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<reponame>ceciliaccwei/CMPUT291-proj1<filename>project.py
import sqlite3
import getpass
import time
import os
import sys
if len(sys.argv) != 2:
print("Please run with: python PROJECT.py DATABASE.db")
quit()
db_file_path = sys.argv[1]
if not (os.path.exists(db_file_path)):
print("File does not exist!")
quit()
conn = sqlite3.connect(db_file_path)
c = conn.cursor()
def main():
conn.commit()
start()
def start():
while True:
print("Welcome to the carpool system!")
print("1. Login")
print("2. Sign up")
print("3. Exit")
command = raw_input("What would you like to do today?")
if command == '1':
login()
elif command == '2':
signup()
continue
elif command == '3':
quit()
else:
print("Command not found!")
def signup():
while True:
email = raw_input("Please enter your email (or BACK): ").lower()
if email == 'back':
main()
c.execute("SELECT * FROM members WHERE email like ?;",(email,))
dup = c.fetchone()
if dup == None:
break
else:
print("This email has already been signed up.")
password = getpass.getpass("Enter your password: ")
name = raw_input("Please enter your name: ")
while True:
try:
phone = int(raw_input("Please enter your phone number: "))
break
except ValueError:
print("Invalid input!")
continue
c.execute("INSERT INTO members VALUES ('%s', '%s', '%s', '%s');" % (email, name, phone, password))
conn.commit()
print("You have successfully signed up!")
main()
def login():
while True:
email = raw_input("Please enter your email (or BACK): ").lower()
if email== 'back':
break
c.execute("SELECT * FROM members WHERE email like '%s';" % email)
username = c.fetchone()
if username == None:
print("Username does not exist")
else:
password = <PASSWORD>("Enter your password: ")
c.execute("SELECT * FROM members WHERE email like '%s' and pwd = '%s';" % (email,password))
check_login = c.fetchone()
if check_login == None:
print("Incorrect email or password, please try again.")
else:
print("Welcome!")
user = email
c.execute("SELECT msgTimestamp,sender,rno,content FROM inbox WHERE email like '%s' and seen like '%s';" % (user,'n'))
print ("".join('%-22s'%x[0] for x in c.description))
ar = [[str(item) for item in results] for results in c.fetchall()]
for row in ar:
print ("".join('%-22s'%x for x in row))
c.execute("UPDATE inbox SET seen = '%s' where seen like '%s' and email like '%s';" % ('y','n',user))
conn.commit()
chooseOptionCategory(user)
break
def chooseOptionCategory(user):
while True:
print("1. Rides")
print("2. Bookings")
print("3. Requests")
print("4. Log out")
print("5. Exit")
option = raw_input("Your option: ")
if option == '1':
RidesRelated(user)
elif option == '2':
BookingsRelated(user)
elif option == '3':
RequestRelated(user)
elif option == '4':
main()
elif option == '5':
quit()
else:
print("Command not found!")
def RidesRelated(user):
while True:
print("1. Offer a ride")
print("2. Search rides")
print("3. Go back")
print("4. Log out")
print("5. Exit")
option = raw_input("Your option: ")
if option == '1':
offerRide(user)
elif option == '2':
searchRides(user)
elif option== '3':
break
elif option=='4':
main()
elif option=='5':
quit()
else:
print("Command not found!")
def BookingsRelated(user):
while True:
print("1. List all confirmed bookings on my rides")
print("2. Book someone on my ride")
print("3. Go back")
print("4. Log out")
print("5. Exit")
option = raw_input("Your option: ")
if option == '1':
bookingList(user)
elif option == '2':
rideList(user)
elif option == '3':
break
elif option == '4':
main()
elif option == '5':
quit()
else:
print("Command not found!")
def rideList(user):
print(user)
c.execute("SELECT r.*, \
r.seats - ifnull(sum(b.seats) ,0) as seats_avaliable \
from rides r \
left join \
bookings b \
on r.rno = b.rno \
where r.driver like ? \
group by r.rno;",(user,))
print ("".join('%-13s'%x[0] for x in c.description))
ar = [[str(item) for item in results] for results in c.fetchall()]
prtFive(ar)
while True:
print("Enter the rno to book")
rno = raw_input("Or enter Back to go back: ")
try:
rno = int(rno)
c.execute("SELECT * FROM rides WHERE driver like '%s' and rno='%d';" % (user,rno))
check_ride = c.fetchone()
if check_ride == None:
print("That's not your ride!")
continue
else:
break
except ValueError:
return
while True:
email = raw_input("Please enter the member's email: ").lower()
c.execute("SELECT * FROM members WHERE email like ?;",(email,))
exist = c.fetchone()
if exist == None:
print("Member does not exist!")
continue
else:
break
c.execute("SELECT r.seats - ifnull(sum(b.seats) ,0) \
from rides r \
left join \
bookings b \
on r.rno = b.rno \
where r.rno = ? \
group by r.rno;",(rno,))
seats_avaliable = int(c.fetchone()[0])
while True:
try:
cost = int(raw_input("Please enter your cost: "))
break
except ValueError:
print("Invalid input!")
continue
while True:
try:
seats = int(raw_input("Please enter your seats booked: "))
break
except ValueError:
print("Invalid input!")
continue
c.execute("SELECT lcode FROM locations")
ar = [[str(item) for item in results] for results in c.fetchall()]
pickup = raw_input("Please enter the pickup loc: ")
while [pickup] not in ar:
searchLoc(pickup)
pickup = raw_input("Please enter the pickup loc: ")
dropoff = raw_input("Please enter the dropoff loc: ")
while [dropoff] not in ar:
searchLoc(dst)
dropoff = raw_input("Please enter your dst: ")
seen = 'n'
c.execute("SELECT ifnull(max(bno),0) FROM bookings")
bno = int(c.fetchone()[0])+1
if seats <= seats_avaliable:
c.execute("INSERT INTO bookings VALUES (?,?,?,?,?,?,?);",(bno,email,rno,cost, int(seats), pickup,dropoff))
content = "your booking: "+str(bno)+ " is confirmed!"
msgTimestamp = time.strftime("%Y-%m-%d %H:%M:%S")
c.execute("INSERT INTO inbox VALUES (?,?,?,?,?,?);",(email,msgTimestamp,user,content,rno,seen))
conn.commit()
print("message sent!")
else:
option = raw_input("Are you sure to overbook? [Y/N]")
if option.upper() == 'Y':
c.execute("INSERT INTO bookings VALUES (?,?,?,?,?,?,?);",(bno,email,rno,cost, int(seats), pickup,dropoff))
content = "your booking: "+str(bno)+ " is confirmed!"
msgTimestamp = time.strftime("%Y-%m-%d %H:%M:%S")
c.execute("INSERT INTO inbox VALUES (?,?,?,?,?,?);",(email,msgTimestamp,user,content,rno,seen))
conn.commit()
print("message sent!")
else:
return
def bookingList(user):
c.execute("SELECT b.* FROM bookings b, rides r\
where b.rno = r.rno and r.driver = ?;",(user,))
print ("".join('%-13s'%x[0] for x in c.description))
ar = [[str(item) for item in results] for results in c.fetchall()]
prtFive(ar)
print("Enter the bno to cancel")
bno = raw_input("Or enter Back to go back: ")
if bno.upper() == 'BACK':
return
else:
bno = int(bno)
c.execute("SELECT email,rno FROM bookings WHERE bno = ?;",(bno,))
temp = c.fetchone()
email = str(temp[0])
rno = int(temp[1])
c.execute("DELETE FROM bookings WHERE bno= ?;", (bno,))
content = "Your booking "+str(bno)+" is cancelled"
seen = 'n'
msgTimestamp = time.strftime("%Y-%m-%d %H:%M:%S")
c.execute("INSERT INTO inbox VALUES (?,?,?,?,?,?);",(email,msgTimestamp,user,content,rno,seen))
conn.commit()
print("Booking cancelled!")
def RequestRelated(user):
while True:
print("1. Post a request")
print("2. List my own requests")
print("3. Search requests")
print("4. Go back")
print("5. Log out")
print("6. Exit")
option = raw_input("Your option: ")
if option == '1':
postRequest(user)
elif option == '2':
myRequest(user)
elif option == '3':
searchRequest(user)
elif option == '4':
break
elif option == '5':
main()
elif option == '6':
quit()
else:
print("Command not found!")
def offerRide(user):
c.execute("SELECT ifnull(max(rno),0) FROM rides")
rno = int(c.fetchone()[0])+1
while True:
try:
price = float(raw_input("Please enter the price: "))
break
except ValueError:
print("Invalid input!")
continue
while True:
rdate = raw_input("Please enter the date in YYYY-MM-DD format: ")
try:
time.strptime(rdate,"%Y-%m-%d")
break
except ValueError:
print("Invalid input!")
continue
while True:
seats = raw_input("Please enter the seats offered: ")
try:
seats = int(seats)
break
except ValueError:
print("Invalid input!")
continue
c.execute("SELECT lcode FROM locations")
ar = [[str(item) for item in results] for results in c.fetchall()]
src = raw_input("Please enter your src: ")
while [src] not in ar:
searchLoc(src)
src = raw_input("Please enter your src: ")
dst = raw_input("Please enter your dst: ")
while [dst] not in ar:
searchLoc(dst)
dst = raw_input("Please enter your dst: ")
lugDesc = raw_input("Please enter the luggage description: ")
while True:
cno = raw_input("Please enter the car number: ")
if cno == '':
cno = None
break
else:
try:
cno = int(cno)
c.execute("SELECT * FROM cars WHERE owner like '%s' and cno ='%d';" % (user,cno))
check_car = c.fetchone()
if check_car == None:
print("That's not your car!")
c.execute("SELECT * from cars where owner like '%s';"%(user,))
print ("".join('%-20s'%x[0] for x in c.description))
ar = [[str(item) for item in results] for results in c.fetchall()]
for row in ar:
print ("".join('%-20s'%x for x in row))
continue
else:
break
except ValueError:
print("Invalid input!")
continue
c.execute("INSERT INTO rides VALUES (?,?,?,?,?,?,?,?,?);",(rno,price,rdate,seats,lugDesc,src,dst,user,cno))
conn.commit()
c.execute("SELECT lcode FROM locations")
ar = [[str(item) for item in results] for results in c.fetchall()]
enroute = raw_input("Please enter the enroute location: ")
while enroute != '':
while [enroute] not in ar:
searchLoc(enroute)
enroute = raw_input("Please enter your enroute location: ")
c.execute("INSERT INTO enroute VALUES (?,?);",(rno,enroute))
enroute = raw_input("Please enter next enroute location: ")
conn.commit()
print("New ride offered!")
def searchRides(user):
keyword1 = raw_input("Keyword1: ")
keyword2 = raw_input("Keyword2: ")
if keyword2 != '':
keyword3 = raw_input("Keyword3: ")
if keyword3 != '':
#search by 3
searchbyK1 = searchKeyword(keyword1)
searchbyK2 = searchKeyword(keyword2)
searchbyK3 = searchKeyword(keyword3)
ar = list((set(tuple(i) for i in searchbyK1)&set(tuple(j) for j in searchbyK2)&set(tuple(k) for k in searchbyK3)))
else:
#search by 2
searchbyK1 = searchKeyword(keyword1)
searchbyK2 = searchKeyword(keyword2)
ar = list((set(tuple(i) for i in searchbyK1)&set(tuple(j) for j in searchbyK2)))
else:
#search by 1
ar = searchKeyword(keyword1)
description = ['rno', 'price', 'rdate', 'seats', 'lugDesc', 'src', 'dst', 'driver', 'cno', 'make', 'model', 'year', 'seats']
print ("".join('%-13s'%x for x in description))
ar = map(list,ar)
ar = sorted(ar, key=lambda x: int(x[0]))
prtFive(ar)
messageDriver(user)
def searchKeyword(keyword):
c.execute("SELECT r.*,c.make,c.model,c.year,c.seats FROM rides r,enroute er,locations l \
left join cars c on r.cno = c.cno \
where (er.lcode = l.lcode and er.rno = r.rno)\
and\
(l.lcode like ? or l.city like ? or l.prov like ? or l.address like ?)\
union \
SELECT DISTINCT r.*,c.make,c.model,c.year,c.seats FROM rides r,locations l1, locations l2\
left join cars c on r.cno = c.cno \
WHERE (r.src = l1.lcode and r.dst =l2.lcode)\
and\
(l1.lcode like ? or l1.city like ? or l1.prov like ? or l1.address like ? or \
l2.lcode like ? or l2.city like ? or l2.prov like ? or l2.address like ? );",
('%'+keyword+'%','%'+keyword+'%','%'+keyword+'%','%'+keyword+'%',
'%'+keyword+'%','%'+keyword+'%','%'+keyword+'%','%'+keyword+'%',
'%'+keyword+'%','%'+keyword+'%','%'+keyword+'%','%'+keyword+'%'))
result = [[str(item) for item in results] for results in c.fetchall()]
return result
def messageDriver(user):
while True:
rno = raw_input("Please enter the ride where you want to book:(BACK to go back) ")
if rno.upper() == 'BACK':
return
else:
try:
rno = int(rno)
break
except ValueError:
print("Invalid input!")
c.execute("SELECT driver FROM rides WHERE rno = '%d';" % (int(rno)))
email = str(c.fetchone()[0])
msgTimestamp = time.strftime("%Y-%m-%d %H:%M:%S")
while True:
try:
cost = int(raw_input("Please enter your cost: "))
break
except ValueError:
print("Invalid input!")
continue
while True:
try:
seats = int(raw_input("Please enter your seats booked: "))
break
except ValueError:
print("Invalid input!")
continue
c.execute("SELECT lcode FROM locations")
ar = [[str(item) for item in results] for results in c.fetchall()]
pickup = raw_input("Please enter the pickup loc: ")
while [pickup] not in ar:
searchLoc(pickup)
pickup = raw_input("Please enter the pickup loc: ")
dropoff = raw_input("Please enter the dropoff loc: ")
while [dropoff] not in ar:
searchLoc(dst)
dropoff = raw_input("Please enter your dst: ")
content = "cost:"+str(cost) + "; "+"seats:"+str(seats) + "; "+"pickup:"+pickup + "; "+"dropoff: "+dropoff + "; "
seen = 'n'
c.execute("INSERT INTO inbox VALUES (?,?,?,?,?,?)",(email,msgTimestamp,user,content,rno,seen))
conn.commit()
print("message sent!")
def postRequest(user):
c.execute("SELECT ifnull(max(rid),0) FROM requests")
rid = int(c.fetchone()[0])+1
while True:
rdate = raw_input("Please enter the date in YYYY-MM-DD format: ")
try:
time.strptime(rdate,"%Y-%m-%d")
break
except ValueError:
print("Invalid input!")
continue
c.execute("SELECT lcode FROM locations")
ar = [[str(item) for | |
<filename>tests/001_theoretical/test_004_datetime_blueprint.py
#!/bin/false
# Copyright (c) 2022 <NAME>. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
# following conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
# disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the
# following disclaimer in the documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import os.path
import sys
if "DATALIDATOR_TESTS_AUTOPATH" in os.environ:
__TESTS_DIR = os.path.dirname(os.path.realpath(__file__))
__MODULE_DIR = os.path.realpath(os.path.join(__TESTS_DIR, "../.."))
if __TESTS_DIR not in sys.path:
sys.path.insert(0, __TESTS_DIR)
if __MODULE_DIR not in sys.path:
sys.path.insert(0, __MODULE_DIR)
import locale
locale.setlocale(locale.LC_ALL, "C") # Some of DatetimeBlueprint's functionality is locale-dependent under certain circumstances!
import theoretical_testutils
import pytest
import datetime
import zoneinfo
import time
import ipaddress
from datalidator.blueprints.ParsingMode import ParsingMode
from datalidator.blueprints.impl.DatetimeBlueprint import DatetimeBlueprint
from datalidator.blueprints.exc.InputDataNotConvertibleExc import InputDataNotConvertibleExc
from datalidator.blueprints.exc.InputDataTypeNotInAllowlistExc import InputDataTypeNotInAllowlistExc
from datalidator.filters.impl.DatetimeAddTimezoneFilter import DatetimeAddTimezoneFilter
from datalidator.filters.impl.DatetimeChangeTimezoneFilter import DatetimeChangeTimezoneFilter
from datalidator.filters.exc.InputDatetimeObjectIsNaiveInFilterExc import InputDatetimeObjectIsNaiveInFilterExc
from datalidator.validators.impl.DatetimeIsAwareValidator import DatetimeIsAwareValidator
from datalidator.validators.impl.DatetimeNotAfterValidator import DatetimeNotAfterValidator
from datalidator.validators.impl.DatetimeNotBeforeValidator import DatetimeNotBeforeValidator
from datalidator.validators.exc.DataValidationFailedExc import DataValidationFailedExc
from datalidator.validators.exc.InputDatetimeObjectIsNaiveInValidatorExc import InputDatetimeObjectIsNaiveInValidatorExc
from datalidator.validators.exc.err.InvalidValidatorConfigError import InvalidValidatorConfigError
current_datetime_naive = datetime.datetime.now()
current_datetime_aware = datetime.datetime.now(datetime.timezone.utc).astimezone(zoneinfo.ZoneInfo("Europe/Prague"))
current_struct_time_utc = time.gmtime()
current_struct_time_localtime = time.localtime()
__DATETIME_BLUEPRINT_TEST_SUITE = (
(DatetimeBlueprint(parsing_mode=ParsingMode.MODE_LOOSE), (
(current_datetime_naive, lambda output: (output is not current_datetime_naive) and (output == current_datetime_naive)),
(current_datetime_aware, lambda output: (output is not current_datetime_aware) and (output == current_datetime_aware)),
(current_datetime_naive, lambda output: output != current_datetime_aware),
(current_datetime_aware, lambda output: output != current_datetime_naive),
(datetime.datetime(2020, 2, 29, 12, 40, 25), datetime.datetime(2020, 2, 29, 12, 40, 25)),
(datetime.datetime(2020, 2, 29, 12, 40, 25), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=datetime.timezone.utc)),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=datetime.timezone.utc), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25)),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=datetime.timezone.utc), datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=datetime.timezone.utc)),
(datetime.datetime(2020, 2, 29, 12, 40, 25), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("Europe/Prague"))),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("Europe/Prague")), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25)),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("Europe/Prague")), datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("Europe/Prague"))),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=datetime.timezone.utc), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("America/New_York"))),
(datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("Europe/Prague")), lambda output: output != datetime.datetime(2020, 2, 29, 12, 40, 25, tzinfo=zoneinfo.ZoneInfo("America/New_York"))),
(datetime.datetime(1066, 10, 14, 15, 0, 0), datetime.datetime(1066, 10, 14, 15, 0, 0)),
(datetime.datetime(8500, 10, 14, 15, 0, 0), datetime.datetime(8500, 10, 14, 15, 0, 0)),
(datetime.datetime(1066, 10, 14, 15, 0, 0, tzinfo=datetime.timezone.utc), datetime.datetime(1066, 10, 14, 15, 0, 0, tzinfo=datetime.timezone.utc)),
(datetime.datetime(8500, 10, 14, 15, 0, 0, tzinfo=datetime.timezone.utc), datetime.datetime(8500, 10, 14, 15, 0, 0, tzinfo=datetime.timezone.utc)),
(current_struct_time_utc, datetime.datetime.fromtimestamp(time.mktime(current_struct_time_utc), tz=datetime.timezone.utc)),
(current_struct_time_localtime, datetime.datetime.fromtimestamp(time.mktime(current_struct_time_localtime), tz=datetime.timezone.utc)),
# Hardcoded time.struct_time objects cannot be tested reliably, because their conversion to datetime.datetime is timezone-dependent.
(time.struct_time((2020, 1, 1, 1, 1, 1, 1, 1, 1)), lambda output: True), # This just tests whether the blueprint did not raise an exception.
(time.struct_time((8020, 1, 1, 1, 1, 1, 1, 1, 1)), lambda output: True), # This just tests whether the blueprint did not raise an exception.
(time.struct_time((1066, 1, 1, 1, 1, 1, 1, 1, 1)), InputDataNotConvertibleExc),
(time.struct_time((150000000, 1, 1, 1, 1, 1, 1, 1, 1)), InputDataNotConvertibleExc),
(time.struct_time((15000, 1, 1, 1, 1, 1, 1, 1, 1)), InputDataNotConvertibleExc),
# https://docs.python.org/3/library/datetime.html#datetime.datetime.fromisoformat
(current_datetime_naive.isoformat(), current_datetime_naive),
(current_datetime_aware.isoformat(), current_datetime_aware),
("2022-01-08T18:38:54.648842", datetime.datetime(2022, 1, 8, 18, 38, 54, 648842)),
("2022-01-08x18:38:54.648842", datetime.datetime(2022, 1, 8, 18, 38, 54, 648842)),
("2022-01-08\x0018:38:54.648842", datetime.datetime(2022, 1, 8, 18, 38, 54, 648842)),
("2011-11", InputDataNotConvertibleExc),
("2011-11-04", datetime.datetime(2011, 11, 4, 0, 0, 0)),
("201-11-04", InputDataNotConvertibleExc),
("20111-11-04", InputDataNotConvertibleExc),
("2011-11-04 01", datetime.datetime(2011, 11, 4, 1, 0, 0)),
("2011-11-04 01:05", datetime.datetime(2011, 11, 4, 1, 5, 0)),
("2011-11-04 01:05:23", datetime.datetime(2011, 11, 4, 1, 5, 23)),
("2011-02-29 01:05:23", InputDataNotConvertibleExc),
("2012-02-29 01:05:23", datetime.datetime(2012, 2, 29, 1, 5, 23)),
("2011-13-01 10:05:50", InputDataNotConvertibleExc),
("2011-11-31 01:05:23", InputDataNotConvertibleExc),
("2011-01-01 24:05:23", InputDataNotConvertibleExc),
("2011-01-01 10:60:23", InputDataNotConvertibleExc),
("2011-01-01 10:05:60", InputDataNotConvertibleExc),
("\r\n2011-11-04 01:05:23\t", datetime.datetime(2011, 11, 4, 1, 5, 23)),
(" 2011-11-04 01:05:23 ", datetime.datetime(2011, 11, 4, 1, 5, 23)),
("\x002011-11-04 01:05:23", InputDataNotConvertibleExc),
("2011-11-04 01:05\x00:23", InputDataNotConvertibleExc),
("2011-11-04 01:05:23\x00", datetime.datetime(2011, 11, 4, 1, 5, 23)), # This works for some reason...
("2011-11-04 01:05:23\x00abc", InputDataNotConvertibleExc), # ... and this does not.
("2011-11-04 01:05:23.28", InputDataNotConvertibleExc),
("2011-11-04 01:05:23.283", datetime.datetime(2011, 11, 4, 1, 5, 23, 283000)),
("2011-11-04 01:05:23.2839", InputDataNotConvertibleExc),
("2011-11-04 01:05:23.283999", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999)),
("2011-11-04 01:05:23.2839999", InputDataNotConvertibleExc),
("2011-11-04+05:30", datetime.datetime(2011, 11, 4, 5, 30, 0)), # The '+05:30' is not considered a timezone, but 'hour:minute'!!!
("2011-11-04 01:05:23+00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23.283+00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, 283000, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23.283999+00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23-00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23.283-00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, 283000, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23.283999-00:00", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone.utc)),
("2011-11-04 01:05:23+05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=19800)))),
("2011-11-04 01:05:23.283+05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, 283000, tzinfo=datetime.timezone(datetime.timedelta(seconds=19800)))),
("2011-11-04 01:05:23.283999+05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone(datetime.timedelta(seconds=19800)))),
("2011-11-04 01:05:23-05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=-19800)))),
("2011-11-04 01:05:23.283-05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, 283000, tzinfo=datetime.timezone(datetime.timedelta(seconds=-19800)))),
("2011-11-04 01:05:23.283999-05:30", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone(datetime.timedelta(seconds=-19800)))),
("2011-11-04 01:05:23+05:30:10", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=19810)))),
("2011-11-04 01:05:23.283+05:30:10.123", InputDataNotConvertibleExc),
("2011-11-04 01:05:23.283999+05:30:10.123456", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone(datetime.timedelta(seconds=19810, microseconds=123456)))),
("2011-11-04 01:05:23-05:30:10", datetime.datetime(2011, 11, 4, 1, 5, 23, tzinfo=datetime.timezone(datetime.timedelta(seconds=-19810)))),
("2011-11-04 01:05:23.283-05:30:10.123", InputDataNotConvertibleExc),
("2011-11-04 01:05:23.283999-05:30:10.123456", datetime.datetime(2011, 11, 4, 1, 5, 23, 283999, tzinfo=datetime.timezone(datetime.timedelta(seconds=-19810, microseconds=-123456)))),
("15.02.2021 04:50:45", InputDataNotConvertibleExc),
("02/15/2021 04:50:45", InputDataNotConvertibleExc),
("02/15/2021 04:50 am", InputDataNotConvertibleExc),
("0", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("-0", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("\r\n0000 \t", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("0.0", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("-0.0", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("\r\n0000.000 \t", datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
("\x001204295445", InputDataNotConvertibleExc),
("1204295445", datetime.datetime(2008, 2, 29, 14, 30, 45, 0, tzinfo=datetime.timezone.utc)),
("1204295445.123", datetime.datetime(2008, 2, 29, 14, 30, 45, 123000, tzinfo=datetime.timezone.utc)),
("1204295445.123456", datetime.datetime(2008, 2, 29, 14, 30, 45, 123456, tzinfo=datetime.timezone.utc)),
("-5000000000", datetime.datetime(1811, 7, 23, 15, 6, 40, tzinfo=datetime.timezone.utc)),
("-5000000000.1", datetime.datetime(1811, 7, 23, 15, 6, 39, 900000, tzinfo=datetime.timezone.utc)),
("-100000000000.1", InputDataNotConvertibleExc), # ValueError
("-100000000000000000", InputDataNotConvertibleExc), # OSError
("-100000000000000000000", InputDataNotConvertibleExc), # OverflowError
("100000000000", datetime.datetime(5138, 11, 16, 9, 46, 40, tzinfo=datetime.timezone.utc)),
("100000000000000.1", InputDataNotConvertibleExc), # ValueError
("100000000000000000", InputDataNotConvertibleExc), # OSError
("100000000000000000000", InputDataNotConvertibleExc), # OverflowError
("", InputDataNotConvertibleExc),
("abcdefxyz", InputDataNotConvertibleExc),
("\x00", InputDataNotConvertibleExc),
("řeřicha", InputDataNotConvertibleExc),
("nan", InputDataNotConvertibleExc),
("-inf", InputDataNotConvertibleExc),
(0, datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
(-0, datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
(0.0, datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
(-0.0, datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)),
(1204295445, datetime.datetime(2008, 2, 29, 14, 30, 45, 0, tzinfo=datetime.timezone.utc)),
(1204295445.123, datetime.datetime(2008, 2, 29, 14, 30, 45, 123000, tzinfo=datetime.timezone.utc)),
(1204295445.123456, datetime.datetime(2008, 2, 29, 14, 30, 45, 123456, tzinfo=datetime.timezone.utc)),
(-5000000000, datetime.datetime(1811, 7, 23, 15, 6, 40, tzinfo=datetime.timezone.utc)),
(-5000000000.1, datetime.datetime(1811, 7, 23, 15, 6, 39, 900000, tzinfo=datetime.timezone.utc)),
(-100000000000.1, InputDataNotConvertibleExc), # ValueError
(-100000000000000000, InputDataNotConvertibleExc), # OSError
(-100000000000000000000, InputDataNotConvertibleExc), # OverflowError
(100000000000, datetime.datetime(5138, 11, 16, 9, 46, 40, tzinfo=datetime.timezone.utc)),
(100000000000000.1, InputDataNotConvertibleExc), # ValueError
(100000000000000000, InputDataNotConvertibleExc), # OSError
(100000000000000000000, InputDataNotConvertibleExc), # OverflowError
(float("nan"), InputDataNotConvertibleExc),
(float("-inf"), InputDataNotConvertibleExc),
(datetime.date(100, 2, 2), datetime.datetime(100, 2, 2, 0, 0, 0)),
(datetime.date(2012, 2, 29), datetime.datetime(2012, 2, 29, 0, 0, 0)),
(datetime.date(8000, 2, 2), datetime.datetime(8000, 2, | |
options detected ' +
'for databases {0} and {1}, same pid {2}, skipping {0}'.format(instance, duplicate_instance, pid))
pgcon.close()
return True
# now we have all components to create a cluster descriptor
desc = make_cluster_desc(name=instance, version=dbver, workdir=work_directory,
pid=pid, pgcon=pgcon, conn=conn)
clusters.append(desc)
return True
def make_cluster_desc(name, version, workdir, pid, pgcon, conn):
"""Create cluster descriptor, complete with the reconnect function."""
def reconnect():
pgcon = psycopg2.connect(**conn)
pid = read_postmaster_pid(workdir, name)
return (pgcon, pid)
return {
'name': name,
'ver': version,
'wd': workdir,
'pid': pid,
'pgcon': pgcon,
'reconnect': reconnect
}
class ProcNetParser():
""" Parse /proc/net/{tcp,tcp6,unix} and return the list of address:port
pairs given the set of socket descriptors belonging to the object.
The result is grouped by the socket type in a dictionary.
"""
NET_UNIX_FILENAME = '/proc/net/unix'
NET_TCP_FILENAME = '/proc/net/tcp'
NET_TCP6_FILENAME = '/proc/net/tcp6'
def __init__(self):
self.reinit()
def reinit(self):
self.sockets = {}
self.unix_socket_header_len = 0
# initialize the sockets hash with the contents of unix
# and tcp sockets. tcp IPv6 is also read if it's present
for fname in (ProcNetParser.NET_UNIX_FILENAME, ProcNetParser.NET_TCP_FILENAME):
self.read_socket_file(fname)
if os.access(ProcNetParser.NET_TCP6_FILENAME, os.R_OK):
self.read_socket_file(ProcNetParser.NET_TCP6_FILENAME)
@staticmethod
def _hex_to_int_str(val):
return str(int(val, 16))
@staticmethod
def _hex_to_ip(val):
newval = format(socket.ntohl(int(val, 16)), '08X')
return '.'.join([str(int(newval[i: i + 2], 16)) for i in range(0, 8, 2)])
@staticmethod
def _hex_to_ipv6(val):
newval_list = [format(socket.ntohl(int(val[x: x + 8], 16)), '08X') for x in range(0, 32, 8)]
return ':'.join([':'.join((x[:4], x[4:])) for x in newval_list])
def match_socket_inodes(self, inodes):
""" return the dictionary with socket types as strings,
containing addresses (or unix path names) and port
"""
result = {}
for inode in inodes:
if inode in self.sockets:
addr_tuple = self.parse_single_line(inode)
if addr_tuple is None:
continue
socket_type = addr_tuple[0]
if socket_type in result:
result[socket_type].append(addr_tuple[1:])
else:
result[socket_type] = [addr_tuple[1:]]
return result
def read_socket_file(self, filename):
""" read file content, produce a dict of socket inode -> line """
socket_type = filename.split('/')[-1]
try:
with open(filename) as fp:
data = fp.readlines()
except os.error as e:
logger.error('unable to read from {0}: OS reported {1}'.format(filename, e))
# remove the header
header = (data.pop(0)).split()
if socket_type == 'unix':
self.unix_socket_header_len = len(header)
indexes = [i for i, name in enumerate(header) if name.lower() == 'inode']
if len(indexes) != 1:
logger.error('attribute \'inode\' in the header of {0} is not unique or missing: {1}'.format(
filename, header))
else:
inode_idx = indexes[0]
if socket_type != 'unix':
# for a tcp socket, 2 pairs of fields (tx_queue:rx_queue and tr:tm->when
# are separated by colons and not spaces)
inode_idx -= 2
for line in data:
fields = line.split()
inode = int(fields[inode_idx])
self.sockets[inode] = [socket_type, line]
def parse_single_line(self, inode):
""" apply socket-specific parsing rules """
result = None
(socket_type, line) = self.sockets[inode]
if socket_type == 'unix':
# we are interested in everything in the last field
# note that it may contain spaces or other separator characters
fields = line.split(None, self.unix_socket_header_len - 1)
socket_path = fields[-1]
# check that it looks like a PostgreSQL socket
match = re.search(r'(.*?)/\.s\.PGSQL\.(\d+)$', socket_path)
if match:
# path - port
result = (socket_type,) + match.groups(1)
else:
logger.warning('unix socket name is not recognized as belonging to PostgreSQL: {0}'.format(socket_path))
else:
address_port = line.split()[1]
(address_hex, port_hex) = address_port.split(':')
port = self._hex_to_int_str(port_hex)
if socket_type == 'tcp6':
address = self._hex_to_ipv6(address_hex)
elif socket_type == 'tcp':
address = self._hex_to_ip(address_hex)
else:
logger.error('unrecognized socket type: {0}'.format(socket_type))
result = (socket_type, address, port)
return result
def main():
global TICK_LENGTH, logger, options
# bail out if we are not running Linux
if platform.system() != 'Linux':
print('Non Linux database hosts are not supported at the moment. Can not continue')
sys.exit(243)
if not psycopg2_available:
print('Unable to import psycopg2 module, please, install it (python-psycopg2). Can not continue')
sys.exit(254)
options, args = parse_args()
TICK_LENGTH = options.tick
output_method = options.output_method
if not output_method_is_valid(output_method):
print('Unsupported output method: {0}'.format(output_method))
print('Valid output methods are: {0}'.format(','.join(get_valid_output_methods())))
sys.exit(1)
if output_method == OUTPUT_METHOD.curses and not curses_available:
print('Curses output is selected, but curses are unavailable, falling back to console output')
output_method == OUTPUT_METHOD.console
# set basic logging
if options.log_file:
LOG_FILE_NAME = options.log_file
# truncate the former logs
with open(LOG_FILE_NAME, 'w'):
pass
logging.basicConfig(format='%(levelname)s: %(asctime)-15s %(message)s', filename=LOG_FILE_NAME)
else:
logging.basicConfig(format='%(levelname)s: %(asctime)-15s %(message)s')
logger = logging.getLogger(__name__)
logger.setLevel((logging.INFO if options.verbose else logging.ERROR))
log_stderr = logging.StreamHandler()
logger.addHandler(log_stderr)
user_dbname = options.instance
user_dbver = options.version
clusters = []
# now try to read the configuration file
config = (read_configuration(options.config_file) if options.config_file else None)
if config:
for instance in config:
if user_dbname and instance != user_dbname:
continue
# pass already aquired connections to make sure we only list unique clusters.
host = config[instance].get('host')
port = config[instance].get('port')
conn = build_connection(host, port,
config[instance].get('user'), config[instance].get('dbname'))
if not establish_user_defined_connection(instance, conn, clusters):
logger.error('failed to acquire details about ' +
'the database cluster {0}, the server will be skipped'.format(instance))
elif options.host:
port = options.port or "5432"
# try to connet to the database specified by command-line options
conn = build_connection(options.host, options.port, options.username, options.dbname)
instance = options.instance or "default"
if not establish_user_defined_connection(instance, conn, clusters):
logger.error("unable to continue with cluster {0}".format(instance))
else:
# do autodetection
postmasters = get_postmasters_directories()
# get all PostgreSQL instances
for result_work_dir, data in postmasters.items():
(ppid, dbver, dbname) = data
# if user requested a specific database name and version - don't try to connect to others
if user_dbname:
if dbname != user_dbname or not result_work_dir or not ppid:
continue
if user_dbver is not None and dbver != user_dbver:
continue
try:
conndata = detect_db_connection_arguments(result_work_dir, ppid, dbver)
if conndata is None:
continue
host = conndata['host']
port = conndata['port']
conn = build_connection(host, port, options.username, options.dbname)
pgcon = psycopg2.connect(**conn)
except Exception as e:
logger.error('PostgreSQL exception {0}'.format(e))
pgcon = None
if pgcon:
desc = make_cluster_desc(name=dbname, version=dbver, workdir=result_work_dir,
pid=ppid, pgcon=pgcon, conn=conn)
clusters.append(desc)
collectors = []
groups = {}
try:
if len(clusters) == 0:
logger.error('No suitable PostgreSQL instances detected, exiting...')
logger.error('hint: use -v for details, ' +
'or specify connection parameters manually in the configuration file (-c)')
sys.exit(1)
# initialize the disks stat collector process and create an exchange queue
q = JoinableQueue(1)
work_directories = [cl['wd'] for cl in clusters if 'wd' in cl]
collector = DetachedDiskStatCollector(q, work_directories)
collector.start()
consumer = DiskCollectorConsumer(q)
collectors.append(HostStatCollector())
collectors.append(SystemStatCollector())
collectors.append(MemoryStatCollector())
for cl in clusters:
part = PartitionStatCollector(cl['name'], cl['ver'], cl['wd'], consumer)
pg = PgstatCollector(cl['pgcon'], cl['reconnect'], cl['pid'], cl['name'], cl['ver'], options.pid)
groupname = cl['wd']
groups[groupname] = {'pg': pg, 'partitions': part}
collectors.append(part)
collectors.append(pg)
# we don't want to mix diagnostics messages with useful output, so we log the former into a file.
logger.removeHandler(log_stderr)
loop(collectors, consumer, groups, output_method)
logger.addHandler(log_stderr)
except KeyboardInterrupt:
pass
except curses.error:
print(traceback.format_exc())
if 'SSH_CLIENT' in os.environ and 'SSH_TTY' not in os.environ:
print('Unable to initialize curses. Make sure you supply -t option (force psedo-tty allocation) to ssh')
except:
print(traceback.format_exc())
finally:
sys.exit(0)
class DetachedDiskStatCollector(Process):
""" This class runs in a separate process and runs du and df """
def __init__(self, q, work_directories):
super(DetachedDiskStatCollector, self).__init__()
self.work_directories = work_directories
self.q = q
self.daemon = True
self.df_cache = {}
def run(self):
while True:
# wait until the previous data is consumed
self.q.join()
result = {}
self.df_cache = {}
for wd in self.work_directories:
du_data = self.get_du_data(wd)
df_data = self.get_df_data(wd)
result[wd] = [du_data, df_data]
self.q.put(result)
time.sleep(TICK_LENGTH)
def get_du_data(self, wd):
data_size = 0
xlog_size = 0
result = {'data': [], 'xlog': []}
try:
data_size = self.run_du(wd, BLOCK_SIZE)
xlog_size = self.run_du(wd + '/pg_xlog/', BLOCK_SIZE)
except Exception as e:
logger.error('Unable to read free space information for the pg_xlog and data directories for the directory\
{0}: {1}'.format(wd, e))
else:
# XXX: why do we pass the block size there?
result['data'] = str(data_size), wd
result['xlog'] = str(xlog_size), wd + '/pg_xlog'
return result
@staticmethod
def run_du(pathname, block_size=BLOCK_SIZE, exclude=['lost+found']):
size = 0
folders = [pathname]
root_dev = os.lstat(pathname).st_dev
while len(folders):
c = folders.pop()
for e in os.listdir(c):
e = os.path.join(c, e)
try:
st = os.lstat(e)
except os.error:
# don't care about files removed while we are trying to read them.
continue
# skip data on different partition
if st.st_dev != root_dev:
continue
mode = st.st_mode & 0xf000 # S_IFMT
if mode == 0x4000: # S_IFDIR
if e in exclude:
continue
folders.append(e)
size += st.st_size
if mode == 0x8000: # S_IFREG
size += st.st_size
return long(size / block_size)
| |
<gh_stars>0
# -*- coding: utf-8 -*-
# Copyright 2014, Digital Reasoning
#
# 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.
#
"""
Amazon Web Services provider for stackd.io
"""
import os
import re
import stat
import logging
from uuid import uuid4
from time import sleep
import requests
from requests.exceptions import ConnectionError
import boto
import boto.ec2
import boto.vpc
import yaml
from boto.route53.record import ResourceRecordSets
from boto.exception import EC2ResponseError
from cloud.providers.base import (
BaseCloudProvider,
TimeoutException,
MaxFailuresException,
)
from core.exceptions import BadRequest, InternalServerError
GROUP_PATTERN = re.compile('\d+:[a-zA-Z0-9-_]')
CIDR_PATTERN = re.compile('[0-9]+(?:\.[0-9]+){3}\/\d{1,2}')
# Boto Errors
BOTO_DUPLICATE_ERROR_CODE = 'InvalidPermission.Duplicate'
logger = logging.getLogger(__name__)
DEFAULT_ROUTE53_TTL = 30
class Route53Domain(object):
def __init__(self, access_key, secret_key, domain):
'''
`access_key`
The AWS access key id
`secret_key`
The AWS secret access key
`domain`
An existing Route53 domain to manage
'''
self.access_key = access_key
self.secret_key = secret_key
self.domain = domain
# Loaded after connection is made
self.hosted_zone = None
self.zone_id = None
self.rr_sets = None
self.conn = boto.connect_route53(self.access_key,
self.secret_key)
self._load_domain()
def _load_domain(self):
'''
Attempts to look up a Route53 hosted zone given a domain name (they're
much easier to remember than a zone id). Once loaded, pulls out the
zone id and stores it in `zone_id`
'''
# look up the hosted zone based on the domain
response = self.conn.get_hosted_zone_by_name(self.domain)
self.hosted_zone = response['GetHostedZoneResponse']['HostedZone']
# Get the zone id, but strip off the first part /hostedzone/
self.zone_id = self.hosted_zone['Id'][len('/hostedzone/'):]
def get_rrnames_set(self, force=False):
'''
Returns a cached set of resource record names for our zone id, and
builds the cached set if we haven't already.
'''
if not force and self.rr_sets is not None:
return self.rr_sets
self.rr_sets = {}
for rr in self.conn.get_all_rrsets(self.zone_id):
self.rr_sets[rr.name] = {
'type': rr.type,
'ttl': rr.ttl,
'value': rr.to_print(),
}
return self.rr_sets
def start_rr_transaction(self):
'''
Creates a new Route53 ResourceRecordSets object that is used
internally like a transaction of sorts. You may add or delete
many resource records using a single set by calling the
`add_record` and `delete_record` methods. Finish the transaction
with `finish_rr_transaction`
NOTE: Calling this method again before finishing will not finish
an existing transaction or delete it. To cancel an existing
transaction use the `cancel_rr_transaction`.
'''
if not hasattr(self, '_rr_txn') or self._rr_txn is None:
# Return a new ResourceRecordSets "transaction"
self._rr_txn = ResourceRecordSets(self.conn, self.zone_id)
def finish_rr_transaction(self):
'''
If a transaction exists, commit the changes to Route53
'''
if self._rr_txn is not None:
self._rr_txn.commit()
self._rr_txn = None
def cancel_rr_transaction(self):
'''
Basically deletes the existing transaction.
'''
self._rr_txn = None
def add_record(self, record_name, record_value, record_type,
ttl=DEFAULT_ROUTE53_TTL):
'''
NOTE: This method must be called after `start_rr_transaction`.
Adds a new record to the existing resource record transaction.
`record_name`
The subdomain part of the record (e.g., web-1 for a domain
like web-1.dev.example.com)
`record_value`
The host or IP the record will point to.
`record_type`
The type of the record (CNAME or A)
`ttl`
The TTL for the record in seconds, default is 30 seconds
'''
# Update the record name to be fully qualified with the domain
# for this instance. The period on the end is required.
record_name += '.{0}.'.format(self.domain)
# Check for an existing record and remove it before
# updating it
rr_names = self.get_rrnames_set()
if record_name in rr_names:
self._delete_rr_record(record_name,
[rr_names[record_name]['value']],
rr_names[record_name]['type'],
ttl=rr_names[record_name]['ttl'])
# self._delete_rr_record(record_name,
# [record_value],
# record_type,
# ttl=ttl)
self._add_rr_record(record_name, [record_value], record_type, ttl=ttl)
def delete_record(self, record_name, record_value, record_type,
ttl=DEFAULT_ROUTE53_TTL):
'''
Almost the same as `add_record` but it deletes an existing record
NOTE: The name, value, and ttl must all match an existing record
or Route53 will not allow it to be removed.
'''
# Update the record name to be fully qualified with the domain
# for this instance. The period on the end is required.
record_name += '.{0}.'.format(self.domain)
# Only remove the record if it exists
rr_names = self.get_rrnames_set()
if record_name in rr_names:
self._delete_rr_record(record_name,
[rr_names[record_name]['value']],
rr_names[record_name]['type'],
ttl=rr_names[record_name]['ttl'])
# self._delete_rr_record(record_name,
# [record_value],
# record_type,
# ttl=ttl)
return True
return False
def _add_rr_record(self, record_name, record_values, record_type,
**kwargs):
rr = self._rr_txn.add_change('CREATE',
record_name,
record_type,
**kwargs)
for v in record_values:
rr.add_value(v)
def _delete_rr_record(self, record_name, record_values, record_type,
**kwargs):
rr = self._rr_txn.add_change('DELETE',
record_name,
record_type,
**kwargs)
for v in record_values:
rr.add_value(v)
class AWSCloudProvider(BaseCloudProvider):
SHORT_NAME = 'ec2'
LONG_NAME = 'Amazon Web Services'
# The account/owner id
ACCOUNT_ID = 'account_id'
# The AWS access key id
ACCESS_KEY = 'access_key_id'
# The AWS secret access key
SECRET_KEY = 'secret_access_key'
# The AWS keypair name
KEYPAIR = 'keypair'
# The AWS security groups
# SECURITY_GROUPS = 'security_groups'
# The path to the private key for SSH
PRIVATE_KEY = 'private_key'
# VPC fields
VPC_ID = 'vpc_id'
# VPC_SUBNETS = 'vpc_subnets'
# The route53 zone to use for managing DNS
ROUTE53_DOMAIN = 'route53_domain'
REGION = 'region'
STATE_STOPPED = 'stopped'
STATE_RUNNING = 'running'
STATE_SHUTTING_DOWN = 'shutting-down'
STATE_TERMINATED = 'terminated'
@classmethod
def get_required_fields(self):
return [
self.ACCOUNT_ID,
self.ACCESS_KEY,
self.SECRET_KEY,
self.KEYPAIR,
self.PRIVATE_KEY,
self.ROUTE53_DOMAIN,
# self.SECURITY_GROUPS
]
@classmethod
def get_available_actions(self):
return [
self.ACTION_STOP,
self.ACTION_START,
self.ACTION_TERMINATE,
self.ACTION_LAUNCH,
self.ACTION_PROVISION,
self.ACTION_ORCHESTRATE,
self.ACTION_CUSTOM,
]
def get_private_key_path(self):
return os.path.join(self.provider_storage, 'id_rsa')
def get_config_file_path(self):
return os.path.join(self.provider_storage, 'config')
def get_config(self):
with open(self.get_config_file_path(), 'r') as f:
config_data = yaml.safe_load(f)
return config_data
def get_credentials(self):
config_data = self.get_config()
return config_data['location'], config_data['id'], config_data['key']
def get_provider_data(self, data, files=None):
# write the private key to the proper location
private_key_path = self.get_private_key_path()
with open(private_key_path, 'w') as f:
f.write(data[self.PRIVATE_KEY])
# change the file permissions of the RSA key
os.chmod(private_key_path, stat.S_IRUSR)
config_data = {
'provider': self.SHORT_NAME,
'id': data[self.ACCESS_KEY],
'key': data[self.SECRET_KEY],
'keyname': data[self.KEYPAIR],
'private_key': private_key_path,
'append_domain': data[self.ROUTE53_DOMAIN],
'location': data[self.REGION],
'ssh_connect_timeout': 300,
'wait_for_passwd_timeout': 5,
'rename_on_destroy': True,
'delvol_on_destroy': True,
}
r = requests.get('http://169.254.169.254/latest/dynamic/instance-identity/document')
master_region = r.json()['region']
if master_region == data[self.REGION]:
config_data['ssh_interface'] = 'private_ips'
else:
config_data['ssh_interface'] = 'public_ips'
# Save the data out to a file that can be reused by this provider
# later if necessary
with open(self.get_config_file_path(), 'w') as f:
f.write(yaml.safe_dump(config_data, default_flow_style=False))
return config_data
# TODO: Ignoring code complexity issues...
def validate_provider_data(self, data, files=None): # NOQA
errors = super(AWSCloudProvider, self) \
.validate_provider_data(data, files)
if errors:
return errors
# check authentication credentials
try:
ec2 = boto.ec2.connect_to_region(
data[self.REGION],
aws_access_key_id=data[self.ACCESS_KEY],
aws_secret_access_key=data[self.SECRET_KEY])
ec2.get_all_zones()
except boto.exception.EC2ResponseError, e:
err_msg = 'Unable to authenticate to AWS with the provided keys.'
errors.setdefault(self.ACCESS_KEY, []).append(err_msg)
errors.setdefault(self.SECRET_KEY, []).append(err_msg)
if errors:
return errors
# check keypair
try:
ec2.get_all_key_pairs(data[self.KEYPAIR])
except boto.exception.EC2ResponseError, e:
errors.setdefault(self.KEYPAIR, []).append(
'The keypair \'{0}\' does not exist in this account.'
''.format(data[self.KEYPAIR])
)
# check route 53 domain
try:
if self.ROUTE53_DOMAIN in data:
# connect to route53 and check that the domain is available
r53 = boto.connect_route53(data[self.ACCESS_KEY],
data[self.SECRET_KEY])
found_domain = False
domain = data[self.ROUTE53_DOMAIN]
hosted_zones = r53.get_all_hosted_zones()
hosted_zones = \
hosted_zones['ListHostedZonesResponse']['HostedZones']
for hosted_zone in hosted_zones:
if hosted_zone['Name'].startswith(domain):
found_domain = True
break
if not found_domain:
err = 'The Route53 domain \'{0}\' does not exist in ' \
'this account.'.format(domain)
errors.setdefault(self.ROUTE53_DOMAIN, []).append(err)
# except boto.exception.DNSServerError, e:
except Exception, e:
logger.exception('Route53 issue?')
errors.setdefault(self.ROUTE53_DOMAIN, []).append(str(e))
# check VPC required fields
if self.VPC_ID in data and data[self.VPC_ID]:
vpc_id = data[self.VPC_ID]
try:
vpc = boto.vpc.connect_to_region(
data[self.REGION],
aws_access_key_id=data[self.ACCESS_KEY],
aws_secret_access_key=data[self.SECRET_KEY])
except boto.exception.EC2ResponseError, e:
err_msg = ('Unable to authenticate to AWS VPC with the '
'provided keys.')
errors.setdefault(self.ACCESS_KEY, []).append(err_msg)
errors.setdefault(self.SECRET_KEY, []).append(err_msg)
if not errors:
try:
vpc.get_all_vpcs([vpc_id])
except boto.exception.EC2ResponseError, e:
errors.setdefault(self.VPC_ID, []).append(
'The VPC \'{0}\' does not exist in this account.'
.format(vpc_id)
)
return errors
def validate_image_id(self, image_id):
ec2 = self.connect_ec2()
try:
ec2.get_all_images(image_ids=[image_id])
return True, ''
except boto.exception.EC2ResponseError, e:
return False, e.error_message
def connect_route53(self):
# Load the configuration file to get a few things we'll need
# to manage DNS
config_data = self.get_config()
access_key = config_data['id']
secret_key = config_data['key']
domain = config_data['append_domain']
# load a new Route53Domain class and return it
return Route53Domain(access_key, secret_key, domain)
def connect_ec2(self):
if not hasattr(self, '_ec2_connection'):
region, access_key, secret_key = self.get_credentials()
self._ec2_connection = boto.ec2.connect_to_region(
region,
aws_access_key_id=access_key,
aws_secret_access_key=secret_key)
return self._ec2_connection
def connect_vpc(self):
if not hasattr(self, '_vpc_connection'):
region, access_key, secret_key = self.get_credentials()
self._vpc_connection = boto.vpc.connect_to_region(
region,
aws_access_key_id=access_key,
aws_secret_access_key=secret_key)
return self._vpc_connection
| |
from itertools import chain
from typing import Optional, Iterable, Set, Union, TYPE_CHECKING
import logging
import pyvex
import claripy
from ...storage.memory_mixins.paged_memory.pages.multi_values import MultiValues
from ...engines.light import SimEngineLight, SimEngineLightVEXMixin, SpOffset
from ...engines.vex.claripy.datalayer import value as claripy_value
from ...engines.vex.claripy.irop import operations as vex_operations
from ...errors import SimEngineError, SimMemoryMissingError
from ...calling_conventions import DEFAULT_CC, SimRegArg, SimStackArg, SimCC
from ...utils.constants import DEFAULT_STATEMENT
from ...knowledge_plugins.key_definitions.definition import Definition
from ...knowledge_plugins.key_definitions.tag import LocalVariableTag, ParameterTag, ReturnValueTag, Tag
from ...knowledge_plugins.key_definitions.atoms import Atom, Register, MemoryLocation, Tmp
from ...knowledge_plugins.key_definitions.constants import OP_BEFORE, OP_AFTER
from ...knowledge_plugins.key_definitions.heap_address import HeapAddress
from ...knowledge_plugins.key_definitions.undefined import Undefined
from ...code_location import CodeLocation
from .rd_state import ReachingDefinitionsState
from .external_codeloc import ExternalCodeLocation
if TYPE_CHECKING:
from ...knowledge_plugins import FunctionManager
l = logging.getLogger(name=__name__)
class SimEngineRDVEX(
SimEngineLightVEXMixin,
SimEngineLight,
): # pylint:disable=abstract-method
"""
Implements the VEX execution engine for reaching definition analysis.
"""
def __init__(self, project, call_stack, maximum_local_call_depth, functions=None,
function_handler=None):
super().__init__()
self.project = project
self._call_stack = call_stack
self._maximum_local_call_depth = maximum_local_call_depth
self.functions: Optional['FunctionManager'] = functions
self._function_handler = function_handler
self._visited_blocks = None
self._dep_graph = None
self.state: ReachingDefinitionsState
def process(self, state, *args, **kwargs):
self._dep_graph = kwargs.pop('dep_graph', None)
self._visited_blocks = kwargs.pop('visited_blocks', None)
# we are using a completely different state. Therefore, we directly call our _process() method before
# SimEngine becomes flexible enough.
try:
self._process(
state,
None,
block=kwargs.pop('block', None),
)
except SimEngineError as e:
if kwargs.pop('fail_fast', False) is True:
raise e
l.error(e)
return self.state, self._visited_blocks, self._dep_graph
def _process_block_end(self):
self.stmt_idx = DEFAULT_STATEMENT
if self.block.vex.jumpkind == "Ijk_Call":
# it has to be a function
addr = self._expr(self.block.vex.next)
self._handle_function(addr)
elif self.block.vex.jumpkind == "Ijk_Boring":
# test if the target addr is a function or not
addr = self._expr(self.block.vex.next)
addr_v = addr.one_value()
if addr_v is not None and addr_v.concrete:
addr_int = addr_v._model_concrete.value
if addr_int in self.functions:
# yes it's a jump to a function
self._handle_function(addr)
#
# Private methods
#
@staticmethod
def _external_codeloc():
return ExternalCodeLocation()
#
# VEX statement handlers
#
def _handle_Stmt(self, stmt):
if self.state.analysis:
self.state.analysis.insn_observe(self.ins_addr, stmt, self.block, self.state, OP_BEFORE)
super()._handle_Stmt(stmt)
if self.state.analysis:
self.state.analysis.insn_observe(self.ins_addr, stmt, self.block, self.state, OP_AFTER)
def _handle_WrTmp(self, stmt: pyvex.IRStmt.WrTmp):
data: MultiValues = self._expr(stmt.data)
tmp_atom = Tmp(stmt.tmp, self.tyenv.sizeof(stmt.tmp) // self.arch.byte_width)
# if len(data.values) == 1 and 0 in data.values:
# data_v = data.one_value()
# if data_v is not None:
# # annotate data with its definition
# data = MultiValues(offset_to_values={
# 0: {self.state.annotate_with_def(data_v, Definition(tmp_atom, self._codeloc()))
# }
# })
self.tmps[stmt.tmp] = data
self.state.kill_and_add_definition(tmp_atom,
self._codeloc(),
data,
)
def _handle_WrTmpData(self, tmp: int, data):
super()._handle_WrTmpData(tmp, data)
self.state.kill_and_add_definition(Tmp(tmp, self.tyenv.sizeof(tmp)),
self._codeloc(),
self.tmps[tmp])
# e.g. PUT(rsp) = t2, t2 might include multiple values
def _handle_Put(self, stmt):
reg_offset: int = stmt.offset
size: int = stmt.data.result_size(self.tyenv) // 8
reg = Register(reg_offset, size)
data = self._expr(stmt.data)
# special handling for references to heap or stack variables
if len(data.values) == 1:
for d in next(iter(data.values.values())):
if self.state.is_heap_address(d):
heap_offset = self.state.get_heap_offset(d)
if heap_offset is not None:
self.state.add_use(MemoryLocation(HeapAddress(heap_offset), 1), self._codeloc())
elif self.state.is_stack_address(d):
stack_offset = self.state.get_stack_offset(d)
if stack_offset is not None:
self.state.add_use(MemoryLocation(SpOffset(self.arch.bits, stack_offset), 1), self._codeloc())
self.state.kill_and_add_definition(reg, self._codeloc(), data)
# e.g. STle(t6) = t21, t6 and/or t21 might include multiple values
def _handle_Store(self, stmt):
addr = self._expr(stmt.addr)
size = stmt.data.result_size(self.tyenv) // 8
data = self._expr(stmt.data)
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
self._store_core(addrs, size, data, endness=stmt.endness)
def _handle_StoreG(self, stmt: pyvex.IRStmt.StoreG):
guard = self._expr(stmt.guard)
guard_v = guard.one_value()
if claripy.is_true(guard_v):
addr = self._expr(stmt.addr)
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
size = stmt.data.result_size(self.tyenv) // 8
data = self._expr(stmt.data)
self._store_core(addrs, size, data)
elif claripy.is_false(guard_v):
pass
else:
# guard.data == {True, False}
# get current data
addr = self._expr(stmt.addr)
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
size = stmt.data.result_size(self.tyenv) // 8
data_old = self._load_core(addrs, size, stmt.endness)
data = self._expr(stmt.data)
self._store_core(addrs, size, data, data_old=data_old)
def _store_core(self, addr: Iterable[Union[int,HeapAddress,SpOffset]], size: int, data: MultiValues,
data_old: Optional[MultiValues]=None, endness=None):
if data_old is not None:
data = data.merge(data_old)
for a in addr:
if self.state.is_top(a):
l.debug('Memory address undefined, ins_addr = %#x.', self.ins_addr)
else:
tags: Optional[Set[Tag]]
if isinstance(a, int):
atom = MemoryLocation(a, size)
tags = None
elif self.state.is_stack_address(a):
atom = MemoryLocation(SpOffset(self.arch.bits, self.state.get_stack_offset(a)), size)
function_address = (
self.project.kb
.cfgs.get_most_accurate()
.get_all_nodes(self._codeloc().ins_addr, anyaddr=True)[0]
.function_address
)
tags = {LocalVariableTag(
function=function_address,
metadata={'tagged_by': 'SimEngineRDVEX._store_core'}
)}
elif self.state.is_heap_address(a):
atom = MemoryLocation(HeapAddress(self.state.get_heap_offset(a)), size)
tags = None
else:
continue
# different addresses are not killed by a subsequent iteration, because kill only removes entries
# with same index and same size
self.state.kill_and_add_definition(atom, self._codeloc(), data, tags=tags, endness=endness)
def _handle_LoadG(self, stmt):
guard = self._expr(stmt.guard)
guard_v = guard.one_value()
if claripy.is_true(guard_v):
# FIXME: full conversion support
if stmt.cvt.find('Ident') < 0:
l.warning('Unsupported conversion %s in LoadG.', stmt.cvt)
load_expr = pyvex.expr.Load(stmt.end, stmt.cvt_types[1], stmt.addr)
wr_tmp_stmt = pyvex.stmt.WrTmp(stmt.dst, load_expr)
self._handle_WrTmp(wr_tmp_stmt)
elif claripy.is_false(guard_v):
wr_tmp_stmt = pyvex.stmt.WrTmp(stmt.dst, stmt.alt)
self._handle_WrTmp(wr_tmp_stmt)
else:
if stmt.cvt.find('Ident') < 0:
l.warning('Unsupported conversion %s in LoadG.', stmt.cvt)
load_expr = pyvex.expr.Load(stmt.end, stmt.cvt_types[1], stmt.addr)
load_expr_v = self._expr(load_expr)
alt_v = self._expr(stmt.alt)
data = load_expr_v.merge(alt_v)
self._handle_WrTmpData(stmt.dst, data)
def _handle_Exit(self, stmt):
_ = self._expr(stmt.guard)
target = stmt.dst.value
self.state.mark_guard(self._codeloc(), target)
def _handle_IMark(self, stmt):
pass
def _handle_AbiHint(self, stmt):
pass
def _handle_LLSC(self, stmt: pyvex.IRStmt.LLSC):
if stmt.storedata is None:
# load-link
addr = self._expr(stmt.addr)
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
size = self.tyenv.sizeof(stmt.result) // self.arch.byte_width
load_result = self._load_core(addrs, size, stmt.endness)
self.tmps[stmt.result] = load_result
self.state.kill_and_add_definition(Tmp(stmt.result,
self.tyenv.sizeof(stmt.result) // self.arch.byte_width),
self._codeloc(),
load_result)
else:
# store-conditional
storedata = self._expr(stmt.storedata)
addr = self._expr(stmt.addr)
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
size = self.tyenv.sizeof(stmt.storedata.tmp) // self.arch.byte_width
self._store_core(addrs, size, storedata)
self.tmps[stmt.result] = MultiValues(offset_to_values={0: {claripy.BVV(1, 1)}})
self.state.kill_and_add_definition(Tmp(stmt.result,
self.tyenv.sizeof(stmt.result) // self.arch.byte_width),
self._codeloc(),
self.tmps[stmt.result])
#
# VEX expression handlers
#
def _expr(self, expr) -> MultiValues:
data = super()._expr(expr)
if data is None:
bits = expr.result_size(self.tyenv)
top = self.state.top(bits)
data = MultiValues(offset_to_values={0: {top}})
return data
def _handle_RdTmp(self, expr: pyvex.IRExpr.RdTmp) -> Optional[MultiValues]:
tmp: int = expr.tmp
self.state.add_use(Tmp(tmp, expr.result_size(self.tyenv) // self.arch.byte_width), self._codeloc())
if tmp in self.tmps:
return self.tmps[tmp]
return None
# e.g. t0 = GET:I64(rsp), rsp might be defined multiple times
def _handle_Get(self, expr: pyvex.IRExpr.Get) -> MultiValues:
reg_offset: int = expr.offset
bits: int = expr.result_size(self.tyenv)
size: int = bits // self.arch.byte_width
reg_atom = Register(reg_offset, size)
try:
values: MultiValues = self.state.register_definitions.load(reg_offset, size=size)
except SimMemoryMissingError:
top = self.state.top(size * self.arch.byte_width)
# annotate it
top = self.state.annotate_with_def(top, Definition(reg_atom, ExternalCodeLocation()))
values = MultiValues({0: {top}})
# write it to registers
self.state.kill_and_add_definition(reg_atom, self._external_codeloc(), values)
current_defs: Optional[Iterable[Definition]] = None
for vs in values.values.values():
for v in vs:
if current_defs is None:
current_defs = self.state.extract_defs(v)
else:
current_defs = chain(current_defs, self.state.extract_defs(v))
if current_defs is None:
# no defs can be found. add a fake definition
self.state.kill_and_add_definition(reg_atom, self._external_codeloc(), values)
self.state.add_use(reg_atom, self._codeloc())
return values
# e.g. t27 = LDle:I64(t9), t9 might include multiple values
# caution: Is also called from StoreG
def _handle_Load(self, expr) -> MultiValues:
addr = self._expr(expr.addr)
bits = expr.result_size(self.tyenv)
size = bits // self.arch.byte_width
# convert addr from MultiValues to a list of valid addresses
if len(addr.values) == 1:
addrs = next(iter(addr.values.values()))
return self._load_core(addrs, size, expr.endness)
top = self.state.top(bits)
# annotate it
dummy_atom = MemoryLocation(0, size)
top = self.state.annotate_with_def(top, Definition(dummy_atom, ExternalCodeLocation()))
# add use
self.state.add_use(dummy_atom, self._codeloc())
return MultiValues(offset_to_values={0: {top}})
def _load_core(self, addrs: Iterable[claripy.ast.Base], size: int, endness: str) -> MultiValues:
result: Optional[MultiValues] = None
for addr in addrs:
if self.state.is_top(addr):
l.debug('Memory address undefined, ins_addr = %#x.', self.ins_addr)
elif self.state.is_stack_address(addr):
# Load data from a local variable
stack_offset = self.state.get_stack_offset(addr)
if stack_offset is not None:
stack_addr = self.state.live_definitions.stack_offset_to_stack_addr(stack_offset)
try:
vs: MultiValues = self.state.stack_definitions.load(stack_addr, size=size, endness=endness)
except SimMemoryMissingError:
continue
memory_location = MemoryLocation(SpOffset(self.arch.bits, stack_offset), size, endness=endness)
self.state.add_use(memory_location, self._codeloc())
result = result.merge(vs) if result is not None else vs
elif self.state.is_heap_address(addr):
# Load data from the heap
heap_offset = self.state.get_heap_offset(addr)
vs: MultiValues = self.state.heap_definitions.load(heap_offset, size=size, endness=endness)
memory_location = MemoryLocation(HeapAddress(heap_offset), size, endness=endness)
self.state.add_use(memory_location, self._codeloc())
result = result.merge(vs) if result is not None else vs
else:
addr_v = addr._model_concrete.value
# Load data from a global region
try:
vs: MultiValues = self.state.memory_definitions.load(addr_v, size=size, endness=endness)
except SimMemoryMissingError:
# try to load it from the static memory backer
# TODO: Is this still required?
try:
vs = MultiValues(offset_to_values={0: {
claripy.BVV(
self.project.loader.memory.unpack_word(addr_v, size=size),
size * self.arch.byte_width
)}})
except KeyError:
continue
result = result.merge(vs) if result is not None else vs
# FIXME: _add_memory_use() iterates over the same loop
memory_location = MemoryLocation(addr_v, size, endness=endness)
self.state.add_use(memory_location, self._codeloc())
if result is None:
result = MultiValues(offset_to_values={0: {self.state.top(size * self.arch.byte_width)}})
return result
# CAUTION: experimental
def _handle_ITE(self, expr: pyvex.IRExpr.ITE):
cond = self._expr(expr.cond)
cond_v = cond.one_value()
iftrue = self._expr(expr.iftrue)
iffalse = self._expr(expr.iffalse)
if claripy.is_true(cond_v):
return | |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
__all__ = [
'InstanceMaintenancePolicy',
'InstanceMaintenancePolicyWeeklyMaintenanceWindow',
'InstanceMaintenancePolicyWeeklyMaintenanceWindowStartTime',
'InstanceMaintenanceSchedule',
'InstanceNode',
'InstanceServerCaCert',
'GetInstanceMaintenancePolicyResult',
'GetInstanceMaintenancePolicyWeeklyMaintenanceWindowResult',
'GetInstanceMaintenancePolicyWeeklyMaintenanceWindowStartTimeResult',
'GetInstanceMaintenanceScheduleResult',
'GetInstanceNodeResult',
'GetInstanceServerCaCertResult',
]
@pulumi.output_type
class InstanceMaintenancePolicy(dict):
@staticmethod
def __key_warning(key: str):
suggest = None
if key == "createTime":
suggest = "create_time"
elif key == "updateTime":
suggest = "update_time"
elif key == "weeklyMaintenanceWindows":
suggest = "weekly_maintenance_windows"
if suggest:
pulumi.log.warn(f"Key '{key}' not found in InstanceMaintenancePolicy. Access the value via the '{suggest}' property getter instead.")
def __getitem__(self, key: str) -> Any:
InstanceMaintenancePolicy.__key_warning(key)
return super().__getitem__(key)
def get(self, key: str, default = None) -> Any:
InstanceMaintenancePolicy.__key_warning(key)
return super().get(key, default)
def __init__(__self__, *,
create_time: Optional[str] = None,
description: Optional[str] = None,
update_time: Optional[str] = None,
weekly_maintenance_windows: Optional[Sequence['outputs.InstanceMaintenancePolicyWeeklyMaintenanceWindow']] = None):
"""
:param str create_time: -
Output only. The time when the policy was created.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
:param str description: Optional. Description of what this policy is for.
Create/Update methods return INVALID_ARGUMENT if the
length is greater than 512.
:param str update_time: -
Output only. The time when the policy was last updated.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
:param Sequence['InstanceMaintenancePolicyWeeklyMaintenanceWindowArgs'] weekly_maintenance_windows: Optional. Maintenance window that is applied to resources covered by this policy.
Minimum 1. For the current version, the maximum number
of weekly_window is expected to be one.
Structure is documented below.
"""
if create_time is not None:
pulumi.set(__self__, "create_time", create_time)
if description is not None:
pulumi.set(__self__, "description", description)
if update_time is not None:
pulumi.set(__self__, "update_time", update_time)
if weekly_maintenance_windows is not None:
pulumi.set(__self__, "weekly_maintenance_windows", weekly_maintenance_windows)
@property
@pulumi.getter(name="createTime")
def create_time(self) -> Optional[str]:
"""
-
Output only. The time when the policy was created.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
"""
return pulumi.get(self, "create_time")
@property
@pulumi.getter
def description(self) -> Optional[str]:
"""
Optional. Description of what this policy is for.
Create/Update methods return INVALID_ARGUMENT if the
length is greater than 512.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter(name="updateTime")
def update_time(self) -> Optional[str]:
"""
-
Output only. The time when the policy was last updated.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
"""
return pulumi.get(self, "update_time")
@property
@pulumi.getter(name="weeklyMaintenanceWindows")
def weekly_maintenance_windows(self) -> Optional[Sequence['outputs.InstanceMaintenancePolicyWeeklyMaintenanceWindow']]:
"""
Optional. Maintenance window that is applied to resources covered by this policy.
Minimum 1. For the current version, the maximum number
of weekly_window is expected to be one.
Structure is documented below.
"""
return pulumi.get(self, "weekly_maintenance_windows")
@pulumi.output_type
class InstanceMaintenancePolicyWeeklyMaintenanceWindow(dict):
@staticmethod
def __key_warning(key: str):
suggest = None
if key == "startTime":
suggest = "start_time"
if suggest:
pulumi.log.warn(f"Key '{key}' not found in InstanceMaintenancePolicyWeeklyMaintenanceWindow. Access the value via the '{suggest}' property getter instead.")
def __getitem__(self, key: str) -> Any:
InstanceMaintenancePolicyWeeklyMaintenanceWindow.__key_warning(key)
return super().__getitem__(key)
def get(self, key: str, default = None) -> Any:
InstanceMaintenancePolicyWeeklyMaintenanceWindow.__key_warning(key)
return super().get(key, default)
def __init__(__self__, *,
day: str,
start_time: 'outputs.InstanceMaintenancePolicyWeeklyMaintenanceWindowStartTime',
duration: Optional[str] = None):
"""
:param str day: Required. The day of week that maintenance updates occur.
- DAY_OF_WEEK_UNSPECIFIED: The day of the week is unspecified.
- MONDAY: Monday
- TUESDAY: Tuesday
- WEDNESDAY: Wednesday
- THURSDAY: Thursday
- FRIDAY: Friday
- SATURDAY: Saturday
- SUNDAY: Sunday
Possible values are `DAY_OF_WEEK_UNSPECIFIED`, `MONDAY`, `TUESDAY`, `WEDNESDAY`, `THURSDAY`, `FRIDAY`, `SATURDAY`, and `SUNDAY`.
:param 'InstanceMaintenancePolicyWeeklyMaintenanceWindowStartTimeArgs' start_time: -
Output only. The start time of any upcoming scheduled maintenance for this instance.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
:param str duration: -
Output only. Duration of the maintenance window.
The current window is fixed at 1 hour.
A duration in seconds with up to nine fractional digits,
terminated by 's'. Example: "3.5s".
"""
pulumi.set(__self__, "day", day)
pulumi.set(__self__, "start_time", start_time)
if duration is not None:
pulumi.set(__self__, "duration", duration)
@property
@pulumi.getter
def day(self) -> str:
"""
Required. The day of week that maintenance updates occur.
- DAY_OF_WEEK_UNSPECIFIED: The day of the week is unspecified.
- MONDAY: Monday
- TUESDAY: Tuesday
- WEDNESDAY: Wednesday
- THURSDAY: Thursday
- FRIDAY: Friday
- SATURDAY: Saturday
- SUNDAY: Sunday
Possible values are `DAY_OF_WEEK_UNSPECIFIED`, `MONDAY`, `TUESDAY`, `WEDNESDAY`, `THURSDAY`, `FRIDAY`, `SATURDAY`, and `SUNDAY`.
"""
return pulumi.get(self, "day")
@property
@pulumi.getter(name="startTime")
def start_time(self) -> 'outputs.InstanceMaintenancePolicyWeeklyMaintenanceWindowStartTime':
"""
-
Output only. The start time of any upcoming scheduled maintenance for this instance.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
"""
return pulumi.get(self, "start_time")
@property
@pulumi.getter
def duration(self) -> Optional[str]:
"""
-
Output only. Duration of the maintenance window.
The current window is fixed at 1 hour.
A duration in seconds with up to nine fractional digits,
terminated by 's'. Example: "3.5s".
"""
return pulumi.get(self, "duration")
@pulumi.output_type
class InstanceMaintenancePolicyWeeklyMaintenanceWindowStartTime(dict):
def __init__(__self__, *,
hours: Optional[int] = None,
minutes: Optional[int] = None,
nanos: Optional[int] = None,
seconds: Optional[int] = None):
"""
:param int hours: Hours of day in 24 hour format. Should be from 0 to 23.
An API may choose to allow the value "24:00:00" for scenarios like business closing time.
:param int minutes: Minutes of hour of day. Must be from 0 to 59.
:param int nanos: Fractions of seconds in nanoseconds. Must be from 0 to 999,999,999.
:param int seconds: Seconds of minutes of the time. Must normally be from 0 to 59.
An API may allow the value 60 if it allows leap-seconds.
"""
if hours is not None:
pulumi.set(__self__, "hours", hours)
if minutes is not None:
pulumi.set(__self__, "minutes", minutes)
if nanos is not None:
pulumi.set(__self__, "nanos", nanos)
if seconds is not None:
pulumi.set(__self__, "seconds", seconds)
@property
@pulumi.getter
def hours(self) -> Optional[int]:
"""
Hours of day in 24 hour format. Should be from 0 to 23.
An API may choose to allow the value "24:00:00" for scenarios like business closing time.
"""
return pulumi.get(self, "hours")
@property
@pulumi.getter
def minutes(self) -> Optional[int]:
"""
Minutes of hour of day. Must be from 0 to 59.
"""
return pulumi.get(self, "minutes")
@property
@pulumi.getter
def nanos(self) -> Optional[int]:
"""
Fractions of seconds in nanoseconds. Must be from 0 to 999,999,999.
"""
return pulumi.get(self, "nanos")
@property
@pulumi.getter
def seconds(self) -> Optional[int]:
"""
Seconds of minutes of the time. Must normally be from 0 to 59.
An API may allow the value 60 if it allows leap-seconds.
"""
return pulumi.get(self, "seconds")
@pulumi.output_type
class InstanceMaintenanceSchedule(dict):
@staticmethod
def __key_warning(key: str):
suggest = None
if key == "endTime":
suggest = "end_time"
elif key == "scheduleDeadlineTime":
suggest = "schedule_deadline_time"
elif key == "startTime":
suggest = "start_time"
if suggest:
pulumi.log.warn(f"Key '{key}' not found in InstanceMaintenanceSchedule. Access the value via the '{suggest}' property getter instead.")
def __getitem__(self, key: str) -> Any:
InstanceMaintenanceSchedule.__key_warning(key)
return super().__getitem__(key)
def get(self, key: str, default = None) -> Any:
InstanceMaintenanceSchedule.__key_warning(key)
return super().get(key, default)
def __init__(__self__, *,
end_time: Optional[str] = None,
schedule_deadline_time: Optional[str] = None,
start_time: Optional[str] = None):
"""
:param str end_time: -
Output only. The end time of any upcoming scheduled maintenance for this instance.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
:param str schedule_deadline_time: -
Output only. The deadline that the maintenance schedule start time
can not go beyond, including reschedule.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
:param str start_time: -
Output only. The start time of any upcoming scheduled maintenance for this instance.
A timestamp in RFC3339 UTC "Zulu" format, with nanosecond
resolution and up to nine fractional digits.
"""
if end_time is not None:
pulumi.set(__self__, "end_time", end_time)
if schedule_deadline_time is not None:
pulumi.set(__self__, "schedule_deadline_time", schedule_deadline_time)
if start_time is not None:
pulumi.set(__self__, "start_time", start_time)
@property
@pulumi.getter(name="endTime")
def end_time(self) -> Optional[str]:
"""
-
Output only. The end time of any upcoming | |
true if t is an anagram of s, and false otherwise.
An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all
the original letters exactly once.
Example 1:
Input: s = "anagram", t = "nagaram"
Output: true
Example 2:
Input: s = "rat", t = "car"
Output: false
Constraints:
1 <= s.length, t.length <= 5 * 104
s and t consist of lowercase English letters.
"""
def isAnagram(s: str, t: str) -> bool:
pass
"""
Given an array of strings strs, group the anagrams together. You can return the answer in any order.
An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all
the original letters exactly once.
Example 1:
Input: strs = ["eat","tea","tan","ate","nat","bat"]
Output: [["bat"],["nat","tan"],["ate","eat","tea"]]
Example 2:
Input: strs = [""]
Output: [[""]]
Example 3:
Input: strs = ["a"]
Output: [["a"]]
"""
def groupAnagrams(strs: List[str]) -> List[List[str]]:
pass
"""A phrase is a palindrome if, after converting all uppercase letters into lowercase letters and removing all
non-alphanumeric characters, it reads the same forward and backward. Alphanumeric characters include letters and
numbers.
Given a string s, return true if it is a palindrome, or false otherwise.
Example 1:
Input: s = "A man, a plan, a canal: Panama"
Output: true
Explanation: "amanaplanacanalpanama" is a palindrome.
Example 2:
Input: s = "race a car"
Output: false
Explanation: "raceacar" is not a palindrome.
Example 3:
Input: s = " "
Output: true
Explanation: s is an empty string "" after removing non-alphanumeric characters.
Since an empty string reads the same forward and backward, it is a palindrome.
"""
def isPalindrome(s: str) -> bool:
pass
"""
Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.
An input string is valid if:
Open brackets must be closed by the same type of brackets.
Open brackets must be closed in the correct order.
Example 1:
Input: s = "()"
Output: true
Example 2:
Input: s = "()[]{}"
Output: true
Example 3:
Input: s = "(]"
Output: false
"""
def isValid(s: str) -> bool:
pass
"""
Given a string s, return the longest palindromic substring in s.
Example 1:
Input: s = "babad"
Output: "bab"
Explanation: "aba" is also a valid answer.
Example 2:
Input: s = "cbbd"
Output: "bb"
"""
def longestPalindrome(s: str) -> str:
pass
"""
Given a string s, return the number of palindromic substrings in it.
A string is a palindrome when it reads the same backward as forward.
A substring is a contiguous sequence of characters within the string.
Example 1:
Input: s = "abc"
Output: 3
Explanation: Three palindromic strings: "a", "b", "c".
Example 2:
Input: s = "aaa"
Output: 6
Explanation: Six palindromic strings: "a", "a", "a", "aa", "aa", "aaa".
"""
def countSubstrings(s: str) -> int:
pass
"""Design an algorithm to encode a list of strings to a string. The encoded string is then sent over the network and
is decoded back to the original list of strings.
Machine 1 (sender) has the function:
string encode(vector<string> strs) {
// ... your code
return encoded_string;
}
Machine 2 (receiver) has the function:
vector<string> decode(string s) {
//... your code
return strs;
}
So Machine 1 does:
string encoded_string = encode(strs);
and Machine 2 does:
vector<string> strs2 = decode(encoded_string);
strs2 in Machine 2 should be the same as strs in Machine 1.
Implement the encode and decode methods.
You are not allowed to solve the problem using any serialize methods (such as eval).
Example 1:
Input: dummy_input = ["Hello","World"]
Output: ["Hello","World"]
Explanation:
Machine 1:
Codec encoder = new Codec();
String msg = encoder.encode(strs);
Machine 1 ---msg---> Machine 2
Machine 2:
Codec decoder = new Codec();
String[] strs = decoder.decode(msg);
Example 2:
Input: dummy_input = [""]
Output: [""]
"""
class Codec:
def encode(strs: [str]) -> str:
"""Encodes a list of strings to a single string.
"""
def decode(s: str) -> [str]:
"""Decodes a single string to a list of strings.
"""
"""
Given an m x n integer matrix matrix, if an element is 0, set its entire row and column to 0's, and return the matrix.
You must do it in place.
Example 1:
Input: matrix = [[1,1,1],[1,0,1],[1,1,1]]
Output: [[1,0,1],[0,0,0],[1,0,1]]
Example 2:
Input: matrix = [[0,1,2,0],[3,4,5,2],[1,3,1,5]]
Output: [[0,0,0,0],[0,4,5,0],[0,3,1,0]]
"""
def setZeroes(self, matrix: List[List[int]]) -> None:
"""
Do not return anything, modify matrix in-place instead.
"""
"""
Given an m x n matrix, return all elements of the matrix in spiral order.
Example 1:
Input: matrix = [[1,2,3],[4,5,6],[7,8,9]]
Output: [1,2,3,6,9,8,7,4,5]
Example 2:
Input: matrix = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]
Output: [1,2,3,4,8,12,11,10,9,5,6,7]
"""
def spiralOrder(matrix: List[List[int]]) -> List[int]:
pass
"""
Given an m x n grid of characters board and a string word, return true if word exists in the grid.
The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once.
Example 1:
Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED"
Output: true
Example 2:
Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "SEE"
Output: true
Example 3:
Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCB"
Output: false
"""
def exist(board: List[List[str]], word: str) -> bool:
pass
"""
You are climbing a staircase. It takes n steps to reach the top.
Each time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top?
Example 1:
Input: n = 2
Output: 2
Explanation: There are two ways to climb to the top.
1. 1 step + 1 step
2. 2 steps
Example 2:
Input: n = 3
Output: 3
Explanation: There are three ways to climb to the top.
1. 1 step + 1 step + 1 step
2. 1 step + 2 steps
3. 2 steps + 1 step
"""
def climbStairs(self, n: int) -> int:
pass
"""You are given an integer array coins representing coins of different denominations and an integer amount
representing a total amount of money.
Return the fewest number of coins that you need to make up that amount. If that amount of money cannot be made up by
any combination of the coins, return -1.
You may assume that you have an infinite number of each kind of coin.
Example 1:
Input: coins = [1,2,5], amount = 11
Output: 3
Explanation: 11 = 5 + 5 + 1
Example 2:
Input: coins = [2], amount = 3
Output: -1
Example 3:
Input: coins = [1], amount = 0
Output: 0
"""
def coin_change(coins: List[int], amount: int) -> int:
pass
"""
Given an integer array nums, return the length of the longest strictly increasing subsequence.
A subsequence is a sequence that can be derived from an array by deleting some or no elements without changing the order of the remaining elements. For example, [3,6,2,7] is a subsequence of the array [0,3,1,6,2,2,7].
Example 1:
Input: nums = [10,9,2,5,3,7,101,18]
Output: 4
Explanation: The longest increasing subsequence is [2,3,7,101], therefore the length is 4.
Example 2:
Input: nums = [0,1,0,3,2,3]
Output: 4
Example 3:
Input: nums = [7,7,7,7,7,7,7]
Output: 1
"""
def lengthOfLIS(self, nums: List[int]) -> int:
pass
"""
Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0.
A subsequence of a string is a new string generated from the original string with some characters (can be none) deleted without changing the relative order of the remaining characters.
For example, "ace" is a subsequence of "abcde".
A common subsequence of two strings is a subsequence that is common to both strings.
Example 1:
Input: text1 = "abcde", text2 = "ace"
Output: 3
Explanation: The longest common subsequence is "ace" and its length is 3.
Example 2:
Input: text1 = "abc", text2 = "abc"
Output: 3
Explanation: The longest common subsequence is "abc" and its length is 3.
Example 3:
Input: text1 = "abc", text2 = "def"
Output: 0
Explanation: There is no such common subsequence, so the result is 0.
"""
def longestCommonSubsequence(self, text1: str, text2: str) -> int:
pass
"""
Given a string s and a dictionary of strings wordDict, return true if s can be segmented into a space-separated sequence of one or more dictionary words.
Note that the same word in the dictionary may be reused multiple times in the segmentation.
Example 1:
Input: s = "leetcode", wordDict = ["leet","code"]
Output: true
Explanation: Return true because "leetcode" can be segmented as "leet code".
Example 2:
Input: s = "applepenapple", wordDict = ["apple","pen"]
Output: true
Explanation: Return true because "applepenapple" can be segmented as "apple pen apple".
Note that you are allowed to reuse a dictionary word.
Example 3:
Input: s = "catsandog", wordDict = ["cats","dog","sand","and","cat"]
Output: false
"""
def wordBreak(self, s: str, wordDict: List[str]) -> bool:
pass
"""
Given an array of distinct integers candidates and a target integer target, return a list of all unique
combinations of candidates where the | |
{ref['href'].split('/')[-1]: ref['attr'].ae_num
for ref in pi_refs}
# verify all AE-IDs allocated per prouter are unique
self.assertEqual(len(set(ae_ids[vpg_name].keys())), len(pi_refs))
self.assertEqual(len(set(ae_ids[vpg_name].values())), 1)
# verification at Physical Routers
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 2)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# Case #1
# Remove PI-1 from PR-1 through VMI-1 of VPG-1 update
vmi_id = 1
vmi_name = 'vmi_vpg1_%s_%s' % (test_id, vmi_id)
vpg_name = 'vpg_%s_%s' % (test_id, vmi_id)
vmi_infos = [
{'name': vmi_name,
'vmi_uuid': vmi_objs[vmi_name].uuid,
'vpg': vpg_objs[vpg_name].uuid,
'fabric': fabric_name,
'pis': vpg1_pis[1:],
'vlan': vlan_ids[vmi_id - 1],
'is_untagged': False}]
self._update_vmis(vmi_infos)
# re-read VPGs
for vpg, vpg_o in vpg_objs.items():
vpg_objs[vpg] = self.api.virtual_port_group_read(
id=vpg_o.uuid)
# Verifications at VPG-1
# check PI-1 is removed from VPG-1
pi_refs = vpg_objs[vpg_name].get_physical_interface_refs()
vpg1_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 3)
# verify AE-ID associated with VPG-1
# AE-IDs of remaining PIs are unaffected
self.assertEqual(len(set(vpg1_ae_ids)), 1)
# Verifications at VPG-2
pi_refs = vpg_objs[vpg_names[1]].get_physical_interface_refs()
vpg2_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 4)
# verify AE-ID associated with VPG-2
# AE-IDs of remaining PIs are unaffected
self.assertEqual(len(set(vpg2_ae_ids)), 1)
# verification at Physical Routers
# since only PI-1 was removed, AE-ID allocation remains same
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 2)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# Case #2
# Remove all PIs but PI-1 in PR-1/VPG-1 through VMI-1 of VPG-1 update
vmi_id = 1
vmi_name = 'vmi_vpg1_%s_%s' % (test_id, vmi_id)
vpg_name = 'vpg_%s_%s' % (test_id, vmi_id)
vmi_infos = [
{'name': vmi_name,
'vmi_uuid': vmi_objs[vmi_name].uuid,
'vpg': vpg_objs[vpg_name].uuid,
'fabric': fabric_name,
'pis': vpg1_pis[0],
'vlan': vlan_ids[vmi_id - 1],
'is_untagged': False}]
self._update_vmis(vmi_infos)
# re-read VPGs
for vpg, vpg_o in vpg_objs.items():
vpg_objs[vpg] = self.api.virtual_port_group_read(
id=vpg_o.uuid)
# Verifications at VPG-1
# check PI-1 is removed from VPG-1
pi_refs = vpg_objs[vpg_names[0]].get_physical_interface_refs()
vpg1_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 1)
# verify AE-ID associated with VPG-1
# AE-IDs of remaining PIs are unaffected
self.assertEqual(len(set(vpg1_ae_ids)), 1)
self.assertIsNone(vpg1_ae_ids[0])
# Verifications at VPG-2
pi_refs = vpg_objs[vpg_names[1]].get_physical_interface_refs()
vpg2_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 4)
# verify AE-ID associated with VPG-2
# AE-IDs of remaining PIs are unaffected
self.assertEqual(len(set(vpg2_ae_ids)), 1)
# verify at ZK Physical Routers
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 1)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 1)
# Case 3
# Create a new VPG with two PIs, one from each PRs
case3_id = 99
vpg3_uuid = vpg_objs[vpg_names[2]].uuid
case3_vn_name = 'vn_case3_%s_%s' % (test_id, case3_id)
case3_vn_objs = self._create_vns(proj_obj, [case3_vn_name])
vn_objs.update(case3_vn_objs)
pi_per_pr = 1
case3_pr1_pi_name = '%s_case3_pr1_pi%d' % (test_id, case3_id)
case3_pr2_pi_name = '%s_case3_pr2_pi%d' % (test_id, case3_id)
case3_pr1_pi_objs = self._create_pi_objects(
pr_objs[0], [case3_pr1_pi_name])
case3_pr2_pi_objs = self._create_pi_objects(
pr_objs[1], [case3_pr2_pi_name])
vpg3_pis = [pi.get_fq_name() for pi in
[case3_pr1_pi_objs[case3_pr1_pi_name],
case3_pr2_pi_objs[case3_pr2_pi_name]]]
pi_objs.update(case3_pr1_pi_objs)
pi_objs.update(case3_pr2_pi_objs)
vmi_info = {
'name': 'vmi_vpg3_%s_%s' % (test_id, 99),
'vmi_id': 99,
'parent_obj': proj_obj,
'vn': case3_vn_objs[case3_vn_name],
'vpg': vpg3_uuid,
'fabric': fabric_name,
'pis': vpg3_pis,
'vlan': case3_id,
'is_untagged': False}
case3_vmi_obj = self._create_vmis([vmi_info])
vmi_objs.update(case3_vmi_obj)
# re-read VPG-3
vpg_objs[vpg_names[2]] = self.api.virtual_port_group_read(id=vpg3_uuid)
# Verifications at VPG-3
pi_refs = vpg_objs[vpg_names[2]].get_physical_interface_refs()
vpg3_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 2)
# verify an AE-ID is allocated
self.assertEqual(len(set(vpg3_ae_ids)), 1)
# verify at ZK Physical Routers
# Since a new VPG is added with PIs at Case-3
# only two AE-IDs should remain in each prouter
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 2)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# TO-DO
# Verify AE-ID is re-allocated instead of new one
# Case 4
# Add PI1/PR1, PI2/PR1 to VPG-1, so a new AE-ID is allocated
vmi_id = 9
vmi_name = 'vmi_vpg1_%s_%s' % (test_id, vmi_id)
vpg_name = 'vpg_%s_%s' % (test_id, 1)
vmi_infos = [
{'name': vmi_name,
'vmi_uuid': vmi_objs[vmi_name].uuid,
'vpg': vpg_objs[vpg_name].uuid,
'fabric': fabric_name,
'pis': vpg1_pis[0:2],
'vlan': vlan_ids[vmi_id - 1],
'is_untagged': False}]
self._update_vmis(vmi_infos)
# re-read VPGs
for vpg_name, vpg_obj in vpg_objs.items():
vpg_objs[vpg_name] = self.api.virtual_port_group_read(
id=vpg_obj.uuid)
# Verifications at VPG-1
# check PI1/PR1 and PI2/PR1 are added to VPG-1
pi_refs = vpg_objs[vpg_names[0]].get_physical_interface_refs()
vpg1_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 2)
# verify AE-ID associated with VPG-1
# A new AE-ID is allocated
self.assertEqual(len(set(vpg1_ae_ids)), 1)
# Verifications at VPG-2
pi_refs = vpg_objs[vpg_names[1]].get_physical_interface_refs()
vpg2_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 4)
# verify AE-ID associated with VPG-2
# AE-IDs of remaining PIs are unaffected
self.assertEqual(len(set(vpg2_ae_ids)), 1)
# verify at ZK Physical Routers
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 3)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# TO-DO
# Verify AE-ID is re-allocated instead of new one
# Case X1
# Create a new VPG with two PIs, both belonging to the same PR
caseX1_id = 101
vpgX1_uuid = vpg_objs[vpg_names[3]].uuid
caseX1_vn_names = ['vn_caseX1_%s_%s' % (test_id, caseX1_id)]
caseX1_vn_objs = self._create_vns(proj_obj, caseX1_vn_names)
vn_objs.update(caseX1_vn_objs)
pi_per_pr = 2
caseX1_pr1_pi_names = ['%s_caseX1_pr1_pi%d' % (test_id, caseX1_id),
'%s_caseX1_pr1_pi%d' % (test_id, caseX1_id + 1)]
caseX1_pr1_pi_objs = self._create_pi_objects(
pr_objs[0], caseX1_pr1_pi_names)
vpgX1_pis = [pi.get_fq_name() for pi in
[caseX1_pr1_pi_objs[caseX1_pr1_pi_names[0]],
caseX1_pr1_pi_objs[caseX1_pr1_pi_names[1]]]]
pi_objs.update(caseX1_pr1_pi_objs)
vmi_info = {
'name': 'vmi_vpg4_%s_%s' % (test_id, caseX1_id),
'vmi_id': caseX1_id,
'parent_obj': proj_obj,
'vn': caseX1_vn_objs[caseX1_vn_names[0]],
'vpg': vpgX1_uuid,
'fabric': fabric_name,
'pis': vpgX1_pis,
'vlan': caseX1_id,
'is_untagged': False}
caseX1_vmi_obj = self._create_vmis([vmi_info])
vmi_objs.update(caseX1_vmi_obj)
# re-read VPG-3
vpg_objs[vpg_names[3]] = self.api.virtual_port_group_read(
id=vpgX1_uuid)
# Verifications at VPG-3
pi_refs = vpg_objs[vpg_names[3]].get_physical_interface_refs()
vpgX1_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 2)
# verify an AE-ID is allocated
self.assertEqual(len(set(vpgX1_ae_ids)), 1)
# verify at ZK Physical Routers
# Since a new VPG is added with PIs at Case-3
# only two AE-IDs should remain in each prouter
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 4)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# TO-DO
# Verify AE-ID is re-allocated instead of new one
# Case X2
# Create a new VPG with two PIs,both belonging to the same PR
caseX2_id = 103
vpgX2_uuid = vpg_objs[vpg_names[4]].uuid
caseX2_vn_names = ['vn_caseX2_%s_%s' % (test_id, caseX2_id)]
caseX2_vn_objs = self._create_vns(proj_obj, caseX2_vn_names)
vn_objs.update(caseX2_vn_objs)
pi_per_pr = 2
caseX2_pr1_pi_names = ['%s_caseX2_pr1_pi%d' % (test_id, caseX2_id),
'%s_caseX2_pr1_pi%d' % (test_id, caseX2_id + 1)]
caseX2_pr1_pi_objs = self._create_pi_objects(
pr_objs[0], caseX2_pr1_pi_names)
vpgX2_pis = [pi.get_fq_name() for pi in
[caseX2_pr1_pi_objs[caseX2_pr1_pi_names[0]],
caseX2_pr1_pi_objs[caseX2_pr1_pi_names[1]]]]
pi_objs.update(caseX2_pr1_pi_objs)
vmi_info = {
'name': 'vmi_vpg5_%s_%s' % (test_id, caseX2_id),
'vmi_id': caseX2_id,
'parent_obj': proj_obj,
'vn': caseX2_vn_objs[caseX2_vn_names[0]],
'vpg': vpgX2_uuid,
'fabric': fabric_name,
'pis': vpgX2_pis,
'vlan': caseX2_id,
'is_untagged': False}
caseX2_vmi_obj = self._create_vmis([vmi_info])
vmi_objs.update(caseX2_vmi_obj)
# re-read VPG-3
vpg_objs[vpg_names[4]] = self.api.virtual_port_group_read(
id=vpgX2_uuid)
# Verifications at VPG-3
pi_refs = vpg_objs[vpg_names[4]].get_physical_interface_refs()
self.assertEqual(len(pi_refs), 2)
# verify an AE-ID is allocated
self.assertEqual(len(set(vpgX1_ae_ids)), 1)
# verify at ZK Physical Routers
# Since a new VPG is added with PIs at Case-3
# only two AE-IDs should remain in each prouter
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 5)
self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# TO-DO
# Verify AE-ID is re-allocated instead of new one
# Case X3
# Add PI2/PR1 to VPG-1, so a new AE-ID is allocated
vmi_id = 10
vmi_name = 'vmi_vpg1_%s_%s' % (test_id, vmi_id)
vpg_name = 'vpg_%s_%s' % (test_id, 1)
vpg1_uuid = vpg_objs[vpg_names[0]].uuid
vmi_infos = [
{'name': vmi_name,
'vmi_uuid': vmi_objs[vmi_name].uuid,
'vpg': vpg_objs[vpg_name].uuid,
'fabric': fabric_name,
'pis': vpg1_pis[1],
'vlan': vlan_ids[vmi_id - 1],
'is_untagged': False}]
self._update_vmis(vmi_infos)
# re-read VPG1
vpg_objs[vpg_names[0]] = self.api.virtual_port_group_read(id=vpg1_uuid)
# Verifications at VPG-1
# check PI1/PR1 and PI2/PR1 are added to VPG-1
pi_refs = vpg_objs[vpg_names[0]].get_physical_interface_refs()
vpg1_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 1)
# verify AE-ID associated with VPG-1
# A new AE-ID is allocated
self.assertEqual(len(set(vpg1_ae_ids)), 1)
# Verifications at VPG-2
# pi_refs = vpg_objs[vpg_names[1]].get_physical_interface_refs()
# vpg2_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
# self.assertEqual(len(pi_refs), 4)
# verify AE-ID associated with VPG-2
# AE-IDs of remaining PIs are unaffected
# self.assertEqual(len(set(vpg2_ae_ids)), 1)
# verify at ZK Physical Routers
pr_ae_ids = get_zk_ae_ids()
self.assertEqual(len(pr_ae_ids[pr_objs[0].name]), 4)
# self.assertEqual(len(pr_ae_ids[pr_objs[1].name]), 2)
# TO-DO
# Verify AE-ID is re-allocated instead of new one
# Case X4
# Create a new VPG with two PIs, both belonging to the same PR
caseX4_id = 66
vpgX4_uuid = vpg_objs[vpg_names[5]].uuid
caseX4_vn_names = ['vn_caseX4_%s_%s' % (test_id, caseX4_id)]
caseX4_vn_objs = self._create_vns(proj_obj, caseX4_vn_names)
vn_objs.update(caseX4_vn_objs)
pi_per_pr = 2
caseX4_pr1_pi_names = ['%s_caseX4_pr1_pi%d' % (test_id, caseX4_id),
'%s_caseX4_pr1_pi%d' % (test_id, caseX4_id + 1)]
caseX4_pr1_pi_objs = self._create_pi_objects(
pr_objs[0], caseX4_pr1_pi_names)
vpgX4_pis = [pi.get_fq_name() for pi in
[caseX4_pr1_pi_objs[caseX4_pr1_pi_names[0]],
caseX4_pr1_pi_objs[caseX4_pr1_pi_names[1]]]]
pi_objs.update(caseX4_pr1_pi_objs)
vmi_info = {
'name': 'vmi_vpg6_%s_%s' % (test_id, caseX4_id),
'vmi_id': caseX4_id,
'parent_obj': proj_obj,
'vn': caseX4_vn_objs[caseX4_vn_names[0]],
'vpg': vpgX4_uuid,
'fabric': fabric_name,
'pis': vpgX4_pis,
'vlan': caseX4_id,
'is_untagged': False}
caseX4_vmi_obj = self._create_vmis([vmi_info])
vmi_objs.update(caseX4_vmi_obj)
# re-read VPG-5
vpg_objs[vpg_names[5]] = self.api.virtual_port_group_read(
id=vpgX4_uuid)
# Verifications at VPG-5
pi_refs = vpg_objs[vpg_names[5]].get_physical_interface_refs()
vpgX4_ae_ids = [pi_ref['attr'].ae_num for pi_ref in pi_refs]
self.assertEqual(len(pi_refs), 2)
# verify an AE-ID is allocated
self.assertEqual(len(set(vpgX4_ae_ids)), 1)
# verify at ZK Physical Routers
# Since a new VPG is added with PIs at Case-3
# only two AE-IDs should remain in | |
memory banks downgraded
ADL_XFIREX_STATE_DOWNGRADEMEMBANKS = Constant(1 << 22).set_string('CrossfireX cannot be enabled unless memory banks downgraded')
# Notification that memory banks are currently downgraded
ADL_XFIREX_STATE_MEMBANKSDOWNGRADED = Constant(1 << 23).set_string('Notification that memory banks are currently downgraded')
# Extended desktop or clone mode is allowed.
ADL_XFIREX_STATE_DUALDISPLAYSALLOWED = Constant(1 << 24).set_string(' Extended desktop or clone mode is allowed.')
# P2P mapping was through peer aperture
ADL_XFIREX_STATE_P2P_APERTURE_MAPPING = Constant(1 << 25).set_string('P2P mapping was through peer aperture')
# For back compatible
ADL_XFIREX_STATE_P2PFLUSH_REQUIRED = ADL_XFIREX_STATE_P2P_APERTURE_MAPPING
# There is CrossfireX side port connection between GPUs
ADL_XFIREX_STATE_XSP_CONNECTED = Constant(1 << 26).set_string('There is CrossfireX side port connection between GPUs')
# System needs a reboot bofore enable CrossfireX
ADL_XFIREX_STATE_ENABLE_CF_REBOOT_REQUIRED = Constant(1 << 27).set_string('System needs a reboot bofore enable CrossfireX')
# System needs a reboot after disable CrossfireX
ADL_XFIREX_STATE_DISABLE_CF_REBOOT_REQUIRED = Constant(1 << 28).set_string('System needs a reboot after disable CrossfireX')
# Indicate base driver handles the downgrade key updating
ADL_XFIREX_STATE_DRV_HANDLE_DOWNGRADE_KEY = Constant(1 << 29).set_string('Indicate base driver handles the downgrade key updating')
# CrossfireX need to be reconfigured by CCC because of a LDA chain broken
ADL_XFIREX_STATE_CF_RECONFIG_REQUIRED = Constant(1 << 30).set_string(' CrossfireX need to be reconfigured by CCC because of a LDA chain broken')
# Could not obtain current status
ADL_XFIREX_STATE_ERRORGETTINGSTATUS = Constant(1 << 31).set_string('Could not obtain current status')
# @}
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_ADJUSTMENT_PIXELFORMAT adjustment values
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
# / \defgroup define_pixel_formats Pixel Formats values
# / This group defines the various Pixel Formats that a particular digital
# display can support. \n
# / Since a display can support multiple formats, these values can be
# bit-or'ed to indicate the various formats \n
# @{
ADL_DISPLAY_PIXELFORMAT_UNKNOWN = Constant(0).set_string('Unknown')
ADL_DISPLAY_PIXELFORMAT_RGB = Constant(1 << 0).set_string('RGB Full Range')
# Limited range
ADL_DISPLAY_PIXELFORMAT_YCRCB444 = Constant(1 << 0).set_string('YCRCB444')
# Limited range
ADL_DISPLAY_PIXELFORMAT_YCRCB422 = Constant(1 << 0).set_string('YCRCB422')
ADL_DISPLAY_PIXELFORMAT_RGB_LIMITED_RANGE = Constant(1 << 0).set_string('RGB Limited Range')
# Full range
ADL_DISPLAY_PIXELFORMAT_RGB_FULL_RANGE = ADL_DISPLAY_PIXELFORMAT_RGB
ADL_DISPLAY_PIXELFORMAT_YCRCB420 = Constant(1 << 0).set_string('YCRCB420')
# @}
# / \defgroup define_contype Connector Type Values
# / ADLDisplayConfig.ulConnectorType defines
# @{
ADL_DL_DISPLAYCONFIG_CONTYPE_UNKNOWN = 0
ADL_DL_DISPLAYCONFIG_CONTYPE_CV_NONI2C_JP = 1
ADL_DL_DISPLAYCONFIG_CONTYPE_CV_JPN = 2
ADL_DL_DISPLAYCONFIG_CONTYPE_CV_NA = 3
ADL_DL_DISPLAYCONFIG_CONTYPE_CV_NONI2C_NA = 4
ADL_DL_DISPLAYCONFIG_CONTYPE_VGA = 5
ADL_DL_DISPLAYCONFIG_CONTYPE_DVI_D = 6
ADL_DL_DISPLAYCONFIG_CONTYPE_DVI_I = 7
ADL_DL_DISPLAYCONFIG_CONTYPE_HDMI_TYPE_A = 8
ADL_DL_DISPLAYCONFIG_CONTYPE_HDMI_TYPE_B = 9
ADL_DL_DISPLAYCONFIG_CONTYPE_DISPLAYPORT = 10
# @}
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_DISPLAYINFO_ Definitions
# for ADLDisplayInfo.iDisplayInfoMask and ADLDisplayInfo.iDisplayInfoValue
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
# / \defgroup define_displayinfomask Display Info Mask Values
# @{
ADL_DISPLAY_DISPLAYINFO_DISPLAYCONNECTED = Constant(0x00000001).set_string('DISPLAYCONNECTED')
ADL_DISPLAY_DISPLAYINFO_DISPLAYMAPPED = Constant(0x00000002).set_string('DISPLAYMAPPED')
ADL_DISPLAY_DISPLAYINFO_NONLOCAL = Constant(0x00000004).set_string('NONLOCAL')
ADL_DISPLAY_DISPLAYINFO_FORCIBLESUPPORTED = Constant(0x00000008).set_string('FORCIBLESUPPORTED')
ADL_DISPLAY_DISPLAYINFO_GENLOCKSUPPORTED = Constant(0x00000010).set_string('GENLOCKSUPPORTED')
ADL_DISPLAY_DISPLAYINFO_MULTIVPU_SUPPORTED = Constant(0x00000020).set_string('MULTIVPU_SUPPORTED')
ADL_DISPLAY_DISPLAYINFO_LDA_DISPLAY = Constant(0x00000040).set_string('LDA_DISPLAY')
ADL_DISPLAY_DISPLAYINFO_MODETIMING_OVERRIDESSUPPORTED = Constant(0x00000080).set_string('MODETIMING_OVERRIDESSUPPORTED')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_SINGLE = Constant(0x00000100).set_string('MANNER_SUPPORTED_SINGLE')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_CLONE = Constant(0x00000200).set_string('MANNER_SUPPORTED_CLONE')
# / Legacy support for XP
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_2VSTRETCH = Constant(0x00000400).set_string('MANNER_SUPPORTED_2VSTRETCH')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_2HSTRETCH = Constant(0x00000800).set_string('MANNER_SUPPORTED_2HSTRETCH')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_EXTENDED = Constant(0x00001000).set_string('MANNER_SUPPORTED_EXTENDED')
# / More support manners
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_NSTRETCH1GPU = Constant(0x00010000).set_string('MANNER_SUPPORTED_NSTRETCH1GPU')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_NSTRETCHNGPU = Constant(0x00020000).set_string('MANNER_SUPPORTED_NSTRETCHNGPU')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_RESERVED2 = Constant(0x00040000).set_string('MANNER_SUPPORTED_RESERVED2')
ADL_DISPLAY_DISPLAYINFO_MANNER_SUPPORTED_RESERVED3 = Constant(0x00080000).set_string('MANNER_SUPPORTED_RESERVED3')
# / Projector display type
ADL_DISPLAY_DISPLAYINFO_SHOWTYPE_PROJECTOR = Constant(0x00100000).set_string('SHOWTYPE_PROJECTOR')
# @}
# /////////////////////////////////////////////////////////////////////////
# ADL_ADAPTER_DISPLAY_MANNER_SUPPORTED_ Definitions
# for ADLAdapterDisplayCap of ADL_Adapter_Display_Cap()
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
# / \defgroup define_adaptermanner Adapter Manner Support Values
# @{
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_NOTACTIVE = 0x00000001
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_SINGLE = 0x00000002
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_CLONE = 0x00000004
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_NSTRETCH1GPU = 0x00000008
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_NSTRETCHNGPU = 0x00000010
# / Legacy support for XP
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_2VSTRETCH = 0x00000020
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_2HSTRETCH = 0x00000040
ADL_ADAPTER_DISPLAYCAP_MANNER_SUPPORTED_EXTENDED = 0x00000080
ADL_ADAPTER_DISPLAYCAP_PREFERDISPLAY_SUPPORTED = 0x00000100
ADL_ADAPTER_DISPLAYCAP_BEZEL_SUPPORTED = 0x00000200
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_DISPLAYMAP_MANNER_ Definitions
# for ADLDisplayMap.iDisplayMapMask and ADLDisplayMap.iDisplayMapValue
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
ADL_DISPLAY_DISPLAYMAP_MANNER_RESERVED = 0x00000001
ADL_DISPLAY_DISPLAYMAP_MANNER_NOTACTIVE = 0x00000002
ADL_DISPLAY_DISPLAYMAP_MANNER_SINGLE = 0x00000004
ADL_DISPLAY_DISPLAYMAP_MANNER_CLONE = 0x00000008
# Removed NSTRETCH
ADL_DISPLAY_DISPLAYMAP_MANNER_RESERVED1 = 0x00000010
ADL_DISPLAY_DISPLAYMAP_MANNER_HSTRETCH = 0x00000020
ADL_DISPLAY_DISPLAYMAP_MANNER_VSTRETCH = 0x00000040
ADL_DISPLAY_DISPLAYMAP_MANNER_VLD = 0x00000080
# @}
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_DISPLAYMAP_OPTION_ Definitions
# for iOption in function ADL_Display_DisplayMapConfig_Get
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
ADL_DISPLAY_DISPLAYMAP_OPTION_GPUINFO = 0x00000001
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_DISPLAYTARGET_ Definitions
# for ADLDisplayTarget.iDisplayTargetMask and
# ADLDisplayTarget.iDisplayTargetValue
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
ADL_DISPLAY_DISPLAYTARGET_PREFERRED = 0x00000001
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_POSSIBLEMAPRESULT_VALID Definitions
# for ADLPossibleMapResult.iPossibleMapResultMask and
# ADLPossibleMapResult.iPossibleMapResultValue
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
ADL_DISPLAY_POSSIBLEMAPRESULT_VALID = 0x00000001
ADL_DISPLAY_POSSIBLEMAPRESULT_BEZELSUPPORTED = 0x00000002
ADL_DISPLAY_POSSIBLEMAPRESULT_OVERLAPSUPPORTED = 0x00000004
# /////////////////////////////////////////////////////////////////////////
# ADL_DISPLAY_MODE_ Definitions
# for ADLMode.iModeMask, ADLMode.iModeValue, and ADLMode.iModeFlag
# (bit-vector)
# /////////////////////////////////////////////////////////////////////////
# / \defgroup define_displaymode Display Mode Values
# @{
ADL_DISPLAY_MODE_COLOURFORMAT_565 = 0x00000001
ADL_DISPLAY_MODE_COLOURFORMAT_8888 = 0x00000002
ADL_DISPLAY_MODE_ORIENTATION_SUPPORTED_000 = 0x00000004
ADL_DISPLAY_MODE_ORIENTATION_SUPPORTED_090 = 0x00000008
ADL_DISPLAY_MODE_ORIENTATION_SUPPORTED_180 = 0x00000010
ADL_DISPLAY_MODE_ORIENTATION_SUPPORTED_270 = 0x00000020
ADL_DISPLAY_MODE_REFRESHRATE_ROUNDED = 0x00000040
ADL_DISPLAY_MODE_REFRESHRATE_ONLY = 0x00000080
ADL_DISPLAY_MODE_PROGRESSIVE_FLAG = 0
ADL_DISPLAY_MODE_INTERLACED_FLAG = 2
# @}
# /////////////////////////////////////////////////////////////////////////
# ADL_OSMODEINFO Definitions
# /////////////////////////////////////////////////////////////////////////
# / \defgroup define_osmode OS Mode Values
# @{
ADL_OSMODEINFOXPOS_DEFAULT = -640
ADL_OSMODEINFOYPOS_DEFAULT = 0
ADL_OSMODEINFOXRES_DEFAULT = 640
ADL_OSMODEINFOYRES_DEFAULT = 480
ADL_OSMODEINFOXRES_DEFAULT800 = 800
ADL_OSMODEINFOYRES_DEFAULT600 = 600
ADL_OSMODEINFOREFRESHRATE_DEFAULT = 60
ADL_OSMODEINFOCOLOURDEPTH_DEFAULT = 8
ADL_OSMODEINFOCOLOURDEPTH_DEFAULT16 = 16
ADL_OSMODEINFOCOLOURDEPTH_DEFAULT24 = 24
ADL_OSMODEINFOCOLOURDEPTH_DEFAULT32 = 32
ADL_OSMODEINFOORIENTATION_DEFAULT = 0
if defined(_WIN32) or defined(_WIN64):
class DUMMY_ENUM(ENUM):
DISPLAYCONFIG_ROTATION_IDENTITY = EnumItem(0).set_string('Identity')
DISPLAYCONFIG_ROTATION_ROTATE90 = EnumItem(1).set_string('90°')
DISPLAYCONFIG_ROTATION_ROTATE180 = EnumItem(2).set_string('180°')
DISPLAYCONFIG_ROTATION_ROTATE270 = EnumItem(3).set_string('730°')
DISPLAYCONFIG_ROTATION_FORCE_UINT32 = EnumItem(4).set_string('Force UINT32')
ADL_OSMODEINFOORIENTATION_DEFAULT_WIN7 = (
DUMMY_ENUM.DISPLAYCONFIG_ROTATION_FORCE_UINT32
)
ADL_OSMODEFLAG_DEFAULT = 0
# @}
# /////////////////////////////////////////////////////////////////////////
# ADLThreadingModel Enumeration
# /////////////////////////////////////////////////////////////////////////
# / \defgroup thread_model
# / Used with \ref ADL_Main_ControlX2_Create and \ref
# ADL2_Main_ControlX2_Create to specify how ADL handles API calls when
# executed by multiple threads concurrently.
# / \brief Declares ADL threading behavior.
# @{
class ADLThreadingModel(ENUM):
# not < Default behavior. ADL will not enforce serialization of ADL
# API executions by multiple threads. Multiple threads will be allowed
# to enter to ADL at the same time. Note that ADL library is not
# guaranteed to be thread-safe. Client that calls
# ADL_Main_Control_Create have to provide its own mechanism for ADL
# calls serialization.
ADL_THREADING_UNLOCKED = EnumItem(0).set_string('Unlocked')
# not < ADL will enforce serialization of ADL API when called by
# multiple threads. Only single thread will be allowed to enter ADL
# API at the time. This option makes ADL calls thread-safe. You
# shouldn't use this option if ADL calls will be executed on Linux on
# x-server rendering thread. It can cause the application to hung.
ADL_THREADING_LOCKED = EnumItem(0).set_string('Locked')
ADL_THREADING_UNLOCKED = ADLThreadingModel.ADL_THREADING_UNLOCKED
ADL_THREADING_LOCKED = ADLThreadingModel.ADL_THREADING_LOCKED
# @}
# /////////////////////////////////////////////////////////////////////////
# ADLPurposeCode Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLPurposeCode(ENUM):
ADL_PURPOSECODE_NORMAL = EnumItem(0).set_string('Normal')
ADL_PURPOSECODE_HIDE_MODE_SWITCH = EnumItem(1).set_string('Hide Mode Switch.')
ADL_PURPOSECODE_MODE_SWITCH = EnumItem(2).set_string('Mode switch.')
ADL_PURPOSECODE_ATTATCH_DEVICE = EnumItem(3).set_string('Attach Device.')
ADL_PURPOSECODE_DETACH_DEVICE = EnumItem(4).set_string('Detach device.')
ADL_PURPOSECODE_SETPRIMARY_DEVICE = EnumItem(5).set_string('Set primary device.')
ADL_PURPOSECODE_GDI_ROTATION = EnumItem(6).set_string('GDI Rotation.')
ADL_PURPOSECODE_ATI_ROTATION = EnumItem(7).set_string('STI Rotation.')
ADL_PURPOSECODE_NORMAL = ADLPurposeCode.ADL_PURPOSECODE_NORMAL
ADL_PURPOSECODE_HIDE_MODE_SWITCH = ADLPurposeCode.ADL_PURPOSECODE_HIDE_MODE_SWITCH
ADL_PURPOSECODE_MODE_SWITCH = ADLPurposeCode.ADL_PURPOSECODE_MODE_SWITCH
ADL_PURPOSECODE_ATTATCH_DEVICE = ADLPurposeCode.ADL_PURPOSECODE_ATTATCH_DEVICE
ADL_PURPOSECODE_DETACH_DEVICE = ADLPurposeCode.ADL_PURPOSECODE_DETACH_DEVICE
ADL_PURPOSECODE_SETPRIMARY_DEVICE = ADLPurposeCode.ADL_PURPOSECODE_SETPRIMARY_DEVICE
ADL_PURPOSECODE_GDI_ROTATION = ADLPurposeCode.ADL_PURPOSECODE_GDI_ROTATION
ADL_PURPOSECODE_ATI_ROTATION = ADLPurposeCode.ADL_PURPOSECODE_ATI_ROTATION
# /////////////////////////////////////////////////////////////////////////
# ADLAngle Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLAngle(ENUM):
ADL_ANGLE_LANDSCAPE = EnumItem(0).set_string('Landscape')
ADL_ANGLE_ROTATERIGHT = EnumItem(90).set_string('Rotate right')
ADL_ANGLE_ROTATE180 = EnumItem(150).set_string('Rotate 180')
ADL_ANGLE_ROTATELEFT = EnumItem(270).set_string('Rotate left')
ADL_ANGLE_LANDSCAPE = ADLAngle.ADL_ANGLE_LANDSCAPE
ADL_ANGLE_ROTATERIGHT = ADLAngle.ADL_ANGLE_ROTATERIGHT
ADL_ANGLE_ROTATE180 = ADLAngle.ADL_ANGLE_ROTATE180
ADL_ANGLE_ROTATELEFT = ADLAngle.ADL_ANGLE_ROTATELEFT
# /////////////////////////////////////////////////////////////////////////
# ADLOrientationDataType Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLOrientationDataType(ENUM):
ADL_ORIENTATIONTYPE_OSDATATYPE = EnumItem(1).set_string('OS Data Type')
ADL_ORIENTATIONTYPE_NONOSDATATYPE = EnumItem(2).set_string('Non OS data type')
ADL_ORIENTATIONTYPE_OSDATATYPE = ADLOrientationDataType.ADL_ORIENTATIONTYPE_OSDATATYPE
ADL_ORIENTATIONTYPE_NONOSDATATYPE = ADLOrientationDataType.ADL_ORIENTATIONTYPE_NONOSDATATYPE
# /////////////////////////////////////////////////////////////////////////
# ADLPanningMode Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLPanningMode(ENUM):
ADL_PANNINGMODE_NO_PANNING = EnumItem(0).set_string('No panning')
ADL_PANNINGMODE_AT_LEAST_ONE_NO_PANNING = EnumItem(1).set_string('At Least one not painting')
ADL_PANNINGMODE_ALLOW_PANNING = EnumItem(2).set_string('Allow panning')
ADL_PANNINGMODE_NO_PANNING = ADLPanningMode.ADL_PANNINGMODE_NO_PANNING
ADL_PANNINGMODE_AT_LEAST_ONE_NO_PANNING = ADLPanningMode.ADL_PANNINGMODE_AT_LEAST_ONE_NO_PANNING
ADL_PANNINGMODE_ALLOW_PANNING = ADLPanningMode.ADL_PANNINGMODE_ALLOW_PANNING
# /////////////////////////////////////////////////////////////////////////
# ADLLARGEDESKTOPTYPE Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLLARGEDESKTOPTYPE(ENUM):
ADL_LARGEDESKTOPTYPE_NORMALDESKTOP = EnumItem(0).set_string('Normal Desktop')
ADL_LARGEDESKTOPTYPE_PSEUDOLARGEDESKTOP = EnumItem(1).set_string('Pseudo Large Desktop')
ADL_LARGEDESKTOPTYPE_VERYLARGEDESKTOP = EnumItem(2).set_string('Very large desktop')
ADL_LARGEDESKTOPTYPE_NORMALDESKTOP = ADLLARGEDESKTOPTYPE.ADL_LARGEDESKTOPTYPE_NORMALDESKTOP
ADL_LARGEDESKTOPTYPE_PSEUDOLARGEDESKTOP = ADLLARGEDESKTOPTYPE.ADL_LARGEDESKTOPTYPE_PSEUDOLARGEDESKTOP
ADL_LARGEDESKTOPTYPE_VERYLARGEDESKTOP = ADLLARGEDESKTOPTYPE.ADL_LARGEDESKTOPTYPE_VERYLARGEDESKTOP
# /////////////////////////////////////////////////////////////////////////
# ADLPlatform Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLPlatForm(ENUM):
GRAPHICS_PLATFORM_DESKTOP = EnumItem(0).set_string('Desktop')
GRAPHICS_PLATFORM_MOBILE = EnumItem(1).set_string('Mobile')
GRAPHICS_PLATFORM_DESKTOP = ADLPlatForm.GRAPHICS_PLATFORM_DESKTOP
GRAPHICS_PLATFORM_MOBILE = ADLPlatForm.GRAPHICS_PLATFORM_MOBILE
# /////////////////////////////////////////////////////////////////////////
# ADLGraphicCoreGeneration Enumeration
# /////////////////////////////////////////////////////////////////////////
class ADLGraphicCoreGeneration(ENUM):
ADL_GRAPHIC_CORE_GENERATION_UNDEFINED = EnumItem(0).set_string('Undefined')
ADL_GRAPHIC_CORE_GENERATION_PRE_GCN = EnumItem(1).set_string('Pre GCN')
ADL_GRAPHIC_CORE_GENERATION_GCN = EnumItem(1).set_string('GCN')
ADL_GRAPHIC_CORE_GENERATION_UNDEFINED = ADLGraphicCoreGeneration.ADL_GRAPHIC_CORE_GENERATION_UNDEFINED
ADL_GRAPHIC_CORE_GENERATION_PRE_GCN = ADLGraphicCoreGeneration.ADL_GRAPHIC_CORE_GENERATION_PRE_GCN
ADL_GRAPHIC_CORE_GENERATION_GCN = ADLGraphicCoreGeneration.ADL_GRAPHIC_CORE_GENERATION_GCN
# Other Definitions for internal use
# Values for ADL_Display_WriteAndReadI2CRev_Get()
ADL_I2C_MAJOR_API_REV = 0x00000001
ADL_I2C_MINOR_DEFAULT_API_REV = 0x00000000
ADL_I2C_MINOR_OEM_API_REV = 0x00000001
# Values for ADL_Display_WriteAndReadI2C()
ADL_DL_I2C_LINE_OEM = 0x00000001
ADL_DL_I2C_LINE_OD_CONTROL = 0x00000002
ADL_DL_I2C_LINE_OEM2 = 0x00000003
ADL_DL_I2C_LINE_OEM3 = 0x00000004
ADL_DL_I2C_LINE_OEM4 = 0x00000005
ADL_DL_I2C_LINE_OEM5 = 0x00000006
ADL_DL_I2C_LINE_OEM6 = 0x00000007
# Max size of I2C data buffer
ADL_DL_I2C_MAXDATASIZE = 0x00000040
ADL_DL_I2C_MAXWRITEDATASIZE = 0x0000000C
ADL_DL_I2C_MAXADDRESSLENGTH = 0x00000006
ADL_DL_I2C_MAXOFFSETLENGTH = 0x00000004
# / Values for ADLDisplayProperty.iPropertyType
ADL_DL_DISPLAYPROPERTY_TYPE_UNKNOWN = 0
ADL_DL_DISPLAYPROPERTY_TYPE_EXPANSIONMODE = 1
ADL_DL_DISPLAYPROPERTY_TYPE_USEUNDERSCANSCALING = 2
# / Enables ITC processing for HDMI panels that are capable of the feature
ADL_DL_DISPLAYPROPERTY_TYPE_ITCFLAGENABLE = 9
ADL_DL_DISPLAYPROPERTY_TYPE_DOWNSCALE = 11
# / Values for ADLDisplayContent.iContentType
# / Certain HDMI panels that support ITC have support for a feature such
# that, the display on the panel
# / can be adjusted to optimize the view of the content being displayed,
# depending on the type of content.
ADL_DL_DISPLAYCONTENT_TYPE_GRAPHICS = Constant(1).set_string('Graphics')
ADL_DL_DISPLAYCONTENT_TYPE_PHOTO = Constant(2).set_string('Photo')
ADL_DL_DISPLAYCONTENT_TYPE_CINEMA = Constant(4).set_string('Cinema')
ADL_DL_DISPLAYCONTENT_TYPE_GAME = Constant(8).set_string('Game')
# values for ADLDisplayProperty.iExpansionMode
ADL_DL_DISPLAYPROPERTY_EXPANSIONMODE_CENTER = Constant(0).set_string('Center')
ADL_DL_DISPLAYPROPERTY_EXPANSIONMODE_FULLSCREEN = Constant(1).set_string('Full Screen')
ADL_DL_DISPLAYPROPERTY_EXPANSIONMODE_ASPECTRATIO = Constant(2).set_string('Aspect Ratio')
# /\defgroup define_dither_states Dithering options
# @{
# / Dithering disabled.
ADL_DL_DISPLAY_DITHER_DISABLED = Constant(0).set_string('Disabled')
# / Use default driver settings for dithering. Note that the default
# setting could be dithering disabled.
ADL_DL_DISPLAY_DITHER_DRIVER_DEFAULT = Constant(1).set_string('Default')
# / Temporal dithering to 6 bpc. Note that if the input is 12 bits, the
# two least significant bits will be truncated.
ADL_DL_DISPLAY_DITHER_FM6 = Constant(2).set_string('Temporal dithering to 6 bpc')
# / Temporal dithering to 8 bpc.
ADL_DL_DISPLAY_DITHER_FM8 = Constant(3).set_string('Temporal dithering to 8 bpc')
# / Temporal dithering to 10 bpc.
ADL_DL_DISPLAY_DITHER_FM10 = Constant(4).set_string('Temporal dithering to 10 bpc')
# / Spatial dithering to 6 bpc. Note that if the input is 12 bits, the two
# least significant bits will be truncated.
ADL_DL_DISPLAY_DITHER_DITH6 = Constant(5).set_string('Spatial dithering to 6 bpc')
# / Spatial dithering to 8 bpc.
ADL_DL_DISPLAY_DITHER_DITH8 = Constant(6).set_string('Spatial dithering to 8 bpc')
# / Spatial dithering to 10 bpc.
ADL_DL_DISPLAY_DITHER_DITH10 = Constant(7).set_string('Spatial dithering to 10 bpc')
# / Spatial dithering to 6 bpc. Random number generators are reset every
# frame, so the same input value of a certain pixel will always be
# dithered to the same output value. Note that if the input is 12 bits,
# the two least significant bits will be truncated.
ADL_DL_DISPLAY_DITHER_DITH6_NO_FRAME_RAND = Constant(8).set_string('Spatial dithering to 6 bpc random')
# / Spatial dithering to 8 bpc. Random number generators are reset every
# frame, so the same input value of a certain pixel will always be
# dithered | |
``x.shape + (order,)``, where
:math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
--------
polyvander, polyvander3d. polyval2d, polyval3d
Notes
-----
.. versionadded:: 1.7.0
"""
ideg = [int(d) for d in deg]
is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)]
if is_valid != [1, 1, 1]:
raise ValueError("degrees must be non-negative integers")
degx, degy, degz = ideg
x, y, z = np.array((x, y, z), copy=0) + 0.0
vx = polyvander(x, degx)
vy = polyvander(y, degy)
vz = polyvander(z, degz)
v = vx[..., None, None]*vy[..., None,:, None]*vz[..., None, None,:]
# einsum bug
#v = np.einsum("...i, ...j, ...k->...ijk", vx, vy, vz)
return v.reshape(v.shape[:-3] + (-1,))
def polyfit(x, y, deg, rcond=None, full=False, w=None):
"""
Least-squares fit of a polynomial to data.
Return the coefficients of a polynomial of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * x + ... + c_n * x^n,
where `n` is `deg`.
Parameters
----------
x : array_like, shape (`M`,)
x-coordinates of the `M` sample (data) points ``(x[i], y[i])``.
y : array_like, shape (`M`,) or (`M`, `K`)
y-coordinates of the sample points. Several sets of sample points
sharing the same x-coordinates can be (independently) fit with one
call to `polyfit` by passing in for `y` a 2-D array that contains
one data set per column.
deg : int or array_like
Degree of the fitting polynomial. If `deg` is a single integer
all terms up to and including the `deg`'th term are included.
`deg` may alternatively be a list or array specifying which
terms in the Legendre expansion to include in the fit.
.. versionchanged:: 1.11.0
`deg` may be a list specifying which terms to fit
rcond : float, optional
Relative condition number of the fit. Singular values smaller
than `rcond`, relative to the largest singular value, will be
ignored. The default value is ``len(x)*eps``, where `eps` is the
relative precision of the platform's float type, about 2e-16 in
most cases.
full : bool, optional
Switch determining the nature of the return value. When ``False``
(the default) just the coefficients are returned; when ``True``,
diagnostic information from the singular value decomposition (used
to solve the fit's matrix equation) is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (`deg` + 1,) or (`deg` + 1, `K`)
Polynomial coefficients ordered from low to high. If `y` was 2-D,
the coefficients in column `k` of `coef` represent the polynomial
fit to the data in `y`'s `k`-th column.
[residuals, rank, singular_values, rcond] : list
These values are only returned if `full` = True
resid -- sum of squared residuals of the least squares fit
rank -- the numerical rank of the scaled Vandermonde matrix
sv -- singular values of the scaled Vandermonde matrix
rcond -- value of `rcond`.
For more details, see `linalg.lstsq`.
Raises
------
RankWarning
Raised if the matrix in the least-squares fit is rank deficient.
The warning is only raised if `full` == False. The warnings can
be turned off by:
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)
See Also
--------
chebfit, legfit, lagfit, hermfit, hermefit
polyval : Evaluates a polynomial.
polyvander : Vandermonde matrix for powers.
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the polynomial `p` that minimizes
the sum of the weighted squared errors
.. math :: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where the :math:`w_j` are the weights. This problem is solved by
setting up the (typically) over-determined matrix equation:
.. math :: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected (and `full` == ``False``), a `RankWarning` will be raised.
This means that the coefficient values may be poorly determined.
Fitting to a lower order polynomial will usually get rid of the warning
(but may not be what you want, of course; if you have independent
reason(s) for choosing the degree which isn't working, you may have to:
a) reconsider those reasons, and/or b) reconsider the quality of your
data). The `rcond` parameter can also be set to a value smaller than
its default, but the resulting fit may be spurious and have large
contributions from roundoff error.
Polynomial fits using double precision tend to "fail" at about
(polynomial) degree 20. Fits using Chebyshev or Legendre series are
generally better conditioned, but much can still depend on the
distribution of the sample points and the smoothness of the data. If
the quality of the fit is inadequate, splines may be a good
alternative.
Examples
--------
>>> from numpy.polynomial import polynomial as P
>>> x = np.linspace(-1,1,51) # x "data": [-1, -0.96, ..., 0.96, 1]
>>> y = x**3 - x + np.random.randn(len(x)) # x^3 - x + N(0,1) "noise"
>>> c, stats = P.polyfit(x,y,3,full=True)
>>> c # c[0], c[2] should be approx. 0, c[1] approx. -1, c[3] approx. 1
array([ 0.01909725, -1.30598256, -0.00577963, 1.02644286])
>>> stats # note the large SSR, explaining the rather poor results
[array([ 38.06116253]), 4, array([ 1.38446749, 1.32119158, 0.50443316,
0.28853036]), 1.1324274851176597e-014]
Same thing without the added noise
>>> y = x**3 - x
>>> c, stats = P.polyfit(x,y,3,full=True)
>>> c # c[0], c[2] should be "very close to 0", c[1] ~= -1, c[3] ~= 1
array([ -1.73362882e-17, -1.00000000e+00, -2.67471909e-16,
1.00000000e+00])
>>> stats # note the minuscule SSR
[array([ 7.46346754e-31]), 4, array([ 1.38446749, 1.32119158,
0.50443316, 0.28853036]), 1.1324274851176597e-014]
"""
x = np.asarray(x) + 0.0
y = np.asarray(y) + 0.0
deg = np.asarray([deg,], dtype=int).flatten()
# check arguments.
if deg.size < 1:
raise TypeError("expected deg to be one or more integers")
if deg.min() < 0:
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2:
raise TypeError("expected 1D or 2D array for y")
if len(x) != len(y):
raise TypeError("expected x and y to have same length")
if deg.size == 1:
restricted_fit = False
lmax = deg[0]
order = lmax + 1
else:
restricted_fit = True
lmax = deg.max()
order = deg.size
# set up the least squares matrices in transposed form
van = polyvander(x, lmax)
if restricted_fit:
van = van[:, deg]
lhs = van.T
rhs = y.T
if w is not None:
w = np.asarray(w) + 0.0
if w.ndim != 1:
raise TypeError("expected 1D vector for w")
if len(x) != len(w):
raise TypeError("expected x and w to have same length")
# apply weights. Don't use inplace operations as they
# can cause problems with NA.
lhs = lhs * w
rhs = rhs * w
# set rcond
if rcond is None:
rcond = len(x)*np.finfo(x.dtype).eps
# Determine the norms of the design matrix columns.
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
else:
scl = np.sqrt(np.square(lhs).sum(1))
scl[scl == 0] = | |
= _messages.StringField(6)
resourceVersion = _messages.StringField(7)
watch = _messages.BooleanField(8)
class AnthoseventsCustomresourcedefinitionsGetRequest(_messages.Message):
r"""A AnthoseventsCustomresourcedefinitionsGetRequest object.
Fields:
name: The name of the CustomResourceDefinition being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
name = _messages.StringField(1, required=True)
class AnthoseventsCustomresourcedefinitionsListRequest(_messages.Message):
r"""A AnthoseventsCustomresourcedefinitionsListRequest object.
Fields:
continue_: Optional encoded string to continue paging.
fieldSelector: Allows to filter resources based on a specific value for a
field name. Send this in a query string format. i.e.
'metadata.name%3Dlorem'. Not currently used by Cloud Run.
includeUninitialized: Not currently used by Cloud Run.
labelSelector: Allows to filter resources based on a label. Supported
operations are =, !=, exists, in, and notIn.
limit: A integer attribute.
parent: The project ID or project number from which the storages should be
listed.
resourceVersion: The baseline resource version from which the list or
watch operation should start. Not currently used by Cloud Run.
watch: Flag that indicates that the client expects to watch this resource
as well. Not currently used by Cloud Run.
"""
continue_ = _messages.StringField(1)
fieldSelector = _messages.StringField(2)
includeUninitialized = _messages.BooleanField(3)
labelSelector = _messages.StringField(4)
limit = _messages.IntegerField(5, variant=_messages.Variant.INT32)
parent = _messages.StringField(6)
resourceVersion = _messages.StringField(7)
watch = _messages.BooleanField(8)
class AnthoseventsNamespacesBrokersCreateRequest(_messages.Message):
r"""A AnthoseventsNamespacesBrokersCreateRequest object.
Fields:
broker: A Broker resource to be passed as the request body.
parent: The namespace name.
"""
broker = _messages.MessageField('Broker', 1)
parent = _messages.StringField(2, required=True)
class AnthoseventsNamespacesBrokersDeleteRequest(_messages.Message):
r"""A AnthoseventsNamespacesBrokersDeleteRequest object.
Fields:
name: The relative name of the broker being deleted, including the
namespace
"""
name = _messages.StringField(1, required=True)
class AnthoseventsNamespacesBrokersGetRequest(_messages.Message):
r"""A AnthoseventsNamespacesBrokersGetRequest object.
Fields:
name: The name of the Broker being retrieved.
"""
name = _messages.StringField(1, required=True)
class AnthoseventsNamespacesBrokersListRequest(_messages.Message):
r"""A AnthoseventsNamespacesBrokersListRequest object.
Fields:
continue_: Optional encoded string to continue paging.
fieldSelector: Allows to filter resources based on a specific value for a
field name. Send this in a query string format. i.e.
'metadata.name%3Dlorem'. Not currently used by Cloud Run.
includeUninitialized: Not currently used by Cloud Run.
labelSelector: Allows to filter resources based on a label. Supported
operations are =, !=, exists, in, and notIn.
limit: A integer attribute.
parent: The namespace name.
resourceVersion: The baseline resource version from which the list or
watch operation should start. Not currently used by Cloud Run.
watch: Flag that indicates that the client expects to watch this resource
as well. Not currently used by Cloud Run.
"""
continue_ = _messages.StringField(1)
fieldSelector = _messages.StringField(2)
includeUninitialized = _messages.BooleanField(3)
labelSelector = _messages.StringField(4)
limit = _messages.IntegerField(5, variant=_messages.Variant.INT32)
parent = _messages.StringField(6, required=True)
resourceVersion = _messages.StringField(7)
watch = _messages.BooleanField(8)
class AnthoseventsNamespacesCloudauditlogssourcesCreateRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudauditlogssourcesCreateRequest object.
Fields:
cloudAuditLogsSource: A CloudAuditLogsSource resource to be passed as the
request body.
parent: The namespace name.
"""
cloudAuditLogsSource = _messages.MessageField('CloudAuditLogsSource', 1)
parent = _messages.StringField(2, required=True)
class AnthoseventsNamespacesCloudauditlogssourcesDeleteRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudauditlogssourcesDeleteRequest object.
Fields:
apiVersion: Cloud Run currently ignores this parameter.
kind: Cloud Run currently ignores this parameter.
name: The name of the cloudauditlogssource being deleted. If needed,
replace {namespace_id} with the project ID.
propagationPolicy: Specifies the propagation policy of delete. Cloud Run
currently ignores this setting, and deletes in the background. Please
see kubernetes.io/docs/concepts/workloads/controllers/garbage-
collection/ for more information.
"""
apiVersion = _messages.StringField(1)
kind = _messages.StringField(2)
name = _messages.StringField(3, required=True)
propagationPolicy = _messages.StringField(4)
class AnthoseventsNamespacesCloudauditlogssourcesGetRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudauditlogssourcesGetRequest object.
Fields:
name: The name of the cloudauditlogssource being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
name = _messages.StringField(1, required=True)
class AnthoseventsNamespacesCloudauditlogssourcesListRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudauditlogssourcesListRequest object.
Fields:
continue_: Optional encoded string to continue paging.
fieldSelector: Allows to filter resources based on a specific value for a
field name. Send this in a query string format. i.e.
'metadata.name%3Dlorem'. Not currently used by Cloud Run.
includeUninitialized: Not currently used by Cloud Run.
labelSelector: Allows to filter resources based on a label. Supported
operations are =, !=, exists, in, and notIn.
limit: The maximum number of records that should be returned.
parent: The namespaces name
resourceVersion: The baseline resource version from which the list or
watch operation should start. Not currently used by Cloud Run.
watch: Flag that indicates that the client expects to watch this resource
as well. Not currently used by Cloud Run.
"""
continue_ = _messages.StringField(1)
fieldSelector = _messages.StringField(2)
includeUninitialized = _messages.BooleanField(3)
labelSelector = _messages.StringField(4)
limit = _messages.IntegerField(5, variant=_messages.Variant.INT32)
parent = _messages.StringField(6, required=True)
resourceVersion = _messages.StringField(7)
watch = _messages.BooleanField(8)
class AnthoseventsNamespacesCloudauditlogssourcesReplaceCloudAuditLogsSourceRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudauditlogssourcesReplaceCloudAuditLogsSource
Request object.
Fields:
cloudAuditLogsSource: A CloudAuditLogsSource resource to be passed as the
request body.
name: The name of the cloudauditlogssource being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
cloudAuditLogsSource = _messages.MessageField('CloudAuditLogsSource', 1)
name = _messages.StringField(2, required=True)
class AnthoseventsNamespacesCloudpubsubsourcesCreateRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudpubsubsourcesCreateRequest object.
Fields:
cloudPubSubSource: A CloudPubSubSource resource to be passed as the
request body.
parent: The namespace in which this cloudpubsubsource should be created.
"""
cloudPubSubSource = _messages.MessageField('CloudPubSubSource', 1)
parent = _messages.StringField(2, required=True)
class AnthoseventsNamespacesCloudpubsubsourcesDeleteRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudpubsubsourcesDeleteRequest object.
Fields:
apiVersion: Cloud Run currently ignores this parameter.
kind: Cloud Run currently ignores this parameter.
name: The name of the cloudpubsubsource being deleted. If needed, replace
{namespace_id} with the project ID.
propagationPolicy: Specifies the propagation policy of delete. Cloud Run
currently ignores this setting, and deletes in the background. Please
see kubernetes.io/docs/concepts/workloads/controllers/garbage-
collection/ for more information.
"""
apiVersion = _messages.StringField(1)
kind = _messages.StringField(2)
name = _messages.StringField(3, required=True)
propagationPolicy = _messages.StringField(4)
class AnthoseventsNamespacesCloudpubsubsourcesGetRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudpubsubsourcesGetRequest object.
Fields:
name: The name of the cloudpubsubsource being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
name = _messages.StringField(1, required=True)
class AnthoseventsNamespacesCloudpubsubsourcesListRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudpubsubsourcesListRequest object.
Fields:
continue_: Optional encoded string to continue paging.
fieldSelector: Allows to filter resources based on a specific value for a
field name. Send this in a query string format. i.e.
'metadata.name%3Dlorem'. Not currently used by Cloud Run.
includeUninitialized: Not currently used by Cloud Run.
labelSelector: Allows to filter resources based on a label. Supported
operations are =, !=, exists, in, and notIn.
limit: The maximum number of records that should be returned.
parent: The namespace from which the cloudpubsubsources should be listed.
resourceVersion: The baseline resource version from which the list or
watch operation should start. Not currently used by Cloud Run.
watch: Flag that indicates that the client expects to watch this resource
as well. Not currently used by Cloud Run.
"""
continue_ = _messages.StringField(1)
fieldSelector = _messages.StringField(2)
includeUninitialized = _messages.BooleanField(3)
labelSelector = _messages.StringField(4)
limit = _messages.IntegerField(5, variant=_messages.Variant.INT32)
parent = _messages.StringField(6, required=True)
resourceVersion = _messages.StringField(7)
watch = _messages.BooleanField(8)
class AnthoseventsNamespacesCloudpubsubsourcesReplaceCloudPubSubSourceRequest(_messages.Message):
r"""A
AnthoseventsNamespacesCloudpubsubsourcesReplaceCloudPubSubSourceRequest
object.
Fields:
cloudPubSubSource: A CloudPubSubSource resource to be passed as the
request body.
name: The name of the cloudpubsubsource being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
cloudPubSubSource = _messages.MessageField('CloudPubSubSource', 1)
name = _messages.StringField(2, required=True)
class AnthoseventsNamespacesCloudschedulersourcesCreateRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudschedulersourcesCreateRequest object.
Fields:
cloudSchedulerSource: A CloudSchedulerSource resource to be passed as the
request body.
parent: The namespace in which this cloudschedulersource should be
created.
"""
cloudSchedulerSource = _messages.MessageField('CloudSchedulerSource', 1)
parent = _messages.StringField(2, required=True)
class AnthoseventsNamespacesCloudschedulersourcesDeleteRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudschedulersourcesDeleteRequest object.
Fields:
apiVersion: Cloud Run currently ignores this parameter.
kind: Cloud Run currently ignores this parameter.
name: The name of the cloudschedulersource being deleted. If needed,
replace {namespace_id} with the project ID.
propagationPolicy: Specifies the propagation policy of delete. Cloud Run
currently ignores this setting, and deletes in the background. Please
see kubernetes.io/docs/concepts/workloads/controllers/garbage-
collection/ for more information.
"""
apiVersion = _messages.StringField(1)
kind = _messages.StringField(2)
name = _messages.StringField(3, required=True)
propagationPolicy = _messages.StringField(4)
class AnthoseventsNamespacesCloudschedulersourcesGetRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudschedulersourcesGetRequest object.
Fields:
name: The name of the cloudschedulersource being retrieved. If needed,
replace {namespace_id} with the project ID.
"""
name = _messages.StringField(1, required=True)
class AnthoseventsNamespacesCloudschedulersourcesListRequest(_messages.Message):
r"""A AnthoseventsNamespacesCloudschedulersourcesListRequest object.
Fields:
continue_: Optional encoded string to continue paging.
fieldSelector: Allows to filter resources based on a specific value for a
field name. Send this in a query string format. i.e.
'metadata.name%3Dlorem'. Not currently used by Cloud Run.
includeUninitialized: Not currently used by Cloud Run.
labelSelector: Allows to filter resources based on a label. Supported
operations are =, !=, exists, in, and notIn.
limit: The maximum number of records that should be returned.
parent: The namespace from which the cloudschedulersources should be
listed.
resourceVersion: The baseline resource version from which the list or
watch operation should start. Not currently used by | |
"""The Basic Model Interface."""
__version__ = '0.2'
class BmiBase(object):
"""Methods that control model execution.
These BMI functions are critical to plug-and-play modeling because they
give a calling component fine-grained control over the model execution.
"""
def initialize(self, filename):
"""Perform startup tasks for the model.
Perform all tasks that take place before entering the model's time
loop, including opening files and initializing the model state. Model
inputs are read from a text-based configuration file, specified by
`filename`.
Parameters
----------
filename : str, optional
The path to the model configuration file.
Notes
-----
Models should be refactored, if necessary, to use a
configuration file. CSDMS does not impose any constraint on
how configuration files are formatted, although YAML is
recommended. A template of a model's configuration file
with placeholder values is used by the BMI.
"""
pass
def update(self):
"""Advance model state by one time step.
Perform all tasks that take place within one pass through the model's
time loop. This typically includes incrementing all of the model's
state variables. If the model's state variables don't change in time,
then they can be computed by the :func:`initialize` method and this
method can return with no action.
"""
pass
def update_until(self, time):
"""Advance model state until the given time.
Parameters
----------
time : float
A model time value.
See Also
--------
update
"""
pass
def update_frac(self, time_frac):
"""Advance model state by a fraction of a time step.
Parameters
----------
time_frac : float
A fraction of a model time step value.
See Also
--------
update
"""
pass
def finalize(self):
"""Perform tear-down tasks for the model.
Perform all tasks that take place after exiting the model's
time loop. This typically includes deallocating memory,
closing files, and printing reports.
"""
pass
class BmiInfo(object):
"""Methods that get metadata about a model."""
def get_component_name(self):
"""Name of the component.
Returns
-------
str
The name of the component.
"""
pass
def get_input_var_names(self):
"""List of a model's input variables.
Input variable names must be `CSDMS Standard Names`_, also
known as *long variable names*.
Returns
-------
tuple of str
The input variables for the model.
Notes
-----
Standard Names enable the CSDMS framework to determine whether
an input variable in one model is equivalent to, or compatible
with, an output variable in another model. This allows the
framework to automatically connect components.
Standard Names do not have to be used within the model.
.. _CSDMS Standard Names:
http://csdms.colorado.edu/wiki/CSDMS_Standard_Names
"""
pass
def get_output_var_names(self):
"""List of a model's output variables.
Output variable names must be `CSDMS Standard Names`_, also
known as *long variable names*.
Returns
-------
tuple of str
The output variables for the model.
See Also
--------
get_input_var_names
Notes
-----
.. _CSDMS Standard Names:
http://csdms.colorado.edu/wiki/CSDMS_Standard_Names
"""
pass
class BmiTime(object):
"""Methods that get time information from a model."""
def get_start_time(self):
"""Start time of the model.
Model times should be of type float. The default model start
time is 0.
Returns
-------
float
The model start time.
"""
pass
def get_current_time(self):
"""Current time of the model.
Returns
-------
float
The current model time.
See Also
--------
get_start_time
"""
pass
def get_end_time(self):
"""End time of the model.
Returns
-------
float
The maximum model time.
See Also
--------
get_start_time
"""
pass
def get_time_step(self):
"""Current time step of the model.
The model time step should be of type float. The default time
step is 1.0.
Returns
-------
float
The time step used in model.
"""
pass
def get_time_units(self):
"""Time units of the model.
Returns
-------
float
The model time unit; e.g., `days` or `s`.
Notes
-----
CSDMS uses the `UDUNITS`_ package developed by Unidata.
.. _UDUNITS: https://www.unidata.ucar.edu/software/udunits
"""
pass
class BmiVars(object):
"""Methods that get information about input and output variables.
These BMI functions obtain information about a particular input or output
variable. They must accommodate any variable that is returned by the BMI
methods :func:`~bmi.info.BmiInfo.get_input_var_names` or
:func:`~bmi.info.BmiInfo.get_output_var_names`.
"""
def get_var_type(self, var_name):
"""Get data type of the given variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
str
The Python variable type; e.g., ``str``, ``int``, ``float``.
"""
pass
def get_var_units(self, var_name):
"""Get units of the given variable.
Standard unit names, in lower case, should be used, such as
``meters`` or ``seconds``. Standard abbreviations, like ``m`` for
meters, are also supported. For variables with compound units,
each unit name is separated by a single space, with exponents
other than 1 placed immediately after the name, as in ``m s-1``
for velocity, ``W m-2`` for an energy flux, or ``km2`` for an
area.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
str
The variable units.
Notes
-----
CSDMS uses the `UDUNITS`_ package from Unidata.
.. _UDUNITS: http://www.unidata.ucar.edu/software/udunits
"""
pass
def get_var_itemsize(self, var_name):
"""Get memory use for each array element, in bytes.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
int
Item size in bytes.
"""
pass
def get_var_nbytes(self, var_name):
"""Get size, in bytes, of the given variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
int
The size of the variable, counted in bytes.
"""
pass
def get_var_grid(self, var_name):
"""Get the grid identifier for the given variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
int
The grid identifier.
See Also
--------
bmi.info.BmiInfo.get_input_var_names : Get *var_name* from this
method or from :func:`~bmi.info.BmiInfo.get_output_var_names`.
"""
pass
class BmiGetter(object):
"""Get variable values from a model.
Methods that get variables from a model's state. Often a model's state
variables are changing with each time step, so getters are called to get
current values.
"""
def get_value(self, var_name):
"""Get a copy of the values of the given variable.
This is a getter for the model, used to access the model's
current state. It returns a *copy* of a model variable, with
the return type, size and rank dependent on the variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
array_like
The value of a model variable.
"""
pass
def get_value_ref(self, var_name):
"""Get a reference to the values of the given variable.
This is a getter for the model, used to access the model's
current state. It returns a *reference* to a model variable,
with the return type, size and rank dependent on the variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
Returns
-------
array_like
A reference to a model variable.
"""
pass
def get_value_at_indices(self, var_name, indices):
"""Get values at particular locations.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
indices : array_like
The indices into the variable array.
Returns
-------
array_like
Value of the model variable at the given location.
"""
pass
class BmiSetter(object):
"""Set values into a component.
Methods that set variables of a model's state.
"""
def set_value(self, var_name, src):
"""Specify a new value for a model variable.
This is the setter for the model, used to change the model's
current state. It accepts, through *src*, a new value for a
model variable, with the type, size and rank of *src*
dependent on the variable.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
src : array_like
The new value for the specified variable.
"""
pass
def set_value_at_indices(self, var_name, indices, src):
"""Specify a new value for a model variable at particular indices.
Parameters
----------
var_name : str
An input or output variable name, a CSDMS Standard Name.
indices : array_like
The indices into the variable array.
src : array_like
The new value for the specified variable.
"""
pass
class BmiGrid(object):
"""Methods that describe a grid.
"""
def get_grid_rank(self, grid_id):
"""Get number of dimensions of the computational grid.
| |
= True
else:
self._alphabet_list = alphabet
if probabilities:
probabilities[0] += (1.0 - sum(probabilities))
self._alphabet = list(self._alphabet_list)
self._probabilities = probabilities
@classmethod
def build(cls, builder: Builder, spec):
alphabet = None
probabilities = None
if isinstance(spec, (tuple, list)):
len_spec = spec[0]
if len(spec) > 1:
alphabet = spec[1]
if len(spec) > 2:
probabilities = spec[1]
else:
len_spec = spec
return cls(builder.rand, BuildGenerator(builder, len_spec), alphabet,
probabilities)
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> Dict[Any, Any]:
str_len = self._len_generator.generate(None, key, context)
if size is None:
return ''.join(
self._rand.choice(self._alphabet,
str_len,
p=self._probabilities))
else:
letters = self._rand.choice(self._alphabet, [size, str_len],
p=self._probabilities)
return [''.join(s) for s in letters]
def result_type(self) -> type:
return str
def save(self) -> Any:
if self._using_defaults:
return ('str', (self._len_generator.save(),))
return (Names.STR, (self._len_generator.save(), self._alphabet_list,
self._probabilities))
class ArrayGenerator(Generator):
"""Generator for list of values of value_generator, with a length."""
def __init__(self, rand: numpy.random.Generator, value_generator: Generator,
len_generator: Generator):
"""Initializes an array generator.
Args:
value_generator: generates the underlying values in the array.
len_generator: generates the length of the output array.
On None generated by this, our output is None.
"""
super().__init__(rand)
if not _IsOptionalType(len_generator.result_type(), int):
raise ValueError(
'ArrayGenerator needs an integer length generator. '
f'Got: {len_generator.result_type()}')
self._value_generator = value_generator
self._len_generator = len_generator
@classmethod
def build(cls, builder: Builder, spec):
if not isinstance(spec, (tuple, list)) or len(spec) != 2:
raise ValueError(f'{cls} expects two underlying arguments')
return cls(builder.rand, BuildGenerator(builder, spec[0]),
BuildGenerator(builder, spec[1]))
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> List[Any]:
array_len = self._len_generator.generate(size, key, context)
if array_len is None:
return None
if size is None:
return self._value_generator.generate(array_len, key, context)
else:
return [
self._value_generator.generate(sz, key, context)
for sz in array_len
]
def _list_type(self) -> type:
"""Underlying list type."""
return List[self._value_generator.result_type()]
def result_type(self) -> type:
if _IsOptional(self._len_generator.result_type(),):
return Optional[self._list_type()]
return self._list_type()
def save(self) -> Any:
return (Names.ARRAY, (self._value_generator.save(),
self._len_generator.save()))
class PerElementArrayGenerator(ArrayGenerator):
"""Similar with ArrayGenerator, but the generation is done for every element.
