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pype/vendor/pynput/keyboard/_darwin.py | tws0002/pype | 0 | 1610615 | # coding=utf-8
# pynput
# Copyright (C) 2015-2017 <NAME>
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU 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, see <http://www.gnu.org/licenses/>.
"""
The keyboard implementation for *OSX*.
"""
# pylint: disable=C0111
# The documentation is extracted from the base classes
# pylint: disable=R0903
# We implement stubs
import enum
import Quartz
from pynput._util.darwin import (
get_unicode_to_keycode_map,
keycode_context,
keycode_to_string,
ListenerMixin)
from . import _base
class KeyCode(_base.KeyCode):
def _event(self, modifiers, mapping, is_pressed):
"""This key as a *Quartz* event.
:param set modifiers: The currently active modifiers.
:param mapping: The current keyboard mapping.
:param bool is_press: Whether to generate a press event.
:return: a *Quartz* event
"""
vk = self.vk or mapping.get(self.char, 0)
result = Quartz.CGEventCreateKeyboardEvent(None, vk, is_pressed)
Quartz.CGEventSetFlags(
result,
0
| (Quartz.kCGEventFlagMaskAlternate
if Key.alt in modifiers else 0)
| (Quartz.kCGEventFlagMaskCommand
if Key.cmd in modifiers else 0)
| (Quartz.kCGEventFlagMaskControl
if Key.ctrl in modifiers else 0)
| (Quartz.kCGEventFlagMaskShift
if Key.shift in modifiers else 0))
if vk is None and self.char is not None:
Quartz.CGEventKeyboardSetUnicodeString(
result, len(self.char), self.char)
return result
class Key(enum.Enum):
# Default keys
alt = KeyCode.from_vk(0x3A)
alt_l = KeyCode.from_vk(0x3A)
alt_r = KeyCode.from_vk(0x3D)
alt_gr = KeyCode.from_vk(0x3D)
backspace = KeyCode.from_vk(0x33)
caps_lock = KeyCode.from_vk(0x39)
cmd = KeyCode.from_vk(0x37)
cmd_l = KeyCode.from_vk(0x37)
cmd_r = KeyCode.from_vk(0x36)
ctrl = KeyCode.from_vk(0x3B)
ctrl_l = KeyCode.from_vk(0x3B)
ctrl_r = KeyCode.from_vk(0x3E)
delete = KeyCode.from_vk(0x75)
down = KeyCode.from_vk(0x7D)
end = KeyCode.from_vk(0x77)
enter = KeyCode.from_vk(0x24)
esc = KeyCode.from_vk(0x35)
f1 = KeyCode.from_vk(0x7A)
f2 = KeyCode.from_vk(0x78)
f3 = KeyCode.from_vk(0x63)
f4 = KeyCode.from_vk(0x76)
f5 = KeyCode.from_vk(0x60)
f6 = KeyCode.from_vk(0x61)
f7 = KeyCode.from_vk(0x62)
f8 = KeyCode.from_vk(0x64)
f9 = KeyCode.from_vk(0x65)
f10 = KeyCode.from_vk(0x6D)
f11 = KeyCode.from_vk(0x67)
f12 = KeyCode.from_vk(0x6F)
f13 = KeyCode.from_vk(0x69)
f14 = KeyCode.from_vk(0x6B)
f15 = KeyCode.from_vk(0x71)
f16 = KeyCode.from_vk(0x6A)
f17 = KeyCode.from_vk(0x40)
f18 = KeyCode.from_vk(0x4F)
f19 = KeyCode.from_vk(0x50)
f20 = KeyCode.from_vk(0x5A)
home = KeyCode.from_vk(0x73)
left = KeyCode.from_vk(0x7B)
page_down = KeyCode.from_vk(0x79)
page_up = KeyCode.from_vk(0x74)
right = KeyCode.from_vk(0x7C)
shift = KeyCode.from_vk(0x38)
shift_l = KeyCode.from_vk(0x38)
shift_r = KeyCode.from_vk(0x3C)
space = KeyCode.from_vk(0x31, char=' ')
tab = KeyCode.from_vk(0x30)
up = KeyCode.from_vk(0x7E)
class Controller(_base.Controller):
_KeyCode = KeyCode
_Key = Key
def __init__(self):
super(Controller, self).__init__()
self._mapping = get_unicode_to_keycode_map()
def _handle(self, key, is_press):
with self.modifiers as modifiers:
Quartz.CGEventPost(
Quartz.kCGHIDEventTap,
(key if key not in Key else key.value)._event(
modifiers, self._mapping, is_press))
class Listener(ListenerMixin, _base.Listener):
#: The events that we listen to
_EVENTS = (
Quartz.CGEventMaskBit(Quartz.kCGEventKeyDown) |
Quartz.CGEventMaskBit(Quartz.kCGEventKeyUp) |
Quartz.CGEventMaskBit(Quartz.kCGEventFlagsChanged))
#: A mapping from keysym to special key
_SPECIAL_KEYS = {
key.value.vk: key
for key in Key}
#: The event flags set for the various modifier keys
_MODIFIER_FLAGS = {
Key.alt: Quartz.kCGEventFlagMaskAlternate,
Key.alt_l: Quartz.kCGEventFlagMaskAlternate,
Key.alt_r: Quartz.kCGEventFlagMaskAlternate,
Key.cmd: Quartz.kCGEventFlagMaskCommand,
Key.cmd_l: Quartz.kCGEventFlagMaskCommand,
Key.cmd_r: Quartz.kCGEventFlagMaskCommand,
Key.ctrl: Quartz.kCGEventFlagMaskControl,
Key.ctrl_l: Quartz.kCGEventFlagMaskControl,
Key.ctrl_r: Quartz.kCGEventFlagMaskControl,
Key.shift: Quartz.kCGEventFlagMaskShift,
Key.shift_l: Quartz.kCGEventFlagMaskShift,
Key.shift_r: Quartz.kCGEventFlagMaskShift}
def __init__(self, *args, **kwargs):
super(Listener, self).__init__(*args, **kwargs)
self._flags = 0
self._context = None
self._intercept = self._options.get(
'intercept',
None)
def _run(self):
with keycode_context() as context:
self._context = context
try:
super(Listener, self)._run()
finally:
self._context = None
def _handle(self, dummy_proxy, event_type, event, dummy_refcon):
# Convert the event to a KeyCode; this may fail, and in that case we
# pass None
try:
key = self._event_to_key(event)
except IndexError:
key = None
try:
if event_type == Quartz.kCGEventKeyDown:
# This is a normal key press
self.on_press(key)
elif event_type == Quartz.kCGEventKeyUp:
# This is a normal key release
self.on_release(key)
elif key == Key.caps_lock:
# We only get an event when caps lock is toggled, so we fake
# press and release
self.on_press(key)
self.on_release(key)
else:
# This is a modifier event---excluding caps lock---for which we
# must check the current modifier state to determine whether
# the key was pressed or released
flags = Quartz.CGEventGetFlags(event)
is_press = flags & self._MODIFIER_FLAGS.get(key, 0)
if is_press:
self.on_press(key)
else:
self.on_release(key)
finally:
# Store the current flag mask to be able to detect modifier state
# changes
self._flags = Quartz.CGEventGetFlags(event)
def _event_to_key(self, event):
"""Converts a *Quartz* event to a :class:`KeyCode`.
:param event: The event to convert.
:return: a :class:`pynput.keyboard.KeyCode`
:raises IndexError: if the key code is invalid
"""
vk = Quartz.CGEventGetIntegerValueField(
event, Quartz.kCGKeyboardEventKeycode)
# First try special keys...
if vk in self._SPECIAL_KEYS:
return self._SPECIAL_KEYS[vk]
# ...then try characters...
# TODO: Use Quartz.CGEventKeyboardGetUnicodeString instead
char = keycode_to_string(
self._context, vk, Quartz.CGEventGetFlags(event))
if char:
return KeyCode.from_char(char, vk=vk)
# ...and fall back on a virtual key code
return KeyCode.from_vk(vk)
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319,
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515,
6611,
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8426,
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10818,
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426,
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363,
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297,
7670,
29913,
13,
13,
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396,
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450,
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363,
278,
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3709,
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6720,
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1001,
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18823,
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7670,
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24664,
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794,
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19832,
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725,
403,
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13,
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29918,
29880,
29901,
24664,
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29907,
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794,
21979,
19832,
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725,
403,
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13,
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7670,
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29918,
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29901,
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7670,
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7670,
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24220,
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24220,
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794,
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19832,
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29913,
13,
13,
1678,
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4770,
2344,
12035,
1311,
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334,
5085,
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3579,
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2428,
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3962,
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467,
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5085,
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3032,
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13,
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6213,
13,
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6768,
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657,
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13,
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1678,
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14701,
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535,
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278,
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304,
263,
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445,
1122,
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322,
297,
393,
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13,
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1018,
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13,
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1820,
353,
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3032,
3696,
29918,
517,
29918,
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3696,
29897,
13,
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5174,
11374,
2392,
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13,
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1820,
353,
6213,
13,
13,
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1018,
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13,
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565,
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1853,
1275,
24664,
29920,
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29895,
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794,
2558,
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13,
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396,
910,
338,
263,
4226,
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3965,
13,
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265,
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13,
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338,
263,
4226,
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13,
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265,
29918,
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13,
13,
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1275,
7670,
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2547,
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908,
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13,
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396,
1334,
871,
679,
385,
1741,
746,
26091,
7714,
338,
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577,
591,
25713,
13,
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396,
3965,
322,
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13,
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265,
29918,
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1989,
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13,
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1583,
29889,
265,
29918,
14096,
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1989,
29897,
13,
13,
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1683,
29901,
13,
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396,
910,
338,
263,
878,
3709,
1741,
5634,
735,
22368,
26091,
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5634,
1454,
607,
591,
13,
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396,
1818,
1423,
278,
1857,
878,
3709,
2106,
304,
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3692,
13,
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396,
278,
1820,
471,
15385,
470,
5492,
13,
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13449,
353,
24664,
29920,
29889,
29907,
1692,
794,
2577,
15675,
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3696,
29897,
13,
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338,
29918,
2139,
353,
13449,
669,
1583,
3032,
6720,
4571,
3738,
1001,
29918,
18823,
10749,
29889,
657,
29898,
1989,
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29871,
29900,
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13,
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565,
338,
29918,
2139,
29901,
13,
462,
1678,
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29889,
265,
29918,
2139,
29898,
1989,
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13,
18884,
1683,
29901,
13,
462,
1678,
1583,
29889,
265,
29918,
14096,
29898,
1989,
29897,
13,
13,
4706,
7146,
29901,
13,
9651,
396,
14491,
278,
1857,
7353,
11105,
304,
367,
2221,
304,
6459,
878,
3709,
2106,
13,
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396,
3620,
13,
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1583,
3032,
15764,
353,
24664,
29920,
29889,
29907,
1692,
794,
2577,
15675,
29898,
3696,
29897,
13,
13,
1678,
822,
903,
3696,
29918,
517,
29918,
1989,
29898,
1311,
29892,
1741,
1125,
13,
4706,
9995,
1168,
369,
1372,
263,
334,
2182,
442,
29920,
29930,
1741,
304,
263,
584,
1990,
18078,
2558,
3399,
1412,
13,
13,
4706,
584,
3207,
1741,
29901,
450,
1741,
304,
3588,
29889,
13,
13,
4706,
584,
2457,
29901,
263,
584,
1990,
18078,
29886,
948,
649,
29889,
1989,
3377,
29889,
2558,
3399,
29952,
13,
13,
4706,
584,
336,
4637,
11374,
2392,
29901,
565,
278,
1820,
775,
338,
8340,
13,
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13,
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325,
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353,
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794,
2577,
7798,
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13,
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24664,
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3377,
2624,
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401,
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13,
13,
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396,
3824,
1018,
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6611,
856,
13,
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565,
325,
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297,
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3032,
29903,
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8426,
1964,
29918,
10818,
29903,
29901,
13,
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736,
1583,
3032,
29903,
4162,
8426,
1964,
29918,
10818,
29903,
29961,
29894,
29895,
29962,
13,
13,
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396,
2023,
6098,
1018,
4890,
856,
13,
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396,
14402,
29901,
4803,
24664,
29920,
29889,
29907,
1692,
794,
2558,
3377,
2577,
2525,
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1231,
2012,
13,
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1373,
353,
1820,
401,
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517,
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13,
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3032,
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325,
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24664,
29920,
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29907,
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794,
2577,
15675,
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3696,
876,
13,
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565,
1373,
29901,
13,
9651,
736,
7670,
3399,
29889,
3166,
29918,
3090,
29898,
3090,
29892,
325,
29895,
29922,
29894,
29895,
29897,
13,
13,
4706,
396,
2023,
392,
6416,
1250,
373,
263,
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tests/test_provider_Mongey_kafka_connect.py | mjuenema/python-terrascript | 507 | 3451 | # tests/test_provider_Mongey_kafka-connect.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:20:11 UTC)
def test_provider_import():
import terrascript.provider.Mongey.kafka_connect
def test_resource_import():
from terrascript.resource.Mongey.kafka_connect import kafka_connect_connector
# TODO: Shortcut imports without namespace for official and supported providers.
# TODO: This has to be moved into a required_providers block.
# def test_version_source():
#
# import terrascript.provider.Mongey.kafka_connect
#
# t = terrascript.provider.Mongey.kafka_connect.kafka_connect()
# s = str(t)
#
# assert 'https://github.com/Mongey/terraform-provider-kafka-connect' in s
# assert '0.2.3' in s
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plugins/chatinfo.py | HtmlLoverExe/MultiUserbot1 | 0 | 58166 | import configparser
from datetime import datetime
from pyrogram import Client, Filters, Emoji
config = configparser.ConfigParser()
config.read("config.ini")
prefixes = list(config["prefixes"].keys())
chatinfo_message = {"id": f"{Emoji.ID_BUTTON} <b>Id</b>: <code>[%id%]</code>",
"type": f"{Emoji.JAPANESE_SYMBOL_FOR_BEGINNER} <b>Type</b>: <code>[%type%]</code>",
"title": f"{Emoji.FLEUR_DE_LIS} <b>Title</b>: <code>[%title%]</code>",
"invite_link": f"{Emoji.LINK} <b>Invite Link</b>: <code>[%invite_link%]</code>",
"first_name": f"{Emoji.BLOND_HAIRED_MAN_LIGHT_SKIN_TONE} <b>Name</b>: <code>[%first_name%]</code>",
"last_name": f"{Emoji.BUST_IN_SILHOUETTE} <b>Last Name</b>: <code>[%last_name%]</code>",
"username": f"{Emoji.LINK} <b>Username</b>: <code>[%username%]</code>",
"dc_id": f"{Emoji.DESKTOP_COMPUTER} <b>Dc</b>: <code>[%dc_id%]</code>",
"status": f"{Emoji.MOBILE_PHONE_WITH_ARROW} <b>Status</b>: <code>[%status%]</code>",
"last_online_date": f"{Emoji.TWELVE_O_CLOCK} <b>Last Online Date</b>: <code>["
f"%last_online_date%]</code>",
"next_offline_date": f"{Emoji.SEVEN_THIRTY} <b>Next Offline Date</b>: <code>["
f"%next_offline_date%]</code>",
"is_bot": f"{Emoji.ROBOT_FACE} <b>Is Bot</b>: <code>[%is_bot%]</code>",
"is_contact": f"{Emoji.TELEPHONE} <b>Is Contact</b>: <code>[%is_contact%]</code>",
"is_mutual_contact": f"{Emoji.MOBILE_PHONE} <b>Is Mutual Contact</b>: <code>["
f"%is_mutual_contact%]</code>",
"is_scam": f"{Emoji.CROSS_MARK} <b>Is scam</b>: <code>[%is_scam%]</code>",
"sticker_set_name": f"{Emoji.DIAMOND_WITH_A_DOT} <b>Sticker Set</b>: <code>["
f"%sticker_set_name%]</code>",
"members_count": f"{Emoji.FAMILY_MAN_WOMAN_GIRL_BOY} <b>Members</b>: "
f"<code>[%members_count%]</code>",
"bio": f"{Emoji.TRIDENT_EMBLEM} <b>Bio</b>: <code>[%bio%]</code>"}
@Client.on_message(Filters.user("self") & Filters.command("chatinfo", prefixes=prefixes))
def chatinfo_command(c, msg):
target = c.get_chat(msg.chat.id)
message = "{} Info {}\n\n".format(Emoji.INFORMATION, Emoji.INFORMATION)
for key in chatinfo_message:
try:
message += chatinfo_message[key].replace(
f"[%{key}%]", str(
(
target[key] if key != "next_offline_date" and key != "last_online_date" else (
datetime.fromtimestamp(int(target[key])).strftime("%H:%M:%S %d/%m/%y")
)
) if target[key] else (
target["raise AttributeError()"])
)
) + "\n"
except AttributeError:
pass
bio = c.get_chat(target.id).description
if bio:
message += chatinfo_message["bio"].replace("[%bio%]", c.get_chat(target.id).description)
message += f"\n\n<a href=\"tg://user?id={target.id}\">Profile Link</a>"
msg.edit_text(message)
print("[MultiUserbot] Loaded \"chatinfo.py\" plugin")
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thing/awsiot.py | ymtomoyk/smarthome | 0 | 184892 | from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
import logging
MQTT_PORT = 8883
class MyAWSIoTClient:
def __init__ (self, args):
self.args = args
self.client = AWSIoTMQTTClient (args.clientId)
# Configure logging
logger = logging.getLogger ('AWSIoTPythonSDK.core')
logger.setLevel (logging.DEBUG)
streamHandler = logging.StreamHandler ()
formatter = logging.Formatter ('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
streamHandler.setFormatter (formatter)
logger.addHandler (streamHandler)
# Init AWSIoTMQTTClient
self.client.configureEndpoint (self.args.host, MQTT_PORT)
self.client.configureCredentials (self.args.rootCAPath, self.args.privateKeyPath, self.args.certificatePath)
# AWSIoTMQTTClient connection configuration
self.client.configureAutoReconnectBackoffTime (1, 32, 20)
self.client.configureOfflinePublishQueueing (-1) # Infinite offline Publish queueing
self.client.configureDrainingFrequency (2) # Draining: 2 Hz
self.client.configureConnectDisconnectTimeout (100) # 10 sec
self.client.configureMQTTOperationTimeout (5) # 5 sec
def __message_handler (self, client, userdata, message):
print ('Received a new message: ')
print (message.payload)
print ('from topic: ' + message.topic)
try:
self.callback (message.payload)
finally:
print ("--------------\n\n")
def subscribe (self, callback):
self.callback = callback
self.client.connect ()
self.client.subscribe (self.args.topicReq, 1, self.__message_handler)
def publish (self, message):
print message
self.client.publishAsync (self.args.topicRes, message, 1)
def __del__ (self):
self.client.disconnect ()
# to call __del__() explicitly
def delete (self):
self.__del__ ()
del self
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] |
Lib/site-packages/pyexcel_io/writers/tsvz.py | percevalm/aumyproject | 0 | 85126 | <gh_stars>0
"""
pyexcel_io.fileformat.tsvz
~~~~~~~~~~~~~~~~~~~~~~~~~~
The lower level tsvz file format handler.
:copyright: (c) 2014-2017 by Onni Software Ltd.
:license: New BSD License, see LICENSE for more details
"""
from pyexcel_io.constants import FILE_FORMAT_TSVZ, KEYWORD_TSV_DIALECT
from .csvz import CSVZipBookWriter
class TSVZipBookWriter(CSVZipBookWriter):
""" write zipped tsv file
It is similiar to CSVZipBookWriter, but support tab separated values
"""
def __init__(self):
CSVZipBookWriter.__init__(self)
self._file_type = FILE_FORMAT_TSVZ
def open(self, file_name, **keywords):
keywords["dialect"] = KEYWORD_TSV_DIALECT
CSVZipBookWriter.open(self, file_name, **keywords)
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] |
binaryclassifier/statistics/ks.py | bmathias12/model-analysis | 0 | 181186 | import numpy as np
import pandas as pd
def ks_table(y, quantiles):
"""Function to produce a K-S table"""
if len(set(y)) > 2:
raise ValueError('Function only defined for binary classification')
df = pd.concat([pd.Series(y), pd.Series(quantiles)], axis=1)
df.columns = ['y', 'quantiles']
# Get counts of positive and negative values
count_total = df.groupby('quantiles')['y'].count()
count_pos = df.groupby('quantiles')['y'].sum()
count_neg = count_total - count_pos
# Get cumulative percents
pos_pct_cum = count_pos.cumsum() / float(count_pos.sum()) * 100
neg_pct_cum = count_neg.cumsum() / float(count_neg.sum()) * 100
# Calculate KS
ks_idx = np.abs(pos_pct_cum - neg_pct_cum)
# Output table
out_df = pd.concat([
count_total, count_pos, count_neg, pos_pct_cum, neg_pct_cum, ks_idx
], axis=1)
out_df.columns = [
'count_total', 'count_pos', 'count_neg',
'pos_pct_cum', 'neg_pct_cum', 'ks_idx'
]
return out_df | [
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] |
module5/coroutine_pipelines_filter.py | axel-sirota/advanced-generator-and-coroutines | 5 | 186491 | <gh_stars>1-10
from module4 import coroutine
@coroutine
def filter_by_pattern(pattern, target):
print(f'I am going to filter lines by pattern {pattern}')
while True:
line = yield
if pattern in line:
target.send(line)
def iterate_file(file, target):
with open(file) as f:
for line in f:
target.send(line)
print('Im done!')
@coroutine
def coprint():
while True:
line = yield
print(f'-->{line}\n')
| [
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anoncreds/protocol/primary/primary_proof_builder.py | fabienpe/indy-anoncreds | 16 | 72214 | from typing import Sequence, Dict
from anoncreds.protocol.globals import LARGE_VPRIME, LARGE_MVECT, LARGE_E_START, \
LARGE_ETILDE, \
LARGE_VTILDE, LARGE_UTILDE, LARGE_RTILDE, LARGE_ALPHATILDE, ITERATIONS, \
DELTA
from anoncreds.protocol.primary.primary_proof_common import calcTge, calcTeq
from anoncreds.protocol.types import PrimaryClaim, Predicate, PrimaryInitProof, \
PrimaryEqualInitProof, PrimaryPrecicateGEInitProof, PrimaryProof, \
PrimaryEqualProof, PrimaryPredicateGEProof, \
ID, ClaimInitDataType, ClaimAttributeValues
from anoncreds.protocol.utils import splitRevealedAttrs, fourSquares
from anoncreds.protocol.wallet.prover_wallet import ProverWallet
from config.config import cmod
class PrimaryClaimInitializer:
def __init__(self, wallet: ProverWallet):
self._wallet = wallet
async def genClaimInitData(self, schemaId: ID) -> ClaimInitDataType:
pk = await self._wallet.getPublicKey(schemaId)
ms = await self._wallet.getMasterSecret(schemaId)
vprime = cmod.randomBits(LARGE_VPRIME)
N = pk.N
Rms = pk.Rms
S = pk.S
U = (S ** vprime) * (Rms ** ms) % N
return ClaimInitDataType(U=U, vPrime=vprime)
async def preparePrimaryClaim(self, schemaId: ID, claim: PrimaryClaim):
claimInitDat = await self._wallet.getPrimaryClaimInitData(schemaId)
newV = claim.v + claimInitDat.vPrime
claim = claim._replace(v=newV)
return claim
class PrimaryProofBuilder:
def __init__(self, wallet: ProverWallet):
self._wallet = wallet
async def initProof(self, schemaId, c1: PrimaryClaim,
revealedAttrs: Sequence[str],
predicates: Sequence[Predicate],
m1Tilde, m2Tilde, claimAttributes: Dict[str, ClaimAttributeValues]) -> PrimaryInitProof:
if not c1:
return None
eqProof = await self._initEqProof(schemaId, c1, revealedAttrs,
m1Tilde, m2Tilde, claimAttributes)
geProofs = []
for predicate in predicates:
geProof = await self._initGeProof(schemaId, eqProof, c1,
predicate, claimAttributes)
geProofs.append(geProof)
return PrimaryInitProof(eqProof, geProofs)
async def finalizeProof(self, schemaId, cH,
initProof: PrimaryInitProof) -> PrimaryProof:
if not initProof:
return None
cH = cmod.integer(cH)
eqProof = await self._finalizeEqProof(schemaId, cH,
initProof.eqProof)
geProofs = []
for initGeProof in initProof.geProofs:
geProof = await self._finalizeGeProof(schemaId, cH, initGeProof,
eqProof)
geProofs.append(geProof)
return PrimaryProof(eqProof, geProofs)
async def _initEqProof(self, schemaId, c1: PrimaryClaim,
revealedAttrs: Sequence[str], m1Tilde, m2Tilde, claimAttributes: Dict[str, ClaimAttributeValues]) \
-> PrimaryEqualInitProof:
m2Tilde = m2Tilde if m2Tilde else cmod.integer(
cmod.randomBits(LARGE_MVECT))
revealedAttrs, unrevealedAttrs = splitRevealedAttrs(
claimAttributes, [a.name for a in revealedAttrs])
mtilde = self._getMTilde(unrevealedAttrs)
Ra = cmod.integer(cmod.randomBits(LARGE_VPRIME))
pk = await self._wallet.getPublicKey(ID(schemaId=schemaId))
A, e, v = c1.A, c1.e, c1.v
Aprime = A * (pk.S ** Ra) % pk.N
vprime = (v - e * Ra)
eprime = e - (2 ** LARGE_E_START)
etilde = cmod.integer(cmod.randomBits(LARGE_ETILDE))
vtilde = cmod.integer(cmod.randomBits(LARGE_VTILDE))
Rur = 1 % pk.N
for k, value in unrevealedAttrs.items():
if k in claimAttributes:
Rur = Rur * (pk.R[k] ** mtilde[k])
Rur *= pk.Rms ** m1Tilde
Rur *= pk.Rctxt ** m2Tilde
# T = ((Aprime ** etilde) * Rur * (pk.S ** vtilde)) % pk.N
T = calcTeq(pk, Aprime, etilde, vtilde, mtilde, m1Tilde, m2Tilde,
unrevealedAttrs.keys())
return PrimaryEqualInitProof(c1, Aprime, T, etilde, eprime, vtilde,
vprime, mtilde, m1Tilde, m2Tilde,
unrevealedAttrs.keys(), revealedAttrs)
async def _initGeProof(self, schemaId, eqProof: PrimaryEqualInitProof,
c1: PrimaryClaim, predicate: Predicate, claimAttributes: Dict[str, ClaimAttributeValues]) \
-> PrimaryPrecicateGEInitProof:
# gen U for Delta
pk = await self._wallet.getPublicKey(ID(schemaId=schemaId))
k, value = predicate.attrName, predicate.value
delta = claimAttributes[k].encoded - value
if delta < 0:
raise ValueError("Predicate is not satisfied")
u = fourSquares(delta)
# prepare C list
r = {}
T = {}
CList = []
for i in range(0, ITERATIONS):
r[str(i)] = cmod.integer(cmod.randomBits(LARGE_VPRIME))
T[str(i)] = (pk.Z ** u[str(i)]) * (pk.S ** r[str(i)]) % pk.N
CList.append(T[str(i)])
r[DELTA] = cmod.integer(cmod.randomBits(LARGE_VPRIME))
T[DELTA] = (pk.Z ** delta) * (pk.S ** r[DELTA]) % pk.N
CList.append(T[DELTA])
# prepare Tau List
utilde = {}
rtilde = {}
for i in range(0, ITERATIONS):
utilde[str(i)] = cmod.integer(cmod.randomBits(LARGE_UTILDE))
rtilde[str(i)] = cmod.integer(cmod.randomBits(LARGE_RTILDE))
rtilde[DELTA] = cmod.integer(cmod.randomBits(LARGE_RTILDE))
alphatilde = cmod.integer(cmod.randomBits(LARGE_ALPHATILDE))
TauList = calcTge(pk, utilde, rtilde, eqProof.mTilde[k], alphatilde, T)
return PrimaryPrecicateGEInitProof(CList, TauList, u, utilde, r, rtilde,
alphatilde, predicate, T)
async def _finalizeEqProof(self, schemaId, cH,
initProof: PrimaryEqualInitProof) -> PrimaryEqualProof:
e = initProof.eTilde + (cH * initProof.ePrime)
v = initProof.vTilde + (cH * initProof.vPrime)
m = {}
claimAttributes = await self._wallet.getClaimAttributes(ID(schemaId=schemaId))
for k in initProof.unrevealedAttrs:
m[str(k)] = initProof.mTilde[str(k)] + (
cH * claimAttributes[str(k)].encoded)
ms = await self._wallet.getMasterSecret(ID(schemaId=schemaId))
m1 = initProof.m1Tilde + (cH * ms)
m2 = initProof.m2Tilde + (cH * initProof.c1.m2)
return PrimaryEqualProof(e, v, m, m1, m2, initProof.Aprime,
initProof.revealedAttrs)
async def _finalizeGeProof(self, schemaId, cH,
initProof: PrimaryPrecicateGEInitProof,
eqProof: PrimaryEqualProof) \
-> PrimaryPredicateGEProof:
u = {}
r = {}
urproduct = 0
for i in range(0, ITERATIONS):
u[str(i)] = initProof.uTilde[str(i)] + cH * initProof.u[str(i)]
r[str(i)] = initProof.rTilde[str(i)] + cH * initProof.r[str(i)]
urproduct += initProof.u[str(i)] * initProof.r[str(i)]
r[DELTA] = initProof.rTilde[DELTA] + cH * initProof.r[DELTA]
alpha = initProof.alphaTilde + cH * (initProof.r[DELTA] - urproduct)
k = initProof.predicate.attrName
return PrimaryPredicateGEProof(u, r, alpha, eqProof.m[str(k)],
initProof.T, initProof.predicate)
def _getMTilde(self, unrevealedAttrs):
mtilde = {}
for key, value in unrevealedAttrs.items():
mtilde[key] = cmod.integer(cmod.randomBits(LARGE_MVECT))
return mtilde
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] |
defx/metrics/f1_measure.py | DFKI-NLP/defx | 5 | 32220 | <reponame>DFKI-NLP/defx<filename>defx/metrics/f1_measure.py
from statistics import mean
from typing import Dict, List, Optional, Set
from collections import defaultdict
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import get_lengths_from_binary_sequence_mask
from allennlp.data.vocabulary import Vocabulary
from allennlp.training.metrics.metric import Metric
from defx.util.index_to_relation_and_type_mapping import map_index_to_relation_head_and_type
@Metric.register("f1-measure")
class F1Measure(Metric):
"""
Computes macro-averaged F1 score for relation classification on a token level.
"""
def __init__(self,
vocabulary: Vocabulary,
negative_label: str,
label_namespace: str = "labels",
evaluated_labels: List[str] = None) -> None:
self._label_vocabulary = vocabulary.get_index_to_token_vocabulary(label_namespace)
if evaluated_labels is None:
self.evaluated_labels = list(self._label_vocabulary.values())
self.evaluated_labels.remove(negative_label)
else:
self.evaluated_labels = evaluated_labels
self._true_positives: Dict[str, int] = defaultdict(int)
self._false_positives: Dict[str, int] = defaultdict(int)
self._false_negatives: Dict[str, int] = defaultdict(int)
self._support: Dict[str, int] = defaultdict(int)
assert self._label_vocabulary[0] == negative_label, 'Negative label should have index 0'
self._negative_label_idx = 0
def __call__(self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.Tensor] = None):
"""
Parameters
----------
predictions : ``torch.Tensor``, required.
A tensor of predictions of shape (batch_size, sequence_length, num_classes).
gold_labels : ``torch.Tensor``, required.
A tensor of integer class label of shape (batch_size, sequence_length).
It must be the same shape as the ``predictions`` tensor without the
``num_classes`` dimension.
mask: ``torch.Tensor``, optional (default = None).
A masking tensor the same size as ``gold_labels``.
"""
if mask is None:
mask = torch.ones_like(gold_labels)
predictions, gold_labels, mask = self.unwrap_to_tensors(predictions,
gold_labels,
mask)
num_classes = predictions.size(-1)
if (gold_labels >= num_classes).any():
raise ConfigurationError("A gold label passed to SpanBasedF1Measure contains an "
"id >= {}, the number of classes.".format(num_classes))
sequence_lengths = get_lengths_from_binary_sequence_mask(mask)
argmax_predictions = predictions.max(-1)[1]
# Iterate over timesteps in batch.
batch_size = gold_labels.size(0)
for i in range(batch_size):
sequence_prediction = argmax_predictions[i, :]
sequence_gold_label = gold_labels[i, :]
length = sequence_lengths[i]
if length == 0:
# It is possible to call this metric with sequences which are
# completely padded. These contribute nothing, so we skip these rows.
continue
predicted_tuples = []
gold_tuples = []
for token_idx in range(length):
pred_head_and_type_idx = sequence_prediction[token_idx].item()
pred_head_and_type = map_index_to_relation_head_and_type(
label_vocab=self._label_vocabulary,
head_and_type_idx=pred_head_and_type_idx
)
pred_head, pred_label = pred_head_and_type
if pred_label in self.evaluated_labels:
predicted_tuples.append(
(token_idx, pred_head, pred_label)
)
gold_head_and_type_idx = sequence_gold_label[token_idx].item()
gold_head_and_type = map_index_to_relation_head_and_type(
self._label_vocabulary,
gold_head_and_type_idx
)
gold_head, gold_label = gold_head_and_type
if gold_label in self.evaluated_labels:
gold_tuples.append(
(token_idx, gold_head, gold_label)
)
self._support[gold_label] += 1
for idx_head_and_type in predicted_tuples:
relation_type = idx_head_and_type[2]
if idx_head_and_type in gold_tuples:
self._true_positives[relation_type] += 1
gold_tuples.remove(idx_head_and_type)
else:
self._false_positives[relation_type] += 1
# These tokens weren't predicted.
for idx_head_and_type in gold_tuples:
self._false_negatives[idx_head_and_type[2]] += 1
def get_metric(self, reset: bool = False):
"""
Returns
-------
A Dict per label containing following the span based metrics:
precision : float
recall : float
f1-measure : float
Additionally, an ``overall`` key is included, which provides the precision,
recall and f1-measure for all spans.
