityError:
form.add_error('username', 'Username already taken')
return render(request, 'registration/change_username.html', context)
def change_username_complete(request):
return render(request, 'registration/change_username_complete.html')
@login_required
def profile_delete(request, profile_name):
user = request.user
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = DeleteProfileForm(request.POST or None)
form.helper.form_action = reverse('herders:profile_delete', kwargs={'profile_name': profile_name})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
logout(request)
user.delete()
messages.warning(request, 'Your profile has been permanently deleted.')
return redirect('news:latest_news')
return render(request, 'herders/profile/profile_delete.html', context)
else:
return HttpResponseForbidden("You don't own this profile")
@login_required
def following(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_following', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'view': 'following',
'return_path': return_path,
}
return render(request, 'herders/profile/following/list.html', context)
@login_required
def follow_add(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
new_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.add(new_follower)
messages.info(request, 'Now following %s' % new_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required
def follow_remove(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
removed_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.remove(removed_follower)
messages.info(request, 'Unfollowed %s' % removed_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
def profile(request, profile_name=None):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in.')
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_filter_form = FilterMonsterInstanceForm()
monster_filter_form.helper.form_action = reverse('herders:monster_inventory', kwargs={'profile_name': profile_name})
context = {
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'monster_filter_form': monster_filter_form,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_inventory/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def buildings(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
all_buildings = Building.objects.all().order_by('name')
building_data = []
total_glory_cost = 0
spent_glory = 0
total_guild_cost = 0
spent_guild = 0
for b in all_buildings:
bldg_data = _building_data(summoner, b)
if b.area == Building.AREA_GENERAL:
total_glory_cost += sum(b.upgrade_cost)
spent_glory += bldg_data['spent_upgrade_cost']
elif b.area == Building.AREA_GUILD:
total_guild_cost += sum(b.upgrade_cost)
spent_guild += bldg_data['spent_upgrade_cost']
building_data.append(bldg_data)
context = {
'is_owner': is_owner,
'summoner': summoner,
'profile_name': profile_name,
'buildings': building_data,
'total_glory_cost': total_glory_cost,
'spent_glory': spent_glory,
'glory_progress': float(spent_glory) / total_glory_cost * 100,
'total_guild_cost': total_guild_cost,
'spent_guild': spent_guild,
'guild_progress': float(spent_guild) / total_guild_cost * 100,
}
return render(request, 'herders/profile/buildings/base.html', context)
@login_required
def building_edit(request, profile_name, building_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
base_building = get_object_or_404(Building, pk=building_id)
try:
owned_instance = BuildingInstance.objects.get(owner=summoner, building=base_building)
except BuildingInstance.DoesNotExist:
owned_instance = BuildingInstance.objects.create(owner=summoner, level=0, building=base_building)
form = EditBuildingForm(request.POST or None, instance=owned_instance)
form.helper.form_action = reverse('herders:building_edit', kwargs={'profile_name': profile_name, 'building_id': building_id})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
owned_instance = form.save()
messages.success(request, 'Updated ' + owned_instance.building.name + ' to level ' + str(owned_instance.level))
template = loader.get_template('herders/profile/buildings/building_row_snippet.html')
context = {
'is_owner': is_owner,
'bldg': _building_data(summoner, base_building)
}
response_data = {
'code': 'success',
'instance_id': building_id,
'html': template.render(RequestContext(request, context))
}
else:
template = loader.get_template('herders/profile/buildings/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
def _building_data(summoner, building):
percent_stat = building.affected_stat in Building.PERCENT_STATS
total_upgrade_cost = sum(building.upgrade_cost)
if building.area == Building.AREA_GENERAL:
currency = 'glory_points.png'
else:
currency = 'guild_points.png'
try:
instance = BuildingInstance.objects.get(owner=summoner, building=building)
if instance.level > 0:
stat_bonus = building.stat_bonus[instance.level - 1]
else:
stat_bonus = 0
remaining_upgrade_cost = instance.remaining_upgrade_cost()
except BuildingInstance.DoesNotExist:
instance = None
stat_bonus = 0
remaining_upgrade_cost = total_upgrade_cost
return {
'base': building,
'instance': instance,
'stat_bonus': stat_bonus,
'percent_stat': percent_stat,
'spent_upgrade_cost': total_upgrade_cost - remaining_upgrade_cost,
'total_upgrade_cost': total_upgrade_cost,
'upgrade_progress': float(total_upgrade_cost - remaining_upgrade_cost) / total_upgrade_cost * 100,
'currency': currency,
}
def monster_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to ourself without the view mode or box grouping
if view_mode:
request.session['profile_view_mode'] = view_mode.lower()
if box_grouping:
request.session['profile_group_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Profile view mode cookie set")
view_mode = request.session.get('profile_view_mode', 'list').lower()
box_grouping = request.session.get('profile_group_method', 'grade').lower()
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
monster_queryset = MonsterInstance.committed.filter(owner=summoner)
total_monsters = monster_queryset.count()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if view_mode == 'list':
monster_queryset = monster_queryset.select_related('monster', 'monster__leader_skill', 'monster__awakens_from', 'monster__awakens_to').prefetch_related('monster__skills', 'monster__skills__skill_effect', 'runeinstance_set', 'team_set', 'team_leader')
pieces = MonsterPiece.objects.filter(owner=summoner)
form = FilterMonsterInstanceForm(request.POST or None)
if form.is_valid():
monster_filter = MonsterInstanceFilter(form.cleaned_data, queryset=monster_queryset)
else:
monster_filter = MonsterInstanceFilter(queryset=monster_queryset)
filtered_count = monster_filter.qs.count()
context = {
'monsters': monster_filter,
'monster_pieces': pieces,
'total_count': total_monsters,
'filtered_count': filtered_count,
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'pieces':
context['monster_pieces'] = MonsterPiece.committed.filter(owner=summoner)
template = 'herders/profile/monster_inventory/summoning_pieces.html'
elif view_mode == 'list':
template = 'herders/profile/monster_inventory/list.html'
else:
# Group up the filtered monsters
monster_stable = OrderedDict()
if box_grouping == 'grade':
monster_stable['6*'] = monster_filter.qs.filter(stars=6).order_by('-level', 'monster__element', 'monster__name')
monster_stable['5*'] = monster_filter.qs.filter(stars=5).order_by('-level', 'monster__element', 'monster__name')
monster_stable['4*'] = monster_filter.qs.filter(stars=4).order_by('-level', 'monster__element', 'monster__name')
monster_stable['3*'] = monster_filter.qs.filter(stars=3).order_by('-level', 'monster__element', 'monster__name')
monster_stable['2*'] = monster_filter.qs.filter(stars=2).order_by('-level', 'monster__element', 'monster__name')
monster_stable['1*'] = monster_filter.qs.filter(stars=1).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'level':
monster_stable['40'] = monster_filter.qs.filter(level=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['39-31'] = monster_filter.qs.filter(level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['30-21'] = monster_filter.qs.filter(level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['20-11'] = monster_filter.qs.filter(level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['10-1'] = monster_filter.qs.filter(level__lte=10).order_by('-level', '-stars', 'monster__element', 'monster__name')
elif box_grouping == 'attribute':
monster_stable['water'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'priority':
monster_stable['High'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_HIGH).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Medium'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_MED).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Low'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_LOW).order_by('-level', 'monster__element', 'monster__name')
monster_stable['None'] = monster_filter.qs.select_related('monster').filter(owner=summoner).filter(Q(priority=None) | Q(priority=0)).order_by('-level', 'monster__element', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
context['box_grouping'] = box_grouping
template = 'herders/profile/monster_inventory/box.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.info(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def profile_storage(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path
template = loader.get_template('herders/essence_storage.html')
if request.method == 'POST' and form.is_valid():
form.save()
messages.success(request, 'Updated essence storage.')
response_data = {
'code': 'success'
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def quick_fodder_menu(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
template = loader.get_template('herders/profile/monster_inventory/quick_fodder_menu.html')
response_data = {
'code': 'success',
'html': template.render(),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = AddMonsterInstanceForm(request.POST or None)
else:
form = AddMonsterInstanceForm(initial=request.GET.dict())
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
context = {
'profile_name': profile_name,
'instance': new_monster,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_monster.pk.hex,
'html': template.render(RequestContext(request, context)),
}
else:
form.helper.form_action = reverse('herders:monster_instance_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/add_monster_form.html')
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_monster_form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
new_monster = MonsterInstance.committed.create(owner=summoner, monster=monster_to_add, stars=int(stars),
level=int(level), fodder=True, notes='',
priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_bulk_add(request, profile_name):
return_path = reverse('herders:profile_default', kwargs={'profile_name': profile_name})
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
BulkAddFormset = modelformset_factory(MonsterInstance, form=BulkAddMonsterInstanceForm,
formset=BulkAddMonsterInstanceFormset, extra=5, max_num=50)
if request.method == 'POST':
formset = BulkAddFormset(request.POST)
else:
formset = BulkAddFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'bulk_add_formset_action': request.path + '?next=' + return_path,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if formset.is_valid():
new_instances = formset.save(commit=False)
for new_instance in new_instances:
try:
if new_instance.monster:
new_instance.owner = summoner
if new_instance.monster.archetype == Monster.TYPE_MATERIAL:
new_instance.priority = MonsterInstance.PRIORITY_DONE
new_instance.save()
messages.success(request, 'Added %s to your collection.' % new_instance)
except ObjectDoesNotExist:
# Blank form, don't care
pass
return redirect(return_path)
else:
raise PermissionDenied("Trying to bulk add to profile you don't own")
context['bulk_add_formset'] = formset
return render(request, 'herders/profile/monster_inventory/bulk_add_form.html', context)
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
request.path
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
context = {
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'instance': instance,
'is_owner': is_owner,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_view/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def monster_instance_view_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
instance_runes = [
instance.runeinstance_set.filter(slot=1).first(),
instance.runeinstance_set.filter(slot=2).first(),
instance.runeinstance_set.filter(slot=3).first(),
instance.runeinstance_set.filter(slot=4).first(),
instance.runeinstance_set.filter(slot=5).first(),
instance.runeinstance_set.filter(slot=6).first(),
]
context = {
'runes': instance_runes,
'instance': instance,
'profile_name': profile_name,
'is_owner': is_owner,
}
return render(request, 'herders/profile/monster_view/runes.html', context)
def monster_instance_view_stats(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
context = {
'instance': instance,
'bldg_stats': instance.get_building_stats(),
'guild_stats': instance.get_building_stats(Building.AREA_GUILD),
}
return render(request, 'herders/profile/monster_view/stats.html', context)
def monster_instance_view_skills(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
context = {
'instance': instance,
'skills': skills,
}
return render(request, 'herders/profile/monster_view/skills.html', context)
def monster_instance_view_info(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
if instance.monster.is_awakened:
ingredient_in = instance.monster.fusion_set.all()
elif instance.monster.can_awaken and instance.monster.awakens_to:
ingredient_in = instance.monster.awakens_to.fusion_set.all()
else:
ingredient_in = []
if instance.monster.is_awakened and instance.monster.awakens_from:
product_of = instance.monster.awakens_from.product.first()
elif instance.monster.can_awaken:
product_of = instance.monster.product.first()
else:
product_of = []
context = {
'instance': instance,
'profile_name': profile_name,
'fusion_ingredient_in': ingredient_in,
'fusion_product_of': product_of,
}
return render(request, 'herders/profile/monster_view/notes_info.html', context)
@login_required()
def monster_instance_remove_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
try:
instance = MonsterInstance.committed.get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
else:
for rune in instance.runeinstance_set.all():
rune.assigned_to = None
rune.save()
instance.save()
messages.success(request, 'Removed all runes from ' + str(instance))
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
instance = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
form = EditMonsterInstanceForm(request.POST or None, instance=instance)
form.helper.form_action = request.path
if len(skills) >= 1 and skills[0]['skill'].max_level > 1:
form.helper['skill_1_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_1", data_skill_field=form['skill_1_level'].auto_id),
)
form.helper['skill_1_level'].wrap(Field, min=1, max=skills[0]['skill'].max_level)
form.fields['skill_1_level'].label = skills[0]['skill'].name + " Level"
else:
form.helper['skill_1_level'].wrap(Div, css_class="hidden")
if len(skills) >= 2 and skills[1]['skill'].max_level > 1:
form.helper['skill_2_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_2", data_skill_field=form['skill_2_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_2_level'].wrap(Field, min=1, max=skills[1]['skill'].max_level)
form.fields['skill_2_level'].label = skills[1]['skill'].name + " Level"
else:
form.helper['skill_2_level'].wrap(Div, css_class="hidden")
if len(skills) >= 3 and skills[2]['skill'].max_level > 1:
form.helper['skill_3_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_3", data_skill_field=form['skill_3_level'].auto_id),
min=1,
max=skills[2]['skill'].max_level,
)
form.helper['skill_3_level'].wrap(Field, min=1, max=skills[2]['skill'].max_level)
form.fields['skill_3_level'].label = skills[2]['skill'].name + " Level"
else:
form.helper['skill_3_level'].wrap(Div, css_class="hidden")
if len(skills) >= 4 and skills[3]['skill'].max_level > 1:
form.helper['skill_4_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_4", data_skill_field=form['skill_4_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_4_level'].wrap(Field, min=1, max=skills[3]['skill'].max_level)
form.fields['skill_4_level'].label = skills[3]['skill'].name + " Level"
else:
form.helper['skill_4_level'].wrap(Div, css_class="hidden")
if not instance.monster.fusion_food:
form.helper['ignore_for_fusion'].wrap(Div, css_class="hidden")
if request.method == 'POST' and form.is_valid():
mon = form.save()
messages.success(request, 'Successfully edited ' + str(mon))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'instance': mon,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': mon.pk.hex,
'html': template.render(RequestContext(request, context)),
}
else:
# Return form filled in and errors shown
template = loader.get_template('herders/profile/monster_view/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'edit_monster_form': form}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
messages.warning(request, 'Deleted ' + str(monster))
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = PowerUpMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_power_up', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
context = {
'profile_name': request.user.username,
'monster': monster,
'is_owner': is_owner,
'form': form,
'view': 'profile',
}
validation_errors = {}
response_data = {
'code': 'error'
}
if is_owner:
if request.method == 'POST' and form.is_valid():
food_monsters = form.cleaned_data['monster']
# Check that monster is not being fed to itself
if monster in food_monsters:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if not form.cleaned_data['ignore_evolution']:
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars or higher." % monster.stars
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.warning(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
response_data['code'] = 'success'
else:
response_data['code'] = 'edit'
return JsonResponse(response_data)
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
template = loader.get_template('herders/profile/monster_view/power_up_form.html')
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
response_data['html'] = template.render(RequestContext(request, context))
return JsonResponse(response_data)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
template = loader.get_template('herders/profile/monster_view/awaken_form.html')
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_awaken', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if is_owner:
if not monster.monster.is_awakened:
if request.method == 'POST' and form.is_valid():
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
summoner.storage_magic_high -= monster.monster.awaken_mats_magic_high
summoner.storage_magic_mid -= monster.monster.awaken_mats_magic_mid
summoner.storage_magic_low -= monster.monster.awaken_mats_magic_low
summoner.storage_fire_high -= monster.monster.awaken_mats_fire_high
summoner.storage_fire_mid -= monster.monster.awaken_mats_fire_mid
summoner.storage_fire_low -= monster.monster.awaken_mats_fire_low
summoner.storage_water_high -= monster.monster.awaken_mats_water_high
summoner.storage_water_mid -= monster.monster.awaken_mats_water_mid
summoner.storage_water_low -= monster.monster.awaken_mats_water_low
summoner.storage_wind_high -= monster.monster.awaken_mats_wind_high
summoner.storage_wind_mid -= monster.monster.awaken_mats_wind_mid
summoner.storage_wind_low -= monster.monster.awaken_mats_wind_low
summoner.storage_dark_high -= monster.monster.awaken_mats_dark_high
summoner.storage_dark_mid -= monster.monster.awaken_mats_dark_mid
summoner.storage_dark_low -= monster.monster.awaken_mats_dark_low
summoner.storage_light_high -= monster.monster.awaken_mats_light_high
summoner.storage_light_mid -= monster.monster.awaken_mats_light_mid
summoner.storage_light_low -= monster.monster.awaken_mats_light_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
response_data = {
'code': 'success',
'removeElement': '#awakenMonsterButton',
}
else:
storage = summoner.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().iteritems():
available_essences[element] = OrderedDict()
for size, cost in essences.iteritems():
if cost > 0:
available_essences[element][size] = {
'qty': storage[element][size],
'sufficient': storage[element][size] >= cost,
}
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {
'awaken_form': form,
'available_essences': available_essences,
'instance': monster,
}))
}
else:
error_template = loader.get_template('herders/profile/monster_already_awakened.html')
response_data = {
'code': 'error',
'html': error_template.render(RequestContext(request, {}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
messages.success(request, 'Succesfully copied ' + str(newmonster))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'is_owner': True,
'instance': newmonster,
}
response_data = {
'code': 'success',
'instance_id': newmonster.pk.hex,
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = MonsterPieceForm(request.POST or None)
else:
form = MonsterPieceForm()
form.helper.form_action = reverse('herders:monster_piece_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_pieces = form.save(commit=False)
new_pieces.owner = request.user.summoner
new_pieces.save()
messages.success(request, 'Added %s to your collection.' % new_pieces)
response_data = {
'code': 'success'
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if is_owner:
form = MonsterPieceForm(request.POST or None, instance=pieces)
form.helper.form_action = request.path
if request.method == 'POST' and form.is_valid():
new_piece = form.save()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': new_piece,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_piece.pk.hex,
'html': template.render(RequestContext(request, context))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_summon(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if pieces.can_summon():
new_monster = MonsterInstance.committed.create(owner=summoner, monster=pieces.monster,
stars=pieces.monster.base_stars, level=1, fodder=False,
notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
# Remove the pieces, delete if 0
pieces.pieces -= pieces.PIECE_REQUIREMENTS[pieces.monster.base_stars]
pieces.save()
if pieces.pieces <= 0:
pieces.delete()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': pieces,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': pieces.pk.hex,
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == pieces.owner:
messages.warning(request, 'Deleted ' + str(pieces))
pieces.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
fusions = Fusion.objects.all()
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'fusions': fusions,
}
return render(request, 'herders/profile/fusion/base.html', context)
def fusion_progress_detail(request, profile_name, monster_slug):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
try:
fusion = Fusion.objects.get(product__bestiary_slug=monster_slug)
except Fusion.DoesNotExist:
raise Http404()
else:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.committed.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).exists()
# Scan summoner's collection for instances each ingredient
fusion_ready = True
for ingredient in fusion.ingredients.all().select_related('awakens_from', 'awakens_to'):
owned_ingredients = MonsterInstance.committed.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
owned_ingredient_pieces = MonsterPiece.committed.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).first()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
if owned_ingredient_pieces:
acquired = owned_ingredient_pieces.can_summon()
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
if not complete:
fusion_ready = False
# Check if this ingredient is fusable
if not acquired:
try:
sub_fusion = Fusion.objects.get(product=ingredient.awakens_from)
except Fusion.DoesNotExist:
sub_fusion_awakening_cost = None
else:
awakened_sub_fusion_ingredients = MonsterInstance.committed.filter(
monster__pk__in=sub_fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
sub_fusion_awakening_cost = sub_fusion.total_awakening_cost(awakened_sub_fusion_ingredients)
else:
sub_fusion_awakening_cost = None
ingredient_progress = {
'instance': ingredient,
'owned': owned_ingredients,
'pieces': owned_ingredient_pieces,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
'sub_fusion_cost': sub_fusion_awakening_cost,
}
ingredients.append(ingredient_progress)
awakened_owned_ingredients = MonsterInstance.committed.filter(
monster__pk__in=fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
total_cost = fusion.total_awakening_cost(awakened_owned_ingredients)
essences_satisfied, total_missing = fusion.missing_awakening_cost(summoner)
# Determine the total/missing essences including sub-fusions
if fusion.sub_fusion_available():
total_sub_fusion_cost = deepcopy(total_cost)
for ingredient in ingredients:
if ingredient['sub_fusion_cost']:
for element, sizes in total_sub_fusion_cost.iteritems():
for size, qty in sizes.iteritems():
total_sub_fusion_cost[element][size] += ingredient['sub_fusion_cost'][element][size]
# Now determine what's missing based on owner's storage
storage = summoner.get_storage()
sub_fusion_total_missing = {
element: {
size: total_sub_fusion_cost[element][size] - storage[element][size] if total_sub_fusion_cost[element][size] > storage[element][size] else 0
for size, qty in element_sizes.items()
}
for element, element_sizes in total_sub_fusion_cost.items()
}
sub_fusion_mats_satisfied = True
for sizes in total_sub_fusion_cost.itervalues():
for qty in sizes.itervalues():
if qty > 0:
sub_fusion_mats_satisfied = False
else:
sub_fusion_total_missing = None
sub_fusion_mats_satisfied = None
progress = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_mats_cost': total_cost,
'awakening_mats_sufficient': essences_satisfied,
'awakening_mats_missing': total_missing,
'sub_fusion_mats_missing': sub_fusion_total_missing,
'sub_fusion_mats_sufficient': sub_fusion_mats_satisfied,
'ready': fusion_ready,
}
context['fusion'] = progress
return render(request, 'herders/profile/fusion/fusion_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_edit(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = EditTeamGroupForm(request.POST or None, instance=team_group)
if is_owner:
if form.is_valid() and request.method == 'POST':
form.save()
return redirect(return_path)
else:
return PermissionDenied("Editing a group you don't own")
context = {
'profile_name': profile_name,
'summoner': summoner,
'form': form,
'group_id': group_id,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
return render(request, 'herders/profile/teams/team_group_edit.html', context)
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.warning(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader and team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/team_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.committed.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.committed.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.warning(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def runes(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
filter_form = FilterRuneForm(auto_id="filter_id_%s")
filter_form.helper.form_action = reverse('herders:rune_inventory', kwargs={'profile_name': profile_name})
context = {
'view': 'runes',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'old_rune_count': RuneInstance.objects.filter(owner=summoner, substats__isnull=True).count(),
'rune_filter_form': filter_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/runes/base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to base profile URL
if view_mode:
request.session['rune_inventory_view_mode'] = view_mode.lower()
if box_grouping:
request.session['rune_inventory_box_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Rune view mode cookie set")
view_mode = request.session.get('rune_inventory_view_mode', 'box').lower()
box_grouping = request.session.get('rune_inventory_box_method', 'slot').lower()
if view_mode == 'crafts':
return rune_inventory_crafts(request, profile_name)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
rune_queryset = RuneInstance.committed.filter(owner=summoner).select_related('assigned_to', 'assigned_to__monster')
total_count = rune_queryset.count()
form = FilterRuneForm(request.POST or None)
if form.is_valid():
rune_filter = RuneInstanceFilter(form.cleaned_data, queryset=rune_queryset)
else:
rune_filter = RuneInstanceFilter(None, queryset=rune_queryset)
filtered_count = rune_filter.qs.count()
context = {
'runes': rune_filter,
'total_count': total_count,
'filtered_count': filtered_count,
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'box':
rune_box = OrderedDict()
if box_grouping == 'slot':
rune_box['Slot 1'] = rune_filter.qs.filter(slot=1)
rune_box['Slot 2'] = rune_filter.qs.filter(slot=2)
rune_box['Slot 3'] = rune_filter.qs.filter(slot=3)
rune_box['Slot 4'] = rune_filter.qs.filter(slot=4)
rune_box['Slot 5'] = rune_filter.qs.filter(slot=5)
rune_box['Slot 6'] = rune_filter.qs.filter(slot=6)
elif box_grouping == 'grade':
rune_box['6*'] = rune_filter.qs.filter(stars=6)
rune_box['5*'] = rune_filter.qs.filter(stars=5)
rune_box['4*'] = rune_filter.qs.filter(stars=4)
rune_box['3*'] = rune_filter.qs.filter(stars=3)
rune_box['2*'] = rune_filter.qs.filter(stars=2)
rune_box['1*'] = rune_filter.qs.filter(stars=1)
elif box_grouping == 'equipped':
rune_box['Not Equipped'] = rune_filter.qs.filter(assigned_to__isnull=True)
rune_box['Equipped'] = rune_filter.qs.filter(assigned_to__isnull=False)
elif box_grouping == 'type':
for (type, type_name) in RuneInstance.TYPE_CHOICES:
rune_box[type_name] = rune_filter.qs.filter(type=type)
context['runes'] = rune_box
context['box_grouping'] = box_grouping
template = 'herders/profile/runes/inventory.html'
elif view_mode == 'grid':
template = 'herders/profile/runes/inventory_grid.html'
else:
template = 'herders/profile/runes/inventory_table.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory_crafts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
craft_box = OrderedDict()
for (craft, craft_name) in RuneInstance.CRAFT_CHOICES:
craft_box[craft_name] = OrderedDict()
for rune, rune_name in RuneInstance.TYPE_CHOICES:
craft_box[craft_name][rune_name] = RuneCraftInstance.committed.filter(owner=summoner, type=craft, rune=rune).order_by('stat', 'quality')
context['crafts'] = craft_box
return render(request, 'herders/profile/runes/inventory_crafts.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def rune_counts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
response_data = {
'code': 'success',
'counts': summoner.get_rune_counts()
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_add(request, profile_name):
form = AddRuneInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_rune = form.save(commit=False)
new_rune.owner = request.user.summoner
new_rune.save()
messages.success(request, 'Added ' + str(new_rune))
# Send back blank form
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
# Check for any pre-filled GET parameters
slot = request.GET.get('slot', None)
assigned_to = request.GET.get('assigned_to', None)
form = AddRuneInstanceForm(initial={
'assigned_to': assigned_to,
'slot': slot if slot is not None else 1,
})
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
# Return form filled in and errors shown
response_data = {
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
return JsonResponse(response_data)
@login_required
def rune_edit(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneInstanceForm(request.POST or None, instance=rune, auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
template = loader.get_template('herders/profile/runes/add_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm(auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_assign(request, profile_name, instance_id, slot=None):
rune_queryset = RuneInstance.committed.filter(owner=request.user.summoner, assigned_to=None)
filter_form = AssignRuneForm(request.POST or None, initial={'slot': slot})
filter_form.helper.form_action = reverse('herders:rune_assign', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if slot:
rune_queryset = rune_queryset.filter(slot=slot)
rune_filter = RuneInstanceFilter(request.POST, queryset=rune_queryset)
if request.method == 'POST':
template = loader.get_template('herders/profile/runes/assign_results.html')
response_data = {
'code': 'results',
'html': template.render(RequestContext(request, {
'filter': rune_filter,
'profile_name': profile_name,
'instance_id': instance_id,
}))
}
else:
template = loader.get_template('herders/profile/runes/assign_form.html')
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {
'filter': rune_filter,
'form': filter_form,
'profile_name': profile_name,
'instance_id': instance_id,
}))
}
return JsonResponse(response_data)
@login_required
def rune_assign_choice(request, profile_name, instance_id, rune_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
rune = get_object_or_404(RuneInstance, pk=rune_id)
if rune.assigned_to is not None:
# TODO: Warn about removing from other monster?
pass
# Check for existing rune.
existing_runes = monster.runeinstance_set.filter(slot=rune.slot)
for existing_rune in existing_runes:
existing_rune.assigned_to = None
rune.assigned_to = monster
rune.save()
monster.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
@login_required
def rune_unassign(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
rune.assigned_to = None
rune.save()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def rune_unassign_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
assigned_mons = []
assigned_runes = RuneInstance.committed.filter(owner=summoner, assigned_to__isnull=False)
number_assigned = assigned_runes.count()
if is_owner:
for rune in assigned_runes:
if rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
rune.assigned_to = None
rune.save()
# Resave monster instances that had runes removed to recalc stats
for mon in assigned_mons:
mon.save()
messages.success(request, 'Unassigned ' + str(number_assigned) + ' rune(s).')
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
messages.warning(request, 'Deleted ' + str(rune))
rune.delete()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Delete the runes
death_row = RuneInstance.committed.filter(owner=summoner)
number_killed = death_row.count()
assigned_mons = []
for rune in death_row:
if rune.assigned_to and rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
death_row.delete()
# Delete the crafts
RuneCraftInstance.committed.filter(owner=summoner).delete()
messages.warning(request, 'Deleted ' + str(number_killed) + ' runes.')
for mon in assigned_mons:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_resave_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
for r in RuneInstance.objects.filter(owner=summoner, substats__isnull=True):
r.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_add(request, profile_name):
form = AddRuneCraftInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_craft = form.save(commit=False)
new_craft.owner = request.user.summoner
new_craft.save()
messages.success(request, 'Added ' + new_craft.get_type_display() + ' ' + str(new_craft))
# Send back blank form
form = AddRuneCraftInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
@login_required
def rune_craft_edit(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneCraftInstanceForm(request.POST or None, instance=craft)
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_delete(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
messages.warning(request, 'Deleted ' + craft.get_rune_display() + ' ' + str(craft))
craft.delete()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
Defined an auto_id for the monster filter form so the fields do not generate the same IDs as other forms.
from collections import OrderedDict
from copy import deepcopy
from django.http import Http404, HttpResponseForbidden, JsonResponse, HttpResponse
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied, ObjectDoesNotExist
from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import Group
from django.contrib.auth.decorators import login_required
from django.db import IntegrityError
from django.forms.models import modelformset_factory
from django.shortcuts import render, redirect, get_object_or_404
from django.template import loader, RequestContext
from bestiary.models import Monster, Fusion, Building
from .forms import *
from .filters import *
from .models import Summoner, BuildingInstance, MonsterInstance, MonsterPiece, TeamGroup, Team, RuneInstance, RuneCraftInstance
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_user.groups.add(Group.objects.get(name='Summoners'))
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile_default', profile_name=user.username)
except IntegrityError:
form.add_error('username', 'Username already taken')
context = {'form': form}
return render(request, 'herders/register.html', context)
@login_required
def change_username(request):
user = request.user
form = CrispyChangeUsernameForm(request.POST or None)
context = {
'form': form,
}
if request.method == 'POST' and form.is_valid():
try:
user.username = form.cleaned_data['username']
user.save()
return redirect('username_change_complete')
except IntegrityError:
form.add_error('username', 'Username already taken')
return render(request, 'registration/change_username.html', context)
def change_username_complete(request):
return render(request, 'registration/change_username_complete.html')
@login_required
def profile_delete(request, profile_name):
user = request.user
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = DeleteProfileForm(request.POST or None)
form.helper.form_action = reverse('herders:profile_delete', kwargs={'profile_name': profile_name})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
logout(request)
user.delete()
messages.warning(request, 'Your profile has been permanently deleted.')
return redirect('news:latest_news')
return render(request, 'herders/profile/profile_delete.html', context)
else:
return HttpResponseForbidden("You don't own this profile")
@login_required
def following(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_following', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'view': 'following',
'return_path': return_path,
}
return render(request, 'herders/profile/following/list.html', context)
@login_required
def follow_add(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
new_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.add(new_follower)
messages.info(request, 'Now following %s' % new_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required
def follow_remove(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
removed_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.remove(removed_follower)
messages.info(request, 'Unfollowed %s' % removed_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
def profile(request, profile_name=None):
if profile_name is None:
if request.user.is_authenticated():
profile_name = request.user.username
else:
raise Http404('No user profile specified and not logged in.')
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_filter_form = FilterMonsterInstanceForm(auto_id='id_filter_%s')
monster_filter_form.helper.form_action = reverse('herders:monster_inventory', kwargs={'profile_name': profile_name})
context = {
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'monster_filter_form': monster_filter_form,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_inventory/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def buildings(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
all_buildings = Building.objects.all().order_by('name')
building_data = []
total_glory_cost = 0
spent_glory = 0
total_guild_cost = 0
spent_guild = 0
for b in all_buildings:
bldg_data = _building_data(summoner, b)
if b.area == Building.AREA_GENERAL:
total_glory_cost += sum(b.upgrade_cost)
spent_glory += bldg_data['spent_upgrade_cost']
elif b.area == Building.AREA_GUILD:
total_guild_cost += sum(b.upgrade_cost)
spent_guild += bldg_data['spent_upgrade_cost']
building_data.append(bldg_data)
context = {
'is_owner': is_owner,
'summoner': summoner,
'profile_name': profile_name,
'buildings': building_data,
'total_glory_cost': total_glory_cost,
'spent_glory': spent_glory,
'glory_progress': float(spent_glory) / total_glory_cost * 100,
'total_guild_cost': total_guild_cost,
'spent_guild': spent_guild,
'guild_progress': float(spent_guild) / total_guild_cost * 100,
}
return render(request, 'herders/profile/buildings/base.html', context)
@login_required
def building_edit(request, profile_name, building_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
base_building = get_object_or_404(Building, pk=building_id)
try:
owned_instance = BuildingInstance.objects.get(owner=summoner, building=base_building)
except BuildingInstance.DoesNotExist:
owned_instance = BuildingInstance.objects.create(owner=summoner, level=0, building=base_building)
form = EditBuildingForm(request.POST or None, instance=owned_instance)
form.helper.form_action = reverse('herders:building_edit', kwargs={'profile_name': profile_name, 'building_id': building_id})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
owned_instance = form.save()
messages.success(request, 'Updated ' + owned_instance.building.name + ' to level ' + str(owned_instance.level))
template = loader.get_template('herders/profile/buildings/building_row_snippet.html')
context = {
'is_owner': is_owner,
'bldg': _building_data(summoner, base_building)
}
response_data = {
'code': 'success',
'instance_id': building_id,
'html': template.render(RequestContext(request, context))
}
else:
template = loader.get_template('herders/profile/buildings/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
def _building_data(summoner, building):
percent_stat = building.affected_stat in Building.PERCENT_STATS
total_upgrade_cost = sum(building.upgrade_cost)
if building.area == Building.AREA_GENERAL:
currency = 'glory_points.png'
else:
currency = 'guild_points.png'
try:
instance = BuildingInstance.objects.get(owner=summoner, building=building)
if instance.level > 0:
stat_bonus = building.stat_bonus[instance.level - 1]
else:
stat_bonus = 0
remaining_upgrade_cost = instance.remaining_upgrade_cost()
except BuildingInstance.DoesNotExist:
instance = None
stat_bonus = 0
remaining_upgrade_cost = total_upgrade_cost
return {
'base': building,
'instance': instance,
'stat_bonus': stat_bonus,
'percent_stat': percent_stat,
'spent_upgrade_cost': total_upgrade_cost - remaining_upgrade_cost,
'total_upgrade_cost': total_upgrade_cost,
'upgrade_progress': float(total_upgrade_cost - remaining_upgrade_cost) / total_upgrade_cost * 100,
'currency': currency,
}
def monster_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to ourself without the view mode or box grouping
if view_mode:
request.session['profile_view_mode'] = view_mode.lower()
if box_grouping:
request.session['profile_group_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Profile view mode cookie set")
view_mode = request.session.get('profile_view_mode', 'list').lower()
box_grouping = request.session.get('profile_group_method', 'grade').lower()
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
monster_queryset = MonsterInstance.committed.filter(owner=summoner)
total_monsters = monster_queryset.count()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if view_mode == 'list':
monster_queryset = monster_queryset.select_related('monster', 'monster__leader_skill', 'monster__awakens_from', 'monster__awakens_to').prefetch_related('monster__skills', 'monster__skills__skill_effect', 'runeinstance_set', 'team_set', 'team_leader')
pieces = MonsterPiece.objects.filter(owner=summoner)
form = FilterMonsterInstanceForm(request.POST or None, auto_id='id_filter_%s')
if form.is_valid():
monster_filter = MonsterInstanceFilter(form.cleaned_data, queryset=monster_queryset)
else:
monster_filter = MonsterInstanceFilter(queryset=monster_queryset)
filtered_count = monster_filter.qs.count()
context = {
'monsters': monster_filter,
'monster_pieces': pieces,
'total_count': total_monsters,
'filtered_count': filtered_count,
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'pieces':
context['monster_pieces'] = MonsterPiece.committed.filter(owner=summoner)
template = 'herders/profile/monster_inventory/summoning_pieces.html'
elif view_mode == 'list':
template = 'herders/profile/monster_inventory/list.html'
else:
# Group up the filtered monsters
monster_stable = OrderedDict()
if box_grouping == 'grade':
monster_stable['6*'] = monster_filter.qs.filter(stars=6).order_by('-level', 'monster__element', 'monster__name')
monster_stable['5*'] = monster_filter.qs.filter(stars=5).order_by('-level', 'monster__element', 'monster__name')
monster_stable['4*'] = monster_filter.qs.filter(stars=4).order_by('-level', 'monster__element', 'monster__name')
monster_stable['3*'] = monster_filter.qs.filter(stars=3).order_by('-level', 'monster__element', 'monster__name')
monster_stable['2*'] = monster_filter.qs.filter(stars=2).order_by('-level', 'monster__element', 'monster__name')
monster_stable['1*'] = monster_filter.qs.filter(stars=1).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'level':
monster_stable['40'] = monster_filter.qs.filter(level=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['39-31'] = monster_filter.qs.filter(level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['30-21'] = monster_filter.qs.filter(level__gt=20).filter(level__lte=30).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['20-11'] = monster_filter.qs.filter(level__gt=10).filter(level__lte=20).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['10-1'] = monster_filter.qs.filter(level__lte=10).order_by('-level', '-stars', 'monster__element', 'monster__name')
elif box_grouping == 'attribute':
monster_stable['water'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'priority':
monster_stable['High'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_HIGH).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Medium'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_MED).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Low'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_LOW).order_by('-level', 'monster__element', 'monster__name')
monster_stable['None'] = monster_filter.qs.select_related('monster').filter(owner=summoner).filter(Q(priority=None) | Q(priority=0)).order_by('-level', 'monster__element', 'monster__name')
else:
raise Http404('Invalid sort method')
context['monster_stable'] = monster_stable
context['box_grouping'] = box_grouping
template = 'herders/profile/monster_inventory/box.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.info(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def profile_storage(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
form = EditEssenceStorageForm(request.POST or None, instance=request.user.summoner)
form.helper.form_action = request.path
template = loader.get_template('herders/essence_storage.html')
if request.method == 'POST' and form.is_valid():
form.save()
messages.success(request, 'Updated essence storage.')
response_data = {
'code': 'success'
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def quick_fodder_menu(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
template = loader.get_template('herders/profile/monster_inventory/quick_fodder_menu.html')
response_data = {
'code': 'success',
'html': template.render(),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = AddMonsterInstanceForm(request.POST or None)
else:
form = AddMonsterInstanceForm(initial=request.GET.dict())
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
context = {
'profile_name': profile_name,
'instance': new_monster,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_monster.pk.hex,
'html': template.render(RequestContext(request, context)),
}
else:
form.helper.form_action = reverse('herders:monster_instance_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/add_monster_form.html')
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_monster_form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
new_monster = MonsterInstance.committed.create(owner=summoner, monster=monster_to_add, stars=int(stars),
level=int(level), fodder=True, notes='',
priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_bulk_add(request, profile_name):
return_path = reverse('herders:profile_default', kwargs={'profile_name': profile_name})
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
BulkAddFormset = modelformset_factory(MonsterInstance, form=BulkAddMonsterInstanceForm,
formset=BulkAddMonsterInstanceFormset, extra=5, max_num=50)
if request.method == 'POST':
formset = BulkAddFormset(request.POST)
else:
formset = BulkAddFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'bulk_add_formset_action': request.path + '?next=' + return_path,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if formset.is_valid():
new_instances = formset.save(commit=False)
for new_instance in new_instances:
try:
if new_instance.monster:
new_instance.owner = summoner
if new_instance.monster.archetype == Monster.TYPE_MATERIAL:
new_instance.priority = MonsterInstance.PRIORITY_DONE
new_instance.save()
messages.success(request, 'Added %s to your collection.' % new_instance)
except ObjectDoesNotExist:
# Blank form, don't care
pass
return redirect(return_path)
else:
raise PermissionDenied("Trying to bulk add to profile you don't own")
context['bulk_add_formset'] = formset
return render(request, 'herders/profile/monster_inventory/bulk_add_form.html', context)
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
request.path
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
context = {
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'instance': instance,
'is_owner': is_owner,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_view/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def monster_instance_view_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
instance_runes = [
instance.runeinstance_set.filter(slot=1).first(),
instance.runeinstance_set.filter(slot=2).first(),
instance.runeinstance_set.filter(slot=3).first(),
instance.runeinstance_set.filter(slot=4).first(),
instance.runeinstance_set.filter(slot=5).first(),
instance.runeinstance_set.filter(slot=6).first(),
]
context = {
'runes': instance_runes,
'instance': instance,
'profile_name': profile_name,
'is_owner': is_owner,
}
return render(request, 'herders/profile/monster_view/runes.html', context)
def monster_instance_view_stats(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
context = {
'instance': instance,
'bldg_stats': instance.get_building_stats(),
'guild_stats': instance.get_building_stats(Building.AREA_GUILD),
}
return render(request, 'herders/profile/monster_view/stats.html', context)
def monster_instance_view_skills(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
context = {
'instance': instance,
'skills': skills,
}
return render(request, 'herders/profile/monster_view/skills.html', context)
def monster_instance_view_info(request, profile_name, instance_id):
try:
instance = MonsterInstance.committed.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
if instance.monster.is_awakened:
ingredient_in = instance.monster.fusion_set.all()
elif instance.monster.can_awaken and instance.monster.awakens_to:
ingredient_in = instance.monster.awakens_to.fusion_set.all()
else:
ingredient_in = []
if instance.monster.is_awakened and instance.monster.awakens_from:
product_of = instance.monster.awakens_from.product.first()
elif instance.monster.can_awaken:
product_of = instance.monster.product.first()
else:
product_of = []
context = {
'instance': instance,
'profile_name': profile_name,
'fusion_ingredient_in': ingredient_in,
'fusion_product_of': product_of,
}
return render(request, 'herders/profile/monster_view/notes_info.html', context)
@login_required()
def monster_instance_remove_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
try:
instance = MonsterInstance.committed.get(pk=instance_id)
except ObjectDoesNotExist:
raise Http404()
else:
for rune in instance.runeinstance_set.all():
rune.assigned_to = None
rune.save()
instance.save()
messages.success(request, 'Removed all runes from ' + str(instance))
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
instance = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
form = EditMonsterInstanceForm(request.POST or None, instance=instance)
form.helper.form_action = request.path
if len(skills) >= 1 and skills[0]['skill'].max_level > 1:
form.helper['skill_1_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_1", data_skill_field=form['skill_1_level'].auto_id),
)
form.helper['skill_1_level'].wrap(Field, min=1, max=skills[0]['skill'].max_level)
form.fields['skill_1_level'].label = skills[0]['skill'].name + " Level"
else:
form.helper['skill_1_level'].wrap(Div, css_class="hidden")
if len(skills) >= 2 and skills[1]['skill'].max_level > 1:
form.helper['skill_2_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_2", data_skill_field=form['skill_2_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_2_level'].wrap(Field, min=1, max=skills[1]['skill'].max_level)
form.fields['skill_2_level'].label = skills[1]['skill'].name + " Level"
else:
form.helper['skill_2_level'].wrap(Div, css_class="hidden")
if len(skills) >= 3 and skills[2]['skill'].max_level > 1:
form.helper['skill_3_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_3", data_skill_field=form['skill_3_level'].auto_id),
min=1,
max=skills[2]['skill'].max_level,
)
form.helper['skill_3_level'].wrap(Field, min=1, max=skills[2]['skill'].max_level)
form.fields['skill_3_level'].label = skills[2]['skill'].name + " Level"
else:
form.helper['skill_3_level'].wrap(Div, css_class="hidden")
if len(skills) >= 4 and skills[3]['skill'].max_level > 1:
form.helper['skill_4_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_4", data_skill_field=form['skill_4_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_4_level'].wrap(Field, min=1, max=skills[3]['skill'].max_level)
form.fields['skill_4_level'].label = skills[3]['skill'].name + " Level"
else:
form.helper['skill_4_level'].wrap(Div, css_class="hidden")
if not instance.monster.fusion_food:
form.helper['ignore_for_fusion'].wrap(Div, css_class="hidden")
if request.method == 'POST' and form.is_valid():
mon = form.save()
messages.success(request, 'Successfully edited ' + str(mon))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'instance': mon,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': mon.pk.hex,
'html': template.render(RequestContext(request, context)),
}
else:
# Return form filled in and errors shown
template = loader.get_template('herders/profile/monster_view/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'edit_monster_form': form}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
messages.warning(request, 'Deleted ' + str(monster))
monster.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = PowerUpMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_power_up', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
context = {
'profile_name': request.user.username,
'monster': monster,
'is_owner': is_owner,
'form': form,
'view': 'profile',
}
validation_errors = {}
response_data = {
'code': 'error'
}
if is_owner:
if request.method == 'POST' and form.is_valid():
food_monsters = form.cleaned_data['monster']
# Check that monster is not being fed to itself
if monster in food_monsters:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if not form.cleaned_data['ignore_evolution']:
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars or higher." % monster.stars
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.warning(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
response_data['code'] = 'success'
else:
response_data['code'] = 'edit'
return JsonResponse(response_data)
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
template = loader.get_template('herders/profile/monster_view/power_up_form.html')
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
response_data['html'] = template.render(RequestContext(request, context))
return JsonResponse(response_data)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
template = loader.get_template('herders/profile/monster_view/awaken_form.html')
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_awaken', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if is_owner:
if not monster.monster.is_awakened:
if request.method == 'POST' and form.is_valid():
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
summoner.storage_magic_high -= monster.monster.awaken_mats_magic_high
summoner.storage_magic_mid -= monster.monster.awaken_mats_magic_mid
summoner.storage_magic_low -= monster.monster.awaken_mats_magic_low
summoner.storage_fire_high -= monster.monster.awaken_mats_fire_high
summoner.storage_fire_mid -= monster.monster.awaken_mats_fire_mid
summoner.storage_fire_low -= monster.monster.awaken_mats_fire_low
summoner.storage_water_high -= monster.monster.awaken_mats_water_high
summoner.storage_water_mid -= monster.monster.awaken_mats_water_mid
summoner.storage_water_low -= monster.monster.awaken_mats_water_low
summoner.storage_wind_high -= monster.monster.awaken_mats_wind_high
summoner.storage_wind_mid -= monster.monster.awaken_mats_wind_mid
summoner.storage_wind_low -= monster.monster.awaken_mats_wind_low
summoner.storage_dark_high -= monster.monster.awaken_mats_dark_high
summoner.storage_dark_mid -= monster.monster.awaken_mats_dark_mid
summoner.storage_dark_low -= monster.monster.awaken_mats_dark_low
summoner.storage_light_high -= monster.monster.awaken_mats_light_high
summoner.storage_light_mid -= monster.monster.awaken_mats_light_mid
summoner.storage_light_low -= monster.monster.awaken_mats_light_low
summoner.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
response_data = {
'code': 'success',
'removeElement': '#awakenMonsterButton',
}
else:
storage = summoner.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().iteritems():
available_essences[element] = OrderedDict()
for size, cost in essences.iteritems():
if cost > 0:
available_essences[element][size] = {
'qty': storage[element][size],
'sufficient': storage[element][size] >= cost,
}
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {
'awaken_form': form,
'available_essences': available_essences,
'instance': monster,
}))
}
else:
error_template = loader.get_template('herders/profile/monster_already_awakened.html')
response_data = {
'code': 'error',
'html': error_template.render(RequestContext(request, {}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
messages.success(request, 'Succesfully copied ' + str(newmonster))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'is_owner': True,
'instance': newmonster,
}
response_data = {
'code': 'success',
'instance_id': newmonster.pk.hex,
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = MonsterPieceForm(request.POST or None)
else:
form = MonsterPieceForm()
form.helper.form_action = reverse('herders:monster_piece_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_pieces = form.save(commit=False)
new_pieces.owner = request.user.summoner
new_pieces.save()
messages.success(request, 'Added %s to your collection.' % new_pieces)
response_data = {
'code': 'success'
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if is_owner:
form = MonsterPieceForm(request.POST or None, instance=pieces)
form.helper.form_action = request.path
if request.method == 'POST' and form.is_valid():
new_piece = form.save()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': new_piece,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_piece.pk.hex,
'html': template.render(RequestContext(request, context))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_summon(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if pieces.can_summon():
new_monster = MonsterInstance.committed.create(owner=summoner, monster=pieces.monster,
stars=pieces.monster.base_stars, level=1, fodder=False,
notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
# Remove the pieces, delete if 0
pieces.pieces -= pieces.PIECE_REQUIREMENTS[pieces.monster.base_stars]
pieces.save()
if pieces.pieces <= 0:
pieces.delete()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': pieces,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': pieces.pk.hex,
'html': template.render(RequestContext(request, context))
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == pieces.owner:
messages.warning(request, 'Deleted ' + str(pieces))
pieces.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
fusions = Fusion.objects.all()
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'fusions': fusions,
}
return render(request, 'herders/profile/fusion/base.html', context)
def fusion_progress_detail(request, profile_name, monster_slug):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
try:
fusion = Fusion.objects.get(product__bestiary_slug=monster_slug)
except Fusion.DoesNotExist:
raise Http404()
else:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.committed.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).exists()
# Scan summoner's collection for instances each ingredient
fusion_ready = True
for ingredient in fusion.ingredients.all().select_related('awakens_from', 'awakens_to'):
owned_ingredients = MonsterInstance.committed.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
owned_ingredient_pieces = MonsterPiece.committed.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).first()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
if owned_ingredient_pieces:
acquired = owned_ingredient_pieces.can_summon()
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
if not complete:
fusion_ready = False
# Check if this ingredient is fusable
if not acquired:
try:
sub_fusion = Fusion.objects.get(product=ingredient.awakens_from)
except Fusion.DoesNotExist:
sub_fusion_awakening_cost = None
else:
awakened_sub_fusion_ingredients = MonsterInstance.committed.filter(
monster__pk__in=sub_fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
sub_fusion_awakening_cost = sub_fusion.total_awakening_cost(awakened_sub_fusion_ingredients)
else:
sub_fusion_awakening_cost = None
ingredient_progress = {
'instance': ingredient,
'owned': owned_ingredients,
'pieces': owned_ingredient_pieces,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
'sub_fusion_cost': sub_fusion_awakening_cost,
}
ingredients.append(ingredient_progress)
awakened_owned_ingredients = MonsterInstance.committed.filter(
monster__pk__in=fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
total_cost = fusion.total_awakening_cost(awakened_owned_ingredients)
essences_satisfied, total_missing = fusion.missing_awakening_cost(summoner)
# Determine the total/missing essences including sub-fusions
if fusion.sub_fusion_available():
total_sub_fusion_cost = deepcopy(total_cost)
for ingredient in ingredients:
if ingredient['sub_fusion_cost']:
for element, sizes in total_sub_fusion_cost.iteritems():
for size, qty in sizes.iteritems():
total_sub_fusion_cost[element][size] += ingredient['sub_fusion_cost'][element][size]
# Now determine what's missing based on owner's storage
storage = summoner.get_storage()
sub_fusion_total_missing = {
element: {
size: total_sub_fusion_cost[element][size] - storage[element][size] if total_sub_fusion_cost[element][size] > storage[element][size] else 0
for size, qty in element_sizes.items()
}
for element, element_sizes in total_sub_fusion_cost.items()
}
sub_fusion_mats_satisfied = True
for sizes in total_sub_fusion_cost.itervalues():
for qty in sizes.itervalues():
if qty > 0:
sub_fusion_mats_satisfied = False
else:
sub_fusion_total_missing = None
sub_fusion_mats_satisfied = None
progress = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_mats_cost': total_cost,
'awakening_mats_sufficient': essences_satisfied,
'awakening_mats_missing': total_missing,
'sub_fusion_mats_missing': sub_fusion_total_missing,
'sub_fusion_mats_sufficient': sub_fusion_mats_satisfied,
'ready': fusion_ready,
}
context['fusion'] = progress
return render(request, 'herders/profile/fusion/fusion_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_edit(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = EditTeamGroupForm(request.POST or None, instance=team_group)
if is_owner:
if form.is_valid() and request.method == 'POST':
form.save()
return redirect(return_path)
else:
return PermissionDenied("Editing a group you don't own")
context = {
'profile_name': profile_name,
'summoner': summoner,
'form': form,
'group_id': group_id,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
return render(request, 'herders/profile/teams/team_group_edit.html', context)
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.warning(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader and team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/team_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.committed.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.committed.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.warning(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def runes(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
filter_form = FilterRuneForm(auto_id="filter_id_%s")
filter_form.helper.form_action = reverse('herders:rune_inventory', kwargs={'profile_name': profile_name})
context = {
'view': 'runes',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'old_rune_count': RuneInstance.objects.filter(owner=summoner, substats__isnull=True).count(),
'rune_filter_form': filter_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/runes/base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to base profile URL
if view_mode:
request.session['rune_inventory_view_mode'] = view_mode.lower()
if box_grouping:
request.session['rune_inventory_box_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Rune view mode cookie set")
view_mode = request.session.get('rune_inventory_view_mode', 'box').lower()
box_grouping = request.session.get('rune_inventory_box_method', 'slot').lower()
if view_mode == 'crafts':
return rune_inventory_crafts(request, profile_name)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
rune_queryset = RuneInstance.committed.filter(owner=summoner).select_related('assigned_to', 'assigned_to__monster')
total_count = rune_queryset.count()
form = FilterRuneForm(request.POST or None)
if form.is_valid():
rune_filter = RuneInstanceFilter(form.cleaned_data, queryset=rune_queryset)
else:
rune_filter = RuneInstanceFilter(None, queryset=rune_queryset)
filtered_count = rune_filter.qs.count()
context = {
'runes': rune_filter,
'total_count': total_count,
'filtered_count': filtered_count,
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'box':
rune_box = OrderedDict()
if box_grouping == 'slot':
rune_box['Slot 1'] = rune_filter.qs.filter(slot=1)
rune_box['Slot 2'] = rune_filter.qs.filter(slot=2)
rune_box['Slot 3'] = rune_filter.qs.filter(slot=3)
rune_box['Slot 4'] = rune_filter.qs.filter(slot=4)
rune_box['Slot 5'] = rune_filter.qs.filter(slot=5)
rune_box['Slot 6'] = rune_filter.qs.filter(slot=6)
elif box_grouping == 'grade':
rune_box['6*'] = rune_filter.qs.filter(stars=6)
rune_box['5*'] = rune_filter.qs.filter(stars=5)
rune_box['4*'] = rune_filter.qs.filter(stars=4)
rune_box['3*'] = rune_filter.qs.filter(stars=3)
rune_box['2*'] = rune_filter.qs.filter(stars=2)
rune_box['1*'] = rune_filter.qs.filter(stars=1)
elif box_grouping == 'equipped':
rune_box['Not Equipped'] = rune_filter.qs.filter(assigned_to__isnull=True)
rune_box['Equipped'] = rune_filter.qs.filter(assigned_to__isnull=False)
elif box_grouping == 'type':
for (type, type_name) in RuneInstance.TYPE_CHOICES:
rune_box[type_name] = rune_filter.qs.filter(type=type)
context['runes'] = rune_box
context['box_grouping'] = box_grouping
template = 'herders/profile/runes/inventory.html'
elif view_mode == 'grid':
template = 'herders/profile/runes/inventory_grid.html'
else:
template = 'herders/profile/runes/inventory_table.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory_crafts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
craft_box = OrderedDict()
for (craft, craft_name) in RuneInstance.CRAFT_CHOICES:
craft_box[craft_name] = OrderedDict()
for rune, rune_name in RuneInstance.TYPE_CHOICES:
craft_box[craft_name][rune_name] = RuneCraftInstance.committed.filter(owner=summoner, type=craft, rune=rune).order_by('stat', 'quality')
context['crafts'] = craft_box
return render(request, 'herders/profile/runes/inventory_crafts.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def rune_counts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
response_data = {
'code': 'success',
'counts': summoner.get_rune_counts()
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_add(request, profile_name):
form = AddRuneInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_rune = form.save(commit=False)
new_rune.owner = request.user.summoner
new_rune.save()
messages.success(request, 'Added ' + str(new_rune))
# Send back blank form
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
# Check for any pre-filled GET parameters
slot = request.GET.get('slot', None)
assigned_to = request.GET.get('assigned_to', None)
form = AddRuneInstanceForm(initial={
'assigned_to': assigned_to,
'slot': slot if slot is not None else 1,
})
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
# Return form filled in and errors shown
response_data = {
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
return JsonResponse(response_data)
@login_required
def rune_edit(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneInstanceForm(request.POST or None, instance=rune, auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
template = loader.get_template('herders/profile/runes/add_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm(auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'add_rune_form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_assign(request, profile_name, instance_id, slot=None):
rune_queryset = RuneInstance.committed.filter(owner=request.user.summoner, assigned_to=None)
filter_form = AssignRuneForm(request.POST or None, initial={'slot': slot})
filter_form.helper.form_action = reverse('herders:rune_assign', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if slot:
rune_queryset = rune_queryset.filter(slot=slot)
rune_filter = RuneInstanceFilter(request.POST, queryset=rune_queryset)
if request.method == 'POST':
template = loader.get_template('herders/profile/runes/assign_results.html')
response_data = {
'code': 'results',
'html': template.render(RequestContext(request, {
'filter': rune_filter,
'profile_name': profile_name,
'instance_id': instance_id,
}))
}
else:
template = loader.get_template('herders/profile/runes/assign_form.html')
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {
'filter': rune_filter,
'form': filter_form,
'profile_name': profile_name,
'instance_id': instance_id,
}))
}
return JsonResponse(response_data)
@login_required
def rune_assign_choice(request, profile_name, instance_id, rune_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
rune = get_object_or_404(RuneInstance, pk=rune_id)
if rune.assigned_to is not None:
# TODO: Warn about removing from other monster?
pass
# Check for existing rune.
existing_runes = monster.runeinstance_set.filter(slot=rune.slot)
for existing_rune in existing_runes:
existing_rune.assigned_to = None
rune.assigned_to = monster
rune.save()
monster.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
@login_required
def rune_unassign(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
rune.assigned_to = None
rune.save()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def rune_unassign_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
assigned_mons = []
assigned_runes = RuneInstance.committed.filter(owner=summoner, assigned_to__isnull=False)
number_assigned = assigned_runes.count()
if is_owner:
for rune in assigned_runes:
if rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
rune.assigned_to = None
rune.save()
# Resave monster instances that had runes removed to recalc stats
for mon in assigned_mons:
mon.save()
messages.success(request, 'Unassigned ' + str(number_assigned) + ' rune(s).')
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
messages.warning(request, 'Deleted ' + str(rune))
rune.delete()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Delete the runes
death_row = RuneInstance.committed.filter(owner=summoner)
number_killed = death_row.count()
assigned_mons = []
for rune in death_row:
if rune.assigned_to and rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
death_row.delete()
# Delete the crafts
RuneCraftInstance.committed.filter(owner=summoner).delete()
messages.warning(request, 'Deleted ' + str(number_killed) + ' runes.')
for mon in assigned_mons:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_resave_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
for r in RuneInstance.objects.filter(owner=summoner, substats__isnull=True):
r.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_add(request, profile_name):
form = AddRuneCraftInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_craft = form.save(commit=False)
new_craft.owner = request.user.summoner
new_craft.save()
messages.success(request, 'Added ' + new_craft.get_type_display() + ' ' + str(new_craft))
# Send back blank form
form = AddRuneCraftInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
@login_required
def rune_craft_edit(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneCraftInstanceForm(request.POST or None, instance=craft)
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
response_data = {
'code': 'success',
'html': template.render(RequestContext(request, {'form': form}))
}
else:
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(RequestContext(request, {'form': form}))
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_delete(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
raise Http404
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
messages.warning(request, 'Deleted ' + craft.get_rune_display() + ' ' + str(craft))
craft.delete()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
|
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RDismo(RPackage):
"""Functions for species distribution modeling, that is, predicting
entire geographic distributions form occurrences at a number of sites
and the environment at these sites."""
homepage = "https://cloud.r-project.org/package=dismo"
url = "https://cloud.r-project.org/src/contrib/dismo_1.1-4.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/dismo"
version('1.1-4', sha256='f2110f716cd9e4cca5fd2b22130c6954658aaf61361d2fe688ba22bbfdfa97c8')
depends_on('r@3.2.0:', type=('build', 'run'))
depends_on('r-raster@2.5-2:', type=('build', 'run'))
depends_on('r-sp@1.2-0:', type=('build', 'run'))
add version 1.3-3 to r-dismo (#20883)
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RDismo(RPackage):
"""Species Distribution Modeling
Methods for species distribution modeling, that is, predicting the
environmental similarity of any site to that of the locations of known
occurrences of a species."""
homepage = "https://cloud.r-project.org/package=dismo"
url = "https://cloud.r-project.org/src/contrib/dismo_1.1-4.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/dismo"
version('1.3-3', sha256='fd65331ac18a4287ba0856b90508ddd0e2738c653eecc5f3eb2b14e1d06949ca')
version('1.1-4', sha256='f2110f716cd9e4cca5fd2b22130c6954658aaf61361d2fe688ba22bbfdfa97c8')
depends_on('r@3.2.0:', type=('build', 'run'))
depends_on('r-raster@2.5-2:', type=('build', 'run'))
depends_on('r-sp@1.2-0:', type=('build', 'run'))
depends_on('java@8:', when='@1.3-3:')
|
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 7 21:14:25 2017
@author: Suhas Somnath, Chris Smith
"""
from __future__ import division, print_function, unicode_literals, absolute_import
import sys
import numpy as np
from collections import Iterable
import datetime
from dtype_utils import contains_integers
__all__ = ['clean_string_att', 'get_aux_dset_slicing', 'make_indices_matrix', 'INDICES_DTYPE', 'VALUES_DTYPE',
'Dimension', 'build_ind_val_matrices', 'calc_chunks', 'create_spec_inds_from_vals']
if sys.version_info.major == 3:
unicode = str
INDICES_DTYPE = np.uint32
VALUES_DTYPE = np.float32
class Dimension(object):
def __init__(self, name, units, values):
"""
Simple object that describes a dimension in a dataset by its name, units, and values
Parameters
----------
name : str / unicode
Name of the dimension. For example 'Bias'
units : str / unicode
Units for this dimension. For example: 'V'
values : array-like, or int
Values over which this dimension was varied. A linearly increasing set of values will be generated if an
integer is provided instead of an array.
"""
if not isinstance(name, (str, unicode)):
raise TypeError('name should be a string')
name = name.strip()
if len(name) < 1:
raise ValueError('name should not be an empty string')
if not isinstance(units, (str, unicode)):
raise TypeError('units should be a string')
if isinstance(values, int):
if values < 1:
raise ValueError('values should at least be specified as a positive integer')
values = np.arange(values)
if not isinstance(values, (np.ndarray, list, tuple)):
raise TypeError('values should be array-like')
self.name = name
self.units = units
self.values = values
def __repr__(self):
return '{} ({}) : {}'.format(self.name, self.units, self.values)
def get_aux_dset_slicing(dim_names, last_ind=None, is_spectroscopic=False):
"""
Returns a dictionary of slice objects to help in creating region references in the position or spectroscopic
indices and values datasets
Parameters
------------
dim_names : iterable
List of strings denoting the names of the position axes or spectroscopic dimensions arranged in the same order
that matches the dimensions in the indices / values dataset
last_ind : (Optional) unsigned int, default = None
Last pixel in the positon or spectroscopic matrix. Useful in experiments where the
parameters have changed (eg. BEPS new data format) during the experiment.
is_spectroscopic : bool, optional. default = True
set to True for position datasets and False for spectroscopic datasets
Returns
------------
slice_dict : dictionary
Dictionary of tuples containing slice objects corresponding to
each position axis.
"""
if not isinstance(dim_names, Iterable):
raise TypeError('dim_names should be and Iterable')
if not len(dim_names) > 0:
raise ValueError('dim_names should not be empty')
if not np.all([isinstance(x, (str, unicode)) for x in dim_names]):
raise TypeError('dim_names should contain strings')
slice_dict = dict()
for spat_ind, curr_dim_name in enumerate(dim_names):
val = (slice(last_ind), slice(spat_ind, spat_ind + 1))
if is_spectroscopic:
val = val[::-1]
slice_dict[str(curr_dim_name)] = val
return slice_dict
def make_indices_matrix(num_steps, is_position=True):
"""
Makes an ancillary indices matrix given the number of steps in each dimension. In other words, this function builds
a matrix whose rows correspond to unique combinations of the multiple dimensions provided.
Parameters
------------
num_steps : List / numpy array
Number of steps in each spatial or spectral dimension
Note that the axes must be ordered from fastest varying to slowest varying
is_position : bool, optional, default = True
Whether the returned matrix is meant for position (True) indices (tall and skinny) or spectroscopic (False)
indices (short and wide)
Returns
--------------
indices_matrix : 2D unsigned int numpy array
arranged as [steps, spatial dimension]
"""
if not isinstance(num_steps, (tuple, list, np.ndarray)):
raise TypeError('num_steps should be a list / tuple / numpy array')
if not contains_integers(num_steps, min_val=1 + int(len(num_steps) > 0)):
raise ValueError('num_steps should contain integers greater than equal to 1 (empty dimension) or 2')
num_steps = np.array(num_steps)
spat_dims = max(1, len(np.where(num_steps > 1)[0]))
indices_matrix = np.zeros(shape=(np.prod(num_steps), spat_dims), dtype=INDICES_DTYPE)
dim_ind = 0
for indx, curr_steps in enumerate(num_steps):
if curr_steps > 1:
part1 = np.prod(num_steps[:indx + 1])
if indx > 0:
part2 = np.prod(num_steps[:indx])
else:
part2 = 1
if indx + 1 == len(num_steps):
part3 = 1
else:
part3 = np.prod(num_steps[indx + 1:])
indices_matrix[:, dim_ind] = np.tile(np.floor(np.arange(part1) / part2), part3)
dim_ind += 1
if not is_position:
indices_matrix = indices_matrix.T
return indices_matrix
def clean_string_att(att_val):
"""
Replaces any unicode objects within lists with their string counterparts to ensure compatibility with python 3.
If the attribute is indeed a list of unicodes, the changes will be made in-place
Parameters
----------
att_val : object
Attribute object
Returns
-------
att_val : object
Attribute object
"""
try:
if isinstance(att_val, Iterable):
if type(att_val) in [unicode, str]:
return att_val
elif np.any([type(x) in [str, unicode, bytes, np.str_] for x in att_val]):
return np.array(att_val, dtype='S')
if type(att_val) == np.str_:
return str(att_val)
return att_val
except TypeError:
raise TypeError('Failed to clean: {}'.format(att_val))
def build_ind_val_matrices(unit_values, is_spectral=True):
"""
Builds indices and values matrices using given unit values for each dimension.
Parameters
----------
unit_values : list / tuple
Sequence of values vectors for each dimension
is_spectral : bool (optional), default = True
If true, returns matrices for spectroscopic datasets, else returns matrices for Position datasets
Returns
-------
ind_mat : 2D numpy array
Indices matrix
val_mat : 2D numpy array
Values matrix
"""
if not isinstance(unit_values, (list, tuple)):
raise TypeError('unit_values should be a list or tuple')
if not np.all([np.array(x).ndim == 1 for x in unit_values]):
raise ValueError('unit_values should only contain 1D array')
lengths = [len(x) for x in unit_values]
tile_size = [np.prod(lengths[x:]) for x in range(1, len(lengths))] + [1]
rep_size = [1] + [np.prod(lengths[:x]) for x in range(1, len(lengths))]
val_mat = np.zeros(shape=(len(lengths), np.prod(lengths)))
ind_mat = np.zeros(shape=val_mat.shape, dtype=np.uint32)
for ind, ts, rs, vec in zip(range(len(lengths)), tile_size, rep_size, unit_values):
val_mat[ind] = np.tile(np.repeat(vec, rs), ts)
ind_mat[ind] = np.tile(np.repeat(np.arange(len(vec)), rs), ts)
if not is_spectral:
val_mat = val_mat.T
ind_mat = ind_mat.T
return INDICES_DTYPE(ind_mat), VALUES_DTYPE(val_mat)
def create_spec_inds_from_vals(ds_spec_val_mat):
"""
Create new Spectroscopic Indices table from the changes in the
Spectroscopic Values
Parameters
----------
ds_spec_val_mat : array-like,
Holds the spectroscopic values to be indexed
Returns
-------
ds_spec_inds_mat : numpy array of uints the same shape as ds_spec_val_mat
Indices corresponding to the values in ds_spec_val_mat
"""
if not isinstance(ds_spec_val_mat, np.ndarray):
raise TypeError('ds_spec_val_mat must be a numpy array')
if ds_spec_val_mat.ndim != 2:
raise ValueError('ds_spec_val_mat must be a 2D array arranged as [dimension, values]')
ds_spec_inds_mat = np.zeros_like(ds_spec_val_mat, dtype=np.int32)
"""
Find how quickly the spectroscopic values are changing in each row
and the order of row from fastest changing to slowest.
"""
change_count = [len(np.where([row[i] != row[i - 1] for i in range(len(row))])[0]) for row in ds_spec_val_mat]
change_sort = np.argsort(change_count)[::-1]
"""
Determine everywhere the spectroscopic values change and build
index table based on those changed
"""
indices = np.zeros(ds_spec_val_mat.shape[0])
for jcol in range(1, ds_spec_val_mat.shape[1]):
this_col = ds_spec_val_mat[change_sort, jcol]
last_col = ds_spec_val_mat[change_sort, jcol - 1]
"""
Check if current column values are different than those
in last column.
"""
changed = np.where(this_col != last_col)[0]
"""
If only one row changed, increment the index for that
column
If more than one row has changed, increment the index for
the last row that changed and set all others to zero
"""
if len(changed) == 1:
indices[changed] += 1
elif len(changed > 1):
for change in changed[:-1]:
indices[change] = 0
indices[changed[-1]] += 1
"""
Store the indices for the current column in the dataset
"""
ds_spec_inds_mat[change_sort, jcol] = indices
return ds_spec_inds_mat
def calc_chunks(dimensions, dtype_byte_size, unit_chunks=None, max_chunk_mem=10240):
"""
Calculate the chunk size for the HDF5 dataset based on the dimensions and the
maximum chunk size in memory
Parameters
----------
dimensions : array_like of int
Shape of the data to be chunked
dtype_byte_size : unsigned int
Size of an entry in the data in bytes
unit_chunks : array_like of int, optional
Unit size of the chunking in each dimension. Must be the same size as
the shape of `ds_main`. Default None, `unit_chunks` is set to 1 in all
dimensions
max_chunk_mem : int, optional
Maximum size of the chunk in memory in bytes. Default 10240b or 10kb per h5py recommendations
Returns
-------
chunking : tuple of int
Calculated maximum size of a chunk in each dimension that is as close to the
requested `max_chunk_mem` as posible while having steps based on the input
`unit_chunks`.
"""
if not isinstance(dimensions, (list, tuple)):
raise TypeError('dimensions should either be a tuple or list')
if not isinstance(dtype_byte_size, int):
raise TypeError('dtype_byte_size should be an integer')
if unit_chunks is not None:
if not isinstance(unit_chunks, (tuple, list)):
raise TypeError('unit_chunks should either be a tuple or list')
'''
Ensure that dimensions is an array
'''
dimensions = np.asarray(dimensions, dtype=np.uint)
'''
Set the unit_chunks to all ones if not given. Ensure it is an array if it is.
'''
if unit_chunks is None:
unit_chunks = np.ones_like(dimensions)
else:
unit_chunks = np.asarray(unit_chunks, dtype=np.uint)
if unit_chunks.shape != dimensions.shape:
raise ValueError('Unit chunk size must have the same shape as the input dataset.')
'''
Save the original size of unit_chunks to use for incrementing the chunk size during
loop
'''
base_chunks = unit_chunks.copy()
'''
Loop until chunk_size is greater than the maximum chunk_mem or the chunk_size is equal to
that of dimensions
'''
while np.prod(unit_chunks) * dtype_byte_size <= max_chunk_mem:
'''
Check if all chunk dimensions are greater or equal to the
actual dimensions. Exit the loop if true.
'''
if np.all(unit_chunks >= dimensions):
break
'''
Find the index of the next chunk to be increased and increment it by the base_chunk
size
'''
ichunk = np.argmax(dimensions / unit_chunks)
unit_chunks[ichunk] += base_chunks[ichunk]
'''
Ensure that the size of the chunks is between one and the dimension size.
'''
unit_chunks = np.clip(unit_chunks, np.ones_like(unit_chunks), dimensions)
chunking = tuple(unit_chunks)
return chunking
def write_dset_to_txt(input_file, output_file='output.csv'):
"""
Output an h5 file or a PycroDataset in csv format
Parameters
----------
input_file : str
path to the h5 input file that is to be translated into csv format
output_file : str, optional
path that the output file should be written to
"""
try:
import h5py
import pycroscopy as px
import os
except ImportError:
print('something is not installed properly')
if not isinstance(input_file, str):
raise TypeError('input_file should be a path to an h5 file')
h5File = h5py.File(input_file)
pdRaw = px.PycroDataset(h5File['Measurement_000/Channel_000/Raw_Data'])
specVals = pdRaw.h5_spec_vals
posVals = pdRaw.h5_pos_vals
dimUnits = pdRaw.spec_dim_descriptors
pdPosDims = pdRaw.pos_dim_labels
pdSpecDims = pdRaw.spec_dim_labels
header = ''
for idx, spec in enumerate(specVals):
"""
Obtain the units from the spectral dimension descriptors then
create each line of the header with a spacer between the dimensions and the data
"""
unitStart = dimUnits[idx].find('(') + 1
unitEnd = dimUnits[idx].find(')')
unit = dimUnits[idx][unitStart:unitEnd]
header = header + ','.join(str(freq) + ' ' + unit for freq in spec) + '\n'
header = header + ','.join('--------------------------------------------------------------' for idx in specVals[0])
"""
Create the spectral and position labels for the dataset in string form then
create the position value array in string form, right-strip the last comma from the
string to deliver the correct number of values, append all of the labels and values together,
save the data and header to a temporary csv output
"""
specLabel = ''
for dim in pdSpecDims:
specLabel = specLabel + ','.join('' for idx in pdPosDims) + str(dim) + ',\n'
posLabel = ','.join(posL for posL in pdPosDims) + ',\n'
posValOut = ''
for val, posDim in enumerate(posVals):
posValOut = posValOut + ','.join(str(posVal) for posVal in posVals[val]) + ',\n'
posValOut = posValOut.rstrip('\n')
output = specLabel + posLabel + posValOut
np.savetxt('temp.csv', pdRaw, delimiter=',', header=header, comments='')
left_dset = output.splitlines()
with open('temp.csv', 'r+') as f, open(output_file, 'w') as b:
for left_line, right_line in zip(left_dset, f):
right_line = left_line + right_line
b.write(right_line)
os.remove('temp.csv')
return output_file
Add returns
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 7 21:14:25 2017
@author: Suhas Somnath, Chris Smith
"""
from __future__ import division, print_function, unicode_literals, absolute_import
import sys
import numpy as np
from collections import Iterable
import datetime
from dtype_utils import contains_integers
__all__ = ['clean_string_att', 'get_aux_dset_slicing', 'make_indices_matrix', 'INDICES_DTYPE', 'VALUES_DTYPE',
'Dimension', 'build_ind_val_matrices', 'calc_chunks', 'create_spec_inds_from_vals']
if sys.version_info.major == 3:
unicode = str
INDICES_DTYPE = np.uint32
VALUES_DTYPE = np.float32
class Dimension(object):
def __init__(self, name, units, values):
"""
Simple object that describes a dimension in a dataset by its name, units, and values
Parameters
----------
name : str / unicode
Name of the dimension. For example 'Bias'
units : str / unicode
Units for this dimension. For example: 'V'
values : array-like, or int
Values over which this dimension was varied. A linearly increasing set of values will be generated if an
integer is provided instead of an array.
"""
if not isinstance(name, (str, unicode)):
raise TypeError('name should be a string')
name = name.strip()
if len(name) < 1:
raise ValueError('name should not be an empty string')
if not isinstance(units, (str, unicode)):
raise TypeError('units should be a string')
if isinstance(values, int):
if values < 1:
raise ValueError('values should at least be specified as a positive integer')
values = np.arange(values)
if not isinstance(values, (np.ndarray, list, tuple)):
raise TypeError('values should be array-like')
self.name = name
self.units = units
self.values = values
def __repr__(self):
return '{} ({}) : {}'.format(self.name, self.units, self.values)
def get_aux_dset_slicing(dim_names, last_ind=None, is_spectroscopic=False):
"""
Returns a dictionary of slice objects to help in creating region references in the position or spectroscopic
indices and values datasets
Parameters
------------
dim_names : iterable
List of strings denoting the names of the position axes or spectroscopic dimensions arranged in the same order
that matches the dimensions in the indices / values dataset
last_ind : (Optional) unsigned int, default = None
Last pixel in the positon or spectroscopic matrix. Useful in experiments where the
parameters have changed (eg. BEPS new data format) during the experiment.
is_spectroscopic : bool, optional. default = True
set to True for position datasets and False for spectroscopic datasets
Returns
------------
slice_dict : dictionary
Dictionary of tuples containing slice objects corresponding to
each position axis.
"""
if not isinstance(dim_names, Iterable):
raise TypeError('dim_names should be and Iterable')
if not len(dim_names) > 0:
raise ValueError('dim_names should not be empty')
if not np.all([isinstance(x, (str, unicode)) for x in dim_names]):
raise TypeError('dim_names should contain strings')
slice_dict = dict()
for spat_ind, curr_dim_name in enumerate(dim_names):
val = (slice(last_ind), slice(spat_ind, spat_ind + 1))
if is_spectroscopic:
val = val[::-1]
slice_dict[str(curr_dim_name)] = val
return slice_dict
def make_indices_matrix(num_steps, is_position=True):
"""
Makes an ancillary indices matrix given the number of steps in each dimension. In other words, this function builds
a matrix whose rows correspond to unique combinations of the multiple dimensions provided.
Parameters
------------
num_steps : List / numpy array
Number of steps in each spatial or spectral dimension
Note that the axes must be ordered from fastest varying to slowest varying
is_position : bool, optional, default = True
Whether the returned matrix is meant for position (True) indices (tall and skinny) or spectroscopic (False)
indices (short and wide)
Returns
--------------
indices_matrix : 2D unsigned int numpy array
arranged as [steps, spatial dimension]
"""
if not isinstance(num_steps, (tuple, list, np.ndarray)):
raise TypeError('num_steps should be a list / tuple / numpy array')
if not contains_integers(num_steps, min_val=1 + int(len(num_steps) > 0)):
raise ValueError('num_steps should contain integers greater than equal to 1 (empty dimension) or 2')
num_steps = np.array(num_steps)
spat_dims = max(1, len(np.where(num_steps > 1)[0]))
indices_matrix = np.zeros(shape=(np.prod(num_steps), spat_dims), dtype=INDICES_DTYPE)
dim_ind = 0
for indx, curr_steps in enumerate(num_steps):
if curr_steps > 1:
part1 = np.prod(num_steps[:indx + 1])
if indx > 0:
part2 = np.prod(num_steps[:indx])
else:
part2 = 1
if indx + 1 == len(num_steps):
part3 = 1
else:
part3 = np.prod(num_steps[indx + 1:])
indices_matrix[:, dim_ind] = np.tile(np.floor(np.arange(part1) / part2), part3)
dim_ind += 1
if not is_position:
indices_matrix = indices_matrix.T
return indices_matrix
def clean_string_att(att_val):
"""
Replaces any unicode objects within lists with their string counterparts to ensure compatibility with python 3.
If the attribute is indeed a list of unicodes, the changes will be made in-place
Parameters
----------
att_val : object
Attribute object
Returns
-------
att_val : object
Attribute object
"""
try:
if isinstance(att_val, Iterable):
if type(att_val) in [unicode, str]:
return att_val
elif np.any([type(x) in [str, unicode, bytes, np.str_] for x in att_val]):
return np.array(att_val, dtype='S')
if type(att_val) == np.str_:
return str(att_val)
return att_val
except TypeError:
raise TypeError('Failed to clean: {}'.format(att_val))
def build_ind_val_matrices(unit_values, is_spectral=True):
"""
Builds indices and values matrices using given unit values for each dimension.
Parameters
----------
unit_values : list / tuple
Sequence of values vectors for each dimension
is_spectral : bool (optional), default = True
If true, returns matrices for spectroscopic datasets, else returns matrices for Position datasets
Returns
-------
ind_mat : 2D numpy array
Indices matrix
val_mat : 2D numpy array
Values matrix
"""
if not isinstance(unit_values, (list, tuple)):
raise TypeError('unit_values should be a list or tuple')
if not np.all([np.array(x).ndim == 1 for x in unit_values]):
raise ValueError('unit_values should only contain 1D array')
lengths = [len(x) for x in unit_values]
tile_size = [np.prod(lengths[x:]) for x in range(1, len(lengths))] + [1]
rep_size = [1] + [np.prod(lengths[:x]) for x in range(1, len(lengths))]
val_mat = np.zeros(shape=(len(lengths), np.prod(lengths)))
ind_mat = np.zeros(shape=val_mat.shape, dtype=np.uint32)
for ind, ts, rs, vec in zip(range(len(lengths)), tile_size, rep_size, unit_values):
val_mat[ind] = np.tile(np.repeat(vec, rs), ts)
ind_mat[ind] = np.tile(np.repeat(np.arange(len(vec)), rs), ts)
if not is_spectral:
val_mat = val_mat.T
ind_mat = ind_mat.T
return INDICES_DTYPE(ind_mat), VALUES_DTYPE(val_mat)
def create_spec_inds_from_vals(ds_spec_val_mat):
"""
Create new Spectroscopic Indices table from the changes in the
Spectroscopic Values
Parameters
----------
ds_spec_val_mat : array-like,
Holds the spectroscopic values to be indexed
Returns
-------
ds_spec_inds_mat : numpy array of uints the same shape as ds_spec_val_mat
Indices corresponding to the values in ds_spec_val_mat
"""
if not isinstance(ds_spec_val_mat, np.ndarray):
raise TypeError('ds_spec_val_mat must be a numpy array')
if ds_spec_val_mat.ndim != 2:
raise ValueError('ds_spec_val_mat must be a 2D array arranged as [dimension, values]')
ds_spec_inds_mat = np.zeros_like(ds_spec_val_mat, dtype=np.int32)
"""
Find how quickly the spectroscopic values are changing in each row
and the order of row from fastest changing to slowest.
"""
change_count = [len(np.where([row[i] != row[i - 1] for i in range(len(row))])[0]) for row in ds_spec_val_mat]
change_sort = np.argsort(change_count)[::-1]
"""
Determine everywhere the spectroscopic values change and build
index table based on those changed
"""
indices = np.zeros(ds_spec_val_mat.shape[0])
for jcol in range(1, ds_spec_val_mat.shape[1]):
this_col = ds_spec_val_mat[change_sort, jcol]
last_col = ds_spec_val_mat[change_sort, jcol - 1]
"""
Check if current column values are different than those
in last column.
"""
changed = np.where(this_col != last_col)[0]
"""
If only one row changed, increment the index for that
column
If more than one row has changed, increment the index for
the last row that changed and set all others to zero
"""
if len(changed) == 1:
indices[changed] += 1
elif len(changed > 1):
for change in changed[:-1]:
indices[change] = 0
indices[changed[-1]] += 1
"""
Store the indices for the current column in the dataset
"""
ds_spec_inds_mat[change_sort, jcol] = indices
return ds_spec_inds_mat
def calc_chunks(dimensions, dtype_byte_size, unit_chunks=None, max_chunk_mem=10240):
"""
Calculate the chunk size for the HDF5 dataset based on the dimensions and the
maximum chunk size in memory
Parameters
----------
dimensions : array_like of int
Shape of the data to be chunked
dtype_byte_size : unsigned int
Size of an entry in the data in bytes
unit_chunks : array_like of int, optional
Unit size of the chunking in each dimension. Must be the same size as
the shape of `ds_main`. Default None, `unit_chunks` is set to 1 in all
dimensions
max_chunk_mem : int, optional
Maximum size of the chunk in memory in bytes. Default 10240b or 10kb per h5py recommendations
Returns
-------
chunking : tuple of int
Calculated maximum size of a chunk in each dimension that is as close to the
requested `max_chunk_mem` as posible while having steps based on the input
`unit_chunks`.
"""
if not isinstance(dimensions, (list, tuple)):
raise TypeError('dimensions should either be a tuple or list')
if not isinstance(dtype_byte_size, int):
raise TypeError('dtype_byte_size should be an integer')
if unit_chunks is not None:
if not isinstance(unit_chunks, (tuple, list)):
raise TypeError('unit_chunks should either be a tuple or list')
'''
Ensure that dimensions is an array
'''
dimensions = np.asarray(dimensions, dtype=np.uint)
'''
Set the unit_chunks to all ones if not given. Ensure it is an array if it is.
'''
if unit_chunks is None:
unit_chunks = np.ones_like(dimensions)
else:
unit_chunks = np.asarray(unit_chunks, dtype=np.uint)
if unit_chunks.shape != dimensions.shape:
raise ValueError('Unit chunk size must have the same shape as the input dataset.')
'''
Save the original size of unit_chunks to use for incrementing the chunk size during
loop
'''
base_chunks = unit_chunks.copy()
'''
Loop until chunk_size is greater than the maximum chunk_mem or the chunk_size is equal to
that of dimensions
'''
while np.prod(unit_chunks) * dtype_byte_size <= max_chunk_mem:
'''
Check if all chunk dimensions are greater or equal to the
actual dimensions. Exit the loop if true.
'''
if np.all(unit_chunks >= dimensions):
break
'''
Find the index of the next chunk to be increased and increment it by the base_chunk
size
'''
ichunk = np.argmax(dimensions / unit_chunks)
unit_chunks[ichunk] += base_chunks[ichunk]
'''
Ensure that the size of the chunks is between one and the dimension size.
'''
unit_chunks = np.clip(unit_chunks, np.ones_like(unit_chunks), dimensions)
chunking = tuple(unit_chunks)
return chunking
def write_dset_to_txt(input_file, output_file='output.csv'):
"""
Output an h5 file or a PycroDataset in csv format
Parameters
----------
input_file : str
path to the h5 input file that is to be translated into csv format
output_file : str, optional
path that the output file should be written to
Returns
-------
output_file: str
"""
try:
import h5py
import pycroscopy as px
import os
except ImportError:
print('something is not installed properly')
if not isinstance(input_file, str):
raise TypeError('input_file should be a path to an h5 file')
h5File = h5py.File(input_file)
pdRaw = px.PycroDataset(h5File['Measurement_000/Channel_000/Raw_Data'])
specVals = pdRaw.h5_spec_vals
posVals = pdRaw.h5_pos_vals
dimUnits = pdRaw.spec_dim_descriptors
pdPosDims = pdRaw.pos_dim_labels
pdSpecDims = pdRaw.spec_dim_labels
header = ''
for idx, spec in enumerate(specVals):
"""
Obtain the units from the spectral dimension descriptors then
create each line of the header with a spacer between the dimensions and the data
"""
unitStart = dimUnits[idx].find('(') + 1
unitEnd = dimUnits[idx].find(')')
unit = dimUnits[idx][unitStart:unitEnd]
header = header + ','.join(str(freq) + ' ' + unit for freq in spec) + '\n'
header = header + ','.join('--------------------------------------------------------------' for idx in specVals[0])
"""
Create the spectral and position labels for the dataset in string form then
create the position value array in string form, right-strip the last comma from the
string to deliver the correct number of values, append all of the labels and values together,
save the data and header to a temporary csv output
"""
specLabel = ''
for dim in pdSpecDims:
specLabel = specLabel + ','.join('' for idx in pdPosDims) + str(dim) + ',\n'
posLabel = ','.join(posL for posL in pdPosDims) + ',\n'
posValOut = ''
for val, posDim in enumerate(posVals):
posValOut = posValOut + ','.join(str(posVal) for posVal in posVals[val]) + ',\n'
posValOut = posValOut.rstrip('\n')
output = specLabel + posLabel + posValOut
np.savetxt('temp.csv', pdRaw, delimiter=',', header=header, comments='')
left_dset = output.splitlines()
with open('temp.csv', 'r+') as f, open(output_file, 'w') as b:
for left_line, right_line in zip(left_dset, f):
right_line = left_line + right_line
b.write(right_line)
os.remove('temp.csv')
return output_file
|
# future
from __future__ import annotations
# stdlib
from typing import Any
from typing import Dict
from typing import Iterable
from typing import Optional
from typing import TYPE_CHECKING
from typing import Tuple
from typing import Union
# third party
import numpy as np
from scipy.ndimage.interpolation import rotate
# relative
from ..common.serde.serializable import serializable
from .broadcastable import is_broadcastable
from .config import DEFAULT_FLOAT_NUMPY_TYPE
from .config import DEFAULT_INT_NUMPY_TYPE
from .passthrough import is_acceptable_simple_type # type: ignore
from .smpc.utils import get_shape
if TYPE_CHECKING:
# relative
from .autodp.phi_tensor import PhiTensor
@serializable(recursive_serde=True)
class lazyrepeatarray:
"""
A class representing Differential Privacy metadata (minimum and maximum values) in a way that saves RAM/CPU.
We store large arrays of a single repeating value as a single tuple (shape) and a single value (int/float/etc)
e.g. np.array([8,8,8,8,8,8]) = lazyrepeatarray(data=8, shape=(6,))
Think like the opposite of np.broadcast, repeated values along an axis are collapsed but the .shape
attribute of the higher dimensional projection is retained for operations.
...
Attributes:
data: int/float
the actual value that is repeating.
shape: tuple
the shape that the fully expanded array would be.
Methods:
to_numpy():
expands the lazyrepeatarray into the full sized numpy array it was representing.
"""
__attr_allowlist__ = ["data", "shape"]
def __init__(self, data: np.ndarray, shape: Tuple[int, ...]) -> None:
"""
data: the raw data values without repeats
shape: the shape of 'data' if repeats were included
"""
# NOTE: all additional arguments are assumed to be broadcast if dims are shorter
# than that of data. Example: if data.shape == (2,3,4) and
# min_vals.shape == (2,3), then it's assumed that the full min_vals.shape is
# actually (2,3,4) where the last dim is simply copied.
# Example2: if data.shape == (2,3,4) and min_vals.shape == (2,1,4), then the
# middle dimension is supposed to be copied to be min_vals.shape == (2,3,4)
# if necessary. This is just to keep the memory footprint (and computation)
# as small as possible.
if isinstance(data, (bool, int, float)):
data = np.array(data)
if isinstance(data, int):
data = data.astype(DEFAULT_INT_NUMPY_TYPE) # type: ignore
if isinstance(data, float):
data = data.astype(DEFAULT_FLOAT_NUMPY_TYPE) # type: ignore
# verify broadcasting works on shapes
if -1 not in shape:
np.broadcast_shapes(data.shape, shape)
self.data = data
self.shape = shape
if isinstance(shape, Iterable):
for val in shape:
if val < 0:
raise ValueError(f"Invalid shape: {shape}")
def __getitem__(self, item: Union[str, int, slice]) -> lazyrepeatarray:
if self.data.shape == self.shape:
output = self.data[item]
return lazyrepeatarray(data=output, shape=output.shape)
elif self.data.size == 1:
test_arr = np.ones(self.shape)[
item
] # TODO: Is there a better way to determine output shape?
return lazyrepeatarray(data=self.data, shape=test_arr.shape)
else:
raise NotImplementedError
def __add__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
return self.__class__(data=self.data + other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
if self.data.shape == other.data.shape:
return self.__class__(data=self.data + other.data, shape=self.shape)
else:
return self.__class__(data=self.data + other.data, shape=self.shape)
def __sub__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
res = self.data - other
return self.__class__(data=res, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
if self.data.shape == other.data.shape:
return self.__class__(data=self.data - other.data, shape=self.shape)
else:
return self.__class__(data=self.data - other.data, shape=self.shape)
def __mul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
return self.__class__(data=self.data * other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
"Cannot broadcast arrays with shapes for LazyRepeatArray Multiplication:"
+ f" {self.shape} & {other.shape}"
)
else:
return self.__class__(data=self.data * other.data, shape=self.shape)
def __matmul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
new_shape = get_shape("__matmul__", self.shape, other.shape)
if self.data.size == 1:
return self.__class__(
data=np.matmul(np.ones(self.shape), other * self.data),
shape=new_shape,
)
return self.__class__(data=self.data.__matmul__(other), shape=new_shape)
if self.shape[-1] != other.shape[-2]:
raise Exception(
f"cannot matrix multiply tensors with different shapes: {self.shape} and {other.shape}"
)
result = self.to_numpy() @ other.to_numpy()
return self.__class__(data=result, shape=result.shape)
def __lshift__(self, other: Any) -> lazyrepeatarray:
if is_acceptable_simple_type(other):
return self.__class__(data=self.data << other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
return self.__class__(data=self.data << other.data, shape=self.shape)
def __rshift__(self, other: Any) -> lazyrepeatarray:
if is_acceptable_simple_type(other):
return self.__class__(data=self.data >> other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
return self.__class__(data=self.data >> other.data, shape=self.shape)
def zeros_like(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(np.zeros_like(self.to_numpy(), *args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def __rtruediv__(self, other: Any) -> lazyrepeatarray:
res = (1 / self.data) * other
return lazyrepeatarray(data=res, shape=self.shape)
def __rmatmul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
new_shape = get_shape("__matmul__", other.shape, self.shape)
if other.size == 1:
return self.__class__(
data=np.matmul(np.ones(other.shape), other * self.data),
shape=new_shape,
)
return self.__class__(
data=self.to_numpy().__rmatmul__(other), shape=new_shape
)
if other.shape[-1] != self.shape[0]:
raise Exception(
"cannot matrix multiply tensors with different shapes: {self.shape} and {other.shape}"
)
result = self.to_numpy().__rmatmul__(other.to_numpy())
return self.__class__(data=result, shape=result.shape)
def __pow__(self, exponent: int) -> lazyrepeatarray:
if exponent == 2:
return self * self
raise Exception("not sure how to do this yet")
def pad(self, pad_width: int, mode: str = "reflect") -> lazyrepeatarray:
if mode == "reflect":
new_shape = tuple([i + pad_width * 2 for i in self.shape])
if self.data.shape == self.shape:
return lazyrepeatarray(
data=np.pad(self.data, pad_width=pad_width, mode="reflect"),
shape=new_shape,
)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=new_shape)
else:
raise NotImplementedError
else:
raise NotImplementedError
def horizontal_flip(self) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=np.fliplr(self.data), shape=self.shape)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def vertical_flip(self) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=np.flipud(self.data), shape=self.shape)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def rotate(self, angle: int) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=rotate(self.data, angle), shape=self.shape)
elif self.data.size == 1:
# TODO: This is almost certainly incorrect
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def reshape(self, target_shape: Tuple) -> lazyrepeatarray:
# TODO: Can we reshape without creating new objects
if self.data.shape == self.shape:
return lazyrepeatarray(
data=self.data.reshape(target_shape), shape=target_shape
)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=target_shape)
else:
if not np.broadcast_shapes(self.data.shape, target_shape):
raise NotImplementedError(
f"data= {self.data.shape}, shape: {self.shape}"
)
else:
return lazyrepeatarray(data=self.data, shape=target_shape)
def copy(self, order: Optional[str] = "K") -> lazyrepeatarray:
return self.__class__(data=self.data.copy(order=order), shape=self.shape)
@property
def size(self) -> int:
return np.prod(self.shape)
def sum(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(self.to_numpy().sum(*args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def ones_like(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(np.ones_like(self.to_numpy(), *args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def __eq__(self, other: Any) -> lazyrepeatarray: # type: ignore
if isinstance(other, lazyrepeatarray):
if self.shape == other.shape:
return lazyrepeatarray(data=self.data == other.data, shape=self.shape)
else:
result = (self.to_numpy() == other.to_numpy()).all()
return lazyrepeatarray(data=np.array([result]), shape=result.shape)
if isinstance(other, np.ndarray):
try:
_ = np.broadcast_shapes(self.shape, other.shape)
result = (self.to_numpy() == other).all()
return lazyrepeatarray(data=np.array([result]), shape=other.shape)
except Exception as e:
print(
"Failed to compare lazyrepeatarray with "
+ f"{self.shape} == {other.shape} to numpy by broadcasting. {e}"
)
raise e
return self == other
def __le__(self, other: Any) -> lazyrepeatarray: # type: ignore
if isinstance(other, lazyrepeatarray):
if self.shape == other.shape:
return lazyrepeatarray(data=self.data <= other.data, shape=self.shape)
else:
result = (self.to_numpy() <= other.to_numpy()).all()
return lazyrepeatarray(data=np.array([result]), shape=result.shape)
if isinstance(other, np.ndarray):
try:
_ = np.broadcast_shapes(self.shape, other.shape)
result = (self.to_numpy() <= other).all()
return lazyrepeatarray(data=np.array([result]), shape=other.shape)
except Exception as e:
print(
"Failed to compare lazyrepeatarray with "
+ f"{self.shape} == {other.shape} to numpy by broadcasting. {e}"
)
raise e
return self <= other
def concatenate(
self, other: lazyrepeatarray, *args: Any, **kwargs: Any
) -> lazyrepeatarray:
if not isinstance(other, lazyrepeatarray):
raise NotImplementedError
dummy_res = np.concatenate(
(np.empty(self.shape), np.empty(other.shape)), *args, **kwargs
)
return lazyrepeatarray(data=self.data, shape=dummy_res.shape)
@property
def dtype(self) -> np.dtype:
return self.data.dtype
def astype(self, np_type: np.dtype) -> lazyrepeatarray:
return self.__class__(self.data.astype(np_type), self.shape)
def to_numpy(self) -> np.ndarray:
return np.broadcast_to(self.data, self.shape)
def __repr__(self) -> str:
return f"<lazyrepeatarray data: {self.data} -> shape: {self.shape}>"
def __bool__(self) -> bool:
return self.data.__bool__()
def all(self) -> bool:
return self.data.all()
def any(self) -> bool:
return self.data.any()
def transpose(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
dummy_res = self.to_numpy().transpose(*args, **kwargs)
return lazyrepeatarray(
data=self.data.transpose(*args, **kwargs), shape=dummy_res.shape
)
# As the min and max values calculation is the same regardless of the tensor type,
# We centralize this method as baseline for calculation for min/max values
def compute_min_max(
x_min_vals: lazyrepeatarray,
x_max_vals: lazyrepeatarray,
other: Union[PhiTensor, int, float, np.ndarray],
op_str: str,
*args: Any,
**kwargs: Dict[Any, Any],
) -> Tuple[lazyrepeatarray, lazyrepeatarray]:
min_vals: lazyrepeatarray
max_vals: lazyrepeatarray
if op_str in ["__add__", "__matmul__", "__rmatmul__", "__lshift__", "__rshift__"]:
if is_acceptable_simple_type(other):
min_vals = getattr(x_min_vals, op_str)(other)
max_vals = getattr(x_max_vals, op_str)(other)
elif hasattr(other, "min_vals") and hasattr(other, "max_vals"):
min_vals = getattr(x_min_vals, op_str)(other.min_vals) # type: ignore
max_vals = getattr(x_max_vals, op_str)(other.max_vals) # type: ignore
else:
raise ValueError(
f"Not supported type for lazy repeat array computation: {type(other)}"
)
elif op_str in ["__sub__", "__mul__"]:
if is_acceptable_simple_type(other):
min_vals = getattr(x_min_vals, op_str)(other)
max_vals = getattr(x_max_vals, op_str)(other)
elif hasattr(other, "min_vals") and hasattr(other, "max_vals"):
min_min = getattr(x_min_vals.data, op_str)(other.min_vals.data) # type: ignore
min_max = getattr(x_min_vals.data, op_str)(other.max_vals.data) # type: ignore
max_min = getattr(x_max_vals.data, op_str)(other.min_vals.data) # type: ignore
max_max = getattr(x_max_vals.data, op_str)(other.max_vals.data) # type: ignore
_min_vals = np.minimum.reduce([min_min, min_max, max_min, max_max])
_max_vals = np.maximum.reduce([min_min, min_max, max_min, max_max])
min_vals = x_min_vals.copy()
min_vals.data = _min_vals
max_vals = x_max_vals.copy()
max_vals.data = _max_vals
else:
raise ValueError(
f"Not supported type for lazy repeat array computation: {type(other)}"
)
elif op_str in [
"__gt__",
"__lt__",
"__le__",
"__ge__",
"__eq__",
"__ne__",
"__xor__",
]:
min_vals = x_min_vals * 0
max_vals = (x_max_vals * 0) + 1
elif op_str == "sum":
min_vals = lazyrepeatarray(data=np.array(x_min_vals.sum(axis=None)), shape=())
max_vals = lazyrepeatarray(data=np.array(x_max_vals.sum(axis=None)), shape=())
elif op_str in ["__pos__", "sort"]:
min_vals = x_min_vals
max_vals = x_max_vals
elif op_str == "trace":
# NOTE: This is potentially expensive
min_val_data = x_min_vals.to_numpy().trace(*args, **kwargs)
min_vals = lazyrepeatarray(data=min_val_data, shape=min_val_data.shape)
max_val_data = x_max_vals.to_numpy().trace(*args, **kwargs)
max_vals = lazyrepeatarray(data=max_val_data, shape=max_val_data.shape)
elif op_str == "repeat":
dummy_res = np.empty(x_min_vals.shape).repeat(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data.min(), shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data.max(), shape=dummy_res.shape)
elif op_str == "min":
dummy_res = np.empty(x_min_vals.shape).min(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data, shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data, shape=dummy_res.shape)
elif op_str == "max":
dummy_res = np.empty(x_min_vals.shape).max(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data, shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data, shape=dummy_res.shape)
elif op_str == "ones_like":
min_vals = x_min_vals.ones_like(*args, **kwargs)
max_vals = x_max_vals.ones_like(*args, **kwargs)
elif op_str == "copy":
min_vals = x_min_vals.copy(*args, **kwargs) # type: ignore
max_vals = x_max_vals.copy(*args, **kwargs) # type: ignore
elif op_str == "__round__":
min_vals = lazyrepeatarray(
data=x_min_vals.data.round(*args, **kwargs), shape=x_min_vals.shape
)
max_vals = lazyrepeatarray(
data=x_max_vals.data.round(*args, **kwargs), shape=x_max_vals.shape
elif op_str == "__pow__":
if x_min_vals.data <= 0 <= x_max_vals.data:
# If data is in range [-5, 5], it's possible the minimum is 0 and not (-5)^2
min_data = min(0, (x_min_vals.data.__pow__(*args, **kwargs)).min())
else:
min_data = x_min_vals.data.__pow__(*args, **kwargs)
min_vals = lazyrepeatarray(data=min_data, shape=x_min_vals.shape)
max_vals = lazyrepeatarray(
data=x_max_vals.data.__pow__(*args, **kwargs), shape=x_max_vals.shape
)
elif op_str == "argsort":
min_vals = x_min_vals * 0
max_vals = x_max_vals * 0 + np.prod(x_max_vals.shape)
else:
raise ValueError(f"Invaid Operation for LazyRepeatArray: {op_str}")
return (min_vals, max_vals)
fixed error in _round_ op
# future
from __future__ import annotations
# stdlib
from typing import Any
from typing import Dict
from typing import Iterable
from typing import Optional
from typing import TYPE_CHECKING
from typing import Tuple
from typing import Union
# third party
import numpy as np
from scipy.ndimage.interpolation import rotate
# relative
from ..common.serde.serializable import serializable
from .broadcastable import is_broadcastable
from .config import DEFAULT_FLOAT_NUMPY_TYPE
from .config import DEFAULT_INT_NUMPY_TYPE
from .passthrough import is_acceptable_simple_type # type: ignore
from .smpc.utils import get_shape
if TYPE_CHECKING:
# relative
from .autodp.phi_tensor import PhiTensor
@serializable(recursive_serde=True)
class lazyrepeatarray:
"""
A class representing Differential Privacy metadata (minimum and maximum values) in a way that saves RAM/CPU.