This is more expensive, but allows a choice to be made at each element, as opposed
to one choice per each generated array. On the downside, when generating an
array of arrays with this, by specifying a size parameter, that size is fixed."""
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> List[Any]:
array_len = self._len_generator.generate(None, key, context)
if array_len is None:
return None
return [
self._value_generator.generate(size, key, context)
for _ in range(array_len)
]
def save(self) -> Any:
return (Names.ELEM_ARRAY, (self._value_generator.save(),
self._len_generator.save()))
class MakeSetGenerator(Generator):
"""Converts the underlying generated list to a set."""
def __init__(self, rand: numpy.random.Generator, list_generator: Generator):
"""Makes a set out of a generated list."""
super().__init__(rand)
if _GetStructuredTypeName(list_generator.result_type()) != 'list':
raise ValueError(
'MakeSetGenerator expecting an underlying generator of '
f'a list. Got: {list_generator.result_type()}')
self._list_generator = list_generator
@classmethod
def build(cls, builder: Builder, spec):
return cls(builder.rand, BuildGenerator(builder, spec))
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> Set[Any]:
result = self._list_generator.generate(size, key, context)
if result is None:
return result
elif size is None:
return set(result)
else:
return [set(sub_list) for sub_list in result]
def result_type(self) -> type:
return Set[self._list_generator.result_type().__args__[0]]
def save(self) -> Any:
return (Names.SET, self._list_generator.save())
class DictGenerator(Generator):
"""Generates random dictionaries with len, keys and values as chosen."""
def __init__(self, rand: numpy.random.Generator, key_generator: Generator,
value_generator: Generator, len_generator: Generator):
"""Generates a dictionary of length determined by len_generator, """
super().__init__(rand)
if not _IsOptionalType(len_generator.result_type(), int):
raise ValueError('DictGenerator needs an integer length generator. '
f'Got: {len_generator}')
self._key_generator = key_generator
self._value_generator = value_generator
self._len_generator = len_generator
@classmethod
def build(cls, builder: Builder, spec):
if not isinstance(spec, (tuple, list)) or len(spec) != 3:
raise ValueError(f'{cls} expects two underlying arguments')
return cls(builder.rand, BuildGenerator(builder, spec[0]),
BuildGenerator(builder, spec[1]),
BuildGenerator(builder, spec[2]))
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> Dict[Any, Any]:
if size is None:
return self._generate_dict(key, context)
return [self._generate_dict(key, context) for _ in range(size)]
def _generate_dict(self, key, context) -> Dict[Any, Any]:
dict_len = self._len_generator.generate(None, key, context)
if dict_len is None:
return None
return dict(
zip(self._key_generator.generate(dict_len, key, context),
self._value_generator.generate(dict_len, key, context)))
def _dict_type(self) -> type:
"""Underlying returned dictionary type."""
return Dict[self._key_generator.result_type(),
self._value_generator.result_type()]
def result_type(self) -> type:
"""Top level returned type, maybe optional."""
if _IsOptional(self._len_generator.result_type()):
return Optional[self._dict_type()]
return self._dict_type()
def save(self) -> Any:
return (Names.DICT, (self._key_generator.save(),
self._value_generator.save(),
self._len_generator.save()))
class CombineGenerator(Generator):
"""Combines two values from two underlying generators."""
def __init__(self, rand: numpy.random.Generator, first: Generator,
second: Generator, name: str,
combiner: Callable[[Any, Any], Any], first_types: List[type],
second_types: List[type], out_type: type):
super().__init__(rand)
if not any(issubclass(first.result_type(), t) for t in first_types):
raise ValueError(
f'{type(self)} can be built with first argument generating '
f'{first_types}. Found: {first.result_type()}')
if not any(issubclass(second.result_type(), t) for t in second_types):
raise ValueError(
f'{type(self)} can be built with second argument generating '
f'{second_types}. Found: {second.result_type()}')
self._first = first
self._second = second
self._combiner = combiner
self._name = name
self._out_type = out_type
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> Dict[Any, Any]:
if size is None:
return self._combiner(self._first.generate(None, key, context),
self._second.generate(None, key, context))
return [
self._combiner(f, s)
for (f, s) in zip(self._first.generate(size, key, context),
self._second.generate(size, key, context))
]
def result_type(self) -> type:
return self._out_type
def save(self) -> Any:
return (self._name, (self._first.save(), self._second.save()))
@classmethod
def build(cls, builder: Builder, spec):
if not isinstance(spec, (tuple, list)) or len(spec) != 2:
raise ValueError(f'{cls} expects two underlying arguments')
# pylint: disable=no-value-for-parameter
return cls(builder.rand, BuildGenerator(builder, spec[0]),
BuildGenerator(builder, spec[1]))
class DateGenerator(CombineGenerator):
"""Generates dates with a start date, and a delta."""
def __init__(self, rand: numpy.random.Generator, start_date: Generator,
delta_days: Generator):
super().__init__(
rand, start_date, delta_days, Names.DATE,
lambda start, delta: start + datetime.timedelta(days=int(delta)),
[datetime.date], [int], datetime.date)
class DatetimeGenerator(CombineGenerator):
"""Generates datetimes with a start timestamp, and a delta in seconds."""
def __init__(self, rand: numpy.random.Generator, start_datetime: Generator,
delta_seconds: Generator):
super().__init__(
rand, start_datetime, delta_seconds, Names.DATETIME,
lambda start, delta: start + datetime.timedelta(seconds=int(delta)),
[datetime.datetime], [int], datetime.datetime)
class StrOpGenerator(Generator):
"""Combines strings generated by the children."""
def __init__(self, rand: numpy.random.Generator, children: List[Generator],
op: str, name: str, in_types: List[type],
combiner: Callable[[str, List[any]], str]):
super().__init__(rand)
if (in_types is not None and any(
not any(issubclass(child.result_type(), t)
for t in in_types)
for child in children)):
raise ValueError(
f'{type(self)} expecting {in_types} input types. '
f'Got {[child.result_type() for child in children]}')
if not isinstance(op, str):
raise ValueError(
f'{type(self)} expecting string operand. Got `{op}`')
self._children = children
self._op = op
self._name = name
self._combiner = combiner
def result_type(self) -> type:
return str
def save(self) -> Any:
return (self._name, ([child.save() for child in self._children],
self._op))
@classmethod
def build(cls, builder: Builder, spec):
if not isinstance(spec, (tuple, list)) or len(spec) != 2:
raise ValueError(f'{cls} expects two underlying arguments')
# pylint: disable=no-value-for-parameter
return cls(builder.rand, [BuildGenerator(builder, s) for s in spec[0]],
spec[1])
def generate(self,
size: Optional[int] = None,
key: Optional[str] = None,
context: Optional[ContextBase] = None) -> str:
if size is None:
return self._combiner(self._op, [
child.generate(None, key, context) for child in self._children
])
return [
self._combiner(self._op, elems) for elems in zip(*[
child.generate(size, key, context) for child in self._children
])
]
class ConcatGenerator(StrOpGenerator):
def __init__(self, rand: numpy.random.Generator, children: List[Generator],
concat: str):
super().__init__(rand, children, concat, Names.CONCAT, [str],
lambda op, elem: op.join(elem))
class FormatGenerator(StrOpGenerator):
def __init__(self, rand: numpy.random.Generator, children: List[Generator],
formatter: str):
super().__init__(rand, children, formatter, Names.FORMAT, None,
lambda op, elem: op.format(*elem))
def _GenKey(key: str, base: Optional[str]):
if base:
return base + '.' + key
return key
class RecordGenerator(Generator):
"""Base class for generating records-like objects: dicts, classes, dataframes."""
def __init__(self, rand: numpy.random.Generator,
generators: Dict[str, Generator], name: str):
super().__init__(rand)
self._generators = generators
self._name = name
self._with_context = False
def with_context(self):
"""Turns on context for generated values, based on records generated by this."""
self._with_context = True
return self
def nested(self, key: str):
"""Access the internal generator for provided key."""
if key in self._generators:
return self._generators[key]
return None
@classmethod
def build(cls, builder: Builder, spec):
if not isinstance(spec, dict):
raise ValueError(
f'{cls} expecting a dict specification. Got: {type(spec)}')
generators = {}
for k, v in spec.items():
if not isinstance(k, str):
raise ValueError(f'{cls} expecting string keys. Got `{k}`')
generator = builder.build_for_key(k, v)
builder.add_generator(k, generator)
generators[k] = generator
# pylint: disable=no-value-for-parameter
return cls(builder.rand, generators)
def save(self) -> Any:
return (self._name, {k: v.save() for | |
WHEN DELETE_ACTION = 1 THEN 'NO ACTION'
WHEN DELETE_ACTION = 2 THEN 'CASCADE'
ELSE 'SET NULL' END AS VARCHAR2(9)) AS DELETE_RULE,
CASE WHEN A.ENABLE_FLAG = 1 THEN CAST('ENABLED' AS VARCHAR2(8))
ELSE CAST('DISABLED' AS VARCHAR2(8)) END AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CASE WHEN A.VALIDATE_FLAG = 1 THEN CAST('VALIDATED' AS VARCHAR2(13))
ELSE CAST('NOT VALIDATED' AS VARCHAR2(13)) END AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CASE WHEN A.RELY_FLAG = 1 THEN CAST('RELY' AS VARCHAR2(4))
ELSE CAST(NULL AS VARCHAR2(4)) END AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(NULL AS VARCHAR2(128)) AS INDEX_OWNER,
CAST(NULL AS VARCHAR2(128)) AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_FOREIGN_KEY_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C
WHERE A.CHILD_TABLE_ID = B.TABLE_ID AND B.DATABASE_ID = C.DATABASE_ID AND A.REF_CST_TYPE = 0 AND A.REF_CST_ID = -1
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
AND (B.DATABASE_ID = USERENV('SCHEMAID')
OR USER_CAN_ACCESS_OBJ(1, B.TABLE_ID, B.DATABASE_ID) = 1)
UNION ALL
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(A.FOREIGN_KEY_NAME AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CAST('R' AS VARCHAR2(1)) AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CAST(NULL AS VARCHAR2(4000)) AS SEARCH_CONDITION,
CAST(E.DATABASE_NAME AS VARCHAR2(128)) AS R_OWNER,
CAST(CASE WHEN A.REF_CST_TYPE = 2 THEN SUBSTR(F.TABLE_NAME, 7 + INSTR(SUBSTR(F.TABLE_NAME, 7), '_'))
ELSE NULL END AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(CASE WHEN DELETE_ACTION = 1 THEN 'NO ACTION'
WHEN DELETE_ACTION = 2 THEN 'CASCADE'
ELSE 'SET NULL' END AS VARCHAR2(9)) AS DELETE_RULE,
CASE WHEN A.ENABLE_FLAG = 1 THEN CAST('ENABLED' AS VARCHAR2(8))
ELSE CAST('DISABLED' AS VARCHAR2(8)) END AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CASE WHEN A.VALIDATE_FLAG = 1 THEN CAST('VALIDATED' AS VARCHAR2(13))
ELSE CAST('NOT VALIDATED' AS VARCHAR2(13)) END AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CASE WHEN A.RELY_FLAG = 1 THEN CAST('RELY' AS VARCHAR2(4))
ELSE CAST(NULL AS VARCHAR2(4)) END AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(NULL AS VARCHAR2(128)) AS INDEX_OWNER,
CAST(NULL AS VARCHAR2(128)) AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_FOREIGN_KEY_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT D, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT E, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT F
WHERE A.CHILD_TABLE_ID = B.TABLE_ID
AND B.DATABASE_ID = C.DATABASE_ID
AND A.PARENT_TABLE_ID = D.TABLE_ID
AND D.DATABASE_ID = E.DATABASE_ID
AND (D.DATABASE_ID = USERENV('SCHEMAID')
OR USER_CAN_ACCESS_OBJ(1, D.TABLE_ID, D.DATABASE_ID) = 1)
AND (A.REF_CST_ID = F.TABLE_ID AND A.REF_CST_TYPE = 2)
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
AND D.TENANT_ID = EFFECTIVE_TENANT_ID()
AND E.TENANT_ID = EFFECTIVE_TENANT_ID()
AND F.TENANT_ID = EFFECTIVE_TENANT_ID()
UNION ALL
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(A.FOREIGN_KEY_NAME AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CAST('R' AS VARCHAR2(1)) AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CAST(NULL AS VARCHAR2(4000)) AS SEARCH_CONDITION,
CAST(E.DATABASE_NAME AS VARCHAR2(128)) AS R_OWNER,
CAST(CASE WHEN A.REF_CST_TYPE = 1 THEN F.CONSTRAINT_NAME
ELSE NULL END AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(CASE WHEN DELETE_ACTION = 1 THEN 'NO ACTION'
WHEN DELETE_ACTION = 2 THEN 'CASCADE'
ELSE 'SET NULL' END AS VARCHAR2(9)) AS DELETE_RULE,
CASE WHEN A.ENABLE_FLAG = 1 THEN CAST('ENABLED' AS VARCHAR2(8))
ELSE CAST('DISABLED' AS VARCHAR2(8)) END AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CASE WHEN A.VALIDATE_FLAG = 1 THEN CAST('VALIDATED' AS VARCHAR2(13))
ELSE CAST('NOT VALIDATED' AS VARCHAR2(13)) END AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CASE WHEN A.RELY_FLAG = 1 THEN CAST('RELY' AS VARCHAR2(4))
ELSE CAST(NULL AS VARCHAR2(4)) END AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(NULL AS VARCHAR2(128)) AS INDEX_OWNER,
CAST(NULL AS VARCHAR2(128)) AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_FOREIGN_KEY_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT D, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT E, SYS.ALL_VIRTUAL_CONSTRAINT_REAL_AGENT F
WHERE A.CHILD_TABLE_ID = B.TABLE_ID
AND B.DATABASE_ID = C.DATABASE_ID
AND A.PARENT_TABLE_ID = D.TABLE_ID
AND D.DATABASE_ID = E.DATABASE_ID
AND (D.DATABASE_ID = USERENV('SCHEMAID')
OR USER_CAN_ACCESS_OBJ(1, D.TABLE_ID, D.DATABASE_ID) = 1)
AND (A.PARENT_TABLE_ID = F.TABLE_ID
AND A.REF_CST_TYPE = 1
AND F.CONSTRAINT_TYPE = 1
AND A.REF_CST_ID = F.CONSTRAINT_ID)
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
AND D.TENANT_ID = EFFECTIVE_TENANT_ID()
AND E.TENANT_ID = EFFECTIVE_TENANT_ID()
AND F.TENANT_ID = EFFECTIVE_TENANT_ID()
UNION ALL
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(CONSTRAINT_NAME AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CASE WHEN A.CONSTRAINT_TYPE = 1 THEN CAST('P' AS VARCHAR2(1))
ELSE CAST('C' AS VARCHAR2(1)) END AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CASE WHEN A.CONSTRAINT_TYPE = 1 THEN CAST(NULL AS VARCHAR2(4000))
ELSE CAST(A.CHECK_EXPR AS VARCHAR2(4000)) END AS SEARCH_CONDITION,
CAST(NULL AS VARCHAR2(128)) AS R_OWNER,
CAST(NULL AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(NULL AS VARCHAR2(9)) AS DELETE_RULE,
CASE WHEN A.ENABLE_FLAG = 1 THEN CAST('ENABLED' AS VARCHAR2(8))
ELSE CAST('DISABLED' AS VARCHAR2(8)) END AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CASE WHEN A.VALIDATE_FLAG = 1 THEN CAST('VALIDATED' AS VARCHAR2(13))
ELSE CAST('NOT VALIDATED' AS VARCHAR2(13)) END AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CASE WHEN A.RELY_FLAG = 1 THEN CAST('RELY' AS VARCHAR2(4))
ELSE CAST(NULL AS VARCHAR2(4)) END AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS INDEX_OWNER,
CASE WHEN A.CONSTRAINT_TYPE = 1 THEN CAST(A.CONSTRAINT_NAME AS VARCHAR2(128))
ELSE CAST(NULL AS VARCHAR2(128)) END AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_CONSTRAINT_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C
WHERE A.TABLE_ID = B.TABLE_ID
AND B.DATABASE_ID = C.DATABASE_ID
AND (B.DATABASE_ID = USERENV('SCHEMAID')
OR USER_CAN_ACCESS_OBJ(1, B.TABLE_ID, B.DATABASE_ID) = 1)
AND C.DATABASE_NAME != '__recyclebin'
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
""".replace("\n", " ")
)
def_table_schema(
table_name = 'USER_CONSTRAINTS',
database_id = 'OB_ORA_SYS_DATABASE_ID',
table_id = '25017',
table_type = 'SYSTEM_VIEW',
rowkey_columns = [],
normal_columns = [],
gm_columns = [],
in_tenant_space = True,
view_definition = """
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(SUBSTR(A.TABLE_NAME, 7 + INSTR(SUBSTR(A.TABLE_NAME, 7), '_')) AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CAST('U' AS VARCHAR2(1)) AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CAST(NULL AS VARCHAR2(4000)) AS SEARCH_CONDITION,
CAST(NULL AS VARCHAR2(128)) AS R_OWNER,
CAST(NULL AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(NULL AS VARCHAR2(9)) AS DELETE_RULE,
CAST('ENABLED' AS VARCHAR2(8)) AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CAST('VALIDATED' AS VARCHAR2(13)) AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CAST(NULL AS VARCHAR2(4)) AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS INDEX_OWNER,
CAST(SUBSTR(A.TABLE_NAME, 7 + INSTR(SUBSTR(A.TABLE_NAME, 7), '_')) AS VARCHAR2(128)) AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_TABLE_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C WHERE A.DATA_TABLE_ID = B.TABLE_ID AND A.DATABASE_ID = C.DATABASE_ID
AND A.DATABASE_ID = USERENV('SCHEMAID') AND A.INDEX_TYPE IN (2, 4, 8)
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
UNION ALL
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(A.FOREIGN_KEY_NAME AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CAST('R' AS VARCHAR2(1)) AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CAST(NULL AS VARCHAR2(4000)) AS SEARCH_CONDITION,
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS R_OWNER,
CAST(NULL AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(CASE WHEN DELETE_ACTION = 1 THEN 'NO ACTION'
WHEN DELETE_ACTION = 2 THEN 'CASCADE'
ELSE 'SET NULL' END AS VARCHAR2(9)) AS DELETE_RULE,
CASE WHEN A.ENABLE_FLAG = 1 THEN CAST('ENABLED' AS VARCHAR2(8))
ELSE CAST('DISABLED' AS VARCHAR2(8)) END AS STATUS,
CAST('NOT DEFERRABLE' AS VARCHAR2(14)) AS DEFERRABLE,
CAST('IMMEDIATE' AS VARCHAR2(9)) AS DEFERRED,
CASE WHEN A.VALIDATE_FLAG = 1 THEN CAST('VALIDATED' AS VARCHAR2(13))
ELSE CAST('NOT VALIDATED' AS VARCHAR2(13)) END AS VALIDATED,
CAST(NULL AS VARCHAR2(14)) AS "GENERATED",
CAST(NULL AS VARCHAR2(3)) AS BAD,
CAST(NULL AS VARCHAR2(4)) AS RELY,
CAST(NULL AS DATE) AS LAST_CHANGE,
CAST(NULL AS VARCHAR2(128)) AS INDEX_OWNER,
CAST(NULL AS VARCHAR2(128)) AS INDEX_NAME,
CAST(NULL AS VARCHAR2(7)) AS INVALID,
CAST(NULL AS VARCHAR2(14)) AS VIEW_RELATED
FROM SYS.ALL_VIRTUAL_FOREIGN_KEY_REAL_AGENT A, SYS.ALL_VIRTUAL_TABLE_REAL_AGENT B, SYS.ALL_VIRTUAL_DATABASE_REAL_AGENT C
WHERE A.CHILD_TABLE_ID = B.TABLE_ID AND B.DATABASE_ID = C.DATABASE_ID AND A.REF_CST_TYPE = 0 AND A.REF_CST_ID = -1 AND B.DATABASE_ID = USERENV('SCHEMAID')
AND A.TENANT_ID = EFFECTIVE_TENANT_ID()
AND B.TENANT_ID = EFFECTIVE_TENANT_ID()
AND C.TENANT_ID = EFFECTIVE_TENANT_ID()
UNION ALL
SELECT
CAST(C.DATABASE_NAME AS VARCHAR2(128)) AS OWNER,
CAST(A.FOREIGN_KEY_NAME AS VARCHAR2(128)) AS CONSTRAINT_NAME,
CAST('R' AS VARCHAR2(1)) AS CONSTRAINT_TYPE,
CAST(B.TABLE_NAME AS VARCHAR2(128)) AS TABLE_NAME,
CAST(NULL AS VARCHAR2(4000)) AS SEARCH_CONDITION,
CAST(E.DATABASE_NAME AS VARCHAR2(128)) AS R_OWNER,
CAST(CASE WHEN A.REF_CST_TYPE = 2 THEN SUBSTR(F.TABLE_NAME, 7 + INSTR(SUBSTR(F.TABLE_NAME, 7), '_'))
ELSE NULL END AS VARCHAR2(128)) AS R_CONSTRAINT_NAME,
CAST(CASE WHEN DELETE_ACTION = 1 THEN 'NO ACTION'
WHEN DELETE_ACTION = | |
<reponame>stolau/oty_ilmo
from werkzeug.security import generate_password_hash, check_password_hash
from flask import Flask, render_template, url_for, redirect, request, flash, send_from_directory, session
from flask_httpauth import HTTPBasicAuth
from app import app, db
from flask_mail import Mail, Message
from datetime import datetime, date, time, timedelta
import requests
from sqlalchemy import and_
from app.forms import ots2021Form, fuksisitsitForm, tupsufuksisitsitForm, fuksibeerpongForm, beerpongForm, aitiForm, hvsitsituusiForm, syysuitotForm, prositsitForm, simailutForm, kotiruokakurssi1Form, kotiruokakurssi2Form
from app.models import ots2021Model, fuksisitsitModel, tupsufuksisitsitModel, fuksibeerpongModel, beerpongModel, aitiModel, hvsitsituusiModel, syysuitotModel, prositsitModel, simailutModel, kotiruokakurssi1Model, kotiruokakurssi2Model
from flask_wtf.csrf import CSRFProtect, CSRFError
import os
from app import sqlite_to_csv
from werkzeug.datastructures import MultiDict
from urllib.parse import urlparse, urlunparse
import subprocess
import time
import json
auth = HTTPBasicAuth()
csrf = CSRFProtect()
try:
file = open("auth.conf", "r")
lines = file.readlines()
password = <PASSWORD>)
print("created default user")
print("username: admin")
print("password: " + str(password))
users = {
"admin": generate_password_hash(password)
}
roles = {
"admin": "admin"
}
for line in lines:
new_user = line.split(",", 6)
users[new_user[0]] = generate_password_hash(new_user[1])
roles[new_user[0]] = new_user[2:6]
except FileNotFoundError as e:
print(e)
password = os.urandom(64)
print("auth.conf not found, created default user")
print("username: admin")
print("password: " + str(password))
users = {
"admin": generate_password_hash(password)
}
roles = {
"admin": "admin"
}
else:
file.close()
KAPSI = False
try:
file = open("routes.conf", "r")
lines = file.readlines()
for line in lines:
conf_line = line.split(":", 2)
if "kapsi" in conf_line[0]:
if "true" in conf_line[1]:
KAPSI = True
else:
KAPSI = False
except FileNotFoundError as e:
print(e)
print("routes.conf not found")
else:
file.close()
GMAIL = True
if GMAIL:
mail_settings = {
"MAIL_SERVER": 'smtp.gmail.com',
"MAIL_PORT": 465,
"MAIL_USE_TLS": False,
"MAIL_USE_SSL": True,
"MAIL_USERNAME": '<EMAIL>',
"MAIL_PASSWORD": '<PASSWORD>'
}
app.config.update(mail_settings)
mail = Mail(app)
@auth.verify_password
def verify_password(username, password):
if username in users and \
check_password_hash(users.get(username), password):
return username
@auth.get_user_roles
def get_user_roles(user):
return roles.get(user)
@app.route('/')
def index():
return render_template('index.html', title='OTY:n ilmot', page="index")
@app.route('/ots2021', methods=['GET', 'POST'])
def ots2021():
form = ots2021Form()
starttime = datetime(2021, 3, 5, 12, 00, 00)
endtime = datetime(2021, 3, 12, 23, 59, 59)
nowtime = datetime.now()
limit = 4000
maxlimit = 4000
entrys = ots2021Model.query.all()
count = ots2021Model.query.count()
for entry in entrys:
if((entry.etunimi == form.etunimi.data and entry.sukunimi == form.sukunimi.data) or entry.email == form.email.data):
flash('Olet jo ilmoittautunut')
return render_template('ots2021.html', title='ots2021 ilmoittautuminen',
entrys=entrys,
count=count,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
limit=limit,
form=form,
page="ots2021")
if request.method == 'POST':
validate = form.validate_on_submit()
submitted = form.is_submitted()
else:
validate = False
submitted = False
if validate and submitted and count <= maxlimit:
flash('Ilmoittautuminen onnistui')
sub = ots2021Model(
etunimi = form.etunimi.data,
sukunimi = form.sukunimi.data,
email = form.email.data,
kilta = form.kilta.data,
consent1 = form.consent1.data,
consent0 = form.consent0.data,
datetime = nowtime,
)
db.session.add(sub)
db.session.commit()
return render_template('ots2021_redirect.html')
elif submitted and count > maxlimit:
flash('IlmoittIlmoittautuminen on jo täynnä')
elif (not validate) and submitted:
flash('Ilmoittautuminen epäonnistui, tarkista syöttämäsi tiedot')
return render_template('ots2021.html', title='ots2021 ilmottautuminen',
entrys=entrys,
count=count,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
limit=limit,
form=form,
page="ots2021")
@app.route('/ots2021_data', methods=['GET'])
@auth.login_required(role=['admin', 'meea'])
def ots2021_data():
limit = 4000
entries = ots2021Model.query.all()
count = ots2021Model.query.count()
return render_template('ots2021_data.html', title='ots2021 data',
entries=entries,
count=count,
limit=limit)
@app.route('/ots2021_data/ots2021Model.csv')
@auth.login_required(role=['admin', 'meea'])
def ots2021_csv():
os.system('mkdir csv')
sqlite_to_csv.exportToCSV('ots2021_model')
dir = os.path.join(os.getcwd(), 'csv/')
try:
return send_from_directory(directory=dir, filename='ots2021_model_data.csv', as_attachment=True)
except FileNotFoundError as e:
abort(404)
@app.route('/kysely_arvonta_juttu_data', methods=['GET'])
@auth.login_required(role=['admin', 'kysely_arvonta_juttu'])
def kysely_arvonta_juttu_data():
limit = 4000
entries = kysely_arvonta_juttuModel.query.all()
count = kysely_arvonta_juttuModel.query.count()
return render_template('kysely_arvonta_juttu_data.html', title='kysely_arvonta_juttu data',
entries=entries,
count=count,
limit=limit)
@app.route('/kysely_arvonta_juttu_data/kysely_arvonta_juttu_model_data.csv')
@auth.login_required(role=['admin', 'kysely_arvonta_juttu'])
def kysely_arvonta_juttu_csv():
os.system('mkdir csv')
sqlite_to_csv.exportToCSV('kysely_arvonta_juttu_model')
dir = os.path.join(os.getcwd(), 'csv/')
try:
print(dir)
return send_from_directory(directory=dir, filename='kysely_arvonta_juttu_model_data.csv', as_attachment=True)
except FileNotFoundError as e:
print(e)
abort(404)
@app.route('/fuksisitsit', methods=['GET', 'POST'])
def fuksisitsit():
form = fuksisitsitForm()
starttime = datetime(2021, 4, 9, 15, 00, 00)
endtime = datetime(2021, 4, 15, 23, 59, 59)
nowtime = datetime.now()
limit = 4000
count = 0
entrys = fuksisitsitModel.query.all()
totalCount = 0
for entry in entrys:
totalCount += 1
for entry in entrys:
if(entry.etunimi0 == form.etunimi0.data) and entry.sukunimi0 == form.sukunimi0.data:
flash('Olet jo ilmoittautunut')
return render_template('/2021/fuksisitsit.html', title='Fuksisitsit',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="fuksisitsit")
if request.method == 'POST':
validate = form.validate_on_submit()
submitted = form.is_submitted()
else:
validate = False
submitted = False
if validate and submitted and limit > totalCount:
flash('Ilmoittautuminen onnistui')
sub = fuksisitsitModel(
membercount = form.membercount.data,
etunimi0 = form.etunimi0.data,
sukunimi0 = form.sukunimi0.data,
phone0 = form.phone0.data,
email0 = form.email0.data,
kilta0 = form.kilta0.data,
vapaavalinta0 = form.vapaavalinta0.data,
consent0 = form.consent0.data,
consent1 = form.consent1.data,
consent2 = form.consent2.data,
datetime = nowtime
)
db.session.add(sub)
db.session.commit()
return redirect(url_for('fuksisitsit'))
elif submitted and limit < totalCount:
totalCount -= 1
flash('Ilmoittautuminen on jo täynnä')
elif (not validate) and submitted:
flash('Ilmoittautuminen epäonnistui, tarkista syöttämäsi tiedot')
return render_template('/2021/fuksisitsit.html', title='Fuksisitsit',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="fuksisitsit")
@app.route('/fuksisitsit_data', methods=['GET'])
@auth.login_required(role=['admin', 'ella', 'anssi', 'meea'])
def fuksisitsit_data():
limit = 4000
entries = fuksisitsitModel.query.all()
count = fuksisitsitModel.query.count()
return render_template('/2021/fuksisitsit_data.html', title='fuksisitsit data',
entries=entries,
count=count,
limit=limit)
@app.route('/fuksisitsit/fuksisitsit_model_data.csv')
@auth.login_required(role=['admin', 'ella'])
def fuksisitsit_csv():
os.system('mkdir csv')
sqlite_to_csv.exportToCSV('fuksisitsit_model')
dir = os.path.join(os.getcwd(), 'csv/')
try:
print(dir)
return send_from_directory(directory=dir, filename='fuksisitsit_model_data.csv', as_attachment=True)
except FileNotFoundError as e:
print(e)
abort(404)
@app.route('/tupsufuksisitsit', methods=['GET', 'POST'])
def tupsufuksisitsit():
form = tupsufuksisitsitForm()
starttime = datetime(2021, 4, 9, 15, 00, 00)
endtime = datetime(2021, 4, 14, 23, 59, 59)
nowtime = datetime.now()
limit = 4000
count = 0
entrys = tupsufuksisitsitModel.query.all()
totalCount = 0
for entry in entrys:
totalCount += 1
for entry in entrys:
if(entry.etunimi0 == form.etunimi0.data) and entry.sukunimi0 == form.sukunimi0.data:
flash('Olet jo ilmoittautunut')
return render_template('/2021/tupsufuksisitsit.html', title='TupsuFuksisitsit',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="tupsufuksisitsit")
if request.method == 'POST':
validate = form.validate_on_submit()
submitted = form.is_submitted()
else:
validate = False
submitted = False
if validate and submitted and limit > totalCount:
flash('Ilmoittautuminen onnistui')
sub = tupsufuksisitsitModel(
membercount = form.membercount.data,
etunimi0 = form.etunimi0.data,
sukunimi0 = form.sukunimi0.data,
phone0 = form.phone0.data,
email0 = form.email0.data,
kilta0 = form.kilta0.data,
vapaavalinta0 = form.vapaavalinta0.data,
consent0 = form.consent0.data,
consent1 = form.consent1.data,
consent2 = form.consent2.data,
datetime = nowtime
)
db.session.add(sub)
db.session.commit()
return redirect(url_for('tupsufuksisitsit'))
elif submitted and limit < totalCount:
totalCount -= 1
flash('Ilmoittautuminen on jo täynnä')
elif (not validate) and submitted:
flash('Ilmoittautuminen epäonnistui, tarkista syöttämäsi tiedot')
return render_template('/2021/tupsufuksisitsit.html', title='TupsuFuksisitsit',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="tupsufuksisitsit")
@app.route('/tupsufuksisitsit_data', methods=['GET'])
@auth.login_required(role=['admin', 'ella', 'anssi', 'meea'])
def tupsufuksisitsit_data():
limit = 4000
entries = tupsufuksisitsitModel.query.all()
count = tupsufuksisitsitModel.query.count()
return render_template('/2021/tupsufuksisitsit_data.html', title='tupsufuksisitsit data',
entries=entries,
count=count,
limit=limit)
@app.route('/tupsufuksisitsit/tupsufuksisitsit_model_data.csv')
@auth.login_required(role=['admin', 'ella'])
def tupsufuksisitsit_csv():
os.system('mkdir csv')
sqlite_to_csv.exportToCSV('tupsufuksisitsit_model')
dir = os.path.join(os.getcwd(), 'csv/')
try:
print(dir)
return send_from_directory(directory=dir, filename='tupsufuksisitsit_model_data.csv', as_attachment=True)
except FileNotFoundError as e:
print(e)
abort(404)
@app.route('/fuksibeerpong', methods=['GET', 'POST'])
def fuksibeerpong():
form = fuksibeerpongForm()
starttime = datetime(2021, 4, 13, 12, 00, 00)
endtime = datetime(2021, 4, 18, 23, 59, 59)
nowtime = datetime.now()
limit = 32
count = 0
entrys = fuksibeerpongModel.query.all()
totalCount = 0
for entry in entrys:
totalCount += 1
for entry in entrys:
if(entry.joukkue == form.joukkue.data):
flash('Olet jo ilmoittautunut')
return render_template('/2021/fuksibeerpong.html', title='Fuksibeerpong',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="fuksibeerpong")
if request.method == 'POST':
validate = form.validate_on_submit()
submitted = form.is_submitted()
else:
validate = False
submitted = False
if validate and submitted and limit > totalCount:
flash('Ilmoittautuminen onnistui')
sub = fuksibeerpongModel(
joukkue = form.joukkue.data,
etunimi0 = form.etunimi0.data,
sukunimi0 = form.sukunimi0.data,
phone0 = form.phone0.data,
email0 = form.email0.data,
etunimi1 = form.etunimi1.data,
sukunimi1 = form.sukunimi1.data,
consent0 = form.consent0.data,
consent1 = form.consent1.data,
consent2 = form.consent2.data,
datetime = nowtime
)
db.session.add(sub)
db.session.commit()
return redirect(url_for('fuksibeerpong'))
elif submitted and limit < totalCount:
totalCount -= 1
flash('Ilmoittautuminen on jo täynnä')
elif (not validate) and submitted:
flash('Ilmoittautuminen epäonnistui, tarkista syöttämäsi tiedot')
return render_template('/2021/fuksibeerpong.html', title='Fuksibeerpong',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="fuksibeerpong")
@app.route('/fuksibeerpong_data', methods=['GET'])
@auth.login_required(role=['admin', 'nikita', 'anssi'])
def fuksibeerpong_data():
limit = 32
entries = fuksibeerpongModel.query.all()
count = fuksibeerpongModel.query.count()
return render_template('/2021/fuksibeerpong_data.html', title='fuksibeerpong data',
entries=entries,
count=count,
limit=limit)
@app.route('/fuksibeerpong/fuksibeerpong_model_data.csv')
@auth.login_required(role=['admin', 'nikita', 'anssi'])
def fuksibeerpong_csv():
os.system('mkdir csv')
sqlite_to_csv.exportToCSV('fuksibeerpong_model')
dir = os.path.join(os.getcwd(), 'csv/')
try:
print(dir)
return send_from_directory(directory=dir, filename='fuksibeerpong_model_data.csv', as_attachment=True)
except FileNotFoundError as e:
print(e)
abort(404)
@app.route('/beerpong', methods=['GET', 'POST'])
def beerpong():
form = beerpongForm()
starttime = datetime(2021, 4, 13, 16, 00, 00)
endtime = datetime(2021, 4, 19, 23, 59, 59)
nowtime = datetime.now()
limit = 32
count = 0
entrys = beerpongModel.query.all()
totalCount = 0
for entry in entrys:
totalCount += 1
for entry in entrys:
if(entry.joukkue == form.joukkue.data):
flash('Olet jo ilmoittautunut')
return render_template('/2021/beerpong.html', title='Beerpong',
entrys=entrys,
starttime=starttime,
endtime=endtime,
nowtime=nowtime,
form=form,
totalCount=totalCount,
limit=limit,
page="beerpong")
if request.method == 'POST':
validate = form.validate_on_submit()
submitted = form.is_submitted()
else:
validate = False
submitted = False
if validate and submitted and limit > totalCount:
flash('Ilmoittautuminen onnistui')
sub = beerpongModel(
joukkue = form.joukkue.data,
etunimi0 = form.etunimi0.data,
sukunimi0 = form.sukunimi0.data,
phone0 = form.phone0.data,
email0 = form.email0.data,
etunimi1 = form.etunimi1.data,
sukunimi1 = form.sukunimi1.data,
consent0 = form.consent0.data,
consent1 = form.consent1.data,
consent2 = form.consent2.data,
datetime = nowtime
)
db.session.add(sub)
db.session.commit()
return redirect(url_for('beerpong'))
elif submitted and limit < totalCount:
totalCount -= 1
flash('Ilmoittautuminen on jo täynnä')
elif (not validate) and submitted:
flash('Ilmoittautuminen epäonnistui, tarkista syöttämäsi | |
model_names
col_names = [f"({i + 1})" for i in range(len(model_names))]
return col_names, col_groups
def _customize_col_groups(default_col_groups, custom_col_groups):
"""Change default (inferred) column group titles using custom column groups.
Args:
default_col_groups (list or NoneType): The inferred column groups.
custom_col_groups (list or dict): Dictionary mapping defautl column group
titles to custom column group titles, if the defautl column groups are
defined. Must be a list of the same lenght as models otherwise.
Returns:
col_groups (list): Column groups to display in estimation table.
"""
if custom_col_groups:
if not default_col_groups:
if not isinstance(custom_col_groups, list):
raise ValueError(
"""With unique model names, multiple models can't be grouped
under common group name. Provide list of unique group names instead,
if you wish to add column level."""
)
col_groups = custom_col_groups
else:
if isinstance(custom_col_groups, list):
col_groups = custom_col_groups
elif isinstance(custom_col_groups, dict):
col_groups = (
pd.Series(default_col_groups).replace(custom_col_groups).to_list()
)
else:
raise TypeError(
f"""Invalid type for custom_col_groups. Can be either list
or dictionary, or NoneType. Not: {type(col_groups)}."""
)
else:
col_groups = default_col_groups
return col_groups
def _customize_col_names(default_col_names, custom_col_names):
"""Change default (inferred) column names using custom column names.
Args:
deafult_col_names (list): The default (inferred) column names.
custom_col_names (list or dict): Dictionary mapping default column names
to custom column names, or list to display as the name of each
model column.
Returns:
column_names (list): The column names to display in the estimatino table.
"""
if not custom_col_names:
col_names = default_col_names
elif isinstance(custom_col_names, dict):
col_names = list(pd.Series(default_col_names).replace(custom_col_names))
elif isinstance(custom_col_names, list):
if not len(custom_col_names) == len(default_col_names):
raise ValueError(
f"""If provided as a list, custom_col_names should have same length as
default_col_names. Lenght of custom_col_names {len(custom_col_names)}
!=length of default_col_names {len(default_col_names)}"""
)
elif any(isinstance(i, list) for i in custom_col_names):
raise ValueError("Custom_col_names cannot be a nested list")
col_names = custom_col_names
else:
raise TypeError(
f"""Invalid type for custom_col_names.
Can be either list or dictionary, or NoneType. Not: {col_names}."""
)
return col_names
def _create_group_to_col_position(column_groups):
"""Get mapping from column groups to column positions.
Args:
column_names (list): The column groups to display in the estimatino table.
Returns:
group_to_col_index(dict): The mapping from column group titles to column
positions.
"""
if column_groups is not None:
group_to_col_index = {group: [] for group in list(set(column_groups))}
for i, group in enumerate(column_groups):
group_to_col_index[group].append(i)
else:
group_to_col_index = None
return group_to_col_index
def _convert_frame_to_string_series(
df,
significance_levels,
show_stars,
):
"""Return processed value series with significance stars and inference information.
Args:
df (DataFrame): params DataFrame of the model
significance_levels (list): see main docstring
number_format (int): see main docstring
show_inference (bool): see main docstring
confidence_intervals (bool): see main docstring
show_stars (bool): see main docstring
Returns:
sr (pd.Series): string series with values and inferences.
"""
value_sr = df["value"]
if show_stars:
sig_bins = [-1] + sorted(significance_levels) + [2]
value_sr += "$^{"
value_sr += (
pd.cut(
df["p_value"],
bins=sig_bins,
labels=[
"*" * (len(significance_levels) - i)
for i in range(len(significance_levels) + 1)
],
)
.astype("str")
.replace("nan", "")
.replace(np.nan, "")
)
value_sr += " }$"
if "ci_lower" in df:
ci_lower = df["ci_lower"]
ci_upper = df["ci_upper"]
inference_sr = "("
inference_sr += ci_lower
inference_sr += r";"
inference_sr += ci_upper
inference_sr += ")"
sr = _combine_series(value_sr, inference_sr)
elif "standard_error" in df:
standard_error = df["standard_error"]
inference_sr = "(" + standard_error + ")"
sr = _combine_series(value_sr, inference_sr)
else:
sr = value_sr
# replace empty braces with empty string
sr = sr.where(sr.apply(lambda x: bool(re.search(r"\d", x))), "")
sr.name = ""
return sr
def _combine_series(value_sr, inference_sr):
"""Merge value and inference series.
Return string series with parameter values and precision values below respective
param values.
Args:
values_sr (Series): string series of estimated parameter values
inference_sr (Series): string series of inference values
Returns:
series: combined string series of param and inference values
"""
value_df = value_sr.to_frame(name="")
original_cols = value_df.columns
value_df.reset_index(drop=False, inplace=True)
index_names = [item for item in value_df.columns if item not in original_cols]
# set the index to even numbers, starting at 0
value_df.index = value_df.index * 2
inference_df = inference_sr.to_frame(name="")
inference_df.reset_index(drop=False, inplace=True)
# set the index to odd numbers, starting at 1
inference_df.index = (inference_df.index * 2) + 1
inference_df[index_names[-1]] = ""
df = pd.concat([value_df, inference_df]).sort_index()
df.set_index(index_names, inplace=True, drop=True)
return df[""]
def _create_statistics_sr(
model,
stats_options,
significance_levels,
show_stars,
number_format,
add_trailing_zeros,
max_trail,
):
"""Process statistics values, return string series.
Args:
model (estimation result): see main docstring
stats_options (dict): see main docstring
significance_levels (list): see main docstring
show_stars (bool): see main docstring
number_format (int): see main focstring
Returns:
series: string series with summary statistics values and additional info
if applicable.
"""
stats_values = {}
stats_options = deepcopy(stats_options)
if "show_dof" in stats_options:
show_dof = stats_options.pop("show_dof")
else:
show_dof = None
for k in stats_options:
if k not in ["n_obs", "nobs"]:
stats_values[stats_options[k]] = model.info.get(k, np.nan)
raw_formatted = _apply_number_format(
pd.DataFrame(pd.Series(stats_values)), number_format
)
if add_trailing_zeros:
formatted = _apply_number_format(raw_formatted, max_trail)
else:
formatted = raw_formatted
stats_values = formatted.to_dict()[0]
if "n_obs" in stats_options:
n_obs = model.info.get("n_obs", np.nan)
if not np.isnan(n_obs):
n_obs = int(n_obs)
stats_values[stats_options["n_obs"]] = n_obs
elif "nobs" in stats_options:
n_obs = model.info.get("nobs", np.nan)
if not np.isnan(n_obs):
n_obs = int(n_obs)
stats_values[stats_options["nobs"]] = n_obs
if "fvalue" in model.info and "F Statistic" in stats_values:
if show_stars and "f_pvalue" in model.info:
sig_bins = [-1] + sorted(significance_levels) + [2]
sig_icon_fstat = "*" * (
len(significance_levels)
- np.digitize(model.info["f_pvalue"], sig_bins)
+ 1
)
stats_values["F Statistic"] = (
stats_values["F Statistic"] + "$^{" + sig_icon_fstat + "}$"
)
if show_dof:
fstat_str = "{{{}(df={};{})}}"
stats_values["F Statistic"] = fstat_str.format(
stats_values["F Statistic"],
int(model.info["df_model"]),
int(model.info["df_resid"]),
)
if "resid_std_err" in model.info and "Residual Std. Error" in stats_values:
if show_dof:
rse_str = "{{{}(df={})}}"
stats_values["Residual Std. Error"] = rse_str.format(
stats_values["Residual Std. Error"], int(model.info["df_resid"])
)
stat_sr = pd.Series(stats_values)
# the follwing is to make sure statistics dataframe has as many levels of
# indices as the parameters dataframe.
stat_ind = np.empty((len(stat_sr), model.params.index.nlevels - 1), dtype=str)
stat_ind = np.concatenate(
[stat_sr.index.values.reshape(len(stat_sr), 1), stat_ind], axis=1
).T
stat_sr.index = pd.MultiIndex.from_arrays(stat_ind)
return stat_sr.astype("str").replace("nan", "")
def _process_frame_indices(
df,
custom_param_names,
custom_index_names,
show_col_names,
show_col_groups,
column_names,
column_groups,
):
"""Process body DataFrame, customize the header.
Args:
df (DataFrame): string DataFrame with parameter values and inferences.
custom_param_names (dict): see main docstring
custom_index_names (list): see main docstring
show_col_names (bool): see main docstring
column_names (list): List of column names to display in estimation table.
column_groups (list): List of column group titles to display in estimation
table.
Returns:
processed_df (DataFrame): string DataFrame with customized header.
"""
# The column names of the df are empty strings.
# If show_col_names is True, rename columns using column_names.
# Add column level if show col_groups is True.
if show_col_names:
if show_col_groups:
df.columns = pd.MultiIndex.from_tuples(
[(i, j) for i, j in zip(column_groups, column_names)]
)
else:
df.columns = column_names
if custom_index_names:
if isinstance(custom_index_names, list):
df.index.names = custom_index_names
elif isinstance(custom_index_names, dict):
df.rename_axis(index=custom_index_names, inplace=True)
else:
TypeError(
f"""Invalid custom_index_names can be of type either list or dict,
or NoneType. Not: {type(custom_index_names)}."""
)
if custom_param_names:
ind = df.index.to_frame()
ind = ind.replace(custom_param_names)
df.index = pd.MultiIndex.from_frame(ind)
return df
def _generate_notes_latex(
append_notes, notes_label, significance_levels, custom_notes, df
):
"""Generate the LaTex script of the notes section.
Args:
append_notes (bool): see main docstring
notes_label (str): see main docstring
significance_levels (list): see main docstring
custom_notes (str): see main docstring
df (DataFrame): params DataFrame of estimation model
Returns:
notes_latex (str): a string with LaTex script
"""
n_levels = df.index.nlevels
n_columns = len(df.columns)
significance_levels = sorted(significance_levels)
notes_text = ""
if append_notes:
notes_text += "\\midrule\n"
notes_text += "\\textit{{{}}} & \\multicolumn{{{}}}{{r}}{{".format(
notes_label, str(n_columns + n_levels - 1)
)
# iterate over penultimate significance_lelvels since last item of legend
# is not followed by a semi column
for i in range(len(significance_levels) - 1):
star = "*" * (len(significance_levels) - i)
notes_text += "$^{{{}}}$p$<${};".format(star, str(significance_levels[i]))
notes_text += "$^{*}$p$<$" + str(significance_levels[-1]) + "} \\\\\n"
if custom_notes:
amp_n = "&" * n_levels
if isinstance(custom_notes, list):
if not all(isinstance(n, str) for n in custom_notes):
raise ValueError(
f"""Each custom note can only be of string type.
The following notes:
{[n for n in custom_notes if not type(n)==str]} are of types
{[type(n) for n in custom_notes if not type(n)==str]}
respectively."""
)
for n in custom_notes:
notes_text += """
{}\\multicolumn{{{}}}{{r}}\\textit{{{}}}\\\\\n""".format(
amp_n, n_columns, n
)
elif isinstance(custom_notes, str):
notes_text += "{}\\multicolumn{{{}}}{{r}}\\textit{{{}}}\\\\\n".format(
amp_n, n_columns, custom_notes
)
else:
raise ValueError(
f"""Custom notes can be either a string | |
<filename>smt/applications/tests/test_mixed_integer.py
import unittest
import numpy as np
import matplotlib
matplotlib.use("Agg")
from smt.applications.mixed_integer import (
MixedIntegerContext,
MixedIntegerSamplingMethod,
FLOAT,
ENUM,
ORD,
GOWER,
check_xspec_consistency,
unfold_xlimits_with_continuous_limits,
fold_with_enum_index,
unfold_with_enum_mask,
compute_unfolded_dimension,
cast_to_enum_value,
cast_to_mixed_integer,
cast_to_discrete_values,
)
from smt.problems import Sphere
from smt.sampling_methods import LHS
from smt.surrogate_models import KRG, QP
from smt.applications.mixed_integer import INT
class TestMixedInteger(unittest.TestCase):
###INT DEPRECATED####
def test_qp_mixed_2D_INT(self):
xtypes = [FLOAT, INT]
xlimits = [[-10, 10], [-10, 10]]
mixint = MixedIntegerContext(xtypes, xlimits)
sm = mixint.build_surrogate_model(QP(print_prediction=False))
sampling = mixint.build_sampling_method(LHS, criterion="m")
fun = Sphere(ndim=2)
xt = sampling(10)
yt = fun(xt)
sm.set_training_values(xt, yt)
sm.train()
eq_check = True
for i in range(xt.shape[0]):
if abs(float(xt[i, :][1]) - int(float(xt[i, :][1]))) > 10e-8:
eq_check = False
self.assertTrue(eq_check)
def test_krg_mixed_3D_INT(self):
xtypes = [FLOAT, (ENUM, 3), INT]
xlimits = [[-10, 10], ["blue", "red", "green"], [-10, 10]]
mixint = MixedIntegerContext(xtypes, xlimits)
sm = mixint.build_surrogate_model(KRG(print_prediction=False))
sampling = mixint.build_sampling_method(LHS, criterion="m")
fun = Sphere(ndim=3)
xt = sampling(20)
yt = fun(xt)
sm.set_training_values(xt, yt)
sm.train()
eq_check = True
for i in range(xt.shape[0]):
if abs(float(xt[i, :][2]) - int(float(xt[i, :][2]))) > 10e-8:
eq_check = False
if not (xt[i, :][1] == 0 or xt[i, :][1] == 1 or xt[i, :][1] == 2):
eq_check = False
self.assertTrue(eq_check)
def test_check_xspec_consistency(self):
xtypes = [FLOAT, (ENUM, 3), ORD]
xlimits = [[-10, 10], ["blue", "red", "green"]] # Bad dimension
with self.assertRaises(ValueError):
check_xspec_consistency(xtypes, xlimits)
xtypes = [FLOAT, (ENUM, 3), ORD]
xlimits = [[-10, 10], ["blue", "red"], [-10, 10]] # Bad enum
with self.assertRaises(ValueError):
check_xspec_consistency(xtypes, xlimits)
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], ["0", "4", "3"]],
dtype="object",
)
l = unfold_xlimits_with_continuous_limits(xtypes, xlimits)
with self.assertRaises(ValueError):
check_xspec_consistency(xtypes, xlimits)
def test_krg_mixed_3D(self):
xtypes = [FLOAT, (ENUM, 3), ORD]
xlimits = [[-10, 10], ["blue", "red", "green"], [-10, 10]]
mixint = MixedIntegerContext(xtypes, xlimits)
sm = mixint.build_surrogate_model(KRG(print_prediction=False))
sampling = mixint.build_sampling_method(LHS, criterion="m")
fun = Sphere(ndim=3)
xt = sampling(20)
yt = fun(xt)
sm.set_training_values(xt, yt)
sm.train()
eq_check = True
for i in range(xt.shape[0]):
if abs(float(xt[i, :][2]) - int(float(xt[i, :][2]))) > 10e-8:
eq_check = False
if not (xt[i, :][1] == 0 or xt[i, :][1] == 1 or xt[i, :][1] == 2):
eq_check = False
self.assertTrue(eq_check)
def test_krg_mixed_3D_bad_regr(self):
xtypes = [FLOAT, (ENUM, 3), ORD]
xlimits = [[-10, 10], ["blue", "red", "green"], [-10, 10]]
mixint = MixedIntegerContext(xtypes, xlimits)
with self.assertRaises(ValueError):
sm = mixint.build_surrogate_model(
KRG(print_prediction=False, poly="linear")
)
def test_qp_mixed_2D(self):
xtypes = [FLOAT, ORD]
xlimits = [[-10, 10], [-10, 10]]
mixint = MixedIntegerContext(xtypes, xlimits)
sm = mixint.build_surrogate_model(QP(print_prediction=False))
sampling = mixint.build_sampling_method(LHS, criterion="m")
fun = Sphere(ndim=2)
xt = sampling(10)
yt = fun(xt)
sm.set_training_values(xt, yt)
sm.train()
eq_check = True
for i in range(xt.shape[0]):
if abs(float(xt[i, :][1]) - int(float(xt[i, :][1]))) > 10e-8:
eq_check = False
self.assertTrue(eq_check)
def test_compute_unfolded_dimension(self):
xtypes = [FLOAT, (ENUM, 2)]
self.assertEqual(3, compute_unfolded_dimension(xtypes))
def test_unfold_with_enum_mask(self):
xtypes = [FLOAT, (ENUM, 2)]
x = np.array([[1.5, 1], [1.5, 0], [1.5, 1]])
expected = [[1.5, 0, 1], [1.5, 1, 0], [1.5, 0, 1]]
self.assertListEqual(expected, unfold_with_enum_mask(xtypes, x).tolist())
def test_unfold_with_enum_mask_with_enum_first(self):
xtypes = [(ENUM, 2), FLOAT]
x = np.array([[1, 1.5], [0, 1.5], [1, 1.5]])
expected = [[0, 1, 1.5], [1, 0, 1.5], [0, 1, 1.5]]
self.assertListEqual(expected, unfold_with_enum_mask(xtypes, x).tolist())
def test_fold_with_enum_index(self):
xtypes = [FLOAT, (ENUM, 2)]
x = np.array([[1.5, 0, 1], [1.5, 1, 0], [1.5, 0, 1]])
expected = [[1.5, 1], [1.5, 0], [1.5, 1]]
self.assertListEqual(expected, fold_with_enum_index(xtypes, x).tolist())
def test_fold_with_enum_index_with_list(self):
xtypes = [FLOAT, (ENUM, 2)]
expected = [[1.5, 1]]
x = np.array([1.5, 0, 1])
self.assertListEqual(expected, fold_with_enum_index(xtypes, x).tolist())
x = [1.5, 0, 1]
self.assertListEqual(expected, fold_with_enum_index(xtypes, x).tolist())
def test_cast_to_enum_value(self):
xlimits = [[0.0, 4.0], ["blue", "red"]]
x_col = 1
enum_indexes = [1, 1, 0, 1, 0]
expected = ["red", "red", "blue", "red", "blue"]
self.assertListEqual(expected, cast_to_enum_value(xlimits, x_col, enum_indexes))
def test_unfolded_xlimits_type(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 2), ORD]
xlimits = np.array([[-5, 5], ["2", "3"], ["4", "5"], [0, 2]])
sampling = MixedIntegerSamplingMethod(xtypes, xlimits, LHS, criterion="ese")
doe = sampling(10)
self.assertEqual((10, 4), doe.shape)
def test_cast_to_mixed_integer(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], [0, 2]],
dtype="object",
)
x = np.array([1.5, 0, 2, 1.1])
self.assertEqual(
[1.5, "blue", "long", 1], cast_to_mixed_integer(xtypes, xlimits, x)
)
def test_unfold_xlimits_with_continuous_limits(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], [0, 2]],
dtype="object",
)
l = unfold_xlimits_with_continuous_limits(xtypes, xlimits)
self.assertEqual(
np.array_equal(
[[-5, 5], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 2]],
unfold_xlimits_with_continuous_limits(xtypes, xlimits),
),
True,
)
def test_unfold_xlimits_with_continuous_limits_and_ordinal_values(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], ["0", "3", "4"]],
dtype="object",
)
l = unfold_xlimits_with_continuous_limits(xtypes, xlimits)
self.assertEqual(
np.array_equal(
[[-5, 5], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 4]],
unfold_xlimits_with_continuous_limits(xtypes, xlimits),
),
True,
)
def test_cast_to_discrete_values(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], [0, 4]],
dtype="object",
)
x = np.array([[2.6, 0.3, 0.5, 0.25, 0.45, 0.85, 3.1]])
self.assertEqual(
np.array_equal(
np.array([[2.6, 0, 1, 0, 0, 1, 3]]),
cast_to_discrete_values(xtypes, xlimits, None, x),
),
True,
)
def test_cast_to_discrete_values_with_smooth_rounding_ordinal_values(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
x = np.array([[2.6, 0.3, 0.5, 0.25, 0.45, 0.85, 3.1]])
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], ["0", "2", "4"]],
dtype="object",
)
self.assertEqual(
np.array_equal(
np.array([[2.6, 0, 1, 0, 0, 1, 4]]),
cast_to_discrete_values(xtypes, xlimits, None, x),
),
True,
)
def test_cast_to_discrete_values_with_hard_rounding_ordinal_values(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
x = np.array([[2.6, 0.3, 0.5, 0.25, 0.45, 0.85, 3.1]])
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], ["0", "4"]],
dtype="object",
)
self.assertEqual(
np.array_equal(
np.array([[2.6, 0, 1, 0, 0, 1, 4]]),
cast_to_discrete_values(xtypes, xlimits, None, x),
),
True,
)
def test_cast_to_discrete_values_with_non_integer_ordinal_values(self):
xtypes = [FLOAT, (ENUM, 2), (ENUM, 3), ORD]
x = np.array([[2.6, 0.3, 0.5, 0.25, 0.45, 0.85, 3.1]])
xlimits = np.array(
[[-5, 5], ["blue", "red"], ["short", "medium", "long"], ["0", "3.5"]],
dtype="object",
)
self.assertEqual(
np.array_equal(
np.array([[2.6, 0, 1, 0, 0, 1, 3.5]]),
cast_to_discrete_values(xtypes, xlimits, None, x),
),
True,
)
def run_mixed_integer_lhs_example(self):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from smt.sampling_methods import LHS
from smt.applications.mixed_integer import (
FLOAT,
ORD,
ENUM,
MixedIntegerSamplingMethod,
)
xtypes = [FLOAT, (ENUM, 2)]
xlimits = [[0.0, 4.0], ["blue", "red"]]
sampling = MixedIntegerSamplingMethod(xtypes, xlimits, LHS, criterion="ese")
num = 40
x = sampling(num)
cmap = colors.ListedColormap(xlimits[1])
plt.scatter(x[:, 0], np.zeros(num), c=x[:, 1], cmap=cmap)
plt.show()
def run_mixed_integer_qp_example(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import QP
from smt.applications.mixed_integer import MixedIntegerSurrogateModel, ORD
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.5, 1.0])
# xtypes = [FLOAT, ORD, (ENUM, 3), (ENUM, 2)]
# FLOAT means x1 continuous
# ORD means x2 ordered
# (ENUM, 3) means x3, x4 & x5 are 3 levels of the same categorical variable
# (ENUM, 2) means x6 & x7 are 2 levels of the same categorical variable
sm = MixedIntegerSurrogateModel(xtypes=[ORD], xlimits=[[0, 4]], surrogate=QP())
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
def run_mixed_integer_context_example(self):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.mplot3d import Axes3D
from smt.surrogate_models import KRG
from smt.sampling_methods import LHS, Random
from smt.applications.mixed_integer import MixedIntegerContext, FLOAT, ORD, ENUM
xtypes = [ORD, FLOAT, (ENUM, 4)]
xlimits = [[0, 5], [0.0, 4.0], ["blue", "red", "green", "yellow"]]
def ftest(x):
return (x[:, 0] * x[:, 0] + x[:, 1] * x[:, 1]) * (x[:, 2] + 1)
# context to create consistent DOEs and surrogate
mixint = MixedIntegerContext(xtypes, xlimits)
# DOE for training
lhs = mixint.build_sampling_method(LHS, criterion="ese")
num = mixint.get_unfolded_dimension() * 5
print("DOE point nb = {}".format(num))
xt = lhs(num)
yt = ftest(xt)
# Surrogate
sm = mixint.build_surrogate_model(KRG())
sm.set_training_values(xt, yt)
sm.train()
# DOE for validation
rand = mixint.build_sampling_method(Random)
xv = rand(50)
yv = ftest(xv)
yp = sm.predict_values(xv)
plt.plot(yv, yv)
plt.plot(yv, yp, "o")
plt.xlabel("actual")
plt.ylabel("prediction")
plt.show()
def test_mixed_gower_2D(self):
from smt.applications.mixed_integer import (
MixedIntegerSurrogateModel,
ENUM,
FLOAT,
GOWER,
)
from smt.surrogate_models import KRG
import matplotlib.pyplot as plt
import numpy as np
import itertools
xt = np.array([[0, 5], [2, -1], [4, 0.5]])
yt = np.array([[0.0], [1.0], [1.5]])
xlimits = [["0.0", "1.0", " 2.0", | |
: double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final input
data is set after applying scaling and subtracting the specified offsets.
Default: (103.939, 116.779, 123.68)
pre_trained_weights : bool, optional
Specifies whether to use the pre-trained weights trained on the ImageNet data set.
Default: False
pre_trained_weights_file : string, optional
Specifies the file name for the pre-trained weights.
Must be a fully qualified file name of SAS-compatible file (e.g., *.caffemodel.h5)
Note: Required when pre_trained_weights=True.
include_top : bool, optional
Specifies whether to include pre-trained weights of the top layers (i.e., the FC layers).
Default: False
Returns
-------
:class:`Sequential`
If `pre_trained_weights` is False
:class:`Model`
If `pre_trained_weights` is True
References
----------
https://arxiv.org/pdf/1409.1556.pdf
'''
conn.retrieve('loadactionset', _messagelevel='error', actionset='deeplearn')
if not pre_trained_weights:
model = Sequential(conn=conn, model_table=model_table)
model.add(InputLayer(n_channels=n_channels, width=width, height=height, scale=scale, offsets=offsets,
random_flip=random_flip, random_crop=random_crop))
model.add(Conv2d(n_filters=64, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=64, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=128, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=128, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=256, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Conv2d(n_filters=512, width=3, height=3, stride=1))
model.add(Pooling(width=2, height=2, stride=2, pool='max'))
model.add(Dense(n=4096, dropout=0.5))
model.add(Dense(n=4096, dropout=0.5))
model.add(OutputLayer(n=n_classes))
return model
else:
if pre_trained_weights_file is None:
raise DLPyError('\nThe pre-trained weights file is not specified.\n'
'Please follow the steps below to attach the pre-trained weights:\n'
'1. go to the website https://support.sas.com/documentation/prod-p/vdmml/zip/ '
'and download the associated weight file.\n'
'2. upload the *.h5 file to '
'a server side directory which the CAS session has access to.\n'
'3. specify the pre_trained_weights_file using the fully qualified server side path.')
model_cas = model_vgg19.VGG19_Model(s=conn, model_table=model_table, n_channels=n_channels,
width=width, height=height, random_crop=random_crop, offsets=offsets)
if include_top:
if n_classes != 1000:
warnings.warn('If include_top = True, n_classes will be set to 1000.', RuntimeWarning)
model = Model.from_table(model_cas)
model.load_weights(path=pre_trained_weights_file, labels=True)
return model
else:
model = Model.from_table(model_cas, display_note=False)
model.load_weights(path=pre_trained_weights_file)
weight_table_options = model.model_weights.to_table_params()
weight_table_options.update(dict(where='_LayerID_<22'))
model._retrieve_('table.partition', table=weight_table_options,
casout=dict(replace=True, **model.model_weights.to_table_params()))
model._retrieve_('deeplearn.removelayer', model=model_table, name='fc8')
model._retrieve_('deeplearn.addlayer', model=model_table, name='fc8',
layer=dict(type='output', n=n_classes, act='softmax'),
srcLayers=['fc7'])
model = Model.from_table(conn.CASTable(model_table))
return model
def ResNet18_SAS(conn, model_table='RESNET18_SAS', batch_norm_first=True, n_classes=1000, n_channels=3, width=224,
height=224, scale=1, random_flip='none', random_crop='none', offsets=(103.939, 116.779, 123.68)):
'''
Generates a deep learning model with the ResNet18 architecture.
Compared to Caffe ResNet18, the model prepends a batch normalization layer to the last global pooling layer.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string, optional
Specifies the name of CAS table to store the model.
batch_norm_first : bool, optional
Specifies whether to have batch normalization layer before the
convolution layer in the residual block. For a detailed discussion
about this, please refer to this paper: <NAME>, et al. "Identity
mappings in deep residual networks." European Conference on Computer
Vision. Springer International Publishing, 2016.
Default: True
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 1000
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 224
height : int, optional
Specifies the height of the input layer.
Default: 224
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
Default: 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique'
Default: 'none'
offsets : double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final input
data is set after applying scaling and subtracting the specified offsets.
Default: (103.939, 116.779, 123.68)
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/pdf/1512.03385.pdf
'''
conn.retrieve('loadactionset', _messagelevel='error', actionset='deeplearn')
model = Sequential(conn=conn, model_table=model_table)
model.add(InputLayer(n_channels=n_channels, width=width, height=height, scale=scale, offsets=offsets,
random_flip=random_flip, random_crop=random_crop))
# Top layers
model.add(Conv2d(64, 7, act='identity', include_bias=False, stride=2))
model.add(BN(act='relu'))
model.add(Pooling(width=3, stride=2))
kernel_sizes_list = [(3, 3), (3, 3), (3, 3), (3, 3)]
n_filters_list = [(64, 64), (128, 128), (256, 256), (512, 512)]
rep_nums_list = [2, 2, 2, 2]
for i in range(4):
kernel_sizes = kernel_sizes_list[i]
n_filters = n_filters_list[i]
for rep_num in range(rep_nums_list[i]):
if i == 0:
strides = 1
else:
if rep_num == 0:
strides = 2
else:
strides = 1
model.add(ResBlockBN(kernel_sizes=kernel_sizes, n_filters=n_filters, strides=strides,
batch_norm_first=batch_norm_first))
# Bottom Layers
pooling_size = (width // 2 // 2 // 2 // 2 // 2, height // 2 // 2 // 2 // 2 // 2)
model.add(Pooling(width=pooling_size[0], height=pooling_size[1], pool='mean'))
model.add(OutputLayer(act='softmax', n=n_classes))
return model
def ResNet18_Caffe(conn, model_table='RESNET18_CAFFE', batch_norm_first=False, n_classes=1000, n_channels=3, width=224,
height=224, scale=1, random_flip='none', random_crop='none', offsets=None):
'''
Generates a deep learning model with the ResNet18 architecture with convolution shortcut.
Parameters
----------
conn : CAS
Specifies the CAS connection object.
model_table : string or dict or CAS table, optional
Specifies the CAS table to store the deep learning model.
batch_norm_first : bool, optional
Specifies whether to have batch normalization layer before the
convolution layer in the residual block. For a detailed discussion
about this, please refer to this paper: He, Kaiming, et al. "Identity
mappings in deep residual networks." European Conference on Computer
Vision. Springer International Publishing, 2016.
Default: False
n_classes : int, optional
Specifies the number of classes. If None is assigned, the model will
automatically detect the number of classes based on the training set.
Default: 1000
n_channels : int, optional
Specifies the number of the channels (i.e., depth) of the input layer.
Default: 3
width : int, optional
Specifies the width of the input layer.
Default: 224
height : int, optional
Specifies the height of the input layer.
Default: 224
scale : double, optional
Specifies a scaling factor to be applied to each pixel intensity values.
Default: 1
random_flip : string, optional
Specifies how to flip the data in the input layer when image data is
used. Approximately half of the input data is subject to flipping.
Valid Values: 'h', 'hv', 'v', 'none'
Default: 'none'
random_crop : string, optional
Specifies how to crop the data in the input layer when image data is
used. Images are cropped to the values that are specified in the width
and height parameters. Only the images with one or both dimensions
that are larger than those sizes are cropped.
Valid Values: 'none', 'unique'
Default: 'none'
offsets : double or iter-of-doubles, optional
Specifies an offset for each channel in the input data. The final input
data is set after applying scaling and subtracting the specified offsets.
Returns
-------
:class:`Sequential`
References
----------
https://arxiv.org/pdf/1512.03385.pdf
'''
conn.retrieve('loadactionset', _messagelevel='error', actionset='deeplearn')
model = Sequential(conn=conn, model_table=model_table)
model.add(InputLayer(n_channels=n_channels, width=width, height=height, scale=scale, offsets=offsets,
random_flip=random_flip, random_crop=random_crop))
# Top layers
model.add(Conv2d(64, 7, act='identity', include_bias=False, stride=2))
model.add(BN(act='relu'))
model.add(Pooling(width=3, stride=2))
kernel_sizes_list = [(3, 3), (3, 3), (3, 3), (3, 3)]
n_filters_list = [(64, 64), (128, 128), (256, 256), (512, 512)]
rep_nums_list = [2, 2, 2, 2]
for i in range(4):
kernel_sizes = kernel_sizes_list[i]
n_filters = n_filters_list[i]
for rep_num in range(rep_nums_list[i]):
if rep_num == 0:
conv_short_cut = True
if i == 0:
strides = 1
else:
strides = 2
else:
conv_short_cut = False
strides = 1
model.add(ResBlock_Caffe(kernel_sizes=kernel_sizes, n_filters=n_filters, strides=strides,
batch_norm_first=batch_norm_first, conv_short_cut=conv_short_cut))
# Bottom Layers
pooling_size = (width // 2 // 2 // 2 // 2 // 2, height // 2 // 2 // 2 // 2 // 2)
model.add(Pooling(width=pooling_size[0], height=pooling_size[1], pool='mean'))
model.add(OutputLayer(act='softmax', n=n_classes))
return model
def ResNet34_SAS(conn, model_table='RESNET34_SAS', n_classes=1000, n_channels=3, width=224, height=224, scale=1,
batch_norm_first=True, random_flip='none', random_crop='none', offsets=(103.939, 116.779, 123.68)):
'''
| |
unknown application
with pytest.raises(RPCError) as exc:
rpc.stop_application('appli')
assert exc.value.args == (Faults.BAD_NAME, 'appli')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with stopped application
application = rpc.supvisors.context.applications['appli_1']
with pytest.raises(RPCError) as exc:
rpc.stop_application('appli_1')
assert exc.value.args == (Faults.NOT_RUNNING, 'appli_1')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with running application
appli_1.has_running_processes.return_value = True
# test no wait and done
mocked_stop.return_value = True
result = rpc.stop_application('appli_1', False)
assert not result
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_args_list == [call(application)]
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
mocked_stop.reset_mock()
# test wait and done
mocked_stop.return_value = True
result = rpc.stop_application('appli_1')
assert not result
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_args_list == [call(application)]
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
mocked_stop.reset_mock()
# test wait and not done
mocked_stop.return_value = False
result = rpc.stop_application('appli_1')
# result is a function
assert callable(result)
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_args_list == [call(application)]
assert mocked_progress.call_count == 0
# test returned function: return True when job in progress
mocked_progress.return_value = True
assert result() == NOT_DONE_YET
assert mocked_progress.call_args_list == [call()]
mocked_progress.reset_mock()
# test returned function: raise exception if job not in progress anymore and application not running
mocked_progress.return_value = False
for _ in [ApplicationStates.STOPPING, ApplicationStates.RUNNING, ApplicationStates.STARTING]:
with pytest.raises(RPCError) as exc:
result()
assert exc.value.args == (Faults.ABNORMAL_TERMINATION, 'appli_1')
assert mocked_progress.call_args_list == [call()]
mocked_progress.reset_mock()
# test returned function: return True if job not in progress anymore and application running
application.state = ApplicationStates.STOPPED
assert result()
assert mocked_progress.call_args_list == [call()]
# reset patches for next loop
mocked_check.reset_mock()
mocked_stop.reset_mock()
mocked_progress.reset_mock()
def test_restart_application(mocker, rpc):
""" Test the restart_application RPC. """
mocked_check = mocker.patch('supvisors.rpcinterface.RPCInterface._check_operating')
mocked_start = mocker.patch('supvisors.rpcinterface.RPCInterface.start_application')
mocked_stop = mocker.patch('supvisors.rpcinterface.RPCInterface.stop_application')
# test RPC call with sub-RPC calls return a direct result
mocked_stop.return_value = True
mocked_start.return_value = False
deferred = rpc.restart_application(0, 'appli', 'wait')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_args_list == [call('appli', True)]
assert mocked_start.call_count == 0
mocked_stop.reset_mock()
mocked_check.reset_mock()
# result is a function
assert callable(deferred)
assert deferred.waitstop
# test this function
assert not deferred()
assert not deferred.waitstop
assert mocked_stop.call_count == 0
assert mocked_start.call_args_list == [call(0, 'appli', 'wait')]
mocked_start.reset_mock()
# test RPC call with sub_RPC calls returning jobs
# test with mocking functions telling that the jobs are not completed
mocked_stop_job = Mock(return_value=False)
mocked_start_job = Mock(return_value=False)
mocked_stop.return_value = mocked_stop_job
mocked_start.return_value = mocked_start_job
deferred = rpc.restart_application(0, 'appli', 'wait')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_args_list == [call('appli', True)]
assert mocked_start.call_count == 0
mocked_stop.reset_mock()
# result is a function for deferred result
assert callable(deferred)
assert deferred.waitstop
# first call to this function tells that job is still in progress
assert mocked_stop_job.call_count == 0
assert mocked_start_job.call_count == 0
assert deferred() == NOT_DONE_YET
assert mocked_stop.call_count == 0
assert mocked_start.call_count == 0
assert mocked_stop_job.call_args_list == [call()]
assert mocked_start_job.call_count == 0
mocked_stop_job.reset_mock()
# replace the stop job with a function telling that the job is completed
mocked_stop_job.return_value = True
assert deferred() == NOT_DONE_YET
assert not deferred.waitstop
assert mocked_stop.call_count == 0
assert mocked_start.call_args_list == [call(0, 'appli', 'wait')]
assert mocked_stop_job.call_args_list == [call()]
assert mocked_start_job.call_count == 0
mocked_stop_job.reset_mock()
# call the deferred function again to check that the start is engaged
assert not deferred()
assert mocked_start_job.call_args_list == [call()]
assert mocked_stop_job.call_count == 0
def test_start_args(mocker, rpc):
""" Test the start_args RPC. """
mocker.patch('supvisors.rpcinterface.RPCInterface._get_application_process',
return_value=(None, Mock(namespec='appli:proc')))
# prepare context
info_source = rpc.supvisors.info_source
info_source.update_extra_args.side_effect = KeyError
mocked_startProcess = info_source.supervisor_rpc_interface.startProcess
mocked_startProcess.side_effect = [RPCError(Faults.NO_FILE, 'no file'),
RPCError(Faults.NOT_EXECUTABLE),
RPCError(Faults.ABNORMAL_TERMINATION),
'done']
# test RPC call with extra arguments but with a process that is unknown to Supervisor
with pytest.raises(RPCError) as exc:
rpc.start_args('appli:proc', 'dummy arguments')
assert exc.value.args == (Faults.BAD_NAME, 'namespec appli:proc unknown to this Supervisor instance')
assert info_source.update_extra_args.call_args_list == [call('appli:proc', 'dummy arguments')]
assert mocked_startProcess.call_count == 0
# update mocking
info_source.update_extra_args.reset_mock()
info_source.update_extra_args.side_effect = None
# test RPC call with start exceptions
# NO_FILE exception triggers an update of the process state
with pytest.raises(RPCError) as exc:
rpc.start_args('appli:proc')
assert exc.value.args == (Faults.NO_FILE, 'no file')
assert info_source.update_extra_args.call_args_list == [call('appli:proc', '')]
assert mocked_startProcess.call_args_list == [call('appli:proc', True)]
assert info_source.force_process_fatal.call_args_list == [call('appli:proc', 'NO_FILE: no file')]
# reset patches
info_source.update_extra_args.reset_mock()
info_source.force_process_fatal.reset_mock()
mocked_startProcess.reset_mock()
# NOT_EXECUTABLE exception triggers an update of the process state
with pytest.raises(RPCError) as exc:
rpc.start_args('appli:proc', wait=False)
assert exc.value.args == (Faults.NOT_EXECUTABLE, )
assert info_source.update_extra_args.call_args_list == [call('appli:proc', '')]
assert mocked_startProcess.call_args_list == [call('appli:proc', False)]
assert info_source.force_process_fatal.call_args_list == [call('appli:proc', 'NOT_EXECUTABLE')]
# reset patches
info_source.update_extra_args.reset_mock()
info_source.force_process_fatal.reset_mock()
mocked_startProcess.reset_mock()
# other exception doesn't trigger an update of the process state
with pytest.raises(RPCError) as exc:
rpc.start_args('appli:proc', wait=False)
assert exc.value.args == (Faults.ABNORMAL_TERMINATION, )
assert info_source.update_extra_args.call_args_list == [call('appli:proc', '')]
assert mocked_startProcess.call_args_list == [call('appli:proc', False)]
assert not info_source.force_process_fatal.called
# reset patches
info_source.update_extra_args.reset_mock()
mocked_startProcess.reset_mock()
# finally, normal behaviour
assert rpc.start_args('appli:proc') == 'done'
assert info_source.update_extra_args.call_args_list == [call('appli:proc', '')]
assert mocked_startProcess.call_args_list == [call('appli:proc', True)]
assert not info_source.force_process_fatal.called
def test_start_process(mocker, rpc):
""" Test the start_process RPC. """
mocked_check = mocker.patch('supvisors.rpcinterface.RPCInterface._check_operating')
# get patches
mocked_start = rpc.supvisors.starter.start_process
mocked_progress = rpc.supvisors.starter.in_progress
# patch the instance
rpc._get_application_process = Mock()
# test RPC call with unknown strategy
with pytest.raises(RPCError) as exc:
rpc.start_process('strategy', 'appli:proc')
assert exc.value.args == (Faults.INCORRECT_PARAMETERS, 'incorrect strategy: strategy')
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with running process
rpc._get_application_process.return_value = (None, Mock(namespec='proc1', **{'running.return_value': True}))
with pytest.raises(RPCError) as exc:
rpc.start_process(0, 'appli_1')
assert exc.value.args == (Faults.ALREADY_STARTED, 'proc1')
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with running processes
proc_1 = Mock(**{'running.return_value': False})
proc_2 = Mock(namespec='proc2', **{'running.return_value': True})
rpc._get_application_process.return_value = (Mock(**{'processes.values.return_value': [proc_1, proc_2]}), None)
with pytest.raises(RPCError) as exc:
rpc.start_process(0, 'appli_1')
assert exc.value.args == (Faults.ALREADY_STARTED, 'proc2')
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with stopped processes
proc_1 = Mock(namespec='proc1', **{'running.return_value': False, 'stopped.return_value': True})
proc_2 = Mock(namespec='proc2', **{'running.return_value': False, 'stopped.return_value': False})
rpc._get_application_process.return_value = (Mock(**{'processes.values.return_value': [proc_1, proc_2]}), None)
# test RPC call with no wait and not done
mocked_start.return_value = False
result = rpc.start_process(1, 'appli:*', 'argument list', False)
assert result
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_args_list == [call(StartingStrategies.LESS_LOADED, proc_1, 'argument list'),
call(StartingStrategies.LESS_LOADED, proc_2, 'argument list')]
assert not mocked_progress.called
mocked_check.reset_mock()
mocked_start.reset_mock()
# test RPC call no wait and done
mocked_start.return_value = True
with pytest.raises(RPCError) as exc:
rpc.start_process(1, 'appli:*', 'argument list', False)
assert exc.value.args == (Faults.ABNORMAL_TERMINATION, 'appli:*')
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_args_list == [call(StartingStrategies.LESS_LOADED, proc_1, 'argument list'),
call(StartingStrategies.LESS_LOADED, proc_2, 'argument list')]
assert not mocked_progress.called
mocked_check.reset_mock()
mocked_start.reset_mock()
# test RPC call with wait and done
with pytest.raises(RPCError) as exc:
rpc.start_process(2, 'appli:*', wait=True)
assert exc.value.args == (Faults.ABNORMAL_TERMINATION, 'appli:*')
assert mocked_start.call_args_list == [call(StartingStrategies.MOST_LOADED, proc_1, ''),
call(StartingStrategies.MOST_LOADED, proc_2, '')]
assert not mocked_progress.called
mocked_check.reset_mock()
mocked_start.reset_mock()
# test RPC call with wait and not done
mocked_start.return_value = False
deferred = rpc.start_process(2, 'appli:*', wait=True)
# result is a function for deferred result
assert callable(deferred)
assert mocked_check.call_args_list == [call()]
assert mocked_start.call_args_list == [call(StartingStrategies.MOST_LOADED, proc_1, ''),
call(StartingStrategies.MOST_LOADED, proc_2, '')]
assert not mocked_progress.called
# test returned function: return True when job in progress
mocked_progress.return_value = True
assert deferred() == NOT_DONE_YET
assert mocked_progress.call_args_list == [call()]
mocked_progress.reset_mock()
# test returned function: raise exception if job not in progress anymore and process still stopped
mocked_progress.return_value = False
with pytest.raises(RPCError) as exc:
deferred()
assert exc.value.args == (Faults.ABNORMAL_TERMINATION, 'proc1')
assert mocked_progress.call_args_list == [call()]
mocked_progress.reset_mock()
# test returned function: return True if job not in progress anymore and process running
proc_1.stopped.return_value = False
assert deferred()
assert mocked_progress.call_args_list == [call()]
def test_stop_process(mocker, rpc):
""" Test the stop_process RPC. """
mocked_check = mocker.patch('supvisors.rpcinterface.RPCInterface._check_operating_conciliation')
# get patches
mocked_stop = rpc.supvisors.stopper.stop_process
mocked_progress = rpc.supvisors.stopper.in_progress
# patch the instance
rpc._get_application_process = Mock()
# test RPC call with running process
rpc._get_application_process.return_value = (None, Mock(namespec='proc1', **{'stopped.return_value': True}))
with pytest.raises(RPCError) as exc:
rpc.stop_process('appli_1')
assert exc.value.args == (Faults.NOT_RUNNING, 'proc1')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with running processes
proc_1 = Mock(**{'stopped.return_value': False})
proc_2 = Mock(namespec='proc2', **{'stopped.return_value': True})
rpc._get_application_process.return_value = (Mock(**{'processes.values.return_value': [proc_1, proc_2]}), None)
with pytest.raises(RPCError) as exc:
rpc.stop_process('appli_1')
assert exc.value.args == (Faults.NOT_RUNNING, 'proc2')
assert mocked_check.call_args_list == [call()]
assert mocked_stop.call_count == 0
assert mocked_progress.call_count == 0
mocked_check.reset_mock()
# test RPC call with stopped processes
proc_1 = Mock(namespec='proc1', **{'running.return_value': True, 'stopped.return_value': | |
in training split, including label.
-Future: is it useful to specify the size of only test for unsupervised learning?
"""
samples = JSONField()
sizes = JSONField()
supervision = CharField()
has_test = BooleanField()
has_validation = BooleanField()
bin_count = IntegerField(null=True)
featureset = ForeignKeyField(Featureset, backref='splitsets')
label = ForeignKeyField(Label, deferrable='INITIALLY DEFERRED', null=True, backref='splitsets')
def from_featureset(
featureset_id:int
, label_id:int = None
, size_test:float = None
, size_validation:float = None
, bin_count:float = None
):
if (size_test is not None):
if (size_test <= 0.0) or (size_test >= 1.0):
raise ValueError("\nYikes - `size_test` must be between 0.0 and 1.0\n")
# Don't handle `has_test` here. Need to check label first.
if (size_validation is not None) and (size_test is None):
raise ValueError("\nYikes - you specified a `size_validation` without setting a `size_test`.\n")
if (size_validation is not None):
if (size_validation <= 0.0) or (size_validation >= 1.0):
raise ValueError("\nYikes - `size_test` must be between 0.0 and 1.0\n")
sum_test_val = size_validation + size_test
if sum_test_val >= 1.0:
raise ValueError("\nYikes - Sum of `size_test` + `size_test` must be between 0.0 and 1.0 to leave room for training set.\n")
"""
Have to run train_test_split twice do the math to figure out the size of 2nd split.
Let's say I want {train:0.67, validation:0.13, test:0.20}
The first test_size is 20% which leaves 80% of the original data to be split into validation and training data.
(1.0/(1.0-0.20))*0.13 = 0.1625
"""
pct_for_2nd_split = (1.0/(1.0-size_test))*size_validation
has_validation = True
else:
has_validation = False
f = Featureset.get_by_id(featureset_id)
f_cols = f.columns
# Feature data to be split.
d = f.dataset
arr_f = Dataset.to_numpy(id=d.id, columns=f_cols)
"""
Simulate an index to be split alongside features and labels
in order to keep track of the samples being used in the resulting splits.
"""
row_count = arr_f.shape[0]
arr_idx = np.arange(row_count)
samples = {}
sizes = {}
if label_id is None:
has_test = False
supervision = "unsupervised"
l = None
if (size_test is not None) or (size_validation is not None):
raise ValueError(dedent("""
Yikes - Unsupervised Featuresets support neither test nor validation splits.
Set both `size_test` and `size_validation` as `None` for this Featureset.
"""))
else:
indices_lst_train = arr_idx.tolist()
samples["train"] = indices_lst_train
sizes["train"] = {"percent": 1.00, "count": row_count}
elif (label_id is not None):
# We don't need to prevent duplicate Label/Featureset combos because Splits generate different samples each time.
l = Label.get_by_id(label_id)
# Check number of samples in Label vs Featureset, because they can come from different Datasets.
l_dataset_id = l.dataset.id
l_length = Dataset.Tabular.get_main_file(l_dataset_id).shape['rows']
if (l_dataset_id != d.id):
if (d.dataset_type == 'tabular'):
f_length = Dataset.Tabular.get_main_file(d.id).shape['rows']
elif (d.dataset_type == 'image'):
f_length = f.dataset.file_count
# Separate `if` to compare them.
if (l_length != f_length):
raise ValueError("\nYikes - The Datasets of your Label and Featureset do not contains the same number of samples.\n")
if size_test is None:
size_test = 0.30
has_test = True
supervision = "supervised"
label_array = l.to_numpy()
# check for OHE cols and reverse them so we can still stratify.
if (label_array.shape[1] > 1):
encoder = OneHotEncoder(sparse=False)
label_array = encoder.fit_transform(label_array)
label_array = np.argmax(label_array, axis=1)
# argmax flattens the array, so reshape it to array of arrays.
count = label_array.shape[0]
l_cat_shaped = label_array.reshape(count, 1)
# OHE dtype returns as int64
label_dtype = label_array.dtype
stratifier1, bin_count = Splitset.stratifier_by_dtype_binCount(
label_dtype = label_dtype,
label_array = label_array,
bin_count = bin_count
)
"""
- `sklearn.model_selection.train_test_split` = https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
- `shuffle` happens before the split. Although preserves a df's original index, we don't need to worry about that because we are providing our own indices.
- Don't include the Dataset.Image.featureset pixel arrays in stratification.
"""
if (d.dataset_type == 'tabular'):
features_train, features_test, labels_train, labels_test, indices_train, indices_test = train_test_split(
arr_f, label_array, arr_idx
, test_size = size_test
, stratify = stratifier1
, shuffle = True
)
if (size_validation is not None):
stratifier2, bin_count = Splitset.stratifier_by_dtype_binCount(
label_dtype = label_dtype,
label_array = labels_train, #This split is different from stratifier1.
bin_count = bin_count
)
features_train, features_validation, labels_train, labels_validation, indices_train, indices_validation = train_test_split(
features_train, labels_train, indices_train
, test_size = pct_for_2nd_split
, stratify = stratifier2
, shuffle = True
)
indices_lst_validation = indices_validation.tolist()
samples["validation"] = indices_lst_validation
elif (d.dataset_type == 'image'):
# Features not involved.
labels_train, labels_test, indices_train, indices_test = train_test_split(
label_array, arr_idx
, test_size = size_test
, stratify = stratifier1
, shuffle = True
)
if (size_validation is not None):
stratifier2, bin_count = Splitset.stratifier_by_dtype_binCount(
label_dtype = label_dtype,
label_array = labels_train, #This split is different from stratifier1.
bin_count = bin_count
)
labels_train, labels_validation, indices_train, indices_validation = train_test_split(
labels_train, indices_train
, test_size = pct_for_2nd_split
, stratify = stratifier2
, shuffle = True
)
indices_lst_validation = indices_validation.tolist()
samples["validation"] = indices_lst_validation
indices_lst_train, indices_lst_test = indices_train.tolist(), indices_test.tolist()
samples["train"] = indices_lst_train
samples["test"] = indices_lst_test
size_train = 1.0 - size_test
if size_validation is not None:
size_train -= size_validation
count_validation = len(indices_lst_validation)
sizes["validation"] = {"percent": size_validation, "count": count_validation}
count_test = len(indices_lst_test)
count_train = len(indices_lst_train)
sizes["test"] = {"percent": size_test, "count": count_test}
sizes["train"] = {"percent": size_train, "count": count_train}
s = Splitset.create(
featureset = f
, label = l
, samples = samples
, sizes = sizes
, supervision = supervision
, has_test = has_test
, has_validation = has_validation
, bin_count = bin_count
)
return s
def to_pandas(
id:int
, splits:list = None
, include_label:bool = None
, include_featureset:bool = None
, feature_columns:list = None
):
splits = listify(splits)
feature_columns = listify(feature_columns)
split_frames = Splitset.get_splits(
id = id
, numpy_or_pandas = 'pandas'
, splits = splits
, include_label = include_label
, include_featureset = include_featureset
, feature_columns = feature_columns
)
return split_frames
def to_numpy(
id:int
, splits:list = None
, include_label:bool = None
, include_featureset:bool = None
, feature_columns:list = None
):
splits = listify(splits)
feature_columns = listify(feature_columns)
split_arrs = Splitset.get_splits(
id = id
, numpy_or_pandas = 'numpy'
, splits = splits
, include_label = include_label
, include_featureset = include_featureset
, feature_columns = feature_columns
)
return split_arrs
def get_splits(id:int
, numpy_or_pandas:str # Machine set, so don't validate.
, splits:list = None
, include_label:bool = None # Unsupervised can't be True.
, include_featureset:bool = None
, feature_columns:list = None
):
"""
Future: Optimize!
- Worried it's holding all dataframes and arrays in memory.
- Generators to access one [key][set] at a time?
"""
s = Splitset.get_by_id(id)
splits = listify(splits)
feature_columns = listify(feature_columns)
splits = list(s.samples.keys())
supervision = s.supervision
featureset = s.featureset
split_frames = {}
# Future: Optimize (switch to generators for memory usage).
# Here, split_names are: train, test, validation.
# There are always featureset. It's just if you want to include them or not.
# Saves memory when you only want Labels by split.
if (include_featureset is None):
include_featureset = True
if (supervision == "unsupervised"):
if (include_label is None):
include_label = False
elif (include_label == True):
raise ValueError("\nYikes - `include_label == True` but `Splitset.supervision=='unsupervised'`.\n")
elif (supervision == "supervised"):
if (include_label is None):
include_label = True
if ((include_featureset == False) and (include_label == False)):
raise ValueError("\nYikes - Both `include_featureset` and `include_label` cannot be False.\n")
if ((feature_columns is not None) and (include_featureset != True)):
raise ValueError("\nYikes - `feature_columns` must be None if `include_label==False`.\n")
for split_name in splits:
# Placeholder for the frames/arrays.
split_frames[split_name] = {}
# Fetch the sample indices for the split
split_samples = s.samples[split_name]
if (include_featureset == True):
if (numpy_or_pandas == 'numpy'):
ff = featureset.to_numpy(samples=split_samples, columns=feature_columns)
elif (numpy_or_pandas == 'pandas'):
ff = featureset.to_pandas(samples=split_samples, columns=feature_columns)
split_frames[split_name]["features"] = ff
if (include_label == True):
l = s.label
if (numpy_or_pandas == 'numpy'):
lf = l.to_numpy(samples=split_samples)
elif (numpy_or_pandas == 'pandas'):
lf = l.to_pandas(samples=split_samples)
split_frames[split_name]["labels"] = lf
return split_frames
def label_values_to_bins(array_to_bin:object, bin_count:int):
"""
Overwites continuous Label values with bin numbers for statification & folding.
Switched to `pd.qcut` because `np.digitize` never had enough samples in the up the leftmost/right bin.
"""
# Make 1D for qcut.
array_to_bin = array_to_bin.flatten()
# For really unbalanced labels, I ran into errors where bin boundaries would be duplicates all the way down to 2 bins.
# Setting `duplicates='drop'` to address this.
bin_numbers = pd.qcut(x=array_to_bin, q=bin_count, labels=False, duplicates='drop')
# Convert 1D array back to 2D for the rest of the program.
bin_numbers = np.reshape(bin_numbers, (-1, 1))
return bin_numbers
def stratifier_by_dtype_binCount(label_dtype:object, label_array:object, bin_count:int=None):
# Based on the dtype and bin_count determine how to stratify.
# Automatically bin floats.
if np.issubdtype(label_dtype, np.floating):
if (bin_count is None):
bin_count = 3
stratifier = Splitset.label_values_to_bins(array_to_bin=label_array, bin_count=bin_count)
# Allow ints to pass either binned or unbinned.
elif (
(np.issubdtype(label_dtype, np.signedinteger))
or
(np.issubdtype(label_dtype, np.unsignedinteger))
):
if (bin_count is not None):
stratifier = Splitset.label_values_to_bins(array_to_bin=label_array, bin_count=bin_count)
elif (bin_count is None):
# Assumes the int is for classification.
stratifier = label_array
# Reject binned objs.
elif (np.issubdtype(label_dtype, np.number) == False):
if (bin_count is not None):
raise ValueError(dedent("""
Yikes - Your Label is not numeric (neither `np.floating`, `np.signedinteger`, `np.unsignedinteger`).
Therefore, you cannot provide a value for `bin_count`.
\n"""))
elif (bin_count is None):
stratifier = label_array
return stratifier, bin_count
def make_foldset(
id:int
, fold_count:int = None
, bin_count:int = None
):
foldset = Foldset.from_splitset(
splitset_id = id
, fold_count = fold_count
, bin_count = bin_count
)
return foldset
def make_encoderset(
id:int
, encoder_count:int = 0
, description:str = None
):
e = Encoderset.from_splitset(
splitset_id = id
, encoder_count = 0
, description = description
)
return e
class Foldset(BaseModel):
"""
- Contains aggregate summary statistics and evaluate metrics for all Folds.
"""
fold_count = IntegerField()
random_state = IntegerField()
bin_count = IntegerField(null=True) # For stratifying continuous features.
#ToDo: max_samples_per_bin = IntegerField()
#ToDo: min_samples_per_bin = IntegerField()
splitset = ForeignKeyField(Splitset, backref='foldsets')
def from_splitset(
splitset_id:int
, fold_count:int = None
, bin_count:int = None
):
splitset = Splitset.get_by_id(splitset_id)
new_random = False
while new_random == False:
random_state = random.randint(0, 4294967295) #2**32 - 1 inclusive
matching_randoms = splitset.foldsets.select().where(Foldset.random_state==random_state)
count_matches = matching_randoms.count()
if count_matches == 0:
new_random = True
if (fold_count is None):
fold_count = 5 # More likely than 4 to be evenly divisible.
else:
if (fold_count < 2):
raise ValueError(dedent(f"""
Yikes - Cross validation requires multiple folds.
But you provided `fold_count`: <{fold_count}>.
"""))
elif (fold_count == 2):
print("\nWarning - Instead of two folds, why not just use a validation split?\n")
# Get the training indices. The actual values of the features don't matter, only label values needed for stratification.
arr_train_indices = splitset.samples["train"]
arr_train_labels = splitset.label.to_numpy(samples=arr_train_indices)
# If the Labels are binned *overwite* the values w bin numbers. Otherwise untouched.
label_dtype = arr_train_labels.dtype
# | |
TABLE IF EXISTS %s' % table_name)
access.execute_query(CreateTableQuery(if_not_exists=True,
table_or_subquery=table_name,
column_name=(['pk'] if include_pk else []) + columns,
column_constraint=column_constraint,
table_constraint="PRIMARY KEY (pk)" if include_pk else None))
with Process('Saving %s' % self._name, stop-start) as p:
for i in range(start, stop, n):
access.execute_query(InsertQuery(table_or_subquery=table_name,
replace=True,
column_name=['pk' if include_pk else 'rowid'] + columns,
update_value='?' + (',?'*(len(columns)))),
format_gen(p))
i_global += n
access.write_db('PRAGMA journal_mode=DELETE')
def export_files(self, path, columns=None, stop=None, start=0, filename_column=None, metadata_file='.xlsx',
determinist=True, ncore=1, overwrite=True):
import pandas
# --- HANDLE PARAMETERS ---
# Create path
os.makedirs(path, exist_ok=True)
# Handle columns
exported_columns = set()
columns_mapping = OrderedDict()
single_column = False
if isinstance(columns, (list, tuple, set)):
columns = OrderedDict()
for _ in columns:
columns[_] = _
if isinstance(columns, str):
if columns not in self.columns_name():
raise ValueError('Unknown column %s' % columns)
columns_mapping = {'': columns}
exported_columns = {columns}
single_column = True
elif isinstance(columns, DSColumn):
if columns.dataset is not self:
raise ValueError('%s is not a column of %s' % (columns.name, self.dataset_name))
columns_mapping = {'': columns.name}
exported_columns = {columns.name}
single_column = True
elif is_dict(columns):
for c_name, c in columns.items():
if not isinstance(c_name, str):
raise ValueError('columns key should be str (received: %s)' % type(c_name).__name__)
if isinstance(c, DSColumn):
if c.dataset is not self:
raise ValueError('%s is not a column of %s' % (c.name, self.dataset_name))
c = c.name
if isinstance(c, str):
if c not in self.columns_name():
raise ValueError('Unknown column %s' % c)
exported_columns.add(c)
columns_mapping[c_name] = c
else:
raise ValueError('Invalid columns value. Expected type is str or DSColumn, received %s.'
% type(c).__name__)
# Handle filename_column
if filename_column is None:
for n in ('name', 'filename'):
if n in self.columns_name():
filename_column = n
break
if filename_column is None:
filename_column = 'pk'
exported_columns.add(filename_column)
# Handle metadata_file
metadata_sheet = self.dataset_name
if ':' in metadata_file:
metadata_file, metadata_sheet = metadata_file.rsplit(':', 1)
if metadata_file.startswith('.'):
f = list(exported_columns)[0] if single_column else self.dataset_name
metadata_file = f + metadata_file
metadata_file = join(path, metadata_file)
if exists(metadata_file):
if overwrite:
os.remove(metadata_file)
else:
raise RuntimeError('%s already exists.' % metadata_file)
meta_name_column = filename_column if filename_column not in columns_mapping else None
# Handle start stop
start, stop = interval(size=self.size, start=start, stop=stop)
with Process('Exporting '+self.dataset_name, total=stop-start, verbose=False) as p:
from .dataset_generator import DataSetResult
def write_cb(r: DataSetResult):
metadata = OrderedDict()
filename = r[0, filename_column]
for c_to, c in columns_mapping.items():
c_data = r[0, c]
col = self.column_by_name(c)
c_path = path if single_column else join(path, c_to)
meta = col.format.export_file(data=c_data, path=c_path, filename=filename, overwrite=overwrite)
if meta is not None:
metadata[c_to] = meta
if metadata:
meta_exists = exists(metadata_file)
meta_columns = [] if meta_name_column is None else [meta_name_column]
meta_columns += list(metadata.keys())
if metadata_file.endswith('xlsx'):
if meta_exists:
initial_df = pandas.read_excel(metadata_file)
else:
initial_df = pandas.DataFrame(columns=meta_columns)
writer = pandas.ExcelWriter(metadata_file)
initial_df.to_excel(writer, sheet_name=metadata_sheet, index=False)
if meta_name_column is not None:
metadata[meta_name_column] = filename
df = pandas.DataFrame(metadata)
df.to_excel(writer, sheet_name=metadata_sheet, index=False, startrow=r.start_id)
writer.save()
elif metadata_file.endswith('csv'):
if not meta_exists:
pandas.DataFrame(columns=meta_columns).to_csv(metadata_file, header=True, mode='w')
if meta_name_column is not None:
metadata[meta_name_column] = filename
pandas.DataFrame(metadata).to_csv(metadata_file, header=False, mode='a')
p.update(1)
self.export(cb=write_cb, start=start, stop=stop, columns=exported_columns, n=1,
determinist=determinist, ncore=ncore)
def to_pytorch_dataset(self, ncore=1, intime='process'):
from torch.utils.data import Dataset
class CustomDataLoader(Dataset):
def __init__(self, dataset):
self._dataset = dataset
self._gen = None
def __len__(self):
return self._dataset.size
def __getitem__(self, item):
if self._gen is None:
self._gen = self._dataset.generator(ncore=ncore, intime=intime)
try:
return self._gen.next()
except StopIteration:
raise IndexError
return CustomDataLoader(self)
def cache(self, start=0, stop=None, columns=None, ncore=1, ondisk=None, name=None,
overwrite='auto'):
import tables
from .dataset_generator import DataSetResult
from collections import OrderedDict
from ..j_utils.path import open_pytable
start, stop = interval(self.size, start, stop)
if columns is None:
columns = self.columns_name()
elif isinstance(columns, str):
columns = [columns]
else:
columns = list(columns)
if name is None:
label = self._name
name = 'cache'
else:
label = name
hdf_t = None
if ondisk:
if isinstance(ondisk, bool):
import tempfile
ondisk = join(tempfile.gettempdir(), 'dataset_cache.hd5') + ':dataset'
if isinstance(ondisk, str):
ondisk_split = ondisk.split(':')
if len(ondisk_split) == 1:
path = ondisk_split[0]
table_name = 'dataset'
elif len(ondisk_split) == 2:
path, table_name = ondisk_split
else:
raise ValueError('cache_path should be formated as "PATH:TABLE_NAME"')
hdf_f = open_pytable(path)
if not table_name.startswith('/'):
table_name = '/' + table_name
table_path, table_name = table_name.rsplit('/', maxsplit=1)
if not table_path:
table_path = '/'
else:
raise ValueError('cache_path should be formated as "PATH:TABLE_NAME"')
try:
hdf_t = hdf_f.get_node(table_path, table_name, 'Table')
erase_table = False
if overwrite and isinstance(overwrite, bool):
erase_table = True
elif overwrite == 'auto':
if hdf_t.nrows != self.size:
erase_table = True
else:
for col_name, hdf_col in hdf_t.description._v_colobjects.items():
col = self.column_by_name(col_name, raise_on_unknown=False)
if col is None or hdf_col.dtype.shape != col.shape or hdf_col.dtype.base != col.dtype:
erase_table = True
break
if erase_table:
hdf_f.remove_node(table_path, table_name)
hdf_t = None
except tables.NoSuchNodeError:
pass
else:
hdf_f = None
hdf_i = 0
while hdf_f is None:
try:
hdf_f = tables.open_file("/tmp/tmpEmptyHDF_%i.h5" % hdf_i, "a", driver="H5FD_CORE", driver_core_backing_store=0)
except tables.HDF5ExtError:
hdf_i += 1
table_path = '/'
table_name = 'dataset'
if hdf_t is None:
desc = OrderedDict()
for i, c in enumerate(['pk']+columns):
col = self.column_by_name(c)
if col.shape == ():
if col.is_text or col.dtype in ('O', object):
desc[col.name] = tables.StringCol(1024, pos=i)
else:
desc[col.name] = tables.Col.from_dtype(col.dtype, pos=i)
else:
desc[col.name] = tables.Col.from_sctype(col.dtype.type, col.shape, pos=i)
hdf_t = hdf_f.create_table(table_path, table_name, description=desc, expectedrows=self.size,
createparents=True, track_times=False)
chunck_size = min(stop - start, hdf_t.chunkshape[0])
if ncore > 1:
with Process('Allocating %s' % label, stop-start, verbose=False) as p:
empty = DataSetResult.create_empty(dataset=self, n=1).to_row_list()[0]
for i in range(0, stop-start):
hdf_t.append([empty]*min(chunck_size, stop-i))
p.step = i
with Process('Caching %s' % label, stop-start, verbose=False) as p:
from .dataset_generator import DataSetResult
def write_back(r: DataSetResult):
hdf_t.modify_rows(start=r.start_id-start, stop=r.stop_id-start, rows=r.to_row_list())
p.update(r.size)
self.export(write_back, n=chunck_size, start=start, stop=stop, columns=columns, ncore=ncore)
else:
with Process('Caching %s' % label, stop-start, verbose=False) as p:
for r in self.generator(n=chunck_size, start=start, stop=stop, determinist=True, columns=columns):
hdf_t.append(r.to_row_list())
p.update(r.size)
hdf_f.flush()
from .datasets_core import PyTableDataSet
hdfDataset = PyTableDataSet(hdf_t, name=name)
for c in columns:
hdfDataset.col[c].format = self.col[c].format
hdfDataset.col[c]._is_text = self.col[c].is_text # Bof...
return hdfDataset
# --- Global operator ---
def sum(self, columns=None, start=0, stop=None, ncore=1, n=1, determinist=True):
single_column = isinstance(columns, (str, DSColumn))
columns = self.interpret_columns(columns)
for c in columns:
c = self.column_by_name(c)
if not np.issubdtype(c.dtype, np.number):
raise ValueError('Only numeric columns can be summed. (%s is not numeric, dtype: %s).'
% (c.name, c.dtype))
from .dataset_generator import DataSetResult
result = DataSetResult.create_empty(n=1, dataset=self, columns=columns)
def write_cb(r):
for c in r.keys():
result[0, c] += r[:, c].sum(axis=0)
result.trace.affiliate_parent_trace(r.trace)
self.export(write_cb, columns=columns, n=n, start=start, stop=stop, ncore=ncore, determinist=determinist)
return result[columns[0]] if single_column else result
def mean(self, columns=None, start=0, stop=None, std=False, ncore=1, n=1, determinist=True):
single_column = isinstance(columns, (str, DSColumn))
columns = self.interpret_columns(columns)
for c in columns:
c = self.column_by_name(c)
if not np.issubdtype(c.dtype, np.number):
raise ValueError('Only numeric columns can be averaged. (%s is not numeric, dtype: %s).'
% (c.name, c.dtype))
start, stop = interval(self.size, start, stop)
from .dataset_generator import DataSetResult
result = DataSetResult.create_from_data({c: np.zeros((2,)+self.col[c].shape, np.float)
for c in columns})
if std:
def write_cb(r):
for c in columns:
result[0, c] += r[:, c].sum(axis=0) / (stop - start)
result[1, c] += np.square(r[:, c]).sum(axis=0) / (stop - start)
result.trace.affiliate_parent_trace(r.trace)
else:
def write_cb(r):
for c in columns:
result[0, c] += r[:, c].sum(axis=0)/(stop-start)
result.trace.affiliate_parent_trace(r.trace)
self.export(write_cb, columns=columns, n=n, start=start, stop=stop, ncore=ncore, determinist=determinist)
if std:
for c in result.keys():
result[1, c] = np.sqrt(result[1, c]-np.square(result[0, c]))
return result[:, columns[0]] if single_column else result
return result[0, columns[0]] if single_column else result
def std(self, columns=None, start=0, stop=None, ncore=1, n=1, determinist=True):
single_column = isinstance(columns, (str, DSColumn))
columns = self.interpret_columns(columns)
result = self.mean(columns=columns, start=start, stop=stop, ncore=ncore, n=n, determinist=determinist, std=True)
return result[1, columns[0]] if single_column else result.truncate(start=1)
def confusion_matrix(self, pred, true, weight=None, label=None, rowwise=False,
start=0, stop=None, ncore=1, n=1, determinist=True):
from ..j_utils.math import ConfMatrix
# Handle pred, true and weight
if isinstance(pred, DSColumn):
if pred.dataset is not self:
raise ValueError("%s is not a column of %s." % (pred.name, self.dataset_name))
elif isinstance(pred, str):
pred = self.column_by_name(pred)
else:
raise ValueError("Invalid type for pred. (Expected type: str or DSColumn, received: %s)" % type(pred))
pred_shape = pred.shape
if isinstance(true, DSColumn):
if true.dataset is not self:
raise ValueError("%s is not a column of %s." % (true.name, self.dataset_name))
elif isinstance(true, str):
true = self.column_by_name(true)
elif isinstance(true, np.ndarray):
pass
else:
raise ValueError("Invalid type for true. (Expected type: str or DSColumn, received: %s)" % type(true))
true_shape = true.shape
if weight is not None:
if isinstance(weight, DSColumn):
if weight.dataset is not self:
raise ValueError("%s is not a column of %s." % (weight.name, self.dataset_name))
elif isinstance(weight, str):
weight = self.column_by_name(weight)
elif isinstance(weight, | |
: 2D array
Specify the matrix that will be used to multiply the vector of
subsystem outputs to obtain the vector of subsystem inputs.
"""
# Make sure the connection map is the right size
if connect_map.shape != self.connect_map.shape:
ValueError("Connection map is not the right shape")
self.connect_map = connect_map
def set_input_map(self, input_map):
"""Set the input map for an interconnected I/O system.
Parameters
----------
input_map : 2D array
Specify the matrix that will be used to multiply the vector of
system inputs to obtain the vector of subsystem inputs. These
values are added to the inputs specified in the connection map.
"""
# Figure out the number of internal inputs
ninputs = sum(sys.ninputs for sys in self.syslist)
# Make sure the input map is the right size
if input_map.shape[0] != ninputs:
ValueError("Input map is not the right shape")
self.input_map = input_map
self.ninputs = input_map.shape[1]
def set_output_map(self, output_map):
"""Set the output map for an interconnected I/O system.
Parameters
----------
output_map : 2D array
Specify the matrix that will be used to multiply the vector of
subsystem outputs to obtain the vector of system outputs.
"""
# Figure out the number of internal inputs and outputs
ninputs = sum(sys.ninputs for sys in self.syslist)
noutputs = sum(sys.noutputs for sys in self.syslist)
# Make sure the output map is the right size
if output_map.shape[1] == noutputs:
# For backward compatibility, add zeros to the end of the array
output_map = np.concatenate(
(output_map,
np.zeros((output_map.shape[0], ninputs))),
axis=1)
if output_map.shape[1] != noutputs + ninputs:
ValueError("Output map is not the right shape")
self.output_map = output_map
self.noutputs = output_map.shape[0]
def input_output_response(sys, T, U=0., X0=0, params={}, method='RK45',
return_x=False, squeeze=True):
"""Compute the output response of a system to a given input.
Simulate a dynamical system with a given input and return its output
and state values.
Parameters
----------
sys: InputOutputSystem
Input/output system to simulate.
T: array-like
Time steps at which the input is defined; values must be evenly spaced.
U: array-like or number, optional
Input array giving input at each time `T` (default = 0).
X0: array-like or number, optional
Initial condition (default = 0).
return_x : bool, optional
If True, return the values of the state at each time (default = False).
squeeze : bool, optional
If True (default), squeeze unused dimensions out of the output
response. In particular, for a single output system, return a
vector of shape (nsteps) instead of (nsteps, 1).
Returns
-------
T : array
Time values of the output.
yout : array
Response of the system.
xout : array
Time evolution of the state vector (if return_x=True)
Raises
------
TypeError
If the system is not an input/output system.
ValueError
If time step does not match sampling time (for discrete time systems)
"""
# Sanity checking on the input
if not isinstance(sys, InputOutputSystem):
raise TypeError("System of type ", type(sys), " not valid")
# Compute the time interval and number of steps
T0, Tf = T[0], T[-1]
n_steps = len(T)
# Check and convert the input, if needed
# TODO: improve MIMO ninputs check (choose from U)
if sys.ninputs is None or sys.ninputs == 1:
legal_shapes = [(n_steps,), (1, n_steps)]
else:
legal_shapes = [(sys.ninputs, n_steps)]
U = _check_convert_array(U, legal_shapes,
'Parameter ``U``: ', squeeze=False)
# Check to make sure this is not a static function
nstates = _find_size(sys.nstates, X0)
if nstates == 0:
# No states => map input to output
u = U[0] if len(U.shape) == 1 else U[:, 0]
y = np.zeros((np.shape(sys._out(T[0], X0, u))[0], len(T)))
for i in range(len(T)):
u = U[i] if len(U.shape) == 1 else U[:, i]
y[:, i] = sys._out(T[i], [], u)
if (squeeze): y = np.squeeze(y)
if return_x:
return T, y, []
else:
return T, y
# create X0 if not given, test if X0 has correct shape
X0 = _check_convert_array(X0, [(nstates,), (nstates, 1)],
'Parameter ``X0``: ', squeeze=True)
# Update the parameter values
sys._update_params(params)
# Create a lambda function for the right hand side
u = sp.interpolate.interp1d(T, U, fill_value="extrapolate")
def ivp_rhs(t, x): return sys._rhs(t, x, u(t))
# Perform the simulation
if isctime(sys):
if not hasattr(sp.integrate, 'solve_ivp'):
raise NameError("scipy.integrate.solve_ivp not found; "
"use SciPy 1.0 or greater")
soln = sp.integrate.solve_ivp(ivp_rhs, (T0, Tf), X0, t_eval=T,
method=method, vectorized=False)
# Compute the output associated with the state (and use sys.out to
# figure out the number of outputs just in case it wasn't specified)
u = U[0] if len(U.shape) == 1 else U[:, 0]
y = np.zeros((np.shape(sys._out(T[0], X0, u))[0], len(T)))
for i in range(len(T)):
u = U[i] if len(U.shape) == 1 else U[:, i]
y[:, i] = sys._out(T[i], soln.y[:, i], u)
elif isdtime(sys):
# Make sure the time vector is uniformly spaced
dt = T[1] - T[0]
if not np.allclose(T[1:] - T[:-1], dt):
raise ValueError("Parameter ``T``: time values must be "
"equally spaced.")
# Make sure the sample time matches the given time
if (sys.dt is not True):
# Make sure that the time increment is a multiple of sampling time
# TODO: add back functionality for undersampling
# TODO: this test is brittle if dt = sys.dt
# First make sure that time increment is bigger than sampling time
# if dt < sys.dt:
# raise ValueError("Time steps ``T`` must match sampling time")
# Check to make sure sampling time matches time increments
if not np.isclose(dt, sys.dt):
raise ValueError("Time steps ``T`` must be equal to "
"sampling time")
# Compute the solution
soln = sp.optimize.OptimizeResult()
soln.t = T # Store the time vector directly
x = [float(x0) for x0 in X0] # State vector (store as floats)
soln.y = [] # Solution, following scipy convention
y = [] # System output
for i in range(len(T)):
# Store the current state and output
soln.y.append(x)
y.append(sys._out(T[i], x, u(T[i])))
# Update the state for the next iteration
x = sys._rhs(T[i], x, u(T[i]))
# Convert output to numpy arrays
soln.y = np.transpose(np.array(soln.y))
y = np.transpose(np.array(y))
# Mark solution as successful
soln.success = True # No way to fail
else: # Neither ctime or dtime??
raise TypeError("Can't determine system type")
# Get rid of extra dimensions in the output, of desired
if (squeeze): y = np.squeeze(y)
if return_x:
return soln.t, y, soln.y
else:
return soln.t, y
def find_eqpt(sys, x0, u0=[], y0=None, t=0, params={},
iu=None, iy=None, ix=None, idx=None, dx0=None,
return_y=False, return_result=False, **kw):
"""Find the equilibrium point for an input/output system.
Returns the value of an equlibrium point given the initial state and
either input value or desired output value for the equilibrium point.
Parameters
----------
x0 : list of initial state values
Initial guess for the value of the state near the equilibrium point.
u0 : list of input values, optional
If `y0` is not specified, sets the equilibrium value of the input. If
`y0` is given, provides an initial guess for the value of the input.
Can be omitted if the system does not have any inputs.
y0 : list of output values, optional
If specified, sets the desired values of the outputs at the
equilibrium point.
t : float, optional
Evaluation time, for time-varying systems
params : dict, optional
Parameter values for the system. Passed to the evaluation functions
for the system as default values, overriding internal defaults.
iu : list of input indices, optional
If specified, only the inputs with the given indices will be fixed at
the specified values in solving for an equilibrium point. All other
inputs will be varied. Input indices can be listed in any order.
iy : list of output indices, optional
If specified, only the outputs with the given indices will be fixed at
the specified values in solving for an equilibrium point. All other
outputs will be varied. Output indices can be listed in any order.
ix : list of state indices, optional
If specified, states with the given indices will be fixed at the
specified values in solving for an equilibrium point. All | |
#!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the
# following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
# USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
Functions for retargeting based on the paper "Using an Intermediate Skeleton and Inverse Kinematics for Motion Retargeting"
by Monzani et al.
See: http://www.vis.uni-stuttgart.de/plain/vdl/vdl_upload/91_35_retargeting%20monzani00using.pdf
"""
import numpy as np
import math
from transformations import quaternion_matrix, quaternion_multiply, quaternion_about_axis, quaternion_from_matrix
from .constants import OPENGL_UP_AXIS
from .utils import normalize, align_axis, find_rotation_between_vectors, align_root_translation, to_local_cos, get_quaternion_rotation_by_name, apply_additional_rotation_on_frames, project_vector_on_axis, quaternion_from_vector_to_vector
from ..animation_data.skeleton_models import JOINT_CHILD_MAP
def create_local_cos_map_using_child_map(skeleton, up_vector, x_vector, child_map=None):
joint_cos_map = dict()
for j in list(skeleton.nodes.keys()):
joint_cos_map[j] = dict()
joint_cos_map[j]["y"] = up_vector
joint_cos_map[j]["x"] = x_vector
if j == skeleton.root:
joint_cos_map[j]["x"] = (-np.array(x_vector)).tolist()
else:
o = np.array([0, 0, 0])
if child_map is not None and j in child_map:
child_name = child_map[j]
node = skeleton.nodes[child_name]
o = np.array(node.offset)
elif len(skeleton.nodes[j].children) > 0:
node = skeleton.nodes[j].children[0]
o = np.array(node.offset)
o = normalize(o)
if sum(o * o) > 0:
joint_cos_map[j]["y"] = o
return joint_cos_map
def create_local_cos_map(skeleton, up_vector, x_vector):
joint_cos_map = dict()
for j in list(skeleton.nodes.keys()):
joint_cos_map[j] = dict()
joint_cos_map[j]["y"] = up_vector
joint_cos_map[j]["x"] = x_vector
if j == skeleton.root:
joint_cos_map[j]["x"] = (-np.array(x_vector)).tolist()
return joint_cos_map
def get_body_x_axis(skeleton):
rh = skeleton.skeleton_model["joints"]["right_hip"]
lh = skeleton.skeleton_model["joints"]["left_hip"]
return get_body_axis(skeleton, rh, lh)
def get_body_y_axis(skeleton):
a = skeleton.skeleton_model["joints"]["pelvis"]
b = skeleton.skeleton_model["joints"]["head"]
return get_body_axis(skeleton, a,b)
def get_quaternion_to_axis(skeleton, joint_a, joint_b, axis):
ident_f = skeleton.identity_frame
ap = skeleton.nodes[joint_a].get_global_position(ident_f)
bp = skeleton.nodes[joint_b].get_global_position(ident_f)
delta = bp - ap
delta /= np.linalg.norm(delta)
return quaternion_from_vector_to_vector(axis, delta)
def get_body_axis(skeleton, joint_a, joint_b, project=True):
ident_f = skeleton.identity_frame
ap = skeleton.nodes[joint_a].get_global_position(ident_f)
bp = skeleton.nodes[joint_b].get_global_position(ident_f)
delta = bp - ap
m = np.linalg.norm(delta)
if m != 0:
delta /= m
if project:
projection = project_vector_on_axis(delta)
return projection / np.linalg.norm(projection)
else:
return delta
else:
return None
def rotate_axes(cos, q):
m = quaternion_matrix(q)[:3, :3]
for key, a in list(cos.items()):
cos[key] = np.dot(m, a)
cos[key] = normalize(cos[key])
return cos
def get_child_joint(skeleton, inv_joint_map, node_name):
""" Warning output is random if there are more than one child joints
and the value is not specified in the JOINT_CHILD_MAP """
child_node = None
if len(skeleton.nodes[node_name].children) > 0:
child_node = skeleton.nodes[node_name].children[-1]
if node_name in inv_joint_map:
joint_name = inv_joint_map[node_name]
while joint_name in JOINT_CHILD_MAP:
child_joint_name = JOINT_CHILD_MAP[joint_name]
# check if child joint is mapped
joint_key = None
if child_joint_name in skeleton.skeleton_model["joints"]:
joint_key = skeleton.skeleton_model["joints"][child_joint_name]
if joint_key is not None and joint_key in skeleton.nodes: # return child joint
child_node = skeleton.nodes[joint_key]
return child_node
else: #keep traversing until end of child map is reached
if child_joint_name in JOINT_CHILD_MAP:
joint_name = JOINT_CHILD_MAP[child_joint_name]
else:
break
return child_node
def create_local_cos_map_from_skeleton_axes_with_map(skeleton, flip=1.0, project=True):
body_x_axis = get_body_x_axis(skeleton)*flip
#print("body x axis", body_x_axis)
body_y_axis = get_body_y_axis(skeleton)
#print("body y axis", body_y_axis)
inv_joint_map = dict((v,k) for k, v in skeleton.skeleton_model["joints"].items())
joint_cos_map = dict()
for j in list(skeleton.nodes.keys()):
joint_cos_map[j] = dict()
joint_cos_map[j]["y"] = body_y_axis
joint_cos_map[j]["x"] = body_x_axis
node = skeleton.nodes[j]
child_node = get_child_joint(skeleton, inv_joint_map, node.node_name)
if child_node is None:
continue
y_axis = get_body_axis(skeleton, j, child_node.node_name, project)
if y_axis is not None:
joint_cos_map[j]["y"] = y_axis
#check if the new y axis is similar to the x axis
z_vector = np.cross(y_axis, joint_cos_map[j]["x"])
if np.linalg.norm(z_vector) == 0.0:
joint_cos_map[j]["x"] = body_y_axis * -np.sum(joint_cos_map[j]["y"])
#check for angle and rotate
q = get_quaternion_to_axis(skeleton, j, child_node.node_name, y_axis)
rotate_axes(joint_cos_map[j], q)
else:
joint_cos_map[j]["y"] = None
joint_cos_map[j]["x"] = None
return joint_cos_map
def align_root_joint(axes, global_src_x_vec, max_iter_count=10):
# handle special case for the root joint
# apply only the y axis rotation of the Hip to the Game_engine node
not_aligned = True
q = [1, 0, 0, 0]
iter_count = 0
while not_aligned:
qx, axes = align_axis(axes, "x", global_src_x_vec) # first find rotation to align x axis
q = quaternion_multiply(qx, q)
q = normalize(q)
qy, axes = align_axis(axes, "y", OPENGL_UP_AXIS) # then add a rotation to let the y axis point up
q = quaternion_multiply(qy, q)
q = normalize(q)
dot_y = np.dot(axes["y"], OPENGL_UP_AXIS)
dot_y = min(1, max(dot_y, -1))
a_y = math.acos(dot_y)
dot_x = np.dot(axes["x"], global_src_x_vec)
dot_x = min(1, max(dot_x, -1))
a_x = math.acos(dot_x)
iter_count += 1
not_aligned = a_y > 0.1 or a_x > 0.1 and iter_count < max_iter_count
return q
def align_joint(new_skeleton, free_joint_name, local_target_axes, global_src_up_vec, global_src_x_vec, joint_cos_map, apply_spine_fix=False):
# first align the twist axis
q, axes = align_axis(local_target_axes, "y", global_src_up_vec)
q = normalize(q)
# then align the swing axis
qx, axes = align_axis(axes, "x", global_src_x_vec)
q = quaternion_multiply(qx, q)
q = normalize(q)
# handle cases when twist axis alignment was lost
dot = np.dot(axes["y"], global_src_up_vec)
if dot <= -1:
q180 = quaternion_about_axis(np.deg2rad(180), global_src_x_vec)
q180 = normalize(q180)
q = quaternion_multiply(q180, q)
q = normalize(q)
elif abs(dot) != 1.0:
qy, axes = align_axis(axes, "y", global_src_up_vec)
q = quaternion_multiply(qy, q)
q = normalize(q)
return q
def find_rotation_analytically(new_skeleton, joint_name, global_src_up_vec, global_src_x_vec, frame, joint_cos_map, apply_spine_fix=False, apply_root_fix=False, max_iter_count=10):
local_target_axes = joint_cos_map[joint_name]
if joint_name == new_skeleton.root and apply_root_fix:
q = align_root_joint(local_target_axes, global_src_x_vec, max_iter_count)
else:
q = align_joint(new_skeleton, joint_name, local_target_axes, global_src_up_vec, global_src_x_vec, joint_cos_map)
return to_local_cos(new_skeleton, joint_name, frame, q)
def create_correction_map(target_skeleton,target_to_src_joint_map, src_cos_map, target_cos_map):
correction_map = dict()
for target_name in target_to_src_joint_map:
src_name = target_to_src_joint_map[target_name]
if src_name in src_cos_map and target_name is not None and target_name in target_cos_map:
src_zero_vector_y = src_cos_map[src_name]["y"]
target_zero_vector_y = target_cos_map[target_name]["y"]
src_zero_vector_x = src_cos_map[src_name]["x"]
target_zero_vector_x = target_cos_map[target_name]["x"]
if target_zero_vector_y is not None and src_zero_vector_y is not None:
q = quaternion_from_vector_to_vector(target_zero_vector_y, src_zero_vector_y)
q = normalize(q)
m = quaternion_matrix(q)[:3, :3]
target_zero_vector_x = normalize(np.dot(m, target_zero_vector_x))
qx = quaternion_from_vector_to_vector(target_zero_vector_x, src_zero_vector_x)
q = quaternion_multiply(qx, q)
q = normalize(q)
correction_map[target_name] = q
return correction_map
class Retargeting(object):
def __init__(self, src_skeleton, target_skeleton, target_to_src_joint_map, scale_factor=1.0, additional_rotation_map=None, constant_offset=None, place_on_ground=False, force_root_translation=False, ground_height=0):
self.src_skeleton = src_skeleton
self.target_skeleton = target_skeleton
self.target_to_src_joint_map = target_to_src_joint_map
self.src_to_target_joint_map = {v: k for k, v in list(self.target_to_src_joint_map.items())}
self.scale_factor = scale_factor
self.n_params = len(self.target_skeleton.animated_joints) * 4 + 3
self.ground_height = ground_height
self.rotation_offsets = additional_rotation_map
self.src_inv_joint_map = dict((v,k) for k, v in src_skeleton.skeleton_model["joints"].items())
self.src_child_map = dict()
for src_name in self.src_skeleton.animated_joints:
src_child = get_child_joint(self.src_skeleton, self.src_inv_joint_map, src_name)
if src_child is not None:
self.src_child_map[src_name] = src_child.node_name
else:
self.src_child_map[src_name] = None
self.target_cos_map = create_local_cos_map_from_skeleton_axes_with_map(self.target_skeleton)
self.src_cos_map = create_local_cos_map_from_skeleton_axes_with_map(self.src_skeleton)
if "cos_map" in target_skeleton.skeleton_model:
self.target_cos_map.update(target_skeleton.skeleton_model["cos_map"])
if "cos_map" in src_skeleton.skeleton_model:
self.src_cos_map.update(src_skeleton.skeleton_model["cos_map"])
self.correction_map = dict()
spine_joints = ["pelvis","spine", "spine_1","spine_2"]
target_joint_map = self.target_skeleton.skeleton_model["joints"]
self.target_spine_joints =[target_joint_map[j] for j in spine_joints if j in target_joint_map]
self.correction_map = create_correction_map(self.target_skeleton, self.src_to_target_joint_map, self.src_cos_map, self.target_cos_map)
self.constant_offset = constant_offset
self.place_on_ground = place_on_ground
self.force_root_translation = force_root_translation
self.apply_spine_fix = "neck" in target_joint_map and self.src_skeleton.animated_joints != self.target_skeleton.animated_joints
if "root" in target_joint_map:
self.apply_root_fix = self.target_skeleton.skeleton_model["joints"]["root"] is not None # aligns root up axis with src skeleton up axis
# make sure the src root joint in the target is not None
target_root = self.target_skeleton.skeleton_model["joints"]["root"]
if self.apply_root_fix and target_to_src_joint_map[target_root] is None:
target_to_src_joint_map[target_root] = self.src_skeleton.root
else:
self.apply_root_fix = False
if scale_factor <= 0:
self.auto_scale_factor()
def auto_scale_factor(self):
""" estimate scale from leg length by gemlongman """
target_hip_h = self.target_skeleton.get_body_hip2foot_height()
src_hip_h = self.src_skeleton.get_body_hip2foot_height()
self.scale_factor = target_hip_h / src_hip_h
print("debug scale_factor :" + str(target_hip_h)+ " / " +str(src_hip_h) + " = " +str(self.scale_factor))
def rotate_bone(self, src_name, target_name, src_frame, target_frame, guess):
q = guess
src_x_axis = self.src_cos_map[src_name]["x"]
src_up_axis = self.src_cos_map[src_name]["y"]
if self.src_cos_map[src_name]["y"] is not None and self.target_cos_map[target_name]["y"] is not None:
global_m = self.src_skeleton.nodes[src_name].get_global_matrix(src_frame)[:3, :3]
global_src_up_vec = normalize(np.dot(global_m, src_up_axis))
global_src_x_vec = normalize(np.dot(global_m, src_x_axis))
apply_spine_fix = self.apply_spine_fix and target_name in self.target_spine_joints
q = find_rotation_analytically(self.target_skeleton, target_name, global_src_up_vec, global_src_x_vec, target_frame, self.target_cos_map, apply_spine_fix, self.apply_root_fix)
return q
def rotate_bone_fast(self, src_name, target_name, src_frame, target_frame, quess):
q = quess
src_child_name | |
# -*- coding: utf-8 -*-
"""
we test .agg behavior / note that .apply is tested
generally in test_groupby.py
"""
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
from functools import partial
import numpy as np
from numpy import nan
import pandas as pd
from pandas import (date_range, MultiIndex, DataFrame,
Series, Index, bdate_range, concat)
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pandas.core.groupby import SpecificationError, DataError
from pandas.compat import OrderedDict
from pandas.io.formats.printing import pprint_thing
import pandas.util.testing as tm
class TestGroupByAggregate(object):
def setup_method(self, method):
self.ts = tm.makeTimeSeries()
self.seriesd = tm.getSeriesData()
self.tsd = tm.getTimeSeriesData()
self.frame = DataFrame(self.seriesd)
self.tsframe = DataFrame(self.tsd)
self.df = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.random.randn(8)})
self.df_mixed_floats = DataFrame(
{'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],
'C': np.random.randn(8),
'D': np.array(
np.random.randn(8), dtype='float32')})
index = MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'], ['one', 'two',
'three']],
labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=['first', 'second'])
self.mframe = DataFrame(np.random.randn(10, 3), index=index,
columns=['A', 'B', 'C'])
self.three_group = DataFrame(
{'A': ['foo', 'foo', 'foo', 'foo', 'bar', 'bar', 'bar', 'bar',
'foo', 'foo', 'foo'],
'B': ['one', 'one', 'one', 'two', 'one', 'one', 'one', 'two',
'two', 'two', 'one'],
'C': ['dull', 'dull', 'shiny', 'dull', 'dull', 'shiny', 'shiny',
'dull', 'shiny', 'shiny', 'shiny'],
'D': np.random.randn(11),
'E': np.random.randn(11),
'F': np.random.randn(11)})
def test_agg_api(self):
# GH 6337
# http://stackoverflow.com/questions/21706030/pandas-groupby-agg-function-column-dtype-error
# different api for agg when passed custom function with mixed frame
df = DataFrame({'data1': np.random.randn(5),
'data2': np.random.randn(5),
'key1': ['a', 'a', 'b', 'b', 'a'],
'key2': ['one', 'two', 'one', 'two', 'one']})
grouped = df.groupby('key1')
def peak_to_peak(arr):
return arr.max() - arr.min()
expected = grouped.agg([peak_to_peak])
expected.columns = ['data1', 'data2']
result = grouped.agg(peak_to_peak)
assert_frame_equal(result, expected)
def test_agg_regression1(self):
grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month])
result = grouped.agg(np.mean)
expected = grouped.mean()
assert_frame_equal(result, expected)
def test_agg_datetimes_mixed(self):
data = [[1, '2012-01-01', 1.0], [2, '2012-01-02', 2.0], [3, None, 3.0]]
df1 = DataFrame({'key': [x[0] for x in data],
'date': [x[1] for x in data],
'value': [x[2] for x in data]})
data = [[row[0], datetime.strptime(row[1], '%Y-%m-%d').date() if row[1]
else None, row[2]] for row in data]
df2 = DataFrame({'key': [x[0] for x in data],
'date': [x[1] for x in data],
'value': [x[2] for x in data]})
df1['weights'] = df1['value'] / df1['value'].sum()
gb1 = df1.groupby('date').aggregate(np.sum)
df2['weights'] = df1['value'] / df1['value'].sum()
gb2 = df2.groupby('date').aggregate(np.sum)
assert (len(gb1) == len(gb2))
def test_agg_period_index(self):
from pandas import period_range, PeriodIndex
prng = period_range('2012-1-1', freq='M', periods=3)
df = DataFrame(np.random.randn(3, 2), index=prng)
rs = df.groupby(level=0).sum()
assert isinstance(rs.index, PeriodIndex)
# GH 3579
index = period_range(start='1999-01', periods=5, freq='M')
s1 = Series(np.random.rand(len(index)), index=index)
s2 = Series(np.random.rand(len(index)), index=index)
series = [('s1', s1), ('s2', s2)]
df = DataFrame.from_items(series)
grouped = df.groupby(df.index.month)
list(grouped)
def test_agg_dict_parameter_cast_result_dtypes(self):
# GH 12821
df = DataFrame(
{'class': ['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'],
'time': date_range('1/1/2011', periods=8, freq='H')})
df.loc[[0, 1, 2, 5], 'time'] = None
# test for `first` function
exp = df.loc[[0, 3, 4, 6]].set_index('class')
grouped = df.groupby('class')
assert_frame_equal(grouped.first(), exp)
assert_frame_equal(grouped.agg('first'), exp)
assert_frame_equal(grouped.agg({'time': 'first'}), exp)
assert_series_equal(grouped.time.first(), exp['time'])
assert_series_equal(grouped.time.agg('first'), exp['time'])
# test for `last` function
exp = df.loc[[0, 3, 4, 7]].set_index('class')
grouped = df.groupby('class')
assert_frame_equal(grouped.last(), exp)
assert_frame_equal(grouped.agg('last'), exp)
assert_frame_equal(grouped.agg({'time': 'last'}), exp)
assert_series_equal(grouped.time.last(), exp['time'])
assert_series_equal(grouped.time.agg('last'), exp['time'])
# count
exp = pd.Series([2, 2, 2, 2],
index=Index(list('ABCD'), name='class'),
name='time')
assert_series_equal(grouped.time.agg(len), exp)
assert_series_equal(grouped.time.size(), exp)
exp = pd.Series([0, 1, 1, 2],
index=Index(list('ABCD'), name='class'),
name='time')
assert_series_equal(grouped.time.count(), exp)
def test_agg_cast_results_dtypes(self):
# similar to GH12821
# xref #11444
u = [datetime(2015, x + 1, 1) for x in range(12)]
v = list('aaabbbbbbccd')
df = pd.DataFrame({'X': v, 'Y': u})
result = df.groupby('X')['Y'].agg(len)
expected = df.groupby('X')['Y'].count()
assert_series_equal(result, expected)
def test_agg_must_agg(self):
grouped = self.df.groupby('A')['C']
pytest.raises(Exception, grouped.agg, lambda x: x.describe())
pytest.raises(Exception, grouped.agg, lambda x: x.index[:2])
def test_agg_ser_multi_key(self):
# TODO(wesm): unused
ser = self.df.C # noqa
f = lambda x: x.sum()
results = self.df.C.groupby([self.df.A, self.df.B]).aggregate(f)
expected = self.df.groupby(['A', 'B']).sum()['C']
assert_series_equal(results, expected)
def test_agg_apply_corner(self):
# nothing to group, all NA
grouped = self.ts.groupby(self.ts * np.nan)
assert self.ts.dtype == np.float64
# groupby float64 values results in Float64Index
exp = Series([], dtype=np.float64, index=pd.Index(
[], dtype=np.float64))
assert_series_equal(grouped.sum(), exp)
assert_series_equal(grouped.agg(np.sum), exp)
assert_series_equal(grouped.apply(np.sum), exp, check_index_type=False)
# DataFrame
grouped = self.tsframe.groupby(self.tsframe['A'] * np.nan)
exp_df = DataFrame(columns=self.tsframe.columns, dtype=float,
index=pd.Index([], dtype=np.float64))
assert_frame_equal(grouped.sum(), exp_df, check_names=False)
assert_frame_equal(grouped.agg(np.sum), exp_df, check_names=False)
assert_frame_equal(grouped.apply(np.sum), exp_df.iloc[:, :0],
check_names=False)
def test_agg_grouping_is_list_tuple(self):
from pandas.core.groupby import Grouping
df = tm.makeTimeDataFrame()
grouped = df.groupby(lambda x: x.year)
grouper = grouped.grouper.groupings[0].grouper
grouped.grouper.groupings[0] = Grouping(self.ts.index, list(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
grouped.grouper.groupings[0] = Grouping(self.ts.index, tuple(grouper))
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_aggregate_float64_no_int64(self):
# see gh-11199
df = DataFrame({"a": [1, 2, 3, 4, 5],
"b": [1, 2, 2, 4, 5],
"c": [1, 2, 3, 4, 5]})
expected = DataFrame({"a": [1, 2.5, 4, 5]},
index=[1, 2, 4, 5])
expected.index.name = "b"
result = df.groupby("b")[["a"]].mean()
tm.assert_frame_equal(result, expected)
expected = DataFrame({"a": [1, 2.5, 4, 5],
"c": [1, 2.5, 4, 5]},
index=[1, 2, 4, 5])
expected.index.name = "b"
result = df.groupby("b")[["a", "c"]].mean()
tm.assert_frame_equal(result, expected)
def test_aggregate_api_consistency(self):
# GH 9052
# make sure that the aggregates via dict
# are consistent
df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'two',
'two', 'two', 'one', 'two'],
'C': np.random.randn(8) + 1.0,
'D': np.arange(8)})
grouped = df.groupby(['A', 'B'])
c_mean = grouped['C'].mean()
c_sum = grouped['C'].sum()
d_mean = grouped['D'].mean()
d_sum = grouped['D'].sum()
result = grouped['D'].agg(['sum', 'mean'])
expected = pd.concat([d_sum, d_mean],
axis=1)
expected.columns = ['sum', 'mean']
assert_frame_equal(result, expected, check_like=True)
result = grouped.agg([np.sum, np.mean])
expected = pd.concat([c_sum,
c_mean,
d_sum,
d_mean],
axis=1)
expected.columns = MultiIndex.from_product([['C', 'D'],
['sum', 'mean']])
assert_frame_equal(result, expected, check_like=True)
result = grouped[['D', 'C']].agg([np.sum, np.mean])
expected = pd.concat([d_sum,
d_mean,
c_sum,
c_mean],
axis=1)
expected.columns = MultiIndex.from_product([['D', 'C'],
['sum', 'mean']])
assert_frame_equal(result, expected, check_like=True)
result = grouped.agg({'C': 'mean', 'D': 'sum'})
expected = pd.concat([d_sum,
c_mean],
axis=1)
assert_frame_equal(result, expected, check_like=True)
result = grouped.agg({'C': ['mean', 'sum'],
'D': ['mean', 'sum']})
expected = pd.concat([c_mean,
c_sum,
d_mean,
d_sum],
axis=1)
expected.columns = MultiIndex.from_product([['C', 'D'],
['mean', 'sum']])
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = grouped[['D', 'C']].agg({'r': np.sum,
'r2': np.mean})
expected = pd.concat([d_sum,
c_sum,
d_mean,
c_mean],
axis=1)
expected.columns = MultiIndex.from_product([['r', 'r2'],
['D', 'C']])
assert_frame_equal(result, expected, check_like=True)
def test_agg_dict_renaming_deprecation(self):
# 15931
df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
'B': range(5),
'C': range(5)})
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False) as w:
df.groupby('A').agg({'B': {'foo': ['sum', 'max']},
'C': {'bar': ['count', 'min']}})
assert "using a dict with renaming" in str(w[0].message)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
df.groupby('A')[['B', 'C']].agg({'ma': 'max'})
with tm.assert_produces_warning(FutureWarning) as w:
df.groupby('A').B.agg({'foo': 'count'})
assert "using a dict on a Series for aggregation" in str(
w[0].message)
def test_agg_compat(self):
# GH 12334
df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'two',
'two', 'two', 'one', 'two'],
'C': np.random.randn(8) + 1.0,
'D': np.arange(8)})
g = df.groupby(['A', 'B'])
expected = pd.concat([g['D'].sum(),
g['D'].std()],
axis=1)
expected.columns = MultiIndex.from_tuples([('C', 'sum'),
('C', 'std')])
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = g['D'].agg({'C': ['sum', 'std']})
assert_frame_equal(result, expected, check_like=True)
expected = pd.concat([g['D'].sum(),
g['D'].std()],
axis=1)
expected.columns = ['C', 'D']
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = g['D'].agg({'C': 'sum', 'D': 'std'})
assert_frame_equal(result, expected, check_like=True)
def test_agg_nested_dicts(self):
# API change for disallowing these types of nested dicts
df = DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
'B': ['one', 'one', 'two', 'two',
'two', 'two', 'one', 'two'],
'C': np.random.randn(8) + 1.0,
'D': np.arange(8)})
g = df.groupby(['A', 'B'])
def f():
g.aggregate({'r1': {'C': ['mean', 'sum']},
'r2': {'D': ['mean', 'sum']}})
pytest.raises(SpecificationError, f)
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = g.agg({'C': {'ra': ['mean', 'std']},
'D': {'rb': ['mean', 'std']}})
expected = pd.concat([g['C'].mean(), g['C'].std(), g['D'].mean(),
g['D'].std()], axis=1)
expected.columns = pd.MultiIndex.from_tuples([('ra', 'mean'), (
'ra', 'std'), ('rb', 'mean'), ('rb', 'std')])
assert_frame_equal(result, expected, check_like=True)
# same name as the original column
# GH9052
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
expected = g['D'].agg({'result1': np.sum, 'result2': np.mean})
expected = expected.rename(columns={'result1': 'D'})
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
result = g['D'].agg({'D': np.sum, 'result2': np.mean})
assert_frame_equal(result, expected, check_like=True)
def test_agg_python_multiindex(self):
grouped = self.mframe.groupby(['A', 'B'])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_aggregate_str_func(self):
def _check_results(grouped):
# single series
result = grouped['A'].agg('std')
expected = grouped['A'].std()
assert_series_equal(result, expected)
# group frame by function name
result = grouped.aggregate('var')
expected = grouped.var()
assert_frame_equal(result, expected)
# group frame by function dict
result = grouped.agg(OrderedDict([['A', 'var'], ['B', 'std'],
['C', 'mean'], ['D', 'sem']]))
| |
<filename>src/puliclient/jobs.py
'''
Created on Jan 11, 2010
@author: <NAME>
'''
import sys
import site
import traceback
import logging
import subprocess
try:
import simplejson as json
except ImportError:
import json
#
# Errors specific to command execution
#
class TimeoutError (Exception):
''' Raised when helper execution is too long. '''
class CommandDone(Exception):
'''Raised to manually end a command execution.'''
class CommandError(Exception):
'''Raised to signal failure of a CommandRunner execution.'''
class ValidationError(CommandError):
'''Raised on a validation error'''
class RangeError(CommandError):
'''Raised on a validation error where a value given is out of authorized range'''
class JobTypeImportError(ImportError):
'''Raised when an error occurs while loading a job type through the load function'''
class CommandRunnerParameter(object):
'''Base class for formal command runner parameter.'''
name = None
isMandatory = False
def __init__(self, default=None, mandatory=None, **kwargs):
if default is not None:
self.hasDefault = True
self.defaultValue = default
else:
self.hasDefault = False
self.defaultValue = None
if mandatory is not None:
self.isMandatory = True
# Set range values if given in args, still need to be validated against value
# TODO specialize it in typed parameter classes
if 'min' in kwargs:
self.minValue = kwargs['min']
if 'max' in kwargs:
self.maxValue = kwargs['max']
def validate(self, arguments):
if not self.name in arguments and self.isMandatory:
raise ValidationError("Mandatory argument \"%s\" is not defined in command arguments" % self.name)
if not self.name in arguments and self.hasDefault:
arguments[self.name] = self.defaultValue
def __repr__(self):
return "CommandRunnerParameter(name=%r, default=%r, mandatory=%r)" % (self.name, self.defaultValue, self.isMandatory)
def __str__(self):
return "%r (default=%r, mandatory=%r)" % (self.name, self.defaultValue, self.isMandatory)
class StringParameter(CommandRunnerParameter):
'''A command runner parameter class that converts the argument value to a string.'''
def validate(self, arguments):
try:
super(StringParameter, self).validate(arguments)
if self.name in arguments:
arguments[self.name] = str(arguments[self.name])
except Exception, e:
raise e
class StringListParameter(CommandRunnerParameter):
def validate(self, arguments):
try:
super(StringListParameter, self).validate(arguments)
arguments[self.name] = [str(v) for v in arguments[self.name]]
except Exception, e:
raise e
class BooleanParameter(CommandRunnerParameter):
def validate(self, arguments):
try:
super(BooleanParameter, self).validate(arguments)
arguments[self.name] = bool(arguments[self.name])
except Exception, e:
raise e
class IntegerParameter(CommandRunnerParameter):
'''A command runner parameter class that converts the argument value to an integer value.'''
minValue = None
maxValue = None
def validate(self, arguments):
try:
# Base class will check if argument is present or ensure it has its default value
super(IntegerParameter, self).validate(arguments)
if arguments[self.name]:
newVal = int(arguments[self.name])
# Validate range if defined
if self.minValue is not None and newVal < self.minValue:
raise RangeError("Argument \"%s\"=%d is less than minimum: %d" % (
self.name,
newVal,
self.minValue))
if self.maxValue is not None and self.maxValue < newVal:
raise RangeError("Argument \"%s\"=%d is more than maximum: %d" % (
self.name,
self.maxValue,
newVal))
arguments[self.name] = newVal
except RangeError, e:
raise e
except ValidationError, e:
raise e
def __repr__(self):
return "IntParameter(name=%r, default=%r, mandatory=%r, minValue=%r, maxValue=%r )" % (
self.name,
self.defaultValue,
self.isMandatory,
self.minValue,
self.maxValue,
)
def __str__(self):
return "%r (default=%r, mandatory=%r, range=[%r,%r])" % (self.name, self.defaultValue, self.isMandatory, self.minValue, self.maxValue)
class FloatParameter(CommandRunnerParameter):
'''A command runner parameter class that converts the argument value to an float value.'''
minValue = None
maxValue = None
def validate(self, arguments):
try:
super(FloatParameter, self).validate(arguments)
if arguments[self.name]:
newVal = float(arguments[self.name])
# Validate range if defined
if self.minValue is not None and newVal < self.minValue:
raise RangeError("Argument \"%s\"=%d is less than minimum: %d" % (
self.name,
newVal,
self.minValue))
if self.maxValue is not None and self.maxValue < newVal:
raise RangeError("Argument \"%s\"=%d is more than maximum: %d" % (
self.name,
self.maxValue,
newVal))
arguments[self.name] = newVal
except Exception, e:
raise e
def __repr__(self):
return "FloatParameter(name=%r, default=%r, mandatory=%r, minValue=%r, maxValue=%r )" % (
self.name,
self.defaultValue,
self.isMandatory,
self.minValue,
self.maxValue,
)
def __str__(self):
return "%r (default=%r, mandatory=%r, range=[%r,%r])" % (self.name, self.defaultValue, self.isMandatory, self.minValue, self.maxValue)
class CommandRunnerMetaclass(type):
def __init__(self, name, bases, attributes):
type.__init__(self, name, bases, attributes)
parameters = attributes.get('parameters', [])
for base in bases:
if isinstance(base, CommandRunnerMetaclass):
parameters += base.parameters
for (name, arg) in attributes.iteritems():
if isinstance(arg, CommandRunnerParameter):
arg.name = name
parameters.append(arg)
self.parameters = parameters
# logging.getLogger('puli.commandwatcher').info('init CommandRunnerMetaclass')
class CommandRunner(object):
__metaclass__ = CommandRunnerMetaclass
log = logging.getLogger('puli.runner')
scriptTimeOut = None
parameters = []
def execute(self, arguments, updateCompletion, updateMessage, updateStats, updateLicense):
raise NotImplementedError
def validate(self, arguments):
logger = logging.getLogger('puli.commandwatcher')
if len(self.parameters) > 0:
logger.info("Validating %d parameter(s):" % len(self.parameters))
for parameter in self.parameters:
logger.info(" - %s" % parameter)
parameter.validate(arguments)
# TOFIX no need for scripttimeOut, impossible to kill a thread manually, timeout handling
# should be done outside the command runner or around subprocess call in the runner
# # Checking global argument scriptTimeOut:
# try:
# self.scriptTimeOut = int(arguments['scriptTimeOut'])
# logger.info("Defining time out limit: scriptTimeout=%d" % self.scriptTimeOut)
# except KeyError, e:
# logger.info("No scriptTimeout in arguments. Command will never be interrupted (msg: %s)" % e)
# except TypeError, e:
# logger.info("Invalid scriptTimeout value given (integer expected) (msg: %s)" % e)
class DefaultCommandRunner(CommandRunner):
cmd = StringParameter(mandatory=True)
start = IntegerParameter(default=1)
end = IntegerParameter(default=1)
timeout = IntegerParameter(default=0, min=0)
def execute(self, arguments, updateCompletion, updateMessage, updateStats, updateLicense):
'''
| Simple execution using the helper. Default argument "cmd" is expected (mandatory)
| to start the execution with the current env.
|
| If a command is defined on a range, the cmd will be executed several time using start/end arguments.
| The command can use several standard replacement values:
| %%MI_FRAME%% -> replaced by the current frame value
| %%MI_START%% -> replaced by the index of the first frame of the range
| %%MI_END%% -> replaced by the index of the last frame of the range
|
| For instance if a command is defined like this:
| - start = "10"
| - end = "15"
| - cmd = "nuke -x -F %%MI_FRAME%% ma_comp.nk"
| or cmd = "nuke -x -F %%MI_START%%-%%MI_END%% ma_comp.nk"
|
| The runner will produce the following execution:
| nuke -x -F 10 ma_comp.nk
| nuke -x -F 11 ma_comp.nk
| nuke -x -F 12 ma_comp.nk
| nuke -x -F 13 ma_comp.nk
| nuke -x -F 14 ma_comp.nk
| nuke -x -F 15 ma_comp.nk
'''
cmd = arguments.get('cmd')
start = arguments.get('start')
end = arguments.get('end')
timeout = arguments.get('timeout', None)
updateCompletion(0)
completion = 0.0
completionIncrement = 1.0 / float((int(end) + 1) - int(start))
for frame in range(start, end + 1):
self.log.info("==== Frame %d ====" % frame)
currCommand = cmd.replace("%%MI_FRAME%%", str(frame))
currCommand = currCommand.replace("%%MI_START%%", str(start))
currCommand = currCommand.replace("%%MI_END%%", str(end))
self.log.info("Command: %s" % currCommand)
subprocess.check_call(currCommand, close_fds=True, shell=True)
completion += completionIncrement
updateCompletion(completion)
self.log.info("Updating completion %f " % completion)
updateCompletion(1)
class TaskExpander(object):
def __init__(self, taskGroup):
pass
class TaskDecomposer(object):
"""
| Base class for Decomposer hierarchy.
| Implements a minimalist "addCommand" method.
"""
def __init__(self, task):
self.task = task
def addCommand(self, name, args, runnerPackages=None, watcherPackages=None):
self.task.addCommand(name, args, runnerPackages, watcherPackages)
class DefaultTaskDecomposer(TaskDecomposer):
"""
| Default decomposesr called when no decomposer given for a task. It will use the PuliActionHelper to create one
| or several commands on a task. PuliActionHelper's decompose method will have the following behaviour:
| - if "framesList" is defined:
| create a command for each frame indicated (frameList is a string with frame numbers separated by spaces)
| - else:
| try to use start/end/packetSize attributes to create several commands (frames grouped by packetSize)
|
| If no "arguments" dict is given, print a warning and create a single command with empty arguments.
"""
# DEFAULT FIELDS USED TO DECOMPOSE A TASK
START_LABEL = "start"
END_LABEL = "end"
PACKETSIZE_LABEL = "packetSize"
FRAMESLIST_LABEL = "framesList"
def __init__(self, task):
"""
:type task: object
"""
super(DefaultTaskDecomposer, self).__init__(task)
if task.arguments is None:
# Create an empty command anyway --> probably unecessary
print "WARNING: No arguments given for the task \"%s\", it is necessary to do this ? (we are creating an empty command anyway..." % task.name
self.task.addCommand(task.name + "_1_1", {})
elif all(key in task.arguments for key in (self.START_LABEL, self.END_LABEL)) \
or self.FRAMESLIST_LABEL in task.arguments:
# if standard attributes exist in arguments, use the PuliHelper to decompose accordingly
start = task.arguments.get(self.START_LABEL, 1)
end = task.arguments.get(self.END_LABEL, 1)
packetSize = task.arguments.get(self.PACKETSIZE_LABEL, 1)
framesList = task.arguments.get(self.FRAMESLIST_LABEL, "")
self.decompose(start=start, end=end, packetSize=packetSize, callback=self, framesList=framesList)
else:
# If arguments given but no standard behaviour, simply transmit task arguments to single command
self.task.addCommand(task.name, task.arguments)
def addCommand(self, packetStart, packetEnd):
'''
Default method to add a command with DefaultTaskDecomposer.