"""
all_tags: Set[str] = set()
all_tags.update(self._true_positives.keys())
all_tags.update(self._false_positives.keys())
all_tags.update(self._false_negatives.keys())
all_metrics = {}
overall_precision_values = []
overall_recall_values = []
overall_f1_values = []
for tag in all_tags:
precision, recall, f1_measure = self._compute_metrics(self._true_positives[tag],
self._false_positives[tag],
self._false_negatives[tag])
precision_key = "precision" + "-" + tag
recall_key = "recall" + "-" + tag
f1_key = "f1-measure" + "-" + tag
support_key = "support" + "-" + tag
all_metrics[precision_key] = precision
all_metrics[recall_key] = recall
all_metrics[f1_key] = f1_measure
all_metrics[support_key] = self._support[tag]
if tag in self.evaluated_labels:
overall_precision_values.append(precision)
overall_recall_values.append(recall)
overall_f1_values.append(f1_measure)
# If no samples are given, simply return 0
if len(overall_precision_values) < 1:
all_metrics["precision-overall"] = 0
all_metrics["recall-overall"] = 0
all_metrics["f1-measure-overall"] = 0
else:
all_metrics["precision-overall"] = mean(overall_precision_values)
all_metrics["recall-overall"] = mean(overall_recall_values)
all_metrics["f1-measure-overall"] = mean(overall_f1_values)
if reset:
self.reset()
return all_metrics
@staticmethod
def _compute_metrics(true_positives: int, false_positives: int, false_negatives: int):
precision = float(true_positives) / float(true_positives + false_positives + 1e-13)
recall = float(true_positives) / float(true_positives + false_negatives + 1e-13)
f1_measure = 2. * ((precision * recall) / (precision + recall + 1e-13))
return precision, recall, f1_measure
def reset(self):
self._true_positives = defaultdict(int)
self._false_positives = defaultdict(int)
self._false_negatives = defaultdict(int)
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] |
src/dlg_windows.py | seung-lab/SBEMimage | 0 | 48752 | <gh_stars>0
# -*- coding: utf-8 -*-
# ==============================================================================
# SBEMimage, ver. 2.0
# Acquisition control software for serial block-face electron microscopy
# (c) 2018-2019 <NAME> Institute for Biomedical Research, Basel.
# This software is licensed under the terms of the MIT License.
# See LICENSE.txt in the project root folder.
# ==============================================================================
"""This module contains all dialog windows."""
import os
import re
import string
import threading
import datetime
import glob
import json
import validators
import requests
import shutil
from random import random
from time import sleep, time
from validate_email import validate_email
from math import atan, sqrt
from queue import Queue
from PIL import Image
from skimage.io import imread
from skimage.feature import register_translation
import numpy as np
from imreg_dft import translation
from zipfile import ZipFile
from PyQt5.uic import loadUi
from PyQt5.QtCore import Qt, QObject, QSize, pyqtSignal
from PyQt5.QtGui import QPixmap, QIcon, QPalette, QColor, QFont
from PyQt5.QtWidgets import QApplication, QDialog, QMessageBox, \
QFileDialog, QLineEdit, QDialogButtonBox
import utils
import acq_func
class Trigger(QObject):
"""Custom signal for updating GUI from within running threads."""
s = pyqtSignal()
#------------------------------------------------------------------------------
class ConfigDlg(QDialog):
"""Ask the user to select a configuration file. The previously used
configuration is preselected in the list widget. If no previously used
configuration found, use default.ini. If status.dat does not exists,
show warning message box."""
def __init__(self, VERSION):
super().__init__()
loadUi('..\\gui\\config_dlg.ui', self)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.label_version.setText('Version ' + VERSION)
self.labelIcon.setPixmap(QPixmap('..\\img\\logo.png'))
self.label_website.setText('<a href="https://github.com/SBEMimage">'
'https://github.com/SBEMimage</a>')
self.label_website.setOpenExternalLinks(True)
self.setFixedSize(self.size())
self.show()
self.abort = False
# Populate the list widget with existing .ini files
inifile_list = []
for file in os.listdir('..\\cfg'):
if file.endswith('.ini'):
inifile_list.append(file)
self.listWidget_filelist.addItems(inifile_list)
# Which .ini file was used previously? Check in status.dat
if os.path.isfile('..\\cfg\\status.dat'):
status_file = open('..\\cfg\\status.dat', 'r')
last_inifile = status_file.readline()
status_file.close()
try:
last_item_used = self.listWidget_filelist.findItems(
last_inifile, Qt.MatchExactly)[0]
self.listWidget_filelist.setCurrentItem(last_item_used)
except:
# If the file indicated in status.dat does not exist,
# select default.ini.
# This dialog is called from SBEMimage.py only if default.ini
# is found in cfg directory.
default_item = self.listWidget_filelist.findItems(
'default.ini', Qt.MatchExactly)[0]
self.listWidget_filelist.setCurrentItem(default_item)
else:
# If status.dat does not exists, the program crashed or a second
# instance is running. Display a warning and select default.ini
default_item = self.listWidget_filelist.findItems(
'default.ini', Qt.MatchExactly)[0]
self.listWidget_filelist.setCurrentItem(default_item)
QMessageBox.warning(
self, 'Problem detected: Crash or other SBEMimage instance '
'running',
'WARNING: SBEMimage appears to have crashed during the '
'previous run, or there is already another instance of '
'SBEMimage running. Please either close the other instance '
'or abort this one.\n\n'
'If you are restarting a stack after a crash, doublecheck '
'all settings before restarting!',
QMessageBox.Ok)
def reject(self):
self.abort = True
super().reject()
def get_ini_file(self):
if not self.abort:
return self.listWidget_filelist.currentItem().text()
else:
return 'abort'
#------------------------------------------------------------------------------
class SaveConfigDlg(QDialog):
"""Save current configuration in a new config file."""
def __init__(self):
super().__init__()
loadUi('..\\gui\\save_config_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.lineEdit_cfgFileName.setText('')
self.file_name = None
def get_file_name(self):
return self.file_name
def accept(self):
# Replace spaces in file name with underscores.
without_spaces = self.lineEdit_cfgFileName.text().replace(' ', '_')
self.lineEdit_cfgFileName.setText(without_spaces)
# Check whether characters in name are permitted.
# Use may not overwrite "default.ini"
reg = re.compile('^[a-zA-Z0-9_-]+$')
if (reg.match(self.lineEdit_cfgFileName.text())
and self.lineEdit_cfgFileName.text().lower != 'default'):
self.file_name = self.lineEdit_cfgFileName.text() + '.ini'
super().accept()
else:
QMessageBox.warning(
self, 'Error',
'Name contains forbidden characters.',
QMessageBox.Ok)
#------------------------------------------------------------------------------
class SEMSettingsDlg(QDialog):
"""Let user change SEM beam settings (target EHT, taget beam current).
Display current working distance and stigmation.
"""
def __init__(self, sem):
super().__init__()
self.sem = sem
loadUi('..\\gui\\sem_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Display current target settings:
self.doubleSpinBox_EHT.setValue(self.sem.get_eht())
self.spinBox_beamCurrent.setValue(self.sem.get_beam_current())
# Display current focus/stig:
self.lineEdit_currentFocus.setText(
'{0:.6f}'.format(sem.get_wd() * 1000))
self.lineEdit_currentStigX.setText('{0:.6f}'.format(sem.get_stig_x()))
self.lineEdit_currentStigY.setText('{0:.6f}'.format(sem.get_stig_y()))
def accept(self):
self.sem.set_eht(self.doubleSpinBox_EHT.value())
self.sem.set_beam_current(self.spinBox_beamCurrent.value())
super().accept()
#------------------------------------------------------------------------------
class MicrotomeSettingsDlg(QDialog):
"""Adjust stage motor limits and wait interval after stage moves."""
def __init__(self, microtome, sem, microtome_active=True):
super().__init__()
self.microtome = microtome
self.sem = sem
self.microtom_active = microtome_active
loadUi('..\\gui\\microtome_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# If microtome not active, change selection label:
if microtome_active:
self.label_selectedStage.setText('Microtome stage active.')
# Display settings that can only be changed in DM:
self.lineEdit_knifeCutSpeed.setText(
str(self.microtome.get_knife_cut_speed()))
self.lineEdit_knifeRetractSpeed.setText(
str(self.microtome.get_knife_retract_speed()))
self.checkBox_useOscillation.setChecked(
self.microtome.is_oscillation_enabled())
# Settings changeable in GUI:
self.doubleSpinBox_waitInterval.setValue(
self.microtome.get_stage_move_wait_interval())
current_motor_limits = self.microtome.get_motor_limits()
current_calibration = self.microtome.get_stage_calibration()
speed_x, speed_y = self.microtome.get_motor_speeds()
else:
self.label_selectedStage.setText('SEM stage active.')
# Display stage limits. Not editable for SEM.
self.spinBox_stageMaxX.setMaximum(200000)
self.spinBox_stageMaxY.setMaximum(200000)
self.spinBox_stageMinX.setMaximum(0)
self.spinBox_stageMinY.setMaximum(0)
self.spinBox_stageMinX.setEnabled(False)
self.spinBox_stageMaxX.setEnabled(False)
self.spinBox_stageMinY.setEnabled(False)
self.spinBox_stageMaxY.setEnabled(False)
current_motor_limits = self.sem.get_motor_limits()
current_calibration = self.sem.get_stage_calibration()
speed_x, speed_y = self.sem.get_motor_speeds()
self.doubleSpinBox_waitInterval.setValue(
self.sem.get_stage_move_wait_interval())
# Show current calibration:
self.spinBox_stageMinX.setValue(current_motor_limits[0])
self.spinBox_stageMaxX.setValue(current_motor_limits[1])
self.spinBox_stageMinY.setValue(current_motor_limits[2])
self.spinBox_stageMaxY.setValue(current_motor_limits[3])
# Other settings that can be changed in SBEMimage,
# but in a different dialog (CalibrationDgl):
self.lineEdit_scaleFactorX.setText(str(current_calibration[0]))
self.lineEdit_scaleFactorY.setText(str(current_calibration[1]))
self.lineEdit_rotationX.setText(str(current_calibration[2]))
self.lineEdit_rotationY.setText(str(current_calibration[3]))
# Motor speeds:
self.lineEdit_speedX.setText(str(speed_x))
self.lineEdit_speedY.setText(str(speed_y))
def accept(self):
if self.microtom_active:
self.microtome.set_stage_move_wait_interval(
self.doubleSpinBox_waitInterval.value())
self.microtome.set_motor_limits([
self.spinBox_stageMinX.value(), self.spinBox_stageMaxX.value(),
self.spinBox_stageMinY.value(), self.spinBox_stageMaxY.value()])
else:
self.sem.set_stage_move_wait_interval(
self.doubleSpinBox_waitInterval.value())
super().accept()
#------------------------------------------------------------------------------
class KatanaSettingsDlg(QDialog):
"""Adjust settings for the katana microtome."""
def __init__(self, microtome):
super().__init__()
self.microtome = microtome
loadUi('..\\gui\\katana_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Set up COM port selector
self.comboBox_portSelector.addItems(utils.get_serial_ports())
self.comboBox_portSelector.setCurrentIndex(0)
self.comboBox_portSelector.currentIndexChanged.connect(
self.reconnect)
self.display_connection_status()
self.display_current_settings()
def reconnect(self):
pass
def display_connection_status(self):
# Show message in dialog whether or not katana is connected.
pal = QPalette(self.label_connectionStatus.palette())
if self.microtome.connected:
# Use red colour if not connected
pal.setColor(QPalette.WindowText, QColor(Qt.black))
self.label_connectionStatus.setPalette(pal)
self.label_connectionStatus.setText('katana microtome connected.')
else:
pal.setColor(QPalette.WindowText, QColor(Qt.red))
self.label_connectionStatus.setPalette(pal)
self.label_connectionStatus.setText('katana microtome is not connected.')
def display_current_settings(self):
self.spinBox_knifeCutSpeed.setValue(
self.microtome.get_knife_cut_speed())
self.spinBox_knifeFastSpeed.setValue(
self.microtome.get_knife_fast_speed())
cut_window_start, cut_window_end = self.microtome.get_cut_window()
self.spinBox_cutWindowStart.setValue(cut_window_start)
self.spinBox_cutWindowEnd.setValue(cut_window_end)
self.checkBox_useOscillation.setChecked(
self.microtome.is_oscillation_enabled())
self.spinBox_oscAmplitude.setValue(
self.microtome.get_oscillation_amplitude())
self.spinBox_oscFrequency.setValue(
self.microtome.get_oscillation_frequency())
if not self.microtome.simulation_mode and self.microtome.connected:
self.doubleSpinBox_zPosition.setValue(self.microtome.get_stage_z())
z_range_min, z_range_max = self.microtome.get_stage_z_range()
self.doubleSpinBox_zRangeMin.setValue(z_range_min)
self.doubleSpinBox_zRangeMax.setValue(z_range_max)
# Retraction clearance is stored in nanometres, display in micrometres
self.doubleSpinBox_retractClearance.setValue(
self.microtome.get_retract_clearance() / 1000)
def accept(self):
new_cut_speed = self.spinBox_knifeCutSpeed.value()
new_fast_speed = self.spinBox_knifeFastSpeed.value()
new_cut_start = self.spinBox_cutWindowStart.value()
new_cut_end = self.spinBox_cutWindowEnd.value()
new_osc_frequency = self.spinBox_oscFrequency.value()
new_osc_amplitude = self.spinBox_oscAmplitude. value()
# retract_clearance in nanometres
new_retract_clearance = (
self.doubleSpinBox_retractClearance.value() * 1000)
# End position of cut window must be smaller than start position:
if new_cut_end < new_cut_start:
self.microtome.set_knife_cut_speed(new_cut_speed)
self.microtome.set_knife_fast_speed(new_fast_speed)
self.microtome.set_cut_window(new_cut_start, new_cut_end)
self.microtome.set_oscillation_enabled(
self.checkBox_useOscillation.isChecked())
self.microtome.set_oscillation_frequency(new_osc_frequency)
self.microtome.set_oscillation_amplitude(new_osc_amplitude)
self.microtome.set_retract_clearance(new_retract_clearance)
super().accept()
else:
QMessageBox.warning(
self, 'Invalid input',
'The start position of the cutting window must be larger '
'than the end position.',
QMessageBox.Ok)
#------------------------------------------------------------------------------
class StageCalibrationDlg(QDialog):
"""Calibrate the stage (rotation and scaling) and the motor speeds."""
def __init__(self, config, stage, sem):
super().__init__()
self.base_dir = config['acq']['base_dir']
self.stage = stage
self.sem = sem
self.current_eht = self.sem.get_eht()
self.x_shift_vector = [0, 0]
self.y_shift_vector = [0, 0]
self.finish_trigger = Trigger()
self.finish_trigger.s.connect(self.process_results)
self.update_calc_trigger = Trigger()
self.update_calc_trigger.s.connect(self.update_log)
self.calc_exception = None
self.busy = False
loadUi('..\\gui\\stage_calibration_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.arrow_symbol1.setPixmap(QPixmap('..\\img\\arrow.png'))
self.arrow_symbol2.setPixmap(QPixmap('..\\img\\arrow.png'))
self.lineEdit_EHT.setText('{0:.2f}'.format(self.current_eht))
params = self.stage.get_stage_calibration()
self.doubleSpinBox_stageScaleFactorX.setValue(params[0])
self.doubleSpinBox_stageScaleFactorY.setValue(params[1])
self.doubleSpinBox_stageRotationX.setValue(params[2])
self.doubleSpinBox_stageRotationY.setValue(params[3])
speed_x, speed_y = self.stage.get_motor_speeds()
self.doubleSpinBox_motorSpeedX.setValue(speed_x)
self.doubleSpinBox_motorSpeedY.setValue(speed_y)
self.comboBox_dwellTime.addItems(map(str, self.sem.DWELL_TIME))
self.comboBox_dwellTime.setCurrentIndex(4)
self.comboBox_package.addItems(['imreg_dft', 'skimage'])
self.pushButton_startImageAcq.clicked.connect(
self.start_calibration_procedure)
if config['sys']['simulation_mode'] == 'True':
self.pushButton_startImageAcq.setEnabled(False)
self.pushButton_helpCalibration.clicked.connect(self.show_help)
self.pushButton_calcStage.clicked.connect(
self.calculate_stage_parameters_from_user_input)
self.pushButton_calcMotor.clicked.connect(
self.calculate_motor_parameters)
def calculate_motor_parameters(self):
"""Calculate the motor speeds from the duration measurements provided
by the user and let user confirm the new speeds."""
duration_x = self.doubleSpinBox_durationX.value()
duration_y = self.doubleSpinBox_durationY.value()
motor_speed_x = 1000 / duration_x
motor_speed_y = 1000 / duration_y
user_choice = QMessageBox.information(
self, 'Calculated parameters',
'Results:\nMotor speed X: ' + '{0:.2f}'.format(motor_speed_x)
+ ';\nMotor speed Y: ' + '{0:.2f}'.format(motor_speed_y)
+ '\n\nDo you want to use these values?',
QMessageBox.Ok | QMessageBox.Cancel)
if user_choice == QMessageBox.Ok:
self.doubleSpinBox_motorSpeedX.setValue(motor_speed_x)
self.doubleSpinBox_motorSpeedY.setValue(motor_speed_y)
def show_help(self):
QMessageBox.information(
self, 'Stage calibration procedure',
'If you click on "Start automatic calibration", '
'three images will be acquired and saved in the current base '
'directory: start.tif, shift_x.tif, shift_y.tif. '
'You can set the pixel size and dwell time for these images and '
'specify how far the stage should move along the X and the Y axis. '
'The X/Y moves must be small enough to allow some overlap between '
'the test images. The frame size is set automatically. '
'Make sure that structure is visible in the images, and that the '
'beam is focused.\n'
'The current stage position will be used as the starting '
'position. The recommended starting position is the centre of the '
'stage (0, 0).\n'
'Shift vectors between the acquired images will be computed using '
'a function from the selected package (imreg_dft or skimage). '
'Angles and scale factors will then be computed from these '
'shifts.\n\n'
'Alternatively, you can manually provide the pixel shifts by '
'looking at the calibration images start.tif, shift_x.tif, and '
'shift_y.tif and measuring the difference (with ImageJ, for '
'example) in the XY pixel position for some feature in the image. '
'Click on "Calculate" to calculate the calibration parameters '
'from these shifts.',
QMessageBox.Ok)
def start_calibration_procedure(self):
"""Acquire three images to be used for the stage calibration"""
# TODO: error handling!
reply = QMessageBox.information(
self, 'Start calibration procedure',
'This will acquire three images and save them in the current base '
'directory: start.tif, shift_x.tif, shift_y.tif. '
'Structure must be visible in the images, and the beam must be '
'focused.\nThe current stage position will be used as the starting '
'position. The recommended starting position is the centre of the '
'stage (0, 0). Angles and scale factors will be computed from the '
'shifts between the acquired test images.\n'
'Proceed?',
QMessageBox.Ok | QMessageBox.Cancel)
if reply == QMessageBox.Ok:
# Show update in text field:
self.busy = True
self.plainTextEdit_calibLog.setPlainText('Acquiring images...')
self.pushButton_startImageAcq.setText('Busy')
self.pushButton_startImageAcq.setEnabled(False)
self.pushButton_calcStage.setEnabled(False)
thread = threading.Thread(target=self.stage_calibration_acq_thread)
thread.start()
def stage_calibration_acq_thread(self):
"""Acquisition thread for three images used for the stage calibration.
Frame settings are fixed for now. Currently no error handling.
XY shifts are computed from images.
"""
shift = self.spinBox_shift.value()
pixel_size = self.spinBox_pixelsize.value()
dwell_time = self.sem.DWELL_TIME[self.comboBox_dwellTime.currentIndex()]
# Use frame size 4 if available, otherwise 3:
if len(self.sem.STORE_RES) > 4:
# Merlin
frame_size_selector = 4
else:
# Sigma
frame_size_selector = 3
self.sem.apply_frame_settings(
frame_size_selector, pixel_size, dwell_time)
start_x, start_y = self.stage.get_xy()
# First image:
self.sem.acquire_frame(self.base_dir + '\\start.tif')
# X shift:
self.stage.move_to_xy((start_x + shift, start_y))
# Second image:
self.sem.acquire_frame(self.base_dir + '\\shift_x.tif')
# Y shift:
self.stage.move_to_xy((start_x, start_y + shift))
# Third image:
self.sem.acquire_frame(self.base_dir + '\\shift_y.tif')
# Back to initial position:
self.stage.move_to_xy((start_x, start_y))
# Show in log that calculations begin:
self.update_calc_trigger.s.emit()
# Load images
start_img = imread(self.base_dir + '\\start.tif', as_gray=True)
shift_x_img = imread(self.base_dir + '\\shift_x.tif', as_gray=True)
shift_y_img = imread(self.base_dir + '\\shift_y.tif', as_gray=True)
self.calc_exception = None
try:
if self.comboBox_package.currentIndex() == 0: # imreg_dft selected
# [::-1] to use x, y, z order
x_shift = translation(
start_img, shift_x_img, filter_pcorr=3)['tvec'][::-1]
y_shift = translation(
start_img, shift_y_img, filter_pcorr=3)['tvec'][::-1]
else: # use skimage.register_translation
x_shift = register_translation(start_img, shift_x_img)[0][::-1]
y_shift = register_translation(start_img, shift_y_img)[0][::-1]
self.x_shift_vector = [x_shift[0], x_shift[1]]
self.y_shift_vector = [y_shift[0], y_shift[1]]
except Exception as e:
self.calc_exception = e
self.finish_trigger.s.emit()
def update_log(self):
self.plainTextEdit_calibLog.appendPlainText(
'Now computing pixel shifts...')
def process_results(self):
self.pushButton_startImageAcq.setText('Start')
self.pushButton_startImageAcq.setEnabled(True)
self.pushButton_calcStage.setEnabled(True)
if self.calc_exception is None:
# Show the vectors in the textbox and the spinboxes:
self.plainTextEdit_calibLog.setPlainText(
'Shift_X: [{0:.1f}, {1:.1f}], '
'Shift_Y: [{2:.1f}, {3:.1f}]'.format(
*self.x_shift_vector, *self.y_shift_vector))
# Absolute values for the GUI
self.spinBox_x2x.setValue(abs(self.x_shift_vector[0]))
self.spinBox_x2y.setValue(abs(self.x_shift_vector[1]))
self.spinBox_y2x.setValue(abs(self.y_shift_vector[0]))
self.spinBox_y2y.setValue(abs(self.y_shift_vector[1]))
# Now calculate parameters:
self.calculate_stage_parameters()
else:
QMessageBox.warning(
self, 'Error',
'An exception occured while computing the translations: '
+ str(self.calc_exception),
QMessageBox.Ok)
self.busy = False
def calculate_stage_parameters(self):
shift = self.spinBox_shift.value()
pixel_size = self.spinBox_pixelsize.value()
# Use absolute values for now, TODO: revisit for the Sigma stage
delta_xx, delta_xy = (
abs(self.x_shift_vector[0]), abs(self.x_shift_vector[1]))
delta_yx, delta_yy = (
abs(self.y_shift_vector[0]), abs(self.y_shift_vector[1]))
# Rotation angles:
rot_x = atan(delta_xy/delta_xx)
rot_y = atan(delta_yx/delta_yy)
# Scale factors:
scale_x = shift / (sqrt(delta_xx**2 + delta_xy**2) * pixel_size / 1000)
scale_y = shift / (sqrt(delta_yx**2 + delta_yy**2) * pixel_size / 1000)
# alternative calc
# x_abs = np.linalg.norm([self.x_shift_vector[0], self.x_shift_vector[1]])
# y_abs = np.linalg.norm([self.y_shift_vector[0], self.y_shift_vector[1]])
# rot_x_alt = np.arccos(shift * self.x_shift_vector[0] / (shift * x_abs))
# rot_y_alt = np.arccos(shift * self.y_shift_vector[1] / (shift * y_abs))
# GUI cannot handle negative values
# if rot_x < 0:
# rot_x += 2 * 3.141592
# if rot_y < 0:
# rot_y += 2 * 3.141592
# Scale factors:
# scale_x_alt = shift / (x_abs * pixel_size / 1000)
# scale_y_alt = shift / (y_abs * pixel_size / 1000)
self.busy = False
user_choice = QMessageBox.information(
self, 'Calculated parameters',
'Results:\n'
+ 'Scale factor X: ' + '{0:.5f}'.format(scale_x)
+ ';\nScale factor Y: ' + '{0:.5f}'.format(scale_y)
+ '\nRotation X: ' + '{0:.5f}'.format(rot_x)
+ ';\nRotation Y: ' + '{0:.5f}'.format(rot_y)
+ '\n\nDo you want to use these values?',
QMessageBox.Ok | QMessageBox.Cancel)
if user_choice == QMessageBox.Ok:
self.doubleSpinBox_stageScaleFactorX.setValue(scale_x)
self.doubleSpinBox_stageScaleFactorY.setValue(scale_y)
self.doubleSpinBox_stageRotationX.setValue(rot_x)
self.doubleSpinBox_stageRotationY.setValue(rot_y)
def calculate_stage_parameters_from_user_input(self):
"""Calculate the rotation angles and scale factors from the user input.
The user provides the pixel position of any object that can be
identified in all three acquired test images. From this, the program
calculates the difference the object was shifted in pixels, and the
angle with respect to the x or y axis.
"""
# Pixel positions:
x1x = self.spinBox_x1x.value()
x1y = self.spinBox_x1y.value()
x2x = self.spinBox_x2x.value()
x2y = self.spinBox_x2y.value()
y1x = self.spinBox_y1x.value()
y1y = self.spinBox_y1y.value()
y2x = self.spinBox_y2x.value()
y2y = self.spinBox_y2y.value()
# Distances in pixels
delta_xx = abs(x1x - x2x)
delta_xy = abs(x1y - x2y)
delta_yx = abs(y1x - y2x)
delta_yy = abs(y1y - y2y)
if delta_xx == 0 or delta_yy == 0:
QMessageBox.warning(
self, 'Error computing stage calibration',
'Please check your input values.',
QMessageBox.Ok)
else:
self.plainTextEdit_calibLog.setPlainText(
'Using pixel shifts specified on the right side as input...')
self.x_shift_vector = [delta_xx, delta_xy]
self.y_shift_vector = [delta_yx, delta_yy]
self.calculate_stage_parameters()
def accept(self):
if not self.busy:
stage_params = [
self.doubleSpinBox_stageScaleFactorX.value(),
self.doubleSpinBox_stageScaleFactorY.value(),
self.doubleSpinBox_stageRotationX.value(),
self.doubleSpinBox_stageRotationY.value()]
self.stage.set_stage_calibration(self.current_eht, stage_params)
success = self.stage.set_motor_speeds(
self.doubleSpinBox_motorSpeedX.value(),
self.doubleSpinBox_motorSpeedY.value())
if not success:
QMessageBox.warning(
self, 'Error updating motor speeds',
'Motor calibration could not be updated in DM script.',
QMessageBox.Ok)
super().accept()
def reject(self):
if not self.busy:
super().reject()
def closeEvent(self, event):
if not self.busy:
event.accept()
else:
event.ignore()
#------------------------------------------------------------------------------
class MagCalibrationDlg(QDialog):
"""Calibrate the relationship between magnification and pixel size."""
def __init__(self, sem, ovm):
super().__init__()
self.sem = sem
self.ovm = ovm
loadUi('..\\gui\\mag_calibration_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.spinBox_calibrationFactor.setValue(
self.sem.get_mag_px_size_factor())
self.comboBox_frameWidth.addItems(['2048', '4096'])
self.comboBox_frameWidth.setCurrentIndex(1)
self.pushButton_calculate.clicked.connect(
self.calculate_calibration_factor)
def calculate_calibration_factor(self):
"""Calculate the mag calibration factor from the frame width, the
magnification and the pixel size.
"""
frame_width = int(str(self.comboBox_frameWidth.currentText()))
pixel_size = self.doubleSpinBox_pixelSize.value()
mag = self.spinBox_mag.value()
new_factor = mag * frame_width * pixel_size
user_choice = QMessageBox.information(
self, 'Calculated calibration factor',
'Result:\nNew magnification calibration factor: %d '
'\n\nDo you want to use this value?' % new_factor,
QMessageBox.Ok | QMessageBox.Cancel)
if user_choice == QMessageBox.Ok:
self.spinBox_calibrationFactor.setValue(new_factor)
def accept(self):
self.sem.set_mag_px_size_factor(
self.spinBox_calibrationFactor.value())
# Update the magnifications of all OVs:
self.ovm.calculate_ov_mag_from_pixel_size()
super().accept()
#------------------------------------------------------------------------------
class CutDurationDlg(QDialog):
def __init__(self, microtome):
super().__init__()
self.microtome = microtome
loadUi('..\\gui\\cut_duration_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.doubleSpinBox_cutDuration.setValue(
self.microtome.get_full_cut_duration())
def accept(self):
self.microtome.set_full_cut_duration(
self.doubleSpinBox_cutDuration.value())
super().accept()
#------------------------------------------------------------------------------
class OVSettingsDlg(QDialog):
"""Let the user change all settings for each overview image."""
def __init__(self, ovm, sem, current_ov,
main_window_queue, main_window_trigger):
super().__init__()
self.ovm = ovm
self.sem = sem
self.current_ov = current_ov
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
loadUi('..\\gui\\overview_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Set up OV selector:
self.comboBox_OVSelector.addItems(self.ovm.get_ov_str_list())
self.comboBox_OVSelector.setCurrentIndex(self.current_ov)
self.comboBox_OVSelector.currentIndexChanged.connect(self.change_ov)
# Set up other comboboxes:
store_res_list = [
'%d × %d' % (res[0], res[1]) for res in self.sem.STORE_RES]
self.comboBox_frameSize.addItems(store_res_list)
self.comboBox_frameSize.currentIndexChanged.connect(
self.update_pixel_size)
self.comboBox_dwellTime.addItems(map(str, self.sem.DWELL_TIME))
# Update pixel size when mag changed:
self.spinBox_magnification.valueChanged.connect(self.update_pixel_size)
# Add and delete button:
self.pushButton_save.clicked.connect(self.save_current_settings)
self.pushButton_addOV.clicked.connect(self.add_ov)
self.pushButton_deleteOV.clicked.connect(self.delete_ov)
self.update_buttons()
self.show_current_settings()
self.show_frame_size()
def show_current_settings(self):
self.comboBox_frameSize.setCurrentIndex(
self.ovm.get_ov_size_selector(self.current_ov))
self.spinBox_magnification.setValue(
self.ovm.get_ov_magnification(self.current_ov))
self.doubleSpinBox_pixelSize.setValue(
self.ovm.get_ov_pixel_size(self.current_ov))
self.comboBox_dwellTime.setCurrentIndex(
self.ovm.get_ov_dwell_time_selector(self.current_ov))
self.spinBox_acqInterval.setValue(
self.ovm.get_ov_acq_interval(self.current_ov))
self.spinBox_acqIntervalOffset.setValue(
self.ovm.get_ov_acq_interval_offset(self.current_ov))
def update_pixel_size(self):
"""Calculate pixel size from current magnification and display it."""
pixel_size = (
self.sem.MAG_PX_SIZE_FACTOR
/ (self.sem.STORE_RES[self.comboBox_frameSize.currentIndex()][0]
* self.spinBox_magnification.value()))
self.doubleSpinBox_pixelSize.setValue(pixel_size)
def show_frame_size(self):
"""Calculate and show frame size depending on user selection."""
frame_size_selector = self.ovm.get_ov_size_selector(self.current_ov)
pixel_size = self.ovm.get_ov_pixel_size(self.current_ov)
width = self.sem.STORE_RES[frame_size_selector][0] * pixel_size / 1000
height = self.sem.STORE_RES[frame_size_selector][1] * pixel_size / 1000
self.label_frameSize.setText('{0:.1f} × '.format(width)
+ '{0:.1f}'.format(height))
def change_ov(self):
self.current_ov = self.comboBox_OVSelector.currentIndex()
self.update_buttons()
self.show_current_settings()
def update_buttons(self):
"""Update labels on buttons and disable/enable delete button
depending on which OV is selected. OV 0 cannot be deleted.
Only the last OV can be deleted. Reason: preserve identities of
overviews during stack acq.