We store large arrays of a single repeating value as a single tuple (shape) and a single value (int/float/etc)
e.g. np.array([8,8,8,8,8,8]) = lazyrepeatarray(data=8, shape=(6,))
Think like the opposite of np.broadcast, repeated values along an axis are collapsed but the .shape
attribute of the higher dimensional projection is retained for operations.
...
Attributes:
data: int/float
the actual value that is repeating.
shape: tuple
the shape that the fully expanded array would be.
Methods:
to_numpy():
expands the lazyrepeatarray into the full sized numpy array it was representing.
"""
__attr_allowlist__ = ["data", "shape"]
def __init__(self, data: np.ndarray, shape: Tuple[int, ...]) -> None:
"""
data: the raw data values without repeats
shape: the shape of 'data' if repeats were included
"""
# NOTE: all additional arguments are assumed to be broadcast if dims are shorter
# than that of data. Example: if data.shape == (2,3,4) and
# min_vals.shape == (2,3), then it's assumed that the full min_vals.shape is
# actually (2,3,4) where the last dim is simply copied.
# Example2: if data.shape == (2,3,4) and min_vals.shape == (2,1,4), then the
# middle dimension is supposed to be copied to be min_vals.shape == (2,3,4)
# if necessary. This is just to keep the memory footprint (and computation)
# as small as possible.
if isinstance(data, (bool, int, float)):
data = np.array(data)
if isinstance(data, int):
data = data.astype(DEFAULT_INT_NUMPY_TYPE) # type: ignore
if isinstance(data, float):
data = data.astype(DEFAULT_FLOAT_NUMPY_TYPE) # type: ignore
# verify broadcasting works on shapes
if -1 not in shape:
np.broadcast_shapes(data.shape, shape)
self.data = data
self.shape = shape
if isinstance(shape, Iterable):
for val in shape:
if val < 0:
raise ValueError(f"Invalid shape: {shape}")
def __getitem__(self, item: Union[str, int, slice]) -> lazyrepeatarray:
if self.data.shape == self.shape:
output = self.data[item]
return lazyrepeatarray(data=output, shape=output.shape)
elif self.data.size == 1:
test_arr = np.ones(self.shape)[
item
] # TODO: Is there a better way to determine output shape?
return lazyrepeatarray(data=self.data, shape=test_arr.shape)
else:
raise NotImplementedError
def __add__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
return self.__class__(data=self.data + other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
if self.data.shape == other.data.shape:
return self.__class__(data=self.data + other.data, shape=self.shape)
else:
return self.__class__(data=self.data + other.data, shape=self.shape)
def __sub__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
res = self.data - other
return self.__class__(data=res, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
if self.data.shape == other.data.shape:
return self.__class__(data=self.data - other.data, shape=self.shape)
else:
return self.__class__(data=self.data - other.data, shape=self.shape)
def __mul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
return self.__class__(data=self.data * other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
"Cannot broadcast arrays with shapes for LazyRepeatArray Multiplication:"
+ f" {self.shape} & {other.shape}"
)
else:
return self.__class__(data=self.data * other.data, shape=self.shape)
def __matmul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
new_shape = get_shape("__matmul__", self.shape, other.shape)
if self.data.size == 1:
return self.__class__(
data=np.matmul(np.ones(self.shape), other * self.data),
shape=new_shape,
)
return self.__class__(data=self.data.__matmul__(other), shape=new_shape)
if self.shape[-1] != other.shape[-2]:
raise Exception(
f"cannot matrix multiply tensors with different shapes: {self.shape} and {other.shape}"
)
result = self.to_numpy() @ other.to_numpy()
return self.__class__(data=result, shape=result.shape)
def __lshift__(self, other: Any) -> lazyrepeatarray:
if is_acceptable_simple_type(other):
return self.__class__(data=self.data << other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
return self.__class__(data=self.data << other.data, shape=self.shape)
def __rshift__(self, other: Any) -> lazyrepeatarray:
if is_acceptable_simple_type(other):
return self.__class__(data=self.data >> other, shape=self.shape)
if not is_broadcastable(self.shape, other.shape):
raise Exception(
f"Cannot broadcast arrays with shapes: {self.shape} & {other.shape}"
)
return self.__class__(data=self.data >> other.data, shape=self.shape)
def zeros_like(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(np.zeros_like(self.to_numpy(), *args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def __rtruediv__(self, other: Any) -> lazyrepeatarray:
res = (1 / self.data) * other
return lazyrepeatarray(data=res, shape=self.shape)
def __rmatmul__(self, other: Any) -> lazyrepeatarray:
"""
THIS MIGHT LOOK LIKE COPY-PASTED CODE!
Don't touch it. It's going to get more complicated.
"""
if is_acceptable_simple_type(other):
new_shape = get_shape("__matmul__", other.shape, self.shape)
if other.size == 1:
return self.__class__(
data=np.matmul(np.ones(other.shape), other * self.data),
shape=new_shape,
)
return self.__class__(
data=self.to_numpy().__rmatmul__(other), shape=new_shape
)
if other.shape[-1] != self.shape[0]:
raise Exception(
"cannot matrix multiply tensors with different shapes: {self.shape} and {other.shape}"
)
result = self.to_numpy().__rmatmul__(other.to_numpy())
return self.__class__(data=result, shape=result.shape)
def __pow__(self, exponent: int) -> lazyrepeatarray:
if exponent == 2:
return self * self
raise Exception("not sure how to do this yet")
def pad(self, pad_width: int, mode: str = "reflect") -> lazyrepeatarray:
if mode == "reflect":
new_shape = tuple([i + pad_width * 2 for i in self.shape])
if self.data.shape == self.shape:
return lazyrepeatarray(
data=np.pad(self.data, pad_width=pad_width, mode="reflect"),
shape=new_shape,
)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=new_shape)
else:
raise NotImplementedError
else:
raise NotImplementedError
def horizontal_flip(self) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=np.fliplr(self.data), shape=self.shape)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def vertical_flip(self) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=np.flipud(self.data), shape=self.shape)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def rotate(self, angle: int) -> lazyrepeatarray:
if self.data.shape == self.shape:
return lazyrepeatarray(data=rotate(self.data, angle), shape=self.shape)
elif self.data.size == 1:
# TODO: This is almost certainly incorrect
return lazyrepeatarray(data=self.data, shape=self.shape)
else:
raise NotImplementedError
def reshape(self, target_shape: Tuple) -> lazyrepeatarray:
# TODO: Can we reshape without creating new objects
if self.data.shape == self.shape:
return lazyrepeatarray(
data=self.data.reshape(target_shape), shape=target_shape
)
elif self.data.size == 1:
return lazyrepeatarray(data=self.data, shape=target_shape)
else:
if not np.broadcast_shapes(self.data.shape, target_shape):
raise NotImplementedError(
f"data= {self.data.shape}, shape: {self.shape}"
)
else:
return lazyrepeatarray(data=self.data, shape=target_shape)
def copy(self, order: Optional[str] = "K") -> lazyrepeatarray:
return self.__class__(data=self.data.copy(order=order), shape=self.shape)
@property
def size(self) -> int:
return np.prod(self.shape)
def sum(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(self.to_numpy().sum(*args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def ones_like(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
res = np.array(np.ones_like(self.to_numpy(), *args, **kwargs))
return lazyrepeatarray(data=res, shape=res.shape)
def __eq__(self, other: Any) -> lazyrepeatarray: # type: ignore
if isinstance(other, lazyrepeatarray):
if self.shape == other.shape:
return lazyrepeatarray(data=self.data == other.data, shape=self.shape)
else:
result = (self.to_numpy() == other.to_numpy()).all()
return lazyrepeatarray(data=np.array([result]), shape=result.shape)
if isinstance(other, np.ndarray):
try:
_ = np.broadcast_shapes(self.shape, other.shape)
result = (self.to_numpy() == other).all()
return lazyrepeatarray(data=np.array([result]), shape=other.shape)
except Exception as e:
print(
"Failed to compare lazyrepeatarray with "
+ f"{self.shape} == {other.shape} to numpy by broadcasting. {e}"
)
raise e
return self == other
def __le__(self, other: Any) -> lazyrepeatarray: # type: ignore
if isinstance(other, lazyrepeatarray):
if self.shape == other.shape:
return lazyrepeatarray(data=self.data <= other.data, shape=self.shape)
else:
result = (self.to_numpy() <= other.to_numpy()).all()
return lazyrepeatarray(data=np.array([result]), shape=result.shape)
if isinstance(other, np.ndarray):
try:
_ = np.broadcast_shapes(self.shape, other.shape)
result = (self.to_numpy() <= other).all()
return lazyrepeatarray(data=np.array([result]), shape=other.shape)
except Exception as e:
print(
"Failed to compare lazyrepeatarray with "
+ f"{self.shape} == {other.shape} to numpy by broadcasting. {e}"
)
raise e
return self <= other
def concatenate(
self, other: lazyrepeatarray, *args: Any, **kwargs: Any
) -> lazyrepeatarray:
if not isinstance(other, lazyrepeatarray):
raise NotImplementedError
dummy_res = np.concatenate(
(np.empty(self.shape), np.empty(other.shape)), *args, **kwargs
)
return lazyrepeatarray(data=self.data, shape=dummy_res.shape)
@property
def dtype(self) -> np.dtype:
return self.data.dtype
def astype(self, np_type: np.dtype) -> lazyrepeatarray:
return self.__class__(self.data.astype(np_type), self.shape)
def to_numpy(self) -> np.ndarray:
return np.broadcast_to(self.data, self.shape)
def __repr__(self) -> str:
return f"<lazyrepeatarray data: {self.data} -> shape: {self.shape}>"
def __bool__(self) -> bool:
return self.data.__bool__()
def all(self) -> bool:
return self.data.all()
def any(self) -> bool:
return self.data.any()
def transpose(self, *args: Any, **kwargs: Any) -> lazyrepeatarray:
dummy_res = self.to_numpy().transpose(*args, **kwargs)
return lazyrepeatarray(
data=self.data.transpose(*args, **kwargs), shape=dummy_res.shape
)
# As the min and max values calculation is the same regardless of the tensor type,
# We centralize this method as baseline for calculation for min/max values
def compute_min_max(
x_min_vals: lazyrepeatarray,
x_max_vals: lazyrepeatarray,
other: Union[PhiTensor, int, float, np.ndarray],
op_str: str,
*args: Any,
**kwargs: Dict[Any, Any],
) -> Tuple[lazyrepeatarray, lazyrepeatarray]:
min_vals: lazyrepeatarray
max_vals: lazyrepeatarray
if op_str in ["__add__", "__matmul__", "__rmatmul__", "__lshift__", "__rshift__"]:
if is_acceptable_simple_type(other):
min_vals = getattr(x_min_vals, op_str)(other)
max_vals = getattr(x_max_vals, op_str)(other)
elif hasattr(other, "min_vals") and hasattr(other, "max_vals"):
min_vals = getattr(x_min_vals, op_str)(other.min_vals) # type: ignore
max_vals = getattr(x_max_vals, op_str)(other.max_vals) # type: ignore
else:
raise ValueError(
f"Not supported type for lazy repeat array computation: {type(other)}"
)
elif op_str in ["__sub__", "__mul__"]:
if is_acceptable_simple_type(other):
min_vals = getattr(x_min_vals, op_str)(other)
max_vals = getattr(x_max_vals, op_str)(other)
elif hasattr(other, "min_vals") and hasattr(other, "max_vals"):
min_min = getattr(x_min_vals.data, op_str)(other.min_vals.data) # type: ignore
min_max = getattr(x_min_vals.data, op_str)(other.max_vals.data) # type: ignore
max_min = getattr(x_max_vals.data, op_str)(other.min_vals.data) # type: ignore
max_max = getattr(x_max_vals.data, op_str)(other.max_vals.data) # type: ignore
_min_vals = np.minimum.reduce([min_min, min_max, max_min, max_max])
_max_vals = np.maximum.reduce([min_min, min_max, max_min, max_max])
min_vals = x_min_vals.copy()
min_vals.data = _min_vals
max_vals = x_max_vals.copy()
max_vals.data = _max_vals
else:
raise ValueError(
f"Not supported type for lazy repeat array computation: {type(other)}"
)
elif op_str in [
"__gt__",
"__lt__",
"__le__",
"__ge__",
"__eq__",
"__ne__",
"__xor__",
]:
min_vals = x_min_vals * 0
max_vals = (x_max_vals * 0) + 1
elif op_str == "sum":
min_vals = lazyrepeatarray(data=np.array(x_min_vals.sum(axis=None)), shape=())
max_vals = lazyrepeatarray(data=np.array(x_max_vals.sum(axis=None)), shape=())
elif op_str in ["__pos__", "sort"]:
min_vals = x_min_vals
max_vals = x_max_vals
elif op_str == "trace":
# NOTE: This is potentially expensive
min_val_data = x_min_vals.to_numpy().trace(*args, **kwargs)
min_vals = lazyrepeatarray(data=min_val_data, shape=min_val_data.shape)
max_val_data = x_max_vals.to_numpy().trace(*args, **kwargs)
max_vals = lazyrepeatarray(data=max_val_data, shape=max_val_data.shape)
elif op_str == "repeat":
dummy_res = np.empty(x_min_vals.shape).repeat(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data.min(), shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data.max(), shape=dummy_res.shape)
elif op_str == "min":
dummy_res = np.empty(x_min_vals.shape).min(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data, shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data, shape=dummy_res.shape)
elif op_str == "max":
dummy_res = np.empty(x_min_vals.shape).max(*args, **kwargs)
min_vals = lazyrepeatarray(data=x_min_vals.data, shape=dummy_res.shape)
max_vals = lazyrepeatarray(data=x_max_vals.data, shape=dummy_res.shape)
elif op_str == "ones_like":
min_vals = x_min_vals.ones_like(*args, **kwargs)
max_vals = x_max_vals.ones_like(*args, **kwargs)
elif op_str == "copy":
min_vals = x_min_vals.copy(*args, **kwargs) # type: ignore
max_vals = x_max_vals.copy(*args, **kwargs) # type: ignore
elif op_str == "__round__":
min_vals = lazyrepeatarray(
data=x_min_vals.data.round(*args, **kwargs), shape=x_min_vals.shape
)
max_vals = lazyrepeatarray(
data=x_max_vals.data.round(*args, **kwargs), shape=x_max_vals.shape
)
elif op_str == "__pow__":
if x_min_vals.data <= 0 <= x_max_vals.data:
# If data is in range [-5, 5], it's possible the minimum is 0 and not (-5)^2
min_data = min(0, (x_min_vals.data.__pow__(*args, **kwargs)).min())
else:
min_data = x_min_vals.data.__pow__(*args, **kwargs)
min_vals = lazyrepeatarray(data=min_data, shape=x_min_vals.shape)
max_vals = lazyrepeatarray(
data=x_max_vals.data.__pow__(*args, **kwargs), shape=x_max_vals.shape
)
elif op_str == "argsort":
min_vals = x_min_vals * 0
max_vals = x_max_vals * 0 + np.prod(x_max_vals.shape)
else:
raise ValueError(f"Invaid Operation for LazyRepeatArray: {op_str}")
return (min_vals, max_vals)
|
# Copyright (C) 2010-2012 Cuckoo Sandbox Developers.
# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org
# See the file 'docs/LICENSE' for copying permission.
import os
import logging
import ConfigParser
from lib.cuckoo.common.exceptions import CuckooMachineError
from lib.cuckoo.common.constants import CUCKOO_ROOT
log = logging.getLogger(__name__)
class Dictionary(dict):
"""Cuckoo custom dict."""
def __getattr__(self, key):
return self.get(key, None)
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class MachineManager(object):
"""Base abstract class for analysis machine manager."""
def __init__(self):
self.module_name = ""
self.options = None
self.machines = []
def set_options(self, options):
"""Set machine manager options.
@param options: machine manager options dict.
"""
self.options = options
def initialize(self, module_name):
"""Read and load machines configuration, try to check the configuration.
@param module_name: module name.
"""
# Load.
self._initialize(module_name)
# Run initialization checks.
self._initialize_check()
def _initialize(self, module_name):
"""Read configuration.
@param module_name: module name.
"""
self.module_name = module_name
mmanager_opts = self.options.get(module_name)
for machine_id in mmanager_opts["machines"].strip().split(","):
try:
machine_opts = self.options.get(machine_id)
machine = Dictionary()
machine.id = machine_id
machine.label = machine_opts["label"]
machine.platform = machine_opts["platform"]
machine.ip = machine_opts["ip"]
machine.locked = False
self.machines.append(machine)
except AttributeError:
log.warning("Configuration details about machine %s are missing. Continue" % machine_id)
continue
def _initialize_check(self):
"""Runs all checks when a machine manager is initialized.
@note: in machine manager modules you may override or superclass this method.
@raise CuckooMachineError: if a misconfiguration or a unkown vm state is found.
"""
# Checks if machines configured are really available.
try:
configured_vm = self._list()
for machine in self.machines:
if machine.label not in configured_vm:
raise CuckooMachineError("Configured machine %s was not detected or it's not in proper state" % machine.label)
except NotImplementedError:
pass
def availables(self):
"""How many machines are free.
@return: free machines count.
"""
count = 0
for machine in self.machines:
if not machine.locked:
count += 1
return count
def acquire(self, machine_id=None, platform=None):
"""Acquire a machine to start analysis.
@param machine_id: machine ID.
@param platform: machine platform.
@return: machine or None.
"""
if machine_id:
for machine in self.machines:
if machine.id == machine_id and not machine.locked:
machine.locked = True
return machine
elif platform:
for machine in self.machines:
if machine.platform == platform and not machine.locked:
machine.locked = True
return machine
else:
for machine in self.machines:
if not machine.locked:
machine.locked = True
return machine
return None
def release(self, label=None):
"""Release a machine.
@param label: machine name.
"""
if label:
for machine in self.machines:
if machine.label == label:
machine.locked = False
def running(self):
"""Returns running virutal machines.
@return: running virtual machines list.
"""
return [m for m in self.machines if m.locked]
def start(self, label=None):
"""Start a machine.
@param label: machine name.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
def stop(self, label=None):
"""Stop a machine.
@param label: machine name.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
def _list(self):
"""Lists virtual machines configured.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Processing(object):
"""Base abstract class for processing module."""
def __init__(self):
self.analysis_path = ""
self.logs_path = ""
def set_path(self, analysis_path):
"""Set paths.
@param analysis_path: analysis folder path.
"""
self.analysis_path = analysis_path
self.log_path = os.path.join(self.analysis_path, "analysis.log")
self.conf_path = os.path.join(self.analysis_path, "analysis.conf")
self.file_path = os.path.realpath(os.path.join(self.analysis_path, "binary"))
self.dropped_path = os.path.join(self.analysis_path, "files")
self.logs_path = os.path.join(self.analysis_path, "logs")
self.shots_path = os.path.join(self.analysis_path, "shots")
self.pcap_path = os.path.join(self.analysis_path, "dump.pcap")
def run(self):
"""Start processing.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Signature(object):
"""Base abstract class for signature."""
name = ""
description = ""
severity = 1
categories = []
authors = []
references = []
alert = False
enabled = True
def __init__(self):
self.data = []
def run(self, results=None):
"""Start signature processing.
@param results: analysis results.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Report(object):
"""Base abstract class for reporting module."""
def __init__(self):
self.analysis_path = ""
self.reports_path = ""
self.options = None
def set_path(self, analysis_path):
"""Set analysis folder path.
@param analysis_path: analysis folder path.
"""
self.analysis_path = analysis_path
self.conf_path = os.path.join(self.analysis_path, "analysis.conf")
self.reports_path = os.path.join(self.analysis_path, "reports")
if not os.path.exists(self.reports_path):
os.mkdir(self.reports_path)
def set_options(self, options):
"""Set report options.
@param options: report options dict.
"""
self.options = options
def run(self):
"""Start report processing.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
Docstring minor fix
# Copyright (C) 2010-2012 Cuckoo Sandbox Developers.
# This file is part of Cuckoo Sandbox - http://www.cuckoosandbox.org
# See the file 'docs/LICENSE' for copying permission.
import os
import logging
import ConfigParser
from lib.cuckoo.common.exceptions import CuckooMachineError
from lib.cuckoo.common.constants import CUCKOO_ROOT
log = logging.getLogger(__name__)
class Dictionary(dict):
"""Cuckoo custom dict."""
def __getattr__(self, key):
return self.get(key, None)
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class MachineManager(object):
"""Base abstract class for analysis machine manager."""
def __init__(self):
self.module_name = ""
self.options = None
self.machines = []
def set_options(self, options):
"""Set machine manager options.
@param options: machine manager options dict.
"""
self.options = options
def initialize(self, module_name):
"""Read and load machines configuration, try to check the configuration.
@param module_name: module name.
"""
# Load.
self._initialize(module_name)
# Run initialization checks.
self._initialize_check()
def _initialize(self, module_name):
"""Read configuration.
@param module_name: module name.
"""
self.module_name = module_name
mmanager_opts = self.options.get(module_name)
for machine_id in mmanager_opts["machines"].strip().split(","):
try:
machine_opts = self.options.get(machine_id)
machine = Dictionary()
machine.id = machine_id
machine.label = machine_opts["label"]
machine.platform = machine_opts["platform"]
machine.ip = machine_opts["ip"]
machine.locked = False
self.machines.append(machine)
except AttributeError:
log.warning("Configuration details about machine %s are missing. Continue" % machine_id)
continue
def _initialize_check(self):
"""Runs checks against virtualization software when a machine manager is initialized.
@note: in machine manager modules you may override or superclass this method.
@raise CuckooMachineError: if a misconfiguration or a unkown vm state is found.
"""
try:
configured_vm = self._list()
for machine in self.machines:
if machine.label not in configured_vm:
raise CuckooMachineError("Configured machine %s was not detected or it's not in proper state" % machine.label)
except NotImplementedError:
pass
def availables(self):
"""How many machines are free.
@return: free machines count.
"""
count = 0
for machine in self.machines:
if not machine.locked:
count += 1
return count
def acquire(self, machine_id=None, platform=None):
"""Acquire a machine to start analysis.
@param machine_id: machine ID.
@param platform: machine platform.
@return: machine or None.
"""
if machine_id:
for machine in self.machines:
if machine.id == machine_id and not machine.locked:
machine.locked = True
return machine
elif platform:
for machine in self.machines:
if machine.platform == platform and not machine.locked:
machine.locked = True
return machine
else:
for machine in self.machines:
if not machine.locked:
machine.locked = True
return machine
return None
def release(self, label=None):
"""Release a machine.
@param label: machine name.
"""
if label:
for machine in self.machines:
if machine.label == label:
machine.locked = False
def running(self):
"""Returns running virutal machines.
@return: running virtual machines list.
"""
return [m for m in self.machines if m.locked]
def start(self, label=None):
"""Start a machine.
@param label: machine name.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
def stop(self, label=None):
"""Stop a machine.
@param label: machine name.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
def _list(self):
"""Lists virtual machines configured.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Processing(object):
"""Base abstract class for processing module."""
def __init__(self):
self.analysis_path = ""
self.logs_path = ""
def set_path(self, analysis_path):
"""Set paths.
@param analysis_path: analysis folder path.
"""
self.analysis_path = analysis_path
self.log_path = os.path.join(self.analysis_path, "analysis.log")
self.conf_path = os.path.join(self.analysis_path, "analysis.conf")
self.file_path = os.path.realpath(os.path.join(self.analysis_path, "binary"))
self.dropped_path = os.path.join(self.analysis_path, "files")
self.logs_path = os.path.join(self.analysis_path, "logs")
self.shots_path = os.path.join(self.analysis_path, "shots")
self.pcap_path = os.path.join(self.analysis_path, "dump.pcap")
def run(self):
"""Start processing.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Signature(object):
"""Base abstract class for signature."""
name = ""
description = ""
severity = 1
categories = []
authors = []
references = []
alert = False
enabled = True
def __init__(self):
self.data = []
def run(self, results=None):
"""Start signature processing.
@param results: analysis results.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
class Report(object):
"""Base abstract class for reporting module."""
def __init__(self):
self.analysis_path = ""
self.reports_path = ""
self.options = None
def set_path(self, analysis_path):
"""Set analysis folder path.
@param analysis_path: analysis folder path.
"""
self.analysis_path = analysis_path
self.conf_path = os.path.join(self.analysis_path, "analysis.conf")
self.reports_path = os.path.join(self.analysis_path, "reports")
if not os.path.exists(self.reports_path):
os.mkdir(self.reports_path)
def set_options(self, options):
"""Set report options.
@param options: report options dict.
"""
self.options = options
def run(self):
"""Start report processing.
@raise NotImplementedError: this method is abstract.
"""
raise NotImplementedError
|
from collections import OrderedDict
from copy import deepcopy
from django.core.mail import mail_admins
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied, ObjectDoesNotExist
from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import Group
from django.contrib.auth.decorators import login_required
from django.db import IntegrityError
from django.forms.models import modelformset_factory
from django.http import HttpResponseForbidden, JsonResponse, HttpResponse, HttpResponseBadRequest
from django.db.models import FieldDoesNotExist
from django.shortcuts import render, redirect, get_object_or_404
from django.template import loader, RequestContext, Context
from django.template.context_processors import csrf
from .forms import *
from .filters import *
from .models import Summoner, Monster, Fusion, Building, BuildingInstance, MonsterInstance, MonsterPiece, TeamGroup, Team, RuneInstance, RuneCraftInstance, Storage
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
if User.objects.filter(username=form.cleaned_data['username']).exists():
form.add_error('username', 'Username already taken')
else:
new_user = None
new_summoner = None
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_user.groups.add(Group.objects.get(name='Summoners'))
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile_default', profile_name=user.username)
except IntegrityError as e:
if new_user is not None:
new_user.delete()
if new_summoner is not None:
new_summoner.delete()
form.add_error(None, 'There was an issue completing your registration. Please try again.')
mail_admins(
subject='Error during user registration',
message='{}'.format(e),
fail_silently=True,
)
context = {'form': form}
return render(request, 'herders/register.html', context)
@login_required
def change_username(request):
user = request.user
form = CrispyChangeUsernameForm(request.POST or None)
context = {
'form': form,
}
if request.method == 'POST' and form.is_valid():
try:
user.username = form.cleaned_data['username']
user.save()
return redirect('username_change_complete')
except IntegrityError:
form.add_error('username', 'Username already taken')
return render(request, 'registration/change_username.html', context)
def change_username_complete(request):
return render(request, 'registration/change_username_complete.html')
@login_required
def profile_delete(request, profile_name):
user = request.user
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = DeleteProfileForm(request.POST or None)
form.helper.form_action = reverse('herders:profile_delete', kwargs={'profile_name': profile_name})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
logout(request)
user.delete()
messages.warning(request, 'Your profile has been permanently deleted.')
return redirect('news:latest_news')
return render(request, 'herders/profile/profile_delete.html', context)
else:
return HttpResponseForbidden("You don't own this profile")
@login_required
def following(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_following', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'view': 'following',
'return_path': return_path,
}
return render(request, 'herders/profile/following/list.html', context)
@login_required
def follow_add(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
new_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.add(new_follower)
messages.info(request, 'Now following %s' % new_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required
def follow_remove(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
removed_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.remove(removed_follower)
messages.info(request, 'Unfollowed %s' % removed_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
def profile(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_filter_form = FilterMonsterInstanceForm(auto_id='id_filter_%s')
monster_filter_form.helper.form_action = reverse('herders:monster_inventory', kwargs={'profile_name': profile_name})
context = {
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'monster_filter_form': monster_filter_form,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_inventory/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def buildings(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'summoner': summoner,
'is_owner': is_owner,
'profile_name': profile_name,
}
return render(request, 'herders/profile/buildings/base.html', context)
def buildings_inventory(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
all_buildings = Building.objects.all().order_by('name')
building_data = []
total_glory_cost = 0
spent_glory = 0
total_guild_cost = 0
spent_guild = 0
for b in all_buildings:
bldg_data = _building_data(summoner, b)
if b.area == Building.AREA_GENERAL:
total_glory_cost += sum(b.upgrade_cost)
spent_glory += bldg_data['spent_upgrade_cost']
elif b.area == Building.AREA_GUILD:
total_guild_cost += sum(b.upgrade_cost)
spent_guild += bldg_data['spent_upgrade_cost']
building_data.append(bldg_data)
context = {
'is_owner': is_owner,
'summoner': summoner,
'profile_name': profile_name,
'buildings': building_data,
'total_glory_cost': total_glory_cost,
'spent_glory': spent_glory,
'glory_progress': float(spent_glory) / total_glory_cost * 100,
'total_guild_cost': total_guild_cost,
'spent_guild': spent_guild,
'guild_progress': float(spent_guild) / total_guild_cost * 100,
}
return render(request, 'herders/profile/buildings/inventory.html', context)
@login_required
def building_edit(request, profile_name, building_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
base_building = get_object_or_404(Building, pk=building_id)
try:
owned_instance = BuildingInstance.objects.get(owner=summoner, building=base_building)
except BuildingInstance.DoesNotExist:
owned_instance = BuildingInstance.objects.create(owner=summoner, level=0, building=base_building)
form = EditBuildingForm(request.POST or None, instance=owned_instance)
form.helper.form_action = reverse('herders:building_edit', kwargs={'profile_name': profile_name, 'building_id': building_id})
context = {
'form': form,
}
context.update(csrf(request))
if is_owner:
if request.method == 'POST' and form.is_valid():
owned_instance = form.save()
messages.success(request,'Updated ' + owned_instance.building.name + ' to level ' + str(owned_instance.level))
response_data = {
'code': 'success',
}
else:
template = loader.get_template('herders/profile/buildings/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
def _building_data(summoner, building):
percent_stat = building.affected_stat in Building.PERCENT_STATS
total_upgrade_cost = sum(building.upgrade_cost)
if building.area == Building.AREA_GENERAL:
currency = 'glory_points.png'
else:
currency = 'guild_points.png'
try:
instance = BuildingInstance.objects.get(owner=summoner, building=building)
if instance.level > 0:
stat_bonus = building.stat_bonus[instance.level - 1]
else:
stat_bonus = 0
remaining_upgrade_cost = instance.remaining_upgrade_cost()
except BuildingInstance.DoesNotExist:
instance = None
stat_bonus = 0
remaining_upgrade_cost = total_upgrade_cost
except BuildingInstance.MultipleObjectsReturned:
# Should only be 1 ever - use the first and delete the others.
instance = BuildingInstance.objects.filter(owner=summoner, building=building).first()
BuildingInstance.objects.filter(owner=summoner, building=building).exclude(pk=instance.pk).delete()
return _building_data(summoner, building)
return {
'base': building,
'instance': instance,
'stat_bonus': stat_bonus,
'percent_stat': percent_stat,
'spent_upgrade_cost': total_upgrade_cost - remaining_upgrade_cost,
'total_upgrade_cost': total_upgrade_cost,
'upgrade_progress': float(total_upgrade_cost - remaining_upgrade_cost) / total_upgrade_cost * 100,
'currency': currency,
}
def monster_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to ourself without the view mode or box grouping
if view_mode:
request.session['profile_view_mode'] = view_mode.lower()
if box_grouping:
request.session['profile_group_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Profile view mode cookie set")
view_mode = request.session.get('profile_view_mode', 'box').lower()
box_grouping = request.session.get('profile_group_method', 'grade').lower()
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
monster_queryset = MonsterInstance.objects.filter(owner=summoner).select_related('monster', 'monster__awakens_from')
total_monsters = monster_queryset.count()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if view_mode == 'list':
monster_queryset = monster_queryset.select_related(
'monster__leader_skill', 'monster__awakens_to'
).prefetch_related(
'monster__skills', 'runeinstance_set', 'team_set', 'team_leader', 'tags'
)
form = FilterMonsterInstanceForm(request.POST or None, auto_id='id_filter_%s')
if form.is_valid():
monster_filter = MonsterInstanceFilter(form.cleaned_data, queryset=monster_queryset)
else:
monster_filter = MonsterInstanceFilter(queryset=monster_queryset)
filtered_count = monster_filter.qs.count()
context = {
'monsters': monster_filter.qs,
'total_count': total_monsters,
'filtered_count': filtered_count,
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'pieces':
context['monster_pieces'] = MonsterPiece.objects.filter(owner=summoner).select_related('monster')
template = 'herders/profile/monster_inventory/summoning_pieces.html'
elif view_mode == 'list':
template = 'herders/profile/monster_inventory/list.html'
else:
# Group up the filtered monsters
monster_stable = OrderedDict()
if box_grouping == 'grade' or box_grouping == 'stars':
monster_stable['6*'] = monster_filter.qs.filter(stars=6).order_by('-level', 'monster__element', 'monster__name')
monster_stable['5*'] = monster_filter.qs.filter(stars=5).order_by('-level', 'monster__element', 'monster__name')
monster_stable['4*'] = monster_filter.qs.filter(stars=4).order_by('-level', 'monster__element', 'monster__name')
monster_stable['3*'] = monster_filter.qs.filter(stars=3).order_by('-level', 'monster__element', 'monster__name')
monster_stable['2*'] = monster_filter.qs.filter(stars=2).order_by('-level', 'monster__element', 'monster__name')
monster_stable['1*'] = monster_filter.qs.filter(stars=1).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'natural_stars':
nat5 = (Q(monster__base_stars=6) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=5) & Q(monster__is_awakened=False))
nat4 = (Q(monster__base_stars=5) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=4) & Q(monster__is_awakened=False))
nat3 = (Q(monster__base_stars=4) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=3) & Q(monster__is_awakened=False))
nat2 = (Q(monster__base_stars=3) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=2) & Q(monster__is_awakened=False))
nat1 = (Q(monster__base_stars=2) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=1) & Q(monster__is_awakened=False))
monster_stable['Natural 5*'] = monster_filter.qs.filter(nat5).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 4*'] = monster_filter.qs.filter(nat4).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 3*'] = monster_filter.qs.filter(nat3).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 2*'] = monster_filter.qs.filter(nat2).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 1*'] = monster_filter.qs.filter(nat1).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'level':
monster_stable['40'] = monster_filter.qs.filter(level=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['39-31'] = monster_filter.qs.filter(level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['30-21'] = monster_filter.qs.filter(level__gt=20).filter(level__lte=30).order_by( '-level', '-stars', 'monster__element', 'monster__name')
monster_stable['20-11'] = monster_filter.qs.filter(level__gt=10).filter(level__lte=20).order_by( '-level', '-stars', 'monster__element', 'monster__name')
monster_stable['10-1'] = monster_filter.qs.filter(level__lte=10).order_by('-level', '-stars', 'monster__element', 'monster__name')
elif box_grouping == 'element' or box_grouping == 'attribute':
monster_stable['water'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'archetype':
monster_stable['attack'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_ATTACK).order_by('-stars', '-level', 'monster__name')
monster_stable['hp'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_HP).order_by('-stars', '-level', 'monster__name')
monster_stable['support'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_SUPPORT).order_by('-stars', '-level', 'monster__name')
monster_stable['defense'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_DEFENSE).order_by('-stars', '-level', 'monster__name')
monster_stable['material'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_MATERIAL).order_by('-stars', '-level', 'monster__name')
monster_stable['other'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_NONE).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'priority':
monster_stable['High'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_HIGH).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Medium'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_MED).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Low'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_LOW).order_by('-level', 'monster__element', 'monster__name')
monster_stable['None'] = monster_filter.qs.select_related('monster').filter(owner=summoner).filter(Q(priority=None) | Q(priority=0)).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'family':
for mon in monster_filter.qs:
if mon.monster.is_awakened and mon.monster.awakens_from is not None:
family_name = mon.monster.awakens_from.name
else:
family_name = mon.monster.name
if family_name not in monster_stable:
monster_stable[family_name] = []
monster_stable[family_name].append(mon)
# Sort ordered dict alphabetically by family name
monster_stable = OrderedDict(sorted(monster_stable.items(), key=lambda family:family[0]))
else:
return HttpResponseBadRequest('Invalid sort method')
context['monster_stable'] = monster_stable
context['box_grouping'] = box_grouping.replace('_', ' ')
template = 'herders/profile/monster_inventory/box.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.info(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def storage(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
craft_mats = []
essence_mats = []
for field_name in Storage.ESSENCE_FIELDS:
essence_mats.append({
'name': summoner.storage._meta.get_field(field_name).help_text,
'field_name': field_name,
'element': field_name.split('_')[0],
'qty': getattr(summoner.storage, field_name)
})
for field_name in Storage.CRAFT_FIELDS:
craft_mats.append({
'name': summoner.storage._meta.get_field(field_name).help_text,
'field_name': field_name,
'qty': getattr(summoner.storage, field_name)
})
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'essence_mats': essence_mats,
'craft_mats': craft_mats,
}
return render(request, 'herders/profile/storage/base.html', context=context)
else:
return HttpResponseForbidden()
@login_required
def storage_update(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner and request.POST:
field_name = request.POST.get('name')
try:
new_value = int(request.POST.get('value'))
except ValueError:
return HttpResponseBadRequest('Invalid Entry')
essence_size = None
if 'essence' in field_name:
# Split the actual field name off from the size
try:
field_name, essence_size = field_name.split('.')
size_map = {
'low': Storage.ESSENCE_LOW,
'mid': Storage.ESSENCE_MID,
'high': Storage.ESSENCE_HIGH,
}
essence_size = size_map[essence_size]
except (ValueError, KeyError):
return HttpResponseBadRequest()
try:
Storage._meta.get_field(field_name)
except FieldDoesNotExist:
return HttpResponseBadRequest()
else:
if essence_size is not None:
# Get a copy of the size array and set the correct index to new value
essence_list = getattr(summoner.storage, field_name)
essence_list[essence_size] = new_value
new_value = essence_list
setattr(summoner.storage, field_name, new_value)
summoner.storage.save()
return HttpResponse()
else:
return HttpResponseForbidden()
@login_required
def quick_fodder_menu(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
template = loader.get_template('herders/profile/monster_inventory/quick_fodder_menu.html')
response_data = {
'code': 'success',
'html': template.render(),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = AddMonsterInstanceForm(request.POST or None)
else:
form = AddMonsterInstanceForm(initial=request.GET.dict())
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
context = {
'profile_name': profile_name,
'instance': new_monster,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_monster.pk.hex,
'html': template.render(context),
}
else:
form.helper.form_action = reverse('herders:monster_instance_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/add_monster_form.html')
# Return form filled in and errors shown
context = {'add_monster_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
new_monster = MonsterInstance.objects.create(owner=summoner, monster=monster_to_add, stars=int(stars), level=int(level), fodder=True, notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_bulk_add(request, profile_name):
return_path = reverse('herders:profile_default', kwargs={'profile_name': profile_name})
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
BulkAddFormset = modelformset_factory(MonsterInstance, form=BulkAddMonsterInstanceForm, formset=BulkAddMonsterInstanceFormset, extra=5, max_num=50)
if request.method == 'POST':
formset = BulkAddFormset(request.POST)
else:
formset = BulkAddFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'bulk_add_formset_action': request.path + '?next=' + return_path,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if formset.is_valid():
new_instances = formset.save(commit=False)
for new_instance in new_instances:
try:
if new_instance.monster:
new_instance.owner = summoner
if new_instance.monster.archetype == Monster.TYPE_MATERIAL:
new_instance.priority = MonsterInstance.PRIORITY_DONE
new_instance.save()
messages.success(request, 'Added %s to your collection.' % new_instance)
except ObjectDoesNotExist:
# Blank form, don't care
pass
return redirect(return_path)
else:
raise PermissionDenied("Trying to bulk add to profile you don't own")
context['bulk_add_formset'] = formset
return render(request, 'herders/profile/monster_inventory/bulk_add_form.html', context)
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
request.path
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'view': 'profile',
}
try:
context['instance'] = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return render(request, 'herders/profile/monster_view/not_found.html', context)
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_view/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def monster_instance_view_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
instance_runes = [
instance.runeinstance_set.filter(slot=1).first(),
instance.runeinstance_set.filter(slot=2).first(),
instance.runeinstance_set.filter(slot=3).first(),
instance.runeinstance_set.filter(slot=4).first(),
instance.runeinstance_set.filter(slot=5).first(),
instance.runeinstance_set.filter(slot=6).first(),
]
context = {
'runes': instance_runes,
'instance': instance,
'profile_name': profile_name,
'is_owner': is_owner,
}
return render(request, 'herders/profile/monster_view/runes.html', context)
def monster_instance_view_stats(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
context = {
'instance': instance,
'max_stats': instance.get_max_level_stats(),
'bldg_stats': instance.get_building_stats(),
'guild_stats': instance.get_building_stats(Building.AREA_GUILD),
}
return render(request, 'herders/profile/monster_view/stats.html', context)
def monster_instance_view_skills(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
context = {
'instance': instance,
'skills': skills,
}
return render(request, 'herders/profile/monster_view/skills.html', context)
def monster_instance_view_info(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
if instance.monster.is_awakened:
ingredient_in = instance.monster.fusion_set.all()
elif instance.monster.can_awaken and instance.monster.awakens_to:
ingredient_in = instance.monster.awakens_to.fusion_set.all()
else:
ingredient_in = []
if instance.monster.is_awakened and instance.monster.awakens_from:
product_of = instance.monster.awakens_from.product.first()
elif instance.monster.can_awaken:
product_of = instance.monster.product.first()
else:
product_of = []
context = {
'instance': instance,
'profile_name': profile_name,
'fusion_ingredient_in': ingredient_in,
'fusion_product_of': product_of,
'skillups': instance.get_possible_skillups(),
}
return render(request, 'herders/profile/monster_view/notes_info.html', context)
@login_required()
def monster_instance_remove_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
try:
instance = MonsterInstance.objects.get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
else:
for rune in instance.runeinstance_set.all():
rune.assigned_to = None
rune.save()
instance.save()
messages.success(request, 'Removed all runes from ' + str(instance))
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
instance = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
form = EditMonsterInstanceForm(request.POST or None, instance=instance)
form.helper.form_action = request.path
if len(skills) >= 1 and skills[0]['skill'].max_level > 1:
form.helper['skill_1_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_1", data_skill_field=form['skill_1_level'].auto_id),
)
form.helper['skill_1_level'].wrap(Field, min=1, max=skills[0]['skill'].max_level)
form.fields['skill_1_level'].label = skills[0]['skill'].name + " Level"
else:
form.helper['skill_1_level'].wrap(Div, css_class="hidden")
if len(skills) >= 2 and skills[1]['skill'].max_level > 1:
form.helper['skill_2_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_2", data_skill_field=form['skill_2_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_2_level'].wrap(Field, min=1, max=skills[1]['skill'].max_level)
form.fields['skill_2_level'].label = skills[1]['skill'].name + " Level"
else:
form.helper['skill_2_level'].wrap(Div, css_class="hidden")
if len(skills) >= 3 and skills[2]['skill'].max_level > 1:
form.helper['skill_3_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_3", data_skill_field=form['skill_3_level'].auto_id),
min=1,
max=skills[2]['skill'].max_level,
)
form.helper['skill_3_level'].wrap(Field, min=1, max=skills[2]['skill'].max_level)
form.fields['skill_3_level'].label = skills[2]['skill'].name + " Level"
else:
form.helper['skill_3_level'].wrap(Div, css_class="hidden")
if len(skills) >= 4 and skills[3]['skill'].max_level > 1:
form.helper['skill_4_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_4", data_skill_field=form['skill_4_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_4_level'].wrap(Field, min=1, max=skills[3]['skill'].max_level)
form.fields['skill_4_level'].label = skills[3]['skill'].name + " Level"
else:
form.helper['skill_4_level'].wrap(Div, css_class="hidden")
if not instance.monster.homunculus:
form.helper['custom_name'].wrap(Div, css_class="hidden")
if request.method == 'POST' and form.is_valid():
mon = form.save()
messages.success(request, 'Successfully edited ' + str(mon))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'instance': mon,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': mon.pk.hex,
'html': template.render(context),
}
else:
# Return form filled in and errors shown
template = loader.get_template('herders/profile/monster_view/edit_form.html')
context = {'edit_monster_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
messages.warning(request, 'Deleted ' + str(monster))
monster.delete()
return redirect(return_path)
else:
return HttpResponseBadRequest()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = PowerUpMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_power_up', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
context = {
'profile_name': request.user.username,
'monster': monster,
'is_owner': is_owner,
'form': form,
'view': 'profile',
}
validation_errors = {}
response_data = {
'code': 'error'
}
if is_owner:
if request.method == 'POST' and form.is_valid():
food_monsters = form.cleaned_data['monster']
# Check that monster is not being fed to itself
if monster in food_monsters:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if not form.cleaned_data['ignore_evolution']:
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars or higher." % monster.stars
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.warning(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
response_data['code'] = 'success'
else:
response_data['code'] = 'edit'
return JsonResponse(response_data)
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
template = loader.get_template('herders/profile/monster_view/power_up_form.html')
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
context.update(csrf(request))
response_data['html'] = template.render(context)
return JsonResponse(response_data)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
template = loader.get_template('herders/profile/monster_view/awaken_form.html')
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_awaken', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if is_owner:
if not monster.monster.is_awakened:
if request.method == 'POST' and form.is_valid():
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
summoner.storage.magic_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_magic_high
summoner.storage.magic_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_magic_mid
summoner.storage.magic_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_magic_low
summoner.storage.fire_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_fire_high
summoner.storage.fire_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_fire_mid
summoner.storage.fire_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_fire_low
summoner.storage.water_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_water_high
summoner.storage.water_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_water_mid
summoner.storage.water_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_water_low
summoner.storage.wind_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_wind_high
summoner.storage.wind_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_wind_mid
summoner.storage.wind_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_wind_low
summoner.storage.dark_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_dark_high
summoner.storage.dark_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_dark_mid
summoner.storage.dark_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_dark_low
summoner.storage.light_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_light_high
summoner.storage.light_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_light_mid
summoner.storage.light_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_light_low
summoner.storage.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
response_data = {
'code': 'success',
'removeElement': '#awakenMonsterButton',
}
else:
storage = summoner.storage.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().items():
available_essences[element] = OrderedDict()
for size, cost in essences.items():
if cost > 0:
available_essences[element][size] = {
'qty': storage[element][size],
'sufficient': storage[element][size] >= cost,
}
context = {
'awaken_form': form,
'available_essences': available_essences,
'instance': monster,
}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
error_template = loader.get_template('herders/profile/monster_already_awakened.html')
response_data = {
'code': 'error',
'html': error_template.render()
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
messages.success(request, 'Succesfully copied ' + str(newmonster))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'is_owner': True,
'instance': newmonster,
}
response_data = {
'code': 'success',
'instance_id': newmonster.pk.hex,
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = MonsterPieceForm(request.POST or None)
else:
form = MonsterPieceForm()
form.helper.form_action = reverse('herders:monster_piece_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_pieces = form.save(commit=False)
new_pieces.owner = request.user.summoner
new_pieces.save()
messages.success(request, 'Added %s to your collection.' % new_pieces)
response_data = {
'code': 'success'
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if is_owner:
form = MonsterPieceForm(request.POST or None, instance=pieces)
form.helper.form_action = request.path
if request.method == 'POST' and form.is_valid():
new_piece = form.save()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': new_piece,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_piece.pk.hex,
'html': template.render(context),
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_summon(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if pieces.can_summon():
new_monster = MonsterInstance.objects.create(owner=summoner, monster=pieces.monster, stars=pieces.monster.base_stars, level=1, fodder=False, notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
# Remove the pieces, delete if 0
pieces.pieces -= pieces.PIECE_REQUIREMENTS[pieces.monster.base_stars]
pieces.save()
response_data = {
'code': 'success',
}
if pieces.pieces <= 0:
pieces.delete()
else:
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': pieces,
'is_owner': is_owner,
}
response_data['instance_id'] = pieces.pk.hex
response_data['html'] = template.render(context),
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == pieces.owner:
messages.warning(request, 'Deleted ' + str(pieces))
pieces.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
fusions = Fusion.objects.all()
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'fusions': fusions,
}
return render(request, 'herders/profile/fusion/base.html', context)
def fusion_progress_detail(request, profile_name, monster_slug):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
try:
fusion = Fusion.objects.get(product__bestiary_slug=monster_slug)
except Fusion.DoesNotExist:
return HttpResponseBadRequest()
else:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.objects.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).exists()
# Scan summoner's collection for instances each ingredient
fusion_ready = True
for ingredient in fusion.ingredients.all().select_related('awakens_from', 'awakens_to'):
owned_ingredients = MonsterInstance.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
owned_ingredient_pieces = MonsterPiece.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).first()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
if owned_ingredient_pieces:
acquired = owned_ingredient_pieces.can_summon()
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
if not complete:
fusion_ready = False
# Check if this ingredient is fusable
sub_fusion = None
sub_fusion_awakening_cost = None
try:
sub_fusion = Fusion.objects.get(product=ingredient.awakens_from)
except Fusion.DoesNotExist:
pass
else:
if not acquired:
awakened_sub_fusion_ingredients = MonsterInstance.objects.filter(
monster__pk__in=sub_fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
sub_fusion_awakening_cost = sub_fusion.total_awakening_cost(awakened_sub_fusion_ingredients)
ingredient_progress = {
'instance': ingredient,
'owned': owned_ingredients,
'pieces': owned_ingredient_pieces,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
'is_fuseable': True if sub_fusion else False,
'sub_fusion_cost': sub_fusion_awakening_cost,
}
ingredients.append(ingredient_progress)
awakened_owned_ingredients = MonsterInstance.objects.filter(
monster__pk__in=fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
total_cost = fusion.total_awakening_cost(awakened_owned_ingredients)
essences_satisfied, total_missing = fusion.missing_awakening_cost(summoner)
# Determine the total/missing essences including sub-fusions
if fusion.sub_fusion_available():
total_sub_fusion_cost = deepcopy(total_cost)
for ingredient in ingredients:
if ingredient['sub_fusion_cost']:
for element, sizes in total_sub_fusion_cost.items():
for size, qty in sizes.items():
total_sub_fusion_cost[element][size] += ingredient['sub_fusion_cost'][element][size]
# Now determine what's missing based on owner's storage
storage = summoner.storage.get_storage()
sub_fusion_total_missing = {
element: {
size: total_sub_fusion_cost[element][size] - storage[element][size] if total_sub_fusion_cost[element][size] > storage[element][size] else 0
for size, qty in element_sizes.items()
}
for element, element_sizes in total_sub_fusion_cost.items()
}
sub_fusion_mats_satisfied = True
for sizes in total_sub_fusion_cost.values():
for qty in sizes.values():
if qty > 0:
sub_fusion_mats_satisfied = False
else:
sub_fusion_total_missing = None
sub_fusion_mats_satisfied = None
progress = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_mats_cost': total_cost,
'awakening_mats_sufficient': essences_satisfied,
'awakening_mats_missing': total_missing,
'sub_fusion_mats_missing': sub_fusion_total_missing,
'sub_fusion_mats_sufficient': sub_fusion_mats_satisfied,
'ready': fusion_ready,
}
context['fusion'] = progress
return render(request, 'herders/profile/fusion/fusion_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_edit(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = EditTeamGroupForm(request.POST or None, instance=team_group)
if is_owner:
if form.is_valid() and request.method == 'POST':
form.save()
return redirect(return_path)
else:
return PermissionDenied("Editing a group you don't own")
context = {
'profile_name': profile_name,
'summoner': summoner,
'form': form,
'group_id': group_id,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
return render(request, 'herders/profile/teams/team_group_edit.html', context)
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.warning(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader and team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/team_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
edit_form.full_clean() # re-clean due to updated querysets after form initialization
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save(commit=False)
team.owner = summoner
team.save()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.warning(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def runes(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
filter_form = FilterRuneForm(auto_id="filter_id_%s")
filter_form.helper.form_action = reverse('herders:rune_inventory', kwargs={'profile_name': profile_name})
context = {
'view': 'runes',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'old_rune_count': RuneInstance.objects.filter(owner=summoner, substats__isnull=True).count(),
'rune_filter_form': filter_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/runes/base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to base profile URL
if view_mode:
request.session['rune_inventory_view_mode'] = view_mode.lower()
if box_grouping:
request.session['rune_inventory_box_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Rune view mode cookie set")
view_mode = request.session.get('rune_inventory_view_mode', 'box').lower()
box_grouping = request.session.get('rune_inventory_box_method', 'slot').lower()
if view_mode == 'crafts':
return rune_inventory_crafts(request, profile_name)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
rune_queryset = RuneInstance.objects.filter(owner=summoner).select_related('assigned_to', 'assigned_to__monster')
total_count = rune_queryset.count()
form = FilterRuneForm(request.POST or None)
if form.is_valid():
rune_filter = RuneInstanceFilter(form.cleaned_data, queryset=rune_queryset)
else:
rune_filter = RuneInstanceFilter(None, queryset=rune_queryset)
filtered_count = rune_filter.qs.count()
context = {
'runes': rune_filter.qs,
'total_count': total_count,
'filtered_count': filtered_count,
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'box':
rune_box = []
if box_grouping == 'slot':
rune_box.append({
'name': 'Slot 1',
'runes': rune_filter.qs.filter(slot=1)
})
rune_box.append({
'name': 'Slot 2',
'runes': rune_filter.qs.filter(slot=2)
})
rune_box.append({
'name': 'Slot 3',
'runes': rune_filter.qs.filter(slot=3)
})
rune_box.append({
'name': 'Slot 4',
'runes': rune_filter.qs.filter(slot=4)
})
rune_box.append({
'name': 'Slot 5',
'runes': rune_filter.qs.filter(slot=5)
})
rune_box.append({
'name': 'Slot 6',
'runes': rune_filter.qs.filter(slot=6)
})
elif box_grouping == 'grade':
rune_box.append({
'name': '6*',
'runes': rune_filter.qs.filter(stars=6)
})
rune_box.append({
'name': '5*',
'runes': rune_filter.qs.filter(stars=5)
})
rune_box.append({
'name': '4*',
'runes': rune_filter.qs.filter(stars=4)
})
rune_box.append({
'name': '3*',
'runes': rune_filter.qs.filter(stars=3)
})
rune_box.append({
'name': '2*',
'runes': rune_filter.qs.filter(stars=2)
})
rune_box.append({
'name': '1*',
'runes': rune_filter.qs.filter(stars=1)
})
elif box_grouping == 'equipped':
rune_box.append({
'name': 'Not Equipped',
'runes': rune_filter.qs.filter(assigned_to__isnull=True)
})
# Create a dictionary of monster PKs and their equipped runes
monsters = OrderedDict()
for rune in rune_filter.qs.filter(assigned_to__isnull=False).select_related('assigned_to', 'assigned_to__monster').order_by('assigned_to__monster__name', 'slot'):
if rune.assigned_to.pk not in monsters:
monsters[rune.assigned_to.pk] = {
'name': str(rune.assigned_to),
'runes': []
}
monsters[rune.assigned_to.pk]['runes'].append(rune)
for monster_runes in monsters.values():
rune_box.append(monster_runes)
elif box_grouping == 'type':
for (type, type_name) in RuneInstance.TYPE_CHOICES:
rune_box.append({
'name': type_name,
'runes': rune_filter.qs.filter(type=type)
})
context['runes'] = rune_box
context['box_grouping'] = box_grouping
template = 'herders/profile/runes/inventory.html'
elif view_mode == 'grid':
template = 'herders/profile/runes/inventory_grid.html'
else:
template = 'herders/profile/runes/inventory_table.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory_crafts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
craft_box = OrderedDict()
for (craft, craft_name) in RuneInstance.CRAFT_CHOICES:
craft_box[craft_name] = OrderedDict()
for rune, rune_name in RuneInstance.TYPE_CHOICES:
craft_box[craft_name][rune_name] = RuneCraftInstance.objects.filter(owner=summoner, type=craft, rune=rune).order_by('stat', 'quality')
# Immemorial
craft_box[craft_name]['Immemorial'] = RuneCraftInstance.objects.filter(owner=summoner, type=craft, rune__isnull=True).order_by('stat', 'quality')
context['crafts'] = craft_box
return render(request, 'herders/profile/runes/inventory_crafts.html', context)
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def rune_add(request, profile_name):
form = AddRuneInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the rune instance
new_rune = form.save(commit=False)
new_rune.owner = request.user.summoner
new_rune.save()
messages.success(request, 'Added ' + str(new_rune))
# Send back blank form
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
# Check for any pre-filled GET parameters
slot = request.GET.get('slot', None)
assigned_to = request.GET.get('assigned_to', None)
try:
assigned_monster = MonsterInstance.objects.get(owner=request.user.summoner, pk=assigned_to)
except MonsterInstance.DoesNotExist:
assigned_monster = None
form = AddRuneInstanceForm(initial={
'assigned_to': assigned_monster,
'slot': slot if slot is not None else 1,
})
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
# Return form filled in and errors shown
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_edit(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneInstanceForm(request.POST or None, instance=rune, auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
template = loader.get_template('herders/profile/runes/add_form.html')
if is_owner:
context = {'add_rune_form': form}
context.update(csrf(request))
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm(auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'add_rune_form': form}
context.update(csrf(request))
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_assign(request, profile_name, instance_id, slot=None):
rune_queryset = RuneInstance.objects.filter(owner=request.user.summoner, assigned_to=None)
filter_form = AssignRuneForm(request.POST or None, initial={'slot': slot}, prefix='assign')
filter_form.helper.form_action = reverse('herders:rune_assign', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if slot:
rune_queryset = rune_queryset.filter(slot=slot)
if request.method == 'POST' and filter_form.is_valid():
rune_filter = RuneInstanceFilter(filter_form.cleaned_data, queryset=rune_queryset)
template = loader.get_template('herders/profile/runes/assign_results.html')
context = {
'filter': rune_filter.qs,
'profile_name': profile_name,
'instance_id': instance_id,
}
context.update(csrf(request))
response_data = {
'code': 'results',
'html': template.render(context)
}
else:
rune_filter = RuneInstanceFilter(queryset=rune_queryset)
template = loader.get_template('herders/profile/runes/assign_form.html')
context = {
'filter': rune_filter.qs,
'form': filter_form,
'profile_name': profile_name,
'instance_id': instance_id,
}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_assign_choice(request, profile_name, instance_id, rune_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
rune = get_object_or_404(RuneInstance, pk=rune_id)
if rune.assigned_to is not None:
# TODO: Warn about removing from other monster?
pass
# Check for existing rune.
existing_runes = monster.runeinstance_set.filter(slot=rune.slot)
for existing_rune in existing_runes:
existing_rune.assigned_to = None
rune.assigned_to = monster
rune.save()
monster.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
@login_required
def rune_unassign(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
rune.assigned_to = None
rune.save()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def rune_unassign_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
assigned_mons = []
assigned_runes = RuneInstance.objects.filter(owner=summoner, assigned_to__isnull=False)
number_assigned = assigned_runes.count()
if is_owner:
for rune in assigned_runes:
if rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
rune.assigned_to = None
rune.save()
# Resave monster instances that had runes removed to recalc stats
for mon in assigned_mons:
mon.save()
messages.success(request, 'Unassigned ' + str(number_assigned) + ' rune(s).')
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
messages.warning(request, 'Deleted ' + str(rune))
rune.delete()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Delete the runes
death_row = RuneInstance.objects.filter(owner=summoner)
number_killed = death_row.count()
assigned_mons = []
for rune in death_row:
if rune.assigned_to and rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
death_row.delete()
# Delete the crafts
RuneCraftInstance.objects.filter(owner=summoner).delete()
messages.warning(request, 'Deleted ' + str(number_killed) + ' runes.')
for mon in assigned_mons:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_resave_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
for r in RuneInstance.objects.filter(owner=summoner, substats__isnull=True):
r.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_add(request, profile_name):
form = AddRuneCraftInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_craft = form.save(commit=False)
new_craft.owner = request.user.summoner
new_craft.save()
messages.success(request, 'Added ' + new_craft.get_type_display() + ' ' + str(new_craft))
# Send back blank form
form = AddRuneCraftInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_craft_edit(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneCraftInstanceForm(request.POST or None, instance=craft)
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_delete(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
messages.warning(request, 'Deleted ' + craft.get_rune_display() + ' ' + str(craft))
craft.delete()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
Resolved an issue saving updated team roster
from collections import OrderedDict
from copy import deepcopy
from django.core.mail import mail_admins
from django.core.urlresolvers import reverse
from django.core.exceptions import PermissionDenied, ObjectDoesNotExist
from django.contrib import messages
from django.contrib.auth import authenticate, login, logout
from django.contrib.auth.models import Group
from django.contrib.auth.decorators import login_required
from django.db import IntegrityError
from django.forms.models import modelformset_factory
from django.http import HttpResponseForbidden, JsonResponse, HttpResponse, HttpResponseBadRequest
from django.db.models import FieldDoesNotExist
from django.shortcuts import render, redirect, get_object_or_404
from django.template import loader, RequestContext, Context
from django.template.context_processors import csrf
from .forms import *
from .filters import *
from .models import Summoner, Monster, Fusion, Building, BuildingInstance, MonsterInstance, MonsterPiece, TeamGroup, Team, RuneInstance, RuneCraftInstance, Storage
def register(request):
form = RegisterUserForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
if User.objects.filter(username=form.cleaned_data['username']).exists():
form.add_error('username', 'Username already taken')
else:
new_user = None
new_summoner = None
try:
# Create the user
new_user = User.objects.create_user(
username=form.cleaned_data['username'],
password=form.cleaned_data['password'],
email=form.cleaned_data['email'],
)
new_user.save()
new_user.groups.add(Group.objects.get(name='Summoners'))
new_summoner = Summoner.objects.create(
user=new_user,
summoner_name=form.cleaned_data['summoner_name'],
public=form.cleaned_data['is_public'],
)
new_summoner.save()
# Automatically log them in
user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])
if user is not None:
if user.is_active:
login(request, user)
return redirect('herders:profile_default', profile_name=user.username)
except IntegrityError as e:
if new_user is not None:
new_user.delete()
if new_summoner is not None:
new_summoner.delete()
form.add_error(None, 'There was an issue completing your registration. Please try again.')
mail_admins(
subject='Error during user registration',
message='{}'.format(e),
fail_silently=True,
)
context = {'form': form}
return render(request, 'herders/register.html', context)
@login_required
def change_username(request):
user = request.user
form = CrispyChangeUsernameForm(request.POST or None)
context = {
'form': form,
}
if request.method == 'POST' and form.is_valid():
try:
user.username = form.cleaned_data['username']
user.save()
return redirect('username_change_complete')
except IntegrityError:
form.add_error('username', 'Username already taken')
return render(request, 'registration/change_username.html', context)
def change_username_complete(request):
return render(request, 'registration/change_username_complete.html')
@login_required
def profile_delete(request, profile_name):
user = request.user
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = DeleteProfileForm(request.POST or None)
form.helper.form_action = reverse('herders:profile_delete', kwargs={'profile_name': profile_name})
context = {
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
logout(request)
user.delete()
messages.warning(request, 'Your profile has been permanently deleted.')
return redirect('news:latest_news')
return render(request, 'herders/profile/profile_delete.html', context)
else:
return HttpResponseForbidden("You don't own this profile")
@login_required
def following(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_following', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'view': 'following',
'return_path': return_path,
}
return render(request, 'herders/profile/following/list.html', context)
@login_required
def follow_add(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
new_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.add(new_follower)
messages.info(request, 'Now following %s' % new_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required
def follow_remove(request, profile_name, follow_username):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
removed_follower = get_object_or_404(Summoner, user__username=follow_username)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
summoner.following.remove(removed_follower)
messages.info(request, 'Unfollowed %s' % removed_follower.user.username)
return redirect(return_path)
else:
return HttpResponseForbidden()
def profile(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
# Determine if the person logged in is the one requesting the view
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_filter_form = FilterMonsterInstanceForm(auto_id='id_filter_%s')
monster_filter_form.helper.form_action = reverse('herders:monster_inventory', kwargs={'profile_name': profile_name})
context = {
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'monster_filter_form': monster_filter_form,
'view': 'profile',
}
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_inventory/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def buildings(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'summoner': summoner,
'is_owner': is_owner,
'profile_name': profile_name,
}
return render(request, 'herders/profile/buildings/base.html', context)
def buildings_inventory(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
all_buildings = Building.objects.all().order_by('name')
building_data = []
total_glory_cost = 0
spent_glory = 0
total_guild_cost = 0
spent_guild = 0
for b in all_buildings:
bldg_data = _building_data(summoner, b)
if b.area == Building.AREA_GENERAL:
total_glory_cost += sum(b.upgrade_cost)
spent_glory += bldg_data['spent_upgrade_cost']
elif b.area == Building.AREA_GUILD:
total_guild_cost += sum(b.upgrade_cost)
spent_guild += bldg_data['spent_upgrade_cost']
building_data.append(bldg_data)
context = {
'is_owner': is_owner,
'summoner': summoner,
'profile_name': profile_name,
'buildings': building_data,
'total_glory_cost': total_glory_cost,
'spent_glory': spent_glory,
'glory_progress': float(spent_glory) / total_glory_cost * 100,
'total_guild_cost': total_guild_cost,
'spent_guild': spent_guild,
'guild_progress': float(spent_guild) / total_guild_cost * 100,
}
return render(request, 'herders/profile/buildings/inventory.html', context)
@login_required
def building_edit(request, profile_name, building_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
base_building = get_object_or_404(Building, pk=building_id)
try:
owned_instance = BuildingInstance.objects.get(owner=summoner, building=base_building)
except BuildingInstance.DoesNotExist:
owned_instance = BuildingInstance.objects.create(owner=summoner, level=0, building=base_building)
form = EditBuildingForm(request.POST or None, instance=owned_instance)
form.helper.form_action = reverse('herders:building_edit', kwargs={'profile_name': profile_name, 'building_id': building_id})
context = {
'form': form,
}
context.update(csrf(request))
if is_owner:
if request.method == 'POST' and form.is_valid():
owned_instance = form.save()
messages.success(request,'Updated ' + owned_instance.building.name + ' to level ' + str(owned_instance.level))
response_data = {
'code': 'success',
}
else:
template = loader.get_template('herders/profile/buildings/edit_form.html')
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
def _building_data(summoner, building):
percent_stat = building.affected_stat in Building.PERCENT_STATS
total_upgrade_cost = sum(building.upgrade_cost)
if building.area == Building.AREA_GENERAL:
currency = 'glory_points.png'
else:
currency = 'guild_points.png'
try:
instance = BuildingInstance.objects.get(owner=summoner, building=building)
if instance.level > 0:
stat_bonus = building.stat_bonus[instance.level - 1]
else:
stat_bonus = 0
remaining_upgrade_cost = instance.remaining_upgrade_cost()
except BuildingInstance.DoesNotExist:
instance = None
stat_bonus = 0
remaining_upgrade_cost = total_upgrade_cost
except BuildingInstance.MultipleObjectsReturned:
# Should only be 1 ever - use the first and delete the others.
instance = BuildingInstance.objects.filter(owner=summoner, building=building).first()
BuildingInstance.objects.filter(owner=summoner, building=building).exclude(pk=instance.pk).delete()
return _building_data(summoner, building)
return {
'base': building,
'instance': instance,
'stat_bonus': stat_bonus,
'percent_stat': percent_stat,
'spent_upgrade_cost': total_upgrade_cost - remaining_upgrade_cost,
'total_upgrade_cost': total_upgrade_cost,
'upgrade_progress': float(total_upgrade_cost - remaining_upgrade_cost) / total_upgrade_cost * 100,
'currency': currency,
}
def monster_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to ourself without the view mode or box grouping
if view_mode:
request.session['profile_view_mode'] = view_mode.lower()
if box_grouping:
request.session['profile_group_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Profile view mode cookie set")
view_mode = request.session.get('profile_view_mode', 'box').lower()
box_grouping = request.session.get('profile_group_method', 'grade').lower()
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
monster_queryset = MonsterInstance.objects.filter(owner=summoner).select_related('monster', 'monster__awakens_from')
total_monsters = monster_queryset.count()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if view_mode == 'list':
monster_queryset = monster_queryset.select_related(
'monster__leader_skill', 'monster__awakens_to'
).prefetch_related(
'monster__skills', 'runeinstance_set', 'team_set', 'team_leader', 'tags'
)
form = FilterMonsterInstanceForm(request.POST or None, auto_id='id_filter_%s')
if form.is_valid():
monster_filter = MonsterInstanceFilter(form.cleaned_data, queryset=monster_queryset)
else:
monster_filter = MonsterInstanceFilter(queryset=monster_queryset)
filtered_count = monster_filter.qs.count()
context = {
'monsters': monster_filter.qs,
'total_count': total_monsters,
'filtered_count': filtered_count,
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'pieces':
context['monster_pieces'] = MonsterPiece.objects.filter(owner=summoner).select_related('monster')
template = 'herders/profile/monster_inventory/summoning_pieces.html'
elif view_mode == 'list':
template = 'herders/profile/monster_inventory/list.html'
else:
# Group up the filtered monsters
monster_stable = OrderedDict()
if box_grouping == 'grade' or box_grouping == 'stars':
monster_stable['6*'] = monster_filter.qs.filter(stars=6).order_by('-level', 'monster__element', 'monster__name')
monster_stable['5*'] = monster_filter.qs.filter(stars=5).order_by('-level', 'monster__element', 'monster__name')
monster_stable['4*'] = monster_filter.qs.filter(stars=4).order_by('-level', 'monster__element', 'monster__name')
monster_stable['3*'] = monster_filter.qs.filter(stars=3).order_by('-level', 'monster__element', 'monster__name')
monster_stable['2*'] = monster_filter.qs.filter(stars=2).order_by('-level', 'monster__element', 'monster__name')
monster_stable['1*'] = monster_filter.qs.filter(stars=1).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'natural_stars':
nat5 = (Q(monster__base_stars=6) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=5) & Q(monster__is_awakened=False))
nat4 = (Q(monster__base_stars=5) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=4) & Q(monster__is_awakened=False))
nat3 = (Q(monster__base_stars=4) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=3) & Q(monster__is_awakened=False))
nat2 = (Q(monster__base_stars=3) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=2) & Q(monster__is_awakened=False))
nat1 = (Q(monster__base_stars=2) & Q(monster__is_awakened=True)) | (Q(monster__base_stars=1) & Q(monster__is_awakened=False))
monster_stable['Natural 5*'] = monster_filter.qs.filter(nat5).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 4*'] = monster_filter.qs.filter(nat4).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 3*'] = monster_filter.qs.filter(nat3).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 2*'] = monster_filter.qs.filter(nat2).order_by('-stars', '-level', 'monster__name')
monster_stable['Natural 1*'] = monster_filter.qs.filter(nat1).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'level':
monster_stable['40'] = monster_filter.qs.filter(level=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['39-31'] = monster_filter.qs.filter(level__gt=30).filter(level__lt=40).order_by('-level', '-stars', 'monster__element', 'monster__name')
monster_stable['30-21'] = monster_filter.qs.filter(level__gt=20).filter(level__lte=30).order_by( '-level', '-stars', 'monster__element', 'monster__name')
monster_stable['20-11'] = monster_filter.qs.filter(level__gt=10).filter(level__lte=20).order_by( '-level', '-stars', 'monster__element', 'monster__name')
monster_stable['10-1'] = monster_filter.qs.filter(level__lte=10).order_by('-level', '-stars', 'monster__element', 'monster__name')
elif box_grouping == 'element' or box_grouping == 'attribute':
monster_stable['water'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WATER).order_by('-stars', '-level', 'monster__name')
monster_stable['fire'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_FIRE).order_by('-stars', '-level', 'monster__name')
monster_stable['wind'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_WIND).order_by('-stars', '-level', 'monster__name')
monster_stable['light'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_LIGHT).order_by('-stars', '-level', 'monster__name')
monster_stable['dark'] = monster_filter.qs.filter(monster__element=Monster.ELEMENT_DARK).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'archetype':
monster_stable['attack'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_ATTACK).order_by('-stars', '-level', 'monster__name')
monster_stable['hp'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_HP).order_by('-stars', '-level', 'monster__name')
monster_stable['support'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_SUPPORT).order_by('-stars', '-level', 'monster__name')
monster_stable['defense'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_DEFENSE).order_by('-stars', '-level', 'monster__name')
monster_stable['material'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_MATERIAL).order_by('-stars', '-level', 'monster__name')
monster_stable['other'] = monster_filter.qs.filter(monster__archetype=Monster.TYPE_NONE).order_by('-stars', '-level', 'monster__name')
elif box_grouping == 'priority':
monster_stable['High'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_HIGH).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Medium'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_MED).order_by('-level', 'monster__element', 'monster__name')
monster_stable['Low'] = monster_filter.qs.select_related('monster').filter(owner=summoner, priority=MonsterInstance.PRIORITY_LOW).order_by('-level', 'monster__element', 'monster__name')
monster_stable['None'] = monster_filter.qs.select_related('monster').filter(owner=summoner).filter(Q(priority=None) | Q(priority=0)).order_by('-level', 'monster__element', 'monster__name')
elif box_grouping == 'family':
for mon in monster_filter.qs:
if mon.monster.is_awakened and mon.monster.awakens_from is not None:
family_name = mon.monster.awakens_from.name
else:
family_name = mon.monster.name
if family_name not in monster_stable:
monster_stable[family_name] = []
monster_stable[family_name].append(mon)
# Sort ordered dict alphabetically by family name
monster_stable = OrderedDict(sorted(monster_stable.items(), key=lambda family:family[0]))
else:
return HttpResponseBadRequest('Invalid sort method')
context['monster_stable'] = monster_stable
context['box_grouping'] = box_grouping.replace('_', ' ')
template = 'herders/profile/monster_inventory/box.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def profile_edit(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
user_form = EditUserForm(request.POST or None, instance=request.user)
summoner_form = EditSummonerForm(request.POST or None, instance=request.user.summoner)
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'user_form': user_form,
'summoner_form': summoner_form,
}
if is_owner:
if request.method == 'POST' and summoner_form.is_valid() and user_form.is_valid():
summoner_form.save()
user_form.save()
messages.info(request, 'Your profile has been updated.')
return redirect(return_path)
else:
return render(request, 'herders/profile/profile_edit.html', context)
else:
return HttpResponseForbidden()
@login_required
def storage(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
craft_mats = []
essence_mats = []
for field_name in Storage.ESSENCE_FIELDS:
essence_mats.append({
'name': summoner.storage._meta.get_field(field_name).help_text,
'field_name': field_name,
'element': field_name.split('_')[0],
'qty': getattr(summoner.storage, field_name)
})
for field_name in Storage.CRAFT_FIELDS:
craft_mats.append({
'name': summoner.storage._meta.get_field(field_name).help_text,
'field_name': field_name,
'qty': getattr(summoner.storage, field_name)
})
context = {
'is_owner': is_owner,
'profile_name': profile_name,
'summoner': summoner,
'essence_mats': essence_mats,
'craft_mats': craft_mats,
}
return render(request, 'herders/profile/storage/base.html', context=context)
else:
return HttpResponseForbidden()
@login_required
def storage_update(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner and request.POST:
field_name = request.POST.get('name')
try:
new_value = int(request.POST.get('value'))
except ValueError:
return HttpResponseBadRequest('Invalid Entry')
essence_size = None
if 'essence' in field_name:
# Split the actual field name off from the size
try:
field_name, essence_size = field_name.split('.')
size_map = {
'low': Storage.ESSENCE_LOW,
'mid': Storage.ESSENCE_MID,
'high': Storage.ESSENCE_HIGH,
}
essence_size = size_map[essence_size]
except (ValueError, KeyError):
return HttpResponseBadRequest()
try:
Storage._meta.get_field(field_name)
except FieldDoesNotExist:
return HttpResponseBadRequest()
else:
if essence_size is not None:
# Get a copy of the size array and set the correct index to new value
essence_list = getattr(summoner.storage, field_name)
essence_list[essence_size] = new_value
new_value = essence_list
setattr(summoner.storage, field_name, new_value)
summoner.storage.save()
return HttpResponse()
else:
return HttpResponseForbidden()
@login_required
def quick_fodder_menu(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
template = loader.get_template('herders/profile/monster_inventory/quick_fodder_menu.html')
response_data = {
'code': 'success',
'html': template.render(),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = AddMonsterInstanceForm(request.POST or None)
else:
form = AddMonsterInstanceForm(initial=request.GET.dict())
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_monster = form.save(commit=False)
new_monster.owner = request.user.summoner
new_monster.save()
messages.success(request, 'Added %s to your collection.' % new_monster)
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
context = {
'profile_name': profile_name,
'instance': new_monster,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_monster.pk.hex,
'html': template.render(context),
}
else:
form.helper.form_action = reverse('herders:monster_instance_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/add_monster_form.html')
# Return form filled in and errors shown
context = {'add_monster_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_quick_add(request, profile_name, monster_id, stars, level):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster_to_add = get_object_or_404(Monster, pk=monster_id)
if is_owner:
new_monster = MonsterInstance.objects.create(owner=summoner, monster=monster_to_add, stars=int(stars), level=int(level), fodder=True, notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
return redirect(return_path)
else:
return HttpResponseForbidden()
@login_required()
def monster_instance_bulk_add(request, profile_name):
return_path = reverse('herders:profile_default', kwargs={'profile_name': profile_name})
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
BulkAddFormset = modelformset_factory(MonsterInstance, form=BulkAddMonsterInstanceForm, formset=BulkAddMonsterInstanceFormset, extra=5, max_num=50)
if request.method == 'POST':
formset = BulkAddFormset(request.POST)
else:
formset = BulkAddFormset()
context = {
'profile_name': request.user.username,
'return_path': return_path,
'is_owner': is_owner,
'bulk_add_formset_action': request.path + '?next=' + return_path,
'view': 'profile',
}
if is_owner:
if request.method == 'POST':
if formset.is_valid():
new_instances = formset.save(commit=False)
for new_instance in new_instances:
try:
if new_instance.monster:
new_instance.owner = summoner
if new_instance.monster.archetype == Monster.TYPE_MATERIAL:
new_instance.priority = MonsterInstance.PRIORITY_DONE
new_instance.save()
messages.success(request, 'Added %s to your collection.' % new_instance)
except ObjectDoesNotExist:
# Blank form, don't care
pass
return redirect(return_path)
else:
raise PermissionDenied("Trying to bulk add to profile you don't own")
context['bulk_add_formset'] = formset
return render(request, 'herders/profile/monster_inventory/bulk_add_form.html', context)
def monster_instance_view(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
request.path
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'view': 'profile',
}
try:
context['instance'] = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return render(request, 'herders/profile/monster_view/not_found.html', context)
if is_owner or summoner.public:
return render(request, 'herders/profile/monster_view/base.html', context)
else:
return render(request, 'herders/profile/not_public.html')
def monster_instance_view_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
instance_runes = [
instance.runeinstance_set.filter(slot=1).first(),
instance.runeinstance_set.filter(slot=2).first(),
instance.runeinstance_set.filter(slot=3).first(),
instance.runeinstance_set.filter(slot=4).first(),
instance.runeinstance_set.filter(slot=5).first(),
instance.runeinstance_set.filter(slot=6).first(),
]
context = {
'runes': instance_runes,
'instance': instance,
'profile_name': profile_name,
'is_owner': is_owner,
}
return render(request, 'herders/profile/monster_view/runes.html', context)
def monster_instance_view_stats(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
context = {
'instance': instance,
'max_stats': instance.get_max_level_stats(),
'bldg_stats': instance.get_building_stats(),
'guild_stats': instance.get_building_stats(Building.AREA_GUILD),
}
return render(request, 'herders/profile/monster_view/stats.html', context)
def monster_instance_view_skills(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
context = {
'instance': instance,
'skills': skills,
}
return render(request, 'herders/profile/monster_view/skills.html', context)
def monster_instance_view_info(request, profile_name, instance_id):
try:
instance = MonsterInstance.objects.select_related('monster', 'monster__leader_skill').prefetch_related('monster__skills').get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
if instance.monster.is_awakened:
ingredient_in = instance.monster.fusion_set.all()
elif instance.monster.can_awaken and instance.monster.awakens_to:
ingredient_in = instance.monster.awakens_to.fusion_set.all()
else:
ingredient_in = []
if instance.monster.is_awakened and instance.monster.awakens_from:
product_of = instance.monster.awakens_from.product.first()
elif instance.monster.can_awaken:
product_of = instance.monster.product.first()
else:
product_of = []
context = {
'instance': instance,
'profile_name': profile_name,
'fusion_ingredient_in': ingredient_in,
'fusion_product_of': product_of,
'skillups': instance.get_possible_skillups(),
}
return render(request, 'herders/profile/monster_view/notes_info.html', context)
@login_required()
def monster_instance_remove_runes(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
try:
instance = MonsterInstance.objects.get(pk=instance_id)
except ObjectDoesNotExist:
return HttpResponseBadRequest()
else:
for rune in instance.runeinstance_set.all():
rune.assigned_to = None
rune.save()
instance.save()
messages.success(request, 'Removed all runes from ' + str(instance))
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
instance = get_object_or_404(MonsterInstance, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Reconcile skill level with actual skill from base monster
skills = []
skill_levels = [
instance.skill_1_level,
instance.skill_2_level,
instance.skill_3_level,
instance.skill_4_level,
]
for idx in range(0, instance.monster.skills.count()):
skills.append({
'skill': instance.monster.skills.all()[idx],
'level': skill_levels[idx]
})
form = EditMonsterInstanceForm(request.POST or None, instance=instance)
form.helper.form_action = request.path
if len(skills) >= 1 and skills[0]['skill'].max_level > 1:
form.helper['skill_1_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_1", data_skill_field=form['skill_1_level'].auto_id),
)
form.helper['skill_1_level'].wrap(Field, min=1, max=skills[0]['skill'].max_level)
form.fields['skill_1_level'].label = skills[0]['skill'].name + " Level"
else:
form.helper['skill_1_level'].wrap(Div, css_class="hidden")
if len(skills) >= 2 and skills[1]['skill'].max_level > 1:
form.helper['skill_2_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_2", data_skill_field=form['skill_2_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_2_level'].wrap(Field, min=1, max=skills[1]['skill'].max_level)
form.fields['skill_2_level'].label = skills[1]['skill'].name + " Level"
else:
form.helper['skill_2_level'].wrap(Div, css_class="hidden")
if len(skills) >= 3 and skills[2]['skill'].max_level > 1:
form.helper['skill_3_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_3", data_skill_field=form['skill_3_level'].auto_id),
min=1,
max=skills[2]['skill'].max_level,
)
form.helper['skill_3_level'].wrap(Field, min=1, max=skills[2]['skill'].max_level)
form.fields['skill_3_level'].label = skills[2]['skill'].name + " Level"
else:
form.helper['skill_3_level'].wrap(Div, css_class="hidden")
if len(skills) >= 4 and skills[3]['skill'].max_level > 1:
form.helper['skill_4_level'].wrap(
FieldWithButtons,
StrictButton("Max", name="Set_Max_Skill_4", data_skill_field=form['skill_4_level'].auto_id),
min=1,
max=skills[1]['skill'].max_level,
)
form.helper['skill_4_level'].wrap(Field, min=1, max=skills[3]['skill'].max_level)
form.fields['skill_4_level'].label = skills[3]['skill'].name + " Level"
else:
form.helper['skill_4_level'].wrap(Div, css_class="hidden")
if not instance.monster.homunculus:
form.helper['custom_name'].wrap(Div, css_class="hidden")
if request.method == 'POST' and form.is_valid():
mon = form.save()
messages.success(request, 'Successfully edited ' + str(mon))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'instance': mon,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': mon.pk.hex,
'html': template.render(context),
}
else:
# Return form filled in and errors shown
template = loader.get_template('herders/profile/monster_view/edit_form.html')
context = {'edit_monster_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == monster.owner:
messages.warning(request, 'Deleted ' + str(monster))
monster.delete()
return redirect(return_path)
else:
return HttpResponseBadRequest()
@login_required()
def monster_instance_power_up(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
form = PowerUpMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_power_up', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
context = {
'profile_name': request.user.username,
'monster': monster,
'is_owner': is_owner,
'form': form,
'view': 'profile',
}
validation_errors = {}
response_data = {
'code': 'error'
}
if is_owner:
if request.method == 'POST' and form.is_valid():
food_monsters = form.cleaned_data['monster']
# Check that monster is not being fed to itself
if monster in food_monsters:
validation_errors['base_food_same'] = "You can't feed a monster to itself. "
is_evolution = request.POST.get('evolve', False)
# Perform validation checks for evolve action
if is_evolution:
# Check constraints on evolving (or not, if form element was set)
# Check monster level and stars
if monster.stars >= 6:
validation_errors['base_monster_stars'] = "%s is already at 6 stars." % monster.monster.name
if not form.cleaned_data['ignore_evolution']:
if monster.level != monster.max_level_from_stars():
validation_errors['base_monster_level'] = "%s is not at max level for the current star rating (Lvl %s)." % (monster.monster.name, monster.monster.max_level_from_stars())
# Check number of fodder monsters
if len(food_monsters) < monster.stars:
validation_errors['food_monster_quantity'] = "Evolution requres %s food monsters." % monster.stars
# Check fodder star ratings - must be same as monster
for food in food_monsters:
if food.stars != monster.stars:
if 'food_monster_stars' not in validation_errors:
validation_errors['food_monster_stars'] = "All food monsters must be %s stars or higher." % monster.stars
# Perform the stars++ if no errors
if not validation_errors:
# Level up stars
monster.stars += 1
monster.level = 1
monster.save()
messages.success(request, 'Successfully evolved %s to %s<span class="glyphicon glyphicon-star"></span>' % (monster.monster.name, monster.stars), extra_tags='safe')
if not validation_errors:
# Delete the submitted monsters
for food in food_monsters:
if food.owner == request.user.summoner:
messages.warning(request, 'Deleted %s' % food)
food.delete()
else:
raise PermissionDenied("Trying to delete a monster you don't own")
# Redirect back to return path if evolved, or go to edit screen if power up
if is_evolution:
response_data['code'] = 'success'
else:
response_data['code'] = 'edit'
return JsonResponse(response_data)
else:
raise PermissionDenied("Trying to power up or evolve a monster you don't own")
template = loader.get_template('herders/profile/monster_view/power_up_form.html')
# Any errors in the form will fall through to here and be displayed
context['validation_errors'] = validation_errors
context.update(csrf(request))
response_data['html'] = template.render(context)
return JsonResponse(response_data)
@login_required()
def monster_instance_awaken(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
monster = get_object_or_404(MonsterInstance, pk=instance_id)
template = loader.get_template('herders/profile/monster_view/awaken_form.html')
form = AwakenMonsterInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:monster_instance_awaken', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if is_owner:
if not monster.monster.is_awakened:
if request.method == 'POST' and form.is_valid():
# Subtract essences from inventory if requested
if form.cleaned_data['subtract_materials']:
summoner = Summoner.objects.get(user=request.user)
summoner.storage.magic_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_magic_high
summoner.storage.magic_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_magic_mid
summoner.storage.magic_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_magic_low
summoner.storage.fire_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_fire_high
summoner.storage.fire_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_fire_mid
summoner.storage.fire_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_fire_low
summoner.storage.water_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_water_high
summoner.storage.water_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_water_mid
summoner.storage.water_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_water_low
summoner.storage.wind_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_wind_high
summoner.storage.wind_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_wind_mid
summoner.storage.wind_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_wind_low
summoner.storage.dark_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_dark_high
summoner.storage.dark_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_dark_mid
summoner.storage.dark_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_dark_low
summoner.storage.light_essence[Storage.ESSENCE_HIGH] -= monster.monster.awaken_mats_light_high
summoner.storage.light_essence[Storage.ESSENCE_MID] -= monster.monster.awaken_mats_light_mid
summoner.storage.light_essence[Storage.ESSENCE_LOW] -= monster.monster.awaken_mats_light_low
summoner.storage.save()
# Perform the awakening by instance's monster source ID
monster.monster = monster.monster.awakens_to
monster.save()
response_data = {
'code': 'success',
'removeElement': '#awakenMonsterButton',
}
else:
storage = summoner.storage.get_storage()
available_essences = OrderedDict()
for element, essences in monster.monster.get_awakening_materials().items():
available_essences[element] = OrderedDict()
for size, cost in essences.items():
if cost > 0:
available_essences[element][size] = {
'qty': storage[element][size],
'sufficient': storage[element][size] >= cost,
}
context = {
'awaken_form': form,
'available_essences': available_essences,
'instance': monster,
}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
error_template = loader.get_template('herders/profile/monster_already_awakened.html')
response_data = {
'code': 'error',
'html': error_template.render()
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_instance_duplicate(request, profile_name, instance_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
# Check for proper owner before copying
if request.user.summoner == monster.owner:
newmonster = monster
newmonster.pk = None
newmonster.save()
messages.success(request, 'Succesfully copied ' + str(newmonster))
view_mode = request.session.get('profile_view_mode', 'list').lower()
if view_mode == 'list':
template = loader.get_template('herders/profile/monster_inventory/monster_list_row_snippet.html')
else:
template = loader.get_template('herders/profile/monster_inventory/monster_box_snippet.html')
context = {
'profile_name': profile_name,
'is_owner': True,
'instance': newmonster,
}
response_data = {
'code': 'success',
'instance_id': newmonster.pk.hex,
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_add(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if request.method == 'POST':
form = MonsterPieceForm(request.POST or None)
else:
form = MonsterPieceForm()
form.helper.form_action = reverse('herders:monster_piece_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if request.method == 'POST' and form.is_valid():
# Create the monster instance
new_pieces = form.save(commit=False)
new_pieces.owner = request.user.summoner
new_pieces.save()
messages.success(request, 'Added %s to your collection.' % new_pieces)
response_data = {
'code': 'success'
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def monster_piece_edit(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
template = loader.get_template('herders/profile/monster_inventory/monster_piece_form.html')
if is_owner:
form = MonsterPieceForm(request.POST or None, instance=pieces)
form.helper.form_action = request.path
if request.method == 'POST' and form.is_valid():
new_piece = form.save()
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': new_piece,
'is_owner': is_owner,
}
response_data = {
'code': 'success',
'instance_id': new_piece.pk.hex,
'html': template.render(context),
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context),
}
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_summon(request, profile_name, instance_id):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
if pieces.can_summon():
new_monster = MonsterInstance.objects.create(owner=summoner, monster=pieces.monster, stars=pieces.monster.base_stars, level=1, fodder=False, notes='', priority=MonsterInstance.PRIORITY_DONE)
messages.success(request, 'Added %s to your collection.' % new_monster)
# Remove the pieces, delete if 0
pieces.pieces -= pieces.PIECE_REQUIREMENTS[pieces.monster.base_stars]
pieces.save()
response_data = {
'code': 'success',
}
if pieces.pieces <= 0:
pieces.delete()
else:
template = loader.get_template('herders/profile/monster_inventory/monster_piece_snippet.html')
context = {
'piece': pieces,
'is_owner': is_owner,
}
response_data['instance_id'] = pieces.pk.hex
response_data['html'] = template.render(context),
return JsonResponse(response_data)
else:
raise PermissionDenied()
@login_required()
def monster_piece_delete(request, profile_name, instance_id):
return_path = request.GET.get(
'next',
reverse('herders:profile_default', kwargs={'profile_name': profile_name})
)
pieces = get_object_or_404(MonsterPiece, pk=instance_id)
# Check for proper owner before deleting
if request.user.summoner == pieces.owner:
messages.warning(request, 'Deleted ' + str(pieces))
pieces.delete()
return redirect(return_path)
else:
return HttpResponseForbidden()
def fusion_progress(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
fusions = Fusion.objects.all()
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'fusions': fusions,
}
return render(request, 'herders/profile/fusion/base.html', context)
def fusion_progress_detail(request, profile_name, monster_slug):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'view': 'fusion',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
try:
fusion = Fusion.objects.get(product__bestiary_slug=monster_slug)
except Fusion.DoesNotExist:
return HttpResponseBadRequest()
else:
level = 10 + fusion.stars * 5
ingredients = []
# Check if fusion has been completed already
fusion_complete = MonsterInstance.objects.filter(
Q(owner=summoner), Q(monster=fusion.product) | Q(monster=fusion.product.awakens_to)
).exists()
# Scan summoner's collection for instances each ingredient
fusion_ready = True
for ingredient in fusion.ingredients.all().select_related('awakens_from', 'awakens_to'):
owned_ingredients = MonsterInstance.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).order_by('-stars', '-level', '-monster__is_awakened')
owned_ingredient_pieces = MonsterPiece.objects.filter(
Q(owner=summoner),
Q(monster=ingredient) | Q(monster=ingredient.awakens_from),
).first()
# Determine if each individual requirement is met using highest evolved/leveled monster that is not ignored for fusion
for owned_ingredient in owned_ingredients:
if not owned_ingredient.ignore_for_fusion:
acquired = True
evolved = owned_ingredient.stars >= fusion.stars
leveled = owned_ingredient.level >= level
awakened = owned_ingredient.monster.is_awakened
complete = acquired & evolved & leveled & awakened
break
else:
if owned_ingredient_pieces:
acquired = owned_ingredient_pieces.can_summon()
else:
acquired = False
evolved = False
leveled = False
awakened = False
complete = False
if not complete:
fusion_ready = False
# Check if this ingredient is fusable
sub_fusion = None
sub_fusion_awakening_cost = None
try:
sub_fusion = Fusion.objects.get(product=ingredient.awakens_from)
except Fusion.DoesNotExist:
pass
else:
if not acquired:
awakened_sub_fusion_ingredients = MonsterInstance.objects.filter(
monster__pk__in=sub_fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
sub_fusion_awakening_cost = sub_fusion.total_awakening_cost(awakened_sub_fusion_ingredients)
ingredient_progress = {
'instance': ingredient,
'owned': owned_ingredients,
'pieces': owned_ingredient_pieces,
'complete': complete,
'acquired': acquired,
'evolved': evolved,
'leveled': leveled,
'awakened': awakened,
'is_fuseable': True if sub_fusion else False,
'sub_fusion_cost': sub_fusion_awakening_cost,
}
ingredients.append(ingredient_progress)
awakened_owned_ingredients = MonsterInstance.objects.filter(
monster__pk__in=fusion.ingredients.values_list('pk', flat=True),
ignore_for_fusion=False,
owner=summoner,
)
total_cost = fusion.total_awakening_cost(awakened_owned_ingredients)
essences_satisfied, total_missing = fusion.missing_awakening_cost(summoner)
# Determine the total/missing essences including sub-fusions
if fusion.sub_fusion_available():
total_sub_fusion_cost = deepcopy(total_cost)
for ingredient in ingredients:
if ingredient['sub_fusion_cost']:
for element, sizes in total_sub_fusion_cost.items():
for size, qty in sizes.items():
total_sub_fusion_cost[element][size] += ingredient['sub_fusion_cost'][element][size]
# Now determine what's missing based on owner's storage
storage = summoner.storage.get_storage()
sub_fusion_total_missing = {
element: {
size: total_sub_fusion_cost[element][size] - storage[element][size] if total_sub_fusion_cost[element][size] > storage[element][size] else 0
for size, qty in element_sizes.items()
}
for element, element_sizes in total_sub_fusion_cost.items()
}
sub_fusion_mats_satisfied = True
for sizes in total_sub_fusion_cost.values():
for qty in sizes.values():
if qty > 0:
sub_fusion_mats_satisfied = False
else:
sub_fusion_total_missing = None
sub_fusion_mats_satisfied = None
progress = {
'instance': fusion.product,
'acquired': fusion_complete,
'stars': fusion.stars,
'level': level,
'cost': fusion.cost,
'ingredients': ingredients,
'awakening_mats_cost': total_cost,
'awakening_mats_sufficient': essences_satisfied,
'awakening_mats_missing': total_missing,
'sub_fusion_mats_missing': sub_fusion_total_missing,
'sub_fusion_mats_sufficient': sub_fusion_mats_satisfied,
'ready': fusion_ready,
}
context['fusion'] = progress
return render(request, 'herders/profile/fusion/fusion_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def teams(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
add_team_group_form = AddTeamGroupForm()
context = {
'view': 'teams',
'profile_name': profile_name,
'summoner': summoner,
'return_path': return_path,
'is_owner': is_owner,
'add_team_group_form': add_team_group_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/teams_base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def team_list(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Get team objects for the summoner
team_groups = TeamGroup.objects.filter(owner=summoner)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
'team_groups': team_groups,
}
return render(request, 'herders/profile/teams/team_list.html', context)
@login_required
def team_group_add(request, profile_name):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddTeamGroupForm(request.POST or None)
if is_owner:
if form.is_valid() and request.method == 'POST':
# Create the monster instance
new_group = form.save(commit=False)
new_group.owner = request.user.summoner
new_group.save()
return redirect(return_path)
else:
return PermissionDenied("Attempting to add group to profile you don't own.")