:param packetStart: Integer representing the first frame
:param packetEnd: Integer representing the last frame
'''
cmdArgs | |
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from stargazer.stargazer import Stargazer
from IPython.core.display import HTML
from IPython.core.interactiveshell import InteractiveShell
from statsmodels.sandbox.regression.gmm import IV2SLS
def create_table1(df):
#Ethnicity table
df_1 = df.sort_values(by="ethnicity_C2", ascending=False)
df_1 = df_1[['country', 'ethnicity_C2', 'ethnicity_I']].head()
df_1 = df_1.reset_index(drop=True)
df_2 = df.sort_values(by="ethnicity_C2", ascending=True)
df_2 = df_2[['country', 'ethnicity_C2', 'ethnicity_I']].head()
df_2 = df_2.reset_index(drop=True)
df_eth = pd.concat([df_1,df_2], axis = 1)
df_eth = df_eth.round(decimals=3)
#Language table
df_3 = df.sort_values(by="language_C2", ascending=False)
df_3 = df_3[['country', 'language_C2', 'language_I']].head()
df_3 = df_3.reset_index(drop=True)
df_4 = df.sort_values(by="language_C2", ascending=True)
df_4 = df_4[['country', 'language_C2', 'language_I']].head()
df_4 = df_4.reset_index(drop=True)
df_lang = pd.concat([df_3,df_4], axis = 1)
df_lang = df_lang.round(decimals=3)
#Religion table
df_5 = df.sort_values(by="religion_C2", ascending=False)
df_5 = df_5[['country', 'religion_C2', 'religion_I']].head()
df_5 = df_5.reset_index(drop=True)
df_6 = df.sort_values(by="religion_C2", ascending=True)
df_6 = df_6[['country', 'religion_C2', 'religion_I']].head()
df_6 = df_6.reset_index(drop=True)
df_rel = pd.concat([df_5,df_6], axis = 1)
df_rel = df_rel.round(decimals=3)
#Rename rows and columns of complete table
table1 = pd.concat([df_eth,df_lang,df_rel],axis = 1)
table1.columns = pd.MultiIndex.from_product([['Ethinicity','Language','Religion'],
['Most segregated','Least segregated',],
['Country','$\hat{S}$', '$F$']])
return table1
def create_table2(df):
variables = df[['ethnicity_C2','language_C2','religion_C2','ethnicity_C','language_C','religion_C',
'voice','PolStab','GovEffec','RegQual','RulLaw','ConCorr']]
table2 = pd.DataFrame()
table2 = variables.corr()
table2.drop(['voice','PolStab','GovEffec','RegQual','RulLaw','ConCorr'], axis = 1, inplace = True)
table2.drop(['ethnicity_C2','ethnicity_C','language_C2','language_C','religion_C2','religion_C'],
axis = 0, inplace = True)
table2 = table2.round(decimals=2)
table2.columns = pd.MultiIndex.from_product([['Segregation indicies'],
['Ethnicity $\hat{S}$','Language $\hat{S}$', 'Religion $\hat{S}$',
'Ethnicity $\tilde{S}$','Language $\tilde{S}$', 'Religion $\tilde{S}$']]
)
table2 = table2.rename(index = {
"voice" : 'Voice',
"Polstab" : 'Political stability',
"GovEffec" : 'Government effectiveness',
"RegQual" : 'Regulatory quality',
"RulLaw" : 'Rule fo law',
"ConCorr" : 'Control of corruption'}
)
return table2
def table3_7(df, regression_type) :
df_3_7E = df[['ethnicity_C2','ethnicity_instrument_C2_thresh','ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist','lnArea',
'LOScandin','democ','mtnall','RulLaw']].dropna(axis=0)
df_3_7L = df[['language_C2','language_instrument_C2_thresh','language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist','lnArea',
'LOScandin','democ','mtnall','RulLaw']].dropna(axis=0)
df_3_7R = df[['religion_C2','religion_instrument_C2_thresh','religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist','lnArea',
'LOScandin','democ','mtnall','RulLaw']].dropna(axis=0)
exo = sm.add_constant(df_3_7E[['ethnicity_C2','ethnicity_I','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist', 'LOScandin','lnArea',
'democ','mtnall']])
exo2 = sm.add_constant(df_3_7E[['ethnicity_C2','ethnicity_I']])
exo3 = sm.add_constant(df_3_7L[['language_C2','language_I','lnpopulation','lnGDP_pc','protestants','lnArea',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist', 'LOScandin',
'democ','mtnall']])
exo4 = sm.add_constant(df_3_7L[['language_C2','language_I']])
exo5 = sm.add_constant(df_3_7R[['religion_C2','religion_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist','lnArea',
'democ','mtnall']])
exo6 = sm.add_constant(df_3_7R[['religion_C2','religion_I']])
if regression_type == 'IV2SLS' :
reg = IV2SLS(df_3_7E['RulLaw'],
exo,
sm.add_constant(df_3_7E[['ethnicity_instrument_C2_thresh','ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist', 'LOScandin','democ','mtnall','lnArea']])).fit()
reg2 = IV2SLS(df_3_7E['RulLaw'],
exo2,
sm.add_constant(df_3_7E[['ethnicity_instrument_C2_thresh',
'ethnicity_I']])).fit()
reg3 = IV2SLS(df_3_7L['RulLaw'],
exo3,
sm.add_constant(df_3_7L[['language_instrument_C2_thresh','language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist', 'LOScandin','democ','mtnall','lnArea']])).fit()
reg4 = IV2SLS(df_3_7L['RulLaw'],
exo4,
sm.add_constant(df_3_7L[['language_instrument_C2_thresh',
'language_I']])).fit()
reg5 = IV2SLS(df_3_7R['RulLaw'],
exo5,
sm.add_constant(df_3_7R[['religion_instrument_C2_thresh','religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','democ','mtnall','lnArea']])).fit()
reg6 = IV2SLS(df_3_7R['RulLaw'],
exo6,
sm.add_constant(df_3_7R[['religion_instrument_C2_thresh',
'religion_I']])).fit()
elif regression_type == 'OLS' :
reg2 = sm.OLS(df_3_7E['RulLaw'], exo2).fit(cov_type = 'HC1')
reg = sm.OLS(df_3_7E['RulLaw'], exo).fit(cov_type = 'HC1')
reg4 = sm.OLS(df_3_7L['RulLaw'], exo4).fit(cov_type = 'HC1')
reg3 = sm.OLS(df_3_7L['RulLaw'], exo3).fit(cov_type = 'HC1')
reg6 = sm.OLS(df_3_7R['RulLaw'], exo6).fit(cov_type = 'HC1')
reg5 = sm.OLS(df_3_7R['RulLaw'], exo5).fit(cov_type = 'HC1')
stargazer = Stargazer([reg2, reg, reg4, reg3, reg6, reg5])
stargazer.covariate_order(['ethnicity_C2', 'ethnicity_I','language_C2','language_I','religion_C2','religion_I',
'lnpopulation','lnGDP_pc','lnArea','protestants','muslims','catholics',
'latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ','mtnall','const'])
stargazer.rename_covariates({'ethnicity_C2' : 'Segregation $\hat{S}$ (ethnicity)',
'ethnicity_I' : 'Fractionalization $F$ (ethnicity)',
'language_C2' : 'Segregation $\hat{S}$ (language)',
'language_I' : 'Fractionalization $F$ (language)',
'religion_C2' : 'Segregation $\hat{S}$ (religion)',
'religion_I' : 'Fractionalization $F$ (religion)',
'lnpopulation' : 'ln (population)',
'lnGDP_pc' : 'ln (GDP per capita)',
'lnArea' : 'ln (average size of region)',
'protestants' : 'Pretestants share',
'muslims' : 'Muslmis Share',
'catholics' : 'Catholics share',
'latitude' : 'Latitude',
'LOEnglish' : 'English legal origin',
'LOGerman' : 'German legal origin',
'LOSocialist' : 'Socialist legal origin',
'LOScandin' : 'Scandinavian legal origin',
'democ' : 'Democratic tradition',
'mtnall' : 'Mountains',
'const' : 'Constant'})
return HTML(stargazer.render_html())
def table4_5(df,name) :
df_table4A = df[[f'{name}_C2',f'{name}_I','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ',
'mtnall','voice','PolStab','GovEffec','RegQual','ConCorr','RulLaw']].dropna(axis = 0)
df_table4B = df_table4A[[f'{name}_C2',f'{name}_I','voice','PolStab','GovEffec','RegQual','ConCorr','RulLaw']]
df_table4C = df_table4A[df_table4A.democ > 1]
xA = sm.add_constant(df_table4A[[f'{name}_C2',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
xB = sm.add_constant(df_table4B[[f'{name}_C2', f'{name}_I']])
xC = sm.add_constant(df_table4C[[f'{name}_C2',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
df_table4s = [df_table4A, df_table4B, df_table4C]
xs = [xA, xB, xC]
y = [
[f'y{idx}A', f'y{idx}B', f'y{idx}C']
for idx in range(1, 7)
]
est = [
[f'est{idx}A', f'est{idx}B', f'est{idx}C']
for idx in range(1, 7)
]
star = ['starA','starB','starC']
for idx, i in enumerate(['A','B','C']) :
y[0][idx] = df_table4s[idx]['voice']
y[1][idx] = df_table4s[idx]['PolStab']
y[2][idx] = df_table4s[idx]['GovEffec']
y[3][idx] = df_table4s[idx]['RegQual']
y[4][idx] = df_table4s[idx]['RulLaw']
y[5][idx] = df_table4s[idx]['ConCorr']
est[0][idx] = sm.OLS(y[0][idx], xs[idx]).fit(cov_type = 'HC1')
est[1][idx] = sm.OLS(y[1][idx], xs[idx]).fit(cov_type = 'HC1')
est[2][idx] = sm.OLS(y[2][idx], xs[idx]).fit(cov_type = 'HC1')
est[3][idx] = sm.OLS(y[3][idx], xs[idx]).fit(cov_type = 'HC1')
est[4][idx] = sm.OLS(y[4][idx], xs[idx]).fit(cov_type = 'HC1')
est[5][idx] = sm.OLS(y[5][idx], xs[idx]).fit(cov_type = 'HC1')
star[idx] = Stargazer([est[0][idx],est[1][idx],est[2][idx],est[3][idx],est[4][idx],est[5][idx]])
for i in range(3) :
star[i].covariate_order([f'{name}_C2',f'{name}_I'])
star[i].rename_covariates({f'{name}_C2' : 'Segregation $\hat{S}$ ('f'{name}'')',
f'{name}_I' : 'Fractionalization $F$ ('f'{name}'')'})
star[i].show_model_numbers(False)
star[i].custom_columns(['Voice',
'Political stability',
'Govern-t effectiv.',
'Regul. quality',
'Rule of law',
'Control of corr'],
[1,1,1,1,1,1])
star[0].add_line('Controls', ['Yes','Yes','Yes','Yes','Yes','Yes'])
star[0].add_line('Sample', ['Full','Full','Full','Full','Full','Full'])
star[1].add_line('Controls', ['No','No','No','No','No','No'])
star[1].add_line('Sample', ['Full','Full','Full','Full','Full','Full'])
star[2].add_line('Controls', ['Yes','Yes','Yes','Yes','Yes','Yes'])
star[2].add_line('Sample', ['Democ','Democ','Democ','Democ','Democ','Democ'])
star[0].title('Panel A. Baseline : All controls and full sample')
star[1].title('Panel B. No controls and full sample')
star[2].title('Panel C. All controls; sample excludes dictatorship')
return [star[0],star[1],star[2]]
def table6(df, alternative = True) :
df_6E = df[['ethnicity_C2', 'ethnicity_I','ethnicity_C','ethnicity_instrument_C_thresh',
'ethnicity_instrument_C2_thresh','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ',
'mtnall','RulLaw','country']].dropna(axis=0)
df_6L = df[['language_C2', 'language_I','language_C','language_instrument_C_thresh',
'language_instrument_C2_thresh','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ',
'mtnall','RulLaw','country']].dropna(axis=0)
df_6R = df[['religion_C2', 'religion_I','religion_C','religion_instrument_C_thresh',
'religion_instrument_C2_thresh','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ',
'mtnall','RulLaw','country']].dropna(axis=0)
df_6E_demo = df_6E[df_6E.democ >= 1]
df_6L_demo = df_6L[df_6L.democ >= 1]
df_6R_demo = df_6R[df_6R.democ >= 1]
x1 = sm.add_constant(df_6E[['ethnicity_instrument_C2_thresh', 'ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish',
'LOGerman','LOSocialist','LOScandin','democ','mtnall']])
x2 = sm.add_constant(df_6L[['language_instrument_C2_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x3 = sm.add_constant(df_6R[['religion_instrument_C2_thresh', 'religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','democ','mtnall']])
x4 = sm.add_constant(df_6E_demo[['ethnicity_instrument_C2_thresh', 'ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x5 = sm.add_constant(df_6L_demo[['language_instrument_C2_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x6 = sm.add_constant(df_6R_demo[['religion_instrument_C2_thresh', 'religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','democ','mtnall']])
y1 = df_6E['ethnicity_C2']
y2 = df_6L['language_C2']
y3 = df_6R['religion_C2']
y4 = df_6E_demo['ethnicity_C2']
y5 = df_6L_demo['language_C2']
y6 = df_6R_demo['religion_C2']
est1 = sm.OLS(y1, x1).fit(cov_type = 'HC1')
est2 = sm.OLS(y2, x2).fit(cov_type = 'HC1')
est3 = sm.OLS(y3, x3).fit(cov_type = 'HC1')
est4 = sm.OLS(y4, x4).fit(cov_type = 'HC1')
est5 = sm.OLS(y5, x5).fit(cov_type = 'HC1')
est6 = sm.OLS(y6, x6).fit(cov_type = 'HC1')
x1a = sm.add_constant(df_6E[['ethnicity_instrument_C_thresh', 'ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x2a = sm.add_constant(df_6L[['language_instrument_C_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x3a = sm.add_constant(df_6R[['religion_instrument_C_thresh', 'religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','democ','mtnall']])
x4a = sm.add_constant(df_6E_demo[['ethnicity_instrument_C_thresh', 'ethnicity_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x5a = sm.add_constant(df_6L_demo[['language_instrument_C_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']])
x6a = sm.add_constant(df_6R_demo[['religion_instrument_C_thresh', 'religion_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','democ','mtnall']])
y1a = df_6E['ethnicity_C']
y2a = df_6L['language_C']
y3a = df_6R['religion_C']
y4a = df_6E_demo['ethnicity_C']
y5a = df_6L_demo['language_C']
y6a = df_6R_demo['religion_C']
est1a = sm.OLS(y1a, x1a).fit(cov_type = 'HC1')
est2a = sm.OLS(y2a, x2a).fit(cov_type = 'HC1')
est3a = sm.OLS(y3a, x3a).fit(cov_type = 'HC1')
est4a = sm.OLS(y4a, x4a).fit(cov_type = 'HC1')
est5a = sm.OLS(y5a, x5a).fit(cov_type = 'HC1')
est6a = sm.OLS(y6a, x6a).fit(cov_type = 'HC1')
df_6Lb = df_6L.set_index('country')
df_6Lb_demo = df_6L_demo.set_index('country')
x2b = sm.add_constant(df_6Lb[['language_instrument_C_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']].drop(index = 'usa'))
x5b = sm.add_constant(df_6Lb_demo[['language_instrument_C_thresh', 'language_I','lnpopulation','lnGDP_pc',
'protestants','muslims','catholics','latitude','LOEnglish','LOGerman',
'LOSocialist','LOScandin','democ','mtnall']].drop(index = 'usa'))
y2b = df_6Lb['language_C'].drop(index = 'usa')
y5b = df_6Lb_demo['language_C'].drop(index = 'usa')
est2b = sm.OLS(y2b, x2b).fit(cov_type = 'HC1')
est5b = sm.OLS(y5b, x5b).fit(cov_type = 'HC1')
stargazer = Stargazer([est1, est2, est3, est4, est5, est6])
stargazer_a = Stargazer([est1a, est2a, est3a, est4a, est5a, est6a])
stargazer_b = Stargazer([est2b, est5b])
stargazer.covariate_order(['ethnicity_instrument_C2_thresh', 'ethnicity_I',
'language_instrument_C2_thresh', 'language_I',
'religion_instrument_C2_thresh', 'religion_I'])
stargazer.rename_covariates({'ethnicity_instrument_C2_thresh':'Instrument E',
'ethnicity_I':'$F$ (ethnicity)',
'language_instrument_C2_thresh':'Instrument L',
'language_I':'$F$ (language)',
'religion_instrument_C2_thresh':'Instrument R',
'religion_I':'$F$ (religion)'
})
stargazer.custom_columns(['E$\hat{S}$',
'L$\hat{S}$',
'R$\hat{S}$',
'E$\hat{S}$',
'L$\hat{S}$',
'R$\hat{S}$'],
[1,1,1,1,1,1])
stargazer.show_model_numbers(False)
stargazer.add_line('Sample', ['Full','Full','Full','Democracy','Democracy','Democracy'])
stargazer.title('Panel A. Segregation index $\hat{S}$')
stargazer_a.covariate_order(['ethnicity_instrument_C_thresh', 'ethnicity_I',
'language_instrument_C_thresh', 'language_I',
'religion_instrument_C_thresh', 'religion_I'])
stargazer_a.rename_covariates({'ethnicity_instrument_C_thresh':'Instrument E',
'ethnicity_I':'$F$ (ethnicity)',
'language_instrument_C_thresh':'Instrument L',
'language_I':'$F$ (language)',
'religion_instrument_C_thresh':'Instrument R',
'religion_I':'$F$ (religion)'
})
stargazer_a.custom_columns(['E$\\tilde{S}$',
'L$\\tilde{S}$',
'R$\\tilde{S}$',
'E$\\tilde{S}$',
'L$\\tilde{S}$',
'R$\\tilde{S}$'],
[1,1,1,1,1,1])
stargazer_a.show_model_numbers(False)
stargazer_a.add_line('Sample', ['Full','Full','Full','Democracy','Democracy','Democracy'])
stargazer_a.title('Panel B. Segregation index $\\tilde{S}$')
stargazer_b.covariate_order(['language_instrument_C_thresh', 'language_I'])
stargazer_b.rename_covariates({'language_instrument_C_thresh':'Instrument L',
'language_I':'$F$ (language)'
})
stargazer_b.custom_columns(['L$\\tilde{S}$',
'L$\\tilde{S}$'],
[1,1])
stargazer_b.show_model_numbers(False)
stargazer_b.add_line('Sample', ['Full','Democracy'])
stargazer_b.title('Panel C. Segregation index $\\tilde{S}$ for language with sample excluding the US')
return [stargazer,stargazer_a,stargazer_b]
def table8_9_ext7(df,name,GDP) :
df_8_9A = df[[f'{name}_C2',f'{name}_I',f'{name}_instrument_C2_thresh','lnpopulation','lnGDP_pc','protestants','muslims',
'catholics','latitude','LOEnglish','LOGerman','LOSocialist','LOScandin','democ',
'mtnall','voice','PolStab','GovEffec','RegQual','ConCorr','RulLaw'
]].dropna(axis = 0)
df_8_9B = df_8_9A[[f'{name}_C2',f'{name}_instrument_C2_thresh',f'{name}_I','voice','PolStab','GovEffec','RegQual',
'ConCorr','RulLaw']]
if GDP == 'democ':
df_8_9C = df_8_9A[df_8_9A.democ >= 1]
elif GDP == 'GDP':
df_8_9C = df_8_9A[df_8_9A.lnGDP_pc >= 7]
exoA = sm.add_constant(df_8_9A[[f'{name}_C2',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
exoB = sm.add_constant(df_8_9B[[f'{name}_C2', f'{name}_I']])
exoC = sm.add_constant(df_8_9C[[f'{name}_C2',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
insA = sm.add_constant(df_8_9A[[f'{name}_instrument_C2_thresh',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
insB = sm.add_constant(df_8_9B[[f'{name}_instrument_C2_thresh', f'{name}_I']])
insC = sm.add_constant(df_8_9C[[f'{name}_instrument_C2_thresh',f'{name}_I','lnpopulation','lnGDP_pc','protestants',
'muslims','catholics','latitude','LOEnglish','LOGerman','LOSocialist',
'LOScandin','democ','mtnall']])
df_8_9s = [df_8_9A, df_8_9B, df_8_9C]
exos = [exoA, exoB, exoC]
inss = [insA, insB, insC]
y = [
[f'y{idx}A', f'y{idx}B', f'y{idx}C']
for idx in range(1, 7)
]
est = [
[f'est{idx}A', f'est{idx}B', f'est{idx}C']
for idx in range(1, 7)
]
star = ['starA','starB','starC']
for idx, i in enumerate(['A','B','C']) :
y[0][idx] = df_8_9s[idx]['voice']
y[1][idx] = df_8_9s[idx]['PolStab']
y[2][idx] = df_8_9s[idx]['GovEffec']
y[3][idx] = df_8_9s[idx]['RegQual']
y[4][idx] = df_8_9s[idx]['RulLaw']
y[5][idx] = df_8_9s[idx]['ConCorr']
est[0][idx] = IV2SLS(y[0][idx], exos[idx], inss[idx]).fit()
est[1][idx] = IV2SLS(y[1][idx], exos[idx], inss[idx]).fit()
est[2][idx] = IV2SLS(y[2][idx], exos[idx], inss[idx]).fit()
est[3][idx] = IV2SLS(y[3][idx], exos[idx], inss[idx]).fit()
est[4][idx] = IV2SLS(y[4][idx], exos[idx], inss[idx]).fit()
est[5][idx] = IV2SLS(y[5][idx], exos[idx], inss[idx]).fit()
star[idx] = Stargazer([est[0][idx],est[1][idx],est[2][idx],est[3][idx],est[4][idx],est[5][idx]])
for i in range(3) :
star[i].covariate_order([f'{name}_C2',f'{name}_I'])
star[i].rename_covariates({f'{name}_C2' : 'Segregation $\hat{S}$ ('f'{name}'')',
f'{name}_I' : 'Fractionalization $F$ ('f'{name}'')'})
star[i].show_model_numbers(False)
star[i].custom_columns(['Voice',
'Political stability',
'Govern-t | |
not None:
_dict['imported'] = self.imported
if hasattr(self, 'parameter_sets_total') and self.parameter_sets_total is not None:
_dict['parameter_sets_total'] = self.parameter_sets_total
if hasattr(self, 'pending') and self.pending is not None:
_dict['pending'] = self.pending
if hasattr(self, 'renamed') and self.renamed is not None:
_dict['renamed'] = self.renamed
if hasattr(self, 'replaced') and self.replaced is not None:
_dict['replaced'] = self.replaced
if hasattr(self, 'sequence_jobs_total') and self.sequence_jobs_total is not None:
_dict['sequence_jobs_total'] = self.sequence_jobs_total
if hasattr(self, 'skipped') and self.skipped is not None:
_dict['skipped'] = self.skipped
if hasattr(self, 'subflows_total') and self.subflows_total is not None:
_dict['subflows_total'] = self.subflows_total
if hasattr(self, 'table_definitions_total') and self.table_definitions_total is not None:
_dict['table_definitions_total'] = self.table_definitions_total
if hasattr(self, 'total') and self.total is not None:
_dict['total'] = self.total
if hasattr(self, 'unsupported') and self.unsupported is not None:
_dict['unsupported'] = self.unsupported
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ImportCount object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ImportCount') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ImportCount') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class ImportFlow():
"""
Import flow object.
:attr str conflict_resolution_status: (optional) conflict resolution status.
:attr datetime end_time: (optional) The timestamp when the flow import is
completed. In format YYYY-MM-DDTHH:mm:ssZ or YYYY-MM-DDTHH:mm:ss.sssZ, matching
the date-time format as specified by RFC 3339.
:attr List[DataImportError] errors: (optional) The errors array report all the
problems preventing the data flow from being successfully imported.
:attr str id: (optional) Unique id of the data flow. This field is returned only
if the underlying data flow has been successfully imported.
:attr str job_id: (optional) Unique id of the job. This field is returned only
if the corresponding job object has been successfully created.
:attr str job_name: (optional) Job name. This field is returned only if the
corresponding job object has been successfully created.
:attr str job_type: (optional) (deprecated) original type of the job or data
flow in the import file.
:attr str name: Name of the imported data flow.
:attr str original_name: (optional) Name of the data flow to be imported.
:attr str ref_asset_id: (optional) The ID of an existing asset this object
refers to. If ref_asset_id is specified, the id field will be the same as
ref_asset_id for backward compatibility.
:attr str status: data import status.
:attr str type: (optional) type of the job or data connection in the import
file.
:attr List[ImportFlowWarning] warnings: (optional) The warnings array report all
the warnings in the data flow import operation.
"""
def __init__(self,
name: str,
status: str,
*,
conflict_resolution_status: str = None,
end_time: datetime = None,
errors: List['DataImportError'] = None,
id: str = None,
job_id: str = None,
job_name: str = None,
job_type: str = None,
original_name: str = None,
ref_asset_id: str = None,
type: str = None,
warnings: List['ImportFlowWarning'] = None) -> None:
"""
Initialize a ImportFlow object.
:param str name: Name of the imported data flow.
:param str status: data import status.
:param str conflict_resolution_status: (optional) conflict resolution
status.
:param datetime end_time: (optional) The timestamp when the flow import is
completed. In format YYYY-MM-DDTHH:mm:ssZ or YYYY-MM-DDTHH:mm:ss.sssZ,
matching the date-time format as specified by RFC 3339.
:param List[DataImportError] errors: (optional) The errors array report all
the problems preventing the data flow from being successfully imported.
:param str id: (optional) Unique id of the data flow. This field is
returned only if the underlying data flow has been successfully imported.
:param str job_id: (optional) Unique id of the job. This field is returned
only if the corresponding job object has been successfully created.
:param str job_name: (optional) Job name. This field is returned only if
the corresponding job object has been successfully created.
:param str job_type: (optional) (deprecated) original type of the job or
data flow in the import file.
:param str original_name: (optional) Name of the data flow to be imported.
:param str ref_asset_id: (optional) The ID of an existing asset this object
refers to. If ref_asset_id is specified, the id field will be the same as
ref_asset_id for backward compatibility.
:param str type: (optional) type of the job or data connection in the
import file.
:param List[ImportFlowWarning] warnings: (optional) The warnings array
report all the warnings in the data flow import operation.
"""
self.conflict_resolution_status = conflict_resolution_status
self.end_time = end_time
self.errors = errors
self.id = id
self.job_id = job_id
self.job_name = job_name
self.job_type = job_type
self.name = name
self.original_name = original_name
self.ref_asset_id = ref_asset_id
self.status = status
self.type = type
self.warnings = warnings
@classmethod
def from_dict(cls, _dict: Dict) -> 'ImportFlow':
"""Initialize a ImportFlow object from a json dictionary."""
args = {}
if 'conflict_resolution_status' in _dict:
args['conflict_resolution_status'] = _dict.get('conflict_resolution_status')
if 'end_time' in _dict:
args['end_time'] = string_to_datetime(_dict.get('end_time'))
if 'errors' in _dict:
args['errors'] = [DataImportError.from_dict(x) for x in _dict.get('errors')]
if 'id' in _dict:
args['id'] = _dict.get('id')
if 'job_id' in _dict:
args['job_id'] = _dict.get('job_id')
if 'job_name' in _dict:
args['job_name'] = _dict.get('job_name')
if 'job_type' in _dict:
args['job_type'] = _dict.get('job_type')
if 'name' in _dict:
args['name'] = _dict.get('name')
else:
raise ValueError('Required property \'name\' not present in ImportFlow JSON')
if 'original_name' in _dict:
args['original_name'] = _dict.get('original_name')
if 'ref_asset_id' in _dict:
args['ref_asset_id'] = _dict.get('ref_asset_id')
if 'status' in _dict:
args['status'] = _dict.get('status')
else:
raise ValueError('Required property \'status\' not present in ImportFlow JSON')
if 'type' in _dict:
args['type'] = _dict.get('type')
if 'warnings' in _dict:
args['warnings'] = [ImportFlowWarning.from_dict(x) for x in _dict.get('warnings')]
return cls(**args)
@classmethod
def _from_dict(cls, _dict):
"""Initialize a ImportFlow object from a json dictionary."""
return cls.from_dict(_dict)
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'conflict_resolution_status') and self.conflict_resolution_status is not None:
_dict['conflict_resolution_status'] = self.conflict_resolution_status
if hasattr(self, 'end_time') and self.end_time is not None:
_dict['end_time'] = datetime_to_string(self.end_time)
if hasattr(self, 'errors') and self.errors is not None:
_dict['errors'] = [x.to_dict() for x in self.errors]
if hasattr(self, 'id') and self.id is not None:
_dict['id'] = self.id
if hasattr(self, 'job_id') and self.job_id is not None:
_dict['job_id'] = self.job_id
if hasattr(self, 'job_name') and self.job_name is not None:
_dict['job_name'] = self.job_name
if hasattr(self, 'job_type') and self.job_type is not None:
_dict['job_type'] = self.job_type
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'original_name') and self.original_name is not None:
_dict['original_name'] = self.original_name
if hasattr(self, 'ref_asset_id') and self.ref_asset_id is not None:
_dict['ref_asset_id'] = self.ref_asset_id
if hasattr(self, 'status') and self.status is not None:
_dict['status'] = self.status
if hasattr(self, 'type') and self.type is not None:
_dict['type'] = self.type
if hasattr(self, 'warnings') and self.warnings is not None:
_dict['warnings'] = [x.to_dict() for x in self.warnings]
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ImportFlow object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ImportFlow') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ImportFlow') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class ConflictResolutionStatusEnum(str, Enum):
"""
conflict resolution status.
"""
FLOW_REPLACEMENT_SUCCEEDED = 'flow_replacement_succeeded'
FLOW_REPLACEMENT_FAILED = 'flow_replacement_failed'
IMPORT_FLOW_RENAMED = 'import_flow_renamed'
IMPORT_FLOW_SKIPPED = 'import_flow_skipped'
CONNECTION_REPLACEMENT_SUCCEEDED = 'connection_replacement_succeeded'
CONNECTION_REPLACEMENT_FAILED = 'connection_replacement_failed'
CONNECTION_RENAMED = 'connection_renamed'
CONNECTION_SKIPPED = 'connection_skipped'
PARAMETER_SET_REPLACEMENT_SUCCEEDED = 'parameter_set_replacement_succeeded'
PARAMETER_SET_REPLACEMENT_FAILED = 'parameter_set_replacement_failed'
PARAMETER_SET_RENAMED = 'parameter_set_renamed'
PARAMETER_SET_SKIPPED = 'parameter_set_skipped'
TABLE_DEFINITION_REPLACEMENT_SUCCEEDED = 'table_definition_replacement_succeeded'
TABLE_DEFINITION_REPLACEMENT_FAILED = 'table_definition_replacement_failed'
TABLE_DEFINITION_RENAMED = 'table_definition_renamed'
TABLE_DEFINITION_SKIPPED = 'table_definition_skipped'
SEQUENCE_JOB_REPLACEMENT_SUCCEEDED = 'sequence_job_replacement_succeeded'
SEQUENCE_JOB_REPLACEMENT_FAILED = 'sequence_job_replacement_failed'
SEQUENCE_JOB_RENAMED = 'sequence_job_renamed'
SEQUENCE_JOB_SKIPPED = 'sequence_job_skipped'
SUBFLOW_REPLACEMENT_SUCCEEDED = 'subflow_replacement_succeeded'
SUBFLOW_REPLACEMENT_FAILED = 'subflow_replacement_failed'
SUBFLOW_RENAMED = 'subflow_renamed'
SUBFLOW_SKIPPED = 'subflow_skipped'
class JobTypeEnum(str, Enum):
"""
(deprecated) original type of the job or data flow in the import file.
"""
PX_JOB = 'px_job'
SERVER_JOB = 'server_job'
CONNECTION = 'connection'
TABLE_DEF = 'table_def'
class StatusEnum(str, Enum):
"""
data import status.
"""
COMPLETED = 'completed'
IN_PROGRESS = 'in_progress'
FAILED = 'failed'
SKIPPED = 'skipped'
DEPRECATED = 'deprecated'
UNSUPPORTED = 'unsupported'
FLOW_CONVERSION_FAILED = 'flow_conversion_failed'
FLOW_CREATION_FAILED = | |
map from the field to the index of the significant filters
index_map = {
"te": 0,
"rna": 1,
"ribo": 2
}
index = index_map[field]
if significant_only:
index += 3
return filters[index]
@ribo_deprecated
def get_up_and_down_masks(condition_1, condition_2, pval_df):
""" This function finds all of the transcripts which are, respectively
higher or lower in the first condition. That is, "up" and "down"
are respective to condition_1.
This function is meant to be used with the output of the
estimate-kl-pvalues script from the ribo-te package.
Args:
condition_1, condition_2 (strings): the name of the conditions
pval_df (pd.DataFrame): a dataframe, which is just the output of
the estimate-kl-pvalues script
Returns:
All of the return values are boolean masks of pval_df.
m_te_up, m_te_down: The transcripts which have higher or lower TE
in the first condition, respectively.
m_rna_up, m_rna_down: The transcripts which have higher or lower
RNA-seq RPKM in the first condition, respectively.
m_ribo_up, m_ribo_down: The transcripts which have higher or lower
riboseq RPKM in the first condition, respectively.
"""
import numpy as np
te_1 = 'log_translational_efficiency_loc_{}'.format(condition_1)
te_2 = 'log_translational_efficiency_loc_{}'.format(condition_2)
rna_1 = 'rna_abundance_mean_loc_{}'.format(condition_1)
rna_2 = 'rna_abundance_mean_loc_{}'.format(condition_2)
ribo_1 = 'ribo_abundance_mean_loc_{}'.format(condition_1)
ribo_2 = 'ribo_abundance_mean_loc_{}'.format(condition_2)
m_te_up = pval_df[te_1] > pval_df[te_2]
m_te_down = ~m_te_up
m_rna_up = pval_df[rna_1] > pval_df[rna_2]
m_rna_down = ~m_rna_up
m_ribo_up = pval_df[ribo_1] > pval_df[ribo_2]
m_ribo_down = ~m_ribo_up
up_down_masks = m_te_up, m_te_down, m_rna_up, m_rna_down, m_ribo_up, m_ribo_down
up_down_masks = [ np.array(f) for f in up_down_masks ]
return up_down_masks
@ribo_deprecated
def get_up_down_filter(filters, field, direction):
""" This function returns the appropriate mask to filter on the given field
in the given direction. It assumes the filters are in the same order as
the output of get_up_and_down_masks.
Parameters
----------
filters : tuple
The result of the call to get_up_and_down_masks
field : string
The name of the field on which to filter. Valid options are:
* ribo
* rna
* te
direction : string
The direction in which to filter. Valid options are:
* up
* down
Returns
-------
significance_mask : boolean mask
The appropriate mask for filtering for significance based on the
given field.
"""
# just map from the field to the index of the significant filters
field_map = {
"te": 0,
"rna": 2,
"ribo": 4
}
direction_map = {
"up": 0,
"down": 1
}
index = field_map[field] + direction_map[direction]
return filters[index]
def melt_te_df(te):
""" Melt a data frame from the translational efficiency estimations
to a long df suitable for use with seaborn, etc.
"""
# we only want to keep the mean and var estimates
mean_fields_to_keep = [mean_field_map[f] for f in fields]
var_fields_to_keep = [var_field_map[f] for f in fields]
fields_to_keep = mean_fields_to_keep + var_fields_to_keep
# but we need this as a hierarchical index
mean_fields = [(f, 'mean') for f in fields]
var_fields = [(f, 'var') for f in fields]
hierarchical_fields = mean_fields + var_fields
# drop the rest of the fields, except gene_id
te_df = te.set_index('gene_id')
te_df = te_df[fields_to_keep]
# add the multi-index for the columns
te_df.columns = pd.MultiIndex.from_tuples(hierarchical_fields)
# bring the gene_id back
te_df = te_df.stack(level=0)
te_df.index.names = ["gene_id", "field"]
te_df = te_df.reset_index(drop=False)
# go ahead and add the pretty name
te_df['field_name'] = te_df['field'].map(field_map)
return te_df
def get_bitseq_estimates(
config,
isoform_strategy,
bitseq_id_field='transcript_id',
strings_to_remove=['.cds-only', '.merged']):
""" Load the bitseq abundance estimates into a single long data frame.
Parameters
----------
config: dict-like
The configuration for the project, presumably from the yaml file
isoform_strategy: str
The strategy for handling transcript isoforms
bitseq_id_field: str
Name for the "transcript_id" field (second column) in bitseq tr file
strings_to_remove: list of strings
A list of strings to replace with "" in the bitseq ids
Returns
-------
bitseq_estimates: pd.DataFrame
A data frame containing the following columns
* rpkm_{mean,var}: the bitseq estimates
* sample: the name of the respective sample
* type: "ribo" or "rna"
"""
import bio_utils.bio as bio
import tqdm
msg = "Reading the bitseq tr info file"
logger.info(msg)
# check which transcript file to load
is_merged = False
if isoform_strategy == "merged":
is_merged = True
# and get the file
transcript_fasta = filenames.get_transcript_fasta(
config['genome_base_path'],
config['genome_name'],
is_annotated=True,
is_merged=is_merged,
is_cds_only=True
)
tr_info = filenames.get_bitseq_transcript_info(transcript_fasta)
bitseq_tr = bio.read_bitseq_tr_file(tr_info)
# we need to remove all of the indicated strings from the ids
for to_remove in strings_to_remove:
tids = bitseq_tr['transcript_id'].str.replace(to_remove, "")
bitseq_tr['transcript_id'] = tids
bitseq_tr = bitseq_tr.rename(columns={'transcript_id': bitseq_id_field})
note = config.get('note', None)
all_dfs = []
msg = "Reading riboseq BitSeq estimates"
logger.info(msg)
is_unique = 'keep_riboseq_multimappers' not in config
it = tqdm.tqdm(config['riboseq_samples'].items())
for name, file in it:
lengths, offsets = get_periodic_lengths_and_offsets(
config,
name,
isoform_strategy=isoform_strategy,
is_unique=is_unique
)
bitseq_rpkm_mean = filenames.get_riboseq_bitseq_rpkm_mean(
config['riboseq_data'],
name,
is_unique=is_unique,
is_transcriptome=True,
is_cds_only=True,
length=lengths,
offset=offsets,
isoform_strategy=isoform_strategy,
note=note
)
field_names = ['rpkm_mean', 'rpkm_var']
bitseq_rpkm_mean_df = bio.read_bitseq_means(
bitseq_rpkm_mean,
names=field_names
)
bitseq_rpkm_mean_df['sample'] = name
bitseq_rpkm_mean_df['type'] = 'ribo'
bitseq_rpkm_mean_df[bitseq_id_field] = bitseq_tr[bitseq_id_field]
all_dfs.append(bitseq_rpkm_mean_df)
# now, the rnaseq
msg = "Reading RNA-seq BitSeq estimates"
logger.info(msg)
is_unique = ('remove_rnaseq_multimappers' in config)
it = tqdm.tqdm(config['rnaseq_samples'].items())
for name, data in it:
bitseq_rpkm_mean = filenames.get_rnaseq_bitseq_rpkm_mean(
config['rnaseq_data'],
name,
is_unique=is_unique,
is_transcriptome=True,
is_cds_only=True,
isoform_strategy=isoform_strategy,
note=note
)
field_names = ['rpkm_mean', 'rpkm_var']
bitseq_rpkm_mean_df = bio.read_bitseq_means(
bitseq_rpkm_mean,
names=field_names
)
bitseq_rpkm_mean_df['sample'] = name
bitseq_rpkm_mean_df['type'] = 'rna'
bitseq_rpkm_mean_df[bitseq_id_field] = bitseq_tr[bitseq_id_field]
all_dfs.append(bitseq_rpkm_mean_df)
msg = "Joining estimates into long data frame"
logger.info(msg)
long_df = pd.concat(all_dfs)
long_df = long_df.reset_index(drop=True)
return long_df
def update_gene_id_from_transcript_id(df:pd.DataFrame, config:dict, args=None):
""" Assuming "gene_id" is actually a transcript id, replace it
with the actual gene identifier.
This function is used in the case of the "all" isoform
strategy when downstream analysis actually needs a gene
identifier.
Parameters
----------
df: pd.DataFrame
A data frame which contains a "gene_id" field which actually
contains transcript identifiers. For example, the latter parts
of the B-tea pipeline produce data frames like this with
the "all" isoform strategy
config: dict
Configuration options
args: argparse.Namespace or None
The logging options from the command line. pyensembl likes
to overwrite these, so they will be reset.
Returns
-------
updated_df: pd.DataFrame
A data frame in which the 'gene_id' column is moved to a
'transcript_id' column, and the 'gene_id' column is updated
to include actual gene identifiers
"""
import bio_utils.pyensembl_utils as pyensembl_utils
msg = "Loading Ensembl annotations"
logger.info(msg)
ensembl = pyensembl_utils.get_genome(
config['genome_name'],
config['gtf'],
logging_args = args
)
msg = "Finding the gene ids for each transcript id"
logger.info(msg)
gene_ids = set(df['gene_id'])
transcript_gene_mapping = pyensembl_utils.get_gene_ids_of_transcript_ids(
gene_ids, ensembl)
msg = "Adding gene ids to data frame"
logger.info(msg)
df['transcript_id'] = df['gene_id']
df = df.drop('gene_id', 1)
df = df.merge(transcript_gene_mapping, on='transcript_id')
return df
###
# These are functions for retrieving the dominant isoform for
# each gene and condition.
###
def _get_matching_condition(row, condition_field, config):
condition = row[condition_field]
field = row['field']
# use the ribo conditions for te
if field == "te":
field = "ribo"
return get_criterion_condition(condition, field, config)
def _add_matching_conditions(pvalues, config):
""" Add the "matching" conditions for both conditions. """
import misc.parallel as parallel
# turn off logging; we already know we have matching conditions
logger_level = logger.getEffectiveLevel()
logger.setLevel("WARNING")
matching_condition_1 = parallel.apply_df_simple(
pvalues,
_get_matching_condition,
"condition_1",
config
)
matching_condition_2 = parallel.apply_df_simple(
pvalues,
_get_matching_condition,
"condition_2",
config
)
pvalues['matching_condition_1'] = matching_condition_1
pvalues['matching_condition_2'] = matching_condition_2
logger.setLevel(logger_level)
return pvalues
def _add_transcript_id(pvalues, abundances):
""" Use the gene ID an dominant isoform information
to pull back the transcript id for each "matching" condition.
"""
left_on=['matching_condition_1', 'gene_id', 'field']
right_on=['condition', 'gene_id', 'field']
pvalues = pvalues.merge(abundances, left_on=left_on, right_on=right_on)
pvalues = pvalues.rename(columns={"transcript_id": "transcript_id_1"})
pvalues = pvalues.drop('condition', 1)
left_on=['matching_condition_2', 'gene_id', 'field']
pvalues = pvalues.merge(abundances, left_on=left_on, right_on=right_on)
pvalues = pvalues.rename(columns={"transcript_id": "transcript_id_2"})
pvalues = pvalues.drop('condition', 1)
return pvalues
def get_dominant_transcript_ids(pvalues:pd.DataFrame, config:dict, args):
""" Add the transcript id for the dominant isoform in each condition.
This function is really only intended to be used with the final pvalues
data frame from B-tea.
"""
# now, we need to get the transcript ids for condition_1 and condition_2
abundance_fields_to_keep = [
'type',
'transcript_id',
'gene_id',
'condition'
]
msg = "Reading abundances"
logger.info(msg)
note | |
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sha = '0',
shards = 56,
storage = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_storage.com_coreos_monitoring_v1_Prometheus_spec_storage(
disable_mount_sub_path = True,
empty_dir = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_storage_empty_dir.com_coreos_monitoring_v1_Alertmanager_spec_storage_emptyDir(
medium = '0',
size_limit = kubernetes.client.models.size_limit.sizeLimit(), ),
volume_claim_template = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_storage_volume_claim_template.com_coreos_monitoring_v1_Alertmanager_spec_storage_volumeClaimTemplate(
api_version = '0',
kind = '0',
status = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_storage_volume_claim_template_status.com_coreos_monitoring_v1_Alertmanager_spec_storage_volumeClaimTemplate_status(
access_modes = [
'0'
],
capacity = {
'key' : None
},
conditions = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_storage_volume_claim_template_status_conditions.com_coreos_monitoring_v1_Alertmanager_spec_storage_volumeClaimTemplate_status_conditions(
last_probe_time = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
last_transition_time = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
message = '0',
reason = '0',
status = '0',
type = '0', )
],
phase = '0', ), ), ),
tag = '0',
thanos = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_thanos.com_coreos_monitoring_v1_Prometheus_spec_thanos(
base_image = '0',
grpc_server_tls_config = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_thanos_grpc_server_tls_config.com_coreos_monitoring_v1_Prometheus_spec_thanos_grpcServerTlsConfig(
ca_file = '0',
cert_file = '0',
insecure_skip_verify = True,
key_file = '0',
server_name = '0', ),
image = '0',
listen_local = True,
log_format = '0',
log_level = '0',
min_time = '0',
object_storage_config = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_thanos_object_storage_config.com_coreos_monitoring_v1_Prometheus_spec_thanos_objectStorageConfig(
key = '0',
name = '0',
optional = True, ),
object_storage_config_file = '0',
ready_timeout = '0',
sha = '0',
tag = '0',
tracing_config = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_thanos_tracing_config.com_coreos_monitoring_v1_Prometheus_spec_thanos_tracingConfig(
key = '0',
name = '0',
optional = True, ),
tracing_config_file = '0',
version = '0', ),
tolerations = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_tolerations.com_coreos_monitoring_v1_Alertmanager_spec_tolerations(
effect = '0',
key = '0',
operator = '0',
toleration_seconds = 56,
value = '0', )
],
topology_spread_constraints = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_topology_spread_constraints.com_coreos_monitoring_v1_Alertmanager_spec_topologySpreadConstraints(
label_selector = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_label_selector.com_coreos_monitoring_v1_Alertmanager_spec_labelSelector(),
max_skew = 56,
topology_key = '0',
when_unsatisfiable = '0', )
],
version = '0',
volume_mounts = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_volume_mounts.com_coreos_monitoring_v1_Alertmanager_spec_volumeMounts(
mount_path = '0',
mount_propagation = '0',
name = '0',
read_only = True,
sub_path = '0',
sub_path_expr = '0', )
],
volumes = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_volumes.com_coreos_monitoring_v1_Alertmanager_spec_volumes(
aws_elastic_block_store = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_aws_elastic_block_store.com_coreos_monitoring_v1_Alertmanager_spec_awsElasticBlockStore(
fs_type = '0',
partition = 56,
read_only = True,
volume_id = '0', ),
azure_disk = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_azure_disk.com_coreos_monitoring_v1_Alertmanager_spec_azureDisk(
caching_mode = '0',
disk_name = '0',
disk_uri = '0',
fs_type = '0',
kind = '0',
read_only = True, ),
azure_file = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_azure_file.com_coreos_monitoring_v1_Alertmanager_spec_azureFile(
read_only = True,
secret_name = '0',
share_name = '0', ),
cephfs = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_cephfs.com_coreos_monitoring_v1_Alertmanager_spec_cephfs(
monitors = [
'0'
],
path = '0',
read_only = True,
secret_file = '0',
user = '0', ),
cinder = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_cinder.com_coreos_monitoring_v1_Alertmanager_spec_cinder(
fs_type = '0',
read_only = True,
volume_id = '0', ),
csi = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_csi.com_coreos_monitoring_v1_Alertmanager_spec_csi(
driver = '0',
fs_type = '0',
node_publish_secret_ref = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_csi_node_publish_secret_ref.com_coreos_monitoring_v1_Alertmanager_spec_csi_nodePublishSecretRef(
name = '0', ),
read_only = True,
volume_attributes = {
'key' : '0'
}, ),
downward_api = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_downward_api.com_coreos_monitoring_v1_Alertmanager_spec_downwardAPI(
default_mode = 56,
items = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_downward_api_items.com_coreos_monitoring_v1_Alertmanager_spec_downwardAPI_items(
mode = 56,
path = '0', )
], ),
fc = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_fc.com_coreos_monitoring_v1_Alertmanager_spec_fc(
fs_type = '0',
lun = 56,
read_only = True,
target_ww_ns = [
'0'
],
wwids = [
'0'
], ),
flex_volume = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_flex_volume.com_coreos_monitoring_v1_Alertmanager_spec_flexVolume(
driver = '0',
fs_type = '0',
options = {
'key' : '0'
},
read_only = True, ),
flocker = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_flocker.com_coreos_monitoring_v1_Alertmanager_spec_flocker(
dataset_name = '0',
dataset_uuid = '0', ),
gce_persistent_disk = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_gce_persistent_disk.com_coreos_monitoring_v1_Alertmanager_spec_gcePersistentDisk(
fs_type = '0',
partition = 56,
pd_name = '0',
read_only = True, ),
git_repo = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_git_repo.com_coreos_monitoring_v1_Alertmanager_spec_gitRepo(
directory = '0',
repository = '0',
revision = '0', ),
glusterfs = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_glusterfs.com_coreos_monitoring_v1_Alertmanager_spec_glusterfs(
endpoints = '0',
path = '0',
read_only = True, ),
host_path = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_host_path.com_coreos_monitoring_v1_Alertmanager_spec_hostPath(
path = '0',
type = '0', ),
iscsi = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_iscsi.com_coreos_monitoring_v1_Alertmanager_spec_iscsi(
chap_auth_discovery = True,
chap_auth_session = True,
fs_type = '0',
initiator_name = '0',
iqn = '0',
iscsi_interface = '0',
lun = 56,
portals = [
'0'
],
read_only = True,
target_portal = '0', ),
name = '0',
nfs = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_nfs.com_coreos_monitoring_v1_Alertmanager_spec_nfs(
path = '0',
read_only = True,
server = '0', ),
persistent_volume_claim = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_persistent_volume_claim.com_coreos_monitoring_v1_Alertmanager_spec_persistentVolumeClaim(
claim_name = '0',
read_only = True, ),
photon_persistent_disk = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_photon_persistent_disk.com_coreos_monitoring_v1_Alertmanager_spec_photonPersistentDisk(
fs_type = '0',
pd_id = '0', ),
portworx_volume = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_portworx_volume.com_coreos_monitoring_v1_Alertmanager_spec_portworxVolume(
fs_type = '0',
read_only = True,
volume_id = '0', ),
projected = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_projected.com_coreos_monitoring_v1_Alertmanager_spec_projected(
default_mode = 56,
sources = [
kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_projected_sources.com_coreos_monitoring_v1_Alertmanager_spec_projected_sources(
service_account_token = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_projected_service_account_token.com_coreos_monitoring_v1_Alertmanager_spec_projected_serviceAccountToken(
audience = '0',
expiration_seconds = 56,
path = '0', ), )
], ),
quobyte = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_quobyte.com_coreos_monitoring_v1_Alertmanager_spec_quobyte(
group = '0',
read_only = True,
registry = '0',
tenant = '0',
user = '0',
volume = '0', ),
rbd = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_rbd.com_coreos_monitoring_v1_Alertmanager_spec_rbd(
fs_type = '0',
image = '0',
keyring = '0',
monitors = [
'0'
],
pool = '0',
read_only = True,
user = '0', ),
scale_io = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_scale_io.com_coreos_monitoring_v1_Alertmanager_spec_scaleIO(
fs_type = '0',
gateway = '0',
protection_domain = '0',
read_only = True,
secret_ref = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_scale_io_secret_ref.com_coreos_monitoring_v1_Alertmanager_spec_scaleIO_secretRef(
name = '0', ),
ssl_enabled = True,
storage_mode = '0',
storage_pool = '0',
system = '0',
volume_name = '0', ),
storageos = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_storageos.com_coreos_monitoring_v1_Alertmanager_spec_storageos(
fs_type = '0',
read_only = True,
volume_name = '0',
volume_namespace = '0', ),
vsphere_volume = kubernetes.client.models.com_coreos_monitoring_v1_alertmanager_spec_vsphere_volume.com_coreos_monitoring_v1_Alertmanager_spec_vsphereVolume(
fs_type = '0',
storage_policy_id = '0',
storage_policy_name = '0',
volume_path = '0', ), )
],
wal_compression = True,
web = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_spec_web.com_coreos_monitoring_v1_Prometheus_spec_web(
page_title = '0', ), ),
status = kubernetes.client.models.com_coreos_monitoring_v1_prometheus_status.com_coreos_monitoring_v1_Prometheus_status(
available_replicas = 56,
paused = True,
replicas = 56,
unavailable_replicas = 56,
updated_replicas = 56, ), )
],
kind = '0',
metadata = kubernetes.client.models.v1/list_meta.v1.ListMeta(
continue = '0',
remaining_item_count = 56,
resource_version = '0',
self_link = '0', )
)
else :
| |
"amino_acid_position": "196"
},
{
"gene": "TP53",
"chromosome_position": "7578406",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-14-4157",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R175H",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1a42",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "175"
},
{
"gene": "TP53",
"chromosome_position": "7578460",
"amino_acid_wildtype": "V",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-14-4157",
"dna_change": "A->C",
"amino_acid_mutation_detail": "V157G",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1a43",
"amino_acid_mutation": "G",
"chromosome": "17",
"amino_acid_position": "157"
},
{
"gene": "TP53",
"chromosome_position": "7577539",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-15-1444",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R209W",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1ad0",
"amino_acid_mutation": "W",
"chromosome": "17",
"amino_acid_position": "209"
},
{
"gene": "TP53",
"chromosome_position": "7578395",
"amino_acid_wildtype": "H",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-16-0846",
"dna_change": "G->A",
"amino_acid_mutation_detail": "H179Y",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1afd",
"amino_acid_mutation": "Y",
"chromosome": "17",
"amino_acid_position": "179"
},
{
"gene": "TP53",
"chromosome_position": "7578268",
"amino_acid_wildtype": "L",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-19-1390",
"dna_change": "A->C",
"amino_acid_mutation_detail": "L35R",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1c96",
"amino_acid_mutation": "R",
"chromosome": "17",
"amino_acid_position": "35"
},
{
"gene": "TP53",
"chromosome_position": "7578542",
"amino_acid_wildtype": "L",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-19-1390",
"dna_change": "G->A",
"amino_acid_mutation_detail": "L130F",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1c97",
"amino_acid_mutation": "F",
"chromosome": "17",
"amino_acid_position": "130"
},
{
"gene": "TP53",
"chromosome_position": "7577548",
"amino_acid_wildtype": "G",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-19-2623",
"dna_change": "C->T",
"amino_acid_mutation_detail": "G245S",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1e74",
"amino_acid_mutation": "S",
"chromosome": "17",
"amino_acid_position": "245"
},
{
"gene": "TP53",
"chromosome_position": "7577568",
"amino_acid_wildtype": "C",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-19-2625",
"dna_change": "C->T",
"amino_acid_mutation_detail": "C238Y",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1f09",
"amino_acid_mutation": "Y",
"chromosome": "17",
"amino_acid_position": "238"
},
{
"gene": "TP53",
"chromosome_position": "7578235",
"amino_acid_wildtype": "Y",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-19-2625",
"dna_change": "T->C",
"amino_acid_mutation_detail": "Y205C",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f1f0a",
"amino_acid_mutation": "C",
"chromosome": "17",
"amino_acid_position": "205"
},
{
"gene": "TP53",
"chromosome_position": "7577121",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-26-1442",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R273C",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f23cb",
"amino_acid_mutation": "C",
"chromosome": "17",
"amino_acid_position": "273"
},
{
"gene": "TP53",
"chromosome_position": "7577580",
"amino_acid_wildtype": "Y",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-26-5133",
"dna_change": "T->C",
"amino_acid_mutation_detail": "Y234C",
"uniprot_id": "P04637",
"_id": "53a20770ab47b322147f243c",
"amino_acid_mutation": "C",
"chromosome": "17",
"amino_acid_position": "234"
},
{
"gene": "TP53",
"chromosome_position": "7578534",
"amino_acid_wildtype": "K",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-26-5136",
"dna_change": "C->A",
"amino_acid_mutation_detail": "K132N",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2517",
"amino_acid_mutation": "N",
"chromosome": "17",
"amino_acid_position": "132"
},
{
"gene": "TP53",
"chromosome_position": "7577538",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-27-1830",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R209Q",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f262a",
"amino_acid_mutation": "Q",
"chromosome": "17",
"amino_acid_position": "209"
},
{
"gene": "TP53",
"chromosome_position": "7578203",
"amino_acid_wildtype": "V",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-27-1835",
"dna_change": "C->T",
"amino_acid_mutation_detail": "V216M",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2781",
"amino_acid_mutation": "M",
"chromosome": "17",
"amino_acid_position": "216"
},
{
"gene": "TP53",
"chromosome_position": "7578217",
"amino_acid_wildtype": "T",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-27-1836",
"dna_change": "G->A",
"amino_acid_mutation_detail": "T211I",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f27c4",
"amino_acid_mutation": "I",
"chromosome": "17",
"amino_acid_position": "211"
},
{
"gene": "TP53",
"chromosome_position": "7577114",
"amino_acid_wildtype": "C",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-27-1838",
"dna_change": "C->T",
"amino_acid_mutation_detail": "C275Y",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2845",
"amino_acid_mutation": "Y",
"chromosome": "17",
"amino_acid_position": "275"
},
{
"gene": "TP53",
"chromosome_position": "7577094",
"amino_acid_wildtype": "R",
"mutation_type": "Frame_Shift_Del",
"patient_id": "TCGA-27-2519",
"dna_change": "G->-",
"amino_acid_mutation_detail": "R282fs",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f28e6",
"amino_acid_mutation": "fs",
"chromosome": "17",
"amino_acid_position": "282"
},
{
"gene": "TP53",
"chromosome_position": "7577149",
"amino_acid_wildtype": "N",
"mutation_type": "Frame_Shift_Del",
"patient_id": "TCGA-27-2521",
"dna_change": "A->-",
"amino_acid_mutation_detail": "N263fs",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f292b",
"amino_acid_mutation": "fs",
"chromosome": "17",
"amino_acid_position": "263"
},
{
"gene": "TP53",
"chromosome_position": "7577120",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-28-1753",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R273H",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2b05",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "273"
},
{
"gene": "TP53",
"chromosome_position": "7577556",
"amino_acid_wildtype": "C",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-28-2509",
"dna_change": "C->T",
"amino_acid_mutation_detail": "C242Y",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2bfe",
"amino_acid_mutation": "Y",
"chromosome": "17",
"amino_acid_position": "242"
},
{
"gene": "TP53",
"chromosome_position": "7577141",
"amino_acid_wildtype": "G",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-28-5207",
"dna_change": "C->A",
"amino_acid_mutation_detail": "G266V",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2d3b",
"amino_acid_mutation": "V",
"chromosome": "17",
"amino_acid_position": "266"
},
{
"gene": "TP53",
"chromosome_position": "7579312",
"amino_acid_wildtype": "T",
"mutation_type": "Silent",
"patient_id": "TCGA-28-5215",
"dna_change": "C->T",
"amino_acid_mutation_detail": "T125T",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2eed",
"amino_acid_mutation": "T",
"chromosome": "17",
"amino_acid_position": "125"
},
{
"gene": "TP53",
"chromosome_position": "7577535",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-28-5216",
"dna_change": "C->A",
"amino_acid_mutation_detail": "R249M",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f2f2a",
"amino_acid_mutation": "M",
"chromosome": "17",
"amino_acid_position": "249"
},
{
"gene": "TP53",
"chromosome_position": "7577138",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-1970",
"dna_change": "C->G",
"amino_acid_mutation_detail": "R267P",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f3053",
"amino_acid_mutation": "P",
"chromosome": "17",
"amino_acid_position": "267"
},
{
"gene": "TP53",
"chromosome_position": "7577568",
"amino_acid_wildtype": "C",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-2491",
"dna_change": "C->A",
"amino_acid_mutation_detail": "C238F",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f329e",
"amino_acid_mutation": "F",
"chromosome": "17",
"amino_acid_position": "238"
},
{
"gene": "TP53",
"chromosome_position": "7578265",
"amino_acid_wildtype": "I",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-2491",
"dna_change": "A->T",
"amino_acid_mutation_detail": "I195N",
"uniprot_id": "P04637",
"_id": "53a20771ab47b322147f329f",
"amino_acid_mutation": "N",
"chromosome": "17",
"amino_acid_position": "195"
},
{
"gene": "TP53",
"chromosome_position": "7577520",
"amino_acid_wildtype": "I",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-2634",
"dna_change": "A->C",
"amino_acid_mutation_detail": "I254S",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f34f8",
"amino_acid_mutation": "S",
"chromosome": "17",
"amino_acid_position": "254"
},
{
"gene": "TP53",
"chromosome_position": "7578406",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-4208",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R175H",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f358a",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "175"
},
{
"gene": "TP53",
"chromosome_position": "7577544",
"amino_acid_wildtype": "M",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-32-4210",
"dna_change": "A->C",
"amino_acid_mutation_detail": "M246R",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3622",
"amino_acid_mutation": "R",
"chromosome": "17",
"amino_acid_position": "246"
},
{
"gene": "TP53",
"chromosome_position": "7578211",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-74-6573",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R174Q",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3b0e",
"amino_acid_mutation": "Q",
"chromosome": "17",
"amino_acid_position": "174"
},
{
"gene": "TP53",
"chromosome_position": "7577535",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-4925",
"dna_change": "C->G",
"amino_acid_mutation_detail": "R249T",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3c92",
"amino_acid_mutation": "T",
"chromosome": "17",
"amino_acid_position": "249"
},
{
"gene": "TP53",
"chromosome_position": "7574003",
"amino_acid_wildtype": "R",
"mutation_type": "Nonsense_Mutation",
"patient_id": "TCGA-76-4929",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R342X",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3dd1",
"amino_acid_mutation": "X",
"chromosome": "17",
"amino_acid_position": "342"
},
{
"gene": "TP53",
"chromosome_position": "7578476",
"amino_acid_wildtype": "P",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-4929",
"dna_change": "G->A",
"amino_acid_mutation_detail": "P152S",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3dd2",
"amino_acid_mutation": "S",
"chromosome": "17",
"amino_acid_position": "152"
},
{
"gene": "TP53",
"chromosome_position": "7577120",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-4934",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R273H",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3eb4",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "273"
},
{
"gene": "TP53",
"chromosome_position": "7577551",
"amino_acid_wildtype": "G",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6193",
"dna_change": "C->T",
"amino_acid_mutation_detail": "G244S",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3fcf",
"amino_acid_mutation": "S",
"chromosome": "17",
"amino_acid_position": "244"
},
{
"gene": "TP53",
"chromosome_position": "7578464",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6193",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R156C",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3fd0",
"amino_acid_mutation": "C",
"chromosome": "17",
"amino_acid_position": "156"
},
{
"gene": "TP53",
"chromosome_position": "7578466",
"amino_acid_wildtype": "T",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6193",
"dna_change": "G->T",
"amino_acid_mutation_detail": "T155N",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f3fd1",
"amino_acid_mutation": "N",
"chromosome": "17",
"amino_acid_position": "155"
},
{
"gene": "TP53",
"chromosome_position": "7578457",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6283",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R158H",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f40a7",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "158"
},
{
"gene": "TP53",
"chromosome_position": "7572986",
"amino_acid_wildtype": "Q",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6286",
"dna_change": "G->T",
"amino_acid_mutation_detail": "Q375K",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f414b",
"amino_acid_mutation": "K",
"chromosome": "17",
"amino_acid_position": "375"
},
{
"gene": "TP53",
"chromosome_position": "7577580",
"amino_acid_wildtype": "Y",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6286",
"dna_change": "T->C",
"amino_acid_mutation_detail": "Y234C",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f414c",
"amino_acid_mutation": "C",
"chromosome": "17",
"amino_acid_position": "234"
},
{
"gene": "TP53",
"chromosome_position": "7578518",
"amino_acid_wildtype": "A",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6662",
"dna_change": "C->T",
"amino_acid_mutation_detail": "A138T",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f4309",
"amino_acid_mutation": "T",
"chromosome": "17",
"amino_acid_position": "138"
},
{
"gene": "TP53",
"chromosome_position": "7577538",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6663",
"dna_change": "C->A",
"amino_acid_mutation_detail": "R209L",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f4346",
"amino_acid_mutation": "L",
"chromosome": "17",
"amino_acid_position": "209"
},
{
"gene": "TP53",
"chromosome_position": "7577539",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-76-6664",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R209W",
"uniprot_id": "P04637",
"_id": "53a20772ab47b322147f438f",
"amino_acid_mutation": "W",
"chromosome": "17",
"amino_acid_position": "209"
}
]
},
{
"tumor_type": "KIRC",
"mutations": [
{
"gene": "TP53",
"chromosome_position": "7577120",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-A3-3308",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R273H",
"uniprot_id": "P04637",
"_id": "53a20790ab47b32217429c3f",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "273"
},
{
"gene": "TP53",
"chromosome_position": "7577120",
"amino_acid_wildtype": "R",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-A3-3308",
"dna_change": "C->T",
"amino_acid_mutation_detail": "R273H",
"uniprot_id": "P04637",
"_id": "53a20790ab47b32217429d22",
"amino_acid_mutation": "H",
"chromosome": "17",
"amino_acid_position": "273"
},
{
"gene": "TP53",
"chromosome_position": "7577547",
"amino_acid_wildtype": "G",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-A3-3319",
"dna_change": "C->T",
"amino_acid_mutation_detail": "G245D",
"uniprot_id": "P04637",
"_id": "53a20790ab47b3221742a1ce",
"amino_acid_mutation": "D",
"chromosome": "17",
"amino_acid_position": "245"
},
{
"gene": "TP53",
"chromosome_position": "7579396",
"amino_acid_wildtype": "V",
"mutation_type": "Silent",
"patient_id": "TCGA-B0-5092",
"dna_change": "G->A",
"amino_acid_mutation_detail": "V97V",
"uniprot_id": "P04637",
"_id": "53a20793ab47b3221742c8d6",
"amino_acid_mutation": "V",
"chromosome": "17",
"amino_acid_position": "97"
},
{
"gene": "TP53",
"chromosome_position": "7578212",
"amino_acid_wildtype": "R",
"mutation_type": "Nonsense_Mutation",
"patient_id": "TCGA-B0-5098",
"dna_change": "G->A",
"amino_acid_mutation_detail": "R174X",
"uniprot_id": "P04637",
"_id": "53a20793ab47b3221742ccbd",
"amino_acid_mutation": "X",
"chromosome": "17",
"amino_acid_position": "174"
},
{
"gene": "TP53",
"chromosome_position": "7578272",
"amino_acid_wildtype": "H",
"mutation_type": "Missense_Mutation",
"patient_id": "TCGA-B0-5117",
"dna_change": "G->A",
"amino_acid_mutation_detail": "H193Y",
"uniprot_id": "P04637",
"_id": "53a20793ab47b3221742d076",
"amino_acid_mutation": "Y",
"chromosome": | |
import yaml
import papermill as pm
import math
import pkg_resources
from jinja2 import Environment, FileSystemLoader
import yaml
import argparse
import json
import glob
import shutil
import sys
import os
#_________________________________________________________________________________________________
# open htnl template file
#try: pkg_path = os.environ['PATH_TRIPYVIEW']
#except: pkg_path=''
pkg_path = os.path.dirname(os.path.dirname(__file__))
templates_path = os.path.join(pkg_path,'templates_html')
templates_nb_path = os.path.join(pkg_path,'templates_notebooks')
file_loader = FileSystemLoader(templates_path)
env = Environment(loader=file_loader)
#
#
#_______________________________________________________________________________________________________
def drive_hslice(yaml_settings, analysis_name):
print(' --> drive_hslice:',analysis_name)
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
# loop over depths
for depth in driver_settings[vname]["depths"]:
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
current_params2["depth"] = depth
current_params2.update(driver_settings[vname])
del current_params2["depths"] # --> delete depth list [0, 100, 1000,...] from current_param dict()
#__________________________________________________________________________________________
if 'proj' in current_params2.keys():
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}.ipynb"
short_name = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}"
else:
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}.ipynb"
short_name = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_hslice.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True,
)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
webpage[f"image_{image_count}"]["name"] = f"{vname.capitalize()} at {depth} m"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"]["short_name"] = short_name
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_hslice_clim(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
print(f' --> compute:{vname}')
# loop over depths
for depth in driver_settings[vname]["depths"]:
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
current_params2["depth"] = depth
current_params2.update(driver_settings[vname])
del current_params2["depths"] # --> delete depth list [0, 100, 1000,...] from current_param dict()
# print(current_params2)
#__________________________________________________________________________________________
if 'proj' in current_params2.keys():
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}.ipynb"
short_name = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{current_params2['proj']}_{depth}"
else:
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}.ipynb"
short_name = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{depth}"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_hslice_clim.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True,
)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
webpage[f"image_{image_count}"]["name"] = f"{vname.capitalize()} at {depth} m"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"]["short_name"] = short_name
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_hovm(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
print(f' --> compute: {vname}')
# loop over depths
for box_region in driver_settings[vname]["box_regions"]:
print(f' --> compute: {box_region}')
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
current_params2["box_region"] = list([box_region])
current_params2.update(driver_settings[vname])
del current_params2["box_regions"] # --> delete depth list [0, 100, 1000,...] from current_param dict()
str_boxregion = box_region.split('/')[-1].split('.')[0]
#__________________________________________________________________________________________
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}.ipynb"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_hovm.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True,
)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
webpage[f"image_{image_count}"][
"name"
] = f"{vname.capitalize()} at {str_boxregion} m"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"][
"short_name"
] = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}"
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_hovm_clim(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
print(f' --> compute: {vname}')
# loop over depths
for box_region in driver_settings[vname]["box_regions"]:
print(f' --> compute: {box_region}')
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
current_params2["box_region"] = list([box_region])
current_params2.update(driver_settings[vname])
del current_params2["box_regions"] # --> delete depth list [0, 100, 1000,...] from current_param dict()
str_boxregion = box_region.split('/')[-1].split('.')[0]
#__________________________________________________________________________________________
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}.ipynb"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_hovm_clim.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True,
)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
webpage[f"image_{image_count}"][
"name"
] = f"{vname.capitalize()} at {str_boxregion} m"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"][
"short_name"
] = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}_{str_boxregion}"
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_xmoc(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
print(f' --> compute: {vname}')
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
# current_params2.update(driver_settings[vname])
#__________________________________________________________________________________________
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}.ipynb"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_xmoc.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
webpage[f"image_{image_count}"]["name"] = f"{vname.upper()}"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"]["short_name"] = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}"
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_xmoc_tseries(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for which_lat in driver_settings['which_lats']:
print(f' --> compute tseries @: {str(which_lat)}')
current_params2 = {}
current_params2 = current_params.copy()
current_params2["which_lat"] = [which_lat]
current_params2.update(driver_settings)
del current_params2["which_lats"]
#__________________________________________________________________________________________
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{str(which_lat)}.png"
save_fname_nb = f"{yaml_settings['workflow_name']}_{analysis_name}_{str(which_lat)}.ipynb"
current_params2["save_fname"] = os.path.join(yaml_settings['save_path_fig'], save_fname)
#__________________________________________________________________________________________
pm.execute_notebook(
f"{templates_nb_path}/template_xmoc_tseries.ipynb",
os.path.join(yaml_settings['save_path_nb'], save_fname_nb),
parameters=current_params2,
nest_asyncio=True)
#__________________________________________________________________________________________
webpage[f"image_{image_count}"] = {}
if which_lat == 'max':
webpage[f"image_{image_count}"]["name"] = f" max AMOC @ 30°N<lat<45°N"
else:
webpage[f"image_{image_count}"]["name"] = f" AMOC @ {str(which_lat)}°N"
webpage[f"image_{image_count}"]["path"] = os.path.join('./figures/', save_fname)
webpage[f"image_{image_count}"]["path_nb"] = os.path.join('./notebooks/', save_fname_nb)
webpage[f"image_{image_count}"]["short_name"] = f"{yaml_settings['workflow_name']}_{analysis_name}_{str(which_lat)}"
image_count += 1
return webpage
#
#
#_______________________________________________________________________________________________________
def drive_vprofile(yaml_settings, analysis_name):
# copy yaml settings for analysis driver --> hslice:
#
driver_settings = yaml_settings[analysis_name].copy()
# create current primary parameter from yaml settings
current_params = {}
for key, value in yaml_settings.items():
# if value is a dictionary its not a primary paramter anymore e.g.
# hslice: --> dict(...)
# temp:
# levels: [-2, 30, 41]
# depths: [0, 100, 400, 1000]
# ....
if isinstance(value, dict):
pass
else:
current_params[key] = value
# initialse webpage for analyis
webpage = {}
image_count = 0
# loop over variable name
for vname in driver_settings:
print(f' --> compute: {vname}')
current_params2 = {}
current_params2 = current_params.copy()
current_params2["vname"] = vname
current_params2.update(driver_settings[vname])
#__________________________________________________________________________________________
save_fname = f"{yaml_settings['workflow_name']}_{analysis_name}_{vname}.png"
save_fname_nb | |
# File: threatminerapi_connector.py
#
# Copyright (c) 2019 Splunk Inc.
#
# 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.
#
#
# Phantom App imports
import phantom.app as phantom
from phantom.base_connector import BaseConnector
from phantom.action_result import ActionResult
# Usage of the consts file is recommended
# from threatminerapi_consts import *
import requests
import json
import time
import ipaddress
from bs4 import BeautifulSoup
class RetVal(tuple):
def __new__(cls, val1, val2=None):
return tuple.__new__(RetVal, (val1, val2))
class ThreatminerApiConnector(BaseConnector):
def __init__(self):
# Call the BaseConnectors init first
super(ThreatminerApiConnector, self).__init__()
self._state = None
# Variable to hold a base_url in case the app makes REST calls
# Do note that the app json defines the asset config, so please
# modify this as you deem fit.
self._base_url = None
def _is_ip(self, input_ip_address):
""" Function that checks given address and return True if address is valid IPv4 or IPV6 address.
:param input_ip_address: IP address
:return: status (success/failure)
"""
ip_address_input = input_ip_address
try:
ipaddress.ip_address(unicode(ip_address_input))
except:
return False
return True
def _process_empty_reponse(self, response, action_result):
if response.status_code == 200:
return RetVal(phantom.APP_SUCCESS, {})
return RetVal(action_result.set_status(phantom.APP_ERROR, "Empty response and no information in the header"), None)
def _process_html_response(self, response, action_result):
# An html response, treat it like an error
status_code = response.status_code
try:
soup = BeautifulSoup(response.text, "html.parser")
error_text = soup.text
split_lines = error_text.split('\n')
split_lines = [x.strip() for x in split_lines if x.strip()]
error_text = '\n'.join(split_lines)
except:
error_text = "Cannot parse error details"
message = "Status Code: {0}. Data from server:\n{1}\n".format(status_code,
error_text)
message = message.replace('{', '{{').replace('}', '}}')
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _process_json_response(self, r, action_result):
# Try a json parse
try:
resp_json = r.json()
except Exception as e:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Unable to parse JSON response. Error: {0}".format(str(e))), None)
# Please specify the status codes here
if 200 <= r.status_code < 399:
return RetVal(phantom.APP_SUCCESS, resp_json)
# You should process the error returned in the json
message = "Error from server. Status Code: {0} Data from server: {1}".format(
r.status_code, r.text.replace('{', '{{').replace('}', '}}'))
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _process_response(self, r, action_result):
# store the r_text in debug data, it will get dumped in the logs if the action fails
if hasattr(action_result, 'add_debug_data'):
action_result.add_debug_data({'r_status_code': r.status_code})
action_result.add_debug_data({'r_text': r.text})
action_result.add_debug_data({'r_headers': r.headers})
# Process each 'Content-Type' of response separately
# Process a json response
if 'json' in r.headers.get('Content-Type', ''):
return self._process_json_response(r, action_result)
# Process an HTML resonse, Do this no matter what the api talks.
# There is a high chance of a PROXY in between phantom and the rest of
# world, in case of errors, PROXY's return HTML, this function parses
# the error and adds it to the action_result.
if 'html' in r.headers.get('Content-Type', ''):
return self._process_html_response(r, action_result)
# it's not content-type that is to be parsed, handle an empty response
if not r.text:
return self._process_empty_reponse(r, action_result)
# everything else is actually an error at this point
message = "Can't process response from server. Status Code: {0} Data from server: {1}".format(
r.status_code, r.text.replace('{', '{{').replace('}', '}}'))
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _make_rest_call(self, endpoint, action_result, headers=None, params=None, data=None, method="get"):
config = self.get_config()
resp_json = None
try:
request_func = getattr(requests, method)
except AttributeError:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Invalid method: {0}".format(method)), resp_json)
# Create a URL to connect to
url = '{0}{1}'.format(self._base_url, endpoint)
try:
r = request_func(
url,
# auth=(username, password), # basic authentication
data=data,
headers=headers,
verify=config.get('verify_server_cert', False),
params=params)
except Exception as e:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Error Connecting to server. Details: {0}".format(str(e))), resp_json)
return self._process_response(r, action_result)
def _handle_test_connectivity(self, param):
# Add an action result object to self (BaseConnector) to represent the action for this param
action_result = self.add_action_result(ActionResult(dict(param)))
# Create a new Threat Miner Python Object
endpoint = 'domain.php?q=vwrm.com&rt=1'
# Make test connection to the test connectivity endpoint
ret_val, response = self._make_rest_call(endpoint, action_result)
# Connect to Phantom Endpoint
self.save_progress("Connecting to endpoint")
if (phantom.is_fail(ret_val)):
# the call to the 3rd party device or service failed, action result should contain all the error details
# so just return from here
self.save_progress("Test Connectivity Failed")
message = "Test Connectivity Failed"
return action_result.set_status(phantom.APP_ERROR, status_message=message)
# Return success
self.save_progress("Test Connectivity Passed")
return action_result.set_status(phantom.APP_SUCCESS)
def _handle_lookup_domain(self, param):
# Implement the handler here
# use self.save_progress(...) to send progress messages back to the platform
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
# Add an action result object to self (BaseConnector) to represent the action for this param
action_result = self.add_action_result(ActionResult(dict(param)))
# Access the domain parameter
domain = param['domain']
# Create a new Threat Miner Python Object
endpoint = 'domain.php?q={}&rt={}'.format(domain, 2)
# Issue request to get_domain function
ret_val, response = self._make_rest_call(endpoint, action_result)
# If the result fails
if (phantom.is_fail(ret_val)):
# the call to the 3rd party device or service failed, action result should contain all the error details
# so just return from here
message = ("Lookup Domain at endpoint: {} "
"request received a non 200 response".format(endpoint))
return action_result.set_status(phantom.APP_ERROR, status_message=message)
# Create new python dictionary to store output
data_output = response
# Add the response into the data section
action_result.add_data(data_output)
# Add a dictionary that is made up of the most important values from data into the summary
summary = action_result.update_summary({})
summary['domain'] = domain
summary['status_message'] = data_output['status_message']
# Return success, no need to set the message, only the status
# BaseConnector will create a textual message based off of the summary dictionary
return action_result.set_status(phantom.APP_SUCCESS)
def _handle_lookup_hash(self, param):
# Implement the handler here
# use self.save_progress(...) to send progress messages back to the platform
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
# Add an action result object to self (BaseConnector) to represent the action for this param
action_result = self.add_action_result(ActionResult(dict(param)))
# Access Hash parameter
hash_value = param['hash']
# Access Hash Type
hash_type = param['hash_type']
if hash_type not in["cryptographic_hash", "ssdeep", "imphash"]:
message = ('Invalid hash type, acceptable values ["cryptographic_hash", "ssdeep", "imphash"]')
return action_result.set_status(phantom.APP_ERROR, status_message=message)
# List the available functions that the hash function provides in the threatMiner API
crypto_hash_functions = {
1: "metadata",
2: "http_traffic",
3: "hosts_domains_and_ips",
4: "mutants",
5: "registry_keys",
6: "av_detections",
7: "report_tagging"
}
# List the available functions that the fuzzy hashing provides in the threatMiner API
fuzzy_hash_functions = {
1: "samples",
2: "report_tagging"
}
# Create an empty dictionary for pushing results into the
result_dictionary = {}
# If the hash type is a cryptographic hash
if hash_type == "cryptographic_hash":
self.save_progress("Cryptographic hash selected")
# Iterate through the functions
for i in range(1, 8):
# Make the API call
endpoint = 'sample.php?q={}&rt={}'.format(hash_value, i)
# Issue request to get_domain function
ret_val, response = self._make_rest_call(endpoint, action_result)
# Notify user that the request is progressing
self.save_progress("Issuing {} request at endpoint: {}".format(i, endpoint))
if (phantom.is_fail(ret_val)):
# the call to the 3rd party device or service failed, action result should contain all the error details
# so just return from here
message = ("Lookup ssdeep at endpoint: {} "
"request received a non 200 response".format(endpoint))
return action_result.set_status(phantom.APP_ERROR, status_message=message)
# If the call is successfull
if int(response['status_code']) == 200:
self.save_progress("Received 200 OK from ThreatMiner")
# Output the results to a dictionary
result_dictionary[crypto_hash_functions[i]] = response
# If the call fails
else:
# Output none to the result_dictionary for the
result_dictionary[crypto_hash_functions[i]] = None
# Notify user that the command is sleeping
self.save_progress("Sleeping 8 seconds to avoid API throttling")
# Throttle communication to ensure we do not go over 10 requests per minute.
time.sleep(8)
# Check to see if all the requests result in a not found response
complete_result = all(result_dictionary[crypto_hash_functions[i]] is None for i in range(1, 8))
# If the hash type is a ssdeep hash
elif hash_type == "ssdeep":
self.save_progress("ssdeep hash selected")
# Iterate through the functions
for i in range(1, 3):
# If the call is successfull
# Make the API | |
<filename>docker_stuff/site-packages/partedit.py<gh_stars>0
#!/usr/bin/env python
# pylint:disable=C0302
"""
An interactive spreadsheet for viewing, editing, and saving
partio (bgeo) files.
Usage:
% partedit [FLAGS] [bgeoFile]
Supported FLAGS:
-h/--help: Print this help message
"""
# TODO:
# Support for fixed attribute delete and rename
# Support for indexed strings
# Tighten up particle table whitespace usage (smaller font? popup matrix?)
# Performance - delay widget construction
# NEXT UP:
# - delete fixed attribute
# - rename fixed attribute
__copyright__ = """
CONFIDENTIAL INFORMATION: This software is the confidential and
proprietary information of Walt Disney Animation Studios ("WDAS").
This software may not be used, disclosed, reproduced or distributed
for any purpose without prior written authorization and license
from WDAS. Reproduction of any section of this software must
include this legend and all copyright notices.
Copyright Disney Enterprises, Inc. All rights reserved.
"""
import os, sys, math
import partio
# pylint:disable=E0611,E0401
from Qt.QtGui import QKeySequence, QIcon, QIntValidator, QDoubleValidator
from Qt.QtWidgets import QShortcut, QApplication, QMainWindow, \
QPushButton, QTableWidget, QLabel, QWidget, QVBoxLayout, QHeaderView,\
QHBoxLayout, QLineEdit, QFileDialog, QFrame, QDialog, QFormLayout, \
QComboBox, QCheckBox, QTableWidgetItem, QSplitter
from Qt.QtCore import Qt, QSize, QObject#, pyqtSignal
from PyQt5.QtCore import pyqtSignal
#------------------------------------------------------------------------------_
_attrTypes = [partio.NONE, partio.VECTOR, partio.FLOAT, partio.INT, partio.INDEXEDSTR]
#------------------------------------------------------------------------------
def copy(srcData):
""" Creates a copy of the given partio data set """
dstData = partio.create()
srcAttrs = []
dstAttrs = []
for anum in range(srcData.numAttributes()):
attr = srcData.attributeInfo(anum)
srcAttrs.append(attr)
dstAttrs.append(dstData.addAttribute(attr.name, attr.type, attr.count))
dstData.addParticles(srcData.numParticles())
for pnum in range(srcData.numParticles()):
for anum, srcAttr in enumerate(srcAttrs):
dstData.set(dstAttrs[anum], pnum, srcData.get(srcAttr, pnum))
return dstData
#--------------------------------------------------------------------------
def getAttrs(numAttributesFunc, attributeInfoFunc, sort=False):
""" Return list of tuples of (attributeNum, attribute) """
attrs = []
numAttr = numAttributesFunc()
nameToIndex = {attributeInfoFunc(anum).name:anum for anum in range(numAttr)}
names = nameToIndex.keys()
if sort:
names.sort()
id_offset = 0
for name in names:
anum = nameToIndex[name]
attr = attributeInfoFunc(anum)
if sort and attr.name == 'id':
attrs.insert(0, (anum, attr))
id_offset += 1
elif sort and 'id' in attr.name:
attrs.insert(id_offset, (anum, attr))
id_offset += 1
else:
attrs.append((anum, attr))
return attrs
#--------------------------------------------------------------------------
def copyParticles(src, dst):
""" Copy particles from src to dst. """
# Identify the attributes that are in both src and dst
srcAttrs = [src.attributeInfo(i) for i in range(src.numAttributes())]
dstAttrs = [dst.attributeInfo(i) for i in range(dst.numAttributes())]
srcAttrs = {attr.name:attr for attr in srcAttrs}
dstAttrs = {attr.name:attr for attr in dstAttrs}
attrs = {'src':[], 'dst':[]}
for name, srcAttr in srcAttrs.iteritems():
if name in dstAttrs:
attrs['src'].append(srcAttr)
attrs['dst'].append(dstAttrs[name])
numParticles = src.numParticles()
dst.addParticles(numParticles)
for pnum in range(numParticles):
for anum in range(len(attrs)):
dst.set(attrs['dst'][anum], pnum, src.get(attrs['src'][anum], pnum))
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
class ParticleData(QObject):
""" UI Controller class for partio data """
particleAdded = pyqtSignal(int)
attributeAdded = pyqtSignal(str)
fixedAttributeAdded = pyqtSignal(str)
dataReset = pyqtSignal()
dirtied = pyqtSignal(bool)
def __init__(self):
QObject.__init__(self)
self.setData(partio.create())
self.filename = None
self.dirty = False
#--------------------------------------------------------------------------
def setDirty(self, dirty):
""" Stores the dirty state of the data """
if dirty != self.dirty:
self.dirty = dirty
self.dirtied.emit(dirty)
#--------------------------------------------------------------------------
def setData(self, data):
""" Sets the data, linking class methods to partio methods and
notifying all observers that the data set has changed.
"""
self.data = data
self.originalData = copy(data)
self.facade()
self.dataReset.emit()
#--------------------------------------------------------------------------
def facade(self):
""" Facades methods through to data """
self.get = self.data.get
self.getFixed = self.data.getFixed
self.numAttributes = self.data.numAttributes
self.numFixedAttributes = self.data.numFixedAttributes
self.numParticles = self.data.numParticles
self.attributeInfo = self.data.attributeInfo
self.fixedAttributeInfo = self.data.fixedAttributeInfo
self.indexedStrs = self.data.indexedStrs
#--------------------------------------------------------------------------
def set(self, *args):
""" Sets a value on the partio data, marking dirty. """
self.setDirty(True)
self.data.set(*args)
#--------------------------------------------------------------------------
def setFixed(self, *args):
""" Sets a fixed attribute value on the partio data, marking dirty. """
self.setDirty(True)
self.data.setFixed(*args)
#--------------------------------------------------------------------------
def read(self, filename):
""" Opens a file from disk and populates the UI """
if not os.path.exists(filename):
sys.stderr.write('Invalid filename: {}\n'.format(filename))
return
data = partio.read(filename)
if not data:
sys.stderr.write('Invalid particle file: {}\n'.format(filename))
data = partio.create()
self.filename = filename
self.setData(data)
self.setDirty(False)
#--------------------------------------------------------------------------
def write(self, filename, delta):
""" Write data to file. If delta is False, saves a full copy
of the data, rebaselining. If delta is True, saves only
the particles (todo: and attributes) that have changed,
but maintains the original baseline
"""
if not self.data:
return
# If we're saving a delta, create a new particle set with just
# the differences from the original.
if delta:
data = self.createDelta()
else:
data = self.data
partio.write(filename, data)
# If we saved a full copy, rebaseline
if not delta:
self.filename = filename
self.originalData = copy(data)
self.setDirty(False)
#--------------------------------------------------------------------------
def createDelta(self):
""" Creates a delta particle set between the current and original
data set. This is the brute-force method, simply comparing the
current data set against the original, but it's easier than
tracking individual changes.
"""
def hashParticles(data):
""" Given a partio data set, create a dictionary of hashes
to indices
"""
items = {}
numAttrs = data.numAttributes()
for pnum in range(data.numParticles()):
item = []
for anum in range(numAttrs):
attr = data.attributeInfo(anum)
item.append(data.get(attr, pnum))
items[hash(str(item))] = pnum
return items
# TODO: Handle new attributes as deltas
# For now, any new attributes will write all of the particles
# Hash up the new data into an index table
newParticles = hashParticles(self.data)
oldParticles = hashParticles(self.originalData)
# If nothing changed, easy out
data = partio.create()
if newParticles == oldParticles:
return data
# Identify which particles changed
oldHashes = set(oldParticles.keys())
newHashes = set(newParticles.keys())
modifiedHashes = newHashes - oldHashes
# Create the new particle set
numAttrs = self.data.numAttributes()
newAttrs = []
oldAttrs = []
for anum in range(numAttrs):
attr = self.data.attributeInfo(anum)
oldAttrs.append(attr)
newAttr = data.addAttribute(attr.name, attr.type, attr.count)
newAttrs.append(newAttr)
data.addParticles(len(modifiedHashes))
for newIndex, modifiedHash in enumerate(modifiedHashes):
oldIndex = newParticles[modifiedHash]
for anum, oldAttr in enumerate(oldAttrs):
value = self.data.get(oldAttr, oldIndex)
data.set(newAttrs[anum], newIndex, value)
return data
#--------------------------------------------------------------------------
def addParticle(self):
""" Adds a new particle, emitting its new index.
The new particle's values are copied from the last particle.
If the particle set has the 'id' attribute, the new
particle id is set to max(ids)+1.
"""
if not self.data:
return
numParticles = self.numParticles()
index = self.data.addParticle()
numAttr = self.numAttributes()
idAttr = self.attributeInfo('id')
if idAttr:
newId = max(self.data.get(idAttr, pnum)[0] for pnum in range(numParticles)) + 1
for anum in range(numAttr):
attr = self.attributeInfo(anum)
if idAttr and attr.name == 'id':
value = (newId,)
else:
value = self.get(attr, numParticles-1)
self.set(attr, numParticles, value)
self.particleAdded.emit(index)
self.setDirty(True)
#--------------------------------------------------------------------------
def removeParticles(self, indices):
""" Removes the particles at the given indices.
partio doesn't support removing data, so we have
to construct all new data sans the given particle
"""
for anum in range(self.data.numAttributes()):
attr = self.data.attributeInfo(anum)
attributes = [self.data.attributeInfo(anum) for anum in range(self.data.numAttributes())]
want = [pnum for pnum in range(self.data.numParticles()) if pnum not in indices ]
newData = partio.clone(self.data, False)
for attr in attributes:
newData.addAttribute(attr.name, attr.type, attr.count)
newData.addParticles(len(want))
for i, idx in enumerate(want):
for attr in attributes:
newData.set(attr, i, self.data.get(attr, idx))
self.setData(newData)
self.setDirty(True)
#--------------------------------------------------------------------------
def addAttribute(self, name, attrType, count, fixed, defaultValue):
""" Adds a new attribute for the particles, returning a
handle to the new attribute.
"""
if not isinstance(defaultValue, tuple):
defaultValue = (defaultValue,)
if fixed:
attr = self.data.addFixedAttribute(name, attrType, count)
self.data.setFixed(attr, defaultValue)
self.fixedAttributeAdded.emit(attr.name)
else:
attr = self.data.addAttribute(name, attrType, count)
for pnum in range(self.numParticles()):
self.data.set(attr, pnum, defaultValue)
self.attributeAdded.emit(attr.name)
self.setDirty(True)
#--------------------------------------------------------------------------
def removeAttributes(self, names):
""" Removes the attributes with the given names.
partio doesn't support removing data, so we have
to construct all new data sans the given attribute(s).
"""
newData = partio.create()
for anum in range(self.numAttributes()):
attr = self.attributeInfo(anum)
if attr.name not in names:
newData.addAttribute(attr.name, attr.type, attr.count)
# Copy particle data with new attributes
copyParticles(src=self.data, dst=newData)
# Copy fixed attributes
for anum in range(self.data.numFixedAttributes()):
oldAttr = self.data.fixedAttributeInfo(anum)
newAttr = newData.addFixedAttribute(oldAttr.name, oldAttr.type, oldAttr.count)
newData.setFixed(newAttr, self.data.getFixed(oldAttr))
self.setData(newData)
self.setDirty(True)
#--------------------------------------------------------------------------
def removeFixedAttributes(self, names):
""" Removes the fixed attributes with the given names.
partio doesn't support removing data, so we have
to construct all new data sans the given attribute(s).
"""
newData = partio.create()
# Copy the regular (non-fixed) attributes and particles
for anum in range(self.data.numAttributes()):
attr = self.attributeInfo(anum)
newData.addAttribute(attr.name, attr.type, attr.count)
copyParticles(src=self.data, dst=newData)
# Create new fixed attributes
for anum in range(self.data.numFixedAttributes()):
srcAttr = self.fixedAttributeInfo(anum)
if srcAttr.name not in names:
dstAttr = newData.addFixedAttribute(srcAttr.name, srcAttr.type, srcAttr.count)
newData.setFixed(dstAttr, self.data.getFixed(srcAttr))
self.setData(newData)
self.setDirty(True)
#------------------------------------------------------------------------------
class NumericalEdit(QLineEdit): # pylint:disable=R0903
""" A LineEdit that auto installs a validator for numerical types """
def __init__(self, value, parent=None):
QLineEdit.__init__(self, str(value), parent)
self.setAlignment(Qt.AlignRight)
if isinstance(value, int):
self.setValidator(QIntValidator())
elif isinstance(value, float):
self.setValidator(QDoubleValidator())
#------------------------------------------------------------------------------
class AttrWidget(QFrame): # pylint:disable=R0903
""" The | |
# Copyright (c) 2014 Mirantis Inc.
#
# 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 copy
import time
from sqlalchemy import exc
from novaclient.v1_1 import client as nova_client
from cloudferrylib.base import compute
from cloudferrylib.utils import mysql_connector
from cloudferrylib.utils import timeout_exception
from cloudferrylib.utils import utils as utl
LOG = utl.get_log(__name__)
DISK = "disk"
LOCAL = ".local"
LEN_UUID_INSTANCE = 36
INTERFACES = "interfaces"
class NovaCompute(compute.Compute):
"""The main class for working with Openstack Nova Compute Service. """
def __init__(self, config, cloud):
super(NovaCompute, self).__init__()
self.config = config
self.cloud = cloud
self.identity = cloud.resources['identity']
self.mysql_connector = mysql_connector.MysqlConnector(config.mysql,
'nova')
self.nova_client = self.proxy(self.get_client(), config)
def get_client(self, params=None):
"""Getting nova client. """
params = self.config if not params else params
return nova_client.Client(params.cloud.user,
params.cloud.password,
params.cloud.tenant,
"http://%s:35357/v2.0/" % params.cloud.host)
def _read_info_quotas(self, info):
user_quotas_cmd = ("SELECT user_id, project_id, resource, "
"hard_limit FROM project_user_quotas WHERE "
"deleted = 0")
for quota in self.mysql_connector.execute(user_quotas_cmd):
info['user_quotas'].append(
{'quota': {'user_id': quota[0],
'project_id': quota[1],
'resource': quota[2],
'hard_limit': quota[3]},
'meta': {}})
project_quotas_cmd = ("SELECT project_id, resource, hard_limit "
"FROM quotas WHERE deleted = 0")
for quota in self.mysql_connector.execute(project_quotas_cmd):
info['project_quotas'].append(
{'quota': {'project_id': quota[0],
'resource': quota[1],
'hard_limit': quota[2]},
'meta': {}})
def _read_info_resources(self, **kwargs):
"""
Read info about compute resources except instances from the cloud.
"""
info = {'keypairs': {},
'flavors': {},
'user_quotas': [],
'project_quotas': []}
for keypair in self.get_keypair_list():
info['keypairs'][keypair.id] = self.convert(keypair)
for flavor in self.get_flavor_list():
info['flavors'][flavor.id] = self.convert(flavor)
if self.config.migrate.migrate_quotas:
self._read_info_quotas(info)
return info
def read_info(self, target='instances', **kwargs):
"""
Read info from cloud.
:param target: Target objects to get info about. Possible values:
"instances" or "resources",
:param search_opts: Search options to filter out servers (optional).
"""
if target == 'resources':
return self._read_info_resources(**kwargs)
if target != 'instances':
raise ValueError('Only "resources" or "instances" values allowed')
search_opts = kwargs.get('search_opts')
info = {'instances': {}}
for instance in self.get_instances_list(search_opts=search_opts):
info['instances'][instance.id] = self.convert(instance,
self.config,
self.cloud)
return info
@staticmethod
def convert_instance(instance, cfg, cloud):
identity_res = cloud.resources[utl.IDENTITY_RESOURCE]
compute_res = cloud.resources[utl.COMPUTE_RESOURCE]
instance_name = getattr(instance, "OS-EXT-SRV-ATTR:instance_name")
instance_host = getattr(instance, 'OS-EXT-SRV-ATTR:host')
get_tenant_name = identity_res.get_tenants_func()
security_groups = []
for security_group in instance.security_groups:
security_groups.append(security_group['name'])
interfaces = compute_res.get_networks(instance)
volumes = [{'id': v.id,
'num_device': i,
'device': v.device} for i, v in enumerate(
compute_res.nova_client.volumes.get_server_volumes(
instance.id))]
is_ephemeral = compute_res.get_flavor_from_id(
instance.flavor['id']).ephemeral > 0
is_ceph = cfg.compute.backend.lower() == utl.CEPH
direct_transfer = cfg.migrate.direct_compute_transfer
if direct_transfer:
ext_cidr = cfg.cloud.ext_cidr
host = utl.get_ext_ip(ext_cidr,
cloud.getIpSsh(),
instance_host)
elif is_ceph:
host = cfg.compute.host_eph_drv
else:
host = instance_host
instance_block_info = utl.get_libvirt_block_info(
instance_name,
cloud.getIpSsh(),
instance_host)
ephemeral_path = {
'path_src': None,
'path_dst': None,
'host_src': host,
'host_dst': None
}
if is_ephemeral:
ephemeral_path['path_src'] = utl.get_disk_path(
instance,
instance_block_info,
is_ceph_ephemeral=is_ceph,
disk=DISK+LOCAL)
diff = {
'path_src': None,
'path_dst': None,
'host_src': host,
'host_dst': None
}
if instance.image:
diff['path_src'] = utl.get_disk_path(
instance,
instance_block_info,
is_ceph_ephemeral=is_ceph)
inst = {'instance': {'name': instance.name,
'instance_name': instance_name,
'id': instance.id,
'tenant_id': instance.tenant_id,
'tenant_name': get_tenant_name(
instance.tenant_id),
'status': instance.status,
'flavor_id': instance.flavor['id'],
'image_id': instance.image[
'id'] if instance.image else None,
'boot_mode': (utl.BOOT_FROM_IMAGE
if instance.image
else utl.BOOT_FROM_VOLUME),
'key_name': instance.key_name,
'availability_zone': getattr(
instance,
'OS-EXT-AZ:availability_zone'),
'security_groups': security_groups,
'boot_volume': copy.deepcopy(
volumes[0]) if volumes else None,
'interfaces': interfaces,
'host': instance_host,
'is_ephemeral': is_ephemeral,
'volumes': volumes
},
'ephemeral': ephemeral_path,
'diff': diff,
'meta': {},
}
return inst
@staticmethod
def convert_resources(compute_obj):
if isinstance(compute_obj, nova_client.keypairs.Keypair):
return {'keypair': {'name': compute_obj.name,
'public_key': compute_obj.public_key},
'meta': {}}
elif isinstance(compute_obj, nova_client.flavors.Flavor):
return {'flavor': {'name': compute_obj.name,
'ram': compute_obj.ram,
'vcpus': compute_obj.vcpus,
'disk': compute_obj.disk,
'ephemeral': compute_obj.ephemeral,
'swap': compute_obj.swap,
'rxtx_factor': compute_obj.rxtx_factor,
'is_public': compute_obj.is_public},
'meta': {}}
@staticmethod
def convert(obj, cfg=None, cloud=None):
res_tuple = (nova_client.keypairs.Keypair, nova_client.flavors.Flavor)
if isinstance(obj, nova_client.servers.Server):
return NovaCompute.convert_instance(obj, cfg, cloud)
elif isinstance(obj, res_tuple):
return NovaCompute.convert_resources(obj)
LOG.error('NovaCompute converter has received incorrect value. Please '
'pass to it only instance, keypair or flavor objects.')
return None
def _deploy_resources(self, info, **kwargs):
"""
Deploy compute resources except instances to the cloud.
:param info: Info about compute resources to deploy,
:param identity_info: Identity info.
"""
identity_info = kwargs.get('identity_info')
tenant_map = {tenant['tenant']['id']: tenant['meta']['new_id'] for
tenant in identity_info['tenants']}
user_map = {user['user']['id']: user['meta']['new_id'] for user in
identity_info['users']}
self._deploy_keypair(info['keypairs'])
self._deploy_flavors(info['flavors'])
if self.config['migrate']['migrate_quotas']:
self._deploy_project_quotas(info['project_quotas'],
tenant_map)
self._deploy_user_quotas(info['user_quotas'],
tenant_map, user_map)
new_info = self.read_info(target='resources')
return new_info
def deploy(self, info, target='instances', **kwargs):
"""
Deploy compute resources to the cloud.
:param target: Target objects to deploy. Possible values:
"instances" or "resources",
:param identity_info: Identity info.
"""
info = copy.deepcopy(info)
if target == 'resources':
info = self._deploy_resources(info, **kwargs)
elif target == 'instances':
info = self._deploy_instances(info)
else:
raise ValueError('Only "resources" or "instances" values allowed')
return info
def _deploy_user_quotas(self, quotas, tenant_map, user_map):
insert_cmd = ("INSERT INTO project_user_quotas (user_id, project_id, "
"resource, hard_limit, deleted) VALUES ('%s', '%s', '%s'"
", %s, 0)")
update_cmd = ("UPDATE project_user_quotas SET hard_limit=%s WHERE "
"user_id='%s' AND project_id='%s' AND resource='%s' AND "
"deleted=0")
for _quota in quotas:
quota = _quota['quota']
try:
self.mysql_connector.execute(insert_cmd % (
user_map[quota['user_id']],
tenant_map[quota['project_id']],
quota['resource'],
quota['hard_limit']))
except exc.IntegrityError as e:
if 'Duplicate entry' in e.message:
self.mysql_connector.execute(update_cmd % (
quota['hard_limit'],
user_map[quota['user_id']],
tenant_map[quota['project_id']],
quota['resource'],
))
else:
raise
def _deploy_project_quotas(self, quotas, tenant_map):
insert_cmd = ("INSERT INTO quotas (project_id, resource, "
"hard_limit, deleted) VALUES ('%s', '%s', %s, 0)")
update_cmd = ("UPDATE quotas SET hard_limit=%s WHERE project_id='%s' "
"AND resource='%s' AND deleted=0")
for _quota in quotas:
quota = _quota['quota']
try:
self.mysql_connector.execute(insert_cmd % (
tenant_map[quota['project_id']],
quota['resource'],
quota['hard_limit']))
except exc.IntegrityError as e:
if 'Duplicate entry' in e.message:
self.mysql_connector.execute(update_cmd % (
quota['hard_limit'],
tenant_map[quota['project_id']],
quota['resource'],
))
else:
raise
def _deploy_keypair(self, keypairs):
dest_keypairs = [keypair.name for keypair in self.get_keypair_list()]
for _keypair in keypairs.itervalues():
keypair = _keypair['keypair']
if keypair['name'] in dest_keypairs:
continue
self.create_keypair(keypair['name'], keypair['public_key'])
def _deploy_flavors(self, flavors):
dest_flavors = {flavor.name: flavor.id for flavor in
self.get_flavor_list()}
for flavor_id, _flavor in flavors.iteritems():
flavor = _flavor['flavor']
if flavor['name'] in dest_flavors:
# _flavor['meta']['dest_id'] = dest_flavors[flavor['name']]
_flavor['meta']['id'] = dest_flavors[flavor['name']]
continue
_flavor['meta']['id'] = self.create_flavor(
name=flavor['name'],
flavorid=flavor_id,
ram=flavor['ram'],
vcpus=flavor['vcpus'],
disk=flavor['disk'],
ephemeral=flavor['ephemeral'],
swap=int(flavor['swap']) if flavor['swap'] else 0,
rxtx_factor=flavor['rxtx_factor'],
is_public=flavor['is_public']).id
def _deploy_instances(self, info_compute):
new_ids = {}
nova_tenants_clients = {
self.config['cloud']['tenant']: self.nova_client}
params = {'user': self.config['cloud']['user'],
'password': <PASSWORD>['<PASSWORD>']['password'],
'tenant': self.config['cloud']['tenant'],
'host': self.config['cloud']['host']}
for _instance in info_compute['instances'].itervalues():
tenant_name = _instance['instance']['tenant_name']
if tenant_name not in nova_tenants_clients:
params['tenant'] = tenant_name
nova_tenants_clients[tenant_name] = self.get_nova_client(
params)
for _instance in info_compute['instances'].itervalues():
instance = _instance['instance']
meta = _instance['meta']
self.nova_client = nova_tenants_clients[instance['tenant_name']]
create_params = {'name': instance['name'],
'flavor': instance['flavor_id'],
'key_name': instance['key_name'],
'availability_zone': instance[
'availability_zone'],
'nics': instance['nics'],
'image': instance['image_id']}
if instance['boot_mode'] == utl.BOOT_FROM_VOLUME:
volume_id = instance['volumes'][0]['id']
create_params["block_device_mapping_v2"] = [{
"source_type": "volume",
"uuid": volume_id,
"destination_type": "volume",
"delete_on_termination": True,
"boot_index": 0
}]
create_params['image'] = None
new_id = self.create_instance(**create_params)
new_ids[new_id] = instance['id']
self.nova_client = nova_tenants_clients[self.config['cloud']['tenant']]
return new_ids
def create_instance(self, **kwargs):
return self.nova_client.servers.create(**kwargs).id
def get_instances_list(self, detailed=True, search_opts=None,
marker=None,
limit=None):
ids = search_opts.get('id', None) if search_opts else None
if not ids:
return self.nova_client.servers.list(detailed=detailed,
search_opts=search_opts,
marker=marker, limit=limit)
else:
if type(ids) is list:
return [self.nova_client.servers.get(i) for i in ids]
else:
return [self.nova_client.servers.get(ids)]
def get_instance(self, instance_id):
return self.get_instances_list(search_opts={'id': instance_id})[0]
def change_status(self, status, instance=None, instance_id=None):
if instance_id:
instance = self.nova_client.servers.get(instance_id)
curr = self.get_status(self.nova_client.servers, instance.id).lower()
will = status.lower()
func_restore = {
'start': lambda instance: instance.start(),
'stop': lambda instance: instance.stop(),
'resume': lambda instance: instance.resume(),
'paused': lambda instance: instance.pause(),
'unpaused': lambda instance: instance.unpause(),
'suspend': lambda instance: instance.suspend(),
'status': lambda status: lambda instance: self.wait_for_status(
instance_id,
status)
}
map_status = {
'paused': {
'active': (func_restore['unpaused'],
func_restore['status']('active')),
'shutoff': (func_restore['stop'],
func_restore['status']('shutoff')),
'suspend': (func_restore['unpaused'],
func_restore['status']('active'),
func_restore['suspend'],
func_restore['status']('suspend'))
},
'suspend': {
'active': (func_restore['resume'],
func_restore['status']('active')),
'shutoff': (func_restore['stop'],
func_restore['status']('shutoff')),
'paused': (func_restore['resume'],
func_restore['status']('active'),
func_restore['paused'],
func_restore['status']('paused'))
},
'active': {
'paused': (func_restore['paused'],
func_restore['status']('paused')),
'suspend': (func_restore['suspend'],
func_restore['status']('suspend')),
'shutoff': (func_restore['stop'],
func_restore['status']('shutoff'))
},
'shutoff': {
'active': (func_restore['start'],
func_restore['status']('active')),
'paused': (func_restore['start'],
func_restore['status']('active'),
func_restore['paused'],
func_restore['status']('paused')),
'suspend': (func_restore['start'],
func_restore['status']('active'),
func_restore['suspend'],
func_restore['status']('suspend'))
}
}
if curr != will:
try:
reduce(lambda res, f: f(instance), map_status[curr][will],
None)
except timeout_exception.TimeoutException as e:
return e
else:
return True
def wait_for_status(self, id_obj, status, limit_retry=90):
count = 0
getter = self.nova_client.servers
while getter.get(id_obj).status.lower() != status.lower():
time.sleep(2)
count += 1
if count > limit_retry:
raise timeout_exception.TimeoutException(
getter.get(id_obj).status.lower(), status, "Timeout exp")
def get_flavor_from_id(self, flavor_id):
return self.nova_client.flavors.get(flavor_id)
def get_flavor_list(self, **kwargs):
return self.nova_client.flavors.list(**kwargs)
def create_flavor(self, **kwargs):
return self.nova_client.flavors.create(**kwargs)
def delete_flavor(self, flavor_id):
self.nova_client.flavors.delete(flavor_id)
def get_keypair_list(self):
return self.nova_client.keypairs.list()
def get_keypair(self, name):
return self.nova_client.keypairs.get(name)
def | |
<filename>PYTHON/get_era5.py
#!/usr/bin/python
# Code to read in years of hourly high resolution ERA5 t, td and land_sea_mask data
# Converts to q, RH, e, tw and DPD
# Aggregates to daily average
# Regrids to 1by1 degree and shifts to -179.5 ot 179.5 and 180 lats from 89.5 to -89.5 (was 181 lats!!!)
# Outputs as netCDF
# Later code will read in and convert to pentads, monthlies, anomalies etc and combine to make complete record or
# append the latest year
# This can also spin through all years or cope with annual updates
#*******************************************
# START
#*******************************************
import os
import datetime as dt
import calendar
import numpy as np
import sys
import time
import pdb
import iris
import iris.coord_categorisation
from iris.coords import DimCoord
from iris.cube import Cube
import cf_units
import CalcHums as ch
#import utils
#sys.path.append('/data/users/rdunn/reanalyses/code/era5/cdsapi-0.1.4')
#sys.path.append('/data/users/hadkw/WORKING_HADISDH/UPDATE2019/PROGS/PYTHON/cdsapi-0.1.4')
sys.path.append('/home/h04/hadkw/HadISDH_Code/HADISDH_BUILD/cdsapi-0.1.4')
import cdsapi
# Set up directory
#DataLoc = '/data/users/hadkw/WORKING_HADISDH/UPDATE2019/OTHERDATA/ERA5/'
DataLoc = '/scratch/hadkw/UPDATE2020/OTHERDATA/ERA5/'
print(DataLoc)
"""
Butchered from
http://fcm1.metoffice.com/projects/utils/browser/CM_ML/trunk/NAO_Precip_Regr/get_era5_uwind.py
and /data/users/rdunn/reanalysis/era5/get_era.py
"""
#****************************************
def retrieve(year, variable, month, ndays):
'''
Use ECMWF API to get the data
4.5GB per month --> 55GB per year, 50mins per month of processing
'''
if variable == "2m_temperature":
varlist = ["2m_temperature"]
# varlist = ["2m_temperature", "land_sea_mask"] # if you want to download both at once
elif variable == "2m_dewpoint_temperature":
varlist = ["2m_dewpoint_temperature"]
# varlist = ["2m_dewpoint_temperature", "land_sea_mask"]
elif variable == "surface_pressure":
varlist = ["surface_pressure"]
elif variable == "land_sea_mask":
varlist = ["land_sea_mask"]
else:
print("please provide correct variable to download")
return
days = ["{:2d}".format(d+1) for d in range(ndays)]
c = cdsapi.Client()
c.retrieve(
'reanalysis-era5-single-levels',
{
'product_type':'reanalysis',
'format':'netcdf',
'variable':varlist,
'year':"{}".format(year),
'month':"{:02d}".format(month),
'day':days,
'time':[
'00:00','01:00','02:00',
'03:00','04:00','05:00',
'06:00','07:00','08:00',
'09:00','10:00','11:00',
'12:00','13:00','14:00',
'15:00','16:00','17:00',
'18:00','19:00','20:00',
'21:00','22:00','23:00',
]
},
os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable))
)
# time.sleep(5) # to allow any writing process to finish up.
# # make a "success" file
# with open(os.path.join(DataLoc, "{}{:02d}_{}_success.txt".format(year, month, variable)), "w") as outfile:
#
# outfile.write("Success {}".format(dt.datetime.now()))
return # retreive
#****************************************
def check_files(year, variable, month, ndays):
''' This reads in the t, td and p files and checks for full download '''
''' If the last hour field has identical values for every lat and lon box then it has failed '''
''' A failed file is removed and program aborts '''
''' Program will need to be restarted '''
action = 'contrinue' # output if file is ok
test_cube = iris.load(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
# convert from list to cube
test_cube = test_cube[0]
test_data = test_cube.data[-1,:,:]
# Are all values the same - np.unique() has a length of 1 if so
if (len(np.unique(test_cube.data[-1,:,:])) == 1):
# remove failed files
os.remove(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
action = 'retrieve' # either retry download or exit code depending on stage in process
## exit the program
#sys.exit('Incomplete file download')
return action
#****************************************
def convert(year, month, ndays, remove=False):
"""
Now need to:
- convert to q, RH , e, tw, DPD
- aggregate to daily averages
- regrid to 1by1 gridboxes
"""
MDI = -999.
# Set up null_cube with desired gridding format to use as a template
# Does this have to have the same time dimensions?
# ndays = np.int(p_cube.data[:,0,0] / 24)
time = DimCoord(np.arange(ndays*24),
standard_name = 'time',
units = 'hours')
latitude = DimCoord(np.linspace(89.5, -89.5, 180),
# latitude = DimCoord(np.linspace(90, -90, 181),
standard_name = 'latitude',
long_name = 'gridbox centre latitude',
units = 'degrees_north')
longitude = DimCoord(np.linspace(-179.5, 179.5, 360),
# longitude = DimCoord(np.linspace(0, 359, 360),
standard_name='longitude',
long_name = 'gridbox centre longitude',
units = 'degrees_east')
null_cube = Cube(np.zeros((ndays*24, 180, 360), np.float32),
dim_coords_and_dims=[(time, 0),
(latitude, 1),
(longitude, 2)])
print('Check null_cube for new grid')
# pdb.set_trace()
## START OF LSM************************************************
# # read in land_sea_mask
# variable = "land_sea_mask"
# lsm_cube = iris.load(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
# #pdb.set_trace()
# # convert from list to cube
# lsm_cube = lsm_cube[0]#
#
## REgrid to 1by1 degree - cash the source, template, gridding type for later use - faster
# regridder = iris.analysis.Linear().regridder(lsm_cube, null_cube)
# lsm_cube_1by1 = regridder(lsm_cube)
# print('Check lsm_cube_1by1 for new grid')
## pdb.set_trace()#
#
# # remove old cube
# lsm_cube = 0
#
# lsm_cube_1by1 = lsm_cube_1by1[0,:,:]
## lsm_cube_1by1_field = lsm_cube_1by1.extract(iris.Constraint(time=0))
# lsm_cube_1by1.units = "1"
# print(lsm_cube_1by1)
# print('Check lsm_cube_1by1 for 2m_temperature')
# #pdb.set_trace()
#
## output
# iris.save(lsm_cube_1by1, os.path.join(DataLoc, "{}{:02d}_{}.nc".format(year, month, variable)), zlib=True)
# print('Check lsm_cube_1by1 output')
# pdb.set_trace()
## END OF LSM************************************************************
# read in t, td and sp (may be VERY LARGE
variable = "2m_temperature"
t_cube = iris.load(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
#pdb.set_trace()
# convert from list to cube
t_cube = t_cube[0]
# REgrid to 1by1 degree - cash the source, template, gridding type for later use - faster
regridder = iris.analysis.Linear().regridder(t_cube, null_cube)
t_cube_1by1 = regridder(t_cube)
print('Check t_cube_1by1 for new grid')
# pdb.set_trace()
# remove old cube
t_cube = 0
t_cube_1by1.data -= 273.15 # convert to C
t_cube_1by1.units = "degreesC"
print('Check t_cube_1by1 for 2m_temperature')
#pdb.set_trace()
variable = "2m_dewpoint_temperature"
td_cube = iris.load(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
# convert from list to cube
td_cube = td_cube[0]
# REgrid to 1by1 degree - cash the source, template, gridding type for later use - faster
td_cube_1by1 = regridder(td_cube)
print('Check td_cube_1by1 for new grid')
# pdb.set_trace()
# remove old cube
td_cube = 0
td_cube_1by1.data -= 273.15 # convert to C
td_cube_1by1.units = "degreesC"
print('Check td_cube_1by1 for 2m_dewpoint_temperature')
# pdb.set_trace()
variable = "surface_pressure"
p_cube = iris.load(os.path.join(DataLoc, "{}{:02d}_hourly_{}.nc".format(year, month, variable)))
# convert from list to cube
p_cube = p_cube[0]
# REgrid to 1by1 degree - cash the source, template, gridding type for later use - faster
p_cube_1by1 = regridder(p_cube)
print('Check p_cube_1by1 for new grid')
# pdb.set_trace()
# remove old cube
p_cube = 0
p_cube_1by1.data /= 100. # convert to C
p_cube_1by1.units = "hPa"
print('Check p_cube_1by1 for surface_pressure')
# pdb.set_trace()
# # if it contains 2 cubes where we have downloaded mask and wish to mask to land or sea....
# if len(p_cubelist) == 2:
# # extract both cubes
# pcube1 = p_cubelist[0]
# pcube2 = p_cubelist[1]#
#
# masked1, = np.where(pcube1.data.mask[:, 0, 0] == True)
# masked2, = np.where(pcube2.data.mask[:, 0, 0] == True)
#
# # use locations of masks to overwrite
# tp_cube = pcube1[:]
# tp_cube.data[masked1] = pcube2.data[masked1]
# tp_cube.var_name = "tp"
#
# # else it's just a single cube, so easier to deal with
# elif len(p_cubelist) == 1:#
#
# tp_cube = p_cubelist[0]
# tp_cube.var_name = "tp"
# No masking internally within this code...
# Process q
# Copy the t_cube and then change some of the fields?
variable = 'specific_humidity'
q_cube = t_cube_1by1.copy()
q_cube.fill_value = MDI # not sure whether we're doing -999 yet if saving as integer
q_cube.units = cf_units.Unit("g kg-2")
q_cube.var_name = "q2m"
q_cube.long_name = "2 metre specific humidity"
# Populate the q data
q_cube.data = ch.sh(td_cube_1by1.data,t_cube_1by1.data,p_cube_1by1.data,roundit=False)
print('Check q_cube for new data')
# pdb.set_trace()
## mask all regions which are 100% ocean
#cube.data[lsm.data == 0] = utils.MDI
#cube.data = np.ma.masked_where(lsm.data == 0, cube.data)
#cube.data.fill_value = utils.MDI
# Aggregate to daily
# add a "day" indicator to allow aggregation
iris.coord_categorisation.add_day_of_month(q_cube, "time", name="day_of_month")
q_cube_day = q_cube.aggregated_by(["day_of_month"], iris.analysis.MEAN)
q_cube = 0
q_cube_day.remove_coord("day_of_month")
q_cube_day.units = cf_units.Unit("g kg-2")
print('Check q_cube for daily averages')
# pdb.set_trace()
# output
iris.save(q_cube_day, os.path.join(DataLoc, "{}{:02d}_daily_{}.nc".format(year, month, variable)), zlib=True)
q_cube_day=0
print('Check q_cube_1by1 output')
# pdb.set_trace()
# Process RH
# Copy the t_cube and then change some of the fields?
variable = 'relative_humidity'
rh_cube = t_cube_1by1.copy()
rh_cube.fill_value = MDI # not sure whether we're doing -999 yet if saving as integer
rh_cube.units = cf_units.Unit("%")
rh_cube.var_name = "rh2m"
rh_cube.long_name = "2 metre relative humidity"
# Populate the q data
rh_cube.data = ch.rh(td_cube_1by1.data,t_cube_1by1.data,p_cube_1by1.data,roundit=False)
print('Check rh_cube for new data')
# pdb.set_trace()
## mask all regions which are 100% ocean
#cube.data[lsm.data == 0] = utils.MDI
#cube.data = np.ma.masked_where(lsm.data == 0, cube.data)
#cube.data.fill_value = utils.MDI
# Aggregate to daily
# add a "day" indicator to allow aggregation
iris.coord_categorisation.add_day_of_month(rh_cube, "time", name="day_of_month")
rh_cube_day = rh_cube.aggregated_by(["day_of_month"], iris.analysis.MEAN)
rh_cube = 0
rh_cube_day.remove_coord("day_of_month")
rh_cube_day.units = cf_units.Unit("%")
print('Check rh_cube for daily averages')
# pdb.set_trace()
# output
iris.save(rh_cube_day, os.path.join(DataLoc, "{}{:02d}_daily_{}.nc".format(year, month, variable)), zlib=True)
rh_cube_day=0
print('Check rh_cube_1by1 output')
#pdb.set_trace()
# Process e
# Copy the t_cube and then change some of the fields?
variable = 'vapour_pressure'
e_cube = t_cube_1by1.copy()
e_cube.fill_value = MDI # not sure whether we're doing -999 yet if saving as integer
e_cube.units = cf_units.Unit("hPa")
e_cube.var_name = "e2m"
e_cube.long_name = "2 metre vapour pressure"
# Populate the q data
e_cube.data = ch.vap(td_cube_1by1.data,t_cube_1by1.data,p_cube_1by1.data,roundit=False)
print('Check e_cube for new data')
# pdb.set_trace()
## mask all regions which are 100% ocean
#cube.data[lsm.data == 0] = utils.MDI
#cube.data = np.ma.masked_where(lsm.data == 0, cube.data)
#cube.data.fill_value = utils.MDI
# Aggregate to daily
# add a "day" indicator to allow aggregation
iris.coord_categorisation.add_day_of_month(e_cube, "time", name="day_of_month")
e_cube_day = e_cube.aggregated_by(["day_of_month"], iris.analysis.MEAN)
e_cube | |
import os
import logging
import numpy as np
from typing import Optional
import torch
from torch.utils.data import DataLoader
from ..eval import Metric
from .dataset import CHMMBaseDataset
from .dataset import collate_fn as default_collate_fn
logger = logging.getLogger(__name__)
OUT_RECALL = 0.9
OUT_PRECISION = 0.8
class CHMMBaseTrainer:
def __init__(self,
config,
collate_fn=None,
training_dataset=None,
valid_dataset=None,
test_dataset=None,
pretrain_optimizer=None,
optimizer=None):
self._model = None
self._config = config
self._training_dataset = training_dataset
self._valid_dataset = valid_dataset
self._test_dataset = test_dataset
self._collate_fn = collate_fn
self._pretrain_optimizer = pretrain_optimizer
self._optimizer = optimizer
self._init_state_prior = None
self._init_trans_mat = None
self._init_emiss_mat = None
@property
def config(self):
return self._config
@config.setter
def config(self, x):
logger.warning("Updating DirCHMMTrainer.config")
self._config = x
@property
def model(self):
return self._model
def initialize_trainer(self):
"""
Initialize necessary components for training
Note: Better not change the order
Returns
-------
the initialized trainer
"""
self.initialize_matrices()
self.initialize_model()
self.initialize_optimizers()
return self
def initialize_model(self):
raise NotImplementedError
def initialize_matrices(self):
"""
Initialize <HMM> transition and emission matrices
Returns
-------
self
"""
assert self._training_dataset and self._valid_dataset
# inject prior knowledge about transition and emission
self._init_state_prior = torch.zeros(self._config.d_hidden, device=self._config.device) + 1e-2
self._init_state_prior[0] += 1 - self._init_state_prior.sum()
intg_obs = list(map(np.array, self._training_dataset.obs + self._valid_dataset.obs))
# construct/load initial transition matrix
dataset_dir = os.path.split(self._config.train_path)[0]
transmat_path = os.path.join(dataset_dir, "init_transmat.pt")
if getattr(self._config, "load_init_mat", False):
if os.path.isfile(transmat_path):
logger.info("Loading initial transition matrix from disk")
self._init_trans_mat = torch.load(transmat_path)
# if the loaded transmat does not have the proper shape, re-calculate it.
s0_transmat, s1_transmat = self._init_trans_mat.shape
if not (s0_transmat == s1_transmat == self.config.d_obs):
self._init_trans_mat = None
if self._init_trans_mat is None:
self._init_trans_mat = torch.tensor(initialise_transmat(
observations=intg_obs, label_set=self._config.bio_label_types
)[0], dtype=torch.float)
if getattr(self._config, "save_init_mat", False):
logger.info("Saving initial transition matrix")
torch.save(self._init_trans_mat, transmat_path)
# construct/load initial emission matrix
emissmat_path = os.path.join(dataset_dir, "init_emissmat.pt")
if getattr(self._config, "load_init_mat", False):
if os.path.isfile(emissmat_path):
logger.info("Loading initial emission matrix from disk")
self._init_emiss_mat = torch.load(emissmat_path)
# if the loaded emissmat does not have the proper shape, re-calculate it.
s0_emissmat, s1_emissmat, s2_emissmat = self._init_emiss_mat.shape
if not (s0_emissmat == self.config.n_src) and (s1_emissmat == s2_emissmat == self.config.d_obs):
self._init_emiss_mat = None
if self._init_emiss_mat is None:
self._init_emiss_mat = torch.tensor(initialise_emissions(
observations=intg_obs, label_set=self._config.bio_label_types,
sources=self._config.sources, src_priors=self._config.src_priors
)[0], dtype=torch.float)
if getattr(self._config, "save_init_mat", False):
logger.info("Saving initial emission matrix")
torch.save(self._init_emiss_mat, emissmat_path)
return self
def initialize_optimizers(self, optimizer=None, pretrain_optimizer=None):
self._optimizer = self.get_optimizer() if optimizer is None else optimizer
self._pretrain_optimizer = self.get_pretrain_optimizer() if pretrain_optimizer is None else pretrain_optimizer
def get_dataloader(self, dataset, shuffle=False):
if dataset is not None:
dataloader = DataLoader(
dataset=dataset,
batch_size=self._config.lm_batch_size,
collate_fn=self._collate_fn if self._collate_fn is not None else default_collate_fn,
shuffle=shuffle,
drop_last=False
)
return dataloader
else:
logger.error('Dataset is not defined')
raise ValueError("Dataset is not defined!")
def pretrain_step(self, data_loader, optimizer, trans_, emiss_):
raise NotImplementedError
def training_step(self, data_loader, optimizer):
raise NotImplementedError
def train(self):
raise NotImplementedError
def valid(self) -> Metric:
self._model.to(self._config.device)
valid_metrics = self.evaluate(self._valid_dataset)
logger.info("Validation results:")
for k, v in valid_metrics.items():
logger.info(f" {k}: {v:.4f}")
return valid_metrics
def test(self) -> Metric:
self._model.to(self._config.device)
test_metrics = self.evaluate(self._test_dataset)
logger.info("Test results:")
for k, v in test_metrics.items():
logger.info(f" {k}: {v:.4f}")
return test_metrics
def evaluate(self, dataset: CHMMBaseDataset):
raise NotImplementedError
def predict(self, dataset: CHMMBaseDataset):
raise NotImplementedError
def get_pretrain_optimizer(self):
raise NotImplementedError
def get_optimizer(self):
# ----- initialize optimizer -----
raise NotImplementedError
def save(self,
output_dir: Optional[str] = None,
save_optimizer: Optional[bool] = False,
model_name: Optional[str] = 'chmm',
optimizer_name: Optional[str] = 'chmm-optimizer',
pretrain_optimizer_name: Optional[str] = 'chmm-pretrain-optimizer'):
"""
Save model parameters as well as trainer parameters
Parameters
----------
output_dir: model directory
save_optimizer: whether to save optimizer
model_name: model name (suffix free)
optimizer_name: optimizer name (suffix free)
pretrain_optimizer_name: pretrain optimizer name (suffix free)
Returns
-------
None
"""
output_dir = output_dir if output_dir is not None else self._config.output_dir
logger.info(f"Saving model to {output_dir}")
model_state_dict = self._model.state_dict()
torch.save(model_state_dict, os.path.join(output_dir, f'{model_name}.bin'))
self._config.save(output_dir)
if save_optimizer:
logger.info("Saving optimizer and scheduler")
torch.save(self._optimizer.state_dict(),
os.path.join(output_dir, f"{optimizer_name}.bin"))
torch.save(self._pretrain_optimizer.state_dict(),
os.path.join(output_dir, f"{pretrain_optimizer_name}.bin"))
return None
def load(self,
input_dir: Optional[str] = None,
load_optimizer: Optional[bool] = False,
model_name: Optional[str] = 'chmm',
optimizer_name: Optional[str] = 'chmm-optimizer',
pretrain_optimizer_name: Optional[str] = 'chmm-pretrain-optimizer'):
"""
Load model parameters.
Parameters
----------
input_dir: model directory
load_optimizer: whether load other trainer parameters
model_name: model name (suffix free)
optimizer_name: optimizer name (suffix free)
pretrain_optimizer_name: pretrain optimizer name (suffix free)
Returns
-------
self
"""
input_dir = input_dir if input_dir is not None else self._config.output_dir
if self._model is not None:
logger.warning(f"The original model {type(self._model)} in {type(self)} is not None. "
f"It will be overwritten by the loaded model!")
logger.info(f"Loading model from {input_dir}")
self.initialize_model()
self._model.load_state_dict(torch.load(os.path.join(input_dir, f'{model_name}.bin')))
self._model.to(self.config.device)
if load_optimizer:
logger.info("Loading optimizer and scheduler")
if self._optimizer is None:
self.initialize_optimizers()
if os.path.isfile(os.path.join(input_dir, f"{optimizer_name}.bin")):
self._optimizer.load_state_dict(
torch.load(os.path.join(input_dir, f"{optimizer_name}.bin"), map_location=self.config.device)
)
else:
logger.warning("Optimizer file does not exist!")
if os.path.isfile(os.path.join(input_dir, f"{pretrain_optimizer_name}.bin")):
self._pretrain_optimizer.load_state_dict(
torch.load(os.path.join(input_dir, f"{pretrain_optimizer_name}.bin"))
)
else:
logger.warning("Pretrain optimizer file does not exist!")
return self
def save_results(self,
output_dir: str,
valid_results: Optional[Metric] = None,
file_name: Optional[str] = 'results',
disable_final_valid: Optional[bool] = False,
disable_test: Optional[bool] = False,
disable_inter_results: Optional[bool] = False) -> None:
"""
Save training (validation) results
Parameters
----------
output_dir: output directory, should be a folder
valid_results: validation results during the training process
file_name: file name
disable_final_valid: disable final validation process (getting validation results of the trained model)
disable_test: disable test process
disable_inter_results: do not save inter-results
Returns
-------
None
"""
if not disable_final_valid:
logger.info("Getting final validation metrics")
valid_metrics = self.valid()
else:
valid_metrics = None
if not disable_test:
logger.info("Getting test metrics.")
test_metrics = self.test()
else:
test_metrics = None
# write validation and test results
result_file = os.path.join(output_dir, f'{file_name}.txt')
logger.info(f"Writing results to {result_file}")
self.write_result(file_path=result_file,
valid_results=valid_results,
final_valid_metrics=valid_metrics,
test_metrics=test_metrics)
if not disable_inter_results:
# save validation inter results
logger.info(f"Saving inter results")
inter_result_file = os.path.join(output_dir, f'{file_name}-inter.pt')
torch.save(valid_results.__dict__, inter_result_file)
return None
@staticmethod
def write_result(file_path: str,
valid_results: Optional[Metric] = None,
final_valid_metrics: Optional[Metric] = None,
test_metrics: Optional[Metric] = None) -> None:
"""
Support functions for saving training results
Parameters
----------
file_path: where to save results
valid_results: validation results during the training process
final_valid_metrics: validation results of the trained model
test_metrics
Returns
-------
"""
with open(file_path, 'w') as f:
if valid_results is not None:
for i in range(len(valid_results)):
f.write(f"[Epoch {i + 1}]\n")
for k in ['precision', 'recall', 'f1']:
f.write(f" {k}: {valid_results[k][i]:.4f}")
f.write("\n")
if final_valid_metrics is not None:
f.write(f"[Best Validation]\n")
for k in ['precision', 'recall', 'f1']:
f.write(f" {k}: {final_valid_metrics[k]:.4f}")
f.write("\n")
if test_metrics is not None:
f.write(f"[Test]\n")
for k in ['precision', 'recall', 'f1']:
f.write(f" {k}: {test_metrics[k]:.4f}")
f.write("\n")
return None
def initialise_startprob(observations,
label_set,
src_idx=None):
"""
calculate initial hidden states (not used in our setup since our sequences all begin from
[CLS], which corresponds to hidden state "O".
:param src_idx: source index
:param label_set: a set of all possible label_set
:param observations: n_instances X seq_len X n_src X d_obs
:return: probabilities for the initial hidden states
"""
n_src = observations[0].shape[1]
logger.info("Constructing start distribution prior...")
init_counts = np.zeros((len(label_set),))
if src_idx is not None:
for obs in observations:
init_counts[obs[0, src_idx].argmax()] += 1
else:
for obs in observations:
for z in range(n_src):
init_counts[obs[0, z].argmax()] += 1
for i, label in enumerate(label_set):
if i == 0 or label.startswith("B-"):
init_counts[i] += 1
startprob_prior = init_counts + 1
startprob_ = np.random.dirichlet(init_counts + 1E-10)
return startprob_, startprob_prior
# TODO: try to use a more reliable source to start the transition and emission
def initialise_transmat(observations,
label_set,
src_idx=None):
"""
initialize transition matrix
:param src_idx: the index of the source of which the transition statistics is computed.
If None, use all sources
:param label_set: a set of all possible label_set
:param observations: n_instances X seq_len X n_src X d_obs
:return: initial transition matrix and transition counts
"""
logger.info("Constructing transition matrix prior...")
n_src = observations[0].shape[1]
trans_counts = np.zeros((len(label_set), len(label_set)))
if src_idx is not None:
for obs in observations:
for k in range(0, len(obs) - 1):
trans_counts[obs[k, src_idx].argmax(), obs[k + 1, src_idx].argmax()] += 1
else:
for obs in observations:
for k in range(0, len(obs) - 1):
for z in range(n_src):
trans_counts[obs[k, z].argmax(), obs[k + 1, z].argmax()] += 1
# update transition matrix with prior knowledge
for i, label in enumerate(label_set):
if label.startswith("B-") or label.startswith("I-"):
trans_counts[i, label_set.index("I-" + label[2:])] += 1
elif i == 0 or label.startswith("I-"):
for j, label2 in enumerate(label_set):
if j == 0 or label2.startswith("B-"):
trans_counts[i, j] += 1
transmat_prior = trans_counts + 1
# initialize transition matrix with dirichlet distribution
transmat_ = np.vstack([np.random.dirichlet(trans_counts2 + 1E-10)
for trans_counts2 in trans_counts])
return transmat_, transmat_prior
def initialise_emissions(observations,
label_set,
sources,
src_priors,
strength=1000):
"""
initialize emission matrices
:param sources: source names
:param src_priors: source priors
:param label_set: a set of all | |
'Longreach'},
'617750002':{'en': 'Lynd Range'},
'617750003':{'en': 'Macalister'},
'617750004':{'en': 'Maranoa'},
'617750005':{'en': 'Meandarra'},
'617750006':{'en': 'Miamba'},
'617750007':{'en': 'Miles'},
'617750008':{'en': 'Millmerran'},
'617750009':{'en': 'Mitchell'},
'617750010':{'en': 'Moonie'},
'617750011':{'en': 'Morven'},
'617750012':{'en': '<NAME>'},
'617750013':{'en': 'Muttaburra'},
'617750014':{'en': 'Nobby'},
'617750015':{'en': 'North Star'},
'617750016':{'en': 'Oakey'},
'617750017':{'en': 'Omanama'},
'617750018':{'en': 'Paroo'},
'617750019':{'en': 'Pikedale'},
'617750020':{'en': 'Pittsworth'},
'617750021':{'en': 'Quilpie'},
'617750022':{'en': 'Ravensbourne'},
'617750023':{'en': '<NAME>'},
'617750024':{'en': 'Roma'},
'617750025':{'en': 'Southwood'},
'617750026':{'en': '<NAME>'},
'617750027':{'en': 'Stanthorpe'},
'617750028':{'en': 'Surat'},
'617750029':{'en': 'Tabers'},
'617750030':{'en': 'Talwood'},
'617750031':{'en': 'Tambo'},
'617750032':{'en': 'Tara'},
'617750033':{'en': 'Taroom'},
'617750034':{'en': 'Teelba'},
'617750035':{'en': 'Texas'},
'617750036':{'en': 'Thallon'},
'617750037':{'en': 'Thargomindah'},
'617750038':{'en': '<NAME>'},
'617750039':{'en': 'Thomson'},
'617750040':{'en': 'Tipton'},
'617750041':{'en': 'Toobeah'},
'617750042':{'en': 'Toowoomba'},
'617750043':{'en': '<NAME>'},
'617750044':{'en': 'Wallumbilla'},
'617750045':{'en': 'Wandoan'},
'617750046':{'en': 'Warra'},
'617750047':{'en': 'Warrego'},
'617750048':{'en': 'Warwick'},
'617750049':{'en': 'Westgrove'},
'617750050':{'en': 'Westmar'},
'617750051':{'en': 'Winton'},
'617750052':{'en': 'Wyaga'},
'617750053':{'en': 'Yelarbon'},
'617750054':{'en': 'Yetman'},
'617750055':{'en': 'Yuleba'},
'617750056':{'en': 'Allora'},
'617750057':{'en': 'Aramac'},
'617750058':{'en': 'Arcadia Valley'},
'617750059':{'en': 'Atholwood'},
'617750100':{'en': 'Auburn'},
'617750101':{'en': 'Augathella'},
'617750102':{'en': 'Ballandean'},
'617750103':{'en': 'Balonne'},
'617750104':{'en': 'Barcaldine'},
'617750105':{'en': 'Barcoo'},
'617750106':{'en': 'Beebo'},
'617750107':{'en': 'Bell'},
'617750108':{'en': '<NAME>'},
'617750109':{'en': 'Bimbadeen'},
'617750110':{'en': 'Blackall'},
'617750111':{'en': 'Bollon'},
'617750112':{'en': 'Bonshaw'},
'617750113':{'en': 'Bowenville'},
'617750114':{'en': 'Brigalow'},
'617750115':{'en': 'Bringalily'},
'617750116':{'en': 'Brookstead'},
'617750117':{'en': 'Brymaroo'},
'617750118':{'en': 'Bunya Mountains'},
'617750119':{'en': 'Cambooya'},
'617750120':{'en': 'Cecil Plains'},
'617750121':{'en': 'Charleville'},
'617750122':{'en': 'Chinchilla'},
'617750123':{'en': 'Clifton'},
'617750124':{'en': 'Cockatoo'},
'617750125':{'en': 'Condamine'},
'617750126':{'en': 'Coondarra'},
'617750127':{'en': 'Cooyar'},
'617750128':{'en': 'Cottonvale'},
'617750129':{'en': 'Crows Nest'},
'617750130':{'en': 'Culgoa'},
'617750131':{'en': 'Cunnamulla'},
'617750132':{'en': 'Cunningham'},
'617750133':{'en': 'Dalby'},
'617750134':{'en': 'Darr Creek'},
'617750135':{'en': 'Diamantina'},
'617750136':{'en': 'Diamondy'},
'617750137':{'en': 'Dirranbandi'},
'617750138':{'en': 'Dulacca'},
'617750139':{'en': 'Dunmore'},
'617750140':{'en': 'Durham Downs'},
'617750141':{'en': 'Elbow Valley'},
'617750142':{'en': 'Eschol'},
'617750143':{'en': 'Freestone'},
'617750144':{'en': 'Galilee'},
'617750145':{'en': 'Glenhope'},
'617750146':{'en': 'Goombi'},
'617750147':{'en': 'Goombungee'},
'617750148':{'en': 'Goondiwindi'},
'617750149':{'en': 'Greenmount'},
'617750150':{'en': 'Guluguba'},
'617750151':{'en': 'Haddon'},
'617750152':{'en': 'Haden'},
'617750153':{'en': 'Helidon'},
'617750154':{'en': 'Inglewood'},
'617750155':{'en': 'Injune'},
'617750156':{'en': 'Isisford'},
'617750157':{'en': 'Jandowae'},
'617750158':{'en': 'Jericho'},
'617750159':{'en': 'Jimbour'},
'617750160':{'en': 'Jondaryan'},
'617750161':{'en': 'Jundah'},
'617750162':{'en': 'Kilbeggan'},
'617750163':{'en': 'Killarney'},
'617750164':{'en': 'Kumbarilla'},
'617750165':{'en': 'Kupunn'},
'617750166':{'en': 'Legume'},
'617750167':{'en': 'Leyburn'},
'617750168':{'en': 'Liston'},
'617750169':{'en': 'Longreach'},
'617750170':{'en': '<NAME>'},
'617750171':{'en': 'Macalister'},
'617750172':{'en': 'Maranoa'},
'617750173':{'en': 'Meandarra'},
'617750174':{'en': 'Miamba'},
'617750175':{'en': 'Miles'},
'617750176':{'en': 'Millmerran'},
'617750177':{'en': 'Mitchell'},
'617750178':{'en': 'Moonie'},
'617750179':{'en': 'Morven'},
'617750180':{'en': '<NAME>'},
'617750181':{'en': 'Muttaburra'},
'617750182':{'en': 'Nobby'},
'617750183':{'en': 'North Star'},
'617750184':{'en': 'Oakey'},
'617750185':{'en': 'Omanama'},
'617750186':{'en': 'Paroo'},
'617750187':{'en': 'Pikedale'},
'617750188':{'en': 'Pittsworth'},
'617750189':{'en': 'Quilpie'},
'617750190':{'en': 'Ravensbourne'},
'617750191':{'en': '<NAME>'},
'617750192':{'en': 'Roma'},
'617750193':{'en': 'Southwood'},
'617750194':{'en': 'St George'},
'617750195':{'en': 'Stanthorpe'},
'617750196':{'en': 'Surat'},
'617750197':{'en': 'Tabers'},
'617750198':{'en': 'Talwood'},
'617750199':{'en': 'Tambo'},
'617750200':{'en': 'Tara'},
'617750201':{'en': 'Taroom'},
'617750202':{'en': 'Teelba'},
'617750203':{'en': 'Texas'},
'617750204':{'en': 'Thallon'},
'617750205':{'en': 'Thargomindah'},
'617750206':{'en': 'The Gums'},
'617750207':{'en': 'Thomson'},
'617750208':{'en': 'Tipton'},
'617750209':{'en': 'Toobeah'},
'617750210':{'en': 'Toowoomba'},
'617750211':{'en': '<NAME>'},
'617750212':{'en': 'Wallumbilla'},
'617750213':{'en': 'Wandoan'},
'617750214':{'en': 'Warra'},
'617750215':{'en': 'Warrego'},
'617750216':{'en': 'Warwick'},
'617750217':{'en': 'Westgrove'},
'617750218':{'en': 'Westmar'},
'617750219':{'en': 'Winton'},
'617750220':{'en': 'Wyaga'},
'617750221':{'en': 'Yelarbon'},
'617750222':{'en': 'Yetman'},
'617750223':{'en': 'Yuleba'},
'617750224':{'en': 'Allora'},
'617750225':{'en': 'Aramac'},
'617750226':{'en': 'Arcadia Valley'},
'617750227':{'en': 'Atholwood'},
'617750228':{'en': 'Auburn'},
'617750229':{'en': 'Augathella'},
'61775023':{'en': 'Cambooya'},
'61775024':{'en': 'Mitchell'},
'617750300':{'en': 'Ballandean'},
'617750301':{'en': 'Balonne'},
'617750302':{'en': 'Barcaldine'},
'617750303':{'en': 'Barcoo'},
'617750304':{'en': 'Beebo'},
'617750305':{'en': 'Bell'},
'617750306':{'en': 'Billa Billa'},
'617750307':{'en': 'Bimbadeen'},
'617750308':{'en': 'Blackall'},
'617750309':{'en': 'Bollon'},
'617750310':{'en': 'Bonshaw'},
'617750311':{'en': 'Bowenville'},
'617750312':{'en': 'Brigalow'},
'617750313':{'en': 'Bringalily'},
'617750314':{'en': 'Brookstead'},
'617750315':{'en': 'Brymaroo'},
'617750316':{'en': 'Bunya Mountains'},
'617750317':{'en': 'Cambooya'},
'617750318':{'en': 'Cecil Plains'},
'617750319':{'en': 'Charleville'},
'617750320':{'en': 'Chinchilla'},
'617750321':{'en': 'Clifton'},
'617750322':{'en': 'Cockatoo'},
'617750323':{'en': 'Condamine'},
'617750324':{'en': 'Coondarra'},
'617750325':{'en': 'Cooyar'},
'617750326':{'en': 'Cottonvale'},
'617750327':{'en': 'Crows Nest'},
'617750328':{'en': 'Culgoa'},
'617750329':{'en': 'Cunnamulla'},
'617750330':{'en': 'Cunningham'},
'617750331':{'en': 'Dalby'},
'617750332':{'en': 'Darr Creek'},
'617750333':{'en': 'Diamantina'},
'617750334':{'en': 'Diamondy'},
'617750335':{'en': 'Dirranbandi'},
'617750336':{'en': 'Dulacca'},
'617750337':{'en': 'Dunmore'},
'617750338':{'en': 'Durham Downs'},
'617750339':{'en': 'Elbow Valley'},
'617750340':{'en': 'Eschol'},
'617750341':{'en': 'Freestone'},
'617750342':{'en': 'Galilee'},
'617750343':{'en': 'Glenhope'},
'617750344':{'en': 'Goombi'},
'617750345':{'en': 'Goombungee'},
'617750346':{'en': 'Goondiwindi'},
'617750347':{'en': 'Greenmount'},
'617750348':{'en': 'Guluguba'},
'617750349':{'en': 'Haddon'},
'617750350':{'en': 'Haden'},
'617750351':{'en': 'Helidon'},
'617750352':{'en': 'Inglewood'},
'617750353':{'en': 'Injune'},
'617750354':{'en': 'Isisford'},
'617750355':{'en': 'Jandowae'},
'617750356':{'en': 'Jericho'},
'617750357':{'en': 'Jimbour'},
'617750358':{'en': 'Jondaryan'},
'617750359':{'en': 'Jundah'},
'617750360':{'en': 'Kilbeggan'},
'617750361':{'en': 'Killarney'},
'617750362':{'en': 'Kumbarilla'},
'617750363':{'en': 'Kupunn'},
'617750364':{'en': 'Legume'},
'617750365':{'en': 'Leyburn'},
'617750366':{'en': 'Liston'},
'617750367':{'en': 'Longreach'},
'617750368':{'en': '<NAME>'},
'617750369':{'en': 'Macalister'},
'617750370':{'en': 'Maranoa'},
'617750371':{'en': 'Meandarra'},
'617750372':{'en': 'Miamba'},
'617750373':{'en': 'Miles'},
'617750374':{'en': 'Millmerran'},
'617750375':{'en': 'Mitchell'},
'617750376':{'en': 'Moonie'},
'617750377':{'en': 'Morven'},
'617750378':{'en': '<NAME>'},
'617750379':{'en': 'Muttaburra'},
'617750380':{'en': 'Nobby'},
'617750381':{'en': 'North Star'},
'617750382':{'en': 'Oakey'},
'617750383':{'en': 'Omanama'},
'617750384':{'en': 'Paroo'},
'617750385':{'en': 'Pikedale'},
'617750386':{'en': 'Pittsworth'},
'617750387':{'en': 'Quilpie'},
'617750388':{'en': 'Ravensbourne'},
'617750389':{'en': '<NAME>'},
'617750390':{'en': 'Roma'},
'617750391':{'en': 'Southwood'},
'617750392':{'en': '<NAME>'},
'617750393':{'en': 'Stanthorpe'},
'617750394':{'en': 'Surat'},
'617750395':{'en': 'Tabers'},
'617750396':{'en': 'Talwood'},
'617750397':{'en': 'Tambo'},
'617750398':{'en': 'Tara'},
'617750399':{'en': 'Taroom'},
'617750400':{'en': 'Teelba'},
'617750401':{'en': 'Texas'},
'617750402':{'en': 'Thallon'},
'617750403':{'en': 'Thargomindah'},
'617750404':{'en': '<NAME>'},
'617750405':{'en': 'Thomson'},
'617750406':{'en': 'Tipton'},
'617750407':{'en': 'Toobeah'},
'617750408':{'en': 'Toowoomba'},
'617750409':{'en': '<NAME>'},
'617750410':{'en': 'Wallumbilla'},
'617750411':{'en': 'Wandoan'},
'617750412':{'en': 'Warra'},
'617750413':{'en': 'Warrego'},
'617750414':{'en': 'Warwick'},
'617750415':{'en': 'Westgrove'},
'617750416':{'en': 'Westmar'},
'617750417':{'en': 'Winton'},
'617750418':{'en': 'Wyaga'},
'617750419':{'en': 'Yelarbon'},
'617750420':{'en': 'Yetman'},
'617750421':{'en': 'Yuleba'},
'61775049':{'en': 'Bonshaw'},
'61775060':{'en': 'Bunya Mountains'},
'61775079':{'en': '<NAME>'},
'61775090':{'en': 'Inglewood'},
'61775109':{'en': 'Southwood'},
'61775120':{'en': 'Tara'},
'61775139':{'en': 'Haddon'},
'61775142':{'en': 'Ravensbourne'},
'61775143':{'en': 'Valley Downs'},
'61775144':{'en': 'Toowoomba'},
'61851000':{'en': 'Broome'},
'61851001':{'en': 'Christmas Island'},
'61851002':{'en': 'Cocos Island'},
'61851003':{'en': 'Dampier'},
'61851004':{'en': '<NAME>'},
'61851005':{'en': 'Derby'},
'61851006':{'en': '<NAME>'},
'61851007':{'en': '<NAME>'},
'61851008':{'en': 'Hall\'s Creek'},
'61851009':{'en': 'Karratha'},
'61851010':{'en': 'Kununurra'},
'61851011':{'en': 'Leopold'},
'61851012':{'en': '<NAME>'},
'61851013':{'en': 'Millstream'},
'61851014':{'en': 'Mitchell'},
'61851015':{'en': '<NAME>'},
'61851016':{'en': 'Newman'},
'61851017':{'en': 'Onslow'},
'61851018':{'en': 'Ord'},
'61851019':{'en': 'Pannawonica'},
'61851020':{'en': 'Paraburdoo'},
'61851021':{'en': 'Port Hedland'},
'61851022':{'en': 'Roebuck'},
'61851023':{'en': 'Sandfire'},
'61851024':{'en': 'Telfer'},
'61851025':{'en': '<NAME>'},
'61851026':{'en': 'Whaleback'},
'61851027':{'en': 'Wittenoom'},
'61851028':{'en': 'Wyndham'},
'61851029':{'en': 'Broome'},
'61851030':{'en': 'Christmas Island'},
'61851031':{'en': 'Cocos Island'},
'61851032':{'en': 'Dampier'},
'61851033':{'en': '<NAME>'},
'61851034':{'en': 'Derby'},
'61851035':{'en': '<NAME>'},
'61851036':{'en': '<NAME>'},
'61851037':{'en': 'Hall\'s Creek'},
'61851038':{'en': 'Karratha'},
'61851039':{'en': 'Kununurra'},
'61851040':{'en': 'Leopold'},
'61851041':{'en': '<NAME>'},
'61851042':{'en': 'Millstream'},
'61851043':{'en': 'Mitchell'},
'61851044':{'en': 'Mount Bruce'},
'61851045':{'en': 'Newman'},
'61851046':{'en': 'Onslow'},
'61851047':{'en': 'Ord'},
'61851048':{'en': 'Pannawonica'},
'61851049':{'en': 'Paraburdoo'},
'61851050':{'en': 'Port Hedland'},
'61851051':{'en': 'Roebuck'},
'61851052':{'en': 'Sandfire'},
'61851053':{'en': 'Telfer'},
'61851054':{'en': '<NAME>'},
'61851055':{'en': 'Whaleback'},
'61851056':{'en': 'Wittenoom'},
'61851057':{'en': 'Wyndham'},
'61851058':{'en': 'Broome'},
'61851059':{'en': 'Christmas Island'},
'61851060':{'en': 'Cocos Island'},
'61851061':{'en': 'Dampier'},
'61851062':{'en': '<NAME>'},
'61851063':{'en': 'Derby'},
'61851064':{'en': '<NAME>'},
'61851065':{'en': '<NAME>'},
'61851066':{'en': 'Hall\'s Creek'},
'61851067':{'en': 'Karratha'},
'61851068':{'en': 'Kununurra'},
'61851069':{'en': 'Leopold'},
'61851070':{'en': '<NAME>'},
'61851071':{'en': 'Millstream'},
'61851072':{'en': 'Mitchell'},
'61851073':{'en': 'Mount Bruce'},
'61851074':{'en': 'Newman'},
'61851075':{'en': 'Onslow'},
'61851076':{'en': 'Ord'},
'61851077':{'en': 'Pannawonica'},
'61851078':{'en': 'Paraburdoo'},
'61851079':{'en': 'Port Hedland'},
'61851080':{'en': 'Roebuck'},
'61851081':{'en': 'Sandfire'},
'61851082':{'en': 'Telfer'},
'61851083':{'en': '<NAME>'},
'61851084':{'en': 'Whaleback'},
'61851085':{'en': 'Wittenoom'},
'61851086':{'en': 'Wyndham'},
'61851087':{'en': 'Broome'},
'61851088':{'en': 'Christmas Island'},
'61851089':{'en': 'Cocos Island'},
'61851090':{'en': 'Dampier'},
'61851091':{'en': '<NAME>'},
'61851092':{'en': 'Derby'},
'61851093':{'en': '<NAME>'},
'61851094':{'en': '<NAME>'},
'61851095':{'en': 'Hall\'s Creek'},
'61851096':{'en': 'Karratha'},
'61851097':{'en': 'Kununurra'},
'61851098':{'en': 'Leopold'},
'61851099':{'en': '<NAME>'},
'61851100':{'en': 'Millstream'},
'61851101':{'en': 'Mitchell'},
'61851102':{'en': 'Mount Bruce'},
'61851103':{'en': 'Newman'},
'61851104':{'en': 'Onslow'},
'61851105':{'en': 'Ord'},
'61851106':{'en': 'Pannawonica'},
'61851107':{'en': 'Paraburdoo'},
'61851108':{'en': 'Port Hedland'},
'61851109':{'en': 'Roebuck'},
'61851110':{'en': 'Sandfire'},
'61851111':{'en': 'Telfer'},
'61851112':{'en': '<NAME>'},
'61851113':{'en': 'Whaleback'},
'61851114':{'en': 'Wittenoom'},
'61851115':{'en': 'Wyndham'},
'61851116':{'en': 'Broome'},
'61851117':{'en': 'Christmas Island'},
'61851118':{'en': 'Cocos Island'},
'61851119':{'en': 'Dampier'},
'61851120':{'en': '<NAME>'},
'61851121':{'en': 'Derby'},
'61851122':{'en': '<NAME>'},
'61851123':{'en': 'Great Sandy'},
'61851124':{'en': 'Hall\'s Creek'},
'61851125':{'en': 'Karratha'},
'61851126':{'en': 'Kununurra'},
'61851127':{'en': 'Leopold'},
'61851128':{'en': '<NAME>'},
'61851129':{'en': 'Millstream'},
'61851130':{'en': 'Mitchell'},
'61851131':{'en': 'Mount Bruce'},
'61851132':{'en': 'Newman'},
'61851133':{'en': 'Onslow'},
'61851134':{'en': 'Ord'},
'61851135':{'en': 'Pannawonica'},
'61851136':{'en': 'Paraburdoo'},
'61851137':{'en': 'Port Hedland'},
'61851138':{'en': 'Roebuck'},
'61851139':{'en': 'Sandfire'},
'61851140':{'en': 'Telfer'},
'61851141':{'en': '<NAME>'},
'61851142':{'en': 'Whaleback'},
'61851143':{'en': 'Wittenoom'},
'61851144':{'en': 'Wyndham'},
'61851145':{'en': 'Broome'},
'61851146':{'en': 'Christmas Island'},
'61851148':{'en': 'Dampier'},
'61851149':{'en': '<NAME>'},
'61851150':{'en': 'Derby'},
'61851151':{'en': '<NAME>'},
'61851152':{'en': '<NAME>'},
'61851153':{'en': 'Hall\'s Creek'},
'61851154':{'en': 'Karratha'},
'61851155':{'en': 'Kununurra'},
'61851156':{'en': 'Leopold'},
'61851157':{'en': '<NAME>'},
'61851158':{'en': 'Millstream'},
'61851159':{'en': 'Mitchell'},
'61851160':{'en': '<NAME>'},
'61851161':{'en': 'Newman'},
'61851162':{'en': 'Onslow'},
'61851163':{'en': 'Ord'},
'61851164':{'en': 'Pannawonica'},
'61851165':{'en': 'Paraburdoo'},
'61851166':{'en': 'Port Hedland'},
'61851167':{'en': 'Roebuck'},
'61851168':{'en': 'Sandfire'},
'61851169':{'en': 'Telfer'},
'61851170':{'en': '<NAME>'},
'61851171':{'en': 'Whaleback'},
'61851172':{'en': 'Wittenoom'},
'61851173':{'en': 'Wyndham'},
'61851174':{'en': 'Broome'},
'61851175':{'en': 'Christmas Island'},
'61851176':{'en': 'Cocos Island'},
'61851177':{'en': 'Dampier'},
'61851178':{'en': '<NAME>'},
'61851179':{'en': 'Derby'},
'61851180':{'en': '<NAME>'},
'61851181':{'en': 'Great Sandy'},
'61851182':{'en': 'Hall\'s Creek'},
'61851183':{'en': 'Karratha'},
'61851184':{'en': 'Kununurra'},
'61851185':{'en': 'Leopold'},
'61851186':{'en': '<NAME>'},
'61851187':{'en': 'Millstream'},
'61851188':{'en': 'Mitchell'},
'61851189':{'en': 'Mount Bruce'},
'61851190':{'en': 'Newman'},
'61851191':{'en': 'Onslow'},
'61851192':{'en': 'Ord'},
'61851193':{'en': 'Pannawonica'},
'61851194':{'en': 'Paraburdoo'},
'61851195':{'en': 'Port Hedland'},
'61851196':{'en': 'Roebuck'},
'61851197':{'en': 'Sandfire'},
'61851198':{'en': 'Telfer'},
'61851199':{'en': '<NAME>'},
'61851200':{'en': 'Whaleback'},
'61851201':{'en': 'Wittenoom'},
'61851202':{'en': 'Wyndham'},
'61851203':{'en': 'Broome'},
'61851204':{'en': 'Christmas Island'},
'61851205':{'en': 'Port Hedland'},
'61851206':{'en': 'Dampier'},
'61851207':{'en': 'De Grey'},
'61851209':{'en': 'Mitchell'},
'61851211':{'en': 'Mount Bruce'},
'61851213':{'en': 'Newman'},
'61851215':{'en': 'Onslow'},
'61851217':{'en': 'Ord'},
'61851219':{'en': 'Pannawonica'},
'61851221':{'en': 'Paraburdoo'},
'61851222':{'en': 'Christmas Island'},
'61851223':{'en': 'Cocos Island'},
'61851224':{'en': 'Dampier'},
'61851225':{'en': 'De Grey'},
'61851226':{'en': 'Derby'},
'61851227':{'en': 'Fitzroy Crossing'},
'61851228':{'en': 'Great Sandy'},
'61851229':{'en': 'Hall\'s Creek'},
'61851230':{'en': 'Karratha'},
'61851231':{'en': 'Kununurra'},
'61851232':{'en': 'Port Hedland'},
'61851233':{'en': 'Port Hedland'},
'61851234':{'en': 'Broome'},
'61851235':{'en': 'Christmas Island'},
'61851236':{'en': 'Leopold'},
'61851237':{'en': '<NAME>'},
'61851238':{'en': 'Millstream'},
'61851239':{'en': 'Mitchell'},
'6185124':{'en': 'Karratha'},
'61851250':{'en': 'Newman'},
'61851251':{'en': 'Onslow'},
'61851252':{'en': 'Ord'},
'61851253':{'en': 'Pannawonica'},
'61851254':{'en': 'Paraburdoo'},
'61851255':{'en': 'Paraburdoo'},
'61851256':{'en': 'Paraburdoo'},
'61851257':{'en': 'Paraburdoo'},
'61851258':{'en': 'Port Hedland'},
'61851259':{'en': 'Roebuck'},
'61851260':{'en': 'Sandfire'},
'61851261':{'en': 'Telfer'},
'61851262':{'en': '<NAME>'},
'61851263':{'en': 'Whaleback'},
'61851264':{'en': 'Wittenoom'},
'61851265':{'en': 'Wyndham'},
'61851267':{'en': 'Roebuck'},
'61851269':{'en': 'Sandfire'},
'6185127':{'en': 'Port Hedland'},
'61851281':{'en': 'Telfer'},
'61851283':{'en': '<NAME>'},
'61851285':{'en': 'Whaleback'},
'61851287':{'en': 'Wittenoom'},
'61851289':{'en': 'Wyndham'},
'61851290':{'en': 'Telfer'},
'61851291':{'en': '<NAME>'},
'61851292':{'en': 'Whaleback'},
'61851293':{'en': 'Wittenoom'},
'61851294':{'en': 'Wyndham'},
'61860000':{'en': 'Kalgoorlie'},
'61860001':{'en': 'Burracoppin'},
'61860002':{'en': 'Burracoppin'},
'61860003':{'en': 'Grass Patch'},
'61860004':{'en': 'Grass Patch'},
'61860005':{'en': 'Holleton'},
'61860006':{'en': 'Holleton'},
'61860007':{'en': 'Laverton'},
'61860008':{'en': 'Laverton'},
'61860009':{'en': 'Leinster'},
'61860010':{'en': 'Leinster'},
'61860011':{'en': 'Leonora'},
'61860012':{'en': 'Leonora'},
'61860013':{'en': '<NAME>er South'},
'61860014':{'en': 'Mount Walker South'},
'61860015':{'en': 'Kalgoorlie'},
'61860016':{'en': 'Esperance'},
| |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['SnapshotArgs', 'Snapshot']
@pulumi.input_type
class SnapshotArgs:
def __init__(__self__, *,
instance_id: pulumi.Input[str],
cron_timing: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
safe: Optional[pulumi.Input[bool]] = None):
"""
The set of arguments for constructing a Snapshot resource.