"""
if self.current_ov == 0:
self.pushButton_deleteOV.setEnabled(False)
else:
self.pushButton_deleteOV.setEnabled(
self.current_ov == self.ovm.get_number_ov() - 1)
# Show current OV number on delete and save buttons
self.pushButton_save.setText(
'Save settings for OV %d' % self.current_ov)
self.pushButton_deleteOV.setText('Delete OV %d' % self.current_ov)
def save_current_settings(self):
self.prev_frame_size = self.ovm.get_ov_size_selector(self.current_ov)
self.ovm.set_ov_size_selector(self.current_ov,
self.comboBox_frameSize.currentIndex())
self.ovm.set_ov_magnification(self.current_ov,
self.spinBox_magnification.value())
self.ovm.set_ov_dwell_time_selector(self.current_ov,
self.comboBox_dwellTime.currentIndex())
self.ovm.set_ov_acq_interval(self.current_ov,
self.spinBox_acqInterval.value())
self.ovm.set_ov_acq_interval_offset(self.current_ov,
self.spinBox_acqIntervalOffset.value())
if self.comboBox_frameSize.currentIndex() != self.prev_frame_size:
# Delete current preview image:
self.ovm.update_ov_file_list(self.current_ov, '')
self.main_window_queue.put('OV SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def add_ov(self):
self.ovm.add_new_ov()
self.current_ov = self.ovm.get_number_ov() - 1
# Update OV selector:
self.comboBox_OVSelector.blockSignals(True)
self.comboBox_OVSelector.clear()
self.comboBox_OVSelector.addItems(self.ovm.get_ov_str_list())
self.comboBox_OVSelector.setCurrentIndex(self.current_ov)
self.comboBox_OVSelector.blockSignals(False)
self.update_buttons()
self.show_current_settings()
self.show_frame_size()
self.main_window_queue.put('OV SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def delete_ov(self):
self.ovm.delete_ov()
self.current_ov = self.ovm.get_number_ov() - 1
# Update OV selector:
self.comboBox_OVSelector.blockSignals(True)
self.comboBox_OVSelector.clear()
self.comboBox_OVSelector.addItems(self.ovm.get_ov_str_list())
self.comboBox_OVSelector.setCurrentIndex(self.current_ov)
self.comboBox_OVSelector.blockSignals(False)
self.update_buttons()
self.show_current_settings()
self.show_frame_size()
self.main_window_queue.put('OV SETTINGS CHANGED')
self.main_window_trigger.s.emit()
#------------------------------------------------------------------------------
class ImportImageDlg(QDialog):
"""Import an image into the viewport."""
def __init__(self, ovm, cs, target_dir):
self.ovm = ovm
self.cs = cs
self.target_dir = target_dir
super().__init__()
loadUi('..\\gui\\import_image_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.pushButton_selectFile.clicked.connect(self.select_file)
self.pushButton_selectFile.setIcon(QIcon('..\\img\\selectdir.png'))
self.pushButton_selectFile.setIconSize(QSize(16, 16))
def select_file(self):
# Let user select image to be imported:
start_path = 'C:\\'
selected_file = str(QFileDialog.getOpenFileName(
self, 'Select image',
start_path,
'Images (*.tif *.png *.bmp *.jpg)'
)[0])
if len(selected_file) > 0:
# Replace forward slashes with backward slashes:
selected_file = selected_file.replace('/', '\\')
self.lineEdit_fileName.setText(selected_file)
self.lineEdit_name.setText(
os.path.splitext(os.path.basename(selected_file))[0])
def accept(self):
selection_success = True
selected_path = self.lineEdit_fileName.text()
selected_filename = os.path.basename(selected_path)
timestamp = str(datetime.datetime.now())
# Remove some characters from timestap to get valid file name:
timestamp = timestamp[:19].translate({ord(c): None for c in ' :-.'})
target_path = (self.target_dir + '\\'
+ os.path.splitext(selected_filename)[0]
+ '_' + timestamp + '.png')
if os.path.isfile(selected_path):
# Copy file to data folder as png:
try:
imported_img = Image.open(selected_path)
imported_img.save(target_path)
except:
QMessageBox.warning(
self, 'Error',
'Could not load image file.',
QMessageBox.Ok)
selection_success = False
if selection_success:
new_img_number = self.ovm.get_number_imported()
self.ovm.add_imported_img()
self.cs.set_imported_img_centre_s(
new_img_number,
[self.doubleSpinBox_posX.value(),
self.doubleSpinBox_posY.value()])
self.ovm.set_imported_img_rotation(
new_img_number, self.spinBox_rotation.value())
self.ovm.set_imported_img_file(
new_img_number, target_path)
self.ovm.set_imported_img_name(new_img_number,
self.lineEdit_name.text())
width, height = imported_img.size
self.ovm.set_imported_img_size_px_py(
new_img_number, width, height)
self.ovm.set_imported_img_pixel_size(
new_img_number, self.doubleSpinBox_pixelSize.value())
self.ovm.set_imported_img_transparency(
new_img_number, self.spinBox_transparency.value())
else:
QMessageBox.warning(self, 'Error',
'Specified file not found.',
QMessageBox.Ok)
selection_success = False
if selection_success:
super().accept()
#------------------------------------------------------------------------------
class AdjustImageDlg(QDialog):
"""Adjust an imported image (size, rotation, transparency)"""
def __init__(self, ovm, cs, selected_img,
main_window_queue, main_window_trigger):
self.ovm = ovm
self.cs = cs
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
self.selected_img = selected_img
super().__init__()
loadUi('..\\gui\\adjust_imported_image_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.lineEdit_selectedImage.setText(
self.ovm.get_imported_img_name(self.selected_img))
pos_x, pos_y = self.cs.get_imported_img_centre_s(self.selected_img)
self.doubleSpinBox_posX.setValue(pos_x)
self.doubleSpinBox_posY.setValue(pos_y)
self.doubleSpinBox_pixelSize.setValue(
self.ovm.get_imported_img_pixel_size(self.selected_img))
self.spinBox_rotation.setValue(
self.ovm.get_imported_img_rotation(self.selected_img))
self.spinBox_transparency.setValue(
self.ovm.get_imported_img_transparency(self.selected_img))
# Use "Apply" button to show changes in viewport
apply_button = self.buttonBox.button(QDialogButtonBox.Apply)
cancel_button = self.buttonBox.button(QDialogButtonBox.Cancel)
cancel_button.setAutoDefault(False)
cancel_button.setDefault(False)
apply_button.setDefault(True)
apply_button.setAutoDefault(True)
apply_button.clicked.connect(self.apply_changes)
def apply_changes(self):
"""Apply the current settings and redraw the image in the viewport."""
self.cs.set_imported_img_centre_s(
self.selected_img,
[self.doubleSpinBox_posX.value(),
self.doubleSpinBox_posY.value()])
self.ovm.set_imported_img_pixel_size(
self.selected_img, self.doubleSpinBox_pixelSize.value())
self.ovm.set_imported_img_rotation(
self.selected_img, self.spinBox_rotation.value())
self.ovm.set_imported_img_transparency(
self.selected_img, self.spinBox_transparency.value())
# Emit signals to reload and redraw:
self.main_window_queue.put('RELOAD IMPORTED' + str(self.selected_img))
self.main_window_trigger.s.emit()
#------------------------------------------------------------------------------
class DeleteImageDlg(QDialog):
"""Delete an imported image from the viewport."""
def __init__(self, ovm):
self.ovm = ovm
super().__init__()
loadUi('..\\gui\\delete_image_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Populate the list widget with existing imported images:
img_list = []
for i in range(self.ovm.get_number_imported()):
img_list.append(str(i) + ' - ' + self.ovm.get_imported_img_name(i))
self.listWidget_imagelist.addItems(img_list)
def accept(self):
selected_img = self.listWidget_imagelist.currentRow()
if selected_img is not None:
self.ovm.delete_imported_img(selected_img)
super().accept()
#------------------------------------------------------------------------------
class GridSettingsDlg(QDialog):
"""Let the user change all settings for each grid."""
def __init__(self, grid_manager, sem, selected_grid,
config, main_window_queue, main_window_trigger):
super().__init__()
self.gm = grid_manager
self.sem = sem
self.current_grid = selected_grid
self.cfg = config
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
loadUi('..\\gui\\grid_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Set up grid selector:
self.comboBox_gridSelector.addItems(self.gm.get_grid_str_list())
self.comboBox_gridSelector.setCurrentIndex(self.current_grid)
self.comboBox_gridSelector.currentIndexChanged.connect(
self.change_grid)
# Set up colour selector:
for i in range(len(utils.COLOUR_SELECTOR)):
rgb = utils.COLOUR_SELECTOR[i]
colour_icon = QPixmap(20, 10)
colour_icon.fill(QColor(rgb[0], rgb[1], rgb[2]))
self.comboBox_colourSelector.addItem(QIcon(colour_icon), '')
store_res_list = [
'%d × %d' % (res[0], res[1]) for res in self.sem.STORE_RES]
self.comboBox_tileSize.addItems(store_res_list)
self.comboBox_tileSize.currentIndexChanged.connect(
self.show_tile_size_and_dose)
self.comboBox_dwellTime.addItems(map(str, self.sem.DWELL_TIME))
self.comboBox_dwellTime.currentIndexChanged.connect(
self.show_tile_size_and_dose)
self.doubleSpinBox_pixelSize.valueChanged.connect(
self.show_tile_size_and_dose)
# Adaptive focus tool button:
self.toolButton_adaptiveFocus.clicked.connect(
self.open_adaptive_focus_dlg)
# Reset wd/stig parameters:
self.pushButton_resetFocusParams.clicked.connect(
self.reset_wd_stig_params)
# Save, add and delete button:
self.pushButton_save.clicked.connect(self.save_current_settings)
self.pushButton_addGrid.clicked.connect(self.add_grid)
self.pushButton_deleteGrid.clicked.connect(self.delete_grid)
self.update_buttons()
self.show_current_settings()
self.show_tile_size_and_dose()
# inactivating add grid in magc_mode (should be done in magc panel instead)
if self.cfg['sys']['magc_mode'] == 'True':
self.pushButton_addGrid.setEnabled(False)
def show_current_settings(self):
self.comboBox_colourSelector.setCurrentIndex(
self.gm.get_display_colour_index(self.current_grid))
# Adaptive focus:
self.checkBox_adaptiveFocus.setChecked(
self.gm.is_adaptive_focus_active(self.current_grid))
self.spinBox_rows.setValue(self.gm.get_number_rows(self.current_grid))
self.spinBox_cols.setValue(self.gm.get_number_cols(self.current_grid))
self.spinBox_overlap.setValue(self.gm.get_overlap(self.current_grid))
self.doubleSpinBox_rotation.setValue(
self.gm.get_rotation(self.current_grid))
self.spinBox_shift.setValue(self.gm.get_row_shift(self.current_grid))
self.doubleSpinBox_pixelSize.setValue(
self.gm.get_pixel_size(self.current_grid))
self.comboBox_tileSize.setCurrentIndex(
self.gm.get_tile_size_selector(self.current_grid))
self.comboBox_dwellTime.setCurrentIndex(
self.gm.get_dwell_time_selector(self.current_grid))
self.spinBox_acqInterval.setValue(
self.gm.get_acq_interval(self.current_grid))
self.spinBox_acqIntervalOffset.setValue(
self.gm.get_acq_interval_offset(self.current_grid))
def show_tile_size_and_dose(self):
"""Calculate and display the tile size and the dose for the current
settings. Updated in real-time as user changes dwell time, frame
resolution and pixel size.
"""
tile_size_selector = self.comboBox_tileSize.currentIndex()
pixel_size = self.doubleSpinBox_pixelSize.value()
width = self.sem.STORE_RES[tile_size_selector][0] * pixel_size / 1000
height = self.sem.STORE_RES[tile_size_selector][1] * pixel_size / 1000
self.label_tileSize.setText('{0:.1f} × '.format(width)
+ '{0:.1f}'.format(height))
current = self.sem.get_beam_current()
dwell_time = float(self.comboBox_dwellTime.currentText())
pixel_size = self.doubleSpinBox_pixelSize.value()
# Show electron dose in electrons per square nanometre.
self.label_dose.setText('{0:.1f}'.format(
utils.calculate_electron_dose(current, dwell_time, pixel_size)))
def change_grid(self):
self.current_grid = self.comboBox_gridSelector.currentIndex()
self.update_buttons()
self.show_current_settings()
self.show_tile_size_and_dose()
def update_buttons(self):
"""Update labels on buttons and disable/enable delete button
depending on which grid is selected. Grid 0 cannot be deleted.
Only the last grid can be deleted. Reason: preserve identities of
grids and tiles within grids.
"""
if self.current_grid == 0:
self.pushButton_deleteGrid.setEnabled(False)
else:
self.pushButton_deleteGrid.setEnabled(
self.current_grid == self.gm.get_number_grids() - 1)
self.pushButton_save.setText(
'Save settings for grid %d' % self.current_grid)
self.pushButton_deleteGrid.setText('Delete grid %d' % self.current_grid)
def add_grid(self):
self.gm.add_new_grid()
self.current_grid = self.gm.get_number_grids() - 1
# Update grid selector:
self.comboBox_gridSelector.blockSignals(True)
self.comboBox_gridSelector.clear()
self.comboBox_gridSelector.addItems(self.gm.get_grid_str_list())
self.comboBox_gridSelector.setCurrentIndex(self.current_grid)
self.comboBox_gridSelector.blockSignals(False)
self.update_buttons()
self.show_current_settings()
self.show_tile_size_and_dose()
self.main_window_queue.put('GRID SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def delete_grid(self):
user_reply = QMessageBox.question(
self, 'Delete grid',
'This will delete grid %d.\n\n'
'Do you wish to proceed?' % self.current_grid,
QMessageBox.Ok | QMessageBox.Cancel)
if user_reply == QMessageBox.Ok:
self.gm.delete_grid()
self.current_grid = self.gm.get_number_grids() - 1
# Update grid selector:
self.comboBox_gridSelector.blockSignals(True)
self.comboBox_gridSelector.clear()
self.comboBox_gridSelector.addItems(self.gm.get_grid_str_list())
self.comboBox_gridSelector.setCurrentIndex(self.current_grid)
self.comboBox_gridSelector.blockSignals(False)
self.update_buttons()
self.show_current_settings()
self.show_tile_size_and_dose()
self.main_window_queue.put('GRID SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def reset_wd_stig_params(self):
user_reply = QMessageBox.question(
self, 'Reset focus/astigmatism parameters',
f'This will reset the focus and astigmatism parameters for '
f'all tiles in grid {self.current_grid}.\n'
f'Proceed?',
QMessageBox.Ok | QMessageBox.Cancel)
if user_reply == QMessageBox.Ok:
self.gm.initialize_wd_stig_map(self.current_grid)
self.main_window_queue.put('GRID SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def save_current_settings(self):
if self.cfg['sys']['magc_mode'] == 'True':
grid_center = self.gm.get_grid_center_s(self.current_grid)
error_msg = ''
self.gm.set_grid_size(self.current_grid,
(self.spinBox_rows.value(),
self.spinBox_cols.value()))
self.gm.set_tile_size_selector(self.current_grid,
self.comboBox_tileSize.currentIndex())
tile_width_p = self.gm.get_tile_width_p(self.current_grid)
input_overlap = self.spinBox_overlap.value()
input_shift = self.spinBox_shift.value()
if -0.3 * tile_width_p <= input_overlap < 0.3 * tile_width_p:
self.gm.set_overlap(self.current_grid, input_overlap)
else:
error_msg = ('Overlap outside of allowed '
'range (-30% .. 30% frame width).')
self.gm.set_rotation(
self.current_grid, self.doubleSpinBox_rotation.value())
if 0 <= input_shift <= tile_width_p:
self.gm.set_row_shift(self.current_grid, input_shift)
else:
error_msg = ('Row shift outside of allowed '
'range (0 .. frame width).')
self.gm.set_display_colour(
self.current_grid, self.comboBox_colourSelector.currentIndex())
self.gm.set_adaptive_focus_enabled(self.current_grid,
self.checkBox_adaptiveFocus.isChecked())
if self.checkBox_adaptiveFocus.isChecked():
self.gm.calculate_focus_gradient(self.current_grid)
# Acquisition parameters:
self.gm.set_pixel_size(self.current_grid,
self.doubleSpinBox_pixelSize.value())
self.gm.set_dwell_time_selector(self.current_grid,
self.comboBox_dwellTime.currentIndex())
self.gm.set_acq_interval(
self.current_grid, self.spinBox_acqInterval.value())
self.gm.set_acq_interval_offset(
self.current_grid, self.spinBox_acqIntervalOffset.value())
# Recalculate grid:
self.gm.calculate_grid_map(self.current_grid)
# Update wd/stig map:
self.gm.initialize_wd_stig_map(self.current_grid)
if self.cfg['sys']['magc_mode'] == 'True':
self.gm.set_grid_center_s(self.current_grid, grid_center)
self.gm.update_source_ROIs_from_grids()
if error_msg:
QMessageBox.warning(self, 'Error', error_msg, QMessageBox.Ok)
else:
self.main_window_queue.put('GRID SETTINGS CHANGED')
self.main_window_trigger.s.emit()
def open_adaptive_focus_dlg(self):
sub_dialog = AdaptiveFocusSettingsDlg(self.gm, self.current_grid)
sub_dialog.exec_()
#------------------------------------------------------------------------------
class AdaptiveFocusSettingsDlg(QDialog):
"""Select the tiles to calculate the gradient for the adaptive focus."""
def __init__(self, gm, current_grid):
super().__init__()
self.gm = gm
self.current_grid = current_grid
loadUi('..\\gui\\adaptive_focus_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.lineEdit_currentGrid.setText('Grid ' + str(current_grid))
self.grid_illustration.setPixmap(QPixmap('..\\img\\grid.png'))
self.af_tiles = self.gm.get_adaptive_focus_tiles(self.current_grid)
# Backup variable for currently selected adaptive focus tiles:
self.prev_af_tiles = self.af_tiles.copy()
# Set up tile selectors for adaptive focus tiles:
number_of_tiles = self.gm.get_number_tiles(self.current_grid)
tile_list_str = ['-']
for tile in range(number_of_tiles):
tile_list_str.append(str(tile))
for i in range(3):
if self.af_tiles[i] >= number_of_tiles:
self.af_tiles[i] = -1
self.comboBox_tileUpperLeft.blockSignals(True)
self.comboBox_tileUpperLeft.addItems(tile_list_str)
self.comboBox_tileUpperLeft.setCurrentIndex(self.af_tiles[0] + 1)
self.comboBox_tileUpperLeft.currentIndexChanged.connect(
self.update_settings)
self.comboBox_tileUpperLeft.blockSignals(False)
self.comboBox_tileUpperRight.blockSignals(True)
self.comboBox_tileUpperRight.addItems(tile_list_str)
self.comboBox_tileUpperRight.setCurrentIndex(self.af_tiles[1] + 1)
self.comboBox_tileUpperRight.currentIndexChanged.connect(
self.update_settings)
self.comboBox_tileUpperRight.blockSignals(False)
self.comboBox_tileLowerLeft.blockSignals(True)
self.comboBox_tileLowerLeft.addItems(tile_list_str)
self.comboBox_tileLowerLeft.setCurrentIndex(self.af_tiles[2] + 1)
self.comboBox_tileLowerLeft.currentIndexChanged.connect(
self.update_settings)
self.comboBox_tileLowerLeft.blockSignals(False)
self.update_settings()
def update_settings(self):
"""Get selected working distances and calculate origin WD and
gradient if possible.
"""
self.af_tiles[0] = self.comboBox_tileUpperLeft.currentIndex() - 1
self.af_tiles[1] = self.comboBox_tileUpperRight.currentIndex() - 1
self.af_tiles[2] = self.comboBox_tileLowerLeft.currentIndex() - 1
if self.af_tiles[0] >= 0:
self.label_t1.setText('Tile ' + str(self.af_tiles[0]) + ':')
wd = self.gm.get_tile_wd(self.current_grid, self.af_tiles[0])
self.doubleSpinBox_t1.setValue(wd * 1000)
else:
self.label_t1.setText('Tile (-) :')
self.doubleSpinBox_t1.setValue(0)
if self.af_tiles[1] >= 0:
self.label_t2.setText('Tile ' + str(self.af_tiles[1]) + ':')
wd = self.gm.get_tile_wd(self.current_grid, self.af_tiles[1])
self.doubleSpinBox_t2.setValue(wd * 1000)
else:
self.label_t2.setText('Tile (-) :')
self.doubleSpinBox_t2.setValue(0)
if self.af_tiles[2] >= 0:
self.label_t3.setText('Tile ' + str(self.af_tiles[2]) + ':')
wd = self.gm.get_tile_wd(self.current_grid, self.af_tiles[2])
self.doubleSpinBox_t3.setValue(wd * 1000)
else:
self.label_t3.setText('Tile (-) :')
self.doubleSpinBox_t3.setValue(0)
self.gm.set_adaptive_focus_tiles(self.current_grid, self.af_tiles)
# Try to calculate focus map:
self.af_success = self.gm.calculate_focus_gradient(self.current_grid)
if self.af_success:
grad = self.gm.get_adaptive_focus_gradient(self.current_grid)
wd = self.gm.get_tile_wd(self.current_grid, 0)
current_status_str = (
'WD: ' + '{0:.6f}'.format(wd * 1000)
+ ' mm;\n' + chr(8710)
+ 'x: ' + '{0:.6f}'.format(grad[0] * 1000)
+ '; ' + chr(8710) + 'y: ' + '{0:.6f}'.format(grad[1] * 1000))
else:
current_status_str = 'Insufficient or incorrect tile selection'
self.textEdit_originGradients.setText(current_status_str)
def accept(self):
if self.af_success:
super().accept()
else:
QMessageBox.warning(
self, 'Error',
'Insufficient or incorrect tile selection. Cannot calculate '
'origin working distance and focus gradient.',
QMessageBox.Ok)
def reject(self):
# Restore previous selection:
self.gm.set_adaptive_focus_tiles(self.current_grid, self.prev_af_tiles)
# Recalculate with previous setting:
self.gm.calculate_focus_gradient(self.current_grid)
super().reject()
#------------------------------------------------------------------------------
class AdaptiveFocusSelectionDlg(QDialog):
def __init__(self, current_af_tiles):
super().__init__()
self.selected = None
loadUi('..\\gui\\adaptive_focus_selection_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.grid_illustration.setPixmap(QPixmap('..\\img\\grid.png'))
if current_af_tiles[0] >= 0:
self.pushButton_pos0.setText(str(current_af_tiles[0]))
else:
self.pushButton_pos0.setText('-')
if current_af_tiles[1] >= 0:
self.pushButton_pos1.setText(str(current_af_tiles[1]))
else:
self.pushButton_pos1.setText('-')
if current_af_tiles[2] >= 0:
self.pushButton_pos2.setText(str(current_af_tiles[2]))
else:
self.pushButton_pos2.setText('-')
self.pushButton_pos0.clicked.connect(self.select_pos0)
self.pushButton_pos1.clicked.connect(self.select_pos1)
self.pushButton_pos2.clicked.connect(self.select_pos2)
def select_pos0(self):
self.selected = 0
super().accept()
def select_pos1(self):
self.selected = 1
super().accept()
def select_pos2(self):
self.selected = 2
super().accept()
#------------------------------------------------------------------------------
class GridRotationDlg(QDialog):
"""Change the rotation angle of a selected grid."""
def __init__(self, selected_grid, gm, cfg, main_window_queue, main_window_trigger):
self.selected_grid = selected_grid
self.gm = gm
self.cfg = cfg
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
self.rotation_in_progress = False
super().__init__()
loadUi('..\\gui\\change_grid_rotation_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.label_description.setText(
f'Rotation of selected grid {self.selected_grid} in degrees:')
# Keep current angle and origin to enable undo option
self.previous_angle = self.gm.get_rotation(selected_grid)
self.previous_origin = self.gm.get_grid_origin_s(selected_grid)
# Set initial values:
self.doubleSpinBox_angle.setValue(self.previous_angle)
# Slider value 0..719 (twice the angle in degrees) for 0.5 degree steps
self.horizontalSlider_angle.setValue(
self.doubleSpinBox_angle.value() * 2)
self.horizontalSlider_angle.valueChanged.connect(self.update_spinbox)
self.doubleSpinBox_angle.valueChanged.connect(self.update_slider)
def keyPressEvent(self, event):
# Catch KeyPressEvent when user presses Enter (otherwise dialog would exit.)
if event.key() == Qt.Key_Enter or event.key() == Qt.Key_Return:
event.accept()
def update_spinbox(self):
self.doubleSpinBox_angle.blockSignals(True)
self.doubleSpinBox_angle.setValue(
self.horizontalSlider_angle.value() / 2)
self.doubleSpinBox_angle.blockSignals(False)
self.update_grid()
def update_slider(self):
self.horizontalSlider_angle.blockSignals(True)
self.horizontalSlider_angle.setValue(
self.doubleSpinBox_angle.value() * 2)
self.horizontalSlider_angle.blockSignals(False)
self.update_grid()
def update_grid(self):
"""Apply the new rotation angle and redraw the viewport"""
self.time_of_last_rotation = time()
if not self.rotation_in_progress:
# Start thread to ensure viewport is drawn with labels and previews
# after rotation completed.
self.rotation_in_progress = True
update_viewport_with_delay_thread = threading.Thread(
target=self.update_viewport_with_delay,
args=())
update_viewport_with_delay_thread.start()
if self.radioButton_pivotCentre.isChecked():
# Get current centre of grid:
centre_dx, centre_dy = self.gm.get_grid_centre_d(self.selected_grid)
# Set new angle
self.gm.set_rotation(
self.selected_grid, self.doubleSpinBox_angle.value())
self.gm.rotate_around_grid_centre(
self.selected_grid, centre_dx, centre_dy)
else:
self.gm.set_rotation(
self.selected_grid, self.doubleSpinBox_angle.value())
self.gm.calculate_grid_map(self.selected_grid)
# Emit signal to redraw:
self.main_window_queue.put('DRAW MV NO LABELS')
self.main_window_trigger.s.emit()
def draw_with_labels(self):
self.main_window_queue.put('DRAW MV')
self.main_window_trigger.s.emit()
def update_viewport_with_delay(self):
"""Redraw the viewport without suppressing labels/previews after at
least 0.3 seconds have passed since last update of the rotation angle."""
finish_trigger = Trigger()
finish_trigger.s.connect(self.draw_with_labels)
current_time = self.time_of_last_rotation
while (current_time - self.time_of_last_rotation < 0.3):
sleep(0.1)
current_time += 0.1
self.rotation_in_progress = False
finish_trigger.s.emit()
def reject(self):
# Revert to previous angle and origin:
self.gm.set_rotation(self.selected_grid, self.previous_angle)
self.gm.set_grid_origin_s(self.selected_grid, self.previous_origin)
self.main_window_queue.put('DRAW MV')
self.main_window_trigger.s.emit()
super().reject()
def accept(self):
# Calculate new grid map with new rotation angle:
self.gm.calculate_grid_map(self.selected_grid)
if self.cfg['sys']['magc_mode'] == 'True':
self.gm.update_source_ROIs_from_grids()
super().accept()
#------------------------------------------------------------------------------
class AcqSettingsDlg(QDialog):
"""Let user adjust acquisition settings."""
def __init__(self, config, stack):
super().__init__()
self.cfg = config
self.stack = stack
loadUi('..\\gui\\acq_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.pushButton_selectDir.clicked.connect(self.select_directory)
self.pushButton_selectDir.setIcon(QIcon('..\\img\\selectdir.png'))
self.pushButton_selectDir.setIconSize(QSize(16, 16))
# Display current settings:
self.lineEdit_baseDir.setText(self.cfg['acq']['base_dir'])
self.lineEdit_baseDir.textChanged.connect(self.update_stack_name)
self.update_stack_name()
self.new_base_dir = ''
self.spinBox_sliceThickness.setValue(self.stack.get_slice_thickness())
self.spinBox_numberSlices.setValue(self.stack.get_number_slices())
self.spinBox_sliceCounter.setValue(self.stack.get_slice_counter())
self.doubleSpinBox_zDiff.setValue(self.stack.get_total_z_diff())
self.checkBox_sendMetaData.setChecked(
self.cfg['sys']['send_metadata'] == 'True')
self.update_server_lineedit()
self.checkBox_sendMetaData.stateChanged.connect(
self.update_server_lineedit)
self.checkBox_EHTOff.setChecked(
self.cfg['acq']['eht_off_after_stack'] == 'True')
self.lineEdit_metaDataServer.setText(
self.cfg['sys']['metadata_server_url'])
self.lineEdit_adminEmail.setText(
self.cfg['sys']['metadata_server_admin'])
self.lineEdit_projectName.setText(
self.cfg['sys']['metadata_project_name'])
# Disable two spinboxes when SEM stage used:
if self.cfg['sys']['use_microtome'] == 'False':
self.spinBox_sliceThickness.setEnabled(False)
self.doubleSpinBox_zDiff.setEnabled(False)
def select_directory(self):
"""Let user select the base directory for the stack acquisition.
Note that the final subfolder name in the directory string is used as
the name of the stack in SBEMimage.
"""
if len(self.cfg['acq']['base_dir']) > 2:
start_path = self.cfg['acq']['base_dir'][:3]
else:
start_path = 'C:\\'
self.lineEdit_baseDir.setText(
str(QFileDialog.getExistingDirectory(
self, 'Select Directory',
start_path,
QFileDialog.ShowDirsOnly)).replace('/', '\\'))
def update_server_lineedit(self):
self.lineEdit_projectName.setEnabled(
self.checkBox_sendMetaData.isChecked())
def update_stack_name(self):
base_dir = self.lineEdit_baseDir.text().rstrip(r'\/ ')
self.label_stackName.setText(base_dir[base_dir.rfind('\\') + 1:])
def accept(self):
success = True
selected_dir = self.lineEdit_baseDir.text()
# Remove trailing slashes and whitespace
modified_dir = selected_dir.rstrip(r'\/ ')
# Replace spaces and forward slashes
modified_dir = modified_dir.replace(' ', '_').replace('/', '\\')
# Notify user if directory was modified
if modified_dir != selected_dir:
self.lineEdit_baseDir.setText(modified_dir)
self.update_stack_name()
QMessageBox.information(
self, 'Base directory name modified',
'The selected base directory was modified by removing '
'trailing slashes and whitespace and replacing spaces with '
'underscores and forward slashes with backslashes.',
QMessageBox.Ok)
# Check if path contains a drive letter
reg = re.compile('^[a-zA-Z]:\\\$')
if not reg.match(modified_dir[:3]):
success = False
QMessageBox.warning(
self, 'Error',
'Please specify the full path to the base directory. It '
'must begin with a drive letter, for example: "D:\\..."',
QMessageBox.Ok)
else:
# If workspace directory does not yet exist, create it to test
# whether path is valid and accessible
workspace_dir = os.path.join(modified_dir, 'workspace')
try:
if not os.path.exists(workspace_dir):
os.makedirs(workspace_dir)
except Exception as e:
success = False
QMessageBox.warning(
self, 'Error',
'The selected base directory is invalid or '
'inaccessible: ' + str(e),
QMessageBox.Ok)
if 5 <= self.spinBox_sliceThickness.value() <= 200:
self.stack.set_slice_thickness(self.spinBox_sliceThickness.value())
number_slices = self.spinBox_numberSlices.value()
self.stack.set_number_slices(number_slices)
if (self.spinBox_sliceCounter.value() <= number_slices
or number_slices == 0):
self.stack.set_slice_counter(self.spinBox_sliceCounter.value())
self.stack.set_total_z_diff(self.doubleSpinBox_zDiff.value())
self.cfg['acq']['eht_off_after_stack'] = str(
self.checkBox_EHTOff.isChecked())
self.cfg['sys']['send_metadata'] = str(
self.checkBox_sendMetaData.isChecked())
if self.checkBox_sendMetaData.isChecked():
metadata_server_url = self.lineEdit_metaDataServer.text()
if not validators.url(metadata_server_url):
QMessageBox.warning(
self, 'Error',
'Metadata server URL is invalid. Change the URL in the '
'system configuration file.',
QMessageBox.Ok)
self.cfg['sys']['metadata_project_name'] = (
self.lineEdit_projectName.text())
if ((number_slices > 0)
and (self.spinBox_sliceCounter.value() > number_slices)):
QMessageBox.warning(
self, 'Error',
'Slice counter must be smaller than or equal to '
'target number of slices.', QMessageBox.Ok)
success = False
if success:
self.cfg['acq']['base_dir'] = modified_dir
super().accept()
#------------------------------------------------------------------------------
class PreStackDlg(QDialog):
"""Let user check the acquisition settings before starting a stack.
Also show settings that can only be changed in DM and let user adjust
them for logging purposes.
"""
def __init__(self, config, ovm, gm, paused):
super().__init__()
self.cfg = config
self.ovm = ovm
self.gm = gm
loadUi('..\\gui\\pre_stack_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Different labels if stack is paused ('Continue' instead of 'Start'):
if paused:
self.pushButton_startAcq.setText('Continue acquisition')
self.setWindowTitle('Continue acquisition')
self.pushButton_startAcq.clicked.connect(self.accept)
boldFont = QFont()
boldFont.setBold(True)
# Show the most relevant current settings for the acquisition:
base_dir = self.cfg['acq']['base_dir']
self.label_stackName.setText(base_dir[base_dir.rfind('\\') + 1:])
self.label_beamSettings.setText(
self.cfg['sem']['eht'] + ' keV, '
+ self.cfg['sem']['beam_current'] + ' pA')
self.label_gridSetup.setText(
self.cfg['overviews']['number_ov'] + ' overview(s), '
+ self.cfg['grids']['number_grids'] + ' grid(s);')
self.label_totalActiveTiles.setText(
str(self.gm.get_total_number_active_tiles()) + ' active tile(s)')
if self.cfg['acq']['use_autofocus'] == 'True':
if int(self.cfg['autofocus']['method']) == 0:
self.label_autofocusActive.setFont(boldFont)
self.label_autofocusActive.setText('Active (SmartSEM)')
elif int(self.cfg['autofocus']['method']) == 1:
self.label_autofocusActive.setFont(boldFont)
self.label_autofocusActive.setText('Active (heuristic)')
else:
self.label_autofocusActive.setText('Inactive')
if self.gm.is_adaptive_focus_active():
self.label_adaptiveActive.setFont(boldFont)
self.label_adaptiveActive.setText('Active')
else:
self.label_adaptiveActive.setText('Inactive')
if (self.gm.is_intervallic_acq_active()
or self.ovm.is_intervallic_acq_active()):
self.label_intervallicActive.setFont(boldFont)
self.label_intervallicActive.setText('Active')
else:
self.label_intervallicActive.setText('Inactive')
if self.cfg['acq']['interrupted'] == 'True':
position = json.loads(self.cfg['acq']['interrupted_at'])
self.label_interruption.setFont(boldFont)
self.label_interruption.setText(
'Yes, in grid ' + str(position[0]) + ' at tile '
+ str(position[1]))
else:
self.label_interruption.setText('None')
self.doubleSpinBox_cutSpeed.setValue(
int(float(self.cfg['microtome']['knife_cut_speed'])) / 1000)
self.doubleSpinBox_retractSpeed.setValue(
int(float(self.cfg['microtome']['knife_retract_speed'])) / 1000)
self.doubleSpinBox_brightness.setValue(
float(self.cfg['sem']['bsd_brightness']))
self.doubleSpinBox_contrast.setValue(
float(self.cfg['sem']['bsd_contrast']))
self.spinBox_bias.setValue(
int(self.cfg['sem']['bsd_bias']))
self.checkBox_oscillation.setChecked(
self.cfg['microtome']['knife_oscillation'] == 'True')
def accept(self):
self.cfg['microtome']['knife_cut_speed'] = str(
int(self.doubleSpinBox_cutSpeed.value() * 1000))
self.cfg['microtome']['knife_retract_speed'] = str(
int(self.doubleSpinBox_retractSpeed.value() * 1000))
self.cfg['sem']['bsd_contrast'] = str(
self.doubleSpinBox_contrast.value())
self.cfg['sem']['bsd_brightness'] = str(
self.doubleSpinBox_brightness.value())
self.cfg['sem']['bsd_bias'] = str(
self.spinBox_bias.value())
self.cfg['microtome']['knife_oscillation'] = str(
self.checkBox_oscillation.isChecked())
super().accept()
#------------------------------------------------------------------------------
class PauseDlg(QDialog):
"""Let the user pause a running acquisition. Two options: (1) Pause as soon
as possible (after the current image is acquired.) (2) Pause after the
current slice is imaged and cut.