@login_required
def team_group_edit(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = EditTeamGroupForm(request.POST or None, instance=team_group)
if is_owner:
if form.is_valid() and request.method == 'POST':
form.save()
return redirect(return_path)
else:
return PermissionDenied("Editing a group you don't own")
context = {
'profile_name': profile_name,
'summoner': summoner,
'form': form,
'group_id': group_id,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
return render(request, 'herders/profile/teams/team_group_edit.html', context)
@login_required
def team_group_delete(request, profile_name, group_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team_group = get_object_or_404(TeamGroup, pk=group_id)
form = DeleteTeamGroupForm(request.POST or None)
form.helper.form_action = request.path
form.fields['reassign_group'].queryset = TeamGroup.objects.filter(owner=summoner).exclude(pk=group_id)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'form': form,
}
if is_owner:
if request.method == 'POST' and form.is_valid():
list_of_teams = Team.objects.filter(group__pk=group_id)
if request.POST.get('delete', False):
list_of_teams.delete()
else:
new_group = form.cleaned_data['reassign_group']
if new_group:
for team in list_of_teams:
team.group = new_group
team.save()
else:
context['validation_errors'] = 'Please specify a group to reassign to.'
if team_group.team_set.count() > 0:
return render(request, 'herders/profile/teams/team_group_delete.html', context)
else:
messages.warning(request, 'Deleted team group %s' % team_group.name)
team_group.delete()
return redirect(return_path)
else:
return PermissionDenied()
def team_detail(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
team = get_object_or_404(Team, pk=team_id)
team_effects = []
if team.leader and team.leader.monster.all_skill_effects():
for effect in team.leader.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
for team_member in team.roster.all():
if team_member.monster.all_skill_effects():
for effect in team_member.monster.all_skill_effects():
if effect not in team_effects:
team_effects.append(effect)
context = {
'view': 'teams',
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'team': team,
'team_buffs': team_effects,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/teams/team_detail.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
@login_required
def team_edit(request, profile_name, team_id=None):
return_path = reverse('herders:teams', kwargs={'profile_name': profile_name})
if team_id:
team = Team.objects.get(pk=team_id)
edit_form = EditTeamForm(request.POST or None, instance=team)
else:
edit_form = EditTeamForm(request.POST or None)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
# Limit form choices to objects owned by the current user.
edit_form.fields['group'].queryset = TeamGroup.objects.filter(owner=summoner)
edit_form.fields['leader'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.fields['roster'].queryset = MonsterInstance.objects.filter(owner=summoner)
edit_form.helper.form_action = request.path + '?next=' + return_path
context = {
'profile_name': profile_name,
'return_path': return_path,
'is_owner': is_owner,
'view': 'teams',
}
if is_owner:
edit_form.full_clean() # re-clean due to updated querysets after form initialization
if request.method == 'POST' and edit_form.is_valid():
team = edit_form.save(commit=False)
team.owner = summoner
team.save()
edit_form.save_m2m()
messages.success(request, 'Saved changes to %s - %s.' % (team.group, team))
return team_detail(request, profile_name, team.pk.hex)
else:
raise PermissionDenied()
context['edit_team_form'] = edit_form
return render(request, 'herders/profile/teams/team_edit.html', context)
@login_required
def team_delete(request, profile_name, team_id):
return_path = request.GET.get(
'next',
reverse('herders:teams', kwargs={'profile_name': profile_name})
)
team = get_object_or_404(Team, pk=team_id)
# Check for proper owner before deleting
if request.user.summoner == team.group.owner:
team.delete()
messages.warning(request, 'Deleted team %s - %s.' % (team.group, team))
return redirect(return_path)
else:
return HttpResponseForbidden()
def runes(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return render(request, 'herders/profile/not_found.html')
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
filter_form = FilterRuneForm(auto_id="filter_id_%s")
filter_form.helper.form_action = reverse('herders:rune_inventory', kwargs={'profile_name': profile_name})
context = {
'view': 'runes',
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
'old_rune_count': RuneInstance.objects.filter(owner=summoner, substats__isnull=True).count(),
'rune_filter_form': filter_form,
}
if is_owner or summoner.public:
return render(request, 'herders/profile/runes/base.html', context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory(request, profile_name, view_mode=None, box_grouping=None):
# If we passed in view mode or sort method, set the session variable and redirect back to base profile URL
if view_mode:
request.session['rune_inventory_view_mode'] = view_mode.lower()
if box_grouping:
request.session['rune_inventory_box_method'] = box_grouping.lower()
if request.session.modified:
return HttpResponse("Rune view mode cookie set")
view_mode = request.session.get('rune_inventory_view_mode', 'box').lower()
box_grouping = request.session.get('rune_inventory_box_method', 'slot').lower()
if view_mode == 'crafts':
return rune_inventory_crafts(request, profile_name)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
rune_queryset = RuneInstance.objects.filter(owner=summoner).select_related('assigned_to', 'assigned_to__monster')
total_count = rune_queryset.count()
form = FilterRuneForm(request.POST or None)
if form.is_valid():
rune_filter = RuneInstanceFilter(form.cleaned_data, queryset=rune_queryset)
else:
rune_filter = RuneInstanceFilter(None, queryset=rune_queryset)
filtered_count = rune_filter.qs.count()
context = {
'runes': rune_filter.qs,
'total_count': total_count,
'filtered_count': filtered_count,
'profile_name': profile_name,
'summoner': summoner,
'is_owner': is_owner,
}
if is_owner or summoner.public:
if view_mode == 'box':
rune_box = []
if box_grouping == 'slot':
rune_box.append({
'name': 'Slot 1',
'runes': rune_filter.qs.filter(slot=1)
})
rune_box.append({
'name': 'Slot 2',
'runes': rune_filter.qs.filter(slot=2)
})
rune_box.append({
'name': 'Slot 3',
'runes': rune_filter.qs.filter(slot=3)
})
rune_box.append({
'name': 'Slot 4',
'runes': rune_filter.qs.filter(slot=4)
})
rune_box.append({
'name': 'Slot 5',
'runes': rune_filter.qs.filter(slot=5)
})
rune_box.append({
'name': 'Slot 6',
'runes': rune_filter.qs.filter(slot=6)
})
elif box_grouping == 'grade':
rune_box.append({
'name': '6*',
'runes': rune_filter.qs.filter(stars=6)
})
rune_box.append({
'name': '5*',
'runes': rune_filter.qs.filter(stars=5)
})
rune_box.append({
'name': '4*',
'runes': rune_filter.qs.filter(stars=4)
})
rune_box.append({
'name': '3*',
'runes': rune_filter.qs.filter(stars=3)
})
rune_box.append({
'name': '2*',
'runes': rune_filter.qs.filter(stars=2)
})
rune_box.append({
'name': '1*',
'runes': rune_filter.qs.filter(stars=1)
})
elif box_grouping == 'equipped':
rune_box.append({
'name': 'Not Equipped',
'runes': rune_filter.qs.filter(assigned_to__isnull=True)
})
# Create a dictionary of monster PKs and their equipped runes
monsters = OrderedDict()
for rune in rune_filter.qs.filter(assigned_to__isnull=False).select_related('assigned_to', 'assigned_to__monster').order_by('assigned_to__monster__name', 'slot'):
if rune.assigned_to.pk not in monsters:
monsters[rune.assigned_to.pk] = {
'name': str(rune.assigned_to),
'runes': []
}
monsters[rune.assigned_to.pk]['runes'].append(rune)
for monster_runes in monsters.values():
rune_box.append(monster_runes)
elif box_grouping == 'type':
for (type, type_name) in RuneInstance.TYPE_CHOICES:
rune_box.append({
'name': type_name,
'runes': rune_filter.qs.filter(type=type)
})
context['runes'] = rune_box
context['box_grouping'] = box_grouping
template = 'herders/profile/runes/inventory.html'
elif view_mode == 'grid':
template = 'herders/profile/runes/inventory_grid.html'
else:
template = 'herders/profile/runes/inventory_table.html'
return render(request, template, context)
else:
return render(request, 'herders/profile/not_public.html', context)
def rune_inventory_crafts(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
context = {
'profile_name': profile_name,
'is_owner': is_owner,
}
if is_owner or summoner.public:
craft_box = OrderedDict()
for (craft, craft_name) in RuneInstance.CRAFT_CHOICES:
craft_box[craft_name] = OrderedDict()
for rune, rune_name in RuneInstance.TYPE_CHOICES:
craft_box[craft_name][rune_name] = RuneCraftInstance.objects.filter(owner=summoner, type=craft, rune=rune).order_by('stat', 'quality')
# Immemorial
craft_box[craft_name]['Immemorial'] = RuneCraftInstance.objects.filter(owner=summoner, type=craft, rune__isnull=True).order_by('stat', 'quality')
context['crafts'] = craft_box
return render(request, 'herders/profile/runes/inventory_crafts.html', context)
else:
return render(request, 'herders/profile/not_public.html')
@login_required
def rune_add(request, profile_name):
form = AddRuneInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the rune instance
new_rune = form.save(commit=False)
new_rune.owner = request.user.summoner
new_rune.save()
messages.success(request, 'Added ' + str(new_rune))
# Send back blank form
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
# Check for any pre-filled GET parameters
slot = request.GET.get('slot', None)
assigned_to = request.GET.get('assigned_to', None)
try:
assigned_monster = MonsterInstance.objects.get(owner=request.user.summoner, pk=assigned_to)
except MonsterInstance.DoesNotExist:
assigned_monster = None
form = AddRuneInstanceForm(initial={
'assigned_to': assigned_monster,
'slot': slot if slot is not None else 1,
})
form.helper.form_action = reverse('herders:rune_add', kwargs={'profile_name': profile_name})
# Return form filled in and errors shown
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_edit(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneInstanceForm(request.POST or None, instance=rune, auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
template = loader.get_template('herders/profile/runes/add_form.html')
if is_owner:
context = {'add_rune_form': form}
context.update(csrf(request))
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm(auto_id='edit_id_%s')
form.helper.form_action = reverse('herders:rune_edit', kwargs={'profile_name': profile_name, 'rune_id': rune_id})
context = {'add_rune_form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'add_rune_form': form}
context.update(csrf(request))
# Return form filled in and errors shown
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_assign(request, profile_name, instance_id, slot=None):
rune_queryset = RuneInstance.objects.filter(owner=request.user.summoner, assigned_to=None)
filter_form = AssignRuneForm(request.POST or None, initial={'slot': slot}, prefix='assign')
filter_form.helper.form_action = reverse('herders:rune_assign', kwargs={'profile_name': profile_name, 'instance_id': instance_id})
if slot:
rune_queryset = rune_queryset.filter(slot=slot)
if request.method == 'POST' and filter_form.is_valid():
rune_filter = RuneInstanceFilter(filter_form.cleaned_data, queryset=rune_queryset)
template = loader.get_template('herders/profile/runes/assign_results.html')
context = {
'filter': rune_filter.qs,
'profile_name': profile_name,
'instance_id': instance_id,
}
context.update(csrf(request))
response_data = {
'code': 'results',
'html': template.render(context)
}
else:
rune_filter = RuneInstanceFilter(queryset=rune_queryset)
template = loader.get_template('herders/profile/runes/assign_form.html')
context = {
'filter': rune_filter.qs,
'form': filter_form,
'profile_name': profile_name,
'instance_id': instance_id,
}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_assign_choice(request, profile_name, instance_id, rune_id):
monster = get_object_or_404(MonsterInstance, pk=instance_id)
rune = get_object_or_404(RuneInstance, pk=rune_id)
if rune.assigned_to is not None:
# TODO: Warn about removing from other monster?
pass
# Check for existing rune.
existing_runes = monster.runeinstance_set.filter(slot=rune.slot)
for existing_rune in existing_runes:
existing_rune.assigned_to = None
rune.assigned_to = monster
rune.save()
monster.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
@login_required
def rune_unassign(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
rune.assigned_to = None
rune.save()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required()
def rune_unassign_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
assigned_mons = []
assigned_runes = RuneInstance.objects.filter(owner=summoner, assigned_to__isnull=False)
number_assigned = assigned_runes.count()
if is_owner:
for rune in assigned_runes:
if rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
rune.assigned_to = None
rune.save()
# Resave monster instances that had runes removed to recalc stats
for mon in assigned_mons:
mon.save()
messages.success(request, 'Unassigned ' + str(number_assigned) + ' rune(s).')
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete(request, profile_name, rune_id):
rune = get_object_or_404(RuneInstance, pk=rune_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
mon = rune.assigned_to
messages.warning(request, 'Deleted ' + str(rune))
rune.delete()
if mon:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_delete_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
# Delete the runes
death_row = RuneInstance.objects.filter(owner=summoner)
number_killed = death_row.count()
assigned_mons = []
for rune in death_row:
if rune.assigned_to and rune.assigned_to not in assigned_mons:
assigned_mons.append(rune.assigned_to)
death_row.delete()
# Delete the crafts
RuneCraftInstance.objects.filter(owner=summoner).delete()
messages.warning(request, 'Deleted ' + str(number_killed) + ' runes.')
for mon in assigned_mons:
mon.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_resave_all(request, profile_name):
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
for r in RuneInstance.objects.filter(owner=summoner, substats__isnull=True):
r.save()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_add(request, profile_name):
form = AddRuneCraftInstanceForm(request.POST or None)
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if request.method == 'POST':
if form.is_valid():
# Create the monster instance
new_craft = form.save(commit=False)
new_craft.owner = request.user.summoner
new_craft.save()
messages.success(request, 'Added ' + new_craft.get_type_display() + ' ' + str(new_craft))
# Send back blank form
form = AddRuneCraftInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_add', kwargs={'profile_name': profile_name})
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'html': template.render(context)
}
return JsonResponse(response_data)
@login_required
def rune_craft_edit(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
form = AddRuneCraftInstanceForm(request.POST or None, instance=craft)
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
template = loader.get_template('herders/profile/runes/add_craft_form.html')
if is_owner:
if request.method == 'POST' and form.is_valid():
rune = form.save()
messages.success(request, 'Saved changes to ' + str(rune))
form = AddRuneInstanceForm()
form.helper.form_action = reverse('herders:rune_craft_edit', kwargs={'profile_name': profile_name, 'craft_id': craft_id})
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'success',
'html': template.render(context)
}
else:
# Return form filled in and errors shown
context = {'form': form}
context.update(csrf(request))
response_data = {
'code': 'error',
'html': template.render(context)
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
@login_required
def rune_craft_delete(request, profile_name, craft_id):
craft = get_object_or_404(RuneCraftInstance, pk=craft_id)
try:
summoner = Summoner.objects.select_related('user').get(user__username=profile_name)
except Summoner.DoesNotExist:
return HttpResponseBadRequest()
is_owner = (request.user.is_authenticated() and summoner.user == request.user)
if is_owner:
messages.warning(request, 'Deleted ' + craft.get_rune_display() + ' ' + str(craft))
craft.delete()
response_data = {
'code': 'success',
}
return JsonResponse(response_data)
else:
return HttpResponseForbidden()
|
from __future__ import division
import random
from itertools import combinations_with_replacement, izip
from pydigree.common import *
from pydigree.ibs import ibs
from pydigree.io.smartopen import smartopen
from pydigree.io.plink import write_plink, write_map
from pydigree.individual import Individual
from pydigree.exceptions import SimulationError
# A base class for simulations to inherit from
class Simulation(object):
def __init__(self, template=None, replications=1000):
self.template = template
self.replications = replications
self.accuracy_threshold = 0.9
self.constraints = {'genotype': {}, 'ibd': {}}
self.trait = None
self.founder_genotype_hooks = []
def set_trait(self, architecture):
self.trait = architecture
def replicate(self):
raise NotImplementedError("This is a base class don't call me")
def get_founder_genotypes(self):
geno_constraints = self.constraints['genotype']
for ind in ped.founders():
ind.clear_genotypes()
if ind not in geno_constraints:
ind.get_genotypes(linkeq=linkeq)
else:
ind.get_constrained_genotypes(geno_constraints[ind],
linkeq=linkeq)
self.run_founder_genotype_hooks()
def run(self, verbose=False, writeibd=False, output_predicate=None, compression=None):
write_map(self.template, '{0}.map'.format(self.prefix))
for x in xrange(self.replications):
print 'Replicate %d' % (x + 1)
self.replicate(
verbose=verbose, writeibd=writeibd, replicatenumber=x)
self.write_data(
x, predicate=output_predicate, compression=compression)
def write_data(self, replicatenumber, predicate=None, compression=None):
filename = '{0}-{1}'.format(self.prefix, (replicatenumber + 1))
write_plink(self.template, filename, predicate=predicate,
mapfile=False, compression=compression)
def _writeibd(self, replicatenumber):
# Warning: Don't call this function! If the individuals in the pedigree dont have
# LABEL genotypes, you're just going to get IBS configurations at each locus, not
# actual IBD calculations.
#
# If you have data you want to identify IBD segments in, check
# pydigree.sgs
with smartopen('{0}-{1}.ibd.gz'.format(self.prefix, replicatenumber + 1), 'w') as of:
for ped in self.template:
for ind1, ind2 in combinations_with_replacement(ped.individuals, 2):
identical = []
for chrom_idx, chromosome in enumerate(ind1.chromosomes):
if ind1 == ind2:
genos = izip(*ind1.genotypes[chrom_idx])
ibd = [2 * (x == y) for x, y in genos]
else:
genos1 = izip(*ind1.genotypes[chrom_idx])
genos2 = izip(*ind2.genotypes[chrom_idx])
ibd = [ibs(g1, g2)
for g1, g2 in izip(genos1, genos2)]
identical.extend(ibd)
outline = [ped.label, ind1.label, ind2.label] + identical
outline = ' '.join([str(x) for x in outline])
of.write('{}\n'.format(outline))
def predicted_trait_accuracy(self, ped):
calls = [(ind.predicted_phenotype(self.trait),
ind.phenotypes['affected'])
for ind in ped
if ind.phenotypes['affected'] is not None]
# Remember: in python the bools True and False are actually alternate
# names for the integers 1 and 0, respectively, so you can do
# arithmetic with them if you so please. Here we sum up all the
# correct predictions and divide by the number of predictions made.
return sum(x == y for x, y in calls) / len(calls)
def read_constraints(self, filename):
if not self.template:
raise ValueError()
with open(filename) as f:
for line in f:
line = line.strip()
if not line or line.startswith('#'):
continue
l = line.split()
if l[0].lower() == 'genotype':
type, ped, id, chr, index, allele, chromatid, method = l
locus = (chr, index)
ind = self.template[ped][id]
self.add_genotype_constraint(ind, locus, allele,
chromatid, method)
elif l[0].lower() == 'ibd':
type, ped, id, ancestor, chr, index, anc_chromatid = l
locus = (chr, index)
ind = self.template[ped][id]
ancestor = self.template[ped][ancestor]
self.add_ibd_constraint(ind, ancestor,
locus, anc_chromatid)
else:
raise ValueError('Not a valid constraint (%s)' % l[0])
def add_genotype_constraint(self, ind, location, allele,
chromatid, method='set'):
if not ind.is_founder():
raise ValueError('Genotype constraints only for founders')
if chromatid not in 'PM':
raise ValueError('Not a valid haplotype. Choose P or M')
chromatid = 1 if chromatid == 'M' else 0
location = tuple(int(x) for x in location)
allele = int(allele)
if ind not in self.constraints['genotype']:
self.constraints['genotype'][ind] = []
c = (location, chromatid, allele, method)
self.constraints['genotype'][ind].append(c)
def add_ibd_constraint(self, ind, ancestor, location, anchap):
if anchap not in 'PM':
raise ValueError('Not a valid haplotype. Choose P or M')
anchap = 1 if anchap == 'M' else 0
location = tuple(int(x) for x in location)
if ind not in self.constraints['ibd']:
self.constraints['ibd'][ind] = []
c = (ancestor, location, anchap)
self.constraints['ibd'][ind].append(c)
def add_founder_genotype_hook(self, func):
founder_genotypes_hooks.append(func)
def run_founder_genotype_hooks(self):
for hook in self.founder_genotypes_hooks:
for founder in self.template.founders():
hook(founder)
code cleanup
from __future__ import division
import random
from itertools import combinations_with_replacement, izip
from pydigree.common import *
from pydigree.ibs import ibs
from pydigree.io.smartopen import smartopen
from pydigree.io.plink import write_plink, write_map
from pydigree.io.base import write_phenotypes
from pydigree.individual import Individual
from pydigree.exceptions import SimulationError
# A base class for simulations to inherit from
class Simulation(object):
def __init__(self, template=None, label=None, replications=1000):
self.template = template
self.label = label if label is not None else 'unlabeled'
self.replications = replications
self.accuracy_threshold = 0.9
self.constraints = {'genotype': {}, 'ibd': {}}
self.trait = None
self.founder_genotype_hooks = []
def set_trait(self, architecture):
self.trait = architecture
def replicate(self):
raise NotImplementedError("This is a base class don't call me")
def get_founder_genotypes(self, linkeq=True):
geno_constraints = self.constraints['genotype']
for ind in self.template.founders():
ind.clear_genotypes()
if ind not in geno_constraints:
ind.get_genotypes(linkeq=linkeq)
else:
ind.get_constrained_genotypes(geno_constraints[ind],
linkeq=linkeq)
self.run_founder_genotype_hooks()
def run(self, verbose=False, writeibd=False, output_predicate=None, compression=None):
write_map(self.template, '{0}.map'.format(self.label))
for x in xrange(self.replications):
print 'Replicate %d' % (x + 1)
self.replicate(
verbose=verbose, writeibd=writeibd, replicatenumber=x)
self.write_data(
x, predicate=output_predicate, compression=compression)
def write_data(self, replicatenumber, predicate=None, compression=None):
filename = '{0}-{1}'.format(self.label, (replicatenumber + 1))
write_plink(self.template, filename, predicate=predicate,
mapfile=False, compression=compression)
write_phenotypes(self.template, filename + '.csv',
predicate=predicate)
def _writeibd(self, replicatenumber):
# Warning: Don't call this function! If the individuals in the pedigree dont have
# LABEL genotypes, you're just going to get IBS configurations at each locus, not
# actual IBD calculations.
#
# If you have data you want to identify IBD segments in, check
# pydigree.sgs
with smartopen('{0}-{1}.ibd.gz'.format(self.label, replicatenumber + 1), 'w') as of:
for ped in self.template:
for ind1, ind2 in combinations_with_replacement(ped.individuals, 2):
identical = []
for chrom_idx, chromosome in enumerate(ind1.chromosomes):
if ind1 == ind2:
genos = izip(*ind1.genotypes[chrom_idx])
ibd = [2 * (x == y) for x, y in genos]
else:
genos1 = izip(*ind1.genotypes[chrom_idx])
genos2 = izip(*ind2.genotypes[chrom_idx])
ibd = [ibs(g1, g2)
for g1, g2 in izip(genos1, genos2)]
identical.extend(ibd)
outline = [ped.label, ind1.label, ind2.label] + identical
outline = ' '.join([str(x) for x in outline])
of.write('{}\n'.format(outline))
def predicted_trait_accuracy(self, ped):
calls = [(ind.predicted_phenotype(self.trait),
ind.phenotypes['affected'])
for ind in ped
if ind.phenotypes['affected'] is not None]
# Remember: in python the bools True and False are actually alternate
# names for the integers 1 and 0, respectively, so you can do
# arithmetic with them if you so please. Here we sum up all the
# correct predictions and divide by the number of predictions made.
return sum(x == y for x, y in calls) / len(calls)
def read_constraints(self, filename):
if not self.template:
raise ValueError()
with open(filename) as f:
for line in f:
line = line.strip()
if not line or line.startswith('#'):
continue
l = line.split()
if l[0].lower() == 'genotype':
type, ped, id, chr, index, allele, chromatid, method = l
locus = (chr, index)
ind = self.template[ped][id]
self.add_genotype_constraint(ind, locus, allele,
chromatid, method)
elif l[0].lower() == 'ibd':
type, ped, id, ancestor, chr, index, anc_chromatid = l
locus = (chr, index)
ind = self.template[ped][id]
ancestor = self.template[ped][ancestor]
self.add_ibd_constraint(ind, ancestor,
locus, anc_chromatid)
else:
raise ValueError('Not a valid constraint (%s)' % l[0])
def add_genotype_constraint(self, ind, location, allele,
chromatid, method='set'):
if not ind.is_founder():
raise ValueError('Genotype constraints only for founders')
if chromatid not in 'PM':
raise ValueError('Not a valid haplotype. Choose P or M')
chromatid = 1 if chromatid == 'M' else 0
location = tuple(int(x) for x in location)
allele = int(allele)
if ind not in self.constraints['genotype']:
self.constraints['genotype'][ind] = []
c = (location, chromatid, allele, method)
self.constraints['genotype'][ind].append(c)
def add_ibd_constraint(self, ind, ancestor, location, anchap):
if anchap not in 'PM':
raise ValueError('Not a valid haplotype. Choose P or M')
anchap = 1 if anchap == 'M' else 0
location = tuple(int(x) for x in location)
if ind not in self.constraints['ibd']:
self.constraints['ibd'][ind] = []
c = (ancestor, location, anchap)
self.constraints['ibd'][ind].append(c)
def add_founder_genotype_hook(self, func):
self.founder_genotype_hooks.append(func)
def run_founder_genotype_hooks(self):
for hook in self.founder_genotype_hooks:
for founder in self.template.founders():
hook(founder)
|
__author__ = 'noe'
import psutil
import numpy as np
from pyemma.coordinates.clustering.interface import AbstractClustering
from pyemma.coordinates.transform.transformer import Transformer
from pyemma.coordinates.io.reader import ChunkedReader
from pyemma.coordinates.io.feature_reader import FeatureReader
from pyemma.coordinates.util.chaining import build_chain
from pyemma.util.log import getLogger
logger = getLogger('Discretizer')
__all__ = ['Discretizer']
class Discretizer(object):
"""
A Discretizer gets a FeatureReader, which defines the features (distances,
angles etc.) of given trajectory data and passes this data in a memory
efficient way through the given pipeline of a Transformer and a clustering.
The clustering object is responsible for assigning the data to discrete
states.
Currently the constructor will calculate everything instantly.
Parameters
----------
reader : a FeatureReader object
reads trajectory data and selects features.
transform : a Transformer object (optional)
the Transformer will be used to e.g reduce dimensionality of inputs.
cluster : a clustering object
used to assign input data to discrete states/ discrete trajectories.
"""
def __init__(self, reader, transform=None, cluster=None, chunksize=None):
# check input
assert isinstance(reader, ChunkedReader), \
'reader is not of the correct type'
if (transform is not None):
assert isinstance(transform, Transformer), \
'transform is not of the correct type'
if cluster is None:
raise ValueError('Must specify a clustering algorithm!')
else:
assert isinstance(cluster, Transformer), \
'cluster is not of the correct type'
if hasattr(reader, 'featurizer'): # reader is a FeatureReader
if reader.featurizer.dimension == 0:
logger.warning("no features selected!")
self.transformers = [reader]
if transform is not None:
self.transformers.append(transform)
self.transformers.append(cluster)
if chunksize is not None:
build_chain(self.transformers, chunksize)
else:
self._chunksize = None
build_chain(self.transformers)
self._estimate_chunksize_from_mem_requirement(reader)
self._parameterized = False
def run(self):
"""
reads all data and discretizes it into discrete trajectories
"""
for trans in self.transformers:
trans.parametrize()
self._parameterized = True
@property
def dtrajs(self):
""" get discrete trajectories """
if not self._parameterized:
logger.info("not yet parametrized, running now.")
self.run()
return self.transformers[-1].dtrajs
@property
def chunksize(self):
return self._chunksize
@chunksize.setter
def chunksize(self, cs):
self._chunksize = cs
# update transformers to use new chunksize
for trans in self.transformers:
trans.chunksize = cs
def save_dtrajs(self, prefix='', output_format='ascii', extension='.dtraj'):
"""saves calculated discrete trajectories. Filenames are taken from
given reader. If data comes from memory dtrajs are written to a default
filename.
Parameters
----------
prefix : str
prepend prefix to filenames.
output_format : str
if format is 'ascii' dtrajs will be written as csv files, otherwise
they will be written as NumPy .npy files.
extension : str
file extension to append (eg. '.itraj')
"""
clustering = self.transformers[-1]
reader = self.transformers[0]
assert isinstance(clustering, AbstractClustering)
trajfiles = None
if isinstance(reader, FeatureReader):
trajfiles = reader.trajfiles
clustering.save_dtrajs(trajfiles, prefix, output_format, extension)
def _estimate_chunksize_from_mem_requirement(self, reader):
"""
estimate memory requirement from chain of transformers and sets a
chunksize accordingly
"""
if not hasattr(reader, 'get_memory_per_frame'):
self.chunksize = 0
return
M = psutil.virtual_memory()[1] # available RAM in bytes
logger.info("available RAM: %i" % M)
const_mem = long(0)
mem_per_frame = long(0)
for trans in self.transformers:
mem_per_frame += trans.get_memory_per_frame()
const_mem += trans.get_constant_memory()
logger.info("per-frame memory requirements: %i" % mem_per_frame)
# maximum allowed chunk size
logger.info("const mem: %i" % const_mem)
chunksize = (M - const_mem) / mem_per_frame
if chunksize < 0:
raise MemoryError(
'Not enough memory for desired transformation chain!')
# is this chunksize sufficient to store full trajectories?
chunksize = min(chunksize, np.max(reader.trajectory_lengths()))
logger.info("resulting chunk size: %i" % chunksize)
# set chunksize
self.chunksize = chunksize
# any memory unused? if yes, we can store results
Mfree = M - const_mem - chunksize * mem_per_frame
logger.info("free memory: %i" % Mfree)
# starting from the back of the pipeline, store outputs if possible
for trans in reversed(self.transformers):
mem_req_trans = trans.n_frames_total() * \
trans.get_memory_per_frame()
if Mfree > mem_req_trans:
Mfree -= mem_req_trans
# TODO: before we are allowed to call this method, we have to ensure all memory requirements are correct!
# trans.operate_in_memory()
logger.info("spending %i bytes to operate in main memory: %s "
% (mem_req_trans, trans.describe()))
[discretizer] reflect module movement
__author__ = 'noe'
import psutil
import numpy as np
from pyemma.coordinates.clustering.interface import AbstractClustering
from pyemma.coordinates.transform.transformer import Transformer
from pyemma.coordinates.io.reader import ChunkedReader
from pyemma.coordinates.io.feature_reader import FeatureReader
from pyemma.coordinates.util.stat.chaining import build_chain
from pyemma.util.log import getLogger
logger = getLogger('Discretizer')
__all__ = ['Discretizer']
class Discretizer(object):
"""
A Discretizer gets a FeatureReader, which defines the features (distances,
angles etc.) of given trajectory data and passes this data in a memory
efficient way through the given pipeline of a Transformer and a clustering.
The clustering object is responsible for assigning the data to discrete
states.
Currently the constructor will calculate everything instantly.
Parameters
----------
reader : a FeatureReader object
reads trajectory data and selects features.
transform : a Transformer object (optional)
the Transformer will be used to e.g reduce dimensionality of inputs.
cluster : a clustering object
used to assign input data to discrete states/ discrete trajectories.
"""
def __init__(self, reader, transform=None, cluster=None, chunksize=None):
# check input
assert isinstance(reader, ChunkedReader), \
'reader is not of the correct type'
if (transform is not None):
assert isinstance(transform, Transformer), \
'transform is not of the correct type'
if cluster is None:
raise ValueError('Must specify a clustering algorithm!')
else:
assert isinstance(cluster, Transformer), \
'cluster is not of the correct type'
if hasattr(reader, 'featurizer'): # reader is a FeatureReader
if reader.featurizer.dimension == 0:
logger.warning("no features selected!")
self.transformers = [reader]
if transform is not None:
self.transformers.append(transform)
self.transformers.append(cluster)
if chunksize is not None:
build_chain(self.transformers, chunksize)
else:
self._chunksize = None
build_chain(self.transformers)
self._estimate_chunksize_from_mem_requirement(reader)
self._parameterized = False
def run(self):
"""
reads all data and discretizes it into discrete trajectories
"""
for trans in self.transformers:
trans.parametrize()
self._parameterized = True
@property
def dtrajs(self):
""" get discrete trajectories """
if not self._parameterized:
logger.info("not yet parametrized, running now.")
self.run()
return self.transformers[-1].dtrajs
@property
def chunksize(self):
return self._chunksize
@chunksize.setter
def chunksize(self, cs):
self._chunksize = cs
# update transformers to use new chunksize
for trans in self.transformers:
trans.chunksize = cs
def save_dtrajs(self, prefix='', output_format='ascii', extension='.dtraj'):
"""saves calculated discrete trajectories. Filenames are taken from
given reader. If data comes from memory dtrajs are written to a default
filename.
Parameters
----------
prefix : str
prepend prefix to filenames.
output_format : str
if format is 'ascii' dtrajs will be written as csv files, otherwise
they will be written as NumPy .npy files.
extension : str
file extension to append (eg. '.itraj')
"""
clustering = self.transformers[-1]
reader = self.transformers[0]
assert isinstance(clustering, AbstractClustering)
trajfiles = None
if isinstance(reader, FeatureReader):
trajfiles = reader.trajfiles
clustering.save_dtrajs(trajfiles, prefix, output_format, extension)
def _estimate_chunksize_from_mem_requirement(self, reader):
"""
estimate memory requirement from chain of transformers and sets a
chunksize accordingly
"""
if not hasattr(reader, 'get_memory_per_frame'):
self.chunksize = 0
return
M = psutil.virtual_memory()[1] # available RAM in bytes
logger.info("available RAM: %i" % M)
const_mem = long(0)
mem_per_frame = long(0)
for trans in self.transformers:
mem_per_frame += trans.get_memory_per_frame()
const_mem += trans.get_constant_memory()
logger.info("per-frame memory requirements: %i" % mem_per_frame)
# maximum allowed chunk size
logger.info("const mem: %i" % const_mem)
chunksize = (M - const_mem) / mem_per_frame
if chunksize < 0:
raise MemoryError(
'Not enough memory for desired transformation chain!')
# is this chunksize sufficient to store full trajectories?
chunksize = min(chunksize, np.max(reader.trajectory_lengths()))
logger.info("resulting chunk size: %i" % chunksize)
# set chunksize
self.chunksize = chunksize
# any memory unused? if yes, we can store results
Mfree = M - const_mem - chunksize * mem_per_frame
logger.info("free memory: %i" % Mfree)
# starting from the back of the pipeline, store outputs if possible
for trans in reversed(self.transformers):
mem_req_trans = trans.n_frames_total() * \
trans.get_memory_per_frame()
if Mfree > mem_req_trans:
Mfree -= mem_req_trans
# TODO: before we are allowed to call this method, we have to ensure all memory requirements are correct!
# trans.operate_in_memory()
logger.info("spending %i bytes to operate in main memory: %s "
% (mem_req_trans, trans.describe()))
|
#!/usr/bin/env python
"""
Created as part of the StratusLab project (http://stratuslab.eu),
co-funded by the European Commission under the Grant Agreement
INSFO-RI-261552.
Copyright (c) 2011, Centre National de la Recherche Scientifique (CNRS)
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.
"""
__version__ = "1.0"
__author__ = "Guillaume PHILIPPON <guillaume.philippon@lal.in2p3.fr>"
import sys
sys.path.append('/var/lib/stratuslab/python')
import os
import re
from optparse import OptionParser, OptionGroup
import ConfigParser
import socket
import httplib2
import json
from StringIO import StringIO
from urllib import urlencode
from subprocess import call
import commands
sample_example="""
[main]
pdisk_user=pdisk
pdisk_passwd=xxxxxx
register_filename=pdisk
getTurlCallback=/path/to/script
vm_dir=/var/lib/one
[iscsi]
iscsiadm=/usr/sbin/iscsiadm
"""
config = ConfigParser.RawConfigParser()
config.read('/etc/stratuslab/pdisk-host.conf')
iscsiadm=config.get("iscsi","iscsiadm")
login=config.get("main","pdisk_user")
pswd=config.get("main","pdisk_passwd")
getTurlCallback=config.get("main","getTurlCallback")
vm_dir=config.get("main","vm_dir")
parser=OptionParser()
parser.add_option("--pdisk-id", dest="persistent_disk_id",
help="Persistent disk id ( pdisk:enpoint:port:disk_uuid )", metavar="PID"
)
parser.add_option("--vm-id", dest="vm_id",
help="VM id", metavar="ID"
)
parser.add_option("--vm-dir", dest="vm_dir",
help="Directory where device will be created", metavar="DIR"
)
parser.add_option("--vm-disk-name", dest="disk_name",
help="Name of disk on Virtual Machine directory"
)
parser.add_option("--target", dest="target",
help="Device name on Virtual Machine"
)
parser.add_option("--turl", dest="turl", metavar="TURL", default="",
help="Transport URL of pdisk (protocol://server/protocol-option-to-access-file)"
)
parser.add_option("--username", dest="username",
help="Username use to interact with pdisk server"
)
parser.add_option("--password", dest="password",
help="Password use to interact with pdisk server"
)
action=OptionGroup(parser," Action command")
action.add_option("--attach", dest="attach", action="store_true",
help="Attach/Detach backend to hypervisor"
)
action.add_option("--register", dest="registration", action="store_true",
help="Register/Unregister persistent disk as used on service"
)
action.add_option("--link", dest="link", action="store_true",
help="Link/Unlink attached disk in Virtual Machine directory"
)
action.add_option("--mount", dest="mount", action="store_true",
help="Mount/Unmount disk into Virtual Machine"
)
action.add_option("--status", dest="status", action="store_true",
help="Display status of current persistent disk"
)
action.add_option("--no-check", dest="no_check", action="store_true",
help="Disable check if device is used"
)
action.add_option("--op", dest="operation", metavar="OP",
help="up : active persistent disk ( register / attach / link / mount ) -- down : desactive persistent disk ( unmount / unlink / detach / unregister )"
)
parser.add_option_group(action)
(options, args) = parser.parse_args()
if not options.operation:
raise parser.error("--op options is mandatory")
if options.attach:
if not options.persistent_disk_id :
raise parser.error("--attach option need --pdisk-id options")
if options.registration:
if not options.persistent_disk_id or not options.vm_id:
raise parser.error("--register need --pdisk-id and --vm-id options")
if options.link:
if not options.persistent_disk_id or ( not vm_dir and not options.vm_dir ) or not options.vm_id or not options.disk_name :
raise parser.error("--link need --pdisk-id, --vm-disk-name, --vm-id options are needed, --vm-dir if not define on configuration file ( /etc/stratuslab/pdisk-host.conf)")
if options.mount:
if not options.persistent_disk_id or not options.vm_id or not options.target:
raise parser.error("--mount need --pdisk-id, --target and --vm-id options")
if not options.persistent_disk_id:
raise parser.error("--pdisk-id is mandatory")
if options.vm_dir:
vm_dir = options.vm_dir
if options.username:
login = options.username
if options.password:
pswd = options.password
"""
Metaclass for general persistent disk client
"""
class PersistentDisk:
def __registration_uri__(self):
return "https://"+self.endpoint+":"+self.port+"/pswd/disks/"+self.disk_uuid+"/"
"""
Register/Unregister mount on pdisk endpoint
"""
def register(self,login,pswd,vm_id):
node=socket.gethostbyname(socket.gethostname())
url = self.__registration_uri__()+"mounts/"
h = httplib2.Http("/tmp/.cache")
h.disable_ssl_certificate_validation=True
h.add_credentials(login,pswd)
data = dict(node=node, vm_id=vm_id,register_only="true")
try:
resp, contents = h.request(url,"POST",urlencode(data))
except httplib2.ServerNotFoundError:
raise RegisterPersistentDiskException('Register : Server '+self.endpoint+' not found')
def unregister(self,login,pswd,vm_id):
node=socket.gethostbyname(socket.gethostname())
url = self.__registration_uri__()+"mounts/"+self.disk_uuid+"_"+vm_id
h = httplib2.Http("/tmp/.cache")
h.add_credentials(login,pswd)
h.disable_ssl_certificate_validation=True
try:
resp, contents = h.request(url,"DELETE")
except httplib2.ServerNotFoundError:
raise RegisterPersistentDiskException('Unregister : Server '+self.endpoint+' not found')
"""
Link/Unlink, create a link between device ( image or physical device) and Virtual Machine directory
"""
def link(self,src,dst):
try:
if os.path.exists(dst):
os.unlink(dst)
os.symlink(src,dst)
except:
raise LinkPersistentDiskException('Link : Error while linking '+src+' to '+dst)
def unlink(self,link):
if os.path.exists(link):
os.unlink(link)
"""
Mount/Unmount display device to a VM
"""
def mount(self,vm_id,disk_name,target_device):
hypervisor_device=vm_dir+"/"+str(vm_id)+"/images/"+disk_name
domain_name="one-"+str(vm_id)
cmd="sudo /usr/bin/virsh attach-disk "+domain_name+" "+hypervisor_device+" "+target_device
retcode=call(cmd,shell=True)
def umount(self,vm_id,target_device):
domain_name="one-"+str(vm_id)
cmd="sudo /usr/bin/virsh detach-disk "+domain_name+" "+target_device
retcode=call(cmd,shell=True)
"""
_copy used to create a a xxxPersistentDisk object from PersistentDisk (xxxPersistentDisk is a inheritated class)
"""
def __copy__(self,pdisk):
self.endpoint = pdisk.endpoint
self.port = pdisk.port
self.disk_uuid = pdisk.disk_uuid
self.protocol = pdisk.protocol
self.server = pdisk.server
self.image = pdisk.image
"""
check_mount used to check if pdisk is already used return true if pdisk is free
"""
def check_mount(self,login,pswd):
url = self.__registration_uri__()
h = httplib2.Http("/tmp/.cache")
h.add_credentials(login,pswd)
h.disable_ssl_certificate_validation=True
resp, contents = h.request(url)
#print contents
if resp.status != 200:
raise CheckPersistentDiskException('Check_mount : error while check '+self.endpoint+' with url '+url)
io = StringIO(contents)
json_output = json.load(io)
# try:
# if json_output['count'] != '0':
# raise CheckPersistentDiskException('Check_mount : pdisk pdisk:'+ self.endpoint+':'+self.port+':'+self.disk_uuid+' is mounted')
return False
# except KeyError:
# return False
"""
__checkTurl__ check and split Transport URL ( proto://server:port/proto_options ) from pdisk id ( pdisk:endpoint:port:disk_uuid )
"""
def __checkTurl__(self,turl):
if turl == "":
__url__ = "iscsi://"+self.endpoint+":3260/iqn.2011-01.eu.stratuslab:"+self.disk_uuid+":1"
else:
__url__ = turl
__uri__ = re.match(r"(?P<protocol>.*)://(?P<server>.*)/(?P<image>.*)", __url__)
try :
self.protocol = __uri__.group('protocol')
self.server = __uri__.group('server')
self.image = __uri__.group('image')
except AttributeError:
raise URIPersistentDiskException('TURL '+ turl + ' not match expression protocol://server/protocol-options')
def __init__(self, pdisk_id, turl):
try:
__pdisk__ = re.match(r"pdisk:(?P<server>.*):(?P<port>.*):(?P<disk_uuid>.*)", pdisk_id)
self.endpoint = __pdisk__.group('server')
self.port = __pdisk__.group('port')
self.disk_uuid = __pdisk__.group('disk_uuid')
self.__checkTurl__(turl)
except AttributeError:
raise PersistentDiskException('URI '+pdisk_id+' not match expression pdisk:endpoint:port:disk_uuid')
class IscsiPersistentDisk(PersistentDisk):
_unix_device_path='/dev/disk/by-path/'
def image_storage(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname( __portal__.group('server') )
dev = "ip-" + __portal_ip__ + ":" + __portal__.group('port') + "-iscsi-" + self.iqn + "-lun-" + self.lun
return self._unix_device_path+dev
def attach(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname(__portal__.group('server'))
reg = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " -o new"
cmd = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " --login"
retcode = call(reg, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
retcode = call(cmd, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
def detach(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname(__portal__.group('server'))
cmd = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " --logout"
unreg = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " -o delete"
retcode = call(cmd, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
retcode = call(unreg, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
def __image2iqn__(self, str):
__iqn__ = re.match(r"(?P<iqn>.*:.*):(?P<lun>.*)", str)
self.iqn = __iqn__.group('iqn')
self.lun = __iqn__.group('lun')
def __init__(self,pdisk_class,turl):
self.__copy__(pdisk_class)
self.__image2iqn__(pdisk_class.image)
class FilePersistentDisk(PersistentDisk):
def __image2file__(self, str):
__file__ = re.match(r"(?P<mount_point>.*)/(?P<full_path>.*)", str)
self.mount_point = __file__.group('mount_point')
self.full_path = __file__.group('full_path')
def image_storage(self):
return self.server+"/"+self.image
def attach(self):
pass
def detach(self):
pass
def __init__(self,pdisk_class,turl):
self.__copy__(pdisk_class)
class PersistentDiskException(Exception):
def __init__(self,value):
self.value = value
class RegisterPersistentDiskException(PersistentDisk):
def __init__(self,value):
self.value = value
class AttachPersistentDiskException(PersistentDisk):
def __init__(self,value):
self.value = value
class URIPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class LinkPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class MountPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class CheckPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class getTurlPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
def __init__():
try:
global_pdisk = PersistentDisk(options.persistent_disk_id,options.turl)
except getTurlPersistentDiskException:
print "Error while try to retrive %s" % options.persistent_disk_id
return -1
global vm_dir
if global_pdisk.protocol == "iscsi" :
pdisk = IscsiPersistentDisk(global_pdisk,options.turl)
elif global_pdisk.protocol == "file" :
pdisk = FilePersistentDisk(global_pdisk,options.turl)
else :
print "Protocol "+global_pdisk.protocol+" not supported"
if options.operation == "up":
try:
if not options.no_check:
pdisk.check_mount(login,pswd)
if options.registration:
pdisk.register(login,pswd,options.vm_id)
if options.attach:
pdisk.attach()
if options.link:
src = pdisk.image_storage()
dst = vm_dir+"/"+str(options.vm_id)+"/images/"+options.disk_name
pdisk.link(src,dst)
if options.mount:
pdisk.mount(options.vm_id,options.disk_name,options.target)
except CheckPersistentDiskException:
print "pdisk is market as used, please check or use --register --op down"
except RegisterPersistentDiskException:
print "Error while try to register on pdisk"
except AttachPersistentDiskException:
print "Error while try to attach backend to hypervisor"
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except LinkPersistentDiskException:
print "Error while try to link %s to %s" % ( src , dst )
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except MountPersistentDiskException:
print "Error while try to mount %s to %s" % ( options.persistent_disk_id , options.vm_id )
if options.link:
pdisk.unlink(dst)
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
elif options.operation == "down":
try:
if options.mount:
pdisk.umount(options.vm_id,options.target)
if options.link:
dst = vm_dir+"/"+str(options.vm_id)+"/images/"+options.disk_name
pdisk.unlink(dst)
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except MountPersistentDiskException:
print "Error while try to umount %s to %s " % ( options.persistent_disk_id , options.vm_id )
except LinkPersistentDiskException:
print "Error while try to unlink %s" % dst
except AttachPersistentDiskException:
print "Error while try to detach %s from backend" % options.persistentd_disk_id
except RegisterPersistentDiskException:
print "Error while try to unregister as unused"
else:
raise parser.error("--op options only allow up or down")
__init__()
Minor doc update
#!/usr/bin/env python
"""
Created as part of the StratusLab project (http://stratuslab.eu),
co-funded by the European Commission under the Grant Agreement
INSFO-RI-261552.
Copyright (c) 2011, Centre National de la Recherche Scientifique (CNRS)
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.
"""
__version__ = "1.0"
__author__ = "Guillaume PHILIPPON <guillaume.philippon@lal.in2p3.fr>"
import sys
sys.path.append('/var/lib/stratuslab/python')
import os
import re
from optparse import OptionParser, OptionGroup
import ConfigParser
import socket
import httplib2
import json
from StringIO import StringIO
from urllib import urlencode
from subprocess import call
import commands
sample_example="""
[main]
pdisk_user=pdisk
pdisk_passwd=xxxxxx
register_filename=pdisk
getTurlCallback=/path/to/script
vm_dir=/var/lib/one
[iscsi]
iscsiadm=/usr/sbin/iscsiadm
"""
config = ConfigParser.RawConfigParser()
config.read('/etc/stratuslab/pdisk-host.conf')
iscsiadm=config.get("iscsi","iscsiadm")
login=config.get("main","pdisk_user")
pswd=config.get("main","pdisk_passwd")
getTurlCallback=config.get("main","getTurlCallback")
vm_dir=config.get("main","vm_dir")
parser=OptionParser()
parser.add_option("--pdisk-id", dest="persistent_disk_id",
help="Persistent disk id ( pdisk:enpoint:port:disk_uuid )", metavar="PID"
)
parser.add_option("--vm-id", dest="vm_id",
help="VM id", metavar="ID"
)
parser.add_option("--vm-dir", dest="vm_dir",
help="Directory where device will be created", metavar="DIR"
)
parser.add_option("--vm-disk-name", dest="disk_name",
help="Name of disk on Virtual Machine directory"
)
parser.add_option("--target", dest="target",
help="Device name on Virtual Machine"
)
parser.add_option("--turl", dest="turl", metavar="TURL", default="",
help="Transport URL of pdisk (protocol://server/protocol-option-to-access-file)"
)
parser.add_option("--username", dest="username",
help="Username use to interact with pdisk server"
)
parser.add_option("--password", dest="password",
help="Password use to interact with pdisk server"
)
action=OptionGroup(parser," Action command")
action.add_option("--attach", dest="attach", action="store_true",
help="Attach/Detach backend to hypervisor"
)
action.add_option("--register", dest="registration", action="store_true",
help="Register/Unregister persistent disk as used on service"
)
action.add_option("--link", dest="link", action="store_true",
help="Link/Unlink attached disk in Virtual Machine directory"
)
action.add_option("--mount", dest="mount", action="store_true",
help="Mount/Unmount disk into Virtual Machine"
)
action.add_option("--status", dest="status", action="store_true",
help="Display status of current persistent disk"
)
action.add_option("--no-check", dest="no_check", action="store_true",
help="Disable check if device is used"
)
action.add_option("--op", dest="operation", metavar="OP",
help="up : active persistent disk ( register / attach / link / mount ) -- down : desactive persistent disk ( unmount / unlink / detach / unregister )"
)
parser.add_option_group(action)
(options, args) = parser.parse_args()
if not options.operation:
raise parser.error("--op options is mandatory")
if options.attach:
if not options.persistent_disk_id :
raise parser.error("--attach option need --pdisk-id options")
if options.registration:
if not options.persistent_disk_id or not options.vm_id:
raise parser.error("--register need --pdisk-id and --vm-id options")
if options.link:
if not options.persistent_disk_id or ( not vm_dir and not options.vm_dir ) or not options.vm_id or not options.disk_name :
raise parser.error("--link need --pdisk-id, --vm-disk-name, --vm-id options are needed, --vm-dir if not define on configuration file ( /etc/stratuslab/pdisk-host.conf)")
if options.mount:
if not options.persistent_disk_id or not options.vm_id or not options.target:
raise parser.error("--mount need --pdisk-id, --target and --vm-id options")
if not options.persistent_disk_id:
raise parser.error("--pdisk-id is mandatory")
if options.vm_dir:
vm_dir = options.vm_dir
if options.username:
login = options.username
if options.password:
pswd = options.password
"""
Metaclass for general persistent disk client
"""
class PersistentDisk:
def __registration_uri__(self):
return "https://"+self.endpoint+":"+self.port+"/pswd/disks/"+self.disk_uuid+"/"
"""
Register/Unregister mount on pdisk endpoint
"""
def register(self,login,pswd,vm_id):
node=socket.gethostbyname(socket.gethostname())
url = self.__registration_uri__()+"mounts/"
h = httplib2.Http("/tmp/.cache")
h.disable_ssl_certificate_validation=True
h.add_credentials(login,pswd)
data = dict(node=node, vm_id=vm_id,register_only="true")
try:
resp, contents = h.request(url,"POST",urlencode(data))
except httplib2.ServerNotFoundError:
raise RegisterPersistentDiskException('Register : Server '+self.endpoint+' not found')
def unregister(self,login,pswd,vm_id):
node=socket.gethostbyname(socket.gethostname())
url = self.__registration_uri__()+"mounts/"+self.disk_uuid+"_"+vm_id
h = httplib2.Http("/tmp/.cache")
h.add_credentials(login,pswd)
h.disable_ssl_certificate_validation=True
try:
resp, contents = h.request(url,"DELETE")
except httplib2.ServerNotFoundError:
raise RegisterPersistentDiskException('Unregister : Server '+self.endpoint+' not found')
"""
Link/Unlink, create a link between device ( image or physical device) and Virtual Machine directory
"""
def link(self,src,dst):
try:
if os.path.exists(dst):
os.unlink(dst)
os.symlink(src,dst)
except:
raise LinkPersistentDiskException('Link : Error while linking '+src+' to '+dst)
def unlink(self,link):
if os.path.exists(link):
os.unlink(link)
"""
Mount/Unmount display device to a VM
"""
def mount(self,vm_id,disk_name,target_device):
hypervisor_device=vm_dir+"/"+str(vm_id)+"/images/"+disk_name
domain_name="one-"+str(vm_id)
cmd="sudo /usr/bin/virsh attach-disk "+domain_name+" "+hypervisor_device+" "+target_device
retcode=call(cmd,shell=True)
def umount(self,vm_id,target_device):
domain_name="one-"+str(vm_id)
cmd="sudo /usr/bin/virsh detach-disk "+domain_name+" "+target_device
retcode=call(cmd,shell=True)
"""
__copy__ used to create a a xxxPersistentDisk object from PersistentDisk (xxxPersistentDisk is a inherited class)
"""
def __copy__(self,pdisk):
self.endpoint = pdisk.endpoint
self.port = pdisk.port
self.disk_uuid = pdisk.disk_uuid
self.protocol = pdisk.protocol
self.server = pdisk.server
self.image = pdisk.image
"""
check_mount used to check if pdisk is already used return true if pdisk is free
"""
def check_mount(self,login,pswd):
url = self.__registration_uri__()
h = httplib2.Http("/tmp/.cache")
h.add_credentials(login,pswd)
h.disable_ssl_certificate_validation=True
resp, contents = h.request(url)
#print contents
if resp.status != 200:
raise CheckPersistentDiskException('Check_mount : error while check '+self.endpoint+' with url '+url)
io = StringIO(contents)
json_output = json.load(io)
# try:
# if json_output['count'] != '0':
# raise CheckPersistentDiskException('Check_mount : pdisk pdisk:'+ self.endpoint+':'+self.port+':'+self.disk_uuid+' is mounted')
return False
# except KeyError:
# return False
"""
__checkTurl__ check and split Transport URL ( proto://server:port/proto_options ) from pdisk id ( pdisk:endpoint:port:disk_uuid )
"""
def __checkTurl__(self,turl):
if turl == "":
__url__ = "iscsi://"+self.endpoint+":3260/iqn.2011-01.eu.stratuslab:"+self.disk_uuid+":1"
else:
__url__ = turl
__uri__ = re.match(r"(?P<protocol>.*)://(?P<server>.*)/(?P<image>.*)", __url__)
try :
self.protocol = __uri__.group('protocol')
self.server = __uri__.group('server')
self.image = __uri__.group('image')
except AttributeError:
raise URIPersistentDiskException('TURL '+ turl + ' not match expression protocol://server/protocol-options')
def __init__(self, pdisk_id, turl):
try:
__pdisk__ = re.match(r"pdisk:(?P<server>.*):(?P<port>.*):(?P<disk_uuid>.*)", pdisk_id)
self.endpoint = __pdisk__.group('server')
self.port = __pdisk__.group('port')
self.disk_uuid = __pdisk__.group('disk_uuid')
self.__checkTurl__(turl)
except AttributeError:
raise PersistentDiskException('URI '+pdisk_id+' not match expression pdisk:endpoint:port:disk_uuid')
class IscsiPersistentDisk(PersistentDisk):
_unix_device_path='/dev/disk/by-path/'
def image_storage(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname( __portal__.group('server') )
dev = "ip-" + __portal_ip__ + ":" + __portal__.group('port') + "-iscsi-" + self.iqn + "-lun-" + self.lun
return self._unix_device_path+dev
def attach(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname(__portal__.group('server'))
reg = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " -o new"
cmd = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " --login"
retcode = call(reg, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
retcode = call(cmd, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
def detach(self):
__portal__ = re.match(r"(?P<server>.*):(?P<port>.*)", self.server)
__portal_ip__ = socket.gethostbyname(__portal__.group('server'))
cmd = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " --logout"
unreg = "sudo "+iscsiadm + " --mode node --portal " + __portal_ip__ + ":" + __portal__.group('port') + " --target " + self.iqn + " -o delete"
retcode = call(cmd, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
retcode = call(unreg, shell=True)
if retcode < 0:
raise AttachPersistentDiskException("Error while attach iSCSI disk to hypervisor")
def __image2iqn__(self, str):
__iqn__ = re.match(r"(?P<iqn>.*:.*):(?P<lun>.*)", str)
self.iqn = __iqn__.group('iqn')
self.lun = __iqn__.group('lun')
def __init__(self,pdisk_class,turl):
self.__copy__(pdisk_class)
self.__image2iqn__(pdisk_class.image)
class FilePersistentDisk(PersistentDisk):
def __image2file__(self, str):
__file__ = re.match(r"(?P<mount_point>.*)/(?P<full_path>.*)", str)
self.mount_point = __file__.group('mount_point')
self.full_path = __file__.group('full_path')
def image_storage(self):
return self.server+"/"+self.image
def attach(self):
pass
def detach(self):
pass
def __init__(self,pdisk_class,turl):
self.__copy__(pdisk_class)
class PersistentDiskException(Exception):
def __init__(self,value):
self.value = value
class RegisterPersistentDiskException(PersistentDisk):
def __init__(self,value):
self.value = value
class AttachPersistentDiskException(PersistentDisk):
def __init__(self,value):
self.value = value
class URIPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class LinkPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class MountPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class CheckPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
class getTurlPersistentDiskException(PersistentDiskException):
def __init__(self,value):
self.value = value
def __init__():
try:
global_pdisk = PersistentDisk(options.persistent_disk_id,options.turl)
except getTurlPersistentDiskException:
print "Error while try to retrive %s" % options.persistent_disk_id
return -1
global vm_dir
if global_pdisk.protocol == "iscsi" :
pdisk = IscsiPersistentDisk(global_pdisk,options.turl)
elif global_pdisk.protocol == "file" :
pdisk = FilePersistentDisk(global_pdisk,options.turl)
else :
print "Protocol "+global_pdisk.protocol+" not supported"
if options.operation == "up":
try:
if not options.no_check:
pdisk.check_mount(login,pswd)
if options.registration:
pdisk.register(login,pswd,options.vm_id)
if options.attach:
pdisk.attach()
if options.link:
src = pdisk.image_storage()
dst = vm_dir+"/"+str(options.vm_id)+"/images/"+options.disk_name
pdisk.link(src,dst)
if options.mount:
pdisk.mount(options.vm_id,options.disk_name,options.target)
except CheckPersistentDiskException:
print "pdisk is market as used, please check or use --register --op down"
except RegisterPersistentDiskException:
print "Error while try to register on pdisk"
except AttachPersistentDiskException:
print "Error while try to attach backend to hypervisor"
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except LinkPersistentDiskException:
print "Error while try to link %s to %s" % ( src , dst )
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except MountPersistentDiskException:
print "Error while try to mount %s to %s" % ( options.persistent_disk_id , options.vm_id )
if options.link:
pdisk.unlink(dst)
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
elif options.operation == "down":
try:
if options.mount:
pdisk.umount(options.vm_id,options.target)
if options.link:
dst = vm_dir+"/"+str(options.vm_id)+"/images/"+options.disk_name
pdisk.unlink(dst)
if options.attach:
pdisk.detach()
if options.registration:
pdisk.unregister(login,pswd,options.vm_id)
except MountPersistentDiskException:
print "Error while try to umount %s to %s " % ( options.persistent_disk_id , options.vm_id )
except LinkPersistentDiskException:
print "Error while try to unlink %s" % dst
except AttachPersistentDiskException:
print "Error while try to detach %s from backend" % options.persistentd_disk_id
except RegisterPersistentDiskException:
print "Error while try to unregister as unused"
else:
raise parser.error("--op options only allow up or down")
__init__()
|
#!/usr/bin/python
import sys,os
import ConfigParser
import mosquitto
import json
import requests
import time
class NEEG_DataCollector(mosquitto.Mosquitto):
def __init__(self,ip = "localhost", port = 1883, clientId = "NEEG2MQTT", user = "driver", password = "1234", prefix = "ElectricGridData"):
mosquitto.Mosquitto.__init__(self,clientId)
self.prefix = prefix
self.ip = ip
self.port = port
self.clientId = clientId
self.user = user
self.password = password
if user != None:
self.username_pw_set(user,password)
self.will_set( topic = "system/" + self.prefix, payload="Offline", qos=1, retain=True)
print "Connecting"
self.connect(ip,keepalive=10)
self.subscribe(self.prefix + "/#", 0)
self.on_connect = self.mqtt_on_connect
self.on_message = self.mqtt_on_message
self.publish(topic = "system/"+ self.prefix, payload="Online", qos=1, retain=True)
self.lastupdate = 0
self.lasttimestamp = 0
self.updateperiod = 120
self.oldvalues = {}
self.running = True
#thread.start_new_thread(self.ControlLoop,())
self.loop_start()
def mqtt_on_connect(self, selfX,mosq, result):
print "MQTT connected!"
#self.subscribe(self.prefix + "/#", 0)
def mqtt_on_message(self, selfX,mosq, msg):
#print("RECIEVED MQTT MESSAGE: "+msg.topic + " " + str(msg.payload))
return
def RunCollection(self):
while(self.running):
#Mark the time
now = time.time()
#Get data
r = requests.get('http://driftsdata.statnett.no/restapi/ProductionConsumption/GetLatestDetailedOverview')
#If we failed retry in a while
if r.status_code != 200:
print "Failed to get data from Nordic Electric Energy grid"
time.sleep(30)
continue
self.lastupdate = now
data = r.json()
self.TranslateAndTransmitt(data)
nextUpdate = self.lastupdate + self.updateperiod
timeToNext = nextUpdate - time.time()
if timeToNext > 0:
time.sleep(timeToNext)
return
def TranslateAndTransmitt(self,data):
timestamp = data['MeasuredAt']
#If this is old data ignore it.
if timestamp == self.lasttimestamp:
return
self.lasttimestamp = timestamp
datadic = {}
for category in data:
if type(data[category]) == type([]):
for item in data[category]:
#Abort if junk
if item[u'value'] == None or item[u'value'] == "" or item[u'titleTranslationId'] == None:
continue
topic = prefix + "/" + category + "/" +item[u'titleTranslationId']
value = item[u'value'].replace(u"\xa0","")
#Has this topic existed before
if topic in self.oldvalues:
#If yes do we have a new value?
if self.oldvalues[topic] == value:
#Same value ignore
continue
#Update
update = json.dumps({"time":timestamp,"value":value})
self.publish(topic,update)
#Save new value
self.oldvalues[topic] = value
return
if __name__ == '__main__':
#Where am I
path = os.path.abspath(os.path.dirname(sys.argv[0]))
#Load config file...
try:
ConfigFile = sys.argv[1]
except:
ConfigFile = path + "/NEEG2MQTT.conf"
try:
f = open(ConfigFile,"r")
f.close()
except:
try:
ConfigFile = path + "/NEEG2MQTT.conf"
f = open(ConfigFile,"r")
f.close()
except:
print "Please provide a valid config file! By argument or as default Plugwise2MQTT.cfg file."
exit(1)
config = ConfigParser.RawConfigParser(allow_no_value=True)
config.read(ConfigFile)
#Load basic config.
ip = config.get("MQTTServer","Address")
port = config.get("MQTTServer","Port")
user = config.get("MQTTServer","User")
password = config.get("MQTTServer","Password")
prefix = config.get("MQTTServer","Prefix")
#Create the data collector the power situation
neeg2mqtt = NEEG_DataCollector(ip, port,"NEEG2MQTT", user, password)
neeg2mqtt.RunCollection()
Some change
#!/usr/bin/python
import sys,os
import ConfigParser
import mosquitto
import json
import requests
import time
class NEEG_DataCollector(mosquitto.Mosquitto):
def __init__(self,ip = "localhost", port = 1883, clientId = "NEEG2MQTT", user = "driver", password = "1234", prefix = "ElectricGridData"):
mosquitto.Mosquitto.__init__(self,clientId)
self.prefix = prefix
self.ip = ip
self.port = port
self.clientId = clientId
self.user = user
self.password = password
if user != None:
self.username_pw_set(user,password)
self.will_set( topic = "system/" + self.prefix, payload="Offline", qos=1, retain=True)
print "Connecting"
self.connect(ip,keepalive=10)
#self.subscribe(self.prefix + "/#", 0)
self.on_connect = self.mqtt_on_connect
self.on_message = self.mqtt_on_message
self.publish(topic = "system/"+ self.prefix, payload="Online", qos=1, retain=True)
self.lastupdate = 0
self.lasttimestamp = 0
self.updateperiod = 120
self.oldvalues = {}
self.running = True
#thread.start_new_thread(self.ControlLoop,())
self.loop_start()
def mqtt_on_connect(self, selfX,mosq, result):
print "MQTT connected!"
#self.subscribe(self.prefix + "/#", 0)
def mqtt_on_message(self, selfX,mosq, msg):
#print("RECIEVED MQTT MESSAGE: "+msg.topic + " " + str(msg.payload))
return
def RunCollection(self):
while(self.running):
#Mark the time
now = time.time()
#Get data
r = requests.get('http://driftsdata.statnett.no/restapi/ProductionConsumption/GetLatestDetailedOverview')
#If we failed retry in a while
if r.status_code != 200:
print "Failed to get data from Nordic Electric Energy grid"
time.sleep(30)
continue
self.lastupdate = now
data = r.json()
self.TranslateAndTransmitt(data)
nextUpdate = self.lastupdate + self.updateperiod
timeToNext = nextUpdate - time.time()
if timeToNext > 0:
time.sleep(timeToNext)
return
def TranslateAndTransmitt(self,data):
timestamp = data['MeasuredAt']
#If this is old data ignore it.
if timestamp == self.lasttimestamp:
return
self.lasttimestamp = timestamp
datadic = {}
for category in data:
if type(data[category]) == type([]):
for item in data[category]:
#Abort if junk
if item[u'value'] == None or item[u'value'] == "" or item[u'titleTranslationId'] == None:
continue
topic = prefix + "/" + category + "/" +item[u'titleTranslationId']
value = item[u'value'].replace(u"\xa0","")
#Has this topic existed before
if topic in self.oldvalues:
#If yes do we have a new value?
if self.oldvalues[topic] == value:
#Same value ignore
continue
#Update
update = json.dumps({"time":timestamp,"value":value})
self.publish(topic,update)
#Save new value
self.oldvalues[topic] = value
return
if __name__ == '__main__':
#Where am I
path = os.path.abspath(os.path.dirname(sys.argv[0]))
#Load config file...
try:
ConfigFile = sys.argv[1]
except:
ConfigFile = path + "/NEEG2MQTT.conf"
try:
f = open(ConfigFile,"r")
f.close()
except:
try:
ConfigFile = path + "/NEEG2MQTT.conf"
f = open(ConfigFile,"r")
f.close()
except:
print "Please provide a valid config file! By argument or as default Plugwise2MQTT.cfg file."
exit(1)
config = ConfigParser.RawConfigParser(allow_no_value=True)
config.read(ConfigFile)
#Load basic config.
ip = config.get("MQTTServer","Address")
port = config.get("MQTTServer","Port")
user = config.get("MQTTServer","User")
password = config.get("MQTTServer","Password")
prefix = config.get("MQTTServer","Prefix")
#Create the data collector the power situation
neeg2mqtt = NEEG_DataCollector(ip, port,"NEEG2MQTT", user, password)
neeg2mqtt.RunCollection()
|
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
import platform
class Vecgeom(CMakePackage):
"""The vectorized geometry library for particle-detector simulation
(toolkits)."""
homepage = "https://gitlab.cern.ch/VecGeom/VecGeom"
url = "https://gitlab.cern.ch/api/v4/projects/VecGeom%2FVecGeom/repository/archive.tar.gz?sha=v0.3.rc"
version('01.01.03', git='https://gitlab.cern.ch/VecGeom/VecGeom.git', tag='v01.01.03', preferred=True)
version('01.00.00', git='https://gitlab.cern.ch/VecGeom/VecGeom.git', tag='v01.00.00')
version('00.05.00', git='https://gitlab.cern.ch/VecGeom/VecGeom.git', tag='v00.05.00')
version('0.3.rc', sha256='a87a9ea4ab126b59ff9c79182bc0911ead3d76dd197194742e2a35ccd341299d')
variant('cxxstd',
default='17',
values=('11', '14', '17'),
multi=False,
description='Use the specified C++ standard when building.')
variant('vector',
default='native',
values=('sse3', 'sse4.2', 'native'),
multi=False,
description='Specify the instruction set for vectorization.')
depends_on('cmake@3.5:', type='build')
def cmake_args(self):
options = [
'-DBACKEND=Scalar',
'-DGEANT4=OFF',
'-DUSOLIDS=ON',
'-DUSOLIDS_VECGEOM=ON',
'-DROOT=OFF',
'-DNO_SPECIALIZATION=ON',
'-DCMAKE_VERBOSE_MAKEFILE=TRUE']
options.append('-DCMAKE_CXX_STANDARD={0}'.
format(self.spec.variants['cxxstd'].value))
arch = platform.machine()
if arch == 'x86_64':
options.append('-DVECGEOM_VECTOR={0}'.
format(self.spec.variants['vector'].value))
else:
options.append('-DVECGEOM_VECTOR=' + arch)
return options
Update and exetend VecGeom (#14520)
* Add new vecgeom versions, add cuda support, automate target options
* Add ROOT, GDML, and external VecCore support to VecGeom
* Address reviewer comments
* Update vecgeom for CUDA
* Update versions
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class Vecgeom(CMakePackage, CudaPackage):
"""The vectorized geometry library for particle-detector simulation
(toolkits)."""
homepage = "https://gitlab.cern.ch/VecGeom/VecGeom"
url = "https://gitlab.cern.ch/VecGeom/VecGeom/-/archive/v1.1.6/VecGeom-v1.1.6.tar.gz"
git = "https://gitlab.cern.ch/VecGeom/VecGeom.git"
maintainers = ['drbenmorgan', 'sethrj']
version('master', branch='master')
version('1.1.6', sha256='c4806a6b67d01b40074b8cc6865d78574a6a1c573be51696f2ecdf98b9cb954a')
version('1.1.5', sha256='da674f3bbc75c30f56c1a2d251fa8930c899f27fa64b03a36569924030d87b95')
version('1.1.3', sha256='ada09e8b6b2fa6c058290302b2cb5a6c2e644192aab1623c31d18c6a2f4c01c8')
version('1.0.1', sha256='1eae7ac9014c608e8d8db5568058b8c0fea1a1dc7a8f54157a3a1c997b6fd9eb')
version('0.5.2', tag='v00.05.02',
commit='a7e0828c915ff936a79e672d1dd84b087a323b51')
version('0.3.rc', sha256='a87a9ea4ab126b59ff9c79182bc0911ead3d76dd197194742e2a35ccd341299d')
_cxxstd_values = ('11', '14', '17')
variant('cxxstd', default='11', values=_cxxstd_values, multi=False,
description='Use the specified C++ standard when building')
variant('gdml', default=True,
description='Support native GDML geometry descriptions')
variant('geant4', default=False,
description='Support Geant4 geometry construction')
variant('root', default=False,
description='Support ROOT geometry construction')
variant('shared', default=True,
description='Build shared libraries')
depends_on('veccore@0.5.2:', type=('build', 'link'), when='@1.1.0:')
depends_on('veccore@0.4.2', type=('build', 'link'), when='@:1.0')
depends_on('veccore+cuda', type=('build', 'link'), when='+cuda')
conflicts('+cuda', when='@:1.1.5')
for std in _cxxstd_values:
depends_on('geant4 cxxstd=' + std, when='+geant4 cxxstd=' + std)
depends_on('root cxxstd=' + std, when='+root cxxstd=' + std)
depends_on('veccore cxxstd=' + std, when='cxxstd=' + std)
depends_on('xerces-c cxxstd=' + std, when='+gdml cxxstd=' + std)
def cmake_args(self):
# Possible target options are from the main CMakeLists.txt, assuming
# "best" is last
target = self.spec.target
vecgeom_arch = "sse2 sse3 ssse3 sse4.1 sse4.2 avx avx2".split()
for feature in reversed(vecgeom_arch):
if feature.replace('.', '_') in target:
target_instructions = feature
break
else:
# No features available (could be 'generic' arch)
target_instructions = 'empty'
define = CMakePackage.define
options = [
define('BACKEND', 'Scalar'),
define('BUILTIN_VECCORE', False),
define('NO_SPECIALIZATION', True),
define('VECGEOM_VECTOR', target_instructions),
self.define_from_variant('BUILD_SHARED_LIBS', 'shared'),
self.define_from_variant('CMAKE_CXX_STANDARD', 'cxxstd'),
self.define_from_variant('CUDA'),
self.define_from_variant('GDML'),
self.define_from_variant('GEANT4'),
self.define_from_variant('ROOT'),
]
# Set testing flags
build_tests = self.run_tests
options.extend([
define('BUILD_TESTING', build_tests),
define('CTEST', build_tests),
define('GDMLTESTING', build_tests and '+gdml' in self.spec),
])
if '+cuda' in self.spec:
arch = self.spec.variants['cuda_arch'].value
if len(arch) != 1 or arch[0] == 'none':
raise InstallError("Exactly one cuda_arch must be specified")
options.append(define('CUDA_ARCH', arch[0]))
if self.spec.satisfies("@:0.5.2"):
options.extend([
define('USOLIDS', True),
define('USOLIDS_VECGEOM', True),
])
return options
|
from copy import copy
from typing import List, Tuple
from typing_extensions import Literal
from gilgamesh.instruction import Instruction, InstructionID
from gilgamesh.opcodes import AddressMode, Op
from gilgamesh.state import State, StateChange
class CPU:
def __init__(self, log, pc: int, p: int, subroutine: int):
self.log = log
self.rom = log.rom
self.pc = pc
self.state = State(p)
self.state_assertion = StateChange()
self.state_change = StateChange()
self.state_stack: List[Tuple[State, StateChange]] = []
self.subroutine = subroutine
@property
def instruction_id(self) -> InstructionID:
return InstructionID(self.pc, self.state.p, self.subroutine)
def copy(self, new_subroutine=False) -> "CPU":
cpu = copy(self)
cpu.state = copy(self.state)
cpu.state_assertion = copy(self.state_assertion)
cpu.state_change = StateChange() if new_subroutine else copy(self.state_change)
return cpu
def run(self) -> None:
keep_going = self.step()
while keep_going:
keep_going = self.step()
def step(self) -> bool:
if self.is_ram(self.pc):
return False
if self.log.is_visited(self.instruction_id):
return False
opcode = self.rom.read_byte(self.pc)
argument = self.rom.read_address(self.pc + 1)
instruction = Instruction(self.log, *self.instruction_id, opcode, argument)
self.log.add_instruction(instruction)
return self.execute(instruction)
def execute(self, instruction: Instruction) -> bool:
self.pc += instruction.size
self._derive_state_assertion(instruction)
if instruction.is_return:
self.log.add_subroutine_state(self.subroutine, self.state_change)
return False
elif instruction.is_interrupt:
return self._unknown_subroutine_state()
elif instruction.is_call:
return self.call(instruction)
elif instruction.is_jump:
return self.jump(instruction)
elif instruction.is_branch:
self.branch(instruction)
elif instruction.is_sep_rep:
self.sep_rep(instruction)
elif instruction.operation == Op.PHP:
self.push_state()
elif instruction.operation == Op.PLP:
self.pop_state()
return True
def branch(self, instruction: Instruction) -> None:
cpu = self.copy()
cpu.run()
target = instruction.absolute_argument
assert target is not None
self.log.add_reference(instruction, target)
self.pc = target
def call(self, instruction: Instruction) -> bool:
target = instruction.absolute_argument
if target is None:
return self._unknown_subroutine_state()
self.log.add_reference(instruction, target)
self.log.add_subroutine(target)
cpu = self.copy(new_subroutine=True)
cpu.subroutine = target
cpu.pc = target
cpu.run()
known = self._propagate_subroutine_state(target)
if not known:
return self._unknown_subroutine_state()
return known
def jump(self, instruction: Instruction) -> bool:
target = instruction.absolute_argument
if target is None:
return self._unknown_subroutine_state()
self.log.add_reference(instruction, target)
self.pc = target
return True
def sep_rep(self, instruction: Instruction) -> None:
arg = instruction.absolute_argument
assert arg is not None
if instruction.operation == Op.SEP:
self.state.set(arg)
self.state_change.set(arg)
else:
self.state.reset(arg)
self.state_change.reset(arg)
self.state_change.apply_assertion(self.state_assertion)
def push_state(self) -> None:
self.state_stack.append((copy(self.state), copy(self.state_change)))
def pop_state(self) -> None:
self.state, self.state_change = self.state_stack.pop()
@staticmethod
def is_ram(address: int) -> bool:
return (address <= 0x001FFF) or (0x7E0000 <= address <= 0x7FFFFF)
def _derive_state_assertion(self, instruction: Instruction) -> None:
saved_assertion = self.log.state_assertions.get(instruction.pc)
if saved_assertion:
if saved_assertion.m is not None:
self.state_assertion.m = saved_assertion.m
if saved_assertion.x is not None:
self.state_assertion.x = saved_assertion.x
return
if (
instruction.address_mode == AddressMode.IMMEDIATE_M
and self.state_change.m is None
):
self.state_assertion.m = self.state.m
elif (
instruction.address_mode == AddressMode.IMMEDIATE_X
and self.state_change.x is None
):
self.state_assertion.x = self.state.x
def _propagate_subroutine_state(self, subroutine: int) -> bool:
state_changes = self.log.get_subroutine_states(subroutine)
if len(state_changes) != 1:
return False
change = next(iter(state_changes))
if change.unknown:
return False
if change.m is not None:
self.state_change.m = self.state.m = change.m
if change.x is not None:
self.state_change.x = self.state.x = change.x
return True
def _unknown_subroutine_state(self) -> Literal[False]:
self.log.add_subroutine_state(self.subroutine, StateChange(unknown=True))
return False
CPU comments
from copy import copy
from typing import List, Tuple
from typing_extensions import Literal
from gilgamesh.instruction import Instruction, InstructionID
from gilgamesh.opcodes import AddressMode, Op
from gilgamesh.state import State, StateChange
class CPU:
def __init__(self, log, pc: int, p: int, subroutine: int):
self.log = log
self.rom = log.rom
# Processor state.
self.pc = pc
self.state = State(p)
# Change in CPU state caused by the execution of the current subroutine.
self.state_change = StateChange()
# What we know about the CPU state based on the
# sequence of instructions we have executed.
self.state_assertion = StateChange()
# Stack formed as a result of sequences of PHP/PLP instructions.
self.state_stack: List[Tuple[State, StateChange]] = []
# The subroutine currently being executed.
self.subroutine = subroutine
@property
def instruction_id(self) -> InstructionID:
# Get the ID of the instruction currently being executed
# in the context of the current subroutine.
return InstructionID(self.pc, self.state.p, self.subroutine)
def copy(self, new_subroutine=False) -> "CPU":
# Copy the current state of the CPU.
cpu = copy(self)
cpu.state = copy(self.state)
cpu.state_assertion = copy(self.state_assertion)
# Don't carry over the state change information to new subroutines.
cpu.state_change = StateChange() if new_subroutine else copy(self.state_change)
return cpu
def run(self) -> None:
keep_going = self.step()
while keep_going:
keep_going = self.step()
def step(self) -> bool:
# We can't analyze code that lives in RAM.
if self.is_ram(self.pc):
return False
# Don't visit the exact same instruction twice.
if self.log.is_visited(self.instruction_id):
return False
opcode = self.rom.read_byte(self.pc)
argument = self.rom.read_address(self.pc + 1)
instruction = Instruction(self.log, *self.instruction_id, opcode, argument)
self.log.add_instruction(instruction)
return self.execute(instruction)
def execute(self, instruction: Instruction) -> bool:
self.pc += instruction.size
# See if we can learn something about the *required*
# state of the CPU based on the current instruction.
self._derive_state_assertion(instruction)
if instruction.is_return:
self.log.add_subroutine_state(self.subroutine, self.state_change)
return False # Terminate the execution of this subroutine.
elif instruction.is_interrupt:
return self._unknown_subroutine_state()
elif instruction.is_call:
return self.call(instruction)
elif instruction.is_jump:
return self.jump(instruction)
elif instruction.is_branch:
self.branch(instruction)
elif instruction.is_sep_rep:
self.sep_rep(instruction)
elif instruction.operation == Op.PHP:
self.push_state()
elif instruction.operation == Op.PLP:
self.pop_state()
return True # Keep executing in the context of this subroutine.
def branch(self, instruction: Instruction) -> None:
# Run a parallel instance of the CPU to follow
# the case in which we don't take the branch.
cpu = self.copy()
cpu.run()
target = instruction.absolute_argument
assert target is not None
# Log the fact that the current instruction references the
# instruction pointed by the branch. Then take the branch.
self.log.add_reference(instruction, target)
self.pc = target
def call(self, instruction: Instruction) -> bool:
target = instruction.absolute_argument
if target is None:
# If we can't reliably derive the address of the subroutine
# being called, we're left in an unknown state.
return self._unknown_subroutine_state()
self.log.add_reference(instruction, target)
self.log.add_subroutine(target)
# Run a parallel instance of the CPU to execute
# the subroutine that is being called.
cpu = self.copy(new_subroutine=True)
cpu.subroutine = target
cpu.pc = target
cpu.run()
# If we univocally know what the return state of the
# called subroutine is, we can propagate it to the
# current CPU state. Otherwise, to be on the safe
# side, we need to stop the execution.
known = self._propagate_subroutine_state(target)
if not known:
return self._unknown_subroutine_state()
return True
def jump(self, instruction: Instruction) -> bool:
target = instruction.absolute_argument
if target is None:
return self._unknown_subroutine_state()
self.log.add_reference(instruction, target)
self.pc = target
return True
def sep_rep(self, instruction: Instruction) -> None:
arg = instruction.absolute_argument
assert arg is not None
if instruction.operation == Op.SEP:
self.state.set(arg)
self.state_change.set(arg)
else:
self.state.reset(arg)
self.state_change.reset(arg)
# Simplify the state change by applying our knowledge
# of the current state. I.e. if we know that the
# processor is operating in 8-bits accumulator mode
# and we switch to that same mode, effectively no
# state change is being performed.
self.state_change.apply_assertion(self.state_assertion)
def push_state(self) -> None:
self.state_stack.append((copy(self.state), copy(self.state_change)))
def pop_state(self) -> None:
self.state, self.state_change = self.state_stack.pop()
@staticmethod
def is_ram(address: int) -> bool:
return (address <= 0x001FFF) or (0x7E0000 <= address <= 0x7FFFFF)
def _derive_state_assertion(self, instruction: Instruction) -> None:
# If we have defined a custom assertion for the
# current instruction, retrieve it and apply it.
saved_assertion = self.log.state_assertions.get(instruction.pc)
if saved_assertion:
if saved_assertion.m is not None:
self.state_assertion.m = saved_assertion.m
if saved_assertion.x is not None:
self.state_assertion.x = saved_assertion.x
return
# If we're executing an instruction with a certain operand size,
# and no state change has been performed in the current subroutine,
# then we can infer that the state of the processor as we enter
# the subroutine *must* be the same in all cases.
if (
instruction.address_mode == AddressMode.IMMEDIATE_M
and self.state_change.m is None
):
self.state_assertion.m = self.state.m
elif (
instruction.address_mode == AddressMode.IMMEDIATE_X
and self.state_change.x is None
):
self.state_assertion.x = self.state.x
def _propagate_subroutine_state(self, subroutine: int) -> bool:
# If the subroutine can return in more than one distinct state,
# we can't reliably propagate the state to the caller.
state_changes = self.log.get_subroutine_states(subroutine)
if len(state_changes) != 1:
return False
change = next(iter(state_changes))
if change.unknown:
return False
if change.m is not None:
self.state_change.m = self.state.m = change.m
if change.x is not None:
self.state_change.x = self.state.x = change.x
return True
def _unknown_subroutine_state(self) -> Literal[False]:
self.log.add_subroutine_state(self.subroutine, StateChange(unknown=True))
return False
|
#!/usr/bin/python
from __future__ import division
import copy
"""
<svg>
<polyline
points="0,0 50,0 150,100 250,100 300,150"
fill="rgb(0,249,249)"
stroke-width="0"
stroke="rgb(0,0,0)"
/>
</svg>
"""
def round_point(point):
return [int(point[0]), int(point[1])]
def scale_point(point, k):
return [k[0]*point[0], k[1]*point[1]]
def shift_point(point, offset):
return [point[0]+offset[0], point[1]+offset[1]]
def round_points(points):
return [round_point(p) for p in points]
def scale_points(points, k):
return [scale_point(p, k) for p in points]
def shift_points(points, offset):
return [shift_point(p, offset) for p in points]
def get_square():
return [[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]
def get_rectangular(w, h):
return scale_points(get_square(), [w, h])
def get_color(text):
t = {
'white': '#fff',
'black': '#000',
'red': '#f00',
'green': '#0f0',
'blue': '#00f'
}
return t.get(text, text)
def close_points(points):
res = points[:]
first = res[0]
last = res[-1]
res.append([last[0], 0])
res.append([first[0], 0])
res.append(first)
return res
def tostr(point):
return '({0}, {1})'.format(point[0], point[1])
class BoundingBox:
def __init__(self, point=None):
if point is None:
self.left_bottom = None
self.right_top = None
else:
self.left_bottom = point[:]
self.right_top = point[:]
def include(self, point):
if self.left_bottom is None or self.right_top is None:
self.left_bottom = point[:]
self.right_top = point[:]
return
self.left_bottom[0] = min(self.left_bottom[0], point[0])
self.left_bottom[1] = min(self.left_bottom[1], point[1])
self.right_top[0] = max(self.right_top[0], point[0])
self.right_top[1] = max(self.right_top[1], point[1])
def width(self):
return self.right_top[0] - self.left_bottom[0]
def height(self):
return self.right_top[1] - self.left_bottom[1]
class Chart:
def __init__(self, width=650, height=75,
padding_top=10, padding_bottom=20,
padding_left=20, padding_right=40,
inverseX=False,
xlabels=[], ylabels=[]):
self.width = width
self.height = height
self.padding_top = padding_top
self.padding_bottom = padding_bottom
self.padding_left = padding_left
self.padding_right = padding_right
self.inverseX = inverseX
self.traces = []
self.texts = []
self.canvas = BoundingBox([0, 0])
self.xlabels = xlabels
self.ylabels = ylabels
self.is_axes_on_top = False
#self.add_frame() # TODO: remove
self.add_background()
self.add_labels()
self.add_axes()
def add_frame(self):
points = get_rectangular(self.width-2, self.height-2)
points = shift_points(points, [1, 1])
data = {}
data['points'] = points
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#ddd'
data['atr']['shape-rendering'] = 'crispEdges'
self.traces.append(data)
def add_background(self):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
points = get_rectangular(w, h)
padding = [self.padding_left, self.padding_bottom]
points = shift_points(points, padding)
data = {}
data['points'] = points
data['atr'] = {}
data['atr']['fill'] = get_color('white')
self.traces.append(data)
def add_axes(self):
d = 3
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
padding = [self.padding_left, self.padding_bottom]
data = {}
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#999'
data['atr']['shape-rendering'] = 'crispEdges'
dx0 = 1 if self.inverseX else d
dx1 = d if self.inverseX else 0
# X top
data = copy.deepcopy(data)
points = [[-dx0, h], [w+dx1, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# X bottom
data = copy.deepcopy(data)
points = [[-dx0, 0], [w+dx1, 0]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# Y left
data = copy.deepcopy(data)
points = [[0, -d], [0, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# Y right
data = copy.deepcopy(data)
points = [[w, -d], [w, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
def add_labels(self):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
padding = [self.padding_left, self.padding_bottom]
data = {}
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#ddd'
data['atr']['shape-rendering'] = 'crispEdges'
if self.is_axes_on_top:
data['atr']['stroke-dasharray'] = '1, 5'
text = {}
text['atr'] = {}
text['atr']['font-family'] = 'Verdana'
text['atr']['font-size'] = 10
text['atr']['fill'] = '#777'
yn = len(self.ylabels)
step = h / (yn-1)
for i in xrange(0, yn):
data = copy.deepcopy(data)
y = i * step
points = [[0, y], [w, y]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
x = w+5 if self.inverseX else -5
text = copy.deepcopy(text)
text['text'] = self.ylabels[i]
point = shift_point([x, y-3], padding)
point = self.to_real_coords(point)
text['atr']['x'] = point[0]
text['atr']['y'] = point[1]
text['atr']['text-anchor'] = 'start' if self.inverseX else 'end'
self.texts.append(text)
xn = len(self.xlabels)
step = w / (xn-1)
for i in xrange(0, xn):
data = copy.deepcopy(data)
x = i * step
points = [[x, 0], [x, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
text = copy.deepcopy(text)
j = xn - i - 1 if self.inverseX else i
text['text'] = self.xlabels[j]
point = shift_point([x, -15], padding)
point = self.to_real_coords(point)
text['atr']['x'] = point[0]
text['atr']['y'] = point[1]
text['atr']['text-anchor'] = 'middle'
self.texts.append(text)
def add(self, ys, xs=None, stroke_width=1, stroke='black',
fill='none'):
ny = len(ys)
if xs is None:
xs = range(0, ny)
n = min(len(xs), ny)
xs = xs[:n]
ys = ys[:n]
points = [[xs[i], ys[i]] for i in xrange(0, n)]
if fill != 'none':
self.is_axes_on_top = True
points = close_points(points)
for p in points:
self.canvas.include(p)
data = {}
data['canvas'] = points
data['atr'] = {}
data['atr']['fill'] = fill
data['atr']['stroke-width'] = stroke_width
data['atr']['stroke'] = get_color(stroke)
self.traces.append(data)
def canvas_to_points(self, canvas):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
xk = w / self.canvas.width()
yk = h / self.canvas.height()
if self.inverseX:
canvas = shift_points(canvas, [-self.canvas.width(), 0])
canvas = scale_points(canvas, [-1, 1])
points = scale_points(canvas, [xk, yk])
points = round_points(points)
padding = [self.padding_left, self.padding_bottom]
points = shift_points(points, padding)
return points
def to_real_coords(self, point):
point = shift_point(point, [0, -self.height])
point = scale_point(point, [1, -1])
return point
def render_points(self, points):
points = [self.to_real_coords(p) for p in points]
coords = ['{0},{1}'.format(p[0], p[1]) for p in points]
pts = ' '.join(coords)
return '\tpoints="{0}"'.format(pts)
def render_trace(self, trace):
res = []
res.append('<polyline')
canvas = trace.get('canvas')
if canvas:
trace['points'] = self.canvas_to_points(canvas)
points = trace.get('points')
if points:
res.append(self.render_points(points))
atr = trace.get('atr')
if atr:
for a in atr:
res.append('\t{0}=\"{1}\"'.format(a, atr[a]))
res.append('/>')
return res
def render_text(self, text):
res = []
res.append('<text')
atr = text.get('atr')
if atr:
for a in atr:
res.append('\t{0}=\"{1}\"'.format(a, atr[a]))
res.append('>')
res.append(str(text['text']))
res.append('</text>')
return res
def render(self):
if self.is_axes_on_top:
self.add_labels()
self.add_axes()
svg = []
svg.append('<svg>')
for t in self.traces:
svg.extend(self.render_trace(t))
for t in self.texts:
svg.extend(self.render_text(t))
svg.append('</svg>')
return svg
def render_to_svg(self, filepath):
with open(filepath, 'w') as f:
lines = self.render()
for line in lines:
f.write(line + '\n')
def main():
xlabels = [0, 2, 4, 6, 8, 10, '12 hours']
ylabels = ['0 %', '50 %', '100 %']
chart = Chart(inverseX=True, xlabels=xlabels, ylabels=ylabels,
height=400)
#import random
#ys = [random.randrange(0, 100) for i in xrange(200)]
# c90c28 dark red
# 2e7eb3 blue
# fa730c orange
# 4aa635 green
color = '#2e7eb3'
ys = [10, 60, 60]
xs = [10, 20, 30]
chart.add(xs=xs, ys=ys, stroke=color, fill=color)
color = '#4aa635'
ys = [40, 70, 100, 98]
xs = [30, 40, 50, 60]
chart.add(xs=xs, ys=ys, stroke=color, fill=color)
chart.render_to_svg('test.svg')
if __name__ == '__main__':
main()
refactoring, formatting
#!/usr/bin/python
from __future__ import division
import copy
def round_point(point):
return [int(point[0]), int(point[1])]
def scale_point(point, k):
return [k[0]*point[0], k[1]*point[1]]
def shift_point(point, offset):
return [point[0]+offset[0], point[1]+offset[1]]
def round_points(points):
return [round_point(p) for p in points]
def scale_points(points, k):
return [scale_point(p, k) for p in points]
def shift_points(points, offset):
return [shift_point(p, offset) for p in points]
def get_square():
return [[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]
def get_rectangular(w, h):
return scale_points(get_square(), [w, h])
def get_color(text):
t = {
'white': '#fff',
'black': '#000',
'red': '#f00',
'green': '#0f0',
'blue': '#00f'
}
return t.get(text, text)
def close_points(points):
res = points[:]
first = res[0]
last = res[-1]
res.append([last[0], 0])
res.append([first[0], 0])
res.append(first)
return res
def tostr(point):
return '({0}, {1})'.format(point[0], point[1])
class BoundingBox:
def __init__(self, point=None):
if point is None:
self.left_bottom = None
self.right_top = None
else:
self.left_bottom = point[:]
self.right_top = point[:]
def include(self, point):
if self.left_bottom is None or self.right_top is None:
self.left_bottom = point[:]
self.right_top = point[:]
return
self.left_bottom[0] = min(self.left_bottom[0], point[0])
self.left_bottom[1] = min(self.left_bottom[1], point[1])
self.right_top[0] = max(self.right_top[0], point[0])
self.right_top[1] = max(self.right_top[1], point[1])
def width(self):
return self.right_top[0] - self.left_bottom[0]
def height(self):
return self.right_top[1] - self.left_bottom[1]
class Chart:
def __init__(self, width=650, height=75,
padding_top=10, padding_bottom=20,
padding_left=20, padding_right=40,
inverseX=False,
xlabels=[], ylabels=[]):
self.width = width
self.height = height
self.padding_top = padding_top
self.padding_bottom = padding_bottom
self.padding_left = padding_left
self.padding_right = padding_right
self.inverseX = inverseX
self.traces = []
self.texts = []
self.canvas = BoundingBox([0, 0])
self.xlabels = xlabels
self.ylabels = ylabels
self.is_axes_on_top = False
#self.add_frame() # TODO: remove
self.add_background()
self.add_labels()
self.add_axes()
def add_frame(self):
points = get_rectangular(self.width-2, self.height-2)
points = shift_points(points, [1, 1])
data = {}
data['points'] = points
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#ddd'
data['atr']['shape-rendering'] = 'crispEdges'
self.traces.append(data)
def add_background(self):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
points = get_rectangular(w, h)
padding = [self.padding_left, self.padding_bottom]
points = shift_points(points, padding)
data = {}
data['points'] = points
data['atr'] = {}
data['atr']['fill'] = get_color('white')
self.traces.append(data)
def add_axes(self):
d = 3
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
padding = [self.padding_left, self.padding_bottom]
data = {}
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#999'
data['atr']['shape-rendering'] = 'crispEdges'
dx0 = 1 if self.inverseX else d
dx1 = d if self.inverseX else 0
# X top
data = copy.deepcopy(data)
points = [[-dx0, h], [w+dx1, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# X bottom
data = copy.deepcopy(data)
points = [[-dx0, 0], [w+dx1, 0]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# Y left
data = copy.deepcopy(data)
points = [[0, -d], [0, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
# Y right
data = copy.deepcopy(data)
points = [[w, -d], [w, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
def add_labels(self):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
padding = [self.padding_left, self.padding_bottom]
data = {}
data['atr'] = {}
data['atr']['fill'] = 'none'
data['atr']['stroke-width'] = 1
data['atr']['stroke'] = '#ddd'
data['atr']['shape-rendering'] = 'crispEdges'
if self.is_axes_on_top:
data['atr']['stroke-dasharray'] = '1, 5'
text = {}
text['atr'] = {}
text['atr']['font-family'] = 'Verdana'
text['atr']['font-size'] = 10
text['atr']['fill'] = '#777'
yn = len(self.ylabels)
step = h / (yn-1)
for i in xrange(0, yn):
data = copy.deepcopy(data)
y = i * step
points = [[0, y], [w, y]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
x = w+5 if self.inverseX else -5
text = copy.deepcopy(text)
text['text'] = self.ylabels[i]
point = shift_point([x, y-3], padding)
point = self.to_real_coords(point)
text['atr']['x'] = point[0]
text['atr']['y'] = point[1]
text['atr']['text-anchor'] = 'start' if self.inverseX else 'end'
self.texts.append(text)
xn = len(self.xlabels)
step = w / (xn-1)
for i in xrange(0, xn):
data = copy.deepcopy(data)
x = i * step
points = [[x, 0], [x, h]]
points = shift_points(points, padding)
data['points'] = points
self.traces.append(data)
text = copy.deepcopy(text)
j = xn - i - 1 if self.inverseX else i
text['text'] = self.xlabels[j]
point = shift_point([x, -15], padding)
point = self.to_real_coords(point)
text['atr']['x'] = point[0]
text['atr']['y'] = point[1]
text['atr']['text-anchor'] = 'middle'
self.texts.append(text)
def add(self, ys, xs=None, stroke_width=1, stroke='black',
fill='none'):
ny = len(ys)
if xs is None:
xs = range(0, ny)
n = min(len(xs), ny)
xs = xs[:n]
ys = ys[:n]
points = [[xs[i], ys[i]] for i in xrange(0, n)]
if fill != 'none':
self.is_axes_on_top = True
points = close_points(points)
for p in points:
self.canvas.include(p)
data = {}
data['canvas'] = points
data['atr'] = {}
data['atr']['fill'] = fill
data['atr']['stroke-width'] = stroke_width
data['atr']['stroke'] = get_color(stroke)
self.traces.append(data)
def canvas_to_points(self, canvas):
w = self.width - self.padding_left - self.padding_right
h = self.height - self.padding_top - self.padding_bottom
xk = w / self.canvas.width()
yk = h / self.canvas.height()
if self.inverseX:
canvas = shift_points(canvas, [-self.canvas.width(), 0])
canvas = scale_points(canvas, [-1, 1])
points = scale_points(canvas, [xk, yk])
points = round_points(points)
padding = [self.padding_left, self.padding_bottom]
points = shift_points(points, padding)
return points
def to_real_coords(self, point):
point = shift_point(point, [0, -self.height])
point = scale_point(point, [1, -1])
return point
def render_points(self, points):
points = [self.to_real_coords(p) for p in points]
coords = ['{0},{1}'.format(p[0], p[1]) for p in points]
pts = ' '.join(coords)
return '\tpoints="{0}"'.format(pts)
def render_trace(self, trace):
res = []
res.append('<polyline')
canvas = trace.get('canvas')
if canvas:
trace['points'] = self.canvas_to_points(canvas)
points = trace.get('points')
if points:
res.append(self.render_points(points))
atr = trace.get('atr')
if atr:
for a in atr:
res.append('\t{0}=\"{1}\"'.format(a, atr[a]))
res.append('/>')
return res
def render_text(self, text):
res = []
res.append('<text')
atr = text.get('atr')
if atr:
for a in atr:
res.append('\t{0}=\"{1}\"'.format(a, atr[a]))
res.append('>')
res.append(str(text['text']))
res.append('</text>')
return res
def render(self):
if self.is_axes_on_top:
self.add_labels()
self.add_axes()
svg = []
svg.append('<svg>')
for t in self.traces:
svg.extend(self.render_trace(t))
for t in self.texts:
svg.extend(self.render_text(t))
svg.append('</svg>')
return svg
def render_to_svg(self, filepath):
with open(filepath, 'w') as f:
lines = self.render()
for line in lines:
f.write(line + '\n')
def main():
xlabels = [0, 2, 4, 6, 8, 10, '12 hours']
ylabels = ['0 %', '50 %', '100 %']
chart = Chart(inverseX=True, xlabels=xlabels, ylabels=ylabels,
height=400)
#import random
#ys = [random.randrange(0, 100) for i in xrange(200)]
# c90c28 dark red
# 2e7eb3 blue
# fa730c orange
# 4aa635 green
color = '#2e7eb3'
ys = [10, 60, 60]
xs = [10, 20, 30]
chart.add(xs=xs, ys=ys, stroke=color, fill=color)
color = '#4aa635'
ys = [40, 70, 100, 98]
xs = [30, 40, 50, 60]
chart.add(xs=xs, ys=ys, stroke=color, fill=color)
chart.render_to_svg('test.svg')
if __name__ == '__main__':
main()
|
# Copyright 2016 PerfKitBenchmarker 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.
"""Class to represent an AWS Virtual Machine object.
Images: aws ec2 describe-images --owners self amazon
All VM specifics are self-contained and the class provides methods to
operate on the VM: boot, shutdown, etc.
"""
import base64
import collections
import json
import logging
import posixpath
import re
import threading
import time
import uuid
from absl import flags
from perfkitbenchmarker import disk
from perfkitbenchmarker import errors
from perfkitbenchmarker import linux_virtual_machine
from perfkitbenchmarker import placement_group
from perfkitbenchmarker import providers
from perfkitbenchmarker import resource
from perfkitbenchmarker import virtual_machine
from perfkitbenchmarker import vm_util
from perfkitbenchmarker import windows_virtual_machine
from perfkitbenchmarker.configs import option_decoders
from perfkitbenchmarker.providers.aws import aws_disk
from perfkitbenchmarker.providers.aws import aws_network
from perfkitbenchmarker.providers.aws import util
from six.moves import range
FLAGS = flags.FLAGS
HVM = 'hvm'
PV = 'paravirtual'
NON_HVM_PREFIXES = ['m1', 'c1', 't1', 'm2']
NON_PLACEMENT_GROUP_PREFIXES = frozenset(
['t2', 'm3', 't3', 't3a', 't4g', 'vt1'])
DRIVE_START_LETTER = 'b'
TERMINATED = 'terminated'
SHUTTING_DOWN = 'shutting-down'
INSTANCE_EXISTS_STATUSES = frozenset(['running', 'stopping', 'stopped'])
INSTANCE_DELETED_STATUSES = frozenset([SHUTTING_DOWN, TERMINATED])
INSTANCE_TRANSITIONAL_STATUSES = frozenset(['pending'])
INSTANCE_KNOWN_STATUSES = (INSTANCE_EXISTS_STATUSES | INSTANCE_DELETED_STATUSES
| INSTANCE_TRANSITIONAL_STATUSES)
HOST_EXISTS_STATES = frozenset(
['available', 'under-assessment', 'permanent-failure'])
HOST_RELEASED_STATES = frozenset(['released', 'released-permanent-failure'])
KNOWN_HOST_STATES = HOST_EXISTS_STATES | HOST_RELEASED_STATES
AWS_INITIATED_SPOT_TERMINATING_TRANSITION_STATUSES = frozenset(
['marked-for-termination', 'marked-for-stop'])
AWS_INITIATED_SPOT_TERMINAL_STATUSES = frozenset(
['instance-terminated-by-price', 'instance-terminated-by-service',
'instance-terminated-no-capacity',
'instance-terminated-capacity-oversubscribed',
'instance-terminated-launch-group-constraint'])
USER_INITIATED_SPOT_TERMINAL_STATUSES = frozenset(
['request-canceled-and-instance-running', 'instance-terminated-by-user'])
# These are the project numbers of projects owning common images.
# Some numbers have corresponding owner aliases, but they are not used here.
AMAZON_LINUX_IMAGE_PROJECT = [
'137112412989', # alias amazon most regions
'210953353124', # alias amazon for af-south-1
'910595266909', # alias amazon for ap-east-1
'071630900071', # alias amazon for eu-south-1
]
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Stretch
# Marketplace AMI exists, but not in all regions
DEBIAN_9_IMAGE_PROJECT = ['379101102735']
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Bullseye
DEBIAN_IMAGE_PROJECT = ['136693071363']
# Owns AMIs lists here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
# Also owns the AMIS listed in
# https://builds.coreos.fedoraproject.org/streams/stable.json
CENTOS_IMAGE_PROJECT = ['125523088429']
MARKETPLACE_IMAGE_PROJECT = ['679593333241'] # alias aws-marketplace
# https://access.redhat.com/articles/2962171
RHEL_IMAGE_PROJECT = ['309956199498']
# https://help.ubuntu.com/community/EC2StartersGuide#Official_Ubuntu_Cloud_Guest_Amazon_Machine_Images_.28AMIs.29
UBUNTU_IMAGE_PROJECT = ['099720109477'] # Owned by canonical
# Some Windows images are also available in marketplace project, but this is the
# one selected by the AWS console.
WINDOWS_IMAGE_PROJECT = ['801119661308'] # alias amazon
UBUNTU_EFA_IMAGE_PROJECT = ['898082745236']
# Processor architectures
ARM = 'arm64'
X86 = 'x86_64'
# Machine type to ARM architecture.
_MACHINE_TYPE_PREFIX_TO_ARM_ARCH = {
'a1': 'cortex-a72',
'c6g': 'graviton2',
'g5g': 'graviton2',
'm6g': 'graviton2',
'r6g': 'graviton2',
't4g': 'graviton2',
'im4g': 'graviton2',
'is4ge': 'graviton2',
'x2g': 'graviton2',
}
# Parameters for use with Elastic Fiber Adapter
_EFA_PARAMS = {
'InterfaceType': 'efa',
'DeviceIndex': 0,
'NetworkCardIndex': 0,
'Groups': '',
'SubnetId': ''
}
# Location of EFA installer
_EFA_URL = ('https://s3-us-west-2.amazonaws.com/aws-efa-installer/'
'aws-efa-installer-{version}.tar.gz')
class AwsTransitionalVmRetryableError(Exception):
"""Error for retrying _Exists when an AWS VM is in a transitional state."""
class AwsDriverDoesntSupportFeatureError(Exception):
"""Raised if there is an attempt to set a feature not supported."""
class AwsUnexpectedWindowsAdapterOutputError(Exception):
"""Raised when querying the status of a windows adapter failed."""
class AwsUnknownStatusError(Exception):
"""Error indicating an unknown status was encountered."""
class AwsImageNotFoundError(Exception):
"""Error indicating no appropriate AMI could be found."""
def GetRootBlockDeviceSpecForImage(image_id, region):
"""Queries the CLI and returns the root block device specification as a dict.