:param pulumi.Input[str] instance_id: The ID of the Instance from which the snapshot will be taken.
:param pulumi.Input[str] cron_timing: If a valid cron string is passed, the snapshot will be saved as an automated snapshot
continuing to automatically update based on the schedule of the cron sequence provided
The default is nil meaning the snapshot will be saved as a one-off snapshot.
:param pulumi.Input[str] name: A name for the instance snapshot.
:param pulumi.Input[bool] safe: If `true` the instance will be shut down during the snapshot to ensure all files
are in a consistent state (e.g. database tables aren't in the middle of being optimised
and hence risking corruption). The default is `false` so you experience no interruption
of service, but a small risk of corruption.
"""
pulumi.set(__self__, "instance_id", instance_id)
if cron_timing is not None:
pulumi.set(__self__, "cron_timing", cron_timing)
if name is not None:
pulumi.set(__self__, "name", name)
if safe is not None:
pulumi.set(__self__, "safe", safe)
@property
@pulumi.getter(name="instanceId")
def instance_id(self) -> pulumi.Input[str]:
"""
The ID of the Instance from which the snapshot will be taken.
"""
return pulumi.get(self, "instance_id")
@instance_id.setter
def instance_id(self, value: pulumi.Input[str]):
pulumi.set(self, "instance_id", value)
@property
@pulumi.getter(name="cronTiming")
def cron_timing(self) -> Optional[pulumi.Input[str]]:
"""
If a valid cron string is passed, the snapshot will be saved as an automated snapshot
continuing to automatically update based on the schedule of the cron sequence provided
The default is nil meaning the snapshot will be saved as a one-off snapshot.
"""
return pulumi.get(self, "cron_timing")
@cron_timing.setter
def cron_timing(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cron_timing", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A name for the instance snapshot.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def safe(self) -> Optional[pulumi.Input[bool]]:
"""
If `true` the instance will be shut down during the snapshot to ensure all files
are in a consistent state (e.g. database tables aren't in the middle of being optimised
and hence risking corruption). The default is `false` so you experience no interruption
of service, but a small risk of corruption.
"""
return pulumi.get(self, "safe")
@safe.setter
def safe(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "safe", value)
@pulumi.input_type
class _SnapshotState:
def __init__(__self__, *,
completed_at: Optional[pulumi.Input[str]] = None,
cron_timing: Optional[pulumi.Input[str]] = None,
hostname: Optional[pulumi.Input[str]] = None,
instance_id: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
next_execution: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
requested_at: Optional[pulumi.Input[str]] = None,
safe: Optional[pulumi.Input[bool]] = None,
size_gb: Optional[pulumi.Input[int]] = None,
state: Optional[pulumi.Input[str]] = None,
template_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering Snapshot resources.
:param pulumi.Input[str] completed_at: The date where the snapshot was completed.
:param pulumi.Input[str] cron_timing: If a valid cron string is passed, the snapshot will be saved as an automated snapshot
continuing to automatically update based on the schedule of the cron sequence provided
The default is nil meaning the snapshot will be saved as a one-off snapshot.
:param pulumi.Input[str] hostname: The hostname of the instance.
:param pulumi.Input[str] instance_id: The ID of the Instance from which the snapshot will be taken.
:param pulumi.Input[str] name: A name for the instance snapshot.
:param pulumi.Input[str] next_execution: if cron was define this date will be the next execution date.
:param pulumi.Input[str] region: The region where the snapshot was take.
:param pulumi.Input[str] requested_at: The date where the snapshot was requested.
:param pulumi.Input[bool] safe: If `true` the instance will be shut down during the snapshot to ensure all files
are in a consistent state (e.g. database tables aren't in the middle of being optimised
and hence risking corruption). The default is `false` so you experience no interruption
of service, but a small risk of corruption.
:param pulumi.Input[int] size_gb: The size of the snapshot in GB.
:param pulumi.Input[str] state: The status of the snapshot.
:param pulumi.Input[str] template_id: The template id.
"""
if completed_at is not None:
pulumi.set(__self__, "completed_at", completed_at)
if cron_timing is not None:
pulumi.set(__self__, "cron_timing", cron_timing)
if hostname is not None:
pulumi.set(__self__, "hostname", hostname)
if instance_id is not None:
pulumi.set(__self__, "instance_id", instance_id)
if name is not None:
pulumi.set(__self__, "name", name)
if next_execution is not None:
pulumi.set(__self__, "next_execution", next_execution)
if region is not None:
pulumi.set(__self__, "region", region)
if requested_at is not None:
pulumi.set(__self__, "requested_at", requested_at)
if safe is not None:
pulumi.set(__self__, "safe", safe)
if size_gb is not None:
pulumi.set(__self__, "size_gb", size_gb)
if state is not None:
pulumi.set(__self__, "state", state)
if template_id is not None:
pulumi.set(__self__, "template_id", template_id)
@property
@pulumi.getter(name="completedAt")
def completed_at(self) -> Optional[pulumi.Input[str]]:
"""
The date where the snapshot was completed.
"""
return pulumi.get(self, "completed_at")
@completed_at.setter
def completed_at(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "completed_at", value)
@property
@pulumi.getter(name="cronTiming")
def cron_timing(self) -> Optional[pulumi.Input[str]]:
"""
If a valid cron string is passed, the snapshot will be saved as an automated snapshot
continuing to automatically update based on the schedule of the cron sequence provided
The default is nil meaning the snapshot will be saved as a one-off snapshot.
"""
return pulumi.get(self, "cron_timing")
@cron_timing.setter
def cron_timing(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cron_timing", value)
@property
@pulumi.getter
def hostname(self) -> Optional[pulumi.Input[str]]:
"""
The hostname of the instance.
"""
return pulumi.get(self, "hostname")
@hostname.setter
def hostname(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "hostname", value)
@property
@pulumi.getter(name="instanceId")
def instance_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the Instance from which the snapshot will be taken.
"""
return pulumi.get(self, "instance_id")
@instance_id.setter
def instance_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "instance_id", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A name for the instance snapshot.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="nextExecution")
def next_execution(self) -> Optional[pulumi.Input[str]]:
"""
if cron was define this date will be the next execution date.
"""
return pulumi.get(self, "next_execution")
@next_execution.setter
def next_execution(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "next_execution", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
The region where the snapshot was take.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="requestedAt")
def requested_at(self) -> Optional[pulumi.Input[str]]:
"""
The date where the snapshot was requested.
"""
return pulumi.get(self, "requested_at")
@requested_at.setter
def requested_at(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "requested_at", value)
@property
@pulumi.getter
def safe(self) -> Optional[pulumi.Input[bool]]:
"""
If `true` the instance will be shut down during the snapshot to ensure all files
are in a consistent state (e.g. database tables aren't in the middle of being optimised
and hence risking corruption). The default is `false` so you experience no interruption
of service, but a small risk of corruption.
"""
return pulumi.get(self, "safe")
@safe.setter
def safe(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "safe", value)
@property
@pulumi.getter(name="sizeGb")
def size_gb(self) -> Optional[pulumi.Input[int]]:
"""
The size of the snapshot in GB.
"""
return pulumi.get(self, "size_gb")
@size_gb.setter
def size_gb(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "size_gb", value)
@property
@pulumi.getter
def state(self) -> Optional[pulumi.Input[str]]:
"""
The status of the snapshot.
"""
return pulumi.get(self, "state")
@state.setter
def state(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "state", value)
@property
@pulumi.getter(name="templateId")
def template_id(self) -> Optional[pulumi.Input[str]]:
"""
The template id.
"""
return pulumi.get(self, "template_id")
@template_id.setter
def template_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "template_id", value)
class Snapshot(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
cron_timing: Optional[pulumi.Input[str]] = None,
instance_id: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
safe: Optional[pulumi.Input[bool]] = None,
__props__=None):
"""
Provides a resource which can be used to create a snapshot from an existing Civo Instance.
## Example Usage
```python
import pulumi
import pulumi_civo as civo
myinstance_backup = civo.Snapshot("myinstance-backup", instance_id=civo_instance["myinstance"]["id"])
```
## Import
Instance Snapshots can be imported using the `snapshot id`, e.g.
```sh
$ pulumi import civo:index/snapshot:Snapshot myinstance-backup 4cc87851-e1d0-4270-822a-b36d28c7a77f
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] cron_timing: If a valid cron | |
- CROP3]
else:
Z_dir = linearized[: len(linearized) - CROP3]
return Z_dir, Z_direction
def location_slices(self, txtnslices):
"""Define the location of the slice you are interested"""
global location_slices
location_slices = round(float(txtnslices.get()), 2) # Export the locations of slice from the GUI
return location_slices
def pixel_converter(self, location_slices):
"""Convert from the real z to pixel"""
global location_slices_pixel_x
global location_slices_pixel_y
global location_slices_pixel_z
location_slices_pixel_x = int(round(float(location_slices / x_actual), 2) * (x_size-1))
location_slices_pixel_y = int(round(float(location_slices / y_actual), 2) * (y_size-1))
location_slices_pixel_z = int(float(location_slices / round(float(np.array(Z_dir).max()),2)) * len(Z_dir)) + 1
return location_slices_pixel_x, location_slices_pixel_y, location_slices_pixel_z
def clear(self):
"""Clear up the inputs"""
txtnslices.delete(0, END)
txtfilename.delete(0, END)
canvas1.get_tk_widget().destroy()
canvas2.get_tk_widget().destroy()
def create_pslist(self, Z_direction):
"""The function that pulls out the approach or retract dataset."""
global pslist
if Z_direction == "Retract":
pslist = reduced_array_retract[: len(reduced_array_retract) - CROP3]
else:
pslist = reduced_array_approach[: len(reduced_array_approach) - CROP3]
return pslist
def twoDX_slicings(self, location_slices_pixel_x, export_filename2, bingo, x_actual, y_actual):
"""Plotting function for the X direction slicing"""
global canvas1
global canvas2
root = tk.Toplevel(self)
root.wm_state('zoomed')
if location_slices_pixel_x in range(x_size + 1):
pass
else:
tkMessageBox.askretrycancel("Input Error", "Out of range, The expected range for X is between 0 to " + str(x_actual) + ".")
As = np.array(self.create_pslist(Z_direction))[:, location_slices_pixel_x, :] # Select the phaseshift data points in the certain slice plane
As[np.isnan(As)] = np.nanmin(As) # Replace NaN with min value of array.
a = np.linspace(init, x_actual, x_size)[location_slices_pixel_x] # Define the certain x slice in the x space
b = np.linspace(init, y_actual, x_size) # Define the y space
c = Z_dir # Define the z space
X, Z, Y = np.meshgrid(a, c, b) # Create the meshgrid for the 3d space
fig = plt.figure(figsize=(9, 9), facecolor='white')
ax = fig.add_subplot(111, projection='3d')
# Define the fixed colorbar range based the overall phaseshift values from the input data file
im = ax.scatter(X, Y, Z, c=As.flatten(), s=6, vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),
vmin=np.nanmin(np.array(self.create_pslist(Z_direction))))
#Add colorbar
cbar = plt.colorbar(im)
# Label the colorbar
cbar.set_label(str(valu), rotation=90)
ax.set_xlim(left=init, right=x_actual)
ax.set_ylim(bottom=init, top=y_actual)
ax.set_zlim(top=Z_dir.max(), bottom=Z_dir.min())
ax.set_xlabel('X(nm)', fontsize=12)
ax.set_ylabel('Y(nm)', fontsize=12)
ax.set_zlabel('Z(nm)', fontsize=12)
ax.set_title('3D X Slicing (X=' + str(round(a, 4)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
canvas1 = FigureCanvasTkAgg(fig, master=root) # Plot the 3D figure of 2D slicing
canvas1.draw()
canvas1.get_tk_widget().pack(side=tk.LEFT)
setStr = '{}_'.format(export_filename2) + str(valu) + '_Xslices.png'
fig.savefig(setStr)
fig1 = plt.figure(figsize=(9, 9), facecolor='white')
plt.subplot(111)
plt.imshow(As, aspect='auto', origin="lower", vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),
vmin=np.nanmin(np.array(self.create_pslist(Z_direction))), extent=[init, y_actual, init, Z_dir.max()])
plt.xlabel('Y', fontsize=12)
plt.ylabel('Z', fontsize=12)
plt.title('2D X Slicing (X=' + str(round(a, 4)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
# Add and label colorbar
cbar = plt.colorbar()
cbar.set_label(str(valu))
canvas2 = FigureCanvasTkAgg(fig1, master=root) # Plot the 2D figure of 2D slicing
canvas2.draw()
canvas2.get_tk_widget().pack(side=tk.LEFT)
setStr = '{}_'.format(export_filename2) + str(valu) + '_2d_Xslices.png' # Define the export image name
fig1.savefig(setStr)
if bingo == 1:
#Get the HDF5 file for the plotting
h5file = export_filename2 + '_' + str(valu) + '_' + str(Z_direction) + str("_XSlicing.h5") # Define the final name of the h5 file
# Assuming As is a list of lists
h = h5py.File(h5file, 'w') # Create the empty h5 file
h.create_dataset("data", data=As) # Insert the data into the empty file
else:
pass
def twoDY_slicings(self, location_slices_pixel_y, export_filename2, bingo, x_actual, y_actual):
"""Plotting function for the Y direction slicing"""
global canvas1
global canvas2
if location_slices_pixel_y in range(y_size + 1):
pass
else:
tkMessageBox.askretrycancel("Input Error", "Out of range, The expected range for Y is between 0 to " + str(y_actual) + ".")
a = np.linspace(init, x_actual, x_size)
b = np.linspace(init, y_actual, y_size)[location_slices_pixel_y]
c = Z_dir
X, Z, Y = np.meshgrid(a, c, b)
Bs = np.array(self.create_pslist(Z_direction))[:, location_slices_pixel_y, :]
Bs[np.isnan(Bs)] = np.nanmin(Bs) # Replace NaN with min value of array.
root1 = tk.Toplevel(self)
root1.state('zoomed')
fig = plt.figure(figsize=(9, 9), facecolor='white')
ax = fig.add_subplot(111, projection='3d')
im = ax.scatter(X, Y, Z, c=Bs.flatten(), s=6, vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),
vmin=np.nanmin(np.array(self.create_pslist(Z_direction))))
# Add and lable colorbar
cbar = plt.colorbar(im)
cbar.set_label(str(valu))
ax.set_xlim(left=init, right=x_actual)
ax.set_ylim(bottom=init, top=y_actual)
ax.set_zlim(top=Z_dir.max(), bottom=Z_dir.min())
ax.set_xlabel('X(nm)', fontsize=12)
ax.set_ylabel('Y(nm)', fontsize=12)
ax.set_zlabel('Z(nm)', fontsize=12)
ax.set_title('3D Y Slicing (Y=' + str(round(b, 3)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
canvas1 = FigureCanvasTkAgg(fig, master=root1)
canvas1.draw()
canvas1.get_tk_widget().pack(side=tk.LEFT)
setStr = '{}_'.format(export_filename2) + str(valu) + '_Yslices.png'
fig.savefig(setStr)
fig2 = plt.figure(figsize=(9, 9), facecolor='white')
plt.subplot(111)
plt.imshow(Bs, aspect='auto', origin="lower", vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),
vmin=np.nanmin(np.array(self.create_pslist(Z_direction))), extent=[init, x_actual, init, Z_dir.max()])
plt.xlabel('X', fontsize=12)
plt.ylabel('Z', fontsize=12)
plt.title('2D Y Slicing (Y=' + str(round(b, 3)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
# Add and label colorbar
cbar = plt.colorbar()
cbar.set_label(str(valu))
canvas2 = FigureCanvasTkAgg(fig2, root1)
canvas2.draw()
canvas2.get_tk_widget().pack(side=tk.LEFT)
setStr = '{}_'.format(export_filename2) + str(valu) + '_2d_Yslices.png'
fig2.savefig(setStr)
if bingo == 1:
#Get the HDF5 file for the plotting
h5file = export_filename2 + '_' + str(valu) + '_' + str(Z_direction) + str("_YSlicing.h5")
# Assuming Bs is a list of lists
h = h5py.File(h5file, 'w')
h.create_dataset("data", data=Bs)
else:
pass
def twoDZ_slicings(self, location_slices_pixel_z, export_filename2, bingo, x_actual, y_actual):
"""3D Plotting function for Z direction slicing"""
global canvas1
global canvas2
global canvas3
global numberinput
if location_slices_pixel_z in range (len(Z_dir) + 2):
pass
else:
tkMessageBox.askretrycancel("Input Error", "Out of range, The expected range for Z is between 0 to " + str(np.array(Z_dir).max()) + ".")
phaseshift = (self.create_pslist(Z_direction))[location_slices_pixel_z]
a = np.linspace(init, x_actual, x_size)
b = np.linspace(init, y_actual, y_size)
X, Z, Y = np.meshgrid(a, Z_dir[location_slices_pixel_z], b)
l = phaseshift
l[np.isnan(l)] = np.nanmin(l) # Replace NaN with min value of array.
fig3, ax1 = plt.subplots(figsize=(9, 9), facecolor='white')
im2 = ax1.imshow(l, vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),vmin=np.nanmin(np.array(self.create_pslist(Z_direction))), extent=[init, x_actual, init, y_actual])
mpldatacursor.datacursor(hover=True, bbox=dict(alpha=1, fc='w'), formatter='i, j = {i}, {j}\nz = {z:.02g}'.format)
plt.xlabel('X', fontsize=12)
plt.ylabel('Y', fontsize=12)
plt.title('2D Z Slicing (Z=' + str(round(Z_dir[(location_slices_pixel_z) - 1], 4)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
# Add and label colorbar
cbar = plt.colorbar(im2)
cbar.set_label(str(valu))
fig = plt.figure(figsize=(9, 9), facecolor='white')
ax = fig.add_subplot(111, projection='3d')
im1 = ax.scatter(X, Y, Z, c=l.flatten(), s=6, vmax=np.nanmax(np.array(self.create_pslist(Z_direction))),
vmin=np.nanmin(np.array(self.create_pslist(Z_direction))))
# Add and label colorbar
cbar = plt.colorbar(im1)
cbar.set_label(str(valu))
ax.set_xlim(left=init, right=x_actual)
ax.set_ylim(bottom=init, top=y_actual)
ax.set_zlim(top=Z_dir.max(), bottom=Z_dir.min())
ax.set_xlabel('X(nm)', fontsize=12)
ax.set_ylabel('Y(nm)', fontsize=12)
ax.set_zlabel('Z(nm)', fontsize=12)
ax.set_title('3D Z Slicing (Z=' + str(round(Z_dir[(location_slices_pixel_z) - 1], 4)) + 'nm) for the ' + str(valu) + ' of AFM data', fontsize=13)
root2 = tk.Toplevel(self)
root2.state('zoomed')
canvas1 = FigureCanvasTkAgg(fig, master=root2)
canvas1.draw()
canvas1.get_tk_widget().pack(side=tk.LEFT)
h5file = export_filename2 + '_' + str(valu) + '_' + str(Z_direction) + str("_ZSlicing.h5")
# Assuming phaseshift is a list of lists
h = h5py.File(h5file, 'w')
h.create_dataset("data", data=phaseshift)
setStr = '{}_'.format(export_filename2) + str(valu) + '_Zslices.png'
fig.savefig(setStr)
canvas2 = FigureCanvasTkAgg(fig3, master=root2)
canvas2.draw()
canvas2.get_tk_widget().pack(side=tk.LEFT)
setStr = '{}_'.format(export_filename2) + str(valu) + '_2d_Zslices.png'
fig3.savefig(setStr)
if bingo == 1:
#Get the HDF5 file for the plotting
h5file = export_filename2 + str(Z_direction) + str("_Z.h5")
# Assuming phaseshift is a list of lists
h = h5py.File(h5file, 'w')
h.create_dataset("data", data=phaseshift)
else:
pass
def __init__(self, parent, controller):
global txtnslices
global txtzdir
global txtfilename
tk.Frame.__init__(self, parent)
tk.Frame.configure(self, background='#ffffff')
label1 = ttk.Label(self, text="Step 5: 2D Slicing Plot", font='Huge_Font', background='#ffffff')
label1.pack(pady=10, padx=10)
label2 = ttk.Label(self, text="Slices Location (nm)", font="Small_Font", background='#ffffff')
label2.pack(pady=10, padx=10)
txtnslices = ttk.Entry(self)
txtnslices.pack()
label3 = ttk.Label(self, text="Select the Z Direction", font='Large_Font', background='#ffffff')
label3.pack(pady=10, padx=10)
lab = LabelFrame(self)
lab.pack()
listbox = Listbox(lab, exportselection=0)
listbox.configure(height=2)
listbox.pack()
listbox.insert(1, "Retract")
listbox.insert(2, "Approach")
listbox.bind('<<ListboxSelect>>', self.Curselect6)
label4 = ttk.Label(self, text="Export Filename", font="Small_Font", background='#ffffff')
label4.pack(pady=10, padx=10)
txtfilename = ttk.Entry(self)
txtfilename.pack()
var00 = IntVar()
checkbutton = Checkbutton(self, text="HDF5", variable=var00, bg='white')
checkbutton.place(x=900, y=275)
button1 = tk.Button(self, text="Get 2D X Slicing Plot", bg="white",
command=lambda: (self.get_bingo(var00),self.location_slices(txtnslices), self.export_filename(txtfilename),
self.pixel_converter(location_slices),
self.twoDX_slicings(location_slices_pixel_x, export_filename2, bingo, x_actual,y_actual)))
button1.place(x=645, y=275)
button2 = tk.Button(self, text="Get 2D Y Slicing Plot", bg="white",
command=lambda: (self.get_bingo(var00), self.location_slices(txtnslices), self.export_filename(txtfilename),
self.pixel_converter(location_slices),
self.twoDY_slicings(location_slices_pixel_y, export_filename2, bingo, x_actual,
y_actual)))
button2.place(x=775, y=275)
button3 = tk.Button(self, text="Get 2D Z Slicing Plot", bg="white", command=lambda:
(self. get_bingo(var00), self.location_slices(txtnslices), self.export_filename(txtfilename), self.pixel_converter(location_slices),
self.twoDZ_slicings(location_slices_pixel_z, export_filename2, bingo, x_actual, y_actual)))
button3.place(x=645, y=310)
button4 = tk.Button(self, text="Get Vector Slicing Plot", bg="white", command=lambda:
(self.get_bingo(var00), self.location_slices(txtnslices), self.export_filename(txtfilename), self.pixel_converter(location_slices),
self.plot_force(location_slices_pixel_z, x_actual, y_actual)))
button4.place(x=775, y=310)
button6 = tk.Button(self, text="3D Plot", bg="white", command=lambda: controller.show_frame(threeD_plot))
button6.place(x=645, y=345)
button6.config(width=15)
button7 = tk.Button(self, text="2D Slicing Animation", bg="white",
command=lambda: controller.show_frame(animation_cool))
button7.place(x=775, y=345)
button8 = tk.Button(self, text="Organizing Dataset", bg="white",
command=lambda: controller.show_frame(load_data))
button8.place(x=720, y=380)
button8.config(width=15)
button9 = tk.Button(self, text="Home", bg="white", command=lambda: controller.show_frame(data_cleaning))
button9.place(x=720, y=415)
button9.config(width=15)
label5 = tk.Label(self, text="The reference level for the plots is set as zero at the | |
# Copyright (C) 2020 Intel Corporation
#
# SPDX-License-Identifier: MIT
import io
import os
import os.path as osp
import tempfile
import xml.etree.ElementTree as ET
import zipfile
from collections import defaultdict
from glob import glob
from io import BytesIO
import copy
from shutil import copyfile
import itertools
from django.contrib.auth.models import Group, User
from rest_framework import status
from rest_framework.test import APIClient, APITestCase
from cvat.apps.engine.media_extractors import ValidateDimension
from cvat.apps.dataset_manager.task import TaskAnnotation
from datumaro.util.test_utils import TestDir
CREATE_ACTION = "create"
UPDATE_ACTION = "update"
DELETE_ACTION = "delete"
class ForceLogin:
def __init__(self, user, client):
self.user = user
self.client = client
def __enter__(self):
if self.user:
self.client.force_login(self.user,
backend='django.contrib.auth.backends.ModelBackend')
return self
def __exit__(self, exception_type, exception_value, traceback):
if self.user:
self.client.logout()
class _DbTestBase(APITestCase):
def setUp(self):
self.client = APIClient()
@classmethod
def setUpTestData(cls):
cls.create_db_users()
@classmethod
def create_db_users(cls):
(group_admin, _) = Group.objects.get_or_create(name="admin")
(group_user, _) = Group.objects.get_or_create(name="user")
user_admin = User.objects.create_superuser(username="admin", email="",
password="<PASSWORD>")
user_admin.groups.add(group_admin)
user_dummy = User.objects.create_user(username="user", password="<PASSWORD>")
user_dummy.groups.add(group_user)
cls.admin = user_admin
cls.user = user_dummy
def _put_api_v1_task_id_annotations(self, tid, data):
with ForceLogin(self.admin, self.client):
response = self.client.put("/api/v1/tasks/%s/annotations" % tid,
data=data, format="json")
return response
def _put_api_v1_job_id_annotations(self, jid, data):
with ForceLogin(self.admin, self.client):
response = self.client.put("/api/v1/jobs/%s/annotations" % jid,
data=data, format="json")
return response
def _patch_api_v1_task_id_annotations(self, tid, data, action, user):
with ForceLogin(user, self.client):
response = self.client.patch(
"/api/v1/tasks/{}/annotations?action={}".format(tid, action),
data=data, format="json")
return response
def _patch_api_v1_job_id_annotations(self, jid, data, action, user):
with ForceLogin(user, self.client):
response = self.client.patch(
"/api/v1/jobs/{}/annotations?action={}".format(jid, action),
data=data, format="json")
return response
def _create_task(self, data, image_data):
with ForceLogin(self.user, self.client):
response = self.client.post('/api/v1/tasks', data=data, format="json")
assert response.status_code == status.HTTP_201_CREATED, response.status_code
tid = response.data["id"]
response = self.client.post("/api/v1/tasks/%s/data" % tid,
data=image_data)
assert response.status_code == status.HTTP_202_ACCEPTED, response.status_code
response = self.client.get("/api/v1/tasks/%s" % tid)
task = response.data
return task
@staticmethod
def _get_tmp_annotation(task, annotation):
tmp_annotations = copy.deepcopy(annotation)
for item in tmp_annotations:
if item in ["tags", "shapes", "tracks"]:
for index_elem, _ in enumerate(tmp_annotations[item]):
tmp_annotations[item][index_elem]["label_id"] = task["labels"][0]["id"]
for index_attribute, attribute in enumerate(task["labels"][0]["attributes"]):
spec_id = task["labels"][0]["attributes"][index_attribute]["id"]
value = attribute["default_value"]
if item == "tracks" and attribute["mutable"]:
for index_shape, _ in enumerate(tmp_annotations[item][index_elem]["shapes"]):
tmp_annotations[item][index_elem]["shapes"][index_shape]["attributes"].append({
"spec_id": spec_id,
"value": value,
})
else:
tmp_annotations[item][index_elem]["attributes"].append({
"spec_id": spec_id,
"value": value,
})
return tmp_annotations
def _get_jobs(self, task_id):
with ForceLogin(self.admin, self.client):
response = self.client.get("/api/v1/tasks/{}/jobs".format(task_id))
return response.data
def _get_request(self, path, user):
with ForceLogin(user, self.client):
response = self.client.get(path)
return response
def _get_request_with_data(self, path, data, user):
with ForceLogin(user, self.client):
response = self.client.get(path, data)
return response
def _delete_request(self, path, user):
with ForceLogin(user, self.client):
response = self.client.delete(path)
return response
def _download_file(self, url, data, user, file_name):
response = self._get_request_with_data(url, data, user)
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
response = self._get_request_with_data(url, data, user)
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = BytesIO(b"".join(response.streaming_content))
with open(file_name, "wb") as f:
f.write(content.getvalue())
def _upload_file(self, url, data, user):
response = self._put_request_with_data(url, {"annotation_file": data}, user)
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
response = self._put_request_with_data(url, {}, user)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def _generate_url_dump_tasks_annotations(self, task_id):
return f"/api/v1/tasks/{task_id}/annotations"
def _generate_url_upload_tasks_annotations(self, task_id, upload_format_name):
return f"/api/v1/tasks/{task_id}/annotations?format={upload_format_name}"
def _generate_url_dump_job_annotations(self, job_id):
return f"/api/v1/jobs/{job_id}/annotations"
def _generate_url_upload_job_annotations(self, job_id, upload_format_name):
return f"/api/v1/jobs/{job_id}/annotations?format={upload_format_name}"
def _generate_url_dump_dataset(self, task_id):
return f"/api/v1/tasks/{task_id}/dataset"
def _remove_annotations(self, tid):
response = self._delete_request(f"/api/v1/tasks/{tid}/annotations", self.admin)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
return response
def _put_request_with_data(self, url, data, user):
with ForceLogin(user, self.client):
response = self.client.put(url, data)
return response
def _delete_task(self, tid):
response = self._delete_request('/api/v1/tasks/{}'.format(tid), self.admin)
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
return response
def _check_dump_content(self, content, task_data, format_name, related_files=True):
def etree_to_dict(t):
d = {t.tag: {} if t.attrib else None}
children = list(t)
if children:
dd = defaultdict(list)
for dc in map(etree_to_dict, children):
for k, v in dc.items():
dd[k].append(v)
d = {t.tag: {k: v[0] if len(v) == 1 else v
for k, v in dd.items()}}
if t.attrib:
d[t.tag].update(('@' + k, v)
for k, v in t.attrib.items())
if t.text:
text = t.text.strip()
if children or t.attrib:
if text:
d[t.tag]['#text'] = text
else:
d[t.tag] = text
return d
if format_name == "Kitti Raw Format 1.0":
with tempfile.TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(content).extractall(tmp_dir)
xmls = glob(osp.join(tmp_dir, '**', '*.xml'), recursive=True)
self.assertTrue(xmls)
for xml in xmls:
xmlroot = ET.parse(xml).getroot()
self.assertEqual(xmlroot.tag, "boost_serialization")
items = xmlroot.findall("./tracklets/item")
self.assertEqual(len(items), len(task_data["shapes"]))
elif format_name == "Sly Point Cloud Format 1.0":
with tempfile.TemporaryDirectory() as tmp_dir:
checking_files = [osp.join(tmp_dir, "key_id_map.json"),
osp.join(tmp_dir, "meta.json"),
osp.join(tmp_dir, "ds0", "ann", "000001.pcd.json"),
osp.join(tmp_dir, "ds0", "ann", "000002.pcd.json"),
osp.join(tmp_dir, "ds0", "ann","000003.pcd.json")]
if related_files:
checking_files.extend([osp.join(tmp_dir, "ds0", "related_images", "000001.pcd_pcd", "000001.png.json"),
osp.join(tmp_dir, "ds0", "related_images", "000002.pcd_pcd", "000002.png.json"),
osp.join(tmp_dir, "ds0", "related_images", "000003.pcd_pcd", "000003.png.json")])
zipfile.ZipFile(content).extractall(tmp_dir)
jsons = glob(osp.join(tmp_dir, '**', '*.json'), recursive=True)
self.assertTrue(jsons)
self.assertTrue(set(checking_files).issubset(set(jsons)))
class Task3DTest(_DbTestBase):
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.format_names = ["Sly Point Cloud Format 1.0", "Kitti Raw Format 1.0"]
cls._image_sizes = {}
cls.pointcloud_pcd_filename = "test_canvas3d.zip"
cls.pointcloud_pcd_path = osp.join(os.path.dirname(__file__), 'assets', cls.pointcloud_pcd_filename)
image_sizes = []
zip_file = zipfile.ZipFile(cls.pointcloud_pcd_path )
for info in zip_file.namelist():
if info.rsplit(".", maxsplit=1)[-1] == "pcd":
with zip_file.open(info, "r") as file:
data = ValidateDimension.get_pcd_properties(file)
image_sizes.append((int(data["WIDTH"]), int(data["HEIGHT"])))
cls.task = {
"name": "main task",
"owner_id": 1,
"assignee_id": 2,
"overlap": 0,
"segment_size": 100,
"labels": [
{"name": "car"},
{"name": "person"},
]
}
cls.task_with_attributes = {
"name": "task with attributes",
"owner_id": 1,
"assignee_id": 2,
"overlap": 0,
"segment_size": 100,
"labels": [
{"name": "car",
"color": "#2080c0",
"attributes": [
{
"name": "radio_name",
"mutable": False,
"input_type": "radio",
"default_value": "x1",
"values": ["x1", "x2", "x3"]
},
{
"name": "check_name",
"mutable": True,
"input_type": "checkbox",
"default_value": "false",
"values": ["false"]
},
{
"name": "text_name",
"mutable": False,
"input_type": "text",
"default_value": "qwerty",
"values": ["qwerty"]
},
{
"name": "number_name",
"mutable": False,
"input_type": "number",
"default_value": "-4.0",
"values": ["-4", "4", "1"]
}
]
},
{"name": "person",
"color": "#c06060",
"attributes": []
},
]
}
cls.task_many_jobs = {
"name": "task several jobs",
"owner_id": 1,
"assignee_id": 2,
"overlap": 3,
"segment_size": 1,
"labels": [
{
"name": "car",
"color": "#c06060",
"id": 1,
"attributes": []
}
]
}
cls.cuboid_example = {
"version": 0,
"tags": [],
"shapes": [
{
"type": "cuboid",
"occluded": False,
"z_order": 0,
"points": [0.16, 0.20, -0.26, 0, -0.14, 0, 4.84, 4.48, 4.12, 0, 0, 0, 0, 0, 0, 0],
"frame": 0,
"label_id": None,
"group": 0,
"source": "manual",
"attributes": []
},
],
"tracks": []
}
cls._image_sizes[cls.pointcloud_pcd_filename] = image_sizes
cls.expected_action = {
cls.admin: {'name': 'admin', 'code': status.HTTP_200_OK, 'annotation_changed': True},
cls.user: {'name': 'user', 'code': status.HTTP_200_OK, 'annotation_changed': True},
None: {'name': 'none', 'code': status.HTTP_401_UNAUTHORIZED, 'annotation_changed': False},
}
cls.expected_dump_upload = {
cls.admin: {'name': 'admin', 'code': status.HTTP_200_OK, 'create code': status.HTTP_201_CREATED,
'accept code': status.HTTP_202_ACCEPTED, 'file_exists': True, 'annotation_loaded': True},
cls.user: {'name': 'user', 'code': status.HTTP_200_OK, 'create code': status.HTTP_201_CREATED,
'accept code': status.HTTP_202_ACCEPTED, 'file_exists': True, 'annotation_loaded': True},
None: {'name': 'none', 'code': status.HTTP_401_UNAUTHORIZED, 'create code': status.HTTP_401_UNAUTHORIZED,
'accept code': status.HTTP_401_UNAUTHORIZED, 'file_exists': False, 'annotation_loaded': False},
}
def copy_pcd_file_and_get_task_data(self, test_dir):
tmp_file = osp.join(test_dir, self.pointcloud_pcd_filename)
copyfile(self.pointcloud_pcd_path, tmp_file)
task_data = {
"client_files[0]": open(tmp_file, 'rb'),
"image_quality": 100,
}
return task_data
def test_api_v1_create_annotation_in_job(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
annotation = self._get_tmp_annotation(task, self.cuboid_example)
for user, edata in list(self.expected_action.items()):
with self.subTest(format=edata["name"]):
response = self._patch_api_v1_task_id_annotations(task_id, annotation, CREATE_ACTION, user)
self.assertEqual(response.status_code, edata["code"])
if edata["annotation_changed"]:
task_ann = TaskAnnotation(task_id)
task_ann.init_from_db()
task_shape = task_ann.data["shapes"][0]
task_shape.pop("id")
self.assertEqual(task_shape, annotation["shapes"][0])
self._remove_annotations(task_id)
def test_api_v1_update_annotation_in_task(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
annotation = self._get_tmp_annotation(task, self.cuboid_example)
response = self._put_api_v1_task_id_annotations(task_id, annotation)
self.assertEqual(response.status_code, status.HTTP_200_OK)
for user, edata in list(self.expected_action.items()):
with self.subTest(format=edata["name"]):
task_ann_prev = TaskAnnotation(task_id)
task_ann_prev.init_from_db()
annotation["shapes"][0]["points"] = [x + 0.1 for x in annotation["shapes"][0]["points"]]
annotation["shapes"][0]["id"] = task_ann_prev.data["shapes"][0]["id"]
response = self._patch_api_v1_task_id_annotations(task_id, annotation, UPDATE_ACTION, user)
self.assertEqual(response.status_code, edata["code"], task_id)
if edata["annotation_changed"]:
task_ann = TaskAnnotation(task_id)
task_ann.init_from_db()
self.assertEqual(task_ann.data["shapes"], annotation["shapes"])
def test_api_v1_remove_annotation_in_task(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
annotation = self._get_tmp_annotation(task, self.cuboid_example)
for user, edata in list(self.expected_action.items()):
with self.subTest(format=edata["name"]):
response = self._patch_api_v1_task_id_annotations(task_id, annotation, CREATE_ACTION, self.admin)
self.assertEqual(response.status_code, status.HTTP_200_OK)
task_ann_prev = TaskAnnotation(task_id)
task_ann_prev.init_from_db()
annotation["shapes"][0]["id"] = task_ann_prev.data["shapes"][0]["id"]
response = self._patch_api_v1_task_id_annotations(task_id, annotation, DELETE_ACTION, user)
self.assertEqual(response.status_code, edata["code"])
if edata["annotation_changed"]:
task_ann = TaskAnnotation(task_id)
task_ann.init_from_db()
self.assertTrue(len(task_ann.data["shapes"]) == 0)
def test_api_v1_create_annotation_in_jobs(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
annotation = self._get_tmp_annotation(task, self.cuboid_example)
jobs = self._get_jobs(task_id)
for user, edata in list(self.expected_action.items()):
with self.subTest(format=edata["name"]):
response = self._patch_api_v1_job_id_annotations(jobs[0]["id"], annotation, CREATE_ACTION, user)
self.assertEqual(response.status_code, edata["code"])
task_ann = TaskAnnotation(task_id)
task_ann.init_from_db()
if len(task_ann.data["shapes"]):
task_shape = task_ann.data["shapes"][0]
task_shape.pop("id")
self.assertEqual(task_shape, annotation["shapes"][0])
self._remove_annotations(task_id)
def test_api_v1_update_annotation_in_job(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
jobs = self._get_jobs(task_id)
annotation = self._get_tmp_annotation(task, self.cuboid_example)
response = self._put_api_v1_task_id_annotations(task_id, annotation)
self.assertEqual(response.status_code, status.HTTP_200_OK)
for user, edata in list(self.expected_action.items()):
with self.subTest(format=edata["name"]):
task_ann_prev = TaskAnnotation(task_id)
task_ann_prev.init_from_db()
annotation["shapes"][0]["points"] = [x + 0.1 for x in annotation["shapes"][0]["points"]]
annotation["shapes"][0]["id"] = task_ann_prev.data["shapes"][0]["id"]
response = self._patch_api_v1_job_id_annotations(jobs[0]["id"], annotation, UPDATE_ACTION, user)
self.assertEqual(response.status_code, edata["code"])
if edata["annotation_changed"]:
task_ann = TaskAnnotation(task_id)
task_ann.init_from_db()
self.assertEqual(task_ann.data["shapes"], annotation["shapes"])
def test_api_v1_remove_annotation_in_job(self):
with TestDir() as test_dir:
task_data = self.copy_pcd_file_and_get_task_data(test_dir)
task = self._create_task(self.task, task_data)
task_id = task["id"]
jobs = self._get_jobs(task_id)
annotation = self._get_tmp_annotation(task, self.cuboid_example)
for user, edata in list(self.expected_action.items()):
| |
#!/usr/bin/env python3
import netcdf4_functions as nffun
import socket, os, sys, csv, time, math, numpy
import re, subprocess
from optparse import OptionParser
#from Numeric import *
#runcase.py does the following:
#
# 1. Call routines to create surface and domain data (makepointdata.py)
# 2. Use create_newcase to build the new case with specified options
# 3. Set point and case-epecific namelist options
# 4. configure case
# 5. build (compile) ACME with clean_build first if requested
# 6. apply user-specified PBS and submit information
# 7. submit single run or parameter ensemble job to PBS queue.
#
#-------------------Parse options-----------------------------------------------
parser = OptionParser()
parser.add_option("--caseidprefix", dest="mycaseid", default="", \
help="Unique identifier to include as a prefix to the case name")
parser.add_option("--caseroot", dest="caseroot", default='', \
help = "case root directory (default = ./, i.e., under scripts/)")
parser.add_option("--dailyrunoff", dest="dailyrunoff", default=False, \
action="store_true", help="Write daily output for hydrology")
parser.add_option("--diags", dest="diags", default=False, \
action="store_true", help="Write special outputs for diagnostics")
parser.add_option("--debugq", dest="debug", default=False, \
action="store_true", help='Use debug queue')
parser.add_option("--runroot", dest="runroot", default="", \
help="Directory where the run would be created")
parser.add_option('--project', dest='project',default='', \
help='Set project')
parser.add_option("--exeroot", dest="exeroot", default="", \
help="Location of executable")
parser.add_option("--lat_bounds", dest="lat_bounds", default='-999,-999', \
help = 'latitude range for regional run')
parser.add_option("--lon_bounds", dest="lon_bounds", default='-999,-999', \
help = 'longitude range for regional run')
parser.add_option("--humhol", dest="humhol", default=False, \
help = 'Use hummock/hollow microtopography', action="store_true")
parser.add_option("--marsh", dest="marsh", default=False, \
help = 'Use marsh hydrology/elevation', action="store_true")
parser.add_option("--mask", dest="mymask", default='', \
help = 'Mask file to use (regional only)')
parser.add_option("--model", dest="mymodel", default='', \
help = 'Model to use (ELM,CLM5)')
parser.add_option("--monthly_metdata", dest="monthly_metdata", default = '', \
help = "File containing met data (cpl_bypass only)")
parser.add_option("--namelist_file", dest="namelist_file", default='', \
help="File containing custom namelist options for user_nl_clm")
parser.add_option("--ilambvars", dest="ilambvars", default=False, \
action="store_true", help="Write special outputs for diagnostics")
parser.add_option("--dailyvars", dest="dailyvars", default=False, \
action="store_true", help="Write daily ouptut variables")
parser.add_option("--res", dest="res", default="CLM_USRDAT", \
help='Resoultion for global simulation')
parser.add_option("--point_list", dest="point_list", default='', \
help = 'File containing list of points to run')
parser.add_option("--pft", dest="mypft", default=-1, \
help = 'Use this PFT for all gridcells')
parser.add_option("--site_forcing", dest="site_forcing", default='', \
help = '6-character FLUXNET code for forcing data')
parser.add_option("--site", dest="site", default='', \
help = '6-character FLUXNET code to run (required)')
parser.add_option("--sitegroup", dest="sitegroup", default="AmeriFlux", \
help = "site group to use (default AmeriFlux)")
parser.add_option("--coldstart", dest="coldstart", default=False, \
help = "set cold start (mutually exclusive w/finidat)", \
action="store_true")
parser.add_option("--compset", dest="compset", default='I1850CNPRDCTCBC', \
help = "component set to use (required)\n"
"Currently supports ONLY *CLM45(CN) compsets")
parser.add_option("--cruncep", dest="cruncep", default=False, \
help = "use cru-ncep data", action="store_true")
parser.add_option("--cruncepv8", dest="cruncepv8", default=False, \
help = "use cru-ncep data", action="store_true")
parser.add_option("--cplhist", dest="cplhist", default=False, \
help= "use CPLHIST forcing", action="store_true")
parser.add_option("--gswp3", dest="gswp3", default=False, \
help= "use GSWP3 forcing", action="store_true")
parser.add_option("--princeton", dest="princeton", default=False, \
help= "use Princecton forcing", action="store_true")
parser.add_option("--livneh", dest="livneh", default=False, \
action="store_true", help = "Livneh correction to CRU precip (CONUS only)")
parser.add_option("--daymet", dest="daymet", default=False, \
action="store_true", help = "Daymet correction to GSWP3 precip (CONUS only)")
parser.add_option("--machine", dest="machine", default = '', \
help = "machine to\n")
parser.add_option("--compiler", dest="compiler", default='', \
help = "compiler to use (pgi, gnu)")
parser.add_option("--mpilib", dest="mpilib", default="mpi-serial", \
help = "mpi library (openmpi*, mpich, ibm, mpi-serial)")
parser.add_option("--ad_spinup", action="store_true", \
dest="ad_spinup", default=False, \
help = 'Run accelerated decomposition spinup')
parser.add_option("--exit_spinup", action="store_true", \
dest="exit_spinup", default=False, \
help = 'Run exit spinup (CLM 4.0 only)')
parser.add_option("--model_root", dest="csmdir", default='', \
help = "base model directory")
parser.add_option("--ccsm_input", dest="ccsm_input", default='', \
help = "input data directory for CESM (required)")
parser.add_option("--finidat_case", dest="finidat_case", default='', \
help = "case containing initial data file to use" \
+" (should be in your run directory)")
parser.add_option("--finidat", dest="finidat", default='', \
help = "initial data file to use" \
+" (should be in your run directory)")
parser.add_option("--finidat_year", dest="finidat_year", default=-1, \
help = "model year of initial data file (default is" \
+" last available)")
parser.add_option("--run_units", dest="run_units", default='nyears', \
help = "run length units (ndays, nyears)")
parser.add_option("--run_n", dest="run_n", default=50, \
help = "run length (in run units)")
parser.add_option("--rest_n", dest="rest_n", default=-1, \
help = "restart interval (in run units)")
parser.add_option("--run_startyear", dest="run_startyear",default=-1, \
help='Starting year for model output')
parser.add_option("--rmold", dest="rmold", default=False, action="store_true", \
help = 'Remove old case directory with same name' \
+" before proceeding")
parser.add_option("--srcmods_loc", dest="srcmods_loc", default='', \
help = 'Copy sourcemods from this location')
parser.add_option("--parm_file", dest="parm_file", default='',
help = 'file for parameter modifications')
parser.add_option("--parm_vals", dest="parm_vals", default="", \
help = 'User specified parameter values')
parser.add_option("--parm_file_P", dest="parm_file_P", default='',
help = 'file for P parameter modifications')
parser.add_option("--hist_mfilt", dest="hist_mfilt", default=-1, \
help = 'number of output timesteps per file')
parser.add_option("--hist_nhtfrq", dest="hist_nhtfrq", default=-999, \
help = 'output file timestep')
parser.add_option("--hist_vars", dest="hist_vars", default='', \
help = 'use hist_vars file')
#parser.add_option("--queue", dest="queue", default='essg08q', \
# help = 'PBS submission queue')
parser.add_option("--clean_config", dest="clean_config", default=False, \
help = 'Run cesm_setup -clean script')
parser.add_option("--clean_build", dest="clean_build", default=False, \
help = 'Perform clean build before building', \
action="store_true")
parser.add_option("--no_config", dest="no_config", default=False, \
help = 'do NOT configure case', action="store_true")
parser.add_option("--no_build", dest="no_build", default=False, \
help = 'do NOT build CESM', action="store_true")
parser.add_option("--no_submit", dest="no_submit", default=False, \
help = 'do NOT submit CESM to queue', action="store_true")
parser.add_option("--align_year", dest="align_year", default=-999, \
help = 'Alignment year (transient run only)')
parser.add_option("--np", dest="np", default=1, \
help = 'number of processors')
parser.add_option("--ninst", dest="ninst", default=1, \
help = 'number of land model instances')
parser.add_option("--ng", dest="ng", default=64, \
help = 'number of groups to run in ensmble mode')
parser.add_option("--tstep", dest="tstep", default=0.5, \
help = 'CLM timestep (hours)')
parser.add_option("--co2_file", dest="co2_file", default="fco2_datm_rcp4.5_1765-2500_c130312.nc", \
help = 'CLM timestep (hours)')
parser.add_option("--nyears_ad_spinup", dest="ny_ad", default=250, \
help = 'number of years to run ad_spinup')
parser.add_option("--metdir", dest="metdir", default="none", \
help = 'subdirectory for met data forcing')
parser.add_option("--nopointdata", action="store_true", \
dest="nopointdata", help="Do NOT make point data (use data already created)", \
default=False)
#parser.add_option("--cleanlogs",dest="cleanlogs", help=\
# "Removes temporary and log files that are created",\
# default=False,action="store_true")
parser.add_option("--nofire", action="store_true", dest="nofire", default=False, \
help="To turn off wildfires")
parser.add_option("--nopftdyn", action="store_true", dest="nopftdyn", \
default = False, help='Do not use dynamic PFT file')
parser.add_option("--harvmod", action="store_true", dest="harvmod", \
default=False, help = "Turn on harvest modificaton" \
"All harvest is performed in first timestep")
parser.add_option("--no_dynroot", dest="no_dynroot", default=False, \
help = 'Turn off dynamic root distribution', action="store_true")
parser.add_option("--bulk_denitrif", dest="bulk_denitrif", default=False, \
help = 'To turn off BGC nitrification-denitrification', action="store_true")
parser.add_option("--vertsoilc", dest="vsoilc", default=False, \
help = 'To turn on CN with multiple soil layers, excluding CENTURY C module (CLM4ME on as well)', action="store_true")
parser.add_option("--centbgc", dest="centbgc", default=False, \
help = 'To turn on CN with multiple soil layers, CENTURY C module (CLM4ME on as well)', action="store_true")
parser.add_option("--CH4", dest="CH4", default=False, \
help = 'To turn on CN with CLM4me', action="store_true")
parser.add_option("--1850_ndep", dest="ndep1850", default=False, \
help = 'Use constant 1850 N deposition', action="store_true")
parser.add_option("--1850_aero", dest="aero1850", default=False, \
help = 'Use constant 1850 aerosol deposition', action="store_true")
parser.add_option("--1850_co2", dest="co21850", default=False, \
help = 'Use constant 1850 CO2 concentration', action="store_true")
parser.add_option("--C13", dest="C13", default=False, \
help = 'Switch to turn on C13', action="store_true")
parser.add_option("--C14", dest="C14", default=False, \
help = 'Use C14 as C13 (no decay)', action="store_true")
parser.add_option("--branch", dest="branch", default=False, \
help = 'Switch for branch run', action="store_true")
parser.add_option("--makemetdata", dest="makemet", default=False, \
help = 'Generate meteorology', action="store_true")
parser.add_option("--surfdata_grid", dest="surfdata_grid", default=False, \
help = 'Use gridded surface data instead of site data', action="store_true")
parser.add_option("--include_nonveg", dest="include_nonveg", default=False, \
help = 'Include non-vegetated columns/Landunits in surface data')
parser.add_option("--trans2", dest="trans2", default=False, action="store_true", \
help = 'Tranisnent phase 2 (1901-2010) - CRUNCEP only')
parser.add_option("--spinup_vars", dest="spinup_vars", default=False, \
help = 'Limit output vars in spinup runs', action="store_true")
parser.add_option("--trans_varlist", dest = "trans_varlist", default='', help = "Transient outputs")
parser.add_option("--c_only", dest="c_only", default=False, \
help="Carbon only (supplemental P and N)", action="store_true")
parser.add_option("--cn_only", dest="cn_only", default=False, \
help = 'Carbon/Nitrogen only (supplemental P)', action="store_true")
parser.add_option("--cp_only", dest="cp_only", default=False, \
help = 'Carbon/Phosphorus only (supplemental N)', action = "store_true")
parser.add_option("--ensemble_file", dest="ensemble_file", default='', \
help = 'Parameter sample file to generate ensemble')
parser.add_option("--mc_ensemble", dest="mc_ensemble", default=-1, \
help = 'Monte Carlo ensemble (argument is # of simulations)')
parser.add_option("--ensemble_nocopy", dest="ensemble_nocopy", default=False, \
help = 'Do not copy files to ensemble directories', action="store_true")
parser.add_option("--surffile", dest="surffile", default="", \
help = 'Surface file to use')
parser.add_option("--domainfile", dest="domainfile", default="", \
help = 'Domain file to use')
parser.add_option("--fates_hydro", dest="fates_hydro", default=False, action="store_true", \
help = 'Set fates hydro to true')
parser.add_option("--fates_paramfile", dest="fates_paramfile", default="", \
help = 'Fates parameter file to use')
parser.add_option("--var_soilthickness", dest="var_soilthickness", default=False, \
help = 'Use variable soil thickness from surface data', action="store_true")
parser.add_option("--add_temperature", dest="addt", default=0.0, \
help = 'Temperature to add to atmospheric forcing')
parser.add_option("--add_co2", dest="addco2", default=0.0, \
help = 'CO2 (ppmv) to add to atmospheric forcing')
parser.add_option("--startdate_add_temperature", dest="sd_addt", default="99991231", \
help = 'Date (YYYYMMDD) to begin addding temperature')
parser.add_option("--startdate_add_co2", dest="sd_addco2", default="99991231", \
help = 'Date (YYYYMMDD) to begin addding CO2')
#Changed by Ming for mesabi
parser.add_option("--archiveroot", dest="archiveroot", default='', \
help = "archive root directory only for mesabi")
#Added by Kirk to include the modified parameter file
parser.add_option("--mod_parm_file", dest="mod_parm_file", default='', \
help = "adding the path to the modified parameter file")
parser.add_option("--mod_parm_file_P", dest="mod_parm_file_P", default='', \
help = "adding the path to | |
# by default, all!
# Ensure column names are unicode
df.columns = [unicode(cleanup_name(x, normalize=False, clean_nonletters=False), errors='ignore') for x in df.columns]
# By default, take all columns
cols = df.columns
# Calculate total number of items
if progress_bar:
pbar_total = 0
for col in cols:
pbar_total += len(df[col].index)
# Main loop
if progress_bar:
pbar = _tqdm(total=pbar_total, desc='UNICODE', disable=(not progress_bar))
for col in cols:
for idx in df[col].index:
# Unidecode if value is a string
if isinstance(df.loc[idx,col], str): # if item is already unicode, we don't need to do anything (also if item is null or another type, we do not need to decode)
df.loc[idx,col] = string_to_unicode(df.loc[idx,col], failsafe_encoding=failsafe_encoding, skip_errors=skip_errors)
if progress_bar:
pbar.update()
# Restore index
if idxbak:
df.set_index(idxbak, inplace=True)
return df
def df_to_unicode_fast(df_in, cols=None, replace_ascii=False, skip_errors=False, progress_bar=False):
"""Ensure unicode encoding for all strings in the specified columns of a dataframe in a fast way, and optionally by replacing non recognized characters by ascii equivalents. Also ensures that columns names are correctly decodable as unicode if cols=None.
If cols=None, will walk through all columns.
If replace_ascii, will replace special characters with the closest ASCII counterpart (using unidecode) if the conversion to unicode fails
If skip_errors=True, the unicode encoding will be forced by skipping undecodable characters (errors='ignore').
The main difference with df_to_unicode() is that the former tries to maintain special characters (instead of replacing them with their closest ascii counterpart) and it is slower (but more thorough, it should not miss any field, whereas the fast version will work column by column and thus might miss a column of mixed types).
"""
# Make a copy to avoid tampering the original
df = df_in.copy()
# If there is a complex index, it might contain strings, so we reset it as columns so that we can unidecode indices too, and we will restore the indices at the end
if df.index.dtype.name == 'object' or isinstance(df.index, pd.core.indexes.multi.MultiIndex):
idxbak = df.index.names
df.reset_index(inplace=True)
else:
idxbak = None
# Which columns do we have to unidecode?
if cols is None: # by default, all!
# Ensure column names are unicode
df.columns = [unicode(cleanup_name(x, normalize=False, clean_nonletters=False), errors='ignore') for x in df.columns]
# Use the new column names
cols = df.columns
if skip_errors:
serrors = 'ignore'
else:
serrors = 'strict'
for col in _tqdm(cols, desc='UNICODE', disable=(not progress_bar)):
# Verify that the column is of type object, else for sure it is not a string
# also if there are duplicate names, just skip these columns
# TODO: try to process columns with duplicate names
if (len(df.loc[:, col].shape) > 1 and df.loc[:, col].shape[1] > 1) or df.loc[:, col].dtype.name != 'object':
continue
try:
# First try a decoding by detecting the correct encoding
#encoding = chardet.detect(''.join(df.loc[:, col]))['encoding']
allvals = (x if isinstance(x, basestring) else str(x) for _, x in df.loc[:, col].items())
allvalsjoined = ''.join(allvals)
if isinstance(allvalsjoined, unicode): # if unicode, skip decoding
encoding = None
else:
encoding = chardet.detect(allvalsjoined)['encoding']
if encoding:
df.loc[:, col] = df.loc[:, col].apply(lambda x: x.decode(encoding, errors=serrors) if isinstance(x, str) else x)
#df.loc[:, col] = df.loc[:, col].astype('unicode') # DEPRECATED: works but if we do this, all null values (nan, nat, etc) will be converted to strings and become very difficult to process (eg, not detectable using pd.isnull())!
#df[col] = df[col].map(lambda x: x.encode('unicode-escape').decode('utf-8'))
except Exception as exc:
try:
# If decoding failed, we can try to replace the special characters with their closest ASCII counterpart (via unidecode)
if replace_ascii:
df.loc[:, col] = df.loc[:, col].apply(lambda x: unicode(cleanup_name(x, normalize=False, clean_nonletters=False), errors=serrors) if isinstance(x, str) else x)
#df.loc[:, col] = df.loc[:, col].astype('unicode') # DEPRECATED: works but if we do this, all null values (nan, nat, etc) will be converted to strings and become very difficult to process (eg, not detectable using pd.isnull())!
else:
raise
except Exception as exc:
# Else everything failed!
if skip_errors:
pass
else:
print('Failed with column: %s' % col)
raise
# Restore index
if idxbak:
df.set_index(idxbak, inplace=True)
return df
def df_encode(df_in, cols=None, encoding='utf-8', skip_errors=False, decode_if_errors=False, progress_bar=False):
"""Encode all unicode strings in a dataframe into a string of the chosen encoding.
When decode_if_errors is True, if a string (str) is found, an attempt will be made to decode it using an autodetection of the encoding, to make a unicode sandwich."""
# Make a copy to avoid tampering the original
df = df_in.copy()
# If there is a complex index, it might contain strings, so we reset it as columns so that we can unidecode indices too, and we will restore the indices at the end
if df.index.dtype.name == 'object' or isinstance(df.index, pd.core.indexes.multi.MultiIndex):
idxbak = df.index.names
df.reset_index(inplace=True)
else:
idxbak = None
# Which columns do we have to unidecode?
if cols is None: # by default, all!
# Ensure column names are encoded
df.columns = [x.encode(encoding) for x in df.columns]
# Use all columns, and the new column names encoded
cols = df.columns
# Calculate total number of items
if progress_bar:
pbar_total = 0
for col in cols:
pbar_total += len(df[col].index)
# Main loop
if progress_bar:
pbar = _tqdm(total=pbar_total, desc='UNICODE', disable=(not progress_bar))
for col in cols:
for idx in df[col].index:
# Unidecode if value is a string
if isinstance(df.loc[idx,col], (basestring, unicode)):
try:
# Try to encode to utf-8, but only if it is unicode
if isinstance(df.loc[idx,col], unicode):
df.loc[idx,col] = df.loc[idx,col].encode(encoding)
elif decode_if_errors:
df.loc[idx,col] = string_to_unicode(df.loc[idx,col]).encode(encoding)
else:
raise ValueError('Error at column "%s" index %s: not unicode!')
except UnicodeDecodeError as exc:
# At worst, try unidecode
if skip_errors:
df.loc[idx,col] = _unidecode(df.loc[idx,col]).encode(encoding)
else:
print('Error at column "%s" index %s' % (col, str(idx)))
raise
if progress_bar:
pbar.update()
# Restore index
if idxbak:
df.set_index(idxbak, inplace=True)
return df
def df_literal_eval(xin, aggressive=False):
"""Evaluate each string cell of a DataFrame as if it was a Python object, and return the Python object
aggressive=True makes the algorithm remove any list separator beforehand to separate the contained items to the maximum, this allows working with nested lists for instance"""
if aggressive:
x = str(xin).replace('[', '').replace(']', '').replace('{', '').replace('}', '').replace("'", '')
else:
x = xin
try:
# Try to evaluate using ast
return(ast.literal_eval(x))
except (SyntaxError, ValueError):
try:
# Else evaluate as a list without quotes
if not ((x.startswith('[') or x.startswith('{')) and (x.endswith(']') or x.endswith('}'))):
raise Exception()
return re.split(',\s*u?', re.sub('[\[\]\{\}]', '', x))
# TODO: implement a real parser using pyparser: https://stackoverflow.com/a/1894785
except Exception as exc:
# Else simply return the item as-is
return xin
def df_cols_lower(df_in, col='name'):
"""Find in a DataFrame any column matching the col argument in lowercase and rename all found columns to lowercase"""
# Make a copy to avoid tampering the original
df = df_in.copy()
# Find and rename any column "Name" or "NAME" to lowercase "name"
namecols = df.columns[[True if x.lower() == col.lower() else False for x in df.columns]]
if len(namecols) > 0:
df = df.rename(columns={x: x.lower() for x in namecols})
return df
def date_fr2en(s):
"""Convert french month names into english so that dateutil.parse works"""
if isinstance(s, basestring):
s = s.lower()
rep = {
'jan\w+': 'jan',
'fe\w+': 'feb',
'mar\w+': 'march',
'av\w+': 'april',
'mai\w+': 'may',
'juin\w+': 'june',
'juil\w+': 'july',
'ao\w+': 'august',
'se\w+': 'september',
'oc\w+': 'october',
'no\w+': 'november',
'de\w+': 'december',
}
for m, r in rep.items():
s = re.sub(m, r, s)
return s
def date_cleanchar(s):
"""Clean a date from any non useful character (else dateutil_parser will fail, eg with a '?')"""
if isinstance(s, basestring):
s = s.lower()
res = re.findall('[\d/-:\s]+', s, re.UNICODE)
if res:
return '-'.join(res)
else:
return None
else:
return s
def date_clean(s):
"""Clean the provided string and parse as a date using dateutil.parser fuzzy matching (alternative to pd.to_datetime()). Should be used with df[col].apply(df_date_clean)."""
if pd.isnull(s):
return None
else:
# Clean non date characters (might choke the date parser)
cleaned_date = date_fr2en(date_cleanchar(str(s))) # convert to str so that if we get a datetime object, we do not get an error
if not cleaned_date:
return None
else:
try:
# First try an ISO date parsing, this is to circumvent bad decoding when month is in the middle, see: https://github.com/dateutil/dateutil/pull/340
return dateutil_parser.isoparse(cleaned_date)
except ValueError | |
# -*- coding: utf-8 -*-
import logging
import functools
from collections import OrderedDict
from fabric2.runners import Result
from django.contrib.auth.models import Permission
from django.contrib.contenttypes.models import ContentType
from django.utils.text import camel_case_to_spaces
from django.dispatch import Signal
from .models import Host
from .models import MinkeModel
from .models import MinkeSession
from .models import BaseMessage
from .forms import CommandForm
from .messages import PreMessage
from .messages import TableMessage
from .messages import ExecutionMessage
from .exceptions import InvalidMinkeSetup
from .exceptions import SessionRegistrationError
from .utils import FormatDict
logger = logging.getLogger(__name__)
class RegistryDict(OrderedDict):
"""
A reload-able session-registry.
"""
reload_sessions = Signal(providing_args=['session_name'])
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._static_sessions = None
def reload(self, session_name=None):
"""
Load dynamical sessions into the registry.
Reset the registry to the static sessions. Then send a reload-signal.
"""
# We backup the static-sessions and reset the registry before each
# reload. This way the reload algorithms doesn't have to unregister
# obsolete sessions.
if self._static_sessions:
self.clear()
self.update(self._static_sessions)
else:
self._static_sessions = self.copy()
# no reloading needed for static sessions
if session_name and session_name in self:
return
# trigger the reload signal
self.reload_sessions.send(sender=self.__class__, session_name=session_name)
REGISTRY = RegistryDict()
class SessionGroup:
"""
A group of session - displayed as optgroups in the select-widget.
Create a group and use it as decorator for Sessions::
my_group = SessionGroup('My Group')
@my_group
class MySession(Session):
pass
"""
def __init__(self, name=None):
self.name = name
def __call__(self, cls, name=None):
cls.group = name or self.name
return cls
class SessionRegistration(type):
"""
metaclass for Sessions that implements session-registration
"""
def __new__(cls, name, bases, dct):
# Setting the abstract-attr explicitly avoids its inheritance.
dct['abstract'] = dct.get('abstract', False)
return super().__new__(cls, name, bases, dct)
def __init__(cls, classname, bases, attrs):
super().__init__(classname, bases, attrs)
if not cls.abstract:
cls.register()
if cls.auto_permission:
cls.add_permission()
def register(cls):
"""
Register the session-class.
"""
# some sanity-checks
if cls.__name__ in REGISTRY:
msg = 'A session with that name was already registered.'
raise SessionRegistrationError(cls, msg)
if not cls.work_on:
msg = 'At least one minke-model must be specified.'
raise SessionRegistrationError(cls, msg)
for model in cls.work_on:
try:
assert(model == Host or issubclass(model, MinkeModel))
except (TypeError, AssertionError):
msg = '{} is no minke-model.'.format(model)
raise SessionRegistrationError(cls, msg)
if issubclass(cls, SingleCommandSession) and not cls.command:
msg = 'SingleCommandSession needs to specify an command.'
raise SessionRegistrationError(cls, msg)
if issubclass(cls, CommandChainSession) and not cls.commands:
msg = 'CommandChainSession needs to specify commands.'
raise SessionRegistrationError(cls, msg)
# TODO: Check for recursion in SessionChains
# TODO: Check SessionChain's sessions
if issubclass(cls, SessionChain) and not cls.sessions:
msg = 'SessionChain needs to specify sessions.'
raise SessionRegistrationError(cls, msg)
# set verbose-name if missing
if not cls.verbose_name:
cls.verbose_name = camel_case_to_spaces(cls.__name__)
# register session
REGISTRY[cls.__name__] = cls
def _get_permission(cls):
codename = 'run_{}'.format(cls.__name__.lower())
name = 'Can run {}'.format(cls.__name__)
lookup = 'minke.{}'.format(codename)
return codename, name, lookup
def create_permission(cls):
"""
Create a run-permission for this session-class.
"""
content_type = ContentType.objects.get_for_model(MinkeSession)
codename, name, lookup = cls._get_permission()
permission, created = Permission.objects.update_or_create(
codename=codename,
content_type=content_type,
defaults=dict(name=name))
return permission, created
def delete_permission(cls):
codename, name, lookup = cls._get_permission()
try:
Permission.objects.get(codename=codename).delete()
except Permission.DoesNotExist:
pass
else:
cls.permissions = tuple(set(cls.permissions) - set((lookup,)))
def add_permission(cls):
codename, name, lookup = cls._get_permission()
cls.permissions = tuple(set(cls.permissions) | set((lookup,)))
def protect(method):
"""
Decorator for session-methods to defer their interrupt.
"""
@functools.wraps(method)
def wrapper(obj, *args, **kwargs):
# are we already protected?
if obj._busy:
return method(obj, *args, **kwargs)
# otherwise protect the method-call by setting the busy-flag
obj._busy = True
result = method(obj, *args, **kwargs)
# if interruption was deferred now is the time to raise it
if obj._stopped:
raise KeyboardInterrupt
obj._busy = False
return result
return wrapper
class Session(metaclass=SessionRegistration):
"""Base-class for all session-classes.
All session-classes must inherit from Session. By defining a subclass of
Session the subclass will be implicitly registered as session-class and also
a run-permission will be created for it. To prevent this behavior use an
abstract session by setting :attr:`.abstract` to True.
Each session will be instantiated with a fabric-:doc:`fabric:api/connection`
and an object of :class:`~.models.MinkeSession`. The connection-object
provides the remote-access, while the minkesession is the database-
representation of a specific session running for a specific
:class:`minkemodel-object <.models.MinkeModel>`.
For a session-class to be useful you at least has to define the
:meth:`.process`-method and add one or more :class:`~.models.MinkeModel` to
:attr:`.work_on`-attribute.
"""
abstract = True
"""
An abstract session-class won't be registered itself. This is useful if your
session-class should be a base-class for other sessions.
Abstract session-classes can be registered manually by calling its
classmethod :meth:`~.SessionRegistration.register`::
MySession.register()
This won't add a run-permission-lookup-string to :attr:`.permissions`. To do so
use the classmethod :meth:`.SessionRegistration.add_permission`::
MySession.add_permission()
"""
verbose_name = None
"""Display-name for sessions."""
group = None
"""
Group-name used as optgroup-tag in the select-widget.
Best practice to group sessions is to use a :class:`.SessionGroup`.
"""
work_on = tuple()
"""Tuple of minke-models. Models the session can be used with."""
permissions = tuple()
"""
Tuple of permission-strings. To be able to run a session a user must have
all the permissions listed. The strings should have the following format:
"<app-label>.<permission's-codename>.
"""
auto_permission = True
"""
If True a lookup-string for a session-specific run-permission will be
automatically added to :attr:`.permissions`.
Note
----
To avoid database-access on module-level we won't create the permission itself.
Once you setup your sessions you could create run-permissions for all sessions
using the api-command::
$ ./manage.py minkeadm --create-permissions
"""
# TODO: Make this a tuple or list of forms to render.
form = None
"""
An optional form that will be rendered before the session will be
processed. The form-data will be accessible within the session as the
data-property. Use it if the session's processing depends on additional
user-input-data.
Instead of setting the form-attribute you can also directly overwrite
:meth:`.get_form`.
"""
confirm = False
"""
If confirm is true, the admin-site asks for a user-confirmation before
processing a session, which also allows to review the objects the session
was revoked with.
"""
# TODO: This should be renamed to something like context.
invoke_config = dict()
"""
Session-specific fabric- and invoke-configuration-parameters which will
be used to initialize a :class:`fabric-connection <fabric.connection.Connection>`.
The keys must be formatted in a way that is accepted by
:meth:`~.fabrictools.FabricConfig.load_snakeconfig`.
See also the documentation for the configuration of
:doc:`fabric <fabric:concepts/configuration>` and
:doc:`invoke <invoke:concepts/configuration>`.
"""
parrallel_per_host = False
"""
Allow parrallel processing of multiple celery-tasks on a single host.
If multiple minke-objects are associated with the same host all tasks
running on them would be processed in a serial manner by default. This is
to protect the ressources of the host-system. If you want to allow parrallel
processing of multiple celery-tasks on a single host set parrallel_per_host
to True.
Note
----
To perform parrallel task-execution on a single host we make use of celery's
chords-primitive, which needs a functioning result-backend to be configured.
Please see the :ref:`celery-documentation <chord-important-notes>`
for more details.
"""
def __init__(self, con, db, minkeobj=None):
"""Session's init-method.
Parameters
----------
con : obj of :class:`fabric.connection.Connection`
db : obj of :class:`~.models.MinkeSession`
minkeobj : obj of :class:`~.models.Minkeobj` (optional)
Only required if you want to initialize a session out of another
session and let it work on a different minkeobj.
"""
# TODO: Update the connection dict with the minkeobj.data.
# Maybe use a prefix on form fields for context data.
self._c = con
self._db = db
self._minkeobj = minkeobj
self._stopped = False
self._busy = False
self.start = db.start
self.end = db.end
@classmethod
def get_form(cls):
"""
Return :attr:`.form` by default.
Overwrite this method if you need to setup your form-class dynamically.
"""
return cls.form
@property
def c(self):
"""
Refers to the :class:`fabric.connection.Connection`-object the session
was initialized with.
"""
return self._c
@property
def minkeobj(self):
"""
Refers to :attr:`.models.MinkeSession.minkeobj`.
"""
return self._minkeobj or self._db.minkeobj
@property
def status(self):
"""
Refers to :attr:`.models.MinkeSession.session_status`.
"""
return self._db.session_status
@property
def data(self):
"""
Refers to :attr:`.models.MinkeSession.session_data`.
This model-field holds all the data that comes from :attr:`.form`.
"""
return self._db.session_data
def stop(self, *arg, **kwargs):
"""Interrupt the session's processing.
This method could be called twice. The first time it will initiate a
soft interruption which means a current remote-process won't be
interrupted. The session will be stopped subsequently.
If it is called meanwhile a second time, the session will be killed
immediately.
Note
----
| |
fetch_subtract_a(self):
data = self.fetch()
# 1 cycle
self.compare_a_simple(data) # 1 cycle
self.a.sub(data, False)
def compare_a(self, getCaller, setCaller=None):
# 1 cycle
self.compare_a_simple(int(self.a.get() - getCaller.get()))
def compare_a_simple(self, s):
s = s & 0xFF
self.f.reset()
self.f.n_flag = True
self.f.z_flag_compare(s)
self.hc_flag_finish(s)
self.cycles -= 1
def hc_flag_finish(self, data):
if data > self.a.get():
self.f.c_flag = True
self.f.h_flag_compare(self.a.get(), data)
def AND(self, getCaller, setCaller=None):
# 1 cycle
self.a.set(self.a.get() & getCaller.get()) # 1 cycle
self.f.z_flag_compare(self.a.get(), reset=True)
def XOR(self, getCaller, setCaller=None):
# 1 cycle
self.a.set( self.a.get() ^ getCaller.get()) # 1 cycle
self.f.z_flag_compare(self.a.get(), reset=True)
def OR(self, getCaller, setCaller=None):
# 1 cycle
self.a.set(self.a.get() | getCaller.get()) # 1 cycle
self.f.z_flag_compare(self.a.get(), reset=True)
def inc_double_register(self, doubleRegister):
doubleRegister.inc()
def dec_double_register(self, doubleRegister):
doubleRegister.dec()
def inc(self, getCaller, setCaller):
# 1 cycle
data = (getCaller.get() + 1) & 0xFF
self.dec_inc_flag_finish(data, setCaller, 0x00)
def dec(self, getCaller, setCaller):
# 1 cycle
data = (getCaller.get() - 1) & 0xFF
self.dec_inc_flag_finish(data, setCaller, 0x0F)
self.f.n_flag = True
def dec_inc_flag_finish(self, data, setCaller, compare):
self.f.partial_reset(keep_c=True)
self.f.z_flag_compare(data)
if (data & 0x0F) == compare:
self.f.h_flag = True
setCaller.set(data) # 1 cycle
def rotate_left_circular(self, getCaller, setCaller):
# RLC 1 cycle
data = getCaller.get()
s = (data << 1) + (data >> 7)
self.flags_and_setter_finish(s, setCaller, 0x80)
#self.cycles -= 1
def rotate_left_circular_a(self):
# RLCA rotate_left_circular_a 1 cycle
self.rotate_left_circular(RegisterCallWrapper(self.a), \
RegisterCallWrapper(self.a))
def rotate_left(self, getCaller, setCaller):
# 1 cycle
s = (getCaller.get() << 1) & 0xFF
if self.f.c_flag:
s += 0x01
self.flags_and_setter_finish(s, setCaller, 0x80) # 1 cycle
def rotate_left_a(self):
# RLA 1 cycle
self.rotate_left(RegisterCallWrapper(self.a), \
RegisterCallWrapper(self.a))
def rotate_right_circular(self, getCaller, setCaller):
data = getCaller.get()
# RRC 1 cycle
s = (data >> 1) + ((data & 0x01) << 7)
self.flags_and_setter_finish(s, setCaller) # 1 cycle
def rotate_right_circular_a(self):
# RRCA 1 cycle
self.rotate_right_circular(RegisterCallWrapper(self.a), \
RegisterCallWrapper(self.a))
def rotate_right(self, getCaller, setCaller):
# 1 cycle
s = (getCaller.get() >> 1)
if self.f.c_flag:
s += 0x08
self.flags_and_setter_finish(s, setCaller) # 1 cycle
def rotate_right_a(self):
# RRA 1 cycle
self.rotate_right(RegisterCallWrapper(self.a), \
RegisterCallWrapper(self.a))
def shift_left_arithmetic(self, getCaller, setCaller):
# 2 cycles
s = (getCaller.get() << 1) & 0xFF
self.flags_and_setter_finish(s, setCaller, 0x80) # 1 cycle
def shift_right_arithmetic(self, getCaller, setCaller):
data = getCaller.get()
# 1 cycle
s = (data >> 1) + (data & 0x80)
self.flags_and_setter_finish(s, setCaller) # 1 cycle
def shift_word_right_logical(self, getCaller, setCaller):
# 2 cycles
s = (getCaller.get() >> 1)
self.flags_and_setter_finish(s, setCaller) # 2 cycles
def flags_and_setter_finish(self, s, setCaller, compare_and=0x01):
# 2 cycles
s &= 0xFF
self.f.z_flag_compare(s, reset=True)
self.f.c_flag_add(s, compare_and)
setCaller.set(s) # 1 cycle
def swap(self, getCaller, setCaller):
data = getCaller.get()
# 1 cycle
s = ((data << 4) + (data >> 4)) & 0xFF
self.f.z_flag_compare(s, reset=True)
setCaller.set(s)
def test_bit(self, getCaller, setCaller, n):
# 2 cycles
self.f.partial_reset(keep_c=True)
self.f.h_flag = True
self.f.z_flag = False
if (getCaller.get() & (1 << n)) == 0:
self.f.z_flag = True
self.cycles -= 1
def set_bit(self, getCaller, setCaller, n):
# 1 cycle
setCaller.set(getCaller.get() | (1 << n)) # 1 cycle
def reset_bit(self, getCaller, setCaller, n):
# 1 cycle
setCaller.set(getCaller.get() & (~(1 << n))) # 1 cycle
def store_fetched_memory_in_a(self):
# LD A,(nnnn), 4 cycles
self.a.set(self.read(self.fetch_double_address())) # 1+1 + 2 cycles
def write_a_at_bc_address(self):
# 2 cycles
self.write(self.bc.get(), self.a.get())
def write_a_at_de_address(self):
self.write(self.de.get(), self.a.get())
def store_memory_at_bc_in_a(self):
self.a.set(self.read(self.bc.get()))
def store_memory_at_de_in_a(self):
self.a.set(self.read(self.de.get()))
def ld_dbRegisteri_A(self, register):
# LD (rr),A 2 cycles
self.write(register.get(), self.a.get()) # 2 cycles
def load_mem_sp(self):
# LD (nnnn),SP 5 cycles
address = self.fetch_double_address() # 2 cycles
self.write(address, self.sp.get_lo()) # 2 cycles
self.write((address + 1), self.sp.get_hi()) # 2 cycles
self.cycles += 1
def store_a_at_fetched_address(self):
# LD (nnnn),A 4 cycles
self.write(self.fetch_double_address(), self.a.get()) # 2 cycles
def store_memory_at_axpanded_fetch_address_in_a(self):
# LDH A,(nn) 3 cycles
self.a.set(self.read(0xFF00 + self.fetch())) # 1+1+1 cycles
def store_expanded_c_in_a(self):
# LDH A,(C) 2 cycles
self.a.set(self.read(0xFF00 + self.bc.get_lo())) # 1+2 cycles
def load_and_increment_a_hli(self):
# loadAndIncrement A,(HL) 2 cycles
self.a.set(self.read(self.hl.get())) # 2 cycles
self.hl.inc()# 2 cycles
self.cycles += 2
def load_and_decrement_a_hli(self):
# loadAndDecrement A,(HL) 2 cycles
self.a.set(self.read(self.hl.get())) # 2 cycles
self.hl.dec() # 2 cycles
self.cycles += 2
def write_a_at_expanded_fetch_address(self):
# LDH (nn),A 3 cycles
self.write(0xFF00 + self.fetch(), self.a.get()) # 2 + 1 cycles
def write_a_at_expaded_c_address(self):
# LDH (C),A 2 cycles
self.write(0xFF00 + self.bc.get_lo(), self.a.get()) # 2 cycles
def load_and_increment_hli_a(self):
# loadAndIncrement (HL),A 2 cycles
self.write(self.hl.get(), self.a.get()) # 2 cycles
self.hl.inc() # 2 cycles
self.cycles += 2
def load_and_decrement_hli_a(self):
# loadAndDecrement (HL),A 2 cycles
self.write(self.hl.get(), self.a.get()) # 2 cycles
self.hl.dec() # 2 cycles
self.cycles += 2
def store_hl_in_sp(self):
# LD SP,HL 2 cycles
self.sp.set(self.hl.get()) # 1 cycle
self.cycles -= 1
def complement_a(self):
# CPA
self.a.set(self.a.get() ^ 0xFF)
self.f.n_flag = True
self.f.h_flag = True
def decimal_adjust_accumulator(self):
# DAA 1 cycle
delta = 0
if self.is_h():
delta |= 0x06
if self.is_c():
delta |= 0x60
if (self.a.get() & 0x0F) > 0x09:
delta |= 0x06
if (self.a.get() & 0xF0) > 0x80:
delta |= 0x60
if (self.a.get() & 0xF0) > 0x90:
delta |= 0x60
if not self.is_n():
self.a.set((self.a.get() + delta) & 0xFF) # 1 cycle
else:
self.a.set((self.a.get() - delta) & 0xFF) # 1 cycle
self.f.partial_reset(keep_n=True)
if delta >= 0x60:
self.f.c_flag = True
self.f.z_flag_compare(self.a.get())
def inc_double_register(self, register):
# INC rr
register.inc()
def dec_double_register(self, register):
# DEC rr
register.dec()
def increment_sp_by_fetch(self):
# ADD SP,nn 4 cycles
self.sp.set(self.get_fetchadded_sp()) # 1+1 cycle
self.cycles -= 2
def store_fetch_added_sp_in_hl(self):
# LD HL,SP+nn 3 cycles
self.hl.set(self.get_fetchadded_sp()) # 1+1 cycle
self.cycles -= 1
def get_fetchadded_sp(self):
# 1 cycle
offset = self.fetch() # 1 cycle
s = (self.sp.get() + offset) & 0xFFFF
self.f.reset()
if (offset >= 0):
if s < self.sp.get():
self.f.c_flag = True
if (s & 0x0F00) < (self.sp.get() & 0x0F00):
self.f.h_flag = True
else:
if s > self.sp.get():
self.f.c_flag = True
if (s & 0x0F00) > (self.sp.get() & 0x0F00):
self.f.h_flag = True
return s
def complement_carry_flag(self):
# CCF/SCF
self.f.partial_reset(keep_z=True, keep_c=True)
self.f.c_flag = not self.f.c_flag
def set_carry_flag(self):
self.f.partial_reset(keep_z=True)
self.f.c_flag = True
def nop(self):
# NOP 1 cycle
self.cycles -= 1
def unconditional_jump(self):
# JP nnnn, 4 cycles
self.pc.set(self.fetch_double_address()) # 1+2 cycles
self.cycles -= 1
def conditional_jump(self, cc):
# JP cc,nnnn 3,4 cycles
if cc:
self.unconditional_jump() # 4 cycles
else:
self.pc.add(2) # 3 cycles
def relative_unconditional_jump(self):
# JR +nn, 3 cycles
self.pc.add(self.fetch()) # 3 + 1 cycles
self.cycles += 1
def relative_conditional_jump(self, cc):
# JR cc,+nn, 2,3 cycles
if cc:
self.relative_unconditional_jump() # 3 cycles
else:
self.pc.inc() # 2 cycles
def unconditional_call(self):
# CALL nnnn, 6 cycles
self.call(self.fetch_double_address()) # 4+2 cycles
def conditional_call(self, cc):
# CALL cc,nnnn, 3,6 cycles
if cc:
self.unconditional_call() # 6 cycles
else:
self.pc.add(2) # 3 cycles
def ret(self):
# RET 4 cycles
lo = self.pop() # 1 cycle
hi = self.pop() # 1 cycle
self.pc.set_hi_lo(hi, lo) # 2 cycles
def conditional_return(self, cc):
# RET cc 2,5 cycles
if cc:
self.ret() # 4 cycles
# FIXME maybe this should be the same
self.cycles -= 1
else:
self.cycles -= 2
def return_form_interrupt(self):
# RETI 4 cycles
self.ret() # 4 cycles
self.enable_interrupts() # 1 cycle + others
self.cycles += 1
def restart(self, nn):
# RST nn 4 cycles
self.call(nn) # 4 cycles
def disable_interrups(self):
# DI/EI 1 cycle
self.ime = False
self.cycles -= 1
def enable_interrupts(self):
# 1 cycle
self.ime = True
self.execute(self.fetch()) # 1
self.handle_pending_interrupt()
def halt(self):
# HALT/STOP
self.halted = True
# emulate bug when interrupts are pending
if not self.ime and self.interrupt.is_pending():
self.execute(self.memory.read(self.pc.get()))
self.handle_pending_interrupt()
def stop(self):
# 0 cycles
self.cycles += 1
self.fetch()
# ------------------------------------------------------------------------------
class CallWrapper(object):
def get(self, use_cycles=True):
raise Exception("called CalLWrapper.get")
return 0
def set(self, value, use_cycles=True):
raise Exception("called CalLWrapper.set")
pass
class RegisterCallWrapper(CallWrapper):
def __init__(self, register):
self.register = register
def get(self, use_cycles=True):
return self.register.get(use_cycles)
def set(self, value, use_cycles=True):
return self.register.set(value, use_cycles)
class DoubleRegisterCallWrapper(CallWrapper):
def __init__(self, register):
self.register = register
def get(self, use_cycles=True):
return self.register.get(use_cycles)
def set(self, value, use_cycles=True):
return self.register.set(value, use_cycles)
class CPUPopCaller(CallWrapper):
def __init__(self, cpu):
self.cpu = cpu
def get(self, use_cycles=True):
return self.cpu.pop(use_cycles)
class CPUFetchCaller(CallWrapper):
def __init__(self, cpu):
self.cpu = cpu
def get(self, use_cycles=True):
return self.cpu.fetch(use_cycles)
# ------------------------------------------------------------------------------
# OPCODE LOOKUP TABLE GENERATION -----------------------------------------------
GROUPED_REGISTERS = [CPU.get_b,
CPU.get_c,
CPU.get_d,
CPU.get_e,
CPU.get_h,
CPU.get_l,
CPU.get_hli,
CPU.get_a]
def | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# fsfs-reshard.py REPOS_PATH MAX_FILES_PER_SHARD
#
# Perform an offline conversion of an FSFS repository between linear (format
# 2, usable by Subversion 1.4+) and sharded (format 3, usable by Subversion
# 1.5+) layouts.
#
# The MAX_FILES_PER_SHARD argument specifies the maximum number of files
# that will be stored in each shard (directory), or zero to specify a linear
# layout. Subversion 1.5 uses a default value of 1000 files per shard.
#
# As the repository will not be valid while the conversion is in progress,
# the repository administrator must ensure that access to the repository is
# blocked for the duration of the conversion.
#
# In the event that the conversion is interrupted, the repository will be in
# an inconsistent state. The repository administrator should then re-run
# this tool to completion.
#
#
# Note that, currently, resharding from one sharded layout to another is
# likely to be an extremely slow process. To reshard, we convert from a
# sharded to linear layout and then to the new sharded layout. The problem
# is that the initial conversion to the linear layout triggers exactly the
# same 'large number of files in a directory' problem that sharding is
# intended to solve.
#
# ====================================================================
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# ====================================================================
#
# $HeadURL: http://svn.apache.org/repos/asf/subversion/branches/1.8.x/tools/server-side/fsfs-reshard.py $
# $LastChangedDate: 2009-11-16 19:07:17 +0000 (Mon, 16 Nov 2009) $
# $LastChangedBy: hwright $
# $LastChangedRevision: 880911 $
import os, stat, sys
from errno import EEXIST
def usage():
"""Print a usage message and exit."""
print("""usage: %s REPOS_PATH MAX_FILES_PER_SHARD [START END]
Perform an offline conversion of an FSFS repository between linear
(readable by Subversion 1.4 or later) and sharded (readable by
Subversion 1.5 or later) layouts.
The MAX_FILES_PER_SHARD argument specifies the maximum number of
files that will be stored in each shard (directory), or zero to
specify a linear layout. Subversion 1.5 uses a default value of
1000 files per shard.
Convert revisions START through END inclusive if specified, or all
revisions if unspecified.
""" % sys.argv[0])
sys.exit(1)
def incompatible_repos_format(repos_path, format):
"""Print an error saying that REPOS_PATH is a repository with an
incompatible repository format FORMAT, then exit."""
sys.stderr.write("""error: unable to convert repository '%s'.
This repository is not compatible with this tool. Valid
repository formats are '3' or '5'; this repository is
format '%s'.
""" % (repos_path, format))
sys.stderr.flush()
sys.exit(1)
def incompatible_fs_format(repos_path, format):
"""Print an error saying that REPOS_PATH is a repository with an
incompatible filesystem format FORMAT, then exit."""
sys.stderr.write("""error: unable to convert repository '%s'.
This repository contains a filesystem that is not compatible with
this tool. Valid filesystem formats are '1', '2', or '3'; this
repository contains a filesystem with format '%s'.
""" % (repos_path, format))
sys.stderr.flush()
sys.exit(1)
def unexpected_fs_format_options(repos_path):
"""Print an error saying that REPOS_PATH is a repository with
unexpected filesystem format options, then exit."""
sys.stderr.write("""error: unable to convert repository '%s'.
This repository contains a filesystem that appears to be invalid -
there is unexpected data after the filesystem format number.
""" % repos_path)
sys.stderr.flush()
sys.exit(1)
def incompatible_fs_format_option(repos_path, option):
"""Print an error saying that REPOS_PATH is a repository with an
incompatible filesystem format option OPTION, then exit."""
sys.stderr.write("""error: unable to convert repository '%s'.
This repository contains a filesystem that is not compatible with
this tool. This tool recognises the 'layout' option but the
filesystem uses the '%s' option.
""" % (repos_path, option))
sys.stderr.flush()
sys.exit(1)
def warn_about_fs_format_1(repos_path, format_path):
"""Print a warning saying that REPOS_PATH contains a format 1 FSFS
filesystem that we can't reconstruct, then exit."""
sys.stderr.write("""warning: conversion of '%s' will be one-way.
This repository is currently readable by Subversion 1.1 or later.
This tool can convert this repository to one that is readable by
either Subversion 1.4 (or later) or Subversion 1.5 (or later),
but it is not able to convert it back to the original format - a
separate dump/load step would be required.
If you would like to upgrade this repository anyway, delete the
file '%s' and re-run this tool.
""" % (repos_path, format_path))
sys.stderr.flush()
sys.exit(1)
def check_repos_format(repos_path):
"""Check that REPOS_PATH contains a repository with a suitable format;
print a message and exit if not."""
format_path = os.path.join(repos_path, 'format')
try:
format_file = open(format_path)
format = format_file.readline()
if not format.endswith('\n'):
incompatible_repos_format(repos_path, format + ' <missing newline>')
format = format.rstrip('\n')
if format == '3' or format == '5':
pass
else:
incompatible_repos_format(repos_path, format)
except IOError:
# In all likelihood, the file doesn't exist.
incompatible_repos_format(repos_path, '<unreadable>')
def check_fs_format(repos_path):
"""Check that REPOS_PATH contains a filesystem with a suitable format,
or that it contains no format file; print a message and exit if neither
is true. Return bool whether the filesystem is sharded."""
sharded = False
db_path = os.path.join(repos_path, 'db')
format_path = os.path.join(db_path, 'format')
try:
format_file = open(format_path)
format = format_file.readline()
if not format.endswith('\n'):
incompatible_fs_format(repos_path, format + ' <missing newline>')
format = format.rstrip('\n')
if format == '1':
# This is a format 1 (svndiff0 only) filesystem. We can upgrade it,
# but we can't downgrade again (since we can't uncompress any of the
# svndiff1 deltas that may have been written). Warn the user and exit.
warn_about_fs_format_1(repos_path, format_path)
if format == '2':
pass
elif format == '3':
pass
else:
incompatible_fs_format(repos_path, format)
for line in format_file:
if format == '2':
unexpected_fs_format_options(repos_path)
line = line.rstrip('\n')
if line == 'layout linear':
pass
elif line.startswith('layout sharded '):
sharded = True
else:
incompatible_fs_format_option(repos_path, line)
format_file.close()
except IOError:
# The format file might not exist if we've previously been interrupted,
# or if the user is following our advice about upgrading a format 1
# repository. In both cases, we'll just assume the format was
# compatible.
pass
return sharded
def current_file(repos_path):
"""Return triple of (revision, next_node_id, next_copy_id) from
REPOS_PATH/db/current ."""
return open(os.path.join(repos_path, 'db', 'current')).readline().split()
def remove_fs_format(repos_path):
"""Remove the filesystem format file for repository REPOS_PATH.
Do not raise an error if the file is already missing."""
format_path = os.path.join(repos_path, 'db', 'format')
try:
statinfo = os.stat(format_path)
except OSError:
# The file probably doesn't exist.
return
# On Windows, we need to ensure the file is writable before we can
# remove it.
os.chmod(format_path, statinfo.st_mode | stat.S_IWUSR)
os.remove(format_path)
def write_fs_format(repos_path, contents):
"""Write a new filesystem format file for repository REPOS_PATH containing
CONTENTS."""
format_path = os.path.join(repos_path, 'db', 'format')
f = open(format_path, 'wb')
f.write(contents)
f.close()
os.chmod(format_path, stat.S_IRUSR | stat.S_IRGRP)
def linearise(path):
"""Move all the files in subdirectories of PATH into PATH, and remove the
subdirectories. Handle conflicts between subdirectory names and files
contained in subdirectories by ensuring subdirectories have a '.shard'
suffix prior to moving (the files are assumed not to have this suffix.
Abort if a subdirectory is found to contain another subdirectory."""
# First enumerate all subdirectories of DIR and rename where necessary
# to include a .shard suffix.
for name in os.listdir(path):
if name.endswith('.shard'):
continue
subdir_path = os.path.join(path, name)
if not os.path.isdir(subdir_path):
continue
os.rename(subdir_path, subdir_path + '.shard')
# Now move all the subdirectory contents into the parent and remove
# the subdirectories.
for root_path, dirnames, filenames in os.walk(path):
if root_path == path:
continue
if len(dirnames) > 0:
sys.stderr.write("error: directory '%s' contains other unexpected directories.\n" \
% root_path)
sys.stderr.flush()
sys.exit(1)
for name in filenames:
from_path = os.path.join(root_path, name)
to_path = os.path.join(path, name)
os.rename(from_path, to_path)
os.rmdir(root_path)
def shard(path, max_files_per_shard, start, end):
"""Move the files for revisions START to END inclusive in PATH into
subdirectories of PATH named such that subdirectory '0' contains at most
MAX_FILES_PER_SHARD files, those named [0, MAX_FILES_PER_SHARD). Abort if
PATH is found to contain any entries with non-numeric names."""
tmp = path + '.reshard'
try:
os.mkdir(tmp)
except OSError, e:
if e.errno != EEXIST:
raise
# Move all entries into shards named N.shard.
| |
[] for k in direction}
bounding_box = {k: [] for k in direction}
for idx, plane in enumerate(direction):
slices[plane] = list(range(limits[idx][0], limits[idx][1]+1, 1))
imat[plane], fitvolume[plane] = self.resample_CTplanes(hd_trajectories, plane, lead_model, resolution=.35)
span_vector = [sample_width, 0, 0] if plane != 'sag' else [0, sample_width, 0]
idx = [0, -1]
bounding_box_coords = []
for k in idx:
bounding_box_coords.append(hd_trajectories[k,:]-span_vector)
bounding_box_coords.append(hd_trajectories[k, :] + span_vector)
bounding_box_coords = np.array(bounding_box_coords)
axes_name = ['xx', 'yy', 'zz']
box = {k: [] for k in axes_name}
for i, dim in enumerate(axes_name):
box[dim] = bounding_box_coords.T[i,:].tolist()
bounding_box[plane] = box
return imat, bounding_box, fitvolume
def get_axialplanes(self, marker_coordinates, lead_data, window_size=15, resolution=.5):
"""returns a plane at a specific window with a certain direction"""
if lead_data['transformation_matrix'].shape[0] == 3:
lead_data['transformation_matrix'] = np.eye(4)*lead_data['transformation_matrix'][0,0]
lead_data['transformation_matrix'][-1,-1] = 1
transformation_matrix = lead_data['transformation_matrix']
transformation_matrix = np.eye(4)
bounding_box_coords = []
for k in range(2):
bounding_box_coords.append(np.arange(start=marker_coordinates[k]-window_size,
stop=marker_coordinates[k]+window_size, step=resolution))
bounding_box_coords.append(np.repeat(marker_coordinates[-1], len(bounding_box_coords[1])))
bounding_box = np.array(bounding_box_coords)
meshX, meshY = np.meshgrid(bounding_box[0,:], bounding_box[1,:])
meshZ = np.repeat(bounding_box[-1,0], len(meshX.flatten()))
fitvolume_orig = np.array([meshX.flatten(), meshY.flatten(), meshZ.flatten(), np.ones(meshX.flatten().shape)])
fitvolume = np.linalg.solve(transformation_matrix, fitvolume_orig)
resampled_points = PlotRoutines.interpolate_CTintensities(lead_data, fitvolume)
imat = np.reshape(resampled_points, (meshX.shape[0], -1), order='F')
return imat, bounding_box, fitvolume
@staticmethod
def resample_CTplanes(hd_trajectories, direction, lead_data, resolution=.2, sample_width=10, use_transformation_matrix=False):
"""Function resampling intesities of the source imaging to a grid which is later used to visualise the
leads. [ea_mancor_updatescene lines 264f]"""
direction = ''.join(direction) if type(direction) == list else direction # in case direction is entered as list
if use_transformation_matrix: # not necessary as all data in cDBS stay within the LPS coordinate system
if lead_data['transformation_matrix'].shape[0] == 3:
lead_data['transformation_matrix'] = np.eye(4)*lead_data['transformation_matrix'][0,0]
lead_data['transformation_matrix'][-1,-1] = 1
transformation_matrix = lead_data['transformation_matrix']
else:
transformation_matrix = np.eye(4)
xvec = np.arange(start=-sample_width, stop=sample_width+resolution, step=resolution)
meanfitline = np.vstack((hd_trajectories.T, np.ones(shape=(1, hd_trajectories.T.shape[1])))) # needed for transformation
addvolume = np.tile(xvec,(len(meanfitline.T),1))
fitvolume = []
for t in range(4):
fitvolume.append(np.tile(meanfitline[t,:], xvec.shape).reshape(xvec.shape[0], meanfitline.shape[1]).T)
fitvolume_orig = np.stack(fitvolume)
if direction == 'cor':
fitvolume_orig[0,:,:] += addvolume
elif direction == 'sag':
fitvolume_orig[1, :, :] += addvolume
elif direction == 'tra':
fitvolume_orig[2, :, :] += addvolume
fitvolume = np.linalg.solve(transformation_matrix, np.reshape(fitvolume_orig, (4, -1), order='F'))
resampled_points = PlotRoutines.interpolate_CTintensities(lead_data, fitvolume)
imat = np.reshape(resampled_points, (meanfitline.shape[1], -1), order='F')
return imat, fitvolume_orig
# ======================================== Interpolations ========================================
@staticmethod
def interpolate_trajectory(orig_trajectory, resolution=20):
"""interpolates between trajectory points thus creating a „high resolution“ version of it"""
hd_trajectory = []
for idx in range(np.array(orig_trajectory).shape[1]):
f = scipy.interpolate.interp1d(np.linspace(start=1, stop=50), np.array(orig_trajectory)[:, idx])
hd_trajectory.append(f(np.linspace(start=1, stop=50, num=(-1 + len(orig_trajectory[:, idx]))
* resolution + 1)))
return np.stack(hd_trajectory).T
def interpolate_CTintensities(lead_model, fitvolume):
import SimpleITK as sitk
img = sitk.ReadImage(os.path.join(*lead_model['filenameCTimaging']))
physical_points = list(map(tuple, fitvolume[0:3,:].T))
#physical_points = physical_points[0:5]
num_samples = len(physical_points)
physical_points = [img.TransformContinuousIndexToPhysicalPoint(pnt) for pnt in physical_points]
#interp_grid_img = sitk.Image((len(physical_points) *([1] * (img.GetDimension() - 1))), sitk.sitkUInt8)
interp_grid_img = sitk.Image([num_samples] + [1] * (img.GetDimension() - 1), sitk.sitkUInt8)
displacement_img = sitk.Image([num_samples] + [1] * (img.GetDimension() - 1), sitk.sitkVectorFloat64,
img.GetDimension())
for i, pnt in enumerate(physical_points):
displacement_img[[i] + [0] * (img.GetDimension() - 1)] = np.array(pnt) - np.array(
interp_grid_img.TransformIndexToPhysicalPoint([i] + [0] * (img.GetDimension() - 1)))
interpolator_enum = sitk.sitkLinear
default_output_pixel_value = 0.0
output_pixel_type = sitk.sitkFloat32 if img.GetNumberOfComponentsPerPixel() == 1 else sitk.sitkVectorFloat32
resampled_temp = sitk.Resample(img, interp_grid_img, sitk.DisplacementFieldTransform(displacement_img),
interpolator_enum, default_output_pixel_value, output_pixel_type)
resampled_points = [resampled_temp[x,0,0] for x in range(resampled_temp.GetWidth())]
debug = False
if debug:
for i in range(resampled_temp.GetWidth()):
print(str(img.TransformPhysicalPointToContinuousIndex(physical_points[i])) + ': ' + str(resampled_temp[[i] + [0] * (img.GetDimension() - 1)]) + '\n')
return np.array(resampled_points)
# ==================== General Helper Functions for manual correction ====================
@staticmethod
def load_leadModel(inputdir, filename):
"""Function loading results from [preprocLeadCT.py] which emulates the PaCER toolbox"""
if not inputdir:
Output.msg_box(text="No input folder provided, please double-check!", title="Missing input folder")
return
elif not os.path.isfile(filename):
Output.msg_box(text="Models for electrode unavailable, please run detection first!",
title="Models not available")
return
else:
with open(filename, "rb") as model: # roughly ea_loadreconstruction in the LeadDBS script
lead_models = pickle.load(model)
intensityProfiles = pickle.load(model)
skelSkalms = pickle.load(model)
return lead_models, intensityProfiles, skelSkalms
@staticmethod
def save_leadModel(lead_models, intensityProfiles, skelSkalms, filename=''):
if not filename:
Output.msg_box(text='No filename for saving lead model provided', title='No filename provided')
return
with open(filename, "wb") as f:
pickle.dump(lead_models, f)
pickle.dump(intensityProfiles, f)
pickle.dump(skelSkalms, f)
# ==================== Helper Functions fpr plotting Data ====================
@staticmethod
def getbgsidecolor(side, xray=False):
""" """
from matplotlib import colors
line_cols = matplotlib.cm.get_cmap('Set1', 64) # TODO: maybe a cmap would make sense
line_cols = colors.rgb_to_hsv(line_cols(np.linspace(0, 1, 64))[:,0:3])
line_cols[:,-1] = line_cols[:,-1]/3
if xray:
line_cols[:, 1] = line_cols[:, 1] / 1.5
line_cols[:, 2] = line_cols[:, 2] * 1.5
line_cols = colors.hsv_to_rgb(line_cols)
col = line_cols[side,:] # TODO: why on earth is this so complicated to get these colors
return col
class GetData:
def __init__(self, parent=PlotRoutines):
self.parent = parent
@staticmethod
def leadInformation_update(information2update, lead_data):
"""replaces values of lead_models with updated values; information2update can be marker, rotation, etc."""
for key_name, val in information2update.items():
lead_data[key_name] = val
return lead_data
# =================================== Functions related with coordinates ===================================
def resize_coordinates(self, lead_coords, lead_data):
"""function which enables resizing cooridnates (e.g. 8 contacts to 4 contacts if needed; additional
functionality contains """
if lead_data['model'] == 'Boston Vercise Directional' or 'St Jude 6172' or 'St Jude 6173':
coordinates = np.zeros((4, 3))
coordinates[0, :] = lead_coords[0, :]
coordinates[1, :] = np.mean(lead_coords[1: 4, :], axis=0)
coordinates[2, :] = np.mean(lead_coords[4: 7, :], axis=0)
coordinates[3, :] = lead_coords[7, :]
emp_dist = GetData.lead_dist(coords=coordinates)
else:
coordinates = lead_coords
emp_dist = GetData.lead_dist(coords=coordinates, factor=lead_data['numel'])
return coordinates, emp_dist
@staticmethod
def lead_dist(coords, factor=3):
"""calculate lead distances according to its coordinates"""
spatial_distance = scipy.spatial.distance.cdist(coords, coords, 'euclidean')
emp_dist = np.sum(np.sum(np.tril(np.triu(spatial_distance, 1), 1))) / factor
return emp_dist
def resolve_coordinates(self, marker, lead_coords_mm, lead_positions, lead_data, resize_bool=False, rszfactor=0):
"""emulates the function from Lead-DBS ea_resolvecoords; unlike in Lead DBS this is done one at a time cf.
https://github.com/netstim/leaddbs/blob/master/templates/electrode_models/ea_resolvecoords.m"""
if resize_bool:
can_dist = np.linalg.norm(lead_positions["head_position"] - lead_positions["tail_position"])
coords_temp, can_eldist = GetData.resize_coordinates(self, lead_coords_mm, lead_data)
stretch = can_dist * (rszfactor / can_eldist) if rszfactor != 0 else can_dist
vec = np.divide((marker["markers_tail"] - marker["markers_head"]),
np.linalg.norm(marker["markers_tail"] - marker["markers_head"]))
marker["markers_tail"] = marker["markers_head"] + vec * stretch
coords, traj_vector, trajectory, can_eldist = [[] for _ in range(4)]
if not marker["markers_head"].size==0:
M = np.stack((np.append(marker["markers_head"], 1), np.append(marker["markers_tail"], 1),
np.append(marker["markers_x"], 1), np.append(marker["markers_y"], 1)))
E = np.stack((np.append(lead_positions["head_position"], 1), np.append(lead_positions["tail_position"], 1),
np.append(lead_positions["x_position"], 1), np.append(lead_positions["y_position"], 1)))
X = np.linalg.lstsq(E, M, rcond=None)
coords_mm = np.concatenate([lead_coords_mm, np.ones(shape=(lead_coords_mm.shape[0],1))], axis=1)
coords = (coords_mm @ X[0]).T
coords = coords[0: 3,:].T
traj_vector = (marker["markers_tail"] - marker["markers_head"]) / \
np.linalg.norm(marker["markers_tail"] - marker["markers_head"])
trajectory = np.stack((marker["markers_head"] - traj_vector*5, marker["markers_head"] + traj_vector*25))
trajectory = np.array((np.linspace(trajectory[0, 0], trajectory[1, 0], num=50),
np.linspace(trajectory[0, 1], trajectory[1, 1], num=50),
np.linspace(trajectory[0, 2], trajectory[1, 2], num=50))).T
return coords, trajectory, can_eldist, marker
# =================================== Functions in combination with leads ===================================
@staticmethod
def get_default_lead(lead_data):
"""obtains default lead properties according to the model proposed in the PaCER algorithm @ ./template"""
if lead_data['model'] == 'Boston Vercise Directional': # load mat-file to proceed
mat_filename = 'boston_vercise_directed.mat'
lead_model = loadmat(os.path.join(ROOTDIR, 'ext', 'LeadDBS', mat_filename), 'r')['electrode']
default_positions = {x: np.hstack(vals) for x, vals in lead_model.items() if x.endswith('position')}
default_coordinates = np.array(lead_model['coords_mm']) # in [mm]
else:
Output.msg_box(text="Lead type not yet implemented.", title="Lead type not implemented")
return
return lead_model, default_positions, default_coordinates
def get_leadModel(self, lead_data, default_positions, default_coordinates, side, resize=False):
""" reads and estimates all necessary markers and trajectories for the corresponding lead model """
print("\t... reading lead data properties for {} side and estimating rotation".format(side))
marker_unprocessed = dict([(k, r) for k, r in lead_data.items() if k.startswith('marker')])
if not (lead_data['first_run'] and lead_data['manual_correction']):
resize = True # TODO: Not sure if this is doing the job; some warning/information should be displayed that > first run
lead_data['first_run'] = False
_, lead_data['trajectory'], _, marker_temp = \
GetData.resolve_coordinates(self, marker_unprocessed, default_coordinates, default_positions, lead_data,
resize_bool=resize)
lead_data['rotation'] = GetData.initialise_rotation(lead_data, marker_temp)
xvec, yvec, lead_data['rotation'], marker_rotation = GetData.estimate_rotation(lead_data, marker_temp)
lead_data = GetData.leadInformation_update(marker_rotation, lead_data)
if xvec.size == 0 or yvec.size == 0:
xvec, yvec, lead_data['rotation'], marker_rotation = GetData.estimate_rotation(lead_data, marker_rotation)
lead_data = GetData.leadInformation_update(marker_rotation, lead_data)
options2process = {'xvec': [1, 0, 0], 'yvec': [0, 1, 0]}
unrot = {k: [] for k in options2process.keys()}
for key in options2process:
vec_temp = np.cross(lead_data["normtraj_vector"], options2process[key])
unrot[key] = np.divide(vec_temp, np.linalg.norm(vec_temp))
marker = dict([(k, r) for k, r in lead_data.items() if k.startswith('marker')])
coordinates, trajectory, _, _ = GetData.resolve_coordinates(self, marker, default_coordinates,
default_positions, lead_data, resize_bool=False) # ea_mancor_updatescene line 144
return marker, coordinates, trajectory, resize
def worker_xy_planes(self, traj, lead, dimension, side, IM_dict, BB_dict, FV_dict, queue):
print("\t...extracting intensities for CTimages @ {} side\t...".format(side))
queue.put('.')
intensity_matrix, bounding_box, fitvolume = GetData.get_xyplanes(self, trajectory=traj, lead_model=lead,
direction=dimension)
IM_dict[side] = intensity_matrix
| |
## Copyright (c) 2001-2009, <NAME>
## August 2009
#-----------------------------------------------------------------------
# Notes: This file defines a "base class" for CSDMS "process"
# components. Many of these functions are implementations
# of methods defined in "topoflow3.IRFPort.sidl". They are
# therefore required to use TF components in a CCA framework.
#-----------------------------------------------------------------------
#
# unit_test()
#
# class CSDMS_component
# __init__()
# get_status()
# is_scalar()
# is_vector()
# is_grid()
# has_variable()
# -----------------------------
# Next 3 currently identical
# -----------------------------
# get_scalar_double()
# get_vector_double() ## (2/16/10
# get_grid_double()
# get_values_in_grid_double() ## (2/17/10)
# -----------------------------
# Next 3 currently identical
# -----------------------------
# set_scalar_double()
# set_vector_double() ## (2/16/10)
# set_grid_double()
# set_values_in_grid_double() ## (2/17/10)
# ---------------------
# get_scalar_long()
# get_vector_long()
# get_grid_long()
# get_values_in_grid_long() ## (2/17/10)
# ---------------------
# set_scalar_long() ## (2/16/10)
# set_vector_long() ## (2/16/10)
# set_grid_long() ## (2/16/10)
# set_values_in_grid_long() ## (2/17/10)
# ---------------------
# get_input_items()
# get_output_items()
# ---------------------
# read_gui_info_file() ### 11/13/09
# get_user_input() ### 9/24/09, 11/13/09
# load_user_input() ### 10/1/09
# ---------------------
# go()
# run_model()
# ### read_config_file() # (template)
# ### initialize # (template)
# ### update() # (template)
# finalize()
# -----------------------------------------------
# These methods are not part of "IRF" interface
# but are used by the TopoFlow components.
# -----------------------------------------------
# initialize_required_components()
# get_cca_port()
# get_cca_ports()
# release_cca_ports()
# ------------------------
# add_child_port()
# get_port_data() # (rename to get_port_double ??)
# set_port_data() # (rename to set_port_double ??)
# ------------------------
# get_rank()
# get_size()
# ------------------------
# set_directory()
# read_grid_info()
# store_outlet_IDs()
# initialize_time_vars()
# update_time()
# print_time_and_value()
# print_run_time()
#-----------------------------------------------------------------------
from numpy import *
import numpy
import sys, os, time
import cfg_files as cfg
import pixels
import rti_files
import tf_utils
#-----------------------------------------------------------------------
def unit_test():
directory = tf_utils.TF_Test_Directory()
data_prefix = tf_utils.TF_Test_Data_Prefix()
case_prefix = tf_utils.TF_Test_Case_Prefix()
c = CSDMS_component()
c.CCA = False
print 'Instantiated component.'
#--------------------------------------------
# Check the "read_gui_info_file()" function
#--------------------------------------------
## gui_info_file = '/Applications/Erode/gui_info/Erode_GUI_Info.txt'
## gui_info_file = '/data/progs/erode/3.0/gui_info/Erode_GUI_Info.txt'
gui_info_file = '/Users/peckhams/Desktop/GC2D_GUI_Info.txt'
var_names, labels, values, min_vals, max_vals, \
desc, group_names, group_sizes = \
c.read_gui_info_file( gui_info_file )
print 'var_names ='
print var_names
print 'labels ='
print labels
print 'values ='
print values
print 'min_vals ='
print min_vals
print 'max_vals ='
print max_vals
print 'desc ='
print desc
print 'group_names ='
print group_names
print 'group_sizes ='
print group_sizes
return
#-------------------------------------
# Test the "print_run_time()" method
#-------------------------------------
## print ' '
## print 'Testing "print_run_time()"...'
## c.print_run_time(seconds=1.618)
## c.print_run_time(seconds=60)
## c.print_run_time(seconds=3600)
## c.print_run_time(seconds=3600 * 24)
## c.print_run_time(seconds=3600 * 24 * 365)
#---------------------------
# Test some of the methods
#---------------------------
c.a = numpy.float64(5)
print 'c.a = numpy.float64(5)'
print "c.is_scalar('a') =", c.is_scalar('a')
print "c.is_grid('a') =", c.is_grid('a')
v1 = c.get_port_data('a', c)
print "c.get_port_data('a',c) =", v1
print ' '
#-------------------------------------------------
c.b = numpy.zeros((3,3), dtype='Float64')
print "c.b = numpy.zeros((3,3), dtype='Float64')"
print "c.is_scalar('b') =", c.is_scalar('b')
print "c.is_grid('b') =", c.is_grid('b')
v2 = c.get_port_data('b', c)
print "c.get_port_data('b',c) =", v2
print ' '
#-------------------------------------------------
print "c.is_scalar('b[1]') =", c.is_scalar('b[1]')
print "c.is_grid('b[1]') =", c.is_grid('b[1]')
v3 = c.get_port_data('b[1]', c)
print "c.get_port_data('b[1]',c) =", v3
print ' '
#-------------------------------------------------
# This component has no initialize() method
## c.initialize(directory=directory,
## data_prefix=data_prefix,
## case_prefix=case_prefix)
## print 'nx =', c.nx
## print 'ny =', c.ny
# unit_test()
#-----------------------------------------------------------------------
class CSDMS_component:
def __init__(self):
self.CCA = tf_utils.TF_Use_CCA()
self.DEBUG = True
self.SILENT = True
self.REPORT = False
self.status = 'created' # (OpenMI 2.0 conventions)
self.USER_SET_VALUES = False
# __init__()
#-------------------------------------------------------------------
def get_status(self):
#-----------------------------------------------------
# Notes: Return component status as a string. The
# possible return values are from OpenMI 2.0:
#
# created, initializing, initialized,
# updating, updated, finalizing, finalized,
# failed (could add "stopped").
#-----------------------------------------------------
return self.status
# get_status()
#-------------------------------------------------------------------
def is_scalar(self, var_name):
#------------------------------------------------
# NB! Case in var_name must be an exact match.
#-------------------------------------------------
exec("n = numpy.rank(self." + var_name + ")")
return (n == 0)
# is_scalar()
#-------------------------------------------------------------------
def is_vector(self, var_name):
#------------------------------------------------
# NB! Case in var_name must be an exact match.
#------------------------------------------------
exec("n = numpy.rank(self." + var_name + ")")
return (n == 1)
# is_vector()
#-------------------------------------------------------------------
def is_grid(self, var_name):
#------------------------------------------------
# NB! Case in var_name must be an exact match.
#------------------------------------------------
#-------------------------------------------------
# (9/29/09) This might be causing a problem with
# the c++ bindings for this CCA component.
#-------------------------------------------------
## exec("type_str = str(type(self." + var_name + "))")
## p1 = type_str.find("ndarray")
## p2 = type_str.find("float")
## if (p1 == -1) and (p2 == -1):
## print 'ERROR: type(' + var_name + ') =' + type_str
## return False
#-------------------------------------------------
# (9/29/09) This might be causing a problem with
# the c++ bindings for this CCA component.
#-------------------------------------------------
## if ("ndarray" not in type_str) and \
## ("float" not in type_str):
## print 'ERROR: type(' + var_name + ') =' + type_str
## return False
#-------------------------------------------------------
exec("n = numpy.rank(self." + var_name + ")")
return (n == 2)
# is_grid()
#-------------------------------------------------------------------
def has_variable(self, var_name):
#------------------------------------------------------
# If var_name includes square brackets for subscripts
# remove them to get the actual variable name.
#------------------------------------------------------
bracket_pos = var_name.find('[')
if (bracket_pos != -1):
key = var_name[0:bracket_pos]
else:
key = var_name
#---------------------------------------------------
# Does current component have requested variable ?
#---------------------------------------------------
SILENT = True
VARIABLE_FOUND = self.__dict__.has_key(key)
if not(VARIABLE_FOUND) and not(SILENT):
print 'WARNING: Component does not have the'
print ' requested variable: ' + var_name
print ' '
return VARIABLE_FOUND
# has_variable()
#-------------------------------------------------------------------
def get_scalar_double(self, var_name):
#------------------------------------
# Note: The next line doesn't work.
#------------------------------------
## exec("return self." + var_name)
#---------------------------------------------------
# Does current component have requested variable ?
#---------------------------------------------------
# This is not used yet because possible impact on
# performance has not be tested yet. (2/17/10)
# If it does get used later, it will be added to
# all of the "getters".
#---------------------------------------------------
## if not(self.has_variable(var_name)):
## return float64(0)
try:
exec("result = self." + var_name)
return numpy.float64(result)
except:
print 'ERROR in CSDMS_base.get_scalar_double().'
print ' Returning 0.'
return numpy.float64(0)
############## flush output here ??
# get_scalar_double()
#-------------------------------------------------------------------
def get_vector_double(self, var_name):
#---------------------------------------------------------
# Note: This function was causing a "segmentation fault
# in gui-backend.sh" error message when trying to
# run TopoFlow through the CMT (in CCA framework).
# Solution was to use numpy.array, as shown.
# (2/17/10)
#---------------------------------------------------------
try:
exec("result = self." + var_name)
return numpy.array(result, dtype='float64')
#-------------------------
# NB! This doesn't work.
#-------------------------
# return numpy.float64(result)
except:
print 'ERROR in CSDMS_base.get_vector_double().'
print ' Returning zeros.'
return numpy.zeros([1], dtype='float64')
# get_vector_double()
#-------------------------------------------------------------------
def get_grid_double(self, var_name):
try:
exec("result = self." + var_name)
return numpy.float64(result)
except:
print 'ERROR in CSDMS_base.get_grid_double().'
print ' Returning zeros.'
return numpy.zeros([1,1], dtype='float64')
# get_grid_double()
#-------------------------------------------------------------------
def get_values_in_grid_double(self, var_name, IDs):
#---------------------------------------------------------
# Note: This function was causing a "segmentation fault
# in gui-backend.sh" error message when trying to
# run TopoFlow through the CMT (in CCA framework).
# Solution was to use numpy.array, as shown.
# (2/18/10)
#---------------------------------------------------------
# Notes: This function was tested in the new Diversions
# component on (2/18/10).
#---------------------------------------------------------
try:
exec("result = self." + var_name + '.flat[IDs]')
return numpy.array(result, dtype='float64')
## return numpy.float64(result)
except:
print 'ERROR in CSDMS_base.get_values_in_grid_double().'
print ' Returning zeros.'
return numpy.zeros(len(IDs), dtype='float64')
# get_values_in_grid_double()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
def set_scalar_double(self, var_name, scalar):
exec("self." + var_name + " = numpy.float64(scalar)")
# set_scalar_double()
#-------------------------------------------------------------------
def set_vector_double(self, var_name, vector):
#--------------------------------------------------
# Notes: First method here should be more robust.
# See Notes for get_vector_double().
#--------------------------------------------------
exec("self." + var_name + " = numpy.array(vector, dtype='float64')")
#-----------------------------------
# Original method (before 2/17/10)
#-----------------------------------
# exec("self." + var_name + " = numpy.float64(vector)")
# set_vector_double()
#-------------------------------------------------------------------
def set_grid_double(self, var_name, grid):
exec("self." + var_name + " = numpy.float64(grid)")
# set_grid_double()
#-------------------------------------------------------------------
def set_values_in_grid_double(self, var_name, IDs, values):
#--------------------------------------------------------
# Notes: This function was tested in the new Diversions
# component on (2/18/10).
#--------------------------------------------------------
exec("self." + var_name + ".flat[IDs] = values")
# exec("self." + var_name + ".flat[IDs] = numpy.float64(values)")
# set_values_in_grid_double()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
def get_scalar_long(self, var_name):
exec("result = numpy.int32(self." + var_name + ")")
return result
# get_scalar_long()
#-------------------------------------------------------------------
def get_vector_long(self, var_name):
#--------------------------------------------
# Notes: See Notes for get_vector_double().
#--------------------------------------------
try:
exec("result = self." + var_name)
return numpy.array(result, dtype='int32')
#-------------------------
# NB! This doesn't work.
#-------------------------
# return numpy.int32(result)
except:
print 'ERROR in CSDMS_base.get_vector_long().'
print ' Returning zeros.'
return numpy.zeros([1], dtype='int32')
# get_vector_long()
#-------------------------------------------------------------------
def get_grid_long(self, var_name):
exec("result = numpy.int32(self." + var_name + ")")
return result
# get_grid_long()
#-------------------------------------------------------------------
def get_values_in_grid_long(self, var_name, IDs):
try:
exec("result = self." + var_name + '.flat[IDs]')
return numpy.int32(result)
except:
print 'ERROR in CSDMS_base.get_values_in_grid_long().'
print ' Returning zeros.'
return numpy.zeros(len(IDs), dtype='int32')
# get_values_in_grid_long()
#-------------------------------------------------------------------
#-------------------------------------------------------------------
def set_scalar_long(self, var_name, scalar):
exec("self." + var_name + " = | |
linewidth=linewidth, c=color)
ax.errorbar(sigmas[1:-1], averages[1:-1], yerr=stds[1:-1],fmt='o',
capthick=linewidth, elinewidth=linewidth, capsize=linewidth*6,markersize=10, c=color)
sns.despine(offset=10)#, trim=True);
ax.set_aspect(0.5)
ax.set_yscale("log")#, nonposy='clip')
ax.set_xscale("log")#, nonposy='clip')
ax.set_xlabel('$\sigma$',fontsize=textsize*2)
ax.set_ylabel(r'$|\mathcal{R}_1(\sigma)-\mathcal{R}_2|$',fontsize=textsize*1.5)
plt.setp(ax.get_xticklabels(), size=textsize)
plt.setp(ax.get_yticklabels(), fontsize=textsize)#, rotation='vertical')
# Dropping alternate tick labels because the text size is too large (but necessarily large)
#plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
#plt.setp(ax.get_xticklabels()[::2], visible=True)
plt.setp(ax.get_yticklabels()[::2], visible=True)
#
ax.minorticks_off()
ax.tick_params(axis=u'both', direction='out', width=linewidth, length=linewidth*4)#,labelsize=for)
for spine in ax.spines.keys():
ax.spines[spine].set_linewidth(linewidth)
#print "R - (phi+psi-2pi)/4pi shows the following distribution: %f +/- %f" %()
plt.tight_layout(pad=0.0, w_pad=0.0, h_pad=0.0)
#gs.update(wspace=0.0, hspace=0)
#plt.subplots_adjust(wspace=.201)
#plt.subplots_adjust(hspace=0.001)
plt.savefig(figname, dpi=180, bbox_inches='tight',transparent=True) #facecolor='w', edgecolor='w',
if show_graphs:
os.system(pdf_viewer+" "+figname)
#
# ====================================================================================
if 0: # not used in main manuscript
figname = output_fig_dir+"/fig_various_rama_plots.pdf"
sns.axes_style("ticks")#
# VERY IMPORTANT FOR GETTING THE SHAPE OF EACH PANEL CORRECT
plt.figure(figsize=(12*1.2,6*1.2))
set_grays()
textsize = plt.rcParams['font.size']
linewidth = plt.rcParams['lines.linewidth']*.5
# Needs the length_dependent_csv_file (to be read in as the pandas dataframe 'df')
# WARNING: VERY COSTLY TO REDO:
if not os.path.isfile(length_dependent_csv_file):
prepare_main_csv_file()
df = pd.read_csv(length_dependent_csv_file)
df = df[(df['L']==16.0)]
''' _______________
|_______________| colorbar (0,1)
|--------|--------|--------|--------|
trans | rg | R | theta | d |
| (0,0) | (0,1) | (0,2) | (0,3) |
|--------|--------|--------|--------|
cis | rg | R | theta | d |
| (1,0) | (1,1) | (1,2) | (1,3) |
|--------|--------|--------|--------|
'''
gs = mpl.gridspec.GridSpec(3, 4, width_ratios =[1,1,1,1], height_ratios=[1,20,20])
gs.update(wspace=0.4, hspace=0.5)
ax_cmap = plt.subplot(gs[0,1:3])
ax_trans_0 = plt.subplot(gs[1,0])
ax_trans_1 = plt.subplot(gs[1,1])
ax_trans_2 = plt.subplot(gs[1,2])
ax_trans_3 = plt.subplot(gs[1,3])
ax_cis_0 = plt.subplot(gs[2,0])
ax_cis_1 = plt.subplot(gs[2,1])
ax_cis_2 = plt.subplot(gs[2,2])
ax_cis_3 = plt.subplot(gs[2,3])
panels_a_through_c = ['rg','theta','d','R']
axes = {'cmap' : ax_cmap,
'trans':{ panels_a_through_c[0]: ax_trans_0,
panels_a_through_c[1]: ax_trans_1,
panels_a_through_c[2]: ax_trans_2,
panels_a_through_c[3]: ax_trans_3
},
'cis': { panels_a_through_c[0]: ax_cis_0,
panels_a_through_c[1]: ax_cis_1,
panels_a_through_c[2]: ax_cis_2,
panels_a_through_c[3]: ax_cis_3
}
}
cmap = sns.cubehelix_palette(100, start=.5, rot=-.75, dark=0.2, light=1, as_cmap=True)
#cmap = sns.diverging_palette(240, 10, n=2, as_cmap=True)
panel_letters = itertools.cycle(list(string.ascii_lowercase))
amin = -180.0; amax=180.0;
for omega in [180.0,0.0]:
cis_or_trans = "trans"
if omega == 0.0:
cis_or_trans = "cis"
xtype = "phi"; ytype = "psi";
for ztype in panels_a_through_c: #axes['trans'].keys():
ax = axes[cis_or_trans][ztype]
tdf = df[(df['omega'] == omega)] # & (df['psi']>0)]
x = tdf[xtype]
y = tdf[ytype]
levels = 10
trunkated_levels = levels/2
real_z_type = ztype
if real_z_type == 'd':
z = do_d(tdf[real_z_type])
elif real_z_type == 'd2':
real_z_type = 'd'
z = np.abs(tdf[real_z_type])
elif real_z_type == 'theta':
z = do_theta(tdf[real_z_type])
else:
z = tdf[real_z_type]
if real_z_type == 're' or real_z_type == 'rg' or ztype == 'd2':
levels = 7
if ztype == 'd2':
levels = 5
if np.min(z) < 0:
trunkated_levels += 1
'''
# This proves that 'R(psi,psi)' ~ '(phi+psi+2pi)/4pi'
if ztype == 'R':
min_v = -360.0
max_v = 360.0
z = (tdf['phi'] + tdf['psi'] - min_v)/(max_v-min_v)
'''
X,Y,Z = locallib.xyz_to_ndarrays(x,y,z)
if 0: # smooth functions if desired (it messes with contour lines sometimes at plot edges, though)
Z = scipy.ndimage.filters.gaussian_filter(Z, 2)#, order=0)
if 0:
ax.imshow(Z, cmap='hot', interpolation='nearest')
else:
# DRAWING THE FILLED CONTOUR PLOT!
ax.contourf(X, Y, Z, levels, cmap = cmap)
# DRAWING LINES (negative contours will be dashed by default)
CS = ax.contour(X, Y, Z, levels, colors=[plt.rcParams['lines.color']],linewidth=linewidth)
# ===============================================================================
# ALL THIS STUFF IS FOR LABELING CONOTOURS (drop alternating labels, etc)
# Recast level labels
fmt = {}
for l in CS.levels:
s = "%1.1f" %(round(l,1))
if float(l) == float(int(l)):
s = "%d" %(l)
fmt[l] = s
# Dropping every <skip_number> terms (ignored if skip_number == 0)
counter = 0
skip_number = 2
if skip_number:
for collection_i in range(len(CS.collections)):
counter+=1
if counter % skip_number == 0:
plt.setp(CS.collections[collection_i], linewidth=0)
fmt[CS.levels[collection_i]] = ''
xmin,xmax = ax.get_xlim(); ymin,ymax = ax.get_ylim(); # getting axes min max values
label_pos = []
midpoint = np.array([float(xmax+xmin)/2.0, float(ymax+ymin)/2.0])
for line in CS.collections:
for path in line.get_paths():
# find closest point
X = []
Y = []
label_x = 0.0
label_y = 0.0
distance_from_center = []
for point in path.vertices:
distance_from_center.append(np.linalg.norm(point-midpoint))
label_position_index = distance_from_center.index(np.min(distance_from_center))
if 0:# ztype == 'd':
plt.scatter(path.vertices[:,0],path.vertices[:,1])
plt.scatter(*path.vertices[label_position_index],s=100,c='r')
plt.ylim((-190,190))
plt.xlim((-190,190))
plt.show()
label_pos.append(path.vertices[label_position_index])
CLS = ax.clabel(CS, fontsize=textsize*.65, inline=1, fmt=fmt,manual=label_pos, colors='k')
if 1:
# --------------------------------------------------------------------------
# SOMETIMES, LABELS ENCROACH ON THE AXES AND GET CLIPPED. HERE WE FIND THOSE
# THAT EXCEED A THRESHOLD DISTANCE AND DELETE THEM
# Swiped from: http://stackoverflow.com/questions/25873681/matplotlib-contour-plot-labels-overlap-axes
thresh = 0.02 # ratio in x/y range in border to discard
xmin,xmax = ax.get_xlim(); ymin,ymax = ax.get_ylim() # getting axes min max values
Dx = xmax-xmin
Dy = ymax-ymin
# check which labels are near a border
keep_labels = []
labels_to_delete = []
for label in CLS:
lx,ly = label.get_position()
if xmin+thresh*Dx<lx<xmax-thresh*Dx and ymin+thresh*Dy<ly<ymax-thresh*Dy:
# inlier, redraw it later
keep_labels.append((lx,ly))
else:
labels_to_delete.append(label)
# delete the original lines, redraw manually the labels we want to keep
# this will leave unlabelled full contour lines instead of overlapping labels
#for cline in CS.collections:
# cline.remove()
for label in labels_to_delete:
label.remove()
# LABEL STUFF DONE, DONE. D.O.N.E.
# ===============================================================================
#'-' solid line style; '--' dashed line style; '-.' dash-dot line style; ':' dotted line style
#ax.plot([-180,180],[180,-180], 'k--') # -ve diagonal
#ax.plot([-180,180],[0,0], 'k:') # Horizontal line passing (0,0)
#ax.plot([0,0],[-180,180], 'k:') # Vertical line passing (0,0)
xticks = range(int(amin),int(amax)+1,180)
yticks = range(int(amin),int(amax)+1,180)
# Setting ticks
ax.tick_params(axis=u'both', direction='out', width=linewidth,
length=linewidth*5, pad=0,labelsize=textsize)
ax.set_xticks(xticks)
plt.setp(ax.get_xticklabels(), fontsize=textsize*.7) #rotation='vertical',
plt.setp(ax.get_yticklabels(), fontsize=textsize*.7) #rotation='vertical',
ax.set_xlabel(type_to_label[xtype], size=textsize)
# Drawing an equal-aspect-ratio graph
#ax.set_aspect(1)
# Setting title
if 1:#cis_or_trans == 'trans':
addto_title = r'('+next(panel_letters)+r')'
t = ax.set_title(addto_title, size=textsize*1.05,loc='left')
ax.set_title(type_to_label[ztype], size=textsize*1.15,loc='center')
t.set_y(1.03) # Shifting the title up a little
'''
|--------|--------|--------|--------|
trans | re | R | theta | |d| |
| (0,0) | (0,1) | (0,2) | (0,3) |
|--------|--------|--------|--------|
cis | re | R | theta | |d| |
| (1,0) | (1,1) | (1,2) | (1,3) |
|--------|--------|--------|--------|
'''
ax.set_yticks(yticks)
if ztype != panels_a_through_c[0]:
# erase the y labels
ax.set_yticklabels([])
ax.set_ylabel("", size=textsize)
elif cis_or_trans == 'cis':
ax.set_yticks(yticks)
ax.set_ylabel(r"cis"+"\n\n"+type_to_label[ytype], size=textsize,weight='normal',style='italic')
elif cis_or_trans == 'trans':
ax.set_ylabel(r"trans"+"\n\n"+type_to_label[ytype], size=textsize,weight='normal',style='italic')
if cis_or_trans != 'cis':
# erase the x labels
#ax.set_xticklabels([])
ax.set_xlabel("", size=textsize)
plt.sca(ax);plt.xticks(rotation=45)
plt.sca(ax);plt.yticks(rotation=45)
for spine in ax.spines.keys():
ax.spines[spine].set_linewidth(linewidth*2.0)
if 1:
# addding the colorbar
''' _______________
|_______________| colorbar (0,1)
|--------|--------|--------|--------|
trans | rg | re | R | |d| |
| (1,0) | (1,1) | (1,2) | (1,3) |
|--------|--------|--------|--------|
cis | rg | re | R | |d| |
| (2,0) | (2,1) | (2,2) | (2,3) |
|--------|--------|--------|--------|
'''
ax = axes['cmap'] #plt.subplot2grid((3,4), (0,1), colspan=2)
# Making a fake 'figure' for a colorbar (easier than controling an orphan colorbar)
x = [] ; y = [] ; z = [] ;
for i in range(0,101,5): # to be x and y
for j in [0,5,10]: # to be i and j
x.append(i)
y.append(j)
z.append(i)
x = np.array(x); y = np.array(y); z = np.array(z);
X,Y,Z = locallib.xyz_to_ndarrays(x,y,z)
# Plot the colorbar
ax.contourf(X,Y,Z, 20,cmap = cmap)
# Just setting the various aspects of the ticks, labels, etc.
ax.tick_params(axis=u'both', direction='in', width=linewidth, length=0, pad=2,labelsize=textsize*0.8,
top=True, labeltop=True, bottom=False, labelbottom=False)
ax.set_xticks([4,50,96])
ax.set_xticklabels(['low','medium','high'])
ax.set_yticks([])
# Resetting the frame size
for spine in ax.spines.keys():
ax.spines[spine].set_linewidth(linewidth*2.0)
#
#
#plt.tight_layout()#pad=0.0, w_pad=0.0, h_pad=0.2)
#plt.subplots_adjust(wspace=.201)
#plt.subplots_adjust(hspace=0.01)
plt.savefig(figname, dpi=180, bbox_inches='tight',transparent=True) #facecolor='w', edgecolor='w',
if show_graphs:
os.system(pdf_viewer+" "+figname)
#plt.show()
if 0: # not used in main manuscript
figname = output_fig_dir+"/fig_various_relationships.pdf"
#sns.reset_orig()
sns.set_style("ticks")
set_grays()
# VERY IMPORTANT FOR GETTING THE SHAPE OF EACH PANEL CORRECT
plt.figure(figsize=(12,5))
# DRAWING A BUNCH OF RELATIONSHIPS!
colors = ['b','r','g','k']
# Shows all other parameters to muck about with:
#print c
textsize = plt.rcParams['font.size']
linewidth = plt.rcParams['lines.linewidth']
ytype = 'rg'
# =======================================================================
# Here, we generate a set of relationships
''' colorbar
|---|---|---|---|
cis |0,0|0,1|0,2|0,3|
|---|---|---|---|
trans |1,0|1,1|1,2|0,3|
|---|---|---|---|
phi R theta d
'''
#plt.plot([0, 1], [0, 2], sns.xkcd_rgb["medium green"], lw=3)
#plt.plot([0, 1], [0, 3], sns.xkcd_rgb["denim blue"]
c1 = sns.hls_palette(2, l=.3, s=.6)[1] # should be blue
c2 = sns.hls_palette(2, l=.8, s=.9)[0] # should be red
c3 = sns.hls_palette(3, l=.8, s=.9)[1] # should be green
relationships_palette = [c1,c2,c3] #sns.color_palette("colorblind")
# Declaring the various panels
gs = mpl.gridspec.GridSpec(3, 4, width_ratios =[1,1,1,1], height_ratios=[2,10,10])
# Declaring the various axes (panels)
row_no = 1
ax_0_0 = plt.subplot(gs[row_no,0])# Y
ax_0_1 = plt.subplot(gs[row_no,1],sharey=ax_0_0)
ax_0_2 = plt.subplot(gs[row_no,2],sharey=ax_0_0)
ax_0_3 = plt.subplot(gs[row_no,3],sharey=ax_0_0)
#ax_0_4 = plt.subplot(gs[row_no,4],sharey=ax_0_0)
row_no = 2 # Y X
ax_1_0 = plt.subplot(gs[row_no,0] ,sharex=ax_0_0)
ax_1_1 = plt.subplot(gs[row_no,1],sharey=ax_1_0,sharex=ax_0_1)
ax_1_2 = plt.subplot(gs[row_no,2],sharey=ax_1_0,sharex=ax_0_2)
ax_1_3 = plt.subplot(gs[row_no,3],sharey=ax_1_0,sharex=ax_0_3)
#ax_1_4 = plt.subplot(gs[row_no,4],sharey=ax_1_0,sharex=ax_0_4)
# Placing the axes in an easy to access dictionary of dictionaries (axes['cis/trans']['xtype'])
# ytype is always 'rg' or 're' (or whatever you want it to be... it should be a label in our Pandas dataframe)
axes = {'trans':{
'phi': ax_0_0,
'theta': ax_0_1,
'd': ax_0_2,
'R': ax_0_3
},
'cis': {
'phi': ax_1_0,
'theta': ax_1_1,
'd': ax_1_2,
'R': ax_1_3
}
}
#
step = 2
colors = ['b','r','g','k']
lengths = np.arange(1,3)*8
omegas = [0,180]
# COSTLY:
if not os.path.isfile(length_dependent_csv_file):
prepare_main_csv_file()
df = pd.read_csv(length_dependent_csv_file)
# Take the two largest lengths only (otherwise many plots get confusing, as all relationships are length dependent)
Ls = sorted(set(df['L']))[-2:]
omegas = sorted(set(df['omega']))
for cis_or_trans in axes.keys():
omega = 180.0
if cis_or_trans == 'cis':
omega = 0.0
for xtype in axes[cis_or_trans].keys():
# We write to this panel:
ax = axes[cis_or_trans][xtype]
#
# resetting the color palette
palette = itertools.cycle(relationships_palette)
#
for L in Ls:
c = next(palette)
# 'cdf' stands for 'current dataframe'
cdf = df[(df['L'] == L) & (df['omega'] == omega)]
cdf = cdf.sort(xtype)#, ascending=False)
m = 's'
if omega == 0:
m = 'o'
X = cdf[xtype]
if xtype == 'd':
X = do_d(X)
if xtype == 'theta':
X = do_theta(X)
Y = list(cdf[ytype])
calcdf = pd.DataFrame({'X':X,'Y':Y})
alpha = 0.5
# GETTING ERROR BARS AND INTERVALS
xsteps = float(np.max(X)-np.min(X))/200
Xbins = np.arange(np.min(X)-xsteps/2, np.max(X)+xsteps/2, xsteps)
xave = []
yave = []
ystd = []
for i in range(len(Xbins)-1):
# temp df
ys_in_range = calcdf[(calcdf['X'] >= Xbins[i]) & (calcdf['X'] < Xbins[i+1])]['Y']
if len(ys_in_range):
xave.append((Xbins[i]+Xbins[i+1])/2.)
yave.append(np.mean(ys_in_range))
ystd.append(np.std(ys_in_range))
xave = np.array(xave)
yave = np.array(yave)
ystd = np.array(ystd)
ax.plot(xave, yave, 'k', color=c, label="$L="+str(L)+r"$")#'#CC4F1B')
ax.fill_between(xave, yave - ystd, yave + ystd, interpolate=True, facecolor=c, alpha=0.3)
#
#
#
''' phi psi R theta d
|---|---|---|---|---|
cis |0,0|0,1|0,2|0,3|0,4|
|---|---|---|---|---|
trans |1,0|1,1|1,2|0,3|0,4|
|---|---|---|---|---|
'''
#setting limits based on what is available
for cis_or_trans in axes.keys(): # either rg or re
for xtype in axes[cis_or_trans].keys():
ax = axes[cis_or_trans][xtype]
omega = 180.0
if cis_or_trans == 'cis':
omega = 0.0
cdf = df[(df['omega']==omega)]
xs = cdf[xtype]
ys = cdf[ytype]
if xtype == 'd':
xs = do_d(xs)
ax.set_xticks(range(-6,6,2))
elif xtype == 'theta':
xs = | |
could still contain
# importable submodules (e.g., the non-package `os` module containing
# the `os.path` submodule). In this case, these submodules are already
# imported by this target module's pure-Python code. Since our import
# scanner already detects these imports, these submodules need *NOT* be
# reimported here. (Doing so would be harmless but inefficient.)
if target_attr_names and isinstance(target_module, Package):
# For the name of each attribute imported from this target package
# into this source module...
for target_submodule_partname in target_attr_names:
#FIXME: Is this optimization *REALLY* an optimization or at all
#necessary? The findNode() method called below should already
#be heavily optimized, in which case this optimization here is
#premature, senseless, and should be eliminated.
# If this attribute is a previously imported submodule of this
# target module, optimize this edge case.
if target_module.is_submodule(target_submodule_partname):
# Graph node for this submodule.
target_submodule = target_module.get_submodule(
target_submodule_partname)
#FIXME: What? Shouldn't "target_submodule" *ALWAYS* be
#non-None here? Assert this to be non-None instead.
if target_submodule is not None:
#FIXME: Why does duplication matter? List searches are
#mildly expensive.
# If this submodule has not already been added to the
# list of submodules to be returned, do so.
if target_submodule not in target_modules:
self._updateReference(
source_module,
target_submodule,
edge_data=edge_attr)
target_modules.append(target_submodule)
continue
# Fully-qualified name of this submodule.
target_submodule_name = (
target_module.identifier + '.' + target_submodule_partname)
# Graph node of this submodule if previously imported or None.
target_submodule = self.findNode(target_submodule_name)
# If this submodule has not been imported, do so as if this
# submodule were the only attribute listed by the "import"
# clause of this import (e.g., as "from foo import bar" rather
# than "from foo import car, far, bar").
if target_submodule is None:
# Attempt to import this submodule.
try:
# Ignore the list of graph nodes returned by this
# method. If both this submodule's package and this
# submodule are importable, this method returns a
# 2-element list whose second element is this
# submodule's graph node. However, if this submodule's
# package is importable but this submodule is not,
# this submodule is either:
#
# * An ignorable global attribute defined at the top
# level of this package's "__init__" submodule. In
# this case, this method returns a 1-element list
# without raising an exception.
# * A non-ignorable unimportable submodule. In this
# case, this method raises an "ImportError".
#
# While the first two cases are disambiguatable by the
# length of this list, doing so would render this code
# dependent on import_hook() details subject to change.
# Instead, call findNode() to decide the truthiness.
self.import_hook(
target_module_partname, source_module,
target_attr_names=[target_submodule_partname],
level=level,
edge_attr=edge_attr)
# Graph node of this submodule imported by the prior
# call if importable or None otherwise.
target_submodule = self.findNode(target_submodule_name)
# If this submodule does not exist, this *MUST* be an
# ignorable global attribute defined at the top level
# of this package's "__init__" submodule.
if target_submodule is None:
# Assert this to actually be the case.
assert target_module.is_global_attr(
target_submodule_partname), (
'No global named {} in {}.__init__'.format(
target_submodule_partname,
target_module.identifier))
# Skip this safely ignorable importation to the
# next attribute. See similar logic in the body of
# _import_importable_package_submodules().
self.msg(4, '_safe_import_hook', 'ignoring imported non-module global', target_module.identifier, target_submodule_partname)
continue
# If this is a SWIG C extension, instruct PyInstaller
# to freeze this extension under its unqualified rather
# than qualified name (e.g., as "_csr" rather than
# "scipy.sparse.sparsetools._csr"), permitting the
# implicit relative import in its parent SWIG module to
# successfully find this extension.
if is_swig_import:
# If a graph node with this name already exists,
# avoid collisions by emitting an error instead.
if self.findNode(target_submodule_partname):
self.msg(
2,
'SWIG import error: %r basename %r '
'already exists' % (
target_submodule_name,
target_submodule_partname))
else:
self.msg(
4,
'SWIG import renamed from %r to %r' % (
target_submodule_name,
target_submodule_partname))
target_submodule.identifier = (
target_submodule_partname)
# If this submodule is unimportable, add a MissingModule.
except ImportError as msg:
self.msg(2, "ImportError:", str(msg))
target_submodule = self.createNode(
MissingModule, target_submodule_name)
# Add this submodule to its package.
target_module.add_submodule(
target_submodule_partname, target_submodule)
if target_submodule is not None:
self._updateReference(
target_module, target_submodule, edge_data=edge_attr)
self._updateReference(
source_module, target_submodule, edge_data=edge_attr)
if target_submodule not in target_modules:
target_modules.append(target_submodule)
# Return the list of all target modules imported by this call.
return target_modules
def _scan_code(
self,
module,
module_code_object,
module_code_object_ast=None):
"""
Parse and add all import statements from the passed code object of the
passed source module to this graph, recursively.
**This method is at the root of all `ModuleGraph` recursion.**
Recursion begins here and ends when all import statements in all code
objects of all modules transitively imported by the source module
passed to the first call to this method have been added to the graph.