"""
def __init__(self):
super().__init__()
loadUi('..\\gui\\pause_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.pause_type = 0
self.pushButton_pauseNow.clicked.connect(self.pause_now)
self.pushButton_pauseAfterSlice.clicked.connect(self.pause_later)
def pause_now(self):
self.pause_type = 1
self.accept()
def pause_later(self):
self.pause_type = 2
self.accept()
def get_user_choice(self):
return self.pause_type
def accept(self):
super().accept()
#------------------------------------------------------------------------------
class ExportDlg(QDialog):
"""Export image list in TrakEM2 format."""
def __init__(self, config):
super().__init__()
self.cfg = config
loadUi('..\\gui\\export_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.pushButton_export.clicked.connect(self.export_list)
self.spinBox_untilSlice.setValue(int(self.cfg['acq']['slice_counter']))
self.show()
def export_list(self):
self.pushButton_export.setText('Busy')
self.pushButton_export.setEnabled(False)
QApplication.processEvents()
base_dir = self.cfg['acq']['base_dir']
target_grid_number = (
str(self.spinBox_gridNumber.value()).zfill(utils.GRID_DIGITS))
pixel_size = self.doubleSpinBox_pixelSize.value()
start_slice = self.spinBox_fromSlice.value()
end_slice = self.spinBox_untilSlice.value()
# Read all imagelist files into memory:
imagelist_str = []
imagelist_data = []
file_list = glob.glob(os.path.join(base_dir,
'meta', 'logs', 'imagelist*.txt'))
file_list.sort()
for file in file_list:
with open(file) as f:
imagelist_str.extend(f.readlines())
if len(imagelist_str) > 0:
# split strings, store entries in variables, find minimum x and y:
min_x = 1000000
min_y = 1000000
for line in imagelist_str:
elements = line.split(';')
# elements[0]: relative path to tile image
# elements[1]: x coordinate in nm
# elements[2]: y coordinate in nm
# elements[3]: z coordinate in nm
# elements[4]: slice number
slice_number = int(elements[4])
grid_number = elements[0][7:11]
if (start_slice <= slice_number <= end_slice
and grid_number == target_grid_number):
x = int(int(elements[1]) / pixel_size)
if x < min_x:
min_x = x
y = int(int(elements[2]) / pixel_size)
if y < min_y:
min_y = y
imagelist_data.append([elements[0], x, y, slice_number])
# Subtract minimum values to obtain bounding box with (0, 0) as
# origin in top-left corner.
for item in imagelist_data:
item[1] -= min_x
item[2] -= min_y
# Write to output file:
try:
output_file = os.path.join(base_dir,
'trakem2_imagelist_slice'
+ str(start_slice)
+ 'to'
+ str(end_slice)
+ '.txt')
with open(output_file, 'w') as f:
for item in imagelist_data:
f.write(item[0] + '\t'
+ str(item[1]) + '\t'
+ str(item[2]) + '\t'
+ str(item[3]) + '\n')
except:
QMessageBox.warning(
self, 'Error',
'An error ocurred while writing the output file.',
QMessageBox.Ok)
else:
QMessageBox.information(
self, 'Export completed',
f'A total of {len(imagelist_data)} tile entries were '
f'processed.\n\nThe output file\n'
f'trakem2_imagelist_slice{start_slice}to{end_slice}.txt\n'
f'was written to the current base directory\n'
f'{base_dir}.',
QMessageBox.Ok)
else:
QMessageBox.warning(
self, 'Error',
'No image metadata found.',
QMessageBox.Ok)
self.pushButton_export.setText('Export')
self.pushButton_export.setEnabled(True)
QApplication.processEvents()
class UpdateDlg(QDialog):
"""Update SBEMimage by downloading latest version from GitHub."""
def __init__(self):
super().__init__()
loadUi('..\\gui\\update_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.pushButton_update.clicked.connect(self.update)
self.show()
def update(self):
self.pushButton_update.setText('Busy')
self.pushButton_update.setEnabled(False)
QApplication.processEvents()
url = "https://github.com/SBEMimage/SBEMimage/archive/master.zip"
try:
response = requests.get(url, stream=True)
with open('master.zip', 'wb') as file:
shutil.copyfileobj(response.raw, file)
del response
except:
QMessageBox.warning(
self, 'Error',
'Could not download current version from GitHub. Check your '
'internet connection. ',
QMessageBox.Ok)
else:
# Get directory of current installation:
install_path = os.path.dirname(
os.path.dirname(os.path.abspath(__file__)))
try:
with ZipFile("master.zip", "r") as zip_object:
for zip_info in zip_object.infolist():
if zip_info.filename[-1] == '/':
continue
# Remove 'SBEMimage-master/':
zip_info.filename = zip_info.filename[17:]
print(zip_info.filename)
zip_object.extract(zip_info, install_path)
except:
QMessageBox.warning(
self, 'Error',
'Could not extract downloaded GitHub archive.',
QMessageBox.Ok)
else:
QMessageBox.information(
self, 'Update complete',
'SBEMimage was updated to the most recent version. '
'You must restart the program to use the updated version.',
QMessageBox.Ok)
self.pushButton_update.setText('Update now')
self.pushButton_update.setEnabled(True)
#------------------------------------------------------------------------------
class EmailMonitoringSettingsDlg(QDialog):
"""Adjust settings for the e-mail monitoring feature."""
def __init__(self, config, stack):
super().__init__()
self.cfg = config
self.stack = stack
loadUi('..\\gui\\email_monitoring_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.lineEdit_notificationEmail.setText(
self.cfg['monitoring']['user_email'])
self.lineEdit_secondaryNotificationEmail.setText(
self.cfg['monitoring']['cc_user_email'])
self.spinBox_reportInterval.setValue(
int(self.cfg['monitoring']['report_interval']))
self.lineEdit_selectedOV.setText(
self.cfg['monitoring']['watch_ov'][1:-1])
self.lineEdit_selectedTiles.setText(
self.cfg['monitoring']['watch_tiles'][1:-1].replace('"', ''))
self.checkBox_sendLogFile.setChecked(
self.cfg['monitoring']['send_logfile'] == 'True')
self.checkBox_sendDebrisErrorLogFiles.setChecked(
self.cfg['monitoring']['send_additional_logs'] == 'True')
self.checkBox_sendViewport.setChecked(
self.cfg['monitoring']['send_viewport'] == 'True')
self.checkBox_sendOverviews.setChecked(
self.cfg['monitoring']['send_ov'] == 'True')
self.checkBox_sendTiles.setChecked(
self.cfg['monitoring']['send_tiles'] == 'True')
self.checkBox_sendOVReslices.setChecked(
self.cfg['monitoring']['send_ov_reslices'] == 'True')
self.checkBox_sendTileReslices.setChecked(
self.cfg['monitoring']['send_tile_reslices'] == 'True')
self.checkBox_allowEmailControl.setChecked(
self.cfg['monitoring']['remote_commands_enabled'] == 'True')
self.checkBox_allowEmailControl.stateChanged.connect(
self.update_remote_option_input)
self.update_remote_option_input()
self.spinBox_remoteCheckInterval.setValue(
int(self.cfg['monitoring']['remote_check_interval']))
self.lineEdit_account.setText(self.cfg['sys']['email_account'])
self.lineEdit_password.setEchoMode(QLineEdit.Password)
self.lineEdit_password.setText(self.stack.get_remote_password())
def update_remote_option_input(self):
status = self.checkBox_allowEmailControl.isChecked()
self.spinBox_remoteCheckInterval.setEnabled(status)
self.lineEdit_password.setEnabled(status)
def accept(self):
error_str = ''
email1 = self.lineEdit_notificationEmail.text()
email2 = self.lineEdit_secondaryNotificationEmail.text()
if validate_email(email1):
self.cfg['monitoring']['user_email'] = email1
else:
error_str = 'Primary e-mail address badly formatted or missing.'
# Second user e-mail is optional
if validate_email(email2) or not email2:
self.cfg['monitoring']['cc_user_email'] = (
self.lineEdit_secondaryNotificationEmail.text())
else:
error_str = 'Secondary e-mail address badly formatted.'
self.cfg['monitoring']['report_interval'] = str(
self.spinBox_reportInterval.value())
success, ov_list = utils.validate_ov_list(
self.lineEdit_selectedOV.text())
if success:
self.cfg['monitoring']['watch_ov'] = str(ov_list)
else:
error_str = 'List of selected overviews badly formatted.'
success, tile_list = utils.validate_tile_list(
self.lineEdit_selectedTiles.text())
if success:
self.cfg['monitoring']['watch_tiles'] = json.dumps(tile_list)
else:
error_str = 'List of selected tiles badly formatted.'
self.cfg['monitoring']['send_logfile'] = str(
self.checkBox_sendLogFile.isChecked())
self.cfg['monitoring']['send_additional_logs'] = str(
self.checkBox_sendDebrisErrorLogFiles.isChecked())
self.cfg['monitoring']['send_viewport'] = str(
self.checkBox_sendViewport.isChecked())
self.cfg['monitoring']['send_ov'] = str(
self.checkBox_sendOverviews.isChecked())
self.cfg['monitoring']['send_tiles'] = str(
self.checkBox_sendTiles.isChecked())
self.cfg['monitoring']['send_ov_reslices'] = str(
self.checkBox_sendOVReslices.isChecked())
self.cfg['monitoring']['send_tile_reslices'] = str(
self.checkBox_sendTileReslices.isChecked())
self.cfg['monitoring']['remote_commands_enabled'] = str(
self.checkBox_allowEmailControl.isChecked())
self.cfg['monitoring']['remote_check_interval'] = str(
self.spinBox_remoteCheckInterval.value())
self.stack.set_remote_password(self.lineEdit_password.text())
if not error_str:
super().accept()
else:
QMessageBox.warning(self, 'Error', error_str, QMessageBox.Ok)
#------------------------------------------------------------------------------
class DebrisSettingsDlg(QDialog):
"""Adjust the options for debris detection and removal: Detection area,
detection method, max. number of sweeps, and what to do when max.
number reached.
"""
def __init__(self, config, ovm):
super().__init__()
self.cfg = config
self.ovm = ovm
loadUi('..\\gui\\debris_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Detection area:
if self.cfg['debris']['auto_detection_area'] == 'True':
self.radioButton_autoSelection.setChecked(True)
else:
self.radioButton_fullSelection.setChecked(True)
# Extra margin around detection area in pixels:
self.spinBox_debrisMargin.setValue(
self.ovm.get_ov_auto_debris_detection_area_margin())
self.spinBox_maxSweeps.setValue(
int(self.cfg['debris']['max_number_sweeps']))
self.doubleSpinBox_diffMean.setValue(
float(self.cfg['debris']['mean_diff_threshold']))
self.doubleSpinBox_diffSD.setValue(
float(self.cfg['debris']['stddev_diff_threshold']))
self.spinBox_diffHistogram.setValue(
int(self.cfg['debris']['histogram_diff_threshold']))
self.spinBox_diffPixels.setValue(
int(self.cfg['debris']['image_diff_threshold']))
self.checkBox_showDebrisArea.setChecked(
self.cfg['debris']['show_detection_area'] == 'True')
self.checkBox_continueAcq.setChecked(
self.cfg['debris']['continue_after_max_sweeps'] == 'True')
# Detection methods:
self.radioButton_methodQuadrant.setChecked(
self.cfg['debris']['detection_method'] == '0')
self.radioButton_methodPixel.setChecked(
self.cfg['debris']['detection_method'] == '1')
self.radioButton_methodHistogram.setChecked(
self.cfg['debris']['detection_method'] == '2')
self.radioButton_methodQuadrant.toggled.connect(
self.update_option_selection)
self.radioButton_methodHistogram.toggled.connect(
self.update_option_selection)
self.update_option_selection()
def update_option_selection(self):
"""Let user only change the parameters for the currently selected
detection method. The other input fields are deactivated.
"""
if self.radioButton_methodQuadrant.isChecked():
self.doubleSpinBox_diffMean.setEnabled(True)
self.doubleSpinBox_diffSD.setEnabled(True)
self.spinBox_diffPixels.setEnabled(False)
self.spinBox_diffHistogram.setEnabled(False)
elif self.radioButton_methodPixel.isChecked():
self.doubleSpinBox_diffMean.setEnabled(False)
self.doubleSpinBox_diffSD.setEnabled(False)
self.spinBox_diffPixels.setEnabled(True)
self.spinBox_diffHistogram.setEnabled(False)
elif self.radioButton_methodHistogram.isChecked():
self.doubleSpinBox_diffMean.setEnabled(False)
self.doubleSpinBox_diffSD.setEnabled(False)
self.spinBox_diffPixels.setEnabled(False)
self.spinBox_diffHistogram.setEnabled(True)
def accept(self):
self.ovm.set_ov_auto_debris_detection_area_margin(
self.spinBox_debrisMargin.value())
self.cfg['debris']['max_number_sweeps'] = str(
self.spinBox_maxSweeps.value())
self.cfg['debris']['mean_diff_threshold'] = str(
self.doubleSpinBox_diffMean.value())
self.cfg['debris']['stddev_diff_threshold'] = str(
self.doubleSpinBox_diffSD.value())
self.cfg['debris']['histogram_diff_threshold'] = str(
self.spinBox_diffHistogram.value())
self.cfg['debris']['image_diff_threshold'] = str(
self.spinBox_diffPixels.value())
self.cfg['debris']['auto_detection_area'] = str(
self.radioButton_autoSelection.isChecked())
self.cfg['debris']['show_detection_area'] = str(
self.checkBox_showDebrisArea.isChecked())
self.cfg['debris']['continue_after_max_sweeps'] = str(
self.checkBox_continueAcq.isChecked())
if self.radioButton_methodQuadrant.isChecked():
self.cfg['debris']['detection_method'] = '0'
elif self.radioButton_methodPixel.isChecked():
self.cfg['debris']['detection_method'] = '1'
elif self.radioButton_methodHistogram.isChecked():
self.cfg['debris']['detection_method'] = '2'
super().accept()
#------------------------------------------------------------------------------
class AskUserDlg(QDialog):
"""Specify for which events the program should let the user decide how
to proceed. The "Ask User" functionality is currently only used for
debris detection. Will be expanded, work in progress...
"""
def __init__(self):
super().__init__()
loadUi('..\\gui\\ask_user_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
#------------------------------------------------------------------------------
class MirrorDriveDlg(QDialog):
"""Select a mirror drive from all available drives."""
def __init__(self, config):
super().__init__()
self.cfg = config
loadUi('..\\gui\\mirror_drive_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.available_drives = []
self.label_text.setText('Please wait. Searching for drives...')
QApplication.processEvents()
# Search for drives in thread. If it gets stuck because drives are
# not accessible, user can still cancel dialog.
t = threading.Thread(target=self.search_drives)
t.start()
def search_drives(self):
# Search for all available drives:
self.available_drives = [
'%s:' % d for d in string.ascii_uppercase
if os.path.exists('%s:' % d)]
if self.available_drives:
self.comboBox_allDrives.addItems(self.available_drives)
current_index = self.comboBox_allDrives.findText(
self.cfg['sys']['mirror_drive'])
if current_index == -1:
current_index = 0
self.comboBox_allDrives.setCurrentIndex(current_index)
# Restore label after searching for available drives:
self.label_text.setText('Select drive for mirroring acquired data:')
def accept(self):
if self.available_drives:
if (self.comboBox_allDrives.currentText()[0]
== self.cfg['acq']['base_dir'][0]):
QMessageBox.warning(
self, 'Error',
'The mirror drive must be different from the '
'base directory drive!', QMessageBox.Ok)
else:
self.cfg['sys']['mirror_drive'] = (
self.comboBox_allDrives.currentText())
super().accept()
#------------------------------------------------------------------------------
class ImageMonitoringSettingsDlg(QDialog):
"""Adjust settings to monitor overviews and tiles. A test if image is
within mean/SD range is performed for all images if option is activated.
Tile-by-tile comparisons are performed for the selected tiles.
"""
def __init__(self, config):
super().__init__()
self.cfg = config
loadUi('..\\gui\\image_monitoring_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.spinBox_meanMin.setValue(
int(self.cfg['monitoring']['mean_lower_limit']))
self.spinBox_meanMax.setValue(
int(self.cfg['monitoring']['mean_upper_limit']))
self.spinBox_stddevMin.setValue(
int(self.cfg['monitoring']['stddev_lower_limit']))
self.spinBox_stddevMax.setValue(
int(self.cfg['monitoring']['stddev_upper_limit']))
self.lineEdit_monitorTiles.setText(
self.cfg['monitoring']['monitor_tiles'][1:-1])
self.lineEdit_monitorTiles.setText(
self.cfg['monitoring']['monitor_tiles'][1:-1].replace('"', ''))
self.doubleSpinBox_meanThreshold.setValue(
float(self.cfg['monitoring']['tile_mean_threshold']))
self.doubleSpinBox_stdDevThreshold.setValue(
float(self.cfg['monitoring']['tile_stddev_threshold']))
def accept(self):
error_str = ''
self.cfg['monitoring']['mean_lower_limit'] = str(
self.spinBox_meanMin.value())
self.cfg['monitoring']['mean_upper_limit'] = str(
self.spinBox_meanMax.value())
self.cfg['monitoring']['stddev_lower_limit'] = str(
self.spinBox_stddevMin.value())
self.cfg['monitoring']['stddev_upper_limit'] = str(
self.spinBox_stddevMax.value())
tile_str = self.lineEdit_monitorTiles.text().strip()
if tile_str == 'all':
self.cfg['monitoring']['monitor_tiles'] = '["all"]'
else:
success, tile_list = utils.validate_tile_list(tile_str)
if success:
self.cfg['monitoring']['monitor_tiles'] = json.dumps(tile_list)
else:
error_str = 'List of selected tiles badly formatted.'
self.cfg['monitoring']['tile_mean_threshold'] = str(
self.doubleSpinBox_meanThreshold.value())
self.cfg['monitoring']['tile_stddev_threshold'] = str(
self.doubleSpinBox_stdDevThreshold.value())
if not error_str:
super().accept()
else:
QMessageBox.warning(self, 'Error', error_str, QMessageBox.Ok)
#------------------------------------------------------------------------------
class AutofocusSettingsDlg(QDialog):
"""Adjust settings for the ZEISS autofocus, the heuristic autofocus,
and tracking the focus/stig when refocusing manually."""
def __init__(self, autofocus, grid_manager, magc_mode=False):
super().__init__()
self.af = autofocus
self.gm = grid_manager
loadUi('..\\gui\\autofocus_settings_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
if self.af.get_method() == 0:
self.radioButton_useSmartSEM.setChecked(True)
elif self.af.get_method() == 1:
self.radioButton_useHeuristic.setChecked(True)
elif self.af.get_method() == 2:
self.radioButton_useTrackingOnly.setChecked(True)
self.radioButton_useSmartSEM.toggled.connect(self.group_box_update)
self.radioButton_useHeuristic.toggled.connect(self.group_box_update)
self.radioButton_useTrackingOnly.toggled.connect(self.group_box_update)
self.group_box_update()
# General settings
self.lineEdit_refTiles.setText(
str(self.af.get_ref_tiles())[1:-1].replace('\'', ''))
if self.af.get_tracking_mode() == 1:
self.lineEdit_refTiles.setEnabled(False)
max_diff = self.af.get_max_wd_stig_diff()
self.doubleSpinBox_maxWDDiff.setValue(max_diff[0] * 1000000)
self.doubleSpinBox_maxStigXDiff.setValue(max_diff[1])
self.doubleSpinBox_maxStigYDiff.setValue(max_diff[2])
self.comboBox_trackingMode.addItems(['Track selected, approx. others',
'Track all active tiles',
'Average over selected'])
self.comboBox_trackingMode.setCurrentIndex(
self.af.get_tracking_mode())
self.comboBox_trackingMode.currentIndexChanged.connect(
self.change_tracking_mode)
# SmartSEM autofocus
self.spinBox_interval.setValue(self.af.get_interval())
self.spinBox_autostigDelay.setValue(self.af.get_autostig_delay())
self.doubleSpinBox_pixelSize.setValue(self.af.get_pixel_size())
# For heuristic autofocus:
deltas = self.af.get_heuristic_deltas()
self.doubleSpinBox_wdDiff.setValue(deltas[0] * 1000000)
self.doubleSpinBox_stigXDiff.setValue(deltas[1])
self.doubleSpinBox_stigYDiff.setValue(deltas[2])
calib = self.af.get_heuristic_calibration()
self.doubleSpinBox_focusCalib.setValue(calib[0])
self.doubleSpinBox_stigXCalib.setValue(calib[1])
self.doubleSpinBox_stigYCalib.setValue(calib[2])
rot, scale = self.af.get_heuristic_rot_scale()
self.doubleSpinBox_stigRot.setValue(rot)
self.doubleSpinBox_stigScale.setValue(scale)
# Disable some settings if MagC mode is active
if magc_mode:
self.radioButton_useHeuristic.setEnabled(False)
self.radioButton_useTrackingOnly.setEnabled(False)
self.comboBox_trackingMode.setEnabled(False)
self.spinBox_interval.setEnabled(False)
# make autostig interval work on grids instead of slices
self.label_fdp_4.setText('Autostig interval (grids) ')
def group_box_update(self):
if self.radioButton_useSmartSEM.isChecked():
zeiss_enabled = True
heuristic_enabled = False
diffs_enabled = True
elif self.radioButton_useHeuristic.isChecked():
zeiss_enabled = False
heuristic_enabled = True
diffs_enabled = True
elif self.radioButton_useTrackingOnly.isChecked():
zeiss_enabled = False
heuristic_enabled = False
diffs_enabled = False
self.groupBox_ZEISS_af.setEnabled(zeiss_enabled)
self.groupBox_heuristic_af.setEnabled(heuristic_enabled)
self.doubleSpinBox_maxWDDiff.setEnabled(diffs_enabled)
self.doubleSpinBox_maxStigXDiff.setEnabled(diffs_enabled)
self.doubleSpinBox_maxStigYDiff.setEnabled(diffs_enabled)
def change_tracking_mode(self):
"""Let user confirm switch to "track all"."""
if self.comboBox_trackingMode.currentIndex() == 1:
response = QMessageBox.information(
self, 'Track all tiles',
'This will select all active tiles for autofocus tracking and '
'overwrite the current selection of reference tiles. '
'Continue?',
QMessageBox.Ok, QMessageBox.Cancel)
if response == QMessageBox.Ok:
self.lineEdit_refTiles.setText(str(
self.gm.get_active_tile_key_list())[1:-1].replace('\'', ''))
self.lineEdit_refTiles.setEnabled(False)
else:
# Revert to tracking mode 0:
self.comboBox_trackingMode.blockSignals(True)
self.comboBox_trackingMode.setCurrentIndex(0)
self.comboBox_trackingMode.blockSignals(False)
else:
self.lineEdit_refTiles.setEnabled(True)
def accept(self):
error_str = ''
if self.radioButton_useSmartSEM.isChecked():
self.af.set_method(0)
elif self.radioButton_useHeuristic.isChecked():
self.af.set_method(1)
elif self.radioButton_useTrackingOnly.isChecked():
self.af.set_method(2)
success, tile_list = utils.validate_tile_list(
self.lineEdit_refTiles.text())
if success:
self.af.set_ref_tiles(tile_list)
else:
error_str = 'List of selected tiles badly formatted.'
self.af.set_tracking_mode(
self.comboBox_trackingMode.currentIndex())
max_diffs = [self.doubleSpinBox_maxWDDiff.value() / 1000000,
self.doubleSpinBox_maxStigXDiff.value(),
self.doubleSpinBox_maxStigYDiff.value()]
self.af.set_max_wd_stig_diff(max_diffs)
self.af.set_interval(self.spinBox_interval.value())
self.af.set_autostig_delay(self.spinBox_autostigDelay.value())
self.af.set_pixel_size(self.doubleSpinBox_pixelSize.value())
deltas = [self.doubleSpinBox_wdDiff.value() / 1000000,
self.doubleSpinBox_stigXDiff.value(),
self.doubleSpinBox_stigYDiff.value()]
self.af.set_heuristic_deltas(deltas)
self.af.set_heuristic_calibration(
[self.doubleSpinBox_focusCalib.value(),
self.doubleSpinBox_stigXCalib.value(),
self.doubleSpinBox_stigYCalib.value()])
self.af.set_heuristic_rot_scale(
[self.doubleSpinBox_stigRot.value(),
self.doubleSpinBox_stigScale.value()])
if not error_str:
super().accept()
else:
QMessageBox.warning(self, 'Error', error_str, QMessageBox.Ok)
#------------------------------------------------------------------------------
class PlasmaCleanerDlg(QDialog):
"""Set parameters for the downstream asher, run it."""
def __init__(self, plc_):
super().__init__()
self.plc = plc_
loadUi('..\\gui\\plasma_cleaner_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('icon.ico'))
self.setFixedSize(self.size())
self.show()
try:
self.spinBox_currentPower.setValue(self.plc.get_power())
self.spinBox_currentDuration.setValue(self.plc.get_duration())
except:
QMessageBox.warning(
self, 'Error',
'Could not read current settings from plasma cleaner.',
QMessageBox.Ok)
self.pushButton_setTargets.clicked.connect(self.set_target_parameters)
self.pushButton_startCleaning.clicked.connect(self.start_cleaning)
self.pushButton_abortCleaning.clicked.connect(self.abort_cleaning)
def set_target_parameters(self):
try:
self.plc.set_power(self.spinBox_targetPower.value())
sleep(0.5)
self.lineEdit_currentPower.setText(str(self.plc.get_power()))
self.plc.set_duration(self.spinBox_targetDuration.value())
sleep(0.5)
self.lineEdit_currentDuration.setText(str(self.plc.get_duration()))
except:
QMessageBox.warning(
self, 'Error',
'An error occured when sending the target settings '
'to the plasma cleaner.',
QMessageBox.Ok)
def start_cleaning(self):
result = QMessageBox.warning(
self, 'About to ignite plasma',
'Are you sure you want to run the plasma cleaner at ' +
self.lineEdit_currentPower.text() + ' W for ' +
self.lineEdit_currentDuration.text() + ' min?',
QMessageBox.Ok | QMessageBox.Cancel)
if result == QMessageBox.Ok:
result = QMessageBox.warning(
self, 'WARNING: Check vacuum Status',
'IMPORTANT: \nPlease confirm with "OK" that the SEM chamber '
'is at HIGH VACUUM.\nIf not, ABORT!',
QMessageBox.Ok | QMessageBox.Abort)
if result == QMessageBox.Ok:
self.pushButton_startCleaning.setEnabled(False)
self.pushButton_abortCleaning.setEnabled(True)
# TODO: Thread, show cleaning status.
self.plc.perform_cleaning()
def abort_cleaning(self):
self.plc.abort_cleaning()
self.pushButton_startCleaning.setEnabled(True)
self.pushButton_startCleaning.setText(
'Start in-chamber cleaning process')
self.pushButton_abortCleaning.setEnabled(False)
#------------------------------------------------------------------------------
class ApproachDlg(QDialog):
"""Remove slices without imaging. User can specify how many slices and
the cutting thickness.
"""
def __init__(self, microtome, main_window_queue, main_window_trigger):
super().__init__()
self.microtome = microtome
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
loadUi('..\\gui\\approach_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Set up trigger and queue to update dialog GUI during approach:
self.progress_trigger = Trigger()
self.progress_trigger.s.connect(self.update_progress)
self.finish_trigger = Trigger()
self.finish_trigger.s.connect(self.finish_approach)
self.spinBox_numberSlices.setRange(1, 100)
self.spinBox_numberSlices.setSingleStep(1)
self.spinBox_numberSlices.setValue(5)
self.spinBox_numberSlices.valueChanged.connect(self.update_progress)
self.pushButton_startApproach.clicked.connect(self.start_approach)
self.pushButton_abortApproach.clicked.connect(self.abort_approach)
self.pushButton_startApproach.setEnabled(True)
self.pushButton_abortApproach.setEnabled(False)
self.slice_counter = 0
self.approach_in_progress = False
self.aborted = False
self.z_mismatch = False
self.max_slices = self.spinBox_numberSlices.value()
self.update_progress()
def add_to_log(self, msg):
self.main_window_queue.put(utils.format_log_entry(msg))
self.main_window_trigger.s.emit()
def update_progress(self):
self.max_slices = self.spinBox_numberSlices.value()
if self.slice_counter > 0:
remaining_time_str = (
' ' + str(int((self.max_slices - self.slice_counter)*12))
+ ' seconds left')
else:
remaining_time_str = ''
self.label_statusApproach.setText(str(self.slice_counter) + '/'
+ str(self.max_slices)
+ remaining_time_str)
self.progressBar_approach.setValue(
int(self.slice_counter/self.max_slices * 100))
def start_approach(self):
self.pushButton_startApproach.setEnabled(False)
self.pushButton_abortApproach.setEnabled(True)
self.buttonBox.setEnabled(False)
self.spinBox_thickness.setEnabled(False)
self.spinBox_numberSlices.setEnabled(False)
self.main_window_queue.put('APPROACH BUSY')
self.main_window_trigger.s.emit()
thread = threading.Thread(target=self.approach_thread)
thread.start()
def finish_approach(self):
# Clear knife
self.add_to_log('3VIEW: Clearing knife.')
self.microtome.clear_knife()
if self.microtome.get_error_state() > 0:
self.add_to_log('CTRL: Error clearing knife.')
self.microtome.reset_error_state()
QMessageBox.warning(self, 'Error',
'Warning: Clearing the knife failed. '
'Try to clear manually.', QMessageBox.Ok)
self.main_window_queue.put('STATUS IDLE')
self.main_window_trigger.s.emit()
# Show message box to user and reset counter and progress bar:
if not self.aborted:
QMessageBox.information(
self, 'Approach finished',
str(self.max_slices) + ' slices have been cut successfully. '
'Total sample depth removed: '
+ str(self.max_slices * self.thickness / 1000) + ' µm.',
QMessageBox.Ok)
self.slice_counter = 0
self.update_progress()
elif self.z_mismatch:
# Show warning message if Z mismatch detected
self.microtome.reset_error_state()
QMessageBox.warning(
self, 'Z position mismatch',
'The current Z position does not match the last known '
'Z position in SBEMimage. Have you manually changed Z? '
'Make sure that the Z position is correct before cutting.',
QMessageBox.Ok)
else:
QMessageBox.warning(
self, 'Approach aborted',
str(self.slice_counter) + ' slices have been cut. '
'Total sample depth removed: '
+ str(self.slice_counter * self.thickness / 1000) + ' µm.',
QMessageBox.Ok)
self.slice_counter = 0
self.update_progress()
self.pushButton_startApproach.setEnabled(True)
self.pushButton_abortApproach.setEnabled(False)
self.buttonBox.setEnabled(True)
self.spinBox_thickness.setEnabled(True)
self.spinBox_numberSlices.setEnabled(True)
self.approach_in_progress = False
def approach_thread(self):
self.approach_in_progress = True
self.aborted = False
self.z_mismatch = False
self.slice_counter = 0
self.max_slices = self.spinBox_numberSlices.value()
self.thickness = self.spinBox_thickness.value()
self.progress_trigger.s.emit()
# Get current z position of stage:
z_position = self.microtome.get_stage_z(wait_interval=1)
if z_position is None or z_position < 0:
# Try again:
z_position = self.microtome.get_stage_z(wait_interval=2)
if z_position is None or z_position < 0:
self.add_to_log(
'CTRL: Error reading Z position. Approach aborted.')
self.microtome.reset_error_state()
self.aborted = True
if self.microtome.get_error_state() == 206:
self.microtome.reset_error_state()
self.z_mismatch = True
self.aborted = True
self.add_to_log(
'CTRL: Z position mismatch. Approach aborted.')
self.main_window_queue.put('UPDATE Z')
self.main_window_trigger.s.emit()
if not self.aborted:
self.microtome.near_knife()
self.add_to_log('3VIEW: Moving knife to near position.')
if self.microtome.get_error_state() > 0:
self.add_to_log(
'CTRL: Error moving knife to near position. '
'Approach aborted.')
self.aborted = True
self.microtome.reset_error_state()
# ====== Approach loop =========
while (self.slice_counter < self.max_slices) and not self.aborted:
# Move to new z position:
z_position = z_position + (self.thickness / 1000)
self.add_to_log(
'3VIEW: Move to new Z: ' + '{0:.3f}'.format(z_position))
self.microtome.move_stage_to_z(z_position)
# Show new Z position in main window:
self.main_window_queue.put('UPDATE Z')
self.main_window_trigger.s.emit()
# Check if there were microtome problems:
if self.microtome.get_error_state() > 0:
self.add_to_log(
'CTRL: Z stage problem detected. Approach aborted.')
self.aborted = True
self.microtome.reset_error_state()
break
self.add_to_log('3VIEW: Cutting in progress ('
+ str(self.thickness) + ' nm cutting thickness).')