Args:
image_id: The EC2 image id to query
region: The EC2 region in which the image resides
Returns:
The root block device specification as returned by the AWS cli,
as a Python dict. If the image is not found, or if the response
is malformed, an exception will be raised.
"""
command = util.AWS_PREFIX + [
'ec2',
'describe-images',
'--region=%s' % region,
'--image-ids=%s' % image_id,
'--query', 'Images[]']
stdout, _ = util.IssueRetryableCommand(command)
images = json.loads(stdout)
assert images
assert len(images) == 1, (
'Expected to receive only one image description for %s' % image_id)
image_spec = images[0]
root_device_name = image_spec['RootDeviceName']
block_device_mappings = image_spec['BlockDeviceMappings']
root_block_device_dict = next((x for x in block_device_mappings if
x['DeviceName'] == root_device_name))
return root_block_device_dict
def GetBlockDeviceMap(machine_type, root_volume_size_gb=None,
image_id=None, region=None):
"""Returns the block device map to expose all devices for a given machine.
Args:
machine_type: The machine type to create a block device map for.
root_volume_size_gb: The desired size of the root volume, in GiB,
or None to the default provided by AWS.
image_id: The image id (AMI) to use in order to lookup the default
root device specs. This is only required if root_volume_size
is specified.
region: The region which contains the specified image. This is only
required if image_id is specified.
Returns:
The json representation of the block device map for a machine compatible
with the AWS CLI, or if the machine type has no local disks, it will
return None. If root_volume_size_gb and image_id are provided, the block
device map will include the specification for the root volume.
Raises:
ValueError: If required parameters are not passed.
"""
mappings = []
if root_volume_size_gb is not None:
if image_id is None:
raise ValueError(
'image_id must be provided if root_volume_size_gb is specified')
if region is None:
raise ValueError(
'region must be provided if image_id is specified')
root_block_device = GetRootBlockDeviceSpecForImage(image_id, region)
root_block_device['Ebs']['VolumeSize'] = root_volume_size_gb
# The 'Encrypted' key must be removed or the CLI will complain
if not FLAGS.aws_vm_hibernate:
root_block_device['Ebs'].pop('Encrypted')
else:
root_block_device['Ebs']['Encrypted'] = True
mappings.append(root_block_device)
if (machine_type in aws_disk.NUM_LOCAL_VOLUMES and
not aws_disk.LocalDriveIsNvme(machine_type)):
for i in range(aws_disk.NUM_LOCAL_VOLUMES[machine_type]):
od = collections.OrderedDict()
od['VirtualName'] = 'ephemeral%s' % i
od['DeviceName'] = '/dev/xvd%s' % chr(ord(DRIVE_START_LETTER) + i)
mappings.append(od)
if mappings:
return json.dumps(mappings)
return None
def IsPlacementGroupCompatible(machine_type):
"""Returns True if VMs of 'machine_type' can be put in a placement group."""
prefix = machine_type.split('.')[0]
return prefix not in NON_PLACEMENT_GROUP_PREFIXES
def GetArmArchitecture(machine_type):
"""Returns the specific ARM processor architecture of the VM."""
# c6g.medium -> c6g, m6gd.large -> m6g, c5n.18xlarge -> c5
prefix = re.split(r'[dn]?\.', machine_type)[0]
return _MACHINE_TYPE_PREFIX_TO_ARM_ARCH.get(prefix)
def GetProcessorArchitecture(machine_type):
"""Returns the processor architecture of the VM."""
if GetArmArchitecture(machine_type):
return ARM
else:
return X86
class AwsDedicatedHost(resource.BaseResource):
"""Object representing an AWS host.
Attributes:
region: The AWS region of the host.
zone: The AWS availability zone of the host.
machine_type: The machine type of VMs that may be created on the host.
client_token: A uuid that makes the creation request idempotent.
id: The host_id of the host.
"""
def __init__(self, machine_type, zone):
super(AwsDedicatedHost, self).__init__()
self.machine_type = machine_type
self.zone = zone
self.region = util.GetRegionFromZone(self.zone)
self.client_token = str(uuid.uuid4())
self.id = None
self.fill_fraction = 0.0
def _Create(self):
create_cmd = util.AWS_PREFIX + [
'ec2',
'allocate-hosts',
'--region=%s' % self.region,
'--client-token=%s' % self.client_token,
'--instance-type=%s' % self.machine_type,
'--availability-zone=%s' % self.zone,
'--auto-placement=off',
'--quantity=1']
vm_util.IssueCommand(create_cmd)
def _Delete(self):
if self.id:
delete_cmd = util.AWS_PREFIX + [
'ec2',
'release-hosts',
'--region=%s' % self.region,
'--host-ids=%s' % self.id]
vm_util.IssueCommand(delete_cmd, raise_on_failure=False)
@vm_util.Retry()
def _Exists(self):
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-hosts',
'--region=%s' % self.region,
'--filter=Name=client-token,Values=%s' % self.client_token]
stdout, _, _ = vm_util.IssueCommand(describe_cmd)
response = json.loads(stdout)
hosts = response['Hosts']
assert len(hosts) < 2, 'Too many hosts.'
if not hosts:
return False
host = hosts[0]
self.id = host['HostId']
state = host['State']
assert state in KNOWN_HOST_STATES, state
return state in HOST_EXISTS_STATES
class AwsVmSpec(virtual_machine.BaseVmSpec):
"""Object containing the information needed to create an AwsVirtualMachine.
Attributes:
use_dedicated_host: bool. Whether to create this VM on a dedicated host.
"""
CLOUD = providers.AWS
@classmethod
def _ApplyFlags(cls, config_values, flag_values):
"""Modifies config options based on runtime flag values.
Can be overridden by derived classes to add support for specific flags.
Args:
config_values: dict mapping config option names to provided values. May
be modified by this function.
flag_values: flags.FlagValues. Runtime flags that may override the
provided config values.
"""
super(AwsVmSpec, cls)._ApplyFlags(config_values, flag_values)
if flag_values['aws_boot_disk_size'].present:
config_values['boot_disk_size'] = flag_values.aws_boot_disk_size
if flag_values['aws_spot_instances'].present:
config_values['use_spot_instance'] = flag_values.aws_spot_instances
if flag_values['aws_spot_price'].present:
config_values['spot_price'] = flag_values.aws_spot_price
if flag_values['aws_spot_block_duration_minutes'].present:
config_values['spot_block_duration_minutes'] = int(
flag_values.aws_spot_block_duration_minutes)
@classmethod
def _GetOptionDecoderConstructions(cls):
"""Gets decoder classes and constructor args for each configurable option.
Returns:
dict. Maps option name string to a (ConfigOptionDecoder class, dict) pair.
The pair specifies a decoder class and its __init__() keyword
arguments to construct in order to decode the named option.
"""
result = super(AwsVmSpec, cls)._GetOptionDecoderConstructions()
result.update({
'use_spot_instance': (option_decoders.BooleanDecoder, {
'default': False
}),
'spot_price': (option_decoders.FloatDecoder, {
'default': None
}),
'spot_block_duration_minutes': (option_decoders.IntDecoder, {
'default': None
}),
'boot_disk_size': (option_decoders.IntDecoder, {
'default': None
})
})
return result
def _GetKeyfileSetKey(region):
"""Returns a key to use for the keyfile set.
This prevents other runs in the same process from reusing the key.
Args:
region: The region the keyfile is in.
"""
return (region, FLAGS.run_uri)
class AwsKeyFileManager(object):
"""Object for managing AWS Keyfiles."""
_lock = threading.Lock()
imported_keyfile_set = set()
deleted_keyfile_set = set()
@classmethod
def ImportKeyfile(cls, region):
"""Imports the public keyfile to AWS."""
with cls._lock:
if _GetKeyfileSetKey(region) in cls.imported_keyfile_set:
return
cat_cmd = ['cat',
vm_util.GetPublicKeyPath()]
keyfile, _ = vm_util.IssueRetryableCommand(cat_cmd)
formatted_tags = util.FormatTagSpecifications('key-pair',
util.MakeDefaultTags())
import_cmd = util.AWS_PREFIX + [
'ec2', '--region=%s' % region,
'import-key-pair',
'--key-name=%s' % cls.GetKeyNameForRun(),
'--public-key-material=%s' % keyfile,
'--tag-specifications=%s' % formatted_tags,
]
_, stderr, retcode = vm_util.IssueCommand(
import_cmd, raise_on_failure=False)
if retcode:
if 'KeyPairLimitExceeded' in stderr:
raise errors.Benchmarks.QuotaFailure(
'KeyPairLimitExceeded in %s: %s' % (region, stderr))
else:
raise errors.Benchmarks.PrepareException(stderr)
cls.imported_keyfile_set.add(_GetKeyfileSetKey(region))
if _GetKeyfileSetKey(region) in cls.deleted_keyfile_set:
cls.deleted_keyfile_set.remove(_GetKeyfileSetKey(region))
@classmethod
def DeleteKeyfile(cls, region):
"""Deletes the imported keyfile for a region."""
with cls._lock:
if _GetKeyfileSetKey(region) in cls.deleted_keyfile_set:
return
delete_cmd = util.AWS_PREFIX + [
'ec2', '--region=%s' % region,
'delete-key-pair',
'--key-name=%s' % cls.GetKeyNameForRun()]
util.IssueRetryableCommand(delete_cmd)
cls.deleted_keyfile_set.add(_GetKeyfileSetKey(region))
if _GetKeyfileSetKey(region) in cls.imported_keyfile_set:
cls.imported_keyfile_set.remove(_GetKeyfileSetKey(region))
@classmethod
def GetKeyNameForRun(cls):
return 'perfkit-key-{0}'.format(FLAGS.run_uri)
class AwsVirtualMachine(virtual_machine.BaseVirtualMachine):
"""Object representing an AWS Virtual Machine."""
CLOUD = providers.AWS
# The IMAGE_NAME_FILTER is passed to the AWS CLI describe-images command to
# filter images by name. This must be set by subclasses, but may be overridden
# by the aws_image_name_filter flag.
IMAGE_NAME_FILTER = None
# The IMAGE_NAME_REGEX can be used to further filter images by name. It
# applies after the IMAGE_NAME_FILTER above. Note that before this regex is
# applied, Python's string formatting is used to replace {virt_type} and
# {disk_type} by the respective virtualization type and root disk type of the
# VM, allowing the regex to contain these strings. This regex supports
# arbitrary Python regular expressions to further narrow down the set of
# images considered.
IMAGE_NAME_REGEX = None
# List of projects that own the AMIs of this OS type. Default to
# AWS Marketplace official image project. Note that opt-in regions may have a
# different image owner than default regions.
IMAGE_OWNER = MARKETPLACE_IMAGE_PROJECT
# Some AMIs use a project code to find the latest (in addition to owner, and
# filter)
IMAGE_PRODUCT_CODE_FILTER = None
# CoreOS only distinguishes between stable and testing images in the
# description
IMAGE_DESCRIPTION_FILTER = None
DEFAULT_ROOT_DISK_TYPE = 'gp2'
DEFAULT_USER_NAME = 'ec2-user'
_lock = threading.Lock()
deleted_hosts = set()
host_map = collections.defaultdict(list)
def __init__(self, vm_spec):
"""Initialize a AWS virtual machine.
Args:
vm_spec: virtual_machine.BaseVirtualMachineSpec object of the vm.
Raises:
ValueError: If an incompatible vm_spec is passed.
"""
super(AwsVirtualMachine, self).__init__(vm_spec)
self.region = util.GetRegionFromZone(self.zone)
self.user_name = FLAGS.aws_user_name or self.DEFAULT_USER_NAME
if self.machine_type in aws_disk.NUM_LOCAL_VOLUMES:
self.max_local_disks = aws_disk.NUM_LOCAL_VOLUMES[self.machine_type]
self.user_data = None
self.network = aws_network.AwsNetwork.GetNetwork(self)
self.placement_group = getattr(vm_spec, 'placement_group',
self.network.placement_group)
self.firewall = aws_network.AwsFirewall.GetFirewall()
self.use_dedicated_host = vm_spec.use_dedicated_host
self.num_vms_per_host = vm_spec.num_vms_per_host
self.use_spot_instance = vm_spec.use_spot_instance
self.spot_price = vm_spec.spot_price
self.spot_block_duration_minutes = vm_spec.spot_block_duration_minutes
self.boot_disk_size = vm_spec.boot_disk_size
self.client_token = str(uuid.uuid4())
self.host = None
self.id = None
self.metadata.update({
'spot_instance':
self.use_spot_instance,
'spot_price':
self.spot_price,
'spot_block_duration_minutes':
self.spot_block_duration_minutes,
'placement_group_strategy':
self.placement_group.strategy
if self.placement_group else placement_group.PLACEMENT_GROUP_NONE,
'aws_credit_specification':
FLAGS.aws_credit_specification
if FLAGS.aws_credit_specification else 'none'
})
self.spot_early_termination = False
self.spot_status_code = None
# See:
# https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/enhanced-networking-os.html
self._smp_affinity_script = 'smp_affinity.sh'
if self.use_dedicated_host and util.IsRegion(self.zone):
raise ValueError(
'In order to use dedicated hosts, you must specify an availability '
'zone, not a region ("zone" was %s).' % self.zone)
if self.use_dedicated_host and self.use_spot_instance:
raise ValueError(
'Tenancy=host is not supported for Spot Instances')
self.allocation_id = None
self.association_id = None
self.aws_tags = {}
@property
def host_list(self):
"""Returns the list of hosts that are compatible with this VM."""
return self.host_map[(self.machine_type, self.zone)]
@property
def group_id(self):
"""Returns the security group ID of this VM."""
return self.network.regional_network.vpc.default_security_group_id
@classmethod
def GetDefaultImage(cls, machine_type, region):
"""Returns the default image given the machine type and region.
If specified, the aws_image_name_filter and aws_image_name_regex flags will
override os_type defaults.
Args:
machine_type: The machine_type of the VM, used to determine virtualization
type.
region: The region of the VM, as images are region specific.
Raises:
AwsImageNotFoundError: If a default image cannot be found.
Returns:
The ID of the latest image, or None if no default image is configured or
none can be found.
"""
# These cannot be REQUIRED_ATTRS, because nesting REQUIRED_ATTRS breaks.
if not cls.IMAGE_OWNER:
raise NotImplementedError('AWS OSMixins require IMAGE_OWNER')
if not cls.IMAGE_NAME_FILTER:
raise NotImplementedError('AWS OSMixins require IMAGE_NAME_FILTER')
if FLAGS.aws_image_name_filter:
cls.IMAGE_NAME_FILTER = FLAGS.aws_image_name_filter
if FLAGS.aws_image_name_regex:
cls.IMAGE_NAME_REGEX = FLAGS.aws_image_name_regex
prefix = machine_type.split('.')[0]
virt_type = PV if prefix in NON_HVM_PREFIXES else HVM
processor_architecture = GetProcessorArchitecture(machine_type)
describe_cmd = util.AWS_PREFIX + [
'--region=%s' % region,
'ec2',
'describe-images',
'--query', ('Images[*].{Name:Name,ImageId:ImageId,'
'CreationDate:CreationDate}'),
'--filters',
'Name=name,Values=%s' % cls.IMAGE_NAME_FILTER,
'Name=block-device-mapping.volume-type,Values=%s' %
cls.DEFAULT_ROOT_DISK_TYPE,
'Name=virtualization-type,Values=%s' % virt_type,
'Name=architecture,Values=%s' % processor_architecture]
if cls.IMAGE_PRODUCT_CODE_FILTER:
describe_cmd.extend(['Name=product-code,Values=%s' %
cls.IMAGE_PRODUCT_CODE_FILTER])
if cls.IMAGE_DESCRIPTION_FILTER:
describe_cmd.extend(['Name=description,Values=%s' %
cls.IMAGE_DESCRIPTION_FILTER])
describe_cmd.extend(['--owners'] + cls.IMAGE_OWNER)
stdout, _ = util.IssueRetryableCommand(describe_cmd)
if not stdout:
raise AwsImageNotFoundError('aws describe-images did not produce valid '
'output.')
if cls.IMAGE_NAME_REGEX:
# Further filter images by the IMAGE_NAME_REGEX filter.
image_name_regex = cls.IMAGE_NAME_REGEX.format(
virt_type=virt_type, disk_type=cls.DEFAULT_ROOT_DISK_TYPE,
architecture=processor_architecture)
images = []
excluded_images = []
for image in json.loads(stdout):
if re.search(image_name_regex, image['Name']):
images.append(image)
else:
excluded_images.append(image)
if excluded_images:
logging.debug('Excluded the following images with regex "%s": %s',
image_name_regex,
sorted(image['Name'] for image in excluded_images))
else:
images = json.loads(stdout)
if not images:
raise AwsImageNotFoundError('No AMIs with given filters found.')
return max(images, key=lambda image: image['CreationDate'])['ImageId']
@vm_util.Retry(max_retries=2)
def _PostCreate(self):
"""Get the instance's data and tag it."""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id]
logging.info('Getting instance %s public IP. This will fail until '
'a public IP is available, but will be retried.', self.id)
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
instance = response['Reservations'][0]['Instances'][0]
self.internal_ip = instance['PrivateIpAddress']
if util.IsRegion(self.zone):
self.zone = str(instance['Placement']['AvailabilityZone'])
assert self.group_id == instance['SecurityGroups'][0]['GroupId'], (
self.group_id, instance['SecurityGroups'][0]['GroupId'])
if FLAGS.aws_efa:
self._ConfigureEfa(instance)
elif 'PublicIpAddress' in instance:
self.ip_address = instance['PublicIpAddress']
else:
raise errors.Resource.RetryableCreationError('Public IP not ready.')
def _ConfigureEfa(self, instance):
"""Configuare EFA and associate Elastic IP.
Args:
instance: dict which contains instance info.
"""
if FLAGS.aws_efa_count > 1:
self._ConfigureElasticIp(instance)
else:
self.ip_address = instance['PublicIpAddress']
if FLAGS.aws_efa_version:
# Download EFA then call InstallEfa method so that subclass can override
self.InstallPackages('curl')
url = _EFA_URL.format(version=FLAGS.aws_efa_version)
tarfile = posixpath.basename(url)
self.RemoteCommand(f'curl -O {url}; tar -xzf {tarfile}')
self._InstallEfa()
# Run test program to confirm EFA working
self.RemoteCommand('cd aws-efa-installer; '
'PATH=${PATH}:/opt/amazon/efa/bin ./efa_test.sh')
def _ConfigureElasticIp(self, instance):
"""Create and associate Elastic IP.
Args:
instance: dict which contains instance info.
"""
network_interface_id = None
for network_interface in instance['NetworkInterfaces']:
# The primary network interface (eth0) for the instance.
if network_interface['Attachment']['DeviceIndex'] == 0:
network_interface_id = network_interface['NetworkInterfaceId']
break
assert network_interface_id is not None
stdout, _, _ = vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'allocate-address',
f'--region={self.region}',
'--domain=vpc'])
response = json.loads(stdout)
self.ip_address = response['PublicIp']
self.allocation_id = response['AllocationId']
util.AddDefaultTags(self.allocation_id, self.region)
stdout, _, _ = vm_util.IssueCommand(
util.AWS_PREFIX + ['ec2', 'associate-address',
f'--region={self.region}',
f'--allocation-id={self.allocation_id}',
f'--network-interface-id={network_interface_id}'])
response = json.loads(stdout)
self.association_id = response['AssociationId']
def _InstallEfa(self):
"""Installs AWS EFA packages.
See https://aws.amazon.com/hpc/efa/
"""
if not self.TryRemoteCommand('ulimit -l | grep unlimited'):
self.RemoteCommand(f'echo "{self.user_name} - memlock unlimited" | '
'sudo tee -a /etc/security/limits.conf')
self.RemoteCommand('cd aws-efa-installer; sudo ./efa_installer.sh -y')
if not self.TryRemoteCommand('ulimit -l | grep unlimited'):
# efa_installer.sh should reboot enabling this change, reboot if necessary
self.Reboot()
def _CreateDependencies(self):
"""Create VM dependencies."""
AwsKeyFileManager.ImportKeyfile(self.region)
# GetDefaultImage calls the AWS CLI.
self.image = self.image or self.GetDefaultImage(self.machine_type,
self.region)
self.AllowRemoteAccessPorts()
if self.use_dedicated_host:
with self._lock:
if (not self.host_list or (self.num_vms_per_host and
self.host_list[-1].fill_fraction +
1.0 / self.num_vms_per_host > 1.0)):
host = AwsDedicatedHost(self.machine_type, self.zone)
self.host_list.append(host)
host.Create()
self.host = self.host_list[-1]
if self.num_vms_per_host:
self.host.fill_fraction += 1.0 / self.num_vms_per_host
def _DeleteDependencies(self):
"""Delete VM dependencies."""
AwsKeyFileManager.DeleteKeyfile(self.region)
if self.host:
with self._lock:
if self.host in self.host_list:
self.host_list.remove(self.host)
if self.host not in self.deleted_hosts:
self.host.Delete()
self.deleted_hosts.add(self.host)
def _Create(self):
"""Create a VM instance."""
placement = []
if not util.IsRegion(self.zone):
placement.append('AvailabilityZone=%s' % self.zone)
if self.use_dedicated_host:
placement.append('Tenancy=host,HostId=%s' % self.host.id)
num_hosts = len(self.host_list)
elif self.placement_group:
if IsPlacementGroupCompatible(self.machine_type):
placement.append('GroupName=%s' % self.placement_group.name)
else:
logging.warning(
'VM not placed in Placement Group. VM Type %s not supported',
self.machine_type)
placement = ','.join(placement)
block_device_map = GetBlockDeviceMap(self.machine_type,
self.boot_disk_size,
self.image,
self.region)
if not self.aws_tags:
# Set tags for the AWS VM. If we are retrying the create, we have to use
# the same tags from the previous call.
self.aws_tags.update(self.vm_metadata)
self.aws_tags.update(util.MakeDefaultTags())
create_cmd = util.AWS_PREFIX + [
'ec2',
'run-instances',
'--region=%s' % self.region,
'--client-token=%s' % self.client_token,
'--image-id=%s' % self.image,
'--instance-type=%s' % self.machine_type,
'--key-name=%s' % AwsKeyFileManager.GetKeyNameForRun(),
'--tag-specifications=%s' %
util.FormatTagSpecifications('instance', self.aws_tags)]
if FLAGS.aws_vm_hibernate:
create_cmd.extend([
'--hibernation-options=Configured=true',
])
if FLAGS.disable_smt:
query_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instance-types',
'--instance-types',
self.machine_type,
'--query',
'InstanceTypes[0].VCpuInfo.DefaultCores'
]
stdout, _, retcode = vm_util.IssueCommand(query_cmd)
cores = int(json.loads(stdout))
create_cmd.append(f'--cpu-options=CoreCount={cores},ThreadsPerCore=1')
if FLAGS.aws_efa:
efas = ['--network-interfaces']
for device_index in range(FLAGS.aws_efa_count):
efa_params = _EFA_PARAMS.copy()
efa_params.update({
'NetworkCardIndex': device_index,
'DeviceIndex': device_index,
'Groups': self.group_id,
'SubnetId': self.network.subnet.id
})
if FLAGS.aws_efa_count == 1:
efa_params['AssociatePublicIpAddress'] = True
efas.append(','.join(f'{key}={value}' for key, value in
sorted(efa_params.items())))
create_cmd.extend(efas)
else:
create_cmd.append('--associate-public-ip-address')
create_cmd.append(f'--subnet-id={self.network.subnet.id}')
if block_device_map:
create_cmd.append('--block-device-mappings=%s' % block_device_map)
if placement:
create_cmd.append('--placement=%s' % placement)
if FLAGS.aws_credit_specification:
create_cmd.append('--credit-specification=%s' %
FLAGS.aws_credit_specification)
if self.user_data:
create_cmd.append('--user-data=%s' % self.user_data)
if self.capacity_reservation_id:
create_cmd.append(
'--capacity-reservation-specification=CapacityReservationTarget='
'{CapacityReservationId=%s}' % self.capacity_reservation_id)
if self.use_spot_instance:
instance_market_options = collections.OrderedDict()
spot_options = collections.OrderedDict()
spot_options['SpotInstanceType'] = 'one-time'
spot_options['InstanceInterruptionBehavior'] = 'terminate'
if self.spot_price:
spot_options['MaxPrice'] = str(self.spot_price)
if self.spot_block_duration_minutes:
spot_options['BlockDurationMinutes'] = self.spot_block_duration_minutes
instance_market_options['MarketType'] = 'spot'
instance_market_options['SpotOptions'] = spot_options
create_cmd.append(
'--instance-market-options=%s' % json.dumps(instance_market_options))
_, stderr, retcode = vm_util.IssueCommand(create_cmd,
raise_on_failure=False)
arm_arch = GetArmArchitecture(self.machine_type)
if arm_arch:
self.host_arch = arm_arch
if self.use_dedicated_host and 'InsufficientCapacityOnHost' in stderr:
if self.num_vms_per_host:
raise errors.Resource.CreationError(
'Failed to create host: %d vms of type %s per host exceeds '
'memory capacity limits of the host' %
(self.num_vms_per_host, self.machine_type))
else:
logging.warning(
'Creation failed due to insufficient host capacity. A new host will '
'be created and instance creation will be retried.')
with self._lock:
if num_hosts == len(self.host_list):
host = AwsDedicatedHost(self.machine_type, self.zone)
self.host_list.append(host)
host.Create()
self.host = self.host_list[-1]
self.client_token = str(uuid.uuid4())
raise errors.Resource.RetryableCreationError()
if 'InsufficientInstanceCapacity' in stderr:
if self.use_spot_instance:
self.spot_status_code = 'InsufficientSpotInstanceCapacity'
self.spot_early_termination = True
raise errors.Benchmarks.InsufficientCapacityCloudFailure(stderr)
if 'SpotMaxPriceTooLow' in stderr:
self.spot_status_code = 'SpotMaxPriceTooLow'
self.spot_early_termination = True
raise errors.Resource.CreationError(stderr)
if 'InstanceLimitExceeded' in stderr or 'VcpuLimitExceeded' in stderr:
raise errors.Benchmarks.QuotaFailure(stderr)
if 'RequestLimitExceeded' in stderr:
if FLAGS.retry_on_rate_limited:
raise errors.Resource.RetryableCreationError(stderr)
else:
raise errors.Benchmarks.QuotaFailure(stderr)
# When launching more than 1 VM into the same placement group, there is an
# occasional error that the placement group has already been used in a
# separate zone. Retrying fixes this error.
if 'InvalidPlacementGroup.InUse' in stderr:
raise errors.Resource.RetryableCreationError(stderr)
if 'Unsupported' in stderr:
raise errors.Benchmarks.UnsupportedConfigError(stderr)
if retcode:
raise errors.Resource.CreationError(
'Failed to create VM: %s return code: %s' % (retcode, stderr))
@vm_util.Retry(
poll_interval=0.5,
log_errors=True,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _WaitForStoppedStatus(self):
"""Returns the status of the VM.
Returns:
Whether the VM is suspended i.e. in a stopped status. If not, raises an
error
Raises:
AwsUnknownStatusError: If an unknown status is returned from AWS.
AwsTransitionalVmRetryableError: If the VM is pending. This is retried.
"""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instance-status',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
'--include-all-instances',
]
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
status = response['InstanceStatuses'][0]['InstanceState']['Name']
if status.lower() != 'stopped':
logging.info('VM has status %s.', status)
raise AwsTransitionalVmRetryableError()
def _BeforeSuspend(self):
"""Prepares the instance for suspend by having the VM sleep for a given duration.
This ensures the VM is ready for hibernation
"""
# Add a timer that waits for a given duration after vm instance is
# created before calling suspend on the vm to ensure that the vm is
# ready for hibernation in aws.
time.sleep(600)
def _PostSuspend(self):
self._WaitForStoppedStatus()
def _Suspend(self):
"""Suspends a VM instance."""
suspend_cmd = util.AWS_PREFIX + [
'ec2',
'stop-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
'--hibernate',
]
try:
vm_util.IssueCommand(suspend_cmd)
except:
raise errors.Benchmarks.KnownIntermittentError(
'Instance is still not ready to hibernate')
self._PostSuspend()
@vm_util.Retry(
poll_interval=0.5,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _WaitForNewIP(self):
"""Checks for a new IP address, waiting if the VM is still pending.
Raises:
AwsTransitionalVmRetryableError: If VM is pending. This is retried.
"""
status_cmd = util.AWS_PREFIX + [
'ec2', 'describe-instances', f'--region={self.region}',
f'--instance-ids={self.id}'
]
stdout, _, _ = vm_util.IssueCommand(status_cmd)
response = json.loads(stdout)
instance = response['Reservations'][0]['Instances'][0]
if 'PublicIpAddress' in instance:
self.ip_address = instance['PublicIpAddress']
else:
logging.info('VM is pending.')
raise AwsTransitionalVmRetryableError()
def _PostResume(self):
self._WaitForNewIP()
def _Resume(self):
"""Resumes a VM instance."""
resume_cmd = util.AWS_PREFIX + [
'ec2',
'start-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
]
vm_util.IssueCommand(resume_cmd)
self._PostResume()
def _Delete(self):
"""Delete a VM instance."""
if self.id:
delete_cmd = util.AWS_PREFIX + [
'ec2',
'terminate-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id]
vm_util.IssueCommand(delete_cmd, raise_on_failure=False)
if hasattr(self, 'spot_instance_request_id'):
cancel_cmd = util.AWS_PREFIX + [
'--region=%s' % self.region,
'ec2',
'cancel-spot-instance-requests',
'--spot-instance-request-ids=%s' % self.spot_instance_request_id]
vm_util.IssueCommand(cancel_cmd, raise_on_failure=False)
if FLAGS.aws_efa:
if self.association_id:
vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'disassociate-address',
f'--region={self.region}',
f'--association-id={self.association_id}'])
if self.allocation_id:
vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'release-address',
f'--region={self.region}',
f'--allocation-id={self.allocation_id}'])
# _Start or _Stop not yet implemented for AWS
def _Start(self):
"""Starts the VM."""
if not self.id:
raise errors.Benchmarks.RunError(
'Expected VM id to be non-null. Please make sure the VM exists.')
start_cmd = util.AWS_PREFIX + [
'ec2', 'start-instances',
f'--region={self.region}',
f'--instance-ids={self.id}'
]
vm_util.IssueCommand(start_cmd)
def _PostStart(self):
self._WaitForNewIP()
def _Stop(self):
"""Stops the VM."""
if not self.id:
raise errors.Benchmarks.RunError(
'Expected VM id to be non-null. Please make sure the VM exists.')
stop_cmd = util.AWS_PREFIX + [
'ec2', 'stop-instances',
f'--region={self.region}',
f'--instance-ids={self.id}'
]
vm_util.IssueCommand(stop_cmd)
def _PostStop(self):
self._WaitForStoppedStatus()
def _UpdateInterruptibleVmStatusThroughApi(self):
if hasattr(self, 'spot_instance_request_id'):
describe_cmd = util.AWS_PREFIX + [
'--region=%s' % self.region,
'ec2',
'describe-spot-instance-requests',
'--spot-instance-request-ids=%s' % self.spot_instance_request_id]
stdout, _, _ = vm_util.IssueCommand(describe_cmd)
sir_response = json.loads(stdout)['SpotInstanceRequests']
self.spot_status_code = sir_response[0]['Status']['Code']
self.spot_early_termination = (
self.spot_status_code in AWS_INITIATED_SPOT_TERMINAL_STATUSES)
@vm_util.Retry(
poll_interval=1,
log_errors=False,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _Exists(self):
"""Returns whether the VM exists.
This method waits until the VM is no longer pending.
Returns:
Whether the VM exists.
Raises:
AwsUnknownStatusError: If an unknown status is returned from AWS.
AwsTransitionalVmRetryableError: If the VM is pending. This is retried.
"""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instances',
'--region=%s' % self.region,
'--filter=Name=client-token,Values=%s' % self.client_token]
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
reservations = response['Reservations']
assert len(reservations) < 2, 'Too many reservations.'
if not reservations:
if not self.create_start_time:
return False
logging.info('No reservation returned by describe-instances. This '
'sometimes shows up immediately after a successful '
'run-instances command. Retrying describe-instances '
'command.')
raise AwsTransitionalVmRetryableError()
instances = reservations[0]['Instances']
assert len(instances) == 1, 'Wrong number of instances.'
status = instances[0]['State']['Name']
self.id = instances[0]['InstanceId']
if self.use_spot_instance:
self.spot_instance_request_id = instances[0]['SpotInstanceRequestId']
if status not in INSTANCE_KNOWN_STATUSES:
raise AwsUnknownStatusError('Unknown status %s' % status)
if status in INSTANCE_TRANSITIONAL_STATUSES:
logging.info('VM has status %s; retrying describe-instances command.',
status)
raise AwsTransitionalVmRetryableError()
# In this path run-instances succeeded, a pending instance was created, but
# not fulfilled so it moved to terminated.
if (status == TERMINATED and
instances[0]['StateReason']['Code'] ==
'Server.InsufficientInstanceCapacity'):
raise errors.Benchmarks.InsufficientCapacityCloudFailure(
instances[0]['StateReason']['Message'])
# In this path run-instances succeeded, a pending instance was created, but
# instance is shutting down due to internal server error. This is a
# retryable command for run-instance.
# Client token needs to be refreshed for idempotency.
if (status == SHUTTING_DOWN and
instances[0]['StateReason']['Code'] == 'Server.InternalError'):
self.client_token = str(uuid.uuid4())
return status in INSTANCE_EXISTS_STATUSES
def _GetNvmeBootIndex(self):
if (aws_disk.LocalDriveIsNvme(self.machine_type) and
aws_disk.EbsDriveIsNvme(self.machine_type)):
# identify boot drive
# If this command ever fails consider 'findmnt -nM / -o source'
cmd = ('realpath /dev/disk/by-label/cloudimg-rootfs '
'| grep --only-matching "nvme[0-9]*"')
boot_drive = self.RemoteCommand(cmd, ignore_failure=True)[0].strip()
if boot_drive:
# get the boot drive index by dropping the nvme prefix
boot_idx = int(boot_drive[4:])
logging.info('found boot drive at nvme index %d', boot_idx)
return boot_idx
else:
logging.warning('Failed to identify NVME boot drive index. Assuming 0.')
return 0
def CreateScratchDisk(self, disk_spec):
"""Create a VM's scratch disk.
Args:
disk_spec: virtual_machine.BaseDiskSpec object of the disk.
Raises:
CreationError: If an NFS disk is listed but the NFS service not created.
"""
# Instantiate the disk(s) that we want to create.
disks = []
nvme_boot_drive_index = self._GetNvmeBootIndex()
for _ in range(disk_spec.num_striped_disks):
if disk_spec.disk_type == disk.NFS:
data_disk = self._GetNfsService().CreateNfsDisk()
else:
data_disk = aws_disk.AwsDisk(disk_spec, self.zone, self.machine_type)
if disk_spec.disk_type == disk.LOCAL:
device_letter = chr(ord(DRIVE_START_LETTER) + self.local_disk_counter)
data_disk.AssignDeviceLetter(device_letter, nvme_boot_drive_index)
# Local disk numbers start at 1 (0 is the system disk).
data_disk.disk_number = self.local_disk_counter + 1
self.local_disk_counter += 1
if self.local_disk_counter > self.max_local_disks:
raise errors.Error('Not enough local disks.')
elif disk_spec.disk_type == disk.NFS:
pass
else:
# Remote disk numbers start at 1 + max_local disks (0 is the system disk
# and local disks occupy [1, max_local_disks]).
data_disk.disk_number = (self.remote_disk_counter +
1 + self.max_local_disks)
self.remote_disk_counter += 1
disks.append(data_disk)
self._CreateScratchDiskFromDisks(disk_spec, disks)
def AddMetadata(self, **kwargs):
"""Adds metadata to the VM."""
util.AddTags(self.id, self.region, **kwargs)
if self.use_spot_instance:
util.AddDefaultTags(self.spot_instance_request_id, self.region)
def InstallCli(self):
"""Installs the AWS cli and credentials on this AWS vm."""
self.Install('awscli')
self.Install('aws_credentials')
def DownloadPreprovisionedData(self, install_path, module_name, filename):
"""Downloads a data file from an AWS S3 bucket with pre-provisioned data.
Use --aws_preprovisioned_data_bucket to specify the name of the bucket.
Args:
install_path: The install path on this VM.
module_name: Name of the module associated with this data file.
filename: The name of the file that was downloaded.
"""
self.InstallCli()
# TODO(deitz): Add retry logic.
self.RemoteCommand(GenerateDownloadPreprovisionedDataCommand(
install_path, module_name, filename))
def ShouldDownloadPreprovisionedData(self, module_name, filename):
"""Returns whether or not preprovisioned data is available."""
self.Install('aws_credentials')
self.Install('awscli')
return FLAGS.aws_preprovisioned_data_bucket and self.TryRemoteCommand(
GenerateStatPreprovisionedDataCommand(module_name, filename))
def IsInterruptible(self):
"""Returns whether this vm is an interruptible vm (spot vm).
Returns: True if this vm is an interruptible vm (spot vm).
"""
return self.use_spot_instance
def WasInterrupted(self):
"""Returns whether this spot vm was terminated early by AWS.
Returns: True if this vm was terminated early by AWS.
"""
return self.spot_early_termination
def GetVmStatusCode(self):
"""Returns the early termination code if any.
Returns: Early termination code.
"""
return self.spot_status_code
def GetResourceMetadata(self):
"""Returns a dict containing metadata about the VM.
Returns:
dict mapping string property key to value.
"""
result = super(AwsVirtualMachine, self).GetResourceMetadata()
result['boot_disk_type'] = self.DEFAULT_ROOT_DISK_TYPE
result['boot_disk_size'] = self.boot_disk_size
if self.use_dedicated_host:
result['num_vms_per_host'] = self.num_vms_per_host
result['efa'] = FLAGS.aws_efa
if FLAGS.aws_efa:
result['efa_version'] = FLAGS.aws_efa_version
result['efa_count'] = FLAGS.aws_efa_count
result['preemptible'] = self.use_spot_instance
return result
class ClearBasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.ClearMixin):
IMAGE_NAME_FILTER = 'clear/images/*/clear-*'
DEFAULT_USER_NAME = 'clear'
class CoreOsBasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CoreOsMixin):
IMAGE_NAME_FILTER = 'fedora-coreos-*-hvm'
# CoreOS only distinguishes between stable and testing in the description
IMAGE_DESCRIPTION_FILTER = 'Fedora CoreOS stable *'
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
DEFAULT_USER_NAME = 'core'
class Debian9BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian9Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Stretch
IMAGE_NAME_FILTER = 'debian-stretch-*64-*'
IMAGE_OWNER = DEBIAN_9_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
def _BeforeSuspend(self):
"""Prepares the aws vm for hibernation."""
raise NotImplementedError()
class Debian10BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian10Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
IMAGE_NAME_FILTER = 'debian-10-*64*'
IMAGE_OWNER = DEBIAN_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
class Debian11BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian11Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
IMAGE_NAME_FILTER = 'debian-11-*64*'
IMAGE_OWNER = DEBIAN_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
class UbuntuBasedAwsVirtualMachine(AwsVirtualMachine):
IMAGE_OWNER = UBUNTU_IMAGE_PROJECT
DEFAULT_USER_NAME = 'ubuntu'
class Ubuntu1604BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu1604Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-xenial-16.04-*64-server-20*'
def _InstallEfa(self):
super(Ubuntu1604BasedAwsVirtualMachine, self)._InstallEfa()
self.Reboot()
self.WaitForBootCompletion()
class Ubuntu1804BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu1804Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-bionic-18.04-*64-server-20*'
class Ubuntu1804EfaBasedAwsVirtualMachine(
UbuntuBasedAwsVirtualMachine, linux_virtual_machine.Ubuntu1804EfaMixin):
IMAGE_OWNER = UBUNTU_EFA_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'Deep Learning AMI GPU CUDA * (Ubuntu 18.04) *'
class Ubuntu2004BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu2004Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-focal-20.04-*64-server-20*'
class JujuBasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.JujuMixin):
"""Class with configuration for AWS Juju virtual machines."""
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-trusty-14.04-*64-server-20*'
class AmazonLinux2BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.AmazonLinux2Mixin):
"""Class with configuration for AWS Amazon Linux 2 virtual machines."""
IMAGE_NAME_FILTER = 'amzn2-ami-*-*-*'
IMAGE_OWNER = AMAZON_LINUX_IMAGE_PROJECT
class Rhel7BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Rhel7Mixin):
"""Class with configuration for AWS RHEL 7 virtual machines."""
# Documentation on finding RHEL images:
# https://access.redhat.com/articles/2962171
IMAGE_NAME_FILTER = 'RHEL-7*_GA*'
IMAGE_OWNER = RHEL_IMAGE_PROJECT
class Rhel8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Rhel8Mixin):
"""Class with configuration for AWS RHEL 8 virtual machines."""
# Documentation on finding RHEL images:
# https://access.redhat.com/articles/2962181
# All RHEL AMIs are HVM. HVM- blocks HVM_BETA.
IMAGE_NAME_FILTER = 'RHEL-8*_HVM-*'
IMAGE_OWNER = RHEL_IMAGE_PROJECT
class CentOs7BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CentOs7Mixin):
"""Class with configuration for AWS CentOS 7 virtual machines."""
# Documentation on finding the CentOS 7 image:
# https://wiki.centos.org/Cloud/AWS#x86_64
IMAGE_NAME_FILTER = 'CentOS 7*'
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
DEFAULT_USER_NAME = 'centos'
def _InstallEfa(self):
logging.info('Upgrading Centos7 kernel, installing kernel headers and '
'rebooting before installing EFA.')
self.RemoteCommand('sudo yum upgrade -y kernel')
self.InstallPackages('kernel-devel')
self.Reboot()
self.WaitForBootCompletion()
super(CentOs7BasedAwsVirtualMachine, self)._InstallEfa()
class CentOs8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CentOs8Mixin):
"""Class with configuration for AWS CentOS 8 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS 8*'
DEFAULT_USER_NAME = 'centos'
class CentOsStream8BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.CentOsStream8Mixin):
"""Class with configuration for AWS CentOS Stream 8 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS Stream 8*'
DEFAULT_USER_NAME = 'centos'
class RockyLinux8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.RockyLinux8Mixin):
"""Class with configuration for AWS Rocky Linux 8 virtual machines."""
IMAGE_OWNER = MARKETPLACE_IMAGE_PROJECT
IMAGE_PRODUCT_CODE_FILTER = 'cotnnspjrsi38lfn8qo4ibnnm'
IMAGE_NAME_FILTER = 'Rocky-8-*'
DEFAULT_USER_NAME = 'rocky'
class CentOsStream9BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.CentOsStream9Mixin):
"""Class with configuration for AWS CentOS Stream 9 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS Stream 9*'
class BaseWindowsAwsVirtualMachine(AwsVirtualMachine,
windows_virtual_machine.BaseWindowsMixin):
"""Support for Windows machines on AWS."""
DEFAULT_USER_NAME = 'Administrator'
IMAGE_OWNER = WINDOWS_IMAGE_PROJECT
def __init__(self, vm_spec):
super(BaseWindowsAwsVirtualMachine, self).__init__(vm_spec)
self.user_data = ('<powershell>%s</powershell>' %
windows_virtual_machine.STARTUP_SCRIPT)
@vm_util.Retry()
def _GetDecodedPasswordData(self):
# Retrieve a base64 encoded, encrypted password for the VM.
get_password_cmd = util.AWS_PREFIX + [
'ec2',
'get-password-data',
'--region=%s' % self.region,
'--instance-id=%s' % self.id]
stdout, _ = util.IssueRetryableCommand(get_password_cmd)
response = json.loads(stdout)
password_data = response['PasswordData']
# AWS may not populate the password data until some time after
# the VM shows as running. Simply retry until the data shows up.
if not password_data:
raise ValueError('No PasswordData in response.')
# Decode the password data.
return base64.b64decode(password_data)
def _PostCreate(self):
"""Retrieve generic VM info and then retrieve the VM's password."""
super(BaseWindowsAwsVirtualMachine, self)._PostCreate()
# Get the decoded password data.
decoded_password_data = self._GetDecodedPasswordData()
# Write the encrypted data to a file, and use openssl to
# decrypt the password.
with vm_util.NamedTemporaryFile() as tf:
tf.write(decoded_password_data)
tf.close()
decrypt_cmd = ['openssl',
'rsautl',
'-decrypt',
'-in',
tf.name,
'-inkey',
vm_util.GetPrivateKeyPath()]
password, _ = vm_util.IssueRetryableCommand(decrypt_cmd)
self.password = password
def GetResourceMetadata(self):
"""Returns a dict containing metadata about the VM.
Returns:
dict mapping metadata key to value.
"""
result = super(BaseWindowsAwsVirtualMachine, self).GetResourceMetadata()
result['disable_interrupt_moderation'] = self.disable_interrupt_moderation
return result
@vm_util.Retry(
max_retries=10,
retryable_exceptions=(AwsUnexpectedWindowsAdapterOutputError,
errors.VirtualMachine.RemoteCommandError))
def DisableInterruptModeration(self):
"""Disable the networking feature 'Interrupt Moderation'."""
# First ensure that the driver supports interrupt moderation
net_adapters, _ = self.RemoteCommand('Get-NetAdapter')
if 'Intel(R) 82599 Virtual Function' not in net_adapters:
raise AwsDriverDoesntSupportFeatureError(
'Driver not tested with Interrupt Moderation in PKB.')
aws_int_dis_path = ('HKLM\\SYSTEM\\ControlSet001\\Control\\Class\\'
'{4d36e972-e325-11ce-bfc1-08002be10318}\\0011')
command = 'reg add "%s" /v *InterruptModeration /d 0 /f' % aws_int_dis_path
self.RemoteCommand(command)
try:
self.RemoteCommand('Restart-NetAdapter -Name "Ethernet 2"')
except IOError:
# Restarting the network adapter will always fail because
# the winrm connection used to issue the command will be
# broken.
pass
int_dis_value, _ = self.RemoteCommand(
'reg query "%s" /v *InterruptModeration' % aws_int_dis_path)
# The second line should look like:
# *InterruptModeration REG_SZ 0
registry_query_lines = int_dis_value.splitlines()
if len(registry_query_lines) < 3:
raise AwsUnexpectedWindowsAdapterOutputError(
'registry query failed: %s ' % int_dis_value)
registry_query_result = registry_query_lines[2].split()
if len(registry_query_result) < 3:
raise AwsUnexpectedWindowsAdapterOutputError(
'unexpected registry query response: %s' % int_dis_value)
if registry_query_result[2] != '0':
raise AwsUnexpectedWindowsAdapterOutputError(
'InterruptModeration failed to disable')
class Windows2012CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2012CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2012-R2_RTM-English-64Bit-Core-*'
class Windows2016CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2016CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2016-English-Core-Base-*'
class Windows2019CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2019CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Core-Base-*'
class Windows2022CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2022CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Core-Base-*'
class Windows2012DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2012DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2012-R2_RTM-English-64Bit-Base-*'
class Windows2016DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2016DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2016-English-Full-Base-*'
class Windows2019DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-Base-*'
class Windows2022DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-Base-*'
class Windows2019DesktopSQLServer2019StandardAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019SQLServer2019Standard):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-SQL_2019_Standard-*'
class Windows2019DesktopSQLServer2019EnterpriseAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019SQLServer2019Enterprise):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-SQL_2019_Enterprise-*'
class Windows2022DesktopSQLServer2019StandardAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022SQLServer2019Standard):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-SQL_2019_Standard-*'
class Windows2022DesktopSQLServer2019EnterpriseAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022SQLServer2019Enterprise):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-SQL_2019_Enterprise-*'
def GenerateDownloadPreprovisionedDataCommand(install_path, module_name,
filename):
"""Returns a string used to download preprovisioned data."""
return 'aws s3 cp --only-show-errors s3://%s/%s/%s %s' % (
FLAGS.aws_preprovisioned_data_bucket, module_name, filename,
posixpath.join(install_path, filename))
def GenerateStatPreprovisionedDataCommand(module_name, filename):
"""Returns a string used to download preprovisioned data."""
return 'aws s3api head-object --bucket %s --key %s/%s' % (
FLAGS.aws_preprovisioned_data_bucket, module_name, filename)
Add AWS c7g support.
PiperOrigin-RevId: 450560510
# Copyright 2016 PerfKitBenchmarker 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.
"""Class to represent an AWS Virtual Machine object.
Images: aws ec2 describe-images --owners self amazon
All VM specifics are self-contained and the class provides methods to
operate on the VM: boot, shutdown, etc.
"""
import base64
import collections
import json
import logging
import posixpath
import re
import threading
import time
import uuid
from absl import flags
from perfkitbenchmarker import disk
from perfkitbenchmarker import errors
from perfkitbenchmarker import linux_virtual_machine
from perfkitbenchmarker import placement_group
from perfkitbenchmarker import providers
from perfkitbenchmarker import resource
from perfkitbenchmarker import virtual_machine
from perfkitbenchmarker import vm_util
from perfkitbenchmarker import windows_virtual_machine
from perfkitbenchmarker.configs import option_decoders
from perfkitbenchmarker.providers.aws import aws_disk
from perfkitbenchmarker.providers.aws import aws_network
from perfkitbenchmarker.providers.aws import util
from six.moves import range
FLAGS = flags.FLAGS
HVM = 'hvm'
PV = 'paravirtual'
NON_HVM_PREFIXES = ['m1', 'c1', 't1', 'm2']
NON_PLACEMENT_GROUP_PREFIXES = frozenset(
['t2', 'm3', 't3', 't3a', 't4g', 'vt1'])
DRIVE_START_LETTER = 'b'
TERMINATED = 'terminated'
SHUTTING_DOWN = 'shutting-down'
INSTANCE_EXISTS_STATUSES = frozenset(['running', 'stopping', 'stopped'])
INSTANCE_DELETED_STATUSES = frozenset([SHUTTING_DOWN, TERMINATED])
INSTANCE_TRANSITIONAL_STATUSES = frozenset(['pending'])
INSTANCE_KNOWN_STATUSES = (INSTANCE_EXISTS_STATUSES | INSTANCE_DELETED_STATUSES
| INSTANCE_TRANSITIONAL_STATUSES)
HOST_EXISTS_STATES = frozenset(
['available', 'under-assessment', 'permanent-failure'])
HOST_RELEASED_STATES = frozenset(['released', 'released-permanent-failure'])
KNOWN_HOST_STATES = HOST_EXISTS_STATES | HOST_RELEASED_STATES
AWS_INITIATED_SPOT_TERMINATING_TRANSITION_STATUSES = frozenset(
['marked-for-termination', 'marked-for-stop'])
AWS_INITIATED_SPOT_TERMINAL_STATUSES = frozenset(
['instance-terminated-by-price', 'instance-terminated-by-service',
'instance-terminated-no-capacity',
'instance-terminated-capacity-oversubscribed',
'instance-terminated-launch-group-constraint'])
USER_INITIATED_SPOT_TERMINAL_STATUSES = frozenset(
['request-canceled-and-instance-running', 'instance-terminated-by-user'])
# These are the project numbers of projects owning common images.
# Some numbers have corresponding owner aliases, but they are not used here.
AMAZON_LINUX_IMAGE_PROJECT = [
'137112412989', # alias amazon most regions
'210953353124', # alias amazon for af-south-1
'910595266909', # alias amazon for ap-east-1
'071630900071', # alias amazon for eu-south-1
]
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Stretch
# Marketplace AMI exists, but not in all regions
DEBIAN_9_IMAGE_PROJECT = ['379101102735']
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Bullseye
DEBIAN_IMAGE_PROJECT = ['136693071363']
# Owns AMIs lists here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
# Also owns the AMIS listed in
# https://builds.coreos.fedoraproject.org/streams/stable.json
CENTOS_IMAGE_PROJECT = ['125523088429']
MARKETPLACE_IMAGE_PROJECT = ['679593333241'] # alias aws-marketplace
# https://access.redhat.com/articles/2962171
RHEL_IMAGE_PROJECT = ['309956199498']
# https://help.ubuntu.com/community/EC2StartersGuide#Official_Ubuntu_Cloud_Guest_Amazon_Machine_Images_.28AMIs.29
UBUNTU_IMAGE_PROJECT = ['099720109477'] # Owned by canonical
# Some Windows images are also available in marketplace project, but this is the
# one selected by the AWS console.
WINDOWS_IMAGE_PROJECT = ['801119661308'] # alias amazon
UBUNTU_EFA_IMAGE_PROJECT = ['898082745236']
# Processor architectures
ARM = 'arm64'
X86 = 'x86_64'
# Machine type to ARM architecture.
_MACHINE_TYPE_PREFIX_TO_ARM_ARCH = {
'a1': 'cortex-a72',
'c6g': 'graviton2',
'c7g': 'graviton2',
'g5g': 'graviton2',
'm6g': 'graviton2',
'r6g': 'graviton2',
't4g': 'graviton2',
'im4g': 'graviton2',
'is4ge': 'graviton2',
'x2g': 'graviton2',
}
# Parameters for use with Elastic Fiber Adapter
_EFA_PARAMS = {
'InterfaceType': 'efa',
'DeviceIndex': 0,
'NetworkCardIndex': 0,
'Groups': '',
'SubnetId': ''
}
# Location of EFA installer
_EFA_URL = ('https://s3-us-west-2.amazonaws.com/aws-efa-installer/'
'aws-efa-installer-{version}.tar.gz')
class AwsTransitionalVmRetryableError(Exception):
"""Error for retrying _Exists when an AWS VM is in a transitional state."""
class AwsDriverDoesntSupportFeatureError(Exception):
"""Raised if there is an attempt to set a feature not supported."""
class AwsUnexpectedWindowsAdapterOutputError(Exception):
"""Raised when querying the status of a windows adapter failed."""
class AwsUnknownStatusError(Exception):
"""Error indicating an unknown status was encountered."""
class AwsImageNotFoundError(Exception):
"""Error indicating no appropriate AMI could be found."""
def GetRootBlockDeviceSpecForImage(image_id, region):
"""Queries the CLI and returns the root block device specification as a dict.
Args:
image_id: The EC2 image id to query
region: The EC2 region in which the image resides
Returns:
The root block device specification as returned by the AWS cli,
as a Python dict. If the image is not found, or if the response
is malformed, an exception will be raised.
"""
command = util.AWS_PREFIX + [
'ec2',
'describe-images',
'--region=%s' % region,
'--image-ids=%s' % image_id,
'--query', 'Images[]']
stdout, _ = util.IssueRetryableCommand(command)
images = json.loads(stdout)
assert images
assert len(images) == 1, (
'Expected to receive only one image description for %s' % image_id)
image_spec = images[0]
root_device_name = image_spec['RootDeviceName']
block_device_mappings = image_spec['BlockDeviceMappings']
root_block_device_dict = next((x for x in block_device_mappings if
x['DeviceName'] == root_device_name))
return root_block_device_dict
def GetBlockDeviceMap(machine_type, root_volume_size_gb=None,
image_id=None, region=None):
"""Returns the block device map to expose all devices for a given machine.
Args:
machine_type: The machine type to create a block device map for.
root_volume_size_gb: The desired size of the root volume, in GiB,
or None to the default provided by AWS.
image_id: The image id (AMI) to use in order to lookup the default
root device specs. This is only required if root_volume_size
is specified.
region: The region which contains the specified image. This is only
required if image_id is specified.
Returns:
The json representation of the block device map for a machine compatible
with the AWS CLI, or if the machine type has no local disks, it will
return None. If root_volume_size_gb and image_id are provided, the block
device map will include the specification for the root volume.
Raises:
ValueError: If required parameters are not passed.
"""
mappings = []
if root_volume_size_gb is not None:
if image_id is None:
raise ValueError(
'image_id must be provided if root_volume_size_gb is specified')
if region is None:
raise ValueError(
'region must be provided if image_id is specified')
root_block_device = GetRootBlockDeviceSpecForImage(image_id, region)
root_block_device['Ebs']['VolumeSize'] = root_volume_size_gb
# The 'Encrypted' key must be removed or the CLI will complain
if not FLAGS.aws_vm_hibernate:
root_block_device['Ebs'].pop('Encrypted')
else:
root_block_device['Ebs']['Encrypted'] = True
mappings.append(root_block_device)
if (machine_type in aws_disk.NUM_LOCAL_VOLUMES and
not aws_disk.LocalDriveIsNvme(machine_type)):
for i in range(aws_disk.NUM_LOCAL_VOLUMES[machine_type]):
od = collections.OrderedDict()
od['VirtualName'] = 'ephemeral%s' % i
od['DeviceName'] = '/dev/xvd%s' % chr(ord(DRIVE_START_LETTER) + i)
mappings.append(od)
if mappings:
return json.dumps(mappings)
return None
def IsPlacementGroupCompatible(machine_type):
"""Returns True if VMs of 'machine_type' can be put in a placement group."""
prefix = machine_type.split('.')[0]
return prefix not in NON_PLACEMENT_GROUP_PREFIXES
def GetArmArchitecture(machine_type):
"""Returns the specific ARM processor architecture of the VM."""
# c6g.medium -> c6g, m6gd.large -> m6g, c5n.18xlarge -> c5
prefix = re.split(r'[dn]?\.', machine_type)[0]
return _MACHINE_TYPE_PREFIX_TO_ARM_ARCH.get(prefix)
def GetProcessorArchitecture(machine_type):
"""Returns the processor architecture of the VM."""
if GetArmArchitecture(machine_type):
return ARM
else:
return X86
class AwsDedicatedHost(resource.BaseResource):
"""Object representing an AWS host.
Attributes:
region: The AWS region of the host.
zone: The AWS availability zone of the host.
machine_type: The machine type of VMs that may be created on the host.
client_token: A uuid that makes the creation request idempotent.
id: The host_id of the host.
"""
def __init__(self, machine_type, zone):
super(AwsDedicatedHost, self).__init__()
self.machine_type = machine_type
self.zone = zone
self.region = util.GetRegionFromZone(self.zone)
self.client_token = str(uuid.uuid4())
self.id = None
self.fill_fraction = 0.0
def _Create(self):
create_cmd = util.AWS_PREFIX + [
'ec2',
'allocate-hosts',
'--region=%s' % self.region,
'--client-token=%s' % self.client_token,
'--instance-type=%s' % self.machine_type,
'--availability-zone=%s' % self.zone,
'--auto-placement=off',
'--quantity=1']
vm_util.IssueCommand(create_cmd)
def _Delete(self):
if self.id:
delete_cmd = util.AWS_PREFIX + [
'ec2',
'release-hosts',
'--region=%s' % self.region,
'--host-ids=%s' % self.id]
vm_util.IssueCommand(delete_cmd, raise_on_failure=False)
@vm_util.Retry()
def _Exists(self):
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-hosts',
'--region=%s' % self.region,
'--filter=Name=client-token,Values=%s' % self.client_token]
stdout, _, _ = vm_util.IssueCommand(describe_cmd)
response = json.loads(stdout)
hosts = response['Hosts']
assert len(hosts) < 2, 'Too many hosts.'
if not hosts:
return False
host = hosts[0]
self.id = host['HostId']
state = host['State']
assert state in KNOWN_HOST_STATES, state
return state in HOST_EXISTS_STATES
class AwsVmSpec(virtual_machine.BaseVmSpec):
"""Object containing the information needed to create an AwsVirtualMachine.
Attributes:
use_dedicated_host: bool. Whether to create this VM on a dedicated host.
"""
CLOUD = providers.AWS
@classmethod
def _ApplyFlags(cls, config_values, flag_values):
"""Modifies config options based on runtime flag values.
Can be overridden by derived classes to add support for specific flags.
Args:
config_values: dict mapping config option names to provided values. May
be modified by this function.
flag_values: flags.FlagValues. Runtime flags that may override the
provided config values.
"""
super(AwsVmSpec, cls)._ApplyFlags(config_values, flag_values)
if flag_values['aws_boot_disk_size'].present:
config_values['boot_disk_size'] = flag_values.aws_boot_disk_size
if flag_values['aws_spot_instances'].present:
config_values['use_spot_instance'] = flag_values.aws_spot_instances
if flag_values['aws_spot_price'].present:
config_values['spot_price'] = flag_values.aws_spot_price
if flag_values['aws_spot_block_duration_minutes'].present:
config_values['spot_block_duration_minutes'] = int(
flag_values.aws_spot_block_duration_minutes)
@classmethod
def _GetOptionDecoderConstructions(cls):
"""Gets decoder classes and constructor args for each configurable option.
Returns:
dict. Maps option name string to a (ConfigOptionDecoder class, dict) pair.
The pair specifies a decoder class and its __init__() keyword
arguments to construct in order to decode the named option.
"""
result = super(AwsVmSpec, cls)._GetOptionDecoderConstructions()
result.update({
'use_spot_instance': (option_decoders.BooleanDecoder, {
'default': False
}),
'spot_price': (option_decoders.FloatDecoder, {
'default': None
}),
'spot_block_duration_minutes': (option_decoders.IntDecoder, {
'default': None
}),
'boot_disk_size': (option_decoders.IntDecoder, {
'default': None
})
})
return result
def _GetKeyfileSetKey(region):
"""Returns a key to use for the keyfile set.
This prevents other runs in the same process from reusing the key.
Args:
region: The region the keyfile is in.
"""
return (region, FLAGS.run_uri)
class AwsKeyFileManager(object):
"""Object for managing AWS Keyfiles."""
_lock = threading.Lock()
imported_keyfile_set = set()
deleted_keyfile_set = set()
@classmethod
def ImportKeyfile(cls, region):
"""Imports the public keyfile to AWS."""
with cls._lock:
if _GetKeyfileSetKey(region) in cls.imported_keyfile_set:
return
cat_cmd = ['cat',
vm_util.GetPublicKeyPath()]
keyfile, _ = vm_util.IssueRetryableCommand(cat_cmd)
formatted_tags = util.FormatTagSpecifications('key-pair',
util.MakeDefaultTags())
import_cmd = util.AWS_PREFIX + [
'ec2', '--region=%s' % region,
'import-key-pair',
'--key-name=%s' % cls.GetKeyNameForRun(),
'--public-key-material=%s' % keyfile,
'--tag-specifications=%s' % formatted_tags,
]
_, stderr, retcode = vm_util.IssueCommand(
import_cmd, raise_on_failure=False)
if retcode:
if 'KeyPairLimitExceeded' in stderr:
raise errors.Benchmarks.QuotaFailure(
'KeyPairLimitExceeded in %s: %s' % (region, stderr))
else:
raise errors.Benchmarks.PrepareException(stderr)
cls.imported_keyfile_set.add(_GetKeyfileSetKey(region))
if _GetKeyfileSetKey(region) in cls.deleted_keyfile_set:
cls.deleted_keyfile_set.remove(_GetKeyfileSetKey(region))
@classmethod
def DeleteKeyfile(cls, region):
"""Deletes the imported keyfile for a region."""
with cls._lock:
if _GetKeyfileSetKey(region) in cls.deleted_keyfile_set:
return
delete_cmd = util.AWS_PREFIX + [
'ec2', '--region=%s' % region,
'delete-key-pair',
'--key-name=%s' % cls.GetKeyNameForRun()]
util.IssueRetryableCommand(delete_cmd)
cls.deleted_keyfile_set.add(_GetKeyfileSetKey(region))
if _GetKeyfileSetKey(region) in cls.imported_keyfile_set:
cls.imported_keyfile_set.remove(_GetKeyfileSetKey(region))
@classmethod
def GetKeyNameForRun(cls):
return 'perfkit-key-{0}'.format(FLAGS.run_uri)
class AwsVirtualMachine(virtual_machine.BaseVirtualMachine):
"""Object representing an AWS Virtual Machine."""
CLOUD = providers.AWS
# The IMAGE_NAME_FILTER is passed to the AWS CLI describe-images command to
# filter images by name. This must be set by subclasses, but may be overridden
# by the aws_image_name_filter flag.
IMAGE_NAME_FILTER = None
# The IMAGE_NAME_REGEX can be used to further filter images by name. It
# applies after the IMAGE_NAME_FILTER above. Note that before this regex is
# applied, Python's string formatting is used to replace {virt_type} and
# {disk_type} by the respective virtualization type and root disk type of the
# VM, allowing the regex to contain these strings. This regex supports
# arbitrary Python regular expressions to further narrow down the set of
# images considered.
IMAGE_NAME_REGEX = None
# List of projects that own the AMIs of this OS type. Default to
# AWS Marketplace official image project. Note that opt-in regions may have a
# different image owner than default regions.
IMAGE_OWNER = MARKETPLACE_IMAGE_PROJECT
# Some AMIs use a project code to find the latest (in addition to owner, and
# filter)
IMAGE_PRODUCT_CODE_FILTER = None
# CoreOS only distinguishes between stable and testing images in the
# description
IMAGE_DESCRIPTION_FILTER = None
DEFAULT_ROOT_DISK_TYPE = 'gp2'
DEFAULT_USER_NAME = 'ec2-user'
_lock = threading.Lock()
deleted_hosts = set()
host_map = collections.defaultdict(list)
def __init__(self, vm_spec):
"""Initialize a AWS virtual machine.
Args:
vm_spec: virtual_machine.BaseVirtualMachineSpec object of the vm.
Raises:
ValueError: If an incompatible vm_spec is passed.
"""
super(AwsVirtualMachine, self).__init__(vm_spec)
self.region = util.GetRegionFromZone(self.zone)
self.user_name = FLAGS.aws_user_name or self.DEFAULT_USER_NAME
if self.machine_type in aws_disk.NUM_LOCAL_VOLUMES:
self.max_local_disks = aws_disk.NUM_LOCAL_VOLUMES[self.machine_type]
self.user_data = None
self.network = aws_network.AwsNetwork.GetNetwork(self)
self.placement_group = getattr(vm_spec, 'placement_group',
self.network.placement_group)
self.firewall = aws_network.AwsFirewall.GetFirewall()
self.use_dedicated_host = vm_spec.use_dedicated_host
self.num_vms_per_host = vm_spec.num_vms_per_host
self.use_spot_instance = vm_spec.use_spot_instance
self.spot_price = vm_spec.spot_price
self.spot_block_duration_minutes = vm_spec.spot_block_duration_minutes
self.boot_disk_size = vm_spec.boot_disk_size
self.client_token = str(uuid.uuid4())
self.host = None
self.id = None
self.metadata.update({
'spot_instance':
self.use_spot_instance,
'spot_price':
self.spot_price,
'spot_block_duration_minutes':
self.spot_block_duration_minutes,
'placement_group_strategy':
self.placement_group.strategy
if self.placement_group else placement_group.PLACEMENT_GROUP_NONE,
'aws_credit_specification':
FLAGS.aws_credit_specification
if FLAGS.aws_credit_specification else 'none'
})
self.spot_early_termination = False
self.spot_status_code = None
# See:
# https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/enhanced-networking-os.html
self._smp_affinity_script = 'smp_affinity.sh'
if self.use_dedicated_host and util.IsRegion(self.zone):
raise ValueError(
'In order to use dedicated hosts, you must specify an availability '
'zone, not a region ("zone" was %s).' % self.zone)
if self.use_dedicated_host and self.use_spot_instance:
raise ValueError(
'Tenancy=host is not supported for Spot Instances')
self.allocation_id = None
self.association_id = None
self.aws_tags = {}
@property
def host_list(self):
"""Returns the list of hosts that are compatible with this VM."""
return self.host_map[(self.machine_type, self.zone)]
@property
def group_id(self):
"""Returns the security group ID of this VM."""
return self.network.regional_network.vpc.default_security_group_id
@classmethod
def GetDefaultImage(cls, machine_type, region):
"""Returns the default image given the machine type and region.
If specified, the aws_image_name_filter and aws_image_name_regex flags will
override os_type defaults.
Args:
machine_type: The machine_type of the VM, used to determine virtualization
type.
region: The region of the VM, as images are region specific.
Raises:
AwsImageNotFoundError: If a default image cannot be found.
Returns:
The ID of the latest image, or None if no default image is configured or
none can be found.
"""
# These cannot be REQUIRED_ATTRS, because nesting REQUIRED_ATTRS breaks.
if not cls.IMAGE_OWNER:
raise NotImplementedError('AWS OSMixins require IMAGE_OWNER')
if not cls.IMAGE_NAME_FILTER:
raise NotImplementedError('AWS OSMixins require IMAGE_NAME_FILTER')
if FLAGS.aws_image_name_filter:
cls.IMAGE_NAME_FILTER = FLAGS.aws_image_name_filter
if FLAGS.aws_image_name_regex:
cls.IMAGE_NAME_REGEX = FLAGS.aws_image_name_regex
prefix = machine_type.split('.')[0]
virt_type = PV if prefix in NON_HVM_PREFIXES else HVM
processor_architecture = GetProcessorArchitecture(machine_type)
describe_cmd = util.AWS_PREFIX + [
'--region=%s' % region,
'ec2',
'describe-images',
'--query', ('Images[*].{Name:Name,ImageId:ImageId,'
'CreationDate:CreationDate}'),
'--filters',
'Name=name,Values=%s' % cls.IMAGE_NAME_FILTER,
'Name=block-device-mapping.volume-type,Values=%s' %
cls.DEFAULT_ROOT_DISK_TYPE,
'Name=virtualization-type,Values=%s' % virt_type,
'Name=architecture,Values=%s' % processor_architecture]
if cls.IMAGE_PRODUCT_CODE_FILTER:
describe_cmd.extend(['Name=product-code,Values=%s' %
cls.IMAGE_PRODUCT_CODE_FILTER])
if cls.IMAGE_DESCRIPTION_FILTER:
describe_cmd.extend(['Name=description,Values=%s' %
cls.IMAGE_DESCRIPTION_FILTER])
describe_cmd.extend(['--owners'] + cls.IMAGE_OWNER)
stdout, _ = util.IssueRetryableCommand(describe_cmd)
if not stdout:
raise AwsImageNotFoundError('aws describe-images did not produce valid '
'output.')
if cls.IMAGE_NAME_REGEX:
# Further filter images by the IMAGE_NAME_REGEX filter.
image_name_regex = cls.IMAGE_NAME_REGEX.format(
virt_type=virt_type, disk_type=cls.DEFAULT_ROOT_DISK_TYPE,
architecture=processor_architecture)
images = []
excluded_images = []
for image in json.loads(stdout):
if re.search(image_name_regex, image['Name']):
images.append(image)
else:
excluded_images.append(image)
if excluded_images:
logging.debug('Excluded the following images with regex "%s": %s',
image_name_regex,
sorted(image['Name'] for image in excluded_images))
else:
images = json.loads(stdout)
if not images:
raise AwsImageNotFoundError('No AMIs with given filters found.')
return max(images, key=lambda image: image['CreationDate'])['ImageId']
@vm_util.Retry(max_retries=2)
def _PostCreate(self):
"""Get the instance's data and tag it."""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id]
logging.info('Getting instance %s public IP. This will fail until '
'a public IP is available, but will be retried.', self.id)
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
instance = response['Reservations'][0]['Instances'][0]
self.internal_ip = instance['PrivateIpAddress']
if util.IsRegion(self.zone):
self.zone = str(instance['Placement']['AvailabilityZone'])
assert self.group_id == instance['SecurityGroups'][0]['GroupId'], (
self.group_id, instance['SecurityGroups'][0]['GroupId'])
if FLAGS.aws_efa:
self._ConfigureEfa(instance)
elif 'PublicIpAddress' in instance:
self.ip_address = instance['PublicIpAddress']
else:
raise errors.Resource.RetryableCreationError('Public IP not ready.')
def _ConfigureEfa(self, instance):
"""Configuare EFA and associate Elastic IP.
Args:
instance: dict which contains instance info.
"""
if FLAGS.aws_efa_count > 1:
self._ConfigureElasticIp(instance)
else:
self.ip_address = instance['PublicIpAddress']
if FLAGS.aws_efa_version:
# Download EFA then call InstallEfa method so that subclass can override
self.InstallPackages('curl')
url = _EFA_URL.format(version=FLAGS.aws_efa_version)
tarfile = posixpath.basename(url)
self.RemoteCommand(f'curl -O {url}; tar -xzf {tarfile}')
self._InstallEfa()
# Run test program to confirm EFA working
self.RemoteCommand('cd aws-efa-installer; '
'PATH=${PATH}:/opt/amazon/efa/bin ./efa_test.sh')
def _ConfigureElasticIp(self, instance):
"""Create and associate Elastic IP.
Args:
instance: dict which contains instance info.
"""
network_interface_id = None
for network_interface in instance['NetworkInterfaces']:
# The primary network interface (eth0) for the instance.
if network_interface['Attachment']['DeviceIndex'] == 0:
network_interface_id = network_interface['NetworkInterfaceId']
break
assert network_interface_id is not None
stdout, _, _ = vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'allocate-address',
f'--region={self.region}',
'--domain=vpc'])
response = json.loads(stdout)
self.ip_address = response['PublicIp']
self.allocation_id = response['AllocationId']
util.AddDefaultTags(self.allocation_id, self.region)
stdout, _, _ = vm_util.IssueCommand(
util.AWS_PREFIX + ['ec2', 'associate-address',
f'--region={self.region}',
f'--allocation-id={self.allocation_id}',
f'--network-interface-id={network_interface_id}'])
response = json.loads(stdout)
self.association_id = response['AssociationId']
def _InstallEfa(self):
"""Installs AWS EFA packages.
See https://aws.amazon.com/hpc/efa/
"""
if not self.TryRemoteCommand('ulimit -l | grep unlimited'):
self.RemoteCommand(f'echo "{self.user_name} - memlock unlimited" | '
'sudo tee -a /etc/security/limits.conf')
self.RemoteCommand('cd aws-efa-installer; sudo ./efa_installer.sh -y')
if not self.TryRemoteCommand('ulimit -l | grep unlimited'):
# efa_installer.sh should reboot enabling this change, reboot if necessary
self.Reboot()
def _CreateDependencies(self):
"""Create VM dependencies."""
AwsKeyFileManager.ImportKeyfile(self.region)
# GetDefaultImage calls the AWS CLI.
self.image = self.image or self.GetDefaultImage(self.machine_type,
self.region)
self.AllowRemoteAccessPorts()
if self.use_dedicated_host:
with self._lock:
if (not self.host_list or (self.num_vms_per_host and
self.host_list[-1].fill_fraction +
1.0 / self.num_vms_per_host > 1.0)):
host = AwsDedicatedHost(self.machine_type, self.zone)
self.host_list.append(host)
host.Create()
self.host = self.host_list[-1]
if self.num_vms_per_host:
self.host.fill_fraction += 1.0 / self.num_vms_per_host
def _DeleteDependencies(self):
"""Delete VM dependencies."""
AwsKeyFileManager.DeleteKeyfile(self.region)
if self.host:
with self._lock:
if self.host in self.host_list:
self.host_list.remove(self.host)
if self.host not in self.deleted_hosts:
self.host.Delete()
self.deleted_hosts.add(self.host)
def _Create(self):
"""Create a VM instance."""
placement = []
if not util.IsRegion(self.zone):
placement.append('AvailabilityZone=%s' % self.zone)
if self.use_dedicated_host:
placement.append('Tenancy=host,HostId=%s' % self.host.id)
num_hosts = len(self.host_list)
elif self.placement_group:
if IsPlacementGroupCompatible(self.machine_type):
placement.append('GroupName=%s' % self.placement_group.name)
else:
logging.warning(
'VM not placed in Placement Group. VM Type %s not supported',
self.machine_type)
placement = ','.join(placement)
block_device_map = GetBlockDeviceMap(self.machine_type,
self.boot_disk_size,
self.image,
self.region)
if not self.aws_tags:
# Set tags for the AWS VM. If we are retrying the create, we have to use
# the same tags from the previous call.
self.aws_tags.update(self.vm_metadata)
self.aws_tags.update(util.MakeDefaultTags())
create_cmd = util.AWS_PREFIX + [
'ec2',
'run-instances',
'--region=%s' % self.region,
'--client-token=%s' % self.client_token,
'--image-id=%s' % self.image,
'--instance-type=%s' % self.machine_type,
'--key-name=%s' % AwsKeyFileManager.GetKeyNameForRun(),
'--tag-specifications=%s' %
util.FormatTagSpecifications('instance', self.aws_tags)]
if FLAGS.aws_vm_hibernate:
create_cmd.extend([
'--hibernation-options=Configured=true',
])
if FLAGS.disable_smt:
query_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instance-types',
'--instance-types',
self.machine_type,
'--query',
'InstanceTypes[0].VCpuInfo.DefaultCores'
]
stdout, _, retcode = vm_util.IssueCommand(query_cmd)
cores = int(json.loads(stdout))
create_cmd.append(f'--cpu-options=CoreCount={cores},ThreadsPerCore=1')
if FLAGS.aws_efa:
efas = ['--network-interfaces']
for device_index in range(FLAGS.aws_efa_count):
efa_params = _EFA_PARAMS.copy()
efa_params.update({
'NetworkCardIndex': device_index,
'DeviceIndex': device_index,
'Groups': self.group_id,
'SubnetId': self.network.subnet.id
})
if FLAGS.aws_efa_count == 1:
efa_params['AssociatePublicIpAddress'] = True
efas.append(','.join(f'{key}={value}' for key, value in
sorted(efa_params.items())))
create_cmd.extend(efas)
else:
create_cmd.append('--associate-public-ip-address')
create_cmd.append(f'--subnet-id={self.network.subnet.id}')
if block_device_map:
create_cmd.append('--block-device-mappings=%s' % block_device_map)
if placement:
create_cmd.append('--placement=%s' % placement)
if FLAGS.aws_credit_specification:
create_cmd.append('--credit-specification=%s' %
FLAGS.aws_credit_specification)
if self.user_data:
create_cmd.append('--user-data=%s' % self.user_data)
if self.capacity_reservation_id:
create_cmd.append(
'--capacity-reservation-specification=CapacityReservationTarget='
'{CapacityReservationId=%s}' % self.capacity_reservation_id)
if self.use_spot_instance:
instance_market_options = collections.OrderedDict()
spot_options = collections.OrderedDict()
spot_options['SpotInstanceType'] = 'one-time'
spot_options['InstanceInterruptionBehavior'] = 'terminate'
if self.spot_price:
spot_options['MaxPrice'] = str(self.spot_price)
if self.spot_block_duration_minutes:
spot_options['BlockDurationMinutes'] = self.spot_block_duration_minutes
instance_market_options['MarketType'] = 'spot'
instance_market_options['SpotOptions'] = spot_options
create_cmd.append(
'--instance-market-options=%s' % json.dumps(instance_market_options))
_, stderr, retcode = vm_util.IssueCommand(create_cmd,
raise_on_failure=False)
arm_arch = GetArmArchitecture(self.machine_type)
if arm_arch:
self.host_arch = arm_arch
if self.use_dedicated_host and 'InsufficientCapacityOnHost' in stderr:
if self.num_vms_per_host:
raise errors.Resource.CreationError(
'Failed to create host: %d vms of type %s per host exceeds '
'memory capacity limits of the host' %
(self.num_vms_per_host, self.machine_type))
else:
logging.warning(
'Creation failed due to insufficient host capacity. A new host will '
'be created and instance creation will be retried.')
with self._lock:
if num_hosts == len(self.host_list):
host = AwsDedicatedHost(self.machine_type, self.zone)
self.host_list.append(host)
host.Create()
self.host = self.host_list[-1]
self.client_token = str(uuid.uuid4())
raise errors.Resource.RetryableCreationError()
if 'InsufficientInstanceCapacity' in stderr:
if self.use_spot_instance:
self.spot_status_code = 'InsufficientSpotInstanceCapacity'
self.spot_early_termination = True
raise errors.Benchmarks.InsufficientCapacityCloudFailure(stderr)
if 'SpotMaxPriceTooLow' in stderr:
self.spot_status_code = 'SpotMaxPriceTooLow'
self.spot_early_termination = True
raise errors.Resource.CreationError(stderr)
if 'InstanceLimitExceeded' in stderr or 'VcpuLimitExceeded' in stderr:
raise errors.Benchmarks.QuotaFailure(stderr)
if 'RequestLimitExceeded' in stderr:
if FLAGS.retry_on_rate_limited:
raise errors.Resource.RetryableCreationError(stderr)
else:
raise errors.Benchmarks.QuotaFailure(stderr)
# When launching more than 1 VM into the same placement group, there is an
# occasional error that the placement group has already been used in a
# separate zone. Retrying fixes this error.
if 'InvalidPlacementGroup.InUse' in stderr:
raise errors.Resource.RetryableCreationError(stderr)
if 'Unsupported' in stderr:
raise errors.Benchmarks.UnsupportedConfigError(stderr)
if retcode:
raise errors.Resource.CreationError(
'Failed to create VM: %s return code: %s' % (retcode, stderr))
@vm_util.Retry(
poll_interval=0.5,
log_errors=True,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _WaitForStoppedStatus(self):
"""Returns the status of the VM.
Returns:
Whether the VM is suspended i.e. in a stopped status. If not, raises an
error
Raises:
AwsUnknownStatusError: If an unknown status is returned from AWS.
AwsTransitionalVmRetryableError: If the VM is pending. This is retried.
"""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instance-status',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
'--include-all-instances',
]
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
status = response['InstanceStatuses'][0]['InstanceState']['Name']
if status.lower() != 'stopped':
logging.info('VM has status %s.', status)
raise AwsTransitionalVmRetryableError()
def _BeforeSuspend(self):
"""Prepares the instance for suspend by having the VM sleep for a given duration.
This ensures the VM is ready for hibernation
"""
# Add a timer that waits for a given duration after vm instance is
# created before calling suspend on the vm to ensure that the vm is
# ready for hibernation in aws.
time.sleep(600)
def _PostSuspend(self):
self._WaitForStoppedStatus()
def _Suspend(self):
"""Suspends a VM instance."""
suspend_cmd = util.AWS_PREFIX + [
'ec2',
'stop-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
'--hibernate',
]
try:
vm_util.IssueCommand(suspend_cmd)
except:
raise errors.Benchmarks.KnownIntermittentError(
'Instance is still not ready to hibernate')
self._PostSuspend()
@vm_util.Retry(
poll_interval=0.5,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _WaitForNewIP(self):
"""Checks for a new IP address, waiting if the VM is still pending.
Raises:
AwsTransitionalVmRetryableError: If VM is pending. This is retried.
"""
status_cmd = util.AWS_PREFIX + [
'ec2', 'describe-instances', f'--region={self.region}',
f'--instance-ids={self.id}'
]
stdout, _, _ = vm_util.IssueCommand(status_cmd)
response = json.loads(stdout)
instance = response['Reservations'][0]['Instances'][0]
if 'PublicIpAddress' in instance:
self.ip_address = instance['PublicIpAddress']
else:
logging.info('VM is pending.')
raise AwsTransitionalVmRetryableError()
def _PostResume(self):
self._WaitForNewIP()
def _Resume(self):
"""Resumes a VM instance."""
resume_cmd = util.AWS_PREFIX + [
'ec2',
'start-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id,
]
vm_util.IssueCommand(resume_cmd)
self._PostResume()
def _Delete(self):
"""Delete a VM instance."""
if self.id:
delete_cmd = util.AWS_PREFIX + [
'ec2',
'terminate-instances',
'--region=%s' % self.region,
'--instance-ids=%s' % self.id]
vm_util.IssueCommand(delete_cmd, raise_on_failure=False)
if hasattr(self, 'spot_instance_request_id'):
cancel_cmd = util.AWS_PREFIX + [
'--region=%s' % self.region,
'ec2',
'cancel-spot-instance-requests',
'--spot-instance-request-ids=%s' % self.spot_instance_request_id]
vm_util.IssueCommand(cancel_cmd, raise_on_failure=False)
if FLAGS.aws_efa:
if self.association_id:
vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'disassociate-address',
f'--region={self.region}',
f'--association-id={self.association_id}'])
if self.allocation_id:
vm_util.IssueCommand(util.AWS_PREFIX +
['ec2', 'release-address',
f'--region={self.region}',
f'--allocation-id={self.allocation_id}'])
# _Start or _Stop not yet implemented for AWS
def _Start(self):
"""Starts the VM."""
if not self.id:
raise errors.Benchmarks.RunError(
'Expected VM id to be non-null. Please make sure the VM exists.')
start_cmd = util.AWS_PREFIX + [
'ec2', 'start-instances',
f'--region={self.region}',
f'--instance-ids={self.id}'
]
vm_util.IssueCommand(start_cmd)
def _PostStart(self):
self._WaitForNewIP()
def _Stop(self):
"""Stops the VM."""
if not self.id:
raise errors.Benchmarks.RunError(
'Expected VM id to be non-null. Please make sure the VM exists.')
stop_cmd = util.AWS_PREFIX + [
'ec2', 'stop-instances',
f'--region={self.region}',
f'--instance-ids={self.id}'
]
vm_util.IssueCommand(stop_cmd)
def _PostStop(self):
self._WaitForStoppedStatus()
def _UpdateInterruptibleVmStatusThroughApi(self):
if hasattr(self, 'spot_instance_request_id'):
describe_cmd = util.AWS_PREFIX + [
'--region=%s' % self.region,
'ec2',
'describe-spot-instance-requests',
'--spot-instance-request-ids=%s' % self.spot_instance_request_id]
stdout, _, _ = vm_util.IssueCommand(describe_cmd)
sir_response = json.loads(stdout)['SpotInstanceRequests']
self.spot_status_code = sir_response[0]['Status']['Code']
self.spot_early_termination = (
self.spot_status_code in AWS_INITIATED_SPOT_TERMINAL_STATUSES)
@vm_util.Retry(
poll_interval=1,
log_errors=False,
retryable_exceptions=(AwsTransitionalVmRetryableError,))
def _Exists(self):
"""Returns whether the VM exists.
This method waits until the VM is no longer pending.
Returns:
Whether the VM exists.
Raises:
AwsUnknownStatusError: If an unknown status is returned from AWS.
AwsTransitionalVmRetryableError: If the VM is pending. This is retried.
"""
describe_cmd = util.AWS_PREFIX + [
'ec2',
'describe-instances',
'--region=%s' % self.region,
'--filter=Name=client-token,Values=%s' % self.client_token]
stdout, _ = util.IssueRetryableCommand(describe_cmd)
response = json.loads(stdout)
reservations = response['Reservations']
assert len(reservations) < 2, 'Too many reservations.'
if not reservations:
if not self.create_start_time:
return False
logging.info('No reservation returned by describe-instances. This '
'sometimes shows up immediately after a successful '
'run-instances command. Retrying describe-instances '
'command.')
raise AwsTransitionalVmRetryableError()
instances = reservations[0]['Instances']
assert len(instances) == 1, 'Wrong number of instances.'
status = instances[0]['State']['Name']
self.id = instances[0]['InstanceId']
if self.use_spot_instance:
self.spot_instance_request_id = instances[0]['SpotInstanceRequestId']
if status not in INSTANCE_KNOWN_STATUSES:
raise AwsUnknownStatusError('Unknown status %s' % status)
if status in INSTANCE_TRANSITIONAL_STATUSES:
logging.info('VM has status %s; retrying describe-instances command.',
status)
raise AwsTransitionalVmRetryableError()
# In this path run-instances succeeded, a pending instance was created, but
# not fulfilled so it moved to terminated.
if (status == TERMINATED and
instances[0]['StateReason']['Code'] ==
'Server.InsufficientInstanceCapacity'):
raise errors.Benchmarks.InsufficientCapacityCloudFailure(
instances[0]['StateReason']['Message'])
# In this path run-instances succeeded, a pending instance was created, but
# instance is shutting down due to internal server error. This is a
# retryable command for run-instance.
# Client token needs to be refreshed for idempotency.
if (status == SHUTTING_DOWN and
instances[0]['StateReason']['Code'] == 'Server.InternalError'):
self.client_token = str(uuid.uuid4())
return status in INSTANCE_EXISTS_STATUSES
def _GetNvmeBootIndex(self):
if (aws_disk.LocalDriveIsNvme(self.machine_type) and
aws_disk.EbsDriveIsNvme(self.machine_type)):
# identify boot drive
# If this command ever fails consider 'findmnt -nM / -o source'
cmd = ('realpath /dev/disk/by-label/cloudimg-rootfs '
'| grep --only-matching "nvme[0-9]*"')
boot_drive = self.RemoteCommand(cmd, ignore_failure=True)[0].strip()
if boot_drive:
# get the boot drive index by dropping the nvme prefix
boot_idx = int(boot_drive[4:])
logging.info('found boot drive at nvme index %d', boot_idx)
return boot_idx
else:
logging.warning('Failed to identify NVME boot drive index. Assuming 0.')
return 0
def CreateScratchDisk(self, disk_spec):
"""Create a VM's scratch disk.
Args:
disk_spec: virtual_machine.BaseDiskSpec object of the disk.
Raises:
CreationError: If an NFS disk is listed but the NFS service not created.
"""
# Instantiate the disk(s) that we want to create.
disks = []
nvme_boot_drive_index = self._GetNvmeBootIndex()
for _ in range(disk_spec.num_striped_disks):
if disk_spec.disk_type == disk.NFS:
data_disk = self._GetNfsService().CreateNfsDisk()
else:
data_disk = aws_disk.AwsDisk(disk_spec, self.zone, self.machine_type)
if disk_spec.disk_type == disk.LOCAL:
device_letter = chr(ord(DRIVE_START_LETTER) + self.local_disk_counter)
data_disk.AssignDeviceLetter(device_letter, nvme_boot_drive_index)
# Local disk numbers start at 1 (0 is the system disk).
data_disk.disk_number = self.local_disk_counter + 1
self.local_disk_counter += 1
if self.local_disk_counter > self.max_local_disks:
raise errors.Error('Not enough local disks.')
elif disk_spec.disk_type == disk.NFS:
pass
else:
# Remote disk numbers start at 1 + max_local disks (0 is the system disk
# and local disks occupy [1, max_local_disks]).
data_disk.disk_number = (self.remote_disk_counter +
1 + self.max_local_disks)
self.remote_disk_counter += 1
disks.append(data_disk)
self._CreateScratchDiskFromDisks(disk_spec, disks)
def AddMetadata(self, **kwargs):
"""Adds metadata to the VM."""
util.AddTags(self.id, self.region, **kwargs)
if self.use_spot_instance:
util.AddDefaultTags(self.spot_instance_request_id, self.region)
def InstallCli(self):
"""Installs the AWS cli and credentials on this AWS vm."""
self.Install('awscli')
self.Install('aws_credentials')
def DownloadPreprovisionedData(self, install_path, module_name, filename):
"""Downloads a data file from an AWS S3 bucket with pre-provisioned data.
Use --aws_preprovisioned_data_bucket to specify the name of the bucket.
Args:
install_path: The install path on this VM.
module_name: Name of the module associated with this data file.
filename: The name of the file that was downloaded.
"""
self.InstallCli()
# TODO(deitz): Add retry logic.
self.RemoteCommand(GenerateDownloadPreprovisionedDataCommand(
install_path, module_name, filename))
def ShouldDownloadPreprovisionedData(self, module_name, filename):
"""Returns whether or not preprovisioned data is available."""
self.Install('aws_credentials')
self.Install('awscli')
return FLAGS.aws_preprovisioned_data_bucket and self.TryRemoteCommand(
GenerateStatPreprovisionedDataCommand(module_name, filename))
def IsInterruptible(self):
"""Returns whether this vm is an interruptible vm (spot vm).
Returns: True if this vm is an interruptible vm (spot vm).
"""
return self.use_spot_instance
def WasInterrupted(self):
"""Returns whether this spot vm was terminated early by AWS.
Returns: True if this vm was terminated early by AWS.
"""
return self.spot_early_termination
def GetVmStatusCode(self):
"""Returns the early termination code if any.
Returns: Early termination code.
"""
return self.spot_status_code
def GetResourceMetadata(self):
"""Returns a dict containing metadata about the VM.
Returns:
dict mapping string property key to value.
"""
result = super(AwsVirtualMachine, self).GetResourceMetadata()
result['boot_disk_type'] = self.DEFAULT_ROOT_DISK_TYPE
result['boot_disk_size'] = self.boot_disk_size
if self.use_dedicated_host:
result['num_vms_per_host'] = self.num_vms_per_host
result['efa'] = FLAGS.aws_efa
if FLAGS.aws_efa:
result['efa_version'] = FLAGS.aws_efa_version
result['efa_count'] = FLAGS.aws_efa_count
result['preemptible'] = self.use_spot_instance
return result
class ClearBasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.ClearMixin):
IMAGE_NAME_FILTER = 'clear/images/*/clear-*'
DEFAULT_USER_NAME = 'clear'
class CoreOsBasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CoreOsMixin):
IMAGE_NAME_FILTER = 'fedora-coreos-*-hvm'
# CoreOS only distinguishes between stable and testing in the description
IMAGE_DESCRIPTION_FILTER = 'Fedora CoreOS stable *'
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
DEFAULT_USER_NAME = 'core'
class Debian9BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian9Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Stretch
IMAGE_NAME_FILTER = 'debian-stretch-*64-*'
IMAGE_OWNER = DEBIAN_9_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
def _BeforeSuspend(self):
"""Prepares the aws vm for hibernation."""
raise NotImplementedError()
class Debian10BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian10Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
IMAGE_NAME_FILTER = 'debian-10-*64*'
IMAGE_OWNER = DEBIAN_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
class Debian11BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Debian11Mixin):
# From https://wiki.debian.org/Cloud/AmazonEC2Image/Buster
IMAGE_NAME_FILTER = 'debian-11-*64*'
IMAGE_OWNER = DEBIAN_IMAGE_PROJECT
DEFAULT_USER_NAME = 'admin'
class UbuntuBasedAwsVirtualMachine(AwsVirtualMachine):
IMAGE_OWNER = UBUNTU_IMAGE_PROJECT
DEFAULT_USER_NAME = 'ubuntu'
class Ubuntu1604BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu1604Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-xenial-16.04-*64-server-20*'
def _InstallEfa(self):
super(Ubuntu1604BasedAwsVirtualMachine, self)._InstallEfa()
self.Reboot()
self.WaitForBootCompletion()
class Ubuntu1804BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu1804Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-bionic-18.04-*64-server-20*'
class Ubuntu1804EfaBasedAwsVirtualMachine(
UbuntuBasedAwsVirtualMachine, linux_virtual_machine.Ubuntu1804EfaMixin):
IMAGE_OWNER = UBUNTU_EFA_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'Deep Learning AMI GPU CUDA * (Ubuntu 18.04) *'
class Ubuntu2004BasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.Ubuntu2004Mixin):
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-focal-20.04-*64-server-20*'
class JujuBasedAwsVirtualMachine(UbuntuBasedAwsVirtualMachine,
linux_virtual_machine.JujuMixin):
"""Class with configuration for AWS Juju virtual machines."""
IMAGE_NAME_FILTER = 'ubuntu/images/*/ubuntu-trusty-14.04-*64-server-20*'
class AmazonLinux2BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.AmazonLinux2Mixin):
"""Class with configuration for AWS Amazon Linux 2 virtual machines."""
IMAGE_NAME_FILTER = 'amzn2-ami-*-*-*'
IMAGE_OWNER = AMAZON_LINUX_IMAGE_PROJECT
class Rhel7BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Rhel7Mixin):
"""Class with configuration for AWS RHEL 7 virtual machines."""
# Documentation on finding RHEL images:
# https://access.redhat.com/articles/2962171
IMAGE_NAME_FILTER = 'RHEL-7*_GA*'
IMAGE_OWNER = RHEL_IMAGE_PROJECT
class Rhel8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.Rhel8Mixin):
"""Class with configuration for AWS RHEL 8 virtual machines."""
# Documentation on finding RHEL images:
# https://access.redhat.com/articles/2962181
# All RHEL AMIs are HVM. HVM- blocks HVM_BETA.
IMAGE_NAME_FILTER = 'RHEL-8*_HVM-*'
IMAGE_OWNER = RHEL_IMAGE_PROJECT
class CentOs7BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CentOs7Mixin):
"""Class with configuration for AWS CentOS 7 virtual machines."""
# Documentation on finding the CentOS 7 image:
# https://wiki.centos.org/Cloud/AWS#x86_64
IMAGE_NAME_FILTER = 'CentOS 7*'
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
DEFAULT_USER_NAME = 'centos'
def _InstallEfa(self):
logging.info('Upgrading Centos7 kernel, installing kernel headers and '
'rebooting before installing EFA.')
self.RemoteCommand('sudo yum upgrade -y kernel')
self.InstallPackages('kernel-devel')
self.Reboot()
self.WaitForBootCompletion()
super(CentOs7BasedAwsVirtualMachine, self)._InstallEfa()
class CentOs8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.CentOs8Mixin):
"""Class with configuration for AWS CentOS 8 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS 8*'
DEFAULT_USER_NAME = 'centos'
class CentOsStream8BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.CentOsStream8Mixin):
"""Class with configuration for AWS CentOS Stream 8 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS Stream 8*'
DEFAULT_USER_NAME = 'centos'
class RockyLinux8BasedAwsVirtualMachine(AwsVirtualMachine,
linux_virtual_machine.RockyLinux8Mixin):
"""Class with configuration for AWS Rocky Linux 8 virtual machines."""
IMAGE_OWNER = MARKETPLACE_IMAGE_PROJECT
IMAGE_PRODUCT_CODE_FILTER = 'cotnnspjrsi38lfn8qo4ibnnm'
IMAGE_NAME_FILTER = 'Rocky-8-*'
DEFAULT_USER_NAME = 'rocky'
class CentOsStream9BasedAwsVirtualMachine(
AwsVirtualMachine, linux_virtual_machine.CentOsStream9Mixin):
"""Class with configuration for AWS CentOS Stream 9 virtual machines."""
# This describes the official AMIs listed here:
# https://wiki.centos.org/Cloud/AWS#Official_CentOS_Linux_:_Public_Images
IMAGE_OWNER = CENTOS_IMAGE_PROJECT
IMAGE_NAME_FILTER = 'CentOS Stream 9*'
class BaseWindowsAwsVirtualMachine(AwsVirtualMachine,
windows_virtual_machine.BaseWindowsMixin):
"""Support for Windows machines on AWS."""
DEFAULT_USER_NAME = 'Administrator'
IMAGE_OWNER = WINDOWS_IMAGE_PROJECT
def __init__(self, vm_spec):
super(BaseWindowsAwsVirtualMachine, self).__init__(vm_spec)
self.user_data = ('<powershell>%s</powershell>' %
windows_virtual_machine.STARTUP_SCRIPT)
@vm_util.Retry()
def _GetDecodedPasswordData(self):
# Retrieve a base64 encoded, encrypted password for the VM.
get_password_cmd = util.AWS_PREFIX + [
'ec2',
'get-password-data',
'--region=%s' % self.region,
'--instance-id=%s' % self.id]
stdout, _ = util.IssueRetryableCommand(get_password_cmd)
response = json.loads(stdout)
password_data = response['PasswordData']
# AWS may not populate the password data until some time after
# the VM shows as running. Simply retry until the data shows up.
if not password_data:
raise ValueError('No PasswordData in response.')