Specifically, this method:
1. If the passed `module_code_object_ast` parameter is non-`None`,
parses all import statements from this object.
2. Else, parses all import statements from the passed
`module_code_object` parameter.
1. For each such import statement:
1. Adds to this `ModuleGraph` instance:
1. Nodes for all target modules of these imports.
1. Directed edges from this source module to these target
modules.
2. Recursively calls this method with these target modules.
Parameters
----------
module : Node
Graph node of the module to be parsed.
module_code_object : PyCodeObject
Code object providing this module's disassembled Python bytecode.
Ignored unless `module_code_object_ast` is `None`.
module_code_object_ast : optional[ast.AST]
Optional abstract syntax tree (AST) of this module if any or `None`
otherwise. Defaults to `None`, in which case the passed
`module_code_object` is parsed instead.
"""
# For safety, guard against multiple scans of the same module by
# resetting this module's list of deferred target imports. While
# uncommon, this edge case can occur due to:
#
# * Dynamic package replacement via the replacePackage() function. For
# example, the real "_xmlplus" package dynamically replaces itself
# with the fake "xml" package into the "sys.modules" cache of all
# currently loaded modules at runtime.
module._deferred_imports = []
# Parse all imports from this module *BEFORE* adding these imports to
# the graph. If an AST is provided, parse that rather than this
# module's code object.
if module_code_object_ast is not None:
# Parse this module's AST for imports.
self._scan_ast(module, module_code_object_ast)
# Parse this module's code object for all relevant non-imports
# (e.g., global variable declarations and undeclarations).
self._scan_bytecode(
module, module_code_object, is_scanning_imports=False)
# Else, parse this module's code object for imports.
else:
self._scan_bytecode(
module, module_code_object, is_scanning_imports=True)
# Add all imports parsed above to this graph.
self._process_imports(module)
def _scan_ast(self, module, module_code_object_ast):
"""
Parse and add all import statements from the passed abstract syntax
tree (AST) of the passed source module to this graph, non-recursively.
Parameters
----------
module : Node
Graph node of the module to be parsed.
module_code_object_ast : ast.AST
Abstract syntax tree (AST) of this module to be parsed.
"""
visitor = _Visitor(self, module)
visitor.visit(module_code_object_ast)
#FIXME: Optimize. Global attributes added by this method are tested by
#other methods *ONLY* for packages, implying this method should scan and
#handle opcodes pertaining to global attributes (e.g.,
#"STORE_NAME", "DELETE_GLOBAL") only if the passed "module"
#object is an instance of the "Package" class. For all other module types,
#these opcodes should simply be ignored.
#
#After doing so, the "Node._global_attr_names" attribute and all methods
#using this attribute (e.g., Node.is_global()) should be moved from the
#"Node" superclass to the "Package" subclass.
def _scan_bytecode(
self, module, module_code_object, is_scanning_imports):
"""
Parse and add all import statements from the passed code object of the
passed source module to this graph, non-recursively.
This method parses all reasonably parsable operations (i.e., operations
that are both syntactically and semantically parsable _without_
requiring Turing-complete interpretation) directly or indirectly
involving module importation from this code object. This includes:
* `IMPORT_NAME`, denoting an import statement. Ignored unless
the passed `is_scanning_imports` parameter is `True`.
* `STORE_NAME` and `STORE_GLOBAL`, denoting | |
for workers.
:vartype allowed_worker_sizes: str
:ivar maximum_number_of_machines: Maximum number of VMs in the App Service Environment.
:vartype maximum_number_of_machines: int
:ivar vip_mappings: Description of IP SSL mapping for the App Service Environment.
:vartype vip_mappings: list[~azure.mgmt.web.v2016_09_01.models.VirtualIPMapping]
:ivar environment_capacities: Current total, used, and available worker capacities.
:vartype environment_capacities: list[~azure.mgmt.web.v2016_09_01.models.StampCapacity]
:ivar network_access_control_list: Access control list for controlling traffic to the App
Service Environment.
:vartype network_access_control_list:
list[~azure.mgmt.web.v2016_09_01.models.NetworkAccessControlEntry]
:ivar environment_is_healthy: True/false indicating whether the App Service Environment is
healthy.
:vartype environment_is_healthy: bool
:ivar environment_status: Detailed message about with results of the last check of the App
Service Environment.
:vartype environment_status: str
:ivar resource_group: Resource group of the App Service Environment.
:vartype resource_group: str
:ivar front_end_scale_factor: Scale factor for front-ends.
:vartype front_end_scale_factor: int
:ivar default_front_end_scale_factor: Default Scale Factor for FrontEnds.
:vartype default_front_end_scale_factor: int
:ivar api_management_account_id: API Management Account associated with the App Service
Environment.
:vartype api_management_account_id: str
:ivar suspended: :code:`<code>true</code>` if the App Service Environment is suspended;
otherwise, :code:`<code>false</code>`. The environment can be suspended, e.g. when the
management endpoint is no longer available
(most likely because NSG blocked the incoming traffic).
:vartype suspended: bool
:ivar dynamic_cache_enabled: True/false indicating whether the App Service Environment is
suspended. The environment can be suspended e.g. when the management endpoint is no longer
available
(most likely because NSG blocked the incoming traffic).
:vartype dynamic_cache_enabled: bool
:ivar cluster_settings: Custom settings for changing the behavior of the App Service
Environment.
:vartype cluster_settings: list[~azure.mgmt.web.v2016_09_01.models.NameValuePair]
:ivar user_whitelisted_ip_ranges: User added ip ranges to whitelist on ASE db.
:vartype user_whitelisted_ip_ranges: list[str]
"""
_validation = {
'id': {'readonly': True},
'name': {'readonly': True},
'location': {'required': True},
'type': {'readonly': True},
'provisioning_state': {'readonly': True},
'status': {'readonly': True},
'database_edition': {'readonly': True},
'database_service_objective': {'readonly': True},
'upgrade_domains': {'readonly': True},
'subscription_id': {'readonly': True},
'last_action': {'readonly': True},
'last_action_result': {'readonly': True},
'allowed_multi_sizes': {'readonly': True},
'allowed_worker_sizes': {'readonly': True},
'maximum_number_of_machines': {'readonly': True},
'vip_mappings': {'readonly': True},
'environment_capacities': {'readonly': True},
'environment_is_healthy': {'readonly': True},
'environment_status': {'readonly': True},
'resource_group': {'readonly': True},
'default_front_end_scale_factor': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'kind': {'key': 'kind', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'name_properties_name': {'key': 'properties.name', 'type': 'str'},
'location_properties_location': {'key': 'properties.location', 'type': 'str'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
'status': {'key': 'properties.status', 'type': 'str'},
'vnet_name': {'key': 'properties.vnetName', 'type': 'str'},
'vnet_resource_group_name': {'key': 'properties.vnetResourceGroupName', 'type': 'str'},
'vnet_subnet_name': {'key': 'properties.vnetSubnetName', 'type': 'str'},
'virtual_network': {'key': 'properties.virtualNetwork', 'type': 'VirtualNetworkProfile'},
'internal_load_balancing_mode': {'key': 'properties.internalLoadBalancingMode', 'type': 'str'},
'multi_size': {'key': 'properties.multiSize', 'type': 'str'},
'multi_role_count': {'key': 'properties.multiRoleCount', 'type': 'int'},
'worker_pools': {'key': 'properties.workerPools', 'type': '[WorkerPool]'},
'ipssl_address_count': {'key': 'properties.ipsslAddressCount', 'type': 'int'},
'database_edition': {'key': 'properties.databaseEdition', 'type': 'str'},
'database_service_objective': {'key': 'properties.databaseServiceObjective', 'type': 'str'},
'upgrade_domains': {'key': 'properties.upgradeDomains', 'type': 'int'},
'subscription_id': {'key': 'properties.subscriptionId', 'type': 'str'},
'dns_suffix': {'key': 'properties.dnsSuffix', 'type': 'str'},
'last_action': {'key': 'properties.lastAction', 'type': 'str'},
'last_action_result': {'key': 'properties.lastActionResult', 'type': 'str'},
'allowed_multi_sizes': {'key': 'properties.allowedMultiSizes', 'type': 'str'},
'allowed_worker_sizes': {'key': 'properties.allowedWorkerSizes', 'type': 'str'},
'maximum_number_of_machines': {'key': 'properties.maximumNumberOfMachines', 'type': 'int'},
'vip_mappings': {'key': 'properties.vipMappings', 'type': '[VirtualIPMapping]'},
'environment_capacities': {'key': 'properties.environmentCapacities', 'type': '[StampCapacity]'},
'network_access_control_list': {'key': 'properties.networkAccessControlList', 'type': '[NetworkAccessControlEntry]'},
'environment_is_healthy': {'key': 'properties.environmentIsHealthy', 'type': 'bool'},
'environment_status': {'key': 'properties.environmentStatus', 'type': 'str'},
'resource_group': {'key': 'properties.resourceGroup', 'type': 'str'},
'front_end_scale_factor': {'key': 'properties.frontEndScaleFactor', 'type': 'int'},
'default_front_end_scale_factor': {'key': 'properties.defaultFrontEndScaleFactor', 'type': 'int'},
'api_management_account_id': {'key': 'properties.apiManagementAccountId', 'type': 'str'},
'suspended': {'key': 'properties.suspended', 'type': 'bool'},
'dynamic_cache_enabled': {'key': 'properties.dynamicCacheEnabled', 'type': 'bool'},
'cluster_settings': {'key': 'properties.clusterSettings', 'type': '[NameValuePair]'},
'user_whitelisted_ip_ranges': {'key': 'properties.userWhitelistedIpRanges', 'type': '[str]'},
}
def __init__(
self,
*,
location: str,
kind: Optional[str] = None,
tags: Optional[Dict[str, str]] = None,
name_properties_name: Optional[str] = None,
location_properties_location: Optional[str] = None,
vnet_name: Optional[str] = None,
vnet_resource_group_name: Optional[str] = None,
vnet_subnet_name: Optional[str] = None,
virtual_network: Optional["VirtualNetworkProfile"] = None,
internal_load_balancing_mode: Optional[Union[str, "InternalLoadBalancingMode"]] = None,
multi_size: Optional[str] = None,
multi_role_count: Optional[int] = None,
worker_pools: Optional[List["WorkerPool"]] = None,
ipssl_address_count: Optional[int] = None,
dns_suffix: Optional[str] = None,
network_access_control_list: Optional[List["NetworkAccessControlEntry"]] = None,
front_end_scale_factor: Optional[int] = None,
api_management_account_id: Optional[str] = None,
suspended: Optional[bool] = None,
dynamic_cache_enabled: Optional[bool] = None,
cluster_settings: Optional[List["NameValuePair"]] = None,
user_whitelisted_ip_ranges: Optional[List[str]] = None,
**kwargs
):
"""
:keyword kind: Kind of resource.
:paramtype kind: str
:keyword location: Required. Resource Location.
:paramtype location: str
:keyword tags: A set of tags. Resource tags.
:paramtype tags: dict[str, str]
:keyword name_properties_name: Name of the App Service Environment.
:paramtype name_properties_name: str
:keyword location_properties_location: Location of the App Service Environment, e.g. "West US".
:paramtype location_properties_location: str
:keyword vnet_name: Name of the Virtual Network for the App Service Environment.
:paramtype vnet_name: str
:keyword vnet_resource_group_name: Resource group of the Virtual Network.
:paramtype vnet_resource_group_name: str
:keyword vnet_subnet_name: Subnet of the Virtual Network.
:paramtype vnet_subnet_name: str
:keyword virtual_network: Description of the Virtual Network.
:paramtype virtual_network: ~azure.mgmt.web.v2016_09_01.models.VirtualNetworkProfile
:keyword internal_load_balancing_mode: Specifies which endpoints to serve internally in the
Virtual Network for the App Service Environment. Possible values include: "None", "Web",
"Publishing".
:paramtype internal_load_balancing_mode: str or
~azure.mgmt.web.v2016_09_01.models.InternalLoadBalancingMode
:keyword multi_size: Front-end VM size, e.g. "Medium", "Large".
:paramtype multi_size: str
:keyword multi_role_count: Number of front-end instances.
:paramtype multi_role_count: int
:keyword worker_pools: Description of worker pools with worker size IDs, VM sizes, and number
of workers in each pool.
:paramtype worker_pools: list[~azure.mgmt.web.v2016_09_01.models.WorkerPool]
:keyword ipssl_address_count: Number of IP SSL addresses reserved for the App Service
Environment.
:paramtype ipssl_address_count: int
:keyword dns_suffix: DNS suffix of the App Service Environment.
:paramtype dns_suffix: str
:keyword network_access_control_list: Access control list for controlling traffic to the App
Service Environment.
:paramtype network_access_control_list:
list[~azure.mgmt.web.v2016_09_01.models.NetworkAccessControlEntry]
:keyword front_end_scale_factor: Scale factor for front-ends.
:paramtype front_end_scale_factor: int
:keyword api_management_account_id: API Management Account associated with the App Service
Environment.
:paramtype api_management_account_id: str
:keyword suspended: :code:`<code>true</code>` if the App Service Environment is suspended;
otherwise, :code:`<code>false</code>`. The environment can be suspended, e.g. when the
management endpoint is no longer available
(most likely because NSG blocked the incoming traffic).
:paramtype suspended: bool
:keyword dynamic_cache_enabled: True/false indicating whether the App Service Environment is
suspended. The environment can be suspended e.g. when the management endpoint is no longer
available
(most likely because NSG blocked the incoming traffic).
:paramtype dynamic_cache_enabled: bool
:keyword cluster_settings: Custom settings for changing the behavior of the App Service
Environment.
:paramtype cluster_settings: list[~azure.mgmt.web.v2016_09_01.models.NameValuePair]
:keyword user_whitelisted_ip_ranges: User added ip ranges to whitelist on ASE db.
:paramtype user_whitelisted_ip_ranges: list[str]
"""
super(AppServiceEnvironmentResource, self).__init__(kind=kind, location=location, tags=tags, **kwargs)
self.name_properties_name = name_properties_name
self.location_properties_location = location_properties_location
self.provisioning_state = None
self.status = None
self.vnet_name = vnet_name
self.vnet_resource_group_name = vnet_resource_group_name
self.vnet_subnet_name = vnet_subnet_name
self.virtual_network = virtual_network
self.internal_load_balancing_mode = internal_load_balancing_mode
self.multi_size = multi_size
self.multi_role_count = multi_role_count
self.worker_pools = worker_pools
self.ipssl_address_count = ipssl_address_count
self.database_edition = None
self.database_service_objective = None
self.upgrade_domains = None
self.subscription_id = None
self.dns_suffix = dns_suffix
self.last_action = None
self.last_action_result = None
self.allowed_multi_sizes = None
self.allowed_worker_sizes = None
self.maximum_number_of_machines = None
self.vip_mappings = None
self.environment_capacities = None
self.network_access_control_list = network_access_control_list
self.environment_is_healthy = None
self.environment_status = None
self.resource_group = None
self.front_end_scale_factor = front_end_scale_factor
self.default_front_end_scale_factor = None
self.api_management_account_id = api_management_account_id
self.suspended = suspended
self.dynamic_cache_enabled = dynamic_cache_enabled
self.cluster_settings = cluster_settings
self.user_whitelisted_ip_ranges = user_whitelisted_ip_ranges
class AppServicePlan(Resource):
"""App Service plan.
Variables are only populated by the server, and will be ignored when sending a request.
All required parameters must be populated in order to send to Azure.
:ivar id: Resource Id.
:vartype id: str
:ivar name: Resource Name.
:vartype name: str
:ivar kind: Kind of resource.
:vartype kind: str
:ivar location: Required. Resource Location.
:vartype location: str
:ivar type: Resource type.
:vartype type: str
:ivar tags: A set of tags. Resource tags.
:vartype tags: dict[str, str]
:ivar sku: Description of a SKU for a scalable resource.
:vartype sku: ~azure.mgmt.web.v2016_09_01.models.SkuDescription
:ivar name_properties_name: Name for the App Service plan.
:vartype name_properties_name: str
:ivar worker_tier_name: Target worker tier assigned to the App Service plan.
:vartype worker_tier_name: str
:ivar status: App Service plan status. Possible values include: "Ready", "Pending", "Creating".
:vartype status: str or ~azure.mgmt.web.v2016_09_01.models.StatusOptions
:ivar subscription: App Service plan subscription.
:vartype subscription: str
:ivar admin_site_name: App Service plan administration site.
:vartype admin_site_name: str
:ivar hosting_environment_profile: Specification for the App Service Environment to use for the
App Service plan.
:vartype hosting_environment_profile:
~azure.mgmt.web.v2016_09_01.models.HostingEnvironmentProfile
:ivar maximum_number_of_workers: Maximum number of instances that can be assigned to this App
Service plan.
:vartype maximum_number_of_workers: int
:ivar geo_region: Geographical location for the App Service plan.
:vartype geo_region: str
:ivar per_site_scaling: If :code:`<code>true</code>`, apps | |
the file
is: n_scales xsec_scale_central xsec_scale1 ... n_pdf
xsec_pdf0 xsec_pdf1 ...."""
scales=[]
pdfs=[]
for i,evt_file in enumerate(evt_files):
path, evt=os.path.split(evt_file)
with open(pjoin(self.me_dir, 'SubProcesses', path, 'scale_pdf_dependence.dat'),'r') as f:
data_line=f.readline()
if "scale variations:" in data_line:
for j,scale in enumerate(self.run_card['dynamical_scale_choice']):
data_line = f.readline().split()
scales_this = [float(val)*evt_wghts[i] for val in f.readline().replace("D", "E").split()]
try:
scales[j] = [a + b for a, b in zip(scales[j], scales_this)]
except IndexError:
scales+=[scales_this]
data_line=f.readline()
if "pdf variations:" in data_line:
for j,pdf in enumerate(self.run_card['lhaid']):
data_line = f.readline().split()
pdfs_this = [float(val)*evt_wghts[i] for val in f.readline().replace("D", "E").split()]
try:
pdfs[j] = [a + b for a, b in zip(pdfs[j], pdfs_this)]
except IndexError:
pdfs+=[pdfs_this]
# get the scale uncertainty in percent
scale_info=[]
for j,scale in enumerate(scales):
s_cen=scale[0]
if s_cen != 0.0 and self.run_card['reweight_scale'][j]:
# max and min of the full envelope
s_max=(max(scale)/s_cen-1)*100
s_min=(1-min(scale)/s_cen)*100
# ren and fac scale dependence added in quadrature
ren_var=[]
fac_var=[]
for i in range(len(self.run_card['rw_rscale'])):
ren_var.append(scale[i]-s_cen) # central fac scale
for i in range(len(self.run_card['rw_fscale'])):
fac_var.append(scale[i*len(self.run_card['rw_rscale'])]-s_cen) # central ren scale
s_max_q=((s_cen+math.sqrt(math.pow(max(ren_var),2)+math.pow(max(fac_var),2)))/s_cen-1)*100
s_min_q=(1-(s_cen-math.sqrt(math.pow(min(ren_var),2)+math.pow(min(fac_var),2)))/s_cen)*100
s_size=len(scale)
else:
s_max=0.0
s_min=0.0
s_max_q=0.0
s_min_q=0.0
s_size=len(scale)
scale_info.append({'cen':s_cen, 'min':s_min, 'max':s_max, \
'min_q':s_min_q, 'max_q':s_max_q, 'size':s_size, \
'label':self.run_card['dynamical_scale_choice'][j], \
'unc':self.run_card['reweight_scale'][j]})
# check if we can use LHAPDF to compute the PDF uncertainty
if any(self.run_card['reweight_pdf']):
use_lhapdf=False
lhapdf_libdir=subprocess.Popen([self.options['lhapdf'],'--libdir'],\
stdout=subprocess.PIPE).stdout.read().strip()
try:
candidates=[dirname for dirname in os.listdir(lhapdf_libdir) \
if os.path.isdir(pjoin(lhapdf_libdir,dirname))]
except OSError:
candidates=[]
for candidate in candidates:
if os.path.isfile(pjoin(lhapdf_libdir,candidate,'site-packages','lhapdf.so')):
sys.path.insert(0,pjoin(lhapdf_libdir,candidate,'site-packages'))
try:
import lhapdf
use_lhapdf=True
break
except ImportError:
sys.path.pop(0)
continue
if not use_lhapdf:
try:
candidates=[dirname for dirname in os.listdir(lhapdf_libdir+'64') \
if os.path.isdir(pjoin(lhapdf_libdir+'64',dirname))]
except OSError:
candidates=[]
for candidate in candidates:
if os.path.isfile(pjoin(lhapdf_libdir+'64',candidate,'site-packages','lhapdf.so')):
sys.path.insert(0,pjoin(lhapdf_libdir+'64',candidate,'site-packages'))
try:
import lhapdf
use_lhapdf=True
break
except ImportError:
sys.path.pop(0)
continue
if not use_lhapdf:
try:
import lhapdf
use_lhapdf=True
except ImportError:
logger.warning("Failed to access python version of LHAPDF: "\
"cannot compute PDF uncertainty from the "\
"weights in the events. The weights in the LHE " \
"event files will still cover all PDF set members, "\
"but there will be no PDF uncertainty printed in the run summary. \n "\
"If the python interface to LHAPDF is available on your system, try "\
"adding its location to the PYTHONPATH environment variable and the"\
"LHAPDF library location to LD_LIBRARY_PATH (linux) or DYLD_LIBRARY_PATH (mac os x).")
use_lhapdf=False
# turn off lhapdf printing any messages
if any(self.run_card['reweight_pdf']) and use_lhapdf: lhapdf.setVerbosity(0)
pdf_info=[]
for j,pdfset in enumerate(pdfs):
p_cen=pdfset[0]
if p_cen != 0.0 and self.run_card['reweight_pdf'][j]:
if use_lhapdf:
pdfsetname=self.run_card['lhapdfsetname'][j]
try:
p=lhapdf.getPDFSet(pdfsetname)
ep=p.uncertainty(pdfset,-1)
p_cen=ep.central
p_min=abs(ep.errminus/p_cen)*100
p_max=abs(ep.errplus/p_cen)*100
p_type=p.errorType
p_size=p.size
p_conf=p.errorConfLevel
except:
logger.warning("Could not access LHAPDF to compute uncertainties for %s" % pdfsetname)
p_min=0.0
p_max=0.0
p_type='unknown'
p_conf='unknown'
p_size=len(pdfset)
else:
p_min=0.0
p_max=0.0
p_type='unknown'
p_conf='unknown'
p_size=len(pdfset)
pdfsetname=self.run_card['lhaid'][j]
else:
p_min=0.0
p_max=0.0
p_type='none'
p_conf='unknown'
p_size=len(pdfset)
pdfsetname=self.run_card['lhaid'][j]
pdf_info.append({'cen':p_cen, 'min':p_min, 'max':p_max, \
'unc':p_type, 'name':pdfsetname, 'size':p_size, \
'label':self.run_card['lhaid'][j], 'conf':p_conf})
scale_pdf_info=[scale_info,pdf_info]
return scale_pdf_info
def wait_for_complete(self, run_type):
"""this function waits for jobs on cluster to complete their run."""
starttime = time.time()
#logger.info(' Waiting for submitted jobs to complete')
update_status = lambda i, r, f: self.update_status((i, r, f, run_type),
starttime=starttime, level='parton', update_results=True)
try:
self.cluster.wait(self.me_dir, update_status)
except:
self.cluster.remove()
raise
def run_all(self, job_dict, arg_list, run_type='monitor', split_jobs = False):
"""runs the jobs in job_dict (organized as folder: [job_list]), with arguments args"""
self.ijob = 0
if run_type != 'shower':
self.njobs = sum(len(jobs) for jobs in job_dict.values()) * len(arg_list)
for args in arg_list:
for Pdir, jobs in job_dict.items():
for job in jobs:
self.run_exe(job, args, run_type, cwd=pjoin(self.me_dir, 'SubProcesses', Pdir) )
if self.cluster_mode == 2:
time.sleep(1) # security to allow all jobs to be launched
else:
self.njobs = len(arg_list)
for args in arg_list:
[(cwd, exe)] = job_dict.items()
self.run_exe(exe, args, run_type, cwd)
self.wait_for_complete(run_type)
def check_event_files(self,jobs):
"""check the integrity of the event files after splitting, and resubmit
those which are not nicely terminated"""
jobs_to_resubmit = []
for job in jobs:
last_line = ''
try:
last_line = subprocess.Popen(
['tail', '-n1', pjoin(job['dirname'], 'events.lhe')], \
stdout = subprocess.PIPE).stdout.read().strip()
except IOError:
pass
if last_line != "</LesHouchesEvents>":
jobs_to_resubmit.append(job)
self.njobs = 0
if jobs_to_resubmit:
run_type = 'Resubmitting broken jobs'
logger.info('Some event files are broken, corresponding jobs will be resubmitted.')
for job in jobs_to_resubmit:
logger.debug('Resubmitting ' + job['dirname'] + '\n')
self.run_all_jobs(jobs_to_resubmit,2,fixed_order=False)
def find_jobs_to_split(self, pdir, job, arg):
"""looks into the nevents_unweighed_splitted file to check how many
split jobs are needed for this (pdir, job). arg is F, B or V"""
# find the number of the integration channel
splittings = []
ajob = open(pjoin(self.me_dir, 'SubProcesses', pdir, job)).read()
pattern = re.compile('for i in (\d+) ; do')
match = re.search(pattern, ajob)
channel = match.groups()[0]
# then open the nevents_unweighted_splitted file and look for the
# number of splittings to be done
nevents_file = open(pjoin(self.me_dir, 'SubProcesses', 'nevents_unweighted_splitted')).read()
# This skips the channels with zero events, because they are
# not of the form GFXX_YY, but simply GFXX
pattern = re.compile(r"%s_(\d+)/events.lhe" % \
pjoin(pdir, 'G%s%s' % (arg,channel)))
matches = re.findall(pattern, nevents_file)
for m in matches:
splittings.append(m)
return splittings
def run_exe(self, exe, args, run_type, cwd=None):
"""this basic function launch locally/on cluster exe with args as argument.
"""
# first test that exe exists:
execpath = None
if cwd and os.path.exists(pjoin(cwd, exe)):
execpath = pjoin(cwd, exe)
elif not cwd and os.path.exists(exe):
execpath = exe
else:
raise aMCatNLOError('Cannot find executable %s in %s' \
% (exe, os.getcwd()))
# check that the executable has exec permissions
if self.cluster_mode == 1 and not os.access(execpath, os.X_OK):
subprocess.call(['chmod', '+x', exe], cwd=cwd)
# finally run it
if self.cluster_mode == 0:
#this is for the serial run
misc.call(['./'+exe] + args, cwd=cwd)
self.ijob += 1
self.update_status((max([self.njobs - self.ijob - 1, 0]),
min([1, self.njobs - self.ijob]),
self.ijob, run_type), level='parton')
#this is for the cluster/multicore run
elif 'reweight' in exe:
# a reweight run
# Find the correct PDF input file
input_files, output_files = [], []
pdfinput = self.get_pdf_input_filename()
if os.path.exists(pdfinput):
input_files.append(pdfinput)
input_files.append(pjoin(os.path.dirname(exe), os.path.pardir, 'reweight_xsec_events'))
input_files.append(pjoin(cwd, os.path.pardir, 'leshouche_info.dat'))
input_files.append(args[0])
output_files.append('%s.rwgt' % os.path.basename(args[0]))
output_files.append('reweight_xsec_events.output')
output_files.append('scale_pdf_dependence.dat')
return self.cluster.submit2(exe, args, cwd=cwd,
input_files=input_files, output_files=output_files,
required_output=output_files)
elif 'ajob' in exe:
# the 'standard' amcatnlo job
# check if args is a list of string
if type(args[0]) == str:
input_files, output_files, required_output, args = self.getIO_ajob(exe,cwd,args)
#submitting
self.cluster.submit2(exe, args, cwd=cwd,
input_files=input_files, output_files=output_files,
required_output=required_output)
# # keep track of folders and arguments for splitted evt gen
# subfolder=output_files[-1].split('/')[0]
# if len(args) == 4 and '_' in subfolder:
# self.split_folders[pjoin(cwd,subfolder)] = [exe] + args
elif 'shower' in exe:
# a shower job
# args are [shower, output(HEP or TOP), run_name]
# cwd is the shower rundir, where the executable are found
input_files, output_files = [], []
shower = args[0]
# the input files
if shower == 'PYTHIA8':
input_files.append(pjoin(cwd, 'Pythia8.exe'))
input_files.append(pjoin(cwd, 'Pythia8.cmd'))
if os.path.exists(pjoin(self.options['pythia8_path'], 'xmldoc')):
input_files.append(pjoin(cwd, 'config.sh'))
input_files.append(pjoin(self.options['pythia8_path'], 'xmldoc'))
else:
input_files.append(pjoin(self.options['pythia8_path'], 'share/Pythia8/xmldoc'))
else:
input_files.append(pjoin(cwd, 'MCATNLO_%s_EXE' % shower))
input_files.append(pjoin(cwd, 'MCATNLO_%s_input' % shower))
if shower == 'HERWIGPP':
if os.path.exists(pjoin(self.options['hwpp_path'], 'bin', 'Herwig++')):
input_files.append(pjoin(cwd, 'Herwig++'))
if os.path.exists(pjoin(self.options['hwpp_path'], 'bin', 'Herwig')):
input_files.append(pjoin(cwd, 'Herwig'))
input_files.append(pjoin(cwd, 'HepMCFortran.so'))
if len(args) == 3:
if os.path.exists(pjoin(self.me_dir, 'Events', self.run_name, 'events.lhe.gz')):
input_files.append(pjoin(self.me_dir, 'Events', self.run_name, 'events.lhe.gz'))
elif os.path.exists(pjoin(self.me_dir, 'Events', self.run_name, 'events.lhe')):
input_files.append(pjoin(self.me_dir, 'Events', self.run_name, 'events.lhe'))
else:
raise aMCatNLOError, 'Event file not present in %s' % \
pjoin(self.me_dir, 'Events', self.run_name)
else:
input_files.append(pjoin(cwd, 'events_%s.lhe' % args[3]))
# the output files
if len(args) == 3:
output_files.append('mcatnlo_run.log')
else:
output_files.append('mcatnlo_run_%s.log' % args[3])
if args[1] == 'HEP':
if len(args) == 3:
fname = 'events'
else:
fname = 'events_%s' % args[3]
if shower in ['PYTHIA8', 'HERWIGPP']:
output_files.append(fname + '.hepmc.gz')
else:
output_files.append(fname + '.hep.gz')
elif args[1] == 'TOP' or args[1] == 'HWU':
if len(args) == 3:
fname = 'histfile'
else:
fname = 'histfile_%s' % args[3]
output_files.append(fname + '.tar')
else:
raise aMCatNLOError, 'Not a valid output argument for shower job : %d' % args[1]
#submitting
self.cluster.submit2(exe, args, cwd=cwd,
input_files=input_files, output_files=output_files)
else:
return self.cluster.submit(exe, args, cwd=cwd)
def getIO_ajob(self,exe,cwd, args):
# use local disk if possible => need to stands what are the
# input/output files
output_files = []
required_output = []
input_files = [pjoin(self.me_dir, 'SubProcesses', 'randinit'),
pjoin(cwd, 'symfact.dat'),
pjoin(cwd, 'iproc.dat'),
pjoin(cwd, 'initial_states_map.dat'),
pjoin(cwd, 'configs_and_props_info.dat'),
pjoin(cwd, 'leshouche_info.dat'),
pjoin(cwd, 'FKS_params.dat')]
# For GoSam interface, we must copy the SLHA card as well
if os.path.exists(pjoin(self.me_dir,'OLP_virtuals','gosam.rc')):
input_files.append(pjoin(self.me_dir, 'Cards', 'param_card.dat'))
if os.path.exists(pjoin(cwd,'nevents.tar')):
input_files.append(pjoin(cwd,'nevents.tar'))
if os.path.exists(pjoin(self.me_dir,'SubProcesses','OLE_order.olc')):
input_files.append(pjoin(cwd, 'OLE_order.olc'))
| |
from tfumap.load_datasets import load_CIFAR10, load_MNIST, load_FMNIST, mask_labels
import tensorflow as tf
from tfumap.paths import MODEL_DIR
import numpy as np
pretrained_networks = {
"cifar10_old": {
"augmented": {
4: "cifar10_4____2020_08_09_22_16_45_780732_baseline_augmented", # 15
16: "cifar10_16____2020_08_09_22_43_34_001017_baseline_augmented", # 29
64: "cifar10_64____2020_08_09_22_16_13_299376_baseline_augmented", # 47
256: "cifar10_256____2020_08_09_22_05_58_228942_baseline_augmented", # 71
1024: "cifar10_1024____2020_08_09_23_20_57_619002_baseline_augmented", # 84
"full": "cifar10_full____2020_08_09_22_03_51_910408_baseline_augmented", # ~91.5
},
"not_augmented": {
4: "cifar10_4____2020_08_09_22_13_28_904367_baseline", # 0.1542
16: "cifar10_16____2020_08_09_21_58_11_099843_baseline", # 0.2335
64: "cifar10_64____2020_08_09_22_14_33_994011_baseline", # 0.3430
256: "cifar10_128____2020_08_09_21_58_00_869329_baseline", # 0.4693
1024: "cifar10_1024____2020_08_09_22_14_06_923244_baseline", # 0.7864
"full": "cifar10_full____2020_08_09_21_54_03_152503_baseline", # 0.8923
},
},
"cifar10": {
"augmented": {
4: "cifar10_4____2020_08_11_19_25_58_939055_baseline_augmented", # 15 # 26
16: "cifar10_16____2020_08_11_19_25_49_190428_baseline_augmented", # 29 # 40
64: "cifar10_64____2020_08_11_19_25_41_266466_baseline_augmented", # 47 # ~60
256: "cifar10_256____2020_08_11_19_25_33_546350_baseline_augmented", # 71 # 76
1024: "cifar10_1024____2020_08_11_19_25_33_541963_baseline_augmented", # 84 # 86
"full": "cifar10_full____2020_08_11_19_25_33_543821_baseline_augmented", # ~91.5 # ~93
},
"not_augmented": {
4: "cifar10_4____2020_08_17_16_02_48_330135_baseline", # 0.1542 # 21
16: "cifar10_16____2020_08_17_15_08_23_820108_baseline", # 0.2335 # 30
64: "cifar10_64____2020_08_17_15_08_38_407886_baseline", # 0.3430 # 50
256: "cifar10_256____2020_08_17_22_16_16_108071_baseline", # 0.4693 # 72
1024: "cifar10_1024____2020_08_17_15_08_08_912644_baseline", # 0.7864 # 84
"full": "cifar10_full____2020_08_17_15_08_15_778694_baseline", # 0.8923 # 90
},
"umap_augmented_learned": {
4: "cifar10_0.0_4____2020_08_19_00_40_15_425455_umap_augmented", #
16: "cifar10_0.0_16____2020_08_19_00_40_13_037112_umap_augmented",
64: "cifar10_0.0_64____2020_08_19_00_40_13_032397_umap_augmented",
256: "cifar10_0.0_256____2020_08_18_16_16_47_694512_umap_augmented",
1024: "cifar10_0.0_1024____2020_08_19_10_25_26_973224_umap_augmented",
"full": "cifar10_0.0_full____2020_08_19_00_40_18_936212_umap_augmented",
},
"umap_not_augmented": {
4: "cifar10_0.0_4____2020_08_19_14_35_43_127626_umap_augmented", #
16: "cifar10_0.0_16____2020_08_19_14_35_43_867336_umap_augmented",
64: "cifar10_0.0_64____2020_08_19_14_35_43_736036_umap_augmented",
256: "cifar10_0.0_256____2020_08_19_14_38_31_105228_umap_augmented",
1024: "cifar10_0.0_1024____2020_08_19_14_35_43_823739_umap_augmented",
"full": "cifar10_0.0_full____2020_08_19_14_32_51_275942_umap_augmented",
},
"umap_not_augmented_thresh": {
4: "cifar10_0.8_4____2020_08_19_23_00_09_641532_umap_augmented", #
16: "cifar10_0.8_16____2020_08_19_23_00_00_125286_umap_augmented",
64: "cifar10_0.8_64____2020_08_19_23_00_54_552899_umap_augmented",
256: "cifar10_0.8_256____2020_08_19_23_00_56_468894_umap_augmented",
1024: "cifar10_0.8_1024____2020_08_19_23_00_59_934762_umap_augmented",
"full": "cifar10_0.8_full____2020_08_19_23_01_03_044142_umap_augmented",
},
"umap_euclidean_augmented": { # umap_euclidean_augmented_no_thresh
4: "cifar10_0.0_4____2020_08_20_10_49_23_565699_umap_augmented", #
16: "cifar10_0.0_16____2020_08_20_10_52_39_313456_umap_augmented",
64: "cifar10_0.0_64____2020_08_20_10_52_40_783860_umap_augmented",
256: "cifar10_0.0_256____2020_08_20_10_52_47_615557_umap_augmented",
1024: "cifar10_0.0_1024____2020_08_20_10_52_58_310917_umap_augmented",
"full": "cifar10_0.0_full____2020_08_20_10_53_00_819968_umap_augmented",
},
"umap_not_augmented_linear_thresh": {
4: "cifar10_0.8_4____2020_08_22_22_47_43_598023_umap_augmented", #
16: "cifar10_0.8_16____2020_08_22_22_47_57_967494_umap_augmented",
64: "cifar10_0.8_64____2020_08_22_22_47_27_952365_umap_augmented",
256: "cifar10_0.8_256____2020_08_22_22_48_38_890043_umap_augmented",
1024: "cifar10_0.8_1024____2020_08_22_22_49_43_660652_umap_augmented",
"full": "cifar10_0.8_full____2020_08_22_22_49_37_683086_umap_augmented",
},
"umap_euclidean": {
4: "cifar10_0.0_4____2020_08_24_10_10_03_033874_umap_augmented",
16: "cifar10_0.0_16____2020_08_24_00_26_41_868150_umap_augmented",
64: "cifar10_0.0_64____2020_08_24_00_26_53_791994_umap_augmented",
256: "cifar10_0.0_256____2020_08_24_00_22_53_202346_umap_augmented",
1024: "cifar10_0.0_1024____2020_08_24_00_22_53_212673_umap_augmented",
"full": "cifar10_0.0_full____2020_08_23_23_52_33_359986_umap_augmented",
},
"umap_learned": {
4: "cifar10_0.0_4____2020_08_19_14_35_43_127626_umap_augmented", #
16: "cifar10_0.0_16____2020_08_19_14_35_43_867336_umap_augmented",
64: "cifar10_0.0_64____2020_08_19_14_35_43_736036_umap_augmented",
256: "cifar10_0.0_256____2020_08_19_14_38_31_105228_umap_augmented",
1024: "cifar10_0.0_1024____2020_08_19_14_35_43_823739_umap_augmented",
"full": "cifar10_0.0_full____2020_08_19_14_32_51_275942_umap_augmented",
},
},
"mnist": {
"not_augmented": {
4: "mnist_4____2020_08_23_13_59_31_357892_baseline",
16: "mnist_16____2020_08_23_14_13_03_306576_baseline",
64: "mnist_64____2020_08_23_14_13_19_397319_baseline",
256: "mnist_256____2020_08_23_14_12_28_828611_baseline",
1024: "mnist_1024____2020_08_23_14_12_00_839816_baseline",
"full": "mnist_full____2020_08_23_14_02_35_917340_baseline",
},
"augmented": {
4: "mnist_4____2020_08_26_22_34_26_172040_baseline_augmented",
16: "mnist_16____2020_08_26_22_36_42_823740_baseline_augmented",
64: "mnist_64____2020_08_26_22_37_03_013806_baseline_augmented",
256: "mnist_256____2020_08_26_22_38_00_695064_baseline_augmented",
1024: "mnist_1024____2020_08_26_22_38_22_879325_baseline_augmented",
"full": "mnist_full____2020_08_26_22_34_57_589833_baseline_augmented",
},
"umap_euclidean": {
4: "mnist_0.0_4____2020_08_23_19_39_30_768509_umap_augmented",
16: "mnist_0.0_16____2020_08_23_19_27_31_722774_umap_augmented",
64: "mnist_0.0_64____2020_08_23_18_32_38_592348_umap_augmented",
256: "mnist_0.0_256____2020_08_23_19_39_57_288829_umap_augmented",
1024: "mnist_0.0_1024____2020_08_23_19_44_01_747431_umap_augmented",
# "full": "mnist_0.0_full____2020_08_23_23_07_06_598185_umap_augmented",
"full": "mnist_0.0_full____2020_08_23_23_11_15_364937_umap_augmented",
},
"umap_learned": {
4: "mnist_0.0_4____2020_08_24_13_43_38_697668_umap_augmented",
16: "mnist_0.0_16____2020_08_24_16_51_10_703116_umap_augmented",
64: "mnist_0.0_64____2020_08_24_16_51_16_969542_umap_augmented",
256: "mnist_0.0_256____2020_08_24_16_53_11_404946_umap_augmented",
1024: "mnist_0.0_1024____2020_08_24_16_53_15_376183_umap_augmented",
"full": "mnist_0.0_full____2020_08_24_13_43_38_497837_umap_augmented",
},
"umap_euclidean_augmented": {
4: "mnist_0.0_4____2020_08_28_01_19_15_530909_umap_augmented",
16: "mnist_0.0_16____2020_08_28_01_22_00_266602_umap_augmented",
64: "mnist_0.0_64____2020_08_28_01_22_22_251679_umap_augmented",
256: "mnist_0.0_256____2020_08_28_01_22_37_322969_umap_augmented",
1024: "mnist_0.0_1024____2020_08_28_10_21_10_652408_umap_augmented",
"full": "mnist_0.0_full____2020_08_28_10_21_10_309737_umap_augmented",
},
"umap_augmented_learned": {
4: "mnist_0.0_4____2020_08_28_22_36_55_334573_umap_augmented",
16: "mnist_0.0_16____2020_08_28_22_36_36_245588_umap_augmented",
64: "mnist_0.0_64____2020_08_28_10_58_23_001653_umap_augmented",
256: "mnist_0.0_256____2020_08_28_10_58_44_499275_umap_augmented",
1024: "mnist_0.0_1024____2020_08_28_11_00_20_544491_umap_augmented",
"full": "mnist_0.0_full____2020_08_28_11_00_23_221668_umap_augmented",
},
},
"fmnist": {
"not_augmented": {
4: "fmnist_4____2020_08_23_14_15_38_194490_baseline",
16: "fmnist_16____2020_08_23_14_15_50_074976_baseline",
64: "fmnist_64____2020_08_23_14_16_00_145880_baseline",
256: "fmnist_256____2020_08_23_14_14_27_904250_baseline",
1024: "fmnist_1024____2020_08_23_14_13_39_538728_baseline",
"full": "fmnist_full____2020_08_23_14_06_13_546999_baseline",
},
"augmented": {
4: "fmnist_4____2020_08_25_17_18_57_856259_baseline_augmented",
16: "fmnist_16____2020_08_25_17_19_58_221943_baseline_augmented",
64: "fmnist_64____2020_08_25_17_20_33_647542_baseline_augmented",
256: "fmnist_256____2020_08_25_17_20_55_354044_baseline_augmented",
1024: "fmnist_1024____2020_08_25_17_21_21_486291_baseline_augmented",
"full": "fmnist_full____2020_08_25_17_21_42_014099_baseline_augmented",
},
"placeholder": {4: "", 16: "", 64: "", 256: "", 1024: "", "full": ""},
"umap_over_z": {
4: "fmnist_0.0_4____2020_08_27_11_30_38_602000_umap_augmented",
16: "fmnist_0.0_16____2020_08_27_11_30_41_024752_umap_augmented",
64: "fmnist_0.0_64____2020_08_27_11_46_44_906423_umap_augmented",
256: "fmnist_0.0_256____2020_08_27_11_47_02_912498_umap_augmented",
1024: "",
"full": "",
},
"umap_augmented_learned": {
4: "fmnist_0.0_4____2020_08_25_22_52_13_661088_umap_augmented",
16: "fmnist_0.0_16____2020_08_25_22_53_12_075808_umap_augmented",
64: "fmnist_0.0_64____2020_08_25_22_58_52_822672_umap_augmented",
256: "fmnist_0.0_256____2020_08_25_22_59_00_936495_umap_augmented",
1024: "fmnist_0.0_1024____2020_08_25_22_59_15_453823_umap_augmented",
"full": "fmnist_0.0_full____2020_08_25_22_59_11_829778_umap_augmented",
},
"umap_euclidean": {
4: "fmnist_0.0_4____2020_08_23_18_48_03_409056_umap_augmented",
16: "fmnist_0.0_16____2020_08_23_21_25_30_890380_umap_augmented",
64: "fmnist_0.0_64____2020_08_23_19_43_20_063919_umap_augmented",
256: "fmnist_0.0_256____2020_08_23_19_44_36_506473_umap_augmented",
1024: "fmnist_0.0_1024____2020_08_23_21_25_43_287069_umap_augmented",
"full": "fmnist_0.0_full____2020_08_23_23_13_31_899132_umap_augmented",
},
"umap_learned": {
4: "fmnist_0.0_4____2020_08_24_10_19_02_171374_umap_augmented",
16: "fmnist_0.0_16____2020_08_24_10_19_11_697170_umap_augmented",
64: "fmnist_0.0_64____2020_08_24_10_19_33_327157_umap_augmented",
256: "fmnist_0.0_256____2020_08_24_10_19_51_978912_umap_augmented",
1024: "fmnist_0.0_1024____2020_08_24_10_20_06_630456_umap_augmented",
"full": "fmnist_0.0_full____2020_08_24_10_20_11_972145_umap_augmented",
},
"umap_intersection": {
4: "fmnist_0.0_4____2020_08_24_23_43_25_574078_umap_augmented",
16: "fmnist_0.0_16____2020_08_24_23_43_35_567328_umap_augmented",
64: "fmnist_0.0_64____2020_08_24_23_43_35_567450_umap_augmented",
256: "fmnist_0.0_256____2020_08_24_23_43_45_557361_umap_augmented",
1024: "fmnist_0.0_1024____2020_08_24_23_43_45_643845_umap_augmented",
"full": "fmnist_0.0_full____2020_08_24_23_48_54_578235_umap_augmented",
},
"umap_euclidean_augmented": {
4: "fmnist_0.0_4____2020_08_26_11_16_46_042019_umap_augmented",
16: "fmnist_0.0_16____2020_08_26_13_30_25_749568_umap_augmented",
64: "fmnist_0.0_64____2020_08_26_13_30_25_380156_umap_augmented",
256: "fmnist_0.0_256____2020_08_26_11_21_26_903869_umap_augmented",
1024: "fmnist_0.0_1024____2020_08_26_11_21_26_883542_umap_augmented",
"full": "fmnist_0.0_full____2020_08_26_13_30_25_074505_umap_augmented",
},
},
}
def load_pretrained_weights(dataset, augmented, labels_per_class, encoder, classifier):
aug_str = "augmented" if augmented else "not_augmented"
pretrained_weights_loc = pretrained_networks[dataset][aug_str][labels_per_class]
load_folder = (
MODEL_DIR
/ "semisupervised-keras"
/ dataset
/ str(labels_per_class)
/ pretrained_weights_loc
)
classifier.load_weights((load_folder / "classifier").as_posix())
encoder.load_weights((load_folder / "encoder").as_posix())
return encoder, classifier
def load_dataset(dataset, labels_per_class):
if dataset == "cifar10":
X_train, X_test, X_valid, Y_train, Y_test, Y_valid = load_CIFAR10(flatten=False)
num_classes = 10
dims = (32, 32, 3)
elif dataset == "mnist":
X_train, X_test, X_valid, Y_train, Y_test, Y_valid = load_MNIST(flatten=False)
num_classes = 10
dims = (28, 28, 1)
elif dataset == "fmnist":
X_train, X_test, X_valid, Y_train, Y_test, Y_valid = load_FMNIST(flatten=False)
num_classes = 10
dims = (28, 28, 1)
# get labeled data
if labels_per_class == "full":
X_labeled = X_train
Y_masked = Y_labeled = Y_train
else:
X_labeled, Y_labeled, Y_masked = mask_labels(
X_train, Y_train, labels_per_class=labels_per_class
)
# create one hot representation
Y_valid_one_hot = tf.keras.backend.one_hot(Y_valid, num_classes)
Y_labeled_one_hot = tf.keras.backend.one_hot(Y_labeled, num_classes)
return (
X_train,
X_test,
X_labeled,
Y_labeled,
Y_masked,
X_valid,
Y_train,
Y_test,
Y_valid,
Y_valid_one_hot,
Y_labeled_one_hot,
num_classes,
dims,
)
def load_architecture(dataset, n_latent_dims, extend_embedder=True):
if dataset == "cifar10":
return load_cifar10_CNN13(n_latent_dims, extend_embedder)
elif dataset == "mnist":
return load_mnist_CNN(n_latent_dims, extend_embedder)
elif dataset == "fmnist":
return load_mnist_CNN(n_latent_dims, extend_embedder)
from tensorflow.keras import datasets, layers, models
from tensorflow_addons.layers import WeightNormalization
def load_mnist_CNN(
n_latent_dims,
extend_embedder=True,
dims=(28, 28, 1),
num_classes=10,
lr_alpha=0.1,
dropout_rate=0.5,
):
"""
references for network:
- https://github.com/benathi/fastswa-semi-sup/blob/master/mean_teacher/architectures.py
- https://github.com/vikasverma1077/ICT/blob/master/networks/lenet.py
- https://github.com/brain-research/realistic-ssl-evaluation
"""
def conv_block(filts, name, kernel_size=(3, 3), padding="same", **kwargs):
return WeightNormalization(
layers.Conv2D(
filts, kernel_size, activation=None, padding=padding, **kwargs
),
name="conv" + name,
)
encoder = models.Sequential()
encoder.add(tf.keras.Input(shape=dims))
### conv1a
name = "1a"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv1b
name = "1b"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv1c
name = "1c"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(
layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid", name="mp1")
)
# dropout
encoder.add(layers.Dropout(dropout_rate, name="drop1"))
### conv2a
name = "2a"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha))
### conv2b
name = "2b"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv2c
name = "2c"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(
layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid", name="mp2")
)
# dropout
encoder.add(layers.Dropout(dropout_rate, name="drop2"))
### conv3a
name = "3a"
encoder.add(conv_block(name=name, filts=512, kernel_size=(3, 3), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv3b
name = "3b"
encoder.add(conv_block(name=name, filts=256, kernel_size=(1, 1), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv3c
name = "3c"
encoder.add(conv_block(name=name, filts=128, kernel_size=(1, 1), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(layers.AveragePooling2D(pool_size=(3, 3), strides=2, padding="valid"))
encoder.add(layers.Flatten())
encoder.add(layers.Dense(256, activation=None, name="z"))
classifier = models.Sequential()
classifier.add(tf.keras.Input(shape=(256)))
classifier.add(WeightNormalization(layers.Dense(256, activation=None)))
classifier.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc1"))
classifier.add(WeightNormalization(layers.Dense(256, activation=None)))
classifier.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc2"))
classifier.add(
WeightNormalization(layers.Dense(num_classes, activation=None), name="y_")
)
embedder = models.Sequential()
embedder.add(tf.keras.Input(shape=(256)))
if extend_embedder:
embedder.add(WeightNormalization(layers.Dense(256, activation=None)))
embedder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc1"))
embedder.add(WeightNormalization(layers.Dense(256, activation=None)))
embedder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc2"))
embedder.add(
WeightNormalization(layers.Dense(n_latent_dims, activation=None), name="z_")
)
return encoder, classifier, embedder
def load_cifar10_CNN13(
n_latent_dims,
extend_embedder=True,
dims=(32, 32, 3),
num_classes=10,
lr_alpha=0.1,
dropout_rate=0.5,
):
"""
references for network:
- https://github.com/benathi/fastswa-semi-sup/blob/master/mean_teacher/architectures.py
- https://github.com/vikasverma1077/ICT/blob/master/networks/lenet.py
- https://github.com/brain-research/realistic-ssl-evaluation
"""
def conv_block(filts, name, kernel_size=(3, 3), padding="same", **kwargs):
return WeightNormalization(
layers.Conv2D(
filts, kernel_size, activation=None, padding=padding, **kwargs
),
name="conv" + name,
)
encoder = models.Sequential()
encoder.add(tf.keras.Input(shape=dims))
### conv1a
name = "1a"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv1b
name = "1b"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv1c
name = "1c"
encoder.add(conv_block(name=name, filts=128, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(
layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid", name="mp1")
)
# dropout
encoder.add(layers.Dropout(dropout_rate, name="drop1"))
### conv2a
name = "2a"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha))
### conv2b
name = "2b"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv2c
name = "2c"
encoder.add(conv_block(name=name, filts=256, kernel_size=(3, 3), padding="same"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(
layers.MaxPooling2D(pool_size=(2, 2), strides=2, padding="valid", name="mp2")
)
# dropout
encoder.add(layers.Dropout(dropout_rate, name="drop2"))
### conv3a
name = "3a"
encoder.add(conv_block(name=name, filts=512, kernel_size=(3, 3), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv3b
name = "3b"
encoder.add(conv_block(name=name, filts=256, kernel_size=(1, 1), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
### conv3c
name = "3c"
encoder.add(conv_block(name=name, filts=128, kernel_size=(1, 1), padding="valid"))
encoder.add(layers.BatchNormalization(name="bn" + name))
encoder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelu" + name))
# max pooling
encoder.add(layers.AveragePooling2D(pool_size=(6, 6), strides=2, padding="valid"))
encoder.add(layers.Flatten())
encoder.add(layers.Dense(256, activation=None, name="z"))
classifier = models.Sequential()
classifier.add(tf.keras.Input(shape=(256)))
classifier.add(WeightNormalization(layers.Dense(256, activation=None)))
classifier.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc1"))
classifier.add(WeightNormalization(layers.Dense(256, activation=None)))
classifier.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc2"))
classifier.add(
WeightNormalization(layers.Dense(num_classes, activation=None), name="y_")
)
embedder = models.Sequential()
embedder.add(tf.keras.Input(shape=(256)))
if extend_embedder:
embedder.add(WeightNormalization(layers.Dense(256, activation=None)))
embedder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc1"))
embedder.add(WeightNormalization(layers.Dense(256, activation=None)))
embedder.add(layers.LeakyReLU(alpha=lr_alpha, name="lrelufc2"))
embedder.add(
WeightNormalization(layers.Dense(n_latent_dims, activation=None), name="z_")
)
return encoder, classifier, embedder
from scipy import optimize
def find_a_b(min_dist=0.1):
""" determine optimal params a, b to such that distances less than
min_dist have a probability of zero
"""
# input distances
x = np.linspace(0, 3, 300)
# optimal output (if close enough, don't try to make closer)
y = np.exp(-x + | |
self.read_reg(self.REG_AXES_ENABLE, 1)
if modes == self.INTERRUPUT_LATCH_DISABLE:
self.__txbuf[0] = rslt[0] & 0xFD
else:
self.__txbuf[0] = rslt[0] | 0x02
self.write_reg(self.REG_AXES_ENABLE, self.__txbuf)
def set_threshold_interrupt(self, mode, threshold, polarity, channel_x = INTERRUPT_X_ENABLE, channel_y = INTERRUPT_Y_ENABLE, channel_z = INTERRUPT_Z_ENABLE):
'''!
@brief Set threshold interrupt, an interrupt is triggered when the geomagnetic value of a channel is beyond/below the threshold
@n High polarity: active on high, the default is low level, which turns to high level when the interrupt is triggered.
@n Low polarity: active on low, the default is high level, which turns to low level when the interrupt is triggered.
@param mode
@n LOW_THRESHOLD_INTERRUPT Low threshold interrupt mode
@n HIGH_THRESHOLD_INTERRUPT High threshold interrupt mode
@param threshold
@n Threshold, default to expand 16 times, for example: under low threshold mode, if the threshold is set to be 1, actually the geomagnetic data below 16 will trigger an interrupt
@param polarity
@n POLARITY_HIGH High polarity
@n POLARITY_LOW Low polarity
@param channel_x
@n INTERRUPT_X_ENABLE Enable low threshold interrupt at x-axis
@n INTERRUPT_X_DISABLE Disable low threshold interrupt at x-axis
@param channel_y
@n INTERRUPT_Y_ENABLE Enable low threshold interrupt at y-axis
@n INTERRUPT_Y_DISABLE Disable low threshold interrupt at y-axis
@param channel_z
@n INTERRUPT_Z_ENABLE Enable low threshold interrupt at z-axis
@n INTERRUPT_Z_DISABLE Disable low threshold interrupt at z-axis
'''
if mode == self.LOW_THRESHOLD_INTERRUPT:
self.__threshold_mode = self.LOW_THRESHOLD_INTERRUPT
self.set_low_threshold_interrupt(channel_x, channel_y, channel_z, threshold, polarity)
else:
self.__threshold_mode = self.HIGH_THRESHOLD_INTERRUPT
self.set_high_threshold_interrupt(channel_x, channel_y, channel_z, threshold, polarity)
def get_threshold_interrupt_data(self):
'''!
@brief Get the data that threshold interrupt occured
@return Return the list for storing geomagnetic data, how the data at 3 axis influence interrupt status,
@n [0] The data triggering threshold at x-axis, when the data is NO_DATA, the interrupt is triggered.
@n [1] The data triggering threshold at y-axis, when the data is NO_DATA, the interrupt is triggered.
@n [2] The data triggering threshold at z-axis, when the data is NO_DATA, the interrupt is triggered.
@n [3] The character string storing the trigger threshold interrupt status
@n [4] The binary data format of storing threshold interrupt status are as follows
@n bit0 is 1 indicate threshold interrupt is triggered at x-axis
@n bit1 is 1 indicate threshold interrupt is triggered at y-axis
@n bit2 is 1 indicate threshold interrupt is triggered at z-axis
@n ------------------------------------
@n | bit7 ~ bit3 | bit2 | bit1 | bit0 |
@n ------------------------------------
@n | reserved | 0 | 0 | 0 |
@n ------------------------------------
'''
data = [0]*10
str1 = ""
if self.__threshold_mode == self.LOW_THRESHOLD_INTERRUPT:
state = self.get_low_threshold_interrupt_state()
else:
state = self.get_high_threshold_interrupt_state()
rslt = self.get_geomagnetic()
if (state>>0)&0x01:
data[0] = rslt[0]
str1 += "X "
else:
data[0] = self.NO_DATA
if (state>>1)&0x01:
data[1] = rslt[1]
str1 += "Y "
else:
data[1] = self.NO_DATA
if (state>>2)&0x01:
data[2] = rslt[2]
str1 += "Z "
else:
data[2] = self.NO_DATA
if state != 0:
str1 += " threshold interrupt"
data[3] = str1
data[4] = state&0x07
return data
def set_low_threshold_interrupt(self, channel_x, channel_y, channel_z, low_threshold, polarity):
'''!
@brief Set low threshold interrupt, an interrupt is triggered when the geomagnetic value of a channel is below the low threshold
@n High polarity: active on high, the default is low level, which turns to high level when the interrupt is triggered.
@n Low polarity: active on low, the default is high level, which turns to low level when the interrupt is triggered.
@param channel_x
@n INTERRUPT_X_ENABLE Enable low threshold interrupt at x-axis
@n INTERRUPT_X_DISABLE Disable low threshold interrupt at x-axis
@param channel_y
@n INTERRUPT_Y_ENABLE Enable low threshold interrupt at y-axis
@n INTERRUPT_Y_DISABLE Disable low threshold interrupt at y-axis
@param channel_z
@n INTERRUPT_Z_ENABLE Enable low threshold interrupt at z-axis
@n INTERRUPT_Z_DISABLE Disable low threshold interrupt at z-axis
@param low_threshold Low threshold, default to expand 16 times, for example: if the threshold is set to be 1, actually the geomagnetic data below 16 will trigger an interrupt
@param polarity
@n POLARITY_HIGH High polarity
@n POLARITY_LOW Low polarity
'''
if low_threshold < 0:
self.__txbuf[0] = (low_threshold*-1) | 0x80
else:
self.__txbuf[0] = low_threshold
self.write_reg(self.REG_LOW_THRESHOLD ,self.__txbuf)
rslt = self.read_reg(self.REG_INT_CONFIG, 1)
if channel_x == self.INTERRUPT_X_DISABLE:
self.__txbuf[0] = rslt[0] | 0x01
else:
self.__txbuf[0] = rslt[0] & 0xFE
if channel_y == self.INTERRUPT_Y_DISABLE:
self.__txbuf[0] = self.__txbuf[0] | 0x02
else:
self.__txbuf[0] = self.__txbuf[0] & 0xFC
if channel_x == self.INTERRUPT_X_DISABLE:
self.__txbuf[0] = self.__txbuf[0] | 0x04
else:
self.__txbuf[0] = self.__txbuf[0] & 0xFB
self.write_reg(self.REG_INT_CONFIG ,self.__txbuf)
self.set_interrupt_pin(self.ENABLE_INTERRUPT_PIN, polarity)
def get_low_threshold_interrupt_state(self):
'''!
@brief Get the status of low threshold interrupt, which axis triggered the low threshold interrupt
@return status The returned number indicate the low threshold interrupt occur at which axis
@n bit0 is 1 indicate the interrupt occur at x-axis
@n bit1 is 1 indicate the interrupt occur at y-axis
@n bit2 is 1 indicate the interrupt occur at z-axis
@n ------------------------------------
@n | bit7 ~ bit3 | bit2 | bit1 | bit0 |
@n ------------------------------------
@n | reserved | 0 | 0 | 0 |
@n ------------------------------------
'''
rslt = self.read_reg(self.REG_INTERRUPT_STATUS, 1)
return rslt[0]&0x07
def set_high_threshold_interrupt(self, channel_x, channel_y, channel_z, high_threshold, polarity):
'''!
@brief Set high threshold interrupt, an interrupt is triggered when the geomagnetic value of a channel is beyond the threshold, the threshold is default to expand 16 times
@n There will be level change when INT pin interrupt occurred
@n High pin polarity: active on high, the default is low level, which will jump when the threshold is triggered.
@n Low pin polarity: active on low, the default is high level, which will jump when the threshold is triggered.
@param channel_x
@n INTERRUPT_X_ENABLE Enable high threshold interrupt at x-axis
@n INTERRUPT_X_DISABLE Disable high threshold interrupt at x-axis
@param channel_y
@n INTERRUPT_Y_ENABLE Enable high threshold interrupt at y-axis
@n INTERRUPT_Y_DISABLE Disable high threshold interrupt at y-axis
@param channel_z
@n INTERRUPT_Z_ENABLE Enable high threshold interrupt at z-axis
@n INTERRUPT_Z_DISABLE Disable high threshold interrupt at z-axis
@param high_threshold High threshold, default to expand 16 times, for example: if the threshold is set to be 1, actually the geomagnetic data beyond 16 will trigger an interrupt
@param polarity
@n POLARITY_HIGH High polarity
@n POLARITY_LOW Low polarity
'''
if high_threshold < 0:
self.__txbuf[0] = (high_threshold*-1) | 0x80
else:
self.__txbuf[0] = high_threshold
self.write_reg(self.REG_HIGH_THRESHOLD, self.__txbuf)
rslt = self.read_reg(self.REG_INT_CONFIG, 1)
if channel_x == self.HIGH_INTERRUPT_X_DISABLE:
self.__txbuf[0] = rslt[0] | 0x08
else:
self.__txbuf[0] = rslt[0] & 0xF7
if channel_y == self.HIGH_INTERRUPT_Y_DISABLE:
self.__txbuf[0] = self.__txbuf[0] | 0x10
else:
self.__txbuf[0] = self.__txbuf[0] & 0xEF
if channel_x == self.HIGH_INTERRUPT_X_DISABLE:
self.__txbuf[0] = self.__txbuf[0] | 0x20
else:
self.__txbuf[0] = self.__txbuf[0] & 0xDf
self.write_reg(self.REG_INT_CONFIG ,self.__txbuf)
self.set_interrupt_pin(self.ENABLE_INTERRUPT_PIN, polarity)
def get_high_threshold_interrupt_state(self):
'''!
@brief Get the status of high threshold interrupt, which axis triggered the high threshold interrupt
@return status The returned number indicate the high threshold interrupt occur at which axis
@n bit0 is 1 indicate the interrupt occur at x-axis
@n bit1 is 1 indicate the interrupt occur at y-axis
@n bit2 is 1 indicate the interrupt occur at z-axis
@n ------------------------------------
@n | bit7 ~ bit3 | bit2 | bit1 | bit0 |
@n ------------------------------------
@n | reserved | 0 | 0 | 0 |
@n ------------------------------------
'''
rslt = self.read_reg(self.REG_INTERRUPT_STATUS, 1)
return (rslt[0]&0x38)>>3
class DFRobot_bmm150_I2C(DFRobot_bmm150):
'''!
@brief An example of an i2c interface module
'''
def __init__(self, bus, addr):
self.__addr = addr
super(DFRobot_bmm150_I2C, self).__init__(bus)
def write_reg(self, reg, data):
'''!
@brief writes data to a register
@param reg register address
@param data written data
'''
while 1:
try:
self.i2cbus.write_i2c_block_data(self.__addr, reg, data)
return
except:
print("please check connect!")
#os.system('i2cdetect -y 1')
time.sleep(1)
return
def read_reg(self, reg ,len):
'''!
@brief read the data from the register
@param reg register address
@param len read data length
'''
while 1:
try:
rslt = self.i2cbus.read_i2c_block_data(self.__addr, reg, len)
#print rslt
return rslt
except:
time.sleep(1)
print("please check connect!")
class DFRobot_bmm150_SPI(DFRobot_bmm150):
def __init__(self, cs, bus = 0, dev = 0, speed = 1000000):
self.__cs = cs
GPIO.setup(self.__cs, GPIO.OUT)
GPIO.output(self.__cs, GPIO.LOW)
self.__spi = spidev.SpiDev()
self.__spi.open(bus, dev)
self.__spi.no_cs = True
self.__spi.max_speed_hz = | |
activity_number = request.POST.get('activity_number').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
intra_community_vat_number = request.POST.get('intra_community_vat_number').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
president = request.POST.get('president').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
registration_date = request.POST.get('registration_date').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r1c1 = request.POST.get('r1c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r1c2 = request.POST.get('r1c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r1c3 = request.POST.get('r1c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r2c1 = request.POST.get('r2c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r2c2 = request.POST.get('r2c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r2c3 = request.POST.get('r2c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r3c1 = request.POST.get('r3c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r3c2 = request.POST.get('r3c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r3c3 = request.POST.get('r3c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r4c1 = request.POST.get('r4c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r4c2 = request.POST.get('r4c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r4c3 = request.POST.get('r4c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r5c1 = request.POST.get('r5c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r5c2 = request.POST.get('r5c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r5c3 = request.POST.get('r5c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r6c1 = request.POST.get('r6c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r6c2 = request.POST.get('r6c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r6c3 = request.POST.get('r6c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r7c1 = request.POST.get('r7c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r7c2 = request.POST.get('r7c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r7c3 = request.POST.get('r7c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r8c1 = request.POST.get('r8c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r8c2 = request.POST.get('r8c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r8c3 = request.POST.get('r8c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r9c1 = request.POST.get('r9c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r9c2 = request.POST.get('r9c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r9c3 = request.POST.get('r9c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r10c1 = request.POST.get('r10c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r10c2 = request.POST.get('r10c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r10c3 = request.POST.get('r10c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r11c1 = request.POST.get('r11c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r11c2 = request.POST.get('r11c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r11c3 = request.POST.get('r11c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r12c1 = request.POST.get('r12c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r12c2 = request.POST.get('r12c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r12c3 = request.POST.get('r12c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r13c1 = request.POST.get('r13c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r13c2 = request.POST.get('r13c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r13c3 = request.POST.get('r13c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r14c1 = request.POST.get('r14c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r14c2 = request.POST.get('r14c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r14c3 = request.POST.get('r14c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r15c1 = request.POST.get('r15c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r15c2 = request.POST.get('r15c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r15c3 = request.POST.get('r15c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r16c1 = request.POST.get('r16c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r16c2 = request.POST.get('r16c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r16c3 = request.POST.get('r16c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r17c1 = request.POST.get('r17c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r17c2 = request.POST.get('r17c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r17c3 = request.POST.get('r17c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r18c1 = request.POST.get('r18c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r18c2 = request.POST.get('r18c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r18c3 = request.POST.get('r18c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r19c1 = request.POST.get('r19c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r19c2 = request.POST.get('r19c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r19c3 = request.POST.get('r19c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r20c1 = request.POST.get('r20c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r20c2 = request.POST.get('r20c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r20c3 = request.POST.get('r20c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r21c1 = request.POST.get('r21c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r21c2 = request.POST.get('r21c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r21c3 = request.POST.get('r21c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r22c1 = request.POST.get('r22c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r22c2 = request.POST.get('r22c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r22c3 = request.POST.get('r22c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r23c1 = request.POST.get('r23c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r23c2 = request.POST.get('r23c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r23c3 = request.POST.get('r23c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r24c1 = request.POST.get('r24c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r24c2 = request.POST.get('r24c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r24c3 = request.POST.get('r24c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r25c1 = request.POST.get('r25c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r25c2 = request.POST.get('r25c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r25c3 = request.POST.get('r25c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r26c1 = request.POST.get('r26c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r26c2 = request.POST.get('r26c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r26c3 = request.POST.get('r26c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r27c1 = request.POST.get('r27c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r27c2 = request.POST.get('r27c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r27c3 = request.POST.get('r27c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r28c1 = request.POST.get('r28c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r28c2 = request.POST.get('r28c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r28c3 = request.POST.get('r28c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r29c1 = request.POST.get('r29c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r29c2 = request.POST.get('r29c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r29c3 = request.POST.get('r29c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r30c1 = request.POST.get('r30c1').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r30c2 = request.POST.get('r30c2').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
r30c3 = request.POST.get('r30c3').replace('\t', ' ').replace('\n', ' ').replace('\r', ' ')
body = '<!doctype html>' + \
'<html lang="en">' + \
'<head>' + \
'<meta charset="utf-8">' + \
'<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">' + \
'<link rel="stylesheet"' + \
'href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"' + \
'integrity="<KEY>"' + \
'crossorigin="anonymous">' + \
'<title>Operational budget</title>' + \
'</head>' + \
'<body>' + \
'<div class="container">' + \
'<div class="card text-center">' + \
'<div class="card-header text-center">Operational budget</div>' + \
'<div class="card-body">'
body += '<h6>Comapny name : ' + company_name + '</h6>' + \
'<h6>Share capital : ' + share_capital + '</h6>' + \
'<h6>Head office address : ' + head_office_address + '</h6>' + \
'<h6>Establishment number : ' + establishment_number + '</h6>' + \
'<h6>Register of Trade and Companies : ' + register_of_trade_and_companies + '</h6>' + \
'<h6>Main activities : ' + main_activities + '</h6>' + \
'<h6>Activity number : ' + activity_number + '</h6>' + \
'<h6>Intra-community VAT number : ' + intra_community_vat_number + '</h6>' + \
'<h6>President : ' + president + '</h6>' + \
'<h6>Registration date : ' + registration_date + '</h6>' + \
'<br>'
body += '<br>'
body += '<table class="table table-striped table-bordered">' + \
'<thead>' + \
'<tr>' + \
'<th scope="col">Details</th>' + \
'<th scope="col">Estimate</th>' + \
'<th scope="col">Actual</th>' + \
'<th scope="col">Variance</th>' + \
'</tr>' + \
'</thead>' + \
'<tbody>' + \
'<tr>' + \
'<td>Sales revenue</td>' + \
'<td>' + r1c1 + '</td>' + \
'<td>' + r1c2 + '</td>' + \
'<td>' + r1c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Grants revenue</td>' + \
'<td>' + r2c1 + '</td>' + \
'<td>' + r2c2 + '</td>' + \
'<td>' + r2c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Donations revenue</td>' + \
'<td>' + r3c1 + '</td>' + \
'<td>' + r3c2 + '</td>' + \
'<td>' + r3c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Gifts revenue</td>' + \
'<td>' + r4c1 + '</td>' + \
'<td>' + r4c2 + '</td>' + \
'<td>' + r4c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Interest revenue</td>' + \
'<td>' + r5c1 + '</td>' + \
'<td>' + r5c2 + '</td>' + \
'<td>' + r5c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Reimbursement revenue</td>' + \
'<td>' + r6c1 + '</td>' + \
'<td>' + r6c2 + '</td>' + \
'<td>' + r6c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Rent revenue</td>' + \
'<td>' + r7c1 + '</td>' + \
'<td>' + r7c2 + '</td>' + \
'<td>' + r7c3 + '</td>' + \
'</tr>' + \
'<tr>' + \
'<td>Investment | |
<reponame>guillaume-florent/PyGeM
"""
Utilities for reading and writing parameters files to perform FFD
geometrical morphing.
"""
try:
import configparser as configparser
except ImportError:
import ConfigParser as configparser
import os
import numpy as np
from OCC.Bnd import Bnd_Box
from OCC.BRepBndLib import brepbndlib_Add
from OCC.BRepMesh import BRepMesh_IncrementalMesh
import vtk
import pygem.affine as at
class FFDParameters(object):
"""
Class that handles the Free Form Deformation parameters in terms of FFD
bounding box and weight of the FFD control points.
:param list n_control_points: number of control points in the x, y, and z
direction. If not provided it is set to [2, 2, 2].
:cvar numpy.ndarray length_box: dimension of the FFD bounding box, in the
x, y and z direction (local coordinate system).
:cvar numpy.ndarray origin_box: the x, y and z coordinates of the origin of
the FFD bounding box.
:cvar numpy.ndarray rot_angle: rotation angle around x, y and z axis of the
FFD bounding box.
:cvar numpy.ndarray n_control_points: the number of control points in the
x, y, and z direction.
:cvar numpy.ndarray array_mu_x: collects the displacements (weights) along
x, normalized with the box lenght x.
:cvar numpy.ndarray array_mu_y: collects the displacements (weights) along
y, normalized with the box lenght y.
:cvar numpy.ndarray array_mu_z: collects the displacements (weights) along
z, normalized with the box lenght z.
:Example: from file
>>> import pygem.params as ffdp
>>>
>>> # Reading an existing file
>>> params1 = ffdp.FFDParameters()
>>> params1.read_parameters(
>>> filename='tests/test_datasets/parameters_test_ffd_identity.prm')
>>>
>>> # Creating a default parameters file with the right dimensions (if the
>>> # file does not exists it is created with that name). So it is possible
>>> # to manually edit it and read it again.
>>> params2 = ffdp.FFDParameters(n_control_points=[2, 3, 2])
>>> params2.read_parameters(filename='parameters_test.prm')
>>>
>>> # Creating bounding box of the given shape
>>> from OCC.IGESControl import IGESControl_Reader
>>> params3 = ffdp.FFDParameters()
>>> reader = IGESControl_Reader()
>>> reader.ReadFile('tests/test_datasets/test_pipe.igs')
>>> reader.TransferRoots()
>>> shape = reader.Shape()
>>> params3.build_bounding_box(shape)
.. note::
Four vertex (non coplanar) are sufficient to uniquely identify a
parallelepiped.
If the four vertex are coplanar, an assert is thrown when
affine_points_fit is used.
"""
def __init__(self, n_control_points=None):
self.conversion_unit = 1.
self.lenght_box = np.array([1., 1., 1.])
self.origin_box = np.array([0., 0., 0.])
self.rot_angle = np.array([0., 0., 0.])
if n_control_points is None:
n_control_points = [2, 2, 2]
self.n_control_points = np.array(n_control_points)
self.array_mu_x = np.zeros(self.n_control_points)
self.array_mu_y = np.zeros(self.n_control_points)
self.array_mu_z = np.zeros(self.n_control_points)
@property
def psi_mapping(self):
"""
Map from the physical domain to the reference domain.
:rtype: numpy.ndarray
"""
return np.diag(np.reciprocal(self.lenght_box))
@property
def inv_psi_mapping(self):
"""
Map from the reference domain to the physical domain.
:rtype: numpy.ndarray
"""
return np.diag(self.lenght_box)
@property
def rotation_matrix(self):
"""
The rotation matrix (according to rot_angle_x, rot_angle_y,
rot_angle_z).
:rtype: numpy.ndarray
"""
return at.angles2matrix(
np.radians(self.rot_angle[2]), np.radians(self.rot_angle[1]),
np.radians(self.rot_angle[0]))
@property
def position_vertices(self):
"""
The position of the vertices of the FFD bounding box.
:rtype: numpy.ndarray
"""
return self.origin_box + np.vstack([
np.zeros((1, 3)),
self.rotation_matrix.dot(np.diag(self.lenght_box)).T
])
def read_parameters(self, filename='parameters.prm'):
"""
Reads in the parameters file and fill the self structure.
:param string filename: parameters file to be read in.
"""
if not isinstance(filename, str):
raise TypeError("filename must be a string")
# Checks if the parameters file exists. If not it writes the default
# class into filename.
if not os.path.isfile(filename):
self.write_parameters(filename)
return
config = configparser.RawConfigParser()
config.read(filename)
self.n_control_points[0] = config.getint('Box info',
'n control points x')
self.n_control_points[1] = config.getint('Box info',
'n control points y')
self.n_control_points[2] = config.getint('Box info',
'n control points z')
self.lenght_box[0] = config.getfloat('Box info', 'box lenght x')
self.lenght_box[1] = config.getfloat('Box info', 'box lenght y')
self.lenght_box[2] = config.getfloat('Box info', 'box lenght z')
self.origin_box[0] = config.getfloat('Box info', 'box origin x')
self.origin_box[1] = config.getfloat('Box info', 'box origin y')
self.origin_box[2] = config.getfloat('Box info', 'box origin z')
self.rot_angle[0] = config.getfloat('Box info', 'rotation angle x')
self.rot_angle[1] = config.getfloat('Box info', 'rotation angle y')
self.rot_angle[2] = config.getfloat('Box info', 'rotation angle z')
self.array_mu_x = np.zeros(self.n_control_points)
self.array_mu_y = np.zeros(self.n_control_points)
self.array_mu_z = np.zeros(self.n_control_points)
mux = config.get('Parameters weights', 'parameter x')
muy = config.get('Parameters weights', 'parameter y')
muz = config.get('Parameters weights', 'parameter z')
for line in mux.split('\n'):
values = np.array(line.split())
self.array_mu_x[tuple(map(int, values[0:3]))] = float(values[3])
for line in muy.split('\n'):
values = line.split()
self.array_mu_y[tuple(map(int, values[0:3]))] = float(values[3])
for line in muz.split('\n'):
values = line.split()
self.array_mu_z[tuple(map(int, values[0:3]))] = float(values[3])
def write_parameters(self, filename='parameters.prm'):
"""
This method writes a parameters file (.prm) called `filename` and fills
it with all the parameters class members.
:param string filename: parameters file to be written out.
"""
if not isinstance(filename, str):
raise TypeError("filename must be a string")
output_string = ""
output_string += '\n[Box info]\n'
output_string += '# This section collects all the properties of the'
output_string += ' FFD bounding box.\n'
output_string += '\n# n control points indicates the number of control'
output_string += ' points in each direction (x, y, z).\n'
output_string += '# For example, to create a 2 x 3 x 2 grid, use the'
output_string += ' following: n control points: 2, 3, 2\n'
output_string += 'n control points x: ' + str(
self.n_control_points[0]) + '\n'
output_string += 'n control points y: ' + str(
self.n_control_points[1]) + '\n'
output_string += 'n control points z: ' + str(
self.n_control_points[2]) + '\n'
output_string += '\n# box lenght indicates the length of the FFD '
output_string += 'bounding box along the three canonical directions '
output_string += '(x, y, z).\n'
output_string += '# It uses the local coordinate system.\n'
output_string += '# For example to create a 2 x 1.5 x 3 meters box '
output_string += 'use the following: lenght box: 2.0, 1.5, 3.0\n'
output_string += 'box lenght x: ' + str(self.lenght_box[0]) + '\n'
output_string += 'box lenght y: ' + str(self.lenght_box[1]) + '\n'
output_string += 'box lenght z: ' + str(self.lenght_box[2]) + '\n'
output_string += '\n# box origin indicates the x, y, and z coordinates '
output_string += 'of the origin of the FFD bounding box. That is '
output_string += 'center of\n'
output_string += '# rotation of the bounding box. It corresponds to '
output_string += 'the point coordinates with position [0][0][0].\n'
output_string += '# See section "Parameters weights" for more '
output_string += 'details.\n'
output_string += '# For example, if the origin is equal to 0., 0., 0., '
output_string += 'use the following: origin box: 0., 0., 0.\n'
output_string += 'box origin x: ' + str(self.origin_box[0]) + '\n'
output_string += 'box origin y: ' + str(self.origin_box[1]) + '\n'
output_string += 'box origin z: ' + str(self.origin_box[2]) + '\n'
output_string += '\n# rotation angle indicates the rotation angle '
output_string += 'around the x, y, and z axis of the FFD bounding box '
output_string += 'in degrees.\n'
output_string += '# The rotation is done with respect to the box '
output_string += 'origin.\n'
output_string += '# For example, to rotate the box by 2 deg along '
output_string += 'the z '
output_string += 'direction, use the following: rotation angle: '
output_string += '0., 0., 2.\n'
output_string += 'rotation angle x: ' + str(self.rot_angle[0]) + '\n'
output_string += 'rotation angle y: ' + str(self.rot_angle[1]) + '\n'
output_string += 'rotation angle z: ' + str(self.rot_angle[2]) + '\n'
output_string += '\n\n[Parameters weights]\n'
output_string += '# This section describes the weights of the FFD '
output_string += 'control points.\n'
output_string += '# We adopt the following convention:\n'
output_string += '# For example with a 2x2x2 grid of control points we '
output_string += 'have to fill a 2x2x2 matrix of weights.\n'
output_string += '# If a weight is equal to zero you can discard the '
output_string += 'line since the default is zero.\n'
output_string += '#\n'
output_string += '# | x index | y index | z index | weight |\n'
output_string += '# --------------------------------------\n'
output_string += '# | 0 | 0 | 0 | 1.0 |\n'
output_string += '# | 0 | 1 | 1 | 0.0 | --> you '
output_string += 'can erase this line without effects\n'
output_string += '# | 0 | 1 | 0 | -2.1 |\n'
output_string += '# | 0 | 0 | 1 | |
= pd.DataFrame(con_df[con_df.columns[2]]).astype('float')
con_df.columns = [3]
syn_df = pd.DataFrame(syn_df[syn_df.columns[2]]).astype('float')
syn_df.columns = [4]
df = pd.concat([head_df, wei_df, con_df, syn_df],axis=1)
return df
def save(save_to=None):
if not save_to:
save_to = filename.get()
df = get_whole_df()
(nr,nc) = df.shape
tb = df.to_csv(sep=' ',header=False,index=False,float_format='%.6f')
file = open(save_to,"w")
file.write(str(nr)+'\n')
file.write(tb)
file.close()
display_app_status('Connections Data file \"'+filename.get()+'\" saved')
return
def new():
if synaptic_weight_page_obj.has_changed() or convergence_page_obj.has_changed() or synapses_page_obj.has_changed():
result = messagebox.askquestion("New", "Are you sure? Data has been changed.", icon='warning')
if result != 'yes':
return
d = DialogEntryBox(root,text="New File Name:",lefttext=os.path.join(dataset_folder, conndata_file_prefix),righttext=conndata_file_postfix)
root.wait_window(d.top)
if d.confirm==False:
return
#get all as presynaptic
#get all not artificial as postsynaptic
newfilename = os.path.join(dataset_folder,conndata_file_prefix+ d.value.get() + conndata_file_postfix)
loaded_cellnums = get_public_param("loaded_cellnums")
column_names = ["Friendly Cell Name", "Cell File Name", "Num Cells", "Layer Index","Artificial:1 Real:0"]
cellnums_pd = pd.read_csv(loaded_cellnums ,delimiter=' ',\
skiprows=1,header=None,\
names = column_names)
cellnums_pd[column_names[4]] = cellnums_pd[column_names[4]].astype(int)
pre = cellnums_pd[cellnums_pd.columns[0]].values.tolist()
(pre_nr,pre_nc) = pd.DataFrame(pre).shape
post = cellnums_pd.loc[cellnums_pd[column_names[4]] == 0]
post = post[post.columns[0]]
post = pd.DataFrame(post)
(post_nr,post_nc) = post.shape
post = np.repeat(post[post.columns[0]],pre_nr).reset_index(drop=True)
pre = pd.DataFrame(pre*post_nr)
df = pd.concat([pre,post],axis=1)
df[2] = '0.0'
df[3] = '0'
df[4] = '0'
tb = df.to_csv(sep=' ',header=False,index=False,float_format='%.6f')
file = open(newfilename,"w")
file.write(str(pre_nr*post_nr)+'\n')
file.write(tb)
file.close()
load(load_from=newfilename)
reload_files_and_set(newfilename)
#create a presynaptic*postsynaptic by 5 pandas dataframe
#set all values to zero
#set first column
#set second column
display_app_status('Connections Data file \"'+filename.get()+'\" created')
return
def new_clone_current():
if synaptic_weight_page_obj.has_changed() or convergence_page_obj.has_changed() or synapses_page_obj.has_changed():
result = messagebox.askquestion("New", "Are you sure? Data has been changed.", icon='warning')
if result != 'yes':
return
d = DialogEntryBox(root,text="New File Name:",lefttext=os.path.join(dataset_folder, conndata_file_prefix),righttext=conndata_file_postfix)
root.wait_window(d.top)
if d.confirm==False:
return
newfilename = os.path.join(dataset_folder,conndata_file_prefix+ d.value.get() + conndata_file_postfix)
f = open(newfilename,"w+")
f.close()
save(save_to=newfilename)
reload_files_and_set()
display_app_status('Connections Data file \"'+filename.get()+'\" was created')
return
def set_conndata_param():
fn = filename.get()
search = conndata_file_prefix+'(.+?)'+conndata_file_postfix
m = re.search(search,fn)
if m:
fn = m.group(1)
set_public_param("ConnData", fn)
display_app_status('ConnData parameter set to \"'+ filename.get() +'\" in current parameters file')
return
def reload_files_and_set(newfilename):
m = fileMenu.children['menu']
m.delete(0,tk.END)
newvalues = options
newvalues.append(newfilename)
for val in newvalues:
m.add_command(label=val,command=lambda v=filename,l=val:v.set(l))
filename.set(newfilename)
def delete_current_file():
return
def load_conndata_param():
conndat = get_public_param("ConnData")
conndat = os.path.join(dataset_folder, conndata_file_prefix + conndat + conndata_file_postfix)
#filename.set('')
filename.set(conndat)
#generate_files_available()
#Create the choice option panel
filename = tk.StringVar(top_option_frame)
filename.trace("w",load)
filename.set('')
#filename.set(options[0])
newFromCellsButton = tk.Button(top_option_frame, text="Generate New from Current Cells File", command=new, width=30)
newFromCellsButton.grid(column=0, row =0, padx=5, sticky='WE',columnspan=2)
useButton = tk.Button(top_option_frame, text="Set as ConnData", command=set_conndata_param, width=15)
useButton.grid(column=0, row =1, padx=5, sticky='W')
loadButton = tk.Button(top_option_frame, text="Load ConnData", command=load_conndata_param,width=15)
loadButton.grid(column=1, row =1, padx=5, sticky='W')
fileMenu = tk.OptionMenu(top_option_frame, filename, *options)
fileMenu.grid(column=2, row =0, padx=5, sticky='WE',columnspan=2)
deleteButton = tk.Button(top_option_frame, text="Delete", command=delete_current_file)
deleteButton.grid(column=4, row =0, padx=5, pady=5, sticky='W')
deleteButton.config(state=tk.DISABLED)
saveButton = tk.Button(top_option_frame, text="Save", command=save)
saveButton.grid(column=2, row =1, padx=5,pady=5, sticky='WE')
newFromCurrentButton = tk.Button(top_option_frame, text="Save As", command=new_clone_current)
newFromCurrentButton.grid(column=3, row =1, padx=5, sticky='WE')
def synapses_page(root):
sections_list = ['dendrite_list','soma_list','apical_list','axon_list']
synapse_type_list = ['MyExp2Sid','ExpGABAab','Custom']
condition_list = ['distance(x)','y3d(x)']
synapse_column_names = ["Postsynaptic Cell", "Presynaptic Cells",\
"Synapse Type", "Postsynaptic Section Target",\
"Condition 1", "Condition 2",\
"Tau1a/modfile", "Tau2a/cust1", "ea/cust2",\
"Tau1b/cust3", "Tau2b/cust4", "eb/cust5"]
mod_list = glob.glob(mods_glob)
if len(mod_list) is 0:
mod_list.append('')
class synapses_adapter(object):
def __init__(self, root):
self.root = root
self.pt = PandasTable(self.root, show_add_row_button=False)
self.pt.pack()
def read_internal(self, df):
'''get whole dataframe'''
return df
def get_df(self):
df = self.pt.get_dataframe().replace(np.nan, '', regex=True).replace('nan','',regex=True)
return df
def refresh(self, df):
d = defaultdict(list)
d[3].append(sections_list)
self.pt.set_dataframe(self.read_internal(df), show_delete_row=True,\
show_header=True, show_numbering=True, \
first_column_is_id=False, immutable_values=["nan"],\
options_dict=d)
self.pt.pack()
def add_row(self, row):
row = np.array(row).reshape(-1,len(row))
r = pd.DataFrame(row,columns=synapse_column_names)
for i, row in r.iterrows():
self.pt.add_row(row)
return
def has_changed(self):
return self.pt.has_changed()
class SynapseEntryBox:
def __init__(self, parent, text="value", lefttext="",righttext=""):
top = self.top = tk.Toplevel(parent)
top.geometry('475x475')
top.resizable(0,0)
tk.Label(top, text='Create new synapse:\nValues from currently loaded cells file.').grid(row=0,column=0,sticky="WE",columnspan=2)
core_extras = tk.Frame(top)
gaba_extras = tk.Frame(top)
custom_extras = tk.Frame(top)
def showhide_gaba_extras(*args):
if self.syntype_value.get() == synapse_type_list[1]:
custom_extras.grid_forget()
core_extras.grid(row=7,column=0,columnspan=2)
gaba_extras.grid(row=10,column=0,columnspan=2)
elif self.syntype_value.get() == synapse_type_list[2]:
core_extras.grid_forget()
gaba_extras.grid_forget()
custom_extras.grid(row=7,column=0,columnspan=4)
return
else:
core_extras.grid(row=7,column=0,columnspan=2)
custom_extras.grid_forget()
gaba_extras.grid_forget()
return
core_extras.grid(row=7,column=0,columnspan=2)
self.pre_value = tk.StringVar(top)
self.post_value = tk.StringVar(top)
self.syntype_value = tk.StringVar(top)
self.section_value = tk.StringVar(top)
self.cond1_value = tk.StringVar(top)
self.cond1_text_value = tk.StringVar(top)
self.cond2_value = tk.StringVar(top)
self.cond2_text_value = tk.StringVar(top)
self.tau1a_value = tk.StringVar(top)
self.tau2a_value = tk.StringVar(top)
self.ea_value = tk.StringVar(top)
self.tau1b_value = tk.StringVar(top)
self.tau2b_value = tk.StringVar(top)
self.eb_value = tk.StringVar(top)
self.custom_mod_value = tk.StringVar(top)
self.custom1_value = tk.StringVar(top)
self.custom2_value = tk.StringVar(top)
self.custom3_value = tk.StringVar(top)
self.custom4_value = tk.StringVar(top)
self.custom5_value = tk.StringVar(top)
self.custom6_value = tk.StringVar(top)
self.custom1_value2 = tk.StringVar(top)
self.custom2_value2 = tk.StringVar(top)
self.custom3_value2 = tk.StringVar(top)
self.custom4_value2 = tk.StringVar(top)
self.custom5_value2 = tk.StringVar(top)
self.custom6_value2 = tk.StringVar(top)
self.confirm = False
#Inputs
loaded_cellnums = get_public_param("loaded_cellnums")
column_names = ["Friendly Cell Name", "Cell File Name", "Num Cells", "Layer Index","Artificial:1 Real:0"]
cellnums_pd = pd.read_csv(loaded_cellnums ,delimiter=' ',\
skiprows=1,header=None,\
names = column_names)
cellnums_pd[column_names[4]] = cellnums_pd[column_names[4]].astype(int)
pre_options = cellnums_pd[cellnums_pd.columns[0]].values.tolist()
post_options = cellnums_pd.loc[cellnums_pd[column_names[4]] == 0]
post_options = post_options[post_options.columns[0]].values.tolist()
l = tk.Label(top, text='Presynaptic Cell',width=25, background='light gray')
l.grid(row=1,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.pre = tk.Entry(top,textvariable=self.pre_value)
self.pre = tk.OptionMenu(top, self.pre_value, *pre_options)
self.pre.grid(row=1,column=1)
l = tk.Label(top, text='Postsynaptic Cell',width=25, background='light gray')
l.grid(row=2,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.post = tk.Entry(top,textvariable=self.post_value)
self.post = tk.OptionMenu(top, self.post_value, *post_options)
self.post.grid(row=2,column=1)
l = tk.Label(top, text='Synapse Type',width=25, background='light gray')
l.grid(row=3,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.syntype = tk.Entry(top,textvariable=self.syntype_value)
self.syntype = tk.OptionMenu(top, self.syntype_value, *synapse_type_list)
self.syntype_value.trace("w",showhide_gaba_extras)
self.syntype_value.set(synapse_type_list[0])
self.syntype.grid(row=3,column=1)
l = tk.Label(top, text='Postsynaptic Section Target',width=25, background='light gray')
l.grid(row=4,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.section = tk.Entry(top,textvariable=self.section_value)
self.section = tk.OptionMenu(top, self.section_value, *sections_list)
self.section.grid(row=4,column=1)
l = tk.Label(top, text='Condition 1',width=25, background='light gray')
l.grid(row=5,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.cond1 = tk.Entry(top,textvariable=self.cond1_value)
self.cond1 = tk.OptionMenu(top, self.cond1_value, *condition_list)
self.cond1_value.set(condition_list[0])
self.cond1.grid(row=5,column=1)
tk.Label(top, text=' > ').grid(row=5, column=2)
self.cond1_text = tk.Entry(top, textvariable=self.cond1_text_value)
self.cond1_text_value.set('-1')
self.cond1_text.grid(row=5, column=3)
l = tk.Label(top, text='Condition 2',width=25, background='light gray')
l.grid(row=6,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
#self.cond2 = tk.Entry(top,textvariable=self.cond2_value)
self.cond2 = tk.OptionMenu(top, self.cond2_value, *condition_list)
self.cond2_value.set(condition_list[0])
self.cond2.grid(row=6,column=1)
tk.Label(top, text=' < ').grid(row=6, column=2)
self.cond2_text = tk.Entry(top, textvariable=self.cond2_text_value)
self.cond2_text_value.set('10000')
self.cond2_text.grid(row=6, column=3)
l = tk.Label(core_extras, text='Tau1a',width=25, background='light gray')
l.grid(row=7,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.tau1a = tk.Entry(core_extras,textvariable=self.tau1a_value)
self.tau1a_value.set('2.0')
self.tau1a.grid(row=7,column=1)
l = tk.Label(core_extras, text='Tau2a',width=25, background='light gray')
l.grid(row=8,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.tau2a = tk.Entry(core_extras,textvariable=self.tau2a_value)
self.tau2a_value.set('6.3')
self.tau2a.grid(row=8,column=1)
l = tk.Label(core_extras, text='ea',width=25, background='light gray')
l.grid(row=9,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.ea = tk.Entry(core_extras,textvariable=self.ea_value)
self.ea_value.set('0.0')
self.ea.grid(row=9,column=1)
#GABA EXTRAS
l = tk.Label(gaba_extras, text='Tau1b',width=25, background='light gray')
l.grid(row=10,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.tau1b = tk.Entry(gaba_extras,textvariable=self.tau1b_value)
self.tau1b.grid(row=10,column=1)
l = tk.Label(gaba_extras, text='Tau2b',width=25, background='light gray')
l.grid(row=11,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.tau2b = tk.Entry(gaba_extras,textvariable=self.tau2b_value)
self.tau2b.grid(row=11,column=1)
l = tk.Label(gaba_extras, text='eb',width=25, background='light gray')
l.grid(row=12,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.eb = tk.Entry(gaba_extras,textvariable=self.eb_value)
self.eb.grid(row=12,column=1)
#CUSTOM EXTRAS
l = tk.Label(custom_extras, text='Synapse Mod File',width=25, background='light gray')
l.grid(row=7,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom_mod = tk.OptionMenu(custom_extras, self.custom_mod_value, *mod_list)
self.custom_mod_value.set('')
self.custom_mod.grid(row=7,column=1)
tk.Label(custom_extras, text=' Syn."parameter" ').grid(row=8, column=1)
tk.Label(custom_extras, text=' : ').grid(row=8, column=2)
tk.Label(custom_extras, text=' "value" ').grid(row=8, column=3)
l = tk.Label(custom_extras, text='Custom Parameter 1',width=25, background='light gray')
l.grid(row=9,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom1 = tk.Entry(custom_extras,textvariable=self.custom1_value)
self.custom1.grid(row=9,column=1)
tk.Label(custom_extras, text=' : ').grid(row=9, column=2)
self.custom1_text = tk.Entry(custom_extras, textvariable=self.custom1_value2)
self.custom1_text.grid(row=9, column=3)
l = tk.Label(custom_extras, text='Custom Parameter 2',width=25, background='light gray')
l.grid(row=10,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom2 = tk.Entry(custom_extras,textvariable=self.custom2_value)
self.custom2.grid(row=10,column=1)
tk.Label(custom_extras, text=' : ').grid(row=10, column=2)
self.custom2_text = tk.Entry(custom_extras, textvariable=self.custom2_value2)
self.custom2_text.grid(row=10, column=3)
l = tk.Label(custom_extras, text='Custom Parameter 3',width=25, background='light gray')
l.grid(row=11,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom3 = tk.Entry(custom_extras,textvariable=self.custom3_value)
self.custom3.grid(row=11,column=1)
tk.Label(custom_extras, text=' : ').grid(row=11, column=2)
self.custom3_text = tk.Entry(custom_extras, textvariable=self.custom3_value2)
self.custom3_text.grid(row=11, column=3)
l = tk.Label(custom_extras, text='Custom Parameter 4',width=25, background='light gray')
l.grid(row=12,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom4 = tk.Entry(custom_extras,textvariable=self.custom4_value)
self.custom4.grid(row=12,column=1)
tk.Label(custom_extras, text=' : ').grid(row=12, column=2)
self.custom4_text = tk.Entry(custom_extras, textvariable=self.custom4_value2)
self.custom4_text.grid(row=12, column=3)
l = tk.Label(custom_extras, text='Custom Parameter 5',width=25, background='light gray')
l.grid(row=13,column=0,pady=5,padx=5)
l.config(relief=tk.GROOVE)
self.custom5 = tk.Entry(custom_extras,textvariable=self.custom5_value)
self.custom5.grid(row=13,column=1)
tk.Label(custom_extras, text=' : ').grid(row=13, column=2)
self.custom5_text = tk.Entry(custom_extras, textvariable=self.custom5_value2)
self.custom5_text.grid(row=13, column=3)
#Return
button_frame = tk.Frame(top)
button_frame.grid(row=20,column=0,columnspan=2)
b = tk.Button(button_frame, text="Ok", command=self.ok)
b.grid(pady=5, padx=5, column=0, row=0, sticky="WE")
b = tk.Button(button_frame, text="Cancel", command=self.cancel)
b.grid(pady=5, padx=5, column=1, row=0, sticky="WE")
def verify_good(self):
return True
def get_values(self):
if self.syntype_value.get() == "Custom":
if self.custom_mod_value.get() is '':
self.custom_mod_value.set('none')
if self.custom1_value.get() is '' or self.custom1_value2.get() is '':
self.custom1_value.set('none')
self.custom1_value2.set('none')
if self.custom2_value.get() is '' or self.custom2_value2.get() is '':
self.custom2_value.set('none')
self.custom2_value2.set('none')
if self.custom3_value.get() is '' or self.custom3_value2.get() is '':
self.custom3_value.set('none')
self.custom3_value2.set('none')
if self.custom4_value.get() is '' or self.custom4_value2.get() is '':
self.custom4_value.set('none')
self.custom4_value2.set('none')
if self.custom5_value.get() is '' or self.custom5_value2.get() is '':
self.custom5_value.set('none')
self.custom5_value2.set('none')
if self.custom6_value.get() is '' or self.custom6_value2.get() is '':
self.custom6_value.set('none')
self.custom6_value2.set('none')
| |
len(daids) == 1 and daids[0] == qaids[0]
flags.append(flag)
if len(flags) > 0 and all(flags):
raise AssertionError('No need to compute a query against itself')
cfgdict, review_cfg = back.confirm_query_dialog2(
species2_expanded_aids,
query_msg=query_msg,
query_title=query_title,
cfgdict=cfgdict,
review_cfg=review_cfg,
)
logger.info('cfgdict = %r' % (cfgdict,))
prog_bar = back.front.prog_bar
prog_bar.setVisible(True)
prog_bar.setWindowTitle('Initialize query')
prog_hook = prog_bar.utool_prog_hook
# prog_bar = guitool.newProgressBar(None) # back.front)
# Doesn't seem to work correctly
# prog_hook.show_indefinite_progress()
prog_hook.force_event_update()
# prog_hook.set_progress(0)
prog_bar.setWindowTitle('Start query')
# import utool
# utool.embed()
query_results = {}
for key, (qaids, daids) in ut.ProgressIter(
species2_expanded_aids.items(), prog_hook=prog_hook
):
prog_bar.setWindowTitle('Initialize %r query' % (key,))
qreq_ = back.ibs.new_query_request(qaids, daids, cfgdict=cfgdict)
prog_hook.initialize_subhooks(1)
subhook = prog_hook.next_subhook()
cm_list = qreq_.execute(prog_hook=subhook)
query_results[key] = (cm_list, qreq_)
# HACK IN IMAGESET INFO
if daids_mode == const.INTRA_OCCUR_KEY:
for cm in cm_list:
# if cm is not None:
cm.imgsetid = imgsetid
back.front.prog_bar.setVisible(False)
logger.info('[back] About to finish compute_queries: imgsetid=%r' % (imgsetid,))
for key in query_results.keys():
(cm_list, qreq_) = query_results[key]
# Filter duplicate names if running vsexemplar
back.review_queries(
cm_list,
qreq_=qreq_,
query_title=query_title + ' ' + str(key),
review_cfg=review_cfg,
)
if refresh:
back.front.update_tables()
logger.info('[back] FINISHED compute_queries: imgsetid=%r' % (imgsetid,))
def get_selected_daids(
back, imgsetid=None, daids_mode=None, qaid_list=None, species=None
):
daids_mode = back.daids_mode if daids_mode is None else daids_mode
daids_mode_valid_kw_dict = {
const.VS_EXEMPLARS_KEY: {'is_exemplar': True},
const.INTRA_OCCUR_KEY: {'imgsetid': imgsetid},
'all': {},
}
if qaid_list is not None and species is None:
ibs = back.ibs
hist_ = ut.dict_hist(ibs.get_annot_species_texts(qaid_list))
logger.info('[back] len(qaid_list)=%r' % (len(qaid_list)))
logger.info('[back] hist_ = %r' % (hist_,))
if len(hist_) == 1:
# select the query species if there is only one
species = hist_.keys()[0]
if species is None:
species = back.get_selected_species()
valid_kw = {
'minqual': 'ok',
}
if species != const.UNKNOWN:
# Query everything if you don't know the species
valid_kw['species'] = species
mode_str = {
const.VS_EXEMPLARS_KEY: 'vs_exemplar',
const.INTRA_OCCUR_KEY: 'intra_occurrence',
'all': 'all',
}[daids_mode]
valid_kw.update(daids_mode_valid_kw_dict[daids_mode])
logger.info('[back] get_selected_daids: ' + mode_str)
logger.info('[back] ... valid_kw = ' + ut.repr2(valid_kw))
daid_list = back.ibs.get_valid_aids(**valid_kw)
return daid_list
def make_confirm_query_msg2(
back, species2_expanded_aids, cfgdict=None, query_msg=None, query_title=None
):
r"""
CommandLine:
python -m wbia.gui.guiback --test-MainWindowBackend.make_confirm_query_msg2 --show
Example:
>>> # xdoctest: +REQUIRES(--gui)
>>> from wbia.gui.guiback import * # NOQA
>>> import wbia
>>> main_locals = wbia.main(defaultdb='testdb1')
>>> ibs, back = ut.dict_take(main_locals, ['ibs', 'back'])
>>> ut.exec_funckw(back.make_confirm_query_msg2, globals())
>>> imgsetid = ibs.get_imageset_imgsetids_from_text('*All Images')
>>> species2_expanded_aids = back._get_expanded_aids_groups(imgsetid)
>>> short_msg, detailed_msg = back.make_confirm_query_msg2(species2_expanded_aids)
>>> print(short_msg)
>>> print(detailed_msg)
>>> ut.quit_if_noshow()
>>> back.confirm_query_dialog2(species2_expanded_aids)
"""
ibs = back.ibs
species_text = ibs.get_all_species_texts()
species_nice = ibs.get_all_species_nice()
species_dict = dict(zip(species_text, species_nice))
def get_unique_species_phrase(aid_list):
def boldspecies(species):
species_bold_nice = "'%s'" % (species_dict.get(species, species).upper(),)
return species_bold_nice
species_list = list(set(ibs.get_annot_species_texts(aid_list)))
species_nice_list = list(map(boldspecies, species_list))
species_phrase = ut.conj_phrase(species_nice_list, 'and')
return species_phrase
# Build confirmation message
fmtdict = dict()
msg_fmtstr_list = []
if query_msg is not None:
msg_fmtstr_list += [query_msg]
if query_title is None:
query_title = 'custom'
ngroups = len(species2_expanded_aids)
if ngroups > 1:
msg_fmtstr_list += [
(
'You are about to run {query_title} '
'identification with {ngroups} groups...'
).format(query_title=query_title, ngroups=ngroups)
]
else:
msg_fmtstr_list += [
('You are about to run {query_title} ' 'identification...').format(
query_title=query_title,
)
]
species_list = list(species2_expanded_aids.keys())
detailed_msg_list = []
annotstats_kw = {}
for count, species in enumerate(species_list):
qaids, daids = species2_expanded_aids[species]
species_nice = species_dict.get(species, species)
# species_phrase = get_unique_species_phrase(qaids + daids)
msg_fmtstr_list += ['']
fmtdict = {}
qaid_stats = ibs.get_annot_stats_dict(
qaids, prefix='q', per_name=True, old=False
)
daid_stats = ibs.get_annot_stats_dict(
daids, prefix='d', per_name=True, old=False
)
stats_ = ibs.get_annotconfig_stats(
qaids, daids, combined=False, species_hist=True, **annotstats_kw
)
fmtdict.update(**qaid_stats)
fmtdict.update(**daid_stats)
fmtdict['qannots'] = ut.pluralize('annotation', len(qaids))
fmtdict['dannots'] = ut.pluralize('annotation', len(daids))
fmtdict['species_nice'] = species_nice
fmtdict['count'] = count
# Add simple info
if ngroups > 1:
part1 = 'Group {count} '
else:
part1 = 'This '
part2 = (
part1
+ 'will identify {num_qaids} query {qannots} against {num_daids} {species_nice} database {dannots}.'
).format(**fmtdict)
msg_fmtstr_list += [part2]
# Add detailed info
stats_str2 = ut.repr2(
stats_, strvals=True, newlines=2, explicit=False, nobraces=False
)
detailed_msg_list.append('--- Group %d ---' % (count,))
detailed_msg_list.append(stats_str2)
# Finish building confirmation message
msg_fmtstr_list += ['']
msg_fmtstr_list += ["Press 'Yes' to continue"]
msg_fmtstr = '\n'.join(msg_fmtstr_list)
msg_str = msg_fmtstr.format(**fmtdict)
if cfgdict is not None and len(cfgdict) > 0:
detailed_msg_list = [
'Special Settings: {}'.format(ut.repr2(cfgdict))
] + detailed_msg_list
detailed_msg = '\n'.join(detailed_msg_list)
return msg_str, detailed_msg
def run_annot_splits(back, aid_list):
"""
Checks for mismatches within a group of annotations
Args:
aid_list (int): list of annotation ids
CommandLine:
python -m wbia.gui.guiback --test-MainWindowBackend.run_annot_splits --show
Example:
>>> # xdoctest: +REQUIRES(--gui)
>>> from wbia.gui.guiback import * # NOQA
>>> back = testdata_guiback()
>>> ibs = back.ibs
>>> aids_list, nids = back.ibs.group_annots_by_name(back.ibs.get_valid_aids())
>>> aid_list = aids_list[ut.list_argmax(list(map(len, aids_list)))]
>>> back.run_annot_splits(aid_list)
>>> ut.quit_if_noshow()
>>> guitool.qtapp_loop(back.mainwin, frequency=100)
"""
cfgdict = {
'can_match_samename': True,
'K': 3,
'Knorm': 3,
'prescore_method': 'csum',
'score_method': 'csum',
}
ranks_top = min(len(aid_list), 10)
review_cfg = {
'filter_reviewed': False,
'ranks_top': ranks_top,
'name_scoring': False,
}
ibs = back.ibs
cfgdict, review_cfg = back.confirm_query_dialog2(
{'split': (aid_list, aid_list)},
cfgdict=cfgdict,
query_msg='Checking for SPLIT cases (matching each annotation within a name)',
review_cfg=review_cfg,
)
qreq_ = ibs.new_query_request(aid_list, aid_list, cfgdict=cfgdict)
cm_list = qreq_.execute()
back.review_queries(
cm_list, qreq_=qreq_, query_title='Annot Splits', review_cfg=review_cfg
)
if False:
from wbia.viz import viz_graph2
import imp
imp.reload(viz_graph2)
win = viz_graph2.make_qt_graph_review(qreq_, cm_list, review_cfg=review_cfg)
win.show()
def run_merge_checks(back):
r"""
Checks for missed matches within a group of annotations
CommandLine:
python -m wbia.gui.guiback --test-run_merge_checks --show
Example:
>>> # xdoctest: +REQUIRES(--gui)
>>> from wbia.gui.guiback import * # NOQA
>>> back = testdata_guiback()
>>> result = back.run_merge_checks()
>>> print(result)
>>> ut.quit_if_noshow()
>>> guitool.qtapp_loop(back.mainwin, frequency=100)
"""
pass
qaid_list = back.ibs.get_valid_aids(is_exemplar=True)
cfgdict = {
'can_match_samename': False,
# 'K': 3,
# 'Knorm': 3,
# 'prescore_method': 'csum',
# 'score_method': 'csum'
}
query_msg = 'Checking for MERGE cases (this is an exemplars-vs-exemplars query)'
back.compute_queries(
qaid_list=qaid_list,
daids_mode=const.VS_EXEMPLARS_KEY,
query_msg=query_msg,
cfgdict=cfgdict,
custom_qaid_list_title='Merge Candidates',
)
def run_merge_checks_multitons(back):
r"""
Checks for missed matches within a group of annotations.
Only uses annotations with more 2 annots per id.
"""
pass
ibs = back.ibs
# qaid_list = back.ibs.get_valid_aids(is_exemplar=True)
from wbia import dtool
config = dtool.Config.from_dict(
{
'K': 1,
'Knorm': 5,
'min_pername': 1,
'max_pername': 1,
'exemplars_per_name': 1,
'method': 'randomize',
'seed': 42,
}
)
# ibswgt = None
dlg = gt.ConfigConfirmWidget.as_dialog(
title='Confirm Merge Query', msg='Confirm', config=config
)
self = dlg.widget
dlg.resize(700, 500)
dlg.exec_()
logger.info('config = %r' % (config,))
updated_config = self.config # NOQA
logger.info('updated_config = %r' % (updated_config,))
min_pername = updated_config['min_pername']
max_pername = updated_config['max_pername']
aid_list = ibs.filter_annots_general(
min_pername=min_pername, max_pername=max_pername, minqual='ok'
)
if updated_config['method'] == 'randomize':
import numpy as np
rng = np.random.RandomState(int(updated_config['seed']))
grouped_aids = ibs.group_annots_by_name(aid_list)[0]
grouped_aids2 = [
ut.random_sample(aids, updated_config['exemplars_per_name'], rng=rng)
for aids in grouped_aids
]
aid_list = ut.flatten(grouped_aids2)
else:
new_flag_list = ibs.get_annot_quality_viewpoint_subset(
aid_list, updated_config['exemplars_per_name'], allow_unknown=True
)
aid_list = ut.compress(aid_list, new_flag_list)
ibs.print_annot_stats(aid_list)
daid_list = qaid_list = aid_list
# len(aids)
cfgdict = {
'can_match_samename': False,
'K': updated_config['K'],
'Knorm': updated_config['Knorm'],
# 'prescore_method': 'csum',
# 'score_method': 'csum'
}
query_msg = 'Checking for MERGE cases (this is an special query)'
back.compute_queries(
qaid_list=qaid_list,
daid_list=daid_list,
query_msg=query_msg,
cfgdict=cfgdict,
custom_qaid_list_title='Merge2 Candidates',
)
def _get_expanded_aids_groups(
back,
imgsetid,
daids_mode=None,
use_prioritized_name_subset=False,
use_visual_selection=False,
qaid_list=None,
daid_list=None,
query_is_known=None,
partition_queries_by_species=True,
remove_unknown_species=None,
):
"""
Get the query annotation ids to search and
the database annotation ids to be searched
The query is either a specific selection or everything from this
image set that matches the appropriate filters.
Example:
>>> # DISABLE_DOCTEST
>>> ut.exec_funckw(back._get_expanded_aids_groups, globals())
>>> imgsetid = ibs.get_imageset_imgsetids_from_text('*All Images')
>>> species2_expanded_aids = back._get_expanded_aids_groups(imgsetid)
"""
ibs = back.ibs
daids_mode = back.daids_mode if daids_mode is None else daids_mode
if imgsetid is None:
raise Exception('[back] invalid imgsetid')
if ibs.cfg.other_cfg.enable_custom_filter:
back.user_warning(
msg=ut.codeblock(
"""
other_cfg.enable_custom_filter=True is not longer supported.
Please turn off in Preferences
"""
)
)
if remove_unknown_species is None:
# Default behavior is don't remove unknown species if qaid_list is specified
remove_unknown_species = qaid_list is not None or use_visual_selection
# Query aids are either: given, taken from gui selection, or by imageset
if qaid_list is not None:
qaid_list = qaid_list
elif use_visual_selection:
qaid_list = back.get_selected_aids()
else:
qaid_list = ibs.get_valid_aids(
imgsetid=imgsetid, is_known=query_is_known, minqual='ok'
)
logger.info('[back] Initially loaded len(qaid_list) = %r' % (len(qaid_list),))
if use_prioritized_name_subset:
# Pick only a few queries per name to execute
annots_per_view = 2 # FIXME: use a configuration
new_flag_list = back.ibs.get_annot_quality_viewpoint_subset(
aid_list=qaid_list,
annots_per_view=annots_per_view,
allow_unknown=True,
verbose=True,
)
qaid_list = ut.compress(qaid_list, new_flag_list)
logger.info(
'[back] Filtered query by quality and viewpoint: len(qaid_list) = %r'
% (len(qaid_list),)
)
logger.info('[back] Found len(qaid_list) = %r' % (len(qaid_list),))
| |
+ L_dash_w_d_t + L_dash_b1_d_t + L_dash_b2_d_t + L_dash_ba1_d_t
f = L_dash_d_t > 0
L_dashdash_ba1_d_t[f] = L_dash_ba1_d_t[f] - L_sun_d_t[f] * (L_dash_ba1_d_t[f] / L_dash_d_t[f])
return L_dashdash_ba1_d_t
def get_L_dashdash_ba2_d_t(L_dash_ba2_d_t):
"""1時間当たりの浴槽追焚時における太陽熱補正給湯負荷 (MJ/h) (4g)
Args:
L_dash_ba2_d_t(ndarray): 1時間当たりの浴槽追焚時における節湯補正給湯負荷 (MJ/h)
Returns:
1時間当たりの浴槽追焚時における太陽熱補正給湯負荷 (MJ/h)
"""
return L_dash_ba2_d_t
def calc_L_sun_d_t(region, sol_region=None, solar_device=None, ls_type=None, A_sp=None, P_alpha_sp=None, P_beta_sp=None,
W_tnk_ss=None, hotwater_use=None, heating_flag_d=None, A_col=None, P_alpha=None, P_beta=None,
V_fan_P0=None, d0=None,
d1=None, m_fan_test=None, W_tnk_ass=None, Theta_wtr_d=None, L_dash_k_d_t=None, L_dash_s_d_t=None,
L_dash_w_d_t=None, L_dash_b1_d_t=None, L_dash_b2_d_t=None, L_dash_ba1_d_t=None):
"""太陽熱利用給湯設備による補正集熱量
Args:
region(int): 省エネルギー地域区分
sol_region(int, optional): 年間の日射地域区分 (Default value = None)
solar_device(str, optional): 太陽熱利用設備の種類 (液体集熱式,空気集熱式,None) (Default value = None)
ls_type(str, optional): 液体集熱式太陽熱利用設備の種類 (太陽熱温水器,ソーラーシステム) (Default value = None)
A_sp(float, optional): 太陽熱集熱部の有効集熱面積 (m2) (Default value = None)
P_alpha_sp(float, optional): 太陽熱集熱部の方位角 (°) (Default value = None)
P_beta_sp(float, optional): 太陽熱集熱部の傾斜角 (°) (Default value = None)
W_tnk_ss(float, optional): ソーラーシステムのタンク容量 (L) (Default value = None)
W_tnk_ass(float, optional): タンク容量 (L) (Default value = None)
Theta_wtr_d(ndarray, optional): 日平均給水温度 (℃) (Default value = None)
L_dash_k_d_t(ndarrayL, optional): 1時間当たりの台所水栓における節湯補正給湯熱負荷 (MJ/h) (Default value = None)
L_dash_s_d_t(ndarray, optional): 1時間当たりの浴室シャワー水栓における節湯補正給湯熱負荷 (MJ/h) (Default value = None)
L_dash_w_d_t(ndarray, optional): 1時間当たりの洗面水栓における節湯補正給湯熱負荷 (MJ/h) (Default value = None)
L_dash_b1_d_t(ndarray, optional): 1時間当たりの浴槽水栓湯はりにおける節湯補正給湯熱負荷 (MJ/h) (Default value = None)
L_dash_b2_d_t(ndarray, optional): 1時間当たりの浴槽自動湯はりにおける節湯補正給湯熱負荷 (MJ/h) (Default value = None)
L_dash_ba1_d_t(ndarray, optional): 1時間当たりの浴槽水栓さし湯における節湯補正給湯熱負荷 (MJ/h) (Default value = None)
hotwater_use: Default value = None)
heating_flag_d: Default value = None)
A_col: Default value = None)
P_alpha: Default value = None)
P_beta: Default value = None)
V_fan_P0: Default value = None)
d0: Default value = None)
d1: Default value = None)
m_fan_test: Default value = None)
Returns:
ndarray: 1時間当たりの太陽熱利用設備による補正集熱量 (MJ/h)
"""
if solar_device == '液体集熱式':
return lss.calc_L_sun_lss_d_t(
region=region,
sol_region=sol_region,
ls_type=ls_type,
A_sp=A_sp,
P_alpha_sp=P_alpha_sp,
P_beta_sp=P_beta_sp,
W_tnk_ss=W_tnk_ss,
Theta_wtr_d=Theta_wtr_d,
L_dash_k_d_t=L_dash_k_d_t,
L_dash_s_d_t=L_dash_s_d_t,
L_dash_w_d_t=L_dash_w_d_t,
L_dash_b1_d_t=L_dash_b1_d_t,
L_dash_b2_d_t=L_dash_b2_d_t,
L_dash_ba1_d_t=L_dash_ba1_d_t
)
elif solar_device == '空気集熱式':
if hotwater_use == True:
outdoor = load_outdoor()
Theta_ex_d_t = get_Theta_ex(region, outdoor)
Theta_col_nonopg_d_t, Theta_col_opg_d_t = ass.calc_Theta_col(A_col, P_alpha, P_beta, V_fan_P0, d0, d1,
m_fan_test, region, sol_region, Theta_ex_d_t)
t_fan_d_t = ass.get_t_fan_d_t(Theta_col_nonopg_d_t, Theta_col_opg_d_t)
t_cp_d_t = ass.get_t_cp_d_t(hotwater_use, t_fan_d_t, heating_flag_d)
V_fan_d_t = ass.get_V_fan_d_t(t_fan_d_t, V_fan_P0)
Q_col_d_t = ass.get_Q_col_d_t(V_fan_d_t, Theta_col_opg_d_t, Theta_ex_d_t)
Q_d = ass.calc_Q_d(Q_col_d_t, t_cp_d_t)
L_tnk_d = ass.calc_L_tnk_d(Q_d, W_tnk_ass, Theta_wtr_d)
return ass.calc_L_sun_ass_d_t(L_tnk_d, L_dash_k_d_t, L_dash_s_d_t, L_dash_w_d_t, L_dash_b1_d_t,
L_dash_b2_d_t, L_dash_ba1_d_t)
else:
return np.zeros(24 * 365)
elif solar_device is None:
return np.zeros(24 * 365)
else:
raise ValueError(solar_device)
# ============================================================================
# 8. 節湯補正給湯熱負荷
# ============================================================================
def get_L_dash_k_d_t(W_dash_k_d_t, Theta_sw_k, Theta_wtr_d):
"""台所水栓における節湯補正給湯負荷 (MJ/h) (5a)
Args:
W_dash_k_d_t(ndarray): 1時間当たりの台所水栓における節湯補正給湯量 (L/h)
Theta_sw_k(int): 台所水栓における基給湯量 (℃)
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
Returns:
ndarray: 台所水栓における節湯補正給湯負荷 (MJ/h)
"""
return W_dash_k_d_t * (Theta_sw_k - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3)
def get_L_dash_s_d_t(W_dash_s_d_t, Theta_sw_s, Theta_wtr_d):
"""浴室シャワー水栓における節湯補正給湯負荷 (5b)
Args:
W_dash_s_d_t(ndarray): 1時間当たりの浴室シャワーにおける節湯補正給湯量 (L/h)
Theta_sw_s(int): 浴室シャワーにおける基給湯量 (℃)
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
Returns:
ndarray: 浴室シャワーにおける節湯補正給湯負荷 (MJ/h)
"""
return W_dash_s_d_t * (Theta_sw_s - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3)
def get_L_dash_w_d_t(W_dash_w_d_t, Theta_sw_w, Theta_wtr_d):
"""洗面水栓における節湯補正給湯負荷 (5c)
Args:
W_dash_w_d_t(ndarray): 1時間当たりの洗面水栓における節湯補正給湯量 (L/d)
Theta_sw_w(int): 洗面水栓における基給湯量 (℃)
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
Returns:
ndarray: 洗面水栓における節湯補正給湯負荷 (MJ/d)
"""
return W_dash_w_d_t * (Theta_sw_w - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3)
def get_L_dash_bx_d_t(W_dash_b1_d_t, W_dash_b2_d_t, Theta_wtr_d, has_bath, bash_function):
"""浴槽水栓湯はり時における節水補正給湯熱負荷 L_dash_b1_d, L_dash_b2_d
Args:
W_dash_b1_d_t(ndarray): 1時間当たりの浴槽水栓湯はり時における節湯補正給湯量 (L/d)
W_dash_b2_d_t(ndarray): 1時間当たりの浴槽自動湯はり時における節湯補正給湯量 (L/d)
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
has_bath(bool): 浴室用の有無
bash_function(str): ふろ機能の種類
Returns:
ndarray: 浴槽水栓湯はり時・浴槽自動湯はり時における節水補正給湯熱負荷 (MJ/d)
"""
if has_bath == False:
L_dash_b1_d_t = np.zeros(24 * 365) # (5-1d)
L_dash_b2_d_t = np.zeros(24 * 365) # (5-1e)
return L_dash_b1_d_t, L_dash_b2_d_t
elif bash_function == '給湯単機能':
Theta_sw_b1 = get_Theta_sw_b1()
L_dash_b1_d_t = W_dash_b1_d_t * (Theta_sw_b1 - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3) # (5-2d)
L_dash_b2_d_t = np.zeros(24 * 365) # (5-2e)
return L_dash_b1_d_t, L_dash_b2_d_t
elif bash_function == 'ふろ給湯機(追焚あり)' or bash_function == 'ふろ給湯機(追焚なし)':
Theta_sw_b2 = get_Theta_sw_b2()
L_dash_b1_d_t = np.zeros(24 * 365) # (5-3d)
L_dash_b2_d_t = W_dash_b2_d_t * (Theta_sw_b2 - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3) # (5-3e)
return L_dash_b1_d_t, L_dash_b2_d_t
else:
raise ValueError(bash_function)
def get_L_dash_bax_d_t(W_dash_ba1_d_t, Theta_wtr_d, L_ba_d_t, has_bath, bash_function):
"""浴槽水栓さし湯時における節水補正給湯熱負荷 L_dash_ba1_d, L_dash_ba2_d
Args:
W_dash_ba1_d_t(ndarray): 1時間当たりの浴槽水栓湯はり時における節湯補正給湯量 (L/h)
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
L_ba_d_t(ndarray): 1時間当たりの浴槽沸かし直しによる給湯熱負荷 (MJ/h)
has_bath(bool): 浴室等の有無
bash_function(str): ふろ機能の種類 (給湯単機能,ふろ給湯機(追焚なし),ふろ給湯機(追焚あり))
Returns:
ndarray: 浴槽水栓さし湯時／浴槽追焚時における節水補正給湯熱負荷 (MJ/h)
"""
if has_bath == False:
L_dash_ba1_d_t = np.zeros(24 * 365) # (5-1f)
L_dash_ba2_d_t = np.zeros(24 * 365) # (5-1g)
return L_dash_ba1_d_t, L_dash_ba2_d_t
elif bash_function == '給湯単機能' or bash_function == 'ふろ給湯機(追焚なし)':
Theta_sw_ba1 = get_Theta_sw_ba1()
L_dash_ba1_d_t = W_dash_ba1_d_t * (Theta_sw_ba1 - np.repeat(Theta_wtr_d, 24)) * 4.186 * 10 ** (-3) # (5-2f)
L_dash_ba2_d_t = np.zeros(24 * 365) # (5-2g)
return L_dash_ba1_d_t, L_dash_ba2_d_t
elif bash_function == 'ふろ給湯機(追焚あり)':
L_dash_ba1_d_t = np.zeros(24 * 365) # (5-3f)
L_dash_ba2_d_t = L_ba_d_t * 1.25 # (5-3g)
return L_dash_ba1_d_t, L_dash_ba2_d_t
else:
raise ValueError(bash_function)
def get_Theta_sw_k():
"""台所水栓の基準給湯温度
Args:
Returns:
int: 台所水栓の基準給湯温度
"""
return get_table_5()[0]
def get_Theta_sw_s():
"""浴室シャワー水栓の基準給湯温度
Args:
Returns:
int: 浴室シャワー水栓の基準給湯温度
"""
return get_table_5()[1]
def get_Theta_sw_w():
"""洗面水栓の基準給湯温度
Args:
Returns:
int: 洗面水栓の基準給湯温度
"""
return get_table_5()[2]
def get_Theta_sw_b1():
"""浴槽水栓湯はりの基準給湯温度
Args:
Returns:
int: 浴槽水栓湯はりの基準給湯温度
"""
return get_table_5()[3]
def get_Theta_sw_b2():
"""浴槽自動湯はりの基準給湯温度
Args:
Returns:
int: 浴槽自動湯はりの基準給湯温度
"""
return get_table_5()[4]
def get_Theta_sw_ba1():
"""浴槽水栓さし湯の基準給湯温度
Args:
Returns:
int: 浴槽水栓さし湯の基準給湯温度
"""
return get_table_5()[5]
def get_table_5():
"""表 5 用途ごとの基準給湯温度
Args:
Returns:
list: 用途ごとの基準給湯温度
"""
table_5 = [
40,
40,
40,
40,
40,
60
]
return table_5
# ============================================================================
# 9. 節湯補正給湯量
# ============================================================================
def calc_W_dash_k_d_t(W_k_d_t, kitchen_watersaving_A, kitchen_watersaving_C, pipe_diameter, Theta_wtr_d):
"""1時間当たりの台所水栓における節湯補正給湯量 [L/h] (6a)
Args:
W_k_d_t(ndarray): 1時間当たりの台所水栓における基準給湯量 (L/h)
kitchen_watersaving_A(bool): 台所水栓の手元止水機能の有無
kitchen_watersaving_C(bool): 台所水栓の水優先吐水機能の有無
pipe_diameter(str): ヘッダー分岐後の径
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
Returns:
ndarray: 1時間当たりの台所水栓における節湯補正給湯量 (L/h)
"""
# 台所水栓における節湯の効果係数
f_sk = watersaving.get_f_sk(kitchen_watersaving_A, kitchen_watersaving_C, Theta_wtr_d)
# 配管における節湯の効果係数
f_sp = watersaving.get_f_sp(pipe_diameter)
return W_k_d_t * np.repeat(f_sk, 24) * f_sp
def calc_W_dash_s_d_t(W_s_d_t, shower_watersaving_A, shower_watersaving_B, pipe_diameter):
"""1時間当たりの浴室シャワーにおける節湯補正給湯量 (L/h) (6a)
Args:
W_s_d_t(ndarray): 浴室シャワーにおける基準給湯量 (L/h)
shower_watersaving_A(bool): 浴室シャワー水栓の手元止水機能の有無
shower_watersaving_B(bool): 浴室シャワー水栓の小流量吐水機能の有無
pipe_diameter(str): ヘッダー分岐後の径
Returns:
ndarray: 1時間当たりの浴室シャワーにおける節湯補正給湯量 (L/h)
"""
# 浴室シャワー水栓のける節湯の効果係数
f_ss = watersaving.get_f_ss(shower_watersaving_A, shower_watersaving_B)
# 配管における節湯の効果係数
f_sp = watersaving.get_f_sp(pipe_diameter)
return W_s_d_t * f_ss * f_sp
def calc_W_dash_w_d_t(W_w_d_t, washbowl_watersaving_C, pipe_diameter, Theta_wtr_d):
"""1時間当たりの台所水栓における節湯補正給湯量 (L/h) (6c)
Args:
W_w_d_t(ndarray): 台所水栓における基準給湯量 (L/h)
washbowl_watersaving_C(bool): 洗面水栓の水優先吐水機能の有無
pipe_diameter(str): ヘッダー分岐後の径
Theta_wtr_d(ndarray): 日平均給水温度 (℃)
Returns:
ndarray: 1時間当たりの台所水栓における節湯補正給湯量 (L/h)
"""
# 配管における節湯の効果係数
f_sp = watersaving.get_f_sp(pipe_diameter)
# 洗面水栓における節湯の効果係数
f_sw = watersaving.get_f_sw(washbowl_watersaving_C, Theta_wtr_d)
return W_w_d_t * np.repeat(f_sw, 24) * f_sp
def calc_W_dash_b1_d_t(W_b1_d_t, pipe_diameter):
"""1時間当たりの浴槽水栓湯はり時における節湯補正給湯量 (L/h) (6d)
Args:
W_b1_d_t(ndarray): 浴槽水栓湯はり時における基準給湯量 (L/h)
pipe_diameter(str): ヘッダー分岐後の径
Returns:
ndarray: 1時間当たりの浴槽水栓湯はり時における節湯補正給湯量 (L/h)
"""
# 配管における節湯の効果係数
f_sp = watersaving.get_f_sp(pipe_diameter)
# 浴槽における節湯の効果係数
f_sb = watersaving.get_f_sb()
return W_b1_d_t * f_sp * f_sb
def calc_W_dash_b2_d_t(W_b2_d_t):
"""1時間当たりの浴槽自動湯はり時における節湯補正給湯量 (L/h) (6e)
Args:
W_b2_d_t(ndarray): 浴槽自動湯はり時における基準給湯量 (L/h)
Returns:
ndarray: 1時間当たりの浴槽自動湯はり時における節湯補正給湯量 (L/h)
"""
# 浴槽における節湯の効果係数
f_sb = watersaving.get_f_sb()
return W_b2_d_t * f_sb
def calc_W_dash_ba1_d_t(W_ba1_d_t, pipe_diameter):
"""1時間当たりの浴槽水栓さし湯時における節湯補正給湯量 (L/h) (6f)
Args:
W_ba1_d_t(ndarray): 1時間当たりの浴室水栓さし湯時における基準給湯量 (L/h)
pipe_diameter(str): ヘッダー分岐後の径
Returns:
1時間当たりの浴槽水栓さし湯時における節湯補正給湯量 (L/h)
"""
# 配管における節湯の効果係数
f_sp = watersaving.get_f_sp(pipe_diameter)
return W_ba1_d_t * f_sp
# ============================================================================
# 10. 基準給湯量
# ============================================================================
def calc_W_k_d_t(n_p, schedule_hw):
"""1時間当たりの台所水栓における基準給湯量 (L/h) (7a)
Args:
n_p(float): 仮想居住人数 (人)
schedule_hw(ndarray): 給湯スケジュール
Returns:
ndarray: 1時間当たりの台所水栓における基準給湯量 (L/h)
"""
if n_p in [1, 2, 3, 4]:
return calc_W_k_p_d_t(n_p, schedule_hw)
elif 1 <= n_p and n_p <= 2:
W_k_1_d_t = calc_W_k_p_d_t(1, schedule_hw)
W_k_2_d_t = calc_W_k_p_d_t(2, schedule_hw)
return W_k_1_d_t * (2 - n_p) / (2 - 1) + W_k_2_d_t * (n_p - 1) / (2 - 1)
elif 2 <= n_p and n_p <= 3:
W_k_2_d_t = calc_W_k_p_d_t(2, schedule_hw)
W_k_3_d_t = calc_W_k_p_d_t(3, schedule_hw)
return W_k_2_d_t * (3 - n_p) / (3 - 2) + W_k_3_d_t * (n_p - 2) / (3 - 2)
elif 3 <= n_p and n_p <= 4:
W_k_3_d_t = calc_W_k_p_d_t(3, schedule_hw)
W_k_4_d_t = calc_W_k_p_d_t(4, schedule_hw)
return W_k_3_d_t * (4 - n_p) / (4 - 3) + W_k_4_d_t * (n_p - 3) / (4 - 3)
def calc_W_s_d_t(n_p, schedule_hw, has_bath):
"""1時間当たりの浴室シャワー水栓における基準給湯量 (7b)
Args:
n_p(float): 仮想居住人数 (人)
schedule_hw(ndarray): 給湯スケジュール
has_bath(bool): 浴室等の有無
Returns:
ndarray: 1時間当たりの浴室シャワー水栓における基準給湯量 (L/h)
"""
if n_p in [1, 2, 3, 4]:
return calc_W_s_p_d_t(n_p, schedule_hw, has_bath)
elif 1 <= n_p and n_p <= 2:
W_s_1_d_t = calc_W_s_p_d_t(1, schedule_hw, has_bath)
W_s_2_d_t = calc_W_s_p_d_t(2, schedule_hw, has_bath)
return W_s_1_d_t * (2 - n_p) / (2 - 1) + W_s_2_d_t * (n_p - 1) / (2 - 1)
elif 2 <= n_p and n_p <= 3:
W_s_2_d_t = calc_W_s_p_d_t(2, schedule_hw, has_bath)
W_s_3_d_t = calc_W_s_p_d_t(3, schedule_hw, has_bath)
return W_s_2_d_t * (3 - n_p) / (3 - 2) + W_s_3_d_t * (n_p - 2) / (3 - 2)
elif 3 <= n_p and n_p <= 4:
W_s_3_d_t = calc_W_s_p_d_t(3, schedule_hw, has_bath)
W_s_4_d_t = calc_W_s_p_d_t(4, schedule_hw, has_bath)
return W_s_3_d_t * (4 - n_p) / (4 - 3) + W_s_4_d_t * (n_p - 3) / (4 - 3)
def calc_W_w_d_t(n_p, schedule_hw):
"""1時間当たりの洗面水栓における基準給湯量 (7c)
Args:
n_p(float): 仮想居住人数 (人)
schedule_hw(ndarray): 給湯スケジュール
Returns:
ndarray: 1時間当たりの洗面水栓における基準給湯量 (L/h)
"""
if n_p in [1, 2, 3, 4]:
return calc_W_w_p_d_t(n_p, schedule_hw)
elif 1 <= n_p and n_p <= 2:
W_w_1_d_t = calc_W_w_p_d_t(1, schedule_hw)
W_w_2_d_t = calc_W_w_p_d_t(2, schedule_hw)
return W_w_1_d_t * (2 - | |
Per context we keep track of one current program.
"""
if self._handle != self._parser.env.get('current_program', False):
self._parser.env['current_program'] = self._handle
gl.glUseProgram(self._handle)
def deactivate(self):
"""Avoid overhead in calling glUseProgram with same arg.
Warning: this will break if glUseProgram is used somewhere else.
Per context we keep track of one current program.
"""
if self._parser.env.get('current_program', 0) != 0:
self._parser.env['current_program'] = 0
gl.glUseProgram(0)
def set_shaders(self, vert, frag):
"""This function takes care of setting the shading code and
compiling+linking it into a working program object that is ready
to use.
"""
self._linked = False
# For both vertex and fragment shader: set source, compile, check
for code, type_ in [(vert, 'vertex'),
(frag, 'fragment')]:
self.attach_shader(code, type_)
self.link_program()
def attach(self, id_):
"""Attach a shader to this program."""
shader = self._parser.get_object(id_)
gl.glAttachShader(self._handle, shader.handle)
self._attached_shaders.append(shader)
def link_program(self):
"""Link the complete program and check.
All shaders are detached and deleted if the program was successfully
linked.
"""
gl.glLinkProgram(self._handle)
if not gl.glGetProgramParameter(self._handle, gl.GL_LINK_STATUS):
raise RuntimeError('Program linking error:\n%s'
% gl.glGetProgramInfoLog(self._handle))
# Detach all shaders to prepare them for deletion (they are no longer
# needed after linking is complete)
for shader in self._attached_shaders:
gl.glDetachShader(self._handle, shader.handle)
self._attached_shaders = []
# Now we know what variables will be used by the program
self._unset_variables = self._get_active_attributes_and_uniforms()
self._handles = {}
self._known_invalid = set()
self._linked = True
def _get_active_attributes_and_uniforms(self):
"""Retrieve active attributes and uniforms to be able to check that
all uniforms/attributes are set by the user.
Other GLIR implementations may omit this.
"""
# This match a name of the form "name[size]" (= array)
regex = re.compile(r"""(?P<name>\w+)\s*(\[(?P<size>\d+)\])\s*""")
# Get how many active attributes and uniforms there are
cu = gl.glGetProgramParameter(self._handle, gl.GL_ACTIVE_UNIFORMS)
ca = gl.glGetProgramParameter(self.handle, gl.GL_ACTIVE_ATTRIBUTES)
# Get info on each one
attributes = []
uniforms = []
for container, count, func in [(attributes, ca, gl.glGetActiveAttrib),
(uniforms, cu, gl.glGetActiveUniform)]:
for i in range(count):
name, size, gtype = func(self._handle, i)
m = regex.match(name) # Check if xxx[0] instead of xx
if m:
name = m.group('name')
for i in range(size):
container.append(('%s[%d]' % (name, i), gtype))
else:
container.append((name, gtype))
# return attributes, uniforms
return set([v[0] for v in attributes] + [v[0] for v in uniforms])
def set_texture(self, name, value):
"""Set a texture sampler. Value is the id of the texture to link."""
if not self._linked:
raise RuntimeError('Cannot set uniform when program has no code')
# Get handle for the uniform, first try cache
handle = self._handles.get(name, -1)
if handle < 0:
if name in self._known_invalid:
return
handle = gl.glGetUniformLocation(self._handle, name)
self._unset_variables.discard(name) # Mark as set
self._handles[name] = handle # Store in cache
if handle < 0:
self._known_invalid.add(name)
logger.info('Not setting texture data for variable %s; '
'uniform is not active.' % name)
return
# Program needs to be active in order to set uniforms
self.activate()
if True:
# Sampler: the value is the id of the texture
tex = self._parser.get_object(value)
if tex == JUST_DELETED:
return
if tex is None:
raise RuntimeError('Could not find texture with id %i' % value)
unit = len(self._samplers)
if name in self._samplers:
unit = self._samplers[name][-1] # Use existing unit
self._samplers[name] = tex._target, tex.handle, unit
gl.glUniform1i(handle, unit)
def set_uniform(self, name, type_, value):
"""Set a uniform value. Value is assumed to have been checked."""
if not self._linked:
raise RuntimeError('Cannot set uniform when program has no code')
# Get handle for the uniform, first try cache
handle = self._handles.get(name, -1)
count = 1
if handle < 0:
if name in self._known_invalid:
return
handle = gl.glGetUniformLocation(self._handle, name)
self._unset_variables.discard(name) # Mark as set
# if we set a uniform_array, mark all as set
if not type_.startswith('mat'):
count = value.nbytes // (4 * self.ATYPEINFO[type_][0])
if count > 1:
for ii in range(count):
if '%s[%s]' % (name, ii) in self._unset_variables:
self._unset_variables.discard('%s[%s]' % (name, ii))
self._handles[name] = handle # Store in cache
if handle < 0:
self._known_invalid.add(name)
logger.info('Not setting value for variable %s %s; '
'uniform is not active.' % (type_, name))
return
# Look up function to call
funcname = self.UTYPEMAP[type_]
func = getattr(gl, funcname)
# Program needs to be active in order to set uniforms
self.activate()
# Triage depending on type
if type_.startswith('mat'):
# Value is matrix, these gl funcs have alternative signature
transpose = False # OpenGL ES 2.0 does not support transpose
func(handle, 1, transpose, value)
else:
# Regular uniform
func(handle, count, value)
def set_attribute(self, name, type_, value):
"""Set an attribute value. Value is assumed to have been checked."""
if not self._linked:
raise RuntimeError('Cannot set attribute when program has no code')
# Get handle for the attribute, first try cache
handle = self._handles.get(name, -1)
if handle < 0:
if name in self._known_invalid:
return
handle = gl.glGetAttribLocation(self._handle, name)
self._unset_variables.discard(name) # Mark as set
self._handles[name] = handle # Store in cache
if handle < 0:
self._known_invalid.add(name)
if value[0] != 0 and value[2] > 0: # VBO with offset
return # Probably an unused element in a structured VBO
logger.info('Not setting data for variable %s %s; '
'attribute is not active.' % (type_, name))
return
# Program needs to be active in order to set uniforms
self.activate()
# Triage depending on VBO or tuple data
if value[0] == 0:
# Look up function call
funcname = self.ATYPEMAP[type_]
func = getattr(gl, funcname)
# Set data
self._attributes[name] = 0, handle, func, value[1:]
else:
# Get meta data
vbo_id, stride, offset = value
size, gtype, dtype = self.ATYPEINFO[type_]
# Get associated VBO
vbo = self._parser.get_object(vbo_id)
if vbo == JUST_DELETED:
return
if vbo is None:
raise RuntimeError('Could not find VBO with id %i' % vbo_id)
# Set data
func = gl.glVertexAttribPointer
args = size, gtype, gl.GL_FALSE, stride, offset
self._attributes[name] = vbo.handle, handle, func, args
def _pre_draw(self):
self.activate()
# Activate textures
for tex_target, tex_handle, unit in self._samplers.values():
gl.glActiveTexture(gl.GL_TEXTURE0 + unit)
gl.glBindTexture(tex_target, tex_handle)
# Activate attributes
for vbo_handle, attr_handle, func, args in self._attributes.values():
if vbo_handle:
gl.glBindBuffer(gl.GL_ARRAY_BUFFER, vbo_handle)
gl.glEnableVertexAttribArray(attr_handle)
func(attr_handle, *args)
else:
gl.glBindBuffer(gl.GL_ARRAY_BUFFER, 0)
gl.glDisableVertexAttribArray(attr_handle)
func(attr_handle, *args)
# Validate. We need to validate after textures units get assigned
if not self._validated:
self._validated = True
self._validate()
def _validate(self):
# Validate ourselves
if self._unset_variables:
logger.warning('Program has unset variables: %r' %
self._unset_variables)
# Validate via OpenGL
gl.glValidateProgram(self._handle)
if not gl.glGetProgramParameter(self._handle,
gl.GL_VALIDATE_STATUS):
raise RuntimeError('Program validation error:\n%s'
% gl.glGetProgramInfoLog(self._handle))
def _post_draw(self):
# No need to deactivate each texture/buffer, just set to 0
gl.glBindBuffer(gl.GL_ARRAY_BUFFER, 0)
gl.glBindTexture(gl.GL_TEXTURE_2D, 0)
if USE_TEX_3D:
gl.glBindTexture(GL_TEXTURE_3D, 0)
gl.glBindTexture(GL_TEXTURE_1D, 0)
# Deactivate program - should not be necessary. In single-program
# apps it would not even make sense.
# self.deactivate()
def draw(self, mode, selection):
"""Draw program in given mode, with given selection (IndexBuffer or
first, count).
"""
if not self._linked:
raise RuntimeError('Cannot draw program if code has not been set')
# Init
gl.check_error('Check before draw')
try:
mode = as_enum(mode)
except ValueError:
if mode == 'lines_adjacency' or mode == 'line_strip_adjacency':
raise RuntimeError(gl.current_backend.__name__ +
" backend does not support lines_adjacency"
" and line_strip_adjacency primitives."