# Do the approach cut (cut, retract, in near position)
self.microtome.do_full_approach_cut()
sleep(self.microtome.get_full_cut_duration() - 5)
if self.microtome.get_error_state() > 0:
self.add_to_log(
'CTRL: Cutting problem detected. Approach aborted.')
self.aborted = True
self.microtome.reset_error_state()
break
else:
self.add_to_log('3VIEW: Approach cut completed.')
self.slice_counter += 1
# Update progress bar and slice counter
self.progress_trigger.s.emit()
# ====== End of approach loop =========
# Signal that thread is done:
self.finish_trigger.s.emit()
def abort_approach(self):
self.aborted = True
self.pushButton_abortApproach.setEnabled(False)
def closeEvent(self, event):
if not self.approach_in_progress:
event.accept()
else:
event.ignore()
def accept(self):
if not self.approach_in_progress:
super().accept()
#------------------------------------------------------------------------------
class GrabFrameDlg(QDialog):
"""Acquires or saves a single frame from SmartSEM."""
def __init__(self, config, sem, main_window_queue, main_window_trigger):
super().__init__()
self.cfg = config
self.sem = sem
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
self.finish_trigger = Trigger()
self.finish_trigger.s.connect(self.scan_complete)
loadUi('..\\gui\\grab_frame_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
timestamp = str(datetime.datetime.now())
# Remove some characters from timestap to get valid file name:
timestamp = timestamp[:19].translate({ord(c): None for c in ' :-.'})
self.file_name = 'image_' + timestamp
self.lineEdit_filename.setText(self.file_name)
frame_size, pixel_size, dwell_time = self.sem.get_grab_settings()
store_res_list = [
'%d × %d' % (res[0], res[1]) for res in self.sem.STORE_RES]
self.comboBox_frameSize.addItems(store_res_list)
self.comboBox_frameSize.setCurrentIndex(frame_size)
self.doubleSpinBox_pixelSize.setValue(pixel_size)
self.comboBox_dwellTime.addItems(map(str, self.sem.DWELL_TIME))
self.comboBox_dwellTime.setCurrentIndex(
self.sem.DWELL_TIME.index(dwell_time))
self.pushButton_scan.clicked.connect(self.scan_frame)
self.pushButton_save.clicked.connect(self.save_frame)
def scan_frame(self):
"""Scan and save a single frame using the current grab settings."""
self.file_name = self.lineEdit_filename.text()
# Save and apply grab settings:
selected_dwell_time = self.sem.DWELL_TIME[
self.comboBox_dwellTime.currentIndex()]
self.sem.set_grab_settings(self.comboBox_frameSize.currentIndex(),
self.doubleSpinBox_pixelSize.value(),
selected_dwell_time)
self.sem.apply_grab_settings()
self.pushButton_scan.setText('Wait')
self.pushButton_scan.setEnabled(False)
self.pushButton_save.setEnabled(False)
QApplication.processEvents()
thread = threading.Thread(target=self.perform_scan)
thread.start()
def perform_scan(self):
"""Acquire a new frame. Executed in a thread because it may take some
time and GUI should not freeze.
"""
self.scan_success = self.sem.acquire_frame(
self.cfg['acq']['base_dir'] + '\\' + self.file_name + '.tif')
self.finish_trigger.s.emit()
def scan_complete(self):
"""This function is called when the scan is complete.
Reset the GUI and show result of grab command.
"""
self.pushButton_scan.setText('Scan and grab')
self.pushButton_scan.setEnabled(True)
self.pushButton_save.setEnabled(True)
if self.scan_success:
self.add_to_log('CTRL: Single frame acquired by user.')
QMessageBox.information(
self, 'Frame acquired',
'The image was acquired and saved as '
+ self.file_name +
'.tif in the current base directory.',
QMessageBox.Ok)
else:
QMessageBox.warning(
self, 'Error',
'An error ocurred while attempting to acquire the frame: '
+ self.sem.get_error_cause(),
QMessageBox.Ok)
self.sem.reset_error_state()
def save_frame(self):
"""Save the image currently visible in SmartSEM."""
self.file_name = self.lineEdit_filename.text()
success = self.sem.save_frame(os.path.join(
self.cfg['acq']['base_dir'], self.file_name + '.tif'))
if success:
self.add_to_log('CTRL: Single frame saved by user.')
QMessageBox.information(
self, 'Frame saved',
'The current image shown in SmartSEM was saved as '
+ self.file_name + '.tif in the current base directory.',
QMessageBox.Ok)
else:
QMessageBox.warning(
self, 'Error',
'An error ocurred while attempting to save the current '
'SmarSEM image: '
+ self.sem.get_error_cause(),
QMessageBox.Ok)
self.sem.reset_error_state()
def add_to_log(self, msg):
"""Use trigger and queue to add an entry to the main log."""
self.main_window_queue.put(utils.format_log_entry(msg))
self.main_window_trigger.s.emit()
#------------------------------------------------------------------------------
class EHTDlg(QDialog):
"""Show EHT status and let user switch beam on or off."""
def __init__(self, sem):
super().__init__()
self.sem = sem
loadUi('..\\gui\\eht_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.pushButton_on.clicked.connect(self.turn_on)
self.pushButton_off.clicked.connect(self.turn_off)
self.update_status()
def update_status(self):
if self.sem.is_eht_on():
pal = QPalette(self.label_EHTStatus.palette())
pal.setColor(QPalette.WindowText, QColor(Qt.red))
self.label_EHTStatus.setPalette(pal)
self.label_EHTStatus.setText('ON')
self.pushButton_on.setEnabled(False)
self.pushButton_off.setEnabled(True)
else:
pal = QPalette(self.label_EHTStatus.palette())
pal.setColor(QPalette.WindowText, QColor(Qt.black))
self.label_EHTStatus.setPalette(pal)
self.label_EHTStatus.setText('OFF')
self.pushButton_on.setEnabled(True)
self.pushButton_off.setEnabled(False)
def turn_on(self):
self.pushButton_on.setEnabled(False)
self.pushButton_on.setText('Wait')
thread = threading.Thread(target=self.send_on_cmd_and_wait)
thread.start()
def turn_off(self):
self.pushButton_off.setEnabled(False)
self.pushButton_off.setText('Wait')
QApplication.processEvents()
thread = threading.Thread(target=self.send_off_cmd_and_wait)
thread.start()
def send_on_cmd_and_wait(self):
self.sem.turn_eht_on()
max_wait_time = 15
while not self.sem.is_eht_on() and max_wait_time > 0:
sleep(1)
max_wait_time -= 1
self.pushButton_on.setText('ON')
self.update_status()
def send_off_cmd_and_wait(self):
self.sem.turn_eht_off()
max_wait_time = 15
while not self.sem.is_eht_off() and max_wait_time > 0:
sleep(1)
max_wait_time -= 1
self.pushButton_off.setText('OFF')
self.update_status()
#------------------------------------------------------------------------------
class FTSetParamsDlg(QDialog):
"""Read working distance and stigmation parameters from user input or
from SmartSEM for setting WD/STIG for individual tiles/OVs in
focus tool.
"""
def __init__(self, sem, current_wd, current_stig_x, current_stig_y,
simulation_mode=False):
super().__init__()
self.sem = sem
loadUi('..\\gui\\focus_tool_set_params_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
if simulation_mode:
self.pushButton_getFromSmartSEM.setEnabled(False)
self.pushButton_getFromSmartSEM.clicked.connect(self.get_from_sem)
if current_wd is not None:
self.doubleSpinBox_currentFocus.setValue(1000 * current_wd)
else:
self.doubleSpinBox_currentFocus.setValue(0)
if current_stig_x is not None:
self.doubleSpinBox_currentStigX.setValue(current_stig_x)
else:
self.doubleSpinBox_currentStigX.setValue(0)
if current_stig_y is not None:
self.doubleSpinBox_currentStigY.setValue(current_stig_y)
else:
self.doubleSpinBox_currentStigY.setValue(0)
def get_from_sem(self):
self.doubleSpinBox_currentFocus.setValue(1000 * self.sem.get_wd())
self.doubleSpinBox_currentStigX.setValue(self.sem.get_stig_x())
self.doubleSpinBox_currentStigY.setValue(self.sem.get_stig_y())
def return_params(self):
return (self.new_wd, self.new_stig_x, self.new_stig_y)
def accept(self):
self.new_wd = self.doubleSpinBox_currentFocus.value() / 1000
self.new_stig_x = self.doubleSpinBox_currentStigX.value()
self.new_stig_y = self.doubleSpinBox_currentStigY.value()
super().accept()
#------------------------------------------------------------------------------
class FTMoveDlg(QDialog):
"""Move the stage to the selected tile or OV position."""
def __init__(self, microtome, coordinate_system, grid_manager,
grid_number, tile_number, ov_number):
super().__init__()
self.microtome = microtome
self.cs = coordinate_system
self.gm = grid_manager
self.ov_number = ov_number
self.grid_number = grid_number
self.tile_number = tile_number
self.error = False
self.finish_trigger = Trigger()
self.finish_trigger.s.connect(self.move_completed)
loadUi('..\\gui\\focus_tool_move_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.pushButton_move.clicked.connect(self.start_move)
if ov_number >= 0:
self.label_moveTarget.setText('OV ' + str(ov_number))
elif (grid_number >= 0) and (tile_number >= 0):
self.label_moveTarget.setText(
'Grid: %d, Tile: %d' % (grid_number, tile_number))
def start_move(self):
self.error = False
self.pushButton_move.setText('Busy... please wait.')
self.pushButton_move.setEnabled(False)
thread = threading.Thread(target=self.move_and_wait)
thread.start()
def move_and_wait(self):
# Load target coordinates
if self.ov_number >= 0:
stage_x, stage_y = self.cs.get_ov_centre_s(self.ov_number)
elif self.tile_number >= 0:
stage_x, stage_y = self.gm.get_tile_coordinates_s(
self.grid_number, self.tile_number)
# Now move the stage
self.microtome.move_stage_to_xy((stage_x, stage_y))
if self.microtome.get_error_state() > 0:
self.error = True
self.microtome.reset_error_state()
# Signal that move complete
self.finish_trigger.s.emit()
def move_completed(self):
if self.error:
QMessageBox.warning(self, 'Error',
'An error was detected during the move. '
'Please try again.',
QMessageBox.Ok)
else:
QMessageBox.information(self, 'Move complete',
'The stage has been moved to the selected position. '
'The Viewport will be updated after pressing OK.',
QMessageBox.Ok)
super().accept()
# Enable button again:
self.pushButton_move.setText('Move again')
self.pushButton_move.setEnabled(True)
#------------------------------------------------------------------------------
class MotorTestDlg(QDialog):
"""Perform a random-walk-like XYZ motor test. Experimental, only for
testing/debugging. Only works with a microtome for now."""
def __init__(self, cfg, microtome, main_window_queue, main_window_trigger):
super().__init__()
self.cfg = cfg
self.microtome = microtome
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
loadUi('..\\gui\\motor_test_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
# Set up trigger and queue to update dialog GUI during approach:
self.progress_trigger = Trigger()
self.progress_trigger.s.connect(self.update_progress)
self.finish_trigger = Trigger()
self.finish_trigger.s.connect(self.test_finished)
self.spinBox_duration.setRange(1, 9999)
self.spinBox_duration.setSingleStep(10)
self.spinBox_duration.setValue(10)
self.pushButton_startTest.clicked.connect(self.start_random_walk)
self.pushButton_abortTest.clicked.connect(self.abort_random_walk)
self.pushButton_startTest.setEnabled(True)
self.pushButton_abortTest.setEnabled(False)
self.test_in_progress = False
self.start_time = None
def add_to_log(self, msg):
self.main_window_queue.put(utils.format_log_entry(msg))
self.main_window_trigger.s.emit()
def update_progress(self):
if self.start_time is not None:
elapsed_time = time() - self.start_time
if elapsed_time > self.duration * 60:
self.test_in_progress = False
self.progressBar.setValue(100)
else:
self.progressBar.setValue(
int(elapsed_time/(self.duration * 60) * 100))
def start_random_walk(self):
self.aborted = False
self.start_z = self.microtome.get_stage_z()
if self.start_z is not None:
self.pushButton_startTest.setEnabled(False)
self.pushButton_abortTest.setEnabled(True)
self.buttonBox.setEnabled(False)
self.spinBox_duration.setEnabled(False)
self.progressBar.setValue(0)
thread = threading.Thread(target=self.random_walk_thread)
thread.start()
else:
self.microtome.reset_error_state()
QMessageBox.warning(self, 'Error',
'Could not read current z stage position',
QMessageBox.Ok)
def abort_random_walk(self):
self.aborted = True
self.test_in_progress = False
def test_finished(self):
self.add_to_log('3VIEW: Motor test finished.')
self.add_to_log('3VIEW: Moving back to starting z position.')
# Safe mode must be set to false because diff likely > 200 nm
self.microtome.move_stage_to_z(self.start_z, safe_mode=False)
if self.microtome.get_error_state() > 0:
self.microtome.reset_error_state()
QMessageBox.warning(
self, 'Error',
'Error moving stage back to starting position. Please '
'check the current z coordinate before (re)starting a stack.',
QMessageBox.Ok)
if self.aborted:
QMessageBox.information(
self, 'Aborted',
'Motor test was aborted by user.'
+ '\nPlease make sure that z coordinate is back at starting '
'position of ' + str(self.start_z) + '.',
QMessageBox.Ok)
else:
QMessageBox.information(
self, 'Test complete',
'Motor test complete.\nA total of '
+ str(self.number_tests) + ' xyz moves were performed.\n'
'Number of errors: ' + str(self.number_errors)
+ '\nPlease make sure that z coordinate is back at starting '
'position of ' + str(self.start_z) + '.',
QMessageBox.Ok)
self.pushButton_startTest.setEnabled(True)
self.pushButton_abortTest.setEnabled(False)
self.buttonBox.setEnabled(True)
self.spinBox_duration.setEnabled(True)
self.test_in_progress = False
def random_walk_thread(self):
self.test_in_progress = True
self.duration = self.spinBox_duration.value()
self.start_time = time()
self.progress_trigger.s.emit()
self.number_tests = 0
self.number_errors = 0
current_x, current_y = 0, 0
current_z = self.start_z
# Open log file:
logfile = open(self.cfg['acq']['base_dir'] + '\\motor_test_log.txt',
'w', buffering=1)
while self.test_in_progress:
# Start 'random' walk
if self.number_tests % 10 == 0:
dist = 300 # longer move every 10th cycle
else:
dist = 50
current_x += (random() - 0.5) * dist
current_y += (random() - 0.5) * dist
if self.number_tests % 2 == 0:
current_z += (random() - 0.5) * 0.2
else:
current_z += 0.025
if current_z < 0:
current_z = 0
# If end of permissable range is reached, go back to starting point
if (abs(current_x) > 600 or
abs(current_y) > 600 or
current_z > 600):
current_x, current_y = 0, 0
current_z = self.start_z
logfile.write('{0:.3f}, '.format(current_x)
+ '{0:.3f}, '.format(current_y)
+ '{0:.3f}'.format(current_z) + '\n')
self.microtome.move_stage_to_xy((current_x, current_y))
if self.microtome.get_error_state() > 0:
self.number_errors += 1
logfile.write('ERROR DURING XY MOVE: '
+ self.microtome.get_error_cause()
+ '\n')
self.microtome.reset_error_state()
else:
self.microtome.move_stage_to_z(current_z, safe_mode=False)
if self.microtome.get_error_state() > 0:
self.number_errors += 1
logfile.write('ERROR DURING Z MOVE: '
+ self.microtome.get_error_cause()
+ '\n')
self.microtome.reset_error_state()
else:
logfile.write('OK\n')
self.number_tests += 1
self.progress_trigger.s.emit()
logfile.write('NUMBER OF ERRORS: ' + str(self.number_errors))
logfile.close()
# Signal that thread is done:
self.finish_trigger.s.emit()
def abort_test(self):
self.aborted = True
self.pushButton_abortTest.setEnabled(False)
def closeEvent(self, event):
if not self.test_in_progress:
event.accept()
else:
event.ignore()
def accept(self):
if not self.approach_in_progress:
super().accept()
#------------------------------------------------------------------------------
class StubOVDlg(QDialog):
"""Acquire a stub overview mosaic image. The user can specify the location
in stage coordinates and the size of the mosaic.
"""
def __init__(self, position, size_selector,
base_dir, slice_counter,
sem, stage, ovm, cs,
main_window_queue, main_window_trigger):
super().__init__()
loadUi('..\\gui\\stub_ov_dlg.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.setFixedSize(self.size())
self.show()
self.base_dir = base_dir
self.slice_counter = slice_counter
self.sem = sem
self.stage = stage
self.ovm = ovm
self.cs = cs
self.main_window_queue = main_window_queue
self.main_window_trigger = main_window_trigger
# Set up trigger and queue to update dialog GUI during approach:
self.acq_thread_trigger = Trigger()
self.acq_thread_trigger.s.connect(self.process_thread_signal)
self.acq_thread_queue = Queue()
self.abort_queue = Queue()
self.acq_in_progress = False
self.pushButton_acquire.clicked.connect(self.acquire_stub_ov)
self.pushButton_abort.clicked.connect(self.abort)
self.spinBox_X.setValue(position[0])
self.spinBox_Y.setValue(position[1])
self.size_selector = size_selector
self.size_list = []
self.durations = []
for i in range(7):
# Show available mosaic sizes and the corresponding estimated
# durations in min
rows, cols = ovm.STUB_OV_SIZE[i][0], ovm.STUB_OV_SIZE[i][1]
width = int((cols * ovm.STUB_OV_FRAME_WIDTH
- (cols-1) * ovm.STUB_OV_OVERLAP)
* ovm.STUB_OV_PIXEL_SIZE / 1000)
height = int((rows * ovm.STUB_OV_FRAME_HEIGHT
- (rows-1) * ovm.STUB_OV_OVERLAP)
* ovm.STUB_OV_PIXEL_SIZE / 1000)
time = int(round((rows * cols * 10 + 20) / 60))
self.size_list.append(str(width) + ' µm × ' + str(height) + ' µm')
self.durations.append('Up to ' + str(time) + ' min')
# Grid size selection:
self.comboBox_sizeSelector.addItems(self.size_list)
self.comboBox_sizeSelector.setCurrentIndex(self.size_selector)
self.comboBox_sizeSelector.currentIndexChanged.connect(
self.update_duration)
self.label_duration.setText(self.durations[2])
self.previous_centre = self.cs.get_stub_ov_centre_s()
self.previous_origin = self.cs.get_stub_ov_origin_s()
self.previous_size_selector = self.ovm.get_stub_ov_size_selector()
def process_thread_signal(self):
"""Process commands from the queue when a trigger signal occurs
while the acquisition of the stub overview is running.
"""
msg = self.acq_thread_queue.get()
if msg == 'UPDATE STAGEPOS':
self.show_new_stage_pos()
elif msg[:15] == 'UPDATE PROGRESS':
percentage = int(msg[15:])
self.progressBar.setValue(percentage)
elif msg == 'STUB OV SUCCESS':
self.main_window_queue.put('STUB OV SUCCESS')
self.main_window_trigger.s.emit()
self.pushButton_acquire.setEnabled(True)
self.pushButton_abort.setEnabled(False)
self.buttonBox.setEnabled(True)
self.spinBox_X.setEnabled(True)
self.spinBox_Y.setEnabled(True)
self.comboBox_sizeSelector.setEnabled(True)
QMessageBox.information(
self, 'Stub Overview acquisition complete',
'The stub overview was completed successfully.',
QMessageBox.Ok)
self.acq_in_progress = False
elif msg == 'STUB OV FAILURE':
self.main_window_queue.put('STUB OV FAILURE')
self.main_window_trigger.s.emit()
# Restore previous origin:
self.cs.set_stub_ov_origin_s(self.previous_origin)
self.cs.set_stub_ov_centre_s(self.previous_centre)
self.ovm.set_stub_ov_size_selector(self.previous_size_selector)
QMessageBox.warning(
self, 'Error during stub overview acquisition',
'An error occurred during the acquisition of the stub '
'overview mosaic. The most likely cause are incorrect '
'settings of the stage X/Y motor ranges or speeds. Home '
'the stage and check whether the range limits specified '
'in SBEMimage are correct.',
QMessageBox.Ok)
self.acq_in_progress = False
self.close()
elif msg == 'STUB OV ABORT':
self.main_window_queue.put('STATUS IDLE')
self.main_window_trigger.s.emit()
# Restore previous origin:
self.cs.set_stub_ov_origin_s(self.previous_origin)
self.cs.set_stub_ov_centre_s(self.previous_centre)
self.ovm.set_stub_ov_size_selector(self.previous_size_selector)
QMessageBox.information(
self, 'Stub Overview acquisition aborted',
'The stub overview acquisition was aborted.',
QMessageBox.Ok)
self.acq_in_progress = False
self.close()
def update_duration(self):
self.label_duration.setText(self.durations[
self.comboBox_sizeSelector.currentIndex()])
def show_new_stage_pos(self):
self.main_window_queue.put('UPDATE XY')
self.main_window_trigger.s.emit()
def add_to_log(self, msg):
self.main_window_queue.put(utils.format_log_entry(msg))
self.main_window_trigger.s.emit()
def acquire_stub_ov(self):
"""Acquire the stub overview. Acquisition routine runs in
a thread.
"""
# Start acquisition only if EHT is on:
if self.sem.is_eht_on():
self.acq_in_progress = True
# Save previous stub OV origin in case user aborts acq:
position = (self.spinBox_X.value(), self.spinBox_Y.value())
size_selector = self.comboBox_sizeSelector.currentIndex()
self.add_to_log(
'CTRL: User-requested acquisition of stub OV mosaic started.')
self.pushButton_acquire.setEnabled(False)
self.pushButton_abort.setEnabled(True)
self.buttonBox.setEnabled(False)
self.spinBox_X.setEnabled(False)
self.spinBox_Y.setEnabled(False)
self.comboBox_sizeSelector.setEnabled(False)
self.progressBar.setValue(0)
self.main_window_queue.put('STUB OV BUSY')
self.main_window_trigger.s.emit()
QApplication.processEvents()
stub_acq_thread = threading.Thread(
target=acq_func.acquire_stub_ov,
args=(self.base_dir, self.slice_counter,
self.sem, self.stage,
position, size_selector,
self.ovm, self.cs,
self.acq_thread_queue,
self.acq_thread_trigger,
self.abort_queue,))
stub_acq_thread.start()
else:
QMessageBox.warning(
self, 'EHT off',
'EHT / high voltage is off. Please turn '
'it on before starting the acquisition.',
QMessageBox.Ok)
def abort(self):
if self.abort_queue.empty():
self.abort_queue.put('ABORT')
self.pushButton_abort.setEnabled(False)
def closeEvent(self, event):
if not self.acq_in_progress:
event.accept()
else:
event.ignore()
#------------------------------------------------------------------------------
class AboutBox(QDialog):
"""Show the About dialog box with info about SBEMimage and the current
version and release date.
"""
def __init__(self, VERSION):
super().__init__()
loadUi('..\\gui\\about_box.ui', self)
self.setWindowModality(Qt.ApplicationModal)
self.setWindowIcon(QIcon('..\\img\\icon_16px.ico'))
self.label_version.setText('Version ' + VERSION)
self.labelIcon.setPixmap(QPixmap('..\\img\\logo.png'))
self.setFixedSize(self.size())
self.show()
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] |
sequence_labeler.py | kamei86i/rnn-sequence-labeler | 0 | 80955 | <filename>sequence_labeler.py
import tensorflow as tf
from tensorflow.contrib import rnn
import utils.data as du
import numpy as np
import random
np.random.seed(1337)
random.seed = 1337
class SequenceLabeler:
def __init__(self, sequence_length, embedding, cell_size, num_classes, hls, gcap=5, lam=0.1, verbose=False):
self.max_sequence_length = sequence_length
self.embedding = embedding
self.cell_size = cell_size
self.num_classes = num_classes
self.hls = hls
self.verbose=verbose
self.gcap = gcap
self.lam = lam
def build_network(self):
with tf.variable_scope('input'):
self.input_x = tf.placeholder(dtype=tf.int32, shape=[None, self.max_sequence_length], name="input_x")
if self.verbose:
print(self.input_x)
self.input_y = tf.placeholder(dtype=tf.int32, shape=[None, self.max_sequence_length, self.num_classes], name="input_y")
if self.verbose:
print(self.input_y)
self.dkp = tf.placeholder(dtype=tf.float32, name="dropout")
if self.verbose:
print(self.dkp)
self.mask = tf.not_equal(self.input_x, tf.constant(0, dtype=tf.int32), name='mask')
if self.verbose:
print(self.mask)
self.correct_labels = tf.argmax(self.input_y, axis=2, name='argmax_labels')
if self.verbose:
print(self.correct_labels)
self.sequence_lengths = tf.reduce_sum(tf.cast(self.mask, tf.int32), axis=1, name='sequence_lengths')
if self.verbose:
print(self.sequence_lengths)
with tf.variable_scope('embedding_layer'):
emb_w = tf.Variable(trainable=self.embedding['trainable'], initial_value=self.embedding['weights'], dtype=tf.float32, name="embedding")
token_embedded = tf.nn.embedding_lookup(emb_w, self.input_x, name="embedding_token")
token_embedded = tf.nn.dropout(token_embedded, self.dkp, name="dropout_embedding")
self.emb_loss = tf.nn.l2_loss(emb_w, name="embedding_loss")
if self.verbose:
print(token_embedded)
with tf.variable_scope('bi_rnn'):
with tf.variable_scope('forward'):
fw_cell = rnn.GRUCell(num_units=self.cell_size)
with tf.variable_scope('backward'):
bw_cell = rnn.GRUCell(num_units=self.cell_size)
with tf.variable_scope('rnn'):
self.output, _ = tf.nn.bidirectional_dynamic_rnn(fw_cell, bw_cell, token_embedded,
self.sequence_lengths, dtype=tf.float32)
if self.verbose:
print(self.output)
self.output = tf.concat(self.output, axis=2, name="concat")
self.output = tf.nn.dropout(self.output, self.dkp, name="rnn_dropout")
if self.verbose:
print(self.output)
with tf.variable_scope('sequence_classifier'):
in_size = self.cell_size * 2
self.classify = tf.unstack(self.output, axis=1, name="unstack")
if self.verbose:
print(self.classify)
self.sequence_cl_loss = 0.0
if self.hls > 0:
wh = tf.get_variable(name="W_hidden", dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer(),
shape=[in_size, self.hls])
bh = tf.get_variable(name="b_hidden", dtype=tf.float32, initializer=tf.zeros_initializer(),
shape=[self.hls])
self.sequence_cl_loss += tf.nn.l2_loss(wh) + tf.nn.l2_loss(bh)
self.classify = [tf.nn.relu(tf.nn.xw_plus_b(x, wh, bh), name="hidden") for x in self.classify]
in_size = self.hls
wc = tf.get_variable(name="W_classify", dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer(),
shape=[in_size, self.num_classes])
bc = tf.get_variable(name="b_classify", dtype=tf.float32, initializer=tf.zeros_initializer(),
shape=[self.num_classes])
self.sequence_cl_loss += tf.nn.l2_loss(wc) + tf.nn.l2_loss(bc)
self.scores = [tf.nn.xw_plus_b(x, wc, bc, name="scores") for x in self.classify]
if self.verbose:
print(self.scores)
self.scores = tf.stack(self.scores, axis=1, name="stack")
if self.verbose:
print(self.scores)
with tf.variable_scope('loss'):
self.losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.correct_labels, logits=self.scores)
if self.verbose:
print(self.losses)
self.losses = tf.multiply(self.losses, tf.cast(self.mask, dtype=tf.float32), name='masked_losses')
if self.verbose:
print(self.losses)
self.loss1 = tf.divide(tf.reduce_sum(self.losses, axis=1), tf.cast(self.sequence_lengths, dtype=tf.float32), name="masked_mean_losses")
self.loss = tf.reduce_mean(self.loss1, name="mean_loss") + self.lam * self.emb_loss + self.lam * self.sequence_cl_loss
if self.verbose:
print(self.loss)
tf.summary.scalar('loss', self.loss)
with tf.variable_scope('measures'):
self.predictions = tf.argmax(self.scores, axis=2, name='argmax_predictions')
if self.verbose:
print(self.predictions)
self.correct_predictions = tf.cast(tf.equal(self.predictions, self.correct_labels), dtype=tf.float32, name='correct_predictions')
if self.verbose:
print(self.correct_predictions)
self.correct_predictions = tf.boolean_mask(self.correct_predictions, self.mask, name="masked_correct_predictions")
if self.verbose:
print(self.correct_predictions)
self.accuracy = tf.reduce_mean(self.correct_predictions, name="accuracy")
if self.verbose:
print(self.accuracy)
tf.summary.scalar('accuracy', self.accuracy)
def build_train_ops(self, lr):
with tf.name_scope('train_op'):
self.global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(lr)
gvs = optimizer.compute_gradients(self.loss)
capped_gvs = [(tf.clip_by_norm(grad, self.gcap), var) for grad, var in gvs]
self.train_op = optimizer.apply_gradients(capped_gvs, global_step=self.global_step,
name="train_op")
def summary(self):
self.merged = tf.summary.merge_all()
def train(self, session, batch_x, batch_y, dropout):
feed_dict = {
self.input_x: batch_x,
self.input_y: batch_y,
self.dkp: dropout
}
_, step, loss, summary, accuracy, mask, correct_labels, lengths, losses, loss1, scores = session.run(
[self.train_op, self.global_step, self.loss, self.merged, self.accuracy, self.mask, self.correct_labels, self.sequence_lengths, self.losses, self.loss1, self.scores], feed_dict)
return step, loss, summary, accuracy, mask, correct_labels, lengths, losses, loss1, scores
def predict(self, session, x, y):
feed_dict = {
self.input_x: x,
self.input_y: y,
self.dkp: 1.0
}
loss, accuracy, predictions = session.run(
[self.loss, self.accuracy, self.predictions], feed_dict)
return loss, accuracy, predictions
if __name__ == '__main__':
emb = du.initialize_random_embeddings(100, 20)
sl = SequenceLabeler(20, emb, 16, 5, 12, True)
sl.build_network() | [
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] |
website/forms.py | imanbayati/my-site | 0 | 135609 | from ctypes import resize
from dataclasses import field
from django import forms
from website.models import Contact,Newsletter
class NameForm(forms.Form):
name = forms.CharField(label='Your Name',max_length=225)
email = forms.EmailField(label='Your Email')
subject = forms.CharField(label='Your Subject',max_length=300)
message = forms.CharField(label='Your Message',widget=forms.Textarea(attrs={'cols':40,'rows':10}))
class Contactform(forms.ModelForm):
class Meta:
model = Contact
fields = '__all__'
class Newsletterform(forms.ModelForm):
class Meta:
model = Newsletter
fields = '__all__' | [
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django/feedreader/urls.py | devm1023/angular-Django | 0 | 149158 | <gh_stars>0
from django.conf.urls import url
from . import views
urlpatterns = [
url(regex=r'^recententries$', view=views.get_recent_entries, name='entries'),
url(regex=r'^deletefeed', view=views.delete_feed, name='delete_feed'),
url(regex=r'^deletegroup', view=views.delete_group, name='delete_group'),
url(regex=r'^feeds$', view=views.get_feeds, name='feeds'),
url(regex=r'^groups$', view=views.get_groups, name='groups'),
url(regex=r'^markallread$', view=views.mark_all_read, name='mark_all_read'),
url(regex=r'^savefeed', view=views.save_feed, name='save_feed'),
url(regex=r'^savegroup', view=views.save_group, name='save_group'),
url(regex=r'^toggleread$', view=views.toggle_read, name='toggle_read'),
]
| [
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FuncionSeleccionPadres.py | BrendaMep/Proyecto1Reynas | 0 | 75659 | import numpy as np
import random
from FuncionAptitud import fitness
lista = [0, 1, 2, 3, 4, 5, 6, 7] # son los valores en los que puede estar la reyna
poblacion = np.empty((50,8))
for i in range(50):
random.shuffle(lista)
for j in range(8):
poblacion[i, j] = lista[j]
def padres(conjunto):
r1 = random.random()
r2 = random.random()
Aptitud = ([])
A_norm = ([])
probabilidad = ([])
prob_acomulada = ([])
posibles_papas = ([])
for i in range(50):
Aptitud.append(fitness(conjunto[i, :])) # contiene todos los ataques
maximo = max(Aptitud) # es el que tiene mas ataques
for i in range(50):
A_norm.append(Aptitud[i]-maximo)
suma = sum(A_norm)
for i in range(50):
probabilidad.append(A_norm[i]/ suma) # probabilidad de cada individuo de la población
prob_acomulada = probabilidad
for i in range(1,50):
prob_acomulada[i] = prob_acomulada[i] + prob_acomulada[i-1]
for i in range(50):
if prob_acomulada[i]>= r1:
posibles_papas.append(conjunto[i])
papa1 = posibles_papas[0]
posibles_papas = ([])
for i in range(50):
if prob_acomulada[i]>= r2:
posibles_papas.append(conjunto[i])
papa2 = posibles_papas[0]
return papa1,papa2
def selec_padres(poblacion):
pob_padres = np.empty((100, 8))
j =0
while j in range(100):
papas = padres(poblacion)
pob_padres[j]= papas[0]
pob_padres[j+1]= papas[1]
j = j+2
return pob_padres
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pytorch3d/implicitron/dataset/dataset_zoo.py | JulianKnodt/pytorch3d | 0 | 39576 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import copy
import json
import os
from typing import Any, Dict, List, Optional, Sequence
from iopath.common.file_io import PathManager
from .implicitron_dataset import ImplicitronDataset, ImplicitronDatasetBase
from .utils import (
DATASET_TYPE_KNOWN,
DATASET_TYPE_TEST,
DATASET_TYPE_TRAIN,
DATASET_TYPE_UNKNOWN,
)
# TODO from dataset.dataset_configs import DATASET_CONFIGS
DATASET_CONFIGS: Dict[str, Dict[str, Any]] = {
"default": {
"box_crop": True,
"box_crop_context": 0.3,
"image_width": 800,
"image_height": 800,
"remove_empty_masks": True,
}
}
# fmt: off
CO3D_CATEGORIES: List[str] = list(reversed([
"baseballbat", "banana", "bicycle", "microwave", "tv",
"cellphone", "toilet", "hairdryer", "couch", "kite", "pizza",
"umbrella", "wineglass", "laptop",
"hotdog", "stopsign", "frisbee", "baseballglove",
"cup", "parkingmeter", "backpack", "toyplane", "toybus",
"handbag", "chair", "keyboard", "car", "motorcycle",
"carrot", "bottle", "sandwich", "remote", "bowl", "skateboard",
"toaster", "mouse", "toytrain", "book", "toytruck",
"orange", "broccoli", "plant", "teddybear",
"suitcase", "bench", "ball", "cake",
"vase", "hydrant", "apple", "donut",
]))
# fmt: on
_CO3D_DATASET_ROOT: str = os.getenv("CO3D_DATASET_ROOT", "")
def dataset_zoo(
dataset_name: str = "co3d_singlesequence",
dataset_root: str = _CO3D_DATASET_ROOT,
category: str = "DEFAULT",
limit_to: int = -1,
limit_sequences_to: int = -1,
n_frames_per_sequence: int = -1,
test_on_train: bool = False,
load_point_clouds: bool = False,
mask_images: bool = False,
mask_depths: bool = False,
restrict_sequence_name: Sequence[str] = (),
test_restrict_sequence_id: int = -1,
assert_single_seq: bool = False,
only_test_set: bool = False,
aux_dataset_kwargs: dict = DATASET_CONFIGS["default"],
path_manager: Optional[PathManager] = None,
) -> Dict[str, ImplicitronDatasetBase]:
"""
Generates the training / validation and testing dataset objects.