# Decode the password data.
return base64.b64decode(password_data)
def _PostCreate(self):
"""Retrieve generic VM info and then retrieve the VM's password."""
super(BaseWindowsAwsVirtualMachine, self)._PostCreate()
# Get the decoded password data.
decoded_password_data = self._GetDecodedPasswordData()
# Write the encrypted data to a file, and use openssl to
# decrypt the password.
with vm_util.NamedTemporaryFile() as tf:
tf.write(decoded_password_data)
tf.close()
decrypt_cmd = ['openssl',
'rsautl',
'-decrypt',
'-in',
tf.name,
'-inkey',
vm_util.GetPrivateKeyPath()]
password, _ = vm_util.IssueRetryableCommand(decrypt_cmd)
self.password = password
def GetResourceMetadata(self):
"""Returns a dict containing metadata about the VM.
Returns:
dict mapping metadata key to value.
"""
result = super(BaseWindowsAwsVirtualMachine, self).GetResourceMetadata()
result['disable_interrupt_moderation'] = self.disable_interrupt_moderation
return result
@vm_util.Retry(
max_retries=10,
retryable_exceptions=(AwsUnexpectedWindowsAdapterOutputError,
errors.VirtualMachine.RemoteCommandError))
def DisableInterruptModeration(self):
"""Disable the networking feature 'Interrupt Moderation'."""
# First ensure that the driver supports interrupt moderation
net_adapters, _ = self.RemoteCommand('Get-NetAdapter')
if 'Intel(R) 82599 Virtual Function' not in net_adapters:
raise AwsDriverDoesntSupportFeatureError(
'Driver not tested with Interrupt Moderation in PKB.')
aws_int_dis_path = ('HKLM\\SYSTEM\\ControlSet001\\Control\\Class\\'
'{4d36e972-e325-11ce-bfc1-08002be10318}\\0011')
command = 'reg add "%s" /v *InterruptModeration /d 0 /f' % aws_int_dis_path
self.RemoteCommand(command)
try:
self.RemoteCommand('Restart-NetAdapter -Name "Ethernet 2"')
except IOError:
# Restarting the network adapter will always fail because
# the winrm connection used to issue the command will be
# broken.
pass
int_dis_value, _ = self.RemoteCommand(
'reg query "%s" /v *InterruptModeration' % aws_int_dis_path)
# The second line should look like:
# *InterruptModeration REG_SZ 0
registry_query_lines = int_dis_value.splitlines()
if len(registry_query_lines) < 3:
raise AwsUnexpectedWindowsAdapterOutputError(
'registry query failed: %s ' % int_dis_value)
registry_query_result = registry_query_lines[2].split()
if len(registry_query_result) < 3:
raise AwsUnexpectedWindowsAdapterOutputError(
'unexpected registry query response: %s' % int_dis_value)
if registry_query_result[2] != '0':
raise AwsUnexpectedWindowsAdapterOutputError(
'InterruptModeration failed to disable')
class Windows2012CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2012CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2012-R2_RTM-English-64Bit-Core-*'
class Windows2016CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2016CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2016-English-Core-Base-*'
class Windows2019CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2019CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Core-Base-*'
class Windows2022CoreAwsVirtualMachine(
BaseWindowsAwsVirtualMachine, windows_virtual_machine.Windows2022CoreMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Core-Base-*'
class Windows2012DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2012DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2012-R2_RTM-English-64Bit-Base-*'
class Windows2016DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2016DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2016-English-Full-Base-*'
class Windows2019DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-Base-*'
class Windows2022DesktopAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022DesktopMixin):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-Base-*'
class Windows2019DesktopSQLServer2019StandardAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019SQLServer2019Standard):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-SQL_2019_Standard-*'
class Windows2019DesktopSQLServer2019EnterpriseAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2019SQLServer2019Enterprise):
IMAGE_NAME_FILTER = 'Windows_Server-2019-English-Full-SQL_2019_Enterprise-*'
class Windows2022DesktopSQLServer2019StandardAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022SQLServer2019Standard):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-SQL_2019_Standard-*'
class Windows2022DesktopSQLServer2019EnterpriseAwsVirtualMachine(
BaseWindowsAwsVirtualMachine,
windows_virtual_machine.Windows2022SQLServer2019Enterprise):
IMAGE_NAME_FILTER = 'Windows_Server-2022-English-Full-SQL_2019_Enterprise-*'
def GenerateDownloadPreprovisionedDataCommand(install_path, module_name,
filename):
"""Returns a string used to download preprovisioned data."""
return 'aws s3 cp --only-show-errors s3://%s/%s/%s %s' % (
FLAGS.aws_preprovisioned_data_bucket, module_name, filename,
posixpath.join(install_path, filename))
def GenerateStatPreprovisionedDataCommand(module_name, filename):
"""Returns a string used to download preprovisioned data."""
return 'aws s3api head-object --bucket %s --key %s/%s' % (
FLAGS.aws_preprovisioned_data_bucket, module_name, filename)
|
#!/usr/bin/python
from __future__ import unicode_literals, print_function, division
from datetime import datetime, timedelta
api_ok = True
try:
import tvdb_api
import tvdb_exceptions
except:
print("tvdb api not available")
api_ok = False
from operator import itemgetter
def command_ep(bot, user, channel, args):
"""Usage: ep <series name>"""
if not api_ok: return
t = tvdb_api.Tvdb()
now = datetime.now()
# one day resolution maximum
now = now.replace(hour=0, minute=0, second=0, microsecond=0)
# prevent "Series '' not found"
if not args: return
try:
series = t[args]
except tvdb_exceptions.tvdb_shownotfound:
bot.say(channel, "Series '%s' not found" % args)
return
future_episodes = []
all_episodes = []
# find all episodes with airdate > now
for season_no, season in series.items():
for episode_no, episode in season.items():
firstaired = episode['firstaired']
if not firstaired:
continue
airdate = datetime.strptime(firstaired, "%Y-%m-%d")
td = airdate - now
all_episodes.append(episode)
# list all unaired episodes
if td >= timedelta(0, 0, 0):
future_episodes.append(episode)
# if any future episodes were found, find out the one with airdate closest to now
if future_episodes:
# sort the list just in case it's out of order (specials are season 0)
future_episodes = sorted(future_episodes, key=itemgetter('firstaired'))
episode = future_episodes[0]
td = datetime.strptime(episode['firstaired'], "%Y-%m-%d") - now
if td.days == 1:
airdate = "tomorrow"
elif td.days > 1:
airdate = "%s (%d days)" % (episode['firstaired'], td.days)
else:
airdate = "today"
season_ep = "%dx%02d" % (int(episode['combined_season']),int(episode['combined_episodenumber']))
msg = "Next episode of %s %s '%s' airs %s" % (series.data['seriesname'], season_ep, episode['episodename'], airdate)
# no future episodes found, show the latest one
elif all_episodes:
# find latst episode of the show
all_episodes = sorted(all_episodes, key=itemgetter('firstaired'))
episode = all_episodes[-1]
td = now - datetime.strptime(episode['firstaired'], "%Y-%m-%d")
airdate = "%s (%d days ago)" % (episode['firstaired'], td.days)
season_ep = "%dx%02d" % (int(episode['combined_season']),int(episode['combined_episodenumber']))
msg = "Latest episode of %s %s '%s' aired %s" % (series.data['seriesname'], season_ep, episode['episodename'], airdate)
else:
msg = "No new or past episode airdates found for %s" % series.data['seriesname']
bot.say(channel, msg.encode("UTF-8"))
Handle episode numbers returned as floats
Display episode age in years and days
#!/usr/bin/python
from __future__ import unicode_literals, print_function, division
from datetime import datetime, timedelta
api_ok = True
try:
import tvdb_api
import tvdb_exceptions
except:
print("tvdb api not available")
api_ok = False
from operator import itemgetter
def command_ep(bot, user, channel, args):
"""Usage: ep <series name>"""
if not api_ok: return
t = tvdb_api.Tvdb()
now = datetime.now()
# one day resolution maximum
now = now.replace(hour=0, minute=0, second=0, microsecond=0)
# prevent "Series '' not found"
if not args: return
try:
series = t[args]
except tvdb_exceptions.tvdb_shownotfound:
bot.say(channel, "Series '%s' not found" % args)
return
future_episodes = []
all_episodes = []
# find all episodes with airdate > now
for season_no, season in series.items():
for episode_no, episode in season.items():
firstaired = episode['firstaired']
if not firstaired:
continue
airdate = datetime.strptime(firstaired, "%Y-%m-%d")
td = airdate - now
all_episodes.append(episode)
# list all unaired episodes
if td >= timedelta(0, 0, 0):
future_episodes.append(episode)
# if any future episodes were found, find out the one with airdate closest to now
if future_episodes:
# sort the list just in case it's out of order (specials are season 0)
future_episodes = sorted(future_episodes, key=itemgetter('firstaired'))
episode = future_episodes[0]
td = datetime.strptime(episode['firstaired'], "%Y-%m-%d") - now
if td.days == 1:
airdate = "tomorrow"
elif td.days > 1:
airdate = "%s (%d days)" % (episode['firstaired'], td.days)
else:
airdate = "today"
season_ep = "%dx%02d" % (int(episode['combined_season']),int(episode['combined_episodenumber']))
msg = "Next episode of %s %s '%s' airs %s" % (series.data['seriesname'], season_ep, episode['episodename'], airdate)
# no future episodes found, show the latest one
elif all_episodes:
# find latest episode of the show
all_episodes = sorted(all_episodes, key=itemgetter('firstaired'))
episode = all_episodes[-1]
## episode age in years and days
td = now - datetime.strptime(episode['firstaired'], "%Y-%m-%d")
years, days = td.days // 365, td.days % 365
agestr = []
if years > 1:
agestr.append("%d years" % years)
if days > 0:
agestr.append("%d days" % days)
airdate = "%s (%s ago)" % (episode['firstaired'], " ".join(agestr))
season_no = int(episode['combined_season'])
# the episode number is sometimes returned as a float, hack it.
episode_no = int(float(episode['combined_episodenumber']))
season_ep = "%dx%02d" % (season_no, episode_no)
msg = "Latest episode of %s %s '%s' aired %s" % (series.data['seriesname'], season_ep, episode['episodename'], airdate)
else:
msg = "No new or past episode airdates found for %s" % series.data['seriesname']
bot.say(channel, msg.encode("UTF-8"))
|
import json
import os.path as path
import urllib
import urllib2
from datetime import datetime
from time import strptime
from models import *
# sub and dub is 0=both 1=sub only 2=dub only
if path.exists('settings'):
try:
config = open('settings', 'r')
sub_dub = int(config.read())
except:
config = open('settings', 'w')
config.write('0')
config.close()
sub_dub = 0
else:
config = open('settings', 'w')
config.write('0')
config.close()
sub_dub = 0
base_url = 'http://wpc.8c48.edgecastcdn.net'
bitrate = [2000, 3500, 4000]
funimation_url = 'https://www.funimation.com/'
order_types = ['asc', 'desc']
rating_type = ['tvpg', 'tv14', 'tvma', 'nr', 'pg', 'pg13', 'r', 'all']
sort_types = ['alpha', 'date', 'dvd', 'now', 'soon', 'votes', 'episode',
'title', 'sequence']
genre_types = ['all', 'action', 'adventure', 'bishonen', 'bishoujo', 'comedy',
'cyberpunk', 'drama', 'fan service', 'fantasy', 'harem',
'historical', 'horror', 'live action', 'magical girl',
'martial arts', 'mecha', 'moe', 'mystery', 'reverse harem',
'romance', 'school', 'sci fi', 'shonen', 'slice of life',
'space', 'sports', 'super power', 'supernatural', 'yuri']
urls = {
'details': 'mobile/node/{showid}',
'search': 'mobile/shows.json/alpha/asc/nl/all/all?keys={term}',
'shows': 'mobile/shows.json/{sort}/{order}/{limit}/{rating}/{genre}',
'clips': 'mobile/clips.json/sequence/{order}/{showid}/all/all?page={page}',
'trailers': 'mobile/trailers.json/sequence/{order}/{showid}/all/all?page={page}',
'movies': 'mobile/movies.json/{v_type}/{sort}/{order}/all/{showid}?page={page}',
'episodes': 'mobile/episodes.json/{v_type}/sequence/{order}/all/{showid}?page={page}',
'stream': 'http://wpc.8c48.edgecastcdn.net/038C48/SV/480/{video_id}/{video_id}-480-{quality}K.mp4.m3u8?9b303b6c62204a9dcb5ce5f5c607',
}
def fix_keys(d):
def fix_key(key):
return key.lower().replace(' ', '_').replace('-', '_')
def fix(x):
if isinstance(x, dict):
return dict((fix_key(k), fix(v)) for k, v in x.iteritems())
elif isinstance(x, list):
return [fix(i) for i in x]
else:
return x
return fix(d)
def convert_values(d):
for k, v in d.items():
if k == 'video_section' or k == 'aip':
d[k] = v.values() if isinstance(v, dict) else []
elif k == 'votes' or k == 'nid' or k == 'show_id':
d[k] = int(v) if v is not None else 0
elif k == 'episode_number':
d[k] = int(float(v)) if v is not None else 0
elif k == 'post_date':
try:
d[k] = datetime.strptime(v, '%m/%d/%Y')
except TypeError:
d[k] = datetime(*(strptime(v, '%m/%d/%Y')[0:6]))
elif k == 'duration':
d[k] = v
elif k == 'all_terms' or k == 'term':
d[k] = v.split(', ')
elif k == 'similar_shows':
d[k] = [int(i) for i in v.split(',') if isinstance(i, list)]
elif k == 'video_quality':
d[k] = v.values() if isinstance(v, dict) else [d[k]]
elif k == 'promo':
d[k] = v == 'Promo'
elif k == 'type':
d[k] = v[7:]
elif k == 'maturity_rating':
d[k] = str(v)
elif k == 'mpaa':
d[k] = ','.join(v.values()) if isinstance(v, dict) else v
return d
def process_response(data):
# collapse data into list of dicts
data = [i[i.keys()[0]] for i in data[data.keys()[0]]]
# fix dict key names
data = fix_keys(data)
# fix up the values
data = [convert_values(i) for i in data]
if data[0].has_key('group_title'):
return [EpisodeDetail(**i) for i in data]
elif data[0].has_key('maturity_rating'):
return [Show(**i) for i in data]
elif data[0].has_key('episode_number'):
return [Episode(**i) for i in data]
elif data[0].has_key('tv_key_art'):
return [Movie(**i) for i in data]
elif data[0].has_key('funimationid'):
return [Clip(**i) for i in data]
elif data[0].has_key('is_mature'):
return [Trailer(**i) for i in data]
else:
return data
def filter_response(data):
# just check the first object since all will be the same
if data[0].get('sub_dub') is None:
return data
# both
if sub_dub == 0:
return data
# sub
elif sub_dub == 1:
ret = [ep for ep in data if ep.sub]
return ret
# dub
elif sub_dub == 2:
ret = [ep for ep in data if ep.dub]
return ret
else:
# just in case
return data
def process_data(url):
resp = get(url)
data = process_response(resp)
return filter_response(data)
def get_data_url(endpoint, series=0):
if endpoint == 'shows':
params = check_params(sort='alpha')
url = urls[endpoint].format(**params)
return url
if series != 0:
params = check_params(showid=series)
url = urls[endpoint].format(**params)
return url
params = check_params()
url = urls[endpoint].format(**params)
return url
def check_params(showid=0, page=0, sort=None, order=None, limit=None, rating=None, genre=None, term=None):
if sort is None or sort not in sort_types:
sort = 'date'
if order is None or order not in order_types:
order = 'asc'
if limit is None or not limit.isdigit():
limit = 'nl' # no limit
if rating is None or rating not in rating_type:
rating = 'all'
if genre is None or genre not in genre_types:
genre = 'all'
if term is None:
term = ''
v_type = 'subscription'
return locals()
def get(endpoint, params=None):
if endpoint.startswith('http'):
url = endpoint
else:
url = base_url.format(endpoint)
if params is None:
content = urllib2.urlopen(url).read()
else:
content = urllib2.urlopen(url + urllib.urlencode(params)).read()
return json.loads(content)
def stream_url(video_id, quality):
url = urls['stream'].format(**locals())
return url
def qual(episode):
q = len(episode.video_quality) - 1
return bitrate[q]
def get_shows():
show_url = get_data_url('shows')
shows = process_data(funimation_url + show_url)
return shows
def print_shows(show_list):
for item in range(0, len(show_list)):
title = show_list[item].title
print item, ':', title, '- nid:', show_list[item].nid
def print_eps(ep_list):
for item in range(0,len(ep_list)):
title = ep_list[item].title
ep_number = ep_list[item].episode_number
lang = ep_list[item].sub_dub
ep_id = ep_list[item].funimation_id
print ep_number,':',title,'-',lang,':',ep_id
Changed show episode print to print url
import json
import os.path as path
import urllib
import urllib2
from datetime import datetime
from time import strptime
from models import *
# sub and dub is 0=both 1=sub only 2=dub only
if path.exists('settings'):
try:
config = open('settings', 'r')
sub_dub = int(config.read())
except:
config = open('settings', 'w')
config.write('0')
config.close()
sub_dub = 0
else:
config = open('settings', 'w')
config.write('0')
config.close()
sub_dub = 0
base_url = 'http://wpc.8c48.edgecastcdn.net'
bitrate = [2000, 3500, 4000]
funimation_url = 'https://www.funimation.com/'
order_types = ['asc', 'desc']
rating_type = ['tvpg', 'tv14', 'tvma', 'nr', 'pg', 'pg13', 'r', 'all']
sort_types = ['alpha', 'date', 'dvd', 'now', 'soon', 'votes', 'episode',
'title', 'sequence']
genre_types = ['all', 'action', 'adventure', 'bishonen', 'bishoujo', 'comedy',
'cyberpunk', 'drama', 'fan service', 'fantasy', 'harem',
'historical', 'horror', 'live action', 'magical girl',
'martial arts', 'mecha', 'moe', 'mystery', 'reverse harem',
'romance', 'school', 'sci fi', 'shonen', 'slice of life',
'space', 'sports', 'super power', 'supernatural', 'yuri']
urls = {
'details': 'mobile/node/{showid}',
'search': 'mobile/shows.json/alpha/asc/nl/all/all?keys={term}',
'shows': 'mobile/shows.json/{sort}/{order}/{limit}/{rating}/{genre}',
'clips': 'mobile/clips.json/sequence/{order}/{showid}/all/all?page={page}',
'trailers': 'mobile/trailers.json/sequence/{order}/{showid}/all/all?page={page}',
'movies': 'mobile/movies.json/{v_type}/{sort}/{order}/all/{showid}?page={page}',
'episodes': 'mobile/episodes.json/{v_type}/sequence/{order}/all/{showid}?page={page}',
'stream': 'http://wpc.8c48.edgecastcdn.net/038C48/SV/480/{video_id}/{video_id}-480-{quality}K.mp4.m3u8?9b303b6c62204a9dcb5ce5f5c607',
}
def fix_keys(d):
def fix_key(key):
return key.lower().replace(' ', '_').replace('-', '_')
def fix(x):
if isinstance(x, dict):
return dict((fix_key(k), fix(v)) for k, v in x.iteritems())
elif isinstance(x, list):
return [fix(i) for i in x]
else:
return x
return fix(d)
def convert_values(d):
for k, v in d.items():
if k == 'video_section' or k == 'aip':
d[k] = v.values() if isinstance(v, dict) else []
elif k == 'votes' or k == 'nid' or k == 'show_id':
d[k] = int(v) if v is not None else 0
elif k == 'episode_number':
d[k] = int(float(v)) if v is not None else 0
elif k == 'post_date':
try:
d[k] = datetime.strptime(v, '%m/%d/%Y')
except TypeError:
d[k] = datetime(*(strptime(v, '%m/%d/%Y')[0:6]))
elif k == 'duration':
d[k] = v
elif k == 'all_terms' or k == 'term':
d[k] = v.split(', ')
elif k == 'similar_shows':
d[k] = [int(i) for i in v.split(',') if isinstance(i, list)]
elif k == 'video_quality':
d[k] = v.values() if isinstance(v, dict) else [d[k]]
elif k == 'promo':
d[k] = v == 'Promo'
elif k == 'type':
d[k] = v[7:]
elif k == 'maturity_rating':
d[k] = str(v)
elif k == 'mpaa':
d[k] = ','.join(v.values()) if isinstance(v, dict) else v
return d
def process_response(data):
# collapse data into list of dicts
data = [i[i.keys()[0]] for i in data[data.keys()[0]]]
# fix dict key names
data = fix_keys(data)
# fix up the values
data = [convert_values(i) for i in data]
if data[0].has_key('group_title'):
return [EpisodeDetail(**i) for i in data]
elif data[0].has_key('maturity_rating'):
return [Show(**i) for i in data]
elif data[0].has_key('episode_number'):
return [Episode(**i) for i in data]
elif data[0].has_key('tv_key_art'):
return [Movie(**i) for i in data]
elif data[0].has_key('funimationid'):
return [Clip(**i) for i in data]
elif data[0].has_key('is_mature'):
return [Trailer(**i) for i in data]
else:
return data
def filter_response(data):
# just check the first object since all will be the same
if data[0].get('sub_dub') is None:
return data
# both
if sub_dub == 0:
return data
# sub
elif sub_dub == 1:
ret = [ep for ep in data if ep.sub]
return ret
# dub
elif sub_dub == 2:
ret = [ep for ep in data if ep.dub]
return ret
else:
# just in case
return data
def process_data(url):
resp = get(url)
data = process_response(resp)
return filter_response(data)
def get_data_url(endpoint, series=0):
if endpoint == 'shows':
params = check_params(sort='alpha')
url = urls[endpoint].format(**params)
return url
if series != 0:
params = check_params(showid=series)
url = urls[endpoint].format(**params)
return url
params = check_params()
url = urls[endpoint].format(**params)
return url
def check_params(showid=0, page=0, sort=None, order=None, limit=None, rating=None, genre=None, term=None):
if sort is None or sort not in sort_types:
sort = 'date'
if order is None or order not in order_types:
order = 'asc'
if limit is None or not limit.isdigit():
limit = 'nl' # no limit
if rating is None or rating not in rating_type:
rating = 'all'
if genre is None or genre not in genre_types:
genre = 'all'
if term is None:
term = ''
v_type = 'subscription'
return locals()
def get(endpoint, params=None):
if endpoint.startswith('http'):
url = endpoint
else:
url = base_url.format(endpoint)
if params is None:
content = urllib2.urlopen(url).read()
else:
content = urllib2.urlopen(url + urllib.urlencode(params)).read()
return json.loads(content)
def stream_url(video_id, quality):
url = urls['stream'].format(**locals())
return url
def qual(episode):
q = len(episode.video_quality) - 1
return bitrate[q]
def get_shows():
show_url = get_data_url('shows')
shows = process_data(funimation_url + show_url)
return shows
def print_shows(show_list):
for item in range(0, len(show_list)):
title = show_list[item].title
print item, ':', title, '- nid:', show_list[item].nid
def print_eps(ep_list):
for item in range(0,len(ep_list)):
title = ep_list[item].title
ep_number = ep_list[item].episode_number
lang = ep_list[item].sub_dub
ep_url = stream_url(ep_list[item].funimation_id,qual(ep_list[item]))
print ep_number,':',title,'-',lang,':',ep_url |
# -*- coding: utf-8 -*-
from __future__ import print_function
from builtins import object
import websocket
import logging
from purkinje_messages.message import TestCaseStartEvent
class TestMonitorPlugin(object):
"""py.test plugin for monitoring test progress and
capturing results
TODO graceful termination
"""
def __init__(self, websocket_url):
self.reports = []
self._websocket_url = websocket_url
self._websocket = None
try:
self._log('Connecting to WebSocket %s', websocket_url)
self._websocket = websocket.create_connection(websocket_url)
except ValueError as e:
self._log('Invalid WebSocket URL: "%s"',
self._websocket_url)
except Exception as e:
self._log('Error connecting to WebSocket at URL %s: %s',
self._websocket_url, e)
def is_websocket_connected(self):
return self._websocket is not None
def send_event(self, event):
"""Send event via WebSocket connection.
If there is no connection, the event will be dumped to the log
only, so it is possible to run tests with purkinje enabled even
if the server should be down
"""
ser_event = None
try:
ser_event = event.serialize()
if self._websocket:
self._websocket.send(ser_event)
else:
self._log('purkinje server not available; event: %s',
ser_event)
except Exception as e:
logging.exception(e)
self._log('Error while sending event "%s": %s',
ser_event or event.data, e)
def pytest_sessionfinish(self):
self._log('*** py.test session finished ***')
# def pytest_collect_file(self, path, parent):
# print('pytest_collect_file: {}'.format(path))
def pytest_collection_modifyitems(self, session, config, items):
print('pytest_collection_modifyitems: {} {} {}'.format(session,
config,
items))
def pytest_collectstart(self, collector):
self._log('pytest_collectstart: %s', collector)
# import pdb; pdb.set_trace()
def pytest_collectreport(self, report):
self._log('pytest_collectreport: %s', report)
self.send_event(TestCaseStartEvent(text='TODO xyz'))
self.reports.append(report)
#import pdb
#pdb.set_trace()
def _log(self, fmt, *args):
# TODO use print, logging or py.test facility if it exists
fmt = '** testmon: %s **' % fmt
print(fmt % args)
def pytest_addoption(parser):
parser.addoption('--websocket_url')
def pytest_configure(config):
websocket_url = config.getoption('websocket_url')
config.pluginmanager.register(TestMonitorPlugin(websocket_url))
PEP8 fix
# -*- coding: utf-8 -*-
from __future__ import print_function
from builtins import object
import websocket
import logging
from purkinje_messages.message import TestCaseStartEvent
class TestMonitorPlugin(object):
"""py.test plugin for monitoring test progress and
capturing results
TODO graceful termination
"""
def __init__(self, websocket_url):
self.reports = []
self._websocket_url = websocket_url
self._websocket = None
try:
self._log('Connecting to WebSocket %s', websocket_url)
self._websocket = websocket.create_connection(websocket_url)
except ValueError as e:
self._log('Invalid WebSocket URL: "%s"',
self._websocket_url)
except Exception as e:
self._log('Error connecting to WebSocket at URL %s: %s',
self._websocket_url, e)
def is_websocket_connected(self):
return self._websocket is not None
def send_event(self, event):
"""Send event via WebSocket connection.
If there is no connection, the event will be dumped to the log
only, so it is possible to run tests with purkinje enabled even
if the server should be down
"""
ser_event = None
try:
ser_event = event.serialize()
if self._websocket:
self._websocket.send(ser_event)
else:
self._log('purkinje server not available; event: %s',
ser_event)
except Exception as e:
logging.exception(e)
self._log('Error while sending event "%s": %s',
ser_event or event.data, e)
def pytest_sessionfinish(self):
self._log('*** py.test session finished ***')
# def pytest_collect_file(self, path, parent):
# print('pytest_collect_file: {}'.format(path))
def pytest_collection_modifyitems(self, session, config, items):
print('pytest_collection_modifyitems: {} {} {}'.format(session,
config,
items))
def pytest_collectstart(self, collector):
self._log('pytest_collectstart: %s', collector)
# import pdb; pdb.set_trace()
def pytest_collectreport(self, report):
self._log('pytest_collectreport: %s', report)
self.send_event(TestCaseStartEvent(text='TODO xyz'))
self.reports.append(report)
# import pdb
# pdb.set_trace()
def _log(self, fmt, *args):
# TODO use print, logging or py.test facility if it exists
fmt = '** testmon: %s **' % fmt
print(fmt % args)
def pytest_addoption(parser):
parser.addoption('--websocket_url')
def pytest_configure(config):
websocket_url = config.getoption('websocket_url')
config.pluginmanager.register(TestMonitorPlugin(websocket_url))
|
"""
Highly experimental script that compiles the CPython standard library using Cython.
Execute the script either in the CPython 'Lib' directory or pass the
option '--current-python' to compile the standard library of the running
Python interpreter.
Pass '--parallel' to get a parallel build.
Usage example::
$ python cystdlib.py --current-python build_ext -i
"""
import sys
from distutils.core import setup
from Cython.Build import cythonize
from Cython.Compiler import Options
# improve Python compatibility by allowing some broken code
Options.error_on_unknown_names = False
excludes = ['**/test/**/*.py', '**/tests/**/*.py', '**/__init__.py']
broken = [
'idlelib/MultiCall.py',
'email/utils.py',
'multiprocessing/reduction.py',
'multiprocessing/util.py',
'threading.py', # interrupt handling
'lib2to3/fixes/fix_sys_exc.py',
'traceback.py',
]
default_directives = dict(
auto_cpdef=False, # enable when it's safe, see long list of failures below
binding=True,
set_initial_path='SOURCEFILE')
special_directives = [
(['pkgutil.py',
'decimal.py',
'datetime.py',
'optparse.py',
'sndhdr.py',
'opcode.py',
'ntpath.py',
'urllib/request.py',
'plat-*/TYPES.py',
'plat-*/IN.py',
'tkinter/_fix.py',
'lib2to3/refactor.py',
'webbrowser.py',
'shutil.py',
'multiprocessing/forking.py',
'xml/sax/expatreader.py',
'xmlrpc/client.py',
'pydoc.py',
'xml/etree/ElementTree.py',
'posixpath.py',
'inspect.py',
'ctypes/util.py',
'urllib/parse.py',
'warnings.py',
'tempfile.py',
'trace.py',
'heapq.py',
'pickletools.py',
'multiprocessing/connection.py',
'hashlib.py',
'getopt.py',
'os.py',
'types.py',
], dict(auto_cpdef=False)),
]
del special_directives[:] # currently unused
def build_extensions(includes='**/*.py',
excludes=excludes+broken,
special_directives=special_directives,
parallel=None):
if isinstance(includes, str):
includes = [includes]
all_groups = (special_directives or []) + [(includes, {})]
extensions = []
for modules, directives in all_groups:
exclude_now = excludes[:]
for other_modules, _ in special_directives:
if other_modules != modules:
exclude_now.extend(other_modules)
d = dict(default_directives)
d.update(directives)
extensions.extend(
cythonize(modules,
exclude=exclude_now,
exclude_failures=True,
language_level=pyver,
compiler_directives=d,
nthreads=parallel,
))
return extensions
def build(extensions):
try:
setup(name = 'stuff', ext_modules = extensions)
return extensions, True
except:
import traceback
print('error building extensions %s' % ([ext.name for ext in extensions],))
traceback.print_exc()
return extensions, False
def _build(args):
sys_args, ext = args
sys.argv[1:] = sys_args
return build([ext])
if __name__ == '__main__':
import sys
pyver = sys.version_info[0]
try:
sys.argv.remove('--current-python')
except ValueError:
pass
else:
# assume that the stdlib is where the "os" module lives
import os
os.chdir(os.path.dirname(os.__file__))
try:
sys.argv.remove('--parallel')
import multiprocessing
parallel_compiles = multiprocessing.cpu_count() * 2
print("Building in %d parallel processes" % parallel_compiles)
except (ValueError, ImportError):
parallel_compiles = None
extensions = build_extensions(parallel=parallel_compiles)
if parallel_compiles:
pool = multiprocessing.Pool(parallel_compiles)
sys_args = sys.argv[1:]
results = pool.map(_build, [ (sys_args, ext) for ext in extensions ])
pool.close()
pool.join()
for ext, result in results:
if not result:
print("building extension %s failed" % (ext[0].name,))
else:
build(extensions)
enable 'optimize.inline_defnode_calls' in cystdlib.py build script
"""
Highly experimental script that compiles the CPython standard library using Cython.
Execute the script either in the CPython 'Lib' directory or pass the
option '--current-python' to compile the standard library of the running
Python interpreter.
Pass '--parallel' to get a parallel build.
Usage example::
$ python cystdlib.py --current-python build_ext -i
"""
import sys
from distutils.core import setup
from Cython.Build import cythonize
from Cython.Compiler import Options
# improve Python compatibility by allowing some broken code
Options.error_on_unknown_names = False
excludes = ['**/test/**/*.py', '**/tests/**/*.py', '**/__init__.py']
broken = [
'idlelib/MultiCall.py',
'email/utils.py',
'multiprocessing/reduction.py',
'multiprocessing/util.py',
'threading.py', # interrupt handling
'lib2to3/fixes/fix_sys_exc.py',
'traceback.py',
]
default_directives = dict(
auto_cpdef=False, # enable when it's safe, see long list of failures below
binding=True,
set_initial_path='SOURCEFILE')
default_directives['optimize.inline_defnode_calls'] = True
special_directives = [
(['pkgutil.py',
'decimal.py',
'datetime.py',
'optparse.py',
'sndhdr.py',
'opcode.py',
'ntpath.py',
'urllib/request.py',
'plat-*/TYPES.py',
'plat-*/IN.py',
'tkinter/_fix.py',
'lib2to3/refactor.py',
'webbrowser.py',
'shutil.py',
'multiprocessing/forking.py',
'xml/sax/expatreader.py',
'xmlrpc/client.py',
'pydoc.py',
'xml/etree/ElementTree.py',
'posixpath.py',
'inspect.py',
'ctypes/util.py',
'urllib/parse.py',
'warnings.py',
'tempfile.py',
'trace.py',
'heapq.py',
'pickletools.py',
'multiprocessing/connection.py',
'hashlib.py',
'getopt.py',
'os.py',
'types.py',
], dict(auto_cpdef=False)),
]
del special_directives[:] # currently unused
def build_extensions(includes='**/*.py',
excludes=excludes+broken,
special_directives=special_directives,
parallel=None):
if isinstance(includes, str):
includes = [includes]
all_groups = (special_directives or []) + [(includes, {})]
extensions = []
for modules, directives in all_groups:
exclude_now = excludes[:]
for other_modules, _ in special_directives:
if other_modules != modules:
exclude_now.extend(other_modules)
d = dict(default_directives)
d.update(directives)
extensions.extend(
cythonize(modules,
exclude=exclude_now,
exclude_failures=True,
language_level=pyver,
compiler_directives=d,
nthreads=parallel,
))
return extensions
def build(extensions):
try:
setup(name = 'stuff', ext_modules = extensions)
return extensions, True
except:
import traceback
print('error building extensions %s' % ([ext.name for ext in extensions],))
traceback.print_exc()
return extensions, False
def _build(args):
sys_args, ext = args
sys.argv[1:] = sys_args
return build([ext])
if __name__ == '__main__':
import sys
pyver = sys.version_info[0]
try:
sys.argv.remove('--current-python')
except ValueError:
pass
else:
# assume that the stdlib is where the "os" module lives
import os
os.chdir(os.path.dirname(os.__file__))
try:
sys.argv.remove('--parallel')
import multiprocessing
parallel_compiles = multiprocessing.cpu_count() * 2
print("Building in %d parallel processes" % parallel_compiles)
except (ValueError, ImportError):
parallel_compiles = None
extensions = build_extensions(parallel=parallel_compiles)
if parallel_compiles:
pool = multiprocessing.Pool(parallel_compiles)
sys_args = sys.argv[1:]
results = pool.map(_build, [ (sys_args, ext) for ext in extensions ])
pool.close()
pool.join()
for ext, result in results:
if not result:
print("building extension %s failed" % (ext[0].name,))
else:
build(extensions)
|
# -*- coding: utf-8 -*-
"""
werkzeug_dispatch
~~~~~~~~~~~~~~~~~
:copyright: (c) 2014 by Ben Mather.
:license: BSD, see LICENSE for more details.
"""
from werkzeug import Response
import json
class ViewFactory(object):
def get_bindings(self):
raise NotImplementedError()
class Binding(ViewFactory):
def __init__(self, name, method, action, accept='*'):
self.name = name
self.method = method
self.accept = accept
self.action = action
def get_bindings(self):
yield self
class View(ViewFactory):
""" Wraps a function or callable so that it can be bound to a name in a
dispatcher.
"""
def __init__(self, name, action, *, methods={'GET'}, accept='*'):
self._name = name
self._methods = methods
self._accept = accept
self._action = action
def __call__(self, env, req, *args, **kwargs):
return self._action(env, req, *args, **kwargs)
def get_bindings(self):
for method in self._methods:
yield Binding(self._name, method, self, self._accept)
class TemplateView(View):
""" Like `View` but if the value returned from the action is not an
instance of `Response` it is rendered using the named template.
:param name:
:param action: called with environment, request and params to generate
response. See `View`.
:param template: either a string naming the template to be retrieved from
the environment or a callable applied to the result to
create an http `Response` object
"""
def __init__(self, name, action, *, methods={'GET'}, template=None):
super(TemplateView, self).__init__(name, action, methods=methods)
self._template = template
def __call__(self, env, req, *args, **kwargs):
res = self._action(env, req, *args, **kwargs)
if isinstance(res, Response):
return res
return env.get_template(self._template).render(res)
class JsonView(View):
def __init__(self, name, action, *, methods={'GET'}):
super(JsonView, self).__init__(name, action, methods=methods,
accept='text/json')
def __call__(self, env, req, *args, **kwargs):
res = super(JsonView, self).__call__(env, req, *args, **kwargs)
if isinstance(res, Response):
return res
return Response(json.dumps(res))
class ClassView(ViewFactory):
def get_bindings(self):
for method in {'GET', 'HEAD', 'POST', 'PUT', 'DELETE'}: # TODO
if hasattr(self, method):
yield Binding(self.name, method, getattr(self, method))
class Dispatcher(ViewFactory):
def __init__(self, views=[], *, default_view=TemplateView):
"""
:param default_view: callable used to construct new views from
functions decorated with the `expose` method
"""
self._default_view = default_view
self._views = {}
for view in views:
self.add(view)
def expose(self, name, *args, **kwargs):
""" Decorator to expose a function as a view.
Does not modify the wrapped function.
"""
def decorator(f):
self.add(self._default_view(name, f, *args, **kwargs))
return f
return decorator
def add(self, view_factory):
""" Add views from view factory to this dispatcher.
Dispatchers can be nested
"""
for view in view_factory.get_bindings():
if not view.name in self._views:
self._views[view.name] = {}
if not view.method in self._views[view.name]:
self._views[view.name][view.method] = {}
self._views[view.name][view.method] = view.action
def get_bindings(self):
return iter(self._views.items())
def lookup(self, method, name):
if not name in self._views:
return None
if method in self._views[name]:
return self._views[name][method]
elif method == 'HEAD' and 'GET' in self._views[name]:
return self._views[name]['GET']
docs for `Binding`
# -*- coding: utf-8 -*-
"""
werkzeug_dispatch
~~~~~~~~~~~~~~~~~
:copyright: (c) 2014 by Ben Mather.
:license: BSD, see LICENSE for more details.
"""
from werkzeug import Response
import json
class ViewFactory(object):
def get_bindings(self):
raise NotImplementedError()
class Binding(ViewFactory):
"""Represents an action associated with a single combination of endpoint
name and method
`name`
A hashable identifier.
`method`
An http method as an upper case string.
`action`
The action to perform if the binding is matched
`accept`
TODO. A string in the same format as the http accept header
"""
def __init__(self, name, method, action, accept='*'):
self.name = name
self.method = method
self.accept = accept
self.action = action
def get_bindings(self):
yield self
class View(ViewFactory):
""" Wraps a function or callable so that it can be bound to a name in a
dispatcher.
"""
def __init__(self, name, action, *, methods={'GET'}, accept='*'):
self._name = name
self._methods = methods
self._accept = accept
self._action = action
def __call__(self, env, req, *args, **kwargs):
return self._action(env, req, *args, **kwargs)
def get_bindings(self):
for method in self._methods:
yield Binding(self._name, method, self, self._accept)
class TemplateView(View):
""" Like `View` but if the value returned from the action is not an
instance of `Response` it is rendered using the named template.
:param name:
:param action: called with environment, request and params to generate
response. See `View`.
:param template: either a string naming the template to be retrieved from
the environment or a callable applied to the result to
create an http `Response` object
"""
def __init__(self, name, action, *, methods={'GET'}, template=None):
super(TemplateView, self).__init__(name, action, methods=methods)
self._template = template
def __call__(self, env, req, *args, **kwargs):
res = self._action(env, req, *args, **kwargs)
if isinstance(res, Response):
return res
return env.get_template(self._template).render(res)
class JsonView(View):
def __init__(self, name, action, *, methods={'GET'}):
super(JsonView, self).__init__(name, action, methods=methods,
accept='text/json')
def __call__(self, env, req, *args, **kwargs):
res = super(JsonView, self).__call__(env, req, *args, **kwargs)
if isinstance(res, Response):
return res
return Response(json.dumps(res))
class ClassView(ViewFactory):
def get_bindings(self):
for method in {'GET', 'HEAD', 'POST', 'PUT', 'DELETE'}: # TODO
if hasattr(self, method):
yield Binding(self.name, method, getattr(self, method))
class Dispatcher(ViewFactory):
def __init__(self, views=[], *, default_view=TemplateView):
"""
:param default_view: callable used to construct new views from
functions decorated with the `expose` method
"""
self._default_view = default_view
self._views = {}
for view in views:
self.add(view)
def expose(self, name, *args, **kwargs):
""" Decorator to expose a function as a view.
Does not modify the wrapped function.
"""
def decorator(f):
self.add(self._default_view(name, f, *args, **kwargs))
return f
return decorator
def add(self, view_factory):
""" Add views from view factory to this dispatcher.
Dispatchers can be nested
"""
for view in view_factory.get_bindings():
if not view.name in self._views:
self._views[view.name] = {}
if not view.method in self._views[view.name]:
self._views[view.name][view.method] = {}
self._views[view.name][view.method] = view.action
def get_bindings(self):
return iter(self._views.items())
def lookup(self, method, name):
if not name in self._views:
return None
if method in self._views[name]:
return self._views[name][method]
elif method == 'HEAD' and 'GET' in self._views[name]:
return self._views[name]['GET']
|
"""
Author: Ron Lockwood-Childs
Licensed under LGPL v2.1 (see file COPYING for details)
Widget types and instances.
"""
import math
import pygame
from pygame_maker.actors.gui.styles import WidgetStyle
from .. import object_type
from .. import simple_object_instance
from pygame_maker.actions import action
from pygame_maker.actions import action_sequence
import pygame_maker.support.drawing as drawing
import pygame_maker.support.coordinate as coord
import pygame_maker.support.color as color
class DummySurface(pygame.Rect):
"""
An object that can be substituted for a pygame Surface, for checking width
and height (helpful when calculating a minimum widget size without having a
surface to draw it on).
"""
def get_width(self):
return self.width
def get_height(self):
return self.height
class WidgetInstance(simple_object_instance.SimpleObjectInstance):
"""
Base class for an instance of any Widget type.
"""
INSTANCE_SYMBOLS = {
"visible": 0,
"widget_id": "",
"widget_class": "",
"hover": False,
"selected": False,
}
# WidgetInstance subclasses set this dict to add additional symbols with
# default values
WIDGET_INSTANCE_SUBCLASS_SYMBOLS = {
}
THIN_BORDER_WIDTH = 1
MEDIUM_BORDER_WIDTH = 3
THICK_BORDER_WIDTH = 5
@staticmethod
def get_integer_setting(setting, max_value):
"""
Apply an integer setting value appropriately based on whether the
value supplied is signed, unsigned, an integer 'px' size (in pixels),
or a percentage of the given max_value.
"""
value = 0
# self.debug("search '{}', which is type '{}'".format(setting, type(setting).__name__))
if not isinstance(setting, str):
# in case the value is already a number
return setting
num_minfo = WidgetStyle.NUMBER_RE.search(setting)
if num_minfo:
value = int(setting)
num_minfo = WidgetStyle.SIGNED_NUMBER_RE.search(setting)
if num_minfo:
value = int(setting)
px_minfo = WidgetStyle.PX_RE.search(setting)
if px_minfo:
value = int(px_minfo.group(1))
pc_minfo = WidgetStyle.PERCENT_RE.search(setting)
if pc_minfo:
perc = float(pc_minfo.group(1)) / 100.0
if perc > 1.0:
# WidgetStyle doesn't constrain the percentage; force
# maximum to 100%
perc = 1.0
value = int(math.floor(perc * max_value))
return value
def __init__(self, kind, screen, screen_dims, id_, settings=None, **kwargs):
simple_object_instance.SimpleObjectInstance.__init__(self, kind, screen_dims, id_,
settings, **kwargs)
self.screen = screen
self.screen_width = self.screen_dims[0]
self.screen_height = self.screen_dims[1]
self.style_settings = {}
self.style_values = {}
self.symbols["widget_class"] = ""
self.symbols["widget_id"] = "{}".format(self.inst_id)
self.symbols["visible"] = self.kind.visible
style_hash = self.get_widget_instance_style_hash()
style_info_hash = self.game_engine.global_style_settings.get_style(**style_hash)
# print("{}".format(style_info_hash))
style_info = WidgetStyle(style_info_hash)
self.get_widget_settings(style_info)
self.get_inner_setting_values(screen_dims)
# width, height = self.get_element_dimensions()
# self.symbols["width"] = width
# self.symbols["height"] = height
for subclass_sym in self.WIDGET_INSTANCE_SUBCLASS_SYMBOLS.keys():
if subclass_sym not in self.symbols.keys():
self.symbols[subclass_sym] = self.WIDGET_INSTANCE_SUBCLASS_SYMBOLS[subclass_sym]
@property
def visible(self):
return self.symbols["visible"]
@visible.setter
def visible(self, is_visible):
vis = (is_visible is True)
self.symbols["visible"] = vis
@property
def widget_id(self):
return self.symbols["widget_id"]
@widget_id.setter
def widget_id(self, new_id):
self.symbols["widget_id"] = "{}".format(new_id)
@property
def widget_class(self):
return self.symbols["widget_class"]
@widget_class.setter
def widget_class(self, new_class):
self.symbols["widget_class"] = new_class
@property
def hover(self):
return int(self.symbols["hover"])
@hover.setter
def hover(self, hover_on):
self.symbols["hover"] = (hover_on is True)
@property
def selected(self):
return int(self.symbols["selected"])
@selected.setter
def selected(self, is_selected):
self.symbols["selected"] = (is_selected is True)
@property
def width(self):
if "width" in self.symbols.keys():
return self.symbols["width"]
else:
return 0
@width.setter
def width(self, new_width):
int_width = int(new_width)
self.rect.width = int_width
self.symbols["width"] = int_width
@property
def height(self):
return self.symbols["height"]
@height.setter
def height(self, new_height):
int_height = int(new_height)
self.rect.height = int_height
self.symbols["height"] = int_height
@property
def parent(self):
return self.symbols["parent"]
def get_style_setting(self, setting_name, css_properties, parent_settings):
"""
Given a setting's name, the CSS properties, and the widget's parent's
settings, determine its value.
If not found in the following places:
* the instance symbol table (I.E. in object YAML or passed into
constructor)
* CSS properties
* inherited parent settings
then use the default value.
"""
self.debug("WidgetInstance.get_style_setting(" +
"setting_name={}, css_properties={}, parent_settings={})".
format(setting_name, css_properties, parent_settings))
default_setting = WidgetStyle.get_style_entry_default(setting_name)
setting = default_setting
if setting_name in self.symbols.keys():
# settings passed in from object YAML or constructor override
# default CSS
setting = self.symbols[setting_name]
elif setting_name in css_properties.keys():
# check_setting = " ".join(css_properties[setting_name])
check_setting = css_properties[setting_name]
# self.debug("check_setting: {}".format(check_setting))
if check_setting != "initial":
if (WidgetStyle.compare_value_vs_constraint(setting_name, "inherit") and
check_setting == "inherit" and self.parent is not None):
setting = parent_settings[setting_name]
elif WidgetStyle.compare_value_vs_constraint(setting_name, check_setting):
setting = check_setting
return setting
def get_widget_settings(self, css_properties):
"""
Collect all settings for the widget, using (in order of precedence):
1. values set in the constructor or in YAML properties
2. applicable CSS properties
3. values inherited from a parent widget
:param css_properties: Style settings found in the game engine based on
the widget's current properties (id, class name, etc.)
:type css_properties: WidgetStyle
"""
self.debug("WidgetInstance.get_style_settings(css_properties={})".format(css_properties))
parent_settings = None
if self.parent is not None and isinstance(self.parent, WidgetInstance):
# this could result in the parent checking its parent's
# settings..
parent_settings = self.parent.get_widget_settings(css_properties)
for setting_name in WidgetStyle.STYLE_CONSTRAINTS.keys():
# self.debug("Get widget setting {} .. ".format(setting_name))
self.style_settings[setting_name] = self.get_style_setting(setting_name, css_properties,
parent_settings)
def get_outer_setting_values(self, surface):
"""
Calculate values for all margin, border, and padding settings.
:param surface: A (sub-)surface that can report its own width and
height
:type surface: pygame.Surface or DummySurface
"""
self.debug("WidgetInstance.get_outer_setting_values(surface={})".format(surface))
# CSS box model: calculate margin, border, and padding so the remaining
# setting values can be calculated
surface_width = surface.get_width()
surface_height = surface.get_height()
for setting_name in ("margin-left", "border-left-width", "padding-left",
"padding-right", "border-right-width", "margin-right"):
style_val = type(self).get_integer_setting(self.style_settings[setting_name],
surface_width)
self.style_values[setting_name] = style_val
for setting_name in ("margin-top", "border-top-width", "padding-top",
"padding-bottom", "border-bottom-width", "margin-bottom"):
style_val = type(self).get_integer_setting(self.style_settings[setting_name],
surface_height)
self.style_values[setting_name] = style_val
def calculate_outer_dimensions(self):
self.debug("WidgetInstance.calculate_outer_dimensions()")
# calculate width
outer_width = (self.style_values["margin-left"] + self.style_values["margin-right"] +
self.style_values["padding-left"] + self.style_values["padding-right"])
border_left_width = 0
if self.style_settings["border-left-style"] not in ("none", "hidden"):
border_left_width = self.style_values["border-left-width"]
if self.style_settings["border-left-style"] == "double":
border_left_width = border_left_width * 2 + 1
border_right_width = 0
if self.style_settings["border-right-style"] not in ("none", "hidden"):
border_right_width = self.style_values["border-right-width"]
if self.style_settings["border-right-style"] == "double":
border_right_width = border_right_width * 2 + 1
outer_width += border_left_width + border_right_width
# calculate height
outer_height = (self.style_values["margin-top"] + self.style_values["margin-bottom"] +
self.style_values["padding-top"] + self.style_values["padding-bottom"])
border_top_height = 0
if self.style_settings["border-top-style"] not in ("none", "hidden"):
border_top_height = self.style_values["border-top-width"]
if self.style_settings["border-top-style"] == "double":
border_top_height = border_top_height * 2 + 1
border_bottom_height = 0
if self.style_settings["border-bottom-style"] not in ("none", "hidden"):
border_bottom_height = self.style_values["border-bottom-width"]
if self.style_settings["border-bottom-style"] == "double":
border_bottom_height = border_bottom_height * 2 + 1
outer_height += border_top_height + border_bottom_height
return (outer_width, outer_height)
def get_inner_setting_values(self, max_dimensions):
self.debug("WidgetInstance.get_inner_setting_values(max_dimensions={})".format(max_dimensions))
# calculate min-width, width, max-width values
min_width_val = type(self).get_integer_setting(self.style_settings["min-width"],
max_dimensions[0])
if min_width_val > max_dimensions[0]:
min_width_val = max_dimensions[0]
self.style_values["min-width"] = min_width_val
max_width_val = max_dimensions[0]
if self.style_settings["max-width"] != "none":
max_width_val = type(self).get_integer_setting(self.style_settings["max-width"],
max_dimensions[0])
if max_width_val > max_dimensions[0]:
max_width_val = max_dimensions[0]
elif max_width_val < min_width_val:
max_width_val = min_width_val
self.style_values["max-width"] = max_width_val
if self.style_settings["width"] != "auto":
width_val = type(self).get_integer_setting(self.style_settings["width"],
max_dimensions[0])
if width_val < min_width_val:
width_val = min_width_val
if width_val > max_width_val:
width_val = max_width_val
self.style_values["width"] = width_val
# calculate min-height, height, max-height values
min_height_val = type(self).get_integer_setting(self.style_settings["min-height"],
max_dimensions[1])
if min_height_val > max_dimensions[1]:
min_height_val = max_dimensions[1]
self.style_values["min-height"] = min_height_val
max_height_val = max_dimensions[1]
if self.style_settings["max-height"] != "none":
max_height_val = type(self).get_integer_setting(self.style_settings["max-height"],
max_dimensions[1])
if max_height_val > max_dimensions[1]:
max_height_val = max_dimensions[1]
elif max_height_val < min_height_val:
max_height_val = min_height_val
self.style_values["max-height"] = max_height_val
if self.style_settings["height"] != "auto":
height_val = type(self).get_integer_setting(self.style_settings["height"],
max_dimensions[1])
if height_val < min_height_val:
height_val = min_height_val
if height_val > max_height_val:
height_val = max_height_val
self.style_values["height"] = height_val
def get_element_dimensions(self):
"""
Called after get_inner_setting_values() to determine the size of the
widget's content.
The base class will use the 'min-width' and 'min-height' properties as
element dimensions, if the 'width' and 'height' properties are 'auto';
otherwise, it will use the 'width' and/or 'height' properties' values.
Subclasses should start here, and expand as needed to fit widget
content, honoring the 'max-width' and 'max-height' properties.
"""
self.debug("WidgetInstance.get_element_dimensions()")
element_width = self.style_values["min-width"]
element_height = self.style_values["min-height"]
if self.style_settings["width"] != "auto":
element_width = self.style_values["width"]
if self.style_settings["height"] != "auto":
element_height = self.style_values["height"]
return (element_width, element_height)
def get_color_values(self):
self.debug("WidgetInstance.get_color_values()")
color_property_list = ["border-top-color", "border-right-color", "border-bottom-color", "border-left-color",
"background-color", "color"]
# put Color objects into border/background color settings
for color_property in color_property_list:
color_name = self.style_settings[color_property]
default_color = str(WidgetStyle.STYLE_CONSTRAINTS[color_property]["default"])
color_string = color_name
if color_name != "transparent":
minfo = WidgetStyle.WEB_COLOR_RE.match(color_name)
if minfo:
if len(color_name) == 4:
color_string = "#0{}0{}0{}".format(*color_name[1:4])
elif len(color_name) == 5:
color_string = "#0{}0{}0{}0{}".format(*color_name[1:5])
elif len(color_name) == 6:
str_ary = [color_name[idx] for idx in range(1,4)]
str_ary.append(color_name[4:])
color_string = "#0{}0{}0{}{}".format(*str_ary)
elif len(color_name) == 8:
color_string = "{}0{}".format(color_name[:7], color_name[7])
try:
self.style_values[color_property] = color.Color(color_string)
except ValueError:
self.style_values[color_property] = color.Color(default_color)
elif color_property == "color":
# font color isn't supposed to be "transparent", so replace
# this string with the default color
self.style_values[color_property] = color.Color(default_color)
def get_min_size(self):
"""
Calculate the widget's minimum width and height, and return them in a
tuple.
Container widgets may call this to find out how much space it needs
to reserve for each of its child widgets.
The base class only returns the space surrounding the widget's
contents: the sum of margin, border, and padding widths for each side.
Subclasses should call this method, then determine the element's
actual dimensions after taking min/max width and height into account.
"""
self.debug("WidgetInstance.get_min_size()")
# create a surface such that 1% is a minimum of 1 pixel
dummy_surface = DummySurface(0,0,100,100)
min_width = 1
min_height = 1
style_hash = self.get_widget_instance_style_hash()
style_info = WidgetStyle(self.game_engine.global_style_settings.get_style(**style_hash))
self.get_widget_settings(style_info)
self.get_outer_setting_values(dummy_surface)
min_outer_dims = self.calculate_outer_dimensions()
if min_outer_dims[0] > min_width:
min_width = min_outer_dims[0]
if min_outer_dims[1] > min_height:
min_height = min_outer_dims[1]
return (min_width, min_height)
def calculate_top_outer_border_size(self):
top_size = self.style_values["margin-top"] + self.style_values["padding-top"]
border_top_height = 0
if (self.style_settings["border-top-style"] not in ("none", "hidden") and
(self.style_settings["border-top-color"] != "transparent")):
border_top_height = self.style_values["border-top-width"]
if self.style_settings["border-top-style"] == "double":
border_top_height = border_top_height * 2 + 1
top_size += border_top_height
return top_size
def calculate_left_outer_border_size(self):
left_size = self.style_values["margin-left"] + self.style_values["padding-left"]
border_left_width = 0
if (self.style_settings["border-left-style"] not in ("none", "hidden") and
(self.style_settings["border-left-color"] != "transparent")):
border_left_width = self.style_values["border-left-width"]
if self.style_settings["border-left-style"] == "double":
border_left_width = border_left_width * 2 + 1
left_size += border_left_width
return left_size
def draw_border_side(self, screen, side, outer_dims, element_dims, width, color, style):
draw_rect = pygame.Rect(0, 0, 0, 0)
# thick lines are _centered_ on the calculated coordinates, so shift them by 1/2 their width
thick_adj = 0
if width > 1:
thick_adj = int(math.floor((width-1) / 2))
if side == "top":
draw_rect.left = self.style_values["margin-left"]
draw_rect.top = self.style_values["margin-top"] + thick_adj
draw_rect.width = self.style_values["border-left-width"] + \
self.style_values["padding-left"] + element_dims[0] + \
self.style_values["padding-right"]
if draw_rect.width <= 1:
return
elif side == "bottom":
draw_rect.left = self.style_values["margin-left"]
draw_rect.top = (self.calculate_top_outer_border_size() + element_dims[1] +
self.style_values["padding-bottom"] + thick_adj)
draw_rect.width = self.style_values["border-left-width"] + \
self.style_values["padding-left"] + element_dims[0] + \
self.style_values["padding-right"]
if draw_rect.width <= 1:
return
elif side == "left":
draw_rect.left = self.style_values["margin-left"] + thick_adj
draw_rect.top = self.style_values["margin-top"]
draw_rect.height = self.style_values["border-top-width"] + \
self.style_values["padding-top"] + element_dims[1] + \
self.style_values["padding-bottom"]
if draw_rect.height <= 1:
return
elif side == "right":
draw_rect.left = (self.calculate_left_outer_border_size() + element_dims[0] +
self.style_values["padding-right"] + thick_adj)
draw_rect.top = self.style_values["margin-top"]
draw_rect.height = self.style_values["border-top-width"] + \
self.style_values["padding-top"] + element_dims[1] + \
self.style_values["padding-bottom"]
if draw_rect.height <= 1:
return
start_coord = coord.Coordinate(draw_rect.left, draw_rect.top)
end_coord = coord.Coordinate(draw_rect.right, draw_rect.bottom)
# self.debug("Draw {} border from {} to {}, width {}, color {}, style {}".format(
# side, start_coord, end_coord, width, color, style))
drawing.draw_line(screen, start_coord, end_coord, width, color, style)
def draw_border(self, screen, outer_dims):
self.debug("WidgetInstance.draw_border(screen={}, outer_dims={})".
format(screen, outer_dims))
element_dims = self.get_element_dimensions()
for side in ("top", "right", "bottom", "left"):
border_width = self.style_values["border-{}-width".format(side)]
border_style = self.style_settings["border-{}-style".format(side)]
border_color_style = self.style_settings["border-{}-color".format(side)]
border_color = "transparent"
if border_color_style != "transparent":
border_color = self.style_values["border-{}-color".format(side)]
else:
continue
if border_style in ("none", "hidden") or (border_width < 1) or \
(border_color == "transparent"):
continue
self.draw_border_side(screen, side, outer_dims, element_dims, border_width,
border_color, border_style)
def draw(self, screen):
"""
Draw the widget instance to a surface using css properties.
Always recalculate the settings, in case the style has been updated,
or an attribute has changed that may affect the style.
:param screen: A pygame surface upon which to draw the widget
:type screen: :py:class:`pygame.Surface`
"""
self.debug("{} inst {}: WidgetInstance.draw(screen={})".
format(self.kind.name, self.inst_id, screen))
if not self.visible:
return
style_hash = self.get_widget_instance_style_hash()
# self.debug("Find style {} in {} ..".format(style_hash, style_info))
style_info = WidgetStyle(self.game_engine.global_style_settings.get_style(**style_hash))
self.get_widget_settings(style_info)
# self.debug("Style settings: {}".format(self.style_settings))
self.get_outer_setting_values(screen)
outer_dims = self.calculate_outer_dimensions()
max_inner_dims = (screen.get_width() - outer_dims[0], screen.get_height() - outer_dims[1])
self.get_inner_setting_values(max_inner_dims)
self.get_color_values()
# self.debug("Style values: {}".format(self.style_values))
self.draw_border(screen, outer_dims)
def get_widget_instance_style_hash(self):
"""
Collect widget instance style information for comparison with
stylesheet settings.
Subclasses should start here and add attribute matches (e.g. a
checkbutton could match on "checked" attribute "on" or "off")
"""
self.debug("WidgetInstance.get_widget_instance_style_hash()")
props = {
"element_type": self.kind.name,
"element_id": self.widget_id,
}
if len(self.widget_class) > 0:
props["element_class"] = self.widget_class
if self.hover:
props["pseudo_class"] = "hover"
return props
class LabelWidgetInstance(WidgetInstance):
# WIDGET_INSTANCE_SUBCLASS_SYMBOLS = {
# "label": "",
# "font": "",
# "font_size": "initial",
# "font_style": "initial",
# "font_weight": "initial",
# "text_decoration": "initial",
# "text_transform": "initial",
# "text_align": "initial",
# "vertical_align": "initial",
# "color": "black"
# }
FONT_SIZE_CATEGORIES = {
"small": 10,
"medium": 14,
"large": 20
}
def __init__(self, kind, screen, screen_dims, id_, settings=None, **kwargs):
super(LabelWidgetInstance, self).__init__(kind, screen, screen_dims, id_, settings,
**kwargs)
self.font_resource = None
self.get_font_resource()
self._font_point_size = 12
@property
def label(self):
return self.symbols["label"]
@label.setter
def label(self, new_label):
try:
self.symbols["label"] = str(new_label)
except ValueError:
pass
@property
def font(self):
return self.symbols["font"]
@font.setter
def font(self, new_font):
try:
self.symbols["font"] = str(new_font)
self.get_font_resource()
except ValueError:
pass
@property
def font_size(self):
return self.symbols["font_size"]
@font_size.setter
def font_size(self, new_size):
self.symbols["font_size"] = new_size
def get_font_resource(self):
self.debug("LabelWidgetInstance.get_font_resource()")
if (len(self.font) == 0) or (self.font not in
self.kind.game_engine.resources['fonts'].keys()):
# revert to a system font, if found
if hasattr(self.kind.game_engine, 'system_font'):
self.font_resource = self.kind.game_engine.system_font
else:
return None
else:
self.font_resource = self.kind.game_engine.resources['fonts'][self.font]
def calc_label_size(self):
self.debug("LabelWidgetInstance.calc_label_size()")
if self.font_resource is None:
return (0, 0)
if len(self.label) == 0:
return (0, 0)
font_rndr = self.font_resource.get_font_renderer()
return font_rndr.calc_render_size(self.label)
def get_min_size(self):
self.debug("LabelWidgetInstance.get_min_size()")
total_width, total_height = super(LabelWidgetInstance, self).get_min_size()
text_width, text_height = self.calc_label_size()
total_width += text_width
total_height += text_height
return (total_width, total_height)
def get_element_dimensions(self):
self.debug("LabelWidgetInstance.get_element_dimensions()")
element_width = self.style_values["min-width"]
element_height = self.style_values["min-height"]
label_width, label_height = self.calc_label_size()
if self.style_settings["width"] != "auto":
element_width = self.style_values["width"]
elif label_width > element_width:
element_width = label_width
if self.style_settings["height"] != "auto":
element_height = self.style_values["height"]
elif label_height > element_height:
element_height = label_height
return (element_width, element_height)
def draw_text(self, surface):
self.debug("LabelWidgetInstance.draw_text(surface={})".format(surface))
surf_width = surface.get_width()
surf_height = surface.get_height()
font_rndr = self.font_resource.get_font_renderer()
# apply horizontal, vertical alignment
text_width, text_height = font_rndr.calc_render_size(self.label)
top_left = coord.Coordinate(0, 0)
if surf_width > text_width:
if self.style_settings["text-align"] == "center":
top_left.x = (surf_width / 2) - (text_width / 2)
elif self.style_settings["text-align"] == "right":
top_left.x = surf_width - text_width
if surf_height > text_height:
if self.style_settings["vertical-align"] == "middle":
top_left.y = (surf_height / 2) - (text_height / 2)
elif self.style_settings["vertical-align"] == "bottom":
top_left.y = surf_height - text_height
font_rndr.render_text(surface, top_left, self.label, self.style_values["color"])
def draw(self, screen):
self.debug("LabelWidgetInstance.draw(screen={})".format(screen))
# draw any visible borders
super(LabelWidgetInstance, self).draw(screen)
# create a subsurface big enough to hold the element dimensions
label_width, label_height = self.calc_label_size()
if (label_width > 0) and (label_height > 0):
subsurf_width, subsurf_height = self.get_element_dimensions()
subsurf_left = super(LabelWidgetInstance, self).calculate_left_outer_border_size()
subsurf_top = super(LabelWidgetInstance, self).calculate_top_outer_border_size()
subsurf_rect = pygame.Rect(subsurf_left, subsurf_top, subsurf_width, subsurf_height)
subsurf = screen.subsurface(subsurf_rect)
self.draw_text(subsurf)
class WidgetObjectTypeInvalid(Exception):
pass
class WidgetObjectType(object_type.ObjectType):
DEFAULT_VISIBLE = False
# subclasses set this to their own instance type
WIDGET_INSTANCE_TYPE = WidgetInstance
# subclasses can add their own YAML properties by setting this class
# variable to a list of tuples [(entry_name, entry_type), ..] where
# entry_type is usually a standard type: str, int, or bool
WIDGET_SUBCLASS_KW_ENTRIES = []
@classmethod
def gen_kwargs_from_yaml_obj(cls, obj_name, obj_yaml, game_engine):
kwargs = super(WidgetObjectType, cls).gen_kwargs_from_yaml_obj(obj_name, obj_yaml,
game_engine)
kwargs.update({
"visible": WidgetObjectType.DEFAULT_VISIBLE,
})
if "visible" in obj_yaml.keys():
kwargs["visible"] = (obj_yaml["visible"] is True)
for kw_entry, entry_type in cls.WIDGET_SUBCLASS_KW_ENTRIES:
if kw_entry in obj_yaml.keys():
if isinstance(entry_type, bool):
kwargs[kw_entry] = (obj_yaml[kw_entry] is True)
else:
# set the kwarg if the type conversion succeeds
try:
kwargs[kw_entry] = entry_type(obj_yaml[kw_entry])
except ValueError:
pass
return kwargs
def __init__(self, widget_name, game_engine, **kwargs):
super(WidgetObjectType, self).__init__(widget_name, game_engine, **kwargs)
#: Flag whether this widget type is a container for other widgets
self.is_container = False
self.visible = self.DEFAULT_VISIBLE
# default draw action sequence draws the object's sprite
self["draw"] = action_sequence.ActionSequence()
self["draw"].append_action(action.DrawAction("draw_self"))
if kwargs and "visible" in kwargs:
self.visible = kwargs["visible"]
def make_new_instance(self, screen, instance_properties=None):
"""
Generate a new instance of the widget type in response to
:py:meth:`~pygame_maker.actors.object_type.ObjectType.create_instance`
:param screen: The surface the instance will be drawn upon. The
instance can use this surface's (often a sub-surface's) width and
height parameters to determine child widget placement
:type screen: :py:class:`pygame.Surface`
:param instance_properties: A hash of settings to be applied. See
kwargs entry in
:py:meth:`~pygame_maker.actors.simple_object_instance.SimpleObjectInstance.__init__`
:type instance_properties: dict
"""
self.debug("WidgetObjectType.make_new_instance(screen={}, instance_properties={})".format(screen, instance_properties))
screen_dims = (screen.get_width(), screen.get_height())
new_instance = self.WIDGET_INSTANCE_TYPE(self, screen, screen_dims, self._id, instance_properties)
self.instance_list.append(new_instance)
def update(self):
"""
Update all instances of this widget type.
"""
pass
def draw(self, in_event):
"""Draw all visible instances."""
self.debug("WidgetObjectType.draw(in_event={})".format(in_event))
if len(self.instance_list) > 0:
for inst in self.instance_list:
# self.debug("Check inst {}".format(inst))
if inst.parent is not None:
continue
if inst.visible:
# self.debug("Draw visible inst {}".format(inst))
inst.draw(inst.screen)
class LabelWidgetObjectType(WidgetObjectType):
WIDGET_INSTANCE_TYPE = LabelWidgetInstance
widget.py: More pylint cleanups
"""
Author: Ron Lockwood-Childs
Licensed under LGPL v2.1 (see file COPYING for details)
Widget types and instances.
"""
import math
import pygame
from pygame_maker.actors.gui.styles import WidgetStyle
from .. import object_type
from .. import simple_object_instance
from pygame_maker.actions import action
from pygame_maker.actions import action_sequence
import pygame_maker.support.drawing as drawing
import pygame_maker.support.coordinate as coord
import pygame_maker.support.color as color
class DummySurface(pygame.Rect):
"""
An object that can be substituted for a pygame Surface, for checking width
and height (helpful when calculating a minimum widget size without having a
surface to draw it on).
"""
def get_width(self):
return self.width
def get_height(self):
return self.height
class WidgetInstance(simple_object_instance.SimpleObjectInstance):
"""
Base class for an instance of any Widget type.
"""
INSTANCE_SYMBOLS = {
"visible": 0,
"widget_id": "",
"widget_class": "",
"hover": False,
"selected": False,
}
# WidgetInstance subclasses set this dict to add additional symbols with
# default values
WIDGET_INSTANCE_SUBCLASS_SYMBOLS = {
}
THIN_BORDER_WIDTH = 1
MEDIUM_BORDER_WIDTH = 3
THICK_BORDER_WIDTH = 5
@staticmethod
def get_integer_setting(setting, max_value):
"""
Apply an integer setting value appropriately based on whether the
value supplied is signed, unsigned, an integer 'px' size (in pixels),
or a percentage of the given max_value.
"""
value = 0
# self.debug("search '{}', which is type '{}'".format(setting, type(setting).__name__))
if not isinstance(setting, str):
# in case the value is already a number
return setting
num_minfo = WidgetStyle.NUMBER_RE.search(setting)
if num_minfo:
value = int(setting)
num_minfo = WidgetStyle.SIGNED_NUMBER_RE.search(setting)
if num_minfo:
value = int(setting)
px_minfo = WidgetStyle.PX_RE.search(setting)
if px_minfo:
value = int(px_minfo.group(1))
pc_minfo = WidgetStyle.PERCENT_RE.search(setting)
if pc_minfo:
perc = float(pc_minfo.group(1)) / 100.0
if perc > 1.0:
# WidgetStyle doesn't constrain the percentage; force
# maximum to 100%
perc = 1.0
value = int(math.floor(perc * max_value))
return value
def __init__(self, kind, screen, screen_dims, id_, settings=None, **kwargs):
simple_object_instance.SimpleObjectInstance.__init__(self, kind, screen_dims, id_,
settings, **kwargs)
self.screen = screen
self.screen_width = self.screen_dims[0]
self.screen_height = self.screen_dims[1]
self.style_settings = {}
self.style_values = {}
self.symbols["widget_class"] = ""
self.symbols["widget_id"] = "{}".format(self.inst_id)
self.symbols["visible"] = self.kind.visible
style_hash = self.get_widget_instance_style_hash()
style_info_hash = self.game_engine.global_style_settings.get_style(**style_hash)
# print("{}".format(style_info_hash))
style_info = WidgetStyle(style_info_hash)
self.get_widget_settings(style_info)
self.get_inner_setting_values(screen_dims)
# width, height = self.get_element_dimensions()
# self.symbols["width"] = width
# self.symbols["height"] = height
for subclass_sym in self.WIDGET_INSTANCE_SUBCLASS_SYMBOLS.keys():
if subclass_sym not in self.symbols.keys():
self.symbols[subclass_sym] = self.WIDGET_INSTANCE_SUBCLASS_SYMBOLS[subclass_sym]
@property
def visible(self):
return self.symbols["visible"]
@visible.setter
def visible(self, is_visible):
vis = (is_visible is True)
self.symbols["visible"] = vis
@property
def widget_id(self):
return self.symbols["widget_id"]
@widget_id.setter
def widget_id(self, new_id):
self.symbols["widget_id"] = "{}".format(new_id)
@property
def widget_class(self):
return self.symbols["widget_class"]
@widget_class.setter
def widget_class(self, new_class):
self.symbols["widget_class"] = new_class
@property
def hover(self):
return int(self.symbols["hover"])
@hover.setter
def hover(self, hover_on):
self.symbols["hover"] = (hover_on is True)
@property
def selected(self):
return int(self.symbols["selected"])
@selected.setter
def selected(self, is_selected):
self.symbols["selected"] = (is_selected is True)
@property
def width(self):
if "width" in self.symbols.keys():
return self.symbols["width"]
else:
return 0
@width.setter
def width(self, new_width):
int_width = int(new_width)
self.rect.width = int_width
self.symbols["width"] = int_width
@property
def height(self):
return self.symbols["height"]
@height.setter
def height(self, new_height):
int_height = int(new_height)
self.rect.height = int_height
self.symbols["height"] = int_height
@property
def parent(self):
return self.symbols["parent"]
def get_style_setting(self, setting_name, css_properties, parent_settings):
"""
Given a setting's name, the CSS properties, and the widget's parent's
settings, determine its value.
If not found in the following places:
* the instance symbol table (I.E. in object YAML or passed into
constructor)
* CSS properties
* inherited parent settings
then use the default value.
"""
self.debug("WidgetInstance.get_style_setting(" +
"setting_name={}, css_properties={}, parent_settings={})".
format(setting_name, css_properties, parent_settings))
default_setting = WidgetStyle.get_style_entry_default(setting_name)
setting = default_setting
if setting_name in self.symbols.keys():
# settings passed in from object YAML or constructor override
# default CSS
setting = self.symbols[setting_name]
elif setting_name in css_properties.keys():
# check_setting = " ".join(css_properties[setting_name])
check_setting = css_properties[setting_name]
# self.debug("check_setting: {}".format(check_setting))
if check_setting != "initial":
if (WidgetStyle.compare_value_vs_constraint(setting_name, "inherit") and
check_setting == "inherit" and self.parent is not None):
setting = parent_settings[setting_name]
elif WidgetStyle.compare_value_vs_constraint(setting_name, check_setting):
setting = check_setting
return setting
def get_widget_settings(self, css_properties):
"""
Collect all settings for the widget, using (in order of precedence):
1. values set in the constructor or in YAML properties
2. applicable CSS properties
3. values inherited from a parent widget
:param css_properties: Style settings found in the game engine based on
the widget's current properties (id, class name, etc.)
:type css_properties: WidgetStyle
"""
self.debug("WidgetInstance.get_style_settings(css_properties={})".format(css_properties))
parent_settings = None
if self.parent is not None and isinstance(self.parent, WidgetInstance):
# this could result in the parent checking its parent's
# settings..
parent_settings = self.parent.get_widget_settings(css_properties)
for setting_name in WidgetStyle.STYLE_CONSTRAINTS.keys():
# self.debug("Get widget setting {} .. ".format(setting_name))
self.style_settings[setting_name] = self.get_style_setting(setting_name, css_properties,
parent_settings)
def get_outer_setting_values(self, surface):
"""
Calculate values for all margin, border, and padding settings.
:param surface: A (sub-)surface that can report its own width and
height
:type surface: pygame.Surface or DummySurface
"""
self.debug("WidgetInstance.get_outer_setting_values(surface={})".format(surface))
# CSS box model: calculate margin, border, and padding so the remaining
# setting values can be calculated
surface_width = surface.get_width()
surface_height = surface.get_height()
for setting_name in ("margin-left", "border-left-width", "padding-left",
"padding-right", "border-right-width", "margin-right"):
style_val = type(self).get_integer_setting(self.style_settings[setting_name],
surface_width)
self.style_values[setting_name] = style_val
for setting_name in ("margin-top", "border-top-width", "padding-top",
"padding-bottom", "border-bottom-width", "margin-bottom"):
style_val = type(self).get_integer_setting(self.style_settings[setting_name],
surface_height)
self.style_values[setting_name] = style_val
def calculate_outer_dimensions(self):
self.debug("WidgetInstance.calculate_outer_dimensions()")
# calculate width
outer_width = (self.style_values["margin-left"] + self.style_values["margin-right"] +
self.style_values["padding-left"] + self.style_values["padding-right"])
border_left_width = 0
if self.style_settings["border-left-style"] not in ("none", "hidden"):
border_left_width = self.style_values["border-left-width"]
if self.style_settings["border-left-style"] == "double":
border_left_width = border_left_width * 2 + 1
border_right_width = 0
if self.style_settings["border-right-style"] not in ("none", "hidden"):
border_right_width = self.style_values["border-right-width"]
if self.style_settings["border-right-style"] == "double":
border_right_width = border_right_width * 2 + 1
outer_width += border_left_width + border_right_width
# calculate height
outer_height = (self.style_values["margin-top"] + self.style_values["margin-bottom"] +
self.style_values["padding-top"] + self.style_values["padding-bottom"])
border_top_height = 0
if self.style_settings["border-top-style"] not in ("none", "hidden"):
border_top_height = self.style_values["border-top-width"]
if self.style_settings["border-top-style"] == "double":
border_top_height = border_top_height * 2 + 1
border_bottom_height = 0
if self.style_settings["border-bottom-style"] not in ("none", "hidden"):
border_bottom_height = self.style_values["border-bottom-width"]
if self.style_settings["border-bottom-style"] == "double":
border_bottom_height = border_bottom_height * 2 + 1
outer_height += border_top_height + border_bottom_height
return (outer_width, outer_height)
def get_inner_setting_values(self, max_dimensions):
self.debug("WidgetInstance.get_inner_setting_values(max_dimensions={})".
format(max_dimensions))
# calculate min-width, width, max-width values
min_width_val = type(self).get_integer_setting(self.style_settings["min-width"],
max_dimensions[0])
if min_width_val > max_dimensions[0]:
min_width_val = max_dimensions[0]
self.style_values["min-width"] = min_width_val
max_width_val = max_dimensions[0]
if self.style_settings["max-width"] != "none":
max_width_val = type(self).get_integer_setting(self.style_settings["max-width"],
max_dimensions[0])
if max_width_val > max_dimensions[0]:
max_width_val = max_dimensions[0]
elif max_width_val < min_width_val:
max_width_val = min_width_val
self.style_values["max-width"] = max_width_val
if self.style_settings["width"] != "auto":
width_val = type(self).get_integer_setting(self.style_settings["width"],
max_dimensions[0])
if width_val < min_width_val:
width_val = min_width_val
if width_val > max_width_val:
width_val = max_width_val
self.style_values["width"] = width_val
# calculate min-height, height, max-height values
min_height_val = type(self).get_integer_setting(self.style_settings["min-height"],
max_dimensions[1])
if min_height_val > max_dimensions[1]:
min_height_val = max_dimensions[1]
self.style_values["min-height"] = min_height_val
max_height_val = max_dimensions[1]
if self.style_settings["max-height"] != "none":
max_height_val = type(self).get_integer_setting(self.style_settings["max-height"],
max_dimensions[1])
if max_height_val > max_dimensions[1]:
max_height_val = max_dimensions[1]
elif max_height_val < min_height_val:
max_height_val = min_height_val
self.style_values["max-height"] = max_height_val
if self.style_settings["height"] != "auto":
height_val = type(self).get_integer_setting(self.style_settings["height"],
max_dimensions[1])
if height_val < min_height_val:
height_val = min_height_val
if height_val > max_height_val:
height_val = max_height_val
self.style_values["height"] = height_val
def get_element_dimensions(self):
"""
Called after get_inner_setting_values() to determine the size of the
widget's content.
The base class will use the 'min-width' and 'min-height' properties as
element dimensions, if the 'width' and 'height' properties are 'auto';
otherwise, it will use the 'width' and/or 'height' properties' values.
Subclasses should start here, and expand as needed to fit widget
content, honoring the 'max-width' and 'max-height' properties.
"""
self.debug("WidgetInstance.get_element_dimensions()")
element_width = self.style_values["min-width"]
element_height = self.style_values["min-height"]
if self.style_settings["width"] != "auto":
element_width = self.style_values["width"]
if self.style_settings["height"] != "auto":
element_height = self.style_values["height"]
return (element_width, element_height)
def get_color_values(self):
self.debug("WidgetInstance.get_color_values()")
color_property_list = ["border-top-color", "border-right-color", "border-bottom-color",
"border-left-color", "background-color", "color"]
# put Color objects into border/background color settings
for color_property in color_property_list:
color_name = self.style_settings[color_property]
default_color = str(WidgetStyle.STYLE_CONSTRAINTS[color_property]["default"])
color_string = color_name
if color_name != "transparent":
minfo = WidgetStyle.WEB_COLOR_RE.match(color_name)
if minfo:
if len(color_name) == 4:
color_string = "#0{}0{}0{}".format(*color_name[1:4])
elif len(color_name) == 5:
color_string = "#0{}0{}0{}0{}".format(*color_name[1:5])
elif len(color_name) == 6:
str_ary = [color_name[idx] for idx in range(1, 4)]
str_ary.append(color_name[4:])
color_string = "#0{}0{}0{}{}".format(*str_ary)
elif len(color_name) == 8:
color_string = "{}0{}".format(color_name[:7], color_name[7])
try:
self.style_values[color_property] = color.Color(color_string)
except ValueError:
self.style_values[color_property] = color.Color(default_color)
elif color_property == "color":
# font color isn't supposed to be "transparent", so replace
# this string with the default color
self.style_values[color_property] = color.Color(default_color)
def get_min_size(self):
"""
Calculate the widget's minimum width and height, and return them in a
tuple.
Container widgets may call this to find out how much space it needs
to reserve for each of its child widgets.
The base class only returns the space surrounding the widget's
contents: the sum of margin, border, and padding widths for each side.
Subclasses should call this method, then determine the element's
actual dimensions after taking min/max width and height into account.
"""
self.debug("WidgetInstance.get_min_size()")
# create a surface such that 1% is a minimum of 1 pixel
dummy_surface = DummySurface(0, 0, 100, 100)
min_width = 1
min_height = 1
style_hash = self.get_widget_instance_style_hash()
style_info = WidgetStyle(self.game_engine.global_style_settings.get_style(**style_hash))
self.get_widget_settings(style_info)
self.get_outer_setting_values(dummy_surface)
min_outer_dims = self.calculate_outer_dimensions()
if min_outer_dims[0] > min_width:
min_width = min_outer_dims[0]
if min_outer_dims[1] > min_height:
min_height = min_outer_dims[1]
return (min_width, min_height)
def calculate_top_outer_border_size(self):
top_size = self.style_values["margin-top"] + self.style_values["padding-top"]
border_top_height = 0
if (self.style_settings["border-top-style"] not in ("none", "hidden") and
(self.style_settings["border-top-color"] != "transparent")):
border_top_height = self.style_values["border-top-width"]
if self.style_settings["border-top-style"] == "double":
border_top_height = border_top_height * 2 + 1
top_size += border_top_height
return top_size
def calculate_left_outer_border_size(self):
left_size = self.style_values["margin-left"] + self.style_values["padding-left"]
border_left_width = 0
if (self.style_settings["border-left-style"] not in ("none", "hidden") and
(self.style_settings["border-left-color"] != "transparent")):
border_left_width = self.style_values["border-left-width"]
if self.style_settings["border-left-style"] == "double":
border_left_width = border_left_width * 2 + 1
left_size += border_left_width
return left_size
def draw_border_side(self, screen, side, element_dims, bwidth, bcolor, style):
draw_rect = pygame.Rect(0, 0, 0, 0)
# thick lines are _centered_ on the calculated coordinates, so shift
# them by 1/2 their width
thick_adj = 0
if bwidth > 1:
thick_adj = int(math.floor((bwidth-1) / 2))
if side == "top":
draw_rect.left = self.style_values["margin-left"]
draw_rect.top = self.style_values["margin-top"] + thick_adj
draw_rect.width = self.style_values["border-left-width"] + \
self.style_values["padding-left"] + element_dims[0] + \
self.style_values["padding-right"]
if draw_rect.width <= 1:
return
elif side == "bottom":
draw_rect.left = self.style_values["margin-left"]
draw_rect.top = (self.calculate_top_outer_border_size() + element_dims[1] +
self.style_values["padding-bottom"] + thick_adj)
draw_rect.width = self.style_values["border-left-width"] + \
self.style_values["padding-left"] + element_dims[0] + \
self.style_values["padding-right"]
if draw_rect.width <= 1:
return
elif side == "left":
draw_rect.left = self.style_values["margin-left"] + thick_adj
draw_rect.top = self.style_values["margin-top"]
draw_rect.height = self.style_values["border-top-width"] + \
self.style_values["padding-top"] + element_dims[1] + \
self.style_values["padding-bottom"]
if draw_rect.height <= 1:
return
elif side == "right":
draw_rect.left = (self.calculate_left_outer_border_size() + element_dims[0] +
self.style_values["padding-right"] + thick_adj)
draw_rect.top = self.style_values["margin-top"]
draw_rect.height = self.style_values["border-top-width"] + \
self.style_values["padding-top"] + element_dims[1] + \
self.style_values["padding-bottom"]
if draw_rect.height <= 1:
return
start_coord = coord.Coordinate(draw_rect.left, draw_rect.top)
end_coord = coord.Coordinate(draw_rect.right, draw_rect.bottom)
# self.debug("Draw {} border from {} to {}, width {}, color {}, style {}".format(
# side, start_coord, end_coord, width, color, style))
drawing.draw_line(screen, start_coord, end_coord, bwidth, bcolor, style)
def draw_border(self, screen, outer_dims):
self.debug("WidgetInstance.draw_border(screen={}, outer_dims={})".
format(screen, outer_dims))
element_dims = self.get_element_dimensions()
for side in ("top", "right", "bottom", "left"):
border_width = self.style_values["border-{}-width".format(side)]
border_style = self.style_settings["border-{}-style".format(side)]
border_color_style = self.style_settings["border-{}-color".format(side)]
border_color = "transparent"
if border_color_style != "transparent":
border_color = self.style_values["border-{}-color".format(side)]
else:
continue
if border_style in ("none", "hidden") or (border_width < 1) or \
(border_color == "transparent"):
continue
self.draw_border_side(screen, side, element_dims, border_width, border_color,
border_style)
def draw(self, screen):
"""
Draw the widget instance to a surface using css properties.
Always recalculate the settings, in case the style has been updated,
or an attribute has changed that may affect the style.
:param screen: A pygame surface upon which to draw the widget
:type screen: :py:class:`pygame.Surface`
"""
self.debug("{} inst {}: WidgetInstance.draw(screen={})".
format(self.kind.name, self.inst_id, screen))
if not self.visible:
return
style_hash = self.get_widget_instance_style_hash()
# self.debug("Find style {} in {} ..".format(style_hash, style_info))
style_info = WidgetStyle(self.game_engine.global_style_settings.get_style(**style_hash))
self.get_widget_settings(style_info)
# self.debug("Style settings: {}".format(self.style_settings))
self.get_outer_setting_values(screen)
outer_dims = self.calculate_outer_dimensions()
max_inner_dims = (screen.get_width() - outer_dims[0], screen.get_height() - outer_dims[1])
self.get_inner_setting_values(max_inner_dims)
self.get_color_values()
# self.debug("Style values: {}".format(self.style_values))
self.draw_border(screen, outer_dims)
def get_widget_instance_style_hash(self):
"""
Collect widget instance style information for comparison with
stylesheet settings.
Subclasses should start here and add attribute matches (e.g. a
checkbutton could match on "checked" attribute "on" or "off")
"""
self.debug("WidgetInstance.get_widget_instance_style_hash()")
props = {
"element_type": self.kind.name,
"element_id": self.widget_id,
}
if len(self.widget_class) > 0:
props["element_class"] = self.widget_class
if self.hover:
props["pseudo_class"] = "hover"
return props
class LabelWidgetInstance(WidgetInstance):
# WIDGET_INSTANCE_SUBCLASS_SYMBOLS = {
# "label": "",
# "font": "",
# "font_size": "initial",
# "font_style": "initial",
# "font_weight": "initial",
# "text_decoration": "initial",
# "text_transform": "initial",
# "text_align": "initial",
# "vertical_align": "initial",
# "color": "black"
# }
FONT_SIZE_CATEGORIES = {
"small": 10,
"medium": 14,
"large": 20
}
def __init__(self, kind, screen, screen_dims, id_, settings=None, **kwargs):
super(LabelWidgetInstance, self).__init__(kind, screen, screen_dims, id_, settings,
**kwargs)
self.font_resource = None
self.get_font_resource()
self._font_point_size = 12
@property
def label(self):
return self.symbols["label"]
@label.setter
def label(self, new_label):
try:
self.symbols["label"] = str(new_label)
except ValueError:
pass
@property
def font(self):
return self.symbols["font"]
@font.setter
def font(self, new_font):
try:
self.symbols["font"] = str(new_font)
self.get_font_resource()
except ValueError:
pass
@property
def font_size(self):
return self.symbols["font_size"]
@font_size.setter
def font_size(self, new_size):
self.symbols["font_size"] = new_size
def get_font_resource(self):
self.debug("LabelWidgetInstance.get_font_resource()")
if (len(self.font) == 0) or (self.font not in
self.kind.game_engine.resources['fonts'].keys()):
# revert to a system font, if found
if hasattr(self.kind.game_engine, 'system_font'):
self.font_resource = self.kind.game_engine.system_font
else:
return None
else:
self.font_resource = self.kind.game_engine.resources['fonts'][self.font]
def calc_label_size(self):
self.debug("LabelWidgetInstance.calc_label_size()")
if self.font_resource is None:
return (0, 0)
if len(self.label) == 0:
return (0, 0)
font_rndr = self.font_resource.get_font_renderer()
return font_rndr.calc_render_size(self.label)
def get_min_size(self):
self.debug("LabelWidgetInstance.get_min_size()")
total_width, total_height = super(LabelWidgetInstance, self).get_min_size()
text_width, text_height = self.calc_label_size()
total_width += text_width
total_height += text_height
return (total_width, total_height)
def get_element_dimensions(self):
self.debug("LabelWidgetInstance.get_element_dimensions()")
element_width = self.style_values["min-width"]
element_height = self.style_values["min-height"]
label_width, label_height = self.calc_label_size()
if self.style_settings["width"] != "auto":
element_width = self.style_values["width"]
elif label_width > element_width:
element_width = label_width
if self.style_settings["height"] != "auto":
element_height = self.style_values["height"]
elif label_height > element_height:
element_height = label_height
return (element_width, element_height)
def draw_text(self, surface):
self.debug("LabelWidgetInstance.draw_text(surface={})".format(surface))
surf_width = surface.get_width()
surf_height = surface.get_height()
font_rndr = self.font_resource.get_font_renderer()
# apply horizontal, vertical alignment
text_width, text_height = font_rndr.calc_render_size(self.label)
top_left = coord.Coordinate(0, 0)
if surf_width > text_width:
if self.style_settings["text-align"] == "center":
top_left.x = (surf_width / 2) - (text_width / 2)
elif self.style_settings["text-align"] == "right":
top_left.x = surf_width - text_width
if surf_height > text_height:
if self.style_settings["vertical-align"] == "middle":
top_left.y = (surf_height / 2) - (text_height / 2)
elif self.style_settings["vertical-align"] == "bottom":
top_left.y = surf_height - text_height
font_rndr.render_text(surface, top_left, self.label, self.style_values["color"])
def draw(self, screen):
self.debug("LabelWidgetInstance.draw(screen={})".format(screen))
# draw any visible borders
super(LabelWidgetInstance, self).draw(screen)
# create a subsurface big enough to hold the element dimensions
label_width, label_height = self.calc_label_size()
if (label_width > 0) and (label_height > 0):
subsurf_width, subsurf_height = self.get_element_dimensions()
subsurf_left = super(LabelWidgetInstance, self).calculate_left_outer_border_size()
subsurf_top = super(LabelWidgetInstance, self).calculate_top_outer_border_size()
subsurf_rect = pygame.Rect(subsurf_left, subsurf_top, subsurf_width, subsurf_height)
subsurf = screen.subsurface(subsurf_rect)
self.draw_text(subsurf)
class WidgetObjectTypeInvalid(Exception):
pass
class WidgetObjectType(object_type.ObjectType):
DEFAULT_VISIBLE = False
# subclasses set this to their own instance type
WIDGET_INSTANCE_TYPE = WidgetInstance
# subclasses can add their own YAML properties by setting this class
# variable to a list of tuples [(entry_name, entry_type), ..] where
# entry_type is usually a standard type: str, int, or bool
WIDGET_SUBCLASS_KW_ENTRIES = []
@classmethod
def gen_kwargs_from_yaml_obj(cls, obj_name, obj_yaml, game_engine):
kwargs = super(WidgetObjectType, cls).gen_kwargs_from_yaml_obj(obj_name, obj_yaml,
game_engine)
kwargs.update({
"visible": WidgetObjectType.DEFAULT_VISIBLE,
})
if "visible" in obj_yaml.keys():
kwargs["visible"] = (obj_yaml["visible"] is True)
for kw_entry, entry_type in cls.WIDGET_SUBCLASS_KW_ENTRIES:
if kw_entry in obj_yaml.keys():
if isinstance(entry_type, bool):
kwargs[kw_entry] = (obj_yaml[kw_entry] is True)
else:
# set the kwarg if the type conversion succeeds
try:
kwargs[kw_entry] = entry_type(obj_yaml[kw_entry])
except ValueError:
pass
return kwargs
def __init__(self, widget_name, game_engine, **kwargs):
super(WidgetObjectType, self).__init__(widget_name, game_engine, **kwargs)
#: Flag whether this widget type is a container for other widgets
self.is_container = False
self.visible = self.DEFAULT_VISIBLE
# default draw action sequence draws the object's sprite
self["draw"] = action_sequence.ActionSequence()
self["draw"].append_action(action.DrawAction("draw_self"))
if kwargs and "visible" in kwargs:
self.visible = kwargs["visible"]
def make_new_instance(self, screen, instance_properties=None):
"""
Generate a new instance of the widget type in response to
:py:meth:`~pygame_maker.actors.object_type.ObjectType.create_instance`
:param screen: The surface the instance will be drawn upon. The
instance can use this surface's (often a sub-surface's) width and
height parameters to determine child widget placement
:type screen: :py:class:`pygame.Surface`
:param instance_properties: A hash of settings to be applied. See
kwargs entry in
:py:meth:`~pygame_maker.actors.simple_object_instance.SimpleObjectInstance.__init__`
:type instance_properties: dict
"""
self.debug("WidgetObjectType.make_new_instance(screen={}, instance_properties={})".
format(screen, instance_properties))
screen_dims = (screen.get_width(), screen.get_height())
new_instance = self.WIDGET_INSTANCE_TYPE(self, screen, screen_dims, self._id,
instance_properties)
self.instance_list.append(new_instance)
def update(self):
"""
Update all instances of this widget type.
"""
pass
def draw(self, in_event):
"""Draw all visible instances."""
self.debug("WidgetObjectType.draw(in_event={})".format(in_event))
if len(self.instance_list) > 0:
for inst in self.instance_list:
# self.debug("Check inst {}".format(inst))
if inst.parent is not None:
continue
if inst.visible:
# self.debug("Draw visible inst {}".format(inst))
inst.draw(inst.screen)
class LabelWidgetObjectType(WidgetObjectType):
WIDGET_INSTANCE_TYPE = LabelWidgetInstance
|
from __future__ import absolute_import
# Implementation of RAKE - Rapid Automtic Keyword Exraction algorithm
# as described in:
# Rose, S., D. Engel, N. Cramer, and W. Cowley (2010).
# Automatic keyword extraction from indi-vidual documents.
# In M. W. Berry and J. Kogan (Eds.), Text Mining: Applications and Theory.unknown: John Wiley and Sons, Ltd.
import re
import operator
def is_number(s):
try:
float(s) if '.' in s else int(s)
return True
except ValueError:
return False
def SmartStopList():
from .stoplists import SmartStopList
return SmartStopList.words()
def FoxStopList():
from .stoplists import FoxStopList
return FoxStopList.words()
def MySQLStopList():
from .stoplists import MySQLStopList
return MySQLStopList.words()
def NLTKStopList():
from .stoplists import NLTKStopList
return NLTKStopList.words()
def load_stop_words(stop_word_file, divide, delimiter):
"""
Utility function to load stop words from a file and return as a list of words
@param stop_word_file Path and file name of a file containing stop words.
@return list A list of stop words.
"""
stop_words = []
if divide:
for line in open(stop_word_file):
for word in #magic re.split code
if word != '' or ' ':
stop_words.append(word)
else:
for line in open(stop_word_file):
stop_words.append(line)
return stop_words
def load_stop_words_delimiter(stop_word_file, delimiter):
"""
Utility function to load stop words from a file and return as a list of words
@param stop_word_file Path and file name of a file containing stop words.
@return list A list of stop words.
"""
stop_words = []
for line in open(stop_word_file):
for word in line.split(delimiter): # in case more than one per line
#handles .csvs or a comma seperated list, or equivalent.
#'asd,'.split(',') returns ['asd', ''] but 'asd '.split() returns 'asd'
if word !='':
stop_words.append(word)
return stop_words
def separate_words(text, min_word_return_size):
"""
Utility function to return a list of all words that are have a length greater than a specified number of characters.
@param text The text that must be split in to words.
@param min_word_return_size The minimum no of characters a word must have to be included.
"""
splitter = re.compile('[^a-zA-Z0-9_\\+\\-/]')
words = []
for single_word in splitter.split(text):
current_word = single_word.strip().lower()
# leave numbers in phrase, but don't count as words, since they tend to invalidate scores of their phrases
if len(current_word) > min_word_return_size and current_word != '' and not is_number(current_word):
words.append(current_word)
return words
def split_sentences(text):
"""
Utility function to return a list of sentences.
@param text The text that must be split in to sentences.
"""
sentence_delimiters = re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s')
sentences = sentence_delimiters.split(text)
return sentences
def build_stop_word_regex(stop_word_list):
stop_word_regex_list = []
for word in stop_word_list:
word_regex = r'\b' + word + r'(?![\w-])'
stop_word_regex_list.append(word_regex)
stop_word_pattern = re.compile('|'.join(stop_word_regex_list), re.IGNORECASE)
return stop_word_pattern
def generate_candidate_keywords(sentence_list, stopword_pattern):
phrase_list = []
for s in sentence_list:
tmp = re.sub(stopword_pattern, '|', s.strip())
phrases = tmp.split("|")
for phrase in phrases:
phrase = phrase.strip().lower()
if phrase != "":
phrase_list.append(phrase)
return phrase_list
def calculate_word_scores(phraseList):
word_frequency = {}
word_degree = {}
for phrase in phraseList:
word_list = separate_words(phrase, 0)
word_list_length = len(word_list)
word_list_degree = word_list_length - 1
for word in word_list:
word_frequency.setdefault(word, 0)
word_frequency[word] += 1
word_degree.setdefault(word, 0)
word_degree[word] += word_list_degree
for item in word_frequency:
word_degree[item] = word_degree[item] + word_frequency[item]
# Calculate Word scores = deg(w)/frew(w)
word_score = {}
for item in word_frequency:
word_score.setdefault(item, 0)
word_score[item] = word_degree[item] / (word_frequency[item] * 1.0)
return word_score
def generate_candidate_keyword_scores(phrase_list, word_score):
keyword_candidates = {}
for phrase in phrase_list:
keyword_candidates.setdefault(phrase, 0)
word_list = separate_words(phrase, 0)
candidate_score = 0
for word in word_list:
candidate_score += word_score[word]
keyword_candidates[phrase] = candidate_score
return keyword_candidates
class Rake(object):
def __init__(self, stop_words, divide = True, delimiter = ' '):
#lets users call predefined stopwords easily in a platform agnostic manner or use their own list
if isinstance(stop_words, list):
self.__stop_words_pattern = build_stop_word_regex(stop_words)
else:
if divide:
self.__stop_words_pattern = build_stop_word_regex(load_stop_words(stop_words,divide,delimiter))
"""delimiter != ' ' and divide == False:
#send error and stop here
else:
if divide == True:
else:
self.__stop_words_pattern = build_stop_word_regex(load_stop_words_delimiter(stop_words,delimiter))"""
def run(self, text):
sentence_list = split_sentences(text)
phrase_list = generate_candidate_keywords(sentence_list, self.__stop_words_pattern)
word_scores = calculate_word_scores(phrase_list)
keyword_candidates = generate_candidate_keyword_scores(phrase_list, word_scores)
sorted_keywords = sorted(keyword_candidates.items(), key=operator.itemgetter(1), reverse=True)
return sorted_keywords
goes full regex
from __future__ import absolute_import
# Implementation of RAKE - Rapid Automtic Keyword Exraction algorithm
# as described in:
# Rose, S., D. Engel, N. Cramer, and W. Cowley (2010).
# Automatic keyword extraction from indi-vidual documents.
# In M. W. Berry and J. Kogan (Eds.), Text Mining: Applications and Theory.unknown: John Wiley and Sons, Ltd.
import re
import operator
def is_number(s):
try:
float(s) if '.' in s else int(s)
return True
except ValueError:
return False
def SmartStopList():
from .stoplists import SmartStopList
return SmartStopList.words()
def FoxStopList():
from .stoplists import FoxStopList
return FoxStopList.words()
def MySQLStopList():
from .stoplists import MySQLStopList
return MySQLStopList.words()
def NLTKStopList():
from .stoplists import NLTKStopList
return NLTKStopList.words()
def load_stop_words(stop_word_file, divide, delimiter):
"""
Utility function to load stop words from a file and return as a list of words
@param stop_word_file Path and file name of a file containing stop words.