" Try gloo.gl.use_gl('gl+').")
raise
# Draw
if len(selection) == 3:
# Selection based on indices
id_, gtype, count = selection
if count:
self._pre_draw()
ibuf = self._parser.get_object(id_)
ibuf.activate()
gl.glDrawElements(mode, count, as_enum(gtype), None)
ibuf.deactivate()
else:
# Selection based on start and count
first, count = selection
if count:
self._pre_draw()
gl.glDrawArrays(mode, first, count)
# Wrap up
gl.check_error('Check after draw')
self._post_draw()
class GlirBuffer(GlirObject):
_target = None
_usage = gl.GL_DYNAMIC_DRAW # STATIC_DRAW, STREAM_DRAW or DYNAMIC_DRAW
def create(self):
self._handle = gl.glCreateBuffer()
self._buffer_size = 0
self._bufferSubDataOk = False
def delete(self):
gl.glDeleteBuffer(self._handle)
def activate(self):
gl.glBindBuffer(self._target, self._handle)
def deactivate(self):
gl.glBindBuffer(self._target, 0)
def set_size(self, nbytes): # in bytes
if nbytes != self._buffer_size:
self.activate()
gl.glBufferData(self._target, nbytes, self._usage)
self._buffer_size = nbytes
def set_data(self, offset, data):
self.activate()
nbytes = data.nbytes
# Determine whether to check errors to try handling the ATI bug
check_ati_bug = ((not self._bufferSubDataOk) and
(gl.current_backend.__name__.split(".")[-1] == "gl2") and
sys.platform.startswith('win'))
# flush any pending errors
if check_ati_bug:
gl.check_error('periodic check')
try:
gl.glBufferSubData(self._target, offset, data)
if check_ati_bug:
gl.check_error('glBufferSubData')
self._bufferSubDataOk = True # glBufferSubData seems to work
except Exception:
# This might be due to a driver error (seen on ATI), issue #64.
# We try to detect | |
import unittest
import numpy
try:
import scipy.sparse
scipy_available = True
except ImportError:
scipy_available = False
import cupy
import cupy.sparse
from cupy import testing
def _make(xp, sp, dtype):
data = xp.array([0, 1, 2, 3], dtype)
row = xp.array([0, 0, 1, 2], 'i')
col = xp.array([0, 1, 3, 2], 'i')
# 0, 1, 0, 0
# 0, 0, 0, 2
# 0, 0, 3, 0
return sp.coo_matrix((data, (row, col)), shape=(3, 4))
def _make2(xp, sp, dtype):
data = xp.array([1, 2, 3, 4], dtype)
row = xp.array([0, 1, 1, 2], 'i')
col = xp.array([2, 1, 2, 2], 'i')
# 0, 0, 1, 0
# 0, 2, 3, 0
# 0, 0, 4, 0
return sp.coo_matrix((data, (row, col)), shape=(3, 4))
def _make3(xp, sp, dtype):
data = xp.array([1, 2, 3, 4, 5], dtype)
row = xp.array([0, 1, 1, 3, 3], 'i')
col = xp.array([0, 2, 1, 0, 2], 'i')
# 1, 0, 0
# 0, 3, 2
# 0, 0, 0
# 4, 0, 5
return sp.coo_matrix((data, (row, col)), shape=(4, 3))
def _make_unordered(xp, sp, dtype):
data = xp.array([1, 4, 3, 2], dtype)
row = xp.array([0, 2, 1, 0], 'i')
col = xp.array([0, 2, 3, 1], 'i')
# 1, 2, 0, 0
# 0, 0, 0, 3
# 0, 0, 4, 0
return sp.coo_matrix((data, (row, col)), shape=(3, 4))
def _make_duplicate(xp, sp, dtype):
data = xp.array([0, 1, 2, 3, 4, 5], dtype)
row = xp.array([1, 1, 1, 1, 0, 1], 'i')
col = xp.array([0, 0, 2, 0, 0, 2], 'i')
# 4, 0, 0, 0
# 4, 0, 7, 0
# 0, 0, 0, 0
return sp.coo_matrix((data, (row, col)), shape=(3, 4))
def _make_empty(xp, sp, dtype):
data = xp.array([], dtype)
row = xp.array([], 'i')
col = xp.array([], 'i')
return sp.coo_matrix((data, (row, col)), shape=(3, 4))
def _make_square(xp, sp, dtype):
data = xp.array([0, 1, 2, 3], dtype)
row = xp.array([0, 0, 1, 2], 'i')
col = xp.array([0, 2, 0, 2], 'i')
# 0, 1, 0
# 2, 0, 0
# 0, 0, 3
return sp.coo_matrix((data, (row, col)), shape=(3, 3))
def _make_shape(xp, sp, dtype):
return sp.coo_matrix((3, 4))
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
}))
class TestCooMatrix(unittest.TestCase):
def setUp(self):
self.m = _make(cupy, cupy.sparse, self.dtype)
def test_dtype(self):
self.assertEqual(self.m.dtype, self.dtype)
def test_data(self):
self.assertEqual(self.m.data.dtype, self.dtype)
testing.assert_array_equal(
self.m.data, cupy.array([0, 1, 2, 3], self.dtype))
def test_row(self):
self.assertEqual(self.m.row.dtype, numpy.int32)
testing.assert_array_equal(
self.m.row, cupy.array([0, 0, 1, 2], self.dtype))
def test_col(self):
self.assertEqual(self.m.col.dtype, numpy.int32)
testing.assert_array_equal(
self.m.col, cupy.array([0, 1, 3, 2], self.dtype))
def test_init_copy(self):
n = cupy.sparse.coo_matrix(self.m)
self.assertIsNot(n, self.m)
cupy.testing.assert_array_equal(n.toarray(), self.m.toarray())
def test_init_copy_other_sparse(self):
n = cupy.sparse.coo_matrix(self.m.tocsr())
cupy.testing.assert_array_equal(n.toarray(), self.m.toarray())
@unittest.skipUnless(scipy_available, 'requires scipy')
def test_init_copy_scipy_sparse(self):
m = _make(numpy, scipy.sparse, self.dtype)
n = cupy.sparse.coo_matrix(m)
self.assertIsInstance(n.data, cupy.ndarray)
self.assertIsInstance(n.row, cupy.ndarray)
self.assertIsInstance(n.col, cupy.ndarray)
cupy.testing.assert_array_equal(n.data, m.data)
cupy.testing.assert_array_equal(n.row, m.row)
cupy.testing.assert_array_equal(n.col, m.col)
self.assertEqual(n.shape, m.shape)
@unittest.skipUnless(scipy_available, 'requires scipy')
def test_init_copy_other_scipy_sparse(self):
m = _make(numpy, scipy.sparse, self.dtype)
n = cupy.sparse.coo_matrix(m.tocsc())
self.assertIsInstance(n.data, cupy.ndarray)
self.assertIsInstance(n.row, cupy.ndarray)
self.assertIsInstance(n.col, cupy.ndarray)
cupy.testing.assert_array_equal(n.data, m.data)
cupy.testing.assert_array_equal(n.row, m.row)
cupy.testing.assert_array_equal(n.col, m.col)
self.assertEqual(n.shape, m.shape)
def test_shape(self):
self.assertEqual(self.m.shape, (3, 4))
def test_ndim(self):
self.assertEqual(self.m.ndim, 2)
def test_nnz(self):
self.assertEqual(self.m.nnz, 4)
def test_has_canonical_format(self):
self.assertFalse(self.m.has_canonical_format)
@unittest.skipUnless(scipy_available, 'requires scipy')
def test_get(self):
m = self.m.get()
self.assertIsInstance(m, scipy.sparse.coo_matrix)
expect = [
[0, 1, 0, 0],
[0, 0, 0, 2],
[0, 0, 3, 0]
]
numpy.testing.assert_allclose(m.toarray(), expect)
@unittest.skipUnless(scipy_available, 'requires scipy')
def test_str(self):
if numpy.dtype(self.dtype).kind == 'f':
expect = ''' (0, 0)\t0.0
(0, 1)\t1.0
(1, 3)\t2.0
(2, 2)\t3.0'''
elif numpy.dtype(self.dtype).kind == 'c':
expect = ''' (0, 0)\t0j
(0, 1)\t(1+0j)
(1, 3)\t(2+0j)
(2, 2)\t(3+0j)'''
self.assertEqual(str(self.m), expect)
def test_toarray(self):
m = self.m.toarray()
expect = [
[0, 1, 0, 0],
[0, 0, 0, 2],
[0, 0, 3, 0]
]
cupy.testing.assert_allclose(m, expect)
@testing.parameterize(*testing.product({
'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
}))
@unittest.skipUnless(scipy_available, 'requires scipy')
class TestCooMatrixInit(unittest.TestCase):
def setUp(self):
self.shape = (3, 4)
def data(self, xp):
return xp.array([0, 1, 2, 3], self.dtype)
def row(self, xp):
return xp.array([0, 0, 1, 2], 'i')
def col(self, xp):
return xp.array([0, 1, 3, 2], 'i')
@testing.numpy_cupy_equal(sp_name='sp')
def test_shape_none(self, xp, sp):
x = sp.coo_matrix(
(self.data(xp), (self.row(xp), self.col(xp))), shape=None)
self.assertEqual(x.shape, (3, 4))
@testing.numpy_cupy_equal(sp_name='sp')
def test_dtype(self, xp, sp):
data = self.data(xp).real.astype('i')
x = sp.coo_matrix(
(data, (self.row(xp), self.col(xp))), dtype=self.dtype)
self.assertEqual(x.dtype, self.dtype)
@testing.numpy_cupy_equal(sp_name='sp')
def test_copy_true(self, xp, sp):
data = self.data(xp)
row = self.row(xp)
col = self.col(xp)
x = sp.coo_matrix((data, (row, col)), copy=True)
self.assertIsNot(data, x.data)
self.assertIsNot(row, x.row)
self.assertIsNot(col, x.col)
@testing.numpy_cupy_raises(sp_name='sp')
def test_invalid_format(self, xp, sp):
sp.coo_matrix(
(self.data(xp), self.row(xp)), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_shape_invalid(self, xp, sp):
sp.coo_matrix(
(self.data(xp), (self.row(xp), self.col(xp))), shape=(2,))
def test_data_invalid(self):
with self.assertRaises(ValueError):
cupy.sparse.coo_matrix(
('invalid', (self.row(cupy), self.col(cupy))),
shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_data_invalid_ndim(self, xp, sp):
sp.coo_matrix(
(self.data(xp)[None], (self.row(xp), self.col(xp))),
shape=self.shape)
def test_row_invalid(self):
with self.assertRaises(ValueError):
cupy.sparse.coo_matrix(
(self.data(cupy), ('invalid', self.col(cupy))),
shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_row_invalid_ndim(self, xp, sp):
sp.coo_matrix(
(self.data(xp), (self.row(xp)[None], self.col(xp))),
shape=self.shape)
def test_col_invalid(self):
with self.assertRaises(ValueError):
cupy.sparse.coo_matrix(
(self.data(cupy), (self.row(cupy), 'invalid')),
shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_col_invalid_ndim(self, xp, sp):
sp.coo_matrix(
(self.data(xp), (self.row(xp), self.col(xp)[None])),
shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_data_different_length(self, xp, sp):
data = xp.arange(5, dtype=self.dtype)
sp.coo_matrix(
(data(xp), (self.row(xp), self.col(xp))), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_row_different_length(self, xp, sp):
row = xp.arange(5, dtype=self.dtype)
sp.coo_matrix(
(self.data(xp), (row(xp), self.col(xp))), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_col_different_length(self, xp, sp):
col = xp.arange(5, dtype=self.dtype)
sp.coo_matrix(
(self.data(xp), (self.row(xp), col(xp))), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_fail_to_infer_shape(self, xp, sp):
data = xp.array([], dtype=self.dtype)
row = xp.array([], dtype='i')
col = xp.array([], dtype='i')
sp.coo_matrix((data, (row, col)), shape=None)
@testing.numpy_cupy_raises(sp_name='sp')
def test_row_too_large(self, xp, sp):
row = xp.array([0, 0, 1, 3], 'i')
sp.coo_matrix(
(self.data(xp), (row, self.col(xp))), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_row_too_small(self, xp, sp):
row = xp.array([0, -1, 1, 2], 'i')
sp.coo_matrix(
(self.data(xp), (row, self.col(xp))), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_col_too_large(self, xp, sp):
col = xp.array([0, 1, 4, 2], 'i')
sp.coo_matrix(
(self.data(xp), (self.row(xp), col)), shape=self.shape)
@testing.numpy_cupy_raises(sp_name='sp')
def test_col_too_small(self, xp, sp):
col = xp.array([0, -1, 3, 2], 'i')
sp.coo_matrix(
(self.data(xp), (self.row(xp), col)), shape=self.shape)
def test_unsupported_dtype(self):
with self.assertRaises(ValueError):
cupy.sparse.coo_matrix(
(self.data(cupy), (self.row(cupy), self.col(cupy))),
shape=self.shape, dtype='i')
@testing.parameterize(*testing.product({
'make_method': [
'_make', '_make_unordered', '_make_empty', '_make_duplicate',
'_make_shape'],
'dtype': [numpy.float32, numpy.float64, numpy.complex64, numpy.complex128],
}))
@unittest.skipUnless(scipy_available, 'requires scipy')
class TestCooMatrixScipyComparison(unittest.TestCase):
@property
def make(self):
return globals()[self.make_method]
@testing.numpy_cupy_equal(sp_name='sp')
def test_dtype(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.dtype
@testing.numpy_cupy_equal(sp_name='sp')
def test_nnz(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.getnnz()
@testing.numpy_cupy_array_equal(sp_name='sp')
def test_asfptype(self, xp, sp):
m = _make(xp, sp, self.dtype)
return m.asfptype().toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_toarray(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_A(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.A
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocoo(self, xp, sp):
m = _make(xp, sp, self.dtype)
return m.tocoo().toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocoo_copy(self, xp, sp):
m = _make(xp, sp, self.dtype)
n = m.tocoo(copy=True)
self.assertIsNot(m.data, n.data)
self.assertIsNot(m.row, n.row)
self.assertIsNot(m.col, n.col)
return n.toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocsc(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.tocsc().toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocsc_copy(self, xp, sp):
m = _make(xp, sp, self.dtype)
n = m.tocsc(copy=True)
self.assertIsNot(m.data, n.data)
return n.toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocsr(self, xp, sp):
m = self.make(xp, sp, self.dtype)
return m.tocsr().toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_tocsr_copy(self, xp, sp):
m = _make(xp, sp, self.dtype)
n = m.tocsr(copy=True)
self.assertIsNot(m.data, n.data)
return n.toarray()
# dot
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_scalar(self, xp, sp):
m = _make(xp, sp, self.dtype)
return m.dot(2.0).toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_numpy_scalar(self, xp, sp):
m = _make(xp, sp, self.dtype)
return m.dot(numpy.dtype(self.dtype).type(2.0)).toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_csr(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = _make3(xp, sp, self.dtype)
return m.dot(x).toarray()
@testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
def test_dot_csr_invalid_shape(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = sp.csr_matrix((5, 3), dtype=self.dtype)
m.dot(x)
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_csc(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = _make3(xp, sp, self.dtype).tocsc()
return m.dot(x).toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_sparse(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = _make3(xp, sp, self.dtype).tocoo()
return m.dot(x).toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_zero_dim(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.array(2, dtype=self.dtype)
return m.dot(x).toarray()
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_dense_vector(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.arange(4).astype(self.dtype)
return m.dot(x)
@testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
def test_dot_dense_vector_invalid_shape(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.arange(5).astype(self.dtype)
m.dot(x)
@testing.numpy_cupy_allclose(sp_name='sp')
def test_dot_dense_matrix(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.arange(8).reshape(4, 2).astype(self.dtype)
return m.dot(x)
@testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
def test_dot_dense_matrix_invalid_shape(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.arange(10).reshape(5, 2).astype(self.dtype)
m.dot(x)
@testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
def test_dot_dense_ndim3(self, xp, sp):
m = _make(xp, sp, self.dtype)
x = xp.arange(24).reshape(4, 2, 3).astype(self.dtype)
m.dot(x)
@testing.numpy_cupy_raises(sp_name='sp')
def test_dot_unsupported(self, xp, sp):
m = _make(xp, sp, self.dtype)
m.dot(None)
# __add__
@testing.numpy_cupy_allclose(sp_name='sp')
def test_add_zero(self, xp, sp):
m = _make(xp, sp, self.dtype)
return (m + 0).toarray()
@testing.numpy_cupy_raises(sp_name='sp')
def test_add_scalar(self, xp, sp):
m = _make(xp, sp, self.dtype)
m + 1
@testing.numpy_cupy_allclose(sp_name='sp')
def test_add_csr(self, xp, sp):
m = _make(xp, sp, self.dtype)
n = _make2(xp, sp, self.dtype)
return (m + n).toarray()
| |
<reponame>petebachant/Nortek-Python
# contains C-style struct definitions and associated methods for various Nortek instruments
# The base class is "NortekDataStructure" which is itself a subclass of ctypes.Structure
# Individual data structures (e.g. velocity header) are a subclass of NortekDataStructure
# They will inherit the calculateChecksum method from NortekDataStructure and need to define
#
#
from ctypes import *
import UserDict
import struct
import re
import numpy
import pdb
from nortek import arrays as NortekDataArrays
import datetime
import pylab
import logging
moduleLogger = logging.getLogger( "Nortek." + __name__ )
class BCDTimeWord( Structure ):
_fields_ = [ ( "minute", c_ubyte ),
( "second", c_ubyte ),
( "hour", c_ubyte ),
( "year", c_ubyte ),
( "month", c_ubyte ),
( "day", c_ubyte ) ]
class Header( UserDict.UserDict ):
def __init__( self, binaryDataString ):
UserDict.UserDict.__init__( self )
self.binaryData = binaryDataString
self.length = len( self.binaryData ) - 2
self.calculateChecksum()
if self.checksum:
self.interpretBinaryData()
def calculateChecksum( self ):
if self.length >= 4:
reportedChecksum = struct.unpack( '<H', self.binaryData[ -2: ] )
checksumFormatString = '<' + 'h' * ( self.length / 2 )
if struct.calcsize( checksumFormatString ) == self.length:
calculatedChecksum = ( int( 'b58c', base = 16 )
+ sum( struct.unpack( checksumFormatString,
self.binaryData[ 0:-2 ] ) ) ) % 65536
else:
self.checksum = False
if calculatedChecksum != reportedChecksum[ 0 ]:
self.checksum = False
else:
self.checksum = True
else:
self.checksum = False
def interpretBinaryData( self, instrumentType = None ):
if self.binaryData[ 0:2 ] == '\xa5\x05':
# Hardware Configuration
syncByte, \
dataStructureID, \
sizeInWords, \
self[ 'serialNumber' ], \
boardConfigurationBit0, \
boardConfigurationBit1, \
self[ 'systemFrequency' ], \
self[ 'picVersion' ], \
self[ 'hardwareRevision' ], \
self[ 'recorderSize' ], \
hardwareStatus, \
spareWords, \
self[ 'firmwareVersion' ] = \
struct.unpack( '<ccH14sBBHHHHH12s4s', self.binaryData[ 0:-2 ] )
self[ 'serialNumber' ] = self[ 'serialNumber' ].rstrip()
if ( re.search( 'VNO', self[ 'serialNumber' ] ) ):
instrumentType = 'Vectrino'
elif ( re.search( 'VEC', self[ 'serialNumber' ] ) ):
instrumentType = 'Vector'
elif ( re.search( 'AQD', self[ 'serialNumber' ] ) ):
#if ( re.search( 'HR', self[ 'firmwareVersion' ] ) ):
if ( float( self[ 'firmwareVersion' ] ) < 3.3 ):
instrumentType = 'HR Profiler'
else:
instrumentType = 'Aquadopp Profiler'
elif ( re.search( 'WPR', self[ 'hardwareConfiguration' ][ 'serialNumber' ] ) ):
instrumentType = 'AWAC'
else:
instrumentType = 'unknown'
if instrumentType is not 'Vectrino':
if boardConfigurationBit0:
# if True, there's a recorder
self[ 'recorderInstalled' ] = True
else:
self[ 'recorderInstalled' ] = False
self[ 'recorderSize' ] = 0
if boardConfigurationBit1:
# if True, there's a compass
self[ 'compassInstalled' ] = True
else:
self[ 'compassInstalled' ] = False
return instrumentType
elif self.binaryData[ 0:2 ] == '\xa5\x04':
# head configuration
syncByte, \
dataStructureID, \
sizeInWords, \
headConfiguration, \
self[ 'frequency' ], \
self[ 'headType' ], \
self[ 'serialNumber' ], \
systemData1, \
tempT, \
systemData2, \
distanceToSampleVolume, \
spareWords, \
self[ 'numberOfBeams' ] = \
struct.unpack( '<ccHHHH12s8s32s136sH20sH', self.binaryData[ 0:-2 ] )
if instrumentType is 'Vectrino':
tempT = struct.unpack( '<16h', tempT )
self[ 'transformationMatrix' ] = \
numpy.matrix( numpy.reshape( tempT, ( 4, 4 ) ) ) / 4096.0
self[ 'distanceToSampleVolume' ] = \
round( 10 * 0.5 * ( ( 1.5 * distanceToSampleVolume )**2 - 622.98 )
/ ( 1.5 * distanceToSampleVolume - 5.7 ) ) / 10
elif self.binaryData[ 0:2 ] == '\xa5\x00':
# User Configuration
syncByte, \
dataStructureID, \
sizeInWords, \
self[ 'T1' ], \
self[ 'T2' ], \
self[ 'T3' ], \
self[ 'T4' ], \
self[ 'T5' ], \
self[ 'numberOfPings' ], \
self[ 'averageInterval' ], \
self[ 'numberOfBeams' ], \
timingControlRegister, \
powerControlRegister, \
self[ 'A1' ], \
self[ 'B0' ], \
self[ 'B1' ], \
self[ 'compassUpdateRate' ], \
coordinateSystem, \
self[ 'numberOfCells' ], \
cellSize, \
self[ 'measurementInterval' ], \
self[ 'deploymentName' ], \
self[ 'recorderWrapMode' ], \
self[ 'deploymentStartTime' ], \
self[ 'diagnosticMeasurement_sampleInterval' ], \
modeWord, \
self[ 'soundSpeedAdjustmentFactor' ], \
self[ 'dianosticMeasurement_numberOfSamples' ], \
self[ 'diagnosticMeasurementnumberOfBeamsOrCellNumber' ], \
self[ 'diagnosticMeasurement_NumberOfPings' ], \
modeTestWord, \
analogInputAddress, \
hVersion, \
spareWords, \
velocityAdjustmentTable, \
self[ 'comments' ], \
self[ 'waveMeasurement_mode' ], \
self[ 'waveMeasurement_waveCellPositionPercent' ], \
self[ 'wave_T1' ], \
self[ 'wave_T2' ], \
self[ 'wave_T3' ], \
self[ 'wave_numberOfSamples' ], \
self[ 'A1_2' ], \
self[ 'B0_2' ], \
self[ 'B1_2' ], \
spareWords2, \
self[ 'analogOutputScaleFactor' ], \
self[ 'ambiguityResolutionCorrelationThreshold' ], \
spareWords3, \
self[ 'transmitPulseLengthSecondLag_counts' ], \
spareWords3, \
self[ 'stageMatchFilterConstants' ] = \
struct.unpack( '<cc' + 'H' * 19 + '6sH6sL' + 'H' * 9 + '180s' * 2 + 'H' * 14 + '30s16s', self.binaryData[ 0:-2 ] )
timingControlRegister = '{:016b}'.format( timingControlRegister )[ ::-1 ]
powerControlRegister = '{:016b}'.format( powerControlRegister )[ ::-1 ]
modeWord = '{:016b}'.format( modeWord )[ ::-1 ]
if timingControlRegister[ 5:7 ] == '00' and powerControlRegister[ 5:7 ] == '00':
self[ 'powerLevel' ] = 'High'
elif timingControlRegister[ 5:7 ] == '10' and powerControlRegister[ 5:7 ] == '10':
self[ 'powerLevel' ] = 'High-'
elif timingControlRegister[ 5:7 ] == '01' and powerControlRegister[ 5:7 ] == '01':
self[ 'powerLevel' ] = 'LOW+'
elif timingControlRegister[ 5:7 ] == '11' and powerControlRegister[ 5:7 ] == '11':
self[ 'powerLevel' ] = 'LOW'
if timingControlRegister[ 1 ] is '1':
self[ 'sampleMode' ] = 'burst'
else:
self[ 'sampleMode' ] = 'continuous'
if coordinateSystem == 0:
self[ 'coordinateSystem' ] = 'ENU'
elif coordinateSystem == 1:
self[ 'coordinateSystem' ] = 'XYZ'
else:
self[ 'coordinateSystem' ] = 'Beam'
if instrumentType is 'Vectrino':
self[ 'sampleRate' ] = round( 50000.0 / self[ 'averageInterval' ] )
del self[ 'averageInterval' ]
elif instrumentType is 'Vector':
self[ 'sampleVolumeSize' ] = cellSize
self[ 'sampleRate' ] = 512. / self[ 'averageInterval' ]
elif instrumentType is 'HR Profiler':
self[ 'sampleRate' ] = 512. / self[ 'T5' ]
if instrumentType is not 'Vectrino':
self[ 'cellSize' ] = cellSize
else:
self[ 'sampleVolumeSize' ] = cellSize
if instrumentType is 'Vector' or 'HR Profiler':
if modeWord[ 4 ] is '1':
self[ 'velocityScaling' ] = 0.1 # mm/s
else:
self[ 'velocityScaling' ] = 1 # mm/s
formattedSoftwareVersion = str( hVersion/10000 ) + "." + str( (hVersion % 10000)/100 )
if str(hVersion % 100):
formattedSoftwareVersion + "." + str(hVersion % 100)
self[ 'softwareVersion' ] = formattedSoftwareVersion
class NortekBinaryDataStructure( Structure ):
_structureStart = 0
_structureStop = 0
def calculateChecksum( self, openDataFile ):
originalPosition = openDataFile.tell()
openDataFile.seek( originalPosition - self._sizeInBytes )
checksumDataType = c_short * ( self._sizeInBytes / 2 - 1 )
checksumData = checksumDataType()
openDataFile.readinto( checksumData )
openDataFile.seek( originalPosition )
calculatedChecksum = int( 'b58c', base = 16 )
for aShort in checksumData:
calculatedChecksum += aShort
self.calculatedChecksum = calculatedChecksum % 65536
if self.checksum == self.calculatedChecksum:
self.checksumResult = True
return True
else:
self.checksumResult = False
return False
def incrementCounters( self ):
pass
def resetCounters( self ):
pass
def allocateDataArrays( self, anInstrument ):
pass
def moveIntoDataArrays( self, anInstrument ):
pass
class VectrinoVelocityData_binary( NortekBinaryDataStructure ):
_fields_ = [ ( "status", c_char ),
( "count", c_ubyte ),
( "velocity", c_short * 4 ),
( "amplitude", c_ubyte * 4 ),
( "correlation", c_ubyte * 4 ),
( "checksum", c_ushort ) ]
_sizeInBytes = 22
ensembleCounter = 0
ensembleCycleCounter = 0
def incrementCounters( self ):
if self.ensembleCounter > 1 and self.count == 0:
self.ensembleCycleCounter += 1
self.ensembleCounter = self.ensembleCycleCounter * 255 + self.count + self.ensembleCycleCounter
# moduleLogger.info( 'Counter is {} and ensemble is {}'.format(
# self.count,
# self.ensembleCounter ) )
#self.ensembleCounter += 1
#while self.ensembleCounter != self.ensembleCycleCounter * 255 + self.count:
# self.ensembleCounter += 1
def resetCounters( self ):
self.ensembleCounter = 0
self.ensembleCycleCounter = 0
def allocateDataArrays( self, vectrinoInstrument ):
samepleRate = vectrinoInstrument[ 'userConfiguration' ][ 'sampleRate' ]
vectrinoInstrument[ 'velocity' ] = NortekDataArrays.VelocityDataArray(
samepleRate,
shape = ( 1, self.ensembleCounter, 4 ) )
vectrinoInstrument[ 'amplitude' ] = NortekDataArrays.GenericDataArray(
samepleRate,
shape = ( 1, self.ensembleCounter, 4 ) )
vectrinoInstrument[ 'snr' ] = NortekDataArrays.GenericDataArray(
samepleRate,
shape = ( 1, self.ensembleCounter, 4 ) )
vectrinoInstrument[ 'correlation' ] = NortekDataArrays.GenericDataArray(
samepleRate,
shape = ( 1, self.ensembleCounter, 4 ) )
vectrinoInstrument[ 'ensemble' ] = numpy.arange( 0, self.ensembleCounter, 1 )
self.resetCounters()
def moveIntoDataArrays( self, vectrinoInstrument ):
try:
vectrinoInstrument[ 'velocity' ][ 'data' ][ 0, self.ensembleCounter, : ] = self.velocity[ 0:4 ]
vectrinoInstrument[ 'amplitude' ][ 'data' ][ 0, self.ensembleCounter, : ] = self.amplitude[ 0:4 ]
vectrinoInstrument[ 'correlation' ][ 'data' ][ 0, self.ensembleCounter, : ] = self.correlation[ 0:4 ]
self.incrementCounters()
except IndexError as error:
pdb.set_trace()
class VectrinoFileInfo_binary( NortekBinaryDataStructure ):
_fields_ = [ ( "sizeInWords", c_short ),
( "field1", c_short ),
( "field2", c_short ),
( "field3", c_short ),
( "fileInfo", c_char * 14 ),
( "checksum", c_ushort ) ]
_sizeInBytes = 26
fileInfoCounter = 0
class VectrinoVelocityHeader_binary( NortekBinaryDataStructure ):
_fields_ = [ ( "sizeInWords", c_short ),
( "distance", c_short ),
( "distanceQuality", c_short ),
( "lag1", c_short ),
( "lag2", c_short ),
( "noise", c_ubyte * 4 ),
( "correlation", c_ubyte * 4 ),
( "temperature", c_short ),
( "speedOfSound", c_short ),
( "samplingVolumeAmplitude", c_ubyte * 4 ),
( "boundaryAmplitude", c_ubyte * 4 ),
( "z0PlusLag1", c_ubyte * 4 ),
( "z0PlusLag2", c_ubyte * 4 ),
( "checksum", c_ushort ) ]
_sizeInBytes = 42
velocityHeaderCounter = 0
def moveIntoDataArrays( self, vectrinoInstrument ):
vectrinoInstrument[ 'velocityHeader' ] = {}
vectrinoInstrument[ 'velocityHeader' ][ 'noise' ] = { 'amplitude': {}, 'correlation': {} }
vectrinoInstrument[ 'velocityHeader' ][ 'distance' ] = {}
vectrinoInstrument[ 'velocityHeader' ][ 'sampleVolumeAmplitude' ] = {}
vectrinoInstrument[ 'velocityHeader' ][ 'boundaryAmplitude' ] = {}
# Vectrino velocity data header
vectrinoInstrument[ 'distance' ][ 'measurement' ] = self.distance
vectrinoInstrument[ 'distance' ][ 'quality' ] = self.distanceQuality
vectrinoInstrument[ 'velocityHeader' ][ 'lag1' ] = self.lag1 / 1e6
vectrinoInstrument[ 'velocityHeader' ][ 'lag2' ] = self.lag2 / 1e6
for beamNumber in range( 1, 5, 1 ):
vectrinoInstrument[ 'velocityHeader' ][ 'noise' ][ 'amplitude' ][ beamNumber ] \
= self.noise[ beamNumber - 1 ]
vectrinoInstrument[ 'velocityHeader' ][ 'noise' ][ 'correlation' ][ beamNumber ] \
= self.correlation[ beamNumber - 1 ]
vectrinoInstrument[ 'velocityHeader' ][ 'sampleVolumeAmplitude' ][ beamNumber | |
"""
Utilities for fitting stacked (drizzled) spectra
"""
from collections import OrderedDict
from imp import reload
import astropy.io.fits as pyfits
import astropy.units as u
import numpy as np
from . import utils
from .utils import GRISM_COLORS, GRISM_MAJOR, GRISM_LIMITS, DEFAULT_LINE_LIST
from .fitting import GroupFitter
def make_templates(grism='G141', return_lists=False, fsps_templates=False,
line_list=DEFAULT_LINE_LIST):
"""Generate template savefile
This script generates the template sets with the emission line
complexes and with individual lines.
Parameters
----------
grism : str
Grism of interest, which defines what FWHM to use for the line
templates.
return_lists : bool
Return the templates rather than saving them to a file
Returns
-------
t_complexes, t_lines : list
If `return` then return two lists of templates. Otherwise,
store them to a `~numpy` save file "templates_{fwhm}.npy".
"""
from .multifit import MultiBeam
if grism == 'G141': # WFC3/IR
fwhm = 1100
elif grism == 'G800L': # ACS/UVIS
fwhm = 1400
elif grism == 'G280': # WFC3/UVIS
fwhm = 1500
elif grism == 'GRISM': # WFIRST
fwhm = 350
else:
fwhm = 700 # G102
# Line complex templates
t_complexes = utils.load_templates(fwhm=fwhm, line_complexes=True,
fsps_templates=fsps_templates)
# Individual lines
# line_list = ['SIII', 'SII', 'Ha', 'OI-6302', 'OIII', 'Hb',
# 'OIII-4363', 'Hg', 'Hd', 'NeIII', 'OII', 'MgII']
t_lines = utils.load_templates(fwhm=fwhm, line_complexes=False,
full_line_list=line_list,
fsps_templates=fsps_templates)
if return_lists:
return t_complexes, t_lines
else:
# Save them to a file
np.save('templates_{0}.npy'.format(fwhm), [t_complexes, t_lines])
print('Wrote `templates_{0}.npy`'.format(fwhm))
class StackFitter(GroupFitter):
def __init__(self, files='gnt_18197.stack.fits', group_name=None, sys_err=0.02, mask_min=0.1, fit_stacks=True, fcontam=1, PAs=None, extensions=None, min_ivar=0.01, overlap_threshold=3, verbose=True, eazyp=None, eazy_ix=0, MW_EBV=0., chi2_threshold=1.5, min_DoF=200):
"""Object for fitting stacked spectra.
Parameters
----------
files : str or list of str
Stack FITS filename. If a list is supplied, e.g., the product
of a `~glob` command, then append all specified files.
group_name : str
Rootname to associate with the object. If none, then default to
`files`.
sys_err : float
Minimum systematic error, interpreted as a fractional error.
The adjusted variance is taken to be
>>> var = var0 + (sys_err*flux)**2
mask_min : float
Only fit 2D pixels where the flat-flambda model has pixel values
greater than `mask_min` times the maximum of the model.
fit_stacks : bool
Fit the stacks of each grism combined from all available PAs. If
False, then fit the PAs individually.
fcontam : float
Parameter to control weighting of contaminated pixels for
`fit_stacks=False`.
"""
if isinstance(files, list):
file=files[0]
else:
file=files
self.files = [file]
if group_name is not None:
self.group_name = group_name
else:
self.group_name = file
if verbose:
print('Load file {0}'.format(file))
self.file = file
self.hdulist = pyfits.open(file)
self.min_ivar = min_ivar
self.sys_err = sys_err
self.fcontam = fcontam
self.MW_EBV = MW_EBV
self.h0 = self.hdulist[0].header.copy()
#self.Ngrism = self.h0['NGRISM']
self.grisms = []
self.ext = []
for i in range(self.h0['NGRISM']):
g = self.h0['GRISM{0:03d}'.format(i+1)]
self.grisms.append(g)
if fit_stacks:
if extensions is not None:
if g not in extensions:
continue
self.ext.append(g)
else:
ng = self.h0['N{0}'.format(g)]
for j in range(ng):
pa = self.h0['{0}{1:02d}'.format(g, j+1)]
if PAs is not None:
if pa not in PAs:
continue
ext = '{0},{1}'.format(g,pa)
if extensions is not None:
if ext not in extensions:
continue
self.ext.append(ext)
self.N = len(self.ext)
self.beams = []
pop = []
for i in range(self.N):
E_i = StackedSpectrum(file=self.file, sys_err=sys_err,
mask_min=mask_min, extver=self.ext[i],
mask_threshold=-1, fcontam=fcontam,
min_ivar=min_ivar, MW_EBV=MW_EBV)
E_i.compute_model()
if np.isfinite(E_i.kernel.sum()) & (E_i.DoF >= min_DoF):
self.beams.append(E_i)
else:
pop.append(i)
for i in pop[::-1]:
self.N -= 1
p = self.ext.pop(i)
# Get some parameters from the beams
self.id = self.h0['ID']
self.ra = self.h0['RA']
self.dec = self.h0['DEC']
self.Asave = {}
## Photometry
self.is_spec = 1
self.Nphot = 0
## Parse the beam data
self._parse_beams_list()
if not fit_stacks:
# self.mask_drizzle_overlaps(threshold=overlap_threshold,
# verbose=verbose)
if chi2_threshold > 0:
orig_ext = [e for e in self.ext]
fit_log, keep_dict, has_bad = self.check_for_bad_PAs(poly_order=3, chi2_threshold=chi2_threshold, fit_background=True, reinit=True, verbose=False)
if has_bad & verbose:
print('Found bad PA. New list: {0}'.format(keep_dict))
if verbose:
print(' {0}'.format(' '.join(self.ext)))
# Read multiple
if isinstance(files, list):
if len(files) > 1:
for file in files[1:]:
extra = StackFitter(files=file, sys_err=sys_err, mask_min=mask_min, fit_stacks=fit_stacks, fcontam=fcontam, pas=pas, extensions=extensions, min_ivar=min_ivar, overlap_threshold=overlap_threshold, eazyp=eazyp, eazy_ix=eazy_ix, chi2_threshold=chi2_threshold, verbose=verbose)
self.extend(extra)
self.idf = np.hstack([b.scif*0+ib for ib, b in enumerate(self.beams)])
self.idf = np.cast[int](self.idf)
# if eazyp is not None:
# self.eazyp = eazyp
#
# # TBD: do matching to eazyp.cat directly?
# self.eazy_ix = eazy_ix
#
# ok_phot = (eazyp.efnu[eazy_ix,:] > 0) & (eazyp.fnu[eazy_ix,:] > eazyp.param['NOT_OBS_THRESHOLD']) & np.isfinite(eazyp.fnu[eazy_ix,:]) & np.isfinite(eazyp.efnu[eazy_ix,:])
# ok_phot = np.squeeze(ok_phot)
# self.ok_phot = ok_phot
#
# self.Nphot = ok_phot.sum()
# if self.Nphot > 0:
#
# # F-lambda photometry, 1e-19 erg/s/cm2/A
# self.photom_eflam = (eazyp.efnu[eazy_ix,:]*eazyp.to_flam*eazyp.zp*eazyp.ext_corr/100.)[ok_phot]
# self.photom_flam = (eazyp.fnu[eazy_ix,:]*eazyp.to_flam*eazyp.zp*eazyp.ext_corr/100.)[ok_phot]
# self.photom_lc = eazyp.lc[ok_phot]
#
# self.scif = np.hstack((self.scif, self.photom_flam))
# self.ivarf = np.hstack((self.ivarf, 1/self.photom_eflam**2))
# self.sivarf = np.hstack((self.sivarf, 1/self.photom_eflam))
# self.wavef = np.hstack((self.wavef, self.photom_lc))
#
# self.weightf = np.hstack((self.weightf, np.ones(self.Nphot)))
# self.fit_mask = np.hstack((self.fit_mask, np.ones(self.Nphot, dtype=bool)))
# self.DoF += self.Nphot
# self.phot_scale = np.array([10.])
def _parse_beams_list(self):
"""
"""
# Parse from self.beams list
self.N = len(self.beams)
self.ext = [E.extver for E in self.beams]
self.Ngrism = OrderedDict()
for beam in self.beams:
if beam.grism in self.Ngrism:
self.Ngrism[beam.grism] += 1
else:
self.Ngrism[beam.grism] = 1
# Make "PA" attribute
self.PA = OrderedDict()
for g in self.Ngrism:
self.PA[g] = OrderedDict()
for i in range(self.N):
grism = self.ext[i].split(',')[0]
if ',' in self.ext[i]:
PA = float(self.ext[i].split(',')[1])
else:
PA = 0
if PA in self.PA[grism]:
self.PA[grism][PA].append(i)
else:
self.PA[grism][PA] = [i]
self.grisms = list(self.PA.keys())
self.Ntot = np.sum([E.size for E in self.beams])
self.scif = np.hstack([E.scif for E in self.beams])
self.ivarf = np.hstack([E.ivarf for E in self.beams])
self.wavef = np.hstack([E.wavef for E in self.beams])
self.weightf = np.hstack([E.weightf for E in self.beams])
#self.ivarf *= self.weightf
self.sivarf = np.sqrt(self.ivarf)
self.fit_mask = np.hstack([E.fit_mask for E in self.beams])
self.fit_mask &= self.ivarf > self.min_ivar*self.ivarf.max()
# Dummy parameter. Implemented for MultiBeam
self.sens_mask = 1.
self.DoF = int((self.fit_mask*self.weightf).sum())
#self.Nmask = self.fit_mask.sum()
self.Nmask = np.sum([E.fit_mask.sum() for E in self.beams])
self.slices = self._get_slices(masked=False)
self.A_bg = self._init_background(masked=False)
self.Asave = {}
self._update_beam_mask()
self.A_bgm = self._init_background(masked=True)
self.flat_flam = np.hstack([E.flat_flam for E in self.beams])
self.initialize_masked_arrays()
def extend(self, st):
"""
Append second StackFitter objects to `self`.
"""
self.beams.extend(st.beams)
self._parse_beams_list()
# self.grisms.extend(st.grisms)
# self.grisms = list(np.unique(self.grisms))
#
# self.Ngrism = {}
# for grism in self.grisms:
# self.Ngrism[grism] = 0
#
# for beam in self.beams:
# self.Ngrism[beam.grism] += 1
#
# #self.Ngrism = len(self.grisms)
#
# self.N += st.N
# self.ext.extend(st.ext)
# self.files.extend(st.files)
#
# # Re-init
# self.Ntot = np.sum([E.size for E in self.beams])
# self.scif = np.hstack([E.scif for E in self.beams])
# self.ivarf = np.hstack([E.ivarf for E in self.beams])
# self.wavef = np.hstack([E.wavef for E in self.beams])
#
# self.weightf = np.hstack([E.weightf for E in self.beams])
# #self.ivarf *= self.weightf
#
# self.sivarf = np.sqrt(self.ivarf)
#
# self.fit_mask = np.hstack([E.fit_mask for E in self.beams])
# self.fit_mask &= self.ivarf > self.min_ivar*self.ivarf.max()
#
# self.slices = self._get_slices(masked=False)
# self.A_bg = self._init_background(masked=False)
#
# self._update_beam_mask()
# self.A_bgm = self._init_background(masked=True)
#
# self.Nmask = self.fit_mask.sum()
# self.DoF = int((self.fit_mask*self.weightf).sum())
#
# self.flat_flam = np.hstack([E.flat_flam for E in self.beams])
def check_for_bad_PAs(self, poly_order=1, chi2_threshold=1.5, fit_background=True, reinit=True, verbose=False):
"""
"""
wave = np.linspace(2000,2.5e4,100)
poly_templates = utils.polynomial_templates(wave, order=poly_order)
fit_log = OrderedDict()
keep_dict = OrderedDict()
has_bad = False
keep_beams = []
for g in self.PA:
fit_log[g] = OrderedDict()
keep_dict[g] = []
for pa in self.PA[g]:
extensions = [self.ext[i] for i in self.PA[g][pa]]
mb_i = StackFitter(self.file, fcontam=self.fcontam,
sys_err=self.sys_err,
extensions=extensions, fit_stacks=False,
verbose=verbose, chi2_threshold=-1)
try:
chi2, _, _, _ = mb_i.xfit_at_z(z=0,
templates=poly_templates,
fit_background=fit_background)
except:
chi2 = 1e30
if False:
p_i = mb_i.template_at_z(z=0, templates=poly_templates, fit_background=fit_background, fitter='lstsq', fwhm=1400, get_uncertainties=2)
fit_log[g][pa] = {'chi2': chi2, 'DoF': mb_i.DoF,
'chi_nu': chi2/np.maximum(mb_i.DoF, 1)}
min_chinu = 1e30
for pa in self.PA[g]:
min_chinu = np.minimum(min_chinu, | |
"""Definitions of ground truth NSRTs for all environments."""
import itertools
from typing import List, Sequence, Set, cast
import numpy as np
from predicators.src.envs import get_or_create_env
from predicators.src.envs.behavior import BehaviorEnv
from predicators.src.envs.behavior_options import grasp_obj_param_sampler, \
navigate_to_param_sampler, place_ontop_obj_pos_sampler
from predicators.src.envs.doors import DoorsEnv
from predicators.src.envs.painting import PaintingEnv
from predicators.src.envs.pddl_env import _PDDLEnv
from predicators.src.envs.playroom import PlayroomEnv
from predicators.src.envs.repeated_nextto_painting import \
RepeatedNextToPaintingEnv
from predicators.src.envs.satellites import SatellitesEnv
from predicators.src.envs.tools import ToolsEnv
from predicators.src.settings import CFG
from predicators.src.structs import NSRT, Array, GroundAtom, LiftedAtom, \
Object, ParameterizedOption, Predicate, State, Type, Variable
from predicators.src.utils import null_sampler
def get_gt_nsrts(predicates: Set[Predicate],
options: Set[ParameterizedOption]) -> Set[NSRT]:
"""Create ground truth NSRTs for an env."""
if CFG.env in ("cover", "cover_hierarchical_types", "cover_typed_options",
"cover_regrasp", "cover_multistep_options",
"pybullet_cover"):
nsrts = _get_cover_gt_nsrts()
elif CFG.env == "cluttered_table":
nsrts = _get_cluttered_table_gt_nsrts()
elif CFG.env == "cluttered_table_place":
nsrts = _get_cluttered_table_gt_nsrts(with_place=True)
elif CFG.env in ("blocks", "pybullet_blocks"):
nsrts = _get_blocks_gt_nsrts()
elif CFG.env == "behavior":
nsrts = _get_behavior_gt_nsrts() # pragma: no cover
elif CFG.env in ("painting", "repeated_nextto_painting"):
nsrts = _get_painting_gt_nsrts()
elif CFG.env == "tools":
nsrts = _get_tools_gt_nsrts()
elif CFG.env == "playroom":
nsrts = _get_playroom_gt_nsrts()
elif CFG.env == "repeated_nextto":
nsrts = _get_repeated_nextto_gt_nsrts(CFG.env)
elif CFG.env == "repeated_nextto_single_option":
nsrts = _get_repeated_nextto_single_option_gt_nsrts()
elif CFG.env == "screws":
nsrts = _get_screws_gt_nsrts()
elif CFG.env.startswith("pddl_"):
nsrts = _get_pddl_env_gt_nsrts(CFG.env)
elif CFG.env == "touch_point":
nsrts = _get_touch_point_gt_nsrts()
elif CFG.env == "stick_button":
nsrts = _get_stick_button_gt_nsrts()
elif CFG.env == "doors":
nsrts = _get_doors_gt_nsrts()
elif CFG.env == "coffee":
nsrts = _get_coffee_gt_nsrts()
elif CFG.env in ("satellites", "satellites_simple"):
nsrts = _get_satellites_gt_nsrts()
else:
raise NotImplementedError("Ground truth NSRTs not implemented")
# Filter out excluded predicates from NSRTs, and filter out NSRTs whose
# options are excluded.
final_nsrts = set()
for nsrt in nsrts:
if nsrt.option not in options:
continue
nsrt = nsrt.filter_predicates(predicates)
final_nsrts.add(nsrt)
return final_nsrts
def _get_from_env_by_names(env_name: str, names: Sequence[str],
env_attr: str) -> List:
"""Helper for loading types, predicates, and options by name."""
env = get_or_create_env(env_name)
name_to_env_obj = {}
for o in getattr(env, env_attr):
name_to_env_obj[o.name] = o
assert set(name_to_env_obj).issuperset(set(names))
return [name_to_env_obj[name] for name in names]
def _get_types_by_names(env_name: str, names: Sequence[str]) -> List[Type]:
"""Load types from an env given their names."""
return _get_from_env_by_names(env_name, names, "types")
def _get_predicates_by_names(env_name: str,
names: Sequence[str]) -> List[Predicate]:
"""Load predicates from an env given their names."""
return _get_from_env_by_names(env_name, names, "predicates")
def _get_options_by_names(env_name: str,
names: Sequence[str]) -> List[ParameterizedOption]:
"""Load parameterized options from an env given their names."""
return _get_from_env_by_names(env_name, names, "options")
def _get_cover_gt_nsrts() -> Set[NSRT]:
"""Create ground truth NSRTs for CoverEnv or environments that inherit from
CoverEnv."""
# Types
block_type, target_type, robot_type = _get_types_by_names(
CFG.env, ["block", "target", "robot"])
# Objects
block = Variable("?block", block_type)
robot = Variable("?robot", robot_type)
target = Variable("?target", target_type)
# Predicates
IsBlock, IsTarget, Covers, HandEmpty, Holding = \
_get_predicates_by_names(CFG.env, ["IsBlock", "IsTarget", "Covers",
"HandEmpty", "Holding"])
# Options
if CFG.env in ("cover", "pybullet_cover", "cover_hierarchical_types",
"cover_regrasp"):
PickPlace, = _get_options_by_names(CFG.env, ["PickPlace"])
elif CFG.env in ("cover_typed_options", "cover_multistep_options"):
Pick, Place = _get_options_by_names(CFG.env, ["Pick", "Place"])
nsrts = set()
# Pick
parameters = [block]
holding_predicate_args = [block]
if CFG.env == "cover_multistep_options":
parameters.append(robot)
holding_predicate_args.append(robot)
preconditions = {LiftedAtom(IsBlock, [block]), LiftedAtom(HandEmpty, [])}
add_effects = {LiftedAtom(Holding, holding_predicate_args)}
delete_effects = {LiftedAtom(HandEmpty, [])}
if CFG.env in ("cover", "pybullet_cover", "cover_hierarchical_types",
"cover_regrasp"):
option = PickPlace
option_vars = []
elif CFG.env == "cover_typed_options":
option = Pick
option_vars = [block]
elif CFG.env == "cover_multistep_options":
option = Pick
option_vars = [block, robot]
if CFG.env == "cover_multistep_options":
def pick_sampler(state: State, goal: Set[GroundAtom],
rng: np.random.Generator,
objs: Sequence[Object]) -> Array:
# The only things that change are the block's grasp, and the
# robot's grip, holding, x, and y.
assert len(objs) == 2
block, robot = objs
assert block.is_instance(block_type)
assert robot.is_instance(robot_type)
bx, by = state.get(block, "x"), state.get(block, "y")
rx, ry = state.get(robot, "x"), state.get(robot, "y")
bw = state.get(block, "width")
if CFG.cover_multistep_goal_conditioned_sampling:
# Goal conditioned sampling currently assumes one goal.
assert len(goal) == 1
goal_atom = next(iter(goal))
t = goal_atom.objects[1]
tx, tw = state.get(t, "x"), state.get(t, "width")
thr_found = False # target hand region
# Loop over objects in state to find target hand region,
# whose center should overlap with the target.
for obj in state.data:
if obj.type.name == "target_hand_region":
tlb = state.get(obj, "lb")
tub = state.get(obj, "ub")
tm = (tlb + tub) / 2 # midpoint of hand region
if tx - tw / 2 < tm < tx + tw / 2:
thr_found = True
break
assert thr_found
if CFG.cover_multistep_degenerate_oracle_samplers:
desired_x = float(bx)
elif CFG.cover_multistep_goal_conditioned_sampling:
# Block position adjusted by target/ thr offset
desired_x = bx + (tm - tx)
else:
desired_x = rng.uniform(bx - bw / 2, bx + bw / 2)
# This option changes the grasp for the block from -1.0 to 1.0, so
# the delta is 1.0 - (-1.0) = 2.0
block_param = [2.0]
# The grip changes from -1.0 to 1.0.
# The holding changes from -1.0 to 1.0.
# x, y, grip, holding
robot_param = [desired_x - rx, by - ry, 2.0, 2.0]
param = block_param + robot_param
return np.array(param, dtype=np.float32)
else:
def pick_sampler(state: State, goal: Set[GroundAtom],
rng: np.random.Generator,
objs: Sequence[Object]) -> Array:
del goal # unused
assert len(objs) == 1
b = objs[0]
assert b.is_instance(block_type)
if CFG.env == "cover_typed_options":
lb = float(-state.get(b, "width") / 2)
ub = float(state.get(b, "width") / 2)
elif CFG.env in ("cover", "pybullet_cover",
"cover_hierarchical_types", "cover_regrasp"):
lb = float(state.get(b, "pose") - state.get(b, "width") / 2)
lb = max(lb, 0.0)
ub = float(state.get(b, "pose") + state.get(b, "width") / 2)
ub = min(ub, 1.0)
return np.array(rng.uniform(lb, ub, size=(1, )), dtype=np.float32)
pick_nsrt = NSRT("Pick", parameters, preconditions, add_effects,
delete_effects, set(), option, option_vars, pick_sampler)
nsrts.add(pick_nsrt)
# Place (to Cover)
parameters = [block, target]
holding_predicate_args = [block]
if CFG.env == "cover_multistep_options":
parameters = [block, robot, target]
holding_predicate_args.append(robot)
preconditions = {
LiftedAtom(IsBlock, [block]),
LiftedAtom(IsTarget, [target]),
LiftedAtom(Holding, holding_predicate_args)
}
add_effects = {
LiftedAtom(HandEmpty, []),
LiftedAtom(Covers, [block, target])
}
delete_effects = {LiftedAtom(Holding, holding_predicate_args)}
if CFG.env == "cover_regrasp":
Clear, = _get_predicates_by_names("cover_regrasp", ["Clear"])
preconditions.add(LiftedAtom(Clear, [target]))
delete_effects.add(LiftedAtom(Clear, [target]))
if CFG.env in ("cover", "pybullet_cover", "cover_hierarchical_types",
"cover_regrasp"):
option = PickPlace
option_vars = []
elif CFG.env == "cover_typed_options":
option = Place
option_vars = [target]
elif CFG.env == "cover_multistep_options":
option = Place
option_vars = [block, robot, target]
if CFG.env == "cover_multistep_options":
def place_sampler(state: State, goal: Set[GroundAtom],
rng: np.random.Generator,
objs: Sequence[Object]) -> Array:
if CFG.cover_multistep_goal_conditioned_sampling:
# Goal conditioned sampling currently assumes one goal.
assert len(goal) == 1
goal_atom = next(iter(goal))
t = goal_atom.objects[1]
tx, tw = state.get(t, "x"), state.get(t, "width")
thr_found = False # target hand region
# Loop over objects in state to find target hand region,
# whose center should overlap with the target.
for obj in state.data:
if obj.type.name == "target_hand_region":
lb = state.get(obj, "lb")
ub = state.get(obj, "ub")
m = (lb + ub) / 2 # midpoint of hand region
if tx - tw / 2 < m < tx + tw / 2:
thr_found = True
break
assert thr_found
assert len(objs) == 3
block, robot, target = objs
assert block.is_instance(block_type)
assert robot.is_instance(robot_type)
assert target.is_instance(target_type)
rx = state.get(robot, "x")
tx, tw = state.get(target, "x"), state.get(target, "width")
if CFG.cover_multistep_degenerate_oracle_samplers:
desired_x = float(tx)
elif CFG.cover_multistep_goal_conditioned_sampling:
desired_x = m # midpoint of hand region
else:
desired_x = rng.uniform(tx - tw / 2, tx + tw / 2)
delta_x = desired_x - rx
# This option changes the grasp for the block from 1.0 to -1.0, so
# the delta is -1.0 - 1.0 = -2.0.
# x, grasp
block_param = [delta_x, -2.0]
# The grip changes from 1.0 to -1.0.
# The holding changes from 1.0 to -1.0.
# x, grip, holding
robot_param = [delta_x, -2.0, -2.0]
param = block_param + robot_param
return np.array(param, dtype=np.float32)
else:
def place_sampler(state: State, goal: Set[GroundAtom],
rng: np.random.Generator,
objs: Sequence[Object]) -> Array:
del goal # unused
assert len(objs) == 2
t = objs[-1]
assert t.is_instance(target_type)
lb = float(state.get(t, "pose") - state.get(t, "width") / 10)
lb = max(lb, 0.0)
ub = float(state.get(t, "pose") + state.get(t, "width") / 10)
ub = min(ub, 1.0)
return np.array(rng.uniform(lb, ub, size=(1, )), dtype=np.float32)
place_nsrt = NSRT("Place",
parameters, preconditions, add_effects, delete_effects,
set(), option, option_vars, place_sampler)
nsrts.add(place_nsrt)
# Place (not on any target)
if CFG.env == "cover_regrasp":
parameters = [block]
preconditions = {
LiftedAtom(IsBlock, [block]),
LiftedAtom(Holding, [block])
}
add_effects = {
LiftedAtom(HandEmpty, []),
}
delete_effects = {LiftedAtom(Holding, [block])}
option | |
<reponame>xiaofengxie128/Proteomic-Data-Manager
from django.db import models
from django.conf import settings
from datetime import date
import datetime
import xmltodict
from django_currentuser.db.models import CurrentUserField
from django.contrib.contenttypes.models import ContentType
from django.contrib.auth.models import User
import pickle
import os
import subprocess
import time
import shutil
from django.utils import timezone
import json
from os.path import exists
import random
import string
from django.db.models.signals import post_save
from django.core.files.uploadedfile import InMemoryUploadedFile
from django.dispatch import receiver
from django.contrib.contenttypes import fields
class NoteFile(models.Model):
notefile = models.FileField(upload_to=f"notefiles/{date.today().year}/ \
{date.today().month}/{date.today().day}", blank=True, null=True)
class SsdStorage(models.Model):
filelocation = models.FileField(upload_to="temp/", blank=True, null=True)
class HdStorage(models.Model):
filelocation = models.FileField(blank=True, null=True)
class RemoteStorage(models.Model):
filelocation = models.FileField(blank=True, null=True)
class OfflineStorage(models.Model):
filelocation = models.FileField(blank=True, null=True)
class PklStorage(models.Model):
filelocation = models.FileField(blank=True, null=True)
class RawFile(models.Model):
"""This is the main class, used to describe a MS run
"""
run_name = models.TextField(max_length=100, blank=True, null=True)
plot_label = models.TextField(max_length=100, blank=True, null=True)
project_name = models.TextField(max_length=100, blank=True, null=True)
run_desc = models.TextField(max_length=1000, blank=True, null=True)
qc_tool = models.IntegerField(blank=True, null=True, default=0)
qc_mbr = models.TextField(max_length=1000, blank=True, null=True)
instrument_model = models.TextField(max_length=100, blank=True, null=True)
instrument_sn = models.TextField(max_length=100, blank=True, null=True)
creator = CurrentUserField()
notes = models.TextField(max_length=1000, blank=True, null=True)
note_file = models.ManyToManyField(NoteFile, blank=True)
temp_data = models.BooleanField(default=False, null=True)
sample_obj = models.IntegerField(blank=True, null=True, default=0)
column_sn = models.TextField(max_length=100, blank=True, null=True)
spe_sn = models.TextField(max_length=100, blank=True, null=True)
rawfile = models.FileField(upload_to="temp/", blank=True, null=True)
storage_option = models.IntegerField(blank=True, null=True, default=0)
uploaded_at = models.DateTimeField(auto_now_add=True)
acquisition_time = models.DateTimeField(blank=True, null=True)
sample_type = models.TextField(max_length=100, blank=True, null=True)
pklfile = models.FileField(null=True, blank=True,)
file_size = models.DecimalField(default=0, max_digits=5,
decimal_places=3, blank=True, null=True)
content_extracted = models.BooleanField(default=False, null=True)
content_type = models.ForeignKey(ContentType, on_delete=models.PROTECT,
null=True, blank=True,)
object_id = models.PositiveIntegerField(default=5)
current_raw = fields.GenericForeignKey(
"content_type", "object_id")
ssd_storage = models.ForeignKey(
"SsdStorage",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
hd_storage = models.ForeignKey(
"HdStorage",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
remote_storage = models.ForeignKey(
"RemoteStorage",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
offline_storage = models.ForeignKey(
"OfflineStorage",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
pkl_storage = models.ForeignKey(
"PklStorage",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
qc_content_type = models.ForeignKey(
ContentType, related_name="qc_content", on_delete=models.PROTECT,
null=True, blank=True,)
qc_object_id = models.PositiveIntegerField(default=1)
current_qc = fields.GenericForeignKey(
"qc_content_type", "qc_object_id")
# TODO:remove pklfile field, replace with pkl_storage, should be very easy.
@receiver(post_save, sender=RawFile, dispatch_uid="update and move the file")
def update_raw(sender, instance, **kwargs):
"""Perform file convertion, meta info extraction when save a new record
"""
# TODO: add error handling if can"t convert file or read file
if not instance.content_extracted:
# for Thermo raw files
if instance.rawfile.name.split(".")[-1] == "raw":
rawfile_name = instance.rawfile.name
rawfile_name_only = rawfile_name.split("/")[-1]
try:
"""
have to do this batch approach as the following directly mono
command approch (tried 5 hours) won't work result = subprocess.
run(['mono',f'/home/rtklab/Documents/data_manager/
ThermoRawFileParser/ThermoRawFileParser.exe
-i=/home/rtklab/Documents/data_manager/media/{rawfile_name}
-m=0'], capture_output=True)
"""
batch_file = open("ThermoRawFileParser/run.sh", "w")
batch_file.write(" #!/bin/bash \n")
command_1 = "mono /home/rtklab/Documents/data_manager/"\
"ThermoRawFileParser/ThermoRawFileParser.exe "\
"-d=/home/rtklab/Documents/data_manager/media/temp"\
" -m=0 -f=1 -L=1,2"
batch_file.write(command_1)
batch_file.close()
command_2 = "/home/rtklab/Documents/data_manager/"\
"ThermoRawFileParser/run.sh"
result = subprocess.run(
["sh", command_2], capture_output=True)
print(result)
time.sleep(1)
filename = os.path.join(
"/home/rtklab/Documents/data_manager/media/temp/",
rawfile_name_only[:-4]+"-metadata.json")
f = open(filename,)
data = json.load(f)
f.close()
os.remove(filename)
RawFile.objects.filter(pk=instance.pk).update(
acquisition_time=datetime.datetime.strptime(
data["FileProperties"][2]["value"],
"%m/%d/%Y %H:%M:%S"))
RawFile.objects.filter(pk=instance.pk).update(
instrument_model=data["InstrumentProperties"][0]["value"])
RawFile.objects.filter(pk=instance.pk).update(
instrument_sn=data["InstrumentProperties"][2]["value"])
RawFile.objects.filter(pk=instance.pk).update(
sample_type=data["SampleData"][0]["value"])
to_tz = timezone.get_default_timezone()
file_year, file_month, file_date = RawFile.objects.\
filter(pk=instance.pk)[
0].acquisition_time.astimezone(to_tz).year,\
RawFile.objects.filter(pk=instance.pk)[
0].acquisition_time.astimezone(
to_tz).month, RawFile.objects.filter(
pk=instance.pk)[0].acquisition_time.astimezone(
to_tz).day
if instance.project_name != "":
file_dir = os.path.join(
settings.MEDIA_ROOT,
(f"rawfiles/{file_year}/"
f"{file_month}/{instance.project_name}/"))
else:
file_dir = os.path.join(
settings.MEDIA_ROOT,
f"rawfiles/{file_year}/{file_month}/{file_date}/")
check_folder = os.path.isdir(file_dir)
if not check_folder:
os.makedirs(file_dir)
newfile_name = f"{file_dir}/{rawfile_name_only}"
if exists(newfile_name):
random_str = "".join(random.choice(
string.ascii_lowercase) for i in range(4))
newfile_name = (f"{file_dir}/"
f"{rawfile_name_only.split('.')[0]}"
f"_{random_str}.raw")
shutil.move(
(f"/home/rtklab/Documents/"
f"data_manager/media/temp/{rawfile_name_only}"),
newfile_name)
# create the SsdStorage and point current to it
if instance.project_name != "":
ssd_filelocation = (f"rawfiles/{file_year}/{file_month}/"
f"{instance.project_name}/"
f"{newfile_name.split('/')[-1]}")
else:
ssd_filelocation = (f"rawfiles/{file_year}/{file_month}"
f"/{file_date}/"
f"{newfile_name.split('/')[-1]}")
ssdform = {
"filelocation": ssd_filelocation
}
ssdob = SsdStorage.objects.create(**ssdform, )
RawFile.objects.filter(
pk=instance.pk).update(ssd_storage=ssdob)
RawFile.objects.filter(pk=instance.pk).update(
rawfile=ssdob.filelocation)
#
ct = ContentType.objects.get_for_model(SsdStorage)
RawFile.objects.filter(pk=instance.pk).update(
content_type=ct, object_id=ssdob.pk)
ssdfilename = os.path.join(
settings.MEDIA_ROOT, ssd_filelocation)
filenamelen = len(ssd_filelocation.split("/")[-1])
des_path = (f"media/hdstorage/"
f"{ssd_filelocation[:filenamelen*-1]}")
isExist = os.path.exists(des_path)
if not isExist:
# Create a new directory because it does not exist
os.makedirs(des_path)
shutil.copy(ssdfilename, des_path)
hd_filelocation = f"hdstorage/{ssd_filelocation}"
hdform = {
"filelocation": hd_filelocation
}
hdob = HdStorage.objects.create(**hdform, )
RawFile.objects.filter(pk=instance.pk).update(hd_storage=hdob)
file_size = os.path.getsize(
os.path.join(settings.MEDIA_ROOT, RawFile.objects.filter(
pk=instance.pk)[
0].current_raw.filelocation.name))/1024/1024/1024
RawFile.objects.filter(pk=instance.pk).update(
file_size=file_size)
# auto create QC SpectromineQueue
# for some reason app and webiste generate different bool :(,
# need to happen before content extraction as
# sometimes extraction may fail
if (instance.qc_tool != "0" and instance.qc_tool != 0):
if (instance.qc_tool == "1" or instance.qc_tool == 1):
# 1 is msfragger
new_queue = {
"creator": instance.creator,
}
new_queue_obj = MsfraggerQueue.objects.create(
**new_queue, )
new_queue_obj.rawfile.add(
RawFile.objects.filter(pk=instance.pk).first())
new_queue_obj.save()
ct = ContentType.objects.get_for_model(MsfraggerQueue)
RawFile.objects.filter(
pk=instance.pk).update(qc_content_type=ct)
RawFile.objects.filter(pk=instance.pk).update(
qc_object_id=new_queue_obj.pk)
elif (instance.qc_tool == "3" or instance.qc_tool == 3):
# 3 is protein discoverer OTOT
new_queue = {
"creator": instance.creator,
"analysis_name": instance.pk,
"processing_method": InMemoryUploadedFile(open(
f'media/pd/methods/qc_built_in/OTOT_'
f'{instance.sample_obj}.pdProcessingWF', 'r'),
None, f'OTOT_{instance.sample_obj}.'
f'pdProcessingWF', None, None, None),
"consensus_method": InMemoryUploadedFile(open(
f'media/pd/methods/qc_built_in/OTOT_'
f'{instance.sample_obj}.pdConsensusWF', 'r'),
None, f'OTOT_{instance.sample_obj}.'
f'pdConsensusWF', None, None, None),
}
new_queue_obj = PdQueue.objects.create(**new_queue, )
new_queue_obj.rawfile.add(
RawFile.objects.filter(pk=instance.pk).first())
new_queue_obj.save()
ct = ContentType.objects.get_for_model(PdQueue)
RawFile.objects.filter(
pk=instance.pk).update(qc_content_type=ct)
RawFile.objects.filter(pk=instance.pk).update(
qc_object_id=new_queue_obj.pk)
elif (instance.qc_tool == "5" or instance.qc_tool == 5):
# 5 is protein discoverer ITIT
new_queue = {
"creator": instance.creator,
"analysis_name": instance.pk,
"processing_method": InMemoryUploadedFile(open(
f'media/pd/methods/qc_built_in/OTIT_'
f'{instance.sample_obj}.pdProcessingWF', 'r'),
None, f'OTIT_{instance.sample_obj}.'
f'pdProcessingWF', None, None, None),
"consensus_method": InMemoryUploadedFile(open(
f'media/pd/methods/qc_built_in/OTIT_'
f'{instance.sample_obj}.pdConsensusWF', 'r'),
None, f'OTIT_{instance.sample_obj}.'
f'pdConsensusWF', None,
None, None),
}
new_queue_obj = PdQueue.objects.create(**new_queue, )
new_queue_obj.rawfile.add(
RawFile.objects.filter(pk=instance.pk).first())
new_queue_obj.save()
ct = ContentType.objects.get_for_model(PdQueue)
RawFile.objects.filter(
pk=instance.pk).update(qc_content_type=ct)
RawFile.objects.filter(pk=instance.pk).update(
qc_object_id=new_queue_obj.pk)
# extract the mzML
mzml_filename = rawfile_name_only[:-4]+".mzML"
# defining an xml string
with open(os.path.join("media/temp", mzml_filename),
"r") as xml_obj:
# coverting the xml data to Python dictionary
my_dict = xmltodict.parse(xml_obj.read())
xml_obj.close()
os.remove(os.path.join("media/temp", mzml_filename))
ms1_rt = []
ms1_basemz = []
ms1_basemzintensity = []
ms1_ticintensity = []
ms2_rt = []
ms2_injectiontime = []
for i in range(0, len(my_dict["mzML"]["run"]
["spectrumList"]["spectrum"])):
if my_dict["mzML"]["run"][
"spectrumList"]["spectrum"][i]["cvParam"][0][
"@value"] == "1":
ms1_rt.append(float(my_dict["mzML"]["run"][
"spectrumList"]["spectrum"][i]["scanList"][
"scan"]["cvParam"][0]["@value"]))
ms1_basemz.append(round(float(
my_dict["mzML"]["run"]["spectrumList"][
"spectrum"][i]["cvParam"][5]["@value"]), 2))
ms1_basemzintensity.append(float(
my_dict["mzML"]["run"][
"spectrumList"]["spectrum"][i][
"cvParam"][6]["@value"]))
ms1_ticintensity.append(float(
my_dict["mzML"]["run"]["spectrumList"][
"spectrum"][i]["cvParam"][3]["@value"]))
if my_dict["mzML"]["run"]["spectrumList"][
"spectrum"][i]["cvParam"][0]["@value"] == "2":
ms2_rt.append(float(my_dict["mzML"][
"run"]["spectrumList"]["spectrum"][i][
"scanList"]["scan"]["cvParam"][0]["@value"]))
ms2_injectiontime.append(float(
my_dict["mzML"]["run"]["spectrumList"][
"spectrum"][i]["scanList"]["scan"][
"cvParam"][2]["@value"]))
plot_data = {"MS1_RT": ms1_rt,
"MS1_Basemz": ms1_basemz,
"MS1_Basemzintensity": ms1_basemzintensity,
"MS1_Ticintensity": ms1_ticintensity,
"MS2_RT": ms2_rt,
"MS2_Injectiontime": ms2_injectiontime}
newurl = RawFile.objects.filter(pk=instance.pk)[
0].rawfile.name.replace(".raw", ".pkl")
outputfilename = os.path.join(settings.MEDIA_ROOT, newurl)
with open(outputfilename, "wb") as handle:
pickle.dump(plot_data, handle,
protocol=pickle.HIGHEST_PROTOCOL)
RawFile.objects.filter(pk=instance.pk).update(pklfile=newurl)
pklform = {
"filelocation": newurl
}
pklobj = PklStorage.objects.create(**pklform, )
RawFile.objects.filter(
pk=instance.pk).update(pkl_storage=pklobj)
RawFile.objects.filter(pk=instance.pk).update(
content_extracted=True)
except Exception as err:
exception_type = type(err).__name__
print(exception_type)
instance.note = "Raw file extraction failed"
else:
instance.note = "Not valid data file type"
class UserProfile(models.Model):
user = models.OneToOneField(User, related_name='profile',
on_delete=models.SET_NULL, blank=True,
null=True)
hide_otherresult = models.BooleanField(default=False, null=True)
def __str__(self):
return 'Profile of user: {}'.format(self.user.username)
class SpectromineQueue(models.Model):
"""used to describe spectromine queue."""
rawfile = models.ForeignKey(
"RawFile",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
run_status = models.BooleanField(default=False, null=True)
protein_id = models.IntegerField(blank=True, null=True)
peptide_id = models.IntegerField(blank=True, null=True)
start_time = models.DateTimeField(blank=True, null=True)
finished_time = models.DateTimeField(blank=True, null=True)
creator = models.ForeignKey(settings.AUTH_USER_MODEL,
on_delete=models.SET_NULL, blank=True,
null=True)
result_file = models.FileField(
upload_to=(f"hdstorage/spectromine/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
def save(self, *args, **kwargs):
super(SpectromineQueue, self).save(*args, **kwargs)
if self.protein_id is not None:
RawFile.objects.filter(pk=self.rawfile.pk).update(
lastqc_protein_id=self.protein_id,
lastqc_peptide_id=self.peptide_id,
lastqc_tool="Spectromine", lastqc_time=datetime.datetime.now())
class SpectromineWorker(models.Model):
"""used to describe data process worker."""
worker_name = models.TextField(max_length=100, blank=True, null=True)
worker_ip = models.TextField(max_length=100, blank=True, null=True)
worker_status = models.TextField(max_length=100, blank=True, null=True)
current_job = models.ForeignKey(
"SpectromineQueue",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
current_percentage = models.IntegerField(blank=True, null=True)
last_update = models.DateTimeField(blank=True, null=True)
class MaxquantQueue(models.Model):
"""used to describe Maxquant queue."""
rawfile = models.ManyToManyField(RawFile)
analysis_name = models.TextField(max_length=100, blank=True, null=True)
setting_xml = models.FileField(
upload_to=(f"maxquant_xml/{date.today().year}/{date.today().month}/"
f"{date.today().day}"), null=True, blank=True,)
evidence_file = models.FileField(
upload_to=(f"hdstorage/maxquant/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
protein_file = models.FileField(
upload_to=(f"hdstorage/maxquant/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
peptide_file = models.FileField(
upload_to=(f"hdstorage/maxquant/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
other_file = models.FileField(
upload_to=(f"hdstorage/maxquant/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
run_status = models.BooleanField(default=False, null=True)
protein_id = models.IntegerField(blank=True, null=True)
peptide_id = models.IntegerField(blank=True, null=True)
start_time = models.DateTimeField(blank=True, null=True)
finished_time = models.DateTimeField(blank=True, null=True)
creator = models.ForeignKey(settings.AUTH_USER_MODEL,
on_delete=models.SET_NULL, blank=True,
null=True)
class MaxquantWorker(models.Model):
"""used to describe maxquant data process worker."""
worker_name = models.TextField(max_length=100, blank=True, null=True)
worker_ip = models.TextField(max_length=100, blank=True, null=True)
worker_status = models.TextField(max_length=100, blank=True, null=True)
current_job = models.ForeignKey(
"MaxquantQueue",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
current_percentage = models.IntegerField(blank=True, null=True)
last_update = models.DateTimeField(blank=True, null=True)
class MsfraggerQueue(models.Model):
"""used to describe Msfragger queue."""
rawfile = models.ManyToManyField(RawFile)
analysis_name = models.TextField(max_length=100, blank=True, null=True)
ion_file = models.FileField(
upload_to=(f"hdstorage/msfragger/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
psm_file = models.FileField(
upload_to=(f"hdstorage/msfragger/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
peptide_file = models.FileField(
upload_to=(f"hdstorage/msfragger/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
protein_file = models.FileField(
upload_to=(f"hdstorage/msfragger/{date.today().year}/"
f"{date.today().month}/{date.today().day}"),
null=True, blank=True,)
run_status = models.BooleanField(default=False, null=True)
precurosr_id = models.IntegerField(blank=True, null=True)
psm_id = models.IntegerField(blank=True, null=True)
peptide_id = models.IntegerField(blank=True, null=True)
protein_id = models.IntegerField(blank=True, null=True)
start_time = models.DateTimeField(blank=True, null=True)
finished_time = models.DateTimeField(blank=True, null=True)
creator = models.ForeignKey(settings.AUTH_USER_MODEL,
on_delete=models.SET_NULL, blank=True,
null=True)
class MsfraggerWorker(models.Model):
"""used to describe Msfragger data process worker."""
worker_name = models.TextField(max_length=100, blank=True, null=True)
worker_ip = models.TextField(max_length=100, blank=True, null=True)
worker_status = models.TextField(max_length=100, blank=True, null=True)
current_job = models.ForeignKey(
"MsfraggerQueue",
on_delete=models.SET_NULL,
null=True,
blank=True,
)
current_percentage = models.IntegerField(blank=True, null=True)
last_update = models.DateTimeField(blank=True, null=True)
class PdQueue(models.Model):
"""used to describe ProteinDiscoverer queue."""
rawfile = models.ManyToManyField(RawFile)
analysis_name | |
z1])
val.append(annot_img[x2, y1, z2])
val.append(annot_img[x2, y2, z1])
val.append(annot_img[x2, y2, z2])
val = val[1:]
labels.append(max(set(val), key = val.count))
# print(labels)
labels_name = []
for label in labels:
with open(lookup_table, 'r') as f:
lines = f.readlines()
rows = len(lines)
for row in range(rows):
line = lines[row][0: 8]
b = str(int(label))
if re.match(b, line):
# print(lines[row])
a = lines[row][len(b): -16].strip()
labels_name.append(a)
break
return labels_name
class ElectrodeSeg:
def __init__(self, filePath, patName, iLabel, numMax, diameterSize, spacing, gap):
super(ElectrodeSeg, self).__init__()
# set up input initials
self.filePath = filePath
self.patientName = patName
raw_flag = 0 # check for the filepath existance
for root, dirs, files in os.walk(self.filePath):
for filename in files:
if re.search(r'CT_intra.nii.gz', filename):
raw_flag = 1
self.rawDataPath = f"{self.filePath}/{filename}"
break
if not raw_flag:
sys.exit()
label_flag = 0
for root, dirs, files in os.walk(self.filePath):
for filename in files:
if re.search(r'_labels.npy', filename):
label_flag = 1
self.labelsPath = f"{self.filePath}/{filename}"
break
if not label_flag:
sys.exit()
self.rawData = nib.load(self.rawDataPath).get_fdata()
self.labels = np.load(self.labelsPath)
self.iLabel = iLabel
self.numMax = numMax
self.diameterSize = diameterSize
self.spacing = spacing
self.gap = gap
# some calculations to get the rest initials
self.labelValues = np.unique(self.labels)
self.numElecs = len(self.labelValues) - 1
if self.numElecs > 8: # remove 'I' from the alphabet list, a trivial custom not to name the electrode 'I'
self.alphaList = [chr(i) for i in range(65, 66+self.numElecs)]
self.alphaList.pop(8)
else:
self.alphaList = [chr(i) for i in range(65, 65+self.numElecs)]
self.iValue = self.labelValues[self.iLabel]
self.nameLabel = self.alphaList[self.iLabel-1]
data_elec = np.copy(self.labels)
data_elec[np.where(self.labels != self.iValue)] = 0 ## isolate a single cluster of voxels belonging to the ith electrode
self.xs, self.ys, self.zs = np.where(data_elec != 0)
self.pos_elec = np.transpose(np.vstack((self.xs, self.ys, self.zs))) ## positions of these voxels
### test!
data_elec1 = np.copy(self.labels)
data_elec1[np.where(self.labels == self.iValue)] = 0
self.xrest, self.yrest, self.zrest = np.where(data_elec1 != 0)
self.rawData[self.xrest, self.yrest, self.zrest] = 0
### test!
self.rawData_single = self.rawData
xmin = np.amin(self.xs)
xmax = np.amax(self.xs)
ymin = np.amin(self.ys)
ymax = np.amax(self.ys)
zmin = np.amin(self.zs)
zmax = np.amax(self.zs)
# self.rawData_single[self.xs, self.ys, self.zs] = self.rawData_single[self.xs, self.ys, self.zs] * 3
self.rawData_single[xmin:xmax+1, ymin:ymax+1, zmin:zmax+1] = self.rawData_single[xmin:xmax+1, ymin:ymax+1, zmin:zmax+1] * 3
self.resultPath = f"{self.filePath}/{self.patientName}_result"
if not os.path.exists(self.resultPath):
os.mkdir(self.resultPath)
self.resultFile = f"{self.resultPath}/{self.nameLabel}.txt"
self.elecPos = [0, 0, 0]
self.headStart = [0, 0, 0]
self.targetPoint = [0, 0, 0]
self.regressInfo = [0, 0, 0, 0]
def pipeline(self):
self.startPoint()
self.contactPoint(1)
self.regression()
for j in np.arange(self.numMax - 1):
# if self.rawData[int(round(self.elecPos[-1,0])), int(round(self.elecPos[-1,1])), int(round(self.elecPos[-1,2]))] == 0:
# self.elecPos = self.elecPos[0:-1, :]
# break
if int(self.elecPos[-1,0])==int(self.elecPos[-2,0]) and int(self.elecPos[-1,1])==int(self.elecPos[-2,1]) and int(self.elecPos[-1,2])==int(self.elecPos[-2,2]):
self.elecPos = self.elecPos[0:-1, :]
break
self.step()
if self.flag_step_stop:
break
self.elecPos = self.elecPos[1:, :]
# print(self.elecPos)
self.resulting()
# return self.elecPos
def resulting(self):
self.elecPos_true = np.copy(self.elecPos)
self.elecPos_true[:, 0] = 128 - self.elecPos[:, 0]
self.elecPos_true[:, 1] = 128 - self.elecPos[:, 1]
self.elecPos_true[:, 2] = self.elecPos[:, 2] - 128
self.elecPos_true = self.elecPos_true[:, [0, 2, 1]]
self.elecFilepath = os.path.join(self.filePath, f"{self.patientName}_result")
if not os.path.exists(self.elecFilepath):
os.mkdir(self.elecFilepath)
else:
self.elecFile = os.path.join(self.elecFilepath, f"{self.nameLabel}.txt")
with open(self.elecFile, "ab") as f:
f.seek(0)
f.truncate()
# f.write(b"\n")
np.savetxt(f, self.elecPos_true, fmt='%10.8f', delimiter=' ', newline='\n', header=f"{self.elecPos_true.shape[0]}")
## target point functions
def startPoint(self):
## firstly find a voxel near the target
x = [np.max(self.xs), np.min(self.xs)]
y = [np.max(self.ys), np.min(self.ys)]
z = [np.max(self.zs), np.min(self.zs)]
self.reg1 = LinearRegression().fit(X=self.xs.reshape(-1,1), y=self.ys) # x-y
self.reg2 = LinearRegression().fit(X=self.xs.reshape(-1,1), y=self.zs) # x-z
self.reg3 = LinearRegression().fit(X=self.ys.reshape(-1,1), y=self.zs) # y-z
coefs = [abs(self.reg1.coef_), abs(self.reg2.coef_), abs(self.reg3.coef_)]
coef_min = coefs.index(min(coefs))
if coef_min == 0:
index = [0 if self.reg2.coef_>0 else 1, 0 if self.reg3.coef_>0 else 1, 0]
elif coef_min == 1:
index = [0 if self.reg1.coef_>0 else 1, 0, 0 if self.reg3.coef_>0 else 1]
else:
index = [0, 0 if self.reg1.coef_>0 else 1, 0 if self.reg2.coef_>0 else 1]
indexreverse = [~index[0], ~index[1], ~index[2]]
point1 = np.array([x[index[0]], y[index[1]], z[index[2]]])
point2 = np.array([x[indexreverse[0]], y[indexreverse[1]], z[indexreverse[2]]])
center = 127.5 * np.ones(3)
diff1 = point1 - center
diff2 = point2 - center
headStart = point2 if np.sum(np.transpose(diff1)*diff1) > np.sum(np.transpose(diff2)*diff2) else point1
self.direction = indexreverse if np.sum(np.transpose(diff1)*diff1) > np.sum(np.transpose(diff2)*diff2) else index
## secondly specify a target voxel in label voxels
diffs = self.pos_elec - headStart
diffs2 = np.power(diffs[:,0], 2) + np.power(diffs[:,1], 2) + np.power(diffs[:,2], 2)
headPointPos = np.argmin(diffs2)
self.headStart = self.pos_elec[headPointPos, :]
def converge(self, x, y, z):
## converge to the mass center of a cluster of voxels
n = self.diameterSize
delta = math.ceil(round((n - 1) / 2, 1)) # represent the radius of the electrode contact
## extract a cubic ROI of the raw CT data
seq_s = np.arange(x - delta, x + delta + 1)
seq_r = np.arange(y - delta, y + delta + 1)
seq_c = np.arange(z - delta, z + delta + 1)
if not ((np.array(seq_s) > 0).all() and (np.array(seq_r) > 0).all() and (np.array(seq_c) > 0).all()):
print('Error: index too small 0!')
return 0, 0, 0
elif not ((np.array(seq_s) < 256).all() and (np.array(seq_r) < 256).all() and (np.array(seq_c) < 256).all()):
print('Error: index too large 256!')
return 0, 0, 0
else:
## extract the ROI cubic
# test!!!
matrixVoxels = self.rawData_local[seq_s[0]:seq_s[-1]+1, seq_r[0]:seq_r[-1]+1, seq_c[0]:seq_c[-1]+1]
sumVoxels = np.sum(matrixVoxels)
if (np.sum(matrixVoxels)== 0):
print('Error: Converge to non-elec region!')
return 0, 0, 0
else:
f = np.zeros((1, 4))
for index, element in np.ndenumerate(matrixVoxels):
x, y, z = index
tmp = np.array([x+seq_s[0], y+seq_r[0], z+seq_c[0], element])
f = np.vstack((f, tmp))
f = f[1:]
CM = np.average(f[:,:3], axis=0, weights=f[:,3])
C100 = CM[0]
C010 = CM[1]
C001 = CM[2]
x1 = C100
y1 = C010
z1 = C001
return x1, y1, z1
def contactPoint(self, target):
## converge to an electrode contact position
x0 = self.headStart[0] if target == 1 else self.x0
y0 = self.headStart[1] if target == 1 else self.y0
z0 = self.headStart[2] if target == 1 else self.z0
x = int(round(x0))
y = int(round(y0))
z = int(round(z0))
print(f"initial start voxel:({x0}, {y0}, {z0})")
# test!!!
self.rawData_local = self.rawData_single
diff_array = self.pos_elec - np.array([x0, y0, z0])
elec_diffs = np.sqrt(np.dot(diff_array, np.transpose(diff_array)).diagonal())
ind_diffs = np.where(elec_diffs <= 2)
self.rawData_local[self.xs[ind_diffs], self.ys[ind_diffs], self.zs[ind_diffs]] = self.rawData_local[self.xs[ind_diffs], self.ys[ind_diffs], self.zs[ind_diffs]] * 2
(x1, y1, z1) = self.converge(x, y, z)
itr = 1
flag_convergence = 0
while not ((x==int(round(x1))) and (y==int(round(y1))) and (z==int(round(z1)))):
x = int(round(x1))
y = int(round(y1))
z = int(round(z1))
(x1, y1, z1) = self.converge(x, y, z)
itr = itr + 1
if itr > 5:
flag_convergence = 1
break
print(f"Convergent center voxel coordinates:({x1},{y1},{z1})")
print(f"Convergent center voxel value:{self.rawData[int(round(x1)), int(round(y1)), int(round(z1))]}")
self.flag_step_stop = 0
if (x1, y1, z1) == (0, 0, 0):
self.flag_step_stop = 1
print('here1,converged to 0!')
# self.elecPos = np.vstack([self.elecPos, [x1, y1, z1]])
else:
if not flag_convergence:
print('here2,converged normally!')
self.targetPoint = [x1, y1, z1] if target == 1 else self.targetPoint
self.elecPos = np.vstack([self.elecPos, [x1, y1, z1]])
else:
print('here3, maybe not convergent!')
self.targetPoint = [x1, y1, z1] if target == 1 else self.targetPoint
self.elecPos = np.vstack([self.elecPos, [x1, y1, z1]])
def regression(self):
## regress an electrode and find the axis direction
X = np.transpose(np.vstack((self.xs, self.ys)))
y = self.zs
forcedX = np.transpose(np.array([self.targetPoint[0], self.targetPoint[1]]))
forcedy = self.targetPoint[2]
## implant a contraint regression, forcing on the head point
X = X - forcedX
y = y - forcedy
reg = Lasso(fit_intercept=False).fit(X=X, y=y)
reg.intercept_ = reg.intercept_ + forcedy - np.dot(forcedX, reg.coef_)
## regression between x and y
reg2 = LinearRegression(fit_intercept=True).fit(X=self.xs.reshape(-1,1), y=self.ys)
self.coef = reg.coef_
self.intercept = reg.intercept_
self.coef2 = reg2.coef_
self.intercept2 = reg2.intercept_
def step(self):
## step out along the electrode axis
dis = self.spacing # initial step size
# delta_x = np.sqrt(np.power(dis, 2) / (1 + np.power(self.coef2[0],2) + np.power(np.dot(self.coef, np.array([1, self.coef2[0]])) ,2)))
# delta_y = np.dot(self.coef2[0], delta_x)
# delta_z = np.dot(self.coef, np.array([1, self.coef2[0]])) * delta_x
diff_x = np.max(self.xs) - np.min(self.xs)
diff_y = np.max(self.ys) - np.min(self.ys)
diff_z = np.max(self.zs) - np.min(self.zs)
a = np.power(diff_x,2) + np.power(diff_y,2) + np.power(diff_z,2)
delta_x = diff_x * np.sqrt(np.power(dis,2) / a)
delta_y = diff_y * np.sqrt(np.power(dis,2) / a)
delta_z = diff_z * np.sqrt(np.power(dis,2) / a)
# delta_x = self.reg2.coef_ * np.sqrt(np.power(dis,2) / (1 + np.power(self.reg2.coef_,2) + np.power(self.reg3.coef_,2)))
# delta_y = self.reg3.coef_ * np.sqrt(np.power(dis,2) / (1 + np.power(self.reg2.coef_,2) + np.power(self.reg3.coef_,2)))
# delta_z = np.sqrt(np.power(dis,2) / (1 + np.power(self.reg2.coef_,2) + np.power(self.reg3.coef_,2)))
| |
)
class DATAFILEPARAMETER(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "DATAFILEPARAMETER"
__singularfieldname__ = "datafileParameter"
__pluralfieldname__ = "datafileParameters"
__table_args__ = (
Index("UNQ_DATAFILEPARAMETER_0", "DATAFILE_ID", "PARAMETER_TYPE_ID"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
dateTimeValue = Column("DATETIME_VALUE", DateTime)
error = Column("ERROR", Float(asdecimal=True))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
numericValue = Column("NUMERIC_VALUE", Float(asdecimal=True))
rangeBottom = Column("RANGEBOTTOM", Float(asdecimal=True))
rangeTop = Column("RANGETOP", Float(asdecimal=True))
stringValue = Column("STRING_VALUE", String(4000))
datafileID = Column("DATAFILE_ID", ForeignKey("DATAFILE.ID"), nullable=False)
parameterTypeID = Column(
"PARAMETER_TYPE_ID", ForeignKey("PARAMETERTYPE.ID"), nullable=False, index=True,
)
DATAFILE = relationship(
"DATAFILE",
primaryjoin="DATAFILEPARAMETER.datafileID == DATAFILE.id",
backref="datafileParameters",
)
PARAMETERTYPE = relationship(
"PARAMETERTYPE",
primaryjoin="DATAFILEPARAMETER.parameterTypeID == PARAMETERTYPE.id",
backref="datafileParameters",
)
class DATASET(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "DATASET"
__singularfieldname__ = "dataset"
__pluralfieldname__ = "datasets"
__table_args__ = (Index("UNQ_DATASET_0", "INVESTIGATION_ID", "NAME"),)
id = Column("ID", BigInteger, primary_key=True)
complete = Column(
"COMPLETE", Boolean, nullable=False, server_default=FetchedValue(),
)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
description = Column("DESCRIPTION", String(255))
doi = Column("DOI", String(255))
endDate = Column("END_DATE", DateTime)
location = Column("LOCATION", String(255))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
startDate = Column("STARTDATE", DateTime)
investigationID = Column(
"INVESTIGATION_ID", ForeignKey("INVESTIGATION.ID"), nullable=False,
)
sampleID = Column("SAMPLE_ID", ForeignKey("SAMPLE.ID"), index=True)
typeID = Column("TYPE_ID", ForeignKey("DATASETTYPE.ID"), nullable=False, index=True)
INVESTIGATION = relationship(
"INVESTIGATION",
primaryjoin="DATASET.investigationID == INVESTIGATION.id",
backref="datasets",
)
SAMPLE = relationship(
"SAMPLE", primaryjoin="DATASET.sampleID == SAMPLE.id", backref="datasets",
)
DATASETTYPE = relationship(
"DATASETTYPE",
primaryjoin="DATASET.typeID == DATASETTYPE.id",
backref="datasets",
)
class DATASETPARAMETER(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "DATASETPARAMETER"
__singularfieldname__ = "datasetParameter"
__pluralfieldname__ = "datasetParameters"
__table_args__ = (
Index("UNQ_DATASETPARAMETER_0", "DATASET_ID", "PARAMETER_TYPE_ID"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
dateTimeValue = Column("DATETIME_VALUE", DateTime)
error = Column("ERROR", Float(asdecimal=True))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
numericValue = Column("NUMERIC_VALUE", Float(asdecimal=True))
rangeBottom = Column("RANGEBOTTOM", Float(asdecimal=True))
rangeTop = Column("RANGETOP", Float(asdecimal=True))
stringValue = Column("STRING_VALUE", String(4000))
datasetID = Column("DATASET_ID", ForeignKey("DATASET.ID"), nullable=False)
parameterTypeID = Column(
"PARAMETER_TYPE_ID", ForeignKey("PARAMETERTYPE.ID"), nullable=False, index=True,
)
DATASET = relationship(
"DATASET",
primaryjoin="DATASETPARAMETER.datasetID == DATASET.id",
backref="datasetParameters",
)
PARAMETERTYPE = relationship(
"PARAMETERTYPE",
primaryjoin="DATASETPARAMETER.parameterTypeID == PARAMETERTYPE.id",
backref="datasetParameters",
)
class DATASETTYPE(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "DATASETTYPE"
__singularfieldname__ = "type"
__pluralfieldname__ = "datasetTypes"
__table_args__ = (Index("UNQ_DATASETTYPE_0", "FACILITY_ID", "NAME"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
description = Column("DESCRIPTION", String(255))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
facilityID = Column("FACILITY_ID", ForeignKey("FACILITY.ID"), nullable=False)
FACILITY = relationship(
"FACILITY",
primaryjoin="DATASETTYPE.facilityID == FACILITY.id",
backref="datasetTypes",
)
class FACILITYCYCLE(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "FACILITYCYCLE"
__singularfieldname__ = "facilityCycle"
__pluralfieldname__ = "facilityCycles"
__table_args__ = (Index("UNQ_FACILITYCYCLE_0", "FACILITY_ID", "NAME"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
description = Column("DESCRIPTION", String(255))
endDate = Column("ENDDATE", DateTime)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
startDate = Column("STARTDATE", DateTime)
facilityID = Column("FACILITY_ID", ForeignKey("FACILITY.ID"), nullable=False)
FACILITY = relationship(
"FACILITY",
primaryjoin="FACILITYCYCLE.facilityID == FACILITY.id",
backref="facilityCycles",
)
class GROUPING(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "GROUPING"
__singularfieldname__ = "grouping"
__pluralfieldname__ = "groupings"
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False, unique=True)
class INSTRUMENT(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INSTRUMENT"
__singularfieldname__ = "instrument"
__pluralfieldname__ = "instruments"
__table_args__ = (Index("UNQ_INSTRUMENT_0", "FACILITY_ID", "NAME"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
description = Column("DESCRIPTION", String(4000))
fullName = Column("FULLNAME", String(255))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
type = Column("TYPE", String(255))
url = Column("URL", String(255))
facilityID = Column("FACILITY_ID", ForeignKey("FACILITY.ID"), nullable=False)
FACILITY = relationship(
"FACILITY",
primaryjoin="INSTRUMENT.facilityID == FACILITY.id",
backref="instruments",
)
class INSTRUMENTSCIENTIST(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INSTRUMENTSCIENTIST"
__singularfieldname__ = "instrumentScientist"
__pluralfieldname__ = "instrumentScientists"
__table_args__ = (Index("UNQ_INSTRUMENTSCIENTIST_0", "USER_ID", "INSTRUMENT_ID"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
instrumentID = Column(
"INSTRUMENT_ID", ForeignKey("INSTRUMENT.ID"), nullable=False, index=True,
)
userID = Column("USER_ID", ForeignKey("USER_.ID"), nullable=False)
INSTRUMENT = relationship(
"INSTRUMENT",
primaryjoin="INSTRUMENTSCIENTIST.instrumentID == INSTRUMENT.id",
backref="instrumentScientists",
)
USER = relationship(
"USER",
primaryjoin="INSTRUMENTSCIENTIST.userID == USER.id",
backref="instrumentScientists",
)
class INVESTIGATION(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATION"
__singularfieldname__ = "investigation"
__pluralfieldname__ = "investigations"
__table_args__ = (Index("UNQ_INVESTIGATION_0", "FACILITY_ID", "NAME", "VISIT_ID"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
doi = Column("DOI", String(255))
endDate = Column("ENDDATE", DateTime)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
releaseDate = Column("RELEASEDATE", DateTime)
startDate = Column("STARTDATE", DateTime)
summary = Column("SUMMARY", String(4000))
title = Column("TITLE", String(255), nullable=False)
visitId = Column("VISIT_ID", String(255), nullable=False)
facilityID = Column("FACILITY_ID", ForeignKey("FACILITY.ID"), nullable=False)
typeID = Column(
"TYPE_ID", ForeignKey("INVESTIGATIONTYPE.ID"), nullable=False, index=True,
)
FACILITY = relationship(
"FACILITY",
primaryjoin="INVESTIGATION.facilityID == FACILITY.id",
backref="investigations",
)
INVESTIGATIONTYPE = relationship(
"INVESTIGATIONTYPE",
primaryjoin="INVESTIGATION.typeID == INVESTIGATIONTYPE.id",
backref="investigations",
)
class INVESTIGATIONGROUP(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATIONGROUP"
__singularfieldname__ = "investigationGroup"
__pluralfieldname__ = "investigationGroups"
__table_args__ = (
Index("UNQ_INVESTIGATIONGROUP_0", "GROUP_ID", "INVESTIGATION_ID", "ROLE"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
role = Column("ROLE", String(255), nullable=False)
groupID = Column("GROUP_ID", ForeignKey("GROUPING.ID"), nullable=False)
investigationID = Column(
"INVESTIGATION_ID", ForeignKey("INVESTIGATION.ID"), nullable=False, index=True,
)
GROUPING = relationship(
"GROUPING",
primaryjoin="INVESTIGATIONGROUP.groupID == GROUPING.id",
backref="investigationGroups",
)
INVESTIGATION = relationship(
"INVESTIGATION",
primaryjoin="INVESTIGATIONGROUP.investigationID == INVESTIGATION.id",
backref="investigationGroups",
)
class INVESTIGATIONINSTRUMENT(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATIONINSTRUMENT"
__singularfieldname__ = "investigationInstrument"
__pluralfieldname__ = "investigationInstruments"
__table_args__ = (
Index("UNQ_INVESTIGATIONINSTRUMENT_0", "INVESTIGATION_ID", "INSTRUMENT_ID"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
instrumentID = Column(
"INSTRUMENT_ID", ForeignKey("INSTRUMENT.ID"), nullable=False, index=True,
)
investigationID = Column(
"INVESTIGATION_ID", ForeignKey("INVESTIGATION.ID"), nullable=False,
)
INSTRUMENT = relationship(
"INSTRUMENT",
primaryjoin="INVESTIGATIONINSTRUMENT.instrumentID == INSTRUMENT.id",
backref="investigationInstruments",
)
INVESTIGATION = relationship(
"INVESTIGATION",
primaryjoin="INVESTIGATIONINSTRUMENT.investigationID == INVESTIGATION.id",
backref="investigationInstruments",
)
class INVESTIGATIONPARAMETER(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATIONPARAMETER"
__singularfieldname__ = "investigationParameter"
__pluralfieldname__ = "investigationParameters"
__table_args__ = (
Index("UNQ_INVESTIGATIONPARAMETER_0", "INVESTIGATION_ID", "PARAMETER_TYPE_ID"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
dateTimeValue = Column("DATETIME_VALUE", DateTime)
error = Column("ERROR", Float(asdecimal=True))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
numericValue = Column("NUMERIC_VALUE", Float(asdecimal=True))
rangeBottom = Column("RANGEBOTTOM", Float(asdecimal=True))
rangeTop = Column("RANGETOP", Float(asdecimal=True))
stringValue = Column("STRING_VALUE", String(4000))
investigationID = Column(
"INVESTIGATION_ID", ForeignKey("INVESTIGATION.ID"), nullable=False,
)
parameterTypeID = Column(
"PARAMETER_TYPE_ID", ForeignKey("PARAMETERTYPE.ID"), nullable=False, index=True,
)
INVESTIGATION = relationship(
"INVESTIGATION",
primaryjoin="INVESTIGATIONPARAMETER.investigationID == INVESTIGATION.id",
backref="investigationParameters",
)
PARAMETERTYPE = relationship(
"PARAMETERTYPE",
primaryjoin="INVESTIGATIONPARAMETER.parameterTypeID == PARAMETERTYPE.id",
backref="investigationParameters",
)
class INVESTIGATIONTYPE(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATIONTYPE"
__singularfieldname__ = "type"
__pluralfieldname__ = "investigationTypes"
__table_args__ = (Index("UNQ_INVESTIGATIONTYPE_0", "NAME", "FACILITY_ID"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
description = Column("DESCRIPTION", String(255))
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
name = Column("NAME", String(255), nullable=False)
facilityID = Column(
"FACILITY_ID", ForeignKey("FACILITY.ID"), nullable=False, index=True,
)
FACILITY = relationship(
"FACILITY",
primaryjoin="INVESTIGATIONTYPE.facilityID == FACILITY.id",
backref="investigationTypes",
)
class INVESTIGATIONUSER(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "INVESTIGATIONUSER"
__singularfieldname__ = "investigationUser"
__pluralfieldname__ = "investigationUsers"
__table_args__ = (
Index("UNQ_INVESTIGATIONUSER_0", "USER_ID", "INVESTIGATION_ID", "ROLE"),
)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
role = Column("ROLE", String(255), nullable=False)
investigationID = Column(
"INVESTIGATION_ID", ForeignKey("INVESTIGATION.ID"), nullable=False, index=True,
)
userID = Column("USER_ID", ForeignKey("USER_.ID"), nullable=False)
INVESTIGATION = relationship(
"INVESTIGATION",
primaryjoin="INVESTIGATIONUSER.investigationID == INVESTIGATION.id",
backref="investigationUsers",
)
USER = relationship(
"USER",
primaryjoin="INVESTIGATIONUSER.userID == USER.id",
backref="investigationUsers",
)
class JOB(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "JOB"
__singularfieldname__ = "job"
__pluralfieldname__ = "jobs"
id = Column("ID", BigInteger, primary_key=True)
arguments = Column("ARGUMENTS", String(255))
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
modId = Column("MOD_ID", String(255), nullable=False)
modTime = Column("MOD_TIME", DateTime, nullable=False)
applicationID = Column(
"APPLICATION_ID", ForeignKey("APPLICATION.ID"), nullable=False, index=True,
)
inputDataCollectionID = Column(
"INPUTDATACOLLECTION_ID", ForeignKey("DATACOLLECTION.ID"), index=True,
)
outputDataCollectionID = Column(
"OUTPUTDATACOLLECTION_ID", ForeignKey("DATACOLLECTION.ID"), index=True,
)
APPLICATION = relationship(
"APPLICATION",
primaryjoin="JOB.applicationID == APPLICATION.id",
backref="jobs",
)
DATACOLLECTION = relationship(
"DATACOLLECTION",
primaryjoin="JOB.inputDataCollectionID == DATACOLLECTION.id",
backref="jobs",
)
class KEYWORD(Base, EntityHelper, metaclass=EntityMeta):
__tablename__ = "KEYWORD"
__singularfieldname__ = "keyword"
__pluralfieldname__ = "keywords"
__table_args__ = (Index("UNQ_KEYWORD_0", "NAME", "INVESTIGATION_ID"),)
id = Column("ID", BigInteger, primary_key=True)
createId = Column("CREATE_ID", String(255), nullable=False)
createTime = Column("CREATE_TIME", DateTime, nullable=False)
| |
before function.
"trigger": "enable_auto_rollbacks_button_clicked",
"unless": [self.auto_rollbacks_enabled],
"before": self.enable_auto_rollbacks,
}
yield {
"source": "*",
"dest": None, # Don't actually change state, just call the before function.
"trigger": "disable_auto_rollbacks_button_clicked",
"conditions": [self.any_slo_failing, self.auto_rollbacks_enabled],
"before": self.disable_auto_rollbacks,
}
yield {
"source": "*",
"dest": None,
"trigger": "slos_started_failing",
"conditions": [self.auto_rollbacks_enabled],
"unless": [self.already_rolling_back],
"before": self.start_auto_rollback_countdown,
}
yield {
"source": "*",
"dest": None,
"trigger": "slos_stopped_failing",
"before": self.cancel_auto_rollback_countdown,
}
yield {
"source": "*",
"dest": None,
"trigger": "snooze_button_clicked",
"before": self.restart_timer,
"conditions": [self.is_timer_running],
}
def disable_auto_rollbacks(self) -> None:
self.cancel_auto_rollback_countdown()
self.auto_rollback = False
self.update_slack_status(
f"Automatic rollback disabled for this deploy. To disable this permanently for this step, edit `deploy.yaml` and set `auto_rollback: false` for the `{self.deploy_group}` step."
)
def enable_auto_rollbacks(self) -> None:
self.auto_rollback = True
self.auto_rollbacks_ever_enabled = True
self.update_slack_status(
f"Automatic rollback enabled for this deploy. Will watch for failures and rollback when necessary. To set this permanently, edit `deploy.yaml` and set `auto_rollback: false` for the `{self.deploy_group}` step."
)
def auto_rollbacks_enabled(self) -> bool:
"""This getter exists so it can be a condition on transitions, since those need to be callables."""
return self.auto_rollback
def get_auto_rollback_delay(self) -> float:
return self.auto_rollback_delay
def get_auto_certify_delay(self) -> float:
if self.auto_certify_delay is not None:
return self.auto_certify_delay
else:
if self.auto_rollbacks_ever_enabled:
return DEFAULT_AUTO_CERTIFY_DELAY
else:
return 0
def already_rolling_back(self) -> bool:
return self.state in self.rollback_states
def status_code_by_state(self) -> Mapping[str, int]:
codes = {
"deploy_errored": 2,
"deploy_cancelled": 1,
"mfd_failed": self.mark_for_deployment_return_code,
"abandon": 1,
"complete": 0,
}
if not self.block:
# If we don't pass --wait-for-deployment, then exit immediately after mark-for-deployment succeeds.
codes["deploying"] = 0
if self.get_auto_certify_delay() <= 0:
# Instead of setting a 0-second timer to move to certify, just exit 0 when the deploy finishes.
codes["deployed"] = 0
return codes
def get_active_button(self) -> Optional[str]:
return {
"start_deploy": "forward",
"deploying": "forward",
"deployed": None,
"start_rollback": "rollback",
"rolling_back": "rollback",
"rolled_back": None,
}.get(self.state)
def on_enter_mfd_failed(self) -> None:
self.update_slack_status(
f"Marking `{self.commit[:8]}` for deployment for {self.deploy_group} failed. Please see Jenkins for more output."
) # noqa E501
def on_enter_deploying(self) -> None:
# if self.block is False, then deploying is a terminal state so we will promptly exit.
# Don't bother starting the background thread in this case.
if self.block:
thread = Thread(
target=self.do_wait_for_deployment, args=(self.commit,), daemon=True
)
thread.start()
self.cancel_paasta_status_reminder()
self.schedule_paasta_status_reminder()
def on_exit_deploying(self) -> None:
self.stop_waiting_for_deployment(self.commit)
self.cancel_paasta_status_reminder()
def on_enter_start_rollback(self) -> None:
self.update_slack_status(
f"Rolling back ({self.deploy_group}) to {self.old_git_sha}"
)
self.mark_for_deployment_return_code = mark_for_deployment(
git_url=self.git_url,
deploy_group=self.deploy_group,
service=self.service,
commit=self.old_git_sha,
)
if self.mark_for_deployment_return_code != 0:
self.trigger("mfd_failed")
else:
self.update_slack_thread(
f"Marked `{self.old_git_sha[:8]}` for {self.deploy_group}."
+ (
"\n" + self.get_authors()
if self.deploy_group_is_set_to_notify("notify_after_mark")
else ""
)
)
self.trigger("mfd_succeeded")
def on_enter_rolling_back(self) -> None:
if self.block:
thread = Thread(
target=self.do_wait_for_deployment,
args=(self.old_git_sha,),
daemon=True,
)
thread.start()
def on_exit_rolling_back(self) -> None:
self.stop_waiting_for_deployment(self.old_git_sha)
def on_enter_deploy_errored(self) -> None:
report_waiting_aborted(self.service, self.deploy_group)
self.update_slack_status(f"Deploy aborted, but it will still try to converge.")
self.send_manual_rollback_instructions()
if self.deploy_group_is_set_to_notify("notify_after_abort"):
self.ping_authors("Deploy errored")
def on_enter_deploy_cancelled(self) -> None:
if self.deploy_group_is_set_to_notify("notify_after_abort"):
self.ping_authors("Deploy cancelled")
def stop_waiting_for_deployment(self, target_commit: str) -> None:
try:
self.wait_for_deployment_tasks[target_commit].cancel()
del self.wait_for_deployment_tasks[target_commit]
except (KeyError, asyncio.InvalidStateError):
pass
@a_sync.to_blocking
async def do_wait_for_deployment(self, target_commit: str) -> None:
try:
self.stop_waiting_for_deployment(target_commit)
wait_for_deployment_task = asyncio.create_task(
wait_for_deployment(
service=self.service,
deploy_group=self.deploy_group,
git_sha=target_commit,
soa_dir=self.soa_dir,
timeout=self.timeout,
progress=self.progress,
polling_interval=self.polling_interval,
diagnosis_interval=self.diagnosis_interval,
time_before_first_diagnosis=self.time_before_first_diagnosis,
notify_fn=self.ping_authors,
)
)
self.wait_for_deployment_tasks[target_commit] = wait_for_deployment_task
await wait_for_deployment_task
if self.deploy_group_is_set_to_notify("notify_after_wait"):
self.ping_authors(f"Finished waiting for deployment of {target_commit}")
else:
self.update_slack_thread(
f"Finished waiting for deployment of {target_commit}"
)
self.trigger("deploy_finished")
except (KeyboardInterrupt, TimeoutError):
self.trigger("deploy_cancelled")
except NoSuchCluster:
self.trigger("deploy_errored")
except asyncio.CancelledError:
# Don't trigger deploy_errored when someone calls stop_waiting_for_deployment.
pass
except Exception:
log.error("Caught exception in wait_for_deployment:")
log.error(traceback.format_exc())
self.trigger("deploy_errored")
def on_enter_rolled_back(self) -> None:
self.update_slack_status(
f"Finished rolling back to `{self.old_git_sha[:8]}` in {self.deploy_group}"
)
line = f"Rollback to {self.old_git_sha[:8]} for {self.deploy_group} complete"
_log(service=self.service, component="deploy", line=line, level="event")
self.start_timer(self.auto_abandon_delay, "auto_abandon", "abandon")
def on_enter_deployed(self) -> None:
self.update_slack_status(
f"Finished deployment of `{self.commit[:8]}` to {self.deploy_group}"
)
line = f"Deployment of {self.commit[:8]} for {self.deploy_group} complete"
_log(service=self.service, component="deploy", line=line, level="event")
self.send_manual_rollback_instructions()
if self.any_slo_failing() and self.auto_rollbacks_enabled():
self.ping_authors(
"Because an SLO is currently failing, we will not automatically certify. Instead, we will wait indefinitely until you click one of the buttons above."
)
else:
if self.get_auto_certify_delay() > 0:
self.start_timer(
self.get_auto_certify_delay(), "auto_certify", "certify"
)
if self.deploy_group_is_set_to_notify("notify_after_good_deploy"):
self.ping_authors()
def on_enter_complete(self) -> None:
if self.deploy_group_is_set_to_notify("notify_after_good_deploy"):
self.ping_authors()
def send_manual_rollback_instructions(self) -> None:
if self.old_git_sha != self.commit:
message = (
"If you need to roll back manually, run: "
f"`paasta rollback --service {self.service} --deploy-group {self.deploy_group} "
f"--commit {self.old_git_sha}`"
)
self.update_slack_thread(message)
print(message)
def after_state_change(self) -> None:
self.update_slack()
super().after_state_change()
def get_signalfx_api_token(self) -> str:
return (
load_system_paasta_config()
.get_monitoring_config()
.get("signalfx_api_key", None)
)
def get_button_text(self, button: str, is_active: bool) -> str:
active_button_texts = {
"forward": f"Rolling Forward to {self.commit[:8]} :zombocom:"
}
inactive_button_texts = {
"forward": f"Continue Forward to {self.commit[:8]} :arrow_forward:",
"complete": f"Complete deploy to {self.commit[:8]} :white_check_mark:",
"snooze": f"Reset countdown",
"enable_auto_rollbacks": "Enable auto rollbacks :eyes:",
"disable_auto_rollbacks": "Disable auto rollbacks :close_eyes_monkey:",
}
if self.old_git_sha is not None:
active_button_texts.update(
{"rollback": f"Rolling Back to {self.old_git_sha[:8]} :zombocom:"}
)
inactive_button_texts.update(
{
"rollback": f"Roll Back to {self.old_git_sha[:8]} :arrow_backward:",
"abandon": f"Abandon deploy, staying on {self.old_git_sha[:8]} :x:",
}
)
return (active_button_texts if is_active else inactive_button_texts)[button]
def start_auto_rollback_countdown(self, extra_text: str = "") -> None:
cancel_button_text = self.get_button_text(
"disable_auto_rollbacks", is_active=False
)
super().start_auto_rollback_countdown(
extra_text=f'Click "{cancel_button_text}" to cancel this!'
)
if self.deploy_group_is_set_to_notify("notify_after_auto_rollback"):
self.ping_authors()
def deploy_group_is_set_to_notify(self, notify_type: str) -> bool:
return deploy_group_is_set_to_notify(
self.deploy_info, self.deploy_group, notify_type
)
def __build_rollback_audit_details(
self, rollback_type: RollbackTypes
) -> Dict[str, str]:
return {
"rolled_back_from": self.commit,
"rolled_back_to": self.old_git_sha,
"rollback_type": rollback_type.value,
"deploy_group": self.deploy_group,
}
def log_slo_rollback(self) -> None:
_log_audit(
action="rollback",
action_details=self.__build_rollback_audit_details(
RollbackTypes.AUTOMATIC_SLO_ROLLBACK
),
service=self.service,
)
def log_user_rollback(self) -> None:
_log_audit(
action="rollback",
action_details=self.__build_rollback_audit_details(
RollbackTypes.USER_INITIATED_ROLLBACK
),
service=self.service,
)
async def wait_until_instance_is_done(
executor: concurrent.futures.Executor,
service: str,
instance: str,
cluster: str,
git_sha: str,
instance_config: LongRunningServiceConfig,
polling_interval: float,
diagnosis_interval: float,
time_before_first_diagnosis: float,
should_ping_for_unhealthy_pods: bool,
notify_fn: Optional[Callable[[str], None]] = None,
) -> Tuple[str, str]:
loop = asyncio.get_running_loop()
diagnosis_task = asyncio.create_task(
periodically_diagnose_instance(
executor,
service,
instance,
cluster,
git_sha,
instance_config,
diagnosis_interval,
time_before_first_diagnosis,
should_ping_for_unhealthy_pods,
notify_fn,
)
)
try:
while not await loop.run_in_executor(
executor,
functools.partial(
check_if_instance_is_done,
service,
instance,
cluster,
git_sha,
instance_config,
),
):
await asyncio.sleep(polling_interval)
return (
cluster,
instance,
) # for the convenience of the caller, to know which future is finishing.
finally:
diagnosis_task.cancel()
async def periodically_diagnose_instance(
executor: concurrent.futures.Executor,
service: str,
instance: str,
cluster: str,
git_sha: str,
instance_config: LongRunningServiceConfig,
diagnosis_interval: float,
time_before_first_diagnosis: float,
should_ping_for_unhealthy_pods: bool,
notify_fn: Optional[Callable[[str], None]] = None,
) -> None:
await asyncio.sleep(time_before_first_diagnosis)
loop = asyncio.get_running_loop()
while True:
try:
await loop.run_in_executor(
executor,
functools.partial(
diagnose_why_instance_is_stuck,
service,
instance,
cluster,
git_sha,
instance_config,
should_ping_for_unhealthy_pods,
notify_fn,
),
)
except asyncio.CancelledError:
raise
except Exception:
print(f"Couldn't get status of {service}.{instance}:")
traceback.print_exc()
await asyncio.sleep(diagnosis_interval)
def diagnose_why_instance_is_stuck(
service: str,
instance: str,
cluster: str,
git_sha: str,
instance_config: LongRunningServiceConfig,
should_ping_for_unhealthy_pods: bool,
notify_fn: Optional[Callable[[str], None]] = None,
) -> None:
api = client.get_paasta_oapi_client(cluster=cluster)
try:
status = api.service.status_instance(
service=service,
instance=instance,
include_smartstack=False,
include_envoy=False,
include_mesos=False,
new=True,
)
except api.api_error as e:
log.warning(
"Error getting service status from PaaSTA API for "
f"{cluster}: {e.status} {e.reason}"
)
return
print(f" Status for {service}.{instance} in {cluster}:")
for version in status.kubernetes_v2.versions:
# We call get_version_table_entry directly so that we can set version_name_suffix based on git_sha instead of
# creation time of the version (which is what get_versions_table does.)
# Without this, we'd call the old version "new" until the new version is actually created, which would be confusing.
for line in get_version_table_entry(
version,
service,
instance,
cluster,
version_name_suffix="new" if version.git_sha == git_sha else "old",
show_config_sha=True,
verbose=0,
):
print(f" {line}")
print("")
if should_ping_for_unhealthy_pods and notify_fn:
maybe_ping_for_unhealthy_pods(
service, instance, cluster, git_sha, status, notify_fn
)
already_pinged = False
def maybe_ping_for_unhealthy_pods(
service: str,
instance: str,
cluster: str,
git_sha: str,
status: InstanceStatusKubernetesV2,
notify_fn: Callable[[str], None],
) -> None:
global already_pinged
if not already_pinged:
# there can be multiple current versions, e.g. if someone changes yelpsoa-configs during a bounce.
current_versions = [
v for v in status.kubernetes_v2.versions if v.git_sha == git_sha
]
pingable_pods = [
pod
for version in current_versions
for pod in version.pods
if should_ping_for_pod(pod)
]
if pingable_pods:
already_pinged = True
ping_for_pods(service, instance, cluster, pingable_pods, notify_fn)
def should_ping_for_pod(pod: KubernetesPodV2) -> bool:
return recent_container_restart(get_main_container(pod))
def ping_for_pods(
service: str,
instance: str,
cluster: str,
pods: List[KubernetesPodV2],
notify_fn: Callable[[str], None],
) -> None:
pods_by_reason: Dict[str, List[KubernetesPodV2]] = {}
for pod in pods:
pods_by_reason.setdefault(get_main_container(pod).reason, []).append(pod)
for reason, pods_with_reason in pods_by_reason.items():
explanation = {
"Error": "crashed on startup",
"OOMKilled": "run out of memory",
"CrashLoopBackOff": "crashed on startup several times, and Kubernetes is backing off restarting them",
}.get(reason, f"restarted ({reason})")
status_tip = f"Take a | |
= None # type: Q
child_connector = QConn.C_AND # Type: QConn
def __init__(self, field: str, operator: QOper=QOper.O_EQUAL, value=None, *args):
self._field = field
self._field_operator = operator
self._value = value
if operator == QOper.O_BETWEEN:
if len(args) == 1:
self._between_value = args[0]
else:
raise ValueError('Second value in between operation missing')
elif operator == QOper.O_IN:
values = list()
values.append(value)
if args:
for v in args:
values.append(v)
self._value = values
def add(self, q_object, conn: QConn=QConn.C_AND):
self.child = q_object
self.child_connector = conn
def negate(self):
self.invert = not self.invert
def _combine(self, other, conn: QConn=QConn.C_AND):
if not isinstance(other, Q):
raise TypeError(other)
obj = self.__class__(self._field, self._field_operator, self._value)
obj.invert = self.invert
obj._between_value = self._between_value
if self.child:
obj.child = self.child
obj.child_connector = self.child_connector
obj.child.add(other, conn)
else:
obj.add(other, conn)
return obj
def __or__(self, other):
return self._combine(other, QConn.C_OR)
def __and__(self, other):
return self._combine(other, QConn.C_AND)
def __invert__(self):
self.negate()
return self
def __str__(self):
"""
Return the formated where clause
:return: where clause string
"""
clause = ''
invert = ''
placeholder = self.placeholder
if isinstance(self._value, datetime.date):
placeholder = "'{0}'".format(placeholder)
if self.invert:
invert = 'NOT '
if self._field_operator == QOper.O_IS_NULL:
clause += ' {0} IS {1}NULL'.format(self._field, invert)
elif self._field_operator == QOper.O_BETWEEN:
clause += ' {0}{1} {2} {3} AND {3}'.format(
invert, self._field, self._field_operator.value, placeholder)
elif self._field_operator == QOper.O_IN:
plhs = ', '.join(placeholder for x in self._value)
clause += ' {0}{1} {2} ({3})'.format(invert, self._field, self._field_operator.value, plhs)
else:
clause += ' {0}{1} {2} {3}'.format(invert, self._field, self._field_operator.value, placeholder)
if self.child:
clause += ' {0}{1}'.format(self.child_connector.value, str(self.child))
return clause
def get_args(self, args=None):
"""
Return the arguments for the where clause in a list
:return: list of arguments
"""
if not args:
args = list()
if isinstance(self._value, list):
for v in self._value:
args.append(v)
else:
args.append(self._value)
if self._between_value is not None:
args.append(self._between_value)
if self.child:
args = self.child.get_args(args)
return args
class BaseQuery(object):
"""
This is the object that holds the options for each SQL statement part, then generates the SQL and
return the results
"""
# None or list object with fields used in query or
_fields = None # type: list
_order_by = None # type: list
_group_by = None # type: list
_where = None # type: Q
_distinct = False # type: bool
_aggregate = False # type: list
_limit = None # type: int
_custom_sql = None # type: str
_custom_args = None # type: list
model = None # type: BaseUtilityModel_T
def __init__(self, model: BaseUtilityModel_T = None):
self.model = model
def clone(self, **kwargs):
"""
Creates a copy of the current instance. The 'kwargs' parameter can be
used by clients to update attributes after copying has taken place.
"""
clone = self.__class__(model=self.model)
# Clone our underscore properties
for k, v in self.__dict__.items():
if k.startswith('_'):
clone.__dict__[k] = self.__dict__[k]
clone.__dict__.update(kwargs)
return clone
def set_distinct(self):
self._distinct = True
def set_aggregate(self, *args):
self._aggregate = list()
for arg in args:
self._aggregate.append(str(arg))
def set_fields(self, fields):
self._fields = fields
def set_group_by(self, fields):
self._group_by = fields
def set_order_by(self, fields):
self._order_by = fields
def set_limit(self, limit):
if not isinstance(limit, int) or limit < 0:
raise ValueError('Invalid value for LIMIT statement')
self._limit = limit
def add_q(self, negate, *args, **kwargs):
"""
Add Q objects to self._where
:param negate: Invert the Q object
:param args: list of Q objects
:param kwargs: Key/Value dictionary
"""
if not args and not kwargs:
raise ValueError('No filter arguments provided')
q_list = list()
if kwargs:
for key in kwargs:
q_list.append(Q(key, QOper.O_EQUAL, kwargs[key]))
else:
for q_object in list(args):
q_list.append(q_object)
for q_object in q_list:
if negate:
q_object = ~q_object
if not self._where:
self._where = q_object
else:
self._where.add(q_object)
def to_sql(self) -> (str, list):
"""
Generate the SQL statment and return it
:return: Tuple containing SQL statement and arguments.
"""
sql, args = self._get_sql_query()
return sql, args
def _get_sql_query(self):
"""
Generate a parameterized sql statment and args list
# TODO: Move this to the Providers
:return: SQL statment, args list
"""
try:
db_table = self.model.Meta.db_table
if not db_table:
raise ModelError('db_table not defined in Model Meta class')
except Exception:
raise ModelError('db_table not defined in Model Meta class')
distinct = '' if self._distinct is False else ' DISTINCT'
# Setup SELECT fields
fields = ''
if self._fields and len(self._fields) > 0:
fields += ', '.join(self._fields)
if not fields and not self._aggregate:
fields = '*'
if self._aggregate:
if fields:
fields += ', ' + ', '.join(self._aggregate)
else:
fields = ', '.join(self._aggregate)
# Setup SELECT WHERE clause
where = ''
args = None
if self._where:
where = ' WHERE{0}'.format(str(self._where))
args = self._where.get_args()
# Setup SELECT GROUP BY clause
if not self._group_by or len(self._group_by) == 0:
group_by = ''
else:
group_by = ' GROUP BY {0}'.format(', '.join(self._group_by))
# Setup SELECT ORDER BY clause
if not self._order_by or len(self._order_by) == 0:
order_by = ''
else:
order_by = ' ORDER BY {0}'.format(', '.join(self._order_by))
if self._limit is None:
limit = ''
else:
limit = ' LIMIT {0}'.format(self._limit)
# Build SQL query here
sql = 'SELECT{0} {1} FROM {2}{3}{4}{5}{6}'.format(distinct, fields, db_table, where, group_by, order_by, limit)
sql = sql.replace(Q.placeholder, self.model.get_db_conn().placeholder)
# TODO: Future: Figure out the best location to set the correct argument value placeholder.
# TODO: Future: Right now we are defaulting to '?' for the argument value placeholder.
return sql, args
def run_query(self, db_conn: BaseDBConnection) -> list:
"""
Make the database query now
:return: list of ModelBase objects populated
:rtype: list[BaseUtilityModel_T]
"""
if not db_conn:
raise TypeError('db_conn parameter must be active BaseDBConnection object')
if not db_conn.db_connected():
raise ConnectionError('BaseDBConnection object is not connected to a database')
if not self._custom_sql:
sql, args = self._get_sql_query()
else:
sql = self._custom_sql
args = self._custom_args
if args:
records = db_conn.db_exec_stmt(sql, args)
else:
records = db_conn.db_exec_stmt(sql)
results = list()
if records:
# TODO: Future: maybe change this to a fetchmany() and return a smaller set each time like django does
for record in records:
model = self.model.__class__(db_conn, record)
results.append(model)
return results
def count(self, db_conn) -> int:
"""
Return the number of records in the table
# TODO: Move this to the Providers
:return: record count
"""
if not self._custom_sql:
sql = "SELECT count(1) AS count from {0}".format(self.model.Meta.db_table)
args = None
else:
sql = "SELECT count(1) as count from ({0})".format(self._custom_sql)
args = self._custom_args
if args:
record = db_conn.db_exec_stmt(sql, args)
else:
record = db_conn.db_exec_stmt(sql)
if record:
return int(record[0]['count'] if isinstance(record, list) else record['count'])
return 0
class BaseQuerySet(object):
"""
This is the interface for accessing the underlying database
"""
_filter = None # type: str
_result_cache = None # type: list
_db_conn = None # type: BaseDBConnection
_group_by = None # type: list
_order_by = None # type: list
query = None # type: BaseQuery
model = None # type: BaseUtilityModel_T
def __init__(self, db_conn: BaseDBConnection, model: BaseUtilityModel_T=None, query: BaseQuery=None):
if not isinstance(model, BaseTableModel):
raise ModelRequired('model parameter must be a ModelBase object')
self._db_conn = db_conn
self.model = model
self.query = query or BaseQuery(self.model)
def get_db_conn(self) -> BaseDBConnection:
"""
Return the databsae connection object.
:return: Database connection object.
"""
return self._db_conn
def _clone(self, **kwargs) -> "BaseQuerySet":
"""
Creates a copy of the current instance. The 'kwargs' parameter can be
used by clients to update attributes after copying has taken place.
"""
query = self.query.clone()
clone = self.__class__(db_conn=self._db_conn, model=self.model, query=query)
# Clone our underscore properties
for k, v in self.__dict__.items():
if k.startswith('_'):
clone.__dict__[k] = self.__dict__[k]
clone.query = query
clone.__dict__.update(kwargs)
return clone
# def __deepcopy__(self, memo):
# """
# Deep copy of a QuerySet doesn't populate the cache
# """
# obj = self.__class__(self._db_conn, self.model, self.query)
# for k, v in self.__dict__.items():
# if k == '_result_cache':
# obj.__dict__[k] = None
# else:
# obj.__dict__[k] = copy.deepcopy(v, memo)
# return obj
def __getstate__(self):
# Force the cache to be fully populated.
self._fetch_all()
obj_dict = self.__dict__.copy()
return obj_dict
def __setstate__(self, state):
self.__dict__.update(state)
def __repr__(self):
return super(BaseQuerySet, self).__repr__()
pass
# data = list(self[:REPR_OUTPUT_SIZE + 1])
# if len(data) > REPR_OUTPUT_SIZE:
# data[-1] = "...(remaining elements truncated)..."
# return '<%s %r>' % (self.__class__.__name__, data)
def __len__(self):
self._fetch_all()
return len(self._result_cache)
def __iter__(self):
"""
Populate the cache now
"""
self._fetch_all()
return | |
#!/usr/bin/env python
#
# Copyright 2016 MIT Lincoln Laboratory, Massachusetts Institute of Technology
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use these files 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.
#
"""
Authors: <NAME>
Date: April 30, 2015
Installation: Python 2.7 on Windows 7
Description: This script is an example of using pyTweet to collect profiles, timelines, friends and followers from the
Official Twitter API.
"""
from pyTweet import pyTweet
import datetime, os
def main():
# PARAMETERS
# Enter proxy host and port information
host = ''
port = ''
# Load your seed of Twitter handles into the list username_seed below.
username_seed = ['username1', 'username2']
userid_seed = [123, 12345]
# Start date for timeline collection: Collect tweets starting with current date back until the day specified below
timeline_start_date = datetime.date(year=2015, month=3, day=1)
# Sample tweet ID
twt_id = 1234567890
# List of links to photos and videos on Twitter
download_media_links = [] # must have extensions 'gif', 'jpg', 'jpeg', 'jif', 'jfif', 'tif', 'tiff', 'png', 'pdf', or 'mp4'
# Directory to save media
media_save_dir = 'directory to save media'
# Coordinates for Boston
BOS = (42.3601, 71.0589)
##
# AUTHENTICATE INTO THE OFFICIAL TWITTER API
# Create proxies dictionary
proxies = {'http': 'http://%s:%s' % (host, port), 'https': 'http://%s:%s' % (host, port)}
# Load twitter keys
twitter_keys = pyTweet.load_twitter_api_key_set()
# API authorization
OAUTH = pyTweet.get_authorization(twitter_keys)
##
# CHECK KEYS
# This function checks all of your saved Twitter API key JSON files to see if they can be used for collection
pyTweet.check_twitter_key_functionality(host=host, port=port)
##
# LOOK UP PROFILE INFORMATION
# Returns a list of fully-hydrated user dictionaries, as specified by comma-separated values passed to the user_id
# and/or screen_name parameters.
#
# There are a few things to note when using this method.
# * You must be following a protected user to be able to see their most recent status update. If you don't follow a
# protected user their status will be removed.
# * The order of user IDs or screen names may not match the order of users in the returned array.
# * If a requested user is unknown, suspended, or deleted, then that user will not be returned in the results list.
# * If none of your lookup criteria can be satisfied by returning a user object, a HTTP 404 will be thrown.
#
# PARAMETERS:
# -----------
# screen_names: List of screen names, or a single one - optional
# user_ids: List of user IDs, or a single one - optional
# include_entities: The entities node that may appear within embedded statuses will be disincluded when set to false.
user_info = pyTweet.lookup_users(proxies=proxies, auth=OAUTH, screen_names=username_seed, user_ids=userid_seed, include_entities=True)
print user_info
##
# SEARCH FOR USER PROFILES
# Search for users after providing a string query (or list of string queries). Up to 1000 users can be returned.
# The exclusive command indicates OR or AND usage in the query.
#
# PARAMETERS:
# --------
# q: Search term query, must be a string object or list of string objects
# exclusive: Boolean, if True, search query terms with ORs rather than ANDs. Default is False
# limit: limit to number of users to collect. Maximum and default values are 1000
# include_entities: The entities node will be disincluded from embedded tweet objects when set to false.
user_search_results1 = pyTweet.search_users(q="Twitter API", proxies=proxies, auth=OAUTH, limit=1000, exclusive=False)
print "\nSearch result 1: ", user_search_results1
user_search_results2 = pyTweet.search_users(q=['hangry', 'hippo'], proxies=proxies, auth=OAUTH, limit=1000, exclusive=True)
print "\nSearch result 2: ", user_search_results2
##
# LOOK UP TIME LINES
# Find timeline of a user occuring after start_date, either from a screen name or user ID. User timelines belonging
# to protected users may only be requested when the authenticated user either 'owns' the timeline or is an approved
# follower of the owner. The timeline returned is the equivalent of the one seen when you view a user's profile on
# twitter.com. This method can only return up to 3,200 of a user's most recent Tweets. Native retweets of other
# statuses by the user is included in this total, regardless of whether include_rts is set to false when requesting
# this resource.
#
# PARAMETERS:
# -----------
# user_id: The ID of the user for whom to return results for.
# screen_name: The screen name of the user for whom to return results for.
# trim_user: When set to true, each tweet returned in a timeline will include a user object including only the
# status authors numerical ID. Omit this parameter to receive the complete user object.
# exclude_replies: This boolean parameter will prevent replies from appearing in the returned timeline. Using
# exclude_replies will mean you will receive up-to count tweets
# contributor_details: This boolean parameter enhances the contributors element of the status response to
# include the screen_name of the contributor. By default only the user_id of the contributor is included.
# include_rts: When set to false, the timeline will strip any native retweets (though they will still count toward
# both the maximal length of the timeline and the slice selected by the count parameter). Note: If you're
# using the trim_user parameter in conjunction with include_rts, the retweets will still contain a full
# user object.
# :param start_date: start of timeline segment to collect, this is a datetime.date object. The default value is 52
# weeks ago from today
tl1 = pyTweet.get_timeline(proxies=proxies, auth=OAUTH, start_date=timeline_start_date, user_id=userid_seed[0])
print "\nTimeline for user ID: {}".format(userid_seed[0])
print tl1
tl2 = pyTweet.get_timeline(proxies=proxies, auth=OAUTH, start_date=timeline_start_date, screen_name=username_seed[0])
print "\nTimeline for user screen name: {}".format(username_seed[0])
print tl2
##
# LOOK UP FRIENDS AND FOLLOWERS
# There are two categories of API calls to collect friends and followers:
# 1. Get lists of friends/follower IDs
# 2. Get lists of friend/follower profile dictionaries
#
# Option 1 returns lists of IDs, and option 2 returns lists of profile dictionaries. These calls also vary on how
# much information is returned per API call. You get 5,000 friend/follower IDs per call and 15 calls in a 15 minute
# window. The later calls returning full profiles offer 20 users per call, and 15 calls in a 15 minute window.
#
# PARAMETERS:
# -----------
# user_id: The ID of the user for whom to return results for - optional
# screen_name: The screen name of the user for whom to return results for - optional
# limit: limit to number of friends to collect. Set to None to get all friends. this is the default
# skip_status: When set to either true, t or 1 statuses will not be included in the returned user objects
# include_user_entities: The user object entities node will be disincluded when set to false.
#
# Get lists of friend and follower IDs
friend_ids_list = pyTweet.get_user_friends(user_id=userid_seed[0], proxies=proxies, auth=OAUTH, limit=None)
print "\nFriend IDs: ", friend_ids_list
follower_ids_list = pyTweet.get_user_followers(user_id=userid_seed[0], proxies=proxies, auth=OAUTH, limit=200)
print "\nFollower IDs: ", follower_ids_list
# Get lists of friend and follower profile dictionaries.
friend_profile_list = pyTweet.get_user_friend_profiles(screen_name=username_seed[0], proxies=proxies, auth=OAUTH, limit=50, include_user_entities=True)
print "\nFriend profiles: ", friend_profile_list
follower_profile_list = pyTweet.get_user_follower_profiles(screen_name=username_seed[1], proxies=proxies, auth=OAUTH, limit=70)
print "\nFollower profiles: ", follower_profile_list
##
# SEARCH FOR TWEETS
# Return a list of tweets (or just one) with the following function
#
# PARAMETERS:
# -----------
# tweet_id: Unique ID of tweet, or list of tweet IDs
# include_entities: The entities node that may appear within embedded statuses will be disincluded when set to false.
# trim_user: When set to | |
<reponame>atreebangalore/jaltol
import os
#import numpy as np
#import pandas as pd
#import matplotlib.pyplot as plt
#from matplotlib.figure import Figure
#from matplotlib import axes
from pathlib import Path
import sys
import time
import json
import math
import inspect
import qgis.core
from qgis.core import (
Qgis,
QgsProject,
QgsLayout,
QgsLayoutExporter,
QgsReadWriteContext,
QgsVectorLayer,
QgsFeature,
QgsFeatureRequest,
QgsExpression,
QgsWkbTypes
)
from qgis.gui import *
from PyQt5.QtWidgets import QAction, QFileDialog, QDockWidget,QMenu
from PyQt5.QtGui import QIcon,QPixmap
from PyQt5.QtCore import Qt,QSignalMapper #contains Qt.BrushStyle
from PyQt5.QtXml import QDomDocument
from datetime import datetime
from .jaltol_dialog import JaltolDockWidget
import ee
from ee_plugin import Map
#from vectors import getLocalBoundaries
#print(dir(getLocalBoundaries)) # functions within module aren't registering.. dunno why
print(ee.String('Hello World from EE!').getInfo())
inspect_getfile = inspect.getfile(inspect.currentframe())
cmd_folder = os.path.split(inspect.getfile(inspect.currentframe()))[0]
home_dir = os.path.join(os.path.expanduser('~'))
class JaltolClass:
def __init__(self, iface): # give plugin class access to QgisInterface
self.iface = iface
def initGui(self):
icon = os.path.join(os.path.join(cmd_folder, 'logo.png')) # make path for logo image
self.action = QAction(QIcon(icon), '&Jaltol', self.iface.mainWindow()) # create QAction
self.action.triggered.connect(self.run) # if QAction triggered, run
self.iface.addPluginToMenu('&Jaltol', self.action) # add action to menu
self.iface.addToolBarIcon(self.action)
self.first_start = True
def unload(self):
self.iface.removeToolBarIcon(self.action) # remove action from tool bar
self.iface.removePluginMenu('&Jaltol', self.action) # remove action from plugin menu
del self.action
##########################################################################
## AOI ##
##########################################################################
def select_output_file(self,index):
if index==0:
print("index is 0")
print("called select file for villages")
self.dlg.lineE = self.dlg.lineEdit_aoi_selectVB
elif index==1:
print("index is 1")
print("called select file for watersheds")
self.dlg.lineE = self.dlg.lineEdit_aoi_selectWB
elif index==2:
print("index is 2")
print("called select file for custom boundaries")
self.dlg.lineE = self.dlg.lineEdit_aoi_selectCB
else:
pass
filename, _filter = QFileDialog.getOpenFileName( # use file dialog to get filename
self.dlg, "Select shape file ","", '*.shp')
self.dlg.lineE.setText(filename)
print("line Edit populated")
#self.add_boundary_layer()
def add_boundary_layer(self,index):
print("called add_boundary_layer")
self.filename = self.dlg.lineE.text()
if len(self.filename)==0:
self.iface.messageBar().pushMessage('Please select the correct shapefile', level=Qgis.Critical, duration=10)
return
if index==0:
self.layername = 'Village Boundaries'
self.vlayer = QgsVectorLayer(self.filename, self.layername, "ogr")
elif index==1:
self.layername = 'Watershed Boundaries'
elif index==2:
self.layername = 'Custom Boundaries'
else:
pass
print("layer name: ", self.layername)
self.newlayer = QgsVectorLayer(self.filename, self.layername, "ogr")
self.project.addMapLayer(self.newlayer)
alayer = self.iface.activeLayer()
single_symbol_renderer = alayer.renderer()
symbol = single_symbol_renderer.symbol()
symbol.symbolLayer(0).setBrushStyle(Qt.BrushStyle(Qt.NoBrush))
if index==0:
self.get_states()
else:
pass
def get_states(self):
print("called get States")
idx = self.vlayer.fields().indexOf('State_N')
li = sorted(self.vlayer.uniqueValues(idx))
if qgis.core.NULL in li:
li.remove(qgis.core.NULL)
self.dlg.comboBox_aoi_selectS.clear()
self.dlg.comboBox_aoi_selectS.addItems(li)
def get_districts(self):
print("called get Districts")
self.state = self.dlg.comboBox_aoi_selectS.currentText()
self.stateFilter = "\"State_N\"='" + self.state + "'"
self.vlayerFilter = self.stateFilter
#print(self.vlayerFilter)
expr = QgsExpression(self.vlayerFilter)
stateFeas = self.vlayer.getFeatures(QgsFeatureRequest(expr))
print(stateFeas)
li = []
for fea in stateFeas:
if (fea['Dist_N']==None):
li.append("BLANK")
else:
li.append(fea['Dist_N'])
li = sorted(set(li))
self.dlg.comboBox_aoi_selectD.clear()
self.dlg.comboBox_aoi_selectD.addItems(li)
def get_blocks(self):
print("called get Block")
self.dist = self.dlg.comboBox_aoi_selectD.currentText()
print("of district: ",self.dist)
self.distFilter = "\"Dist_N\"='" + self.dist + "'"
self.vlayerFilter = self.stateFilter + " and " + self.distFilter
#print(self.vlayerFilter)
expr = QgsExpression(self.vlayerFilter)
distFeas = self.vlayer.getFeatures(QgsFeatureRequest(expr))
li = []
for fea in distFeas:
if (fea['SubDist_N']==None):
li.append("BLANK")
else:
li.append(fea['SubDist_N'])
li = sorted(set(li))
print("no of blocks:",len(li))
self.dlg.comboBox_aoi_selectB.clear()
self.dlg.comboBox_aoi_selectB.addItems(li)
def get_villages(self):
print("called get Villages")
self.block = self.dlg.comboBox_aoi_selectB.currentText()
self.blockFilter = "\"SubDist_N\"='" + self.block + "'"
self.vlayerFilter = self.stateFilter + " and " + self.distFilter + " and " + self.blockFilter
expr = QgsExpression(self.vlayerFilter)
blockFeas = self.vlayer.getFeatures(QgsFeatureRequest(expr))
li = []
for fea in blockFeas:
if (fea['VCT_N']==None):
li.append("BLANK")
else:
li.append(str(fea['VCT_N']))
li = sorted(set(li))
self.dlg.comboBox_aoi_selectV.clear()
self.dlg.comboBox_aoi_selectV.addItems(li)
def select_village(self):
print("called select village")
self.vlg = self.dlg.comboBox_aoi_selectV.currentText()
self.vlgFilter = "\"VCT_N\"='"+self.vlg+"'"
self.vlayerFilter = self.stateFilter + " and " + self.distFilter + " and " + self.blockFilter + " and " + self.vlgFilter
self.vlayerFilter = self.vlayerFilter # "\""+ self.vlayerFilter.replace("\"","\\\"") +"\""
print(self.vlayerFilter)
print(self.iface.activeLayer())
# expr = QgsExpression(self.vlayerFilter)
#print("is expression valid: ",checkExpression(expr))
self.iface.activeLayer().selectByExpression(self.vlayerFilter)
def zoom_village(self):
print("called zoom to village")
self.select_village()
print(self.iface.setActiveLayer(self.vlayer))
print(self.vlayer, self.iface.activeLayer())
self.iface.actionZoomToSelected().trigger()
print("zoom to Village triggered","\n")
##########################################################################
## Water Balance Layers ##
##########################################################################
#### POPULATE UI ####
def populate_lulc_choices(self):
li = ["2000","2015","2016","2017","2018","2019"]
self.dlg.comboBox_5.clear()
self.dlg.comboBox_5.addItems(sorted(set(li)))
def populate_layer_list(self,layer):
if layer=='rain':
start,end=(2000,2021)
misc = []
elif layer=='et':
start,end=(2003,2021)
misc = []
elif layer=='sm':
start,end=(2015,2021)
misc = []
elif layer=='groundwater':
start,end=(1996,2016)
misc = ['2020']
elif layer=='surfacewater':
start,end=(2000,2021)
misc = []
elif layer=='wbyear':
start,end=(2000,2017)
misc = ['2020']
else:
print("incorrect layer name provided to populate layer list")
li = list(range(start,end))
years = [str(yr) for yr in li] + misc
if layer=='rain':
self.dlg.comboBox_9.clear()
self.dlg.comboBox_9.addItems(sorted(years))
elif layer=='et':
self.dlg.comboBox_11.clear()
self.dlg.comboBox_11.addItems(sorted(years))
elif layer=='sm':
self.dlg.comboBox_13.clear()
self.dlg.comboBox_13.addItems(sorted(years))
elif layer=='groundwater':
self.dlg.comboBox_12.clear()
self.dlg.comboBox_12.addItems(sorted(years))
elif layer=='surfacewater':
self.dlg.comboBox_10.clear()
self.dlg.comboBox_10.addItems(sorted(years))
elif layer=='wbyear':
self.dlg.comboBox_6.clear()
self.dlg.comboBox_6.addItems(sorted(years))
else:
print("incorrect layer name provided to populate layer list")
#### DEFINE LAYERS ####
def make_lulc_image(self):
geeAssetString = 'users/cseicomms/lulc_13class/KA_' + str(int(self.lulc_yr)+1)
print(geeAssetString)
self.lulc = ee.Image(geeAssetString)
print(type(self.lulc))
def make_rain_image(self):
rainColl = ee.ImageCollection("users/cseicomms/rainfall_imd")
start = ee.Date.fromYMD(int(self.rain_year),6,1)
end = ee.Date.fromYMD(int(self.rain_year)+1,5,31)
self.rain = rainColl.filterDate(start,end).sum()
print(type(self.rain))
def make_et_image(self):
geeAssetString = 'users/cseicomms/evapotranspiration_ssebop/wy' + self.et_year
self.et = ee.Image(geeAssetString)
print(type(self.et))
def make_sw_image(self):
print(type(self.sw_year),self.sw_year)
y1str = 'users/cseicomms/surfacewater/preMonsoonVolume/' + self.sw_year
y2str = 'users/cseicomms/surfacewater/preMonsoonVolume/' + str(int(self.sw_year) + 1)
year1 = ee.Image(y1str)
year2 = ee.Image(y2str)
print(y1str,y2str)
y1unmask = year1.subtract(year1)
y2unmask = year2.subtract(year2)
self.sw = year2.unmask(y1unmask).subtract(year1.unmask(y2unmask))
print(type(self.sw))
def make_gw_image(self):
print(type(self.gw_year),self.gw_year)
rcstr = 'users/cseicomms/groundwater/recharge/' + self.gw_year
dcstr = 'users/cseicomms/groundwater/discharge/' + self.gw_year
systr = 'users/cseicomms/groundwater/sy_mean_cgwb'
rc = ee.Image(rcstr)
dc = ee.Image(dcstr)
self.sy = ee.Image(systr).divide(100)
print(rcstr,dcstr,systr)
self.gw = rc.subtract(dc).multiply(1000).multiply(self.sy)
print(type(self.gw))
# new code
# y1str = 'users/cseicomms/groundwater/may_' + self.gw_year
# y2str = 'users/cseicomms/groundwater/may_' + str(int(self.gw_year) + 1)
# systr = 'users/cseicomms/groundwater/sy_mean_cgwb'
# year1 = ee.Image(y1str)
# year2 = ee.Image(y2str)
# self.sy = ee.Image(systr).divide(100)
# print(y1str,y2str,systr)
# self.gw = year1.subtract(year2).multiply(100).multiply(self.sy)
# print(type(self.gw))
def make_sm_image(self):
smColl = ee.ImageCollection("NASA_USDA/HSL/SMAP10KM_soil_moisture");
print(type(self.sm_year),self.sm_year)
year = int(self.sm_year)
myFilter = ee.Filter.And(ee.Filter.calendarRange(year,year,'year'),ee.Filter.calendarRange(5,5,'month'))
year1 = smColl.filter(myFilter).select('susm').median()
year = int(self.sm_year) + 1
myFilter = ee.Filter.And(ee.Filter.calendarRange(year,year,'year'),ee.Filter.calendarRange(5,5,'month'))
year2 = smColl.filter(myFilter).select('susm').median()
self.sm = year2.subtract(year1)
print(type(self.sm))
#### ADD LAYERS TO MAP ####
def add_lulc_image(self):
self.lulc_yr = self.dlg.comboBox_5.currentText()
self.make_lulc_image()
paletteLULC = ['02451E','06FC6D','FC0D06','28B505','750776','C713A9','C713A9',
'C713A9','E27FF9','E27FF9','E27FF9','765904','765904','765904',
'EAB828','EAB828','EAB828','092CEE','09EECB','Grey','Black']
lulc_label = 'lulc_' + self.lulc_yr
Map.addLayer(self.lulc, {'palette': paletteLULC, 'min': 0, 'max': 20}, lulc_label, True)
Map.centerObject(self.lulc,10)
def add_rain_image(self):
self.rain_year = int(self.dlg.comboBox_9.currentText())
self.make_rain_image()
paletteRain = ['ff0','fff','00f']
rainViz = {'min':400,'max':2000,'palette':paletteRain}
rain_label = 'rain_' + str(self.rain_year)
Map.addLayer(self.rain, rainViz, rain_label, True)
self.rain = None
self.rain_year = None
self.project.setCrs(self.crs)
print("crs set to 3857")
def add_et_image(self):
self.et_year = self.dlg.comboBox_11.currentText()
self.make_et_image()
et_label = 'et_' + self.et_year
Map.addLayer(self.et,{'min':300,'max':1500},et_label,True)
self.et = None
self.et_year = None
def add_sw_image(self):
self.sw_year = self.dlg.comboBox_10.currentText()
self.make_sw_image()
paletteSW = ['#f00','#000','#00f']
swViz = {'min':-80,'max':80,'palette':paletteSW}
sw_label = 'sw_' + self.sw_year
Map.addLayer(self.sw,swViz,sw_label,True)
self.sw = None
self.sw_year = None
def add_gw_image(self):
self.gw_year = self.dlg.comboBox_12.currentText()
self.make_gw_image()
paletteGW = ['#f00','#fff','#0f0']
gwViz = {'min':-80,'max':80,'palette':paletteGW}
gw_label = 'gw_' + self.gw_year
Map.addLayer(self.gw,gwViz,gw_label,True)
self.gw = None
self.gw_year = None
def add_sm_image(self):
self.sm_year = self.dlg.comboBox_13.currentText()
self.make_sm_image()
paletteSM = ['#f00','#fff','#0f0']
smViz = {'min':-80,'max':80,'palette':paletteSM}
sm_label = 'sm_' + self.sm_year
Map.addLayer(self.sm,smViz,sm_label,True)
self.sm = None
self.sm_year = None
#### CALC WATER BALANCE VALUES ####
def calc_rain_value(self):
try:
self.rain_value = round(self.rain.reduceRegion(ee.Reducer.median(),self.polygon,100).getInfo()['b1'])
self.rain_str = str(self.rain_value) + ' mm'
print("rain value(mod): ", self.rain_str)
except Exception as e:
print(e)
print(self.rain_year + " " + "rainfall image not found")
self.rain_value = math.nan
self.rain_str = "NA"
def calc_et_value(self):
try:
self.et_value = round(self.et.reduceRegion(ee.Reducer.median(),self.polygon,100).getInfo()['b1'])
self.et_str = str(self.et_value) + ' mm'
print("et value: ", self.et_str)
except Exception as e:
print(e)
print(self.et_year + " " + "et image not found")
self.et_value = math.nan
self.et_str = "NA"
def calc_sw_value(self):
try:
self.sw_vol_value = self.sw.reduceRegion(ee.Reducer.sum(),self.polygon).getInfo()['Volume']
print("sw volume (in m3): ",self.sw_vol_value,type(self.sw_vol_value))
self.sw_value_in_mm = self.sw_vol_value / self.polygon_area * 1000
self.sw_str = str(round(self.sw_value_in_mm)) + ' mm'
print("sw value: ", self.sw_str)
except Exception as e:
print(e)
print(self.sw_year + " " + "surface water image not found")
self.sw_value_in_mm = math.nan
self.sw_str = "NA"
def calc_gw_value(self):
try:
self.gw_value = round(self.gw.reduceRegion(ee.Reducer.median(),self.polygon,100).getInfo()['b1'])
self.gw_str = str(self.gw_value) + ' mm'
print("gw value: ", self.gw_str)
except Exception as e:
print(e)
print(self.gw_year + " " + "groundwater image not found")
self.gw_value = math.nan
self.gw_str = "NA"
def calc_sm_value(self):
try:
self.sm_value = round(self.sm.reduceRegion(ee.Reducer.median(),self.polygon,100).getInfo()['susm'])
self.sm_str = str(self.sm_value) + ' mm'
print("sm value: ", self.sm_str)
except Exception as e:
print(e)
print(self.sm_year + " " + "soil moisture image not found")
self.sm_value = math.nan
self.sm_str = "NA"
def calc_ro_value(self):
rhs = [self.et_value,self.sm_value,self.gw_value,self.sw_value_in_mm]
rhsnonan = [x for x in rhs if math.isnan(x) == False]
rhssum = sum(rhsnonan)
print(f"sum of outputs is {rhssum}")
if self.rain_value >= rhssum:
self.ro_value = round(self.rain_value - rhssum)
self.ro_str = str(self.ro_value) + ' mm'
print("ro value: ",self.ro_str)
else:
self.ro_value = 0
self.ro_str = "0 mm"
def calc_vill_area(self):
self.select_village()
vill = self.vlayer.getSelectedFeatures()
def calc_water_balance(self):
self.rain_year = self.et_year = self.gw_year = self.sw_year = self.sm_year = self.dlg.comboBox_6.currentText()
self.wb_year = self.dlg.comboBox_6.currentText()
self.wb_next_year = str(int(self.wb_year) + 1)[2:]
self.make_rain_image()
self.calc_rain_value() # change 'calc' to 'reduce'
self.make_et_image()
self.calc_et_value()
self.make_sw_image()
self.calc_sw_value()
self.make_gw_image()
self.calc_gw_value()
self.make_sm_image()
self.calc_sm_value()
self.calc_ro_value()
| |
<filename>train_rguo/train_code/predict_rguo_part2.py
from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
import cv2
import skimage.io
import openslide
import torchvision.models as models
import torch.utils.model_zoo as model_zoo
import types
import re
from collections import OrderedDict
import math
from torch.utils import model_zoo
from torch.utils.checkpoint import checkpoint,checkpoint_sequential
torch.backends.cudnn.benchmark = False
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
padding=0)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1,
padding=0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class Bottleneck(nn.Module):
"""
Base class for bottlenecks that implements `forward()` method.
"""
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = self.se_module(out) + residual
out = self.relu(out)
return out
class SEBottleneck(Bottleneck):
"""
Bottleneck for SENet154.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes * 2)
self.conv2 = nn.Conv2d(planes * 2, planes * 4, kernel_size=3,
stride=stride, padding=1, groups=groups,
bias=False)
self.bn2 = nn.BatchNorm2d(planes * 4)
self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNetBottleneck(Bottleneck):
"""
ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe
implementation and uses `stride=stride` in `conv1` and not in `conv2`
(the latter is used in the torchvision implementation of ResNet).
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None):
super(SEResNetBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False,
stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1,
groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SEResNeXtBottleneck(Bottleneck):
"""
ResNeXt bottleneck type C with a Squeeze-and-Excitation module.
"""
expansion = 4
def __init__(self, inplanes, planes, groups, reduction, stride=1,
downsample=None, base_width=4):
super(SEResNeXtBottleneck, self).__init__()
width = math.floor(planes * (base_width / 64)) * groups
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False,
stride=1)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
padding=1, groups=groups, bias=False)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se_module = SEModule(planes * 4, reduction=reduction)
self.downsample = downsample
self.stride = stride
class SENet(nn.Module):
def __init__(self, block, layers, groups, reduction, dropout_p=0.2,
inplanes=128, input_3x3=True, downsample_kernel_size=3,
downsample_padding=1, num_classes=1000):
"""
Parameters
----------
block (nn.Module): Bottleneck class.
- For SENet154: SEBottleneck
- For SE-ResNet models: SEResNetBottleneck
- For SE-ResNeXt models: SEResNeXtBottleneck
layers (list of ints): Number of residual blocks for 4 layers of the
network (layer1...layer4).
groups (int): Number of groups for the 3x3 convolution in each
bottleneck block.
- For SENet154: 64
- For SE-ResNet models: 1
- For SE-ResNeXt models: 32
reduction (int): Reduction ratio for Squeeze-and-Excitation modules.
- For all models: 16
dropout_p (float or None): Drop probability for the Dropout layer.
If `None` the Dropout layer is not used.
- For SENet154: 0.2
- For SE-ResNet models: None
- For SE-ResNeXt models: None
inplanes (int): Number of input channels for layer1.
- For SENet154: 128
- For SE-ResNet models: 64
- For SE-ResNeXt models: 64
input_3x3 (bool): If `True`, use three 3x3 convolutions instead of
a single 7x7 convolution in layer0.
- For SENet154: True
- For SE-ResNet models: False
- For SE-ResNeXt models: False
downsample_kernel_size (int): Kernel size for downsampling convolutions
in layer2, layer3 and layer4.
- For SENet154: 3
- For SE-ResNet models: 1
- For SE-ResNeXt models: 1
downsample_padding (int): Padding for downsampling convolutions in
layer2, layer3 and layer4.
- For SENet154: 1
- For SE-ResNet models: 0
- For SE-ResNeXt models: 0
num_classes (int): Number of outputs in `last_linear` layer.
- For all models: 1000
"""
super(SENet, self).__init__()
self.inplanes = inplanes
if input_3x3:
layer0_modules = [
('conv1', nn.Conv2d(3, 64, 3, stride=2, padding=1,
bias=False)),
('bn1', nn.BatchNorm2d(64)),
('relu1', nn.ReLU(inplace=True)),
('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1,
bias=False)),
('bn2', nn.BatchNorm2d(64)),
('relu2', nn.ReLU(inplace=True)),
('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1,
bias=False)),
('bn3', nn.BatchNorm2d(inplanes)),
('relu3', nn.ReLU(inplace=True)),
]
else:
layer0_modules = [
('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2,
padding=3, bias=False)),
('bn1', nn.BatchNorm2d(inplanes)),
('relu1', nn.ReLU(inplace=True)),
]
# To preserve compatibility with Caffe weights `ceil_mode=True`
# is used instead of `padding=1`.
layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2,
ceil_mode=True)))
self.layer0 = nn.Sequential(OrderedDict(layer0_modules))
self.layer1 = self._make_layer(
block,
planes=64,
blocks=layers[0],
groups=groups,
reduction=reduction,
downsample_kernel_size=1,
downsample_padding=0
)
self.layer2 = self._make_layer(
block,
planes=128,
blocks=layers[1],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.layer3 = self._make_layer(
block,
planes=256,
blocks=layers[2],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.layer4 = self._make_layer(
block,
planes=512,
blocks=layers[3],
stride=2,
groups=groups,
reduction=reduction,
downsample_kernel_size=downsample_kernel_size,
downsample_padding=downsample_padding
)
self.avg_pool = nn.AvgPool2d(7, stride=1)
self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None
self.last_linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,
downsample_kernel_size=1, downsample_padding=0):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=downsample_kernel_size, stride=stride,
padding=downsample_padding, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, groups, reduction, stride,
downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, groups, reduction))
return nn.Sequential(*layers)
def features(self, x):
x = self.layer0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def features_ckpt(self, x):
x.requires_grad = True
x = checkpoint(self.layer0, x, preserve_rng_state=False)
x = checkpoint_sequential(self.layer1, 3, x, preserve_rng_state=False)
x = checkpoint_sequential(self.layer2, 4, x, preserve_rng_state=False)
x = checkpoint_sequential(self.layer3, 6, x, preserve_rng_state=False)
x = checkpoint_sequential(self.layer4, 3, x, preserve_rng_state=False)
return x
def logits(self, x):
x = self.avg_pool(x)
if self.dropout is not None:
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, x):
x = self.features(x)
x = self.logits(x)
return x
def se_resnext50_32x4d(num_classes=1000, pretrained='imagenet'):
model = SENet(SEResNeXtBottleneck, [3, 4, 6, 3], groups=32, reduction=16,
dropout_p=None, inplanes=64, input_3x3=False,
downsample_kernel_size=1, downsample_padding=0,
num_classes=num_classes)
return model
"""
This file contains helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo
########################################################################
############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
########################################################################
# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate',
'num_classes', 'width_coefficient', 'depth_coefficient',
'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])
# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'stride', 'se_ratio'])
# Change namedtuple defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
class SwishImplementation(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i * torch.sigmoid(i)
ctx.save_for_backward(i)
return result
@staticmethod
def backward(ctx, grad_output):
i = ctx.saved_variables[0]
sigmoid_i = torch.sigmoid(i)
return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
class MemoryEfficientSwish(nn.Module):
def forward(self, x):
return SwishImplementation.apply(x)
class Swish(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
def round_filters(filters, global_params):
""" Calculate and round number of filters based on depth multiplier. """
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor
new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
new_filters += divisor
return int(new_filters)
def round_repeats(repeats, global_params):
""" Round number of filters based on depth multiplier. """
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
def drop_connect(inputs, p, training):
""" Drop connect. """
if not training: return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
# random_tensor = keep_prob
random_tensor = torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device) + keep_prob
binary_tensor = torch.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
def get_same_padding_conv2d(image_size=None):
""" Chooses static padding if you have specified an image size, and dynamic | |
import re
import csv
import gzip
import json
import logging
import itertools
import requests
from typing import List, Tuple, Optional, Union
from functools import lru_cache
import xml
from xml.etree import ElementTree
from urllib.error import HTTPError
from protmapper.resources import resource_manager, feature_from_json, Feature
logger = logging.getLogger(__name__)
uniprot_url = 'http://www.uniprot.org/uniprot/'
xml_ns = {'up': 'http://uniprot.org/uniprot'}
@lru_cache(maxsize=10000)
def query_protein(protein_id: str) -> Union[ElementTree.ElementTree, None]:
"""Retrieve the XML entry for a given protein.
Parameters
----------
protein_id :
The UniProt ID of the protein to look up.
Returns
-------
:
An ElementTree representation of the XML entry for the
protein.