Args:
dataset_name: The name of the returned dataset.
dataset_root: The root folder of the dataset.
category: The object category of the dataset.
limit_to: Limit the dataset to the first #limit_to frames.
limit_sequences_to: Limit the dataset to the first
#limit_sequences_to sequences.
n_frames_per_sequence: Randomly sample #n_frames_per_sequence frames
in each sequence.
test_on_train: Construct validation and test datasets from
the training subset.
load_point_clouds: Enable returning scene point clouds from the dataset.
mask_images: Mask the loaded images with segmentation masks.
mask_depths: Mask the loaded depths with segmentation masks.
restrict_sequence_name: Restrict the dataset sequences to the ones
present in the given list of names.
test_restrict_sequence_id: The ID of the loaded sequence.
Active for dataset_name='co3d_singlesequence'.
assert_single_seq: Assert that only frames from a single sequence
are present in all generated datasets.
only_test_set: Load only the test set.
aux_dataset_kwargs: Specifies additional arguments to the
ImplicitronDataset constructor call.
Returns:
datasets: A dictionary containing the
`"dataset_subset_name": torch_dataset_object` key, value pairs.
"""
restrict_sequence_name = tuple(restrict_sequence_name)
aux_dataset_kwargs = dict(aux_dataset_kwargs)
datasets = {}
# TODO:
# - implement loading multiple categories
if dataset_name in ["co3d_singlesequence", "co3d_multisequence"]:
# This maps the common names of the dataset subsets ("train"/"val"/"test")
# to the names of the subsets in the CO3D dataset.
set_names_mapping = _get_co3d_set_names_mapping(
dataset_name,
test_on_train,
only_test_set,
)
# load the evaluation batches
task = dataset_name.split("_")[-1]
batch_indices_path = os.path.join(
dataset_root,
category,
f"eval_batches_{task}.json",
)
if not os.path.isfile(batch_indices_path):
# The batch indices file does not exist.
# Most probably the user has not specified the root folder.
raise ValueError("Please specify a correct dataset_root folder.")
with open(batch_indices_path, "r") as f:
eval_batch_index = json.load(f)
if task == "singlesequence":
assert (
test_restrict_sequence_id is not None and test_restrict_sequence_id >= 0
), (
"Please specify an integer id 'test_restrict_sequence_id'"
+ " of the sequence considered for 'singlesequence'"
+ " training and evaluation."
)
assert len(restrict_sequence_name) == 0, (
"For the 'singlesequence' task, the restrict_sequence_name has"
" to be unset while test_restrict_sequence_id has to be set to an"
" integer defining the order of the evaluation sequence."
)
# a sort-stable set() equivalent:
eval_batches_sequence_names = list(
{b[0][0]: None for b in eval_batch_index}.keys()
)
eval_sequence_name = eval_batches_sequence_names[test_restrict_sequence_id]
eval_batch_index = [
b for b in eval_batch_index if b[0][0] == eval_sequence_name
]
# overwrite the restrict_sequence_name
restrict_sequence_name = [eval_sequence_name]
for dataset, subsets in set_names_mapping.items():
frame_file = os.path.join(dataset_root, category, "frame_annotations.jgz")
assert os.path.isfile(frame_file)
sequence_file = os.path.join(
dataset_root, category, "sequence_annotations.jgz"
)
assert os.path.isfile(sequence_file)
subset_lists_file = os.path.join(dataset_root, category, "set_lists.json")
assert os.path.isfile(subset_lists_file)
# TODO: maybe directly in param list
params = {
**copy.deepcopy(aux_dataset_kwargs),
"frame_annotations_file": frame_file,
"sequence_annotations_file": sequence_file,
"subset_lists_file": subset_lists_file,
"dataset_root": dataset_root,
"limit_to": limit_to,
"limit_sequences_to": limit_sequences_to,
"n_frames_per_sequence": n_frames_per_sequence
if dataset == "train"
else -1,
"subsets": subsets,
"load_point_clouds": load_point_clouds,
"mask_images": mask_images,
"mask_depths": mask_depths,
"pick_sequence": restrict_sequence_name,
"path_manager": path_manager,
}
datasets[dataset] = ImplicitronDataset(**params)
if dataset == "test":
if len(restrict_sequence_name) > 0:
eval_batch_index = [
b for b in eval_batch_index if b[0][0] in restrict_sequence_name
]
datasets[dataset].eval_batches = datasets[
dataset
].seq_frame_index_to_dataset_index(eval_batch_index)
if assert_single_seq:
# check theres only one sequence in all datasets
assert (
len(
{
e["frame_annotation"].sequence_name
for dset in datasets.values()
for e in dset.frame_annots
}
)
<= 1
), "Multiple sequences loaded but expected one"
else:
raise ValueError(f"Unsupported dataset: {dataset_name}")
if test_on_train:
datasets["val"] = datasets["train"]
datasets["test"] = datasets["train"]
return datasets
def _get_co3d_set_names_mapping(
dataset_name: str,
test_on_train: bool,
only_test: bool,
) -> Dict[str, List[str]]:
"""
Returns the mapping of the common dataset subset names ("train"/"val"/"test")
to the names of the corresponding subsets in the CO3D dataset
("test_known"/"test_unseen"/"train_known"/"train_unseen").
"""
single_seq = dataset_name == "co3d_singlesequence"
if only_test:
set_names_mapping = {}
else:
set_names_mapping = {
"train": [
(DATASET_TYPE_TEST if single_seq else DATASET_TYPE_TRAIN)
+ "_"
+ DATASET_TYPE_KNOWN
]
}
if not test_on_train:
prefixes = [DATASET_TYPE_TEST]
if not single_seq:
prefixes.append(DATASET_TYPE_TRAIN)
set_names_mapping.update(
{
dset: [
p + "_" + t
for p in prefixes
for t in [DATASET_TYPE_KNOWN, DATASET_TYPE_UNKNOWN]
]
for dset in ["val", "test"]
}
)
return set_names_mapping
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mp3player.py | athavus/Mp3Player | 2 | 176504 | from pygame import mixer
from PySimpleGUI import PySimpleGUI as sg
sg.theme('DarkPurple1') # Definição do tema da interface do programa
layout = [
[sg.Text('MP3 PLAYER'.center(50), font='Consolas')], # Título do programa
[sg.Text('NOME DA MÚSICA:', font='Consolas'), sg.Input(key='music', size=(42, 1))], # Input da música que será tocada
[sg.Button('▶', size=(55, 1))] # Botão para fazer tocar a música
]
janela = sg.Window('Mp3 player', layout) # Criação da janela
while True:
try: # Tentativa de abrir o arquivo da música e da leitura dos inputs de cima
eventos, valores = janela.read()
musica = open(valores['music'])
except: # Caso o usuário não coloque um valor válido no input, temos uma mensagem de Erro! Porém o programa não se encerra
print('Erro! :/')
else: # Se tudo ocorrer corretamente, chegamos nesta condição onde o player enfim toca a música
if eventos == sg.WIN_CLOSED:
break
if eventos == '▶':
mixer.init() # Iniciador do mixer do Pygame
mixer.music.load(musica) # Comando que carrega a música recebida pelo input
mixer.music.play() # Comando que toca a música que foi recebida pelo input
print(f'Está tocando {valores["music"]}... Aproveite') # Comando que mostra a música que está tocando
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] |
src/pkg/caendr/caendr/services/sql/etl/__init__.py | AndersenLab/CAENDR | 3 | 1602015 | <reponame>AndersenLab/CAENDR
from .strains import load_strains
from .wormbase import load_genes_summary, load_genes, load_orthologs
from .homologs import load_homologs
from .strain_annotated_variants import load_strain_annotated_variants | [
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itp1_8a.py | ShinjiKatoA16/aoj-itp | 0 | 168514 | <reponame>ShinjiKatoA16/aoj-itp
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import sys
from string import ascii_lowercase as asc_l, ascii_uppercase as asc_u
table = str.maketrans(asc_l+asc_u, asc_u+asc_l)
s = sys.stdin.readline()
print(s.translate(table), end='')
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2433,
1495,
13,
2
] |
src/views/help.py | Amin-egn/Recipient | 1 | 165881 | <reponame>Amin-egn/Recipient<gh_stars>1-10
class Help(object):
"""Help View"""
def __init__(self, ui):
self.ui = ui
self.tab = ui.contents.help
# connect signals
self.ui.menu.btnHelp.clicked.connect(self.tab_handler)
def tab_handler(self):
self.ui.contents.showTab(self.ui.contents.HELP)
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app/ext/api/services/file_services.py | fredsonchaves07/foodfy | 0 | 1617476 | from app.ext.api.models.file import Files
from app.ext.database import db
def create_file(filename, path):
file = Files()
file.name = filename
file.path = path
db.session.add(file)
db.session.commit()
return file.as_dict()
def update_file(file_id, filename, path):
file = get_file_by_id(file_id)
file.name = filename
file.path = path
db.session.commit()
return file.as_dict()
def get_file_by_id(file_id):
file = Files.query.filter_by(id=file_id).first()
return file
def delete_file(file_id):
file = get_file_by_id(file_id)
db.session.delete(file)
db.session.commit()
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FastAutoAugment/pointaug/__init__.py | ironluffy/fast-autoaugment | 0 | 152074 | <reponame>ironluffy/fast-autoaugment
from .train import train
from .test import test
from .test import test_model | [
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tools/examples.py | Hellowlol/plexapi | 4 | 19018 | <filename>tools/examples.py
# -*- coding: utf-8 -*-
"""
PlexAPI Examples
As of Plex version 0.9.11 I noticed that you must be logged in
to browse even the plex server locatewd at localhost. You can
run this example suite with the following command:
>> python examples.py -u <USERNAME> -p <PASSWORD> -s <SERVERNAME>
"""
import argparse, sys
from collections import defaultdict
from os.path import dirname, abspath
sys.path.append(dirname(dirname(abspath(__file__))))
from utils import fetch_server, iter_tests, register
@register()
def list_unwatched_movies(plex):
""" Example 1: List all unwatched movies. """
movies = plex.library.section('Movies')
for video in movies.search(unwatched=True, maxresults=10, sort='addedAt:desc'):
print(' %s' % video.title)
@register()
def mark_all_friends_episodes_watched(plex):
""" Example 2: Mark all Friends episodes watched. """
plex.library.section('TV Shows').get('Friends').markWatched()
@register()
def list_connected_clients(plex):
""" Example 3: List clients connected to the server. """
for client in plex.clients():
print(client.title)
@register()
def play_avatar_on_client(plex):
""" Example 4: Play the Movie Avatar on my iPhone.
Note: Client must be on same network as server.
"""
avatar = plex.library.section('Movies').get('Avatar')
client = plex.client('iphone-mike')
client.playMedia(avatar)
@register()
def list_animated_movies(plex):
""" Example 5: List all animated movies from the 90s. """
movies = plex.library.section('Movies')
for video in movies.search(genre='animation', decade=1990):
print(' %s (%s)' % (video.title, video.year))
@register()
def follow_the_talent(plex):
""" Example 6: List all movies directed by the same person as Jurassic Park. """
movies = plex.library.section('Movies')
jurassic_park = movies.get('Jurassic Park')
for movie in movies.search(director=jurassic_park.directors):
print(movie.title)
@register()
def list_files(plex):
""" Example 7: List files for the latest episode of Friends. """
thelastone = plex.library.section('TV Shows').get('Friends').episodes()[-1]
for part in thelastone.iterParts():
print(part.file)
@register()
def get_stream_url(plex):
""" Example 8: Get a URL you can open in VLC, MPV, etc. """
jurassic_park = plex.library.section('Movies').get('Jurassic Park')
print('Try running the following command:')
print('vlc "%s"' % jurassic_park.getStreamURL(videoResolution='800x600'))
@register()
def most_streamed_titles(plex):
""" Example 9: List the most played movies. """
popular = defaultdict(int)
for item in plex.history():
if item.TYPE == 'movie':
popular[item.title] += 1
popular = sorted(popular.items(), key=lambda x:x[1], reverse=True)
for title, count in popular[:5]:
print('%s (%s plays)' % (title, count))
@register()
def most_active_users(plex):
""" Example 10: List the most active users. """
users = defaultdict(int)
for item in plex.history():
print(item.TYPE)
users[item.username] += 1
users = sorted(users.items(), key=lambda x:x[1], reverse=True)
for user, count in users[:5]:
print('%s (%s plays)' % (user, count))
if __name__ == '__main__':
# There are three ways to authenticate:
# 1. If the server is running on localhost, just run without any auth.
# 2. Pass in --username, --password, and --resource.
# 3. Pass in --baseurl, --token
parser = argparse.ArgumentParser(description='Run PlexAPI examples.')
parser.add_argument('-u', '--username', help='Username for your MyPlex account.')
parser.add_argument('-p', '--password', help='Password for your MyPlex account.')
parser.add_argument('-r', '--resource', help='Name of the Plex resource (requires user/pass).')
parser.add_argument('-b', '--baseurl', help='Baseurl needed for auth token authentication')
parser.add_argument('-t', '--token', help='Auth token (instead of user/pass)')
parser.add_argument('-q', '--example', help='Only run the specified example.')
parser.add_argument('-v', '--verbose', default=False, action='store_true', help='Print verbose logging.')
args = parser.parse_args()
plex, account = fetch_server(args)
for example in iter_tests(args.example):
example['func'](plex)
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Templates/Scripts/src/CGM2/CGM22_workflow.py | sremm/qualisys_CGM2_workflow | 0 | 197132 | <reponame>sremm/qualisys_CGM2_workflow
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import logging
import os
import shutil
import pyCGM2
from pyCGM2.Model.CGM2 import cgm
from pyCGM2.Lib.CGM import cgm2_2
from pyCGM2.Utils import files
from pyCGM2.Utils.utils import *
from pyCGM2.qtm import qtmTools
from pyCGM2 import enums
from pyCGM2.Tools import btkTools
from pyCGM2.Lib import eventDetector,analysis,plot
from pyCGM2.Report import normativeDatasets
from pyCGM2.Signal import signal_processing
from pyCGM2.ForcePlates import forceplates
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import argparse
from pyCGM2.Inspect import inspectFilters, inspectProcedures
from pyCGM2 import log;
log.setLogger(level = logging.INFO)
with open('pyCGM2.log', 'w'): pass
MARKERSETS={"Lower limb tracking markers": cgm.CGM1.LOWERLIMB_TRACKING_MARKERS,
"Thorax tracking markers": cgm.CGM1.THORAX_TRACKING_MARKERS,
"Upper limb tracking markers": cgm.CGM1.UPPERLIMB_TRACKING_MARKERS,
"Calibration markers": ["LKNM","RKNM","LMED","RMED","LKAX","LKD1","LKD2","RKAX","RKD1","RKD2"]}
def main():
logging.info("------------------------------------------------")
logging.info("------------QTM - pyCGM2 Workflow---------------")
logging.info("------------------------------------------------")
file="session.xml"
sessionXML = files.readXml(os.getcwd()+"\\",file)
sessionDate = files.getFileCreationDate(os.getcwd()+"\\"+file)
#---------------------------------------------------------------------------
#management of the Processed folder
DATA_PATH = os.getcwd()+"\\"+"processed\\"
files.createDir(DATA_PATH)
staticMeasurement = qtmTools.findStatic(sessionXML)
calibrateFilenameLabelled = qtmTools.getFilename(staticMeasurement)
if not os.path.isfile(DATA_PATH+calibrateFilenameLabelled):
shutil.copyfile(os.getcwd()+"\\"+calibrateFilenameLabelled,DATA_PATH+calibrateFilenameLabelled)
logging.info("qualisys exported c3d file [%s] copied to processed folder"%(calibrateFilenameLabelled))
dynamicMeasurements= qtmTools.findDynamic(sessionXML)
for dynamicMeasurement in dynamicMeasurements:
reconstructFilenameLabelled = qtmTools.getFilename(dynamicMeasurement)
if not os.path.isfile(DATA_PATH+reconstructFilenameLabelled):
shutil.copyfile(os.getcwd()+"\\"+reconstructFilenameLabelled,DATA_PATH+reconstructFilenameLabelled)
logging.info("qualisys exported c3d file [%s] copied to processed folder"%(reconstructFilenameLabelled))
acq=btkTools.smartReader(str(DATA_PATH+reconstructFilenameLabelled))
if btkTools.checkForcePlateExist(acq):
if "5" in btkTools.smartGetMetadata(acq,"FORCE_PLATFORM","TYPE"):
forceplates.correctForcePlateType5(acq)
acq,zeniState = eventDetector.zeni(acq)
if zeniState:
btkTools.smartWriter(acq, str(DATA_PATH + reconstructFilenameLabelled))
cmd = "Mokka.exe \"%s\""%(str(DATA_PATH + reconstructFilenameLabelled))
os.system(cmd)
# --------------------------GLOBAL SETTINGS ------------------------------------
# global setting ( in user/AppData)
if os.path.isfile(pyCGM2.PYCGM2_APPDATA_PATH + "CGM2_2-pyCGM2.settings"):
settings = files.openFile(pyCGM2.PYCGM2_APPDATA_PATH,"CGM2_2-pyCGM2.settings")
else:
settings = files.openFile(pyCGM2.PYCGM2_SETTINGS_FOLDER,"CGM2_2-pyCGM2.settings")
# --------------------------MP ------------------------------------
required_mp,optional_mp = qtmTools.SubjectMp(sessionXML)
# --Check MP
inspectprocedure = inspectProcedures.AnthropometricDataQualityProcedure(required_mp)
inspector = inspectFilters.QualityFilter(inspectprocedure)
inspector.run()
# translators management
translators = files.getTranslators(os.getcwd()+"\\","CGM2_2.translators")
if not translators: translators = settings["Translators"]
# ikweight
ikWeight = files.getIKweightSet(DATA_PATH,"CGM2_2.ikw")
if not ikWeight: ikWeight = settings["Fitting"]["Weight"]
# --------------------------MODEL CALIBRATION -----------------------
logging.info("--------------------------MODEL CALIBRATION -----------------------")
staticMeasurement = qtmTools.findStatic(sessionXML)
calibrateFilenameLabelled = qtmTools.getFilename(staticMeasurement)
logging.info("----- CALIBRATION- static file [%s]--"%(calibrateFilenameLabelled))
leftFlatFoot = toBool(staticMeasurement.Left_foot_normalised_to_static_trial.text)
rightFlatFoot = toBool(staticMeasurement.Right_foot_normalised_to_static_trial.text)
headFlat = toBool(staticMeasurement.Head_normalised_to_static_trial.text)
markerDiameter = float(staticMeasurement.Marker_diameter.text)*1000.0
hjcMethod = settings["Calibration"]["HJC"]
pointSuffix = None
# Calibration checking
# --------------------
acqStatic = btkTools.smartReader(DATA_PATH+calibrateFilenameLabelled)
for key in MARKERSETS.keys():
logging.info("[pyCGM2] Checking of the %s"%(key))
# presence
ip_presence = inspectProcedures.MarkerPresenceQualityProcedure(acqStatic,
markers = MARKERSETS[key])
inspector = inspectFilters.QualityFilter(ip_presence)
inspector.run()
if ip_presence.markersIn !=[]:
ip_gap = inspectProcedures.GapQualityProcedure(acqStatic,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_gap)
inspector.run()
ip_swap = inspectProcedures.SwappingMarkerQualityProcedure(acqStatic,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_swap)
inspector.run()
ip_pos = inspectProcedures.MarkerPositionQualityProcedure(acqStatic,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_pos)
# Calibration operation
# --------------------
logging.info("[pyCGM2] --- calibration operation ---")
model,acqStatic = cgm2_2.calibrate(DATA_PATH,
calibrateFilenameLabelled,
translators,settings,
required_mp,optional_mp,
False,
leftFlatFoot,rightFlatFoot,headFlat,markerDiameter,
hjcMethod,
pointSuffix)
logging.info("----- CALIBRATION- static file [%s]-----> DONE"%(calibrateFilenameLabelled))
# --------------------------MODEL FITTING ----------------------------------
logging.info("--------------------------MODEL FITTING ----------------------------------")
dynamicMeasurements= qtmTools.findDynamic(sessionXML)
modelledC3ds = list()
eventInspectorStates = list()
for dynamicMeasurement in dynamicMeasurements:
reconstructFilenameLabelled = qtmTools.getFilename(dynamicMeasurement)
logging.info("----Processing of [%s]-----"%(reconstructFilenameLabelled))
mfpa = qtmTools.getForcePlateAssigment(dynamicMeasurement)
momentProjection_text = dynamicMeasurement.Moment_Projection.text
if momentProjection_text == "Default":
momentProjection_text = settings["Fitting"]["Moment Projection"]
if momentProjection_text == "Distal":
momentProjection = enums.MomentProjection.Distal
elif momentProjection_text == "Proximal":
momentProjection = enums.MomentProjection.Proximal
elif momentProjection_text == "Global":
momentProjection = enums.MomentProjection.Global
elif momentProjection_text == "JCS":
momentProjection = enums.MomentProjection.JCS
acq = btkTools.smartReader(DATA_PATH+reconstructFilenameLabelled)
# Fitting checking
# --------------------
for key in MARKERSETS.keys():
if key != "Calibration markers":
logging.info("[pyCGM2] Checking of the %s"%(key))
# presence
ip_presence = inspectProcedures.MarkerPresenceQualityProcedure(acq,
markers = MARKERSETS[key])
inspector = inspectFilters.QualityFilter(ip_presence)
inspector.run()
if ip_presence.markersIn !=[]:
ip_gap = inspectProcedures.GapQualityProcedure(acq,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_gap)
inspector.run()
ip_swap = inspectProcedures.SwappingMarkerQualityProcedure(acq,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_swap)
inspector.run()
ip_pos = inspectProcedures.MarkerPositionQualityProcedure(acq,
markers = ip_presence.markersIn)
inspector = inspectFilters.QualityFilter(ip_pos)
# filtering
# -----------------------
# marker
order = int(float(dynamicMeasurement.Marker_lowpass_filter_order.text))
fc = float(dynamicMeasurement.Marker_lowpass_filter_frequency.text)
signal_processing.markerFiltering(acq,order=order, fc =fc)
# management of force plate type 5 and force plate filtering
order = int(float(dynamicMeasurement.Forceplate_lowpass_filter_order.text))
fc = float(dynamicMeasurement.Forceplate_lowpass_filter_frequency.text)
if order!=0 and fc!=0:
acq = btkTools.smartReader(DATA_PATH+reconstructFilenameLabelled)
if btkTools.checkForcePlateExist(acq):
if "5" in btkTools.smartGetMetadata(acq,"FORCE_PLATFORM","TYPE"):
forceplates.correctForcePlateType5(acq)
signal_processing.markerFiltering(acq,order=order, fc =fc)
else:
if btkTools.checkForcePlateExist(acq):
if "5" in btkTools.smartGetMetadata(acq,"FORCE_PLATFORM","TYPE"):
forceplates.correctForcePlateType5(acq)
btkTools.smartWriter(acq,DATA_PATH+reconstructFilenameLabelled)
# event checking
# -----------------------
inspectprocedureEvents = inspectProcedures.GaitEventQualityProcedure(acq)
inspector = inspectFilters.QualityFilter(inspectprocedureEvents)
inspector.run()
eventInspectorStates.append(inspectprocedureEvents.state)
# fitting operation
# -----------------------
logging.info("[pyCGM2] --- Fitting operation ---")
acqGait = cgm2_2.fitting(model,DATA_PATH, reconstructFilenameLabelled,
translators,settings,
markerDiameter,
pointSuffix,
mfpa,momentProjection)
outFilename = reconstructFilenameLabelled#[:-4] + "_CGM1.c3d"
btkTools.smartWriter(acqGait, str(DATA_PATH + outFilename))
modelledC3ds.append(outFilename)
logging.info("----Processing of [%s]-----> DONE"%(reconstructFilenameLabelled))
# --------------------------GAIT PROCESSING -----------------------
if not all(eventInspectorStates):
raise Exception ("[pyCGM2] Impossible to run Gait processing. Badly gait event detection. check the log file")
logging.info("---------------------GAIT PROCESSING -----------------------")
nds = normativeDatasets.Schwartz2008("Free")
types = qtmTools.detectMeasurementType(sessionXML)
for type in types:
modelledTrials = list()
for dynamicMeasurement in dynamicMeasurements:
if qtmTools.isType(dynamicMeasurement,type):
filename = qtmTools.getFilename(dynamicMeasurement)
modelledTrials.append(filename)#.replace(".c3d","_CGM1.c3d"))
subjectMd = {"patientName": sessionXML.find("Last_name").text +" "+ sessionXML.find("First_name").text,
"bodyHeight": sessionXML.find("Height").text,
"bodyWeight": sessionXML.find("Weight").text ,
"diagnosis": sessionXML.find("Diagnosis").text,
"dob": sessionXML.find("Date_of_birth").text,
"sex": sessionXML.find("Sex").text,
"test condition": type,
"gmfcs": sessionXML.find("Gross_Motor_Function_Classification").text,
"fms": sessionXML.find("Functional_Mobility_Scale").text}
analysisInstance = analysis.makeAnalysis(
DATA_PATH,modelledTrials,
subjectInfo=None,
experimentalInfo=None,
modelInfo=None,
pointLabelSuffix=None)
title = type
# spatiotemporal
plot.plot_spatioTemporal(DATA_PATH,analysisInstance,
exportPdf=True,
outputName=title,
show=None,
title=title)
#Kinematics
if model.m_bodypart in [enums.BodyPart.LowerLimb,enums.BodyPart.LowerLimbTrunk, enums.BodyPart.FullBody]:
plot.plot_DescriptiveKinematic(DATA_PATH,analysisInstance,"LowerLimb",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
plot.plot_ConsistencyKinematic(DATA_PATH,analysisInstance,"LowerLimb",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
if model.m_bodypart in [enums.BodyPart.LowerLimbTrunk, enums.BodyPart.FullBody]:
plot.plot_DescriptiveKinematic(DATA_PATH,analysisInstance,"Trunk",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
plot.plot_ConsistencyKinematic(DATA_PATH,analysisInstance,"Trunk",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
if model.m_bodypart in [enums.BodyPart.UpperLimb, enums.BodyPart.FullBody]:
pass # TODO plot upperlimb panel
#Kinetics
if model.m_bodypart in [enums.BodyPart.LowerLimb,enums.BodyPart.LowerLimbTrunk, enums.BodyPart.FullBody]:
plot.plot_DescriptiveKinetic(DATA_PATH,analysisInstance,"LowerLimb",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
plot.plot_ConsistencyKinetic(DATA_PATH,analysisInstance,"LowerLimb",
nds,
exportPdf=True,
outputName=title,
pointLabelSuffix=pointSuffix,
show=False,
title=title)
#MAP
plot.plot_MAP(DATA_PATH,analysisInstance,
nds,
exportPdf=True,
outputName=title,pointLabelSuffix=pointSuffix,
show=False,
title=title)
plt.show(False)
logging.info("----- Gait Processing -----> DONE")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='CGM21 workflow')
# parser.add_argument('--noGaitEventDetection', action='store_true', help='no gait event detection')
# parser.add_argument('--noGaitProcessing', action='store_true', help='no gait processing')
args = parser.parse_args()
main()
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] |
corona.py | taylordotfish/coronavirus-irc-bot | 0 | 134307 | from utils import json_request
irc_formatting = True
def format(number, sign = False):
if irc_formatting:
if sign:
return '\x02\x0304{:+}\x03\x02'.format(number)
else:
return '\x02\x0304{}\x03\x02'.format(number)
else:
if sign:
return '{:+}'.format(number)
else:
return str(number)
def get_country_status(query):
data = json_request('https://api.covid19api.com/summary')
if not data:
return None
for country in data["Countries"]:
if country["Country"].lower() == query or country["Slug"] == query or country["CountryCode"].lower() == query:
confirmed = format(country["TotalConfirmed"])
new_cases = format(country["NewConfirmed"], sign=True)
deaths = format(country["TotalDeaths"])
new_deaths = format(country["NewDeaths"], sign=True)
info = 'Total Cases: {} ({}) - Total Deaths: {} ({})'.format(confirmed, new_cases, deaths, new_deaths)
return info
def get_italy_status():
data = json_request('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-andamento-nazionale.json')
if not data:
return None
latest = data[-1]
yesterday = data[-2]
totale_positivi = format(latest["totale_positivi"])
variazione_totale_positivi = format(latest["variazione_totale_positivi"], sign = True)
nuovi_positivi = format(latest["nuovi_positivi"], sign = True)
deceduti = format(latest["deceduti"])
nuovi_deceduti = format(latest["deceduti"] - yesterday["deceduti"], sign = True)
dimessi = format(latest["dimessi_guariti"])
nuovi_dimessi = format(latest["dimessi_guariti"] - yesterday["dimessi_guariti"], sign = True)
totale_casi = format(latest["totale_casi"])
info = 'Totale attualmente positivi: {} ({}) - Deceduti: {} ({}) - Dimessi guariti: {} ({}) - Totale casi: {} ({})' \
.format(totale_positivi, variazione_totale_positivi, deceduti, nuovi_deceduti, dimessi, nuovi_dimessi, totale_casi, nuovi_positivi)
return info
def get_italy_regione(query):
data = json_request('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-regioni-latest.json')
if not data:
return None
for regione in data:
if regione["denominazione_regione"].lower() == query:
totale_positivi = format(regione["totale_positivi"])
variazione_totale_positivi = format(regione["variazione_totale_positivi"], sign = True)
nuovi_positivi = format(regione["nuovi_positivi"], sign = True)
dimessi_guariti = format(regione["dimessi_guariti"])
deceduti = format(regione["deceduti"])
totale_casi = format(regione["totale_casi"])
tamponi = format(regione["tamponi"])
info = 'Attualmente positivi: {} ({}) - Dimessi guariti: {} - Deceduti: {} - Totale casi: {} ({}) - Tamponi: {}' \
.format(totale_positivi, variazione_totale_positivi, dimessi_guariti, deceduti, totale_casi, nuovi_positivi, tamponi)
return info
def get_italy_province(query):
data = json_request('https://raw.githubusercontent.com/pcm-dpc/COVID-19/master/dati-json/dpc-covid19-ita-province-latest.json')
if not data:
return None
for provincia in data:
if provincia["denominazione_provincia"].lower() == query:
totale_casi = format(provincia["totale_casi"])
info = 'Totale Casi: {}'.format(totale_casi)
return info
def get_global_status():
data = json_request('https://api.covid19api.com/summary')
if not data:
return None
confirmed = format(data["Global"]["TotalConfirmed"])
deaths = format(data["Global"]["TotalDeaths"])
new_cases = format(data["Global"]["NewConfirmed"], sign=True)
new_deaths = format(data["Global"]["NewDeaths"], sign=True)
info = 'Total Cases: {} ({}) - Total Deaths: {} ({})'.format(confirmed, new_cases, deaths, new_deaths)
return info
def elaborate_query(query):
if query == "global":
return get_global_status()
if query == "italy" or query == "italia":
return get_italy_status()
info = get_country_status(query)
if info:
return info
info = get_italy_regione(query)
if info:
return info
info = get_italy_province(query)
if info:
return info
return "I don't know bro"
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] |
VFF/psi_statistics.py | st--/VFF | 1 | 82524 | # Copyright 2016 <NAME>
#
# 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 tensorflow as tf
import numpy as np
def psi1(mean, var, a, b, ms):
# this bit is the same as Kuf on the mean
omegas = 2.0 * np.pi * ms / (b - a)
Kuf_cos = tf.transpose(tf.cos(omegas * (mean - a)))
omegas = omegas[omegas != 0] # don't compute zeros freq.