@return list A list of stop words.
"""
stop_words = []
if divide:
for line in open(stop_word_file):
for word in re.split(delimiter, stopword_file):
if word != '' or ' ':
stop_words.append(word)
else:
for line in open(stop_word_file):
if line != '' or ' ': #I figure this is going to make someones life much easier
stop_words.append(line)
return stop_words
def separate_words(text, min_word_return_size):
"""
Utility function to return a list of all words that are have a length greater than a specified number of characters.
@param text The text that must be split in to words.
@param min_word_return_size The minimum no of characters a word must have to be included.
"""
splitter = re.compile('[^a-zA-Z0-9_\\+\\-/]')
words = []
for single_word in splitter.split(text):
current_word = single_word.strip().lower()
# leave numbers in phrase, but don't count as words, since they tend to invalidate scores of their phrases
if len(current_word) > min_word_return_size and current_word != '' and not is_number(current_word):
words.append(current_word)
return words
def split_sentences(text):
"""
Utility function to return a list of sentences.
@param text The text that must be split in to sentences.
"""
sentence_delimiters = re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s')
sentences = sentence_delimiters.split(text)
return sentences
def build_stop_word_regex(stop_word_list):
stop_word_regex_list = []
for word in stop_word_list:
word_regex = r'\b' + word + r'(?![\w-])'
stop_word_regex_list.append(word_regex)
stop_word_pattern = re.compile('|'.join(stop_word_regex_list), re.IGNORECASE)
return stop_word_pattern
def generate_candidate_keywords(sentence_list, stopword_pattern):
phrase_list = []
for s in sentence_list:
tmp = re.sub(stopword_pattern, '|', s.strip())
phrases = tmp.split("|")
for phrase in phrases:
phrase = phrase.strip().lower()
if phrase != "":
phrase_list.append(phrase)
return phrase_list
def calculate_word_scores(phraseList):
word_frequency = {}
word_degree = {}
for phrase in phraseList:
word_list = separate_words(phrase, 0)
word_list_length = len(word_list)
word_list_degree = word_list_length - 1
for word in word_list:
word_frequency.setdefault(word, 0)
word_frequency[word] += 1
word_degree.setdefault(word, 0)
word_degree[word] += word_list_degree
for item in word_frequency:
word_degree[item] = word_degree[item] + word_frequency[item]
# Calculate Word scores = deg(w)/frew(w)
word_score = {}
for item in word_frequency:
word_score.setdefault(item, 0)
word_score[item] = word_degree[item] / (word_frequency[item] * 1.0)
return word_score
def generate_candidate_keyword_scores(phrase_list, word_score):
keyword_candidates = {}
for phrase in phrase_list:
keyword_candidates.setdefault(phrase, 0)
word_list = separate_words(phrase, 0)
candidate_score = 0
for word in word_list:
candidate_score += word_score[word]
keyword_candidates[phrase] = candidate_score
return keyword_candidates
class Rake(object):
def __init__(self, stop_words, divide = True, delimiter = '\W+'):
#lets users call predefined stopwords easily in a platform agnostic manner or use their own list
if isinstance(stop_words, list):
self.__stop_words_pattern = build_stop_word_regex(stop_words)
else:
self.__stop_words_pattern = build_stop_word_regex(load_stop_words(stop_words,divide,delimiter))
"""delimiter != ' ' and divide == False:
#send error and stop here
else:
if divide == True:
else:
self.__stop_words_pattern = build_stop_word_regex(load_stop_words_delimiter(stop_words,delimiter))"""
def run(self, text):
sentence_list = split_sentences(text)
phrase_list = generate_candidate_keywords(sentence_list, self.__stop_words_pattern)
word_scores = calculate_word_scores(phrase_list)
keyword_candidates = generate_candidate_keyword_scores(phrase_list, word_scores)
sorted_keywords = sorted(keyword_candidates.items(), key=operator.itemgetter(1), reverse=True)
return sorted_keywords
|
import time
timeStamp = int(time.time())
timeArray = time.localtime(timeStamp)
otherStyleTime = time.strftime("%Y/%m/%d %H:%M:%S", timeArray)
txt = open('Diary.txt', 'r+')
txt = open('Diary.txt', 'a+')
print "Here's your diary:"
print txt.read()
print "How are you today?"
content = raw_input(">")
txt.write(otherStyleTime)
txt.write("\n")
txt.write(content)
txt.write("\n")
txt.close
update diary in Mac
import time
timeStamp = int(time.time())
timeArray = time.localtime(timeStamp)
otherStyleTime = time.strftime("%Y/%m/%d %H:%M:%S", timeArray)
txt = open('Diary.txt', 'a+')
txt = open('Diary.txt', 'r+')
print "Here's your diary:"
print txt.read()
txt.close
print "How are you today?"
content = raw_input(">")
txt.write(otherStyleTime)
txt.write("\n")
txt.write(content)
txt.write("\n")
txt.close
|
# Copyright Vertex.AI.
from __future__ import print_function
import hashlib
import os
import platform
import shutil
import sys
from subprocess import call, check_call
MAIN = "__BZL_MAIN__"
REQUIREMENTS = ["__BZL_REQUIREMENTS__"]
VENV_ARGS = __BZL_VENV_ARGS__
def _find_in_runfiles(logical_name):
key = logical_name
if key.startswith('external/'):
key = key[len('external/'):]
try:
return _find_in_runfiles.manifest.get(key, logical_name)
except AttributeError:
_find_in_runfiles.manifest = {}
if 'RUNFILES_DIR' in os.environ:
manifest_filename = os.path.join(os.environ['RUNFILES_DIR'], 'MANIFEST')
if os.path.exists(manifest_filename):
with open(manifest_filename) as manifest:
for line in manifest:
(logical, physical) = line.split(' ', 2)
_find_in_runfiles.manifest[logical] = physical.strip()
return _find_in_runfiles.manifest.get(key, logical_name)
class VirtualEnv(object):
def __init__(self, requirements):
self._requirements = requirements
hasher = hashlib.md5()
for arg in VENV_ARGS:
hasher.update(arg)
for requirement in requirements:
with open(_find_in_runfiles(requirement)) as file_:
hasher.update(file_.read())
self._path = os.path.join(os.path.expanduser('~'), '.t2', 'venv', hasher.hexdigest())
if platform.system() == 'Windows':
self._venv_bin = os.path.join(self._path, 'Scripts')
else:
self._venv_bin = os.path.join(self._path, 'bin')
self._pip = os.path.join(self._venv_bin, 'pip')
self.python = os.path.join(self._venv_bin, 'python')
def make(self):
try:
if not os.path.exists(self._path):
if platform.system() == 'Windows':
vpython = []
else:
vpython = ['-p', 'python2']
check_call(['virtualenv'] + vpython + VENV_ARGS + [self._path])
for requirement in self._requirements:
check_call(
[self.python, self._pip, 'install', '-r',
_find_in_runfiles(requirement)])
except:
if os.path.exists(self._path):
shutil.rmtree(self._path)
raise
env = dict(os.environ)
env['VIRTUAL_ENV'] = self._path
env['PATH'] = os.pathsep.join([self._venv_bin, os.getenv('PATH', "")])
if platform.system() == 'Windows':
env['PATHEXT'] = '.EXE'
return env
def main():
venv = VirtualEnv(REQUIREMENTS)
env = venv.make()
args = [venv.python, MAIN] + sys.argv[1:]
args[1:] = [_find_in_runfiles(arg) for arg in args[1:]]
print('Running in venv: {}'.format(venv._path))
sys.exit(call(args, env=env))
if __name__ == '__main__':
main()
Use pip directly; use RUNFILES_MANIGEST_FILE (#129)
# Copyright Vertex.AI.
from __future__ import print_function
import hashlib
import os
import platform
import shutil
import sys
from subprocess import call, check_call
MAIN = "__BZL_MAIN__"
REQUIREMENTS = ["__BZL_REQUIREMENTS__"]
VENV_ARGS = __BZL_VENV_ARGS__
def _find_in_runfiles(logical_name):
key = logical_name
if key.startswith('external/'):
key = key[len('external/'):]
try:
return _find_in_runfiles.manifest.get(key, logical_name)
except AttributeError:
_find_in_runfiles.manifest = {}
manifest_filename = None
if 'RUNFILES_MANIFEST_FILE' in os.environ:
manifest_filename = os.environ['RUNFILES_MANIFEST_FILE']
elif 'RUNFILES_DIR' in os.environ:
manifest_filename = os.path.join(os.environ['RUNFILES_DIR'], 'MANIFEST')
if manifest_filename and os.path.exists(manifest_filename):
with open(manifest_filename) as manifest:
for line in manifest:
(logical, physical) = line.split(' ', 2)
_find_in_runfiles.manifest[logical] = physical.strip()
return _find_in_runfiles.manifest.get(key, logical_name)
class VirtualEnv(object):
def __init__(self, requirements):
self._requirements = requirements
hasher = hashlib.md5()
for arg in VENV_ARGS:
hasher.update(arg)
for requirement in requirements:
with open(_find_in_runfiles(requirement)) as file_:
hasher.update(file_.read())
self._path = os.path.join(os.path.expanduser('~'), '.t2', 'venv', hasher.hexdigest())
if platform.system() == 'Windows':
self._venv_bin = os.path.join(self._path, 'Scripts')
else:
self._venv_bin = os.path.join(self._path, 'bin')
self._pip = os.path.join(self._venv_bin, 'pip')
self.python = os.path.join(self._venv_bin, 'python')
def make(self):
try:
if not os.path.exists(self._path):
if platform.system() == 'Windows':
vpython = []
else:
vpython = ['-p', 'python2']
check_call(['virtualenv'] + vpython + VENV_ARGS + [self._path])
for requirement in self._requirements:
check_call(
[self._pip, 'install', '-r',
_find_in_runfiles(requirement)])
except:
if os.path.exists(self._path):
shutil.rmtree(self._path)
raise
env = dict(os.environ)
env['VIRTUAL_ENV'] = self._path
env['PATH'] = os.pathsep.join([self._venv_bin, os.getenv('PATH', "")])
if platform.system() == 'Windows':
env['PATHEXT'] = '.EXE'
return env
def main():
venv = VirtualEnv(REQUIREMENTS)
env = venv.make()
args = [venv.python, MAIN] + sys.argv[1:]
args[1:] = [_find_in_runfiles(arg) for arg in args[1:]]
print('Running in venv: {}'.format(venv._path))
sys.exit(call(args, env=env))
if __name__ == '__main__':
main()
|
# built-ins
import itertools as it
import sys
import argparse
import random
import logging
import json
import collections
from copy import deepcopy
# libraries
from numpy import (array, mean, zeros, zeros_like, where, unique,
newaxis, nonzero, median, float, ones, arange, inf, isnan,
flatnonzero, unravel_index, bincount)
import numpy as np
from scipy.stats import sem
from scipy import sparse
from scipy.sparse import lil_matrix
from scipy.misc import comb as nchoosek
from scipy.ndimage.measurements import label
from scipy import ndimage as ndi
import networkx as nx
from networkx import Graph, biconnected_components
from networkx.algorithms.traversal.depth_first_search import dfs_preorder_nodes
from skimage.segmentation import relabel_sequential
from viridis import tree
# local modules
from . import morpho
from . import sparselol as lol
from . import iterprogress as ip
from . import optimized as opt
from .ncut import ncutW
from .mergequeue import MergeQueue
from .evaluate import merge_contingency_table, split_vi, xlogx
from . import evaluate as ev
from . import features
from . import classify
from .classify import get_classifier, \
unique_learning_data_elements, concatenate_data_elements
from .dtypes import label_dtype
arguments = argparse.ArgumentParser(add_help=False)
arggroup = arguments.add_argument_group('Agglomeration options')
arggroup.add_argument('-t', '--thresholds', nargs='+', default=[128],
type=float, metavar='FLOAT',
help='''The agglomeration thresholds. One output file will be written
for each threshold.'''
)
arggroup.add_argument('-l', '--ladder', type=int, metavar='SIZE',
help='Merge any bodies smaller than SIZE.'
)
arggroup.add_argument('-p', '--pre-ladder', action='store_true', default=True,
help='Run ladder before normal agglomeration (default).'
)
arggroup.add_argument('-L', '--post-ladder',
action='store_false', dest='pre_ladder',
help='Run ladder after normal agglomeration instead of before (SLOW).'
)
arggroup.add_argument('-s', '--strict-ladder', type=int, metavar='INT',
default=1,
help='''Specify the strictness of the ladder agglomeration. Level 1
(default): merge anything smaller than the ladder threshold as
long as it's not on the volume border. Level 2: only merge smaller
bodies to larger ones. Level 3: only merge when the border is
larger than or equal to 2 pixels.'''
)
arggroup.add_argument('-M', '--low-memory', action='store_true',
help='''Use less memory at a slight speed cost. Note that the phrase
'low memory' is relative.'''
)
arggroup.add_argument('--disallow-shared-boundaries', action='store_false',
dest='allow_shared_boundaries',
help='''Watershed pixels that are shared between more than 2 labels are
not counted as edges.'''
)
arggroup.add_argument('--allow-shared-boundaries', action='store_true',
default=True,
help='''Count every watershed pixel in every edge in which it participates
(default: True).'''
)
def conditional_countdown(seq, start=1, pred=bool):
"""Count down from 'start' each time pred(elem) is true for elem in seq.
Used to know how many elements of a sequence remain that satisfy a
predicate.
Parameters
----------
seq : iterable
Any sequence.
start : int, optional
The starting element.
pred : function, type(next(seq)) -> bool
A predicate acting on the elements of `seq`.
Examples
--------
>>> seq = range(10)
>>> cc = conditional_countdown(seq, start=5, pred=lambda x: x % 2 == 1)
>>> next(cc)
5
>>> next(cc)
4
>>> next(cc)
4
>>> next(cc)
3
"""
remaining = start
for elem in seq:
if pred(elem):
remaining -= 1
yield remaining
############################
# Merge priority functions #
############################
def oriented_boundary_mean(g, n1, n2):
return mean(g.oriented_probabilities_r[g[n1][n2]['boundary']])
def boundary_mean(g, n1, n2):
return mean(g.probabilities_r[g[n1][n2]['boundary']])
def boundary_median(g, n1, n2):
return median(g.probabilities_r[g[n1][n2]['boundary']])
def approximate_boundary_mean(g, n1, n2):
"""Return the boundary mean as computed by a MomentsFeatureManager.
The feature manager is assumed to have been set up for g at construction.
"""
return g.feature_manager.compute_edge_features(g, n1, n2)[1]
def make_ladder(priority_function, threshold, strictness=1):
def ladder_function(g, n1, n2):
s1 = g.node[n1]['size']
s2 = g.node[n2]['size']
ladder_condition = \
(s1 < threshold and not g.at_volume_boundary(n1)) or \
(s2 < threshold and not g.at_volume_boundary(n2))
if strictness >= 2:
ladder_condition &= ((s1 < threshold) != (s2 < threshold))
if strictness >= 3:
ladder_condition &= len(g[n1][n2]['boundary']) > 2
if ladder_condition:
return priority_function(g, n1, n2)
else:
return inf
return ladder_function
def no_mito_merge(priority_function):
def predict(g, n1, n2):
frozen = (n1 in g.frozen_nodes or
n2 in g.frozen_nodes or
(n1, n2) in g.frozen_edges)
if frozen:
return np.inf
else:
return priority_function(g, n1, n2)
return predict
def mito_merge():
def predict(g, n1, n2):
if n1 in g.frozen_nodes and n2 in g.frozen_nodes:
return np.inf
elif (n1, n2) in g.frozen_edges:
return np.inf
elif n1 not in g.frozen_nodes and n2 not in g.frozen_nodes:
return np.inf
else:
if n1 in g.frozen_nodes:
mito = n1
cyto = n2
else:
mito = n2
cyto = n1
if g.node[mito]['size'] > g.node[cyto]['size']:
return np.inf
else:
return 1.0 - (float(len(g[mito][cyto]['boundary']))/
sum([len(g[mito][x]['boundary']) for x in g.neighbors(mito)]))
return predict
def classifier_probability(feature_extractor, classifier):
def predict(g, n1, n2):
if n1 == g.boundary_body or n2 == g.boundary_body:
return inf
features = np.atleast_2d(feature_extractor(g, n1, n2))
try:
prediction = classifier.predict_proba(features)
prediction_arr = np.array(prediction, copy=False)
if prediction_arr.ndim > 2:
prediction_arr = prediction_arr[0]
try:
prediction = prediction_arr[0][1]
except (TypeError, IndexError):
prediction = prediction_arr[0]
except AttributeError:
prediction = classifier.predict(features)[0]
return prediction
return predict
def ordered_priority(edges):
d = {}
n = len(edges)
for i, (n1, n2) in enumerate(edges):
score = float(i)/n
d[(n1,n2)] = score
d[(n2,n1)] = score
def ord(g, n1, n2):
return d.get((n1,n2), inf)
return ord
def expected_change_vi(feature_extractor, classifier, alpha=1.0, beta=1.0):
prob_func = classifier_probability(feature_extractor, classifier)
def predict(g, n1, n2):
p = prob_func(g, n1, n2) # Prediction from the classifier
# Calculate change in VI if n1 and n2 should not be merged
v = compute_local_vi_change(
g.node[n1]['size'], g.node[n2]['size'], g.volume_size
)
# Return expected change
return (p*alpha*v + (1.0-p)*(-beta*v))
return predict
def compute_local_vi_change(s1, s2, n):
"""Compute change in VI if we merge disjoint sizes s1,s2 in a volume n."""
py1 = float(s1)/n
py2 = float(s2)/n
py = py1+py2
return -(py1*np.log2(py1) + py2*np.log2(py2) - py*np.log2(py))
def compute_true_delta_vi(ctable, n1, n2):
p1 = ctable[n1].sum()
p2 = ctable[n2].sum()
p3 = p1+p2
p1g_log_p1g = xlogx(ctable[n1]).sum()
p2g_log_p2g = xlogx(ctable[n2]).sum()
p3g_log_p3g = xlogx(ctable[n1]+ctable[n2]).sum()
return p3*np.log2(p3) - p1*np.log2(p1) - p2*np.log2(p2) - \
2*(p3g_log_p3g - p1g_log_p1g - p2g_log_p2g)
def expected_change_rand(feature_extractor, classifier, alpha=1.0, beta=1.0):
prob_func = classifier_probability(feature_extractor, classifier)
def predict(g, n1, n2):
p = float(prob_func(g, n1, n2)) # Prediction from the classifier
v = compute_local_rand_change(
g.node[n1]['size'], g.node[n2]['size'], g.volume_size
)
return p*v*alpha + (1.0-p)*(-beta*v)
return predict
def compute_local_rand_change(s1, s2, n):
"""Compute change in rand if we merge disjoint sizes s1,s2 in volume n."""
return float(s1*s2)/nchoosek(n,2)
def compute_true_delta_rand(ctable, n1, n2, n):
"""Compute change in RI obtained by merging rows n1 and n2.
This function assumes ctable is normalized to sum to 1.
"""
localct = n * ctable[(n1, n2), :]
total = localct.data.sum()
sqtotal = (localct.data ** 2).sum()
delta_sxy = 1. / 2 * ((np.array(localct.sum(axis=0)) ** 2).sum() -
sqtotal)
delta_sx = 1. / 2 * (total ** 2 -
(np.array(localct.sum(axis=1)) ** 2).sum())
return (2 * delta_sxy - delta_sx) / nchoosek(n, 2)
def boundary_mean_ladder(g, n1, n2, threshold, strictness=1):
f = make_ladder(boundary_mean, threshold, strictness)
return f(g, n1, n2)
def boundary_mean_plus_sem(g, n1, n2, alpha=-6):
bvals = g.probabilities_r[g[n1][n2]['boundary']]
return mean(bvals) + alpha*sem(bvals)
def random_priority(g, n1, n2):
if n1 == g.boundary_body or n2 == g.boundary_body:
return inf
return random.random()
class Rag(Graph):
"""Region adjacency graph for segmentation of nD volumes.
Parameters
----------
watershed : array of int, shape (M, N, ..., P)
The labeled regions of the image. Note: this is called
`watershed` for historical reasons, but could refer to a
superpixel map of any origin.
probabilities : array of float, shape (M, N, ..., P[, Q])
The probability of each pixel of belonging to a particular
class. Typically, this has the same shape as `watershed`
and represents the probability that the pixel is part of a
region boundary, but it can also have an additional
dimension for probabilities of belonging to other classes,
such as mitochondria (in biological images) or specific
textures (in natural images).
merge_priority_function : callable function, optional
This function must take exactly three arguments as input
(a Rag object and two node IDs) and return a single float.
feature_manager : ``features.base.Null`` object, optional
A feature manager object that controls feature computation
and feature caching.
mask : array of bool, shape (M, N, ..., P)
A mask of the same shape as `watershed`, `True` in the
positions to be processed when making a RAG, `False` in the
positions to ignore.
show_progress : bool, optional
Whether to display an ASCII progress bar during long-
-running graph operations.
connectivity : int in {1, ..., `watershed.ndim`}
When determining adjacency, allow neighbors along
`connectivity` dimensions.
channel_is_oriented : array-like of bool, shape (Q,), optional
For multi-channel images, some channels, for example some
edge detectors, have a specific orientation. In conjunction
with the `orientation_map` argument, specify which channels
have an orientation associated with them.
orientation_map : array-like of float, shape (Q,)
Specify the orientation of the corresponding channel. (2D
images only)
normalize_probabilities : bool, optional
Divide the input `probabilities` by their maximum to ensure
a range in [0, 1].
exclusions : array-like of int, shape (M, N, ..., P), optional
Volume of same shape as `watershed`. Mark points in the
volume with the same label (>0) to prevent them from being
merged during agglomeration. For example, if
`exclusions[45, 92] == exclusions[51, 105] == 1`, then
segments `watershed[45, 92]` and `watershed[51, 105]` will
never be merged, regardless of the merge priority function.
isfrozennode : function, optional
Function taking in a Rag object and a node id and returning
a bool. If the function returns ``True``, the node will not
be merged, regardless of the merge priority function.
isfrozenedge : function, optional
As `isfrozennode`, but the function should take the graph
and *two* nodes, to specify an edge that cannot be merged.
"""
def __init__(self, watershed=array([], label_dtype),
probabilities=array([]),
merge_priority_function=boundary_mean, gt_vol=None,
feature_manager=features.base.Null(), mask=None,
show_progress=False, connectivity=1,
channel_is_oriented=None, orientation_map=array([]),
normalize_probabilities=False, exclusions=array([]),
isfrozennode=None, isfrozenedge=None):
super(Rag, self).__init__(weighted=False)
self.show_progress = show_progress
self.connectivity = connectivity
self.pbar = (ip.StandardProgressBar() if self.show_progress
else ip.NoProgressBar())
self.set_watershed(watershed, connectivity)
self.set_probabilities(probabilities, normalize_probabilities)
self.set_orientations(orientation_map, channel_is_oriented)
self.merge_priority_function = merge_priority_function
self.max_merge_score = -inf
if mask is None:
self.mask = np.ones(self.watershed_r.shape, dtype=bool)
else:
self.mask = morpho.pad(mask, True).ravel()
self.build_graph_from_watershed()
self.set_feature_manager(feature_manager)
self.set_ground_truth(gt_vol)
self.set_exclusions(exclusions)
self.merge_queue = MergeQueue()
self.tree = tree.Ultrametric(self.nodes())
self.frozen_nodes = set()
if isfrozennode is not None:
for node in self.nodes():
if isfrozennode(self, node):
self.frozen_nodes.add(node)
self.frozen_edges = set()
if isfrozenedge is not None:
for n1, n2 in self.edges():
if isfrozenedge(self, n1, n2):
self.frozen_edges.add((n1,n2))
def __copy__(self):
"""Return a copy of the object and attributes.
"""
pr_shape = self.probabilities_r.shape
g = super(Rag, self).copy()
g.watershed_r = g.watershed.ravel()
g.probabilities_r = g.probabilities.reshape(pr_shape)
return g
def copy(self):
"""Return a copy of the object and attributes.
"""
return self.__copy__()
def extent(self, nodeid):
try:
ext = self.extents
full_ext = [ext.indices[ext.indptr[f]:ext.indptr[f+1]]
for f in self.node[nodeid]['fragments']]
return np.concatenate(full_ext).astype(np.intp)
except AttributeError:
extent_array = opt.flood_fill(self.watershed,
np.array(self.node[nodeid]['entrypoint']),
np.fromiter(self.node[nodeid]['fragments'],
dtype=int))
if len(extent_array) != self.node[nodeid]['size']:
sys.stderr.write('Flood fill fail - found %d voxels but size'
'expected %d\n' % (len(extent_array),
self.node[nodeid]['size']))
raveled_indices = np.ravel_multi_index(extent_array.T,
self.watershed.shape)
return set(raveled_indices)
def real_edges(self, *args, **kwargs):
"""Return edges internal to the volume.
The RAG actually includes edges to a "virtual" region that
envelops the entire volume. This function returns the list of
edges that are internal to the volume.
Parameters
----------
*args, **kwargs : arbitrary types
Arguments and keyword arguments are passed through to the
``edges()`` function of the ``networkx.Graph`` class.
Returns
-------
edge_list : list of tuples
A list of pairs of node IDs, which are typically integers.
See Also
--------
real_edges_iter, networkx.Graph.edges
"""
return [e for e in super(Rag, self).edges(*args, **kwargs) if
self.boundary_body not in e[:2]]
def real_edges_iter(self, *args, **kwargs):
"""Return iterator of edges internal to the volume.
The RAG actually includes edges to a "virtual" region that
envelops the entire volume. This function returns the list of
edges that are internal to the volume.
Parameters
----------
*args, **kwargs : arbitrary types
Arguments and keyword arguments are passed through to the
``edges()`` function of the ``networkx.Graph`` class.
Returns
-------
edges_iter : iterator of tuples
An iterator over pairs of node IDs, which are typically
integers.
"""
return (e for e in super(Rag, self).edges_iter(*args, **kwargs) if
self.boundary_body not in e[:2])
def build_graph_from_watershed(self, idxs=None):
"""Build the graph object from the region labels.
The region labels should have been set ahead of time using
``set_watershed()``.
Parameters
----------
idxs : array-like of int, optional
Linear indices into raveled volume array. If provided, the
graph is built only for these indices.
"""
if self.watershed.size == 0:
return # stop processing for empty graphs
if idxs is None:
idxs = arange(self.watershed.size, dtype=self.steps.dtype)
idxs = idxs[self.mask[idxs]] # use only masked idxs
self.add_node(self.boundary_body)
labels = np.unique(self.watershed_r[idxs])
sizes = np.bincount(self.watershed_r)
if not hasattr(self, 'extents'):
self.extents = lol.extents(self.watershed)
for nodeid in labels:
self.add_node(nodeid)
node = self.node[nodeid]
node['size'] = sizes[nodeid]
node['fragments'] = set([nodeid])
node['entrypoint'] = (
np.array(np.unravel_index(self.extent(nodeid)[0],
self.watershed.shape)))
inner_idxs = idxs[self.watershed_r[idxs] != self.boundary_body]
if self.show_progress:
inner_idxs = ip.with_progress(inner_idxs, title='Graph ',
pbar=self.pbar)
for idx in inner_idxs:
nodeid = self.watershed_r[idx]
ns = idx + self.steps
ns = ns[self.mask[ns]]
adj = self.watershed_r[ns]
adj = set(adj)
for v in adj:
if v == nodeid:
continue
if self.has_edge(nodeid, v):
self[nodeid][v]['boundary'].append(idx)
else:
self.add_edge(nodeid, v, boundary=[idx])
def set_feature_manager(self, feature_manager):
"""Set the feature manager and ensure feature caches are computed.
Parameters
----------
feature_manager : ``features.base.Null`` object
The feature manager to be used by this RAG.
Returns
-------
None
"""
self.feature_manager = feature_manager
self.compute_feature_caches()
def compute_feature_caches(self):
"""Use the feature manager to compute node and edge feature caches.
Parameters
----------
None
Returns
-------
None
"""
for n in ip.with_progress(
self.nodes(), title='Node caches ', pbar=self.pbar):
self.node[n]['feature-cache'] = \
self.feature_manager.create_node_cache(self, n)
for n1, n2 in ip.with_progress(
self.edges(), title='Edge caches ', pbar=self.pbar):
self[n1][n2]['feature-cache'] = \
self.feature_manager.create_edge_cache(self, n1, n2)
def set_probabilities(self, probs=array([]), normalize=False):
"""Set the `probabilities` attributes of the RAG.
For various reasons, including removing the need for bounds
checking when looking for neighboring pixels, the volume of
pixel-level probabilities is padded on all faces. In addition,
this function adds an attribute `probabilities_r`, a raveled
view of the padded probabilities array for quick access to
individual voxels using linear indices.
Parameters
----------
probs : array
The input probabilities array.
normalize : bool, optional
If ``True``, the values in the array are scaled to be in
[0, 1].
Returns
-------
None
"""
if len(probs) == 0:
self.probabilities = zeros_like(self.watershed)
self.probabilities_r = self.probabilities.ravel()
probs = probs.astype('float')
if normalize and len(probs) > 1:
probs -= probs.min() # ensure probs.min() == 0
probs /= probs.max() # ensure probs.max() == 1
sp = probs.shape
sw = tuple(array(self.watershed.shape, dtype=int)-\
2*self.pad_thickness*ones(self.watershed.ndim, dtype=int))
p_ndim = probs.ndim
w_ndim = self.watershed.ndim
padding = [inf]+(self.pad_thickness-1)*[0]
if p_ndim == w_ndim:
self.probabilities = morpho.pad(probs, padding)
self.probabilities_r = self.probabilities.ravel()[:,newaxis]
elif p_ndim == w_ndim+1:
axes = list(range(p_ndim-1))
self.probabilities = morpho.pad(probs, padding, axes)
self.probabilities_r = self.probabilities.reshape(
(self.watershed.size, -1))
def set_orientations(self, orientation_map, channel_is_oriented):
"""Set the orientation map of the probability image.
Parameters
----------
orientation_map : array of float
A map of angles of the same shape as the superpixel map.
channel_is_oriented : 1D array-like of bool
A vector having length the number of channels in the
probability map.
Returns
-------
None
"""
if len(orientation_map) == 0:
self.orientation_map = zeros_like(self.watershed)
self.orientation_map_r = self.orientation_map.ravel()
padding = [0]+(self.pad_thickness-1)*[0]
self.orientation_map = morpho.pad(orientation_map, padding).astype(int)
self.orientation_map_r = self.orientation_map.ravel()
if channel_is_oriented is None:
nchannels = 1 if self.probabilities.ndim==self.watershed.ndim \
else self.probabilities.shape[-1]
self.channel_is_oriented = array([False]*nchannels)
self.max_probabilities_r = zeros_like(self.probabilities_r)
self.oriented_probabilities_r = zeros_like(self.probabilities_r)
self.non_oriented_probabilities_r = self.probabilities_r
else:
self.channel_is_oriented = channel_is_oriented
self.max_probabilities_r = \
self.probabilities_r[:, self.channel_is_oriented].max(axis=1)
self.oriented_probabilities_r = \
self.probabilities_r[:, self.channel_is_oriented]
self.oriented_probabilities_r = \
self.oriented_probabilities_r[
list(range(len(self.oriented_probabilities_r))),
self.orientation_map_r]
self.non_oriented_probabilities_r = \
self.probabilities_r[:, ~self.channel_is_oriented]
def set_watershed(self, ws=array([], label_dtype), connectivity=1):
"""Set the initial segmentation volume (watershed).
The initial segmentation is called `watershed` for historical
reasons only.
Parameters
----------
ws : array of int
The initial segmentation.
connectivity : int in {1, ..., `ws.ndim`}, optional
The pixel neighborhood.
Returns
-------
None
"""
ws = ws.astype(label_dtype)
try:
self.boundary_body = np.max(ws) + 1
except ValueError: # empty watershed given
self.boundary_body = 1
self.volume_size = ws.size
if ws.size > 0:
ws, _, inv = relabel_sequential(ws)
self.inverse_watershed_map = inv # translates to original labels
self.watershed = morpho.pad(ws, self.boundary_body)
self.watershed_r = self.watershed.ravel()
self.pad_thickness = 1
self.steps = morpho.raveled_steps_to_neighbors(self.watershed.shape,
connectivity)
def set_ground_truth(self, gt=None):
"""Set the ground truth volume.
This is useful for tracking segmentation accuracy over time.
Parameters
----------
gt : array of int
A ground truth segmentation of the same volume passed to
``set_watershed``.
Returns
-------
None
"""
if gt is not None:
gtm = gt.max()+1
gt_ignore = [0, gtm] if (gt==0).any() else [gtm]
seg_ignore = [0, self.boundary_body] if \
(self.watershed==0).any() else [self.boundary_body]
self.gt = morpho.pad(gt, gtm)
self.rig = merge_contingency_table(self.watershed, self.gt,
ignore_seg=seg_ignore,
ignore_gt=gt_ignore)
else:
self.gt = None
# null pattern to transparently allow merging of nodes.
# Bonus feature: counts how many sp's went into a single node.
try:
self.rig = ones(2 * self.watershed.max() + 1)
except ValueError:
self.rig = ones(2 * self.number_of_nodes() + 1)
def set_exclusions(self, excl):
"""Set an exclusion volume, forbidding certain merges.
Parameters
----------
excl : array of int
Exclusions work as follows: the volume `excl` is the same
shape as the initial segmentation (see ``set_watershed``),
and consists of mostly 0s. Any voxels with *the same*
non-zero label will not be allowed to merge during
agglomeration (provided they were not merged in the initial
segmentation).
This allows manual separation *a priori* of difficult-to-
-segment regions.
Returns
-------
None
"""
if excl.size != 0:
excl = morpho.pad(excl, [0]*self.pad_thickness)
for n in self.nodes():
if excl.size != 0:
eids = unique(excl.ravel()[self.extent(n)])
eids = eids[flatnonzero(eids)]
self.node[n]['exclusions'] = set(list(eids))
else:
self.node[n]['exclusions'] = set()
def build_merge_queue(self):
"""Build a queue of node pairs to be merged in a specific priority.
Returns
-------
mq : MergeQueue object
A MergeQueue is a Python ``deque`` with a specific element
structure: a list of length 4 containing:
- the merge priority (any ordered type)
- a 'valid' flag
- and the two nodes in arbitrary order
The valid flag allows one to "remove" elements from the
queue in O(1) time by setting the flag to ``False``. Then,
one checks the flag when popping elements and ignores those
marked as invalid.
One other specific feature is that there are back-links from
edges to their corresponding queue items so that when nodes
are merged, affected edges can be invalidated and reinserted
in the queue with a new priority.
"""
queue_items = []
for l1, l2 in self.real_edges_iter():
w = self.merge_priority_function(self,l1,l2)
qitem = [w, True, l1, l2]
queue_items.append(qitem)
self[l1][l2]['qlink'] = qitem
self[l1][l2]['weight'] = w
return MergeQueue(queue_items, with_progress=self.show_progress)
def rebuild_merge_queue(self):
"""Build a merge queue from scratch and assign to self.merge_queue.
See Also
--------
build_merge_queue
"""
self.merge_queue = self.build_merge_queue()
def agglomerate(self, threshold=0.5, save_history=False):
"""Merge nodes hierarchically until given edge confidence threshold.
This is the main workhorse of the ``agglo`` module!
Parameters
----------
threshold : float, optional
The edge priority at which to stop merging.
save_history : bool, optional
Whether to save and return a history of all the merges made.
Returns
-------
history : list of tuple of int, optional
The ordered history of node pairs merged.
scores : list of float, optional
The list of merge scores corresponding to the `history`.
evaluation : list of tuple, optional
The split VI after each merge. This is only meaningful if
a ground truth volume was provided at build time.
Notes
-----
This function returns ``None`` when `save_history` is
``False``.
"""
if self.merge_queue.is_empty():
self.merge_queue = self.build_merge_queue()
history, scores, evaluation = [], [], []
while len(self.merge_queue) > 0 and \
self.merge_queue.peek()[0] < threshold:
merge_priority, _, n1, n2 = self.merge_queue.pop()
self.update_frozen_sets(n1, n2)
self.merge_nodes(n1, n2, merge_priority)
if save_history:
history.append((n1,n2))
scores.append(merge_priority)
evaluation.append(
(self.number_of_nodes()-1, self.split_vi())
)
if save_history:
return history, scores, evaluation
def agglomerate_count(self, stepsize=100, save_history=False):
"""Agglomerate until 'stepsize' merges have been made.
This function is like ``agglomerate``, but rather than to a
certain threshold, a certain number of merges are made,
regardless of threshold.
Parameters
----------
stepsize : int, optional
The number of merges to make.
save_history : bool, optional
Whether to save and return a history of all the merges made.
Returns
-------
history : list of tuple of int, optional
The ordered history of node pairs merged.
scores : list of float, optional
The list of merge scores corresponding to the `history`.
evaluation : list of tuple, optional
The split VI after each merge. This is only meaningful if
a ground truth volume was provided at build time.
Notes
-----
This function returns ``None`` when `save_history` is
``False``.
See Also
--------
agglomerate
"""
if self.merge_queue.is_empty():
self.merge_queue = self.build_merge_queue()
history, evaluation = [], []
i = 0
for i in range(stepsize):
if len(self.merge_queue) == 0:
break
merge_priority, _, n1, n2 = self.merge_queue.pop()
i += 1
self.merge_nodes(n1, n2, merge_priority)
if save_history:
history.append((n1, n2))
evaluation.append(
(self.number_of_nodes()-1, self.split_vi())
)
if save_history:
return history, evaluation
def agglomerate_ladder(self, min_size=1000, strictness=2):
"""Merge sequentially all nodes smaller than `min_size`.
Parameters
----------
min_size : int, optional
The smallest allowable segment after ladder completion.
strictness : {1, 2, 3}, optional
`strictness == 1`: all nodes smaller than `min_size` are
merged according to the merge priority function.
`strictness == 2`: in addition to `1`, small nodes can only
be merged to big nodes.
`strictness == 3`: in addition to `2`, nodes sharing less
than one pixel of boundary are not agglomerated.
Returns
-------
None
Notes
-----
Nodes that are on the volume boundary are not agglomerated.
"""
original_merge_priority_function = self.merge_priority_function
self.merge_priority_function = make_ladder(
self.merge_priority_function, min_size, strictness
)
self.rebuild_merge_queue()
self.agglomerate(inf)
self.merge_priority_function = original_merge_priority_function
self.merge_queue.finish()
self.rebuild_merge_queue()
max_score = max([qitem[0] for qitem in self.merge_queue.q])
for n in self.tree.nodes():
self.tree.node[n]['w'] -= max_score
def learn_agglomerate(self, gts, feature_map,
min_num_samples=1,
learn_flat=True,
learning_mode='strict',
labeling_mode='assignment',
priority_mode='active',
memory=True,
unique=True,
random_state=None,
max_num_epochs=10,
min_num_epochs=2,
max_num_samples=np.inf,
classifier='random forest',
active_function=classifier_probability,
mpf=boundary_mean):
"""Agglomerate while comparing to ground truth & classifying merges.
Parameters
----------
gts : array of int or list thereof
The ground truth volume(s) corresponding to the current
probability map.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
min_num_samples : int, optional
Continue training until this many training examples have
been collected.
learn_flat : bool, optional
Do a flat learning on the static graph with no
agglomeration.
learning_mode : {'strict', 'loose'}, optional
In 'strict' mode, if a "don't merge" edge is encountered,
it is added to the training set but the merge is not
executed. In 'loose' mode, the merge is allowed to proceed.
labeling_mode : {'assignment', 'vi-sign', 'rand-sign'}, optional
How to decide whether two nodes should be merged based on
the ground truth segmentations. ``'assignment'`` means the
nodes are assigned to the ground truth node with which they
share the highest overlap. ``'vi-sign'`` means the the VI
change of the switch is used (negative is better).
``'rand-sign'`` means the change in Rand index is used
(positive is better).
priority_mode : string, optional
One of:
``'active'``: Train a priority function with the data
from previous epochs to obtain the next.
``'random'``: Merge edges at random.
``'mixed'``: Alternate between epochs of ``'active'``
and ``'random'``.
``'mean'``: Use the mean boundary value. (In this case,
training is limited to 1 or 2 epochs.)
``'custom'``: Use the function provided by `mpf`.
memory : bool, optional
Keep the training data from all epochs (rather than just
the most recent one).
unique : bool, optional
Remove duplicate feature vectors.
random_state : int, optional
If provided, this parameter is passed to `get_classifier`
to set the random state and allow consistent results across
tests.
max_num_epochs : int, optional
Do not train for longer than this (this argument *may*
override the `min_num_samples` argument).
min_num_epochs : int, optional
Train for no fewer than this number of epochs.
max_num_samples : int, optional
Train for no more than this number of samples.
classifier : string, optional
Any valid classifier descriptor. See
``gala.classify.get_classifier()``
active_function : function (feat. map, classifier) -> function, optional
Use this to create the next priority function after an
epoch.
mpf : function (Rag, node, node) -> float
A merge priority function to use when ``priority_mode`` is
``'custom'``.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
alldata : list of list of array
A list of lists like `data` above: one list for each epoch.
Notes
-----
The gala algorithm [1] uses the default parameters. For the
LASH algorithm [2], use:
- `learning_mode`: ``'loose'``
- `labeling_mode`: ``'rand-sign'``
- `memory`: ``False``
References
----------
.. [1] Nunez-Iglesias et al, Machine learning of hierarchical
clustering to segment 2D and 3D images, PLOS ONE, 2013.
.. [2] Jain et al, Learning to agglomerate superpixel
hierarchies, NIPS, 2011.
See Also
--------
Rag
"""
learning_mode = learning_mode.lower()
labeling_mode = labeling_mode.lower()
priority_mode = priority_mode.lower()
if priority_mode == 'mean' and unique:
max_num_epochs = 2 if learn_flat else 1
if priority_mode in ['random', 'mean'] and not memory:
max_num_epochs = 1
label_type_keys = {'assignment':0, 'vi-sign':1, 'rand-sign':2}
if type(gts) != list:
gts = [gts] # allow using single ground truth as input
master_ctables = [merge_contingency_table(self.get_segmentation(), gt)
for gt in gts]
alldata = []
data = [[],[],[],[]]
for num_epochs in range(max_num_epochs):
ctables = deepcopy(master_ctables)
if len(data[0]) > min_num_samples and num_epochs >= min_num_epochs:
break
if learn_flat and num_epochs == 0:
alldata.append(self.learn_flat(gts, feature_map))
data = unique_learning_data_elements(alldata) if memory \
else alldata[-1]
continue
g = self.copy()
if priority_mode == 'mean':
g.merge_priority_function = boundary_mean
elif num_epochs > 0 and priority_mode == 'active' or \
num_epochs % 2 == 1 and priority_mode == 'mixed':
cl = get_classifier(classifier, random_state=random_state)
feat, lab = classify.sample_training_data(
data[0], data[1][:, label_type_keys[labeling_mode]],
max_num_samples)
cl = cl.fit(feat, lab)
g.merge_priority_function = active_function(feature_map, cl)
elif priority_mode == 'random' or \
(priority_mode == 'active' and num_epochs == 0):
g.merge_priority_function = random_priority
elif priority_mode == 'custom':
g.merge_priority_function = mpf
g.show_progress = False # bug in MergeQueue usage causes
# progressbar crash.
g.rebuild_merge_queue()
alldata.append(g.learn_epoch(ctables, feature_map,
learning_mode=learning_mode,
labeling_mode=labeling_mode))
if memory:
if unique:
data = unique_learning_data_elements(alldata)
else:
data = concatenate_data_elements(alldata)
else:
data = alldata[-1]
logging.debug('data size %d at epoch %d'%(len(data[0]), num_epochs))
return data, alldata
def learn_flat(self, gts, feature_map):
"""Learn all edges on the graph, but don't agglomerate.
Parameters
----------
gts : array of int or list thereof
The ground truth volume(s) corresponding to the current
probability map.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
See Also
--------
learn_agglomerate
"""
if type(gts) != list:
gts = [gts] # allow using single ground truth as input
ctables = [merge_contingency_table(self.get_segmentation(), gt)
for gt in gts]
assignments = [ev.assignment_table(ct) for ct in ctables]
return list(map(array, zip(*[
self.learn_edge(e, ctables, assignments, feature_map)
for e in self.real_edges()])))
def learn_edge(self, edge, ctables, assignments, feature_map):
"""Determine whether an edge should be merged based on ground truth.
Parameters
----------
edge : (int, int) tuple
An edge in the graph.
ctables : list of array
A list of contingency tables determining overlap between the
current segmentation and the ground truth.
assignments : list of array
Similar to the contingency tables, but each row is thresholded
so each segment corresponds to exactly one ground truth segment.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector.
Returns
-------
features : 1D array of float
The feature vector for that edge.
labels : 1D array of float, length 3
The labels determining whether the edge should be merged.
A value of `-1` means "should merge", while `1` means "should
not merge". The columns correspond to the three labeling
methods: assignment, VI sign, or RI sign.
weights : 1D array of float, length 2
The VI and RI change of the merge.
nodes : tuple of int
The given edge.
"""
n1, n2 = edge
features = feature_map(self, n1, n2).ravel()
# Calculate weights for weighting data points
s1, s2 = [self.node[n]['size'] for n in [n1, n2]]
weights = \
compute_local_vi_change(s1, s2, self.volume_size), \
compute_local_rand_change(s1, s2, self.volume_size)
# Get the fraction of times that n1 and n2 assigned to
# same segment in the ground truths
cont_labels = [
[(-1)**(a[n1]==a[n2]).toarray().all() for a in assignments],
[compute_true_delta_vi(ctable, n1, n2) for ctable in ctables],
[-compute_true_delta_rand(ctable, n1, n2, self.volume_size)
for ctable in ctables]
]
labels = [np.sign(mean(cont_label)) for cont_label in cont_labels]
if any(map(isnan, labels)) or any([label == 0 for label in labels]):
logging.debug('NaN or 0 labels found. ' +
' '.join(map(str, [labels, (n1, n2)])))
labels = [1 if i==0 or isnan(i) or n1 in self.frozen_nodes or
n2 in self.frozen_nodes or (n1, n2) in self.frozen_edges else
i for i in labels]
return features, labels, weights, (n1, n2)
def learn_epoch(self, ctables, feature_map,
learning_mode='permissive', labeling_mode='assignment'):
"""Learn the agglomeration process using various strategies.
Parameters
----------
ctables : array of float or list thereof
One or more contingency tables between own segments and gold
standard segmentations
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
learning_mode : {'strict', 'permissive'}, optional
If ``'strict'``, don't proceed with a merge when it goes against
the ground truth. For historical reasons, 'loose' is allowed as
a synonym for 'strict'.
labeling_mode : {'assignment', 'vi-sign', 'rand-sign'}, optional
Which label to use for `learning_mode`. Note that all labels
are saved in the end.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
"""
label_type_keys = {'assignment':0, 'vi-sign':1, 'rand-sign':2}
assignments = [ev.csrRowExpandableCSR(asst)
for asst in map(ev.assignment_table, ctables)]
g = self
data = []
while len(g.merge_queue) > 0:
merge_priority, valid, n1, n2 = g.merge_queue.pop()
if g.boundary_body in (n1, n2):
continue
dat = g.learn_edge((n1,n2), ctables, assignments, feature_map)
data.append(dat)
label = dat[1][label_type_keys[labeling_mode]]
if learning_mode != 'strict' or label < 0:
node_id = g.merge_nodes(n1, n2, merge_priority)
for ctable, assignment in zip(ctables, assignments):
ctable[node_id] = ctable[n1] + ctable[n2]
ctable[n1] = 0
ctable[n2] = 0
assignment[node_id] = (ctable[node_id] ==
ctable[node_id].max())
assignment[n1] = 0
assignment[n2] = 0
return list(map(array, zip(*data)))
def replay_merge_history(self, merge_seq, labels=None, num_errors=1):
"""Agglomerate according to a merge sequence, optionally labeled.
Parameters
----------
merge_seq : iterable of pair of int
The sequence of node IDs to be merged.
labels : iterable of int in {-1, 0, 1}, optional
A sequence matching `merge_seq` specifying whether a merge
should take place or not. -1 or 0 mean "should merge", 1
otherwise.
Returns
-------
n : int
Number of elements consumed from `merge_seq`
e : (int, int)
Last merge pair observed.
Notes
-----
The merge sequence and labels *must* be generators if you don't want
to manually keep track of how much has been consumed. The merging
continues until `num_errors` false merges have been encountered, or
until the sequence is fully consumed.
"""
if labels is None:
labels1 = it.repeat(False)
labels2 = it.repeat(False)
else:
labels1 = (label > 0 for label in labels)
labels2 = (label > 0 for label in labels)
counter = it.count()
errors_remaining = conditional_countdown(labels2, num_errors)
nodes = None
for nodes, label, errs, count in \
zip(merge_seq, labels1, errors_remaining, counter):
n1, n2 = nodes
if not label:
self.merge_nodes(n1, n2)
elif errs == 0:
break
return next(counter), nodes
def rename_node(self, old, new):
"""Rename node `old` to `new`, updating edges and weights.
Parameters
----------
old : int
The node being renamed.
new : int
The new node id.
"""
self.add_node(new, attr_dict=self.node[old])
self.add_edges_from(
[(new, v, self[old][v]) for v in self.neighbors(old)])
for v in self.neighbors(new):
qitem = self[new][v].get('qlink', None)
if qitem is not None:
if qitem[2] == old:
qitem[2] = new
else:
qitem[3] = new
self.remove_node(old)
def merge_nodes(self, n1, n2, merge_priority=0.0):
"""Merge two nodes, while updating the necessary edges.
Parameters
----------
n1, n2 : int
Nodes determining the edge for which to update the UCM.
merge_priority : float, optional
The merge priority of the merge.
Returns
-------
node_id : int
The id of the node resulting from the merge.
Notes
-----
Additionally, the RIG (region intersection graph), the
contingency matrix to the ground truth (if provided) is
updated.
"""
if len(self.node[n1]['exclusions'] & self.node[n2]['exclusions']) > 0:
return
else:
self.node[n1]['exclusions'].update(self.node[n2]['exclusions'])
w = self[n1][n2].get('weight', merge_priority)
self.node[n1]['size'] += self.node[n2]['size']
self.node[n1]['fragments'].update(self.node[n2]['fragments'])
self.feature_manager.update_node_cache(self, n1, n2,
self.node[n1]['feature-cache'], self.node[n2]['feature-cache'])
new_neighbors = [n for n in self.neighbors(n2) if n != n1]
for n in new_neighbors:
self.merge_edge_properties((n2, n), (n1, n))
try:
self.merge_queue.invalidate(self[n1][n2]['qlink'])
except KeyError:
pass
node_id = self.tree.merge(n1, n2, w)
self.remove_node(n2)
self.rename_node(n1, node_id)
self.rig[node_id] = self.rig[n1] + self.rig[n2]
self.rig[n1] = 0
self.rig[n2] = 0
return node_id
def merge_subgraph(self, subgraph=None, source=None):
"""Merge a (typically) connected set of nodes together.
Parameters
----------
subgraph : agglo.Rag, networkx.Graph, or list of int (node id)
A subgraph to merge.
source : int (node id), optional
Merge the subgraph to this node.
"""
if type(subgraph) not in [Rag, Graph]: # input is node list
subgraph = self.subgraph(subgraph)
if len(subgraph) == 0:
return
for subsubgraph in nx.connected_component_subgraphs(subgraph):
node_dfs = list(dfs_preorder_nodes(subsubgraph, source))
# dfs_preorder_nodes returns iter, convert to list
source_node, other_nodes = node_dfs[0], node_dfs[1:]
for current_node in other_nodes:
source_node = self.merge_nodes(source_node, current_node)
def split_node(self, u, n=2, **kwargs):
"""Use normalized cuts [1] to split a node/segment.
Parameters
----------
u : int (node id)
Which node to split.
n : int, optional
How many segments to split it into.
Returns
-------
None
References
----------
.. [1] Shi, J., and Malik, J. (2000). Normalized cuts and image
segmentation. Pattern Analysis and Machine Intelligence.
"""
node_extent = self.extent(u)
labels = unique(self.watershed_r[node_extent])
self.remove_node(u)
self.build_graph_from_watershed(idxs=node_extent)
self.ncut(num_clusters=n, nodes=labels, **kwargs)
def separate_fragments(self, f0, f1):
"""Ensure fragments (watersheds) f0 and f1 are in different nodes.
If f0 and f1 are the same segment, split that segment at the
lowest common ancestor of f0 and f1 in the merge tree, then add an
exclusion. Otherwise, simply add an exclusion.
Parameters
----------
f0, f1 : int
The fragments to be separated.
Returns
-------
s0, s1 : int
The separated segments resulting from the break. If the
fragments were already in separate segments, return the
highest ancestor of each fragment on the merge tree.
"""
lca = tree.lowest_common_ancestor(self.tree, f0, f1)
if lca is not None:
s0, s1 = self.tree.children(lca)
self.delete_merge(lca)
else:
s0 = self.tree.highest_ancestor(f0)
s1 = self.tree.highest_ancestor(f1)
return s0, s1
def delete_merge(self, tree_node):
"""Delete the merge represented by `tree_node`.
Parameters
----------
tree_node : int
A node that may not be currently in the graph, but was at
some point in its history.
"""
highest = self.tree.highest_ancestor(tree_node)
if highest != tree_node:
leaves = self.tree.leaves(tree_node)
# the graph doesn't keep nodes in the history, only the
# most recent nodes. So, we only need to find that one and
# update its fragment list.
self.node[highest]['fragments'].difference_update(leaves)
self.tree.remove_node(tree_node)
def merge_edge_properties(self, src, dst):
"""Merge the properties of edge src into edge dst.
Parameters
----------
src, dst : (int, int)
Edges being merged.
Returns
-------
None
"""
u, v = dst
w, x = src
if not self.has_edge(u,v):
self.add_edge(u, v, attr_dict=self[w][x])
else:
self[u][v]['boundary'].extend(self[w][x]['boundary'])
self.feature_manager.update_edge_cache(self, (u, v), (w, x),
self[u][v]['feature-cache'], self[w][x]['feature-cache'])
try:
self.merge_queue.invalidate(self[w][x]['qlink'])
except KeyError:
pass
self.update_merge_queue(u, v)
def update_merge_queue(self, u, v):
"""Update the merge queue item for edge (u, v). Add new by default.
Parameters
----------
u, v : int (node id)
Edge being updated.
Returns
-------
None
"""
if self.boundary_body in [u, v]:
return
if 'qlink' in self[u][v]:
self.merge_queue.invalidate(self[u][v]['qlink'])
if not self.merge_queue.is_null_queue:
w = self.merge_priority_function(self,u,v)
new_qitem = [w, True, u, v]
self[u][v]['qlink'] = new_qitem
self[u][v]['weight'] = w
self.merge_queue.push(new_qitem)
def get_segmentation(self, threshold=None):
"""Return the unpadded segmentation represented by the graph.
Remember that the segmentation volume is padded with an
"artificial" segment that envelops the volume. This function
simply removes the wrapping and returns a segmented volume.
Parameters
----------
threshold : float, optional
Get the segmentation at the given threshold. If no
threshold is given, return the segmentation at the current
level of agglomeration.
Returns
-------
seg : array of int
The segmentation of the volume presently represented by the
graph.
"""
if threshold is None:
# a threshold of np.inf is the same as no threshold on the
# tree when getting the map (see below). Thus, using a
# threshold of `None` (the default), we get the segmentation
# implied by the current merge tree.
threshold = np.inf
elif threshold > self.max_merge_score:
# If a higher threshold is required than has been merged, we
# continue the agglomeration until that threshold is hit.
self.agglomerate(threshold)
m = self.tree.get_map(threshold)
seg = m[self.watershed]
if self.pad_thickness > 1: # volume has zero-boundaries
seg = morpho.remove_merged_boundaries(seg, self.connectivity)
return morpho.juicy_center(seg, self.pad_thickness)
def build_volume(self, nbunch=None):
"""Return the segmentation induced by the graph.
Parameters
----------
nbunch : iterable of int (node id), optional
A list of nodes for which to build the volume. All nodes
are used if this is not provided.
Returns
-------
seg : array of int
The segmentation implied by the graph.
Notes
-----
This function is very similar to ``get_segmentation``, but it
builds the segmentation from the bottom up, rather than using
the currently-stored segmentation.
"""
v = zeros_like(self.watershed)
vr = v.ravel()
if nbunch is None:
nbunch = self.nodes()
for n in nbunch:
vr[self.extent(n)] = n
return morpho.juicy_center(v,self.pad_thickness)
def build_boundary_map(self, ebunch=None):
"""Return a map of the current merge priority.
Parameters
----------
ebunch : iterable of (int, int), optional
The list of edges for which to build a map. Use all edges
if not provided.
Returns
-------
bm : array of float
The image of the edge weights.
"""
if len(self.merge_queue) == 0:
self.rebuild_merge_queue()
m = zeros(self.watershed.shape, 'float')
mr = m.ravel()
if ebunch is None:
ebunch = self.real_edges_iter()
ebunch = sorted([(self[u][v]['weight'], u, v) for u, v in ebunch])
for w, u, v in ebunch:
b = self[u][v]['boundary']
mr[b] = w
if hasattr(self, 'ignored_boundary'):
m[self.ignored_boundary] = inf
return morpho.juicy_center(m, self.pad_thickness)
def remove_obvious_inclusions(self):
"""Merge any nodes with only one edge to their neighbors."""
for n in self.nodes():
if self.degree(n) == 1:
self.merge_nodes(self.neighbors(n)[0], n)
def remove_inclusions(self):
"""Merge any segments fully contained within other segments.
In 3D EM images, inclusions are not biologically plausible, so
this function can be used to remove them.
Parameters
----------
None
Returns
-------
None
"""
bcc = list(biconnected_components(self))
if len(bcc) > 1:
container = [i for i, s in enumerate(bcc) if
self.boundary_body in s][0]
del bcc[container] # remove the main graph
bcc = list(map(list, bcc))
for cc in bcc:
cc.sort(key=lambda x: self.node[x]['size'], reverse=True)
bcc.sort(key=lambda x: self.node[x[0]]['size'])
for cc in bcc:
self.merge_subgraph(cc, cc[0])
def orphans(self):
"""List all the nodes that do not touch the volume boundary.
Parameters
----------
None
Returns
-------
orphans : list of int (node id)
A list of node ids.
Notes
-----
"Orphans" are not biologically plausible in EM data, so we can
flag them with this function for further scrutiny.
"""
return [n for n in self.nodes() if not self.at_volume_boundary(n)]
def compute_orphans(self):
"""Find all the segments that do not touch the volume boundary.
Parameters
----------
None
Returns
-------
orphans : list of int (node id)
A list of node ids.
Notes
-----
This function differs from ``orphans`` in that it does not use
the graph, but rather computes orphans directly from the
segmentation.
"""
return morpho.orphans(self.get_segmentation())
def is_traversed_by_node(self, n):
"""Determine whether a body traverses the volume.
This is defined as touching the volume boundary at two distinct
locations.
Parameters
----------
n : int (node id)
The node being inspected.
Returns
-------
tr : bool
Whether the segment "traverses" the volume being segmented.
"""
if not self.at_volume_boundary(n) or n == self.boundary_body:
return False
v = zeros(self.watershed.shape, 'uint8')
v.ravel()[self[n][self.boundary_body]['boundary']] = 1
_, n = label(v, ones([3]*v.ndim))
return n > 1
def traversing_bodies(self):
"""List all bodies that traverse the volume."""
return [n for n in self.nodes() if self.is_traversed_by_node(n)]
def non_traversing_bodies(self):
"""List bodies that are not orphans and do not traverse the volume."""
return [n for n in self.nodes() if self.at_volume_boundary(n) and
not self.is_traversed_by_node(n) and n != self.boundary_body]
def raveler_body_annotations(self, traverse=False):
"""Return JSON-compatible dict formatted for Raveler annotations."""
orphans = self.compute_orphans()
non_traversing_bodies = self.compute_non_traversing_bodies() \
if traverse else []
data = \
[{'status':'not sure', 'comment':'orphan', 'body ID':int(o)}
for o in orphans] +\
[{'status':'not sure', 'comment':'does not traverse',
'body ID':int(n)} for n in non_traversing_bodies]
metadata = {'description':'body annotations', 'file version':2}
return {'data':data, 'metadata':metadata}
def at_volume_boundary(self, n):
"""Return True if node n touches the volume boundary."""
return self.has_edge(n, self.boundary_body) or n == self.boundary_body
def should_merge(self, n1, n2):
return self.rig[n1].argmax() == self.rig[n2].argmax()
def get_pixel_label(self, n1, n2):
boundary = array(self[n1][n2]['boundary'])
min_idx = boundary[self.probabilities_r[boundary,0].argmin()]
if self.should_merge(n1, n2):
return min_idx, 2
else:
return min_idx, 1
def pixel_labels_array(self, false_splits_only=False):
ar = zeros_like(self.watershed_r)
labels = [self.get_pixel_label(*e) for e in self.real_edges()]
if false_splits_only:
labels = [l for l in labels if l[1] == 2]
ids, ls = list(map(array,zip(*labels)))
ar[ids] = ls.astype(ar.dtype)
return ar.reshape(self.watershed.shape)
def split_vi(self, gt=None):
if self.gt is None and gt is None:
return array([0,0])
elif self.gt is not None:
return split_vi(self.rig)
else:
return split_vi(self.get_segmentation(), gt, [0], [0])
def boundary_indices(self, n1, n2):
return self[n1][n2]['boundary']
def get_edge_coordinates(self, n1, n2, arbitrary=False):
"""Find where in the segmentation the edge (n1, n2) is most visible."""
return get_edge_coordinates(self, n1, n2, arbitrary)
def write(self, fout, output_format='GraphML'):
if output_format == 'Plaza JSON':
self.write_plaza_json(fout)
else:
raise ValueError('Unsupported output format for agglo.Rag: %s'
% output_format)
def write_plaza_json(self, fout, synapsejson=None, offsetz=0):
"""Write graph to Steve Plaza's JSON spec."""
json_vals = {}
if synapsejson is not None:
synapse_file = open(synapsejson)
json_vals1 = json.load(synapse_file)
body_count = {}
for item in json_vals1["data"]:
bodyid = ((item["T-bar"])["body ID"])
if bodyid in body_count:
body_count[bodyid] += 1
else:
body_count[bodyid] = 1
for psd in item["partners"]:
bodyid = psd["body ID"]
if bodyid in body_count:
body_count[bodyid] += 1
else:
body_count[bodyid] = 1
json_vals["synapse_bodies"] = []
for body, count in body_count.items():
temp = [body, count]
json_vals["synapse_bodies"].append(temp)
edge_list = [
{'location': list(map(int, self.get_edge_coordinates(i, j)[-1::-1])),
'node1': int(i), 'node2': int(j),
'edge_size': len(self[i][j]['boundary']),
'size1': self.node[i]['size'],
'size2': self.node[j]['size'],
'weight': float(self[i][j]['weight'])}
for i, j in self.real_edges()
]
json_vals['edge_list'] = edge_list
with open(fout, 'w') as f:
json.dump(json_vals, f, indent=4)
def ncut(self, num_clusters=10, kmeans_iters=5, sigma=255.0*20, nodes=None,
**kwargs):
"""Run normalized cuts on the current set of superpixels.
Keyword arguments:
num_clusters -- number of clusters to compute
kmeans_iters -- # iterations to run kmeans when clustering
sigma -- sigma value when setting up weight matrix
Return value: None
"""
if nodes is None:
nodes = self.nodes()
# Compute weight matrix
W = self.compute_W(self.merge_priority_function, nodes=nodes)
# Run normalized cut
labels, eigvec, eigval = ncutW(W, num_clusters, kmeans_iters, **kwargs)
# Merge nodes that are in same cluster
self.cluster_by_labels(labels, nodes)
def cluster_by_labels(self, labels, nodes=None):
"""Merge all superpixels with the same label (1 label per 1 sp)"""
if nodes is None:
nodes = array(self.nodes())
if not (len(labels) == len(nodes)):
raise ValueError('Number of labels should be %d but is %d.',
self.number_of_nodes(), len(labels))
for l in unique(labels):
inds = nonzero(labels==l)[0]
nodes_to_merge = nodes[inds]
node1 = nodes_to_merge[0]
for node in nodes_to_merge[1:]:
self.merge_nodes(node1, node)
def compute_W(self, merge_priority_function, sigma=255.0*20, nodes=None):
""" Computes the weight matrix for clustering"""
if nodes is None:
nodes = array(self.nodes())
n = len(nodes)
nodes2ind = dict(zip(nodes, range(n)))
W = lil_matrix((n,n))
for u, v in self.real_edges(nodes):
try:
i, j = nodes2ind[u], nodes2ind[v]
except KeyError:
continue
w = merge_priority_function(self,u,v)
W[i,j] = W[j,i] = np.exp(-w**2/sigma)
return W
def update_frozen_sets(self, n1, n2):
self.frozen_nodes.discard(n1)
self.frozen_nodes.discard(n2)
for x, y in self.frozen_edges.copy():
if n2 in [x, y]:
self.frozen_edges.discard((x, y))
if x == n2:
self.frozen_edges.add((n1, y))
if y == n2:
self.frozen_edges.add((x, n1))
def get_edge_coordinates(g, n1, n2, arbitrary=False):
"""Find where in the segmentation the edge (n1, n2) is most visible."""
boundary = g[n1][n2]['boundary']
if arbitrary:
# quickly get an arbitrary point on the boundary
idx = boundary.pop(); boundary.append(idx)
coords = unravel_index(idx, g.watershed.shape)
else:
boundary_idxs = unravel_index(boundary, g.watershed.shape)
coords = [bincount(dimcoords).argmax() for dimcoords in boundary_idxs]
return array(coords) - g.pad_thickness
def is_mito_boundary(g, n1, n2, channel=2, threshold=0.5):
return max(np.mean(g.probabilities_r[g[n1][n2]['boundary'], c])
for c in channel) > threshold
def is_mito(g, n, channel=2, threshold=0.5):
return max(np.mean(g.probabilities_r[g.extent(n), c])
for c in channel) > threshold
def best_possible_segmentation(ws, gt):
"""Build the best possible segmentation given a superpixel map."""
ws = Rag(ws)
assignment = ev.assignment_table(ws.get_segmentation(), gt).tocsc()
for gt_node in range(assignment.shape[1]):
i, j = assignment.indptr[gt_node : gt_node+2]
ws.merge_subgraph(assignment.indices[i:j])
return ws.get_segmentation()
Use function, not dict key, to find boundaries
This is the first step to replacing the boundary dictionary with
a sparselol containing the same information.
# built-ins
import itertools as it
import sys
import argparse
import random
import logging
import json
import collections
from copy import deepcopy
# libraries
from numpy import (array, mean, zeros, zeros_like, where, unique,
newaxis, nonzero, median, float, ones, arange, inf, isnan,
flatnonzero, unravel_index, bincount)
import numpy as np
from scipy.stats import sem
from scipy import sparse
from scipy.sparse import lil_matrix
from scipy.misc import comb as nchoosek
from scipy.ndimage.measurements import label
from scipy import ndimage as ndi
import networkx as nx
from networkx import Graph, biconnected_components
from networkx.algorithms.traversal.depth_first_search import dfs_preorder_nodes
from skimage.segmentation import relabel_sequential
from viridis import tree
# local modules
from . import morpho
from . import sparselol as lol
from . import iterprogress as ip
from . import optimized as opt
from .ncut import ncutW
from .mergequeue import MergeQueue
from .evaluate import merge_contingency_table, split_vi, xlogx
from . import evaluate as ev
from . import features
from . import classify
from .classify import get_classifier, \
unique_learning_data_elements, concatenate_data_elements
from .dtypes import label_dtype
arguments = argparse.ArgumentParser(add_help=False)
arggroup = arguments.add_argument_group('Agglomeration options')
arggroup.add_argument('-t', '--thresholds', nargs='+', default=[128],
type=float, metavar='FLOAT',
help='''The agglomeration thresholds. One output file will be written
for each threshold.'''
)
arggroup.add_argument('-l', '--ladder', type=int, metavar='SIZE',
help='Merge any bodies smaller than SIZE.'
)
arggroup.add_argument('-p', '--pre-ladder', action='store_true', default=True,
help='Run ladder before normal agglomeration (default).'
)
arggroup.add_argument('-L', '--post-ladder',
action='store_false', dest='pre_ladder',
help='Run ladder after normal agglomeration instead of before (SLOW).'
)
arggroup.add_argument('-s', '--strict-ladder', type=int, metavar='INT',
default=1,
help='''Specify the strictness of the ladder agglomeration. Level 1
(default): merge anything smaller than the ladder threshold as
long as it's not on the volume border. Level 2: only merge smaller
bodies to larger ones. Level 3: only merge when the border is
larger than or equal to 2 pixels.'''
)
arggroup.add_argument('-M', '--low-memory', action='store_true',
help='''Use less memory at a slight speed cost. Note that the phrase
'low memory' is relative.'''
)
arggroup.add_argument('--disallow-shared-boundaries', action='store_false',
dest='allow_shared_boundaries',
help='''Watershed pixels that are shared between more than 2 labels are
not counted as edges.'''
)
arggroup.add_argument('--allow-shared-boundaries', action='store_true',
default=True,
help='''Count every watershed pixel in every edge in which it participates
(default: True).'''
)
def conditional_countdown(seq, start=1, pred=bool):
"""Count down from 'start' each time pred(elem) is true for elem in seq.
Used to know how many elements of a sequence remain that satisfy a
predicate.
Parameters
----------
seq : iterable
Any sequence.
start : int, optional
The starting element.
pred : function, type(next(seq)) -> bool
A predicate acting on the elements of `seq`.
Examples
--------
>>> seq = range(10)
>>> cc = conditional_countdown(seq, start=5, pred=lambda x: x % 2 == 1)
>>> next(cc)
5
>>> next(cc)
4
>>> next(cc)
4
>>> next(cc)
3
"""
remaining = start
for elem in seq:
if pred(elem):
remaining -= 1
yield remaining
############################
# Merge priority functions #
############################
def oriented_boundary_mean(g, n1, n2):
return mean(g.oriented_probabilities_r[g.boundary(n1, n2)])
def boundary_mean(g, n1, n2):
return mean(g.probabilities_r[g.boundary(n1, n2)])
def boundary_median(g, n1, n2):
return median(g.probabilities_r[g.boundary(n1, n2)])
def approximate_boundary_mean(g, n1, n2):
"""Return the boundary mean as computed by a MomentsFeatureManager.
The feature manager is assumed to have been set up for g at construction.
"""
return g.feature_manager.compute_edge_features(g, n1, n2)[1]
def make_ladder(priority_function, threshold, strictness=1):
def ladder_function(g, n1, n2):
s1 = g.node[n1]['size']
s2 = g.node[n2]['size']
ladder_condition = \
(s1 < threshold and not g.at_volume_boundary(n1)) or \
(s2 < threshold and not g.at_volume_boundary(n2))
if strictness >= 2:
ladder_condition &= ((s1 < threshold) != (s2 < threshold))
if strictness >= 3:
ladder_condition &= len(g.boundary(n1, n2)) > 2
if ladder_condition:
return priority_function(g, n1, n2)
else:
return inf
return ladder_function
def no_mito_merge(priority_function):
def predict(g, n1, n2):
frozen = (n1 in g.frozen_nodes or
n2 in g.frozen_nodes or
(n1, n2) in g.frozen_edges)
if frozen:
return np.inf
else:
return priority_function(g, n1, n2)
return predict
def mito_merge():
def predict(g, n1, n2):
if n1 in g.frozen_nodes and n2 in g.frozen_nodes:
return np.inf
elif (n1, n2) in g.frozen_edges:
return np.inf
elif n1 not in g.frozen_nodes and n2 not in g.frozen_nodes:
return np.inf
else:
if n1 in g.frozen_nodes:
mito = n1
cyto = n2
else:
mito = n2
cyto = n1
if g.node[mito]['size'] > g.node[cyto]['size']:
return np.inf
else:
return 1.0 - (float(len(g.boundary(mito, cyto)))/
sum([len(g.boundary(mito, x)) for x in g.neighbors(mito)]))
return predict
def classifier_probability(feature_extractor, classifier):
def predict(g, n1, n2):
if n1 == g.boundary_body or n2 == g.boundary_body:
return inf
features = np.atleast_2d(feature_extractor(g, n1, n2))
try:
prediction = classifier.predict_proba(features)
prediction_arr = np.array(prediction, copy=False)
if prediction_arr.ndim > 2:
prediction_arr = prediction_arr[0]
try:
prediction = prediction_arr[0][1]
except (TypeError, IndexError):
prediction = prediction_arr[0]
except AttributeError:
prediction = classifier.predict(features)[0]
return prediction
return predict
def ordered_priority(edges):
d = {}
n = len(edges)
for i, (n1, n2) in enumerate(edges):
score = float(i)/n
d[(n1,n2)] = score
d[(n2,n1)] = score
def ord(g, n1, n2):
return d.get((n1,n2), inf)
return ord
def expected_change_vi(feature_extractor, classifier, alpha=1.0, beta=1.0):
prob_func = classifier_probability(feature_extractor, classifier)
def predict(g, n1, n2):
p = prob_func(g, n1, n2) # Prediction from the classifier
# Calculate change in VI if n1 and n2 should not be merged
v = compute_local_vi_change(
g.node[n1]['size'], g.node[n2]['size'], g.volume_size
)
# Return expected change
return (p*alpha*v + (1.0-p)*(-beta*v))
return predict
def compute_local_vi_change(s1, s2, n):
"""Compute change in VI if we merge disjoint sizes s1,s2 in a volume n."""
py1 = float(s1)/n
py2 = float(s2)/n
py = py1+py2
return -(py1*np.log2(py1) + py2*np.log2(py2) - py*np.log2(py))
def compute_true_delta_vi(ctable, n1, n2):
p1 = ctable[n1].sum()
p2 = ctable[n2].sum()
p3 = p1+p2
p1g_log_p1g = xlogx(ctable[n1]).sum()
p2g_log_p2g = xlogx(ctable[n2]).sum()
p3g_log_p3g = xlogx(ctable[n1]+ctable[n2]).sum()
return p3*np.log2(p3) - p1*np.log2(p1) - p2*np.log2(p2) - \
2*(p3g_log_p3g - p1g_log_p1g - p2g_log_p2g)
def expected_change_rand(feature_extractor, classifier, alpha=1.0, beta=1.0):
prob_func = classifier_probability(feature_extractor, classifier)
def predict(g, n1, n2):
p = float(prob_func(g, n1, n2)) # Prediction from the classifier
v = compute_local_rand_change(
g.node[n1]['size'], g.node[n2]['size'], g.volume_size
)
return p*v*alpha + (1.0-p)*(-beta*v)
return predict
def compute_local_rand_change(s1, s2, n):
"""Compute change in rand if we merge disjoint sizes s1,s2 in volume n."""
return float(s1*s2)/nchoosek(n,2)
def compute_true_delta_rand(ctable, n1, n2, n):
"""Compute change in RI obtained by merging rows n1 and n2.
This function assumes ctable is normalized to sum to 1.
"""
localct = n * ctable[(n1, n2), :]
total = localct.data.sum()
sqtotal = (localct.data ** 2).sum()
delta_sxy = 1. / 2 * ((np.array(localct.sum(axis=0)) ** 2).sum() -
sqtotal)
delta_sx = 1. / 2 * (total ** 2 -
(np.array(localct.sum(axis=1)) ** 2).sum())
return (2 * delta_sxy - delta_sx) / nchoosek(n, 2)
def boundary_mean_ladder(g, n1, n2, threshold, strictness=1):
f = make_ladder(boundary_mean, threshold, strictness)
return f(g, n1, n2)
def boundary_mean_plus_sem(g, n1, n2, alpha=-6):
bvals = g.probabilities_r[g.boundary(n1, n2)]
return mean(bvals) + alpha*sem(bvals)
def random_priority(g, n1, n2):
if n1 == g.boundary_body or n2 == g.boundary_body:
return inf
return random.random()
class Rag(Graph):
"""Region adjacency graph for segmentation of nD volumes.
Parameters
----------
watershed : array of int, shape (M, N, ..., P)
The labeled regions of the image. Note: this is called
`watershed` for historical reasons, but could refer to a
superpixel map of any origin.
probabilities : array of float, shape (M, N, ..., P[, Q])
The probability of each pixel of belonging to a particular
class. Typically, this has the same shape as `watershed`
and represents the probability that the pixel is part of a
region boundary, but it can also have an additional
dimension for probabilities of belonging to other classes,
such as mitochondria (in biological images) or specific
textures (in natural images).
merge_priority_function : callable function, optional
This function must take exactly three arguments as input
(a Rag object and two node IDs) and return a single float.
feature_manager : ``features.base.Null`` object, optional
A feature manager object that controls feature computation
and feature caching.
mask : array of bool, shape (M, N, ..., P)
A mask of the same shape as `watershed`, `True` in the
positions to be processed when making a RAG, `False` in the
positions to ignore.
show_progress : bool, optional
Whether to display an ASCII progress bar during long-
-running graph operations.
connectivity : int in {1, ..., `watershed.ndim`}
When determining adjacency, allow neighbors along
`connectivity` dimensions.
channel_is_oriented : array-like of bool, shape (Q,), optional
For multi-channel images, some channels, for example some
edge detectors, have a specific orientation. In conjunction
with the `orientation_map` argument, specify which channels
have an orientation associated with them.
orientation_map : array-like of float, shape (Q,)
Specify the orientation of the corresponding channel. (2D
images only)
normalize_probabilities : bool, optional
Divide the input `probabilities` by their maximum to ensure
a range in [0, 1].
exclusions : array-like of int, shape (M, N, ..., P), optional
Volume of same shape as `watershed`. Mark points in the
volume with the same label (>0) to prevent them from being
merged during agglomeration. For example, if
`exclusions[45, 92] == exclusions[51, 105] == 1`, then
segments `watershed[45, 92]` and `watershed[51, 105]` will
never be merged, regardless of the merge priority function.
isfrozennode : function, optional
Function taking in a Rag object and a node id and returning
a bool. If the function returns ``True``, the node will not
be merged, regardless of the merge priority function.
isfrozenedge : function, optional
As `isfrozennode`, but the function should take the graph
and *two* nodes, to specify an edge that cannot be merged.
"""
def __init__(self, watershed=array([], label_dtype),
probabilities=array([]),
merge_priority_function=boundary_mean, gt_vol=None,
feature_manager=features.base.Null(), mask=None,
show_progress=False, connectivity=1,
channel_is_oriented=None, orientation_map=array([]),
normalize_probabilities=False, exclusions=array([]),
isfrozennode=None, isfrozenedge=None):
super(Rag, self).__init__(weighted=False)
self.show_progress = show_progress
self.connectivity = connectivity
self.pbar = (ip.StandardProgressBar() if self.show_progress
else ip.NoProgressBar())
self.set_watershed(watershed, connectivity)
self.set_probabilities(probabilities, normalize_probabilities)
self.set_orientations(orientation_map, channel_is_oriented)
self.merge_priority_function = merge_priority_function
self.max_merge_score = -inf
if mask is None:
self.mask = np.ones(self.watershed_r.shape, dtype=bool)
else:
self.mask = morpho.pad(mask, True).ravel()
self.build_graph_from_watershed()
self.set_feature_manager(feature_manager)
self.set_ground_truth(gt_vol)
self.set_exclusions(exclusions)
self.merge_queue = MergeQueue()
self.tree = tree.Ultrametric(self.nodes())
self.frozen_nodes = set()
if isfrozennode is not None:
for node in self.nodes():
if isfrozennode(self, node):
self.frozen_nodes.add(node)
self.frozen_edges = set()
if isfrozenedge is not None:
for n1, n2 in self.edges():
if isfrozenedge(self, n1, n2):
self.frozen_edges.add((n1,n2))
def __copy__(self):
"""Return a copy of the object and attributes.
"""
pr_shape = self.probabilities_r.shape
g = super(Rag, self).copy()
g.watershed_r = g.watershed.ravel()
g.probabilities_r = g.probabilities.reshape(pr_shape)
return g
def copy(self):
"""Return a copy of the object and attributes.
"""
return self.__copy__()
def extent(self, nodeid):
try:
ext = self.extents
full_ext = [ext.indices[ext.indptr[f]:ext.indptr[f+1]]
for f in self.node[nodeid]['fragments']]
return np.concatenate(full_ext).astype(np.intp)
except AttributeError:
extent_array = opt.flood_fill(self.watershed,
np.array(self.node[nodeid]['entrypoint']),
np.fromiter(self.node[nodeid]['fragments'],
dtype=int))
if len(extent_array) != self.node[nodeid]['size']:
sys.stderr.write('Flood fill fail - found %d voxels but size'
'expected %d\n' % (len(extent_array),
self.node[nodeid]['size']))
raveled_indices = np.ravel_multi_index(extent_array.T,
self.watershed.shape)
return set(raveled_indices)
def boundary(self, u, v):
edge_dict = self[u][v]
try:
return edge_dict['boundary']
except KeyError:
pass # not using old system
ids = edge_dict['boundary-ids']
b = self.boundaries
all_bounds = [b.indices[b.indptr[i]:b.indptr[i+1]] for i in ids]
return np.concatenate(all_bounds).astype(np.intp)
def real_edges(self, *args, **kwargs):
"""Return edges internal to the volume.
The RAG actually includes edges to a "virtual" region that
envelops the entire volume. This function returns the list of
edges that are internal to the volume.
Parameters
----------
*args, **kwargs : arbitrary types
Arguments and keyword arguments are passed through to the
``edges()`` function of the ``networkx.Graph`` class.
Returns
-------
edge_list : list of tuples
A list of pairs of node IDs, which are typically integers.
See Also
--------
real_edges_iter, networkx.Graph.edges
"""
return [e for e in super(Rag, self).edges(*args, **kwargs) if
self.boundary_body not in e[:2]]
def real_edges_iter(self, *args, **kwargs):
"""Return iterator of edges internal to the volume.
The RAG actually includes edges to a "virtual" region that
envelops the entire volume. This function returns the list of
edges that are internal to the volume.
Parameters
----------
*args, **kwargs : arbitrary types
Arguments and keyword arguments are passed through to the
``edges()`` function of the ``networkx.Graph`` class.
Returns
-------
edges_iter : iterator of tuples
An iterator over pairs of node IDs, which are typically
integers.
"""
return (e for e in super(Rag, self).edges_iter(*args, **kwargs) if
self.boundary_body not in e[:2])
def build_graph_from_watershed(self, idxs=None):
"""Build the graph object from the region labels.
The region labels should have been set ahead of time using
``set_watershed()``.
Parameters
----------
idxs : array-like of int, optional
Linear indices into raveled volume array. If provided, the
graph is built only for these indices.
"""
if self.watershed.size == 0:
return # stop processing for empty graphs
if idxs is None:
idxs = arange(self.watershed.size, dtype=self.steps.dtype)
idxs = idxs[self.mask[idxs]] # use only masked idxs
self.add_node(self.boundary_body)
labels = np.unique(self.watershed_r[idxs])
sizes = np.bincount(self.watershed_r)
if not hasattr(self, 'extents'):
self.extents = lol.extents(self.watershed)
for nodeid in labels:
self.add_node(nodeid)
node = self.node[nodeid]
node['size'] = sizes[nodeid]
node['fragments'] = set([nodeid])
node['entrypoint'] = (
np.array(np.unravel_index(self.extent(nodeid)[0],
self.watershed.shape)))
inner_idxs = idxs[self.watershed_r[idxs] != self.boundary_body]
if self.show_progress:
inner_idxs = ip.with_progress(inner_idxs, title='Graph ',
pbar=self.pbar)
for idx in inner_idxs:
nodeid = self.watershed_r[idx]
ns = idx + self.steps
ns = ns[self.mask[ns]]
adj = self.watershed_r[ns]
adj = set(adj)
for v in adj:
if v == nodeid:
continue
if self.has_edge(nodeid, v):
self[nodeid][v]['boundary'].append(idx)
else:
self.add_edge(nodeid, v, boundary=[idx])
def set_feature_manager(self, feature_manager):
"""Set the feature manager and ensure feature caches are computed.
Parameters
----------
feature_manager : ``features.base.Null`` object
The feature manager to be used by this RAG.
Returns
-------
None
"""
self.feature_manager = feature_manager
self.compute_feature_caches()
def compute_feature_caches(self):
"""Use the feature manager to compute node and edge feature caches.
Parameters
----------
None
Returns
-------
None
"""
for n in ip.with_progress(
self.nodes(), title='Node caches ', pbar=self.pbar):
self.node[n]['feature-cache'] = \
self.feature_manager.create_node_cache(self, n)
for n1, n2 in ip.with_progress(
self.edges(), title='Edge caches ', pbar=self.pbar):
self[n1][n2]['feature-cache'] = \
self.feature_manager.create_edge_cache(self, n1, n2)
def set_probabilities(self, probs=array([]), normalize=False):
"""Set the `probabilities` attributes of the RAG.
For various reasons, including removing the need for bounds
checking when looking for neighboring pixels, the volume of
pixel-level probabilities is padded on all faces. In addition,
this function adds an attribute `probabilities_r`, a raveled
view of the padded probabilities array for quick access to
individual voxels using linear indices.
Parameters
----------
probs : array
The input probabilities array.
normalize : bool, optional
If ``True``, the values in the array are scaled to be in
[0, 1].
Returns
-------
None
"""
if len(probs) == 0:
self.probabilities = zeros_like(self.watershed)
self.probabilities_r = self.probabilities.ravel()
probs = probs.astype('float')
if normalize and len(probs) > 1:
probs -= probs.min() # ensure probs.min() == 0
probs /= probs.max() # ensure probs.max() == 1
sp = probs.shape
sw = tuple(array(self.watershed.shape, dtype=int)-\
2*self.pad_thickness*ones(self.watershed.ndim, dtype=int))
p_ndim = probs.ndim
w_ndim = self.watershed.ndim
padding = [inf]+(self.pad_thickness-1)*[0]
if p_ndim == w_ndim:
self.probabilities = morpho.pad(probs, padding)
self.probabilities_r = self.probabilities.ravel()[:,newaxis]
elif p_ndim == w_ndim+1:
axes = list(range(p_ndim-1))
self.probabilities = morpho.pad(probs, padding, axes)
self.probabilities_r = self.probabilities.reshape(
(self.watershed.size, -1))
def set_orientations(self, orientation_map, channel_is_oriented):
"""Set the orientation map of the probability image.
Parameters
----------
orientation_map : array of float
A map of angles of the same shape as the superpixel map.
channel_is_oriented : 1D array-like of bool
A vector having length the number of channels in the
probability map.
Returns
-------
None
"""
if len(orientation_map) == 0:
self.orientation_map = zeros_like(self.watershed)
self.orientation_map_r = self.orientation_map.ravel()
padding = [0]+(self.pad_thickness-1)*[0]
self.orientation_map = morpho.pad(orientation_map, padding).astype(int)
self.orientation_map_r = self.orientation_map.ravel()
if channel_is_oriented is None:
nchannels = 1 if self.probabilities.ndim==self.watershed.ndim \
else self.probabilities.shape[-1]
self.channel_is_oriented = array([False]*nchannels)
self.max_probabilities_r = zeros_like(self.probabilities_r)
self.oriented_probabilities_r = zeros_like(self.probabilities_r)
self.non_oriented_probabilities_r = self.probabilities_r
else:
self.channel_is_oriented = channel_is_oriented
self.max_probabilities_r = \
self.probabilities_r[:, self.channel_is_oriented].max(axis=1)
self.oriented_probabilities_r = \
self.probabilities_r[:, self.channel_is_oriented]
self.oriented_probabilities_r = \
self.oriented_probabilities_r[
list(range(len(self.oriented_probabilities_r))),
self.orientation_map_r]
self.non_oriented_probabilities_r = \
self.probabilities_r[:, ~self.channel_is_oriented]
def set_watershed(self, ws=array([], label_dtype), connectivity=1):
"""Set the initial segmentation volume (watershed).
The initial segmentation is called `watershed` for historical
reasons only.
Parameters
----------
ws : array of int
The initial segmentation.
connectivity : int in {1, ..., `ws.ndim`}, optional
The pixel neighborhood.
Returns
-------
None
"""
ws = ws.astype(label_dtype)
try:
self.boundary_body = np.max(ws) + 1
except ValueError: # empty watershed given
self.boundary_body = 1
self.volume_size = ws.size
if ws.size > 0:
ws, _, inv = relabel_sequential(ws)
self.inverse_watershed_map = inv # translates to original labels
self.watershed = morpho.pad(ws, self.boundary_body)
self.watershed_r = self.watershed.ravel()
self.pad_thickness = 1
self.steps = morpho.raveled_steps_to_neighbors(self.watershed.shape,
connectivity)
def set_ground_truth(self, gt=None):
"""Set the ground truth volume.
This is useful for tracking segmentation accuracy over time.
Parameters
----------
gt : array of int
A ground truth segmentation of the same volume passed to
``set_watershed``.
Returns
-------
None
"""
if gt is not None:
gtm = gt.max()+1
gt_ignore = [0, gtm] if (gt==0).any() else [gtm]
seg_ignore = [0, self.boundary_body] if \
(self.watershed==0).any() else [self.boundary_body]
self.gt = morpho.pad(gt, gtm)
self.rig = merge_contingency_table(self.watershed, self.gt,
ignore_seg=seg_ignore,
ignore_gt=gt_ignore)
else:
self.gt = None
# null pattern to transparently allow merging of nodes.
# Bonus feature: counts how many sp's went into a single node.
try:
self.rig = ones(2 * self.watershed.max() + 1)
except ValueError:
self.rig = ones(2 * self.number_of_nodes() + 1)
def set_exclusions(self, excl):
"""Set an exclusion volume, forbidding certain merges.
Parameters
----------
excl : array of int
Exclusions work as follows: the volume `excl` is the same
shape as the initial segmentation (see ``set_watershed``),
and consists of mostly 0s. Any voxels with *the same*
non-zero label will not be allowed to merge during
agglomeration (provided they were not merged in the initial
segmentation).
This allows manual separation *a priori* of difficult-to-
-segment regions.
Returns
-------
None
"""
if excl.size != 0:
excl = morpho.pad(excl, [0]*self.pad_thickness)
for n in self.nodes():
if excl.size != 0:
eids = unique(excl.ravel()[self.extent(n)])
eids = eids[flatnonzero(eids)]
self.node[n]['exclusions'] = set(list(eids))
else:
self.node[n]['exclusions'] = set()
def build_merge_queue(self):
"""Build a queue of node pairs to be merged in a specific priority.
Returns
-------
mq : MergeQueue object
A MergeQueue is a Python ``deque`` with a specific element
structure: a list of length 4 containing:
- the merge priority (any ordered type)
- a 'valid' flag
- and the two nodes in arbitrary order
The valid flag allows one to "remove" elements from the
queue in O(1) time by setting the flag to ``False``. Then,
one checks the flag when popping elements and ignores those
marked as invalid.
One other specific feature is that there are back-links from
edges to their corresponding queue items so that when nodes
are merged, affected edges can be invalidated and reinserted
in the queue with a new priority.
"""
queue_items = []
for l1, l2 in self.real_edges_iter():
w = self.merge_priority_function(self,l1,l2)
qitem = [w, True, l1, l2]
queue_items.append(qitem)
self[l1][l2]['qlink'] = qitem
self[l1][l2]['weight'] = w
return MergeQueue(queue_items, with_progress=self.show_progress)
def rebuild_merge_queue(self):
"""Build a merge queue from scratch and assign to self.merge_queue.
See Also
--------
build_merge_queue
"""
self.merge_queue = self.build_merge_queue()
def agglomerate(self, threshold=0.5, save_history=False):
"""Merge nodes hierarchically until given edge confidence threshold.
This is the main workhorse of the ``agglo`` module!
Parameters
----------
threshold : float, optional
The edge priority at which to stop merging.
save_history : bool, optional
Whether to save and return a history of all the merges made.
Returns
-------
history : list of tuple of int, optional
The ordered history of node pairs merged.
scores : list of float, optional
The list of merge scores corresponding to the `history`.
evaluation : list of tuple, optional
The split VI after each merge. This is only meaningful if
a ground truth volume was provided at build time.
Notes
-----
This function returns ``None`` when `save_history` is
``False``.
"""
if self.merge_queue.is_empty():
self.merge_queue = self.build_merge_queue()
history, scores, evaluation = [], [], []
while len(self.merge_queue) > 0 and \
self.merge_queue.peek()[0] < threshold:
merge_priority, _, n1, n2 = self.merge_queue.pop()
self.update_frozen_sets(n1, n2)
self.merge_nodes(n1, n2, merge_priority)
if save_history:
history.append((n1,n2))
scores.append(merge_priority)
evaluation.append(
(self.number_of_nodes()-1, self.split_vi())
)
if save_history:
return history, scores, evaluation
def agglomerate_count(self, stepsize=100, save_history=False):
"""Agglomerate until 'stepsize' merges have been made.
This function is like ``agglomerate``, but rather than to a
certain threshold, a certain number of merges are made,
regardless of threshold.
Parameters
----------
stepsize : int, optional
The number of merges to make.
save_history : bool, optional
Whether to save and return a history of all the merges made.
Returns
-------
history : list of tuple of int, optional
The ordered history of node pairs merged.
scores : list of float, optional
The list of merge scores corresponding to the `history`.
evaluation : list of tuple, optional
The split VI after each merge. This is only meaningful if
a ground truth volume was provided at build time.
Notes
-----
This function returns ``None`` when `save_history` is
``False``.
See Also
--------
agglomerate
"""
if self.merge_queue.is_empty():
self.merge_queue = self.build_merge_queue()
history, evaluation = [], []
i = 0
for i in range(stepsize):
if len(self.merge_queue) == 0:
break
merge_priority, _, n1, n2 = self.merge_queue.pop()
i += 1
self.merge_nodes(n1, n2, merge_priority)
if save_history:
history.append((n1, n2))
evaluation.append(
(self.number_of_nodes()-1, self.split_vi())
)
if save_history:
return history, evaluation
def agglomerate_ladder(self, min_size=1000, strictness=2):
"""Merge sequentially all nodes smaller than `min_size`.
Parameters
----------
min_size : int, optional
The smallest allowable segment after ladder completion.
strictness : {1, 2, 3}, optional
`strictness == 1`: all nodes smaller than `min_size` are
merged according to the merge priority function.
`strictness == 2`: in addition to `1`, small nodes can only
be merged to big nodes.
`strictness == 3`: in addition to `2`, nodes sharing less
than one pixel of boundary are not agglomerated.
Returns
-------
None
Notes
-----
Nodes that are on the volume boundary are not agglomerated.
"""
original_merge_priority_function = self.merge_priority_function
self.merge_priority_function = make_ladder(
self.merge_priority_function, min_size, strictness
)
self.rebuild_merge_queue()
self.agglomerate(inf)
self.merge_priority_function = original_merge_priority_function
self.merge_queue.finish()
self.rebuild_merge_queue()
max_score = max([qitem[0] for qitem in self.merge_queue.q])
for n in self.tree.nodes():
self.tree.node[n]['w'] -= max_score
def learn_agglomerate(self, gts, feature_map,
min_num_samples=1,
learn_flat=True,
learning_mode='strict',
labeling_mode='assignment',
priority_mode='active',
memory=True,
unique=True,
random_state=None,
max_num_epochs=10,
min_num_epochs=2,
max_num_samples=np.inf,
classifier='random forest',
active_function=classifier_probability,
mpf=boundary_mean):
"""Agglomerate while comparing to ground truth & classifying merges.
Parameters
----------
gts : array of int or list thereof
The ground truth volume(s) corresponding to the current
probability map.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
min_num_samples : int, optional
Continue training until this many training examples have
been collected.
learn_flat : bool, optional
Do a flat learning on the static graph with no
agglomeration.
learning_mode : {'strict', 'loose'}, optional
In 'strict' mode, if a "don't merge" edge is encountered,
it is added to the training set but the merge is not
executed. In 'loose' mode, the merge is allowed to proceed.
labeling_mode : {'assignment', 'vi-sign', 'rand-sign'}, optional
How to decide whether two nodes should be merged based on
the ground truth segmentations. ``'assignment'`` means the
nodes are assigned to the ground truth node with which they
share the highest overlap. ``'vi-sign'`` means the the VI
change of the switch is used (negative is better).
``'rand-sign'`` means the change in Rand index is used
(positive is better).
priority_mode : string, optional
One of:
``'active'``: Train a priority function with the data
from previous epochs to obtain the next.
``'random'``: Merge edges at random.
``'mixed'``: Alternate between epochs of ``'active'``
and ``'random'``.
``'mean'``: Use the mean boundary value. (In this case,
training is limited to 1 or 2 epochs.)
``'custom'``: Use the function provided by `mpf`.
memory : bool, optional
Keep the training data from all epochs (rather than just
the most recent one).
unique : bool, optional
Remove duplicate feature vectors.
random_state : int, optional
If provided, this parameter is passed to `get_classifier`
to set the random state and allow consistent results across
tests.
max_num_epochs : int, optional
Do not train for longer than this (this argument *may*
override the `min_num_samples` argument).
min_num_epochs : int, optional
Train for no fewer than this number of epochs.
max_num_samples : int, optional
Train for no more than this number of samples.
classifier : string, optional
Any valid classifier descriptor. See
``gala.classify.get_classifier()``
active_function : function (feat. map, classifier) -> function, optional
Use this to create the next priority function after an
epoch.
mpf : function (Rag, node, node) -> float
A merge priority function to use when ``priority_mode`` is
``'custom'``.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
alldata : list of list of array
A list of lists like `data` above: one list for each epoch.
Notes
-----
The gala algorithm [1] uses the default parameters. For the
LASH algorithm [2], use:
- `learning_mode`: ``'loose'``
- `labeling_mode`: ``'rand-sign'``
- `memory`: ``False``
References
----------
.. [1] Nunez-Iglesias et al, Machine learning of hierarchical
clustering to segment 2D and 3D images, PLOS ONE, 2013.
.. [2] Jain et al, Learning to agglomerate superpixel
hierarchies, NIPS, 2011.
See Also
--------
Rag
"""
learning_mode = learning_mode.lower()
labeling_mode = labeling_mode.lower()
priority_mode = priority_mode.lower()
if priority_mode == 'mean' and unique:
max_num_epochs = 2 if learn_flat else 1
if priority_mode in ['random', 'mean'] and not memory:
max_num_epochs = 1
label_type_keys = {'assignment':0, 'vi-sign':1, 'rand-sign':2}
if type(gts) != list:
gts = [gts] # allow using single ground truth as input
master_ctables = [merge_contingency_table(self.get_segmentation(), gt)
for gt in gts]
alldata = []
data = [[],[],[],[]]
for num_epochs in range(max_num_epochs):
ctables = deepcopy(master_ctables)
if len(data[0]) > min_num_samples and num_epochs >= min_num_epochs:
break
if learn_flat and num_epochs == 0:
alldata.append(self.learn_flat(gts, feature_map))
data = unique_learning_data_elements(alldata) if memory \
else alldata[-1]
continue
g = self.copy()
if priority_mode == 'mean':
g.merge_priority_function = boundary_mean
elif num_epochs > 0 and priority_mode == 'active' or \
num_epochs % 2 == 1 and priority_mode == 'mixed':
cl = get_classifier(classifier, random_state=random_state)
feat, lab = classify.sample_training_data(
data[0], data[1][:, label_type_keys[labeling_mode]],
max_num_samples)
cl = cl.fit(feat, lab)
g.merge_priority_function = active_function(feature_map, cl)
elif priority_mode == 'random' or \
(priority_mode == 'active' and num_epochs == 0):
g.merge_priority_function = random_priority
elif priority_mode == 'custom':
g.merge_priority_function = mpf
g.show_progress = False # bug in MergeQueue usage causes
# progressbar crash.
g.rebuild_merge_queue()
alldata.append(g.learn_epoch(ctables, feature_map,
learning_mode=learning_mode,
labeling_mode=labeling_mode))
if memory:
if unique:
data = unique_learning_data_elements(alldata)
else:
data = concatenate_data_elements(alldata)
else:
data = alldata[-1]
logging.debug('data size %d at epoch %d'%(len(data[0]), num_epochs))
return data, alldata
def learn_flat(self, gts, feature_map):
"""Learn all edges on the graph, but don't agglomerate.
Parameters
----------
gts : array of int or list thereof
The ground truth volume(s) corresponding to the current
probability map.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
See Also
--------
learn_agglomerate
"""
if type(gts) != list:
gts = [gts] # allow using single ground truth as input
ctables = [merge_contingency_table(self.get_segmentation(), gt)
for gt in gts]
assignments = [ev.assignment_table(ct) for ct in ctables]
return list(map(array, zip(*[
self.learn_edge(e, ctables, assignments, feature_map)
for e in self.real_edges()])))
def learn_edge(self, edge, ctables, assignments, feature_map):
"""Determine whether an edge should be merged based on ground truth.
Parameters
----------
edge : (int, int) tuple
An edge in the graph.
ctables : list of array
A list of contingency tables determining overlap between the
current segmentation and the ground truth.
assignments : list of array
Similar to the contingency tables, but each row is thresholded
so each segment corresponds to exactly one ground truth segment.
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector.
Returns
-------
features : 1D array of float
The feature vector for that edge.
labels : 1D array of float, length 3
The labels determining whether the edge should be merged.
A value of `-1` means "should merge", while `1` means "should
not merge". The columns correspond to the three labeling
methods: assignment, VI sign, or RI sign.
weights : 1D array of float, length 2
The VI and RI change of the merge.
nodes : tuple of int
The given edge.