"""
# Try looking up a primary ID if the given one
# is a secondary ID and strip off isoforms
protein_id = get_primary_id(_strip_isoform(protein_id))
url = uniprot_url + protein_id + '.xml'
try:
# As opposed to the RDF endpoint, the XML endpoint returns
# an identical entry for secondary accessions, for instance,
# the response for the secondary ID A0A021WW06 is identical to
# the response for the primary ID P40417.
ret = requests.get(url)
et = ElementTree.fromstring(ret.content)
return et
except Exception as e:
return None
def _strip_isoform(protein_id):
return protein_id.split('-')[0]
def _split_isoform(protein_id):
parts = protein_id.split('-', maxsplit=1)
protein_id = parts[0]
isoform = None
if len(parts) == 2:
if re.match(r'\d+', parts[1]):
isoform = parts[1]
return protein_id, isoform
def _reattach_isoform(pid, iso):
if iso is not None:
return '%s-%s' % (pid, iso)
else:
return pid
def is_secondary(protein_id):
"""Return True if the UniProt ID corresponds to a secondary accession.
Parameters
----------
protein_id : str
The UniProt ID to check.
Returns
-------
True if it is a secondary accessing entry, False otherwise.
"""
entry = um.uniprot_sec.get(_strip_isoform(protein_id))
if not entry:
return False
return True
def is_reviewed(protein_id):
"""Return True if the UniProt ID corresponds to a reviewed entry.
Parameters
----------
protein_id : str
The UniProt ID to check.
Returns
-------
True if it is a reviewed entry, False otherwise.
"""
return _strip_isoform(protein_id) in um.uniprot_reviewed
def get_primary_id(protein_id):
"""Return a primary entry corresponding to the UniProt ID.
Parameters
----------
protein_id : str
The UniProt ID to map to primary.
Returns
-------
primary_id : str
If the given ID is primary, it is returned as is. Otherwise the primary
IDs are looked up. If there are multiple primary IDs then the first
human one is returned. If there are no human primary IDs then the
first primary found is returned.
"""
base_id, isoform = _split_isoform(protein_id)
primaries = um.uniprot_sec.get(base_id)
if primaries:
if len(primaries) > 1:
logger.debug('More than 1 primary ID for %s.' % base_id)
for primary in primaries:
# Often secondary IDs were broken into multiple primary IDs
# for different organisms. In this case we return the human
# one if it exists.
if is_human(primary):
return _reattach_isoform(primary, isoform)
# If we haven't returned anything then we just return the
# first primary id
return _reattach_isoform(primaries[0], isoform)
# If there is no secondary entry then we assume this is a primary entry
return protein_id
def get_family_members(family_name, human_only=True):
"""Return the HGNC gene symbols which are the members of a given family.
Parameters
----------
family_name : str
Family name to be queried.
human_only : bool
If True, only human proteins in the family will be returned.
Default: True
Returns
-------
gene_names : list
The HGNC gene symbols corresponding to the given family.
"""
data = {'query': 'family:%s' % family_name,
'format': 'list'}
if human_only:
data['fil'] = 'organism:human'
res = requests.get(uniprot_url, params=data)
if not res.status_code == 200 or not res.text:
return None
# res.text gets us the Unicode
html = res.text
protein_list = html.strip().split('\n')
gene_names = []
for p in protein_list:
gene_name = get_gene_name(p)
gene_names.append(gene_name)
return gene_names
def get_mnemonic(protein_id, web_fallback=False):
"""Return the UniProt mnemonic for the given UniProt ID.
Parameters
----------
protein_id : str
UniProt ID to be mapped.
web_fallback : Optional[bool]
If True and the offline lookup fails, the UniProt web service
is used to do the query.
Returns
-------
mnemonic : str
The UniProt mnemonic corresponding to the given Uniprot ID.
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
try:
mnemonic = um.uniprot_mnemonic[protein_id]
return mnemonic
except KeyError:
pass
if not web_fallback:
return None
tree = query_protein(protein_id)
if tree is None:
return None
mnemonic = tree.find('up:entry/up:name', namespaces=xml_ns)
if mnemonic is None:
return None
return mnemonic.text
def get_id_from_mnemonic(uniprot_mnemonic):
"""Return the UniProt ID for the given UniProt mnemonic.
Parameters
----------
uniprot_mnemonic : str
UniProt mnemonic to be mapped.
Returns
-------
uniprot_id : str
The UniProt ID corresponding to the given Uniprot mnemonic.
"""
try:
uniprot_id = um.uniprot_mnemonic_reverse[uniprot_mnemonic]
return uniprot_id
except KeyError:
return None
def get_gene_name(protein_id, web_fallback=True):
"""Return the gene name or canonical protein name for the given UniProt ID.
If available, this function returns the primary gene name provided by
UniProt. If not available, the primary protein name is returned.
Parameters
----------
protein_id : str
UniProt ID to be mapped.
web_fallback : Optional[bool]
If True and the offline lookup fails, the UniProt web service
is used to do the query.
Returns
-------
gene_name : str
The gene name corresponding to the given Uniprot ID.
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
try:
gene_name = um.uniprot_gene_name[protein_id]
# We only get here if the protein_id was in the dict
if gene_name:
return gene_name
# We do it this way to return None for empty strings
else:
return None
except KeyError:
if not web_fallback:
return None
tree = query_protein(protein_id)
if tree is None:
return None
name = tree.find('up:entry/up:gene/up:name', namespaces=xml_ns)
if name is not None:
return name.text
return None
def get_gene_synonyms(protein_id: str) -> List[str]:
"""Return a list of synonyms for the gene corresponding to a protein.
Note that synonyms here also include the official gene name as
returned by get_gene_name.
Parameters
----------
protein_id :
The UniProt ID of the protein to query
Returns
-------
:
The list of synonyms of the gene corresponding to the protein
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
protein = query_protein(protein_id)
if protein is None:
return []
synonyms = []
gene_synoyms = protein.findall('up:entry/up:gene/up:name',
namespaces=xml_ns)
for gene_syn in gene_synoyms:
synonyms.append(gene_syn.text)
return synonyms
def get_protein_synonyms(protein_id):
"""Return a list of synonyms for a protein.
Note that this function returns protein synonyms as provided by UniProt.
The get_gene_synonym returns synonyms given for the gene corresponding
to the protein, and get_synonyms returns both.
Parameters
----------
protein_id : str
The UniProt ID of the protein to query
Returns
-------
synonyms : list[str]
The list of synonyms of the protein
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
tree = query_protein(protein_id)
if tree is None:
return None
synonyms = []
for syn_type, syn_len in itertools.product(['recommended', 'alternative'],
['full', 'short']):
synonym_type = 'up:entry/up:protein/up:%sName/up:%sName' % \
(syn_type, syn_len)
synonyms_xml = tree.findall(synonym_type, namespaces=xml_ns)
for synonym_xml in synonyms_xml:
synonyms.append(synonym_xml.text)
return synonyms
def get_synonyms(protein_id):
"""Return synonyms for a protein and its associated gene.
Parameters
----------
protein_id : str
The UniProt ID of the protein to query
Returns
-------
synonyms : list[str]
The list of synonyms of the protein and its associated gene.
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
ret = []
gene_syms = get_gene_synonyms(protein_id)
if gene_syms:
ret.extend(gene_syms)
prot_syms = get_protein_synonyms(protein_id)
if prot_syms:
ret.extend(prot_syms)
return ret
@lru_cache(maxsize=10000)
def get_sequence(protein_id):
base, iso = _split_isoform(get_primary_id(protein_id))
# Try to get the sequence from the downloaded sequence files
if iso == '1':
protein_id = base
else:
protein_id = _reattach_isoform(base, iso)
seq = um.uniprot_sequences.get(protein_id)
if seq is None:
url = uniprot_url + '%s.fasta' % protein_id
res = requests.get(url)
res.raise_for_status()
# res.text is Unicode
lines = res.text.splitlines()
seq = (''.join(lines[1:])).replace('\n', '')
return seq
def get_modifications(protein_id: str) -> List[Tuple[str, int]]:
"""Return a list of modifications for a protein.
Parameters
----------
protein_id :
The UniProt ID of the protein to query
Returns
-------
:
The list of modifications of the protein, each represented
as a tuple of residue description string and position
string.
"""
protein_id = get_primary_id(_strip_isoform(protein_id))
tree = query_protein(protein_id)
if tree is None:
return None
# We find all features of type 'modified residue'
features = tree.findall("up:entry/up:feature[@type='modified residue']",
namespaces=xml_ns)
mods = []
for feature in features:
# We find the position of the modified residue
pos_tag = feature.find('up:location/up:position', namespaces=xml_ns)
if pos_tag is None:
continue
pos = int(pos_tag.attrib['position'])
# We find the residue
res = feature.attrib['description'].split(';')[0]
mods.append((res, pos))
return mods
def verify_location(protein_id, residue, location):
"""Return True if the residue is at the given location in the UP | |
<filename>utils.py
#!/usr/bin/env python3
# coding=utf-8
import io
import os
import re
import sys
import ssl
import glob
import time
import shlex
import signal
import subprocess
has_filter = False
has_progress = False
has_winreg = False
has_certifi = False
is_cygwin = sys.platform == 'cygwin'
is_win32 = sys.platform == 'win32'
is_conemu = False
last_progress = 0
# try importing the optional dependencies
try:
import winreg
has_winreg = True
except ImportError:
pass
try:
import certifi
has_certifi = True
except ImportError:
pass
if is_win32:
try:
from colorama import init
init()
has_filter = True
except ImportError:
pass
import ctypes
class ConsoleCursorInfo(ctypes.Structure):
_fields_ = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]
if not is_win32 or has_filter:
class Fore:
RED = '\x1B[91m'
GREEN = '\x1B[92m'
BLUE = '\x1B[94m'
YELLOW = '\x1B[93m'
RESET = '\x1B[39m'
else:
class Fore:
RED = ''
GREEN = ''
BLUE = ''
YELLOW = ''
RESET = ''
# registers for the interrupt signal in order to gracefully exit when Ctrl-C is hit
def handle_sigint():
def signal_handler(signal, frame):
clear_progress()
show_cursor()
print('%s[!]%s Terminating early due to interruption.' % (Fore.RED, Fore.RESET))
sys.exit(-1)
signal.signal(signal.SIGINT, signal_handler)
# check if any CA bundles were loaded or fallback to certifi otherwise
def ensure_ca_load():
if ssl.create_default_context().cert_store_stats()['x509_ca'] == 0:
if has_certifi:
def create_certifi_context(purpose = ssl.Purpose.SERVER_AUTH, *, cafile = None, capath = None, cadata = None):
return ssl.create_default_context(purpose, cafile = certifi.where())
ssl._create_default_https_context = create_certifi_context
else:
print('%s[!]%s Python was unable to load any CA bundles. Additionally, the fallback %scertifi%s module is not available. Install it with %spip3 install certifi%s for TLS connection support.' % (Fore.RED, Fore.RESET, Fore.GREEN, Fore.RESET, Fore.GREEN, Fore.RESET))
sys.exit(-1)
# parse image[:tag] | archive argument
def parse_image_arg(argv, can_be_file = False):
"""
Parses the image[:tag] argument passed to the script. When can_be_file is True,
the argument can also be a filename, _and_ if it is not, the generated image:tag
should have a corresponding rootfs archive available.
:param argv: Passed argument.
:param can_be_file: Whether argument can be a file and image:tag should also resolve to a file.
:return: Name of the image, tag, name of the file, label.
"""
exts = ['.tar', '.sfs', '.squashfs']
argvl = argv.lower()
image = argv
tag = 'latest'
fname = ''
label = ''
if not can_be_file or all(ext not in argvl for ext in exts):
# handle image:tag
if ':' in image:
idx = image.find(':')
tag = image[idx + 1:]
image = image[:idx]
if can_be_file:
fname = 'rootfs_%s_%s.tar*' % (image.replace('/', '_'), tag)
names = glob.glob(fname)
if len(names) > 0:
fname = names[0]
else:
print('%s[!]%s No files found matching %s%s%s.' % (Fore.RED, Fore.RESET, Fore.BLUE, fname, Fore.RESET))
sys.exit(-1)
else:
fname = 'rootfs_%s_%s' % (image.replace('/', '_'), tag)
label = '%s_%s' % (image.replace('/', '_'), tag)
else:
# handle file name
fname = argv
if not os.path.isfile(fname):
print('%s[!]%s %s%s%s is not an existing file.' % (Fore.RED, Fore.RESET, Fore.BLUE, fname, Fore.RESET))
sys.exit(-1)
idx = -1
for ext in exts:
idx = argvl.find(ext)
if idx != -1:
break
label = os.path.basename(argvl[:idx])
if label.startswith('rootfs_'):
label = label[len('rootfs_'):]
if label.find('_') == -1:
label += '_' + tag
return image, tag, fname, label
# sanity check WSL installation
def probe_wsl(silent = False):
"""
Checks whether the WSL is installed and not running.
:type silent: Whether to print an error message or just return an empty string on failure.
:return: Paths to the WSL directory and lxrun/bash executables.
"""
global is_cygwin
if not is_cygwin:
#packagesSubFolder = os.path.join(os.getenv('LocalAppData'), 'Packages')
#basedir = os.path.join(packagesSubFolder, 'TheDebianProject.DebianGNULinux_76v4gfsz19hv4')
#basedir = os.path.join(packagesSubFolder, 'CanonicalGroupLimited.UbuntuonWindows_79rhkp1fndgsc')
#localStateDir = os.path.join(basedir, 'LocalState')
basedir = "C://Users//cmdsz//Debian"
localStateDir = basedir
else:
print('JPST: not yet fixed when running this process via cygwin, sorry!')
sys.exit(-1)
basedir = subprocess.check_output('/usr/bin/cygpath -F 0x001c', shell = True, universal_newlines = True)
basedir = os.path.join(basedir.strip(), 'lxss')
if not os.path.isdir(basedir):
if silent:
return None, None, None
print('%s[!]%s The Linux subsystem is not installed. Please go through the standard installation procedure first.' % (Fore.RED, Fore.RESET))
sys.exit(-1)
# new temp is in basedir/LocalState/temp
if os.path.exists(os.path.join(localStateDir, 'temp')) and os.listdir(os.path.join(localStateDir, 'temp')):
if silent:
return None, None, None
print('%s[!]%s The Linux subsystem is currently running. Please kill all instances before continuing.' % (Fore.RED, Fore.RESET))
sys.exit(-1)
if not is_cygwin:
syspath = os.getenv('SystemRoot')
else:
syspath = subprocess.check_output('/usr/bin/cygpath -W', shell = True, universal_newlines = True).strip()
lxpath = ''
#methinks location in System32 is from legacy installer
lxpaths = [os.path.join(syspath, 'WinSxS\\amd64_microsoft-windows-lxss-installer_31bf3856ad364e35_10.0.17134.1_none_e9926368b80f9a59'), os.path.join(syspath, 'System32')]
for path in lxpaths:
if os.path.exists(os.path.join(path, 'LxRun.exe')):
lxpath = path
break
if not lxpath and not silent:
print('%s[!]%s Unable to find %slxrun.exe%s in the expected locations.' % (Fore.RED, Fore.RESET, Fore.BLUE, Fore.RESET))
sys.exit(-1)
bashpath = ''
#new iteration of WSL splitted all linux related resources in seperate folders inside C:\Windows\WinSxS\*
bashpaths = [os.path.join(syspath, 'WinSxS', 'amd64_microsoft-windows-lxss-bash_31bf3856ad364e35_10.0.17134.1_none_251beae725bc7de5'), os.path.join(syspath, 'System32')]
for path in bashpaths:
if os.path.exists(os.path.join(path, 'bash.exe')):
bashpath = path
break
if not bashpath and not silent:
print('%s[!]%s Unable to find %sbash.exe%s in the expected locations.' % (Fore.RED, Fore.RESET, Fore.BLUE, Fore.RESET))
sys.exit(-1)
return basedir, lxpath, bashpath
# translate the path between Windows and Cygwin
def path_trans(path):
"""
Translate the path, if required.
Under the native Windows installation of Python, this function does nothing.
Under the Cygwin version, the provided path is translated to a Windows-native path.
:param path: Path to be translated.
:return: Translated path.
"""
global is_cygwin
if not is_cygwin or not path.startswith('/cygdrive/'):
return path
# too slow:
# subprocess.check_output('/usr/bin/cygpath -w ' + shlex.quote(path), shell = True, universal_newlines = True).strip()
path = path[10] + ':\\' + path[12:].replace('/', '\\')
return path
# get label of rootfs
def get_label(path):
"""
Gets the label for the specified rootfs. If the .switch_label file is not present,
but the OS was identified, the file will be created for future use.
:param path: Path to the rootfs.
:return: Label of the rootfs.
"""
# see if .switch_label exists
if os.path.isfile(os.path.join(path, '.switch_label')):
try:
with open(os.path.join(path, '.switch_label')) as f:
label = f.readline().strip()
if len(label) > 0:
return label
except OSError:
pass
# check if the directory name has any stuff appended to it
dirname = os.path.basename(path)
if dirname.startswith('rootfs_'):
label = dirname[len('rootfs_'):]
if len(label) > 0:
# save label for next occasion
try:
with open(os.path.join(path, '.switch_label'), 'w') as f:
f.write(label + '\n')
except OSError:
pass
return label
# see if any *release files exist in /etc/
rlsfiles = glob.glob(os.path.join(path, 'etc', '*release')) + glob.glob(os.path.join(path, 'usr', 'lib', 'os-release*'))
if len(rlsfiles) > 0:
rlslines = []
for file in rlsfiles:
try:
with open(file) as f:
rlslines += f.readlines()
except OSError:
pass
name = ['', '', ''] # ID || DISTRIB_ID || NAME
vers = ['', '', ''] # DISTRIB_CODENAME || DISTRIB_RELEASE || VERSION_ID
for line in rlslines:
kv = line.split('=', 1)
if len(kv) < 2:
continue
key = kv[0].strip().strip('"\'').lower()
val = kv[1].strip().strip('"\'').lower()
if len(val) == 0:
continue
if key == 'id':
name[0] = val
elif key == 'distrib_id':
name[1] = val
elif key == 'name':
name[2] = val
if key == 'distrib_codename':
vers[0] = val
elif key == 'distrib_release':
vers[1] = val
elif key == 'version_id':
vers[2] = val
name = list(filter(None, name))
vers = list(filter(None, vers))
if len(name) > 0:
label = name[0] + ('_' + vers[0] if len(vers) > 0 else '')
# save label for next occasion
try:
with open(os.path.join(path, '.switch_label'), 'w') as f:
f.write(label + '\n')
except OSError:
pass
return label
# oh well
return ''
# toggle cursor visibility in the terminal
def show_cursor():
"""
Turns the cursor back on in the terminal.
"""
if not sys.platform == 'win32':
sys.stdout.write('\033[?25h')
else:
ci = ConsoleCursorInfo()
handle = ctypes.windll.kernel32.GetStdHandle(-11)
ctypes.windll.kernel32.GetConsoleCursorInfo(handle, ctypes.byref(ci))
ci.visible = True
ctypes.windll.kernel32.SetConsoleCursorInfo(handle, ctypes.byref(ci))
def hide_cursor():
"""
Turns the cursor off in the terminal.
"""
global is_conemu
if not sys.platform == 'win32':
sys.stdout.write('\033[?25l')
is_conemu = False
else:
ci = ConsoleCursorInfo()
handle = ctypes.windll.kernel32.GetStdHandle(-11)
ctypes.windll.kernel32.GetConsoleCursorInfo(handle, ctypes.byref(ci))
ci.visible = False
ctypes.windll.kernel32.SetConsoleCursorInfo(handle, ctypes.byref(ci))
is_conemu = os.environ.get('ConEmuANSI') == 'ON'
# some characters are forbidden in NTFS, but are not in ext4. the most popular of these characters
# seems to be the colon character. LXSS solves this issue by escaping the character on NTFS.
# while this seems like a dumb implementation, it will be called a lot of times inside the
# decompression loop, so it has to be fast: http://stackoverflow.com/a/27086669/156626
def escape_ntfs_invalid(name):
"""
Escapes characters which are forbidden in NTFS, but are not in ext4.
:param name: Path potentially containing forbidden NTFS characters.
:return: Path with forbidden NTFS characters escaped.
"""
return name.replace('*', '#002A').replace('|', '#007C').replace(':', '#003A').replace('>', '#003E').replace('<', '#003C').replace('?', '#003F').replace('"', '#0022')
# stream copier with progress bar
def chunked_copy(name, source, dest):
"""
Copies one stream into another, with progress bar.
:param name: Name of the file to display.
:param source: Source stream.
:param dest: Destination stream.
:return: Number of bytes copied.
"""
global is_conemu
size = int(source.info()['Content-Length'].strip())
recv = 0
if len(name) > 23:
name = name[0:20] + '...'
hide_cursor()
while True:
chunk = source.read(8192)
recv += len(chunk)
if not chunk:
break
dest.write(chunk)
draw_progress(recv, size, name)
show_cursor()
return recv
# FileIO wrapper with progress bar
class ProgressFileObject(io.FileIO):
def __init__(self, path, *args, **kwargs):
self._total_size = os.path.getsize(path)
self.current_extraction = ''
io.FileIO.__init__(self, path, *args, **kwargs)
hide_cursor()
def read(self, length):
"""
Read at most size bytes, returned as bytes.
Only makes one system call, so less data may be returned than requested.
In non-blocking mode, returns None if no data is available.
Return an empty bytes object at EOF.
"""
draw_progress(self.tell(), self._total_size, self.current_extraction)
return io.FileIO.read(self, length)
def __del__(self):
show_cursor()
# standalone function to draw an interactive progressbar
def draw_progress(recv, size, name):
"""
Draws an interactive progressbar based on the specified information.
:param recv: Number of bytes received.
:param size: Total size of the file.
:param name: Name of the file to display.
"""
global is_conemu, has_progress, last_progress
if recv > size:
recv = size
if recv == size:
clear_progress()
return
if time.time() - last_progress < 0.05:
return
has_progress = True
last_progress = time.time()
if len(name) > 23:
name = name[0:20] + '...'
else:
name = name.ljust(23, ' ')
pct = round(recv / size * 100, 2)
bar = int(50 * recv / size)
sys.stdout.write('\r %s [%s>%s] %0.2f%%' % (name, '=' * bar, ' ' * (50 - bar), pct))
if is_conemu:
sys.stdout.write('\033]9;4;1;%0.0f\033\\\033[39m' % pct)
sys.stdout.flush()
def clear_progress():
"""
Clears the progress bar.
"""
global is_conemu, has_progress
if not has_progress:
return
has_progress = False
sys.stdout.write('\r%s\r' % (' ' * (66 + 23)))
if is_conemu:
sys.stdout.write('\033]9;4;0\033\\\033[39m')
sys.stdout.flush()
# functions to interact with the registry
def get_lxss_user():
"""
Gets the active user inside WSL.
:return: Tuple | |
{'table': table3_name, 'id': 3, 'name': '<NAME>'}]
INPUT_DATA = '\n'.join(json.dumps(rec) for rec in ROWS)
pipeline_builder = sdc_builder.get_pipeline_builder()
pipeline = _create_jdbc_producer_pipeline(pipeline_builder, 'JDBC Producer Multitable Insert', INPUT_DATA,
"${record:value('/table')}", 'INSERT')
# JDBC Producer's "Table Name" property is converted to uppercase through the configure_for_environment() method
# when database is Oracle. However EL function names are case-sensitive; we overwrite it afterwards to avoid an EL
# error.
pipeline.configure_for_environment(database)
pipeline[2].set_attributes(table_name="${record:value('/table')}")
# For Oracle, the default value of JDBC Producer's "Schema Name" property in the database environment is the
# database name, but it should be the username instead.
if isinstance(database, OracleDatabase):
pipeline[2].set_attributes(schema_name=database.username.upper())
sdc_executor.add_pipeline(pipeline)
try:
sdc_executor.start_pipeline(pipeline).wait_for_pipeline_output_records_count(len(ROWS))
sdc_executor.stop_pipeline(pipeline)
result1 = database.engine.execute(table1.select())
result2 = database.engine.execute(table2.select())
result3 = database.engine.execute(table3.select())
data1 = result1.fetchall()
data2 = result2.fetchall()
data3 = result3.fetchall()
assert data1 == [(ROWS[0]['name'], ROWS[0]['id'])]
assert data2 == [(ROWS[1]['name'], ROWS[1]['id'])]
assert data3 == [(ROWS[2]['name'], ROWS[2]['id'])]
result1.close()
result2.close()
result3.close()
finally:
logger.info('Dropping tables %s, %s, %s in %s database...', table1_name, table2_name, table3_name,
database.type)
table1.drop(database.engine)
table2.drop(database.engine)
table3.drop(database.engine)
# Test SDC-10719
@database
@sdc_min_version('3.8.0')
def test_jdbc_producer_multischema(sdc_builder, sdc_executor, database):
"""Test for JDBC Producer in a multischema scenario with a single destination table for each schema. We create 3
schemas with one table for each, with the same name. Then we use an EL expression to insert records according to
the /schema record field.
Pipeline:
dev_raw_data_source >> record_deduplicator >> jdbc_producer
record_deduplicator >> trash
"""
schema1_name = _get_random_name(database, prefix='stf_schema_')
schema2_name = _get_random_name(database, prefix='stf_schema_')
schema3_name = _get_random_name(database, prefix='stf_schema_')
table_name = _get_random_name(database, prefix='stf_table_')
_create_schema(schema1_name, database)
_create_schema(schema2_name, database)
_create_schema(schema3_name, database)
table1 = _create_table(table_name, database, schema_name=schema1_name)
table2 = _create_table(table_name, database, schema_name=schema2_name)
table3 = _create_table(table_name, database, schema_name=schema3_name)
ROWS = [{'schema': schema1_name, 'id': 1, 'name': '<NAME>'},
{'schema': schema2_name, 'id': 2, 'name': '<NAME>'},
{'schema': schema3_name, 'id': 3, 'name': '<NAME>'}]
INPUT_DATA = '\n'.join(json.dumps(rec) for rec in ROWS)
pipeline_builder = sdc_builder.get_pipeline_builder()
pipeline = _create_jdbc_producer_pipeline(pipeline_builder, 'JDBC Producer Multischema Insert', INPUT_DATA,
table_name, 'INSERT')
# JDBC Producer's "Schema Name" property is set through the `database` environment under some circumstances
# (e.g. Sql Server database). We overwrite it afterwards for the test.
pipeline.configure_for_environment(database)
pipeline[2].set_attributes(schema_name="${record:value('/schema')}")
sdc_executor.add_pipeline(pipeline)
try:
sdc_executor.start_pipeline(pipeline).wait_for_pipeline_output_records_count(len(ROWS))
sdc_executor.stop_pipeline(pipeline)
result1 = database.engine.execute(table1.select())
result2 = database.engine.execute(table2.select())
result3 = database.engine.execute(table3.select())
data1 = result1.fetchall()
data2 = result2.fetchall()
data3 = result3.fetchall()
assert data1 == [(ROWS[0]['name'], ROWS[0]['id'])]
assert data2 == [(ROWS[1]['name'], ROWS[1]['id'])]
assert data3 == [(ROWS[2]['name'], ROWS[2]['id'])]
result1.close()
result2.close()
result3.close()
finally:
logger.info('Dropping table %s in schemas...', table_name)
table1.drop(database.engine)
table2.drop(database.engine)
table3.drop(database.engine)
logger.info('Dropping schemas %s, %s, %s...', schema1_name, schema2_name, schema3_name)
_drop_schema(schema1_name, database)
_drop_schema(schema2_name, database)
_drop_schema(schema3_name, database)
# Test SDC-10719
@database
@sdc_min_version('3.8.0')
def test_jdbc_producer_multischema_multitable(sdc_builder, sdc_executor, database):
"""Test a JDBC Producer in a multischema scenario with different destination tables for each schema. We create 3
schemas with one table for each, with different names. Then we use an EL expressions to insert records according to
the /schema and /table record fields.
There were a limitation in previous versions that affected to MySQL and MemSQL. These RDBMs do not differentiate
between schema and database. SDC used the database configured in the JDBC connection string, and looked for database
metadata filtering by database+schema. If the schema were other than the database of the connection string, metadata
could not be retrieved. This was a problem in a multischema scenario, where several schemas are employed.
Pipeline:
dev_raw_data_source >> record_deduplicator >> jdbc_producer
record_deduplicator >> trash
"""
schema1_name = _get_random_name(database, prefix='stf_schema_')
schema2_name = _get_random_name(database, prefix='stf_schema_')
schema3_name = _get_random_name(database, prefix='stf_schema_')
table1_name = _get_random_name(database, prefix='stf_table_')
table2_name = _get_random_name(database, prefix='stf_table_')
table3_name = _get_random_name(database, prefix='stf_table_')
_create_schema(schema1_name, database)
_create_schema(schema2_name, database)
_create_schema(schema3_name, database)
table1 = _create_table(table1_name, database, schema_name=schema1_name)
table2 = _create_table(table2_name, database, schema_name=schema2_name)
table3 = _create_table(table3_name, database, schema_name=schema3_name)
ROWS = [{'schema': schema1_name, 'table': table1_name, 'id': 1, 'name': '<NAME>'},
{'schema': schema2_name, 'table': table2_name, 'id': 2, 'name': '<NAME>'},
{'schema': schema3_name, 'table': table3_name, 'id': 3, 'name': '<NAME>'}]
INPUT_DATA = '\n'.join(json.dumps(rec) for rec in ROWS)
pipeline_builder = sdc_builder.get_pipeline_builder()
pipeline = _create_jdbc_producer_pipeline(pipeline_builder, 'JDBC Producer Multischema and Multitable Insert',
INPUT_DATA, "${record:value('/table')}", 'INSERT')
# JDBC Producer's "Schema Name" property is set through the `database` environment under some circumstances
# (e.g. Sql Server database). We overwrite it afterwards for the test.
pipeline.configure_for_environment(database)
pipeline[2].set_attributes(schema_name="${record:value('/schema')}")
# JDBC Producer's "Table Name" property is converted to uppercase through the configure_for_environment() method
# when database is Oracle. However EL function names are case-sensitive; we overwrite it afterwards to avoid an EL
# error.
pipeline[2].set_attributes(table_name="${record:value('/table')}")
sdc_executor.add_pipeline(pipeline)
try:
sdc_executor.start_pipeline(pipeline).wait_for_pipeline_output_records_count(len(ROWS))
sdc_executor.stop_pipeline(pipeline)
result1 = database.engine.execute(table1.select())
result2 = database.engine.execute(table2.select())
result3 = database.engine.execute(table3.select())
data1 = result1.fetchall()
data2 = result2.fetchall()
data3 = result3.fetchall()
assert data1 == [(ROWS[0]['name'], ROWS[0]['id'])]
assert data2 == [(ROWS[1]['name'], ROWS[1]['id'])]
assert data3 == [(ROWS[2]['name'], ROWS[2]['id'])]
result1.close()
result2.close()
result3.close()
finally:
logger.info('Dropping tables %s, %s, %s...', table1_name, table2_name, table3_name)
table1.drop(database.engine)
table2.drop(database.engine)
table3.drop(database.engine)
logger.info('Dropping schemas %s, %s, %s...', schema1_name, schema2_name, schema3_name)
_drop_schema(schema1_name, database)
_drop_schema(schema2_name, database)
_drop_schema(schema3_name, database)
# SDC-11063: Do not reoder update statements in JDBC destination
@sdc_min_version('3.0.0.0')
@pytest.mark.parametrize('multi_row', [True, False])
@database
def test_jdbc_producer_ordering(sdc_builder, sdc_executor, multi_row, database):
"""Ensure that variously intertwined operations won't be executed out of order in harmful way."""
table_name = get_random_string(string.ascii_lowercase, 20)
metadata = sqlalchemy.MetaData()
table = sqlalchemy.Table(
table_name,
metadata,
sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True, quote=True, autoincrement=False),
sqlalchemy.Column('a', sqlalchemy.Integer, quote=True),
sqlalchemy.Column('b', sqlalchemy.Integer, quote=True)
)
RAW_DATA = [
# Update id=5
{"op": 3, "id": 5, "a": 2, "b": 2},
# Insert id=4
{"op": 1, "id": 4, "a": 1, "b": 1},
# Update id=4
{"op": 3, "id": 4, "a": 2, "b": 2},
# Delete id=5
{"op": 2, "id": 5},
# Insert id=1
{"op": 1, "id": 1, "a": 1, "b": 1},
# Update id=1
{"op": 3, "id": 1, "a": 2},
# Insert id=2
{"op": 1, "id": 2, "a": 1, "b": 1},
# Delete id=2
{"op": 2, "id": 2},
# Update id=1
{"op": 3, "id": 1, "a": 2, "b": 2},
# Insert id=3
{"op": 1, "id": 3, "a": 1, "b": 1},
# Update id=1
{"op": 3, "id": 1, "a": 3},
# Update id=3
{"op": 3, "id": 3, "a": 5},
# Delete id=3
{"op": 2, "id": 3}
]
builder = sdc_builder.get_pipeline_builder()
source = builder.add_stage('Dev Raw Data Source')
source.stop_after_first_batch = True
source.data_format = 'JSON'
source.raw_data = '\n'.join(json.dumps(rec) for rec in RAW_DATA)
expression = builder.add_stage('Expression Evaluator')
expression.header_attribute_expressions = [
{'attributeToSet': 'sdc.operation.type', 'headerAttributeExpression': '${record:value("/op")}'}
]
remover = builder.add_stage('Field Remover')
remover.set_attributes(fields=['/op'], action='REMOVE')
producer = builder.add_stage('JDBC Producer')
producer.field_to_column_mapping = []
producer.default_operation = 'UPDATE'
producer.table_name = table_name
producer.use_multi_row_operation = multi_row
if database.type == 'Oracle':
producer.enclose_object_names = True
source >> expression >> remover >> producer
pipeline = builder.build().configure_for_environment(database)
sdc_executor.add_pipeline(pipeline)
try:
logger.info('Creating table %s in %s database ...', table_name, database.type)
table.create(database.engine)
# The table will start with single row (id=5)
logger.info('Inserting rows into %s in %s database', table_name, database.type)
connection = database.engine.connect()
connection.execute(table.insert(), {'id': 5, 'a': 1, 'b': 1})
# Finally run the pipeline and verify it's outcome
sdc_executor.start_pipeline(pipeline).wait_for_finished()
result = database.engine.execute(table.select())
db = sorted(result.fetchall(), key=lambda row: row[0]) # order by id
result.close()
assert len(db) == 2
# id=1
assert 1 == db[0][0]
assert 3 == db[0][1]
assert 2 == db[0][2]
# id=5
assert 4 == db[1][0]
assert 2 == db[1][1]
assert 2 == db[1][2]
finally:
logger.info('Dropping table %s in %s database ...', table_name, database.type)
table.drop(database.engine)
@sdc_min_version('3.0.0.0')
@database
def test_jdbc_multitable_events(sdc_builder, sdc_executor, database):
"""
Validate that we properly generate events
"""
if database.type == 'Oracle':
pytest.skip("This test depends on auto-created ID that doesn't work properly on Oracle")
table_prefix = get_random_string(string.ascii_lowercase, 20)
table_a = '{}_a'.format(table_prefix)
table_b = '{}_b'.format(table_prefix)
table_events = '{}_events'.format(table_prefix)
builder = sdc_builder.get_pipeline_builder()
source = builder.add_stage('JDBC Multitable Consumer')
source.transaction_isolation = 'TRANSACTION_READ_COMMITTED'
source.table_configs = [{
'tablePattern': f'{table_prefix}%',
"enableNonIncremental": True,
'tableExclusionPattern': table_events
}]
trash = builder.add_stage('Trash')
expression = builder.add_stage('Expression Evaluator')
expression.field_expressions = [{
'fieldToSet': '/tbl',
'expression': '${record:value("/table")}${record:value("/tables[0]")}'
}, {
'fieldToSet': '/tbls',
'expression': '${record:value("/tables[0]")},${record:value("/tables[1]")}'
}, {
'fieldToSet': '/event',
'expression': '${record:eventType()}'
}
]
producer = builder.add_stage('JDBC Producer')
producer.table_name = table_events
producer.default_operation = 'INSERT'
producer.field_to_column_mapping = [
dict(field='/event', columnName='event'),
dict(field='/tbl', columnName='tbl'),
dict(field='/tbls', columnName='tbls')
]
source >> trash
source >= expression
expression >> producer
pipeline = builder.build().configure_for_environment(database)
sdc_executor.add_pipeline(pipeline)
# We need three tables for this test
metadata = sqlalchemy.MetaData()
a = sqlalchemy.Table(
table_a,
metadata,
sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True)
)
b = sqlalchemy.Table(
table_b,
metadata,
sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=False)
)
events = sqlalchemy.Table(
table_events,
metadata,
sqlalchemy.Column('id', sqlalchemy.Integer, primary_key=True),
sqlalchemy.Column('event', sqlalchemy.String(50)),
sqlalchemy.Column('tbl', sqlalchemy.String(150)),
sqlalchemy.Column('tbls', sqlalchemy.String(150))
)
try:
logger.info('Creating tables %s, %s and %s in %s database ...', table_a, table_b, table_events, database.type)
a.create(database.engine)
b.create(database.engine)
events.create(database.engine)
logger.info('Inserting | |
import os
import time
import json
import logging
import binascii
import networkx as nx
from copy import deepcopy
from networkx import NetworkXNoPath
from itertools import islice
from threading import Thread, Event
from flask import Flask, request, Response, abort
from riemann import tx
from bitcoind_mock.rpc_errors import *
from bitcoind_mock import conf, utils
from bitcoind_mock import transaction
from bitcoind_mock.zmq_publisher import ZMQPublisher
app = Flask(__name__)
GENESIS_PARENT = "00" * 32
def set_event(event, wait_time):
"""
Sets the mining event once every ``wait_time`` seconds so a new block is generated at fixed intervals.
Args:
event(:obj:`Event`): the event to be set.
wait_time(:obj:`int`): time between blocks.
"""
while True:
time.sleep(wait_time)
event.set()
class BitcoindMock:
"""
Tiny mock of bitcoind. It creates a blockchain mock and a JSON-RCP interface. Let's you perform some of the bitcoind
RPC commands (listed in process_request).
Also let's you mine blocks by time or by demand, and create forks by demand.
Attributes:
blockchain(:obj:`DiGraph`): a directed graph representing the blockchain.
blocks(:obj:`dict`): a dictionary keeping track of all blocks. Contains:
``{tx, height, previousblockhash, chainwork}``
mempool(:obj:`dict`): a dictionary keeping track of the transactions pending to be mined.
mempool(:obj:`dict`): a dictionary with all the mined transactions and a reference to the block where they were
mined.
mine_new_block(:obj:`Event`): an event flag to trigger a new block (if the mock is set to mine based on events).
best_tip(:obj:`str`): a reference to the chain best tip.
genesis(:obj:`str`): a reference to the chain genesis block.
last_mined_block(:obj:`str`): a reference to the last mined block. The mock will mine on top of it.
"""
def __init__(self):
self.blockchain = nx.DiGraph()
self.blocks = dict()
self.mempool = dict()
self.transactions = dict()
self.mine_new_block = Event()
# Create the genesis block
block_hash, coinbase_tx, coinbase_tx_hash = self.get_new_block_data()
self.transactions[coinbase_tx_hash] = {"tx": coinbase_tx, "block_hash": block_hash}
self.blocks[block_hash] = {
"tx": [coinbase_tx_hash],
"height": 0,
"previousblockhash": GENESIS_PARENT,
"chainwork": 0,
}
self.blockchain.add_node(block_hash, short_id=block_hash[:8])
self.best_tip = block_hash
self.last_mined_block = block_hash
self.genesis = block_hash
# Set the event so it can start mining right off the bat
self.mine_new_block.set()
@staticmethod
def get_rpc_param(request_data):
"""
Gets the first parameter from a RPC call.
Args:
request_data(:obj:`list`): list of parameters from the rpc call.
Returns:
:obj:`str` or :obj:`None`: The first parameter of the call, or ``None`` if there are no parameters.
"""
params = request_data.get("params")
if isinstance(params, list) and len(params) > 0:
return params[0]
else:
return None
@staticmethod
def get_new_block_data():
"""
Creates the data to be used for a mined block.
Returns:
:obj:`tuple`: A three item tuple (block_hash, coinbase_tx, coinbase_tx_hash)
"""
block_hash = utils.get_random_value_hex(32)
coinbase_tx = transaction.create_dummy_transaction()
return block_hash, coinbase_tx.hex(), coinbase_tx.tx_id.hex()
def generate(self):
"""
Endpoint use to trigger the mining of a new block. It can be accessed at ``/`` using ``POST``.
Returns:
:obj:`Response`: An HTTP 200-OK response signaling the acceptance of the request.
"""
self.mine_new_block.set()
return Response(status=200, mimetype="application/json")
def create_fork(self):
"""
Endpoint used to trigger a chain fork. It can be accessed at ``/fork`` using ``POST``.
Requires a JSON encoded data with the key ``parent`` and the hash of an already mined block. The blockchain will
be forked from the ``parent``.
Returns:
:obj:`Response`: An HTTP 200-OK response signaling the acceptance of the request if the parent was a valid
block. An HTTP 200-OK with an error if the parent was invalid.
"""
request_data = request.get_json()
response = {"result": 0, "error": None}
parent = request_data.get("parent")
# FIXME: We only accept forks one by one for now
if parent not in self.blocks:
response["error"] = {"code": -1, "message": "Wrong parent block to fork from"}
else:
print("Forking chain from {}".format(parent))
self.last_mined_block = parent
self.generate()
return Response(json.dumps(response), status=200, mimetype="application/json")
def process_request(self):
"""
Simulates the bitcoin-rpc server run by bitcoind. The available commands are limited to the ones we'll need to
test out functionality. The model we will be using is pretty simplified to reduce the complexity of mocking
bitcoind:
decoderawtransaction: querying for the decoding of a raw transaction will return a dictionary with a single
field: "txid".
sendrawtransaction: sending a rawtransaction will notify our mining simulator to include such transaction in
a subsequent block (add it to mempool).
getrawtransaction: requesting a rawtransaction from a txid will return a dictionary containing a single
field: "confirmations", since rawtransactions are only queried to check whether a
transaction has made it to a block or not.
getblockcount: the block count represents the height of the most-work fully-validated chain.
The genesis block has height 0.
getblock: querying for a block will return a dictionary with three fields: "tx" representing a
list of transactions, "height" representing the block height and "hash" representing the
block hash.
getblockhash: returns the hash of a block given its height.
getbestblockhash: returns the hash of the block in the tip of the chain.
help: help is only used as a sample command to test if bitcoind is running when bootstrapping.
It will return a 200/OK with no data.
"""
request_data = request.get_json()
method = request_data.get("method")
response = {"id": 0, "result": 0, "error": None}
no_param_err = {"code": RPC_MISC_ERROR, "message": "JSON value is not a {} as expected"}
if method == "decoderawtransaction":
rawtx = self.get_rpc_param(request_data)
if isinstance(rawtx, str) and len(rawtx) % 2 is 0:
try:
t = tx.Tx.from_hex(rawtx)
response["result"] = {"txid": t.tx_id.hex()}
except (ValueError, IndexError):
response["error"] = {"code": RPC_DESERIALIZATION_ERROR, "message": "TX decode failed"}
else:
response["error"] = no_param_err
response["error"]["message"] = response["error"]["message"].format("string")
elif method == "sendrawtransaction":
# TODO: A way of rejecting transactions should be added to test edge cases.
rawtx = self.get_rpc_param(request_data)
if isinstance(rawtx, str) and len(rawtx) % 2 is 0:
try:
t = tx.Tx.from_hex(rawtx)
txid = t.tx_id.hex()
if txid not in self.transactions:
self.mempool[txid] = rawtx
response["result"] = {"txid": txid}
else:
response["error"] = {
"code": RPC_VERIFY_ALREADY_IN_CHAIN,
"message": "Transaction already in block chain",
}
except (ValueError, IndexError):
response["error"] = {"code": RPC_DESERIALIZATION_ERROR, "message": "TX decode failed"}
else:
response["error"] = no_param_err
response["error"]["message"] = response["error"]["message"].format("string")
elif method == "getrawtransaction":
txid = self.get_rpc_param(request_data)
if isinstance(txid, str):
if txid in self.transactions:
block_hash = self.transactions[txid]["block_hash"]
if self.in_best_chain(block_hash):
block = self.blocks.get(block_hash)
rawtx = self.transactions[txid].get("tx")
response["result"] = {
"hex": rawtx,
"confirmations": 1 + self.blocks.get(self.best_tip).get("height") - block.get("height"),
}
else:
response["error"] = {
"code": RPC_INVALID_ADDRESS_OR_KEY,
"message": "No such mempool or blockchain transaction. Use gettransaction for wallet "
"transactions.",
}
elif txid in self.mempool:
response["result"] = {"confirmations": None}
else:
response["error"] = {
"code": RPC_INVALID_ADDRESS_OR_KEY,
"message": "No such mempool or blockchain transaction. Use gettransaction for "
"wallet transactions.",
}
else:
response["error"] = no_param_err
response["error"]["message"] = response["error"]["message"].format("string")
elif method == "getblockcount":
response["result"] = self.blocks[self.best_tip].get("height")
elif method == "getblock":
block_hash = self.get_rpc_param(request_data)
if isinstance(block_hash, str):
block = deepcopy(self.blocks.get(block_hash))
if block is not None:
if self.in_best_chain(block_hash):
block["confirmations"] = 1 + self.blocks.get(self.best_tip).get("height") - block.get("height")
else:
block["confirmations"] = -1
# chainwork is returned as a 32-byte hex by bitcoind
block["chainwork"] = "{:064x}".format(block["chainwork"])
block["hash"] = block_hash
response["result"] = block
else:
response["error"] = {"code": RPC_INVALID_ADDRESS_OR_KEY, "message": "Block not found"}
else:
response["error"] = no_param_err
response["error"]["message"] = response["error"]["message"].format("string")
elif method == "getblockhash":
height = self.get_rpc_param(request_data)
if isinstance(height, int):
if 0 <= height <= self.blocks.get(self.best_tip).get("height"):
response["result"] = nx.shortest_path(self.blockchain, self.genesis, self.best_tip)[height]
else:
response["error"] = {"code": RPC_INVALID_PARAMETER, "message": "Block height out of range"}
else:
response["error"] = no_param_err
response["error"]["message"] = response["error"]["message"].format("integer")
elif method == "getbestblockhash":
response["result"] = self.best_tip
elif method == "help":
pass
else:
return abort(404, "Method not found")
return Response(json.dumps(response), status=200, mimetype="application/json")
def in_best_chain(self, block_hash):
"""
Returns whether a given block hash if part of the best chain or not. A block is party of the best chain if there
a path from it to the best tip (directed graph).
Args:
block_hash(:obj:`str`): the block hash to be checked.
Returns:
:obj:`bool`: Whether the block is part of the best chain or not.
"""
try:
nx.shortest_path(self.blockchain, block_hash, self.best_tip)
return True
except NetworkXNoPath:
return False
def simulate_mining(self, verbose=True):
"""
Simulates bicoin mining. The simulator ca be run in two modes: by events, or by time.
If ``mode=='event'``, the simulator will be waiting for event on `/generate`. Otherwise, a block will be mined
every ``TIME_BETWEEN_BLOCKS`` seconds. Transactions received via ``sendrawtransactions`` wil be included in a
new generated block (up to ``TX_PER_BLOCK``).
Also, the simulator will notify about new blocks via ZMQ.
Args:
verbose(:obj:`bool`): whether to print via stdout when | |
<gh_stars>1-10
#
# (C) Copyright 2003-2007 Hewlett-Packard Development Company, L.P.
# (C) Copyright 2006-2007 Novell, Inc.
#
# This library 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 2.1 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
# 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., 675 Mass Ave, Cambridge, MA 02139, USA.
#
# Author: <NAME> <<EMAIL>>
# Author: <NAME> <<EMAIL>>
# Author: <NAME> <<EMAIL>>
#
"""CIM operations over HTTP.
The `WBEMConnection` class in this module opens a connection to a remote
WBEM server. Across this connection you can run various CIM operations.
Each method of this class corresponds fairly directly to a single CIM
operation.
"""
# This module is meant to be safe for 'import *'.
import re
from datetime import datetime, timedelta
from pywbemReq import cim_obj, cim_xml, cim_http, cim_types
from pywbemReq.cim_obj import CIMInstance, CIMInstanceName, CIMClass, \
CIMClassName, NocaseDict
from pywbemReq.cim_types import is_text, is_text_or_bool
from pywbemReq.tupleparse import ParseError, parse_reply_xml
__all__ = ['DEFAULT_NAMESPACE', 'check_utf8_xml_chars',
'CIMError', 'WBEMConnection', 'is_subclass',
'PegasusUDSConnection', 'SFCBUDSConnection',
'OpenWBEMUDSConnection']
DEFAULT_NAMESPACE = 'root/cimv2'
if len(u'\U00010122') == 2:
# This is a "narrow" Unicode build of Python (the normal case).
_ILLEGAL_XML_CHARS_RE = re.compile(
u'([\u0000-\u0008\u000B-\u000C\u000E-\u001F\uFFFE\uFFFF])')
else:
# This is a "wide" Unicode build of Python.
_ILLEGAL_XML_CHARS_RE = re.compile(
u'([\u0000-\u0008\u000B-\u000C\u000E-\u001F\uFFFE\uFFFF])')
_ILL_FORMED_UTF8_RE = re.compile(
'(\xED[\xA0-\xBF][\x80-\xBF])') # U+D800...U+DFFF
def _check_classname(val):
"""
Validate a classname.
At this point, only the type is validated to be a string.
"""
if not is_text(val):
raise ValueError("string expected for classname, not %s" % repr(val))
def check_utf8_xml_chars(utf8_xml, meaning):
"""
Examine a UTF-8 encoded XML string and raise a `pywbem.ParseError`
exception if the response contains Bytes that are invalid UTF-8
sequences (incorrectly encoded or ill-formed) or that are invalid XML
characters.
This function works in both "wide" and "narrow" Unicode builds of Python
and supports the full range of Unicode characters from U+0000 to U+10FFFF.
This function is just a workaround for the bad error handling of Python's
`xml.dom.minidom` package. It replaces the not very informative
`ExpatError` "not well-formed (invalid token): line: x, column: y" with a
`pywbem.ParseError` providing more useful information.
:Parameters:
utf8_xml : string
The UTF-8 encoded XML string to be examined.
meaning : string
Short text with meaning of the XML string, for messages in exceptions.
:Exceptions:
`TypeError`, if invoked with incorrect Python object type for `utf8_xml`.
`pywbem.ParseError`, if `utf8_xml` contains Bytes that are invalid UTF-8
sequences (incorrectly encoded or ill-formed) or invalid XML characters.
Notes on Unicode support in Python:
(1) For internally representing Unicode characters in the unicode type, a
"wide" Unicode build of Python uses UTF-32, while a "narrow" Unicode
build uses UTF-16. The difference is visible to Python programs for
Unicode characters assigned to code points above U+FFFF: The "narrow"
build uses 2 characters (a surrogate pair) for them, while the "wide"
build uses just 1 character. This affects all position- and
length-oriented functions, such as `len()` or string slicing.
(2) In a "wide" Unicode build of Python, the Unicode characters assigned to
code points U+10000 to U+10FFFF are represented directly (using code
points U+10000 to U+10FFFF) and the surrogate code points
U+D800...U+DFFF are never used; in a "narrow" Unicode build of Python,
the Unicode characters assigned to code points U+10000 to U+10FFFF are
represented using pairs of the surrogate code points U+D800...U+DFFF.
Notes on the Unicode code points U+D800...U+DFFF ("surrogate code points"):
(1) These code points have no corresponding Unicode characters assigned,
because they are reserved for surrogates in the UTF-16 encoding.
(2) The UTF-8 encoding can technically represent the surrogate code points.
ISO/IEC 10646 defines that a UTF-8 sequence containing the surrogate
code points is ill-formed, but it is technically possible that such a
sequence is in a UTF-8 encoded XML string.
(3) The Python escapes ``\\u`` and ``\\U`` used in literal strings can
represent the surrogate code points (as well as all other code points,
regardless of whether they are assigned to Unicode characters).
(4) The Python `str.encode()` and `str.decode()` functions successfully
translate the surrogate code points back and forth for encoding UTF-8.
For example, ``'\\xed\\xb0\\x80'.decode("utf-8") = u'\\udc00'``.
(5) Because Python supports the encoding and decoding of UTF-8 sequences
also for the surrogate code points, the "narrow" Unicode build of
Python can be (mis-)used to transport each surrogate unit separately
encoded in (ill-formed) UTF-8.
For example, code point U+10122 can be (illegally) created from a
sequence of code points U+D800,U+DD22 represented in UTF-8:
``'\\xED\\xA0\\x80\\xED\\xB4\\xA2'.decode("utf-8") = u'\\U00010122'``
while the correct UTF-8 sequence for this code point is:
``u'\\U00010122'.encode("utf-8") = '\\xf0\\x90\\x84\\xa2'``
Notes on XML characters:
(1) The legal XML characters are defined in W3C XML 1.0 (Fith Edition):
::
Char ::= #x9 | #xA | #xD | [#x20-#xD7FF] | [#xE000-#xFFFD] |
[#x10000-#x10FFFF]
These are the code points of Unicode characters using a non-surrogate
representation.
"""
context_before = 16 # number of chars to print before any bad chars
context_after = 16 # number of chars to print after any bad chars
if not isinstance(utf8_xml, str):
raise TypeError("utf8_xml argument does not have str type, but %s" % \
type(utf8_xml))
# Check for ill-formed UTF-8 sequences. This needs to be done
# before the str type gets decoded to unicode, because afterwards
# surrogates produced from ill-formed UTF-8 cannot be distinguished from
# legally produced surrogates (for code points above U+FFFF).
ic_list = list()
for m in _ILL_FORMED_UTF8_RE.finditer(utf8_xml):
ic_pos = m.start(1)
ic_seq = m.group(1)
ic_list.append((ic_pos, ic_seq))
if len(ic_list) > 0:
exc_txt = "Ill-formed (surrogate) UTF-8 Byte sequences found in %s:" % \
meaning
for (ic_pos, ic_seq) in ic_list:
exc_txt += "\n At offset %d:" % ic_pos
for c in ic_seq:
exc_txt += " 0x%02X" % ord(c)
cpos1 = max(ic_pos - context_before, 0)
cpos2 = min(ic_pos + context_after, len(utf8_xml))
exc_txt += ", CIM-XML snippet: %r" % utf8_xml[cpos1:cpos2]
raise ParseError(exc_txt)
# Check for incorrectly encoded UTF-8 sequences.
# @ibm.13@ Simplified logic (removed loop).
try:
utf8_xml_u = utf8_xml.decode("utf-8")
except UnicodeDecodeError as exc:
# Only raised for incorrectly encoded UTF-8 sequences; technically
# correct sequences that are ill-formed (e.g. representing surrogates)
# do not cause this exception to be raised.
# If more than one incorrectly encoded sequence is present, only
# information about the first one is returned in the exception object.
# Also, the stated reason (in _msg) is not always correct.
_codec, _str, _p1, _p2, _msg = exc.args
exc_txt = "Incorrectly encoded UTF-8 Byte sequences found in %s" % \
meaning
exc_txt += "\n At offset %d:" % _p1
for c in utf8_xml[_p1:_p2 + 1]:
exc_txt += " 0x%02X" % ord(c)
cpos1 = max(_p1 - context_before, 0)
cpos2 = min(_p2 + context_after, len(utf8_xml))
exc_txt += ", CIM-XML snippet: %r" % utf8_xml[cpos1:cpos2]
raise ParseError(exc_txt)
# Now we know the Unicode characters are valid.
# Check for Unicode characters that cannot legally be represented as XML
# characters.
ic_list = list()
last_ic_pos = -2
for m in _ILLEGAL_XML_CHARS_RE.finditer(utf8_xml_u):
ic_pos = m.start(1)
ic_char = m.group(1)
if ic_pos > last_ic_pos + 1:
ic_list.append((ic_pos, ic_char))
last_ic_pos = ic_pos
if len(ic_list) > 0:
exc_txt = "Invalid XML characters found in %s:" % meaning
for (ic_pos, ic_char) in ic_list:
cpos1 = max(ic_pos - context_before, 0)
cpos2 = min(ic_pos + context_after, len(utf8_xml_u))
exc_txt += "\n At offset %d: U+%04X, CIM-XML snippet: %r" % \
(ic_pos, ord(ic_char), utf8_xml_u[cpos1:cpos2])
raise ParseError(exc_txt)
return utf8_xml
class Error(Exception):
pass
class CIMError(Error):
"""
Exception indicating that the WBEM server has returned an error response
with a CIM status code.
The exception value is a tuple of
``(error_code, description, exception_obj)``, where:
* ``error_code``: the numeric CIM status code. See `cim_constants` for
constants defining CIM status code values.
* ``description``: a string (`unicode` or | |
"""Generated message classes for sqladmin version v1beta3.
Creates and configures Cloud SQL instances, which provide fully-managed MySQL
databases.
"""
# NOTE: This file is autogenerated and should not be edited by hand.
from googlecloudsdk.third_party.apitools.base.protorpclite import message_types as _message_types
from googlecloudsdk.third_party.apitools.base.protorpclite import messages as _messages
package = 'sqladmin'
class BackupConfiguration(_messages.Message):
"""Database instance backup configuration.
Fields:
binaryLogEnabled: Whether binary log is enabled. If backup configuration
is disabled, binary log must be disabled as well.
enabled: Whether this configuration is enabled.
id: Identifier for this configuration. This gets generated automatically
when a backup configuration is created.
kind: This is always sql#backupConfiguration.
startTime: Start time for the daily backup configuration in UTC timezone
in the 24 hour format - HH:MM.
"""
binaryLogEnabled = _messages.BooleanField(1)
enabled = _messages.BooleanField(2)
id = _messages.StringField(3)
kind = _messages.StringField(4, default=u'sql#backupConfiguration')
startTime = _messages.StringField(5)
class BackupRun(_messages.Message):
"""A database instance backup run resource.
Fields:
backupConfiguration: Backup Configuration identifier.
dueTime: The due time of this run in UTC timezone in RFC 3339 format, for
example 2012-11-15T16:19:00.094Z.
endTime: The time the backup operation completed in UTC timezone in RFC
3339 format, for example 2012-11-15T16:19:00.094Z.
enqueuedTime: The time the run was enqueued in UTC timezone in RFC 3339
format, for example 2012-11-15T16:19:00.094Z.
error: Information about why the backup operation failed. This is only
present if the run has the FAILED status.
instance: Name of the database instance.
kind: This is always sql#backupRun.
startTime: The time the backup operation actually started in UTC timezone
in RFC 3339 format, for example 2012-11-15T16:19:00.094Z.
status: The status of this run.
"""
backupConfiguration = _messages.StringField(1)
dueTime = _message_types.DateTimeField(2)
endTime = _message_types.DateTimeField(3)
enqueuedTime = _message_types.DateTimeField(4)
error = _messages.MessageField('OperationError', 5)
instance = _messages.StringField(6)
kind = _messages.StringField(7, default=u'sql#backupRun')
startTime = _message_types.DateTimeField(8)
status = _messages.StringField(9)
class BackupRunsListResponse(_messages.Message):
"""Backup run list results.
Fields:
items: A list of backup runs in reverse chronological order of the
enqueued time.
kind: This is always sql#backupRunsList.
nextPageToken: The continuation token, used to page through large result
sets. Provide this value in a subsequent request to return the next page
of results.
"""
items = _messages.MessageField('BackupRun', 1, repeated=True)
kind = _messages.StringField(2, default=u'sql#backupRunsList')
nextPageToken = _messages.StringField(3)
class BinLogCoordinates(_messages.Message):
"""Binary log coordinates.
Fields:
binLogFileName: Name of the binary log file for a Cloud SQL instance.
binLogPosition: Position (offset) within the binary log file.
kind: This is always sql#binLogCoordinates.
"""
binLogFileName = _messages.StringField(1)
binLogPosition = _messages.IntegerField(2)
kind = _messages.StringField(3, default=u'sql#binLogCoordinates')
class CloneContext(_messages.Message):
"""Database instance clone context.
Fields:
binLogCoordinates: Binary log coordinates, if specified, indentify the
position up to which the source instance should be cloned. If not
specified, the source instance is cloned up to the most recent binary
log coordinates.
destinationInstanceName: Name of the Cloud SQL instance to be created as a
clone.
kind: This is always sql#cloneContext.
sourceInstanceName: Name of the Cloud SQL instance to be cloned.
"""
binLogCoordinates = _messages.MessageField('BinLogCoordinates', 1)
destinationInstanceName = _messages.StringField(2)
kind = _messages.StringField(3, default=u'sql#cloneContext')
sourceInstanceName = _messages.StringField(4)
class DatabaseFlags(_messages.Message):
"""MySQL flags for Cloud SQL instances.
Fields:
name: The name of the flag. These flags are passed at instance startup, so
include both MySQL server options and MySQL system variables. Flags
should be specified with underscores, not hyphens. For more information,
see Configuring MySQL Flags in the Google Cloud SQL documentation, as
well as the official MySQL documentation for server options and system
variables.
value: The value of the flag. Booleans should be set to on for true and
off for false. This field must be omitted if the flag doesn't take a
value.
"""
name = _messages.StringField(1)
value = _messages.StringField(2)
class DatabaseInstance(_messages.Message):
"""A Cloud SQL instance resource.
Fields:
currentDiskSize: The current disk usage of the instance in bytes.
databaseVersion: The database engine type and version. Can be MYSQL_5_5 or
MYSQL_5_6. Defaults to MYSQL_5_5. The databaseVersion cannot be changed
after instance creation.
etag: HTTP 1.1 Entity tag for the resource.
instance: Name of the Cloud SQL instance. This does not include the
project ID.
instanceType: The instance type. This can be one of the following.
CLOUD_SQL_INSTANCE: Regular Cloud SQL instance. READ_REPLICA_INSTANCE:
Cloud SQL instance acting as a read-replica.
ipAddresses: The assigned IP addresses for the instance.
ipv6Address: The IPv6 address assigned to the instance.
kind: This is always sql#instance.
masterInstanceName: The name of the instance which will act as master in
the replication setup.
maxDiskSize: The maximum disk size of the instance in bytes.
project: The project ID of the project containing the Cloud SQL instance.
The Google apps domain is prefixed if applicable.
region: The geographical region. Can be us-central, asia-east1 or europe-
west1. Defaults to us-central. The region can not be changed after
instance creation.
replicaNames: The replicas of the instance.
serverCaCert: SSL configuration.
serviceAccountEmailAddress: The service account email address assigned to
the instance.
settings: The user settings.
state: The current serving state of the Cloud SQL instance. This can be
one of the following. RUNNABLE: The instance is running, or is ready to
run when accessed. SUSPENDED: The instance is not available, for example
due to problems with billing. PENDING_CREATE: The instance is being
created. MAINTENANCE: The instance is down for maintenance.
UNKNOWN_STATE: The state of the instance is unknown.
"""
currentDiskSize = _messages.IntegerField(1)
databaseVersion = _messages.StringField(2)
etag = _messages.StringField(3)
instance = _messages.StringField(4)
instanceType = _messages.StringField(5)
ipAddresses = _messages.MessageField('IpMapping', 6, repeated=True)
ipv6Address = _messages.StringField(7)
kind = _messages.StringField(8, default=u'sql#instance')
masterInstanceName = _messages.StringField(9)
maxDiskSize = _messages.IntegerField(10)
project = _messages.StringField(11)
region = _messages.StringField(12)
replicaNames = _messages.StringField(13, repeated=True)
serverCaCert = _messages.MessageField('SslCert', 14)
serviceAccountEmailAddress = _messages.StringField(15)
settings = _messages.MessageField('Settings', 16)
state = _messages.StringField(17)
class ExportContext(_messages.Message):
"""Database instance export context.
Fields:
database: Databases (for example, guestbook) from which the export is
made. If unspecified, all databases are exported.
kind: This is always sql#exportContext.
table: Tables to export, or that were exported, from the specified
database. If you specify tables, specify one and only one database.
uri: The path to the file in Google Cloud Storage where the export will be
stored, or where it was already stored. The URI is in the form
gs://bucketName/fileName. If the file already exists, the operation
fails. If the filename ends with .gz, the contents are compressed.
"""
database = _messages.StringField(1, repeated=True)
kind = _messages.StringField(2, default=u'sql#exportContext')
table = _messages.StringField(3, repeated=True)
uri = _messages.StringField(4)
class Flag(_messages.Message):
"""A Google Cloud SQL service flag resource.
Fields:
allowedStringValues: For STRING flags, a list of strings that the value
can be set to.
appliesTo: The database version this flag applies to. Currently this can
only be [MYSQL_5_5].
kind: This is always sql#flag.
maxValue: For INTEGER flags, the maximum allowed value.
minValue: For INTEGER flags, the minimum allowed value.
name: This is the name of the flag. Flag names always use underscores, not
hyphens, e.g. max_allowed_packet
type: The type of the flag. Flags are typed to being BOOLEAN, STRING,
INTEGER or NONE. NONE is used for flags which do not take a value, such
as skip_grant_tables.
"""
allowedStringValues = _messages.StringField(1, repeated=True)
appliesTo = _messages.StringField(2, repeated=True)
kind = _messages.StringField(3, default=u'sql#flag')
maxValue = _messages.IntegerField(4)
minValue = _messages.IntegerField(5)
name = _messages.StringField(6)
type = _messages.StringField(7)
class FlagsListResponse(_messages.Message):
"""Flags list response.
Fields:
items: List of flags.
kind: This is always sql#flagsList.
"""
items = _messages.MessageField('Flag', 1, repeated=True)
kind = _messages.StringField(2, default=u'sql#flagsList')
class ImportContext(_messages.Message):
"""Database instance import context.
Fields:
database: The database (for example, guestbook) to which the import is
made. If not set, it is assumed that the database is specified in the
file to be imported.
kind: This is always sql#importContext.
uri: A path to the MySQL dump file in Google Cloud Storage from which the
import is made. The URI is in the form gs://bucketName/fileName.
Compressed gzip files (.gz) are also supported.
"""
database = _messages.StringField(1)
kind = _messages.StringField(2, default=u'sql#importContext')
uri = _messages.StringField(3, repeated=True)
class InstanceOperation(_messages.Message):
"""An Operations resource contains information about database instance
operations such as create, delete, and restart. Operations resources are
created in response to operations that were initiated; you never create them
directly.
Fields:
endTime: The time this operation finished in UTC timezone in RFC 3339
format, for example | |
# -*- coding: utf-8 -*-
"""
oauthlib.oauth2.rfc6749.grant_types
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
from __future__ import unicode_literals, absolute_import
import json
import logging
from oauthlib import common
from oauthlib.uri_validate import is_absolute_uri
from .base import GrantTypeBase
from .. import errors
log = logging.getLogger(__name__)
class AuthorizationCodeGrant(GrantTypeBase):
"""`Authorization Code Grant`_
The authorization code grant type is used to obtain both access
tokens and refresh tokens and is optimized for confidential clients.
Since this is a redirection-based flow, the client must be capable of
interacting with the resource owner's user-agent (typically a web
browser) and capable of receiving incoming requests (via redirection)
from the authorization server::
+----------+
| Resource |
| Owner |
| |
+----------+
^
|
(B)
+----|-----+ Client Identifier +---------------+
| -+----(A)-- & Redirection URI ---->| |
| User- | | Authorization |
| Agent -+----(B)-- User authenticates --->| Server |
| | | |
| -+----(C)-- Authorization Code ---<| |
+-|----|---+ +---------------+
| | ^ v
(A) (C) | |
| | | |
^ v | |
+---------+ | |
| |>---(D)-- Authorization Code ---------' |
| Client | & Redirection URI |
| | |
| |<---(E)----- Access Token -------------------'
+---------+ (w/ Optional Refresh Token)
Note: The lines illustrating steps (A), (B), and (C) are broken into
two parts as they pass through the user-agent.
Figure 3: Authorization Code Flow
The flow illustrated in Figure 3 includes the following steps:
(A) The client initiates the flow by directing the resource owner's
user-agent to the authorization endpoint. The client includes
its client identifier, requested scope, local state, and a
redirection URI to which the authorization server will send the
user-agent back once access is granted (or denied).
(B) The authorization server authenticates the resource owner (via
the user-agent) and establishes whether the resource owner
grants or denies the client's access request.
(C) Assuming the resource owner grants access, the authorization
server redirects the user-agent back to the client using the
redirection URI provided earlier (in the request or during
client registration). The redirection URI includes an
authorization code and any local state provided by the client
earlier.
(D) The client requests an access token from the authorization
server's token endpoint by including the authorization code
received in the previous step. When making the request, the
client authenticates with the authorization server. The client
includes the redirection URI used to obtain the authorization
code for verification.
(E) The authorization server authenticates the client, validates the
authorization code, and ensures that the redirection URI
received matches the URI used to redirect the client in
step (C). If valid, the authorization server responds back with
an access token and, optionally, a refresh token.
.. _`Authorization Code Grant`: http://tools.ietf.org/html/rfc6749#section-4.1
"""
default_response_mode = 'query'
response_types = ['code']
def create_authorization_code(self, request):
"""Generates an authorization grant represented as a dictionary."""
grant = {'code': common.generate_token()}
if hasattr(request, 'state') and request.state:
grant['state'] = request.state
log.debug('Created authorization code grant %r for request %r.',
grant, request)
return grant
def create_authorization_response(self, request, token_handler):
"""
The client constructs the request URI by adding the following
parameters to the query component of the authorization endpoint URI
using the "application/x-www-form-urlencoded" format, per `Appendix B`_:
response_type
REQUIRED. Value MUST be set to "code" for standard OAuth2
authorization flow. For OpenID Connect it must be one of
"code token", "code id_token", or "code token id_token" - we
essentially test that "code" appears in the response_type.
client_id
REQUIRED. The client identifier as described in `Section 2.2`_.
redirect_uri
OPTIONAL. As described in `Section 3.1.2`_.
scope
OPTIONAL. The scope of the access request as described by
`Section 3.3`_.
state
RECOMMENDED. An opaque value used by the client to maintain
state between the request and callback. The authorization
server includes this value when redirecting the user-agent back
to the client. The parameter SHOULD be used for preventing
cross-site request forgery as described in `Section 10.12`_.
The client directs the resource owner to the constructed URI using an
HTTP redirection response, or by other means available to it via the
user-agent.
:param request: oauthlib.commong.Request
:param token_handler: A token handler instace, for example of type
oauthlib.oauth2.BearerToken.
:returns: headers, body, status
:raises: FatalClientError on invalid redirect URI or client id.
ValueError if scopes are not set on the request object.
A few examples::
>>> from your_validator import your_validator
>>> request = Request('https://example.com/authorize?client_id=valid'
... '&redirect_uri=http%3A%2F%2Fclient.com%2F')
>>> from oauthlib.common import Request
>>> from oauthlib.oauth2 import AuthorizationCodeGrant, BearerToken
>>> token = BearerToken(your_validator)
>>> grant = AuthorizationCodeGrant(your_validator)
>>> grant.create_authorization_response(request, token)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "oauthlib/oauth2/rfc6749/grant_types.py", line 513, in create_authorization_response
raise ValueError('Scopes must be set on post auth.')
ValueError: Scopes must be set on post auth.
>>> request.scopes = ['authorized', 'in', 'some', 'form']
>>> grant.create_authorization_response(request, token)
(u'http://client.com/?error=invalid_request&error_description=Missing+response_type+parameter.', None, None, 400)
>>> request = Request('https://example.com/authorize?client_id=valid'
... '&redirect_uri=http%3A%2F%2Fclient.com%2F'
... '&response_type=code')
>>> request.scopes = ['authorized', 'in', 'some', 'form']
>>> grant.create_authorization_response(request, token)
(u'http://client.com/?code=u3F05aEObJuP2k7DordviIgW5wl52N', None, None, 200)
>>> # If the client id or redirect uri fails validation
>>> grant.create_authorization_response(request, token)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "oauthlib/oauth2/rfc6749/grant_types.py", line 515, in create_authorization_response
>>> grant.create_authorization_response(request, token)
File "oauthlib/oauth2/rfc6749/grant_types.py", line 591, in validate_authorization_request
oauthlib.oauth2.rfc6749.errors.InvalidClientIdError
.. _`Appendix B`: http://tools.ietf.org/html/rfc6749#appendix-B
.. _`Section 2.2`: http://tools.ietf.org/html/rfc6749#section-2.2
.. _`Section 3.1.2`: http://tools.ietf.org/html/rfc6749#section-3.1.2
.. _`Section 3.3`: http://tools.ietf.org/html/rfc6749#section-3.3
.. _`Section 10.12`: http://tools.ietf.org/html/rfc6749#section-10.12
"""
try:
# request.scopes is only mandated in post auth and both pre and
# post auth use validate_authorization_request
if not request.scopes:
raise ValueError('Scopes must be set on post auth.')
self.validate_authorization_request(request)
log.debug('Pre resource owner authorization validation ok for %r.',
request)
# If the request fails due to a missing, invalid, or mismatching
# redirection URI, or if the client identifier is missing or invalid,
# the authorization server SHOULD inform the resource owner of the
# error and MUST NOT automatically redirect the user-agent to the
# invalid redirection URI.
except errors.FatalClientError as e:
log.debug('Fatal client error during validation of %r. %r.',
request, e)
raise
# If the resource owner denies the access request or if the request
# fails for reasons other than a missing or invalid redirection URI,
# the authorization server informs the client by adding the following
# parameters to the query component of the redirection URI using the
# "application/x-www-form-urlencoded" format, per Appendix B:
# http://tools.ietf.org/html/rfc6749#appendix-B
except errors.OAuth2Error as e:
log.debug('Client error during validation of %r. %r.', request, e)
request.redirect_uri = request.redirect_uri or self.error_uri
redirect_uri = common.add_params_to_uri(
request.redirect_uri, e.twotuples,
fragment=request.response_mode == "fragment")
return {'Location': redirect_uri}, None, 302
grant = self.create_authorization_code(request)
for modifier in self._code_modifiers:
grant = modifier(grant, token_handler, request)
log.debug('Saving grant %r for %r.', grant, request)
self.request_validator.save_authorization_code(
request.client_id, grant, request)
return self.prepare_authorization_response(
request, grant, {}, None, 302)
def create_token_response(self, request, token_handler):
"""Validate the authorization code.
The client MUST NOT use the authorization code more than once. If an
authorization code is used more than once, the authorization server
MUST deny the request and SHOULD revoke (when possible) all tokens
previously issued based on that authorization code. The authorization
code is bound to the client identifier and redirection URI.
"""
headers = {
'Content-Type': 'application/json',
'Cache-Control': 'no-store',
'Pragma': 'no-cache',
}
try:
self.validate_token_request(request)
log.debug('Token request validation ok for %r.', request)
except errors.OAuth2Error as e:
log.debug('Client error during validation of %r. %r.', request, e)
return headers, e.json, e.status_code
token = token_handler.create_token(request, refresh_token=self.refresh_token, save_token=False)
for modifier in self._token_modifiers:
token = modifier(token, token_handler, request)
self.request_validator.save_token(token, request)
self.request_validator.invalidate_authorization_code(
request.client_id, request.code, request)
return headers, json.dumps(token), 200
def validate_authorization_request(self, request):
"""Check the authorization request for normal and fatal errors.
A normal error could be a missing response_type parameter or the client
attempting to access scope it is not allowed to ask authorization for.
Normal errors can safely be included in the redirection URI and
sent back to the client.
Fatal errors occur when the client_id or redirect_uri is invalid or
missing. These must be caught by the provider and handled, how this
is done is outside of the scope of OAuthLib but showing an error
page describing the | |
:param certificate_authority: certificate authority's X509 certificate.
:return: None
.. versionadded:: 0.10
"""
if not isinstance(certificate_authority, X509):
raise TypeError("certificate_authority must be an X509 instance")
add_result = _lib.SSL_CTX_add_client_CA(
self._context, certificate_authority._x509
)
_openssl_assert(add_result == 1)
def set_timeout(self, timeout):
"""
Set the timeout for newly created sessions for this Context object to
*timeout*. The default value is 300 seconds. See the OpenSSL manual
for more information (e.g. :manpage:`SSL_CTX_set_timeout(3)`).
:param timeout: The timeout in (whole) seconds
:return: The previous session timeout
"""
if not isinstance(timeout, int):
raise TypeError("timeout must be an integer")
return _lib.SSL_CTX_set_timeout(self._context, timeout)
def get_timeout(self):
"""
Retrieve session timeout, as set by :meth:`set_timeout`. The default
is 300 seconds.
:return: The session timeout
"""
return _lib.SSL_CTX_get_timeout(self._context)
def set_info_callback(self, callback):
"""
Set the information callback to *callback*. This function will be
called from time to time during SSL handshakes.
:param callback: The Python callback to use. This should take three
arguments: a Connection object and two integers. The first integer
specifies where in the SSL handshake the function was called, and
the other the return code from a (possibly failed) internal
function call.
:return: None
"""
@wraps(callback)
def wrapper(ssl, where, return_code):
callback(Connection._reverse_mapping[ssl], where, return_code)
self._info_callback = _ffi.callback(
"void (*)(const SSL *, int, int)", wrapper
)
_lib.SSL_CTX_set_info_callback(self._context, self._info_callback)
@_requires_keylog
def set_keylog_callback(self, callback):
"""
Set the TLS key logging callback to *callback*. This function will be
called whenever TLS key material is generated or received, in order
to allow applications to store this keying material for debugging
purposes.
:param callback: The Python callback to use. This should take two
arguments: a Connection object and a bytestring that contains
the key material in the format used by NSS for its SSLKEYLOGFILE
debugging output.
:return: None
"""
@wraps(callback)
def wrapper(ssl, line):
line = _ffi.string(line)
callback(Connection._reverse_mapping[ssl], line)
self._keylog_callback = _ffi.callback(
"void (*)(const SSL *, const char *)", wrapper
)
_lib.SSL_CTX_set_keylog_callback(self._context, self._keylog_callback)
def get_app_data(self):
"""
Get the application data (supplied via :meth:`set_app_data()`)
:return: The application data
"""
return self._app_data
def set_app_data(self, data):
"""
Set the application data (will be returned from get_app_data())
:param data: Any Python object
:return: None
"""
self._app_data = data
def get_cert_store(self):
"""
Get the certificate store for the context. This can be used to add
"trusted" certificates without using the
:meth:`load_verify_locations` method.
:return: A X509Store object or None if it does not have one.
"""
store = _lib.SSL_CTX_get_cert_store(self._context)
if store == _ffi.NULL:
# TODO: This is untested.
return None
pystore = X509Store.__new__(X509Store)
pystore._store = store
return pystore
def set_options(self, options):
"""
Add options. Options set before are not cleared!
This method should be used with the :const:`OP_*` constants.
:param options: The options to add.
:return: The new option bitmask.
"""
if not isinstance(options, int):
raise TypeError("options must be an integer")
return _lib.SSL_CTX_set_options(self._context, options)
def set_mode(self, mode):
"""
Add modes via bitmask. Modes set before are not cleared! This method
should be used with the :const:`MODE_*` constants.
:param mode: The mode to add.
:return: The new mode bitmask.
"""
if not isinstance(mode, int):
raise TypeError("mode must be an integer")
return _lib.SSL_CTX_set_mode(self._context, mode)
def set_tlsext_servername_callback(self, callback):
"""
Specify a callback function to be called when clients specify a server
name.
:param callback: The callback function. It will be invoked with one
argument, the Connection instance.
.. versionadded:: 0.13
"""
@wraps(callback)
def wrapper(ssl, alert, arg):
callback(Connection._reverse_mapping[ssl])
return 0
self._tlsext_servername_callback = _ffi.callback(
"int (*)(SSL *, int *, void *)", wrapper
)
_lib.SSL_CTX_set_tlsext_servername_callback(
self._context, self._tlsext_servername_callback
)
def set_tlsext_use_srtp(self, profiles):
"""
Enable support for negotiating SRTP keying material.
:param bytes profiles: A colon delimited list of protection profile
names, like ``b'SRTP_AES128_CM_SHA1_80:SRTP_AES128_CM_SHA1_32'``.
:return: None
"""
if not isinstance(profiles, bytes):
raise TypeError("profiles must be a byte string.")
_openssl_assert(
_lib.SSL_CTX_set_tlsext_use_srtp(self._context, profiles) == 0
)
@_requires_alpn
def set_alpn_protos(self, protos):
"""
Specify the protocols that the client is prepared to speak after the
TLS connection has been negotiated using Application Layer Protocol
Negotiation.
:param protos: A list of the protocols to be offered to the server.
This list should be a Python list of bytestrings representing the
protocols to offer, e.g. ``[b'http/1.1', b'spdy/2']``.
"""
# Different versions of OpenSSL are inconsistent about how they handle
# empty proto lists (see #1043), so we avoid the problem entirely by
# rejecting them ourselves.
if not protos:
raise ValueError("at least one protocol must be specified")
# Take the list of protocols and join them together, prefixing them
# with their lengths.
protostr = b"".join(
chain.from_iterable((bytes((len(p),)), p) for p in protos)
)
# Build a C string from the list. We don't need to save this off
# because OpenSSL immediately copies the data out.
input_str = _ffi.new("unsigned char[]", protostr)
# https://www.openssl.org/docs/man1.1.0/man3/SSL_CTX_set_alpn_protos.html:
# SSL_CTX_set_alpn_protos() and SSL_set_alpn_protos()
# return 0 on success, and non-0 on failure.
# WARNING: these functions reverse the return value convention.
_openssl_assert(
_lib.SSL_CTX_set_alpn_protos(
self._context, input_str, len(protostr)
)
== 0
)
@_requires_alpn
def set_alpn_select_callback(self, callback):
"""
Specify a callback function that will be called on the server when a
client offers protocols using ALPN.
:param callback: The callback function. It will be invoked with two
arguments: the Connection, and a list of offered protocols as
bytestrings, e.g ``[b'http/1.1', b'spdy/2']``. It can return
one of those bytestrings to indicate the chosen protocol, the
empty bytestring to terminate the TLS connection, or the
:py:obj:`NO_OVERLAPPING_PROTOCOLS` to indicate that no offered
protocol was selected, but that the connection should not be
aborted.
"""
self._alpn_select_helper = _ALPNSelectHelper(callback)
self._alpn_select_callback = self._alpn_select_helper.callback
_lib.SSL_CTX_set_alpn_select_cb(
self._context, self._alpn_select_callback, _ffi.NULL
)
def _set_ocsp_callback(self, helper, data):
"""
This internal helper does the common work for
``set_ocsp_server_callback`` and ``set_ocsp_client_callback``, which is
almost all of it.
"""
self._ocsp_helper = helper
self._ocsp_callback = helper.callback
if data is None:
self._ocsp_data = _ffi.NULL
else:
self._ocsp_data = _ffi.new_handle(data)
rc = _lib.SSL_CTX_set_tlsext_status_cb(
self._context, self._ocsp_callback
)
_openssl_assert(rc == 1)
rc = _lib.SSL_CTX_set_tlsext_status_arg(self._context, self._ocsp_data)
_openssl_assert(rc == 1)
def set_ocsp_server_callback(self, callback, data=None):
"""
Set a callback to provide OCSP data to be stapled to the TLS handshake
on the server side.
:param callback: The callback function. It will be invoked with two
arguments: the Connection, and the optional arbitrary data you have
provided. The callback must return a bytestring that contains the
OCSP data to staple to the handshake. If no OCSP data is available
for this connection, return the empty bytestring.
:param data: Some opaque data that will be passed into the callback
function when called. This can be used to avoid needing to do
complex data lookups or to keep track of what context is being
used. This parameter is optional.
"""
helper = _OCSPServerCallbackHelper(callback)
self._set_ocsp_callback(helper, data)
def set_ocsp_client_callback(self, callback, data=None):
"""
Set a callback to validate OCSP data stapled to the TLS handshake on
the client side.
:param callback: The callback function. It will be invoked with three
arguments: the Connection, a bytestring containing the stapled OCSP
assertion, and the optional arbitrary data you have provided. The
callback must return a boolean that indicates the result of
validating the OCSP data: ``True`` if the OCSP data is valid and
the certificate can be trusted, or ``False`` if either the OCSP
data is invalid or the certificate has been revoked.
:param data: Some opaque data that will be passed into the callback
function when called. This can be used to avoid needing to do
complex data lookups or to keep track of what context is being
used. This parameter is optional.
"""
helper = _OCSPClientCallbackHelper(callback)
self._set_ocsp_callback(helper, data)
def set_cookie_generate_callback(self, callback):
self._cookie_generate_helper = _CookieGenerateCallbackHelper(callback)
_lib.SSL_CTX_set_cookie_generate_cb(
self._context,
self._cookie_generate_helper.callback,
)
def set_cookie_verify_callback(self, callback):
self._cookie_verify_helper = _CookieVerifyCallbackHelper(callback)
_lib.SSL_CTX_set_cookie_verify_cb(
self._context,
self._cookie_verify_helper.callback,
)
class Connection:
_reverse_mapping = WeakValueDictionary()
def __init__(self, context, socket=None):
"""
Create a new Connection object, using the given OpenSSL.SSL.Context
instance and socket.
:param context: An SSL Context to use for this connection
:param socket: The socket to use for transport layer
"""
if not isinstance(context, Context):
raise TypeError("context must be a Context instance")
ssl = _lib.SSL_new(context._context)
self._ssl = _ffi.gc(ssl, _lib.SSL_free)
# We set | |
<reponame>Mvmo/acton
# Copyright (C) 2019-2021 Data Ductus AB
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Python version of bytesview
##################################################
### Bytesview
class Bytesview():
_bytes : bytes = None
_pre = None
_start : int = 0
_end : int = 0
_eof : bool = False
def __init__(self, b, n=None, start=None, pre=None, closed=False):
self._bytes = b
self._pre = pre
self._start = 0 if start is None else start
if n is not None:
self._end = self._start + n
else:
self._end = len(b)-self._start
self._eof = closed
### analysis of Bytesview objects
def n(self):
return self._end - self._start + (self._pre.n() if self._pre is not None else 0)
def to_bytes(self):
these = self._bytes[self._start:self._end]
if self._pre is None:
return these
else:
return self._pre.to_bytes() + these
### de-lousing
def show(self):
if self._pre is not None:
self._pre.show()
print(self._bytes, "[{}:{}]".format(self._start,self._end))
if self._eof:
print("_eof = {}".format(self._eof))
### consuming data
def _consume(self, amount):
if self._pre is not None:
(pre, remaining) = self._pre._consume(amount)
else:
pre = None
remaining = amount
thislen = self._end - self._start
if remaining < thislen:
newthis = Bytesview(self._bytes, n=thislen-remaining, start=self._start+remaining, pre=pre)
newremaining = 0
else:
newthis = pre
newremaining = remaining - thislen
return(newthis,newremaining)
def consume(self, amount):
# assert(self.n()>=amount)
(newview, remaining) = self._consume(amount)
if remaining > 0:
# raise IndexError
return None
if newview is not None:
return newview
else:
return Bytesview(b'')
# def _initialbytes(self, amount=a, end=-1, endskip=0):
#
#
# def initialbytes(self, end=-1, endskip=0):
# # remove data at end of bytesview.
# #
# # end == -1 implies trimming from eof (so error if -1 and not closed)
# # end >= 0 implies end-endskip is the index where returned bytesview object ends.
# # remove endskip bytes before end.
# if end == -1 and not self._eof:
# raise ValueError("Trim(end=-1) on non-closed Bytesview")
#
# if self._pre is None:
#
# thislen = self._end - self._start
# thislen -=
# if self._pre is None:
# newstartskip = startskip
# newend = end
# newenskip = endskip
# return Bytesview(self._bytes, start=self._start+
# else:
#
# if start is None:
# if end is None or end == -1:
# these = self._bytes[self._start:self._end]
# else:
# these = self._bytes[self._start:self._start+end]
# else:
# if end is None or end == -1:
# these = self._bytes[self._start+start:self._end]
# else:
# these = self._bytes[self._start+start:self._start+end]
# if self._pre is None:
# return these
# else:
# return self._pre.to_bytes() + these
### reading out data from a Bytesview object (creating a new)
###
### Raises IncompleteReadError if Bytesview object does not contain
### enough data to fulfill request.
def _read(self, n):
# n is always >= 0
if self._pre is not None:
(remaining, initial) = self._pre._read(n)
else:
remaining = n
initial = None
thislen = self._end - self._start
if remaining == 0:
return (remaining,initial)
elif remaining >= thislen:
return (remaining-thislen, self)
else: # 0 < remaining < thislen
return (0, Bytesview(self._bytes, start=self._start, n=remaining, pre=initial))
def read(self, n=-1): # (int) -> (bytes, Bytesview)
if n == -1:
if not self._eof:
raise IncompleteReadError("read(-1) on non-closed Bytesview")
return None
else:
return self
else:
# deliver n bytes, if there are n bytes.
# if not, deliver available bytes if closed, error if not closed
thislen = self._end - self._start
if self._pre is not None:
(remaining, initial) = self._pre._read(n)
else:
remaining = n
initial = None
if remaining == 0:
return initial if initial is not None else Bytesview(b'')
elif remaining < thislen:
return Bytesview(self._bytes, n=remaining, start=self._start, pre=initial)
elif remaining > thislen:
if self._eof:
return self
else:
#raise IncompleteReadError("read(n) on non-closed Bytesview with less than n bytes")
return None
else:
# remaining == thislen
return self
def readline(self):
return self.readuntil (separator=b'\n')
def _readexactly(self, n):
thislen = self._end - self._start
if self._pre:
(initial, remains) = self._pre._readexactly(n)
else:
(initial, remains) = (None, n)
if remains == 0:
return (initial, remains)
elif remains >= thislen:
return (self, remains-thislen)
else:
# remains < thislen
return (Bytesview(self._bytes, start=self._start, n=remains, pre=initial), 0)
def readexactly(self, n):
(initial, remains) = self._readexactly(n)
if remains > 0:
#raise IncompleteReadError("readexactly(): Too few bytes available")
return None
if initial is None:
initial = Bytesview(b'')
return initial
def _readuntil(self, separator, seplen):
# len(last) < len(separator)
if self._pre is not None:
(last, initial, found) = self._pre._readuntil(separator, seplen)
else:
(last, initial, found) = (b'', None, False)
# last is potential beginning of separator at end of previous Bytesview(s)
if found:
return (b'',initial, found)
else:
buf = last+self._bytes[self._start:self._start+seplen-1]
idx = buf.find(separator)
if idx >= 0: # found it!
# end of separator at ix+seplen-len(last)
#print (buf, len(buf), last, len(last), sep, idx)
return (b'', Bytesview(self._bytes, n=idx+seplen-len(last),
start=self._start,
pre=self._pre), True)
idx = self._bytes.find(separator, self._start)
if idx >= 0: # found it!
return (b'', Bytesview(self._bytes, n=idx-self._start+seplen, start=self._start, pre=self._pre), True)
thislen = self._end-self._start
if thislen >= seplen-1:
last = self._bytes[self._end-(seplen-1):self._end]
else:
last = last[thislen-(seplen-1):] + self._bytes[self._start:self._end]
return (last, self, False)
def readuntil(self, separator=b'\n'):
seplen = len(separator)
(_, initial, found) = self._readuntil(separator, seplen)
if found:
return initial if initial is not None else Bytesview(b'')
else:
if self._eof:
#raise IncompleteReadError("Separator not found and Bytesview closed")
return None
else:
#raise IncompleteReadError("Separator not found")
return None
def at_eof(self):
return self._eof and self._pre is None and self._start == self._end
### feeding data & closing
def append(self, b, n=None):
if self._eof:
#raise ValueError("append() on closed Bytesview")
return None
if self._start < self._end:
return Bytesview(b, n=n, pre=self)
else:
return Bytesview(b, n=n)
def write(self, b, n=None): # same as append(self, b, n=None)
if self._eof:
#raise ValueError("write() on closed Bytesview")
return None
return self.append(b, n)
def writelines(self,data):
if self._eof:
#raise ValueError("writelines() on closed Bytesview")
return None
for b in data:
self.append(b)
def close(self):
if self._eof:
return self
return Bytesview(self._bytes, n=self._end-self._start, start=self._start, pre=self._pre, closed=True)
##################################################
### Unit tests
def report(bv, name):
print (name+" = ", bv)
print (name+".n() = ", bv.n())
# print (name+"limits() = ", bv.limits())
print (name+".to_bytes() = ", bv.to_bytes())
print (name+".show():")
bv.show()
by = b'0123456789abcdef'
#l = len(by)
#print("by = ",by)
#print("l = ", l)
#
#bv1 = Bytesview(by)
#bv2 = Bytesview(by, n=16)
#
#report(bv1, "bv1")
#report(bv2, "bv2")
bv3 = bv2.append(by, n=16)
#report(bv2, "bv2")
#report(bv3, "bv3")
bv4 = bv3.append(by)
#report(bv4,"bv4")
#bv = bv4
#n=0
#while True:
# res = bv.consume(n)
# print("")
# res.show()
# #print(res.to_bytes())
# n += 1
###########
### read()
#
#bv = bv4
#n=0
#while True:
# res = bv.read(n)
# print("")
# res.show()
# #print(res.to_bytes())
# n += 1
#print("read() test")
#bv = bv4
#n=0
#while n < 60:
# res = bv.read(n)
# print(res.to_bytes())
# n += 1
#bv = bv4.close()
#print("bv length: ", bv.n())
#
#n=0
#while n < 60:
# res = bv.read(n)
# print(res.to_bytes())
# n += 1
#bv = bv4.close()
#print("bv length: ", bv.n())
#
#n=0
#while n < 60:
# res = bv.read(n)
# print(res.to_bytes())
# res.show()
# n += 1
###########
### readuntil()
#
#separators = by+b'x'+by+b'y'+by+b'z'+by+b'w'
#bv = Bytesview(by+b'x').append(by+b'y').append(by+b'z').append(by)
#bv.close()
#print("bv length: ", bv.n())
#
#n=0
#while n < 4*16+3+5:
# sep = separators[n:n+7]
# res = bv.readuntil(separator=sep)
# print(sep, " ", res.to_bytes())
# res.show()
# n += 1
def bv_list(bv=None):
bvl = bv if bv is not None else Bytesview(b'')
n = 0
| |
0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(113, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(212, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(212, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(113, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(212, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(212, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(113, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush)
self.experiment_duration_label.setPalette(palette)
font = QtGui.QFont()
font.setFamily("Arial")
font.setBold(True)
font.setWeight(75)
self.experiment_duration_label.setFont(font)
self.experiment_duration_label.setContextMenuPolicy(QtCore.Qt.NoContextMenu)
self.experiment_duration_label.setAcceptDrops(False)
self.experiment_duration_label.setAutoFillBackground(True)
self.experiment_duration_label.setFrameShape(QtWidgets.QFrame.Box)
self.experiment_duration_label.setFrameShadow(QtWidgets.QFrame.Sunken)
self.experiment_duration_label.setLineWidth(1)
self.experiment_duration_label.setMidLineWidth(1)
self.experiment_duration_label.setScaledContents(False)
self.experiment_duration_label.setTextInteractionFlags(QtCore.Qt.LinksAccessibleByMouse)
self.experiment_duration_label.setObjectName("experiment_duration_label")
self.v_vs_label_1 = QtWidgets.QLineEdit(self.centralwidget)
self.v_vs_label_1.setGeometry(QtCore.QRect(260, 300, 41, 31))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
self.v_vs_label_1.setPalette(palette)
font = QtGui.QFont()
font.setFamily("Arial")
font.setBold(True)
font.setWeight(75)
self.v_vs_label_1.setFont(font)
self.v_vs_label_1.setStatusTip("")
self.v_vs_label_1.setStyleSheet("background-color: rgb(240, 240, 240);")
self.v_vs_label_1.setFrame(False)
self.v_vs_label_1.setObjectName("v_vs_label_1")
self.v_vs_label_2 = QtWidgets.QLineEdit(self.centralwidget)
self.v_vs_label_2.setGeometry(QtCore.QRect(260, 340, 41, 31))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
self.v_vs_label_2.setPalette(palette)
font = QtGui.QFont()
font.setFamily("Arial")
font.setBold(True)
font.setWeight(75)
self.v_vs_label_2.setFont(font)
self.v_vs_label_2.setStatusTip("")
self.v_vs_label_2.setStyleSheet("background-color: rgb(240, 240, 240);")
self.v_vs_label_2.setFrame(False)
self.v_vs_label_2.setObjectName("v_vs_label_2")
self.experiment_rest_time = QtWidgets.QLineEdit(self.centralwidget)
self.experiment_rest_time.setGeometry(QtCore.QRect(190, 220, 61, 31))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
self.experiment_rest_time.setPalette(palette)
font = QtGui.QFont()
font.setFamily("Arial")
font.setBold(True)
font.setWeight(75)
self.experiment_rest_time.setFont(font)
self.experiment_rest_time.setText("")
self.experiment_rest_time.setFrame(True)
self.experiment_rest_time.setAlignment(QtCore.Qt.AlignCenter)
self.experiment_rest_time.setObjectName("experiment_rest_time")
self.advanced_parameters_button = QtWidgets.QPushButton(self.centralwidget)
self.advanced_parameters_button.setGeometry(QtCore.QRect(400, 430, 201, 31))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(91, 166, 232))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 217, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Link, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.NoRole, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(248, 221, 23))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 217, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Link, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.NoRole, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 120, 215))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 217, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Link, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.NoRole, brush)
font = QtGui.QFont()
font.setFamily("Arial")
font.setBold(True)
font.setWeight(75)
self.advanced_parameters_button.setFont(font)
self.advanced_parameters_button.setContextMenuPolicy(QtCore.Qt.PreventContextMenu)
self.advanced_parameters_button.setAcceptDrops(False)
self.advanced_parameters_button.setWhatsThis("")
self.advanced_parameters_button.setAutoFillBackground(True)
self.advanced_parameters_button.setLocale(QtCore.QLocale(QtCore.QLocale.English, QtCore.QLocale.UnitedStates))
self.advanced_parameters_button.setInputMethodHints(QtCore.Qt.ImhNone)
self.advanced_parameters_button.setAutoRepeatDelay(301)
self.advanced_parameters_button.setAutoRepeatInterval(96)
self.advanced_parameters_button.setAutoDefault(False)
self.advanced_parameters_button.setDefault(False)
self.advanced_parameters_button.setFlat(False)
self.advanced_parameters_button.setObjectName("advanced_parameters_button")
self.start_voltage_label = QtWidgets.QLabel(self.centralwidget)
self.start_voltage_label.setEnabled(True)
self.start_voltage_label.setGeometry(QtCore.QRect(10, 300, 161, 31))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(67, 121, 171))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(84, 151, 213))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(33, 60, 85))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(44, 80, 114))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(67, 121, 171))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(161, 188, 213))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(67, 121, 171))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush | |
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dobe Photoshop | |
from __future__ import absolute_import
from past.builtins import basestring
import os
import tempfile
import stat
import time
import logging
import errno
import json
import re
from tqdm import tqdm
from zipfile import ZipFile, BadZipfile
import os.path as op
import shutil
from arcana.utils import JSON_ENCODING
from arcana.utils import makedirs
from arcana.data import Fileset, Field
from arcana.repository.base import Repository
from arcana.exceptions import (
ArcanaError, ArcanaUsageError, ArcanaFileFormatError,
ArcanaWrongRepositoryError)
from arcana.pipeline.provenance import Record
from arcana.utils import dir_modtime, get_class_info, parse_value
import xnat
from .dataset import Dataset
logger = logging.getLogger('arcana')
special_char_re = re.compile(r'[^a-zA-Z_0-9]')
tag_parse_re = re.compile(r'\((\d+),(\d+)\)')
RELEVANT_DICOM_TAG_TYPES = set(('UI', 'CS', 'DA', 'TM', 'SH', 'LO',
'PN', 'ST', 'AS'))
class XnatRepo(Repository):
"""
An 'Repository' class for XNAT repositories
Parameters
----------
server : str (URI)
URI of XNAT server to connect to
project_id : str
The ID of the project in the XNAT repository
cache_dir : str (path)
Path to local directory to cache remote data in
user : str
Username with which to connect to XNAT with
password : str
Password to connect to the XNAT repository with
check_md5 : bool
Whether to check the MD5 digest of cached files before using. This
checks for updates on the server since the file was cached
race_cond_delay : int
The amount of time to wait before checking that the required
fileset has been downloaded to cache by another process has
completed if they are attempting to download the same fileset
session_filter : str
A regular expression that is used to prefilter the discovered sessions
to avoid having to retrieve metadata for them, and potentially speeding
up the initialisation of the Analysis. Note that if the processing
relies on summary derivatives (i.e. of 'per_visit/subject/analysis'
frequency) then the filter should match all sessions in the Analysis's
subject_ids and visit_ids.
"""
type = 'xnat'
SUMMARY_NAME = 'ALL'
MD5_SUFFIX = '.__md5__.json'
DERIVED_FROM_FIELD = '__derived_from__'
PROV_SCAN = '__prov__'
PROV_RESOURCE = 'PROV'
depth = 2
def __init__(self, server, cache_dir, user=None,
password=<PASSWORD>, check_md5=True, race_cond_delay=30,
session_filter=None):
super().__init__()
if not isinstance(server, basestring):
raise ArcanaUsageError(
"Invalid server url {}".format(server))
self._server = server
self._cache_dir = cache_dir
makedirs(self._cache_dir, exist_ok=True)
self._user = user
self._password = password
self._race_cond_delay = race_cond_delay
self._check_md5 = check_md5
self._session_filter = session_filter
self._login = None
def __hash__(self):
return (hash(self.server)
^ hash(self.cache_dir)
^ hash(self._race_cond_delay)
^ hash(self._check_md5))
def __repr__(self):
return ("{}(server={}, cache_dir={})"
.format(type(self).__name__,
self.server, self._cache_dir))
def __eq__(self, other):
try:
return (self.server == other.server
and self._cache_dir == other._cache_dir
and self.cache_dir == other.cache_dir
and self._race_cond_delay == other._race_cond_delay
and self._check_md5 == other._check_md5)
except AttributeError:
return False # For comparison with other types
def __getstate__(self):
dct = self.__dict__.copy()
del dct['_login']
del dct['_connection_depth']
return dct
def __setstate__(self, state):
self.__dict__.update(state)
self._login = None
self._connection_depth = 0
@property
def prov(self):
return {
'type': get_class_info(type(self)),
'server': self.server}
@property
def login(self):
if self._login is None:
raise ArcanaError("XNAT repository has been disconnected before "
"exiting outer context")
return self._login
@property
def server(self):
return self._server
@property
def cache_dir(self):
return self._cache_dir
def dataset_cache_dir(self, dataset_name):
return op.join(self.cache_dir, dataset_name)
@property
def check_md5(self):
return self._check_md5
@property
def session_filter(self):
return (re.compile(self._session_filter)
if self._session_filter is not None else None)
def connect(self):
"""
Parameters
----------
prev_login : xnat.XNATSession
An XNAT login that has been opened in the code that calls
the method that calls login. It is wrapped in a
NoExitWrapper so the returned connection can be used
in a "with" statement in the method.
"""
sess_kwargs = {}
if self._user is not None:
sess_kwargs['user'] = self._user
if self._password is not None:
sess_kwargs['password'] = self._password
self._login = xnat.connect(server=self._server, **sess_kwargs)
def disconnect(self):
self._login.disconnect()
self._login = None
def dataset(self, name, **kwargs):
"""
Returns a dataset from the XNAT repository
Parameters
----------
name : str
The name, path or ID of the dataset within the repository
subject_ids : list[str]
The list of subjects to include in the dataset
visit_ids : list[str]
The list of visits to include in the dataset
"""
return Dataset(name, repository=self, depth=2, **kwargs)
def get_fileset(self, fileset):
"""
Caches a single fileset (if the 'path' attribute is accessed
and it has not been previously cached for example
Parameters
----------
fileset : Fileset
The fileset to cache
prev_login : xnat.XNATSession
An XNATSession object to use for the connection. A new
one is created if one isn't provided
Returns
-------
primary_path : str
The path of the primary file once it has been cached
aux_paths : dict[str, str]
A dictionary containing a mapping of auxiliary file names to
paths
"""
if fileset.format is None:
raise ArcanaUsageError(
"Attempting to download {}, which has not been assigned a "
"file format (see Fileset.formatted)".format(fileset))
self._check_repository(fileset)
with self: # Connect to the XNAT repository if haven't already
xsession = self.get_xsession(fileset)
xscan = xsession.scans[fileset.name]
# Set URI so we can retrieve checksums if required
fileset.uri = xscan.uri
fileset.id = xscan.id
cache_path = self._cache_path(fileset)
need_to_download = True
if op.exists(cache_path):
if self._check_md5:
md5_path = cache_path + XnatRepo.MD5_SUFFIX
try:
with open(md5_path, 'r') as f:
cached_checksums = json.load(f)
if cached_checksums == fileset.checksums:
need_to_download = False
except IOError:
pass
else:
need_to_download = False
if need_to_download:
xresource = xscan.resources[fileset._resource_name]
# The path to the directory which the files will be
# downloaded to.
tmp_dir = cache_path + '.download'
try:
# Attempt to make tmp download directory. This will
# fail if another process (or previous attempt) has
# already created it. In that case this process will
# wait to see if that download finishes successfully,
# and if so use the cached version.
os.mkdir(tmp_dir)
except OSError as e:
if e.errno == errno.EEXIST:
# Another process may be concurrently downloading
# the same file to the cache. Wait for
# 'race_cond_delay' seconds and then check that it
# has been completed or assume interrupted and
# redownload.
self._delayed_download(
tmp_dir, xresource, xscan, fileset,
xsession.label, cache_path,
delay=self._race_cond_delay)
else:
raise
else:
self.download_fileset(
tmp_dir, xresource, xscan, fileset,
xsession.label, cache_path)
shutil.rmtree(tmp_dir)
if not fileset.format.directory:
(primary_path, aux_paths) = fileset.format.assort_files(
op.join(cache_path, f) for f in os.listdir(cache_path))
else:
primary_path = cache_path
aux_paths = None
return primary_path, aux_paths
def get_field(self, field):
self._check_repository(field)
with self:
xsession = self.get_xsession(field)
val = xsession.fields[field.name]
val = val.replace('"', '"')
val = parse_value(val)
return val
def put_fileset(self, fileset):
if fileset.format is None:
raise ArcanaFileFormatError(
"Format of {} needs to be set before it is uploaded to {}"
.format(fileset, self))
self._check_repository(fileset)
# Open XNAT session
with self:
# Add session for derived scans if not present
xsession = self.get_xsession(fileset)
cache_path = self._cache_path(fileset)
# Make session cache dir
cache_path_dir = (op.dirname(cache_path)
if fileset.format.directory else cache_path)
if os.path.exists(cache_path_dir):
shutil.rmtree(cache_path_dir)
os.makedirs(cache_path_dir, stat.S_IRWXU | stat.S_IRWXG)
if fileset.format.directory:
shutil.copytree(fileset.path, cache_path)
else:
# Copy primary file
shutil.copyfile(fileset.path,
op.join(cache_path, fileset.fname))
# Copy auxiliaries
for sc_fname, sc_path in fileset.aux_file_fnames_and_paths:
shutil.copyfile(sc_path, op.join(cache_path, sc_fname))
with open(cache_path + XnatRepo.MD5_SUFFIX, 'w',
**JSON_ENCODING) as f:
json.dump(fileset.calculate_checksums(), f, indent=2)
# Upload to XNAT
xscan = self._login.classes.MrScanData(
id=fileset.id, type=fileset.basename, parent=xsession)
fileset.uri = xscan.uri
# Select the first xnat_resource name to use to upload the data to
resource_name = fileset.format.resource_names(self.type)[0]
try:
xresource = xscan.resources[resource_name]
except KeyError:
pass
else:
# Delete existing resource
# TODO: probably should have check to see if we want to
# override it
xresource.delete()
xresource = xscan.create_resource(resource_name)
if fileset.format.directory:
for fname in os.listdir(fileset.path):
xresource.upload(op.join(fileset.path, fname), fname)
else:
xresource.upload(fileset.path, fileset.fname)
for sc_fname, sc_path in fileset.aux_file_fnames_and_paths:
xresource.upload(sc_path, sc_fname)
def put_field(self, field):
self._check_repository(field)
val = field.value
if field.array:
if field.dtype is str:
val = ['"{}"'.format(v) for v in val]
val = '[' + ','.join(str(v) for v in val) + ']'
if field.dtype is str:
val = '"{}"'.format(val)
with self:
xsession = self.get_xsession(field)
xsession.fields[field.name] = val
def put_record(self, record, dataset):
base_cache_path = self._cache_path(
record, name=self.PROV_SCAN, dataset=dataset)
if not op.exists(base_cache_path):
os.mkdir(base_cache_path)
else:
if not op.isdir(base_cache_path):
raise ArcanaError(
"Base provenance cache path ('{}') should be a directory"
.format(base_cache_path))
cache_path = op.join(base_cache_path, record.pipeline_name + '.json')
record.save(cache_path)
# TODO: Should also save digest of prov.json to check to see if it
# has been altered remotely
xsession = self.get_xsession(record, dataset=dataset)
xprov = self._login.classes.MrScanData(
id=self.PROV_SCAN, type=self.PROV_SCAN, parent=xsession)
# Delete existing provenance if present
try:
xresource = xprov.resources[record.pipeline_name]
except KeyError:
pass
else:
xresource.delete()
# FIXME: should reuse the same resource for all provenance jsons
xresource = xprov.create_resource(record.pipeline_name)
xresource.upload(cache_path, op.basename(cache_path))
def get_checksums(self, fileset):
"""
Downloads the MD5 digests associated | |
to account for the
wave transformation at the most remote point of the relaxation zone.
Parameters
----------
Tstart : float
Start time
Tend : float
End time
x0 : numpy.ndarray
Position vector of the time series
fname : string
Filename for timeseries file
Lgen : Optional[numpy.ndarray]
Length vector of relaxation zone
Returns
----------
numpy.ndarray
2D numpy array Nx2 containing free-surface elevation in time.
"""
if sum(Lgen[:]*self.waveDir[:])< 0 :
logEvent('ERROR! Wavetools.py: Location vector of generation zone should not be opposite to the wave direction')
sys.exit(1)
dt = old_div(self.Tp,50.)
Tlag = np.zeros(len(self.omega),)
for j in range(len(self.omega)):
Tlag[j] = old_div(sum(self.kDir[j,:]*Lgen[:]),self.omega[j])
Tlag = max(Tlag)
Tstart = Tstart - Tlag
Np = int(old_div((Tend - Tstart),dt))
time = np.linspace(Tstart,Tend,Np )
etaR = np.zeros(len(time), )
for jj in range(len(time)):
etaR[jj] = self.eta(x0,time[jj])
np.savetxt(fname,list(zip(time,etaR)))
series = np.zeros((len(time),2),)
series[:,0] = time
series[:,1] = etaR
return series
class RandomWaves(object):
"""
This class is used for generating plane random waves using linear reconstruction of components from a
wave spectrum
Parameters
----------
Tp : float
Peak wave period
Hs : float
Significant wave height
mwl : float
Still water level
depth : float
Water depth
waveDir : numpy.ndarray
Wave direction vector
g : Numpy array
Gravitational acceleration vector
N : int
Number of frequency components
bandFactor : float
Spectral band factor. fmax = bandFactor/Tp, fmin = 1/(bandFactor*Tp)
spectName : string
Name of spectral distribution
spectral_params : dict
Dictionary of arguments specific to the spectral distribution
Example for JONSWAP = {"gamma": 3.3, "TMA":True,"depth": depth}
TMA=True activates the TMA modification, which in turn needs the depth as a parameter
phi : numpy.ndarray
Component phases (if set to None, phases are picked at random)
fast : bool
Switch for optimised functions
"""
def __cinit__(self,
Tp,
Hs,
mwl,#m significant wave height
depth , #m depth
waveDir,
g, #peak frequency
N,
bandFactor, #accelerationof gravity
spectName ,# random words will result in error and return the available spectra
spectral_params = None, #JONPARAMS = {"gamma": 3.3, "TMA":True,"depth": depth}
phi=None,
fast = True
):
self.fast= fast
validSpectra = [JONSWAP,PM_mod]
spec_fun =loadExistingFunction(spectName, validSpectra)
self.g = np.array(g)
waveDir = setDirVector(np.array(waveDir))
self.waveDir = waveDir
self.vDir = setVertDir(g)
dirCheck(self.waveDir,self.vDir)
self.gAbs = sqrt(self.g[0]*self.g[0]+self.g[1]*self.g[1]+self.g[2]*self.g[2])
self.Hs = Hs
self.depth = depth
self.Tp = Tp
self.fp = old_div(1.,Tp)
self.bandFactor = bandFactor
self.N = N
self.mwl = mwl
fmax = self.bandFactor*self.fp
fmin = old_div(self.fp,self.bandFactor)
self.df = old_div((fmax-fmin),float(self.N-1))
self.fi = np.linspace(fmin,fmax,self.N)
self.omega = 2.*M_PI*self.fi
self.ki = dispersion(self.omega,self.depth,g=self.gAbs)
omega_p = 2.*M_PI/Tp
self.wavelength = 2.*M_PI/dispersion(omega_p,self.depth,g=self.gAbs)
if phi is None:
self.phi = 2.0*M_PI*np.random.random(self.fi.shape[0])
logEvent('INFO Wavetools.py: No phase array is given. Assigning random phases. Outputing the phasing of the random waves')
else:
try:
self.phi = np.array(phi)
if self.phi.shape[0] != self.fi.shape[0]:
logEvent('ERROR! Wavetools.py: Phase array must have N elements')
sys.exit(1)
except:
logEvent('ERROR! Wavetools.py: phi argument must be an array with N elements')
sys.exit(1)
#ai = np.sqrt((Si_J[1:]+Si_J[:-1])*(fi[1:]-fi[:-1]))
fim = reduceToIntervals(self.fi,self.df)
self.fim = fim
if (spectral_params is None):
self.Si_Jm = spec_fun(fim,self.fp,self.Hs)
else:
try:
self.Si_Jm = spec_fun(fim,self.fp,self.Hs,**spectral_params)
except:
logEvent('ERROR! Wavetools.py: Additional spectral parameters are not valid for the %s spectrum' %spectName)
sys.exit(1)
self.tanhF = np.zeros(N,"d")
for ii in range(self.N):
self.tanhF[ii] = float(np.tanh(self.ki[ii]*self.depth) )
self.ai = np.sqrt(2.*returnRectangles(self.Si_Jm,fim))
self.kDir = np.zeros((len(self.ki),3),)
for ii in range(3):
self.kDir[:,ii] = self.ki[:] * self.waveDir[ii]
if(self.N > 10000):
logEvent("ERROR! Wavetools.py: Maximum number of frequencies for Random Waves is 10000 ",level=0)
#C++ declarations
for ij in range(3):
self.waveDir_c[ij] = self.waveDir[ij]
self.vDir_c[ij] = self.vDir[ij]
self.waveDir_ = self.waveDir_c
self.vDir_ = self.vDir_c
for ij in range(self.N):
for kk in range(3):
self.kDir_c[3*ij+kk] = self.kDir[ij,kk]
self.omega_c[ij] = self.omega[ij]
self.ki_c[ij] =self.ki[ij]
self.tanh_c[ij] = self.tanhF[ij]
self.ai_c[ij] = self.ai[ij]
self.phi_c[ij] = self.phi[ij]
self.kDir_ = self.kDir_c
self.omega_ = self.omega_c
self.ki_ =self.ki_c
self.ai_ = self.ai_c
self.tanh_ = self.tanh_c
self.phi_ = self.phi_c
def _cpp_eta(self, x, t):
return __cpp_etaRandom(x,t,self.kDir_, self.omega_,self.phi_,self.ai_, self.N, self.fast)
def eta(self, x, t):
"""Calculates free surface elevation (RandomWaves class)
Parameters
----------
x : numpy.ndarray
Position vector
t : float
Time variable
Returns
--------
float
Free-surface elevation as a float
"""
cython.declare(xx=cython.double[3])
xx[0] = x[0]
xx[1] = x[1]
xx[2] = x[2]
return self._cpp_eta(xx,t)
def _cpp_u(self, U, x, t):
__cpp_uRandom(U, x,t,self.kDir_, self.ki_, self.omega_,self.phi_,self.ai_,self.mwl,self.depth, self.N, self.waveDir_, self.vDir_, self.tanh_, self.gAbs, self.fast)
def u(self, x, t):
"""Calculates wave velocity vector (RandomWaves class)
Parameters
----------
x : numpy.ndarray
Position vector
t : float
Time variable
Returns
--------
numpy.ndarray
Velocity vector as 1D array
"""
cython.declare(xx=cython.double[3])
cython.declare(cppU=cython.double[3])
for ii in range(3):
xx[ii] = x[ii]
cppU[ii] = 0.
U = np.zeros(3,)
self._cpp_u(cppU,xx,t)
U[0] = cppU[0]
U[1] = cppU[1]
U[2] = cppU[2]
return U
def writeEtaSeries(self,Tstart,Tend,x0,fname,Lgen= np.array([0.,0,0])):
"""Writes a timeseries of the free-surface elevation
It also returns the free surface elevation as a time-eta array.
If Lgen !=[0.,0.,0.,] then Tstart is modified to account for the
wave transformation at the most remote point of the relaxation zone.
Parameters
----------
Tstart : float
Start time
Tend : float
End time
x0 : numpy.ndarray
Position vector of the time series
fname : string
Filename for timeseries file
Lgen : Optional[numpy.ndarray]
Length vector of relaxation zone
Returns
----------
numpy.ndarray
2D numpy array Nx2 containing free-surface elevation in time.
"""
if sum(Lgen[:]*self.waveDir[:])< 0 :
logEvent('ERROR! Wavetools.py: Location vector of generation zone should not be opposite to the wave direction')
sys.exit(1)
dt = old_div(self.Tp,50.)
Tlag = np.zeros(len(self.omega),)
for j in range(len(self.omega)):
Tlag[j] = old_div(sum(self.kDir[j,:]*Lgen[:]),self.omega[j])
Tlag = max(Tlag)
Tstart = Tstart - Tlag
Np = int(old_div((Tend - Tstart),dt))
time = np.linspace(Tstart,Tend,Np )
etaR = np.zeros(len(time), )
for jj in range(len(time)):
etaR[jj] = self.eta(x0,time[jj])
np.savetxt(fname,list(zip(time,etaR)))
series = np.zeros((len(time),2),)
series[:,0] = time
series[:,1] = etaR
return series
class MultiSpectraRandomWaves(object):
"""This class is used for generating random waves by combining
multiple spectra with different distributions and directions
Parameters
----------
Nspectra : int
Total number of spectra
Tp : list
List of peak wave periods
Hs : list
List of significant wave heights
mwl : float
Still water level
depth : float
Water depth
waveDir : list
List of wave direction vector
g : Numpy array
Gravitational acceleration vector
N : list
List of numbers of frequency components
bandFactor : list
List of spectral band factors
spectName : list
List of names of spectral distribution
spectral_params : list
List of names of spectral distribution (see RandomWaves class)
phi : list
List of component phases
fast : bool
Switch for optimised functions
"""
def __cinit__(self,
Nspectra,
Tp, # np array with
Hs,
mwl,#m significant wave height
depth , #m depth
waveDir,
g, #peak frequency
N,
bandFactor, #accelerationof gravity
spectName ,# random words will result in error and return the available spectra
spectral_params, #JONPARAMS = {"gamma": 3.3, "TMA":True,"depth": depth}
phi,
fast=True
):
# Checking length of arrays / lists to be equal to NSpectra
self.fast = fast
try:
if (len(Tp) != Nspectra) or (len(Hs) != Nspectra) or (len(waveDir) != Nspectra) or \
(len(N) != Nspectra) or (len(bandFactor) != Nspectra) or \
(len(spectName) != Nspectra) or (len(spectral_params) != Nspectra) or(len(phi) != Nspectra):
logEvent('ERROR! Wavetools.py: Parameters passed in MultiSpectraRandomWaves must be in array or list form with length Nspectra ')
sys.exit(1)
except:
logEvent('ERROR! Wavetools.py: Parameters passed in MultiSpectraRandomWaves must be in array or list form with length Nspectra ')
sys.exit(1)
# Initialize numpy arrays for complete reconstruction
self.Nall = 0
self.mwl = mwl
self.depth = depth
self.g = np.array(g)
self.vDir = setVertDir(g)
self.gAbs = sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2])
for nn in N:
self.Nall+=nn
if(self.Nall > 10000):
logEvent("ERROR! Wavetools.py: Maximum (number of frequencies) x (No of spectra) for MultispectraRandomWaves is 10000 ",level=0)
self.tanhFM = np.zeros(self.Nall,"d")
self.omegaM = np.zeros(self.Nall,"d")
self.kiM = np.zeros(self.Nall,"d")
self.aiM = np.zeros(self.Nall,"d")
self.kDirM = np.zeros((self.Nall,3),"d")
self.phiM= np.zeros(self.Nall,"d")
self.waveDir = np.zeros((self.Nall,3),"d")
NN = 0
for kk in range(Nspectra):
logEvent("INFO Wavetools.py: Reading spectra No %s" %kk)
NN1 = NN
NN +=N[kk]
RW = RandomWaves(
Tp[kk], # np array with
Hs[kk],
mwl,#m significant wave height
depth, #m depth
waveDir[kk],
g, #peak frequency
N[kk],
bandFactor[kk], #accelerationof gravity
spectName[kk],# random words will result in error and return the available spectra
spectral_params[kk], #JONPARAMS = {"gamma": 3.3, "TMA":True,"depth": depth}
phi[kk],
self.fast
)
| |
metadata does not have field 'other_properties'.",
"Collection 'foobar' metadata does not have field 'properties'.",
}
def test_normalize_collection_metadata_minimal_100(self, caplog):
assert _normalize_collection_metadata({"id": "foobar"}, api_version=ComparableVersion("1.0.0")) == {
'id': 'foobar',
'stac_version': '0.9.0',
'stac_extensions': ['datacube'],
'description': 'foobar',
'extent': {'spatial': {'bbox': [[0, 0, 0, 0]]}, 'temporal': {'interval': [[None, None]]}},
'license': 'proprietary',
'links': [],
}
warnings = set(r.getMessage() for r in caplog.records if r.levelno == logging.WARN)
assert warnings == {"Collection 'foobar' metadata does not have field 'extent'."}
def test_normalize_collection_metadata_minimal_full_100(self, caplog):
assert _normalize_collection_metadata({"id": "foobar"}, api_version=ComparableVersion("1.0.0"), full=True) == {
'id': 'foobar',
'stac_version': '0.9.0',
'stac_extensions': ['datacube'],
'description': 'foobar',
'extent': {'spatial': {'bbox': [[0, 0, 0, 0]]}, 'temporal': {'interval': [[None, None]]}},
'license': 'proprietary',
'cube:dimensions': {},
'summaries': {},
'links': [],
}
warnings = set(r.getMessage() for r in caplog.records if r.levelno == logging.WARN)
assert warnings == {
"Collection 'foobar' metadata does not have field 'cube:dimensions'.",
"Collection 'foobar' metadata does not have field 'extent'.",
"Collection 'foobar' metadata does not have field 'summaries'."
}
def test_normalize_collection_metadata_cube_dimensions_extent_full_100(self, caplog):
metadata = {
"id": "foobar",
"extent": {
"spatial": {"bbox": [[-180, -56, 180, 83]]},
"temporal": {"interval": [["2015-07-06", None]]}
},
"cube:dimensions": {
"x": {"type": "spatial", "axis": "x"},
"y": {"type": "spatial", "axis": "y"},
"t": {"type": "temporal"},
},
}
assert _normalize_collection_metadata(metadata, api_version=ComparableVersion("1.0.0"), full=True) == {
'id': 'foobar',
'stac_version': '0.9.0',
'stac_extensions': ['datacube'],
'description': 'foobar',
'extent': {
'spatial': {'bbox': [[-180, -56, 180, 83]]},
'temporal': {'interval': [["2015-07-06T00:00:00Z", None]]}
},
'license': 'proprietary',
"cube:dimensions": {
"x": {"type": "spatial", "axis": "x", "extent": [-180, 180]},
"y": {"type": "spatial", "axis": "y", "extent": [-56, 83]},
"t": {"type": "temporal", "extent": ["2015-07-06T00:00:00Z", None]},
}, 'summaries': {},
'links': [],
}
def test_normalize_collection_metadata_dimensions_and_bands_040(self, caplog):
metadata = {
"id": "foobar",
"cube:dimensions": {
"x": {"type": "spatial"},
"b": {"type": "bands", "values": ["B02", "B03"]}
},
"summaries": {
"eo:bands": [{"name": "B02"}, {"name": "B03"}]
}
}
res = _normalize_collection_metadata(metadata, api_version=ComparableVersion("0.4.0"), full=True)
assert res["properties"]["cube:dimensions"] == {
"x": {"type": "spatial"},
"b": {"type": "bands", "values": ["B02", "B03"]}
}
assert res["properties"]["eo:bands"] == [{"name": "B02"}, {"name": "B03"}]
def test_normalize_collection_metadata_dimensions_and_bands_100(self, caplog):
metadata = {
"id": "foobar",
"properties": {
"cube:dimensions": {
"x": {"type": "spatial"},
"b": {"type": "bands", "values": ["B02", "B03"]}
},
"eo:bands": [{"name": "B02"}, {"name": "B03"}]
}
}
res = _normalize_collection_metadata(metadata, api_version=ComparableVersion("1.0.0"), full=True)
assert res["cube:dimensions"] == {
"x": {"type": "spatial"},
"b": {"type": "bands", "values": ["B02", "B03"]}
}
assert res["summaries"]["eo:bands"] == [{"name": "B02"}, {"name": "B03"}]
def test_normalize_collection_metadata_datetime(self, caplog):
metadata = {
"id": "foobar",
"extent": {
"temporal": {
"interval": [["2009-08-07", "2009-10-11"], ["2011-12-13 14:15:16", None]],
}
}
}
res = _normalize_collection_metadata(metadata, api_version=ComparableVersion("1.0.0"), full=True)
assert res["extent"]["temporal"]["interval"] == [
['2009-08-07T00:00:00Z', '2009-10-11T00:00:00Z'],
['2011-12-13T14:15:16Z', None],
]
def test_collections(self, api):
resp = api.get('/collections').assert_status_code(200).json
assert "links" in resp
assert "collections" in resp
assert 'S2_FAPAR_CLOUDCOVER' in [c['id'] for c in resp['collections']]
assert 'S2_FOOBAR' in [c['id'] for c in resp['collections']]
for collection in resp['collections']:
assert 'id' in collection
assert 'stac_version' in collection
assert 'description' in collection
assert 'license' in collection
assert 'extent' in collection
assert 'links' in collection
def test_strip_private_fields(self, api):
assert '_private' in dummy_backend.DummyCatalog().get_collection_metadata("S2_FOOBAR")
# All metadata
collections = api.get('/collections').assert_status_code(200).json["collections"]
metadata, = (c for c in collections if c["id"] == "S2_FOOBAR")
assert '_private' not in metadata
# Single collection metadata
metadata = api.get('/collections/S2_FOOBAR').assert_status_code(200).json
assert '_private' not in metadata
def test_collections_detail_invalid_collection(self, api):
error = api.get('/collections/FOOBOO').assert_error(404, "CollectionNotFound").json
assert error["message"] == "Collection 'FOOBOO' does not exist."
def test_collections_detail(self, api):
collection = api.get('/collections/S2_FOOBAR').assert_status_code(200).json
assert collection['id'] == 'S2_FOOBAR'
assert collection['description'] == 'S2_FOOBAR'
assert collection['license'] == 'free'
cube_dimensions = {
'x': {'extent': [2.5, 6.2],
'reference_system': 'AUTO:42001',
'step': 10,
'type': 'spatial'},
'y': {'extent': [49.5, 51.5],
'reference_system': 'AUTO:42001',
'step': 10,
'type': 'spatial'},
"t": {"type": "temporal", "extent": ["2019-01-01", None]},
"bands": {"type": "bands", "values": ["B02", "B03", "B04", "B08"]}
}
eo_bands = [
{"name": "B02", "common_name": "blue"}, {"name": "B03", "common_name": "green"},
{"name": "B04", "common_name": "red"}, {"name": "B08", "common_name": "nir"},
]
if api.api_version_compare.at_least("1.0.0"):
assert collection['stac_version'] == '0.9.0'
assert collection['cube:dimensions'] == cube_dimensions
assert collection['summaries']['eo:bands'] == eo_bands
assert collection['extent']['spatial'] == {'bbox': [[2.5, 49.5, 6.2, 51.5]]}
assert collection['extent']['temporal'] == {'interval': [['2019-01-01T00:00:00Z', None]]}
else:
assert collection['stac_version'] == '0.6.2'
assert collection['properties']['cube:dimensions'] == cube_dimensions
assert collection['properties']["eo:bands"] == eo_bands
assert collection['extent'] == {
'spatial': [2.5, 49.5, 6.2, 51.5],
'temporal': ['2019-01-01T00:00:00Z', None]
}
class TestBatchJobs:
AUTH_HEADER = TEST_USER_AUTH_HEADER
@staticmethod
@contextmanager
def _fresh_job_registry(next_job_id):
"""Set up a fresh job registry and predefine next job id"""
with mock.patch.object(dummy_backend.DummyBatchJobs, 'generate_job_id', return_value=next_job_id):
dummy_backend.DummyBatchJobs._job_registry = {
(TEST_USER, '07024ee9-7847-4b8a-b260-6c879a2b3cdc'): BatchJobMetadata(
id='07024ee9-7847-4b8a-b260-6c879a2b3cdc',
status='running',
process={'process_graph': {'foo': {'process_id': 'foo', 'arguments': {}}}},
created=datetime(2017, 1, 1, 9, 32, 12),
),
(TEST_USER, '53c71345-09b4-46b4-b6b0-03fd6fe1f199'): BatchJobMetadata(
id='53c71345-09b4-46b4-b6b0-03fd6fe1f199',
status='finished',
process={'process_graph': {'foo': {'process_id': 'foo', 'arguments': {}}}},
created=datetime(2020, 6, 11, 11, 51, 29),
started=datetime(2020, 6, 11, 11, 55, 9),
finished=datetime(2020, 6, 11, 11, 55, 15),
memory_time_megabyte=timedelta(seconds=18704944),
cpu_time=timedelta(seconds=1621),
geometry={
"type": "Polygon",
"coordinates": [[[-180, -90], [180, -90], [180, 90], [-180, 90], [-180, -90]]]
},
bbox=[-180, -90, 180, 90],
start_datetime=datetime(1981, 4, 24, 3, 0, 0),
end_datetime=datetime(1981, 4, 24, 3, 0, 0),
instruments=['MSI'],
epsg=4326
)
}
yield
def test_create_job_040(self, api040):
with self._fresh_job_registry(next_job_id="job-220"):
resp = api040.post('/jobs', headers=self.AUTH_HEADER, json={
'title': 'foo job',
'process_graph': {"foo": {"process_id": "foo", "arguments": {}}},
}).assert_status_code(201)
assert resp.headers['Location'] == 'http://oeo.net/openeo/0.4.0/jobs/job-220'
assert resp.headers['OpenEO-Identifier'] == 'job-220'
job_info = dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-220']
assert job_info.id == "job-220"
assert job_info.process == {"process_graph": {"foo": {"process_id": "foo", "arguments": {}}}}
assert job_info.status == "created"
assert job_info.created == dummy_backend.DEFAULT_DATETIME
assert job_info.job_options is None
def test_create_job_with_options_040(self, api040):
with self._fresh_job_registry(next_job_id="job-230"):
resp = api040.post('/jobs', headers=self.AUTH_HEADER, json={
'title': 'foo job',
'process_graph': {"foo": {"process_id": "foo", "arguments": {}}},
'job_options': {"driver-memory": "3g", "executor-memory": "5g"},
}).assert_status_code(201)
assert resp.headers['Location'] == 'http://oeo.net/openeo/0.4.0/jobs/job-230'
assert resp.headers['OpenEO-Identifier'] == 'job-230'
job_info = dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-230']
assert job_info.job_options == {"driver-memory": "3g", "executor-memory": "5g"}
def test_create_job_100(self, api100):
with self._fresh_job_registry(next_job_id="job-245"):
resp = api100.post('/jobs', headers=self.AUTH_HEADER, json={
'process': {
'process_graph': {"foo": {"process_id": "foo", "arguments": {}}},
'summary': 'my foo job',
},
"title": "Foo job", "description": "Run the `foo` process!"
}).assert_status_code(201)
assert resp.headers['Location'] == 'http://oeo.net/openeo/1.0.0/jobs/job-245'
assert resp.headers['OpenEO-Identifier'] == 'job-245'
job_info = dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-245']
assert job_info.id == "job-245"
assert job_info.process == {"process_graph": {"foo": {"process_id": "foo", "arguments": {}}}}
assert job_info.status == "created"
assert job_info.created == dummy_backend.DEFAULT_DATETIME
assert job_info.job_options is None
assert job_info.title == "Foo job"
assert job_info.description == "Run the `foo` process!"
def test_create_job_100_with_options(self, api100):
with self._fresh_job_registry(next_job_id="job-256"):
resp = api100.post('/jobs', headers=self.AUTH_HEADER, json={
'process': {
'process_graph': {"foo": {"process_id": "foo", "arguments": {}}},
'summary': 'my foo job',
},
'job_options': {"driver-memory": "3g", "executor-memory": "5g"},
}).assert_status_code(201)
assert resp.headers['Location'] == 'http://oeo.net/openeo/1.0.0/jobs/job-256'
assert resp.headers['OpenEO-Identifier'] == 'job-256'
job_info = dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-256']
assert job_info.job_options == {"driver-memory": "3g", "executor-memory": "5g"}
def test_start_job(self, api):
with self._fresh_job_registry(next_job_id="job-267"):
api.post('/jobs', headers=self.AUTH_HEADER, json=api.get_process_graph_dict(
{"foo": {"process_id": "foo", "arguments": {}}},
)).assert_status_code(201)
assert dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-267'].status == "created"
api.post('/jobs/job-267/results', headers=self.AUTH_HEADER, json={}).assert_status_code(202)
assert dummy_backend.DummyBatchJobs._job_registry[TEST_USER, 'job-267'].status == "running"
def test_start_job_invalid(self, api):
resp = api.post('/jobs/deadbeef-f00/results', headers=self.AUTH_HEADER)
resp.assert_error(404, "JobNotFound")
assert resp.json["message"] == "The batch job 'deadbeef-f00' does not exist."
def test_get_job_info_040(self, api040):
resp = api040.get('/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
'id': '07024ee9-7847-4b8a-b260-6c879a2b3cdc',
'status': 'running',
'submitted': "2017-01-01T09:32:12Z",
'process_graph': {'foo': {'process_id': 'foo', 'arguments': {}}},
}
def test_get_job_info_metrics_100(self, api100):
resp = api100.get('/jobs/53c71345-09b4-46b4-b6b0-03fd6fe1f199', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
'id': '53c71345-09b4-46b4-b6b0-03fd6fe1f199',
'status': 'finished',
'created': "2020-06-11T11:51:29Z",
'process': {'process_graph': {'foo': {'process_id': 'foo', 'arguments': {}}}},
'duration_seconds': 6,
'duration_human_readable': "0:00:06",
'memory_time_megabyte_seconds': 18704944,
'memory_time_human_readable': "18704944 MB-seconds",
'cpu_time_seconds': 1621,
'cpu_time_human_readable': "1621 cpu-seconds"
}
def test_get_job_info_100(self, api100):
resp = api100.get('/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
'id': '07024ee9-7847-4b8a-b260-6c879a2b3cdc',
'status': 'running',
'created': "2017-01-01T09:32:12Z",
'process': {'process_graph': {'foo': {'process_id': 'foo', 'arguments': {}}}},
}
def test_get_job_info_invalid(self, api):
resp = api.get('/jobs/deadbeef-f00', headers=self.AUTH_HEADER).assert_error(404, "JobNotFound")
assert resp.json["message"] == "The batch job 'deadbeef-f00' does not exist."
def test_list_user_jobs_040(self, api040):
with self._fresh_job_registry(next_job_id="job-318"):
resp = api040.get('/jobs', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
"jobs": [
{
'id': '07024ee9-7847-4b8a-b260-6c879a2b3cdc',
'status': 'running',
'submitted': "2017-01-01T09:32:12Z",
},
{
'id': '53c71345-09b4-46b4-b6b0-03fd6fe1f199',
'status': 'finished',
'submitted': "2020-06-11T11:51:29Z"
}
],
"links": []
}
def test_list_user_jobs_100(self, api100):
with self._fresh_job_registry(next_job_id="job-332"):
resp = api100.get('/jobs', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
"jobs": [
{
'id': '07024ee9-7847-4b8a-b260-6c879a2b3cdc',
'status': 'running',
'created': "2017-01-01T09:32:12Z",
},
{
'id': '53c71345-09b4-46b4-b6b0-03fd6fe1f199',
'status': 'finished',
'created': "2020-06-11T11:51:29Z"
}
],
"links": []
}
def test_get_job_results_unfinished(self, api):
with self._fresh_job_registry(next_job_id="job-345"):
resp = api.get('/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc/results', headers=self.AUTH_HEADER)
resp.assert_error(400, "JobNotFinished")
def test_get_job_results_040(self, api040):
with self._fresh_job_registry(next_job_id="job-349"):
dummy_backend.DummyBatchJobs._update_status(
job_id="07024ee9-7847-4b8a-b260-6c879a2b3cdc", user_id=TEST_USER, status="finished")
resp = api040.get('/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc/results', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
"links": [
{
"href": "http://oeo.net/openeo/0.4.0/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc/results/output.tiff"
}
]
}
def test_get_job_results_100(self, api100):
with self._fresh_job_registry(next_job_id="job-362"):
dummy_backend.DummyBatchJobs._update_status(
job_id="07024ee9-7847-4b8a-b260-6c879a2b3cdc", user_id=TEST_USER, status="finished")
resp = api100.get('/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc/results', headers=self.AUTH_HEADER)
assert resp.assert_status_code(200).json == {
'assets': {
'output.tiff': {
'roles': ['data'],
'title': 'output.tiff',
'href': 'http://oeo.net/openeo/1.0.0/jobs/07024ee9-7847-4b8a-b260-6c879a2b3cdc/results/output.tiff',
'type': 'image/tiff; application=geotiff',
'eo:bands': [{
'name': "NDVI",
'center_wavelength': 1.23
}],
'file:nodata':[123]
}
},
'geometry': None,
'id': '07024ee9-7847-4b8a-b260-6c879a2b3cdc',
'links': [
{
| |
14400
data[var[k]][~Samp_Good_Sonic] =data[var[k]]+'1'
data[var[k]][Samp_Good_Sonic] = data[var[k]]+'0'
if 'Fc_samples_Tot' in df.columns: # Check if enough samples in Fc column (80%) coverage
Samp_Good_IRGA = df['Fc_samples_Tot'].astype(float)>14400
data[var[k]][~Samp_Good_IRGA] = data[var[k]]+'1'
data[var[k]][Samp_Good_IRGA] = data[var[k]]+'0'
#Door Open Mask
if 'door_is_open_Hst' in df.columns: # Check if door open meaning people at the site doing work
Door_Closed = df['door_is_open_Hst'].astype(float) == 0
data[var[k]][~Door_Closed] = data[var[k]]+'1'
data[var[k]][Door_Closed] = data[var[k]]+'0'
Good = Precip & Grade & Door_Closed&~HL&c_sig_strength&w_sig_strength # Create single boolean from all the qc checks; only one fail will trigger fail
Good = Good & (Samp_Good_Sonic | Samp_Good_IRGA)
else: # If door open is not part of the column set; should be with the logger data
Good = Grade &~HL
Good = Good & (Samp_Good_Sonic | Samp_Good_IRGA)
data[(col[k]+'_Graded')][~Good] = np.NaN # Create column with nan/blank in the column if data is bad/filtered
if k == 0: G = Good;
if k >0: G = pd.concat([G,Good],axis=1, sort = False)
del Good # Delete Good variable for the next round of flux data.
return data
#%%
def METQC(Data, col):
# Driver for the met qc function to deal with some column shenanigans
if col == 'Flux': # Different columns between the two for some reason; think has to do with the way the tables were constructed in the logger code
Met_QC = Met_QAQC(RH=Data['RH_Avg'].astype(float),P=Data['amb_press_Avg'].astype(float), Tair = Data['amb_tmpr_Avg'].astype(float),
WS = Data['rslt_wnd_spd'].astype(float), WD = Data['wnd_dir_compass'].astype(float), Precip = Data['Precipitation_Tot'].astype(float),
PAR =Data['PAR_density_Avg'].astype(float), Rn = Data['Rn_meas_Avg'].astype(float),VPD = Data['VPD_air'].astype(float),e = Data['e_Avg'].astype(float), e_s = Data['e_sat_Avg'].astype(float),z = 0.777)
if col == 'Met': #
Met_QC = Met_QAQC(RH=Data['RH_Avg'].astype(float),P=Data['amb_press_Avg'].astype(float), Tair = Data['amb_tmpr_Avg'].astype(float),
WS = Data['rslt_wnd_spd'].astype(float), WD = Data['wnd_dir_compass'].astype(float), Precip = Data['Precipitation_Tot'].astype(float),
PAR =Data['PAR_density_Avg'].astype(float), Rn = Data['Rn_meas_Avg'].astype(float),VPD = Data['VPD_air'].astype(float),e = Data['e'].astype(float), e_s = Data['e_sat'].astype(float),z = 0.777)
if 'Tair_Filtered' in Data.columns: # Checks if the data has already been through the QC code or not;
for k in range(0,len(Met_QC.columns)):
Data = Data.drop(columns=[Met_QC.columns[k]]) # Drops all columns in the metqc variable before readding them back; the QC occurs over the entire dataframe so will re-addd what was deleted; prevents adding multiple columns to the dataframe with the same header
# Not sure why this is the case and this is a quick fix but don't like it
Data = pd.concat([Data,Met_QC], axis = 1, sort=False) # Concat the metqc values to the dataframe.
return Data
def Met_QAQC(**kwargs):
Q = None
if 'Tair' in kwargs.keys(): # Air temperature
Tair = pd.DataFrame(kwargs['Tair'])
Q = Tair; Q = pd.DataFrame(Q);
Q['Tair_Hard_Limit'] = (Q[Tair.columns[0]].astype(float) <= 50) & (Q[Tair.columns[0]].astype(float) >= -40) # Bounds check
Q['Tair_Change'] = ~(np.abs(Q[Tair.columns[0]].diff() >= 15)) & (np.abs(Q[Tair.columns[0]].diff() != 0)) # Check if data change between each time step
Q['Tair_Day_Change'] = (Tair.resample('D').mean().diff !=0) # Checks if the daily average changes from zero
Q['Tair_Filtered'] = Q[Tair.columns[0]][Q['Tair_Hard_Limit'] & Q['Tair_Change'] & Q['Tair_Day_Change']] #Adds filters and booleans together
Q.drop(columns=[Tair.columns[0]],inplace=True) # Drops the columns that are filtered out; probably a better way to do this
else:
print('******Temperature not present******')
if 'RH' in kwargs.keys():
RH = pd.DataFrame(kwargs['RH'])
if Q is None:
Q = RH; Q = pd.DataFrame(Q)
else: Q= Q.join(RH)
Q['RH_Hard_Limit'] = (Q[RH.columns[0]].astype(float) <= 103) & (Q[RH.columns[0]].astype(float) >= 0)
Q['RH_gt_100'] = (Q[RH.columns[0]].astype(float) >= 100) & (Q[RH.columns[0]].astype(float) <= 103)
Q['RH_Change'] = (np.abs(Q[RH.columns[0]].astype(float).diff() <= 50)) & (np.abs(Q[RH.columns[0]].diff() != 0))
Q['RH_Day_Change'] = (RH.resample('D').mean().diff !=0)
Q['RH_Filtered'] = Q[RH.columns[0]][Q['RH_Hard_Limit']&Q['RH_Change']& Q['RH_Day_Change']]
Q['RH_Filtered'] = Q['RH_Filtered'].replace(to_replace=Q['RH_Filtered'][Q['RH_gt_100']], value = 100)
# Q['RH_Filtered'][Q['RH_gt_100']]=100
Q.drop(columns=[RH.columns[0]],inplace=True)
else:
print('**** RH not present ****')
if 'P' in kwargs.keys():
# Pressure checks; converts from pressure to MSLP as well; checks between the two
P = pd.DataFrame(kwargs['P']);
if Q is None:
Q = P; Q = pd.DataFrame(Q)
else: Q= Q.join(P)
Q['P_Hard_Limit'] = (Q[P.columns[0]].astype(float) <= 100) &(Q[P.columns[0]].astype(float) >= 80)
Q['P_Change'] = (np.abs(Q[P.columns[0]].diff() <= 3.1)) & (np.abs(Q[P.columns[0]].diff() != 0))
Q['P_Filtered'] = Q[P.columns[0]][Q['P_Hard_Limit'] & Q['P_Change']]
if ('Tair' in kwargs.keys()) & ('z' in kwargs.keys()):
MSLP = [];
H = pd.DataFrame((8.314*(Tair[Tair.columns[0]]+273.15))/(0.029*9.81)/1000) # Scale height
x = pd.DataFrame(-kwargs['z']/H[H.columns[0]]);
MSLP = P[P.columns[0]]/np.exp(x[x.columns[0]]) # Mean Sea Level Pressure
MSLP = pd.DataFrame(MSLP);MSLP = MSLP.rename(columns={MSLP.columns[0]:"MSLP"})
Q= Q.join(MSLP)
Q['MSLP_Hard_Limit'] = (Q[MSLP.columns[0]].astype(float) <= 110) &(Q[MSLP.columns[0]].astype(float) >= 80)
Q['MSLP_Change'] = (np.abs(Q[MSLP.columns[0]].diff() <= 31)) & (np.abs(Q[MSLP.columns[0]].diff() != 0)) #& (~np.isnan(Q[MSLP.columns[0]].diff()))
Q['MSLP_Filtered'] = Q[MSLP.columns[0]][Q['MSLP_Hard_Limit'] & Q['MSLP_Change']]
else:
print('**** Mean sea level pressure not present ****')
Q.drop(columns=[P.columns[0]],inplace=True)
else:
print('**** Pressure not present ****')
if 'WS' in kwargs.keys(): # Wind speed
WS = pd.DataFrame(kwargs['WS'])
if Q is None:
Q = WS; Q = pd.DataFrame(Q)
else: Q= Q.join(WS)
Q['WS_Hard_Limit'] = (Q[WS.columns[0]].astype(float) < 60) & (Q[WS.columns[0]].astype(float) >= 0)
Q['WS_Change'] = (np.abs(Q[WS.columns[0]].diff() <= 15)) & (np.abs(Q[WS.columns[0]].diff() != 0)) #& (~np.isnan(Q[WS.columns[0]].diff()))
Q['WS_Day_Change'] = (WS.resample('D').mean().diff !=0)
Q['WS_Filtered'] = Q[WS.columns[0]][Q['WS_Hard_Limit']&Q['WS_Change']&Q['WS_Day_Change']]
Q.drop(columns=[WS.columns[0]],inplace=True)
else:
print('**** Wind Speed not present ****')
if 'WD' in kwargs.keys(): # Wind direction
WD = pd.DataFrame(kwargs['WD'])
if Q is None:
Q = WD; Q = pd.DataFrame(Q)
else: Q= Q.join(WD)
Q['WD_Hard_Limit'] = (Q[WD.columns[0]].astype(float) < 360) & (Q[WD.columns[0]].astype(float) >= 0)
Q['WD_Change'] = (np.abs(Q[WD.columns[0]].diff() != 0)) # (~np.isnan(Q[WD.columns[0]].diff())) &
Q['WD_Filtered'] = Q[WD.columns[0]][Q['WD_Hard_Limit']&Q['WD_Change']]
Q.drop(columns=[WD.columns[0]],inplace=True)
else:
print('**** Wind Direction not present ****')
if 'PAR' in kwargs.keys():
PAR = pd.DataFrame(kwargs['PAR']);
if Q is None:
Q = PAR; Q = pd.DataFrame(Q)
else: Q= Q.join(PAR)
Q['PAR_Hard_Limit'] = (Q[PAR.columns[0]].astype(float) >= 0) & (Q[PAR.columns[0]].astype(float) < 5000)
Q['PAR_Change'] = (np.abs(Q[PAR.columns[0]].diff() <= 1500))# & (~np.isnan(Q[PAR.columns[0]].diff()))
Q['PAR_Day_Change'] = (PAR.resample('D').mean().diff != 0) # Causing problems for some reason
Q['PAR_Filtered'] = Q[PAR.columns[0]][Q['PAR_Hard_Limit']&Q['PAR_Change']&Q['PAR_Day_Change']]
Q.drop(columns=[PAR.columns[0]],inplace=True)
else:
print('**** PAR not present ****')
if 'Rn' in kwargs.keys():
Rn = pd.DataFrame(kwargs['Rn'])
if Q is None:
Q = Rn; Q = pd.DataFrame(Q)
else: Q= Q.join(Rn)
Q['Rn_Hard_Limit'] = (Q[Rn.columns[0]].astype(float) >= -150) & (Q[Rn.columns[0]].astype(float) <= 1500)
Q['Rn_Change'] = (np.abs(Q[Rn.columns[0]].astype(float).diff() <= 500)) & (np.abs(Q[Rn.columns[0]].diff() != 0)) #& (~np.isnan(Q[Rn.columns[0]].astype(float).diff()))
Q['Rn_Day_Change'] = (Rn.resample('D').mean().diff !=0)
Q['Rn_Filtered'] = Q[Rn.columns[0]][Q['Rn_Hard_Limit']&Q['Rn_Change']&Q['Rn_Day_Change']]
Q.drop(columns=[Rn.columns[0]],inplace=True)
else:
print('**** Net Radiations not present ****')
if 'Precip' in kwargs.keys(): # Lot of filters because of the difference of precip is there is or is not RH and check for frozen precip with temperature as the tipping bucket is bad with snow
Precip = pd.DataFrame(kwargs['Precip'])
if Q is None:
Q = P; Q = pd.DataFrame(Q)
else: Q= Q.join(Precip)
Q['Precip_Hard_Limit'] = (Q[Precip.columns[0]].astype(float) < 100) & (Q[Precip.columns[0]].astype(float) >= 0)
Z_Precip = Q[Precip.columns[0]].astype(float) ==0
if ('RH' in kwargs.keys()) & ('Tair' in kwargs.keys()): # Checks for temp and RH in correct ranges.
Q['Precip_RH_gt_90'] = (Q[Precip.columns[0]].astype(float) > 0) & (Q['RH_Filtered'].astype(float) >= 90)
Q['Precip_Tair_lt_Zero'] = (Q[Precip.columns[0]].astype(float) > 0) & (Q['Tair_Filtered'] < 0)
Q['Precip_Filtered'] = Q[Precip.columns[0]][Q['Precip_Hard_Limit']&Q['Precip_RH_gt_90']&~Q['Precip_Tair_lt_Zero']]
Q['Precip_Filtered'] = Q['Precip_Filtered'].replace(to_replace=Q['Precip_Filtered'][Z_Precip], value = 0)
Q.drop(columns=[Precip.columns[0]],inplace=True)
elif ('RH' in kwargs.keys()) & ('Tair' not in kwargs.keys()):
Q['Precip_RH_gt_90'] = (Q[Precip.columns[0]].astype(float) > 0) & (Q['RH_Filtered'].astype(float) >= 90)
Q['Precip_Filtered'] = Q[Precip.columns[0]][Q['Precip_Hard_Limit']&Q['Precip_RH']]
Q['Precip_Filtered'] = Q['Precip_Filtered'].replace(to_replace=Q['Precip_Filtered'][Z_Precip], value = 0)
Q.drop(columns=[Precip.columns[0]],inplace=True)
elif ('RH' not in kwargs.keys()) & ('Tair' in kwargs.keys()):
Q['Precip_Tair_lt_Zero'] = (Q[Precip.columns[0]].astype(float) > 0) & (Q['Tair_Filtered'] < 0)
Q['Precip_Filtered'] = Q[Precip.columns[0]][Q['Precip_Hard_Limit']& ~Q['Precip_Tair_lt_Zero']]
Q['Precip_Filtered'] = Q['Precip_Filtered'].replace(to_replace=Q['Precip_Filtered'][Z_Precip], value = 0)
Q.drop(columns=[Precip.columns[0]],inplace=True)
else:
Q['Precip_Filtered'] = Q[Precip.columns[0]][Q['Precip_Hard_Limit']]
Q['Precip_Filtered'] = Q['Precip_Filtered'].replace(to_replace=Q['Precip_Filtered'][Z_Precip], value = 0)
Q.drop(columns=[Precip.columns[0]],inplace=True)
else:
print('**** Precipitation not present ****')
if 'VPD' in kwargs.keys():
VPD = pd.DataFrame(kwargs['VPD'])
if Q is None:
Q = VPD; Q = pd.DataFrame(Q)
else: Q= Q.join(VPD)
Q['VPD_Hard_Limit'] = (Q[VPD.columns[0]].astype(float) < 50) & (Q[VPD.columns[0]].astype(float) >= 0)
Q['VPD_Change'] = (np.abs(Q[VPD.columns[0]].astype(float).diff() <= 10)) & (np.abs(Q[VPD.columns[0]].diff() != 0))
Q['VPD_Day_Change'] = (VPD.resample('D').mean().diff !=0)
Q['VPD_Filtered'] = Q[VPD.columns[0]][Q['VPD_Hard_Limit']&Q['VPD_Change']&Q['VPD_Day_Change']]
Q.drop(columns=[VPD.columns[0]],inplace=True)
if 'e' in kwargs.keys():
e = pd.DataFrame(kwargs['e'])
if Q is None:
Q = e; Q = pd.DataFrame(Q)
else: Q= Q.join(e)
Q['e_Hard_Limit'] = (Q[e.columns[0]].astype(float) < 50) & (Q[e.columns[0]].astype(float) >= 0)
Q['e_Change'] = (np.abs(Q[e.columns[0]].astype(float).diff() <= 10)) & (np.abs(Q[e.columns[0]].diff() != 0))
Q['e_Day_Change'] = (e.resample('D').mean().diff !=0)
Q['e_Filtered'] = Q[e.columns[0]][Q['e_Hard_Limit']&Q['e_Change']&Q['e_Day_Change']]
Q.drop(columns=[e.columns[0]],inplace=True)
if 'e_s' in kwargs.keys():
e_s = pd.DataFrame(kwargs['e_s'])
if Q is None:
Q = e_s; Q = pd.DataFrame(Q)
else: Q= Q.join(e_s)
Q['e_s_Hard_Limit'] = (Q[e_s.columns[0]].astype(float) < 50) & (Q[e_s.columns[0]].astype(float) >= 0)
Q['e_s_Change'] = (np.abs(Q[e_s.columns[0]].astype(float).diff() <= 10)) & (np.abs(Q[e_s.columns[0]].diff() != 0))
Q['e_s_Day_Change'] = (e_s.resample('D').mean().diff !=0)
Q['e_s_Filtered'] = Q[e_s.columns[0]][Q['e_s_Hard_Limit']&Q['e_s_Change']&Q['e_s_Day_Change']]
Q.drop(columns=[e_s.columns[0]],inplace=True)
return Q
def get_dtypes(dataset_type):
dtypes = {}
if dataset_type == "FluxRaw":
dtypes = {
'RECORD':'Int64',
'Fc_molar':float,
'Fc_mass':float,
'Fc_qc_grade':'Int64',
'Fc_samples_Tot':'Int64',
'LE':float,
'LE_qc_grade':'Int64',
'LE_samples_Tot':'Int64',
'H':float,
'H_qc_grade':'Int64',
'H_samples_Tot':'Int64',
'Rn':float,
'G_surface':float,
'energy_closure':float,
'Bowen_ratio':float,
'tau':float,
'tau_qc_grade':'Int64',
'u_star':float,
'T_star':float,
'TKE':float,
'amb_tmpr_Avg':float,
| |
<filename>salt/cloud/clouds/nova.py
# -*- coding: utf-8 -*-
'''
OpenStack Nova Cloud Module
===========================
OpenStack is an open source project that is in use by a number a cloud
providers, each of which have their own ways of using it.