Kuf_sin = tf.transpose(tf.sin(omegas * (mean - a)))
# this bit depends on the variance
a = tf.transpose(tf.exp(-tf.square(omegas) * var / 2))
Psi1_cos = Kuf_cos * a
Psi1_sin = Kuf_sin * a
return tf.concat([Psi1_cos, Psi1_sin], axis=0)
def psi2(mean, var, a, b, ms):
# mean and var must be N x 1
# this bit is the same as Kuf on the mean
omegas = 2.0 * np.pi * ms / (b - a)
omegas_add = tf.reshape(omegas, (-1, 1)) + tf.reshape(omegas, (1, -1))
omegas_diff = tf.reshape(omegas, (-1, 1)) - tf.reshape(omegas, (1, -1))
omegas_add = tf.expand_dims(omegas_add, 0)
omegas_diff = tf.expand_dims(omegas_diff, 0)
exp_add = tf.exp(-tf.expand_dims(var, 1) * tf.square(omegas_add) / 2)
exp_diff = tf.exp(-var * tf.square(omegas_diff) / 2)
# TODO
def uniform(a, b, ms, low, up):
# here's the cosine part.
omegas_cos = 2.0 * np.pi * ms / (b - a)
w = omegas_cos.reshape(-1, 1)
m = omegas_cos.reshape(1, -1)
# integral_a^b cos(w x) cos(m x) dx = (-m sin(a m) cos(a w)+w cos(a m) sin(a w)+m sin(b m) cos(b w)-w cos(b m) sin(b w))/(m^2-w^2)
coscos = (
-m * tf.sin(low * m) * tf.cos(low * w)
+ w * tf.cos(low * m) * tf.sin(low * w)
+ m * tf.sin(up * m) * tf.cos(up * w)
- w * tf.cos(up * m) * tf.sin(up * w)
) / (tf.square(m) - tf.square(w))
# integral_a^b cos^2(w x) dx = (2 w (b-a)-sin(2 a w)+sin(2 b w))/(4 w)
cos2 = (2 * w * (up - low) - tf.sin(2 * low * w) + tf.sin(2 * up * w)) / (4 * w)
# here's the sin part
omegas_sin = omegas_cos[omegas_cos != 0] # don't compute omega=0
w = omegas_sin.reshape(-1, 1)
m = omegas_sin.reshape(1, -1)
# integral_a^b sin(w x) sin(m x) dx = (-w sin(a m) cos(a w)+m cos(a m) sin(a w)+w sin(b m) cos(b w)-m cos(b m) sin(b w))/(m^2-w^2)
sinsin = (
-w * tf.sin(low * m) * tf.cos(low * w)
+ m * tf.cos(low * m) * tf.sin(low * w)
+ w * tf.sin(up * m) * tf.cos(up * w)
- m * tf.cos(up * m) * tf.sin(up * w)
) / (tf.square(m) - tf.square(w))
# integral_a^b sin^2(w x) dx = (2 w (b-a)+sin(2 a w)-sin(2 b w))/(4 w)
sin2 = (2 * w * (up - low) + tf.sin(2 * a * w) - tf.sin(2 * up * w)) / (4 * w)
# here the 'cross' part
# integral_a^b cos(w x) sin(m x) dx = (w sin(a m) sin(a w)+m cos(a m) cos(a w)-w sin(b m) sin(b w)-m cos(b m) cos(b w))/(m^2-w^2)
w = omegas_cos.reshape(-1, 1)
m = omegas_sin.reshape(1, -1)
sincos = (
w * tf.sin(low * m) * tf.sin(low * w)
+ m * tf.cos(low * m) * tf.cos(low * w)
- w * tf.sin(up * m) * tf.sin(up * w)
- m * tf.cos(up * m) * tf.cos(up * w)
) / (tf.square(m) - tf.square(w))
return coscos, cos2, sinsin, sin2, sincos
# integral_a^b sin(w x) sin(m x) dx = (-w sin(a m) cos(a w)+m cos(a m) sin(a w)+w sin(b m) cos(b w)-m cos(b m) sin(b w))/(m^2-w^2)
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] |
Recommender/build.py | teamclouday/Mooner | 0 | 1617393 | <filename>Recommender/build.py
# This file will build a database for each of the ids in crawled csv
# How it works
# For each of the ids, read through the tweets
# run Sentiment model and Topic model on them
# calculate a representative value for these features
# store the result into a database (could be a csv file)
from model import *
# define topic category map
# 0 1 2 3 4 5
# my_topics = ['computers', 'science', 'sports', 'religions', 'politics', 'others']
# topic_map = {
# 0: 5,
# 1: 0,
# 2: 0,
# 3: 0,
# 4: 0,
# 5: 0,
# 6: 5,
# 7: 2,
# 8: 2,
# 9: 2,
# 10: 2,
# 11: 1,
# 12: 1,
# 13: 1,
# 14: 1,
# 15: 3,
# 16: 4,
# 17: 4,
# 18: 4,
# 19: 3
# }
if __name__ == "__main__":
if not os.path.exists("data.csv"):
# load models
model_senti = ModelSentiment()
model_topic = ModelTopic()
# load tweets dataframe
tweets_data = pd.read_csv(os.path.join("..", "DataProcess", "tweets_200_processed.csv"))
# preprocess all text
tweets_data["Tweet"] = tweets_data["Tweet"].apply(preprocess)
# setup output data
output = []
# get all user ids
userids = tweets_data["User ID"].unique()
# groupby ids
data = tweets_data.groupby(["User ID"])
print("Number of IDs = {}".format(len(userids)))
for i, userid in enumerate(userids):
user_topics = np.zeros(20)
tweets = data.get_group(userid)["Tweet"].tolist()
print("Current ID = {} - With {} tweets".format(userid, len(tweets)), end="")
pos_neg = np.array(model_senti.run(tweets))
print("\tPostive Negative decided", end="")
topics = np.array(model_topic.run(tweets))
print("\tTopic extracted")
pos_neg = pos_neg * 2 - 1 # convert from range [0, 1] to range [-1, 1]
topics = topics * 100 # convert probability from [0, 1] to [0, 100]
for sign, dist in zip(pos_neg, topics):
user_topics += sign * dist
user_topics /= len(tweets) # take average
# # process postive or negatives
# for sign, topic in zip(pos_neg, topics):
# # if not strong, then ignore
# # if sign > 0.6 and sign < 0.7:
# # continue
# # else decide the sign
# sign = -1 if sign <= 0.65 else 1
# topic = topic_map[topic] # find target index
# user_topics[topic] += sign
# # average the topics
# user_topics = [x / len(tweets) for x in user_topics]
user_topics = user_topics.tolist()
output.append([userid] + user_topics) # store the data
print("Processed - {} IDs left".format(len(userids) - i - 1))
df = pd.DataFrame(output, columns=['ID',
'alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc'])
df.to_csv("data.csv", index=False)
print("Complete") | [
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] |
post/admin.py | bramvankooten/dienst2 | 1 | 164430 | from __future__ import unicode_literals
from django.contrib import admin
from post.models import *
@admin.register(Category)
class CategoryAdmin(admin.ModelAdmin):
"""CategoryAdmin"""
search_fields = ("name",)
list_display = ("name", "grouping", "counting")
@admin.register(Contact)
class SourceAdmin(admin.ModelAdmin):
"""SourceAdmin"""
search_fields = ("name",)
list_filter = ("location",)
list_display = ("name", "location")
@admin.register(Item)
class ItemAdmin(admin.ModelAdmin):
"""ItemAdmin"""
date_hierarchy = "date"
search_fields = ("description",)
list_filter = ("sender", "recipient", "category")
list_display = ("description", "date", "sender", "recipient", "category")
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8216,
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13,
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376,
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613,
376,
15452,
613,
376,
4361,
29886,
993,
613,
376,
7320,
1159,
13,
2
] |
gollahalli_cms/welcome/tests/test_apps.py | akshaybabloo/gollahalli-cms | 1 | 1606831 | <filename>gollahalli_cms/welcome/tests/test_apps.py<gh_stars>1-10
from django.apps import apps
from django.test import TestCase
from welcome.apps import WelcomeConfig
class WelcomeConfigTests(TestCase):
"""
Test case for ``EditorConfig``
"""
def test_welcome_config(self):
"""
Testing ``EditorConfig`` class.
"""
app = WelcomeConfig
self.assertEqual(app.name, 'welcome')
self.assertEqual(apps.get_app_config('welcome').name, 'welcome')
| [
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] |
main.py | jakebailey/pyright-action-test | 0 | 118715 | <reponame>jakebailey/pyright-action-test<filename>main.py
from typing import List
x: List[int] = []
x.append(1234)
x.append("not a number")
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cache.py | ivclab/Label_Reuse_Semisupervised | 2 | 53497 | import torch
from torch import nn
class Cache(nn.Module):
def __init__(self, n_entries, entry_size):
super(Cache, self).__init__()
self.n_entries = n_entries
self.entry_size = entry_size
self.register_buffer(
name='idx_sparse',
tensor=torch.zeros((n_entries, entry_size), dtype=torch.long)
)
self.register_buffer(
name='value_sparse',
tensor=torch.zeros((n_entries, entry_size))
)
def forward(self):
return
def read(self, idx):
return self.idx_sparse[idx], self.value_sparse[idx]
def write(self, idx, idx_sparse, value_sparse):
self.idx_sparse[idx] = idx_sparse
self.value_sparse[idx] = value_sparse
return | [
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ldbcache.py | Exahilosys/ldbcache | 0 | 39112 | import pathing
import collections
import itertools
import copy
__all__ = ('Tree', 'Cache', 'Entry', 'EntryCache', 'RowCache', 'BulkRowCache',
'AlikeBulkRowCache')
class Tree(dict):
"""
Simple **BTree** implementation.
"""
__slots__ = ()
def _make(self):
"""
Create a subtree; called with no arguments.
"""
return ()
def _crawl(self, keys, stop = True, fail = False):
"""
Reach the node corresponding to ``keys``.
If set to not ``stop``, create subtrees.
Yield are nodes along the path.
"""
node = self
keys = iter(keys)
while True:
yield node
try:
key = next(keys)
except StopIteration:
break
try:
node = node[key]
except KeyError:
if stop:
if fail:
raise
return
args = self._make()
node[key] = node = self.__class__(*args)
def select(self, keys):
"""
Get the value corresponding to ``keys``.
"""
(*trees, value) = self._crawl(keys, fail = True)
return value
def create(self, keys, value):
"""
Place a value at ``keys``, creating subtrees.
"""
(*keys, key) = keys
(*trees, tree) = self._crawl(keys, stop = False)
tree[key] = value
def _brush(self, keys):
"""
Remove the final value and all empty subtrees along ``keys``.
"""
trees = self._crawl(keys)
pairs = tuple(zip(trees, keys))
for (tree, key) in reversed(pairs):
yield tree.pop(key)
if tree:
break
def delete(self, keys):
"""
Remove and return the value at ``keys``, deleting empty subtrees.
"""
(value, *trees) = self._brush(keys)
return value
def switch(self, old, new):
"""
Move the value at ``old`` to ``new``, deleting and creating subtrees.
"""
node = Tree.delete(self, old)
Tree.create(self, new, node)
return node
class Cache(Tree):
"""
Same as :class:`.Tree`, but declaring its expected depth allows for floor
traversing.
"""
__slots__ = ('_depth',)
def __init__(self, depth):
super().__init__()
self._depth = depth
@property
def depth(self):
"""
Get the expected depth.
"""
return self._depth
def _make(self):
depth = self._depth - 1
return (depth,)
def traverse(self):
"""
Yield ``(keys, value)`` pairs.
"""
yield from pathing.derive(self, min = self._depth, max = self._depth)
def entries(self):
"""
Yield all floor values.
"""
for (keys, value) in self.traverse():
yield value
def __repr__(self):
entries = tuple(self.entries())
size = len(entries)
return f'<Cache({size})>'
class Entry:
"""
:class:`dict`\-like attribute-accessible and read-only representation of
data.
.. code-block:: py
>>> entry = Entry({'name': 'Pup', 'age': 6})
>>> entry.name
'Pup'
>>> list(entry)
['name', 'age']
"""
__slots__ = ('__data__',)
def __init__(self, data = None, direct = False):
self.__data__ = {} if data is None else data if direct else data.copy()
def __getitem__(self, key):
return self.__data__[key]
def __getattr__(self, key):
try:
value = self[key]
except KeyError as error:
raise AttributeError(*error.args) from None
return value
def __copy__(self):
data = self.__data__.copy()
fake = self.__class__(data, True)
return fake
def __deepcopy__(self, memo):
fake = copy.copy(self)
data = fake.__data__
for (key, value) in data.items():
data[key] = copy.deepcopy(value)
return fake
def __iter__(self):
yield from self.__data__.keys()
def __repr__(self):
items = self.__data__.items()
make = '{0}={1}'.format
show = '|'.join(itertools.starmap(make, items))
return f'<{self.__class__.__name__}({show})>'
def _create(value):
"""
Create or return an entry from the value.
"""
return value if isinstance(value, Entry) else Entry(value)
def _modify(entry, data):
"""
Modify an :class:`.Entry` with the data.
"""
entry.__data__.update(data)
class EntryCache(Cache):
"""
Store, create and modify :class:`.Entry` instances.
"""
__slots__ = ('_manage',)
_Asset = collections.namedtuple('Asset', 'create modify')
def __init__(self, depth, create = _create, modify = _modify):
super().__init__(depth)
self._manage = self._Asset(create, modify)
def _make(self, *args, **kwargs):
args = super()._make()
return (*args, self._manage.create, self._manage.modify)
def create(self, keys, data):
"""
Create and put an entry at ``keys``.
"""
entry = self._manage.create(data)
super().create(keys, entry)
return entry
def modify(self, keys, data):
"""
Modify the entry at ``keys``\'s with ``data`` and return ``(old, new)``.
"""
entry = Tree.select(self, keys)
dummy = copy.deepcopy(entry)
self._manage.modify(entry, data)
return (dummy, entry)
class RowCache(EntryCache):
"""
Knowing primary allows for better handling.
"""
__slots__ = ('_primary',)
def __init__(self, primary):
super().__init__(len(primary))
self._primary = primary
@property
def primary(self):
"""
Get the primary keys.
"""
return self._primary
def _make(self):
primary = self._primary[:1]
return (primary,)
def query(self, data):
"""
Get all available values against the ``data``\'s primary keys.
"""
store = []
try:
store.extend(data[key] for key in self._primary)
except KeyError:
pass
return tuple(store)
def create(self, data):
"""
Create and put an entry in the spot designated by its primary keys.
"""
keys = self.query(data)
result = super().create(keys, data)
return result
def modify(self, keys, data):
"""
Modify an entry and change its spot if necessary.
"""
result = super().modify(keys, data)
new = self.query(data)
size = len(new)
old = keys[:size]
if not tuple(old) == new:
super().switch(old, new)
return result
class BulkRowCache(RowCache):
"""
Similar to its base, but data inputs should be arrays of data.
"""
__slots__ = ()
def create(self, data):
"""
Create and return all entries.
"""
results = []
for data in data:
result = super().create(data)
results.append(result)
return tuple(results)
def modify(self, keys, data):
"""
Modify all entries at ``keys`` and change their spot if necessary.
The keys don't need to be a full path to specific entries.
.. tip::
- Assume two entries exist at ``(0, 1, 2)`` and ``(0, 1, 4)``.
- Updating ``(0, 1)`` requires ``data`` to be a two-item array.
- That array should contain the respective data in order.
"""
node = super().select(keys)
depth = self._depth - len(keys)
items = pathing.derive(node, max = depth, min = depth)
try:
(subkeys, _entries) = zip(*items)
except ValueError:
subkeys = ()
results = []
for (subkeys, data) in zip(subkeys, data):
curkeys = (*keys, *subkeys)
result = super().modify(curkeys, data)
results.append(result)
return results
class SoftBulkRowCache(BulkRowCache):
"""
Lookup methods return an array of entries, or empty instead of failing.
"""
__slots__ = ()
@classmethod
def _flat(cls, node):
"""
Turn the node into either a tree's entries or a single-item array.
"""
return tuple(node.entries()) if isinstance(node, cls) else (node,)
def select(self, keys):
"""
Refer to :meth:`.Tree.select`.
"""
try:
node = super().select(keys)
except KeyError:
result = ()
else:
result = self._flat(node)
return result
def modify(self, keys, data):
"""
Refer to :meth:`.BulkRowCache.modify`.
"""
try:
result = super().modify(keys, data)
except KeyError:
result = ()
return result
def delete(self, keys):
"""
Refer to :meth:`.Tree.delete`.
"""
try:
node = super().delete(keys)
except KeyError:
result = ()
else:
result = self._flat(node)
return result
class AlikeBulkRowCache(SoftBulkRowCache):
"""
Active methods accept ``(keys, data)`` for consistency.
"""
__slots__ = ()
def create(self, keys, data):
"""
``data`` is used, ``keys`` is not.
Refer to :meth:`.BulkRowCache.create`.
"""
result = super().create(data)
return result
def modify(self, keys, data):
"""
``data`` and ``keys`` are used.
Refer to :meth:`.SoftBulkRowCache.modify`.
"""
result = super().modify(keys, data)
return result
def delete(self, keys, data):
"""
``keys`` is used, ``data`` is not.
Refer to :meth:`.SoftBulkRowCache.delete`.
"""
result = super().delete(keys)
return result
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1014,
28737,
3412,
4954,
8149,
29952,
1412,
13,
4706,
9995,
13,
13,
4706,
10697,
353,
1583,
3032,
29883,
1610,
29880,
29898,
8149,
29897,
13,
4706,
11000,
353,
18761,
29898,
7554,
29898,
28737,
29892,
6611,
876,
13,
4706,
363,
313,
8336,
29892,
1820,
29897,
297,
18764,
287,
29898,
29886,
7121,
1125,
13,
9651,
7709,
5447,
29889,
7323,
29898,
1989,
29897,
13,
9651,
565,
5447,
29901,
13,
18884,
2867,
13,
13,
1678,
822,
5217,
29898,
1311,
29892,
6611,
1125,
13,
13,
4706,
9995,
13,
4706,
15154,
322,
736,
278,
995,
472,
4954,
8149,
29952,
1673,
21228,
4069,
1014,
28737,
29889,
13,
4706,
9995,
13,
13,
4706,
313,
1767,
29892,
334,
28737,
29897,
353,
1583,
3032,
1182,
1878,
29898,
8149,
29897,
13,
13,
4706,
736,
995,
13,
13,
1678,
822,
4607,
29898,
1311,
29892,
2030,
29892,
716,
1125,
13,
13,
4706,
9995,
13,
4706,
25249,
278,
995,
472,
4954,
1025,
16159,
304,
4954,
1482,
29952,
1673,
21228,
322,
4969,
1014,
28737,
29889,
13,
4706,
9995,
13,
13,
4706,
2943,
353,
15472,
29889,
8143,
29898,
1311,
29892,
2030,
29897,
13,
4706,
15472,
29889,
3258,
29898,
1311,
29892,
716,
29892,
2943,
29897,
13,
13,
4706,
736,
2943,
13,
13,
13,
1990,
28540,
29898,
9643,
1125,
13,
13,
1678,
9995,
13,
1678,
19491,
408,
584,
1990,
29901,
1412,
9643,
1673,
541,
25136,
967,
3806,
10809,
6511,
363,
11904,
13,
1678,
13310,
292,
29889,
13,
1678,
9995,
13,
13,
1678,
4770,
2536,
1862,
1649,
353,
6702,
29918,
19488,
742,
29897,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
10809,
1125,
13,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
13,
4706,
1583,
3032,
19488,
353,
10809,
13,
13,
1678,
732,
6799,
13,
1678,
822,
10809,
29898,
1311,
1125,
13,
13,
4706,
9995,
13,
4706,
3617,
278,
3806,
10809,
29889,
13,
4706,
9995,
13,
13,
4706,
736,
1583,
3032,
19488,
13,
13,
1678,
822,
903,
5675,
29898,
1311,
1125,
13,
13,
4706,
10809,
353,
1583,
3032,
19488,
448,
29871,
29896,
13,
13,
4706,
736,
313,
19488,
29892,
29897,
13,
13,
1678,
822,
29370,
29898,
1311,
1125,
13,
13,
4706,
9995,
13,
4706,
612,
969,
4954,
29898,
8149,
29892,
995,
3569,
29952,
11000,
29889,
13,
4706,
9995,
13,
13,
4706,
7709,
515,
2224,
292,
29889,
672,
573,
29898,
1311,
29892,
1375,
353,
1583,
3032,
19488,
29892,
4236,
353,
1583,
3032,
19488,
29897,
13,
13,
1678,
822,
9976,
29898,
1311,
1125,
13,
13,
4706,
9995,
13,
4706,
612,
969,
599,
11904,
1819,
29889,
13,
4706,
9995,
13,
13,
4706,
363,
313,
8149,
29892,
995,
29897,
297,
1583,
29889,
3018,
3901,
7295,
13,
13,
9651,
7709,
995,
13,
13,
1678,
822,
4770,
276,
558,
12035,
1311,
1125,
13,
13,
4706,
9976,
353,
18761,
29898,
1311,
29889,
26586,
3101,
13,
4706,
2159,
353,
7431,
29898,
26586,
29897,
13,
13,
4706,
736,
285,
29915,
29966,
10408,
3319,
2311,
1800,
16299,
13,
13,
13,
1990,
28236,
29901,
13,
13,
1678,
9995,
13,
1678,
584,
1990,
18078,
8977,
29952,
29905,
29899,
4561,
5352,
29899,
5943,
1821,
322,
1303,
29899,
6194,
8954,
310,
13,
1678,
848,
29889,
13,
13,
1678,
6317,
775,
29899,
1271,
1057,
11451,
13,
13,
4706,
8653,
6251,
353,
28236,
3319,
29915,
978,
2396,
525,
29925,
786,
742,
525,
482,
2396,
29871,
29953,
1800,
13,
4706,
8653,
6251,
29889,
978,
13,
4706,
525,
29925,
786,
29915,
13,
4706,
8653,
1051,
29898,
8269,
29897,
13,
4706,
6024,
978,
742,
525,
482,
2033,
13,
1678,
9995,
13,
13,
1678,
4770,
2536,
1862,
1649,
353,
6702,
1649,
1272,
1649,
742,
29897,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
848,
353,
6213,
29892,
1513,
353,
7700,
1125,
13,
13,
4706,
1583,
17255,
1272,
1649,
353,
6571,
565,
848,
338,
6213,
1683,
848,
565,
1513,
1683,
848,
29889,
8552,
580,
13,
13,
1678,
822,
4770,
657,
667,
12035,
1311,
29892,
1820,
1125,
13,
13,
4706,
736,
1583,
17255,
1272,
1649,
29961,
1989,
29962,
13,
13,
1678,
822,
4770,
657,
5552,
12035,
1311,
29892,
1820,
1125,
13,
13,
4706,
1018,
29901,
13,
9651,
995,
353,
1583,
29961,
1989,
29962,
13,
4706,
5174,
7670,
2392,
408,
1059,
29901,
13,
9651,
12020,
23833,
2392,
10456,
2704,
29889,
5085,
29897,
515,
6213,
13,
13,
4706,
736,
995,
13,
13,
1678,
822,
4770,
8552,
12035,
1311,
1125,
13,
13,
4706,
848,
353,
1583,
17255,
1272,
26914,
8552,
580,
13,
4706,
25713,
353,
1583,
17255,
1990,
12035,
1272,
29892,
5852,
29897,
13,
13,
4706,
736,
25713,
13,
13,
1678,
822,
4770,
24535,
8552,
12035,
1311,
29892,
2626,
29877,
1125,
13,
13,
4706,
25713,
353,
3509,
29889,
8552,
29898,
1311,
29897,
13,
4706,
848,
353,
25713,
17255,
1272,
1649,
13,
4706,
363,
313,
1989,
29892,
995,
29897,
297,
848,
29889,
7076,
7295,
13,
9651,
848,
29961,
1989,
29962,
353,
3509,
29889,
24535,
8552,
29898,
1767,
29897,
13,
13,
4706,
736,
25713,
13,
13,
1678,
822,
4770,
1524,
12035,
1311,
1125,
13,
13,
4706,
7709,
515,
1583,
17255,
1272,
26914,
8149,
580,
13,
13,
1678,
822,
4770,
276,
558,
12035,
1311,
1125,
13,
13,
4706,
4452,
353,
1583,
17255,
1272,
26914,
7076,
580,
13,
4706,
1207,
353,
22372,
29900,
29913,
3790,
29896,
29913,
4286,
4830,
13,
4706,
1510,
353,
525,
29989,
4286,
7122,
29898,
1524,
8504,
29889,
8508,
1958,
29898,
5675,
29892,
4452,
876,
13,
13,
4706,
736,
285,
29915,
29966,
29912,
1311,
17255,
1990,
1649,
17255,
978,
1649,
2119,
29912,
4294,
1800,
16299,
13,
13,
13,
1753,
903,
3258,
29898,
1767,
1125,
13,
13,
1678,
9995,
13,
1678,
6204,
470,
736,
385,
6251,
515,
278,
995,
29889,
13,
1678,
9995,
13,
13,
1678,
736,
995,
565,
338,
8758,
29898,
1767,
29892,
28236,
29897,
1683,
28236,
29898,
1767,
29897,
13,
13,
13,
1753,
903,
1545,
1598,
29898,
8269,
29892,
848,
1125,
13,
13,
1678,
9995,
13,
1678,
3382,
1598,
385,
584,
1990,
29901,
1412,
9634,
29952,
411,
278,
848,
29889,
13,
1678,
9995,
13,
13,
1678,
6251,
17255,
1272,
26914,
5504,
29898,
1272,
29897,
13,
13,
13,
1990,
28236,
10408,
29898,
10408,
1125,
13,
13,
1678,
9995,
13,
1678,
14491,
29892,
1653,
322,
6623,
584,
1990,
29901,
1412,
9634,
29952,
8871,
29889,
13,
1678,
9995,
13,
13,
1678,
4770,
2536,
1862,
1649,
353,
6702,
29918,
1171,
482,
742,
29897,
13,
13,
1678,
903,
26405,
353,
16250,
29889,
17514,
23583,
877,
26405,
742,
525,
3258,
6623,
1495,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
10809,
29892,
1653,
353,
903,
3258,
29892,
6623,
353,
903,
1545,
1598,
1125,
13,
13,
4706,
2428,
2141,
1649,
2344,
12035,
19488,
29897,
13,
4706,
1583,
3032,
1171,
482,
353,
1583,
3032,
26405,
29898,
3258,
29892,
6623,
29897,
13,
13,
1678,
822,
903,
5675,
29898,
1311,
29892,
334,
5085,
29892,
3579,
19290,
1125,
13,
13,
4706,
6389,
353,
2428,
2141,
29918,
5675,
580,
13,
13,
4706,
736,
3070,
5085,
29892,
1583,
3032,
1171,
482,
29889,
3258,
29892,
1583,
3032,
1171,
482,
29889,
1545,
1598,
29897,
13,
13,
1678,
822,
1653,
29898,
1311,
29892,
6611,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
6204,
322,
1925,
385,
6251,
472,
4954,
8149,
29952,
1412,
13,
4706,
9995,
13,
13,
4706,
6251,
353,
1583,
3032,
1171,
482,
29889,
3258,
29898,
1272,
29897,
13,
4706,
2428,
2141,
3258,
29898,
8149,
29892,
6251,
29897,
13,
13,
4706,
736,
6251,
13,
13,
1678,
822,
6623,
29898,
1311,
29892,
6611,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
3382,
1598,
278,
6251,
472,
4954,
8149,
16159,
20333,
29879,
411,
4954,
1272,
16159,
322,
736,
4954,
29898,
1025,
29892,
716,
3569,
1412,
13,
4706,
9995,
13,
13,
4706,
6251,
353,
15472,
29889,
2622,
29898,
1311,
29892,
6611,
29897,
13,
4706,
20254,
353,
3509,
29889,
24535,
8552,
29898,
8269,
29897,
13,
4706,
1583,
3032,
1171,
482,
29889,
1545,
1598,
29898,
8269,
29892,
848,
29897,
13,
13,
4706,
736,
313,
29881,
11770,
29892,
6251,
29897,
13,
13,
13,
1990,
11438,
10408,
29898,
9634,
10408,
1125,
13,
13,
1678,
9995,
13,
1678,
19320,
292,
7601,
6511,
363,
2253,
11415,
29889,
13,
1678,
9995,
13,
13,
1678,
4770,
2536,
1862,
1649,
353,
6702,
29918,
16072,
742,
29897,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
7601,
1125,
13,
13,
4706,
2428,
2141,
1649,
2344,
12035,
2435,
29898,
16072,
876,
13,
13,
4706,
1583,
3032,
16072,
353,
7601,
13,
13,
1678,
732,
6799,
13,
1678,
822,
7601,
29898,
1311,
1125,
13,
13,
4706,
9995,
13,
4706,
3617,
278,
7601,
6611,
29889,
13,
4706,
9995,
13,
13,
4706,
736,
1583,
3032,
16072,
13,
13,
1678,
822,
903,
5675,
29898,
1311,
1125,
13,
13,
4706,
7601,
353,
1583,
3032,
16072,
7503,
29896,
29962,
13,
13,
4706,
736,
313,
16072,
29892,
29897,
13,
13,
1678,
822,
2346,
29898,
1311,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
3617,
599,
3625,
1819,
2750,
278,
4954,
1272,
16159,
20333,
29879,
7601,
6611,
29889,
13,
4706,
9995,
13,
13,
4706,
3787,
353,
5159,
13,
13,
4706,
1018,
29901,
13,
9651,
3787,
29889,
21843,
29898,
1272,
29961,
1989,
29962,
363,
1820,
297,
1583,
3032,
16072,
29897,
13,
4706,
5174,
7670,
2392,
29901,
13,
9651,
1209,
13,
13,
4706,
736,
18761,
29898,
8899,
29897,
13,
13,
1678,
822,
1653,
29898,
1311,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
6204,
322,
1925,
385,
6251,
297,
278,
9758,
25373,
491,
967,
7601,
6611,
29889,
13,
4706,
9995,
13,
13,
4706,
6611,
353,
1583,
29889,
1972,
29898,
1272,
29897,
13,
4706,
1121,
353,
2428,
2141,
3258,
29898,
8149,
29892,
848,
29897,
13,
13,
4706,
736,
1121,
13,
13,
1678,
822,
6623,
29898,
1311,
29892,
6611,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
3382,
1598,
385,
6251,
322,
1735,
967,
9758,
565,
5181,
29889,
13,
4706,
9995,
13,
13,
4706,
1121,
353,
2428,
2141,
1545,
1598,
29898,
8149,
29892,
848,
29897,
13,
13,
4706,
716,
353,
1583,
29889,
1972,
29898,
1272,
29897,
13,
4706,
2159,
353,
7431,
29898,
1482,
29897,
13,
4706,
2030,
353,
6611,
7503,
2311,
29962,
13,
4706,
565,
451,
18761,
29898,
1025,
29897,
1275,
716,
29901,
13,
9651,
2428,
2141,
15123,
29898,
1025,
29892,
716,
29897,
13,
13,
4706,
736,
1121,
13,
13,
13,
1990,
8313,
29895,
4301,
10408,
29898,
4301,
10408,
1125,
13,
13,
1678,
9995,
13,
1678,
13999,
304,
967,
2967,
29892,
541,
848,
10970,
881,
367,
7049,
310,
848,
29889,
13,
1678,
9995,
13,
13,
1678,
4770,
2536,
1862,
1649,
353,
3861,
13,
13,
1678,
822,
1653,
29898,
1311,
29892,
848,
1125,
13,
13,
4706,
9995,
13,
4706,
6204,
322,
736,
599,
9976,
29889,
13,
4706,
9995,
13,
13,
4706,
2582,
353,
5159,
13,
4706,
363,
848,
297,
848,
29901,
13,
9651,
1121,
353,
2428,
2141,
3258,
29898,
1272,
29897,
13,
9651,
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2
] |
web/service/NetService.py | hao707822882/Bichon | 0 | 88296 | <gh_stars>0
# _*_ coding:utf-8 _*_
from web.broker.BrokerService import BrokerService
from com.Config import Config
__author__ = 'Administrator'
class NetService(object):
def __init__(self):
self.before = {}
pass
'''获取net详细信息'''
def getNetInfo(self, hostKey):
broker = BrokerService.getBroker(hostKey)
return broker.getNetInfo()
'''
{
"data": {
"Loopback Pseudo-Interface 1": [
0,
0,
0,
0,
0,
0,
0,
0
],
"\u672c\u5730\u8fde\u63a5 2": [
29258726,
276679541,
230171,
275475,
0,
0,
0,
0
]
},
"time": 1453348072.861
}
'''
def getOutSpeed(self, host, data):
beforeData = self.before.get(host)
nowTime = data["time"]
nowData = {}
self.before[host] = data
if beforeData is not None:
beforeTime = beforeData["time"]
beforeData = beforeData["data"]
for beforeKey in beforeData:
if beforeKey.encode("utf-8") == Config.eht:
beforeData = beforeData[beforeKey]
break
else:
return 0
data = data["data"]
for nowKey in data:
if nowKey.encode("utf-8") == Config.eht:
nowData = data[beforeKey]
break
if nowData is not None:
allUp = nowData[0] - beforeData[0]
time = nowTime - beforeTime
return allUp / 1024 / time
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29900,
29906,
29946,
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] |
doppel/__init__.py | bburns632/doppel-cli | 0 | 151194 | <filename>doppel/__init__.py
__all__ = [
'PackageAPI',
'PackageCollection'
]
from doppel.PackageAPI import PackageAPI
from doppel.PackageCollection import PackageCollection
from doppel.DoppelTestError import DoppelTestError
from doppel.reporters import SimpleReporter
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merger.py | theianrobertson/starbucks-names | 0 | 193526 | <gh_stars>0
"""Merge a name and a cup"""
import glob
import random
import os
import sys
from PIL import Image, ImageFilter
import config
def crop_image(image):
"""Crop an image to not include whatever is just white
Parameters
----------
image : PIL image
"""
max_x = 0
min_x = image.width - 1
max_y = 0
min_y = image.height - 1
for x in range(image.width):
for y in range(image.height):
if image.getpixel((x, y)) != (255, 255, 255, 255):
if x < min_x:
min_x = x
if x > max_x:
max_x = x
if y < min_y:
min_y = y
if y > max_y:
max_y = y
return image.crop((min_x, min_y, max_x, max_y))
def merge_images(cup, name_file, new_file):
"""Merge two images
Parameters
----------
cup : dict
Dictionary with info about the cup
name_file : str
String with the name file
new_file : str
The new file name
"""
cup_file = os.path.join(config.CUPS_DIR, cup['filename'])
cup_image = Image.open(cup_file)
name_image = Image.open(name_file)
name_image = crop_image(name_image)
#Resize the name image
name_size = (
cup['x_bottom'] - cup['x_top'], cup['y_bottom'] - cup['y_top'])
name_image = name_image.resize(name_size, Image.ANTIALIAS)
#Just use all-black
name_blurred = Image.new('RGB',name_image.size,(0,0,0))
#Create a mask (invisible if >= 240 in greyscale)
mask = name_image.convert('L')
mask = Image.eval(mask, lambda x: abs(x - 255))
#Paste into the base image and save.