"""
n1, n2 = edge
features = feature_map(self, n1, n2).ravel()
# Calculate weights for weighting data points
s1, s2 = [self.node[n]['size'] for n in [n1, n2]]
weights = \
compute_local_vi_change(s1, s2, self.volume_size), \
compute_local_rand_change(s1, s2, self.volume_size)
# Get the fraction of times that n1 and n2 assigned to
# same segment in the ground truths
cont_labels = [
[(-1)**(a[n1]==a[n2]).toarray().all() for a in assignments],
[compute_true_delta_vi(ctable, n1, n2) for ctable in ctables],
[-compute_true_delta_rand(ctable, n1, n2, self.volume_size)
for ctable in ctables]
]
labels = [np.sign(mean(cont_label)) for cont_label in cont_labels]
if any(map(isnan, labels)) or any([label == 0 for label in labels]):
logging.debug('NaN or 0 labels found. ' +
' '.join(map(str, [labels, (n1, n2)])))
labels = [1 if i==0 or isnan(i) or n1 in self.frozen_nodes or
n2 in self.frozen_nodes or (n1, n2) in self.frozen_edges else
i for i in labels]
return features, labels, weights, (n1, n2)
def learn_epoch(self, ctables, feature_map,
learning_mode='permissive', labeling_mode='assignment'):
"""Learn the agglomeration process using various strategies.
Parameters
----------
ctables : array of float or list thereof
One or more contingency tables between own segments and gold
standard segmentations
feature_map : function (Rag, node, node) -> array of float
The map from node pairs to a feature vector. This must
consist either of uncached features or of the cache used
when building the graph.
learning_mode : {'strict', 'permissive'}, optional
If ``'strict'``, don't proceed with a merge when it goes against
the ground truth. For historical reasons, 'loose' is allowed as
a synonym for 'strict'.
labeling_mode : {'assignment', 'vi-sign', 'rand-sign'}, optional
Which label to use for `learning_mode`. Note that all labels
are saved in the end.
Returns
-------
data : list of array
Four arrays containing:
- the feature vectors, shape ``(n_samples, n_features)``.
- the labels, shape ``(n_samples, 3)``. A value of `-1`
means "should merge", while `1` means "should
not merge". The columns correspond to the three
labeling methods: assignment, VI sign, or RI sign.
- the VI and RI change of each merge, ``(n_edges, 2)``.
- the list of merged edges ``(n_edges, 2)``.
"""
label_type_keys = {'assignment':0, 'vi-sign':1, 'rand-sign':2}
assignments = [ev.csrRowExpandableCSR(asst)
for asst in map(ev.assignment_table, ctables)]
g = self
data = []
while len(g.merge_queue) > 0:
merge_priority, valid, n1, n2 = g.merge_queue.pop()
if g.boundary_body in (n1, n2):
continue
dat = g.learn_edge((n1,n2), ctables, assignments, feature_map)
data.append(dat)
label = dat[1][label_type_keys[labeling_mode]]
if learning_mode != 'strict' or label < 0:
node_id = g.merge_nodes(n1, n2, merge_priority)
for ctable, assignment in zip(ctables, assignments):
ctable[node_id] = ctable[n1] + ctable[n2]
ctable[n1] = 0
ctable[n2] = 0
assignment[node_id] = (ctable[node_id] ==
ctable[node_id].max())
assignment[n1] = 0
assignment[n2] = 0
return list(map(array, zip(*data)))
def replay_merge_history(self, merge_seq, labels=None, num_errors=1):
"""Agglomerate according to a merge sequence, optionally labeled.
Parameters
----------
merge_seq : iterable of pair of int
The sequence of node IDs to be merged.
labels : iterable of int in {-1, 0, 1}, optional
A sequence matching `merge_seq` specifying whether a merge
should take place or not. -1 or 0 mean "should merge", 1
otherwise.
Returns
-------
n : int
Number of elements consumed from `merge_seq`
e : (int, int)
Last merge pair observed.
Notes
-----
The merge sequence and labels *must* be generators if you don't want
to manually keep track of how much has been consumed. The merging
continues until `num_errors` false merges have been encountered, or
until the sequence is fully consumed.
"""
if labels is None:
labels1 = it.repeat(False)
labels2 = it.repeat(False)
else:
labels1 = (label > 0 for label in labels)
labels2 = (label > 0 for label in labels)
counter = it.count()
errors_remaining = conditional_countdown(labels2, num_errors)
nodes = None
for nodes, label, errs, count in \
zip(merge_seq, labels1, errors_remaining, counter):
n1, n2 = nodes
if not label:
self.merge_nodes(n1, n2)
elif errs == 0:
break
return next(counter), nodes
def rename_node(self, old, new):
"""Rename node `old` to `new`, updating edges and weights.
Parameters
----------
old : int
The node being renamed.
new : int
The new node id.
"""
self.add_node(new, attr_dict=self.node[old])
self.add_edges_from(
[(new, v, self[old][v]) for v in self.neighbors(old)])
for v in self.neighbors(new):
qitem = self[new][v].get('qlink', None)
if qitem is not None:
if qitem[2] == old:
qitem[2] = new
else:
qitem[3] = new
self.remove_node(old)
def merge_nodes(self, n1, n2, merge_priority=0.0):
"""Merge two nodes, while updating the necessary edges.
Parameters
----------
n1, n2 : int
Nodes determining the edge for which to update the UCM.
merge_priority : float, optional
The merge priority of the merge.
Returns
-------
node_id : int
The id of the node resulting from the merge.
Notes
-----
Additionally, the RIG (region intersection graph), the
contingency matrix to the ground truth (if provided) is
updated.
"""
if len(self.node[n1]['exclusions'] & self.node[n2]['exclusions']) > 0:
return
else:
self.node[n1]['exclusions'].update(self.node[n2]['exclusions'])
w = self[n1][n2].get('weight', merge_priority)
self.node[n1]['size'] += self.node[n2]['size']
self.node[n1]['fragments'].update(self.node[n2]['fragments'])
self.feature_manager.update_node_cache(self, n1, n2,
self.node[n1]['feature-cache'], self.node[n2]['feature-cache'])
new_neighbors = [n for n in self.neighbors(n2) if n != n1]
for n in new_neighbors:
self.merge_edge_properties((n2, n), (n1, n))
try:
self.merge_queue.invalidate(self[n1][n2]['qlink'])
except KeyError:
pass
node_id = self.tree.merge(n1, n2, w)
self.remove_node(n2)
self.rename_node(n1, node_id)
self.rig[node_id] = self.rig[n1] + self.rig[n2]
self.rig[n1] = 0
self.rig[n2] = 0
return node_id
def merge_subgraph(self, subgraph=None, source=None):
"""Merge a (typically) connected set of nodes together.
Parameters
----------
subgraph : agglo.Rag, networkx.Graph, or list of int (node id)
A subgraph to merge.
source : int (node id), optional
Merge the subgraph to this node.
"""
if type(subgraph) not in [Rag, Graph]: # input is node list
subgraph = self.subgraph(subgraph)
if len(subgraph) == 0:
return
for subsubgraph in nx.connected_component_subgraphs(subgraph):
node_dfs = list(dfs_preorder_nodes(subsubgraph, source))
# dfs_preorder_nodes returns iter, convert to list
source_node, other_nodes = node_dfs[0], node_dfs[1:]
for current_node in other_nodes:
source_node = self.merge_nodes(source_node, current_node)
def split_node(self, u, n=2, **kwargs):
"""Use normalized cuts [1] to split a node/segment.
Parameters
----------
u : int (node id)
Which node to split.
n : int, optional
How many segments to split it into.
Returns
-------
None
References
----------
.. [1] Shi, J., and Malik, J. (2000). Normalized cuts and image
segmentation. Pattern Analysis and Machine Intelligence.
"""
node_extent = self.extent(u)
labels = unique(self.watershed_r[node_extent])
self.remove_node(u)
self.build_graph_from_watershed(idxs=node_extent)
self.ncut(num_clusters=n, nodes=labels, **kwargs)
def separate_fragments(self, f0, f1):
"""Ensure fragments (watersheds) f0 and f1 are in different nodes.
If f0 and f1 are the same segment, split that segment at the
lowest common ancestor of f0 and f1 in the merge tree, then add an
exclusion. Otherwise, simply add an exclusion.
Parameters
----------
f0, f1 : int
The fragments to be separated.
Returns
-------
s0, s1 : int
The separated segments resulting from the break. If the
fragments were already in separate segments, return the
highest ancestor of each fragment on the merge tree.
"""
lca = tree.lowest_common_ancestor(self.tree, f0, f1)
if lca is not None:
s0, s1 = self.tree.children(lca)
self.delete_merge(lca)
else:
s0 = self.tree.highest_ancestor(f0)
s1 = self.tree.highest_ancestor(f1)
return s0, s1
def delete_merge(self, tree_node):
"""Delete the merge represented by `tree_node`.
Parameters
----------
tree_node : int
A node that may not be currently in the graph, but was at
some point in its history.
"""
highest = self.tree.highest_ancestor(tree_node)
if highest != tree_node:
leaves = self.tree.leaves(tree_node)
# the graph doesn't keep nodes in the history, only the
# most recent nodes. So, we only need to find that one and
# update its fragment list.
self.node[highest]['fragments'].difference_update(leaves)
self.tree.remove_node(tree_node)
def merge_edge_properties(self, src, dst):
"""Merge the properties of edge src into edge dst.
Parameters
----------
src, dst : (int, int)
Edges being merged.
Returns
-------
None
"""
u, v = dst
w, x = src
if not self.has_edge(u,v):
self.add_edge(u, v, attr_dict=self[w][x])
else:
self[u][v]['boundary'].extend(self[w][x]['boundary'])
self.feature_manager.update_edge_cache(self, (u, v), (w, x),
self[u][v]['feature-cache'], self[w][x]['feature-cache'])
try:
self.merge_queue.invalidate(self[w][x]['qlink'])
except KeyError:
pass
self.update_merge_queue(u, v)
def update_merge_queue(self, u, v):
"""Update the merge queue item for edge (u, v). Add new by default.
Parameters
----------
u, v : int (node id)
Edge being updated.
Returns
-------
None
"""
if self.boundary_body in [u, v]:
return
if 'qlink' in self[u][v]:
self.merge_queue.invalidate(self[u][v]['qlink'])
if not self.merge_queue.is_null_queue:
w = self.merge_priority_function(self,u,v)
new_qitem = [w, True, u, v]
self[u][v]['qlink'] = new_qitem
self[u][v]['weight'] = w
self.merge_queue.push(new_qitem)
def get_segmentation(self, threshold=None):
"""Return the unpadded segmentation represented by the graph.
Remember that the segmentation volume is padded with an
"artificial" segment that envelops the volume. This function
simply removes the wrapping and returns a segmented volume.
Parameters
----------
threshold : float, optional
Get the segmentation at the given threshold. If no
threshold is given, return the segmentation at the current
level of agglomeration.
Returns
-------
seg : array of int
The segmentation of the volume presently represented by the
graph.
"""
if threshold is None:
# a threshold of np.inf is the same as no threshold on the
# tree when getting the map (see below). Thus, using a
# threshold of `None` (the default), we get the segmentation
# implied by the current merge tree.
threshold = np.inf
elif threshold > self.max_merge_score:
# If a higher threshold is required than has been merged, we
# continue the agglomeration until that threshold is hit.
self.agglomerate(threshold)
m = self.tree.get_map(threshold)
seg = m[self.watershed]
if self.pad_thickness > 1: # volume has zero-boundaries
seg = morpho.remove_merged_boundaries(seg, self.connectivity)
return morpho.juicy_center(seg, self.pad_thickness)
def build_volume(self, nbunch=None):
"""Return the segmentation induced by the graph.
Parameters
----------
nbunch : iterable of int (node id), optional
A list of nodes for which to build the volume. All nodes
are used if this is not provided.
Returns
-------
seg : array of int
The segmentation implied by the graph.
Notes
-----
This function is very similar to ``get_segmentation``, but it
builds the segmentation from the bottom up, rather than using
the currently-stored segmentation.
"""
v = zeros_like(self.watershed)
vr = v.ravel()
if nbunch is None:
nbunch = self.nodes()
for n in nbunch:
vr[self.extent(n)] = n
return morpho.juicy_center(v,self.pad_thickness)
def build_boundary_map(self, ebunch=None):
"""Return a map of the current merge priority.
Parameters
----------
ebunch : iterable of (int, int), optional
The list of edges for which to build a map. Use all edges
if not provided.
Returns
-------
bm : array of float
The image of the edge weights.
"""
if len(self.merge_queue) == 0:
self.rebuild_merge_queue()
m = zeros(self.watershed.shape, 'float')
mr = m.ravel()
if ebunch is None:
ebunch = self.real_edges_iter()
ebunch = sorted([(self[u][v]['weight'], u, v) for u, v in ebunch])
for w, u, v in ebunch:
b = self.boundary(u, v)
mr[b] = w
if hasattr(self, 'ignored_boundary'):
m[self.ignored_boundary] = inf
return morpho.juicy_center(m, self.pad_thickness)
def remove_obvious_inclusions(self):
"""Merge any nodes with only one edge to their neighbors."""
for n in self.nodes():
if self.degree(n) == 1:
self.merge_nodes(self.neighbors(n)[0], n)
def remove_inclusions(self):
"""Merge any segments fully contained within other segments.
In 3D EM images, inclusions are not biologically plausible, so
this function can be used to remove them.
Parameters
----------
None
Returns
-------
None
"""
bcc = list(biconnected_components(self))
if len(bcc) > 1:
container = [i for i, s in enumerate(bcc) if
self.boundary_body in s][0]
del bcc[container] # remove the main graph
bcc = list(map(list, bcc))
for cc in bcc:
cc.sort(key=lambda x: self.node[x]['size'], reverse=True)
bcc.sort(key=lambda x: self.node[x[0]]['size'])
for cc in bcc:
self.merge_subgraph(cc, cc[0])
def orphans(self):
"""List all the nodes that do not touch the volume boundary.
Parameters
----------
None
Returns
-------
orphans : list of int (node id)
A list of node ids.
Notes
-----
"Orphans" are not biologically plausible in EM data, so we can
flag them with this function for further scrutiny.
"""
return [n for n in self.nodes() if not self.at_volume_boundary(n)]
def compute_orphans(self):
"""Find all the segments that do not touch the volume boundary.
Parameters
----------
None
Returns
-------
orphans : list of int (node id)
A list of node ids.
Notes
-----
This function differs from ``orphans`` in that it does not use
the graph, but rather computes orphans directly from the
segmentation.
"""
return morpho.orphans(self.get_segmentation())
def is_traversed_by_node(self, n):
"""Determine whether a body traverses the volume.
This is defined as touching the volume boundary at two distinct
locations.
Parameters
----------
n : int (node id)
The node being inspected.
Returns
-------
tr : bool
Whether the segment "traverses" the volume being segmented.
"""
if not self.at_volume_boundary(n) or n == self.boundary_body:
return False
v = zeros(self.watershed.shape, 'uint8')
v.ravel()[self.boundary(n, self.boundary_body)] = 1
_, n = label(v, ones([3]*v.ndim))
return n > 1
def traversing_bodies(self):
"""List all bodies that traverse the volume."""
return [n for n in self.nodes() if self.is_traversed_by_node(n)]
def non_traversing_bodies(self):
"""List bodies that are not orphans and do not traverse the volume."""
return [n for n in self.nodes() if self.at_volume_boundary(n) and
not self.is_traversed_by_node(n) and n != self.boundary_body]
def raveler_body_annotations(self, traverse=False):
"""Return JSON-compatible dict formatted for Raveler annotations."""
orphans = self.compute_orphans()
non_traversing_bodies = self.compute_non_traversing_bodies() \
if traverse else []
data = \
[{'status':'not sure', 'comment':'orphan', 'body ID':int(o)}
for o in orphans] +\
[{'status':'not sure', 'comment':'does not traverse',
'body ID':int(n)} for n in non_traversing_bodies]
metadata = {'description':'body annotations', 'file version':2}
return {'data':data, 'metadata':metadata}
def at_volume_boundary(self, n):
"""Return True if node n touches the volume boundary."""
return self.has_edge(n, self.boundary_body) or n == self.boundary_body
def should_merge(self, n1, n2):
return self.rig[n1].argmax() == self.rig[n2].argmax()
def get_pixel_label(self, n1, n2):
boundary = self.boundary(n1, n2)
min_idx = boundary[self.probabilities_r[boundary,0].argmin()]
if self.should_merge(n1, n2):
return min_idx, 2
else:
return min_idx, 1
def pixel_labels_array(self, false_splits_only=False):
ar = zeros_like(self.watershed_r)
labels = [self.get_pixel_label(*e) for e in self.real_edges()]
if false_splits_only:
labels = [l for l in labels if l[1] == 2]
ids, ls = list(map(array,zip(*labels)))
ar[ids] = ls.astype(ar.dtype)
return ar.reshape(self.watershed.shape)
def split_vi(self, gt=None):
if self.gt is None and gt is None:
return array([0,0])
elif self.gt is not None:
return split_vi(self.rig)
else:
return split_vi(self.get_segmentation(), gt, [0], [0])
def get_edge_coordinates(self, n1, n2, arbitrary=False):
"""Find where in the segmentation the edge (n1, n2) is most visible."""
return get_edge_coordinates(self, n1, n2, arbitrary)
def write(self, fout, output_format='GraphML'):
if output_format == 'Plaza JSON':
self.write_plaza_json(fout)
else:
raise ValueError('Unsupported output format for agglo.Rag: %s'
% output_format)
def write_plaza_json(self, fout, synapsejson=None, offsetz=0):
"""Write graph to Steve Plaza's JSON spec."""
json_vals = {}
if synapsejson is not None:
synapse_file = open(synapsejson)
json_vals1 = json.load(synapse_file)
body_count = {}
for item in json_vals1["data"]:
bodyid = ((item["T-bar"])["body ID"])
if bodyid in body_count:
body_count[bodyid] += 1
else:
body_count[bodyid] = 1
for psd in item["partners"]:
bodyid = psd["body ID"]
if bodyid in body_count:
body_count[bodyid] += 1
else:
body_count[bodyid] = 1
json_vals["synapse_bodies"] = []
for body, count in body_count.items():
temp = [body, count]
json_vals["synapse_bodies"].append(temp)
edge_list = [
{'location': list(map(int, self.get_edge_coordinates(i, j)[-1::-1])),
'node1': int(i), 'node2': int(j),
'edge_size': len(self.boundary(i, j)),
'size1': self.node[i]['size'],
'size2': self.node[j]['size'],
'weight': float(self[i][j]['weight'])}
for i, j in self.real_edges()
]
json_vals['edge_list'] = edge_list
with open(fout, 'w') as f:
json.dump(json_vals, f, indent=4)
def ncut(self, num_clusters=10, kmeans_iters=5, sigma=255.0*20, nodes=None,
**kwargs):
"""Run normalized cuts on the current set of superpixels.
Keyword arguments:
num_clusters -- number of clusters to compute
kmeans_iters -- # iterations to run kmeans when clustering
sigma -- sigma value when setting up weight matrix
Return value: None
"""
if nodes is None:
nodes = self.nodes()
# Compute weight matrix
W = self.compute_W(self.merge_priority_function, nodes=nodes)
# Run normalized cut
labels, eigvec, eigval = ncutW(W, num_clusters, kmeans_iters, **kwargs)
# Merge nodes that are in same cluster
self.cluster_by_labels(labels, nodes)
def cluster_by_labels(self, labels, nodes=None):
"""Merge all superpixels with the same label (1 label per 1 sp)"""
if nodes is None:
nodes = array(self.nodes())
if not (len(labels) == len(nodes)):
raise ValueError('Number of labels should be %d but is %d.',
self.number_of_nodes(), len(labels))
for l in unique(labels):
inds = nonzero(labels==l)[0]
nodes_to_merge = nodes[inds]
node1 = nodes_to_merge[0]
for node in nodes_to_merge[1:]:
self.merge_nodes(node1, node)
def compute_W(self, merge_priority_function, sigma=255.0*20, nodes=None):
""" Computes the weight matrix for clustering"""
if nodes is None:
nodes = array(self.nodes())
n = len(nodes)
nodes2ind = dict(zip(nodes, range(n)))
W = lil_matrix((n,n))
for u, v in self.real_edges(nodes):
try:
i, j = nodes2ind[u], nodes2ind[v]
except KeyError:
continue
w = merge_priority_function(self,u,v)
W[i,j] = W[j,i] = np.exp(-w**2/sigma)
return W
def update_frozen_sets(self, n1, n2):
self.frozen_nodes.discard(n1)
self.frozen_nodes.discard(n2)
for x, y in self.frozen_edges.copy():
if n2 in [x, y]:
self.frozen_edges.discard((x, y))
if x == n2:
self.frozen_edges.add((n1, y))
if y == n2:
self.frozen_edges.add((x, n1))
def get_edge_coordinates(g, n1, n2, arbitrary=False):
"""Find where in the segmentation the edge (n1, n2) is most visible."""
boundary = g.boundary(n1, n2)
if arbitrary:
# quickly get an arbitrary point on the boundary
idx = boundary.pop(); boundary.append(idx)
coords = unravel_index(idx, g.watershed.shape)
else:
boundary_idxs = unravel_index(boundary, g.watershed.shape)
coords = [bincount(dimcoords).argmax() for dimcoords in boundary_idxs]
return array(coords) - g.pad_thickness
def is_mito_boundary(g, n1, n2, channel=2, threshold=0.5):
return max(np.mean(g.probabilities_r[g.boundary(n1, n2), c])
for c in channel) > threshold
def is_mito(g, n, channel=2, threshold=0.5):
return max(np.mean(g.probabilities_r[g.extent(n), c])
for c in channel) > threshold
def best_possible_segmentation(ws, gt):
"""Build the best possible segmentation given a superpixel map."""
ws = Rag(ws)
assignment = ev.assignment_table(ws.get_segmentation(), gt).tocsc()
for gt_node in range(assignment.shape[1]):
i, j = assignment.indptr[gt_node : gt_node+2]
ws.merge_subgraph(assignment.indices[i:j])
return ws.get_segmentation()
|
#!/usr/bin/env python
"""
author: Stefano Gariazzo <gariazzo@ific.uv.es>
Read the .tex files in a folder and creates a .bib file for the compilation of the .tex document.
You should use INSPIRES keys for the bibtex entries in order to have a good behaviour of the code.
Usage:
python bibtexImporter.py "folder/where/tex/files/are/" "name_of_output.bib"
assuming that you want to compile some tex file(s):
"folder/where/tex/files/are/*.tex"
and that your .bib file should be called:
"folder/where/tex/files/are/name_of_output.bib".
The code reads the texs to detect which entries must be saved in the output .bib file,
checks the .bib files in the main local database (path configured in the script) to copy the entries that are already stored there
or fetches the missing ones in INSPIRES. The downloaded information is saved both in the local database and in the output file.
"""
import sys,re,os
import urllib2
#configuration: folder and main file where to save the downloaded entries
bibfolder = '/home/gariazzo/Latex/bib/'
saveInFile = "tmp.bib"
################################################################
# unicode characters replacements.
# INSPIRES has the bad habit of using non-ascii characters that can't be saved properly in non-unicode files.
# just convert the bad characters into standard ones.
unicode_to_latex = {
u"\u00C0": "A",
u"\u00C1": "A",
u"\u00C2": "A",
u"\u00C3": "A",
u"\u00C4": "A",
u"\u00C5": "AA",
u"\u00C6": "AE",
u"\u00C7": "C",
u"\u00C8": "E",
u"\u00C9": "E",
u"\u00CA": "E",
u"\u00CB": "E",
u"\u00CC": "I",
u"\u00CD": "I",
u"\u00CE": "I",
u"\u00CF": "I",
u"\u00D0": "DH",
u"\u00D1": "N",
u"\u00D2": "O",
u"\u00D3": "O",
u"\u00D4": "O",
u"\u00D5": "O",
u"\u00D6": "O",
u"\u00D8": "O",
u"\u00D9": "U",
u"\u00DA": "U",
u"\u00DB": "U",
u"\u00DC": "U",
u"\u00DD": "Y",
u"\u00DE": "TH",
u"\u00DF": "ss",
u"\u00E0": "a",
u"\u00E1": "a",
u"\u00E2": "a",
u"\u00E3": "a",
u"\u00E4": "a",
u"\u00E5": "aa",
u"\u00E6": "ae",
u"\u00E7": "c",
u"\u00E8": "e",
u"\u00E9": "e",
u"\u00EA": "e",
u"\u00EB": "e",
u"\u00EC": "i",
u"\u00ED": "i",
u"\u00EE": "i",
u"\u00EF": "i",
u"\u00F0": "dh",
u"\u00F1": "n",
u"\u00F2": "o",
u"\u00F3": "o",
u"\u00F4": "o",
u"\u00F5": "o",
u"\u00F6": "o",
u"\u00F8": "o",
u"\u00F9": "u",
u"\u00FA": "u",
u"\u00FB": "u",
u"\u00FC": "u",
u"\u00FD": "y",
u"\u00FE": "th",
u"\u00FF": "y",
u"\u0100": "A",
u"\u0101": "a",
u"\u0102": "A",
u"\u0103": "a",
u"\u0104": "A",
u"\u0105": "a",
u"\u0410": "A",
u"\u0106": "C",
u"\u0107": "c",
u"\u0108": "C",
u"\u0109": "c",
u"\u010A": "C",
u"\u010B": "c",
u"\u010C": "C",
u"\u010D": "c",
u"\u010E": "D",
u"\u010F": "d",
u"\u0110": "DJ",
u"\u0111": "dj",
u"\u0112": "E",
u"\u0113": "e",
u"\u0114": "E",
u"\u0115": "e",
u"\u0116": "E",
u"\u0117": "e",
u"\u0118": "E",
u"\u0119": "e",
u"\u011A": "E",
u"\u011B": "e",
u"\u011C": "G",
u"\u011D": "g",
u"\u011E": "G",
u"\u011F": "g",
u"\u0120": "G",
u"\u0121": "g",
u"\u0122": "G",
u"\u0123": "g",
u"\u0124": "H",
u"\u0125": "h",
u"\u0128": "I",
u"\u0129": "i",
u"\u012A": "I",
u"\u012B": "i",
u"\u012C": "I",
u"\u012D": "i",
u"\u012E": "I",
u"\u012F": "i",
u"\u0130": "I",
u"\u0131": "i",
u"\u0132": "IJ",
u"\u0133": "ij",
u"\u0134": "J",
u"\u0135": "j",
u"\u0136": "K",
u"\u0137": "k",
u"\u0139": "L",
u"\u013A": "l",
u"\u013B": "L",
u"\u013C": "l",
u"\u013D": "L",
u"\u013E": "l",
u"\u0141": "L",
u"\u0142": "l",
u"\u0143": "N",
u"\u0144": "n",
u"\u0145": "N",
u"\u0146": "n",
u"\u0147": "N",
u"\u0148": "n",
u"\u0149": "n",
u"\u014A": "NG",
u"\u014B": "ng",
u"\u014C": "O",
u"\u014D": "o",
u"\u014E": "O",
u"\u014F": "o",
u"\u0150": "O",
u"\u0151": "o",
u"\u0152": "OE",
u"\u0153": "oe",
u"\u0154": "R",
u"\u0155": "r",
u"\u0156": "R",
u"\u0157": "r",
u"\u0158": "R",
u"\u0159": "r",
u"\u015A": "S",
u"\u015B": "s",
u"\u015C": "S",
u"\u015D": "s",
u"\u015E": "S",
u"\u015F": "s",
u"\u0160": "S",
u"\u0161": "s",
u"\u0162": "T",
u"\u0163": "t",
u"\u0164": "T",
u"\u0165": "t",
u"\u0168": "U",
u"\u0169": "u",
u"\u016A": "U",
u"\u016B": "u",
u"\u016C": "U",
u"\u016D": "u",
u"\u016E": "U",
u"\u016F": "u",
u"\u0170": "U",
u"\u0171": "u",
u"\u0172": "U",
u"\u0173": "u",
u"\u0174": "W",
u"\u0175": "w",
u"\u0176": "Y",
u"\u0177": "y",
u"\u0178": "Y",
u"\u0179": "Z",
u"\u017A": "z",
u"\u017B": "Z",
u"\u017C": "z",
u"\u017D": "Z",
u"\u017E": "z",
u"\u01F5": "g",
u"\u03CC": "o",
u"\u2013": "-",
u"\u207b": "-",
u"\u2014": "--",
u"\u2015": "---",
u"\u2018": "'",
u"\u2019": "'",
u"\xa0": " ",
}
translation_table = dict([(ord(k), unicode(v)) for k, v in unicode_to_latex.items()])
def parse_accents_str(string):
"""needed to remove bad unicode characters that cannot printed well"""
if string is not None and string is not "":
string = string.translate(translation_table)
return string
def writeToFile(text, stream, k):
"""write to file with try/except to avoid problems with unicode"""
try:
stream.write(text)
return True
except UnicodeEncodeError:
print "the current entry '%s' cannot be saved since it contains a bad unicode character!"%m
return False
def retrieveurl(bibkey):
"""search Inspires for the missing entries"""
url="http://inspirehep.net/search?p=" + bibkey + "&sf=&so=d&rm=&rg=1000&sc=0&of=hx&em=B";
print "looking for '%s' in %s"%(bibkey, url)
response = urllib2.urlopen(url)
data = response.read() # a `bytes` object
text = data.decode('utf-8')
i1=text.find("<pre>")
i2=text.find("</pre>")
if i1>0 and i2>0:
text=text[i1+5:i2]
else:
text=""
return text
#read which bib files are in the main folder
l=os.listdir(bibfolder)
bibs=[]
for e in l:
if e.find('.bib')>0 and e.find('.bak')<0:
bibs.append(e)
#read the folder name, scan it for the existing .tex files to be compiled
keysfold=sys.argv[1]
l=os.listdir(keysfold)
texs=[]
for e in l:
if e.find('.tex')>0 and e.find('.bac')<0 and e.find('~')<0:
texs.append(e)
#name of the output .bib file to be saved
outfile=sys.argv[2]
#print some information
print "reading keys from "+keysfold+" folder:"
print " "+" ".join(texs)
print "bib entries from "+bibfolder+" directory:"
print " "+" ".join(bibs)
print "saving in "+keysfold+outfile+"\n"
if not os.path.isfile(keysfold+outfile):
with open(keysfold+outfile,'a'):
os.utime(keysfold+outfile,None)
#save content of tex files in a string:
keyscont=""
for t in texs:
with open(keysfold+t) as r:
keyscont += r.read()
#if existing, read output file (to detect which bibtex entries are already there:)
with open(keysfold+outfile) as r:
outcont = r.read()
#read the local bibtex database in the specified folder
allbib=""
for b in bibs:
with open(bibfolder+b) as r:
allbib += r.read()
#regular expression utilities
cite=re.compile('\\\\cite\{([A-Za-z]*:[0-9]*[a-z]*[,]?[\n ]*|[A-Za-z0-9\-][,]?[\n ]*)*\}',re.MULTILINE) #find \cite{...}
bibel=re.compile('@[a-zA-Z]*\{([A-Za-z]*:[0-9]*[a-z]*)?,',re.MULTILINE|re.DOTALL) #find the @Article(or other)...}, entry for the key "m"
bibty=re.compile('@[a-zA-Z]*\{',re.MULTILINE|re.DOTALL) #find the @Article(or other) entry for the key "m"
#You can add here more fields that you have in your local bib file but that are not necessary for compilation:
unw1=re.compile('[ ]*(Owner|Timestamp|__markedentry|File)+[ ]*=.*?,[\n]*') #remove unwanted fields
unw2=re.compile('[ ]*(Owner|Timestamp|__markedentry|File)+[ ]*=.*?[\n ]*\}') #remove unwanted fields
unw3=re.compile('[ ]*Abstract[ ]*=[ ]*[{]+(.*?)[}]+,',re.MULTILINE) #remove Abstract field
#use regex to detect "\cite{...}" commands in the tex files:
citaz=[m for m in cite.finditer(keyscont)]
strs=[]
#for each "\cite{...}", extract the cited bibtex keys
for c in citaz:
b=c.group().replace(r'\cite{','')
d=b.replace(' ','')
b=d.replace('\n','')
d=b.replace(r'}','')
a=d.split(',')
for e in a:
if e not in strs:
strs.append(e)
print "keys found: %d"%len(strs)
missing=[]
warnings=0
#read the list of bibtex needed keys and check which ones are already in the main bibliography files
for s in strs:
if s not in outcont:
missing.append(s)
print "missing: %d"%len(missing)
notfound=""
keychange=""
#enters the main loop. If entries exist locally, they are just copied, else INSPIRES will be searched for them
for m in missing:
art=re.compile('@[a-zA-Z]*\{'+m+',.*?@',re.MULTILINE|re.DOTALL) #find the @Article(or other) entry for the key "m"
t=[j for j in art.finditer(allbib)]
#local bibtexs match: remove unwanted stuff and copy to output file
if len(t)>0:
a=t[0].group()
bib='@'+a.replace('@','')
for u in unw1.finditer(bib):
bib=bib.replace(u.group(),'')
for u in unw2.finditer(bib):
bib=bib.replace(u.group(),'')
for u in unw3.finditer(bib):
bib=bib.replace(u.group(),'')
bibf = '\n'.join([line for line in bib.split('\n') if line.strip() ])
with open(keysfold+outfile,"a") as o:
if writeToFile(bibf+"\n", o, m):
print "- %s inserted!"%m
#entry missing in local database: search inspires
else:
new=parse_accents_str(retrieveurl(m))#open search url
if len(new):
#sometimes inspires changes the bibtex keys after some time.
#save the entry in the output .bib file and give a warning if it happened for the current entry
with open(keysfold+outfile,"a") as o:
if not new.find(m)>0:
warnings+=1
t=[j.group() for j in bibel.finditer(new)]
t1=[]
for s in t:
for u in bibty.finditer(s):
s=s.replace(u.group(),'')
s=s.replace(',','')
t1.append(s)
keychange+= "--> WARNING! %s has a new key: %s\n"%(m,t1[0])
#first, save the bibtex as it is in the main database or temporary file:
for s in t1:
if m not in allbib and str(s) not in allbib:
with open(bibfolder+saveInFile,"a") as o:
if writeToFile(new+"\n",o,m):
print "'%s' (new key '%s') retrieved by InspireHEP and inserted into %s file - %d bytes"%(m,s,saveInFile,len(new))
if str(s) not in outcont:
if writeToFile(new+"\n",o,m):
print "... and it was inserted in the .bib file"
else:
notfound+="-- warning: missing entry for %s\n"%m
warnings+=1
#print resume of what has been done: keys that don't exist in INSPIRES, entries whose key has been changed, total number of warnings
print "finished!\n"
print notfound
print keychange
print "--> %d warning(s) occurred!"%warnings
python3 compatibility
#!/usr/bin/env python
"""
author: Stefano Gariazzo <gariazzo@ific.uv.es>
Read the .tex files in a folder and creates a .bib file for the compilation of the .tex document.
You should use INSPIRES keys for the bibtex entries in order to have a good behaviour of the code.
Usage:
python bibtexImporter.py "folder/where/tex/files/are/" "name_of_output.bib"
assuming that you want to compile some tex file(s):
"folder/where/tex/files/are/*.tex"
and that your .bib file should be called:
"folder/where/tex/files/are/name_of_output.bib".
The code reads the texs to detect which entries must be saved in the output .bib file,
checks the .bib files in the main local database (path configured in the script) to copy the entries that are already stored there
or fetches the missing ones in INSPIRES. The downloaded information is saved both in the local database and in the output file.
"""
import sys,re,os
try:
# For Python 3.0 and later
from urllib.request import urlopen
except ImportError:
# Fall back to Python 2's urllib2
from urllib2 import urlopen
#configuration: folder and main file where to save the downloaded entries
bibfolder = '/home/gariazzo/Latex/bib/'
saveInFile = "tmp.bib"
################################################################
# unicode characters replacements.
# INSPIRES has the bad habit of using non-ascii characters that can't be saved properly in non-unicode files.
# just convert the bad characters into standard ones.
unicode_to_latex = {
u"\u00C0": "A",
u"\u00C1": "A",
u"\u00C2": "A",
u"\u00C3": "A",
u"\u00C4": "A",
u"\u00C5": "AA",
u"\u00C6": "AE",
u"\u00C7": "C",
u"\u00C8": "E",
u"\u00C9": "E",
u"\u00CA": "E",
u"\u00CB": "E",
u"\u00CC": "I",
u"\u00CD": "I",
u"\u00CE": "I",
u"\u00CF": "I",
u"\u00D0": "DH",
u"\u00D1": "N",
u"\u00D2": "O",
u"\u00D3": "O",
u"\u00D4": "O",
u"\u00D5": "O",
u"\u00D6": "O",
u"\u00D8": "O",
u"\u00D9": "U",
u"\u00DA": "U",
u"\u00DB": "U",
u"\u00DC": "U",
u"\u00DD": "Y",
u"\u00DE": "TH",
u"\u00DF": "ss",
u"\u00E0": "a",
u"\u00E1": "a",
u"\u00E2": "a",
u"\u00E3": "a",
u"\u00E4": "a",
u"\u00E5": "aa",
u"\u00E6": "ae",
u"\u00E7": "c",
u"\u00E8": "e",
u"\u00E9": "e",
u"\u00EA": "e",
u"\u00EB": "e",
u"\u00EC": "i",
u"\u00ED": "i",
u"\u00EE": "i",
u"\u00EF": "i",
u"\u00F0": "dh",
u"\u00F1": "n",
u"\u00F2": "o",
u"\u00F3": "o",
u"\u00F4": "o",
u"\u00F5": "o",
u"\u00F6": "o",
u"\u00F8": "o",
u"\u00F9": "u",
u"\u00FA": "u",
u"\u00FB": "u",
u"\u00FC": "u",
u"\u00FD": "y",
u"\u00FE": "th",
u"\u00FF": "y",
u"\u0100": "A",
u"\u0101": "a",
u"\u0102": "A",
u"\u0103": "a",
u"\u0104": "A",
u"\u0105": "a",
u"\u0410": "A",
u"\u0106": "C",
u"\u0107": "c",
u"\u0108": "C",
u"\u0109": "c",
u"\u010A": "C",
u"\u010B": "c",
u"\u010C": "C",
u"\u010D": "c",
u"\u010E": "D",
u"\u010F": "d",
u"\u0110": "DJ",
u"\u0111": "dj",
u"\u0112": "E",
u"\u0113": "e",
u"\u0114": "E",
u"\u0115": "e",
u"\u0116": "E",
u"\u0117": "e",
u"\u0118": "E",
u"\u0119": "e",
u"\u011A": "E",
u"\u011B": "e",
u"\u011C": "G",
u"\u011D": "g",
u"\u011E": "G",
u"\u011F": "g",
u"\u0120": "G",
u"\u0121": "g",
u"\u0122": "G",
u"\u0123": "g",
u"\u0124": "H",
u"\u0125": "h",
u"\u0128": "I",
u"\u0129": "i",
u"\u012A": "I",
u"\u012B": "i",
u"\u012C": "I",
u"\u012D": "i",
u"\u012E": "I",
u"\u012F": "i",
u"\u0130": "I",
u"\u0131": "i",
u"\u0132": "IJ",
u"\u0133": "ij",
u"\u0134": "J",
u"\u0135": "j",
u"\u0136": "K",
u"\u0137": "k",
u"\u0139": "L",
u"\u013A": "l",
u"\u013B": "L",
u"\u013C": "l",
u"\u013D": "L",
u"\u013E": "l",
u"\u0141": "L",
u"\u0142": "l",
u"\u0143": "N",
u"\u0144": "n",
u"\u0145": "N",
u"\u0146": "n",
u"\u0147": "N",
u"\u0148": "n",
u"\u0149": "n",
u"\u014A": "NG",
u"\u014B": "ng",
u"\u014C": "O",
u"\u014D": "o",
u"\u014E": "O",
u"\u014F": "o",
u"\u0150": "O",
u"\u0151": "o",
u"\u0152": "OE",
u"\u0153": "oe",
u"\u0154": "R",
u"\u0155": "r",
u"\u0156": "R",
u"\u0157": "r",
u"\u0158": "R",
u"\u0159": "r",
u"\u015A": "S",
u"\u015B": "s",
u"\u015C": "S",
u"\u015D": "s",
u"\u015E": "S",
u"\u015F": "s",
u"\u0160": "S",
u"\u0161": "s",
u"\u0162": "T",
u"\u0163": "t",
u"\u0164": "T",
u"\u0165": "t",
u"\u0168": "U",
u"\u0169": "u",
u"\u016A": "U",
u"\u016B": "u",
u"\u016C": "U",
u"\u016D": "u",
u"\u016E": "U",
u"\u016F": "u",
u"\u0170": "U",
u"\u0171": "u",
u"\u0172": "U",
u"\u0173": "u",
u"\u0174": "W",
u"\u0175": "w",
u"\u0176": "Y",
u"\u0177": "y",
u"\u0178": "Y",
u"\u0179": "Z",
u"\u017A": "z",
u"\u017B": "Z",
u"\u017C": "z",
u"\u017D": "Z",
u"\u017E": "z",
u"\u01F5": "g",
u"\u03CC": "o",
u"\u2013": "-",
u"\u207b": "-",
u"\u2014": "--",
u"\u2015": "---",
u"\u2018": "'",
u"\u2019": "'",
u"\xa0": " ",
}
try:
translation_table = dict([(ord(k), unicode(v)) for k, v in unicode_to_latex.items()])
except NameError:
translation_table = dict([(ord(k), str(v)) for k, v in unicode_to_latex.items()])
def parse_accents_str(string):
"""needed to remove bad unicode characters that cannot printed well"""
if string is not None and string is not "":
string = string.translate(translation_table)
return string
def writeToFile(text, filename, k):
"""write to file with try/except to avoid problems with unicode"""
try:
with open(filename,"a") as stream:
stream.write(text)
return True
except UnicodeEncodeError:
print("the current entry '%s' cannot be saved since it contains a bad unicode character!"%m)
return False
def retrieveurl(bibkey):
"""search Inspires for the missing entries"""
url="http://inspirehep.net/search?p=" + bibkey + "&sf=&so=d&rm=&rg=1000&sc=0&of=hx&em=B";
print("looking for '%s' in %s"%(bibkey, url))
response = urlopen(url)
data = response.read() # a `bytes` object
text = data.decode('utf-8')
i1=text.find("<pre>")
i2=text.find("</pre>")
if i1>0 and i2>0:
text=text[i1+5:i2]
else:
text=""
return text
#read which bib files are in the main folder
l=os.listdir(bibfolder)
bibs=[]
for e in l:
if e.find('.bib')>0 and e.find('.bak')<0:
bibs.append(e)
#read the folder name, scan it for the existing .tex files to be compiled
keysfold=sys.argv[1]
l=os.listdir(keysfold)
texs=[]
for e in l:
if e.find('.tex')>0 and e.find('.bac')<0 and e.find('~')<0:
texs.append(e)
#name of the output .bib file to be saved
outfile=sys.argv[2]
#print some information
print("reading keys from "+keysfold+" folder:")
print(" "+" ".join(texs))
print("bib entries from "+bibfolder+" directory:")
print(" "+" ".join(bibs))
print("saving in "+keysfold+outfile+"\n")
if not os.path.isfile(keysfold+outfile):
with open(keysfold+outfile,'a'):
os.utime(keysfold+outfile,None)
#save content of tex files in a string:
keyscont=""
for t in texs:
with open(keysfold+t) as r:
keyscont += r.read()
#if existing, read output file (to detect which bibtex entries are already there:)
with open(keysfold+outfile) as r:
outcont = r.read()
#read the local bibtex database in the specified folder
allbib=""
for b in bibs:
with open(bibfolder+b) as r:
allbib += r.read()
#regular expression utilities
cite=re.compile('\\\\cite\{([A-Za-z]*:[0-9]*[a-z]*[,]?[\n ]*|[A-Za-z0-9\-][,]?[\n ]*)*\}',re.MULTILINE) #find \cite{...}
bibel=re.compile('@[a-zA-Z]*\{([A-Za-z]*:[0-9]*[a-z]*)?,',re.MULTILINE|re.DOTALL) #find the @Article(or other)...}, entry for the key "m"
bibty=re.compile('@[a-zA-Z]*\{',re.MULTILINE|re.DOTALL) #find the @Article(or other) entry for the key "m"
#You can add here more fields that you have in your local bib file but that are not necessary for compilation:
unw1=re.compile('[ ]*(Owner|Timestamp|__markedentry|File)+[ ]*=.*?,[\n]*') #remove unwanted fields
unw2=re.compile('[ ]*(Owner|Timestamp|__markedentry|File)+[ ]*=.*?[\n ]*\}') #remove unwanted fields
unw3=re.compile('[ ]*Abstract[ ]*=[ ]*[{]+(.*?)[}]+,',re.MULTILINE) #remove Abstract field
#use regex to detect "\cite{...}" commands in the tex files:
citaz=[m for m in cite.finditer(keyscont)]
strs=[]
#for each "\cite{...}", extract the cited bibtex keys
for c in citaz:
b=c.group().replace(r'\cite{','')
d=b.replace(' ','')
b=d.replace('\n','')
d=b.replace(r'}','')
a=d.split(',')
for e in a:
if e not in strs:
strs.append(e)
print("keys found: %d"%len(strs))
missing=[]
warnings=0
#read the list of bibtex needed keys and check which ones are already in the main bibliography files
for s in strs:
if s not in outcont:
missing.append(s)
print("missing: %d"%len(missing))
notfound=""
keychange=""
#enters the main loop. If entries exist locally, they are just copied, else INSPIRES will be searched for them
for m in missing:
art=re.compile('@[a-zA-Z]*\{'+m+',.*?@',re.MULTILINE|re.DOTALL) #find the @Article(or other) entry for the key "m"
t=[j for j in art.finditer(allbib)]
#local bibtexs match: remove unwanted stuff and copy to output file
if len(t)>0:
a=t[0].group()
bib='@'+a.replace('@','')
for u in unw1.finditer(bib):
bib=bib.replace(u.group(),'')
for u in unw2.finditer(bib):
bib=bib.replace(u.group(),'')
for u in unw3.finditer(bib):
bib=bib.replace(u.group(),'')
bibf = '\n'.join([line for line in bib.split('\n') if line.strip() ])
if writeToFile(bibf+"\n", keysfold+outfile, m):
print("- %s inserted!"%m)
#entry missing in local database: search inspires
else:
new=parse_accents_str(retrieveurl(m))#open search url
if len(new):
#sometimes inspires changes the bibtex keys after some time.
#save the entry in the output .bib file and give a warning if it happened for the current entry
if not new.find(m)>0:
warnings+=1
t=[j.group() for j in bibel.finditer(new)]
t1=[]
for s in t:
for u in bibty.finditer(s):
s=s.replace(u.group(),'')
s=s.replace(',','')
t1.append(s)
keychange+= "--> WARNING! %s has a new key: %s\n"%(m,t1[0])
#first, save the bibtex as it is in the main database or temporary file:
for s in t1:
if m not in allbib and str(s) not in allbib:
if writeToFile(new+"\n",bibfolder+saveInFile,m):
print("'%s' (new key '%s') retrieved by InspireHEP and inserted into %s file - %d bytes"%(m,s,saveInFile,len(new)))
if str(s) not in outcont:
if writeToFile(new+"\n",keysfold+outfile,m):
print("... and it was inserted in the .bib file")
else:
notfound+="-- warning: missing entry for %s\n"%m
warnings+=1
#print resume of what has been done: keys that don't exist in INSPIRES, entries whose key has been changed, total number of warnings
print("finished!\n")
print(notfound)
print(keychange)
print("--> %d warning(s) occurred!"%warnings)
|
from django.db import models
from django import forms
from wheelcms_axle.content import Content
from wheelcms_axle.node import Node
from wheelcms_spokes.page import PageBase, PageType, PageForm
from wheelcms_axle.content import type_registry
from wheelcms_axle.impexp import WheelSerializer
class CategorySerializer(WheelSerializer):
extra = WheelSerializer.extra + ('items', )
def serialize_extra_items(self, field, o):
""" serialize 'items'. Since it's a m2m, field will be a string """
res = []
for i in o.items.all():
res.append(dict(name="item", value=i.node.path))
return dict(name="items", value=res)
def deserialize_extra_items(self, extra, tree, model):
items = []
for item in tree.findall("items/item"):
items.append(item.text)
def delay_items():
for i in items:
## make absolute path relative to basenode. Relative path
## may be "" / None which means the root, or (relatively)
## self.basenode
if not i or i == '/':
n = self.basenode
else:
n = self.basenode.child(i.lstrip('/'))
# import pytest; pytest.set_trace()
model.items.add(n.content())
return delay_items
class Category(PageBase):
items = models.ManyToManyField(Content, related_name="categories")
## manytomany to content
def __unicode__(self):
return self.title
class CategoryForm(PageForm):
## exclude categories?
class Meta(PageForm.Meta):
model = Category
items = forms.ModelMultipleChoiceField(queryset=Content.objects.all(),
required=False)
class CategoryType(PageType):
model = Category
title = "A category"
form = CategoryForm
serializer = CategorySerializer
@property
def icon(self):
## assume that if this category contains children, they're
## categories themselves and this is more a collection of
## categories.
if self.instance.node.children().exists():
return "categories.png"
return "category.png"
@classmethod
def extend_form(cls, f, *args, **kwargs):
f.fields['categories'] = forms.ModelMultipleChoiceField(
queryset=Category.objects.all(), required=False)
if 'instance' in kwargs:
f.fields['categories'].initial = kwargs['instance'].categories.all()
f.advanced_fields += ["categories"]
@classmethod
def extend_save(cls, form, instance, commit=True):
old_save_m2m = form.save_m2m
def save_m2m():
old_save_m2m()
instance.categories.clear()
for cat in form.cleaned_data['categories']:
instance.categories.add(cat)
form.save_m2m = save_m2m
type_registry.register(CategoryType, extends=Content)
exclude from search. Fixes #636
from django.db import models
from django import forms
from wheelcms_axle.content import Content
from wheelcms_axle.node import Node
from wheelcms_spokes.page import PageBase, PageType, PageForm
from wheelcms_axle.content import type_registry
from wheelcms_axle.impexp import WheelSerializer
class CategorySerializer(WheelSerializer):
extra = WheelSerializer.extra + ('items', )
def serialize_extra_items(self, field, o):
""" serialize 'items'. Since it's a m2m, field will be a string """
res = []
for i in o.items.all():
res.append(dict(name="item", value=i.node.path))
return dict(name="items", value=res)
def deserialize_extra_items(self, extra, tree, model):
items = []
for item in tree.findall("items/item"):
items.append(item.text)
def delay_items():
for i in items:
## make absolute path relative to basenode. Relative path
## may be "" / None which means the root, or (relatively)
## self.basenode
if not i or i == '/':
n = self.basenode
else:
n = self.basenode.child(i.lstrip('/'))
# import pytest; pytest.set_trace()
model.items.add(n.content())
return delay_items
class Category(PageBase):
items = models.ManyToManyField(Content, related_name="categories")
## manytomany to content
def __unicode__(self):
return self.title
class CategoryForm(PageForm):
## exclude categories?
class Meta(PageForm.Meta):
model = Category
items = forms.ModelMultipleChoiceField(queryset=Content.objects.all(),
required=False)
class CategoryType(PageType):
model = Category
title = "A category"
form = CategoryForm
serializer = CategorySerializer
add_to_index = False
@property
def icon(self):
## assume that if this category contains children, they're
## categories themselves and this is more a collection of
## categories.
if self.instance.node.children().exists():
return "categories.png"
return "category.png"
@classmethod
def extend_form(cls, f, *args, **kwargs):
f.fields['categories'] = forms.ModelMultipleChoiceField(
queryset=Category.objects.all(), required=False)
if 'instance' in kwargs:
f.fields['categories'].initial = kwargs['instance'].categories.all()
f.advanced_fields += ["categories"]
@classmethod
def extend_save(cls, form, instance, commit=True):
old_save_m2m = form.save_m2m
def save_m2m():
old_save_m2m()
instance.categories.clear()
for cat in form.cleaned_data['categories']:
instance.categories.add(cat)
form.save_m2m = save_m2m
type_registry.register(CategoryType, extends=Content)
|
"""
Part of this code is based on a similar implementation present in FireWorks (https://pypi.python.org/pypi/FireWorks).
Work done by D. Waroquiers, A. Jain, and M. Kocher.
The main difference wrt the Fireworks implementation is that the QueueAdapter
objects provide a programmatic interface for setting important attributes
such as the number of MPI nodes, the number of OMP threads and the memory requirements.
This programmatic interface is used by the `TaskManager` for optimizing the parameters
of the run before submitting the job (Abinit provides the autoparal option that
allows one to get a list of parallel configuration and their expected efficiency).
"""
from __future__ import print_function, division
import os
import abc
import string
import copy
import getpass
from subprocess import Popen, PIPE
from pymatgen.io.abinitio.launcher import ScriptEditor
from pymatgen.util.string_utils import is_string
import logging
logger = logging.getLogger(__name__)
__all__ = [
"MpiRunner",
"qadapter_class",
]
class Command(object):
"""
From https://gist.github.com/kirpit/1306188
Enables to run subprocess commands in a different thread with TIMEOUT option.
Based on jcollado's solution:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout/4825933#4825933
"""
command = None
process = None
status = None
output, error = '', ''
def __init__(self, command):
if is_string(command):
import shlex
command = shlex.split(command)
self.command = command
def run(self, timeout=None, **kwargs):
""" Run a command then return: (status, output, error). """
def target(**kwargs):
try:
self.process = Popen(self.command, **kwargs)
self.output, self.error = self.process.communicate()
self.status = self.process.returncode
except:
import traceback
self.error = traceback.format_exc()
self.status = -1
# default stdout and stderr
if 'stdout' not in kwargs:
kwargs['stdout'] = PIPE
if 'stderr' not in kwargs:
kwargs['stderr'] = PIPE
# thread
import threading
thread = threading.Thread(target=target, kwargs=kwargs)
thread.start()
thread.join(timeout)
if thread.is_alive():
self.process.terminate()
thread.join()
return self.status, self.output, self.error
class MpiRunner(object):
"""
This object provides an abstraction for the mpirunner provided
by the different MPI libraries. It's main task is handling the
different syntax and options supported by the different mpirunners.
"""
def __init__(self, name, type=None, options=""):
self.name = name
self.type = None
self.options = options
def string_to_run(self, executable, mpi_ncpus, stdin=None, stdout=None, stderr=None):
stdin = "< " + stdin if stdin is not None else ""
stdout = "> " + stdout if stdout is not None else ""
stderr = "2> " + stderr if stderr is not None else ""
if self.has_mpirun:
if self.type is None:
# TODO: better treatment of mpirun syntax.
#se.add_line('$MPIRUN -n $MPI_NCPUS $EXECUTABLE < $STDIN > $STDOUT 2> $STDERR')
num_opt = "-n " + str(mpi_ncpus)
cmd = " ".join([self.name, num_opt, executable, stdin, stdout, stderr])
else:
raise NotImplementedError("type %s is not supported!")
else:
#assert mpi_ncpus == 1
cmd = " ".join([executable, stdin, stdout, stderr])
return cmd
@property
def has_mpirun(self):
"""True if we are running via mpirun, mpiexec ..."""
return self.name is not None
def qadapter_class(qtype):
"""Return the concrete `Adapter` class from a string."""
return {"shell": ShellAdapter,
"slurm": SlurmAdapter,
"pbs": PbsAdapter,
"sge": SGEAdapter,
}[qtype.lower()]
class QueueAdapterError(Exception):
"""Error class for exceptions raise by QueueAdapter."""
class AbstractQueueAdapter(object):
"""
The QueueAdapter is responsible for all interactions with a specific
queue management system. This includes handling all details of queue
script format as well as queue submission and management.
This is the Abstract base class defining the methods that
must be implemented by the concrete classes.
A user should extend this class with implementations that work on
specific queue systems.
"""
__metaclass__ = abc.ABCMeta
Error = QueueAdapterError
def __init__(self, qparams=None, setup=None, modules=None, shell_env=None, omp_env=None,
pre_run=None, post_run=None, mpi_runner=None):
"""
Args:
setup:
String or list of commands to execute during the initial setup.
modules:
String or list of modules to load before running the application.
shell_env:
Dictionary with the environment variables to export
before running the application.
omp_env:
Dictionary with the OpenMP variables.
pre_run:
String or list of commands to execute before launching the calculation.
post_run:
String or list of commands to execute once the calculation is completed.
mpi_runner:
Path to the MPI runner or `MpiRunner` instance. None if not used
"""
# Make defensive copies so that we can change the values at runtime.
self.qparams = qparams.copy() if qparams is not None else {}
if is_string(setup):
setup = [setup]
self.setup = setup[:] if setup is not None else []
self.omp_env = omp_env.copy() if omp_env is not None else {}
if is_string(modules):
modules = [modules]
self.modules = modules[:] if modules is not None else []
self.shell_env = shell_env.copy() if shell_env is not None else {}
self.mpi_runner = mpi_runner
if not isinstance(mpi_runner, MpiRunner):
self.mpi_runner = MpiRunner(mpi_runner)
if is_string(pre_run):
pre_run = [pre_run]
self.pre_run = pre_run[:] if pre_run is not None else []
if is_string(post_run):
post_run = [post_run]
self.post_run = post_run[:] if post_run is not None else []
# Parse the template so that we know the list of supported options.
cls = self.__class__
if hasattr(cls, "QTEMPLATE"):
# Consistency check.
err_msg = ""
for param in self.qparams:
if param not in self.supported_qparams:
err_msg += "Unsupported QUEUE parameter name %s\n" % param
if err_msg:
raise ValueError(err_msg)
def copy(self):
return copy.copy(self)
def deepcopy(self):
return copy.deepcopy(self)
@property
def supported_qparams(self):
"""
Dictionary with the supported parameters that can be passed to the
queue manager (obtained by parsing QTEMPLATE).
"""
try:
return self._supported_qparams
except AttributeError:
import re
self._supported_qparams = re.findall("\$\$\{(\w+)\}", self.QTEMPLATE)
return self._supported_qparams
@property
def has_mpirun(self):
"""True if we are using a mpirunner"""
return bool(self.mpi_runner)
@property
def has_omp(self):
"""True if we are using OpenMP threads"""
return hasattr(self,"omp_env") and bool(getattr(self, "omp_env"))
@property
def tot_ncpus(self):
"""Total number of CPUs employed"""
return self.mpi_ncpus * self.omp_ncpus
@property
def omp_ncpus(self):
"""Number of OpenMP threads."""
if self.has_omp:
return self.omp_env["OMP_NUM_THREADS"]
else:
return 1
@abc.abstractproperty
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
@abc.abstractmethod
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
#@abc.abstractproperty
#def queue_walltime(self):
# """Returns the walltime in seconds."""
#@abc.abstractmethod
#def set_queue_walltime(self):
# """Set the walltime in seconds."""
#@abc.abstractproperty
#def mem_per_cpu(self):
# """The memory per CPU in Megabytes."""
@abc.abstractmethod
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
#@property
#def tot_mem(self):
# """Total memory required by the job n Megabytes."""
# return self.mem_per_cpu * self.mpi_ncpus
@abc.abstractmethod
def cancel(self, job_id):
"""
Cancel the job.
Args:
job_id:
(in) Job identifier.
Returns:
Exit status.
"""
def _make_qheader(self, job_name, qout_path, qerr_path):
"""Return a string with the options that are passed to the resource manager."""
qtemplate = QScriptTemplate(self.QTEMPLATE)
# set substitution dict for replacements into the template and clean null values
subs_dict = {k: v for k,v in self.qparams.items() if v is not None}
# Set job_name and the names for the stderr and stdout of the
# queue manager (note the use of the extensions .qout and .qerr
# so that we can easily locate this file.
subs_dict['job_name'] = job_name.replace('/','_')
subs_dict['_qout_path'] = qout_path
subs_dict['_qerr_path'] = qerr_path
# might contain unused parameters as leftover $$.
unclean_template = qtemplate.safe_substitute(subs_dict)
# Remove lines with leftover $$.
clean_template = []
for line in unclean_template.split('\n'):
if '$$' not in line:
clean_template.append(line)
return '\n'.join(clean_template)
def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path, stdin=None, stdout=None, stderr=None):
"""
Returns a (multi-line) String representing the queue script, e.g. PBS script.
Uses the template_file along with internal parameters to create the script.
Args:
launch_dir:
(str) The directory the job will be launched in.
qout_path
Path of the Queue manager output file.
qerr_path:
Path of the Queue manager error file.
"""
# Construct the header for the Queue Manager.
qheader = self._make_qheader(job_name, qout_path, qerr_path)
# Add the bash section.
se = ScriptEditor()
if self.setup:
se.add_comment("Setup section")
se.add_lines(self.setup)
if self.modules:
se.add_comment("Load Modules")
se.add_line("module purge")
se.load_modules(self.modules)
if self.has_omp:
se.add_comment("OpenMp Environment")
se.declare_vars(self.omp_env)
if self.shell_env:
se.add_comment("Shell Environment")
se.declare_vars(self.shell_env)
# Cd to launch_dir
se.add_line("cd " + os.path.abspath(launch_dir))
if self.pre_run:
se.add_comment("Commands before execution")
se.add_lines(self.pre_run)
# Construct the string to run the executable with MPI and mpi_ncpus.
mpi_ncpus = self.mpi_ncpus
line = self.mpi_runner.string_to_run(executable, mpi_ncpus, stdin=stdin, stdout=stdout, stderr=stderr)
se.add_line(line)
if self.post_run:
se.add_comment("Commands after execution")
se.add_lines(self.post_run)
shell_text = se.get_script_str()
return qheader + shell_text
@abc.abstractmethod
def submit_to_queue(self, script_file):
"""
Submits the job to the queue, probably using subprocess or shutil
Args:
script_file:
(str) name of the script file to use (String)
Returns:
process, queue_id
"""
@abc.abstractmethod
def get_njobs_in_queue(self, username=None):
"""
returns the number of jobs in the queue, probably using subprocess or shutil to
call a command like 'qstat'. returns None when the number of jobs cannot be determined.
Args:
username: (str) the username of the jobs to count (default is to autodetect)
"""
####################
# Concrete classes #
####################
class ShellAdapter(AbstractQueueAdapter):
QTYPE = "shell"
QTEMPLATE = """\
#!/bin/bash
export MPI_NCPUS=$${MPI_NCPUS}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("MPI_NCPUS", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["MPI_NCPUS"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""mem_per_cpu is not available in ShellAdapter."""
def cancel(self, job_id):
return os.system("kill -9 %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
process = Popen(("/bin/bash", script_file), stderr=PIPE)
queue_id = process.pid
return process, queue_id
except:
# random error
raise self.Error("Random Error ...!")
def get_njobs_in_queue(self, username=None):
return None
class SlurmAdapter(AbstractQueueAdapter):
QTYPE = "slurm"
QTEMPLATE = """\
#!/bin/bash
#SBATCH --ntasks=$${ntasks}
#SBATCH --ntasks-per-node=$${ntasks_per_node}
#SBATCH --cpus-per-task=$${cpus_per_task}
#SBATCH --time=$${time}
#SBATCH --partition=$${partition}
#SBATCH --account=$${account}
#SBATCH --job-name=$${job_name}
#SBATCH --nodes=$${nodes}
#SBATCH --mem=$${mem}
#SBATCH --mem-per-cpu=$${mem_per_cpu}
#SBATCH --mail-user=$${mail_user}
#SBATCH --mail-type=$${mail_type}
#SBATCH --constraint=$${constraint}
#SBATCH --gres=$${gres}
#SBATCH --requeue=$${requeue}
#SBATCH --nodelist=$${nodelist}
#SBATCH --propagate=$${propagate}
#SBATCH --output=$${_qout_path}
#SBATCH --error=$${_qerr_path}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ntasks", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ntasks"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["mem_per_cpu"] = int(mem_mb)
# Remove mem if it's defined.
self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("scancel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['sbatch', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. SLURM returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split()[3])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
queue_id = None
logger.warning('Could not parse job id following slurm...')
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
err_msg = ("Error in job submission with SLURM file {f} and cmd {c}\n".format(f=script_file, c=cmd) +
"The error response reads: {}".format(process.stderr.read()))
raise self.Error(err_msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error('Running sbatch caused an error...')
def get_njobs_in_queue(self, username=None):
if username is None:
username = getpass.getuser()
cmd = ['squeue', '-o "%u"', '-u', username]
process = Popen(cmd, shell=False, stdout=PIPE)
process.wait()
# parse the result
if process.returncode == 0:
# lines should have this form
# username
# count lines that include the username in it
outs = process.stdout.readlines()
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to squeue server?
err_msg = ('Error trying to get the number of jobs in the queue using squeue service' +
'The error response reads: {}'.format(process.stderr.read()))
logger.critical(err_msg)
return None
class PbsAdapter(AbstractQueueAdapter):
QTYPE = "pbs"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l mppwidth=$${mppwidth}
#PBS -l nodes=$${nodes}:ppn=$${ppn}
#PBS -N $${job_name}
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("nodes", 1) * self.qparams.get("ppn", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
if "ppn" not in self.qparams: self.qparams["ppn"] = 1
ppnode = self.qparams.get("ppn")
self.qparams["nodes"] = mpi_ncpus // ppnode
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. PBS returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split('.')[0])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-a', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should have this form
# '1339044.sdb username queuename 2012-02-29-16-43 20460 -- -- -- 00:20 C 00:09'
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
class SGEAdapter(AbstractQueueAdapter):
"""
Adapter for Sun Grid Engine (SGE) task submission software.
"""
QTYPE = "sge"
QTEMPLATE = """\
#!/bin/bash
#$ -A $${account}
#$ -N $${job_name}
#$ -l h rt=$${walltime}
#$ -pe $${queue} $${ncpus}
#$ -cwd
#$ -j y
#$ -m n
#$ -e $${_qerr_path}
#$ -o $${_qout_path}
#$ -S /bin/bash
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ncpus", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ncpus"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. PBS returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form
# Your job 1659048 ("NAME_OF_JOB") has been submitted
queue_id = int(process.stdout.read().split(' ')[2])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-a', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should have this form
# '1339044.sdb username queuename 2012-02-29-16-43 20460 -- -- -- 00:20 C 00:09'
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
class QScriptTemplate(string.Template):
delimiter = '$$'
Modified code that probes queue in SGE.
This modification implements the correct way to
apply qstat with SGE.
"""
Part of this code is based on a similar implementation present in FireWorks (https://pypi.python.org/pypi/FireWorks).
Work done by D. Waroquiers, A. Jain, and M. Kocher.
The main difference wrt the Fireworks implementation is that the QueueAdapter
objects provide a programmatic interface for setting important attributes
such as the number of MPI nodes, the number of OMP threads and the memory requirements.
This programmatic interface is used by the `TaskManager` for optimizing the parameters
of the run before submitting the job (Abinit provides the autoparal option that
allows one to get a list of parallel configuration and their expected efficiency).
"""
from __future__ import print_function, division
import os
import abc
import string
import copy
import getpass
from subprocess import Popen, PIPE
from pymatgen.io.abinitio.launcher import ScriptEditor
from pymatgen.util.string_utils import is_string
import logging
logger = logging.getLogger(__name__)
__all__ = [
"MpiRunner",
"qadapter_class",
]
class Command(object):
"""
From https://gist.github.com/kirpit/1306188
Enables to run subprocess commands in a different thread with TIMEOUT option.
Based on jcollado's solution:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout/4825933#4825933
"""
command = None
process = None
status = None
output, error = '', ''
def __init__(self, command):
if is_string(command):
import shlex
command = shlex.split(command)
self.command = command
def run(self, timeout=None, **kwargs):
""" Run a command then return: (status, output, error). """
def target(**kwargs):
try:
self.process = Popen(self.command, **kwargs)
self.output, self.error = self.process.communicate()
self.status = self.process.returncode
except:
import traceback
self.error = traceback.format_exc()
self.status = -1
# default stdout and stderr
if 'stdout' not in kwargs:
kwargs['stdout'] = PIPE
if 'stderr' not in kwargs:
kwargs['stderr'] = PIPE
# thread
import threading
thread = threading.Thread(target=target, kwargs=kwargs)
thread.start()
thread.join(timeout)
if thread.is_alive():
self.process.terminate()
thread.join()
return self.status, self.output, self.error
class MpiRunner(object):
"""
This object provides an abstraction for the mpirunner provided
by the different MPI libraries. It's main task is handling the
different syntax and options supported by the different mpirunners.
"""
def __init__(self, name, type=None, options=""):
self.name = name
self.type = None
self.options = options
def string_to_run(self, executable, mpi_ncpus, stdin=None, stdout=None, stderr=None):
stdin = "< " + stdin if stdin is not None else ""
stdout = "> " + stdout if stdout is not None else ""
stderr = "2> " + stderr if stderr is not None else ""
if self.has_mpirun:
if self.type is None:
# TODO: better treatment of mpirun syntax.
#se.add_line('$MPIRUN -n $MPI_NCPUS $EXECUTABLE < $STDIN > $STDOUT 2> $STDERR')
num_opt = "-n " + str(mpi_ncpus)
cmd = " ".join([self.name, num_opt, executable, stdin, stdout, stderr])
else:
raise NotImplementedError("type %s is not supported!")
else:
#assert mpi_ncpus == 1
cmd = " ".join([executable, stdin, stdout, stderr])
return cmd
@property
def has_mpirun(self):
"""True if we are running via mpirun, mpiexec ..."""
return self.name is not None
def qadapter_class(qtype):
"""Return the concrete `Adapter` class from a string."""
return {"shell": ShellAdapter,
"slurm": SlurmAdapter,
"pbs": PbsAdapter,
"sge": SGEAdapter,
}[qtype.lower()]
class QueueAdapterError(Exception):
"""Error class for exceptions raise by QueueAdapter."""
class AbstractQueueAdapter(object):
"""
The QueueAdapter is responsible for all interactions with a specific
queue management system. This includes handling all details of queue
script format as well as queue submission and management.
This is the Abstract base class defining the methods that
must be implemented by the concrete classes.
A user should extend this class with implementations that work on
specific queue systems.
"""
__metaclass__ = abc.ABCMeta
Error = QueueAdapterError
def __init__(self, qparams=None, setup=None, modules=None, shell_env=None, omp_env=None,
pre_run=None, post_run=None, mpi_runner=None):
"""
Args:
setup:
String or list of commands to execute during the initial setup.
modules:
String or list of modules to load before running the application.
shell_env:
Dictionary with the environment variables to export
before running the application.
omp_env:
Dictionary with the OpenMP variables.
pre_run:
String or list of commands to execute before launching the calculation.
post_run:
String or list of commands to execute once the calculation is completed.
mpi_runner:
Path to the MPI runner or `MpiRunner` instance. None if not used
"""
# Make defensive copies so that we can change the values at runtime.
self.qparams = qparams.copy() if qparams is not None else {}
if is_string(setup):
setup = [setup]
self.setup = setup[:] if setup is not None else []
self.omp_env = omp_env.copy() if omp_env is not None else {}
if is_string(modules):
modules = [modules]
self.modules = modules[:] if modules is not None else []
self.shell_env = shell_env.copy() if shell_env is not None else {}
self.mpi_runner = mpi_runner
if not isinstance(mpi_runner, MpiRunner):
self.mpi_runner = MpiRunner(mpi_runner)
if is_string(pre_run):
pre_run = [pre_run]
self.pre_run = pre_run[:] if pre_run is not None else []
if is_string(post_run):
post_run = [post_run]
self.post_run = post_run[:] if post_run is not None else []
# Parse the template so that we know the list of supported options.
cls = self.__class__
if hasattr(cls, "QTEMPLATE"):
# Consistency check.
err_msg = ""
for param in self.qparams:
if param not in self.supported_qparams:
err_msg += "Unsupported QUEUE parameter name %s\n" % param
if err_msg:
raise ValueError(err_msg)
def copy(self):
return copy.copy(self)
def deepcopy(self):
return copy.deepcopy(self)
@property
def supported_qparams(self):
"""
Dictionary with the supported parameters that can be passed to the
queue manager (obtained by parsing QTEMPLATE).
"""
try:
return self._supported_qparams
except AttributeError:
import re
self._supported_qparams = re.findall("\$\$\{(\w+)\}", self.QTEMPLATE)
return self._supported_qparams
@property
def has_mpirun(self):
"""True if we are using a mpirunner"""
return bool(self.mpi_runner)
@property
def has_omp(self):
"""True if we are using OpenMP threads"""
return hasattr(self,"omp_env") and bool(getattr(self, "omp_env"))
@property
def tot_ncpus(self):
"""Total number of CPUs employed"""
return self.mpi_ncpus * self.omp_ncpus
@property
def omp_ncpus(self):
"""Number of OpenMP threads."""
if self.has_omp:
return self.omp_env["OMP_NUM_THREADS"]
else:
return 1
@abc.abstractproperty
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
@abc.abstractmethod
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
#@abc.abstractproperty
#def queue_walltime(self):
# """Returns the walltime in seconds."""
#@abc.abstractmethod
#def set_queue_walltime(self):
# """Set the walltime in seconds."""
#@abc.abstractproperty
#def mem_per_cpu(self):
# """The memory per CPU in Megabytes."""
@abc.abstractmethod
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
#@property
#def tot_mem(self):
# """Total memory required by the job n Megabytes."""
# return self.mem_per_cpu * self.mpi_ncpus
@abc.abstractmethod
def cancel(self, job_id):
"""
Cancel the job.
Args:
job_id:
(in) Job identifier.
Returns:
Exit status.
"""
def _make_qheader(self, job_name, qout_path, qerr_path):
"""Return a string with the options that are passed to the resource manager."""
qtemplate = QScriptTemplate(self.QTEMPLATE)
# set substitution dict for replacements into the template and clean null values
subs_dict = {k: v for k,v in self.qparams.items() if v is not None}
# Set job_name and the names for the stderr and stdout of the
# queue manager (note the use of the extensions .qout and .qerr
# so that we can easily locate this file.
subs_dict['job_name'] = job_name.replace('/','_')
subs_dict['_qout_path'] = qout_path
subs_dict['_qerr_path'] = qerr_path
# might contain unused parameters as leftover $$.
unclean_template = qtemplate.safe_substitute(subs_dict)
# Remove lines with leftover $$.
clean_template = []
for line in unclean_template.split('\n'):
if '$$' not in line:
clean_template.append(line)
return '\n'.join(clean_template)
def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path, stdin=None, stdout=None, stderr=None):
"""
Returns a (multi-line) String representing the queue script, e.g. PBS script.
Uses the template_file along with internal parameters to create the script.
Args:
launch_dir:
(str) The directory the job will be launched in.
qout_path
Path of the Queue manager output file.
qerr_path:
Path of the Queue manager error file.
"""
# Construct the header for the Queue Manager.
qheader = self._make_qheader(job_name, qout_path, qerr_path)
# Add the bash section.
se = ScriptEditor()
if self.setup:
se.add_comment("Setup section")
se.add_lines(self.setup)
if self.modules:
se.add_comment("Load Modules")
se.add_line("module purge")
se.load_modules(self.modules)
if self.has_omp:
se.add_comment("OpenMp Environment")
se.declare_vars(self.omp_env)
if self.shell_env:
se.add_comment("Shell Environment")
se.declare_vars(self.shell_env)
# Cd to launch_dir
se.add_line("cd " + os.path.abspath(launch_dir))
if self.pre_run:
se.add_comment("Commands before execution")
se.add_lines(self.pre_run)
# Construct the string to run the executable with MPI and mpi_ncpus.
mpi_ncpus = self.mpi_ncpus
line = self.mpi_runner.string_to_run(executable, mpi_ncpus, stdin=stdin, stdout=stdout, stderr=stderr)
se.add_line(line)
if self.post_run:
se.add_comment("Commands after execution")
se.add_lines(self.post_run)
shell_text = se.get_script_str()
return qheader + shell_text
@abc.abstractmethod
def submit_to_queue(self, script_file):
"""
Submits the job to the queue, probably using subprocess or shutil
Args:
script_file:
(str) name of the script file to use (String)
Returns:
process, queue_id
"""
@abc.abstractmethod
def get_njobs_in_queue(self, username=None):
"""
returns the number of jobs in the queue, probably using subprocess or shutil to
call a command like 'qstat'. returns None when the number of jobs cannot be determined.
Args:
username: (str) the username of the jobs to count (default is to autodetect)
"""
####################
# Concrete classes #
####################
class ShellAdapter(AbstractQueueAdapter):
QTYPE = "shell"
QTEMPLATE = """\
#!/bin/bash
export MPI_NCPUS=$${MPI_NCPUS}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("MPI_NCPUS", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["MPI_NCPUS"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""mem_per_cpu is not available in ShellAdapter."""
def cancel(self, job_id):
return os.system("kill -9 %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
process = Popen(("/bin/bash", script_file), stderr=PIPE)
queue_id = process.pid
return process, queue_id
except:
# random error
raise self.Error("Random Error ...!")
def get_njobs_in_queue(self, username=None):
return None
class SlurmAdapter(AbstractQueueAdapter):
QTYPE = "slurm"
QTEMPLATE = """\
#!/bin/bash
#SBATCH --ntasks=$${ntasks}
#SBATCH --ntasks-per-node=$${ntasks_per_node}
#SBATCH --cpus-per-task=$${cpus_per_task}
#SBATCH --time=$${time}
#SBATCH --partition=$${partition}
#SBATCH --account=$${account}
#SBATCH --job-name=$${job_name}
#SBATCH --nodes=$${nodes}
#SBATCH --mem=$${mem}
#SBATCH --mem-per-cpu=$${mem_per_cpu}
#SBATCH --mail-user=$${mail_user}
#SBATCH --mail-type=$${mail_type}
#SBATCH --constraint=$${constraint}
#SBATCH --gres=$${gres}
#SBATCH --requeue=$${requeue}
#SBATCH --nodelist=$${nodelist}
#SBATCH --propagate=$${propagate}
#SBATCH --output=$${_qout_path}
#SBATCH --error=$${_qerr_path}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ntasks", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ntasks"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["mem_per_cpu"] = int(mem_mb)
# Remove mem if it's defined.
self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("scancel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['sbatch', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. SLURM returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split()[3])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
queue_id = None
logger.warning('Could not parse job id following slurm...')
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
err_msg = ("Error in job submission with SLURM file {f} and cmd {c}\n".format(f=script_file, c=cmd) +
"The error response reads: {}".format(process.stderr.read()))
raise self.Error(err_msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error('Running sbatch caused an error...')
def get_njobs_in_queue(self, username=None):
if username is None:
username = getpass.getuser()
cmd = ['squeue', '-o "%u"', '-u', username]
process = Popen(cmd, shell=False, stdout=PIPE)
process.wait()
# parse the result
if process.returncode == 0:
# lines should have this form
# username
# count lines that include the username in it
outs = process.stdout.readlines()
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to squeue server?
err_msg = ('Error trying to get the number of jobs in the queue using squeue service' +
'The error response reads: {}'.format(process.stderr.read()))
logger.critical(err_msg)
return None
class PbsAdapter(AbstractQueueAdapter):
QTYPE = "pbs"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l mppwidth=$${mppwidth}
#PBS -l nodes=$${nodes}:ppn=$${ppn}
#PBS -N $${job_name}
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("nodes", 1) * self.qparams.get("ppn", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
if "ppn" not in self.qparams: self.qparams["ppn"] = 1
ppnode = self.qparams.get("ppn")
self.qparams["nodes"] = mpi_ncpus // ppnode
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. PBS returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split('.')[0])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-a', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should have this form
# '1339044.sdb username queuename 2012-02-29-16-43 20460 -- -- -- 00:20 C 00:09'
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
class SGEAdapter(AbstractQueueAdapter):
"""
Adapter for Sun Grid Engine (SGE) task submission software.
"""
QTYPE = "sge"
QTEMPLATE = """\
#!/bin/bash
#$ -A $${account}
#$ -N $${job_name}
#$ -l h rt=$${walltime}
#$ -pe $${queue} $${ncpus}
#$ -cwd
#$ -j y
#$ -m n
#$ -e $${_qerr_path}
#$ -o $${_qout_path}
#$ -S /bin/bash
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ncpus", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ncpus"] = mpi_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. SGE returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form
# Your job 1659048 ("NAME_OF_JOB") has been submitted
queue_id = int(process.stdout.read().split(' ')[2])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should contain username
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
class QScriptTemplate(string.Template):
delimiter = '$$'
|
# Copyright (c) 2015 Ultimaker B.V.
# Uranium is released under the terms of the AGPLv3 or higher.
from UM.Controller import Controller
from UM.PluginRegistry import PluginRegistry
from UM.Mesh.MeshFileHandler import MeshFileHandler
from UM.Resources import Resources
from UM.Operations.OperationStack import OperationStack
from UM.Event import CallFunctionEvent
from UM.Signal import Signal, SignalEmitter
from UM.Logger import Logger
from UM.Preferences import Preferences
from UM.OutputDevice.OutputDeviceManager import OutputDeviceManager
from UM.i18n import i18nCatalog
from UM.Settings.ContainerStack import ContainerStack
import threading
import argparse
import os
import sys
## Central object responsible for running the main event loop and creating other central objects.
#
# The Application object is a central object for accessing other important objects. It is also
# responsible for starting the main event loop. It is passed on to plugins so it can be easily
# used to access objects required for those plugins.
class Application(SignalEmitter):
## Init method
#
# \param name \type{string} The name of the application.
# \param version \type{string} Version, formatted as major.minor.rev
def __init__(self, name, version, **kwargs):
if(Application._instance != None):
raise ValueError("Duplicate singleton creation")
# If the constructor is called and there is no instance, set the instance to self.
# This is done because we can't make constructor private
Application._instance = self
self._application_name = name
self._version = version
os.putenv("UBUNTU_MENUPROXY","0") # For Ubuntu Unity this makes Qt use its own menu bar rather than pass it on to Unity.
Signal._app = self
Resources.ApplicationIdentifier = name
i18nCatalog.setApplication(self)
Resources.addSearchPath(os.path.join(os.path.dirname(sys.executable), "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "share", "uranium", "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "Resources", "uranium", "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "Resources", self.getApplicationName(), "resources"))
if not hasattr(sys, "frozen"):
Resources.addSearchPath(os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "resources"))
self._main_thread = threading.current_thread()
super().__init__(**kwargs) # Call super to make multiple inheritence work.
self._renderer = None
PluginRegistry.addType("backend", self.setBackend)
PluginRegistry.addType("logger", Logger.addLogger)
PluginRegistry.addType("extension", self.addExtension)
preferences = Preferences.getInstance()
preferences.addPreference("general/language", "en")
try:
preferences.readFromFile(Resources.getPath(Resources.Preferences, self._application_name + ".cfg"))
except FileNotFoundError:
pass
self._controller = Controller(self)
self._mesh_file_handler = MeshFileHandler()
self._extensions = []
self._backend = None
self._output_device_manager = OutputDeviceManager()
self._required_plugins = []
self._operation_stack = OperationStack()
self._plugin_registry = PluginRegistry.getInstance()
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "lib", "uranium"))
self._plugin_registry.addPluginLocation(os.path.join(os.path.dirname(sys.executable), "plugins"))
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "Resources", "uranium", "plugins"))
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "Resources", self.getApplicationName(), "plugins"))
# Locally installed plugins
self._plugin_registry.addPluginLocation(os.path.join(Resources.getStoragePath(Resources.Resources), "plugins"))
if not hasattr(sys, "frozen"):
self._plugin_registry.addPluginLocation(os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "plugins"))
self._plugin_registry.setApplication(self)
self._parsed_command_line = None
self.parseCommandLine()
self._visible_messages = []
self._message_lock = threading.Lock()
self.showMessageSignal.connect(self.showMessage)
self.hideMessageSignal.connect(self.hideMessage)
self._active_container_stack = ContainerStack("empty")
## Emitted when the application window was closed and we need to shut down the application
applicationShuttingDown = Signal()
showMessageSignal = Signal()
hideMessageSignal = Signal()
activeContainerStackChanged = Signal()
def setActiveContainerStack(self, stack):
self._active_container_stack = stack
self.activeContainerStackChanged.emit()
def getActiveContainerStack(self):
return self._active_container_stack
def hideMessage(self, message):
raise NotImplementedError
def showMessage(self, message):
raise NotImplementedError
## Get the version of the application
# \returns version \type{string}
def getVersion(self):
return self._version
## Add a message to the visible message list so it will be displayed.
# This should only be called by message object itself.
# To show a message, simply create it and call its .show() function.
# \param message \type{Message} message object
# \sa Message::show()
#def showMessage(self, message):
# with self._message_lock:
# if message not in self._visible_messages:
# self._visible_messages.append(message)
# self.visibleMessageAdded.emit(message)
visibleMessageAdded = Signal()
## Remove a message from the visible message list so it will no longer be displayed.
# This should only be called by message object itself.
# in principle, this should only be called by the message itself (hide)
# \param message \type{Message} message object
# \sa Message::hide()
#def hideMessage(self, message):
# with self._message_lock:
# if message in self._visible_messages:
# self._visible_messages.remove(message)
# self.visibleMessageRemoved.emit(message)
## Hide message by ID (as provided by built-in id function)
# \param message_id \type{long}
def hideMessageById(self, message_id):
found_message = None
with self._message_lock:
for message in self._visible_messages:
if id(message) == message_id:
found_message = message
if found_message is not None:
self.hideMessageSignal.emit(found_message)
visibleMessageRemoved = Signal()
## Get list of all visible messages
# \returns visible_messages \type{list}
def getVisibleMessages(self):
with self._message_lock:
return self._visible_messages
## Function that needs to be overriden by child classes with a list of plugin it needs.
def _loadPlugins(self):
pass
def getCommandLineOption(self, name, default = None):
if not self._parsed_command_line:
self.parseCommandLine()
Logger.log("d", "Command line options: %s", str(self._parsed_command_line))
return self._parsed_command_line.get(name, default)
## Get name of the application.
# \returns application_name \type{string}
def getApplicationName(self):
return self._application_name
## Set name of the application.
# \param application_name \type{string}
def setApplicationName(self, application_name):
self._application_name = application_name
def getApplicationLanguage(self):
override_lang = os.getenv("URANIUM_LANGUAGE")
if override_lang:
return override_lang
preflang = Preferences.getInstance().getValue("general/language")
if preflang:
return preflang
env_lang = os.getenv("LANGUAGE")
if env_lang:
return env_lang
return "en"
## Application has a list of plugins that it *must* have. If it does not have these, it cannot function.
# These plugins can not be disabled in any way.
# \returns required_plugins \type{list}
def getRequiredPlugins(self):
return self._required_plugins
## Set the plugins that the application *must* have in order to function.
# \param plugin_names \type{list} List of strings with the names of the required plugins
def setRequiredPlugins(self, plugin_names):
self._required_plugins = plugin_names
## Set the backend of the application (the program that does the heavy lifting).
# \param backend \type{Backend}
def setBackend(self, backend):
self._backend = backend
## Get the backend of the application (the program that does the heavy lifting).
# \returns Backend \type{Backend}
def getBackend(self):
return self._backend
## Get the PluginRegistry of this application.
# \returns PluginRegistry \type{PluginRegistry}
def getPluginRegistry(self):
return self._plugin_registry
## Get the Controller of this application.
# \returns Controller \type{Controller}
def getController(self):
return self._controller
## Get the MeshFileHandler of this application.
# \returns MeshFileHandler \type{MeshFileHandler}
def getMeshFileHandler(self):
return self._mesh_file_handler
def getOperationStack(self):
return self._operation_stack
def getOutputDeviceManager(self):
return self._output_device_manager
## Run the main eventloop.
# This method should be reimplemented by subclasses to start the main event loop.
# \exception NotImplementedError
def run(self):
raise NotImplementedError("Run must be implemented by application")
## Return an application-specific Renderer object.
# \exception NotImplementedError
def getRenderer(self):
raise NotImplementedError("getRenderer must be implemented by subclasses.")
## Post a function event onto the event loop.
#
# This takes a CallFunctionEvent object and puts it into the actual event loop.
# \exception NotImplementedError
def functionEvent(self, event):
raise NotImplementedError("functionEvent must be implemented by subclasses.")
## Call a function the next time the event loop runs.
#
# \param function The function to call.
# \param args The positional arguments to pass to the function.
# \param kwargs The keyword arguments to pass to the function.
def callLater(self, function, *args, **kwargs):
event = CallFunctionEvent(function, args, kwargs)
self.functionEvent(event)
## Get the application"s main thread.
def getMainThread(self):
return self._main_thread
## Return the singleton instance of the application object
@classmethod
def getInstance(cls):
# Note: Explicit use of class name to prevent issues with inheritance.
if Application._instance is None:
Application._instance = cls()
return Application._instance
def parseCommandLine(self):
parser = argparse.ArgumentParser(prog = self.getApplicationName())
parser.add_argument("--version", action="version", version="%(prog)s {0}".format(self.getVersion()))
parser.add_argument("--external-backend",
dest="external-backend",
action="store_true", default=False,
help="Use an externally started backend instead of starting it automatically.")
self.addCommandLineOptions(parser)
self._parsed_command_line = vars(parser.parse_args())
## Can be overridden to add additional command line options to the parser.
#
# \param parser \type{argparse.ArgumentParser} The parser that will parse the command line.
def addCommandLineOptions(self, parser):
pass
def addExtension(self, extension):
self._extensions.append(extension)
def getExtensions(self):
return self._extensions
@staticmethod
def getInstallPrefix():
if "python" in os.path.basename(sys.executable):
return os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), ".."))
else:
return os.path.abspath(os.path.join(os.path.dirname(sys.executable), ".."))
_instance = None
Renamed active container stack to globalContainerstack
CURA-1278
# Copyright (c) 2015 Ultimaker B.V.
# Uranium is released under the terms of the AGPLv3 or higher.
from UM.Controller import Controller
from UM.PluginRegistry import PluginRegistry
from UM.Mesh.MeshFileHandler import MeshFileHandler
from UM.Resources import Resources
from UM.Operations.OperationStack import OperationStack
from UM.Event import CallFunctionEvent
from UM.Signal import Signal, SignalEmitter
from UM.Logger import Logger
from UM.Preferences import Preferences
from UM.OutputDevice.OutputDeviceManager import OutputDeviceManager
from UM.i18n import i18nCatalog
from UM.Settings.ContainerStack import ContainerStack
import threading
import argparse
import os
import sys
## Central object responsible for running the main event loop and creating other central objects.
#
# The Application object is a central object for accessing other important objects. It is also
# responsible for starting the main event loop. It is passed on to plugins so it can be easily
# used to access objects required for those plugins.
class Application(SignalEmitter):
## Init method
#
# \param name \type{string} The name of the application.
# \param version \type{string} Version, formatted as major.minor.rev
def __init__(self, name, version, **kwargs):
if(Application._instance != None):
raise ValueError("Duplicate singleton creation")
# If the constructor is called and there is no instance, set the instance to self.
# This is done because we can't make constructor private
Application._instance = self
self._application_name = name
self._version = version
os.putenv("UBUNTU_MENUPROXY","0") # For Ubuntu Unity this makes Qt use its own menu bar rather than pass it on to Unity.
Signal._app = self
Resources.ApplicationIdentifier = name
i18nCatalog.setApplication(self)
Resources.addSearchPath(os.path.join(os.path.dirname(sys.executable), "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "share", "uranium", "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "Resources", "uranium", "resources"))
Resources.addSearchPath(os.path.join(Application.getInstallPrefix(), "Resources", self.getApplicationName(), "resources"))
if not hasattr(sys, "frozen"):
Resources.addSearchPath(os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "resources"))
self._main_thread = threading.current_thread()
super().__init__(**kwargs) # Call super to make multiple inheritence work.
self._renderer = None
PluginRegistry.addType("backend", self.setBackend)
PluginRegistry.addType("logger", Logger.addLogger)
PluginRegistry.addType("extension", self.addExtension)
preferences = Preferences.getInstance()
preferences.addPreference("general/language", "en")
try:
preferences.readFromFile(Resources.getPath(Resources.Preferences, self._application_name + ".cfg"))
except FileNotFoundError:
pass
self._controller = Controller(self)
self._mesh_file_handler = MeshFileHandler()
self._extensions = []
self._backend = None
self._output_device_manager = OutputDeviceManager()
self._required_plugins = []
self._operation_stack = OperationStack()
self._plugin_registry = PluginRegistry.getInstance()
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "lib", "uranium"))
self._plugin_registry.addPluginLocation(os.path.join(os.path.dirname(sys.executable), "plugins"))
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "Resources", "uranium", "plugins"))
self._plugin_registry.addPluginLocation(os.path.join(Application.getInstallPrefix(), "Resources", self.getApplicationName(), "plugins"))
# Locally installed plugins
self._plugin_registry.addPluginLocation(os.path.join(Resources.getStoragePath(Resources.Resources), "plugins"))
if not hasattr(sys, "frozen"):
self._plugin_registry.addPluginLocation(os.path.join(os.path.abspath(os.path.dirname(__file__)), "..", "plugins"))
self._plugin_registry.setApplication(self)
self._parsed_command_line = None
self.parseCommandLine()
self._visible_messages = []
self._message_lock = threading.Lock()
self.showMessageSignal.connect(self.showMessage)
self.hideMessageSignal.connect(self.hideMessage)
self._global_container_stack = ContainerStack("empty")
## Emitted when the application window was closed and we need to shut down the application
applicationShuttingDown = Signal()
showMessageSignal = Signal()
hideMessageSignal = Signal()
globalContainerStackChanged = Signal()
def setGlobalContainerStack(self, stack):
self._global_container_stack = stack
self.globalContainerStackChanged.emit()
def getGlobalContainerStack(self):
return self._global_container_stack
def hideMessage(self, message):
raise NotImplementedError
def showMessage(self, message):
raise NotImplementedError
## Get the version of the application
# \returns version \type{string}
def getVersion(self):
return self._version
## Add a message to the visible message list so it will be displayed.
# This should only be called by message object itself.
# To show a message, simply create it and call its .show() function.
# \param message \type{Message} message object
# \sa Message::show()
#def showMessage(self, message):
# with self._message_lock:
# if message not in self._visible_messages:
# self._visible_messages.append(message)
# self.visibleMessageAdded.emit(message)
visibleMessageAdded = Signal()
## Remove a message from the visible message list so it will no longer be displayed.
# This should only be called by message object itself.
# in principle, this should only be called by the message itself (hide)
# \param message \type{Message} message object
# \sa Message::hide()
#def hideMessage(self, message):
# with self._message_lock:
# if message in self._visible_messages:
# self._visible_messages.remove(message)
# self.visibleMessageRemoved.emit(message)
## Hide message by ID (as provided by built-in id function)
# \param message_id \type{long}
def hideMessageById(self, message_id):
found_message = None
with self._message_lock:
for message in self._visible_messages:
if id(message) == message_id:
found_message = message
if found_message is not None:
self.hideMessageSignal.emit(found_message)
visibleMessageRemoved = Signal()
## Get list of all visible messages
# \returns visible_messages \type{list}
def getVisibleMessages(self):
with self._message_lock:
return self._visible_messages
## Function that needs to be overriden by child classes with a list of plugin it needs.
def _loadPlugins(self):
pass
def getCommandLineOption(self, name, default = None):
if not self._parsed_command_line:
self.parseCommandLine()
Logger.log("d", "Command line options: %s", str(self._parsed_command_line))
return self._parsed_command_line.get(name, default)
## Get name of the application.
# \returns application_name \type{string}
def getApplicationName(self):
return self._application_name
## Set name of the application.
# \param application_name \type{string}
def setApplicationName(self, application_name):
self._application_name = application_name
def getApplicationLanguage(self):
override_lang = os.getenv("URANIUM_LANGUAGE")
if override_lang:
return override_lang
preflang = Preferences.getInstance().getValue("general/language")
if preflang:
return preflang
env_lang = os.getenv("LANGUAGE")
if env_lang:
return env_lang
return "en"
## Application has a list of plugins that it *must* have. If it does not have these, it cannot function.
# These plugins can not be disabled in any way.
# \returns required_plugins \type{list}
def getRequiredPlugins(self):
return self._required_plugins
## Set the plugins that the application *must* have in order to function.
# \param plugin_names \type{list} List of strings with the names of the required plugins
def setRequiredPlugins(self, plugin_names):
self._required_plugins = plugin_names
## Set the backend of the application (the program that does the heavy lifting).
# \param backend \type{Backend}
def setBackend(self, backend):
self._backend = backend
## Get the backend of the application (the program that does the heavy lifting).
# \returns Backend \type{Backend}
def getBackend(self):
return self._backend
## Get the PluginRegistry of this application.
# \returns PluginRegistry \type{PluginRegistry}
def getPluginRegistry(self):
return self._plugin_registry
## Get the Controller of this application.
# \returns Controller \type{Controller}
def getController(self):
return self._controller
## Get the MeshFileHandler of this application.
# \returns MeshFileHandler \type{MeshFileHandler}
def getMeshFileHandler(self):
return self._mesh_file_handler
def getOperationStack(self):
return self._operation_stack
def getOutputDeviceManager(self):
return self._output_device_manager
## Run the main eventloop.
# This method should be reimplemented by subclasses to start the main event loop.
# \exception NotImplementedError
def run(self):
raise NotImplementedError("Run must be implemented by application")
## Return an application-specific Renderer object.
# \exception NotImplementedError
def getRenderer(self):
raise NotImplementedError("getRenderer must be implemented by subclasses.")
## Post a function event onto the event loop.
#
# This takes a CallFunctionEvent object and puts it into the actual event loop.
# \exception NotImplementedError
def functionEvent(self, event):
raise NotImplementedError("functionEvent must be implemented by subclasses.")
## Call a function the next time the event loop runs.
#
# \param function The function to call.
# \param args The positional arguments to pass to the function.
# \param kwargs The keyword arguments to pass to the function.
def callLater(self, function, *args, **kwargs):
event = CallFunctionEvent(function, args, kwargs)
self.functionEvent(event)
## Get the application"s main thread.
def getMainThread(self):
return self._main_thread
## Return the singleton instance of the application object
@classmethod
def getInstance(cls):
# Note: Explicit use of class name to prevent issues with inheritance.
if Application._instance is None:
Application._instance = cls()
return Application._instance
def parseCommandLine(self):
parser = argparse.ArgumentParser(prog = self.getApplicationName())
parser.add_argument("--version", action="version", version="%(prog)s {0}".format(self.getVersion()))
parser.add_argument("--external-backend",
dest="external-backend",
action="store_true", default=False,
help="Use an externally started backend instead of starting it automatically.")
self.addCommandLineOptions(parser)
self._parsed_command_line = vars(parser.parse_args())
## Can be overridden to add additional command line options to the parser.
#
# \param parser \type{argparse.ArgumentParser} The parser that will parse the command line.
def addCommandLineOptions(self, parser):
pass
def addExtension(self, extension):
self._extensions.append(extension)
def getExtensions(self):
return self._extensions
@staticmethod
def getInstallPrefix():
if "python" in os.path.basename(sys.executable):
return os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), ".."))
else:
return os.path.abspath(os.path.join(os.path.dirname(sys.executable), ".."))
_instance = None
|
plot: Update queue delay plot
|
"""Read Minigo game examples from a Bigtable.
"""
import collections
import operator
import os
import re
import struct
import time
import numpy as np
from google.cloud import bigtable
from google.cloud.bigtable import row_filters
import tensorflow as tf
import utils
# Constants
ROW_PREFIX = 'g_{:0>10}_'
ROWCOUNT_PREFIX = 'ct_{:0>10}_'
## Column family and qualifier constants.
#### Column Families
METADATA = 'metadata'
#### Column Qualifiers
#### Note that in CBT, families are strings and qualifiers are bytes.
TABLE_STATE = b'table_state'
WAIT_CELL = b'wait_for_game_number'
MOVE_COUNT = b'move_count'
# Patterns
_game_row_key = re.compile(r'g_(\d+)_m_(\d+)')
def cbt_intvalue(value):
"""Decode a big-endian uint64.
Cloud Bigtable stores integers as big-endian uint64,
and performs this translation when integers are being
set. But when being read, the values need to be
decoded.
"""
return int(struct.unpack('>q', value)[0])
def make_single_array(ds, batch_size=8*1024):
"""Create a single numpy array from a dataset.
Args:
ds: a TF Dataset.
batch_size: how many elements to read per pass
Returns:
a single numpy array.
"""
batches = []
with tf.Session() as sess:
ds = ds.batch(batch_size)
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
try:
while True:
batches.append(sess.run(get_next))
except tf.errors.OutOfRangeError:
pass
return np.concatenate(batches)
def _histogram_move_keys_by_game(sess, ds, batch_size=8*1024):
"""Given dataset of key names, return histogram of moves/game.
Move counts are written by the game players, so
this is mostly useful for repair or backfill.
Args:
sess: TF session
ds: TF dataset containing game move keys.
batch_size: performance tuning parameter
"""
ds = ds.batch(batch_size)
# Turns 'g_0000001234_m_133' into 'g_0000001234'
ds = ds.map(lambda x: tf.strings.substr(x, 0, 12))
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
h = collections.Counter()
try:
while True:
h.update(sess.run(get_next))
except tf.errors.OutOfRangeError:
pass
# NOTE: Cannot be truly sure the count is right till the end.
return h
def _game_keys_as_array(ds):
"""Turn keys of a Bigtable dataset into an array.
Take g_GGG_m_MMM and create GGG.MMM numbers.
Valuable when visualizing the distribution of a given dataset in
the game keyspace.