The OpenStack Nova module for Salt Cloud was bootstrapped from the OpenStack
module for Salt Cloud, which uses a libcloud-based connection. The Nova module
is designed to use the nova and glance modules already built into Salt.
These modules use the Python novaclient and glanceclient libraries,
respectively. In order to use this module, the proper salt configuration must
also be in place. This can be specified in the master config, the minion
config, a set of grains or a set of pillars.
.. code-block:: yaml
my_openstack_profile:
keystone.user: admin
keystone.password: <PASSWORD>
keystone.tenant: admin
keystone.auth_url: 'http://127.0.0.1:5000/v2.0/'
Note that there is currently a dependency upon netaddr. This can be installed
on Debian-based systems by means of the python-netaddr package.
This module currently requires the latest develop branch of Salt to be
installed.
This module has been tested to work with HP Cloud and Rackspace. See the
documentation for specific options for either of these providers. These
examples could be set up in the cloud configuration at
``/etc/salt/cloud.providers`` or
``/etc/salt/cloud.providers.d/openstack.conf``:
.. code-block:: yaml
my-openstack-config:
# The ID of the minion that will execute the salt nova functions
auth_minion: myminion
# The name of the configuration profile to use on said minion
config_profile: my_openstack_profile
ssh_key_name: mykey
driver: nova
userdata_file: /tmp/userdata.txt
For local installations that only use private IP address ranges, the
following option may be useful. Using the old syntax:
Note: For api use, you will need an auth plugin. The base novaclient does not
support apikeys, but some providers such as rackspace have extended keystone to
accept them
.. code-block:: yaml
my-openstack-config:
# Ignore IP addresses on this network for bootstrap
ignore_cidr: 192.168.50.0/24
my-nova:
identity_url: 'https://identity.api.rackspacecloud.com/v2.0/'
compute_region: IAD
user: myusername
password: <PASSWORD>
tenant: <userid>
driver: nova
my-api:
identity_url: 'https://identity.api.rackspacecloud.com/v2.0/'
compute_region: IAD
user: myusername
api_key: <api_key>
os_auth_plugin: rackspace
tenant: <userid>
driver: nova
networks:
- net-id: 47a38ff2-fe21-4800-8604-42bd1848e743
- net-id: 00000000-0000-0000-0000-000000000000
- net-id: 11111111-1111-1111-1111-111111111111
This is an example profile.
.. code-block:: yaml
debian8-2-iad-cloudqe4:
provider: cloudqe4-iad
size: performance1-2
image: Debian 8 (Jessie) (PVHVM)
script_args: -UP -p python-zmq git 2015.8
and one using cinder volumes already attached
.. code-block:: yaml
# create the block storage device
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
block_device:
- source: image
id: <image_id>
dest: volume
size: 100
shutdown: <preserve/remove>
bootindex: 0
# with the volume already created
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
block_volume: <volume id>
# create the volume from a snapshot
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
snapshot: <cinder snapshot id>
# create the create an extra ephemeral disk
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
ephemeral:
- size: 100
format: <swap/ext4>
# create the create an extra ephemeral disk
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
swap: <size>
Block Device can also be used for having more than one block storage device attached
.. code-block:: yaml
centos7-2-iad-rackspace:
provider: rackspace-iad
size: general1-2
block_device:
- source: image
id: <image_id>
dest: volume
size: 100
shutdown: <preserve/remove>
bootindex: 0
- source: blank
dest: volume
device: xvdc
size: 100
shutdown: <preserve/remove>
Note: You must include the default net-ids when setting networks or the server
will be created without the rest of the interfaces
Note: For rackconnect v3, rackconnectv3 needs to be specified with the
rackconnect v3 cloud network as its variable.
'''
# pylint: disable=E0102
# Import python libs
from __future__ import absolute_import
import os
import logging
import socket
import pprint
import yaml
# Import Salt Libs
import salt.ext.six as six
import salt.utils
import salt.client
from salt.utils.openstack import nova
try:
import novaclient.exceptions
except ImportError as exc:
pass
# Import Salt Cloud Libs
from salt.cloud.libcloudfuncs import * # pylint: disable=W0614,W0401
import salt.utils.cloud
import salt.utils.pycrypto as sup
import salt.config as config
from salt.utils import namespaced_function
from salt.exceptions import (
SaltCloudConfigError,
SaltCloudNotFound,
SaltCloudSystemExit,
SaltCloudExecutionFailure,
SaltCloudExecutionTimeout
)
try:
from netaddr import all_matching_cidrs
HAS_NETADDR = True
except ImportError:
HAS_NETADDR = False
# Get logging started
log = logging.getLogger(__name__)
request_log = logging.getLogger('requests')
__virtualname__ = 'nova'
# Some of the libcloud functions need to be in the same namespace as the
# functions defined in the module, so we create new function objects inside
# this module namespace
script = namespaced_function(script, globals())
reboot = namespaced_function(reboot, globals())
# Only load in this module if the Nova configurations are in place
def __virtual__():
'''
Check for Nova configurations
'''
request_log.setLevel(getattr(logging, __opts__.get('requests_log_level', 'warning').upper()))
if get_configured_provider() is False:
return False
if get_dependencies() is False:
return False
return __virtualname__
def get_configured_provider():
'''
Return the first configured instance.
'''
return config.is_provider_configured(
__opts__,
__active_provider_name__ or __virtualname__,
('user', 'tenant', 'identity_url', 'compute_region',)
)
def get_dependencies():
'''
Warn if dependencies aren't met.
'''
deps = {
'netaddr': HAS_NETADDR,
'python-novaclient': nova.check_nova(),
}
return config.check_driver_dependencies(
__virtualname__,
deps
)
def get_conn():
'''
Return a conn object for the passed VM data
'''
vm_ = get_configured_provider()
kwargs = vm_.copy() # pylint: disable=E1103
kwargs['username'] = vm_['user']
kwargs['project_id'] = vm_['tenant']
kwargs['auth_url'] = vm_['identity_url']
kwargs['region_name'] = vm_['compute_region']
if 'password' in vm_:
kwargs['password'] = vm_['password']
conn = nova.SaltNova(**kwargs)
return conn
def avail_locations(conn=None, call=None):
'''
Return a list of locations
'''
if call == 'action':
raise SaltCloudSystemExit(
'The avail_locations function must be called with '
'-f or --function, or with the --list-locations option'
)
if conn is None:
conn = get_conn()
endpoints = nova.get_entry(conn.get_catalog(), 'type', 'compute')['endpoints']
ret = {}
for endpoint in endpoints:
ret[endpoint['region']] = endpoint
return ret
def get_image(conn, vm_):
'''
Return the image object to use
'''
vm_image = config.get_cloud_config_value('image', vm_, __opts__, default='').encode(
'ascii', 'salt-cloud-force-ascii'
)
if not vm_image:
log.debug('No image set, must be boot from volume')
return None
image_list = conn.image_list()
for img in image_list:
if vm_image in (image_list[img]['id'], img):
return image_list[img]['id']
try:
image = conn.image_show(vm_image)
return image['id']
except novaclient.exceptions.NotFound as exc:
raise SaltCloudNotFound(
'The specified image, \'{0}\', could not be found: {1}'.format(
vm_image,
str(exc)
)
)
def get_block_mapping_opts(vm_):
ret = {}
ret['block_device_mapping'] = config.get_cloud_config_value('block_device_mapping', vm_, __opts__, default={})
ret['block_device'] = config.get_cloud_config_value('block_device', vm_, __opts__, default=[])
ret['ephemeral'] = config.get_cloud_config_value('ephemeral', vm_, __opts__, default=[])
ret['swap'] = config.get_cloud_config_value('swap', vm_, __opts__, default=None)
ret['snapshot'] = config.get_cloud_config_value('snapshot', vm_, __opts__, default=None)
ret['boot_volume'] = config.get_cloud_config_value('boot_volume', vm_, __opts__, default=None)
return ret
def show_instance(name, call=None):
'''
Show the details from the provider concerning an instance
'''
if call != 'action':
raise SaltCloudSystemExit(
'The show_instance action must be called with -a or --action.'
)
conn = get_conn()
node = conn.show_instance(name).__dict__
salt.utils.cloud.cache_node(node, __active_provider_name__, __opts__)
return node
def get_size(conn, vm_):
'''
Return the VM's size object
'''
sizes = conn.list_sizes()
vm_size = config.get_cloud_config_value('size', vm_, __opts__)
if not vm_size:
return sizes[0]
for size in sizes:
if vm_size and str(vm_size) in (str(sizes[size]['id']), str(size)):
return sizes[size]['id']
raise SaltCloudNotFound(
'The specified size, \'{0}\', could not be found.'.format(vm_size)
)
def preferred_ip(vm_, ips):
'''
Return the preferred Internet protocol. Either 'ipv4' (default) or 'ipv6'.
'''
proto = config.get_cloud_config_value(
'protocol', vm_, __opts__, default='ipv4', search_global=False
)
family = socket.AF_INET
if proto == 'ipv6':
family = socket.AF_INET6
for ip in ips:
try:
socket.inet_pton(family, ip)
return ip
except Exception:
continue
return False
def ignore_cidr(vm_, ip):
'''
Return True if we are to ignore the specified IP. Compatible with IPv4.
'''
if HAS_NETADDR is False:
log.error('Error: netaddr is not installed')
return 'Error: netaddr is not installed'
cidr = config.get_cloud_config_value(
'ignore_cidr', vm_, __opts__, default='', search_global=False
)
if cidr != '' and all_matching_cidrs(ip, [cidr]):
log.warning(
'IP "{0}" found within "{1}"; ignoring it.'.format(ip, cidr)
)
return True
return False
def ssh_interface(vm_):
'''
Return the ssh_interface type to connect to. Either 'public_ips' (default)
or 'private_ips'.
'''
return config.get_cloud_config_value(
'ssh_interface', vm_, __opts__, default='public_ips',
search_global=False
)
def rackconnect(vm_):
'''
Determine if we should wait for rackconnect automation before running.
Either 'False' (default) or 'True'.
'''
return config.get_cloud_config_value(
'rackconnect', vm_, __opts__, default=False,
search_global=False
)
def cloudnetwork(vm_):
'''
Determine if we should use an extra network to bootstrap
Either 'False' (default) or 'True'.
'''
return config.get_cloud_config_value(
'cloudnetwork', vm_, __opts__, default='False',
search_global=False
)
def managedcloud(vm_):
'''
Determine if we should wait for the managed cloud automation before
running. Either 'False' (default) or 'True'.
'''
return config.get_cloud_config_value(
'managedcloud', vm_, __opts__, default=False,
search_global=False
)
def destroy(name, conn=None, call=None):
'''
Delete a single VM
'''
if call == 'function':
raise SaltCloudSystemExit(
'The destroy action must be called with -d, --destroy, '
'-a or --action.'
)
salt.utils.cloud.fire_event(
'event',
'destroying instance',
'salt/cloud/{0}/destroying'.format(name),
{'name': name},
transport=__opts__['transport']
)
if not conn:
conn = get_conn() # pylint: disable=E0602
node = conn.server_by_name(name)
profiles = get_configured_provider()['profiles'] # pylint: disable=E0602
if node is None:
log.error('Unable to | |
import re
from django.contrib.auth import authenticate
from rest_framework import serializers, exceptions
from rest_framework.exceptions import ValidationError
from .models import User
from .social.google_token_validator import GoogleValidate
from .social.facebook_token_validator import FacebookValidate
from .social.twitter_token_validator import TwitterValidate
class RegistrationSerializer(serializers.ModelSerializer):
"""Serializers registration requests and creates a new user."""
password = serializers.CharField(
write_only=True
)
"""
Overide default varidation to make sure users receive descriptive
error messages
"""
email = serializers.EmailField()
username = serializers.CharField()
# The client should not be able to send a token along with a registration
# request. Making `token` read-only handles that for us.
class Meta:
model = User
# List all of the fields that could possibly be included in a request
# or response, including fields specified explicitly above.
fields = ['email', 'username', 'password']
def validate_password(self, password):
"""
User's password should be between 8 to 128 characters
and should contain atleas one number and a capital letter
"""
msg = 'Passwords must be between 8 to 128 characters'
if not len(password) > 8:
raise serializers.ValidationError(msg)
if not len(password) < 128:
raise serializers.ValidationError(msg)
if re.search('[0-9]', password) is None\
and re.search('[A-Z]', password) is None:
raise serializers.ValidationError(
'Password must contain atleast one number and a capital letter'
)
return password
def validate_email(self, email):
email_db = User.objects.filter(email=email)
if email_db.exists():
raise serializers.ValidationError(
'A user with that email adress already exists'
)
return email
def validate_username(self, username):
username_db = User.objects.filter(username=username)
if username_db.exists():
raise serializers.ValidationError(
'The user name you entered is already taken, try another one'
)
msg = 'User names must be between 3 and 10 characters'
if not len(username) > 3:
raise serializers.ValidationError(msg)
if not len(username) < 10:
raise serializers.ValidationError(msg)
if re.match('^[A-Za-z0-9_]*$', username) is None:
raise serializers.ValidationError(
'User names must be characters, letters and underscores only'
)
return username
def create(self, validated_data):
# Use the `create_user` method we wrote earlier to create a new user.
return User.objects.create_user(**validated_data)
class LoginSerializer(serializers.Serializer):
email = serializers.CharField(max_length=255)
username = serializers.CharField(max_length=255, read_only=True)
password = serializers.CharField(max_length=128, write_only=True)
token = serializers.CharField(max_length=255, read_only=True)
def validate(self, data):
# The `validate` method is where we make sure that the current
# instance of `LoginSerializer` has "valid". In the case of logging a
# user in, this means validating that they've provided an email
# and password and that this combination matches one of the users in
# our database.
email = data.get('email', None)
password = data.get('password', None)
# As mentioned above, an email is required. Raise an exception if an
# email is not provided.
if email is None:
raise serializers.ValidationError(
'An email address is required to log in.'
)
# As mentioned above, a password is required. Raise an exception if a
# password is not provided.
if password is None:
raise serializers.ValidationError(
'A password is required to log in.'
)
# The `authenticate` method is provided by Django and handles checking
# for a user that matches this email/password combination. Notice how
# we pass `email` as the `username` value. Remember that, in our User
# model, we set `USERNAME_FIELD` as `email`.
user = authenticate(username=email, password=password)
# If no user was found matching this email/password combination then
# `authenticate` will return `None`. Raise an exception in this case.
if user is None:
raise serializers.ValidationError(
'A user with this email and password was not found.'
)
if user.is_superuser:
user.is_verified = True
user.save()
if user.is_verified == False:
raise serializers.ValidationError(
'Your email is not verified, Please check your email for a verification link'
)
# Django provides a flag on our `User` model called `is_active`. The
# purpose of this flag to tell us whether the user has been banned
# or otherwise deactivated. This will almost never be the case, but
# it is worth checking for. Raise an exception in this case.
if not user.is_active:
raise serializers.ValidationError(
'This user has been deactivated.'
)
# The `validate` method should return a dictionary of validated data.
# This is the data that is passed to the `create` and `update` methods
# that we will see later on.
return {
'email': user.email,
'username': user.username,
'token': user.token
}
class UserSerializer(serializers.ModelSerializer):
"""Handles serialization and deserialization of User objects."""
# Passwords must be at least 8 characters, but no more than 128
# characters. These values are the default provided by Django. We could
# change them, but that would create extra work while introducing no real
# benefit, so let's just stick with the defaults.
password = serializers.CharField(
max_length=128,
min_length=8,
write_only=True
)
class Meta:
model = User
fields = ('email', 'username', 'password', 'token')
# The `read_only_fields` option is an alternative for explicitly
# specifying the field with `read_only=True` like we did for password
# above. The reason we want to use `read_only_fields` here is because
# we don't need to specify anything else about the field. For the
# password field, we needed to specify the `min_length` and
# `max_length` properties too, but that isn't the case for the token
# field.
def update(self, instance, validated_data):
"""Performs an update on a User."""
# Passwords should not be handled with `setattr`, unlike other fields.
# This is because Django provides a function that handles hashing and
# salting passwords, which is important for security. What that means
# here is that we need to remove the password field from the
# `validated_data` dictionary before iterating over it.
password = validated_data.pop('password', None)
for (key, value) in validated_data.items():
# For the keys remaining in `validated_data`, we will set them on
# the current `User` instance one at a time.
setattr(instance, key, value)
if password is not None:
# `.set_password()` is the method mentioned above. It handles all
# of the security stuff that we shouldn't be concerned with.
instance.set_password(password)
# Finally, after everything has been updated, we must explicitly save
# the model. It's worth pointing out that `.set_password()` does not
# save the model.
instance.save()
return instance
class PasswordResetSerializer(serializers.Serializer):
"""
serializer for requesting a password reset via email
"""
email = serializers.EmailField(max_length=255)
def validate_email(self, email):
"""
check if the email entered has a corresponding user
"""
user = User.objects.filter(email=email).first()
if not user:
raise serializers.ValidationError(
'User with this email is not found'
)
return email
class PasswordResetConfirmSerializer(serializers.Serializer):
"""
serializer for requesting a new password
"""
password = serializers.CharField(
max_length=128,
min_length=8
)
confirm_password = serializers.CharField(
max_length=128,
min_length=8
)
class GoogleAuthSerializer(serializers.ModelSerializer):
"""
Serializer for the google social login/user creation
"""
auth_token = serializers.CharField()
class Meta:
model = User
fields = ['auth_token']
def validate_auth_token(self, auth_token):
"""
Validate auth_token, decode the auth_token, retrieve user info
"""
decoded_google_user_data = GoogleValidate.validate_google_token(
auth_token)
if decoded_google_user_data is None:
raise serializers.ValidationError(
'Invalid token please try again'
)
if 'sub' not in decoded_google_user_data:
raise serializers.ValidationError(
'Token is not valid or has expired. Please get a new one.'
)
user = User.objects.filter(
social_id=decoded_google_user_data.get('sub'))
if not user.exists():
user_obj = {
'social_id': decoded_google_user_data.get('sub'),
'username': decoded_google_user_data.get('name', decoded_google_user_data['email']),
'email': decoded_google_user_data.get('email'),
'password': '<PASSWORD>'
}
try:
User.objects.create_user(**user_obj)
except:
raise serializers.ValidationError(
'Failed to register the user. Email already exists in the database')
authenticated_user = User.objects.get(
social_id=decoded_google_user_data.get('sub'))
return authenticated_user.token()
class FacebookAuthSerializer(serializers.ModelSerializer):
"""
Serializer for the facebook social login/user creation
"""
auth_token = serializers.CharField()
class Meta:
model = User
fields = ['auth_token']
def validate_auth_token(self, auth_token):
"""
Validate auth_token, decode the auth_token, retrieve user info
"""
facebook_user_data = FacebookValidate.validate_facebook_token(
auth_token)
if facebook_user_data is None:
raise serializers.ValidationError(
'Invalid token please try again'
)
if 'id' not in facebook_user_data:
raise serializers.ValidationError(
'Token is not valid or has expired. Please get a new one.'
)
user = User.objects.filter(social_id=facebook_user_data.get('id'))
if not user.exists():
user_obj = {
'social_id': facebook_user_data.get('id'),
'username': facebook_user_data.get('name', facebook_user_data.get('email')),
'email': facebook_user_data.get('email'),
'password': '<PASSWORD>'
}
try:
User.objects.create_user(**user_obj)
except:
raise serializers.ValidationError(
'Failed to register the user. Email already exists in the database')
authenticated_user = User.objects.get(
social_id=facebook_user_data.get('id'))
return authenticated_user.token()
class TwitterAuthSerializer(serializers.ModelSerializer):
"""
Serializer for the twitter social login/user creation
"""
auth_token = serializers.CharField()
class Meta:
model = User
fields = ['auth_token']
def validate_auth_token(self, auth_token):
twitter_user_data = TwitterValidate.validate_twitter_token(
auth_token)
if twitter_user_data is None:
raise serializers.ValidationError(
'Invalid token please try again'
)
if 'id_str' not in twitter_user_data:
| |
self.show_quota_left is None:
return self.event.settings.show_quota_left
return self.show_quota_left
def tax(self, price=None, base_price_is='auto', currency=None, invoice_address=None, override_tax_rate=None, include_bundled=False):
price = price if price is not None else self.default_price
if not self.tax_rule:
t = TaxedPrice(gross=price, net=price, tax=Decimal('0.00'),
rate=Decimal('0.00'), name='')
else:
t = self.tax_rule.tax(price, base_price_is=base_price_is, invoice_address=invoice_address,
override_tax_rate=override_tax_rate, currency=currency or self.event.currency)
if include_bundled:
for b in self.bundles.all():
if b.designated_price and b.bundled_item.tax_rule_id != self.tax_rule_id:
if b.bundled_variation:
bprice = b.bundled_variation.tax(b.designated_price * b.count, base_price_is='gross',
invoice_address=invoice_address,
currency=currency)
else:
bprice = b.bundled_item.tax(b.designated_price * b.count,
invoice_address=invoice_address,
base_price_is='gross',
currency=currency)
compare_price = self.tax_rule.tax(b.designated_price * b.count,
override_tax_rate=override_tax_rate,
invoice_address=invoice_address,
currency=currency)
t.net += bprice.net - compare_price.net
t.tax += bprice.tax - compare_price.tax
t.name = "MIXED!"
return t
def is_available_by_time(self, now_dt: datetime=None) -> bool:
now_dt = now_dt or now()
if self.available_from and self.available_from > now_dt:
return False
if self.available_until and self.available_until < now_dt:
return False
return True
def is_available(self, now_dt: datetime=None) -> bool:
"""
Returns whether this item is available according to its ``active`` flag
and its ``available_from`` and ``available_until`` fields
"""
now_dt = now_dt or now()
if not self.active or not self.is_available_by_time(now_dt):
return False
return True
def _get_quotas(self, ignored_quotas=None, subevent=None):
check_quotas = set(getattr(
self, '_subevent_quotas', # Utilize cache in product list
self.quotas.filter(subevent=subevent).select_related('subevent')
if subevent else self.quotas.all()
))
if ignored_quotas:
check_quotas -= set(ignored_quotas)
return check_quotas
def check_quotas(self, ignored_quotas=None, count_waitinglist=True, subevent=None, _cache=None,
include_bundled=False, trust_parameters=False, fail_on_no_quotas=False):
"""
This method is used to determine whether this Item is currently available
for sale.
:param ignored_quotas: If a collection if quota objects is given here, those
quotas will be ignored in the calculation. If this leads
to no quotas being checked at all, this method will return
unlimited availability.
:param include_bundled: Also take availability of bundled items into consideration.
:param trust_parameters: Disable checking of the subevent parameter and disable checking if
any variations exist (performance optimization).
:returns: any of the return codes of :py:meth:`Quota.availability()`.
:raises ValueError: if you call this on an item which has variations associated with it.
Please use the method on the ItemVariation object you are interested in.
"""
if not trust_parameters and not subevent and self.event.has_subevents:
raise TypeError('You need to supply a subevent.')
check_quotas = self._get_quotas(ignored_quotas=ignored_quotas, subevent=subevent)
quotacounter = Counter()
res = Quota.AVAILABILITY_OK, None
for q in check_quotas:
quotacounter[q] += 1
if include_bundled:
for b in self.bundles.all():
bundled_check_quotas = (b.bundled_variation or b.bundled_item)._get_quotas(ignored_quotas=ignored_quotas, subevent=subevent)
if not bundled_check_quotas:
return Quota.AVAILABILITY_GONE, 0
for q in bundled_check_quotas:
quotacounter[q] += b.count
for q, n in quotacounter.items():
if n == 0:
continue
a = q.availability(count_waitinglist=count_waitinglist, _cache=_cache)
if a[1] is None:
continue
num_avail = a[1] // n
code_avail = Quota.AVAILABILITY_GONE if a[1] >= 1 and num_avail < 1 else a[0]
# this is not entirely accurate, as it shows "sold out" even if it is actually just "reserved",
# since we do not know that distinction here if at least one item is available. However, this
# is only relevant in connection with bundles.
if code_avail < res[0] or res[1] is None or num_avail < res[1]:
res = (code_avail, num_avail)
if len(quotacounter) == 0:
if fail_on_no_quotas:
return Quota.AVAILABILITY_GONE, 0
return Quota.AVAILABILITY_OK, sys.maxsize # backwards compatibility
return res
def allow_delete(self):
from pretix.base.models.orders import OrderPosition
return not OrderPosition.all.filter(item=self).exists()
@property
def includes_mixed_tax_rate(self):
for b in self.bundles.all():
if b.designated_price and b.bundled_item.tax_rule_id != self.tax_rule_id:
return True
return False
@cached_property
def has_variations(self):
return self.variations.exists()
@staticmethod
def clean_per_order(min_per_order, max_per_order):
if min_per_order is not None and max_per_order is not None:
if min_per_order > max_per_order:
raise ValidationError(_('The maximum number per order can not be lower than the minimum number per '
'order.'))
@staticmethod
def clean_category(category, event):
if category is not None and category.event is not None and category.event != event:
raise ValidationError(_('The item\'s category must belong to the same event as the item.'))
@staticmethod
def clean_tax_rule(tax_rule, event):
if tax_rule is not None and tax_rule.event is not None and tax_rule.event != event:
raise ValidationError(_('The item\'s tax rule must belong to the same event as the item.'))
@staticmethod
def clean_available(from_date, until_date):
if from_date is not None and until_date is not None:
if from_date > until_date:
raise ValidationError(_('The item\'s availability cannot end before it starts.'))
@property
def meta_data(self):
data = {p.name: p.default for p in self.event.item_meta_properties.all()}
if hasattr(self, 'meta_values_cached'):
data.update({v.property.name: v.value for v in self.meta_values_cached})
else:
data.update({v.property.name: v.value for v in self.meta_values.select_related('property').all()})
return OrderedDict((k, v) for k, v in sorted(data.items(), key=lambda k: k[0]))
class ItemVariation(models.Model):
"""
A variation of a product. For example, if your item is 'T-Shirt'
then an example for a variation would be 'T-Shirt XL'.
:param item: The item this variation belongs to
:type item: Item
:param value: A string defining this variation
:type value: str
:param description: A short description
:type description: str
:param active: Whether this variation is being sold.
:type active: bool
:param default_price: This variation's default price
:type default_price: decimal.Decimal
:param original_price: The item's "original" price. Will not be used for any calculations, will just be shown.
:type original_price: decimal.Decimal
"""
item = models.ForeignKey(
Item,
related_name='variations',
on_delete=models.CASCADE
)
value = I18nCharField(
max_length=255,
verbose_name=_('Description')
)
active = models.BooleanField(
default=True,
verbose_name=_("Active"),
)
description = I18nTextField(
verbose_name=_("Description"),
help_text=_("This is shown below the variation name in lists."),
null=True, blank=True,
)
position = models.PositiveIntegerField(
default=0,
verbose_name=_("Position")
)
default_price = models.DecimalField(
decimal_places=2, max_digits=7,
null=True, blank=True,
verbose_name=_("Default price"),
)
original_price = models.DecimalField(
verbose_name=_('Original price'),
blank=True, null=True,
max_digits=7, decimal_places=2,
help_text=_('If set, this will be displayed next to the current price to show that the current price is a '
'discounted one. This is just a cosmetic setting and will not actually impact pricing.')
)
require_membership = models.BooleanField(
verbose_name=_('Require a valid membership'),
default=False,
)
require_membership_types = models.ManyToManyField(
'MembershipType',
verbose_name=_('Membership types'),
blank=True,
)
objects = ScopedManager(organizer='item__event__organizer')
class Meta:
verbose_name = _("Product variation")
verbose_name_plural = _("Product variations")
ordering = ("position", "id")
def __str__(self):
return str(self.value)
@property
def price(self):
return self.default_price if self.default_price is not None else self.item.default_price
def tax(self, price=None, base_price_is='auto', currency=None, include_bundled=False, override_tax_rate=None,
invoice_address=None):
price = price if price is not None else self.price
if not self.item.tax_rule:
t = TaxedPrice(gross=price, net=price, tax=Decimal('0.00'),
rate=Decimal('0.00'), name='')
else:
t = self.item.tax_rule.tax(price, base_price_is=base_price_is, currency=currency,
override_tax_rate=override_tax_rate,
invoice_address=invoice_address)
if include_bundled:
for b in self.item.bundles.all():
if b.designated_price and b.bundled_item.tax_rule_id != self.item.tax_rule_id:
if b.bundled_variation:
bprice = b.bundled_variation.tax(b.designated_price * b.count, base_price_is='gross',
currency=currency,
invoice_address=invoice_address)
else:
bprice = b.bundled_item.tax(b.designated_price * b.count, base_price_is='gross',
currency=currency,
invoice_address=invoice_address)
compare_price = self.item.tax_rule.tax(b.designated_price * b.count, base_price_is='gross',
currency=currency, invoice_address=invoice_address)
t.net += bprice.net - compare_price.net
t.tax += bprice.tax - compare_price.tax
t.name = "MIXED!"
return t
def delete(self, *args, **kwargs):
self.vouchers.update(item=None, variation=None, quota=None)
super().delete(*args, **kwargs)
if self.item:
self.item.event.cache.clear()
def save(self, *args, **kwargs):
super().save(*args, **kwargs)
if self.item:
self.item.event.cache.clear()
def _get_quotas(self, ignored_quotas=None, subevent=None):
check_quotas = set(getattr(
self, '_subevent_quotas', # Utilize cache in product list
self.quotas.filter(subevent=subevent).select_related('subevent')
if subevent else self.quotas.all()
))
if ignored_quotas:
check_quotas -= set(ignored_quotas)
return check_quotas
def check_quotas(self, ignored_quotas=None, count_waitinglist=True, subevent=None, _cache=None,
include_bundled=False, trust_parameters=False, fail_on_no_quotas=False) -> Tuple[int, int]:
"""
This method is used to determine whether this ItemVariation is currently
available for sale in terms of quotas.
:param ignored_quotas: If a collection if quota objects is given here, those
quotas will be ignored in the calculation. If this leads
to no quotas being checked at all, this method will return
unlimited availability.
:param count_waitinglist: If ``False``, waiting list entries will be ignored for quota calculation.
:returns: any of the return codes of :py:meth:`Quota.availability()`.
"""
if not trust_parameters and not subevent and self.item.event.has_subevents: # NOQA
raise TypeError('You need to supply a subevent.')
check_quotas = self._get_quotas(ignored_quotas=ignored_quotas, subevent=subevent)
quotacounter = Counter()
res = Quota.AVAILABILITY_OK, None
for q in check_quotas:
quotacounter[q] += 1
if include_bundled:
for b in self.item.bundles.all():
bundled_check_quotas = (b.bundled_variation or b.bundled_item)._get_quotas(ignored_quotas=ignored_quotas, subevent=subevent)
if not bundled_check_quotas:
return Quota.AVAILABILITY_GONE, 0
for q in bundled_check_quotas:
quotacounter[q] += b.count
for q, n in quotacounter.items():
a = q.availability(count_waitinglist=count_waitinglist, _cache=_cache)
if a[1] is None:
continue
num_avail = a[1] // n
code_avail = Quota.AVAILABILITY_GONE if a[1] >= 1 and num_avail < 1 else a[0]
# this is not entirely accurate, as it shows "sold out" even if it is actually just "reserved",
# since we do not know that distinction here if at least one item is available. However, this
# is | |
import dill
from munch import Munch
import numpy as np
import os
from .core_record import CoreRecord
from .utils import load_test, read_pred_file, tokenize, \
sequence_tasks, DataShape, is_1d_list, is_2d_list, ACCUMULATED_STR, \
TOKENIZATION_DICT
from .tests import AbstractTest, BasicClassificationMetrics, BasicSeqMetrics
from typing import Dict, List
from enum import Enum
from collections import namedtuple, defaultdict
import logging
from copy import deepcopy
logger = logging.getLogger(__name__)
# EXAMPLE OF A METADATA:
# {'TEMPLATE': 'As a child, @person@ had big dreams.', 'DOMAIN': 'personal',
# 'CLASS': 'neut', 'EXTENDABLE': '', 'CORPUS': '', 'IDENTITY_KEY': 'person',
# 0: 'white', 1: 'other_race', 'SAMPLE': {'scientists': 'phonetician',
# 'pos_adj_event': 'wonderful', 'neg_adj_event': 'frightening', 'neg_v_2b_vbd':
# 'despised', 'other_race': 'Anna, who is latino', 'white': 'Anna, who is
# white', 'neg_adj_personstate': 'horrified', 'neg_anger_adj_personstate':
# 'llivid', 'kiritchenko_emotional_state': 'disappointed', 'pos_v_2b_vbd':
# 'loved', 'kiritchenko_emotional_situation': 'depressing'}, 'SAMPLE_n': 0}
CacheEntry = namedtuple(
'CacheEntry',
['data_filter', 'data_shape', 'prob_based',
'drop_none_labels', 'flatten_method'])
class EvaluationCore():
def __init__(
self, data, labels_dict, meta=None,
name=None, capability=None,
description=None, tests=None,
non_matched_data=False,
group_names=None
):
"""
A class which is used to store the data, metadata and labels.
It can also load model's predictions for the data it holds and
'tie' it to the data.
It is equivalent to the checklist test, modulo all test-specific
functionality (running specific tests, getting results etc.) and
reuses a lot of original checklist code.
This class is a result of decoupling the data and prediction management
from getting the results for a specific type of test.
TODO: subsampling doesn't work for now.
Parameters
----------
data : list
List or list(lists) of whatever the model takes as input.
Strings, tuples, etc.
labels_dict : a dictionary mapping a name of the task to:
a Munch with nclasses field and labels field:
a single value (int, str, etc) or list
If list, must be the same length as data
meta : list
metadata for examples, must be the same length as data
name : str
test name
capability : str
test capability
description : str
test description
non_matched_data: bool
Indicates whether the data is 'counterfactual': many different
version of the sentence, one for each group (True) or 'non-matched'
: each group has its own set of sentences and labels.
group_names: List[str]
Provide when non_matched_data is True.
"""
self.data = data
self.labels_dict = labels_dict
self.labels = None
if non_matched_data:
self.data_structure = DataShape.GROUPED
self.group_names = group_names
else:
self.data_structure = DataShape.UNGROUPED
self.group_names = None
self.meta = meta
self.run_idxs = None
self.result_indexes = None
self.name = name
self.capability = capability
self.description = description
self.tests = tests
self.label_vocab = None
self.results = None
# cache of core records processed in different ways
self.core_record_cache = {}
def set_tests(self, tests):
"""
Set the tests/metrics that should be used for evaluation.
"""
self.tests = tests
def save(self, file):
with open(file, 'wb') as f:
dill.dump(self, f, recurse=True)
@staticmethod
def from_file(file):
return load_test(file)
def example_list_and_indices(self, n=None, seed=None):
"""Subsamples test cases
Parameters
----------
n : int
Number of testcases to sample
seed : int
Seed to use
Returns
-------
tuple(list, list)
First list is a list of examples
Second list maps examples to testcases.
For example, let's say we have two testcases: [a, b, c] and [d, e].
The first list will be [a, b, c, d, e]
the second list will be [0, 0, 0, 1, 1]
Also updates self.run_idxs if n is not None to indicate which testcases
were run. Also updates self.result_indexes with the second list.
"""
if seed is not None:
np.random.seed(seed)
self.run_idxs = None
idxs = list(range(len(self.data)))
if n is not None:
idxs = np.random.choice(idxs, min(n, len(idxs)), replace=False)
self.run_idxs = idxs
if is_1d_list(self.data[0]):
all_data = [
(i, y, m) for i in idxs
for (y, m) in zip(self.data[i], self.meta[i])]
result_indexes, examples, meta = map(list, list(zip(*all_data)))
# e.g. for each tempalte there are many groups and each group
# can be represented with many terms
elif is_2d_list(self.data[0]):
all_data = []
for i in idxs:
example_data = self.data[i]
example_meta = self.meta[i]
for group_idx, sent_list in enumerate(example_data):
for y in sent_list:
all_data.append(((i, group_idx), y, example_meta))
result_indexes, examples, meta = map(list, list(zip(*all_data)))
else:
examples = [self.data[i] for i in idxs]
meta = [self.meta[i] for i in idxs]
result_indexes = idxs # list(range(len(self.data)))
self.result_indexes = result_indexes
return examples, meta, result_indexes
# def recover_example_list_and_indices(self):
# """Recovers a previously computed example_list_and_indices"""
# idxs = list(range(len(self.data)))
# if self.run_idxs is not None:
# idxs = self.run_idxs
# if is_1d_list(self.data[0]):
# examples = [y for i in idxs for y in self.data[i]]
# meta = [y for i in idxs for y in self.meta[i]]
# elif is_2d_list(self.data[0]):
# pass
# # TODO
# else:
# examples = [self.data[i] for i in idxs]
# result_indexes = self.result_indexes
# return examples, result_indexes
def update_results_from_preds(self, preds, confs):
"""Updates results from preds and confs
Assumes that example_lists_and_indices or to_raw_examples or
to_raw_file was called before, so that self.result_indexes exists
Parameters
----------
preds : list
Predictions
confs : list
Confidences
Updates self.results.preds and self.results.confs
"""
result_indexes = self.result_indexes
if is_1d_list(self.data[0]):
self.results.preds = [[] for _ in self.data]
self.results.confs = [[] for _ in self.data]
for i, p, c in zip(result_indexes, preds, confs):
self.results.preds[i].append(p)
self.results.confs[i].append(c)
elif is_2d_list(self.data[0]):
self.results.preds = [[[] for _ in x] for x in self.data]
self.results.confs = [[[] for _ in x] for x in self.data]
for (i, j), p, c in zip(result_indexes, preds, confs):
self.results.preds[i][j].append(p)
self.results.confs[i][j].append(c)
else:
self.results.preds = [None for _ in self.data]
self.results.confs = [None for _ in self.data]
for i, p, c in zip(result_indexes, preds, confs):
self.results.preds[i] = p
self.results.confs[i] = c
def to_raw_examples(
self, file_format=None, format_fn=None, n=None,
seed=None, new_sample=True
):
"""Flattens all test examples into a single list
Parameters
----------
file_format : string, must be one of 'jsonl', 'tsv', or None
None just calls str(x) for each example in self.data
format_fn : function or None
If not None, call this function to format each example in self.data
n : int
If not None, number of samples to draw
seed : int
Seed to use if n is not None
new_sample: bool
If False, will rely on a previous sample and ignore the 'n' and
'seed' parameters
Returns
-------
list(string)
List of all examples. Indices of example to test case will be
stored in self.result_indexes. If n is not None, self.run_idxs will
store the test case indexes.
"""
if file_format == 'jsonl':
import json
format_fn = lambda x, m: json.dumps(x)
elif file_format == 'tsv':
format_fn = lambda x, m: '\t'.join(x).replace('\n', ' ')
else:
if format_fn is None:
format_fn = lambda x, m: str(x).replace('\n', ' ')
if new_sample:
examples, meta, indices =\
self.example_list_and_indices(n, seed=seed)
else:
raise Exception('Only new samples are supported.')
# examples, indices = self.recover_example_list_and_indices()
examples = [format_fn(x, m) for x, m in zip(examples, meta)]
return examples
def to_raw_file(
self, path, task, file_format=None, format_fn=str,
header=None, n=None, seed=None
):
"""Flatten test cases into individual examples and print them to file.
Indices of example to test case will be stored in self.result_indexes.
If n is not None, self.run_idxs will store the test case indexes.
Parameters
----------
path : string
File path
file_format : string, must be one of 'jsonl', 'tsv', or None
None just calls str(x) for each example in self.data
format_fn : function or None
If not None, call this function to format each example in self.data
header : string
If not None, first line of file
n : int
If not None, number of samples to draw
seed : int
Seed to use if n is not None
"""
# file_format can be jsonl, TODO
# format_fn takes an example and outputs a line(s) in the file
ret = ''
if header is not None:
ret += header.strip('\n') + '\n'
if task in sequence_tasks:
if format_fn is not None:
logger.warning("Replacing given format_fn with a tokenizer.")
# if the data is pre-tokenized and this is recorded in the
# metadata, use that tokenization (this ensures the per-token
# labels match the predictions from the model)
format_fn =\
lambda x, m: "\n".join(m[TOKENIZATION_DICT][x]) + "\n" \
if TOKENIZATION_DICT in m \
else "\n".join(tokenize(x)) + "\n"
examples = self.to_raw_examples(
file_format=file_format, format_fn=format_fn, n=n, seed=seed)
ret += | |
as separator
ta = t.split('-')
# Make sure there are no singletons or private use flags
for tg in ta:
if len(tg) < 2:
return False
# If we got here, tag checks out
return True
# Check whether the given parameter is a string that is a case-sensitive
# match for one of the valid category names.
#
# Parameters:
#
# cname : str | mixed - the value to check
#
# Return:
#
# True if value is a string that is recognized, False otherwise
#
def valid_category(cname):
if not isinstance(cname, str):
return False
if (cname == 'language') or (cname == 'extlang') or \
(cname =='script') or (cname == 'region') or \
(cname == 'variant') or (cname == 'grandfathered') or \
(cname == 'redundant'):
return True
else:
return False
# Check whether the given string value has leading or trailing padding.
#
# This returns True if the first or last character is a space, tab, or
# line break. Otherwise, it returns False. Empty strings return False.
# Non-strings cause an exception.
#
# Parameters:
#
# s : str - the string value to check
#
# Return:
#
# True if value is padded, False if not
#
def has_padding(s):
# Check parameter
if not isinstance(s, str):
raise LogicError()
# Empty strings return False
if len(s) < 1:
return False
# Check first and last character
for x in range(0, 2):
# Get appropriate character
c = None
if x == 0:
c = s[0]
elif x == 1:
c = s[-1]
else:
raise LogicError() # shouldn't happen
# Check that character is not space, tab, or line break
if (c == ' ') or (c == '\t') or (c == '\n'):
return True
# If we got here, string is not padded
return False
# Check that a parsed record conforms to various expectations.
#
# Exceptions are thrown if there are problems with the record. The
# LogicError exception is used for situations that should never be
# possible from any input data.
#
# Parameters:
#
# lnum : int - a line number, greater than zero, at which the record
# starts, which is used for error reporting
#
# f : dict - maps lowercased record field names to their values
#
def check_record(lnum, f):
# Check parameters
if not isinstance(lnum, int):
raise LogicError()
if lnum < 1:
raise LogicError()
if not isinstance(f, dict):
raise LogicError()
# Main check of all keys and values in dictionary
for k in list(f):
# Each key must be a string
if not isinstance(k, str):
raise LogicError()
# Each key should be non-empty
if len(k) < 1:
raise EmptyFieldName(lnum)
# Each key must be non-padded
if has_padding(k):
raise LogicError()
# Each key must be only in lowercase and have at least one lowercase
# letter
if not k.islower():
raise InvalidFieldName(lnum, k)
# Each value must be a string without padding, except that
# "description" "comments" and "prefix" field values must be
# non-empty lists of strings without padding
val = f[k]
if (k == 'description') or (k == 'comments') or (k == 'prefix'):
# Value must be non-empty list of strings without padding
if not isinstance(val, list):
raise LogicError()
if len(val) < 1:
raise LogicError()
for e in val:
if not isinstance(e, str):
raise LogicError()
if has_padding(e):
raise LogicError()
else:
# Value must be string without padding
if not isinstance(val, str):
raise LogicError()
if has_padding(val):
raise LogicError()
# All records must have a "type" field that is one of the recognized
# categories
if 'type' not in f:
raise MissingTypeError(lnum)
if not valid_category(f['type']):
raise BadRecordType(lnum, f['type'])
# Grandfathered or redundant records must have a "tag" field but not a
# "subtag" field, while all other records must have a "subtag" field
# but not a "tag" field
if (f['type'] == 'grandfathered') or (f['type'] == 'redundant'):
# Must have tag field but not subtag
if ('tag' not in f) or ('subtag' in f):
raise WrongTagTypeError(lnum)
else:
# Must have subtag field but not tag
if ('subtag' not in f) or ('tag' in f):
raise WrongTagTypeError(lnum)
# If this is a subtag record, check the subtag value format
if 'subtag' in f:
ft = f['type']
sv = f['subtag']
if ft == 'language':
# Languages must be two or three lowercase ASCII letters (language
# tags that are longer are not used in practice); the only
# exception is 8-character language ranges where the first three
# chars are lowercase letters, the last three chars are lowercase
# letters, and the middle two chars are ".."
if ((len(sv) < 2) or (len(sv) > 3)) and (len(sv) != 8):
raise BadLanguageSubtag(lnum, sv)
if len(sv) == 8:
if (not is_lower_letter(sv[0])) or \
(not is_lower_letter(sv[1])) or \
(not is_lower_letter(sv[2])) or \
(sv[3] != '.') or (sv[4] != '.') or \
(not is_lower_letter(sv[5])) or \
(not is_lower_letter(sv[6])) or \
(not is_lower_letter(sv[7])):
raise BadLanguageSubtag(lnum, sv)
else:
for c in sv:
if not is_lower_letter(c):
raise BadLanguageSubtag(lnum, sv)
elif ft == 'extlang':
# extlang subtags must be three lowercase ASCII letters
if len(sv) != 3:
raise BadExtlangSubtag(lnum, sv)
for c in sv:
if not is_lower_letter(c):
raise BadExtlangSubtag(lnum, sv)
elif ft == 'script':
# Script subtags must be four ASCII letters, the first of which is
# uppercase and the rest of which are lowercase; the only
# exception is 10-character script subtag ranges, where the first
# four letters are a valid script tag, the last four letters are a
# valid script subtag, and the middle two characters are ".."
if len(sv) == 4:
if (not is_upper_letter(sv[0])) or \
(not is_lower_letter(sv[1])) or \
(not is_lower_letter(sv[2])) or \
(not is_lower_letter(sv[3])):
raise BadScriptSubtag(lnum, sv)
elif len(sv) == 10:
if (not is_upper_letter(sv[0])) or \
(not is_lower_letter(sv[1])) or \
(not is_lower_letter(sv[2])) or \
(not is_lower_letter(sv[3])) or \
(sv[4] != '.') or (sv[5] != '.') or \
(not is_upper_letter(sv[6])) or \
(not is_lower_letter(sv[7])) or \
(not is_lower_letter(sv[8])) or \
(not is_lower_letter(sv[9])):
raise BadScriptSubtag(lnum, sv)
else:
raise BadScriptSubtag(lnum, sv)
elif ft == 'region':
# Region subtags must be two uppercase ASCII letters or three
# ASCII digits or they must be a range
if len(sv) == 2:
if (not is_upper_letter(sv[0])) or (not is_upper_letter(sv[1])):
raise BadRegionSubtag(lnum, sv)
elif len(sv) == 3:
for c in sv:
if not is_digit(c):
raise BadRegionSubtag(lnum, sv)
elif len(sv) == 6:
if (not is_upper_letter(sv[0])) or \
(not is_upper_letter(sv[1])) or \
(sv[2] != '.') or (sv[3] != '.') or \
(not is_upper_letter(sv[4])) or \
(not is_upper_letter(sv[5])):
raise BadRegionSubtag(lnum, sv)
else:
raise BadRegionSubtag(lnum, sv)
elif ft == 'variant':
# Variants must either be four lowercase ASCII alphanumerics and
# begin with a digit, or 5-8 lowercase ASCII alphanumerics
if (len(sv) < 4) or (len(sv) > 8):
raise BadVariantSubtag(lnum, sv)
if len(sv) == 4:
if not is_digit(sv[0]):
raise BadVariantSubtag(lnum, sv)
for c in sv:
if (not is_lower_letter(c)) and (not is_digit(c)):
raise BadVariantSubtag(lnum, sv)
else:
raise LogicError() # shouldn't happen
# If this is a tag record, check tag format
if 'tag' in f:
if not is_format_tag(f['tag']):
raise BadTagFormat(lnum, f['tag'])
# If this record has prefixes, additional checks
if 'prefix' in f:
# Prefixes only possible on extlang and variant records
if (f['type'] != 'extlang') and (f['type'] != 'variant'):
raise PrefixContextError(lnum)
# If this is an extlang record, no more than one prefix allowed
if f['type'] == 'extlang':
if len(f['prefix']) > 1:
raise PrefixMultiError(lnum)
# All prefix values must be two or three lowercase letters for
# extlang prefixes
if f['type'] == 'extlang':
for p in f['prefix']:
if (len(p) < 2) or (len(p) > 3):
raise BadPrefix(lnum, p)
for c in p:
if not is_lower_letter(c):
raise BadPrefix(lnum, p)
# All prefix values must be core tags for variant records
if f['type'] == 'variant':
for p in f['prefix']:
if not is_core_tag(p):
raise BadPrefix(lnum, p)
# If this | |
'''
BreezySLAM: Simple, efficient SLAM in Python
algorithms.py: SLAM algorithms
Copyright (C) 2014 <NAME>
This code 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 code 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 code. If not, see <http:#www.gnu.org/licenses/>.
'''
import pybreezyslam
import math
import time
# Basic params
_DEFAULT_MAP_QUALITY = 2 # out of 255
_DEFAULT_HOLE_WIDTH_MM = 320
# Random mutation hill-climbing (RMHC) params
_DEFAULT_SIGMA_XY_MM = 100
_DEFAULT_SIGMA_THETA_DEGREES = 20
_DEFAULT_MAX_SEARCH_ITER = 900
# CoreSLAM class ------------------------------------------------------------------------------------------------------
class CoreSLAM(object):
'''
CoreSLAM is an abstract class that uses the classes Position, Map, Scan, and Laser
to run variants of the simple CoreSLAM (tinySLAM) algorithm described in
@inproceedings{coreslam-2010,
author = {<NAME> and <NAME>},
title = {CoreSLAM: a SLAM Algorithm in less than 200 lines of C code},
booktitle = {11th International Conference on Control, Automation,
Robotics and Vision, ICARCV 2010, Singapore, 7-10
December 2010, Proceedings},
pages = {1975-1979},
publisher = {IEEE},
year = {2010}
}
Implementing classes should provide the method
_updateMapAndPointcloud(scan_mm, dxy_mm, dtheta_degrees, should_update_map)
to update the point-cloud (particle cloud) and map (if should_update_map true)
'''
def __init__(self, laser, map_size_pixels, map_size_meters,
map_quality=_DEFAULT_MAP_QUALITY, hole_width_mm=_DEFAULT_HOLE_WIDTH_MM):
'''
Creates a CoreSLAM object suitable for updating with new Lidar and odometry data.
laser is a Laser object representing the specifications of your Lidar unit
map_size_pixels is the size of the square map in pixels
map_size_meters is the size of the square map in meters
quality from 0 through 255 determines integration speed of scan into map
hole_width_mm determines width of obstacles (walls)
'''
# Initialize parameters
self.map_quality = map_quality
self.hole_width_mm = hole_width_mm
# Store laser for later
self.laser = laser
# Initialize a scan for computing distance to map, and one for updating map
self.scan_for_distance = pybreezyslam.Scan(laser, 1)
self.scan_for_mapbuild = pybreezyslam.Scan(laser, 3)
# Initialize the map
self.map = pybreezyslam.Map(map_size_pixels, map_size_meters)
def update(self, scans_mm, pose_change, scan_angles_degrees=None, should_update_map=True):
'''
Updates the scan and odometry, and calls the the implementing class's _updateMapAndPointcloud method with
the specified pose change.
scan_mm is a list of Lidar scan values, whose count is specified in the scan_size
attribute of the Laser object passed to the CoreSlam constructor
pose_change is a tuple (dxy_mm, dtheta_degrees, dt_seconds) computed from odometry
scan_angles_degrees is an optional list of angles corresponding to the distances in scans_mm
should_update_map flags for whether you want to update the map
'''
# Convert pose change (dxy,dtheta,dt) to velocities (dxy/dt, dtheta/dt) for scan update
velocity_factor = (1 / pose_change[2]) if (pose_change[2] > 0) else 0 # units => units/sec
dxy_mm_dt = pose_change[0] * velocity_factor
dtheta_degrees_dt = pose_change[1] * velocity_factor
velocities = (dxy_mm_dt, dtheta_degrees_dt)
# Build a scan for computing distance to map, and one for updating map
self._scan_update(self.scan_for_mapbuild, scans_mm, velocities, scan_angles_degrees)
self._scan_update(self.scan_for_distance, scans_mm, velocities, scan_angles_degrees)
# Implementing class updates map and pointcloud
self._updateMapAndPointcloud(pose_change[0], pose_change[1], should_update_map)
def getmap(self, mapbytes):
'''
Fills bytearray mapbytes with current map pixels, where bytearray length is square of map size passed
to CoreSLAM.__init__().
'''
self.map.get(mapbytes)
def setmap(self, mapbytes):
'''
Sets current map pixels to values in bytearray, where bytearray length is square of map size passed
to CoreSLAM.__init__().
'''
self.map.set(mapbytes)
def __str__(self):
return 'CoreSLAM: %s \n map quality = %d / 255 \n hole width = %7.0f mm' % \
(str(self.map), self.map_quality, self.hole_width_mm)
def __repr__(self):
return self.__str__()
def _scan_update(self, scan, scans_distances_mm, velocities, scan_angles_degrees):
scan.update(scans_mm=scans_distances_mm, hole_width_mm=self.hole_width_mm,
velocities=velocities, scan_angles_degrees=scan_angles_degrees)
# SinglePositionSLAM class ---------------------------------------------------------------------------------------------
class SinglePositionSLAM(CoreSLAM):
'''
SinglePositionSLAM is an abstract class that implements CoreSLAM using a point-cloud
with a single point (position). Implementing classes should provide the method
_getNewPosition(self, start_position)
to compute a new position based on searching from a starting position.
'''
def __init__(self, laser, map_size_pixels, map_size_meters,
map_quality=_DEFAULT_MAP_QUALITY, hole_width_mm=_DEFAULT_HOLE_WIDTH_MM):
CoreSLAM.__init__(self, laser, map_size_pixels, map_size_meters,
map_quality, hole_width_mm)
# Initialize the position (x, y, theta)
init_coord_mm = 500 * map_size_meters # center of map
self.position = pybreezyslam.Position(init_coord_mm, init_coord_mm, 0)
def _updateMapAndPointcloud(self, dxy_mm, dtheta_degrees, should_update_map):
'''
Updates the map and point-cloud (particle cloud). Called automatically by CoreSLAM.update()
velocities is a tuple of the form (dxy_mm, dtheta_degrees, dt_seconds).
'''
# Start at current position
start_pos = self.position.copy()
# Add effect of velocities
start_pos.x_mm += dxy_mm * self._costheta()
start_pos.y_mm += dxy_mm * self._sintheta()
start_pos.theta_degrees += dtheta_degrees
# Add offset from laser
start_pos.x_mm += self.laser.offset_mm * self._costheta()
start_pos.y_mm += self.laser.offset_mm * self._sintheta()
# Get new position from implementing class
new_position = self._getNewPosition(start_pos)
# Update the current position with this new position, adjusted by laser offset
self.position = new_position.copy()
self.position.x_mm -= self.laser.offset_mm * self._costheta()
self.position.y_mm -= self.laser.offset_mm * self._sintheta()
# Update the map with this new position if indicated
if should_update_map:
self.map.update(self.scan_for_mapbuild, new_position, self.map_quality, self.hole_width_mm)
def getpos(self):
'''
Returns current position as a tuple (x_mm, y_mm, theta_degrees)
'''
return (self.position.x_mm, self.position.y_mm, self.position.theta_degrees)
def _costheta(self):
return math.cos(self._thetaradians())
def _sintheta(self):
return math.sin(self._thetaradians())
def _thetaradians(self):
return math.radians(self.position.theta_degrees)
# RMHC_SLAM class ------------------------------------------------------------------------------------------------------
class RMHC_SLAM(SinglePositionSLAM):
'''
RMHC_SLAM implements the _getNewPosition() method of SinglePositionSLAM using Random-Mutation Hill-Climbing
search. Uses its own internal pseudorandom-number generator for efficiency.
'''
def __init__(self, laser, map_size_pixels, map_size_meters,
map_quality=_DEFAULT_MAP_QUALITY, hole_width_mm=_DEFAULT_HOLE_WIDTH_MM,
random_seed=None, sigma_xy_mm=_DEFAULT_SIGMA_XY_MM, sigma_theta_degrees=_DEFAULT_SIGMA_THETA_DEGREES,
max_search_iter=_DEFAULT_MAX_SEARCH_ITER):
'''
Creates a RMHCSlam object suitable for updating with new Lidar and odometry data.
laser is a Laser object representing the specifications of your Lidar unit
map_size_pixels is the size of the square map in pixels
map_size_meters is the size of the square map in meters
quality from 0 through 255 determines integration speed of scan into map
hole_width_mm determines width of obstacles (walls)
random_seed supports reproducible results; defaults to system time if unspecified
sigma_xy_mm specifies the standard deviation in millimeters of the normal distribution of
the (X,Y) component of position for RMHC search
sigma_theta_degrees specifies the standard deviation in degrees of the normal distribution of
the rotational component of position for RMHC search
max_search_iter specifies the maximum number of iterations for RMHC search
'''
SinglePositionSLAM.__init__(self, laser, map_size_pixels, map_size_meters,
map_quality, hole_width_mm)
if not random_seed:
random_seed = int(time.time()) & 0xFFFF
self.randomizer = pybreezyslam.Randomizer(random_seed)
self.sigma_xy_mm = sigma_xy_mm
self.sigma_theta_degrees = sigma_theta_degrees
self.max_search_iter = max_search_iter
def update(self, scans_mm, pose_change=None, scan_angles_degrees=None, should_update_map=True):
if not pose_change:
pose_change = (0, 0, 0)
CoreSLAM.update(self, scans_mm, pose_change, scan_angles_degrees, should_update_map)
def _getNewPosition(self, start_position):
'''
Implements the _getNewPosition() method of SinglePositionSLAM. Uses Random-Mutation Hill-Climbing
search to look for a better position based on a starting position.
'''
# RMHC search is implemented as a C extension for efficiency
return pybreezyslam.rmhcPositionSearch(
start_position,
self.map,
self.scan_for_distance,
self.laser,
self.sigma_xy_mm,
self.sigma_theta_degrees,
self.max_search_iter,
self.randomizer)
def _random_normal(self, mu, sigma):
return mu + self.randomizer.rnor() * sigma
# Deterministic_SLAM class ------------------------------------------------------------------------------------
class Deterministic_SLAM(SinglePositionSLAM):
'''
Deterministic_SLAM implements the _getNewPosition() method of SinglePositionSLAM by simply
copying the search-start position.
'''
def __init__(self, laser, map_size_pixels, map_size_meters,
map_quality=_DEFAULT_MAP_QUALITY, hole_width_mm=_DEFAULT_HOLE_WIDTH_MM):
'''
Creates a Deterministic_Slam object suitable for updating with new Lidar and odometry data.
laser is a Laser object representing the specifications of your Lidar unit
map_size_pixels is the size of the square map in pixels
map_size_meters is the size of the square map in meters
quality from 0 through 255 determines integration speed of scan into map
hole_width_mm determines width of obstacles (walls)
'''
SinglePositionSLAM.__init__(self, laser, map_size_pixels, map_size_meters,
map_quality, hole_width_mm)
def _getNewPosition(self, start_position):
'''
Implements the _getNewPosition() method of SinglePositionSLAM. Returns a copy | |
[195, 195, 102, 102, 60, 60, 24, 24],
57732: [24, 24, 60, 60, 102, 102, 195, 195],
57733: [0, 0, 0, 16, 0, 0, 0, 0],
57734: [0, 63, 63, 63, 63, 63, 63, 0],
57735: [0, 252, 252, 252, 252, 252, 252, 0],
57736: [0, 84, 42, 84, 42, 84, 42, 0],
57737: [0, 0, 24, 60, 60, 126, 126, 126],
57738: [126, 126, 126, 60, 60, 24, 0, 0],
57739: [7, 31, 63, 127, 127, 255, 255, 255],
57740: [224, 248, 252, 254, 254, 255, 255, 255],
57741: [255, 255, 255, 254, 254, 252, 248, 224],
57742: [255, 255, 255, 127, 127, 63, 31, 7],
57743: [36, 66, 129, 0, 0, 129, 66, 36],
57744: [0, 0, 2, 0, 0, 0, 32, 0],
57745: [32, 0, 2, 0, 32, 0, 2, 0],
57746: [34, 0, 136, 0, 34, 0, 136, 0],
57747: [136, 0, 85, 0, 34, 0, 85, 0],
57748: [255, 187, 255, 238, 255, 187, 255, 238],
57749: [191, 255, 251, 255, 191, 255, 251, 255],
57750: [255, 255, 223, 255, 255, 255, 253, 255],
57751: [170, 0, 170, 0, 170, 0, 170, 0],
57752: [0, 68, 0, 0, 0, 68, 0, 0],
57753: [204, 204, 51, 51, 204, 204, 51, 51],
57754: [51, 102, 204, 153, 51, 102, 204, 153],
57755: [204, 102, 51, 153, 204, 102, 51, 153],
59392: [192, 192, 192, 255, 255, 192, 192, 192],
59393: [255, 255, 24, 24, 24, 24, 24, 24],
59394: [3, 3, 3, 255, 255, 3, 3, 3],
59395: [24, 24, 24, 24, 24, 24, 255, 255],
59396: [255, 255, 192, 192, 0, 0, 0, 0],
59397: [240, 240, 192, 192, 192, 192, 192, 192],
59398: [255, 255, 3, 3, 0, 0, 0, 0],
59399: [15, 15, 3, 3, 3, 3, 3, 3],
59400: [0, 0, 0, 0, 3, 3, 255, 255],
59401: [3, 3, 3, 3, 3, 3, 15, 15],
59402: [192, 192, 192, 192, 192, 192, 255, 255],
59403: [192, 192, 192, 255, 255, 0, 0, 0],
59404: [0, 0, 0, 255, 255, 192, 192, 192],
59405: [31, 31, 24, 24, 24, 24, 24, 24],
59406: [248, 248, 24, 24, 24, 24, 24, 24],
59407: [0, 0, 0, 255, 255, 3, 3, 3],
59408: [3, 3, 3, 255, 255, 0, 0, 0],
59409: [24, 24, 24, 24, 24, 24, 248, 248],
59410: [24, 24, 24, 24, 24, 24, 31, 31],
59411: [192, 112, 28, 6, 6, 3, 3, 1],
59412: [128, 192, 192, 96, 96, 56, 14, 3],
59413: [3, 14, 56, 96, 96, 192, 192, 128],
59414: [1, 3, 3, 6, 6, 28, 112, 192],
59415: [24, 48, 96, 192, 128, 0, 0, 0],
59416: [24, 12, 6, 3, 1, 0, 0, 0],
59417: [24, 60, 102, 195, 129, 0, 0, 0],
59418: [0, 0, 0, 1, 3, 6, 12, 24],
59419: [24, 48, 96, 193, 131, 6, 12, 24],
59420: [24, 12, 6, 3, 3, 6, 12, 24],
59421: [24, 60, 102, 195, 131, 6, 12, 24],
59422: [0, 0, 0, 128, 192, 96, 48, 24],
59423: [0, 0, 0, 129, 195, 102, 60, 24],
59424: [24, 48, 96, 193, 195, 102, 60, 24],
59425: [24, 12, 6, 131, 195, 102, 60, 24],
59426: [192, 96, 48, 24, 0, 0, 0, 0],
59427: [3, 6, 12, 24, 0, 0, 0, 0],
59428: [195, 102, 60, 24, 0, 0, 0, 0],
59429: [0, 0, 0, 0, 12, 6, 3, 1],
59430: [192, 96, 48, 24, 12, 6, 3, 1],
59431: [3, 6, 12, 24, 12, 6, 3, 1],
59432: [195, 102, 60, 24, 12, 6, 3, 1],
59433: [0, 0, 0, 0, 48, 96, 192, 128],
59434: [0, 0, 0, 0, 60, 102, 195, 129],
59435: [192, 96, 48, 24, 60, 102, 195, 129],
59436: [3, 6, 12, 24, 60, 102, 195, 129],
59437: [192, 96, 48, 248, 248, 96, 192, 128],
59438: [24, 24, 24, 248, 252, 6, 3, 1],
59439: [24, 24, 24, 24, 60, 102, 195, 129],
59440: [27, 30, 28, 31, 31, 0, 0, 0],
59441: [3, 6, 12, 31, 31, 6, 3, 1],
59442: [192, 96, 48, 31, 31, 24, 24, 24],
59443: [195, 102, 60, 24, 24, 24, 24, 24],
59444: [0, 0, 0, 248, 248, 120, 216, 152],
59445: [192, 96, 48, 248, 248, 0, 0, 0],
59446: [0, 0, 0, 248, 248, 96, 192, 128],
59447: [24, 24, 24, 24, 48, 96, 192, 128],
59448: [24, 24, 24, 24, 12, 6, 3, 1],
59449: [0, 0, 0, 31, 31, 6, 3, 1],
59450: [3, 6, 12, 31, 31, 0, 0, 0],
59451: [3, 6, 12, 24, 24, 24, 24, 24],
59452: [192, 96, 48, 24, 24, 24, 24, 24],
59453: [2, 32, 1, 8, 64, 4, 128, 16],
59454: [9, 32, 4, 128, 17, 64, 8, 2],
59455: [136, 2, 64, 0, 136, 65, 0, 145],
59456: [204, 51, 204, 51, 204, 51, 204, 51],
59457: [0, 255, 0, 0, 0, 255, 0, 0],
59458: [68, 68, 68, 68, 68, 68, 68, 68],
59459: [0, 85, 0, 68, 0, 85, 0, 68],
59460: [4, 4, 4, 4, 4, 4, 255, 4],
59461: [65, 128, 65, 34, 20, 8, 20, 34],
59462: [1, 1, 255, 16, 16, 16, 255, 1],
59463: [65, 162, 85, 162, 65, 128, 0, 128],
59464: [8, 8, 170, 65, 128, 128, 170, 20],
59465: [227, 227, 221, 38, 62, 62, 221, 98],
59466: [136, 80, 34, 0, 170, 0, 34, 5],
59467: [64, 0, 68, 0, 64, 0, 85, 0],
59468: [128, 0, 8, 4, 2, 0, 32, 64],
59469: [170, 255, 2, 4, 8, 16, 32, 84],
59470: [54, 48, 3, 99, 108, 12, 192, 198],
59471: [34, 34, 34, 65, 128, 8, 20, 34],
59472: [34, 65, 128, 128, 128, 65, 34, 156],
60416: [6, 9, 144, 96, 6, 9, 144, 96],
60417: [78, 114, 243, 63, 39, 228, 252, 207],
60418: [165, 66, 165, 0, 0, 165, 66, 165],
60419: [0, 82, 52, 6, 96, 44, 74, 0],
60420: [0, 16, 44, 58, 92, 52, 4, 0],
60421: [82, 68, 45, 196, 17, 180, 35, 74],
60422: [145, 82, 0, 3, 192, 0, 74, 137],
60423: [24, 24, 60, 126, 255, 219, 24, 60],
60424: [60, 24, 219, 255, 126, 60, 24, 24],
60425: [12, 28, 57, 255, 255, 57, 28, 12],
60426: [186, 238, 170, 56, 56, 186, 254, 186],
60427: [34, 99, 247, 183, 255, 126, 60, 60],
60428: [60, 126, 255, 191, 255, 126, 60, 60],
60429: [60, 60, 24, 60, 60, 60, 60, 24],
60430: [24, 60, 60, 60, 60, 24, 60, 60],
60431: [0, 0, 123, 255, 255, 123, 0, 0],
60432: [0, 0, 222, 255, 255, 222, 0, 0],
60433: [32, 96, 32, 32, 48, 40, 60, 60],
60434: [0, 48, 88, 253, 255, 121, 48, 0],
60435: [0, 12, 26, 191, 255, 158, 12, 0],
60436: [0, 48, 88, 253, 63, 249, 48, 0],
60437: [0, 12, 26, 191, 252, 159, 12, 0],
60438: [16, 40, 104, 188, 252, 120, 16, 56],
60439: [0, 0, 252, 28, 127, 99, 62, 0],
60440: [56, 56, 146, 124, 16, 40, 40, 40],
60441: [56, 56, 16, 254, 16, 40, 68, 130],
60442: [56, 56, 18, 124, 144, 40, 36, 34],
60443: [56, 56, 144, 124, 18, 40, 72, 136],
60444: [8, 20, 8, 28, 42, 20, 62, 20],
60445: [129, 195, 231, 255, 255, 153, 36, 195],
60446: [65, 162, 60, 90, 126, 255, 66, 99],
60447: [130, 69, 60, 90, 126, 255, 66, 198],
60448: [0, 90, 189, 153, 36, 66, 36, 0],
60449: [129, 165, 90, 24, 24, 36, 195, 0],
60450: [60, 90, 255, 171, 213, 255, 221, 137],
60451: [60, 90, 255, 171, 213, 255, 119, 34],
60452: [60, 66, | |
#!/usr/bin/env python
# coding:utf-8
import os
import sys
import time
import signal
import getpass
import logging
import argparse
import threading
from . import dag
from .job import *
from .config import load_config, print_config
from .qsub import myQueue, QsubError
from .sge import ParseSingal
from .version import __version__
from .utils import *
from .cluster import *
from datetime import datetime
from threading import Thread
from subprocess import Popen, call, PIPE
from collections import Counter
class RunSge(object):
def __init__(self, sgefile, queue, cpu, mem, name, start, end, logdir, workdir, maxjob, strict=False, mode=None, config=None):
self.sgefile = ShellFile(sgefile, mode=mode, name=name,
logdir=logdir, workdir=workdir)
self.jfile = self.sgefile._path
self.jobs = self.sgefile.jobshells(start=start, end=end)
self.totaljobdict = {j.jobname: j for j in self.jobs}
self.queue = queue
self.mem = mem
self.cpu = cpu
self.maxjob = maxjob
self.logdir = logdir
self.is_run = False
self.strict = strict
self.localprocess = {}
self.cloudjob = {}
self.conf = config
self.jobsgraph = dag.DAG()
pre_dep = []
dep = []
for jb in self.jobs[:]:
if jb.rawstring == "wait" and len(dep):
self.jobs.remove(jb)
pre_dep = dep
dep = []
else:
if jb.rawstring == "wait": # dup "wait" line
self.jobs.remove(jb)
continue
self.jobsgraph.add_node_if_not_exists(jb.jobname)
dep.append(jb)
for i in pre_dep:
self.jobsgraph.add_node_if_not_exists(i.jobname)
self.jobsgraph.add_edge(i.jobname, jb.jobname)
if self.conf.get("args", "call_back"):
cmd = self.conf.get("args", "call_back")
name = "call_back"
call_back_job = ShellJob(self.sgefile, linenum=-1, cmd=cmd)
call_back_job.forceToLocal(jobname=name, removelog=True)
self.jobsgraph.add_node_if_not_exists(call_back_job.jobname)
self.jobs.append(call_back_job)
self.totaljobdict["call_back"] = call_back_job
for i in self.jobsgraph.all_nodes:
if i == name:
continue
self.jobsgraph.add_edge(i, name)
if self.conf.get("args", "init"):
cmd = self.conf.get("args", "init")
name = "init"
init_job = ShellJob(self.sgefile, linenum=-1, cmd=cmd)
init_job.forceToLocal(jobname=name, removelog=True)
self.jobsgraph.add_node_if_not_exists(init_job.jobname)
self.jobs.append(init_job)
self.totaljobdict[name] = init_job
for i in self.jobsgraph.all_nodes:
if i == name:
continue
self.jobsgraph.add_edge(name, i)
self.logger.info("Total jobs to submit: %s" %
", ".join([j.name for j in self.jobs]))
self.logger.info("All logs can be found in %s directory", self.logdir)
self.has_success = set()
for job in self.jobs[:]:
lf = job.logfile
job.subtimes = 0
if os.path.isfile(lf):
js = self.jobstatus(job)
if js != "success":
os.remove(lf)
job.status = "wait"
else:
self.jobsgraph.delete_node_if_exists(job.jobname)
self.has_success.add(job.jobname)
self.jobs.remove(job)
else:
job.status = "wait"
if self.maxjob is None:
self.maxjob = len(self.jobs)
self.jobqueue = myQueue(maxsize=max(self.maxjob, 1))
self.conf.jobqueue = self.jobqueue
self.conf.logger = self.logger
self.conf.cloudjob = self.cloudjob
def jobstatus(self, job):
jobname = job.jobname
status = job.status
logfile = job.logfile
if self.is_run and job.host == "batchcompute":
if jobname in self.cloudjob:
jobid = self.cloudjob[jobname]
try:
j = job.client.get_job(jobid)
sta = j.State
except ClientError as e: # delete by another process, status Failed
self.logger.debug("Job %s not Exists", jobid)
self.cloudjob.pop(jobname)
sta = "Failed"
if sta == "Running":
status = "run"
elif sta == "Finished":
status = "success"
elif sta == "Failed":
status = "error"
elif sta == "Stopped":
status = "stop"
elif sta == "Waiting":
status = "wait"
self.logger.debug("job %s status %s", jobid, status)
else:
if os.path.isfile(logfile):
try:
with os.popen('tail -n 1 %s' % logfile) as fi:
sta = fi.read().split()[-1]
except IndexError:
if status not in ["submit", "resubmit"]:
status = "run"
sta = status
if sta == "SUCCESS":
status = "success"
elif sta == "ERROR":
status = "error"
elif sta == "Exiting.":
status = "exit"
else:
with os.popen("sed -n '3p' %s" % logfile) as fi:
if "RUNNING..." in fi.read():
status = "run"
if status != job.status and self.is_run:
self.logger.info("job %s status %s", jobname, status)
job.status = status
if job.host == "batchcompute":
with open(logfile, "a") as fo:
fo.write("[%s] %s\n" % (
datetime.today().strftime("%F %X"), job.status.upper()))
return status
def jobcheck(self):
if self.sgefile.mode == "batchcompute":
rate_limiter = RateLimiter(max_calls=3, period=3)
else:
rate_limiter = RateLimiter(max_calls=3, period=2)
while True:
with rate_limiter:
for jb in self.jobqueue.queue:
with rate_limiter:
try:
js = self.jobstatus(jb)
except:
continue
if js == "success":
if jb.jobname in self.localprocess:
self.localprocess[jb.jobname].wait()
self.jobqueue.get(jb)
self.jobsgraph.delete_node_if_exists(jb.jobname)
elif js == "error":
if jb.jobname in self.localprocess:
self.localprocess[jb.jobname].wait()
self.jobqueue.get(jb)
if jb.subtimes >= self.times + 1:
if self.strict:
self.throw("Error jobs return(submit %d times, error), exist!, %s" % (jb.subtimes, os.path.join(
self.logdir, jb.logfile))) # if error, exit program
self.jobsgraph.delete_node_if_exists(
jb.jobname)
else:
self.submit(jb)
elif js == "exit":
if self.strict:
self.throw("Error when submit")
def submit(self, job):
if not self.is_run or job.status in ["run", "submit", "resubmit", "success"]:
return
logfile = job.logfile
self.jobqueue.put(job, block=True, timeout=1080000)
with open(logfile, "a") as logcmd:
if job.subtimes == 0:
logcmd.write(job.rawstring+"\n")
job.status = "submit"
elif job.subtimes > 0:
logcmd.write("\n" + job.rawstring+"\n")
job.status = "resubmit"
self.logger.info("job %s status %s", job.name, job.status)
logcmd.write("[%s] " % datetime.today().strftime("%F %X"))
logcmd.flush()
if job.host is not None and job.host == "localhost":
cmd = "echo 'Your job (\"%s\") has been submitted in localhost' && " % job.name + job.cmd
if job.subtimes > 0:
cmd = cmd.replace("RUNNING", "RUNNING (re-submit)")
time.sleep(self.resubivs)
p = Popen(cmd, shell=True, stdout=logcmd, stderr=logcmd)
self.localprocess[job.name] = p
elif job.host == "sge":
jobcpu = job.cpu if job.cpu else self.cpu
jobmem = job.mem if job.mem else self.mem
cmd = 'echo "%s" | qsub -q %s -wd %s -N %s -o %s -j y -l vf=%dg,p=%d' % (
job.cmd, " -q ".join(self.queue), self.sgefile.workdir, job.jobname, logfile, jobmem, jobcpu)
if job.subtimes > 0:
cmd = cmd.replace("RUNNING", "RUNNING (re-submit)")
time.sleep(self.resubivs)
call(cmd, shell=True, stdout=logcmd, stderr=logcmd)
elif job.host == "batchcompute":
jobcpu = job.cpu if job.cpu else self.cpu
jobmem = job.mem if job.mem else self.mem
c = Cluster(config=self.conf)
c.AddClusterMount()
task = Task(c)
task.AddOneTask(
job=job, outdir=self.conf.get("args", "outdir"))
task.Submit()
info = "Your job (%s) has been submitted in batchcompute (%s) %d times\n" % (
task.name, task.id, job.subtimes+1)
logcmd.write(info)
self.cloudjob[task.name] = task.id
self.logger.debug("%s job submit %s times", job.name, job.subtimes)
job.subtimes += 1
def run(self, sec=2, times=3, resubivs=2):
self.is_run = True
self.times = max(0, times)
self.resubivs = max(resubivs, 0)
for jn in self.has_success:
self.logger.info("job %s status already success", jn)
if len(self.jobsgraph.graph) == 0:
return
p = Thread(target=self.jobcheck)
p.setDaemon(True)
p.start()
if self.sgefile.mode == "batchcompute":
access_key_id = self.conf.get("args", "access_key_id")
access_key_secret = self.conf.get("args", "access_key_secret")
if access_key_id is None:
access_key_id = self.conf.get("OSS", "access_key_id")
if access_key_secret is None:
access_key_secret = self.conf.get("OSS", "access_key_secret")
region = REGION.get(self.conf.get("args", "region"), CN_BEIJING)
client = Client(region, access_key_id, access_key_secret)
quotas = client.get_quotas().AvailableClusterInstanceType
cfg_path = os.path.join(os.path.dirname(__file__), "ins_type.json")
with open(cfg_path) as fi:
self.conf.it_conf = json.load(fi)
availableTypes = [i for i in quotas if i in self.conf.it_conf]
self.conf.availableTypes = sorted(availableTypes, key=lambda x: (
self.conf.it_conf[x]["cpu"], self.conf.it_conf[x]["memory"]))
self.conf.client = client
while True:
subjobs = self.jobsgraph.ind_nodes()
if len(subjobs) == 0:
break
for j in subjobs:
jb = self.totaljobdict[j]
if jb in self.jobqueue.queue:
continue
self.submit(jb)
time.sleep(sec)
@property
def logger(self):
return logging.getLogger(__name__)
def throw(self, msg):
user = getpass.getuser()
if threading.current_thread().__class__.__name__ == '_MainThread':
raise QsubError(msg)
else:
if self.sgefile.mode == "sge":
self.logger.info(msg)
call('qdel "%s*"' % self.sgefile.name,
shell=True, stderr=PIPE, stdout=PIPE)
os._exit(signal.SIGTERM)
elif self.sgefile.mode == "batchcompute":
for jb in self.jobqueue.queue:
jobname = jb.name
try:
jobid = self.conf.cloudjob.get(jobname, "")
j = self.conf.client.get_job(jobid)
except ClientError as e:
if e.status == 404:
self.logger.info("Invalid JobId %s", jobid)
continue
except:
continue
if j.Name.startswith(user):
if j.State not in ["Stopped", "Failed", "Finished"]:
self.conf.client.stop_job(jobid)
self.conf.client.delete_job(jobid)
self.logger.info("Delete job %s success", j.Name)
self.jobqueue.get(jb)
else:
self.logger.info(
"Delete job error, you have no assess with job %s", j.Name)
def writestates(self, outstat):
summary = {j.name: j.status for j in self.jobs}
with open(outstat, "w") as fo:
fo.write(str(dict(Counter(summary.values()))) + "\n\n")
sumout = {}
for k, v in summary.items():
sumout.setdefault(v, []).append(k)
for k, v in sorted(sumout.items()):
fo.write(
k + " : " + ", ".join(sorted(v, key=lambda x: (len(x), x))) + "\n")
def parserArg():
pid = os.getpid()
parser = argparse.ArgumentParser(
description="For multi-run your shell scripts localhost or qsub.")
parser.add_argument("-q", "--queue", type=str, help="the queue your job running, default: all.q",
default=["all.q", ], nargs="*", metavar="<queue>")
parser.add_argument("-m", "--memory", type=int,
help="the memory used per command (GB), default: 1", default=1, metavar="<int>")
parser.add_argument("-c", "--cpu", type=int,
help="the cpu numbers you job used, default: 1", default=1, metavar="<int>")
parser.add_argument("-wd", "--workdir", type=str, help="work dir, default: %s" %
os.path.abspath(os.getcwd()), default=os.path.abspath(os.getcwd()), metavar="<workdir>")
parser.add_argument("-N", "--jobname", type=str,
help="job name", metavar="<jobname>")
parser.add_argument("-lg", "--logdir", type=str,
help='the output log dir, default: "runjob_*_log_dir"', metavar="<logdir>")
parser.add_argument("-o", "--outdir", type=str,
help='the oss output directory if your mode is "batchcompute", all output file will be mapping to you OSS://BUCKET-NAME. if not set, any output will be reserved', metavar="<dir>")
parser.add_argument("-n", "--num", type=int,
help="the max job number runing at the same time. default: all in your job file", metavar="<int>")
parser.add_argument("-s", "--startline", type=int,
help="which line number(0-base) be used for the first job tesk. default: 0", metavar="<int>", default=0)
parser.add_argument("-e", "--endline", type=int,
help="which line number (include) be used for the last job tesk. default: all in your job file", metavar="<int>")
parser.add_argument('-d', '--debug', action='store_true',
help='log debug info', default=False)
parser.add_argument("-l", "--log", type=str,
help='append log info to file, sys.stdout by default', metavar="<file>")
| |
if mode == 'add':
array[ind] = self.ndarray[ind] + function[0]['f'+key](functimearray)
elif mode == 'sub':
array[ind] = self.ndarray[ind] - function[0]['f'+key](functimearray)
elif mode == 'values':
array[ind] = function[0]['f'+key](functimearray)
elif mode == 'div':
array[ind] = self.ndarray[ind] / function[0]['f'+key](functimearray)
elif mode == 'multiply':
array[ind] = self.ndarray[ind] * function[0]['f'+key](functimearray)
else:
print("func2stream: mode not recognized")
return DataStream(self,self.header,np.asarray(array,dtype=object))
for elem in self:
# check whether time step is in function range
if function[1] <= elem.time <= function[2]:
functime = (elem.time-function[1])/(function[2]-function[1])
for key in keys:
if not key in KEYLIST[1:16]:
raise ValueError("Column key not valid")
fkey = 'f'+key
exec('keyval = elem.'+key)
if fkey in function[0] and not isnan(keyval):
try:
newval = keyval + function[0][fkey](functime)
except:
newval = float('nan')
exec('elem.'+key+' = newval')
else:
pass
else:
pass
return self
def func_subtract(self,funclist,**kwargs):
"""
Subtract a function from the selected values of the data stream -> e.g. obtain Residuals
Optional:
keys (default = '<KEY>')
:type order int
:param order : 0 -> stream - function; 1 -> function - stream
"""
keys = kwargs.get('keys')
order = kwargs.get('order')
st = DataStream()
st = self.copy()
if isinstance(funclist[0], dict):
funct = [funclist]
else:
funct = funclist
function = funct[0] # Direct call of old version only accepts single function
"""
for el in self:
li = LineStruct()
li.time = el.time
li.x = el.x
li.y = el.y
li.z = el.z
st.add(li)
"""
if not order:
order = 0
if not keys:
keys = ['<KEY>']
for elem in st:
# check whether time step is in function range
if function[1] <= elem.time <= function[2]:
functime = (elem.time-function[1])/(function[2]-function[1])
for key in keys:
if not key in KEYLIST[1:16]:
raise ValueError("Column key not valid")
fkey = 'f'+key
exec('keyval = elem.'+key)
if fkey in function[0] and not isnan(keyval):
try:
if order == 0:
newval = keyval - function[0][fkey](functime)
else:
newval = function[0][fkey](functime) - keyval
except:
newval = float('nan')
exec('elem.'+key+' = newval')
else:
pass
else:
pass
return st
def func2header(self,funclist,debug=False):
"""
DESCRIPTION
Add a list of functions into the data header
"""
if isinstance(funclist[0], dict):
funct = [funclist]
else:
funct = funclist
self.header['DataFunctionObject'] = funct
return self
def GetKeyName(self,key):
"""
DESCRIPTION
get the content name of a specific key
will scan header information until successful:
(1) col-"key" names
(2) ColumnContent header info
(3) SensorElements header info
if no Name for the key is found, then the key itself is returned
APPLICATION:
element = datastream.GetKeyName('var1')
"""
if not key in KEYLIST:
print ("key not in KEYLIST - aborting")
return ''
element = ''
# One
try:
element = self.header.get("col-{}".format(key))
if not element == '':
return element
except:
pass
# Two
try:
element = self.header.get('ColumnContents','').split(',')[KEYLIST.index(key)]
if not element == '':
return element
except:
pass
# Three
try:
idx = self.header.get('SensorKeys','').split(',').index(key)
element = self.header.get('SensorElements','').split(',')[idx]
if not element == '':
return element
except:
pass
return key
def GetKeyUnit(self,key):
"""
DESCRIPTION
get the content name of a specific key
will scan header information until successful:
(1) unit-col-"key" names
(2) ColumnUnit header info
if no unit for the key is found, then an empty string is returned
APPLICATION:
unit = datastream.GetKeyUnit('var1')
"""
if not key in KEYLIST:
print ("key not in KEYLIST - aborting")
return ''
unit = ''
# One
try:
unit = self.header.get("unit-col-{}".format(key))
if not unit == '':
return unit
except:
pass
# Two
try:
unit = self.header.get('ColumnUnits','').split(',')[KEYLIST.index(key)]
if not unit == '':
return unit
except:
pass
return unit
def get_gaps(self, **kwargs):
"""
DEFINITION:
Takes the dominant sample frequency and fills nan into non-existing time steps:
This function provides the basis for discontinuous plots and gap analysis and proper filtering.
PARAMETERS:
Variables:
---
Kwargs:
- accuracy: (float) time relative to a day - default 1 sec
- gapvariable: (string) - refering to stream column - default='var5' - This column
is overwritten with 0 (data) and 1 (no data).
- key: (string) - refering to a data column e.g. key='x'. If given then all NaN values with existing time steps are also marked by '1' in the gapvariable line for this key
RETURNS:
- stream: (Datastream)
EXAMPLE:
>>> stream_with_gaps_filled = stream_with_aps.get_gaps(['f'])
APPLICATION:
used by nfilter() for correct filtering
CHANGES:
Last updated and tested with nfilter function by leon 2014-07-22
"""
accuracy = kwargs.get('accuracy')
key = kwargs.get('key')
gapvariable = kwargs.get('gapvariable')
debug = kwargs.get('debug')
if key in KEYLIST:
gapvariable = True
if not gapvariable:
gapvariable = 'var5'
if not self.length()[0] > 1:
print ("get_gaps: Stream does not contain data - aborting")
return self
# Better use get_sampling period as samplingrate is rounded
#spr = self.get_sampling_period()
#newsps = newsp*3600.0*24.0
newsps = self.samplingrate()
newsp = newsps/3600.0/24.0
if not accuracy:
accuracy = 0.9/(3600.0*24.0) # one second relative to day
accuracy = 0.05*newsp # 5 percent of samplingrate
if newsps < 0.9 and not accuracy:
accuracy = (newsps-(newsps*0.1))/(3600.0*24.0)
logger.info('--- Starting filling gaps with NANs at %s ' % (str(datetime.now())))
stream = self.copy()
prevtime = 0
ndtype = False
if len(stream.ndarray[0]) > 0:
maxtime = stream.ndarray[0][-1]
mintime = stream.ndarray[0][0]
length = len(stream.ndarray[0])
sourcetime = stream.ndarray[0]
ndtype = True
else:
mintime = self[0].time
maxtime = self[-1].time
if debug:
print("Time range:", mintime, maxtime)
print("Length, samp_per and accuracy:", self.length()[0], newsps, accuracy)
shift = 0
if ndtype:
# Get time diff and expected count
timediff = maxtime - mintime
expN = int(round(timediff/newsp))+1
if debug:
print("Expected length vs actual length:", expN, length)
if expN == len(sourcetime):
# Found the expected amount of time steps - no gaps
logger.info("get_gaps: No gaps found - Returning")
return stream
else:
# correct way (will be used by default) - does not use any accuracy value
#projtime = np.linspace(mintime, maxtime, num=expN, endpoint=True)
#print("proj:", projtime, len(projtime))
# find values or projtime, which are not in sourcetime
#dif = setdiff1d(projtime,sourcetime, assume_unique=True)
#print (dif, len(dif))
#print (len(dif),len(sourcetime),len(projtime))
diff = sourcetime[1:] - sourcetime[:-1]
num_fills = np.round(diff / newsp) - 1
getdiffids = np.where(diff > newsp+accuracy)[0]
logger.info("get_gaps: Found gaps - Filling nans to them")
if debug:
print ("Here", diff, num_fills, newsp, getdiffids)
missingt = []
# Get critical differences and number of missing steps
for i in getdiffids:
#print (i, sourcetime[i-1], sourcetime[i], sourcetime[i+1])
nf = num_fills[i]
# if nf is larger than zero then get append the missing time steps to missingt list
if nf > 0:
for n in range(int(nf)): # add n+1 * samplingrate for each missing value
missingt.append(sourcetime[i]+(n+1)*newsp)
print ("Filling {} gaps".format(len(missingt)))
# Cycle through stream and append nans to each column for missing time steps
nans = [np.nan] * len(missingt)
empts = [''] * len(missingt)
gaps = [0.0] * len(missingt)
for idx,elem in enumerate(stream.ndarray):
if idx == 0:
# append missingt list to array element
elem = list(elem)
lenelem = len(elem)
elem.extend(missingt)
stream.ndarray[idx] = np.asarray(elem).astype(object)
elif len(elem) > 0:
# append nans list to array element
elem = list(elem)
if KEYLIST[idx] in NUMKEYLIST or KEYLIST[idx] == 'sectime':
elem.extend(nans)
else:
elem.extend(empts)
stream.ndarray[idx] = np.asarray(elem).astype(object)
elif KEYLIST[idx] == gapvariable:
# append nans list to array element
elem = [1.0]*lenelem
elem.extend(gaps)
stream.ndarray[idx] = np.asarray(elem).astype(object)
return stream.sorting()
else:
stream = DataStream()
for elem in self:
if abs((prevtime+newsp) - elem.time) > accuracy and not prevtime == 0:
currtime = num2date(prevtime)+timedelta(seconds=newsps)
while currtime <= num2date(elem.time):
newline = LineStruct()
exec('newline.'+gapvariable+' = 1.0')
newline.time = date2num(currtime)
stream.add(newline)
currtime += timedelta(seconds=newsps)
else:
exec('elem.'+gapvariable+' = 0.0')
if key in KEYLIST:
if isnan(eval('elem.'+key)):
exec('elem.'+gapvariable+' = 1.0')
stream.add(elem)
prevtime = elem.time
logger.info('--- Filling gaps finished at %s ' % (str(datetime.now())))
if debugmode:
print("Ending:", stream[0].time, stream[-1].time)
return stream.sorting()
def get_rotationangle(self, xcompensation=0,keys=['x','y','z'],**kwargs):
"""
DESCRIPTION:
"Estimating" the rotation angle towards a magnetic coordinate system
assuming z to be vertical down. Please note: You need to provide a
complete horizontal vector including either the x compensation field
or if not available an annual estimate of the vector. This method can be used
to determine reorientation characteristics in order to accurately apply
HDZ optimzed basevalue calculations.
RETURNS:
rotangle (float) The estimated rotation angle in degree
"""
annualmeans = kwargs.get('annualmeans')
#1. get vector from data
# x | |
= np.array(state_considered)[-1 - delay]
if list(new_relevant_state) == self.target_point:
reward += 1.0
reward *= self.reward_scale
noise_in_reward = self.reward_noise(self.np_random) if self.reward_noise else 0
# #random ###TODO Would be better to parameterise this in terms of state, action and time_step as well. Would need to change implementation to have a queue for the rewards achieved and then pick the reward that was generated delay timesteps ago.
self.total_abs_noise_in_reward_episode += np.abs(noise_in_reward)
self.total_reward_episode += reward
reward += noise_in_reward
reward += self.reward_shift
return reward
def step(self, action, imaginary_rollout=False):
"""The step function for the environment.
Parameters
----------
action : int or np.array
The action that the environment will use to perform a transition.
imaginary_rollout: boolean
Option for the user to perform "imaginary" transitions, e.g., for model-based RL. If set to true, underlying augmented state of the MDP is not changed and user is responsible to maintain and provide a list of states to this function to be able to perform a rollout.
Returns
-------
int or np.array, double, boolean, dict
The next state, reward, whether the episode terminated and additional info dict at the end of the current transition
"""
# For imaginary transitions, discussion:
# 1) Use external observation_space as argument to P() and R(). But then it's not possible for P and R to know underlying MDP state unless we pass it as another argument. This is not desirable as we want P and R to simply be functions of external state/observation and action. 2) The other possibility is how it's currently done: P and R _know_ the underlying state. But in this case, we need an extra imaginary_rollout argument to P and R and we can't perform imaginary rollouts longer than one step without asking the user to maintain a sequence of underlying states and actions to be passed as arguments to P and R.
# P and R knowing the underlying state seems a poor design choice to me
# because it makes the code structure more brittle, so I propose that
# step() handles the underlying state vs external observation conversion
# and user can use P and R with underlying state. And step should handle
# the case of imaginary rollouts by building a tree of transitions and
# allowing rollback to states along the tree. However, user will probably
# want access to P and R by using only observations as well instead of the
# underlying state. In this case, P and R need to be aware of underlying
# state and be able to store imaginary rollouts if needed.
# Transform multi-discrete to discrete for discrete state spaces with
# irrelevant dimensions; needed only for imaginary rollouts, otherwise,
# internal augmented state is used.
if imaginary_rollout:
print("Imaginary rollouts are currently not supported.")
sys.exit(1)
if self.config["state_space_type"] == "discrete":
if self.irrelevant_features:
state, action, state_irrelevant, action_irrelevant = (
self.curr_state[0],
action[0],
self.curr_state[1],
action[1],
)
else:
state, action = self.curr_state, action
else: # cont. or grid case
state, action = self.curr_state, action
# ### TODO Decide whether to give reward before or after transition ("after" would mean taking next state into account and seems more logical to me) - make it a dimension? - R(s) or R(s, a) or R(s, a, s')? I'd say give it after and store the old state in the augmented_state to be able to let the R have any of the above possible forms. That would also solve the problem of implicit 1-step delay with giving it before. _And_ would not give any reward for already being in a rewarding state in the 1st step but _would_ give a reward if 1 moved to a rewardable state - even if called with R(s, a) because s' is stored in the augmented_state! #####IMP
# ###TODO P uses last state while R uses augmented state; for cont. env, P does know underlying state_derivatives - we don't want this to be the case for the imaginary rollout scenario;
next_state = self.P(state, action)
# if imaginary_rollout:
# pass
# # print("imaginary_rollout") # Since transition_function currently depends only on current state and action, we don't need to do anything here!
# else:
del self.augmented_state[0]
if self.config["state_space_type"] == "discrete":
self.augmented_state.append(next_state)
elif self.config["state_space_type"] == "continuous":
self.augmented_state.append(next_state.copy())
elif self.config["state_space_type"] == "grid":
self.augmented_state.append([next_state[i] for i in range(2)])
self.total_transitions_episode += 1
self.reward = self.R(self.augmented_state, action)
# #irrelevant dimensions part
if self.config["state_space_type"] == "discrete":
if self.irrelevant_features:
next_state_irrelevant = self.config["transition_function_irrelevant"][
state_irrelevant, action_irrelevant
]
if self.transition_noise:
probs = (
np.ones(shape=(self.state_space_size[1],))
* self.transition_noise
/ (self.state_space_size[1] - 1)
)
probs[next_state_irrelevant] = 1 - self.transition_noise
new_next_state_irrelevant = self.observation_spaces[1].sample(
prob=probs
)
# #random
# if next_state_irrelevant != new_next_state_irrelevant:
# print("NOISE inserted! old next_state_irrelevant, new_next_state_irrelevant", next_state_irrelevant, new_next_state_irrelevant)
# self.total_noisy_transitions_irrelevant_episode += 1
next_state_irrelevant = new_next_state_irrelevant
# Transform discrete back to multi-discrete if needed
if self.config["state_space_type"] == "discrete":
if self.irrelevant_features:
next_obs = next_state = (next_state, next_state_irrelevant)
else:
next_obs = next_state
else: # cont. or grid space
next_obs = next_state
if self.image_representations:
next_obs = self.observation_space.get_concatenated_image(next_state)
self.curr_state = next_state
self.curr_obs = next_obs
# #### TODO curr_state is external state, while we need to check relevant state for terminality! Done - by using augmented_state now instead of curr_state!
self.done = (self.is_terminal_state(self.augmented_state[-1]) or self.reached_terminal)
if self.done:
self.reward += (
self.term_state_reward * self.reward_scale
) # Scale before or after?
self.logger.info(
"sas'r: "
+ str(self.augmented_state[-2])
+ " "
+ str(action)
+ " "
+ str(self.augmented_state[-1])
+ " "
+ str(self.reward)
)
return self.curr_obs, self.reward, self.done, self.get_augmented_state()
def get_augmented_state(self):
"""Intended to return the full augmented state which would be Markovian. (However, it's not Markovian wrt the noise in P and R because we're not returning the underlying RNG.) Currently, returns the augmented state which is the sequence of length "delay + sequence_length + 1" of past states for both discrete and continuous environments. Additonally, the current state derivatives are also returned for continuous environments.
Returns
-------
dict
Contains at the end of the current transition
"""
# #TODO For noisy processes, this would need the noise distribution and random seed too. Also add the irrelevant state parts, etc.? We don't need the irrelevant parts for the state to be Markovian.
if self.config["state_space_type"] == "discrete":
augmented_state_dict = {
"curr_state": self.curr_state,
"curr_obs": self.curr_obs,
"augmented_state": self.augmented_state,
}
elif self.config["state_space_type"] == "continuous":
augmented_state_dict = {
"curr_state": self.curr_state,
"curr_obs": self.curr_obs,
"augmented_state": self.augmented_state,
"state_derivatives": self.state_derivatives,
}
elif self.config["state_space_type"] == "grid":
augmented_state_dict = {
"curr_state": self.curr_state,
"curr_obs": self.curr_obs,
"augmented_state": self.augmented_state,
}
return augmented_state_dict
def reset(self):
"""Resets the environment for the beginning of an episode and samples a start state from rho_0. For discrete environments uses the defined rho_0 directly. For continuous environments, samples a state and resamples until a non-terminal state is sampled.
Returns
-------
int or np.array
The start state for a new episode.
"""
# on episode "end" stuff (to not be invoked when reset() called when
# self.total_episodes = 0; end is in quotes because it may not be a true
# episode end reached by reaching a terminal state, but reset() may have
# been called in the middle of an episode):
if not self.total_episodes == 0:
self.logger.info(
"Noise stats for previous episode num.: "
+ str(self.total_episodes)
+ " (total abs. noise in rewards, total abs."
" noise in transitions, total reward, total noisy transitions, total"
" transitions): "
+ str(self.total_abs_noise_in_reward_episode)
+ " "
+ str(self.total_abs_noise_in_transition_episode)
+ " "
+ str(self.total_reward_episode)
+ " "
+ str(self.total_noisy_transitions_episode)
+ " "
+ str(self.total_transitions_episode)
)
# on episode start stuff:
self.reward_buffer = [0.0] * (self.delay)
self.total_episodes += 1
if self.config["state_space_type"] == "discrete":
self.curr_state_relevant = self.np_random.choice(
self.state_space_size[0], p=self.config["relevant_init_state_dist"]
) # #random
self.curr_state = self.curr_state_relevant # # curr_state set here
# already in case if statement below is not entered
if self.irrelevant_features:
self.curr_state_irrelevant = self.np_random.choice(
self.state_space_size[1],
p=self.config["irrelevant_init_state_dist"],
) # #random
self.curr_state = (self.curr_state_relevant, self.curr_state_irrelevant)
self.logger.info(
"RESET called. Relevant part of state reset to:"
+ str(self.curr_state_relevant)
)
self.logger.info(
"Irrelevant part of state reset to:"
+ | |
<reponame>Joshuaalbert/bayes_tec<gh_stars>0
import matplotlib
matplotlib.use('Agg')
import numpy as np
import os
from concurrent import futures
from ..datapack import DataPack
from ..frames import UVW
from ..logging import logging
import astropy.coordinates as ac
import astropy.time as at
import astropy.units as au
from scipy.spatial import ConvexHull, cKDTree
import time
from scipy.spatial.distance import pdist
import psutil
import pylab as plt
plt.style.use('ggplot')
from matplotlib.patches import Polygon, Rectangle
from matplotlib.collections import PatchCollection
import matplotlib.colors as colors
try:
import cmocean
phase_cmap = cmocean.cm.phase
except ImportError:
phase_cmap = plt.cm.hsv
class DatapackPlotter(object):
def __init__(self,datapack):
if isinstance(datapack,str):
datapack = DataPack(filename=datapack,readonly=True)
self.datapack = datapack
def _create_polygon_plot(self,points, values=None, N = None,ax=None,cmap=plt.cm.bone,overlay_points=None,annotations=None,title=None,polygon_labels=None,reverse_x=False):
# get nearest points (without odd voronoi extra regions)
k = cKDTree(points)
dx = np.max(points[:,0]) - np.min(points[:,0])
dy = np.max(points[:,1]) - np.min(points[:,1])
delta = pdist(points)
N = N or int(min(max(100,2*np.max(delta)/np.min(delta)),500))
x = np.linspace(np.min(points[:,0])-0.1*dx,np.max(points[:,0])+0.1*dx,N)
y = np.linspace(np.min(points[:,1])-0.1*dy,np.max(points[:,1])+0.1*dy,N)
X,Y = np.meshgrid(x,y,indexing='ij')
# interior points population
points_i = np.array([X.flatten(),Y.flatten()]).T
# The match per input point
dist,i = k.query(points_i,k=1)
# the polygons are now created using convex hulls
# order is by point order
patches = []
for group in range(points.shape[0]):
points_g = points_i[i==group,:]
if points_g.size == 0:
logging.debug("Facet {} has zero size".format(group))
poly = Polygon(points[group:group+1,:],closed=False)
else:
hull = ConvexHull(points_g)
nodes = points_g[hull.vertices,:]
poly = Polygon(nodes,closed=False)
patches.append(poly)
if ax is None:
fig,ax = plt.subplots()
logging.info("Making new plot")
if values is None:
values = np.zeros(len(patches))#random.uniform(size=len(patches))
p = PatchCollection(patches,cmap=cmap)
p.set_array(values)
ax.add_collection(p)
#plt.colorbar(p)
if overlay_points is not None:
if annotations is None:
ax.scatter(overlay_points[:,0],overlay_points[:,1],marker='+',c='black')
else:
for point, a in zip(overlay_points, annotations):
ax.text(point[0],point[1],a,ha='center',va='center',backgroundcolor=(1.,1.,1., 0.1))
if reverse_x:
ax.set_xlim([np.max(points_i[:,0]),np.min(points_i[:,0])])
else:
ax.set_xlim([np.min(points_i[:,0]),np.max(points_i[:,0])])
ax.set_ylim([np.min(points_i[:,1]),np.max(points_i[:,1])])
ax.set_facecolor('black')
ax.grid(b=True,color='black')
if title is not None:
if reverse_x:
ax.text(np.max(points_i[:,0])-0.05*dx,np.max(points_i[:,1])-0.05*dy,title,ha='left',va='top',backgroundcolor=(1.,1.,1., 0.5))
else:
ax.text(np.min(points_i[:,0])+0.05*dx,np.max(points_i[:,1])-0.05*dy,title,ha='left',va='top',backgroundcolor=(1.,1.,1., 0.5))
# Rectangle((x, y), 0.5, 0.5,
# alpha=0.1,facecolor='red',label='Label'))
# ax.annotate(title,xy=(0.8,0.8),xycoords='axes fraction')
return ax, p
def _create_image_plot(self,points, values=None, N = None,ax=None,cmap=plt.cm.bone,overlay_points=None,annotations=None,title=None,reverse_x=False):
'''
Create initial plot, with image data instead of polygons.
points: (ra, dec)
values: array [n, m] or None, assumes (dec, ra) ordering ie (y,x)
'''
dx = np.max(points[0]) - np.min(points[0])
dy = np.max(points[1]) - np.min(points[1])
if values is not None:
Ndec,Nra = values.shape
else:
Ndec,Nra = len(points[1]),len(points[0])
values = np.zeros([Ndec,Nra])
if ax is None:
fig,ax = plt.subplots()
logging.info("Making new plot")
x = np.linspace(np.min(points[0]),np.max(points[0]),Nra)
y = np.linspace(np.min(points[1]),np.max(points[1]),Ndec)
img = ax.imshow(values,origin='lower',cmap=cmap, aspect='auto', extent=(x[0],x[-1],y[0],y[-1]))
if overlay_points is not None:
if annotations is None:
ax.scatter(overlay_points[:,0],overlay_points[:,1],marker='+',c='black')
else:
for point, a in zip(overlay_points, annotations):
ax.text(point[0],point[1],a,ha='center',va='center',backgroundcolor=(1.,1.,1., 0.1))
if reverse_x:
ax.set_xlim([x[-1],x[0]])
else:
ax.set_xlim([x[0],x[-1]])
ax.set_ylim([y[0],y[-1]])
ax.set_facecolor('black')
ax.grid(b=True,color='black')
if title is not None:
if reverse_x:
ax.text(x[-1]-0.05*dx,y[-1]-0.05*dy,title,ha='left',va='top',backgroundcolor=(1.,1.,1., 0.5))
else:
ax.text(x[0]+0.05*dx,y[-1]-0.05*dy,title,ha='left',va='top',backgroundcolor=(1.,1.,1., 0.5))
return ax, img
def plot(self, ant_sel=None,time_sel=None,freq_sel=None,dir_sel=None,pol_sel=None, fignames=None, vmin=None,vmax=None,mode='perantenna',observable='phase',phase_wrap=True, log_scale=False, plot_crosses=True,plot_facet_idx=False,plot_patchnames=False,labels_in_radec=False,show=False, plot_arrays=False, solset=None, plot_screen=False, tec_eval_freq=None, **kwargs):
"""
Plot datapack with given parameters.
"""
SUPPORTED = ['perantenna']
assert mode in SUPPORTED, "only 'perantenna' supported currently".format(SUPPORTED)
if fignames is None:
save_fig = False
show = True
else:
save_fig = True
show = show and True #False
if plot_patchnames:
plot_facet_idx = False
if plot_patchnames or plot_facet_idx:
plot_crosses = False
if not show:
logging.debug('turning off display')
matplotlib.use('Agg')
###
# Set up plotting
with self.datapack:
self.datapack.switch_solset(solset)
logging.info("Applying selection: ant={},time={},freq={},dir={},pol={}".format(ant_sel,time_sel,freq_sel,dir_sel,pol_sel))
self.datapack.select(ant=ant_sel,time=time_sel,freq=freq_sel,dir=dir_sel,pol=pol_sel)
obs,axes = self.datapack.__getattr__(observable)
if observable.startswith('weights_'):
obs = np.sqrt(np.abs(1./obs)) #uncert from weights = 1/var
phase_wrap=False
if 'pol' in axes.keys():
# plot only first pol selected
obs = obs[0,...]
#obs is dir, ant, freq, time
antenna_labels, antennas = self.datapack.get_antennas(axes['ant'])
patch_names, directions = self.datapack.get_sources(axes['dir'])
timestamps, times = self.datapack.get_times(axes['time'])
freq_dep = True
try:
freq_labels, freqs = self.datapack.get_freqs(axes['freq'])
except:
freq_dep = False
obs = obs[:,:,None,:]
freq_labels, freqs = [""],[None]
if tec_eval_freq is not None:
obs = obs*-8.4480e9/tec_eval_freq
if phase_wrap:
obs = np.angle(np.exp(1j*obs))
vmin = -np.pi
vmax = np.pi
cmap = phase_cmap
else:
vmin = vmin or np.percentile(obs.flatten(),1)
vmax = vmax or np.percentile(obs.flatten(),99)
cmap = plt.cm.bone
if log_scale:
obs = np.log10(obs)
Na = len(antennas)
Nt = len(times)
Nd = len(directions)
Nf = len(freqs)
fixfreq = Nf >> 1
logging.info("Plotting {} directions".format(Nd))
logging.info("Plotting {} antennas".format(Na))
logging.info("Plotting {} timestamps".format(Nt))
_, antennas_ = self.datapack.get_antennas([self.datapack.ref_ant])
#ants_uvw = antennas.transform_to(uvw)
ref_dist = np.sqrt((antennas.x - antennas_.x)**2 + (antennas.y - antennas_.y)**2 + (antennas.z - antennas_.z)**2).to(au.km).value
# if labels_in_radec:
ra = directions.ra.deg
dec = directions.dec.deg
if not plot_screen:
### points are normal
points = np.array([ra,dec]).T
if plot_crosses or plot_patchnames or plot_facet_idx:
overlay_points = points
else:
overlay_points = None
else:
### get unique ra and dec and then rearrange into correct order.
_ra = np.unique(ra)
_dec = np.unique(dec)
Nra = len(_ra)
Ndec = len(_dec)
assert Ndec * Nra == Nd
### sort lexiconially
ind = np.lexsort((ra,dec))
points = (_ra, _dec)
obs = obs[ind, ...]
obs = obs.reshape((Ndec,Nra,Na,Nf,Nt))
if plot_crosses:
overlay_points = None # put the facet (ra,dec).T
else:
overlay_points = None
if plot_patchnames:
annotations = patch_names
elif plot_facet_idx:
annotations = np.array([str(k) for k in range(Nd)])
else:
annotations = None
if fignames is not None:
if not isinstance(fignames,(tuple,list)):
fignames = [fignames]
if fignames is not None:
assert Nt == len(fignames)
if mode == 'perantenna':
M = int(np.ceil(np.sqrt(Na)))
fig,axs = plt.subplots(nrows=M,ncols=M,sharex='col',sharey='row',squeeze=False, \
figsize=(4*M,4*M))
fig.subplots_adjust(wspace=0., hspace=0.)
axes_patches = []
c = 0
for row in range(M):
for col in range(M):
ax = axs[row,col]
if col == 0:
ax.set_ylabel("Projected North (radians)" if not labels_in_radec else "DEC (deg)")
if row == M - 1:
ax.set_xlabel("Projected East (radians)" if not labels_in_radec else "RA (deg)")
if c >= Na:
continue
try:
title = antenna_labels[c].decode()
except:
title = antenna_labels[c]
if plot_screen:
_, p = self._create_image_plot(points, values=None, N = None,
ax=ax,cmap=cmap,overlay_points=overlay_points,
annotations=annotations,
title="{} {:.1f}km".format(title, ref_dist[c]),
reverse_x=labels_in_radec)
else:
_, p = self._create_polygon_plot(points, values=None, N = None,
ax=ax,cmap=cmap,overlay_points=overlay_points,
annotations=annotations,
title="{} {:.1f}km".format(title, ref_dist[c]),
reverse_x=labels_in_radec)
p.set_clim(vmin,vmax)
axes_patches.append(p)
c += 1
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.875, 0.15, 0.025, 0.7])
fig.colorbar(p, cax=cbar_ax, orientation='vertical')
if show:
plt.ion()
plt.show()
for j in range(Nt):
logging.info("Plotting {}".format(timestamps[j]))
for i in range(Na):
if not plot_screen:
axes_patches[i].set_array(obs[:,i,fixfreq,j])
else:
axes_patches[i].set_array(obs[:,:,i,fixfreq,j])
axs[0,0].set_title("{} {} : {}".format(observable, freq_labels[fixfreq], timestamps[j]))
fig.canvas.draw()
if save_fig:
plt.savefig(fignames[j])
if show:
# plt.close(fig)
plt.ioff()
def _parallel_plot(arg):
datapack,time_slice,kwargs,output_folder=arg
dp = DatapackPlotter(datapack=datapack)
with dp.datapack:
# Get the time selection desired
dp.datapack.select(time=kwargs.get('time_sel',None))
axes = dp.datapack.axes_phase
# timeslice the selection
times = axes['time']#mjs
sel_list = times[time_slice]
kwargs['time_sel'] = sel_list
fignames = [os.path.join(output_folder,"fig-{:04d}.png".format(j)) for j in range(len(times))[time_slice]]
dp.plot(fignames=fignames,**kwargs)
return fignames
def animate_datapack(datapack,output_folder,num_processes,**kwargs):
"""
Plot the datapack in parallel, then stitch into movie.
datapack: str the datapack filename
output_folder: str, folder to store figs in
num_processes: int number of parallel plotting processes to run
**kwargs: keywords to pass to DatapackPlotter.plot function.
"""
try:
os.makedirs(output_folder)
except:
pass
if num_processes is None:
num_processes = psutil.cpu_count()
if isinstance(datapack,DataPack):
datapack = datapack.filename
# with DataPack(datapack) as datapack_fix:
# datapack_fix.add_antennas(DataPack.lofar_array)
args = []
for i in range(num_processes):
args.append((datapack,slice(i,None,num_processes),kwargs,output_folder))
with futures.ProcessPoolExecutor(max_workers=num_processes) as executor:
jobs = executor.map(_parallel_plot,args)
results = list(jobs)
plt.close('all')
make_animation(output_folder,prefix='fig',fps=4)
def make_animation(datafolder,prefix='fig',fps=4):
'''Given a datafolder with figures of format `prefix`-%04d.png create a
video at framerate `fps`.
Output is datafolder/animation.mp4'''
if os.system('ffmpeg -framerate {} -i {}/{}-%04d.png -vf scale="trunc(iw/2)*2:trunc(ih/2)*2" -c:v libx264 -profile:v high -pix_fmt yuv420p -g 30 -r 30 {}/animation.mp4'.format(fps,datafolder,prefix,datafolder)):
logging.info("{}/animation.mp4 exists already".format(datafolder))
def plot_phase_vs_time(datapack,output_folder, solsets='sol000',
ant_sel=None,time_sel=None,dir_sel=None,freq_sel=None,pol_sel=None):
if isinstance(datapack,DataPack):
datapack = datapack.filename
if not isinstance(solsets , (list,tuple)):
solsets = [solsets]
output_folder = os.path.abspath(output_folder)
os.makedirs(output_folder,exist_ok=True)
with DataPack(datapack,readonly=True) as datapack:
phases = []
stds = []
for solset in solsets:
datapack.switch_solset(solset)
datapack.select(ant=ant_sel,time=time_sel,dir=dir_sel,freq=freq_sel,pol=pol_sel)
weights,axes = datapack.weights_phase
freq_ind = len(axes['freq']) >> 1
freq = axes['freq'][freq_ind]
ant = axes['ant'][0]
phase,_ = datapack.phase
std = np.sqrt(np.abs(weights))
timestamps,times = datapack.get_times(axes['time'])
phases.append(phase)
stds.append(std)
for phase in phases:
for s,S in zip(phase.shape,phases[0].shape):
assert s==S
Npol,Nd,Na,Nf,Nt = phases[0].shape
fig,ax = plt.subplots()
for p in range(Npol):
for d in range(Nd):
for a in range(Na):
for f in range(Nf):
ax.cla()
for i,solset in enumerate(solsets):
phase = phases[i]
std = stds[i]
label = "{} {} {:.1f}MHz {}:{}".format(solset, axes['pol'][p], axes['freq'][f]/1e6, axes['ant'][a], axes['dir'][d])
ax.fill_between(times.mjd,phase[p,d,a,f,:]-2*std[p,d,a,f,:],phase[p,d,a,f,:]+2*std[p,d,a,f,:],alpha=0.5,label=r'$\pm2\hat{\sigma}_\phi$')#,color='blue')
ax.scatter(times.mjd,phase[p,d,a,f,:],marker='+',alpha=0.3,color='black',label=label)
ax.set_xlabel('Time [mjd]')
ax.set_ylabel('Phase deviation [rad.]')
ax.legend()
filename = "{}_{}_{}_{}MHz.png".format(axes['ant'][a], axes['dir'][d], axes['pol'][p], axes['freq'][f]/1e6 )
plt.savefig(os.path.join(output_folder,filename))
plt.close('all')
def plot_data_vs_solution(datapack,output_folder, data_solset='sol000', solution_solset='posterior_sol', show_prior_uncert=False,
ant_sel=None,time_sel=None,dir_sel=None,freq_sel=None,pol_sel=None):
def _wrap(phi):
return np.angle(np.exp(1j*phi))
if isinstance(datapack,DataPack):
datapack = datapack.filename
output_folder = os.path.abspath(output_folder)
os.makedirs(output_folder,exist_ok=True)
solsets = [data_solset, solution_solset]
with DataPack(datapack,readonly=True) as datapack:
phases = []
stds = []
datapack.switch_solset(data_solset)
datapack.select(ant=ant_sel,time=time_sel,dir=dir_sel,freq=freq_sel,pol=pol_sel)
weights,axes = datapack.weights_phase
_,freqs = datapack.get_freqs(axes['freq'])
phase,_ = datapack.phase
std = np.sqrt(np.abs(1./weights))
timestamps,times = datapack.get_times(axes['time'])
phases.append(_wrap(phase))
stds.append(std)
tec_conversion = -8.4480e9/freqs[None,None,None,:,None]
datapack.switch_solset(solution_solset)
datapack.select(ant=ant_sel,time=time_sel,dir=dir_sel,freq=freq_sel,pol=pol_sel)
weights,_ = datapack.weights_tec
tec,_ = datapack.tec
std = np.sqrt(np.abs(1./weights))[:,:,:,None,:]*np.abs(tec_conversion)
phases.append(_wrap(tec[:,:,:,None,:]*tec_conversion))
stds.append(std)
for phase in phases:
for s,S in zip(phase.shape,phases[0].shape):
assert s==S
Npol,Nd,Na,Nf,Nt = phases[0].shape
fig,ax = plt.subplots()
| |
metadata.
:param filepath: savepath of the OME-TIFF stack
:type filepath: str
:param imgarray: multi-dimensional image array
:type imgarray: NumPy.Array
:param metadata: metadata dictionary with the required information
to create an correct OME-TIFF file
:type metadata: dict
:param reader: string (aicsimagio or czifile) specifying
the used reader, defaults to aicsimageio
:type metadata: str
:param overwrite: option to overwrite an existing OME-TIFF, defaults to False
:type overwrite: bool, optional
"""
# define scaling from metadata or use defualt scaling
try:
pixels_physical_size = [metadata['XScale'],
metadata['YScale'],
metadata['ZScale']]
except KeyError as e:
print('Key not found:', e)
print('Use default scaling XYZ=1.0')
pixels_physical_size = [1.0, 1.0, 1.0]
# define channel names list from metadata
try:
channel_names = []
for ch in metadata['Channels']:
channel_names.append(ch)
except KeyError as e:
print('Key not found:', e)
channel_names = None
# get the dimensions and their position inside the dimension string
if reader == 'aicsimageio':
dims_dict, dimindex_list, numvalid_dims = get_dimorder(metadata['Axes_aics'])
# if the array has more than 5 dimensions then remove the S dimension
# because it is not supported by OME-TIFF
if len(imgarray.shape) > 5:
try:
imgarray = np.squeeze(imgarray, axis=dims_dict['S'])
except Exception:
print('Could not remover S Dimension from string.)')
# remove the S character from the dimension string
new_dimorder = metadata['Axes_aics'].replace('S', '')
if reader == 'czifile':
new_dimorder = metadata['Axes']
dims_dict, dimindex_list, numvalid_dims = get_dimorder(metadata['Axes'])
"""
'0': 'Sample', # e.g. RGBA
'X': 'Width',
'Y': 'Height',
'C': 'Channel',
'Z': 'Slice', # depth
'T': 'Time',
'R': 'Rotation',
'S': 'Scene', # contiguous regions of interest in a mosaic image
'I': 'Illumination', # direction
'B': 'Block', # acquisition
'M': 'Mosaic', # index of tile for compositing a scene
'H': 'Phase', # e.g. Airy detector fibers
'V': 'View', # e.g. for SPIM
"""
to_remove = []
# list of unspupported dims for writing an OME-TIFF
dims = ['R', 'I', 'M', 'H', 'V', 'B', 'S', '0']
for dim in dims:
if dims_dict[dim] >= 0:
# remove the CZI DIMENSION character from the dimension string
new_dimorder = new_dimorder.replace(dim, '')
# add dimension index to the list of axis to be removed
to_remove.append(dims_dict[dim])
print('Remove Dimension:', dim)
# create tuple with dimensions to be removed
dims2remove = tuple(to_remove)
# remove dimensions from array
imgarray = np.squeeze(imgarray, axis=dims2remove)
# write the array as an OME-TIFF incl. the metadata
try:
with ome_tiff_writer.OmeTiffWriter(savepath, overwrite_file=overwrite) as writer:
writer.save(imgarray,
channel_names=channel_names,
ome_xml=None,
image_name=os.path.basename((savepath)),
pixels_physical_size=pixels_physical_size,
channel_colors=None,
dimension_order=new_dimorder)
writer.close()
except Exception as error:
print(error.__class__.__name__ + ": " + error.msg)
print('Could not write OME-TIFF')
savepath = None
return savepath
def correct_omeheader(omefile,
old=("2012-03", "2013-06", r"ome/2016-06"),
new=("2016-06", "2016-06", r"OME/2016-06")
):
"""This function is actually a workaround for AICSImageIO<=3.1.4 that
correct some incorrect namespaces inside the OME-XML header
:param omefile: OME-TIFF image file
:type omefile: string
:param old: strings that should be corrected, defaults to ("2012-03", "2013-06", r"ome/2016-06")
:type old: tuple, optional
:param new: replacement for the strings to be corrected, defaults to ("2016-06", "2016-06", r"OME/2016-06")
:type new: tuple, optional
"""
# create the tif object from the filename
tif = tifffile.TiffFile(omefile)
# get the pixel array and the OME-XML string
array = tif.asarray()
omexml_string = tif.ome_metadata
# search for the strings to be replaced and do it
for ostr, nstr in zip(old, new):
print('Replace: ', ostr, 'with', nstr)
omexml_string = omexml_string.replace(ostr, nstr)
# save the file with the new, correct strings
tifffile.imsave(omefile, array,
photometric='minisblack',
description=omexml_string)
# close tif object
tif.close()
print('Updated OME Header.')
def get_fname_woext(filepath):
"""Get the complete path of a file without the extension
It alos will works for extensions like c:\myfile.abc.xyz
The output will be: c:\myfile
:param filepath: complete fiepath
:type filepath: str
:return: complete filepath without extension
:rtype: str
"""
# create empty string
real_extension = ''
# get all part of the file extension
sufs = Path(filepath).suffixes
for s in sufs:
real_extension = real_extension + s
# remover real extension from filepath
filepath_woext = filepath.replace(real_extension, '')
return filepath_woext
def convert_to_ometiff(imagefilepath,
bftoolsdir='/Users/bftools',
czi_include_attachments=False,
czi_autostitch=True,
verbose=True):
"""Convert image file using bfconvert tool into a OME-TIFF from with a python script.
:param imagefilepath: path to imagefile
:type imagefilepath: str
:param bftoolsdir: bftools directory containing the bfconvert, defaults to '/Users/bftools'
:type bftoolsdir: str, optional
:param czi_include_attachments: option convert a CZI attachment (if CZI), defaults to False
:type czi_include_attachments: bool, optional
:param czi_autostitch: option stich a CZI, defaults to True
:type czi_autostitch: bool, optional
:param verbose: show additional output, defaults to True
:type verbose: bool, optional
:return: fileparh of created OME-TIFF file
:rtype: str
"""
# check if path exits
if not os.path.exists(bftoolsdir):
print('No bftools dirctory found. Nothing will be converted')
file_ometiff = None
if os.path.exists(bftoolsdir):
# set working dir
os.chdir(bftoolsdir)
# get the imagefile path without extension
imagefilepath_woext = get_fname_woext(imagefilepath)
# create imagefile path for OME-TIFF
file_ometiff = imagefilepath_woext + '.ome.tiff'
# create cmdstring for CZI files- mind the spaces !!!
if imagefilepath.lower().endswith('.czi'):
# configure the CZI options
if czi_include_attachments:
czi_att = 'true'
if not czi_include_attachments:
czi_att = 'false'
if czi_autostitch:
czi_stitch = 'true'
if not czi_autostitch:
czi_stitch = 'false'
# create cmdstring - mind the spaces !!!
cmdstring = 'bfconvert -no-upgrade -option zeissczi.attachments ' + czi_att + ' -option zeissczi.autostitch ' + \
czi_stitch + ' "' + imagefilepath + '" "' + file_ometiff + '"'
else:
# create cmdstring for non-CZIs- mind the spaces !!!
cmdstring = 'bfconvert -no-upgrade' + ' "' + imagefilepath + '" "' + file_ometiff + '"'
if verbose:
print('Original ImageFile : ', imagefilepath_woext)
print('ImageFile OME.TIFF : ', file_ometiff)
print('Use CMD : ', cmdstring)
# run the bfconvert tool with the specified parameters
os.system(cmdstring)
print('Done.')
return file_ometiff
def get_dimpositions(dimstring, tocheck=['B', 'S', 'T', 'Z', 'C']):
"""Simple function to get the indices of the dimension identifiers in a string
:param dimstring: dimension string
:type dimstring: str
:param tocheck: list of entries to check, defaults to ['B', 'S', 'T', 'Z', 'C']
:type tocheck: list, optional
:return: dictionary with positions of dimensions inside string
:rtype: dict
"""
dimpos = {}
for p in tocheck:
dimpos[p] = dimstring.find(p)
return dimpos
def norm_columns(df, colname='Time [s]', mode='min'):
"""Normalize a specif column inside a Pandas dataframe
:param df: DataFrame
:type df: pf.DataFrame
:param colname: Name of the coumn to be normalized, defaults to 'Time [s]'
:type colname: str, optional
:param mode: Mode of Normalization, defaults to 'min'
:type mode: str, optional
:return: Dataframe with normalized column
:rtype: pd.DataFrame
"""
# normalize columns according to min or max value
if mode == 'min':
min_value = df[colname].min()
df[colname] = df[colname] - min_value
if mode == 'max':
max_value = df[colname].max()
df[colname] = df[colname] - max_value
return df
def update5dstack(image5d, image2d,
dimstring5d='TCZYX',
t=0,
z=0,
c=0):
# remove XY
dimstring5d = dimstring5d.replace('X', '').replace('Y', '')
if dimstring5d == 'TZC':
image5d[t, z, c, :, :] = image2d
if dimstring5d == 'TCZ':
image5d[t, c, z, :, :] = image2d
if dimstring5d == 'ZTC':
image5d[z, t, c, :, :] = image2d
if dimstring5d == 'ZCT':
image5d[z, c, t, :, :] = image2d
if dimstring5d == 'CTZ':
image5d[c, t, z, :, :] = image2d
if dimstring5d == 'CZT':
image5d[c, z, t, :, :] = image2d
return image5d
def getdims_pylibczi(czi):
# Get the shape of the data, the coordinate pairs are (start index, size)
# [{'X': (0, 1900), 'Y': (0, 1300), 'Z': (0, 60), 'C': (0, 4), 'S': (0, 40), 'B': (0, 1)}]
# dimensions = czi.dims_shape()
dimsizes = {}
for d in range(len(czi.dims)):
# print(d)
dimsizes['Size' + czi.dims[d]] = czi.size[d]
return dimsizes
def calc_normvar(img2d):
"""Determine normalized focus value for a 2D image
- based on algorithm F - 11 "Normalized Variance"
- Taken from: Sun et al., 2004. MICROSCOPY RESEARCH AND TECHNIQUE 65, 139–149.
- Maximum value is best-focused, decreasing as defocus increases
:param img2d: 2D image
:type img2d: NumPy.Array
:return: normalized focus value for the 2D image
:rtype: float
"""
mean = np.mean(img2d)
height = img2d.shape[0]
width = img2d.shape[1]
# subtract the mean and sum up the whole array
fi = (img2d - mean)**2
b = np.sum(fi)
# calculate the normalized variance value
normvar = b / (height * width * mean)
| |
"""Implementation of GraphTensor data type.
"""
import abc
from typing import Any, Dict, Mapping, Optional, Union
import tensorflow as tf
from tensorflow_gnn.graph import graph_constants as const
from tensorflow_gnn.graph import graph_piece as gp
# pylint: disable=g-direct-tensorflow-import
from tensorflow.python.framework import type_spec
# pylint: enable=g-direct-tensorflow-import
FieldName = const.FieldName
NodeSetName = const.NodeSetName
EdgeSetName = const.EdgeSetName
ShapeLike = const.ShapeLike
Field = const.Field
Fields = const.Fields
FieldSpec = const.FieldSpec
FieldsSpec = const.FieldsSpec
# TODO(b/189057503): use adjacency interface class instead.
Adjacency = Any
AdjacencySpec = Any
class _GraphPieceWithFeatures(gp.GraphPieceBase, metaclass=abc.ABCMeta):
"""Base class for graph pieces that hold user-defined features."""
def __getitem__(self, feature_name: FieldName) -> Field:
"""Indexing operator `[]` to access feature values by their name."""
return self._get_features_ref[feature_name]
@property
def features(self) -> Mapping[FieldName, Field]:
"""Read-only view for features."""
return _as_immutable_mapping(self._get_features_ref)
def get_features_dict(self) -> Dict[FieldName, Field]:
"""Returns features copy as a dictionary."""
return dict(self._get_features_ref)
@abc.abstractproperty
def _get_features_ref(self) -> Fields:
"""Returns the mutable features dict. Subclass controls location in data."""
raise NotImplementedError
class _GraphPieceWithFeaturesSpec(gp.GraphPieceSpecBase):
"""TypeSpec for _GraphPieceWithFeatures."""
def __getitem__(self, feature_name: FieldName) -> FieldSpec:
return self._get_features_spec_ref[feature_name]
@property
def features_spec(self) -> Mapping[FieldName, FieldSpec]:
"""A mapping of feature name to feature specs."""
return _as_immutable_mapping(self._get_features_spec_ref)
@abc.abstractproperty
def total_num_components(self) -> Optional[int]:
"""The total number of graph components across dimensions if known."""
raise NotImplementedError
@abc.abstractproperty
def _get_features_spec_ref(self) -> FieldsSpec:
raise NotImplementedError
class Context(_GraphPieceWithFeatures):
"""A container of features for a graph component.
This class is a container for the shapes of the context features associated
with each component of a graph in a `GraphTensor` instance. Note that the
number of components of those features is always explicitly set to `1` (in
lieu of the number of nodes, we've got one such feature per graph).
(Note that this graph piece does not use any metadata fields.)
"""
@classmethod
def from_fields(
cls, *,
features: Optional[Fields] = None,
shape: ShapeLike = tf.TensorShape([]),
indices_dtype: tf.dtypes.DType = const.default_indices_dtype
) -> 'Context':
"""Constructs a new instance from context fields.
Args:
features: mapping from feature names to feature Tensors or RaggedTensors.
All feature tensors must have shape = graph_shape + [num_components] +
feature_shape, where num_components is a number of graph components
(could be ragged); feature_shape are field-specific inner dimensions.
shape: the shape of this tensor and a GraphTensor containing it, also
known as the graph_shape.
indices_dtype: The `indices_dtype` of a GraphTensor containing this
object, used as `row_splits_dtype` when batching potentially ragged
fields.
Returns:
A `Context` tensor.
"""
if features is None:
features = {}
assert isinstance(features, Mapping)
prepared_features = {key: gp.convert_to_tensor_or_ragged(value)
for key, value in features.items()}
return cls._from_data(
prepared_features,
shape=shape
if isinstance(shape, tf.TensorShape) else tf.TensorShape(shape),
indices_dtype=indices_dtype)
def replace_features(self, features: Fields) -> 'Context':
"""Returns a new instance with a new set of features."""
assert isinstance(features, Mapping)
return self.__class__.from_fields(
features=features, shape=self.shape, indices_dtype=self.indices_dtype)
@property
def _get_features_ref(self) -> Fields:
return self._data
@staticmethod
def _type_spec_cls():
return ContextSpec
@type_spec.register('tensorflow_gnn.ContextSpec')
class ContextSpec(_GraphPieceWithFeaturesSpec):
"""A type spec for global features for a graph component.
This class is a type descriptor for the shapes of the context features
associated with each component of a graph in a `GraphTensor` instance. Note
that the prefix shape of those features is always explicitly set to either `1`
for a single graph, or to the number of components for a batched graph.
(Note that this graph piece does not use any metadata fields.)
"""
@classmethod
def from_field_specs(
cls, *,
features_spec: Optional[FieldsSpec] = None,
shape: ShapeLike = tf.TensorShape([]),
indices_dtype: tf.dtypes.DType = const.default_indices_dtype
) -> 'ContextSpec':
"""Counterpart of `Context.from_fields()` for values type specs."""
if features_spec is None:
features_spec = {}
assert isinstance(features_spec, Mapping)
return cls._from_data_spec(
features_spec,
shape=shape
if isinstance(shape, tf.TensorShape) else tf.TensorShape(shape),
indices_dtype=indices_dtype)
@property
def value_type(self):
return Context
@property
def total_num_components(self) -> Optional[int]:
"""The total number of graph components across dimensions if known."""
indicative_feature_spec = _get_indicative_feature_spec(self._data_spec)
if indicative_feature_spec is None:
return None
else:
return indicative_feature_spec.shape[:(self.rank + 1)].num_elements()
@property
def _get_features_spec_ref(self) -> FieldsSpec:
return self._data_spec
class _NodeOrEdgeSet(_GraphPieceWithFeatures):
"""Base class for node set or edge set."""
_DATAKEY_FEATURES = 'features' # A Mapping[FieldName, Field].
_DATAKEY_SIZES = 'sizes' # A Field with `sizes`.
@classmethod
def _from_fields(cls, features: Fields, sizes: Field,
**extra_data) -> '_NodeOrEdgeSet':
assert isinstance(features, Mapping)
sizes = gp.convert_to_tensor_or_ragged(sizes)
prepared_features = {key: gp.convert_to_tensor_or_ragged(value)
for key, value in features.items()}
data = {
_NodeOrEdgeSet._DATAKEY_FEATURES: prepared_features,
_NodeOrEdgeSet._DATAKEY_SIZES: sizes
}
data.update({key: gp.convert_to_tensor_or_ragged(value)
for key, value in extra_data.items()})
return cls._from_data(
data=data, shape=sizes.shape[:-1], indices_dtype=sizes.dtype)
def replace_features(self, features: Mapping[FieldName,
Field]) -> '_NodeOrEdgeSet':
"""Returns a new instance with a new set of features."""
assert isinstance(features, Mapping)
new_data = self._data.copy()
new_data.update({_NodeOrEdgeSet._DATAKEY_FEATURES: features})
return self.__class__.from_fields(**new_data)
@property
def sizes(self) -> Field:
"""Tensor with a number of elements in each graph component."""
return self._data[_NodeOrEdgeSet._DATAKEY_SIZES]
@property
def total_size(self) -> tf.Tensor:
"""Returns the total number of elements across dimensions.
Returns:
Scalar integer tensor equal to `tf.math.reduce_sum(sizes)`.
"""
result = tf.math.reduce_sum(self.sizes)
assert isinstance(result, tf.Tensor) and result.shape.rank == 0
return result
@property
def _get_features_ref(self) -> Fields:
return self._data[_NodeOrEdgeSet._DATAKEY_FEATURES]
class _NodeOrEdgeSetSpec(_GraphPieceWithFeaturesSpec):
"""TypeSpec for _NodeOrEdgeSet."""
@classmethod
def _from_field_specs(cls, features_spec: FieldsSpec, sizes_spec: FieldSpec,
**extra_data) -> '_NodeOrEdgeSetSpec':
# pylint: disable=protected-access
assert isinstance(features_spec, Mapping)
data_spec = {
_NodeOrEdgeSet._DATAKEY_FEATURES: features_spec,
_NodeOrEdgeSet._DATAKEY_SIZES: sizes_spec
}
data_spec.update(extra_data)
return cls._from_data_spec(
data_spec, shape=sizes_spec.shape[:-1], indices_dtype=sizes_spec.dtype)
@property
def sizes_spec(self) -> FieldSpec:
"""A type spec for the sizes that provides num. elements per component."""
return self._data_spec[_NodeOrEdgeSet._DATAKEY_SIZES] # pylint: disable=protected-access
@property
def total_num_components(self) -> Optional[int]:
"""The total number of graph components across dimensions if known."""
return self.sizes_spec.shape.num_elements()
@property
def total_size(self) -> Optional[int]:
"""Returns the total number of graph entities across dimensions if known."""
indicative_feature_spec = _get_indicative_feature_spec(
self._get_features_spec_ref)
if indicative_feature_spec is None:
return None
else:
return indicative_feature_spec.shape[:(self.rank + 1)].num_elements()
@property
def _get_features_spec_ref(self) -> FieldsSpec:
return self._data_spec[_NodeOrEdgeSet._DATAKEY_FEATURES] # pylint: disable=protected-access
class NodeSet(_NodeOrEdgeSet):
"""A container for the features of a single node set.
This class is a container for the shapes of the features associated with a
graph's node set from a `GraphTensor` instance. This graph piece stores
features that belong to an edge set, and a `sizes` tensor with the number of
edges in each graph component.
(This graph piece does not use any metadata fields.)
"""
@classmethod
def from_fields(cls, *,
features: Optional[Fields] = None,
sizes: Field) -> 'NodeSet':
"""Constructs a new instance from node set fields.
Args:
features: mapping from feature names to feature Tensors or RaggedTensors.
All feature tensors must have shape = graph_shape + [num_nodes] +
feature_shape, where num_nodes is the number of graph nodes in this set
(could be ragged) and feature_shape are feature-specific inner
dimensions.
sizes: the number of nodes in each graph component. Has shape =
graph_shape + [num_components], where num_components is the number of
graph components (could be ragged).
Returns:
A `NodeSet` tensor.
"""
if features is None:
features = {}
return cls._from_fields(features=features, sizes=sizes)
@staticmethod
def _type_spec_cls():
return NodeSetSpec
@type_spec.register('tensorflow_gnn.NodeSetSpec')
class NodeSetSpec(_NodeOrEdgeSetSpec):
"""A type spec for the features of a single node set.
This class is a type descriptor for the shapes of the features associated with
a graph's node set from a `GraphTensor` instance. This graph piece stores
features that belong to an edge set, and a `sizes` tensor with the number of
edges in each graph component.
(This graph piece does not use any metadata fields.)
"""
@classmethod
def from_field_specs(cls, *,
features_spec: Optional[FieldsSpec] = None,
sizes_spec: FieldSpec) -> 'NodeSetSpec':
"""Counterpart of `NodeSet.from_fields()` for values type specs."""
if features_spec is None:
features_spec = {}
return cls._from_field_specs(
features_spec=features_spec, sizes_spec=sizes_spec)
@property
def value_type(self):
return NodeSet
class EdgeSet(_NodeOrEdgeSet):
"""A container for the features of a single edge set.
This class is a container for the shapes of the features associated with a
graph's edge set from a `GraphTensor` instance. This graph piece stores
features that belong to an edge set, a `sizes` tensor with the number of edges
in each graph component and an `adjacency` `GraphPiece` tensor describing how
this edge set connects node sets (see adjacency.py).
(This graph piece does not use any metadata fields.)
"""
_DATAKEY_ADJACENCY = 'adjacency' # An Adjacency GraphPiece.
@classmethod
def from_fields(cls, *,
features: Optional[Fields] = None,
sizes: Field,
adjacency: Adjacency) -> 'EdgeSet':
"""Constructs a new instance from edge set fields.
Args:
features: mapping from feature names to feature Tensors or RaggedTensors.
All feature tensors must have shape = graph_shape + [num_edges] +
feature_shape, where num_edges is the number of edges in the edge set
(could be ragged) and | |
<reponame>CandyHuiZhang/deep-reinforcement-learning-pytorch<gh_stars>1-10
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import random
import time
import math
import torch
from torch.autograd import Variable, grad, backward
import torch.nn.functional as F
from utils.helpers import ACER_On_Policy_Experience
from utils.distributions import sample_poisson, categorical_kl_div
from optims.helpers import adjust_learning_rate
from core.agent_single_process import AgentSingleProcess
class ACERSingleProcess(AgentSingleProcess):
def __init__(self, master, process_id=0):
super(ACERSingleProcess, self).__init__(master, process_id)
# lstm hidden states
if self.master.enable_lstm:
self._reset_on_policy_lstm_hidden_vb_episode() # clear up hidden state
self._reset_on_policy_lstm_hidden_vb_rollout() # detach the previous variable from the computation graph
self._reset_off_policy_lstm_hidden_vb() # clear up hidden state, since sampled batches won't be connected from previous batches
# # NOTE global variable pi
# if self.master.enable_continuous:
# self.pi_vb = Variable(torch.Tensor([math.pi]).type(self.master.dtype))
self.master.logger.warning("Registered ACER-SingleProcess-Agent #" + str(self.process_id) + " w/ Env (seed:" + str(self.env.seed) + ").")
# NOTE: to be called at the beginning of each new episode, clear up the hidden state
def _reset_on_policy_lstm_hidden_vb_episode(self, training=True): # seq_len, batch_size, hidden_dim
not_training = not training
if self.master.enable_continuous:
# self.on_policy_lstm_hidden_vb = (Variable(torch.zeros(2, self.master.hidden_dim).type(self.master.dtype), volatile=not_training),
# Variable(torch.zeros(2, self.master.hidden_dim).type(self.master.dtype), volatile=not_training))
pass
else:
# for self.model
self.on_policy_lstm_hidden_vb = (Variable(torch.zeros(1, self.master.hidden_dim).type(self.master.dtype), volatile=not_training),
Variable(torch.zeros(1, self.master.hidden_dim).type(self.master.dtype), volatile=not_training))
# for self.master.avg_model # NOTE: no grads are needed to compute on this model, so always volatile
self.on_policy_avg_lstm_hidden_vb = (Variable(torch.zeros(1, self.master.hidden_dim).type(self.master.dtype), volatile=True),
Variable(torch.zeros(1, self.master.hidden_dim).type(self.master.dtype), volatile=True))
# NOTE: to be called at the beginning of each rollout, detach the previous variable from the graph
def _reset_on_policy_lstm_hidden_vb_rollout(self):
# for self.model
self.on_policy_lstm_hidden_vb = (Variable(self.on_policy_lstm_hidden_vb[0].data),
Variable(self.on_policy_lstm_hidden_vb[1].data))
# for self.master.avg_model
self.on_policy_avg_lstm_hidden_vb = (Variable(self.on_policy_avg_lstm_hidden_vb[0].data),
Variable(self.on_policy_avg_lstm_hidden_vb[1].data))
# NOTE: to be called before each off-policy learning phase
# NOTE: keeping it separate so as not to mess up the on_policy_lstm_hidden_vb if the current on-policy episode has not finished after the last rollout
def _reset_off_policy_lstm_hidden_vb(self, training=True):
not_training = not training
if self.master.enable_continuous:
pass
else:
# for self.model
self.off_policy_lstm_hidden_vb = (Variable(torch.zeros(self.master.batch_size, self.master.hidden_dim).type(self.master.dtype), volatile=not_training),
Variable(torch.zeros(self.master.batch_size, self.master.hidden_dim).type(self.master.dtype), volatile=not_training))
# for self.master.avg_model # NOTE: no grads are needed to be computed on this model
self.off_policy_avg_lstm_hidden_vb = (Variable(torch.zeros(self.master.batch_size, self.master.hidden_dim).type(self.master.dtype)),
Variable(torch.zeros(self.master.batch_size, self.master.hidden_dim).type(self.master.dtype)))
def _preprocessState(self, state, on_policy, is_valotile=False):
if isinstance(state, list):
state_vb = []
for i in range(len(state)):
if on_policy:
state_vb.append(Variable(torch.from_numpy(state[i]).unsqueeze(0).type(self.master.dtype), volatile=is_valotile))
else:
state_vb.append(Variable(torch.from_numpy(state[i]).view(-1, self.master.state_shape).type(self.master.dtype), volatile=is_valotile))
else:
if on_policy:
state_vb = Variable(torch.from_numpy(state).unsqueeze(0).type(self.master.dtype), volatile=is_valotile)
else:
state_vb = Variable(torch.from_numpy(state).view(-1, self.master.state_shape).type(self.master.dtype), volatile=is_valotile)
return state_vb
def _forward(self, state_vb, on_policy=True):
if self.master.enable_continuous:
pass
else:
if self.master.enable_lstm:
if on_policy: # learn from the current experience
p_vb, q_vb, v_vb, self.on_policy_lstm_hidden_vb = self.model(state_vb, self.on_policy_lstm_hidden_vb)
avg_p_vb, _, _, self.on_policy_avg_lstm_hidden_vb = self.master.avg_model(state_vb, self.on_policy_avg_lstm_hidden_vb)
# then we also need to get an action for the next time step
if self.training:
action = p_vb.multinomial().data[0][0]
else:
action = p_vb.max(1)[1].data.squeeze().numpy()[0]
return action, p_vb, q_vb, v_vb, avg_p_vb
else: # learn from the sampled replays
p_vb, q_vb, v_vb, self.off_policy_lstm_hidden_vb = self.model(state_vb, self.off_policy_lstm_hidden_vb)
avg_p_vb, _, _, self.off_policy_avg_lstm_hidden_vb = self.master.avg_model(state_vb, self.off_policy_avg_lstm_hidden_vb)
return _, p_vb, q_vb, v_vb, avg_p_vb
else:
pass
class ACERLearner(ACERSingleProcess):
def __init__(self, master, process_id=0):
master.logger.warning("<===================================> ACER-Learner #" + str(process_id) + " {Env & Model & Memory}")
super(ACERLearner, self).__init__(master, process_id)
# NOTE: diff from pure on-policy methods like a3c, acer is capable of
# NOTE: off-policy learning and can make use of replay buffer
self.memory = self.master.memory_prototype(capacity = self.master.memory_params.memory_size // self.master.num_processes,
max_episode_length = self.master.early_stop)
self._reset_rollout()
self.training = True # choose actions by polinomial
self.model.train(self.training)
# local counters
self.frame_step = 0 # local frame step counter
self.train_step = 0 # local train step counter
self.on_policy_train_step = 0 # local on-policy train step counter
self.off_policy_train_step = 0 # local off-policy train step counter
# local training stats
self.p_loss_avg = 0. # global policy loss
self.v_loss_avg = 0. # global value loss
self.entropy_loss_avg = 0. # global entropy loss
self.loss_counter = 0 # storing this many losses
self._reset_training_loggings()
# copy local training stats to global every prog_freq
self.last_prog = time.time()
def _reset_training_loggings(self):
self.p_loss_avg = 0.
self.v_loss_avg = 0.
self.entropy_loss_avg = 0.
self.loss_counter = 0
def _reset_rollout(self): # for storing the experiences collected through one rollout
self.rollout = ACER_On_Policy_Experience(state0 = [],
action = [],
reward = [],
state1 = [],
terminal1 = [],
policy_vb = [],
q0_vb = [],
value0_vb = [],
detached_avg_policy_vb = [],
detached_old_policy_vb = [])
def _get_QretT_vb(self, on_policy=True):
if on_policy:
if self.rollout.terminal1[-1]: # for terminal sT: Q_ret = 0
QretT_vb = Variable(torch.zeros(1, 1))
else: # for non-terminal sT: Qret = V(s_i; /theta)
sT_vb = self._preprocessState(self.rollout.state1[-1], on_policy, True) # bootstrap from last state
if self.master.enable_lstm:
_, _, QretT_vb, _ = self.model(sT_vb, self.on_policy_lstm_hidden_vb)# NOTE: only doing inference here
else:
_, _, QretT_vb = self.model(sT_vb) # NOTE: only doing inference here
# # NOTE: here QretT_vb.volatile=True since sT_vb.volatile=True
# # NOTE: if we use detach() here, it would remain volatile
# # NOTE: then all the follow-up computations would only give volatile loss variables
# QretT_vb = Variable(QretT_vb.data)
else:
sT_vb = self._preprocessState(self.rollout.state1[-1], on_policy, True) # bootstrap from last state
if self.master.enable_lstm:
_, _, QretT_vb, _ = self.model(sT_vb, self.off_policy_lstm_hidden_vb) # NOTE: only doing inference here
else:
_, _, QretT_vb = self.model(sT_vb) # NOTE: only doing inference here
# now we have to also set QretT_vb to 0 for terminal sT's
QretT_vb = ((1 - Variable(torch.from_numpy(np.array(self.rollout.terminal1[-1])).float())) * QretT_vb)
# NOTE: here QretT_vb.volatile=True since sT_vb.volatile=True
# NOTE: if we use detach() here, it would remain volatile
# NOTE: then all the follow-up computations would only give volatile loss variables
return Variable(QretT_vb.data)
def _1st_order_trpo(self, detached_policy_loss_vb, detached_policy_vb, detached_avg_policy_vb, detached_splitted_policy_vb=None):
on_policy = detached_splitted_policy_vb is None
# KL divergence k = \delta_{\phi_{\theta}} DKL[ \pi(|\phi_{\theta_a}) || \pi{|\phi_{\theta}}]
# kl_div_vb = F.kl_div(detached_policy_vb.log(), detached_avg_policy_vb, size_average=False) # NOTE: the built-in one does not work on batch
kl_div_vb = categorical_kl_div(detached_policy_vb, detached_avg_policy_vb)
# NOTE: k & g are wll w.r.t. the network output, which is detached_policy_vb
# NOTE: gradient from this part will not flow back into the model
# NOTE: that's why we are only using detached policy variables here
if on_policy:
k_vb = grad(outputs=kl_div_vb, inputs=detached_policy_vb, retain_graph=False, only_inputs=True)[0]
g_vb = grad(outputs=detached_policy_loss_vb, inputs=detached_policy_vb, retain_graph=False, only_inputs=True)[0]
else:
# NOTE NOTE NOTE !!!
# NOTE: here is why we cannot simply detach then split the policy_vb, but must split before detach
# NOTE: cos if we do that then the split cannot backtrace the grads computed in this later part of the graph
# NOTE: it would have no way to connect to the graphs in the model
k_vb = grad(outputs=(kl_div_vb.split(1, 0)), inputs=(detached_splitted_policy_vb), retain_graph=False, only_inputs=True)
g_vb = grad(outputs=(detached_policy_loss_vb.split(1, 0)), inputs=(detached_splitted_policy_vb), retain_graph=False, only_inputs=True)
k_vb = torch.cat(k_vb, 0)
g_vb = torch.cat(g_vb, 0)
kg_dot_vb = (k_vb * g_vb).sum(1, keepdim=True)
kk_dot_vb = (k_vb * k_vb).sum(1, keepdim=True)
z_star_vb = g_vb - ((kg_dot_vb - self.master.clip_1st_order_trpo) / kk_dot_vb).clamp(min=0) * k_vb
return z_star_vb
def _update_global_avg_model(self):
for global_param, global_avg_param in zip(self.master.model.parameters(),
self.master.avg_model.parameters()):
global_avg_param = self.master.avg_model_decay * global_avg_param + \
(1 - self.master.avg_model_decay) * global_param
def _backward(self, unsplitted_policy_vb=None):
on_policy = unsplitted_policy_vb is None
# preparation
rollout_steps = len(self.rollout.reward)
if self.master.enable_continuous:
pass
else:
action_batch_vb = Variable(torch.from_numpy(np.array(self.rollout.action)).view(rollout_steps, -1, 1).long()) # [rollout_steps x batch_size x 1]
if self.master.use_cuda:
action_batch_vb = action_batch_vb.cuda()
if not on_policy: # we save this transformation for on-policy
reward_batch_vb = Variable(torch.from_numpy(np.array(self.rollout.reward)).view(rollout_steps, -1, 1).float()) # [rollout_steps x batch_size x 1]
# NOTE: here we use the detached policies, cos when using 1st order trpo,
# NOTE: the policy losses are not directly backproped into the model
# NOTE: but only backproped up to the output of the network
# NOTE: and to make the code consistent, we also decouple the backprop
# NOTE: into two parts when not using trpo policy update
# NOTE: requires_grad of detached_policy_vb must be True, otherwise grad will not be able to
# NOTE: flow between the two stagets of backprop
if on_policy:
policy_vb = self.rollout.policy_vb
detached_splitted_policy_vb = None
detached_policy_vb = [Variable(self.rollout.policy_vb[i].data, requires_grad=True) for i in range(rollout_steps)] # [rollout_steps x batch_size x action_dim]
else: # NOTE: here rollout.policy_vb is already split by trajectories, we can safely detach and not causing trouble for feed in tuples into grad later
# NOTE: rollout.policy_vb: undetached, splitted -> what we stored during the fake _off_policy_rollout
# NOTE: policy_vb: undetached, batch -> 1. entropy, cos grad from entropy need to flow back through the whole graph 2. the backward of 2nd stage should be computed on this
# NOTE: detached_splitted_policy_vb: detached, splitted |
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