"""
ds = ds.map(lambda row_key, cell: row_key)
# want 'g_0000001234_m_133' is '0000001234.133' and so forth
ds = ds.map(lambda x:
tf.strings.to_number(tf.strings.substr(x, 2, 10) +
'.' +
tf.strings.substr(x, 15, 3),
out_type=tf.float64))
return make_single_array(ds)
class GameQueue:
"""Queue of games stored in a Cloud Bigtable.
The state of the table is stored in the `table_state`
row, which includes the columns `metadata:game_counter`.
"""
def __init__(self, project_name, instance_name, table_name):
"""Constructor.
Args:
project_name: string name of GCP project having table.
instance_name: string name of CBT instance in project.
table_name: string name of CBT table in instance.
"""
self.bt_table = bigtable.Client(
project_name).instance(
instance_name).table(
table_name)
self.tf_table = tf.contrib.cloud.BigtableClient(
project_name,
instance_name).table(
table_name)
def latest_game_number(self):
"""Return the number of the next game to be written."""
game_counter = b'game_counter'
table_state = self.bt_table.read_row(
TABLE_STATE,
filter_=row_filters.ColumnRangeFilter(
METADATA, game_counter, game_counter))
return cbt_intvalue(table_state.cell_value(METADATA, game_counter))
def bleakest_moves(self, start_game, end_game):
"""Given a range of games, return the bleakest moves.
Returns a list of (game, move, q) sorted by q.
"""
bleak = b'bleakest_q'
rows = self.bt_table.read_rows(
ROW_PREFIX.format(start_game),
ROW_PREFIX.format(end_game),
filter_=row_filters.ColumnRangeFilter(
METADATA, bleak, bleak))
def parse(r):
rk = str(r.row_key, 'utf-8')
g, m = _game_row_key.match(rk).groups()
q = r.cell_value(METADATA, bleak)
return int(g), int(m), float(q)
return sorted([parse(r) for r in rows], key=operator.itemgetter(2))
def require_fresh_games(self, number_fresh):
"""Require a given number of fresh games to be played.
Args:
number_fresh: integer, number of new fresh games needed
Increments the cell `table_state=metadata:wait_for_game_number`
by the given number of games. This will cause
`self.wait_for_fresh_games()` to block until the game
counter has reached this number.
"""
latest = self.latest_game_number()
table_state = self.bt_table.row(TABLE_STATE)
table_state.set_cell(METADATA, WAIT_CELL, int(latest + number_fresh))
table_state.commit()
def wait_for_fresh_games(self, poll_interval=15.0):
"""Block caller until required new games have been played.
Args:
poll_interval: number of seconds to wait between checks
If the cell `table_state=metadata:wait_for_game_number` exists,
then block the caller, checking every `poll_interval` seconds,
until `table_state=metadata:game_counter is at least the value
in that cell.
"""
table_state = self.bt_table.read_row(
TABLE_STATE,
filter_=row_filters.ColumnRangeFilter(
METADATA, WAIT_CELL, WAIT_CELL))
if table_state is None:
utils.dbg('No waiting for new games needed; '
'wait_for_game_number column not in table_state')
return
value = table_state.cell_value(METADATA, WAIT_CELL)
if not value:
utils.dbg('No waiting for new games needed; '
'no value in wait_for_game_number cell '
'in table_state')
return
wait_until_game = cbt_intvalue(value)
latest_game = self.latest_game_number()
while latest_game < wait_until_game:
utils.dbg('Latest game {} not yet at required game {}'.
format(latest_game, wait_until_game))
time.sleep(poll_interval)
latest_game = self.latest_game_number()
def count_moves_in_game_range(self, game_begin, game_end):
"""Count the total moves in a game range.
Args:
game_begin: integer, starting game
game_end: integer, ending game
Uses the `ct_` keyspace for rapid move summary.
"""
rows = self.bt_table.read_rows(
ROWCOUNT_PREFIX.format(game_begin),
ROWCOUNT_PREFIX.format(game_end),
filter_=row_filters.ColumnRangeFilter(
METADATA, MOVE_COUNT, MOVE_COUNT))
return sum([int(r.cell_value(METADATA, MOVE_COUNT)) for r in rows])
def moves_from_games(self, start_game, end_game, moves, shuffle,
column_family, column):
"""Dataset of samples and/or shuffled moves from game range.
Args:
n: an integer indicating how many past games should be sourced.
moves: an integer indicating how many moves should be sampled
from those N games.
column_family: name of the column family containing move examples.
column: name of the column containing move examples.
shuffle: if True, shuffle the selected move examples.
Returns:
A dataset containing no more than `moves` examples, sampled
randomly from the last `n` games in the table.
"""
start_row = ROW_PREFIX.format(start_game)
end_row = ROW_PREFIX.format(end_game)
# NOTE: Choose a probability high enough to guarantee at least the
# required number of moves, by using a slightly lower estimate
# of the total moves, then trimming the result.
total_moves = self.count_moves_in_game_range(start_game, end_game)
probability = moves / (total_moves * 0.99)
utils.dbg('Row range: %s - %s; total moves: %d; probability %.3f' % (
start_row, end_row, total_moves, probability))
shards = 8
ds = self.tf_table.parallel_scan_range(start_row, end_row,
probability=probability,
num_parallel_scans=shards,
columns=[(column_family, column)])
if shuffle:
rds = tf.data.Dataset.from_tensor_slices(
tf.random_shuffle(tf.range(0, shards, dtype=tf.int64)))
ds = rds.apply(
tf.contrib.data.parallel_interleave(
lambda x: ds.shard(shards, x),
cycle_length=shards, block_length=1024))
ds = ds.shuffle(shards * 1024 * 2)
ds = ds.take(moves)
return ds
def _write_move_counts(self, sess, h):
"""Add move counts from the given histogram to the table.
Used to update the move counts in an existing table. Should
not be needed except for backfill or repair.
Args:
sess: TF session to use for doing a Bigtable write.
tf_table: TF Cloud Bigtable to use for writing.
h: a dictionary keyed by game row prefix ("g_0023561") whose values
are the move counts for each game.
"""
def gen():
for k, v in h.items():
# The keys in the histogram may be of type 'bytes'
k = str(k, 'utf-8')
vs = str(v)
yield (k.replace('g_', 'ct_') + '_%d' % v, vs)
yield (k + '_m_000', vs)
mc = tf.data.Dataset.from_generator(gen, (tf.string, tf.string))
wr_op = self.tf_table.write(mc,
column_families=[METADATA],
columns=[MOVE_COUNT])
sess.run(wr_op)
def update_move_counts(self, start_game, end_game, interval=1000):
"""Used to update the move_count cell for older games.
Should not be needed except for backfill or repair.
move_count cells will be updated in both g_<game_id>_m_000 rows
and ct_<game_id>_<move_count> rows.
"""
for g in range(start_game, end_game, interval):
with tf.Session() as sess:
start_row = ROW_PREFIX.format(g)
end_row = ROW_PREFIX.format(g + interval)
print('Range:', start_row, end_row)
start_time = time.time()
ds = self.tf_table.keys_by_range_dataset(start_row, end_row)
h = _histogram_move_keys_by_game(sess, ds)
self._write_move_counts(sess, h)
end_time = time.time()
elapsed = end_time - start_time
print(' games/sec:', len(h)/elapsed)
# TODO(president.jackson): document these
_games = GameQueue(os.environ['PROJECT'],
os.environ['CBT_INSTANCE'],
os.environ['CBT_TABLE'])
def get_unparsed_moves_from_last_n_games(n,
moves=2**21,
shuffle=True,
column_family='tfexample',
column='example'):
"""Get a dataset of serialized TFExamples from the last N games.
Args:
n: an integer indicating how many past games should be sourced.
moves: an integer indicating how many moves should be sampled
from those N games.
column_family: name of the column family containing move examples.
column: name of the column containing move examples.
shuffle: if True, shuffle the selected move examples.
Returns:
A dataset containing no more than `moves` examples, sampled
randomly from the last `n` games in the table.
"""
_games.wait_for_fresh_games()
latest_game = int(_games.latest_game_number())
utils.dbg('Latest game: %s' % latest_game)
if latest_game == 0:
raise ValueError('Cannot find a latest game in the table')
start = int(max(0, latest_game - n))
ds = _games.moves_from_games(start, latest_game, moves, shuffle,
column_family, column)
return ds.map(lambda row_name, s: s)
def count_elements_in_dataset(ds, batch_size=1*1024, parallel_batch=8):
"""Count and return all the elements in the given dataset.
Debugging function. The elements in a dataset cannot be counted
without enumerating all of them. By counting in batch and in
parallel, this method allows rapid traversal of the dataset.
Args:
ds: The dataset whose elements should be counted.
batch_size: the number of elements to count a a time.
parallel_batch: how many batches to count in parallel.
Returns:
The number of elements in the dataset.
"""
with tf.Session() as sess:
dsc = ds.apply(tf.contrib.data.enumerate_dataset())
dsc = dsc.apply(
tf.contrib.data.map_and_batch(lambda c, v: c, batch_size,
num_parallel_batches=parallel_batch))
iterator = dsc.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
counted = 0
try:
while True:
# The numbers in the tensors are 0-based indicies,
# so add 1 to get the number counted.
counted = sess.run(tf.reduce_max(get_next)) + 1
utils.dbg('Counted so far: %d' % counted)
except tf.errors.OutOfRangeError:
pass
utils.dbg('Counted total: %d' % counted)
return counted
Document GameQueue environment variables
"""Read Minigo game examples from a Bigtable.
"""
import collections
import operator
import os
import re
import struct
import time
import numpy as np
from google.cloud import bigtable
from google.cloud.bigtable import row_filters
import tensorflow as tf
import utils
# Constants
ROW_PREFIX = 'g_{:0>10}_'
ROWCOUNT_PREFIX = 'ct_{:0>10}_'
## Column family and qualifier constants.
#### Column Families
METADATA = 'metadata'
#### Column Qualifiers
#### Note that in CBT, families are strings and qualifiers are bytes.
TABLE_STATE = b'table_state'
WAIT_CELL = b'wait_for_game_number'
MOVE_COUNT = b'move_count'
# Patterns
_game_row_key = re.compile(r'g_(\d+)_m_(\d+)')
def cbt_intvalue(value):
"""Decode a big-endian uint64.
Cloud Bigtable stores integers as big-endian uint64,
and performs this translation when integers are being
set. But when being read, the values need to be
decoded.
"""
return int(struct.unpack('>q', value)[0])
def make_single_array(ds, batch_size=8*1024):
"""Create a single numpy array from a dataset.
Args:
ds: a TF Dataset.
batch_size: how many elements to read per pass
Returns:
a single numpy array.
"""
batches = []
with tf.Session() as sess:
ds = ds.batch(batch_size)
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
try:
while True:
batches.append(sess.run(get_next))
except tf.errors.OutOfRangeError:
pass
return np.concatenate(batches)
def _histogram_move_keys_by_game(sess, ds, batch_size=8*1024):
"""Given dataset of key names, return histogram of moves/game.
Move counts are written by the game players, so
this is mostly useful for repair or backfill.
Args:
sess: TF session
ds: TF dataset containing game move keys.
batch_size: performance tuning parameter
"""
ds = ds.batch(batch_size)
# Turns 'g_0000001234_m_133' into 'g_0000001234'
ds = ds.map(lambda x: tf.strings.substr(x, 0, 12))
iterator = ds.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
h = collections.Counter()
try:
while True:
h.update(sess.run(get_next))
except tf.errors.OutOfRangeError:
pass
# NOTE: Cannot be truly sure the count is right till the end.
return h
def _game_keys_as_array(ds):
"""Turn keys of a Bigtable dataset into an array.
Take g_GGG_m_MMM and create GGG.MMM numbers.
Valuable when visualizing the distribution of a given dataset in
the game keyspace.
"""
ds = ds.map(lambda row_key, cell: row_key)
# want 'g_0000001234_m_133' is '0000001234.133' and so forth
ds = ds.map(lambda x:
tf.strings.to_number(tf.strings.substr(x, 2, 10) +
'.' +
tf.strings.substr(x, 15, 3),
out_type=tf.float64))
return make_single_array(ds)
class GameQueue:
"""Queue of games stored in a Cloud Bigtable.
The state of the table is stored in the `table_state`
row, which includes the columns `metadata:game_counter`.
"""
def __init__(self, project_name, instance_name, table_name):
"""Constructor.
Args:
project_name: string name of GCP project having table.
instance_name: string name of CBT instance in project.
table_name: string name of CBT table in instance.
"""
self.bt_table = bigtable.Client(
project_name).instance(
instance_name).table(
table_name)
self.tf_table = tf.contrib.cloud.BigtableClient(
project_name,
instance_name).table(
table_name)
def latest_game_number(self):
"""Return the number of the next game to be written."""
game_counter = b'game_counter'
table_state = self.bt_table.read_row(
TABLE_STATE,
filter_=row_filters.ColumnRangeFilter(
METADATA, game_counter, game_counter))
return cbt_intvalue(table_state.cell_value(METADATA, game_counter))
def bleakest_moves(self, start_game, end_game):
"""Given a range of games, return the bleakest moves.
Returns a list of (game, move, q) sorted by q.
"""
bleak = b'bleakest_q'
rows = self.bt_table.read_rows(
ROW_PREFIX.format(start_game),
ROW_PREFIX.format(end_game),
filter_=row_filters.ColumnRangeFilter(
METADATA, bleak, bleak))
def parse(r):
rk = str(r.row_key, 'utf-8')
g, m = _game_row_key.match(rk).groups()
q = r.cell_value(METADATA, bleak)
return int(g), int(m), float(q)
return sorted([parse(r) for r in rows], key=operator.itemgetter(2))
def require_fresh_games(self, number_fresh):
"""Require a given number of fresh games to be played.
Args:
number_fresh: integer, number of new fresh games needed
Increments the cell `table_state=metadata:wait_for_game_number`
by the given number of games. This will cause
`self.wait_for_fresh_games()` to block until the game
counter has reached this number.
"""
latest = self.latest_game_number()
table_state = self.bt_table.row(TABLE_STATE)
table_state.set_cell(METADATA, WAIT_CELL, int(latest + number_fresh))
table_state.commit()
def wait_for_fresh_games(self, poll_interval=15.0):
"""Block caller until required new games have been played.
Args:
poll_interval: number of seconds to wait between checks
If the cell `table_state=metadata:wait_for_game_number` exists,
then block the caller, checking every `poll_interval` seconds,
until `table_state=metadata:game_counter is at least the value
in that cell.
"""
table_state = self.bt_table.read_row(
TABLE_STATE,
filter_=row_filters.ColumnRangeFilter(
METADATA, WAIT_CELL, WAIT_CELL))
if table_state is None:
utils.dbg('No waiting for new games needed; '
'wait_for_game_number column not in table_state')
return
value = table_state.cell_value(METADATA, WAIT_CELL)
if not value:
utils.dbg('No waiting for new games needed; '
'no value in wait_for_game_number cell '
'in table_state')
return
wait_until_game = cbt_intvalue(value)
latest_game = self.latest_game_number()
while latest_game < wait_until_game:
utils.dbg('Latest game {} not yet at required game {}'.
format(latest_game, wait_until_game))
time.sleep(poll_interval)
latest_game = self.latest_game_number()
def count_moves_in_game_range(self, game_begin, game_end):
"""Count the total moves in a game range.
Args:
game_begin: integer, starting game
game_end: integer, ending game
Uses the `ct_` keyspace for rapid move summary.
"""
rows = self.bt_table.read_rows(
ROWCOUNT_PREFIX.format(game_begin),
ROWCOUNT_PREFIX.format(game_end),
filter_=row_filters.ColumnRangeFilter(
METADATA, MOVE_COUNT, MOVE_COUNT))
return sum([int(r.cell_value(METADATA, MOVE_COUNT)) for r in rows])
def moves_from_games(self, start_game, end_game, moves, shuffle,
column_family, column):
"""Dataset of samples and/or shuffled moves from game range.
Args:
n: an integer indicating how many past games should be sourced.
moves: an integer indicating how many moves should be sampled
from those N games.
column_family: name of the column family containing move examples.
column: name of the column containing move examples.
shuffle: if True, shuffle the selected move examples.
Returns:
A dataset containing no more than `moves` examples, sampled
randomly from the last `n` games in the table.
"""
start_row = ROW_PREFIX.format(start_game)
end_row = ROW_PREFIX.format(end_game)
# NOTE: Choose a probability high enough to guarantee at least the
# required number of moves, by using a slightly lower estimate
# of the total moves, then trimming the result.
total_moves = self.count_moves_in_game_range(start_game, end_game)
probability = moves / (total_moves * 0.99)
utils.dbg('Row range: %s - %s; total moves: %d; probability %.3f' % (
start_row, end_row, total_moves, probability))
shards = 8
ds = self.tf_table.parallel_scan_range(start_row, end_row,
probability=probability,
num_parallel_scans=shards,
columns=[(column_family, column)])
if shuffle:
rds = tf.data.Dataset.from_tensor_slices(
tf.random_shuffle(tf.range(0, shards, dtype=tf.int64)))
ds = rds.apply(
tf.contrib.data.parallel_interleave(
lambda x: ds.shard(shards, x),
cycle_length=shards, block_length=1024))
ds = ds.shuffle(shards * 1024 * 2)
ds = ds.take(moves)
return ds
def _write_move_counts(self, sess, h):
"""Add move counts from the given histogram to the table.
Used to update the move counts in an existing table. Should
not be needed except for backfill or repair.
Args:
sess: TF session to use for doing a Bigtable write.
tf_table: TF Cloud Bigtable to use for writing.
h: a dictionary keyed by game row prefix ("g_0023561") whose values
are the move counts for each game.
"""
def gen():
for k, v in h.items():
# The keys in the histogram may be of type 'bytes'
k = str(k, 'utf-8')
vs = str(v)
yield (k.replace('g_', 'ct_') + '_%d' % v, vs)
yield (k + '_m_000', vs)
mc = tf.data.Dataset.from_generator(gen, (tf.string, tf.string))
wr_op = self.tf_table.write(mc,
column_families=[METADATA],
columns=[MOVE_COUNT])
sess.run(wr_op)
def update_move_counts(self, start_game, end_game, interval=1000):
"""Used to update the move_count cell for older games.
Should not be needed except for backfill or repair.
move_count cells will be updated in both g_<game_id>_m_000 rows
and ct_<game_id>_<move_count> rows.
"""
for g in range(start_game, end_game, interval):
with tf.Session() as sess:
start_row = ROW_PREFIX.format(g)
end_row = ROW_PREFIX.format(g + interval)
print('Range:', start_row, end_row)
start_time = time.time()
ds = self.tf_table.keys_by_range_dataset(start_row, end_row)
h = _histogram_move_keys_by_game(sess, ds)
self._write_move_counts(sess, h)
end_time = time.time()
elapsed = end_time - start_time
print(' games/sec:', len(h)/elapsed)
# PROJECT: the GCP project in which the Cloud Bigtable is located.
# CBT_INSTANCE: identifier of Cloud Bigtable instance in PROJECT.
# CBT_TABLE: identifier of Cloud Bigtable table in CBT_INSTANCE.
_games = GameQueue(os.environ['PROJECT'],
os.environ['CBT_INSTANCE'],
os.environ['CBT_TABLE'])
def get_unparsed_moves_from_last_n_games(n,
moves=2**21,
shuffle=True,
column_family='tfexample',
column='example'):
"""Get a dataset of serialized TFExamples from the last N games.
Args:
n: an integer indicating how many past games should be sourced.
moves: an integer indicating how many moves should be sampled
from those N games.
column_family: name of the column family containing move examples.
column: name of the column containing move examples.
shuffle: if True, shuffle the selected move examples.
Returns:
A dataset containing no more than `moves` examples, sampled
randomly from the last `n` games in the table.
"""
_games.wait_for_fresh_games()
latest_game = int(_games.latest_game_number())
utils.dbg('Latest game: %s' % latest_game)
if latest_game == 0:
raise ValueError('Cannot find a latest game in the table')
start = int(max(0, latest_game - n))
ds = _games.moves_from_games(start, latest_game, moves, shuffle,
column_family, column)
return ds.map(lambda row_name, s: s)
def count_elements_in_dataset(ds, batch_size=1*1024, parallel_batch=8):
"""Count and return all the elements in the given dataset.
Debugging function. The elements in a dataset cannot be counted
without enumerating all of them. By counting in batch and in
parallel, this method allows rapid traversal of the dataset.
Args:
ds: The dataset whose elements should be counted.
batch_size: the number of elements to count a a time.
parallel_batch: how many batches to count in parallel.
Returns:
The number of elements in the dataset.
"""
with tf.Session() as sess:
dsc = ds.apply(tf.contrib.data.enumerate_dataset())
dsc = dsc.apply(
tf.contrib.data.map_and_batch(lambda c, v: c, batch_size,
num_parallel_batches=parallel_batch))
iterator = dsc.make_initializable_iterator()
sess.run(iterator.initializer)
get_next = iterator.get_next()
counted = 0
try:
while True:
# The numbers in the tensors are 0-based indicies,
# so add 1 to get the number counted.
counted = sess.run(tf.reduce_max(get_next)) + 1
utils.dbg('Counted so far: %d' % counted)
except tf.errors.OutOfRangeError:
pass
utils.dbg('Counted total: %d' % counted)
return counted
|
from django.contrib import admin
from django.conf.urls import patterns
from django.http import HttpResponseRedirect
from game.tasks import validate_bot
import itertools
# Register your models here.
from game.models import (
Bot,
Challenge,
UserProfile,
FinalChallenge,
)
from game.tasks import run_match
class UserProfileAdmin(admin.ModelAdmin):
list_display = ('user', 'score', 'current_bot', 'code_update_date')
list_filter = ('current_bot')
class BotAdmin(admin.ModelAdmin):
actions = ['validate_bot']
list_display = ('creation_date', 'modification_date', 'owner', 'valid')
list_filter = ('valid')
def validate_bot(self, request, queryset):
for bot in queryset:
validate_bot.delay(bot.id, bot.code)
class ChallengeAdmin(admin.ModelAdmin):
list_display = ('creation_date', 'requested_by', 'challenger_bot', 'challenged_bot', 'played', 'winner_player', 'canceled')
list_filter = ('canceled', 'final_challenge')
class FinalChallengeAdmin(admin.ModelAdmin):
def get_urls(self):
urls = super(FinalChallengeAdmin, self).get_urls()
my_urls = patterns('',
(r'^final_challenge/$', self.final_challenge)
)
return my_urls + urls
actions = ['final_challenge']
def final_challenge(self, request, queryset):
profiles = UserProfile.objects.filter(user__is_superuser=False, user__is_active=True).all()
if not queryset:
return HttpResponseRedirect('/admin')
final_challenge = queryset[0]
for up_player1, up_player2 in itertools.product(profiles, repeat=2):
if (up_player1 == up_player2
or not up_player1.current_bot
or not up_player2.current_bot):
continue
challenge = Challenge()
challenge.requested_by = up_player1
challenge.challenger_bot = up_player1.current_bot
challenge.challenged_bot = up_player2.current_bot
challenge.final_challenge = final_challenge
challenge.save()
final_challenge.challenge_set.add(challenge)
# dispatch the new task
players = {up_player1.user.username: up_player1.current_bot.code,
up_player2.user.username: up_player2.current_bot.code,
}
run_match.delay(challenge.id, players)
final_challenge.save()
return HttpResponseRedirect('/admin')
admin.site.register(FinalChallenge, FinalChallengeAdmin)
admin.site.register(UserProfile, UserProfileAdmin)
admin.site.register(Challenge, ChallengeAdmin)
admin.site.register(Bot, BotAdmin)
added missing commas to tuple
from django.contrib import admin
from django.conf.urls import patterns
from django.http import HttpResponseRedirect
from game.tasks import validate_bot
import itertools
# Register your models here.
from game.models import (
Bot,
Challenge,
UserProfile,
FinalChallenge,
)
from game.tasks import run_match
class UserProfileAdmin(admin.ModelAdmin):
list_display = ('user', 'score', 'current_bot', 'code_update_date')
list_filter = ('current_bot',)
class BotAdmin(admin.ModelAdmin):
actions = ['validate_bot']
list_display = ('creation_date', 'modification_date', 'owner', 'valid')
list_filter = ('valid',)
def validate_bot(self, request, queryset):
for bot in queryset:
validate_bot.delay(bot.id, bot.code)
class ChallengeAdmin(admin.ModelAdmin):
list_display = ('creation_date', 'requested_by', 'challenger_bot', 'challenged_bot', 'played', 'winner_player', 'canceled')
list_filter = ('canceled', 'final_challenge')
class FinalChallengeAdmin(admin.ModelAdmin):
def get_urls(self):
urls = super(FinalChallengeAdmin, self).get_urls()
my_urls = patterns('',
(r'^final_challenge/$', self.final_challenge)
)
return my_urls + urls
actions = ['final_challenge']
def final_challenge(self, request, queryset):
profiles = UserProfile.objects.filter(user__is_superuser=False, user__is_active=True).all()
if not queryset:
return HttpResponseRedirect('/admin')
final_challenge = queryset[0]
for up_player1, up_player2 in itertools.product(profiles, repeat=2):
if (up_player1 == up_player2
or not up_player1.current_bot
or not up_player2.current_bot):
continue
challenge = Challenge()
challenge.requested_by = up_player1
challenge.challenger_bot = up_player1.current_bot
challenge.challenged_bot = up_player2.current_bot
challenge.final_challenge = final_challenge
challenge.save()
final_challenge.challenge_set.add(challenge)
# dispatch the new task
players = {up_player1.user.username: up_player1.current_bot.code,
up_player2.user.username: up_player2.current_bot.code,
}
run_match.delay(challenge.id, players)
final_challenge.save()
return HttpResponseRedirect('/admin')
admin.site.register(FinalChallenge, FinalChallengeAdmin)
admin.site.register(UserProfile, UserProfileAdmin)
admin.site.register(Challenge, ChallengeAdmin)
admin.site.register(Bot, BotAdmin)
|
"""
Part of this code is based on a similar implementation present in FireWorks (https://pypi.python.org/pypi/FireWorks).
Work done by D. Waroquiers, A. Jain, and M. Kocher.
The main difference wrt the Fireworks implementation is that the QueueAdapter
objects provide a programmatic interface for setting important attributes
such as the number of MPI nodes, the number of OMP threads and the memory requirements.
This programmatic interface is used by the `TaskManager` for optimizing the parameters
of the run before submitting the job (Abinit provides the autoparal option that
allows one to get a list of parallel configuration and their expected efficiency).
"""
from __future__ import print_function, division
import os
import abc
import string
import copy
import getpass
import warnings
from subprocess import Popen, PIPE
from pymatgen.io.abinitio.launcher import ScriptEditor
from pymatgen.util.string_utils import is_string
import logging
logger = logging.getLogger(__name__)
__all__ = [
"MpiRunner",
"qadapter_class",
]
class Command(object):
"""
From https://gist.github.com/kirpit/1306188
Enables to run subprocess commands in a different thread with TIMEOUT option.
Based on jcollado's solution:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout/4825933#4825933
"""
command = None
process = None
status = None
output, error = '', ''
def __init__(self, command):
if is_string(command):
import shlex
command = shlex.split(command)
self.command = command
def run(self, timeout=None, **kwargs):
""" Run a command then return: (status, output, error). """
def target(**kwargs):
try:
self.process = Popen(self.command, **kwargs)
self.output, self.error = self.process.communicate()
self.status = self.process.returncode
except:
import traceback
self.error = traceback.format_exc()
self.status = -1
# default stdout and stderr
if 'stdout' not in kwargs:
kwargs['stdout'] = PIPE
if 'stderr' not in kwargs:
kwargs['stderr'] = PIPE
# thread
import threading
thread = threading.Thread(target=target, kwargs=kwargs)
thread.start()
thread.join(timeout)
if thread.is_alive():
self.process.terminate()
thread.join()
return self.status, self.output, self.error
class MpiRunner(object):
"""
This object provides an abstraction for the mpirunner provided
by the different MPI libraries. It's main task is handling the
different syntax and options supported by the different mpirunners.
"""
def __init__(self, name, type=None, options=""):
self.name = name
self.type = None
self.options = options
def string_to_run(self, executable, mpi_ncpus, stdin=None, stdout=None, stderr=None):
stdin = "< " + stdin if stdin is not None else ""
stdout = "> " + stdout if stdout is not None else ""
stderr = "2> " + stderr if stderr is not None else ""
if self.has_mpirun:
if self.type is None:
# TODO: better treatment of mpirun syntax.
#se.add_line('$MPIRUN -n $MPI_NCPUS $EXECUTABLE < $STDIN > $STDOUT 2> $STDERR')
num_opt = "-n " + str(mpi_ncpus)
cmd = " ".join([self.name, num_opt, executable, stdin, stdout, stderr])
else:
raise NotImplementedError("type %s is not supported!")
else:
#assert mpi_ncpus == 1
cmd = " ".join([executable, stdin, stdout, stderr])
return cmd
@property
def has_mpirun(self):
"""True if we are running via mpirun, mpiexec ..."""
return self.name is not None
def qadapter_class(qtype):
"""Return the concrete `Adapter` class from a string."""
return {"shell": ShellAdapter,
"slurm": SlurmAdapter,
"pbs": PbsAdapter,
"sge": SGEAdapter,
}[qtype.lower()]
class QueueAdapterError(Exception):
"""Error class for exceptions raise by QueueAdapter."""
class AbstractQueueAdapter(object):
"""
The QueueAdapter is responsible for all interactions with a specific
queue management system. This includes handling all details of queue
script format as well as queue submission and management.
This is the Abstract base class defining the methods that
must be implemented by the concrete classes.
A user should extend this class with implementations that work on
specific queue systems.
"""
__metaclass__ = abc.ABCMeta
Error = QueueAdapterError
def __init__(self, qparams=None, setup=None, modules=None, shell_env=None, omp_env=None,
pre_run=None, post_run=None, mpi_runner=None):
"""
Args:
setup:
String or list of commands to execute during the initial setup.
modules:
String or list of modules to load before running the application.
shell_env:
Dictionary with the environment variables to export
before running the application.
omp_env:
Dictionary with the OpenMP variables.
pre_run:
String or list of commands to execute before launching the calculation.
post_run:
String or list of commands to execute once the calculation is completed.
mpi_runner:
Path to the MPI runner or `MpiRunner` instance. None if not used
"""
# Make defensive copies so that we can change the values at runtime.
self.qparams = qparams.copy() if qparams is not None else {}
self._verbatim = []
if is_string(setup):
setup = [setup]
self.setup = setup[:] if setup is not None else []
self.omp_env = omp_env.copy() if omp_env is not None else {}
if is_string(modules):
modules = [modules]
self.modules = modules[:] if modules is not None else []
self.shell_env = shell_env.copy() if shell_env is not None else {}
self.mpi_runner = mpi_runner
if not isinstance(mpi_runner, MpiRunner):
self.mpi_runner = MpiRunner(mpi_runner)
if is_string(pre_run):
pre_run = [pre_run]
self.pre_run = pre_run[:] if pre_run is not None else []
if is_string(post_run):
post_run = [post_run]
self.post_run = post_run[:] if post_run is not None else []
# Parse the template so that we know the list of supported options.
cls = self.__class__
if hasattr(cls, "QTEMPLATE"):
# Consistency check.
err_msg = ""
for param in self.qparams:
if param not in self.supported_qparams:
err_msg += "Unsupported QUEUE parameter name %s\n" % param
err_msg += "Supported are: \n"
for param_sup in self.supported_qparams:
err_msg += " %s \n" % param_sup
if err_msg:
raise ValueError(err_msg)
def copy(self):
return copy.copy(self)
def deepcopy(self):
return copy.deepcopy(self)
@property
def supported_qparams(self):
"""
Dictionary with the supported parameters that can be passed to the
queue manager (obtained by parsing QTEMPLATE).
"""
try:
return self._supported_qparams
except AttributeError:
import re
self._supported_qparams = re.findall("\$\$\{(\w+)\}", self.QTEMPLATE)
return self._supported_qparams
@property
def has_mpirun(self):
"""True if we are using a mpirunner"""
return bool(self.mpi_runner)
@property
def has_omp(self):
"""True if we are using OpenMP threads"""
return hasattr(self, "omp_env") and bool(getattr(self, "omp_env"))
@property
def tot_ncpus(self):
"""Total number of CPUs employed"""
return self.mpi_ncpus * self.omp_ncpus
@property
def omp_ncpus(self):
"""Number of OpenMP threads."""
if self.has_omp:
return self.omp_env["OMP_NUM_THREADS"]
else:
return 1
@abc.abstractmethod
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
@abc.abstractproperty
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
@abc.abstractmethod
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
#@abc.abstractproperty
#def queue_walltime(self):
# """Returns the walltime in seconds."""
#@abc.abstractmethod
#def set_queue_walltime(self):
# """Set the walltime in seconds."""
#@abc.abstractproperty
#def mem_per_cpu(self):
# """The memory per CPU in Megabytes."""
@abc.abstractmethod
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
#@property
#def tot_mem(self):
# """Total memory required by the job n Megabytes."""
# return self.mem_per_cpu * self.mpi_ncpus
@abc.abstractmethod
def cancel(self, job_id):
"""
Cancel the job.
Args:
job_id:
(in) Job identifier.
Returns:
Exit status.
"""
def add_verbatim(self, lines):
"""
Add a list of lines or just a string to the header.
No programmatic interface to change these options is provided
"""
if is_string(lines): lines = [lines]
self._verbatim.extend(lines)
def _make_qheader(self, job_name, qout_path, qerr_path):
"""Return a string with the options that are passed to the resource manager."""
qtemplate = QScriptTemplate(self.QTEMPLATE)
# set substitution dict for replacements into the template and clean null values
subs_dict = {k: v for k, v in self.qparams.items() if v is not None}
# Set job_name and the names for the stderr and stdout of the
# queue manager (note the use of the extensions .qout and .qerr
# so that we can easily locate this file.
subs_dict['job_name'] = job_name.replace('/', '_')
subs_dict['_qout_path'] = qout_path
subs_dict['_qerr_path'] = qerr_path
# might contain unused parameters as leftover $$.
unclean_template = qtemplate.safe_substitute(subs_dict)
# Remove lines with leftover $$.
clean_template = []
for line in unclean_template.split('\n'):
if '$$' not in line:
clean_template.append(line)
# Add verbatim lines
if self._verbatim:
clean_template.extend(self._verbatim)
return '\n'.join(clean_template)
def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path, stdin=None, stdout=None, stderr=None):
"""
Returns a (multi-line) String representing the queue script, e.g. PBS script.
Uses the template_file along with internal parameters to create the script.
Args:
job_name:
Name of the job.
launch_dir:
(str) The directory the job will be launched in.
qout_path
Path of the Queue manager output file.
qerr_path:
Path of the Queue manager error file.
"""
# PBS does not accept job_names longer than 15 chars.
if len(job_name) > 14 and isinstance(self, PbsAdapter):
job_name = job_name[:14]
# Construct the header for the Queue Manager.
qheader = self._make_qheader(job_name, qout_path, qerr_path)
# Add the bash section.
se = ScriptEditor()
if self.setup:
se.add_comment("Setup section")
se.add_lines(self.setup)
se.add_emptyline()
if self.modules:
se.add_comment("Load Modules")
se.add_line("module purge")
se.load_modules(self.modules)
se.add_emptyline()
if self.has_omp:
se.add_comment("OpenMp Environment")
se.declare_vars(self.omp_env)
se.add_emptyline()
if self.shell_env:
se.add_comment("Shell Environment")
se.declare_vars(self.shell_env)
se.add_emptyline()
# Cd to launch_dir
se.add_line("cd " + os.path.abspath(launch_dir))
if self.pre_run:
se.add_comment("Commands before execution")
se.add_lines(self.pre_run)
se.add_emptyline()
# Construct the string to run the executable with MPI and mpi_ncpus.
mpi_ncpus = self.mpi_ncpus
line = self.mpi_runner.string_to_run(executable, mpi_ncpus, stdin=stdin, stdout=stdout, stderr=stderr)
se.add_line(line)
if self.post_run:
se.add_emptyline()
se.add_comment("Commands after execution")
se.add_lines(self.post_run)
shell_text = se.get_script_str()
return qheader + shell_text + "\n"
@abc.abstractmethod
def submit_to_queue(self, script_file):
"""
Submits the job to the queue, probably using subprocess or shutil
Args:
script_file:
(str) name of the script file to use (String)
Returns:
process, queue_id
"""
@abc.abstractmethod
def get_njobs_in_queue(self, username=None):
"""
returns the number of jobs in the queue, probably using subprocess or shutil to
call a command like 'qstat'. returns None when the number of jobs cannot be determined.
Args:
username: (str) the username of the jobs to count (default is to autodetect)
"""
#some method to fix problems
@abc.abstractmethod
def exclude_nodes(self, nodes):
"""
Method to exclude nodes in the calculation
"""
@abc.abstractmethod
def increase_mem(self, factor):
"""
Method to increase the amount of memory asked for, by factor.
"""
@abc.abstractmethod
def increase_time(self, factor):
"""
Method to increase the available wall time asked for, by factor.
"""
@abc.abstractmethod
def increase_cpus(self, factor):
"""
Method to increase the number of cpus asked for.
"""
# @abc.abstractmethod
def increase_resources(self):
"""
Method to generally increase resources.
"""
return False
####################
# Concrete classes #
####################
class ShellAdapter(AbstractQueueAdapter):
QTYPE = "shell"
QTEMPLATE = """\
#!/bin/bash
export MPI_NCPUS=$${MPI_NCPUS}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("MPI_NCPUS", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["MPI_NCPUS"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
def set_mem_per_cpu(self, mem_mb):
"""mem_per_cpu is not available in ShellAdapter."""
def cancel(self, job_id):
return os.system("kill -9 %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
process = Popen(("/bin/bash", script_file), stderr=PIPE)
queue_id = process.pid
return process, queue_id
except:
# random error
raise self.Error("Random Error ...!")
def get_njobs_in_queue(self, username=None):
return None
def exclude_nodes(self, nodes):
return False
def increase_mem(self, factor):
return False
def increase_time(self, factor):
return False
def increase_cpus(self, factor):
return False
class SlurmAdapter(AbstractQueueAdapter):
QTYPE = "slurm"
QTEMPLATE = """\
#!/bin/bash
#SBATCH --ntasks=$${ntasks}
#SBATCH --ntasks-per-node=$${ntasks_per_node}
#SBATCH --cpus-per-task=$${cpus_per_task}
#SBATCH --time=$${time}
#SBATCH --partition=$${partition}
#SBATCH --account=$${account}
#SBATCH --job-name=$${job_name}
#SBATCH --nodes=$${nodes}
#SBATCH --exclude=$${exclude_nodes}
#SBATCH --mem=$${mem}
#SBATCH --mem-per-cpu=$${mem_per_cpu}
#SBATCH --mail-user=$${mail_user}
#SBATCH --mail-type=$${mail_type}
#SBATCH --constraint=$${constraint}
#SBATCH --gres=$${gres}
#SBATCH --requeue=$${requeue}
#SBATCH --nodelist=$${nodelist}
#SBATCH --propagate=$${propagate}
#SBATCH --output=$${_qout_path}
#SBATCH --error=$${_qerr_path}
"""
@property
def limits(self):
"""
the limits for certain parameters set on the cluster.
currently hard coded, should be read at init
the increase functions will not increase beyond thise limits
"""
return {'max_total_tasks': 544, 'max_cpus_per_node': 16, 'mem': 6400000, 'mem_per_cpu': 64000, 'time': 2880}
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ntasks", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ntasks"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
warnings.warn("set_omp_ncpus not availabe for %s" % self.__class__.__name__)
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["mem_per_cpu"] = int(mem_mb)
# Remove mem if it's defined.
self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("scancel %d" % job_id)
def submit_to_queue(self, script_file, submit_err_file="sbatch.err"):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
submit_err_file = os.path.join(os.path.dirname(script_file), submit_err_file)
# submit the job
try:
cmd = ['sbatch', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
# write the err output to file, a error parser may read it and a fixer may know what to do ...
with open(submit_err_file, mode='w') as f:
f.write('sbatch submit process stderr:')
f.write(str(process.stderr.read()))
f.write('qparams:')
f.write(str(self.qparams))
process.wait()
# grab the returncode. SLURM returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split()[3])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
queue_id = None
logger.warning('Could not parse job id following slurm...')
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
err_msg = ("Error in job submission with SLURM file {f} and cmd {c}\n".format(f=script_file, c=cmd) +
"The error response reads: {c}".format(c=process.stderr.read()))
raise self.Error(err_msg)
except Exception as details:
msg = 'Error while submitting job:\n' + str(details)
logger.critical(msg)
with open(submit_err_file, mode='a') as f:
f.write(msg)
try:
print('sometimes we land here, no idea what is happening ... Michiel')
print(details)
print(cmd)
print(process.returncode)
except:
pass
# random error, e.g. no qsub on machine!
raise self.Error('Running sbatch caused an error...')
def exclude_nodes(self, nodes):
try:
if 'exclude_nodes' not in self.qparams.keys():
self.qparams.update({'exclude_nodes': 'node'+nodes[0]})
print('excluded node %s' % nodes[0])
for node in nodes[1:]:
self.qparams['exclude_nodes'] += ',node'+node
print('excluded node %s' % node)
print(self.qparams)
return True
except (KeyError, IndexError):
return False
def increase_cpus(self, factor=1.5):
print('increasing cpus')
try:
if self.qparams['ntasks'] > 1:
# mpi parallel
n = int(self.qparams['ntasks'] * factor)
if n < self.limits['max_total_tasks']:
self.qparams['ntasks'] = n
print('increased ntasks to %s' % n)
return True
else:
raise QueueAdapterError
elif self.qparams['ntasks'] == 1 and self.qparams['cpus_per_task'] > 1:
# open mp parallel
n = int(self.qparams['cpus_per_task'] * factor)
if n < self.limits['max_cpus_per_node']:
self.qparams['cpus_per_task'] = n
return True
else:
raise QueueAdapterError
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def increase_mem(self, factor=1.5):
print('increasing memory')
try:
if 'mem' in self.qparams.keys():
n = int(self.qparams['mem'] * factor)
if n < self.limits['mem']:
self.qparams['mem'] = n
print('increased mem to %s' % n)
return True
else:
raise QueueAdapterError
elif 'mem_per_cpu' in self.qparams.keys():
n = int(self.qparams['mem_per_cpu'] * factor)
if n < self.limits['mem_per_cpu']:
self.qparams['mem'] = n
print('increased mem_per_cpu to %s' % n)
return True
else:
raise QueueAdapterError
else:
raise QueueAdapterError
except (KeyError, IndexError, QueueAdapterError):
return False
def increase_time(self, factor=1.5):
print('increasing time')
days, hours, minutes = 0, 0, 0
try:
# a slurm time parser ;-) forgetting about seconds
# feel free to pull this out and mak time in minutes always
if '-' not in self.qparams['time']:
# "minutes",
# "minutes:seconds",
# "hours:minutes:seconds",
if ':' not in self.qparams['time']:
minutes = int(float(self.qparams['time']))
elif self.qparams['time'].count(':') == 1:
minutes = int(float(self.qparams['time'].split(':')[0]))
else:
minutes = int(float(self.qparams['time'].split(':')[1]))
hours = int(float(self.qparams['time'].split(':')[0]))
else:
# "days-hours",
# "days-hours:minutes",
# "days-hours:minutes:seconds".
days = int(float(self.qparams['time'].split('-')[0]))
hours = int(float(self.qparams['time'].split('-')[1].split(':')[0]))
try:
minutes = int(float(self.qparams['time'].split('-')[1].split(':')[1]))
except IndexError:
pass
time = (days * 24 + hours) * 60 + minutes
time *= factor
if time < self.limits['time']:
self.qparams['time'] = time
print('increased time to %s' % time)
return True
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def get_njobs_in_queue(self, username=None):
if username is None:
username = getpass.getuser()
cmd = ['squeue', '-o "%u"', '-u', username]
process = Popen(cmd, shell=False, stdout=PIPE)
process.wait()
# parse the result
if process.returncode == 0:
# lines should have this form
# username
# count lines that include the username in it
outs = process.stdout.readlines()
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to squeue server?
err_msg = ('Error trying to get the number of jobs in the queue using squeue service' +
'The error response reads: {}'.format(process.stderr.read()))
logger.critical(err_msg)
return None
class PbsAdapter(AbstractQueueAdapter):
QTYPE = "pbs"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -N $${job_name}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l model=$${model}
#PBS -l place=$${place}
#PBS -W group_list=$${group_list}
#PBS -l pvmem=$${pvmem}mb
#PBS -l nodes=$${nodes}:ppn=$${ppn} # OLD SYNTAX
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
@property
def limits(self):
"""
the limits for certain parameters set on the cluster.
currently hard coded, should be read at init
the increase functions will not increase beyond thise limits
"""
return {'max_total_tasks': 3888, 'time': 48, 'max_select': 120}
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("select", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["select"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
self.qparams["ompthreads"] = omp_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["pvmem"] = mem_mb
self.qparams["vmem"] = mem_mb
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the return code. PBS returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split('.')[0])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
raise self.Error(msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-a', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should have this form
# '1339044.sdb username queuename 2012-02-29-16-43 20460 -- -- -- 00:20 C 00:09'
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
print(' ** ')
print(process[1].split('\n'))
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
print(' ** ')
logger.critical(err_msg)
return None
# no need to raise an error, if False is returned the fixer may try something else, we don't need to kill the
# scheduler just yet
def do(self):
return 'this is not FORTRAN'
def exclude_nodes(self, nodes):
logger.warning('exluding nodes, not implemented yet pbs')
return False
def increase_mem(self, factor):
logger.warning('increasing mem, not implemented yet pbs')
return False
def increase_time(self, factor=1.5):
days, hours, minutes = 0, 0, 0
try:
# a pbe time parser [HH:MM]:SS
# feel free to pull this out and mak time in minutes always
n = str(self.qparams['time']).count(':')
if n == 0:
hours = int(float(self.qparams['time']))
elif n > 1:
hours = int(float(self.qparams['time'].split(':')[0]))
minutes = int(float(self.qparams['time'].split(':')[1]))
time = hours * 60 + minutes
time *= factor
if time < self.limits['time']:
self.qparams['time'] = str(int(time / 60)) + ':' + str(int(time - 60 * int(time / 60))) + ':00'
print('increased time to %s minutes' % time)
return True
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def increase_cpus(self, factor):
base_increase = 12
new_cpus = self.qparams['select'] + factor * base_increase
if new_cpus < self.limits['max_select']:
self.qparams['select'] = new_cpus
return True
else:
logger.warning('increasing cpus reached the limit')
return False
def increase_resources(self):
"""
Method to generally increase resources. On typical large machines we only increas cpu's since we use all
mem per cpu per core
"""
if self.increase_cpus(1):
return True
else:
return False
class PbsOldAdapter(PbsAdapter):
QTYPE = "pbsold"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -N $${job_name}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l model=$${model}
#PBS -l place=$${place}
#PBS -W group_list=$${group_list}
####PBS -l select=$${select}:ncpus=1:vmem=$${vmem}mb:mpiprocs=1:ompthreads=$${ompthreads} # New syntax
####PBS -l pvmem=$${pvmem}mb
#PBS -l pmem=$${pmem}mb
####PBS -l mppwidth=$${mppwidth}
#PBS -l nodes=$${nodes}:ppn=$${ppn} # OLD SYNTAX
#PBS -M $${mail_user}
#PBS -m $${mail_type}
# Submission environment
#PBS -V
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["pmem"] = mem_mb
self.qparams["mem"] = mem_mb
class SGEAdapter(AbstractQueueAdapter):
"""
Adapter for Sun Grid Engine (SGE) task submission software.
"""
QTYPE = "sge"
QTEMPLATE = """\
#!/bin/bash
#$ -A $${account}
#$ -N $${job_name}
#$ -l h rt=$${walltime}
#$ -pe $${queue} $${ncpus}
#$ -cwd
#$ -j y
#$ -m n
#$ -e $${_qerr_path}
#$ -o $${_qout_path}
#$ -S /bin/bash
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ncpus", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ncpus"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
warnings.warn("set_omp_ncpus not availabe for %s" % self.__class__.__name__)
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. SGE returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form
# Your job 1659048 ("NAME_OF_JOB") has been submitted
queue_id = int(process.stdout.read().split(' ')[2])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
raise self.Error(msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should contain username
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
def exclude_nodes(self, nodes):
"""
Method to exclude nodes in the calculation
"""
raise NotImplementedError("exclude_nodes")
def increase_mem(self, factor):
"""
Method to increase the amount of memory asked for, by factor.
"""
raise NotImplementedError("increase_mem")
def increase_time(self, factor):
"""
Method to increase the available wall time asked for, by factor.
"""
raise NotImplementedError("increase_time")
def increase_cpus(self, factor):
raise NotImplementedError("increase_cpus")
class QScriptTemplate(string.Template):
delimiter = '$$'
Add PbsAdapter in list of adapters.
"""
Part of this code is based on a similar implementation present in FireWorks (https://pypi.python.org/pypi/FireWorks).
Work done by D. Waroquiers, A. Jain, and M. Kocher.
The main difference wrt the Fireworks implementation is that the QueueAdapter
objects provide a programmatic interface for setting important attributes
such as the number of MPI nodes, the number of OMP threads and the memory requirements.
This programmatic interface is used by the `TaskManager` for optimizing the parameters
of the run before submitting the job (Abinit provides the autoparal option that
allows one to get a list of parallel configuration and their expected efficiency).
"""
from __future__ import print_function, division
import os
import abc
import string
import copy
import getpass
import warnings
from subprocess import Popen, PIPE
from pymatgen.io.abinitio.launcher import ScriptEditor
from pymatgen.util.string_utils import is_string
import logging
logger = logging.getLogger(__name__)
__all__ = [
"MpiRunner",
"qadapter_class",
]
class Command(object):
"""
From https://gist.github.com/kirpit/1306188
Enables to run subprocess commands in a different thread with TIMEOUT option.
Based on jcollado's solution:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout/4825933#4825933
"""
command = None
process = None
status = None
output, error = '', ''
def __init__(self, command):
if is_string(command):
import shlex
command = shlex.split(command)
self.command = command
def run(self, timeout=None, **kwargs):
""" Run a command then return: (status, output, error). """
def target(**kwargs):
try:
self.process = Popen(self.command, **kwargs)
self.output, self.error = self.process.communicate()
self.status = self.process.returncode
except:
import traceback
self.error = traceback.format_exc()
self.status = -1
# default stdout and stderr
if 'stdout' not in kwargs:
kwargs['stdout'] = PIPE
if 'stderr' not in kwargs:
kwargs['stderr'] = PIPE
# thread
import threading
thread = threading.Thread(target=target, kwargs=kwargs)
thread.start()
thread.join(timeout)
if thread.is_alive():
self.process.terminate()
thread.join()
return self.status, self.output, self.error
class MpiRunner(object):
"""
This object provides an abstraction for the mpirunner provided
by the different MPI libraries. It's main task is handling the
different syntax and options supported by the different mpirunners.
"""
def __init__(self, name, type=None, options=""):
self.name = name
self.type = None
self.options = options
def string_to_run(self, executable, mpi_ncpus, stdin=None, stdout=None, stderr=None):
stdin = "< " + stdin if stdin is not None else ""
stdout = "> " + stdout if stdout is not None else ""
stderr = "2> " + stderr if stderr is not None else ""
if self.has_mpirun:
if self.type is None:
# TODO: better treatment of mpirun syntax.
#se.add_line('$MPIRUN -n $MPI_NCPUS $EXECUTABLE < $STDIN > $STDOUT 2> $STDERR')
num_opt = "-n " + str(mpi_ncpus)
cmd = " ".join([self.name, num_opt, executable, stdin, stdout, stderr])
else:
raise NotImplementedError("type %s is not supported!")
else:
#assert mpi_ncpus == 1
cmd = " ".join([executable, stdin, stdout, stderr])
return cmd
@property
def has_mpirun(self):
"""True if we are running via mpirun, mpiexec ..."""
return self.name is not None
def qadapter_class(qtype):
"""Return the concrete `Adapter` class from a string."""
return {"shell": ShellAdapter,
"slurm": SlurmAdapter,
"pbs": PbsAdapter,
"pbsold": PbsOldAdapter,
"sge": SGEAdapter,
}[qtype.lower()]
class QueueAdapterError(Exception):
"""Error class for exceptions raise by QueueAdapter."""
class AbstractQueueAdapter(object):
"""
The QueueAdapter is responsible for all interactions with a specific
queue management system. This includes handling all details of queue
script format as well as queue submission and management.
This is the Abstract base class defining the methods that
must be implemented by the concrete classes.
A user should extend this class with implementations that work on
specific queue systems.
"""
__metaclass__ = abc.ABCMeta
Error = QueueAdapterError
def __init__(self, qparams=None, setup=None, modules=None, shell_env=None, omp_env=None,
pre_run=None, post_run=None, mpi_runner=None):
"""
Args:
setup:
String or list of commands to execute during the initial setup.
modules:
String or list of modules to load before running the application.
shell_env:
Dictionary with the environment variables to export
before running the application.
omp_env:
Dictionary with the OpenMP variables.
pre_run:
String or list of commands to execute before launching the calculation.
post_run:
String or list of commands to execute once the calculation is completed.
mpi_runner:
Path to the MPI runner or `MpiRunner` instance. None if not used
"""
# Make defensive copies so that we can change the values at runtime.
self.qparams = qparams.copy() if qparams is not None else {}
self._verbatim = []
if is_string(setup):
setup = [setup]
self.setup = setup[:] if setup is not None else []
self.omp_env = omp_env.copy() if omp_env is not None else {}
if is_string(modules):
modules = [modules]
self.modules = modules[:] if modules is not None else []
self.shell_env = shell_env.copy() if shell_env is not None else {}
self.mpi_runner = mpi_runner
if not isinstance(mpi_runner, MpiRunner):
self.mpi_runner = MpiRunner(mpi_runner)
if is_string(pre_run):
pre_run = [pre_run]
self.pre_run = pre_run[:] if pre_run is not None else []
if is_string(post_run):
post_run = [post_run]
self.post_run = post_run[:] if post_run is not None else []
# Parse the template so that we know the list of supported options.
cls = self.__class__
if hasattr(cls, "QTEMPLATE"):
# Consistency check.
err_msg = ""
for param in self.qparams:
if param not in self.supported_qparams:
err_msg += "Unsupported QUEUE parameter name %s\n" % param
err_msg += "Supported are: \n"
for param_sup in self.supported_qparams:
err_msg += " %s \n" % param_sup
if err_msg:
raise ValueError(err_msg)
def copy(self):
return copy.copy(self)
def deepcopy(self):
return copy.deepcopy(self)
@property
def supported_qparams(self):
"""
Dictionary with the supported parameters that can be passed to the
queue manager (obtained by parsing QTEMPLATE).
"""
try:
return self._supported_qparams
except AttributeError:
import re
self._supported_qparams = re.findall("\$\$\{(\w+)\}", self.QTEMPLATE)
return self._supported_qparams
@property
def has_mpirun(self):
"""True if we are using a mpirunner"""
return bool(self.mpi_runner)
@property
def has_omp(self):
"""True if we are using OpenMP threads"""
return hasattr(self, "omp_env") and bool(getattr(self, "omp_env"))
@property
def tot_ncpus(self):
"""Total number of CPUs employed"""
return self.mpi_ncpus * self.omp_ncpus
@property
def omp_ncpus(self):
"""Number of OpenMP threads."""
if self.has_omp:
return self.omp_env["OMP_NUM_THREADS"]
else:
return 1
@abc.abstractmethod
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
@abc.abstractproperty
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
@abc.abstractmethod
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
#@abc.abstractproperty
#def queue_walltime(self):
# """Returns the walltime in seconds."""
#@abc.abstractmethod
#def set_queue_walltime(self):
# """Set the walltime in seconds."""
#@abc.abstractproperty
#def mem_per_cpu(self):
# """The memory per CPU in Megabytes."""
@abc.abstractmethod
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
#@property
#def tot_mem(self):
# """Total memory required by the job n Megabytes."""
# return self.mem_per_cpu * self.mpi_ncpus
@abc.abstractmethod
def cancel(self, job_id):
"""
Cancel the job.
Args:
job_id:
(in) Job identifier.
Returns:
Exit status.
"""
def add_verbatim(self, lines):
"""
Add a list of lines or just a string to the header.
No programmatic interface to change these options is provided
"""
if is_string(lines): lines = [lines]
self._verbatim.extend(lines)
def _make_qheader(self, job_name, qout_path, qerr_path):
"""Return a string with the options that are passed to the resource manager."""
qtemplate = QScriptTemplate(self.QTEMPLATE)
# set substitution dict for replacements into the template and clean null values
subs_dict = {k: v for k, v in self.qparams.items() if v is not None}
# Set job_name and the names for the stderr and stdout of the
# queue manager (note the use of the extensions .qout and .qerr
# so that we can easily locate this file.
subs_dict['job_name'] = job_name.replace('/', '_')
subs_dict['_qout_path'] = qout_path
subs_dict['_qerr_path'] = qerr_path
# might contain unused parameters as leftover $$.
unclean_template = qtemplate.safe_substitute(subs_dict)
# Remove lines with leftover $$.
clean_template = []
for line in unclean_template.split('\n'):
if '$$' not in line:
clean_template.append(line)
# Add verbatim lines
if self._verbatim:
clean_template.extend(self._verbatim)
return '\n'.join(clean_template)
def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path, stdin=None, stdout=None, stderr=None):
"""
Returns a (multi-line) String representing the queue script, e.g. PBS script.
Uses the template_file along with internal parameters to create the script.
Args:
job_name:
Name of the job.
launch_dir:
(str) The directory the job will be launched in.
qout_path
Path of the Queue manager output file.
qerr_path:
Path of the Queue manager error file.
"""
# PBS does not accept job_names longer than 15 chars.
if len(job_name) > 14 and isinstance(self, PbsAdapter):
job_name = job_name[:14]
# Construct the header for the Queue Manager.
qheader = self._make_qheader(job_name, qout_path, qerr_path)
# Add the bash section.
se = ScriptEditor()
if self.setup:
se.add_comment("Setup section")
se.add_lines(self.setup)
se.add_emptyline()
if self.modules:
se.add_comment("Load Modules")
se.add_line("module purge")
se.load_modules(self.modules)
se.add_emptyline()
if self.has_omp:
se.add_comment("OpenMp Environment")
se.declare_vars(self.omp_env)
se.add_emptyline()
if self.shell_env:
se.add_comment("Shell Environment")
se.declare_vars(self.shell_env)
se.add_emptyline()
# Cd to launch_dir
se.add_line("cd " + os.path.abspath(launch_dir))
if self.pre_run:
se.add_comment("Commands before execution")
se.add_lines(self.pre_run)
se.add_emptyline()
# Construct the string to run the executable with MPI and mpi_ncpus.
mpi_ncpus = self.mpi_ncpus
line = self.mpi_runner.string_to_run(executable, mpi_ncpus, stdin=stdin, stdout=stdout, stderr=stderr)
se.add_line(line)
if self.post_run:
se.add_emptyline()
se.add_comment("Commands after execution")
se.add_lines(self.post_run)
shell_text = se.get_script_str()
return qheader + shell_text + "\n"
@abc.abstractmethod
def submit_to_queue(self, script_file):
"""
Submits the job to the queue, probably using subprocess or shutil
Args:
script_file:
(str) name of the script file to use (String)
Returns:
process, queue_id
"""
@abc.abstractmethod
def get_njobs_in_queue(self, username=None):
"""
returns the number of jobs in the queue, probably using subprocess or shutil to
call a command like 'qstat'. returns None when the number of jobs cannot be determined.
Args:
username: (str) the username of the jobs to count (default is to autodetect)
"""
#some method to fix problems
@abc.abstractmethod
def exclude_nodes(self, nodes):
"""
Method to exclude nodes in the calculation
"""
@abc.abstractmethod
def increase_mem(self, factor):
"""
Method to increase the amount of memory asked for, by factor.
"""
@abc.abstractmethod
def increase_time(self, factor):
"""
Method to increase the available wall time asked for, by factor.
"""
@abc.abstractmethod
def increase_cpus(self, factor):
"""
Method to increase the number of cpus asked for.
"""
# @abc.abstractmethod
def increase_resources(self):
"""
Method to generally increase resources.
"""
return False
####################
# Concrete classes #
####################
class ShellAdapter(AbstractQueueAdapter):
QTYPE = "shell"
QTEMPLATE = """\
#!/bin/bash
export MPI_NCPUS=$${MPI_NCPUS}
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("MPI_NCPUS", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["MPI_NCPUS"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
def set_mem_per_cpu(self, mem_mb):
"""mem_per_cpu is not available in ShellAdapter."""
def cancel(self, job_id):
return os.system("kill -9 %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
process = Popen(("/bin/bash", script_file), stderr=PIPE)
queue_id = process.pid
return process, queue_id
except:
# random error
raise self.Error("Random Error ...!")
def get_njobs_in_queue(self, username=None):
return None
def exclude_nodes(self, nodes):
return False
def increase_mem(self, factor):
return False
def increase_time(self, factor):
return False
def increase_cpus(self, factor):
return False
class SlurmAdapter(AbstractQueueAdapter):
QTYPE = "slurm"
QTEMPLATE = """\
#!/bin/bash
#SBATCH --ntasks=$${ntasks}
#SBATCH --ntasks-per-node=$${ntasks_per_node}
#SBATCH --cpus-per-task=$${cpus_per_task}
#SBATCH --time=$${time}
#SBATCH --partition=$${partition}
#SBATCH --account=$${account}
#SBATCH --job-name=$${job_name}
#SBATCH --nodes=$${nodes}
#SBATCH --exclude=$${exclude_nodes}
#SBATCH --mem=$${mem}
#SBATCH --mem-per-cpu=$${mem_per_cpu}
#SBATCH --mail-user=$${mail_user}
#SBATCH --mail-type=$${mail_type}
#SBATCH --constraint=$${constraint}
#SBATCH --gres=$${gres}
#SBATCH --requeue=$${requeue}
#SBATCH --nodelist=$${nodelist}
#SBATCH --propagate=$${propagate}
#SBATCH --output=$${_qout_path}
#SBATCH --error=$${_qerr_path}
"""
@property
def limits(self):
"""
the limits for certain parameters set on the cluster.
currently hard coded, should be read at init
the increase functions will not increase beyond thise limits
"""
return {'max_total_tasks': 544, 'max_cpus_per_node': 16, 'mem': 6400000, 'mem_per_cpu': 64000, 'time': 2880}
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ntasks", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ntasks"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
warnings.warn("set_omp_ncpus not availabe for %s" % self.__class__.__name__)
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["mem_per_cpu"] = int(mem_mb)
# Remove mem if it's defined.
self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("scancel %d" % job_id)
def submit_to_queue(self, script_file, submit_err_file="sbatch.err"):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
submit_err_file = os.path.join(os.path.dirname(script_file), submit_err_file)
# submit the job
try:
cmd = ['sbatch', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
# write the err output to file, a error parser may read it and a fixer may know what to do ...
with open(submit_err_file, mode='w') as f:
f.write('sbatch submit process stderr:')
f.write(str(process.stderr.read()))
f.write('qparams:')
f.write(str(self.qparams))
process.wait()
# grab the returncode. SLURM returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split()[3])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
queue_id = None
logger.warning('Could not parse job id following slurm...')
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
err_msg = ("Error in job submission with SLURM file {f} and cmd {c}\n".format(f=script_file, c=cmd) +
"The error response reads: {c}".format(c=process.stderr.read()))
raise self.Error(err_msg)
except Exception as details:
msg = 'Error while submitting job:\n' + str(details)
logger.critical(msg)
with open(submit_err_file, mode='a') as f:
f.write(msg)
try:
print('sometimes we land here, no idea what is happening ... Michiel')
print(details)
print(cmd)
print(process.returncode)
except:
pass
# random error, e.g. no qsub on machine!
raise self.Error('Running sbatch caused an error...')
def exclude_nodes(self, nodes):
try:
if 'exclude_nodes' not in self.qparams.keys():
self.qparams.update({'exclude_nodes': 'node'+nodes[0]})
print('excluded node %s' % nodes[0])
for node in nodes[1:]:
self.qparams['exclude_nodes'] += ',node'+node
print('excluded node %s' % node)
print(self.qparams)
return True
except (KeyError, IndexError):
return False
def increase_cpus(self, factor=1.5):
print('increasing cpus')
try:
if self.qparams['ntasks'] > 1:
# mpi parallel
n = int(self.qparams['ntasks'] * factor)
if n < self.limits['max_total_tasks']:
self.qparams['ntasks'] = n
print('increased ntasks to %s' % n)
return True
else:
raise QueueAdapterError
elif self.qparams['ntasks'] == 1 and self.qparams['cpus_per_task'] > 1:
# open mp parallel
n = int(self.qparams['cpus_per_task'] * factor)
if n < self.limits['max_cpus_per_node']:
self.qparams['cpus_per_task'] = n
return True
else:
raise QueueAdapterError
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def increase_mem(self, factor=1.5):
print('increasing memory')
try:
if 'mem' in self.qparams.keys():
n = int(self.qparams['mem'] * factor)
if n < self.limits['mem']:
self.qparams['mem'] = n
print('increased mem to %s' % n)
return True
else:
raise QueueAdapterError
elif 'mem_per_cpu' in self.qparams.keys():
n = int(self.qparams['mem_per_cpu'] * factor)
if n < self.limits['mem_per_cpu']:
self.qparams['mem'] = n
print('increased mem_per_cpu to %s' % n)
return True
else:
raise QueueAdapterError
else:
raise QueueAdapterError
except (KeyError, IndexError, QueueAdapterError):
return False
def increase_time(self, factor=1.5):
print('increasing time')
days, hours, minutes = 0, 0, 0
try:
# a slurm time parser ;-) forgetting about seconds
# feel free to pull this out and mak time in minutes always
if '-' not in self.qparams['time']:
# "minutes",
# "minutes:seconds",
# "hours:minutes:seconds",
if ':' not in self.qparams['time']:
minutes = int(float(self.qparams['time']))
elif self.qparams['time'].count(':') == 1:
minutes = int(float(self.qparams['time'].split(':')[0]))
else:
minutes = int(float(self.qparams['time'].split(':')[1]))
hours = int(float(self.qparams['time'].split(':')[0]))
else:
# "days-hours",
# "days-hours:minutes",
# "days-hours:minutes:seconds".
days = int(float(self.qparams['time'].split('-')[0]))
hours = int(float(self.qparams['time'].split('-')[1].split(':')[0]))
try:
minutes = int(float(self.qparams['time'].split('-')[1].split(':')[1]))
except IndexError:
pass
time = (days * 24 + hours) * 60 + minutes
time *= factor
if time < self.limits['time']:
self.qparams['time'] = time
print('increased time to %s' % time)
return True
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def get_njobs_in_queue(self, username=None):
if username is None:
username = getpass.getuser()
cmd = ['squeue', '-o "%u"', '-u', username]
process = Popen(cmd, shell=False, stdout=PIPE)
process.wait()
# parse the result
if process.returncode == 0:
# lines should have this form
# username
# count lines that include the username in it
outs = process.stdout.readlines()
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to squeue server?
err_msg = ('Error trying to get the number of jobs in the queue using squeue service' +
'The error response reads: {}'.format(process.stderr.read()))
logger.critical(err_msg)
return None
class PbsAdapter(AbstractQueueAdapter):
QTYPE = "pbs"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -N $${job_name}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l model=$${model}
#PBS -l place=$${place}
#PBS -W group_list=$${group_list}
#PBS -l pvmem=$${pvmem}mb
#PBS -l nodes=$${nodes}:ppn=$${ppn} # OLD SYNTAX
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
@property
def limits(self):
"""
the limits for certain parameters set on the cluster.
currently hard coded, should be read at init
the increase functions will not increase beyond thise limits
"""
return {'max_total_tasks': 3888, 'time': 48, 'max_select': 120}
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("select", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["select"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
self.qparams["ompthreads"] = omp_ncpus
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["pvmem"] = mem_mb
self.qparams["vmem"] = mem_mb
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the return code. PBS returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id
queue_id = int(process.stdout.read().split('.')[0])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
raise self.Error(msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-a', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should have this form
# '1339044.sdb username queuename 2012-02-29-16-43 20460 -- -- -- 00:20 C 00:09'
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
print(' ** ')
print(process[1].split('\n'))
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
print(' ** ')
logger.critical(err_msg)
return None
# no need to raise an error, if False is returned the fixer may try something else, we don't need to kill the
# scheduler just yet
def do(self):
return 'this is not FORTRAN'
def exclude_nodes(self, nodes):
logger.warning('exluding nodes, not implemented yet pbs')
return False
def increase_mem(self, factor):
logger.warning('increasing mem, not implemented yet pbs')
return False
def increase_time(self, factor=1.5):
days, hours, minutes = 0, 0, 0
try:
# a pbe time parser [HH:MM]:SS
# feel free to pull this out and mak time in minutes always
n = str(self.qparams['time']).count(':')
if n == 0:
hours = int(float(self.qparams['time']))
elif n > 1:
hours = int(float(self.qparams['time'].split(':')[0]))
minutes = int(float(self.qparams['time'].split(':')[1]))
time = hours * 60 + minutes
time *= factor
if time < self.limits['time']:
self.qparams['time'] = str(int(time / 60)) + ':' + str(int(time - 60 * int(time / 60))) + ':00'
print('increased time to %s minutes' % time)
return True
else:
raise QueueAdapterError
except (KeyError, QueueAdapterError):
return False
def increase_cpus(self, factor):
base_increase = 12
new_cpus = self.qparams['select'] + factor * base_increase
if new_cpus < self.limits['max_select']:
self.qparams['select'] = new_cpus
return True
else:
logger.warning('increasing cpus reached the limit')
return False
def increase_resources(self):
"""
Method to generally increase resources. On typical large machines we only increas cpu's since we use all
mem per cpu per core
"""
if self.increase_cpus(1):
return True
else:
return False
class PbsOldAdapter(PbsAdapter):
QTYPE = "pbsold"
QTEMPLATE = """\
#!/bin/bash
#PBS -A $${account}
#PBS -N $${job_name}
#PBS -l walltime=$${walltime}
#PBS -q $${queue}
#PBS -l model=$${model}
#PBS -l place=$${place}
#PBS -W group_list=$${group_list}
####PBS -l select=$${select}:ncpus=1:vmem=$${vmem}mb:mpiprocs=1:ompthreads=$${ompthreads} # New syntax
####PBS -l pvmem=$${pvmem}mb
#PBS -l pmem=$${pmem}mb
####PBS -l mppwidth=$${mppwidth}
#PBS -l nodes=$${nodes}:ppn=$${ppn} # OLD SYNTAX
#PBS -M $${mail_user}
#PBS -m $${mail_type}
# Submission environment
#PBS -V
#PBS -o $${_qout_path}
#PBS -e $${_qerr_path}
"""
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
self.qparams["pmem"] = mem_mb
self.qparams["mem"] = mem_mb
class SGEAdapter(AbstractQueueAdapter):
"""
Adapter for Sun Grid Engine (SGE) task submission software.
"""
QTYPE = "sge"
QTEMPLATE = """\
#!/bin/bash
#$ -A $${account}
#$ -N $${job_name}
#$ -l h rt=$${walltime}
#$ -pe $${queue} $${ncpus}
#$ -cwd
#$ -j y
#$ -m n
#$ -e $${_qerr_path}
#$ -o $${_qout_path}
#$ -S /bin/bash
"""
@property
def mpi_ncpus(self):
"""Number of CPUs used for MPI."""
return self.qparams.get("ncpus", 1)
def set_mpi_ncpus(self, mpi_ncpus):
"""Set the number of CPUs used for MPI."""
self.qparams["ncpus"] = mpi_ncpus
def set_omp_ncpus(self, omp_ncpus):
"""Set the number of OpenMP threads."""
self.omp_env["OMP_NUM_THREADS"] = omp_ncpus
warnings.warn("set_omp_ncpus not availabe for %s" % self.__class__.__name__)
def set_mem_per_cpu(self, mem_mb):
"""Set the memory per CPU in Megabytes"""
raise NotImplementedError("")
#self.qparams["mem_per_cpu"] = mem_mb
## Remove mem if it's defined.
#self.qparams.pop("mem", None)
def cancel(self, job_id):
return os.system("qdel %d" % job_id)
def submit_to_queue(self, script_file):
if not os.path.exists(script_file):
raise self.Error('Cannot find script file located at: {}'.format(script_file))
# submit the job
try:
cmd = ['qsub', script_file]
process = Popen(cmd, stdout=PIPE, stderr=PIPE)
process.wait()
# grab the returncode. SGE returns 0 if the job was successful
if process.returncode == 0:
try:
# output should of the form
# Your job 1659048 ("NAME_OF_JOB") has been submitted
queue_id = int(process.stdout.read().split(' ')[2])
logger.info('Job submission was successful and queue_id is {}'.format(queue_id))
except:
# probably error parsing job code
logger.warning("Could not parse job id following qsub...")
queue_id = None
finally:
return process, queue_id
else:
# some qsub error, e.g. maybe wrong queue specified, don't have permission to submit, etc...
msg = ('Error in job submission with PBS file {f} and cmd {c}\n'.format(f=script_file, c=cmd) +
'The error response reads: {}'.format(process.stderr.read()))
raise self.Error(msg)
except:
# random error, e.g. no qsub on machine!
raise self.Error("Running qsub caused an error...")
def get_njobs_in_queue(self, username=None):
# Initialize username
if username is None:
username = getpass.getuser()
# run qstat
qstat = Command(['qstat', '-u', username])
process = qstat.run(timeout=5)
# parse the result
if process[0] == 0:
# lines should contain username
# count lines that include the username in it
# TODO: only count running or queued jobs. or rather, *don't* count jobs that are 'C'.
outs = process[1].split('\n')
njobs = len([line.split() for line in outs if username in line])
logger.info('The number of jobs currently in the queue is: {}'.format(njobs))
return njobs
# there's a problem talking to qstat server?
err_msg = ('Error trying to get the number of jobs in the queue using qstat service\n' +
'The error response reads: {}'.format(process[2]))
logger.critical(err_msg)
return None
def exclude_nodes(self, nodes):
"""
Method to exclude nodes in the calculation
"""
raise NotImplementedError("exclude_nodes")
def increase_mem(self, factor):
"""
Method to increase the amount of memory asked for, by factor.
"""
raise NotImplementedError("increase_mem")
def increase_time(self, factor):
"""
Method to increase the available wall time asked for, by factor.
"""
raise NotImplementedError("increase_time")
def increase_cpus(self, factor):
raise NotImplementedError("increase_cpus")
class QScriptTemplate(string.Template):
delimiter = '$$'
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Cached file-system utility library
# (C) 2016 VRT Systems
#
# vim: set ts=4 sts=4 et tw=78 sw=4 si:
import time
import os
import errno
import collections
import stat
import weakref
from .intnode import _Node
class Node(collections.Mapping):
'''
A file-system node object. This represents a file or directory within
the filesystem. It has a weak reference to the parent node and holds the
metadata for that node.
'''
def __init__(self, cache, abs_path):
self._cache = weakref.ref(cache)
self._node = _Node.get_node(abs_path)
self._atime = time.time()
def _update_atime(self):
self._atime = time.time()
@property
def atime(self):
return self._atime
@property
def atime_since(self):
return time.time() - self.atime
# Python conveniences
def __repr__(self): # pragma: no cover
# Not covered, because it's just for convenience
return '%s(%r, %r)' % (
self.__class__.__name__, self._cache(), self.abs_path)
def __str__(self): # pragma: no cover
# Not covered, because it's just for convenience
if self.is_file:
file_type = 'file'
elif self.is_dir:
file_type = 'dir'
elif self.is_link:
file_type = 'link'
else:
file_type = 'other'
return '%s{%r %s}' % (
self.__class__.__name__, self.abs_path, file_type)
def __bool__(self): # pragma: no cover
'''
Return True if the node exists.
'''
# Not covered, because it's just for convenience
try:
self.stat
return True
except OSError:
return False
# Node properties
@property
def abs_path(self):
'''
Return the node's full absolute path.
'''
return self._node.abs_path
@property
def dir_name(self):
'''
Return the full path of the node's parent directory.
'''
return self._node.dir_name
@property
def base_name(self):
'''
Return the full path of the node's parent.
'''
return self._node.base_name
def join(self, *elements):
'''
Return a path below this node with *elements added.
'''
return os.path.join(self.abs_path, *elements)
def join_node(self, *elements):
'''
Return the node referenced by joining the *elements.
'''
return self._cache()[self.join(*elements)]
@property
def parent(self):
'''
Return the parent node.
'''
return self._cache()[self.dir_name]
@property
def stat(self):
'''
Return the result of os.stat() on this file.
'''
self._update_atime()
return self._node.get_stat(self._cache()._required_time)
@property
def file_type(self):
'''
Returns the file type for the file.
'''
return stat.S_IFMT(self.stat.st_mode)
@property
def is_socket(self): # pragma: no cover
'''
Return true if the file is a socket.
'''
# Not covered by tests: Not all systems implement sockets in the
# filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFSOCK
@property
def is_link(self):
'''
Return true if the file is a symbolic link.
'''
return self.file_type == stat.S_IFLNK
@property
def is_file(self):
'''
Return true if the file is a regular file.
'''
return self.file_type == stat.S_IFREG
@property
def is_dir(self):
'''
Return true if the file is a directory.
'''
return self.file_type == stat.S_IFDIR
@property
def is_block(self): # pragma: no cover
'''
Return true if the file is a block device.
'''
# Not covered by tests: Not all systems implement character devices in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFBLK
@property
def is_char(self): # pragma: no cover
'''
Return true if the file is a character device.
'''
# Not covered by tests: Not all systems implement character devices in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFCHR
@property
def is_fifo(self): # pragma: no cover
'''
Return true if the file is a FIFO.
'''
# Not covered by tests: Not all systems implement FIFOs in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFIFO
# Handling of links.
@property
def target(self):
'''
Returns the name of the file the symlink points to.
'''
return self._node.get_target(self._cache()._required_time)
@property
def abs_target(self):
'''
Returns the absolute path for the target.
'''
target = self.target
if not os.path.isabs(target):
target = self.parent.join(target)
return os.path.abspath(target)
@property
def abs_final_target(self):
'''
Returns the absolute path for the target, following all symlinks.
'''
return os.path.realpath(self.abs_path)
@property
def target_node(self):
'''
Return the filesystem node pointed to by this symlink.
'''
return self._cache()[self.abs_target]
@property
def final_target_node(self):
'''
Return the filesystem node pointed to by this symlink.
'''
return self._cache()[self.abs_final_target]
# Mapping interface for directories.
def __getitem__(self, key):
'''
Return the child filesystem node named 'key'.
'''
self._update_atime()
abs_path = os.path.join(self.abs_path, key)
return self._cache()[abs_path]
def __iter__(self):
'''
Return an iterator for all the children in this directory.
'''
self._update_atime()
return iter(self._node.get_children(\
self._cache()._required_time).copy())
def __len__(self):
'''
Return the number of child elements in the directory.
'''
self._update_atime()
return len(self._node.get_children(\
self._cache()._required_time))
# Searching for child nodes.
def _find(self, predicate, depth_predicate, depth, depth_first):
if self.is_link:
for found in self.final_target_node._find(
predicate, depth_predicate, depth, depth_first):
yield found
return
child_depth = depth+1
show_self = depth_predicate(depth)
show_children = depth_predicate(child_depth)
recurse = depth_predicate(depth, True)
if not self.is_dir:
if show_self and predicate(self):
yield self
return
# Apply predicate to self if not depth first
if (not depth_first) and show_self and predicate(self):
yield self
for child in self.values():
# Apply predicate to child if not a directory
if show_children and (not child.is_dir) and predicate(child):
yield child
# Recurse into child if a directory
elif recurse and child.is_dir:
for found in child._find(predicate, depth_predicate,
child_depth, depth_first):
yield found
# Apply predicate to self if depth first
if depth_first and show_self and predicate(self):
yield self
def find(self, predicate=None, depth=None, min_depth=None, max_depth=None,
depth_first=False):
'''
Attempt to find nodes that match the given predicate. The depth
parameters control the minimum and maximum path depth (with depth
itself overriding both).
Each node is passed to the function called predicate which returns
True or False. If it returns True, find yields that node.
'''
if depth is not None:
depth_predicate = lambda d, r=False : \
r or (d == depth)
elif (min_depth is None) and (max_depth is None):
depth_predicate = lambda d, r=False : True
elif (min_depth is not None) and (max_depth is None):
depth_predicate = lambda d, r=False : \
r or (d >= min_depth)
elif (min_depth is None) and (max_depth is not None):
depth_predicate = lambda d, r=False : \
r or (d <= max_depth)
elif (min_depth is not None) and (max_depth is not None):
depth_predicate = lambda d, r=False : \
r or ((d >= min_depth) and (d <= max_depth))
if predicate is None:
predicate = lambda n : True
for found in self._find(predicate, depth_predicate, 0, depth_first):
yield found
node: Re-work `find` to give more predictable search order.
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Cached file-system utility library
# (C) 2016 VRT Systems
#
# vim: set ts=4 sts=4 et tw=78 sw=4 si:
import time
import os
import errno
import collections
import stat
import weakref
from .intnode import _Node
class Node(collections.Mapping):
'''
A file-system node object. This represents a file or directory within
the filesystem. It has a weak reference to the parent node and holds the
metadata for that node.
'''
def __init__(self, cache, abs_path):
self._cache = weakref.ref(cache)
self._node = _Node.get_node(abs_path)
self._atime = time.time()
def _update_atime(self):
self._atime = time.time()
@property
def atime(self):
return self._atime
@property
def atime_since(self):
return time.time() - self.atime
# Python conveniences
def __repr__(self): # pragma: no cover
# Not covered, because it's just for convenience
return '%s(%r, %r)' % (
self.__class__.__name__, self._cache(), self.abs_path)
def __str__(self): # pragma: no cover
# Not covered, because it's just for convenience
if self.is_file:
file_type = 'file'
elif self.is_dir:
file_type = 'dir'
elif self.is_link:
file_type = 'link'
else:
file_type = 'other'
return '%s{%r %s}' % (
self.__class__.__name__, self.abs_path, file_type)
def __bool__(self): # pragma: no cover
'''
Return True if the node exists.
'''
# Not covered, because it's just for convenience
try:
self.stat
return True
except OSError:
return False
# Node properties
@property
def abs_path(self):
'''
Return the node's full absolute path.
'''
return self._node.abs_path
@property
def dir_name(self):
'''
Return the full path of the node's parent directory.
'''
return self._node.dir_name
@property
def base_name(self):
'''
Return the full path of the node's parent.
'''
return self._node.base_name
def join(self, *elements):
'''
Return a path below this node with *elements added.
'''
return os.path.join(self.abs_path, *elements)
def join_node(self, *elements):
'''
Return the node referenced by joining the *elements.
'''
return self._cache()[self.join(*elements)]
@property
def parent(self):
'''
Return the parent node.
'''
return self._cache()[self.dir_name]
@property
def stat(self):
'''
Return the result of os.stat() on this file.
'''
self._update_atime()
return self._node.get_stat(self._cache()._required_time)
@property
def file_type(self):
'''
Returns the file type for the file.
'''
return stat.S_IFMT(self.stat.st_mode)
@property
def is_socket(self): # pragma: no cover
'''
Return true if the file is a socket.
'''
# Not covered by tests: Not all systems implement sockets in the
# filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFSOCK
@property
def is_link(self):
'''
Return true if the file is a symbolic link.
'''
return self.file_type == stat.S_IFLNK
@property
def is_file(self):
'''
Return true if the file is a regular file.
'''
return self.file_type == stat.S_IFREG
@property
def is_dir(self):
'''
Return true if the file is a directory.
'''
return self.file_type == stat.S_IFDIR
@property
def is_block(self): # pragma: no cover
'''
Return true if the file is a block device.
'''
# Not covered by tests: Not all systems implement character devices in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFBLK
@property
def is_char(self): # pragma: no cover
'''
Return true if the file is a character device.
'''
# Not covered by tests: Not all systems implement character devices in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFCHR
@property
def is_fifo(self): # pragma: no cover
'''
Return true if the file is a FIFO.
'''
# Not covered by tests: Not all systems implement FIFOs in
# the filesystem and there isn't always permission to create them.
# Implementation is "simple enough" that bugs are unlikely.
return self.file_type == stat.S_IFIFO
# Handling of links.
@property
def target(self):
'''
Returns the name of the file the symlink points to.
'''
return self._node.get_target(self._cache()._required_time)
@property
def abs_target(self):
'''
Returns the absolute path for the target.
'''
target = self.target
if not os.path.isabs(target):
target = self.parent.join(target)
return os.path.abspath(target)
@property
def abs_final_target(self):
'''
Returns the absolute path for the target, following all symlinks.
'''
return os.path.realpath(self.abs_path)
@property
def target_node(self):
'''
Return the filesystem node pointed to by this symlink.
'''
return self._cache()[self.abs_target]
@property
def final_target_node(self):
'''
Return the filesystem node pointed to by this symlink.
'''
return self._cache()[self.abs_final_target]
# Mapping interface for directories.
def __getitem__(self, key):
'''
Return the child filesystem node named 'key'.
'''
self._update_atime()
abs_path = os.path.join(self.abs_path, key)
return self._cache()[abs_path]
def __iter__(self):
'''
Return an iterator for all the children in this directory.
'''
self._update_atime()
return iter(self._node.get_children(\
self._cache()._required_time).copy())
def __len__(self):
'''
Return the number of child elements in the directory.
'''
self._update_atime()
return len(self._node.get_children(\
self._cache()._required_time))
# Searching for child nodes.
def _find(self, predicate, depth_predicate, depth, depth_first):
if self.is_link:
for found in self.final_target_node._find(
predicate, depth_predicate, depth, depth_first):
yield found
return
child_depth = depth+1
show_self = (depth == 0) and depth_predicate(depth)
show_children = depth_predicate(child_depth)
recurse = depth_predicate(depth, True)
if not self.is_dir:
if show_self and predicate(self):
yield self
return
def _recurse():
if recurse:
for child in self.values():
if not child.is_dir:
continue
for found in child._find(predicate, depth_predicate,
child_depth, depth_first):
yield found
def _children():
if show_children:
for child in self.values():
if predicate(child):
yield child
def _self():
if show_self and predicate(self):
yield self
# Generators
generators = [_self(), _children(), _recurse()]
if depth_first:
generators.reverse()
for g in generators:
for found in g:
yield found
def find(self, predicate=None, depth=None, min_depth=None, max_depth=None,
depth_first=False):
'''
Attempt to find nodes that match the given predicate. The depth
parameters control the minimum and maximum path depth (with depth
itself overriding both).
Each node is passed to the function called predicate which returns
True or False. If it returns True, find yields that node.
'''
if depth is not None:
depth_predicate = lambda d, r=False : \
r or (d == depth)
elif (min_depth is None) and (max_depth is None):
depth_predicate = lambda d, r=False : True
elif (min_depth is not None) and (max_depth is None):
depth_predicate = lambda d, r=False : \
r or (d >= min_depth)
elif (min_depth is None) and (max_depth is not None):
depth_predicate = lambda d, r=False : \
r or (d <= max_depth)
elif (min_depth is not None) and (max_depth is not None):
depth_predicate = lambda d, r=False : \
r or ((d >= min_depth) and (d <= max_depth))
if predicate is None:
predicate = lambda n : True
for found in self._find(predicate, depth_predicate, 0, depth_first):
yield found
|
# -*- coding: utf-8 -*-
from django.shortcuts import render
from django.http import HttpResponse, Http404
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User, Group
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from reportlab.pdfgen import canvas
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.units import cm
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.platypus import Table, Paragraph
from io import BytesIO
from .settings import BILLJOBS_DEBUG_PDF, BILLJOBS_BILL_LOGO_PATH, \
BILLJOBS_BILL_LOGO_WIDTH, BILLJOBS_BILL_LOGO_HEIGHT, \
BILLJOBS_BILL_PAYMENT_INFO
from .models import Bill
from billjobs.serializers import UserSerializer, GroupSerializer
from .permissions import CustomUserAPIPermission, \
CustomUserDetailAPIPermission, CustomGroupAPIPermission, \
CustomGroupDetailAPIPermission
from textwrap import wrap
class GroupAPI(APIView):
"""
API endpoint to list or create groups
"""
permission_classes = (CustomGroupAPIPermission,)
def get(self, request, format=None):
"""
List groups
"""
if request.user.is_staff is True:
groups = Group.objects.all()
else:
groups = Group.objects.filter(user=request.user)
serializer = GroupSerializer(groups, context={'request': request},
many=True)
return Response(serializer.data, status=status.HTTP_200_OK)
def post(self, request, format=None):
"""
Create a group
"""
serializer = GroupSerializer(data=request.data,
context={'request': request})
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class GroupDetailAPI(APIView):
"""
API endpoint that allow admin and user to retrieve, update and delete a
group
"""
permission_classes = (CustomGroupDetailAPIPermission,)
def get_object(self, pk):
try:
group = Group.objects.get(pk=pk)
self.check_object_permissions(self.request, group)
return group
except Group.DoesNotExist:
raise Http404
def get(self, request, pk, format=None):
group = self.get_object(pk)
serializer = GroupSerializer(group, context={'request': request})
return Response(serializer.data, status=status.HTTP_200_OK)
def put(self, request, pk, format=None):
"""
Update a group instance
"""
group = self.get_object(pk)
serializer = GroupSerializer(group, data=request.data,
context={'request': request})
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_200_OK)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
def delete(self, request, pk, format=None):
"""
Delete a group instance
"""
group = self.get_object(pk)
group.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
class UserAPI(APIView):
"""
API endpoint that allows admin to list or create users
"""
permission_classes = (CustomUserAPIPermission,)
def get(self, request, format=None):
"""
List users for admin
Retrieve user for authenticated user
"""
if request.user.is_staff is True:
users = User.objects.all()
serializer = UserSerializer(users, context={'request': request},
many=True)
else:
users = User.objects.get(pk=request.user.id)
serializer = UserSerializer(users, context={'request': request})
return Response(serializer.data, status=status.HTTP_200_OK)
def post(self, request, format=None):
serializer = UserSerializer(data=request.data,
context={'request': request})
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class UserDetailAPI(APIView):
"""
API endpoint that allows admin to retrieve, update, delete a user
"""
permission_classes = (CustomUserDetailAPIPermission,)
def get_object(self, pk):
try:
user = User.objects.get(pk=pk)
self.check_object_permissions(self.request, user)
return user
except User.DoesNotExist:
raise Http404
def get(self, request, pk, format=None):
user = self.get_object(pk)
serializer = UserSerializer(user, context={'request': request})
return Response(serializer.data, status=status.HTTP_200_OK)
def put(self, request, pk, format=None):
user = self.get_object(pk)
serializer = UserSerializer(user, data=request.data,
context={'request': request}, partial=True)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_200_OK)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
def delete(self, request, pk, format=None):
user = self.get_object(pk)
user.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
@login_required
def generate_pdf(request, bill_id):
bill = Bill.objects.get(id=bill_id)
response = HttpResponse(content_type='application/pdf')
response['Content-Disposition'] = 'attachment; filename="%s.pdf"' % bill.number
# Create a buffer
buffer = BytesIO()
pdf = canvas.Canvas(buffer, pagesize=A4)
# define new 0,0 bottom left with cm as margin
pdf.translate(cm,cm)
# define document width and height with cm as margin
width, height = A4
width = width - 2*cm
height = height - 2*cm
# if debug draw lines for document limit
if BILLJOBS_DEBUG_PDF is True:
pdf.setStrokeColorRGB(1,0,0)
pdf.line(0,0,width,0)
pdf.line(0,0,0,height)
pdf.line(0,height,width,height)
pdf.line(width,height,width,0)
# Put logo on top of pdf original image size is 570px/250px
pdf.drawImage(BILLJOBS_BILL_LOGO_PATH, 0, height-BILLJOBS_BILL_LOGO_HEIGHT,
width=BILLJOBS_BILL_LOGO_WIDTH, height=BILLJOBS_BILL_LOGO_HEIGHT)
# billing information
lh = 15 #define a line height
pdf.setFillColorRGB(0.3,0.3,0.3)
pdf.setFont("Helvetica-Bold", 14)
pdf.drawRightString(width, height-lh, 'Facture');
pdf.setFont("Helvetica-Bold", 10)
pdf.drawRightString(width, height-2*lh, u'Numéro : %s' % bill.number)
pdf.setFont("Helvetica", 10)
pdf.drawRightString(width, height-3*lh, u'Date facturation : %s' % bill.billing_date.strftime('%d/%m/%Y'))
# define new height
nh = height - 90
# seller
pdf.setFillColorRGB(0.95,0.95,0.95)
pdf.setStrokeColorRGB(1,1,1)
# rect(x,y,width,height)
pdf.rect(0, nh-8*lh, width/2-40, 6.4*lh, fill=1)
# reset fill for text color
pdf.setFillColorRGB(0.3,0.3,0.3)
pdf.drawString(10, nh-lh, 'Émetteur')
issuer = Paragraph(bill.issuer_address, getSampleStyleSheet()['Normal'])
issuer.wrapOn(pdf, width*0.25, 6*lh)
issuer.drawOn(pdf, 20, nh-6*lh)
# customer
pdf.drawString(width/2, nh-lh, 'Adressé à')
customer = pdf.beginText()
customer.setTextOrigin(width/2+20, nh-3*lh)
# create text with \n and remove \r
text = '%s %s\n%s' % (bill.user.first_name, bill.user.last_name,
bill.billing_address.replace('\r',''))
# get each line
for line in text.split('\n'):
customer.textOut(line)
customer.moveCursor(0,lh)
pdf.drawText(customer)
pdf.setStrokeColorRGB(0,0,0)
# rect(x,y,width,height)
pdf.rect(width/2, nh-8*lh, width/2, 6.4*lh, fill=0)
# define new height
nh = nh - 10*lh
data = [['Désignation', 'Prix unit. HT', 'Quantité', 'Total HT']]
for line in bill.billline_set.all():
description = '%s - %s\n%s' % (line.service.reference,
line.service.name,
'\n'.join(wrap(line.service.description,62)))
if line.note :
description = '%s\n%s' % (description,
'\n'.join(wrap(line.note,62)))
line = (description, line.service.price, line.quantity, line.total)
data.append(line)
data.append(('TVA non applicable art-293B du CGI', '', 'Total HT',
'%s €' % bill.amount))
data.append(('', '', 'TVA 0%', '0'))
data.append(('', '', 'Total TTC', '%s €' % bill.amount))
# widths in percent of pdf width
colWidths = (width*0.55, width*0.15, width*0.15, width*0.15)
style = [('GRID', (0,0), (-1,0),1, colors.black),
('GRID', (-2,-3), (-1,-1), 1, colors.black),
('BOX', (0,1), (0,-4), 1, colors.black),
('BOX', (1,1), (1,-4), 1, colors.black),
('BOX', (2,1), (2,-4), 1, colors.black),
('BOX', (-1,1), (-1,-4), 1, colors.black),
('ALIGN',(0,0),(0,-1),'LEFT'),
('ALIGN',(1,0),(-1,-1),'CENTER'),
('ALIGN',(-1,0),(-1,-1),'RIGHT'),
('FONTNAME', (0,-3), (0,-3), 'Helvetica-Bold'),
]
table = Table(data, colWidths=colWidths, style=style)
# create table and get width and height
t_width, t_height = table.wrap(0,0)
table.drawOn(pdf, 0, nh-t_height)
p = Paragraph(BILLJOBS_BILL_PAYMENT_INFO, getSampleStyleSheet()['Normal'])
p.wrapOn(pdf, width*0.6, 100)
p.drawOn(pdf, 0, 3*lh)
pdf.line(0, 2*lh, width, 2*lh)
pdf.setFontSize(8)
pdf.drawCentredString(width/2.0, lh, 'Association Loi 1901')
pdf.showPage()
pdf.save()
# get pdf from buffer and return it to response
genpdf = buffer.getvalue()
buffer.close()
response.write(genpdf)
return response
remove rest framework import and views
# -*- coding: utf-8 -*-
from django.shortcuts import render
from django.http import HttpResponse, Http404
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User, Group
from reportlab.pdfgen import canvas
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.units import cm
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.platypus import Table, Paragraph
from io import BytesIO
from .settings import BILLJOBS_DEBUG_PDF, BILLJOBS_BILL_LOGO_PATH, \
BILLJOBS_BILL_LOGO_WIDTH, BILLJOBS_BILL_LOGO_HEIGHT, \
BILLJOBS_BILL_PAYMENT_INFO
from .models import Bill
from textwrap import wrap
@login_required
def generate_pdf(request, bill_id):
bill = Bill.objects.get(id=bill_id)
response = HttpResponse(content_type='application/pdf')
response['Content-Disposition'] = 'attachment; filename="%s.pdf"' % bill.number
# Create a buffer
buffer = BytesIO()
pdf = canvas.Canvas(buffer, pagesize=A4)
# define new 0,0 bottom left with cm as margin
pdf.translate(cm,cm)
# define document width and height with cm as margin
width, height = A4
width = width - 2*cm
height = height - 2*cm
# if debug draw lines for document limit
if BILLJOBS_DEBUG_PDF is True:
pdf.setStrokeColorRGB(1,0,0)
pdf.line(0,0,width,0)
pdf.line(0,0,0,height)
pdf.line(0,height,width,height)
pdf.line(width,height,width,0)
# Put logo on top of pdf original image size is 570px/250px
pdf.drawImage(BILLJOBS_BILL_LOGO_PATH, 0, height-BILLJOBS_BILL_LOGO_HEIGHT,
width=BILLJOBS_BILL_LOGO_WIDTH, height=BILLJOBS_BILL_LOGO_HEIGHT)
# billing information
lh = 15 #define a line height
pdf.setFillColorRGB(0.3,0.3,0.3)
pdf.setFont("Helvetica-Bold", 14)
pdf.drawRightString(width, height-lh, 'Facture');
pdf.setFont("Helvetica-Bold", 10)
pdf.drawRightString(width, height-2*lh, u'Numéro : %s' % bill.number)
pdf.setFont("Helvetica", 10)
pdf.drawRightString(width, height-3*lh, u'Date facturation : %s' % bill.billing_date.strftime('%d/%m/%Y'))
# define new height
nh = height - 90
# seller
pdf.setFillColorRGB(0.95,0.95,0.95)
pdf.setStrokeColorRGB(1,1,1)
# rect(x,y,width,height)
pdf.rect(0, nh-8*lh, width/2-40, 6.4*lh, fill=1)
# reset fill for text color
pdf.setFillColorRGB(0.3,0.3,0.3)
pdf.drawString(10, nh-lh, 'Émetteur')
issuer = Paragraph(bill.issuer_address, getSampleStyleSheet()['Normal'])
issuer.wrapOn(pdf, width*0.25, 6*lh)
issuer.drawOn(pdf, 20, nh-6*lh)
# customer
pdf.drawString(width/2, nh-lh, 'Adressé à')
customer = pdf.beginText()
customer.setTextOrigin(width/2+20, nh-3*lh)
# create text with \n and remove \r
text = '%s %s\n%s' % (bill.user.first_name, bill.user.last_name,
bill.billing_address.replace('\r',''))
# get each line
for line in text.split('\n'):
customer.textOut(line)
customer.moveCursor(0,lh)
pdf.drawText(customer)
pdf.setStrokeColorRGB(0,0,0)
# rect(x,y,width,height)
pdf.rect(width/2, nh-8*lh, width/2, 6.4*lh, fill=0)
# define new height
nh = nh - 10*lh
data = [['Désignation', 'Prix unit. HT', 'Quantité', 'Total HT']]
for line in bill.billline_set.all():
description = '%s - %s\n%s' % (line.service.reference,
line.service.name,
'\n'.join(wrap(line.service.description,62)))
if line.note :
description = '%s\n%s' % (description,
'\n'.join(wrap(line.note,62)))
line = (description, line.service.price, line.quantity, line.total)
data.append(line)
data.append(('TVA non applicable art-293B du CGI', '', 'Total HT',
'%s €' % bill.amount))
data.append(('', '', 'TVA 0%', '0'))
data.append(('', '', 'Total TTC', '%s €' % bill.amount))
# widths in percent of pdf width
colWidths = (width*0.55, width*0.15, width*0.15, width*0.15)
style = [('GRID', (0,0), (-1,0),1, colors.black),
('GRID', (-2,-3), (-1,-1), 1, colors.black),
('BOX', (0,1), (0,-4), 1, colors.black),
('BOX', (1,1), (1,-4), 1, colors.black),
('BOX', (2,1), (2,-4), 1, colors.black),
('BOX', (-1,1), (-1,-4), 1, colors.black),
('ALIGN',(0,0),(0,-1),'LEFT'),
('ALIGN',(1,0),(-1,-1),'CENTER'),
('ALIGN',(-1,0),(-1,-1),'RIGHT'),
('FONTNAME', (0,-3), (0,-3), 'Helvetica-Bold'),
]
table = Table(data, colWidths=colWidths, style=style)
# create table and get width and height
t_width, t_height = table.wrap(0,0)
table.drawOn(pdf, 0, nh-t_height)
p = Paragraph(BILLJOBS_BILL_PAYMENT_INFO, getSampleStyleSheet()['Normal'])
p.wrapOn(pdf, width*0.6, 100)
p.drawOn(pdf, 0, 3*lh)
pdf.line(0, 2*lh, width, 2*lh)
pdf.setFontSize(8)
pdf.drawCentredString(width/2.0, lh, 'Association Loi 1901')
pdf.showPage()
pdf.save()
# get pdf from buffer and return it to response
genpdf = buffer.getvalue()
buffer.close()
response.write(genpdf)
return response
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