cup_image.paste(name_blurred, (cup['x_top'], cup['y_top']), mask=mask)
cup_image.save(new_file, 'JPEG', quality=90)
def main():
try:
name_file = os.path.join(config.NAMES_DIR, sys.argv[1] + '.jpg')
except IndexError:
name_files = glob.glob(os.path.join(config.NAMES_DIR, '*.jpg'))
name_file = random.choice(name_files)
merge_images(
random.choice(config.CUPS),
name_file,
'test_file.jpg')
if __name__ == '__main__':
main() | [
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] |
tests/models.py | sobolevn/django-fakery | 1 | 144879 | <filename>tests/models.py
from decimal import Decimal
from django import VERSION as django_version
from django.contrib.postgres import fields as postgres_fields
from django.db import models
from django_fakery.compat import HAS_GEOS
class Chef(models.Model):
slug = models.SlugField()
first_name = models.CharField(max_length=60)
last_name = models.CharField(max_length=60)
uuid_id = models.UUIDField()
email_address = models.EmailField()
twitter_profile = models.URLField()
def __str__(self):
return "Chef {} {}".format(self.first_name, self.last_name)
class Topping(models.Model):
name = models.CharField(max_length=60)
class Pizza(models.Model):
THICKNESSES = ((0, "thin"), (1, "thick"), (2, "deep dish"))
name = models.CharField(max_length=50)
price = models.DecimalField(null=True, decimal_places=2, max_digits=4)
gluten_free = models.BooleanField(default=False)
vegan = models.BooleanField()
description = models.TextField(blank=True)
thickness = models.CharField(max_length=50, choices=THICKNESSES)
baked_on = models.DateTimeField()
expiration = models.DateField()
rating = models.PositiveSmallIntegerField()
chef = models.ForeignKey(
Chef, on_delete=models.CASCADE, related_name="invented_pizzas"
)
critic = models.ForeignKey(
Chef, null=True, on_delete=models.CASCADE, related_name="reviewed_pizzas"
)
toppings = models.ManyToManyField(Topping, related_name="pizzas")
unique_comment = models.TextField(unique=True)
def get_price(self, tax):
return (Decimal("7.99") + (Decimal("7.99") * Decimal(tax))).quantize(
Decimal("0.01")
)
if HAS_GEOS:
from django.contrib.gis.db import models as geo_models
if django_version < (1, 9, 0):
class Pizzeria(geo_models.Model):
hq = geo_models.PointField()
directions = geo_models.LineStringField()
floor_plan = geo_models.PolygonField()
locations = geo_models.MultiPointField()
routes = geo_models.MultiLineStringField()
delivery_areas = geo_models.MultiPolygonField()
all_the_things = geo_models.GeometryCollectionField()
else:
class Pizzeria(geo_models.Model):
hq = geo_models.PointField()
directions = geo_models.LineStringField()
floor_plan = geo_models.PolygonField()
locations = geo_models.MultiPointField()
routes = geo_models.MultiLineStringField()
delivery_areas = geo_models.MultiPolygonField()
all_the_things = geo_models.GeometryCollectionField()
rast = geo_models.RasterField()
if django_version < (1, 9, 0):
class SpecialtyPizza(models.Model):
toppings = postgres_fields.ArrayField(models.CharField(max_length=20), size=4)
metadata = postgres_fields.HStoreField()
price_range = postgres_fields.IntegerRangeField()
sales = postgres_fields.BigIntegerRangeField()
available_on = postgres_fields.DateTimeRangeField()
season = postgres_fields.DateRangeField()
else:
class SpecialtyPizza(models.Model):
name = models.CharField(max_length=50)
toppings = postgres_fields.ArrayField(models.CharField(max_length=20), size=4)
metadata = postgres_fields.HStoreField()
price_range = postgres_fields.IntegerRangeField()
sales = postgres_fields.BigIntegerRangeField()
available_on = postgres_fields.DateTimeRangeField()
season = postgres_fields.DateRangeField()
nutritional_values = postgres_fields.JSONField()
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src/geometry.py | nyucan/can-racecar-ros | 1 | 166935 | <reponame>nyucan/can-racecar-ros<gh_stars>1-10
#!/usr/bin/env python
import numpy as np
def calculate_theta(x, y):
""" Extimate the theta. From -pi to pi. """
square_sum = np.sqrt(np.square(x) + np.square(y))
sin_theta = np.divide(y, square_sum)
cos_theta = np.divide(x, square_sum)
theta = np.zeros_like(square_sum)
pos_cos = cos_theta >= 0
theta[pos_cos] = np.arcsin(sin_theta[pos_cos])
neg_cos_pos_sin = np.logical_and(cos_theta < 0, sin_theta >= 0)
theta[neg_cos_pos_sin] = np.pi - np.arcsin(sin_theta[neg_cos_pos_sin])
neg_cos_neg_sin = np.logical_and(cos_theta < 0, sin_theta < 0)
theta[neg_cos_neg_sin] = -np.pi - np.arcsin(sin_theta[neg_cos_neg_sin])
theta = np.nan_to_num(theta)
return theta
def calculate_diff_theta(theta1, theta2):
diff = theta1 - theta2
if diff > np.pi:
diff = -diff + 2 * np.pi
elif diff < -np.pi:
diff = diff + 2 * np.pi
diff = -diff
return diff
class Drawer(object):
def __init__(self, x_func, y_func):
self.x_func = x_func
self.y_func = y_func
def draw(self, resolution=200):
theta = np.linspace(0, 2 * np.pi, num=resolution, endpoint=False)
sampled_x = self.x_func(theta)
sampled_y = self.y_func(theta)
return sampled_x, sampled_y
class Path(object):
def __init__(self, xs, ys):
self._x = np.asarray(xs)
self._y = np.asarray(ys)
self._theta = calculate_theta(xs, ys)
self.resolution = len(xs)
def get_x(self):
return self._x
def get_y(self):
return self._y
def distance_to(self, x, y):
# calculate the shortest distance between a point to this curve
# return the distance and the coordinate of the point on the curve
if self._x is None and self._y is None:
self._x, self._y = self.sample()
distance = np.square(self._x - x) + np.square(self._y - y)
dis_min, idx_min = np.min(distance), np.argmin(distance)
return dis_min, (self._x[idx_min], self._y[idx_min])
def contains(self, x, y):
theta_xy = calculate_theta(x, y)
idx = np.argmin(np.abs(self._theta - theta_xy))
origin_to_path = np.square(self._x[idx]) + np.square(self._y[idx])
origin_to_xy = np.square(x) + np.square(y)
return origin_to_xy < origin_to_path
def estimate_derivative(self, theta):
idx = np.argmin(np.abs(self._theta - theta))
next_idx = idx + 1 if idx < self.resolution - 1 else 0
last_idx = idx - 1 if idx > 0 else self.resolution - 1
delta_x = self._x[last_idx] - self._x[next_idx]
delta_y = self._y[last_idx] - self._y[next_idx]
return calculate_theta(delta_x, delta_y)
class OvalPath(Path):
def __init__(self, a, b, resolution=500):
x_func = lambda t: a * np.cos(t)
y_func = lambda t: b * np.sin(t)
drawer = Drawer(x_func, y_func)
super(OvalPath, self).__init__(*drawer.draw(resolution=resolution))
class SimpleTrack(Path):
def __init__(self, scale=2, resolution=500, x_range=(-6, 6)):
self.scale = scale
self.resolution = resolution
self.x_range = x_range
super(SimpleTrack, self).__init__(*self.draw())
def draw(self):
nums = np.array([0.3, 0.4, 0.3]) * (self.resolution / 2)
sampled_x11 = np.linspace(self.x_range[0], self.x_range[0] + 0.35, num=round(nums[0]))
sampled_x12 = np.linspace(self.x_range[0] + 0.35, self.x_range[1] - 0.35, num=round(nums[1]))
sampled_x13 = np.linspace(self.x_range[1] - 0.35, self.x_range[1], num=round(nums[2]))
sampled_x1 = np.concatenate((sampled_x11, sampled_x12, sampled_x13))
sampled_x2 = np.linspace(*self.x_range, num=round(self.resolution / 2))[::-1]
y1 = [self._y2_func(x) for x in sampled_x1]
y2 = [self._y1_func(x) for x in sampled_x2]
sampled_x = np.concatenate((sampled_x1, sampled_x2))
sampled_y = np.asarray(y1 + y2)
return sampled_x, sampled_y
def _y1_func(self, x):
s = self.scale
if x < -2 * s:
return 0
elif x < -1 * s:
return np.sqrt(s ** 2 - (x + 1 * s) ** 2)
elif x < 1 * s:
return s
elif x < 2 * s:
return np.sqrt(s ** 2 - (x - 1 * s) ** 2)
else:
return 0
def _y2_func(self, x):
s = self.scale
if x < -2 * s:
return 0
elif x < -1 * s:
return -np.sqrt(s ** 2 - (x + 1 * s) ** 2)
elif x < 1 * s:
return -s
elif x < 2 * s:
return -np.sqrt(s ** 2 - (x - 1 * s) ** 2)
else:
return 0
oval_path = OvalPath(5, 3, 500)
simple_track = SimpleTrack(scale=3, resolution=1000, x_range=(-6, 6))
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] |
st2api/st2api/controllers/v1/actionalias.py | totalkyos/stack-storm | 1 | 128073 | <gh_stars>1-10
# Licensed to the StackStorm, Inc ('StackStorm') under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pecan
import six
from mongoengine import ValidationError
from st2api.controllers import resource
from st2common import log as logging
from st2common.exceptions.apivalidation import ValueValidationException
from st2common.models.api.action import ActionAliasAPI
from st2common.persistence.actionalias import ActionAlias
from st2common.models.api.base import jsexpose
from st2common.rbac.types import PermissionType
from st2common.rbac.decorators import request_user_has_permission
from st2common.rbac.decorators import request_user_has_resource_api_permission
from st2common.rbac.decorators import request_user_has_resource_db_permission
http_client = six.moves.http_client
LOG = logging.getLogger(__name__)
class ActionAliasController(resource.ContentPackResourceController):
"""
Implements the RESTful interface for ActionAliases.
"""
model = ActionAliasAPI
access = ActionAlias
supported_filters = {
'name': 'name',
'pack': 'pack'
}
query_options = {
'sort': ['pack', 'name']
}
@request_user_has_permission(permission_type=PermissionType.ACTION_ALIAS_LIST)
@jsexpose()
def get_all(self, **kwargs):
return super(ActionAliasController, self)._get_all(**kwargs)
@request_user_has_resource_db_permission(permission_type=PermissionType.ACTION_ALIAS_VIEW)
@jsexpose(arg_types=[str])
def get_one(self, ref_or_id):
return super(ActionAliasController, self)._get_one(ref_or_id)
@jsexpose(body_cls=ActionAliasAPI, status_code=http_client.CREATED)
@request_user_has_resource_api_permission(permission_type=PermissionType.ACTION_ALIAS_CREATE)
def post(self, action_alias):
"""
Create a new ActionAlias.
Handles requests:
POST /actionalias/
"""
try:
action_alias_db = ActionAliasAPI.to_model(action_alias)
LOG.debug('/actionalias/ POST verified ActionAliasAPI and formulated ActionAliasDB=%s',
action_alias_db)
action_alias_db = ActionAlias.add_or_update(action_alias_db)
except (ValidationError, ValueError, ValueValidationException) as e:
LOG.exception('Validation failed for action alias data=%s.', action_alias)
pecan.abort(http_client.BAD_REQUEST, str(e))
return
extra = {'action_alias_db': action_alias_db}
LOG.audit('Action alias created. ActionAlias.id=%s' % (action_alias_db.id), extra=extra)
action_alias_api = ActionAliasAPI.from_model(action_alias_db)
return action_alias_api
@request_user_has_resource_db_permission(permission_type=PermissionType.ACTION_MODIFY)
@jsexpose(arg_types=[str], body_cls=ActionAliasAPI)
def put(self, action_alias_ref_or_id, action_alias):
action_alias_db = self._get_by_ref_or_id(ref_or_id=action_alias_ref_or_id)
LOG.debug('PUT /actionalias/ lookup with id=%s found object: %s', action_alias_ref_or_id,
action_alias_db)
try:
if action_alias.id is not None and action_alias.id is not '' and \
action_alias.id != action_alias_ref_or_id:
LOG.warning('Discarding mismatched id=%s found in payload and using uri_id=%s.',
action_alias.id, action_alias_ref_or_id)
old_action_alias_db = action_alias_db
action_alias_db = ActionAliasAPI.to_model(action_alias)
action_alias_db.id = action_alias_ref_or_id
action_alias_db = ActionAlias.add_or_update(action_alias_db)
except (ValidationError, ValueError) as e:
LOG.exception('Validation failed for action alias data=%s', action_alias)
pecan.abort(http_client.BAD_REQUEST, str(e))
return
extra = {'old_action_alias_db': old_action_alias_db, 'new_action_alias_db': action_alias_db}
LOG.audit('Action alias updated. ActionAlias.id=%s.' % (action_alias_db.id), extra=extra)
action_alias_api = ActionAliasAPI.from_model(action_alias_db)
return action_alias_api
@request_user_has_resource_db_permission(permission_type=PermissionType.ACTION_ALIAS_DELETE)
@jsexpose(arg_types=[str], status_code=http_client.NO_CONTENT)
def delete(self, action_alias_ref_or_id):
"""
Delete an action alias.
Handles requests:
DELETE /actionalias/1
"""
action_alias_db = self._get_by_ref_or_id(ref_or_id=action_alias_ref_or_id)
LOG.debug('DELETE /actionalias/ lookup with id=%s found object: %s', action_alias_ref_or_id,
action_alias_db)
try:
ActionAlias.delete(action_alias_db)
except Exception as e:
LOG.exception('Database delete encountered exception during delete of id="%s".',
action_alias_ref_or_id)
pecan.abort(http_client.INTERNAL_SERVER_ERROR, str(e))
return
extra = {'action_alias_db': action_alias_db}
LOG.audit('Action alias deleted. ActionAlias.id=%s.' % (action_alias_db.id), extra=extra)
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13,
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492,
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13,
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9995,
13,
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1904,
353,
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29909,
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8787,
13,
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353,
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13,
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6969,
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426,
13,
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525,
978,
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525,
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525,
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29915,
13,
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13,
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426,
13,
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525,
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500,
13,
13,
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3827,
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29898,
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1853,
29922,
27293,
1542,
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24705,
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1964,
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13,
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13,
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313,
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511,
4805,
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13,
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2754,
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13,
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29922,
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29979,
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13,
1678,
732,
29926,
14167,
4220,
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1191,
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8768,
11759,
710,
1402,
3573,
29918,
25932,
29922,
4276,
29909,
18849,
8787,
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13,
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272,
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333,
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13,
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462,
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13,
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338,
451,
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320,
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333,
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6632,
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13,
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13209,
273,
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370,
441,
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1124,
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4645,
29889,
23845,
29940,
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29872,
876,
13,
9651,
736,
13,
13,
4706,
4805,
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11117,
2467,
29918,
19973,
29918,
2585,
2396,
3158,
29918,
19973,
29918,
2585,
29913,
13,
4706,
25401,
29889,
15052,
277,
877,
4276,
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11132,
29889,
9123,
29909,
18849,
29889,
333,
16328,
29879,
6169,
1273,
313,
2467,
29918,
19973,
29918,
2585,
29889,
333,
511,
4805,
29922,
17833,
29897,
13,
2
] |
python/testData/codeInsight/controlflow/tryraisefinally.py | jnthn/intellij-community | 2 | 195233 | <reponame>jnthn/intellij-community
try:
raise KeyboardInterrupt
finally:
print 'test' | [
1,
529,
276,
1112,
420,
29958,
29926,
20800,
29876,
29914,
28820,
29899,
23834,
13,
2202,
29901,
13,
1678,
12020,
7670,
3377,
4074,
6685,
13,
4951,
635,
29901,
13,
1678,
1596,
525,
1688,
29915,
2
] |
fjarrsyn/tests/test_agent_setup_mould_act_3.py | anderzzz/fjarrsyn | 0 | 72661 | <filename>fjarrsyn/tests/test_agent_setup_mould_act_3.py
'''Integration test of agent setup of Moulder and Actuator organs with multiple
output Directions and Resource Maps
'''
import pytest
from fjarrsyn.core.agent import Agent
from fjarrsyn.core.instructor import Moulder, Actuator
from fjarrsyn.core.message import Belief, Direction, Resource, MessageOperator
from fjarrsyn.core.scaffold_map import ResourceMap, MapCollection
from fjarrsyn.core.array import EmptyFlashError
REF_REPO = [('v', 0, 1.0), ('h', -1, 0.5), ('v', -1, 1.0), ('h', 1, 0.5)]
REF_E = [15.0, 10.0]
def isclose(a, b, rel_tol=1e-9, abs_tol=0.0):
return abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
def make_decision(s, h, a):
energy_tot = 0.0
if s < 50.0:
return 0, 0.0, 0, 0.0, energy_tot
else:
h_m = 0.5
a_m = 1.0
if h < 1.0:
h_d = -1
energy_tot += 5.0
else:
h_d = 1
energy_tot += 3.0
if a < -0.3:
a_d = -1
energy_tot += 2.0
elif a > 0.3:
a_d = 1
energy_tot += 2.0
else:
a_d = 0
energy_tot += 0.0
return h_d, h_m, a_d, a_m, -1.0 * energy_tot
REPO = []
def move_rule_horizontal(d, m):
REPO.append(('h',d,m))
def move_rule_vertical(d, m):
REPO.append(('v',d,m))
def test_main():
#
# Define Messages
#
belief = Belief('Projectile status', ('speed', 'height', 'angle'))
belief.set_values([100.0, 0.3, 0.2])
direction = Direction('motion', ('horizontal direction',
'horizontal magnitude',
'vertical direction',
'vertical magnitude'))
#
# Define Scaffold and Map for it
#
agent_resources = Resource('internal_resource', ('internal_energy',))
agent_resources.set_values(20.0)
change_energy = ResourceMap('adjust_energy', 'delta', 'internal_energy', ('expend_energy',))
#
# Define Organs and their associated messages
#
moulder = Moulder('take evasive action?', make_decision,
belief, direction,
change_energy)
splitter_1 = MessageOperator(direction, slice_labels=['horizontal direction',
'horizontal magnitude'])
splitter_2 = MessageOperator(direction, slice_labels=['vertical direction',
'vertical magnitude'])
actuator1 = Actuator('move left right', move_rule_horizontal, splitter_1)
actuator2 = Actuator('move up down', move_rule_vertical, splitter_2)
#
# Initialize Agent
#
agent = Agent('test_agent', strict_engine=True)
agent.set_organ(moulder)
agent.set_organ(actuator1)
agent.set_organ(actuator2)
agent.set_scaffold(agent_resources)
agent.set_message(belief)
#
# Decide on direction and execute action
#
agent.mould('take evasive action?')
agent.act('move up down')
agent.act('move left right')
assert (agent.resource['internal_energy'] == REF_E[0])
belief.set_values([100.0, 1.3, -0.5])
agent.mould('take evasive action?')
agent.act('move up down')
agent.act('move left right')
assert (agent.resource['internal_energy'] == REF_E[1])
for e1, e2 in zip(REPO, REF_REPO):
assert(e1 == e2)
try:
agent.act('move up down')
raise AssertionError('Action without preceding moulding did not raise exception')
except EmptyFlashError:
pass
else:
raise AssertionError('Action without preceding moulding did not raise expected exception')
try:
agent.act('move left right')
raise AssertionError('Action without preceding moulding did not raise exception')
except EmptyFlashError:
pass
else:
raise AssertionError('Action without preceding moulding did not raise expected exception')
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__init__.py | Alex2Yang97/recommender | 0 | 44528 | <reponame>Alex2Yang97/recommender
# -*- coding:utf8 -*-
"""
@Author: Zhirui(<NAME>
@Date: 2021/4/25 下午11:07
""" | [
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tests/conftest.py | Evpok/camphr | 0 | 1617824 | import os
from pathlib import Path
import omegaconf
import pytest
import sentencepiece as spm
import torch
from spacy.vocab import Vocab
from camphr.lang.juman import Japanese as Juman
from camphr.lang.mecab import Japanese as Mecab
from camphr.models import create_model
from camphr.pipelines.transformers.model import TRANSFORMERS_MODEL
from .utils import FIXTURE_DIR, TRF_TESTMODEL_PATH, check_juman, check_lang, check_mecab
def pytest_addoption(parser):
parser.addoption(
"--runslow", action="store_true", default=False, help="run slow tests"
)
def pytest_configure(config):
config.addinivalue_line("markers", "slow: mark test as slow to run")
def pytest_collection_modifyitems(config, items):
if config.getoption("--runslow"):
# --runslow given in cli: do not skip slow tests
return
skip_slow = pytest.mark.skip(reason="need --runslow option to run")
for item in items:
if "slow" in item.keywords:
item.add_marker(skip_slow)
@pytest.fixture(scope="session")
def mecab_tokenizer():
if not check_mecab():
pytest.skip("mecab is required")
return Mecab.Defaults.create_tokenizer(dicdir="/usr/local/lib/mecab/dic/ipadic")
@pytest.fixture(scope="session", params=[True, False])
def juman_tokenizer(request):
if not check_juman():
pytest.skip()
return Juman.Defaults.create_tokenizer(juman_kwargs={"jumanpp": request.param})
@pytest.fixture(scope="session")
def spiece_path():
return str(FIXTURE_DIR / "spiece.model")
@pytest.fixture(scope="session")
def spiece(spiece_path):
s = spm.SentencePieceProcessor()
s.load(spiece_path)
return s
@pytest.fixture(scope="session")
def vocab():
return Vocab()
@pytest.fixture
def cuda():
return torch.device("cuda")
@pytest.fixture(scope="session", params=["cuda", "cpu"])
def device(request):
if request.param == "cpu":
return torch.device("cpu")
if not torch.cuda.is_available():
pytest.skip("cuda is required")
return torch.device("cuda")
ALL_LANGS = ["ja_mecab", "ja_juman"]
@pytest.fixture(scope="session", params=ALL_LANGS)
def lang(request):
if not check_lang(request.param):
pytest.skip(f"No requirements for {request.param}")
return request.param
@pytest.fixture(scope="session", params=TRF_TESTMODEL_PATH)
def trf_name_or_path(request):
if "bert-base-japanese" in request.param and not check_mecab():
pytest.skip("mecab is required")
return request.param
@pytest.fixture(scope="session", params=TRF_TESTMODEL_PATH)
def trf_testmodel_path(request) -> str:
return request.param
@pytest.fixture(scope="session")
def trf_model_config(lang, trf_name_or_path, device):
return omegaconf.OmegaConf.create(
f"""
lang:
name: {lang}
optimizer:
class: torch.optim.SGD
params:
lr: 0.01
pipeline:
{TRANSFORMERS_MODEL}:
trf_name_or_path: {trf_name_or_path}
"""
)
@pytest.fixture(scope="module")
def nlp_trf_model(trf_model_config, device):
_nlp = create_model(trf_model_config)
_nlp.to(device)
return _nlp
@pytest.fixture
def chdir(tmp_path: Path):
tmp_path.mkdir(exist_ok=True)
cwd = os.getcwd()
os.chdir(tmp_path)
yield
os.chdir(cwd)
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] |
tests/commands/test_release.py | williamirick/hatch | 2,549 | 172858 | import os
from click.testing import CliRunner
from twine.utils import TEST_REPOSITORY
from hatch.cli import hatch
from hatch.env import install_packages
from hatch.settings import SETTINGS_FILE, copy_default_settings, save_settings
from hatch.utils import env_vars, temp_chdir, temp_move_path
from hatch.venv import create_venv, venv
from ..utils import requires_internet
PACKAGE_NAME = 'e00f69943529ccc38058'
USERNAME = '__token__'
PASSWORD = (
'<KEY>'
'<KEY>'
)
ENV_VARS = {'TWINE_PASSWORD': PASSWORD}
@requires_internet
def test_cwd():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
os.chdir(os.path.join(d, 'dist'))
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-u', USERNAME, '-t'])
assert result.exit_code == 0
@requires_internet
def test_username_env():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
os.chdir(os.path.join(d, 'dist'))
with temp_move_path(SETTINGS_FILE, d):
settings = copy_default_settings()
settings['pypi_username'] = ''
save_settings(settings)
extra_env_vars = {'TWINE_USERNAME': USERNAME, **ENV_VARS}
with env_vars(extra_env_vars):
result = runner.invoke(hatch, ['release', '-t'])
assert result.exit_code == 0
@requires_internet
def test_cwd_dist_exists():
with temp_chdir():
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-u', USERNAME, '-t'])
assert result.exit_code == 0
@requires_internet
def test_package():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
os.chdir(package_dir)
install_packages(['-e', '.'])
os.chdir(d)
result = runner.invoke(hatch, ['release', PACKAGE_NAME, '-u', USERNAME, '-t'])
assert result.exit_code == 0
def test_package_not_exist():
with temp_chdir() as d:
runner = CliRunner()
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
result = runner.invoke(hatch, ['release', PACKAGE_NAME, '-u', USERNAME, '-t'])
assert result.exit_code == 1
assert '`{}` is not an editable package.'.format(PACKAGE_NAME) in result.output
@requires_internet
def test_local():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
install_packages(['-e', package_dir])
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME, '-t'])
assert result.exit_code == 0
def test_local_not_exist():
with temp_chdir() as d:
runner = CliRunner()
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir):
result = runner.invoke(hatch, ['release', '-l'])
assert result.exit_code == 1
assert 'There are no local packages available.' in result.output
@requires_internet
def test_local_multiple():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', 'ok', '--basic', '-ne'])
runner.invoke(hatch, ['new', 'ko', '--basic', '-ne'])
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir):
install_packages(['-e', os.path.join(d, 'ok')])
install_packages(['-e', os.path.join(d, 'ko')])
result = runner.invoke(hatch, ['release', '-l'])
assert result.exit_code == 1
assert (
'There are multiple local packages available. '
'Select one with the optional argument.'
) in result.output
@requires_internet
def test_path_relative():
with temp_chdir():
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', 'dist', '-u', USERNAME, '-t'])
print(result.output)
assert result.exit_code == 0
@requires_internet
def test_path_full():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['new', 'ko', '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
build_dir = os.path.join(d, PACKAGE_NAME, 'dist')
os.chdir(os.path.join(d, 'ko'))
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', build_dir, '-u', USERNAME, '-t'])
assert result.exit_code == 0
def test_path_full_not_exist():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
full_path = os.path.join(d, 'dist')
result = runner.invoke(hatch, ['release', '-p', full_path])
assert result.exit_code == 1
assert 'Directory `{}` does not exist.'.format(full_path) in result.output
@requires_internet
def test_config_username():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with temp_move_path(SETTINGS_FILE, d):
settings = copy_default_settings()
settings['pypi_username'] = USERNAME
save_settings(settings)
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', 'dist', '-t'])
assert result.exit_code == 0
def test_config_not_exist():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with temp_move_path(SETTINGS_FILE, d):
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', 'dist', '-t'])
assert result.exit_code == 1
assert 'Unable to locate config file. Try `hatch config --restore`.' in result.output
def test_config_username_empty():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with temp_move_path(SETTINGS_FILE, d):
settings = copy_default_settings()
settings['pypi_username'] = ''
save_settings(settings)
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', 'dist', '-t'])
assert result.exit_code == 1
assert (
'A username must be supplied via -u/--username, '
'in {} as pypi_username, or in the TWINE_USERNAME environment variable.'.format(SETTINGS_FILE)
) in result.output
def test_strict():
with temp_chdir():
runner = CliRunner()
runner.invoke(hatch, ['init', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build'])
with env_vars(ENV_VARS):
result = runner.invoke(hatch, ['release', '-p', 'dist', '-u', USERNAME, '-t', '-s'])
assert result.exit_code == 1
def test_repository_local():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
# Make sure there's no configuration
with temp_move_path(os.path.expanduser("~/.pypirc"), d):
with venv(venv_dir, evars=ENV_VARS):
install_packages(['-e', package_dir])
# Will error, since there's no configuration parameter for
# this URL
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME, '-r', TEST_REPOSITORY])
assert result.exit_code == 1
@requires_internet
def test_repository_url_local():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
install_packages(['-e', package_dir])
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME,
'--repo-url', TEST_REPOSITORY])
assert result.exit_code == 0
@requires_internet
def test_repository_and_repository_url_local():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
install_packages(['-e', package_dir])
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME,
'--repo', TEST_REPOSITORY,
'--repo-url', TEST_REPOSITORY])
assert result.exit_code == 0
@requires_internet
def test_repository_env_vars():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
extra_env_vars = {'TWINE_REPOSITORY': TEST_REPOSITORY, 'TWINE_REPOSITORY_URL': TEST_REPOSITORY, **ENV_VARS}
with venv(venv_dir, evars=extra_env_vars):
install_packages(['-e', package_dir])
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME])
assert result.exit_code == 0
@requires_internet
def test_repository_and_test():
with temp_chdir() as d:
runner = CliRunner()
runner.invoke(hatch, ['new', PACKAGE_NAME, '--basic', '-ne'])
runner.invoke(hatch, ['build', '-p', PACKAGE_NAME])
package_dir = os.path.join(d, PACKAGE_NAME)
venv_dir = os.path.join(d, 'venv')
create_venv(venv_dir)
with venv(venv_dir, evars=ENV_VARS):
install_packages(['-e', package_dir])
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME,
'-r', TEST_REPOSITORY,
'-t'])
assert result.exit_code == 1
assert "Cannot specify both --test and --repo." in result.output
with venv(venv_dir, evars=ENV_VARS):
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME,
'--repo-url', TEST_REPOSITORY,
'-t'])
assert result.exit_code == 1
assert "Cannot specify both --test and --repo-url." in result.output
with venv(venv_dir, evars=ENV_VARS):
result = runner.invoke(hatch, ['release', '-l', '-u', USERNAME,
'-r', TEST_REPOSITORY,
'-ru', TEST_REPOSITORY,
'-t'])
assert result.exit_code == 1
assert "Cannot specify both --test and --repo." in result.output
assert "Cannot specify both --test and --repo-url." in result.output
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] |
test_pkg.py | rtmigo/neatest_py | 3 | 99015 | from chkpkg import Package
if __name__ == "__main__":
with Package() as pkg:
pkg.run_python_code('import neatest; neatest.print_version()')
pkg.run_shell_code('neatest --version')
print("\nPackage is OK!")
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pythonQuiz.py | Lakhankumawat/Python-Quiz | 0 | 76219 | <reponame>Lakhankumawat/Python-Quiz
def q1():
print("hello" * 5)
def q2():
x, y = 4, 5
y, x = x, y
print(x)
print(y)
def q3():
z = 9
lst = [0] * z
print(lst[:-1])
def q4():
def help(x):
return x % 2 == 0
lst = [i**2 for i in range(10)]
lst = filter(help, lst)
print(list(lst)[::2])
def q5():
z = 0
for i in range(1, 10):
if (i + 1 // 2) % 7 == 0:
break
else:
z += int(i % 2 == 0)
print(z)
else:
print('end')
def q6():
import math
for i in range(1, 100):
if math.sqrt(i) == (i - 5)**2:
print(i)
break
else:
print('no')
def q7():
class A:
def __init__(self, x):
self.x = x
def __eq__(self, o):
return self.x == o.x
a = A(2)
b = A(2)
print(a == b)
print(a is b)
print(b is a)
print(a is a)
print(a == a)
def q8():
class A:
def __init__(self, x, y):
self.x = x
self.y = y
def print(self):
print(self.x, self.y)
class B(A):
def print(self):
print(self.y, self.x)
class C(B):
def __init__(self, x, y, z):
super().__init__(x, y)
self.z = z
d = A(2, 4)
e = B(4, 5)
g = C(3, 4, 7)
g.x = g.z
d.print()
e.print()
g.print()
def q9():
def x(z):
def q(x, y):
x = y + z + x
print(x)
return q
for i in range(10):
func = x(i)
func(i, i-1)
def q10():
def d(f):
def w(*args, **kwargs):
r = f(*args, **kwargs)
r += 1
return r
return w
@d
def a(x):
return x + 1
print(a(5))
def q11():
print(*list(map(lambda x: chr(ord(x) + 1), ["a", "b", "c"])))
def q12():
x = 0.1
y = 0.10000000000000001
print(x == y)
def q13():
x = [True, 1, "a", "b", "2"]
print(any(x))
def q14():
x = ["a", 1, 2, 3, 4]
y = z = x
z[1] = 7
y[3] = 2
x[2] = 9
print(x)
print(y)
print(z)
def q15():
print(1 == True)
print("1" == 1)
def q16():
x = b'1001'
y = b'1010'
z = x + y
print(z)
def q17():
x = 0b1001
y = 0b1010
z = x + y
print(z)
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Protheus_WebApp/Modules/SIGAAGR/OGA250TestCase.py | 98llm/tir-script-samples | 17 | 171464 | <reponame>98llm/tir-script-samples
from tir import Webapp
import unittest
class OGA250(unittest.TestCase):
@classmethod
def setUpClass(inst):
from datetime import datetime
DateSystem = datetime.today().strftime('%d/%m/%Y')
inst.oHelper = Webapp()
inst.oHelper.Setup('SIGAAGR',DateSystem,'T1','D MG 01 ')
def test_OGA250_CT001(self):
#Cenário 001: Entrada por produção - nova comercialização
self.oHelper.Program('OGA250')
self.oHelper.AddParameter("MV_AGRA001", "", ".T.", ".T.", ".T.")
self.oHelper.AddParameter("MV_AGRO002", "", ".T.", ".T.", ".T.")
self.oHelper.AddParameter("MV_SIGAAGR","",".T.",".T.",".T.")
self.oHelper.AddParameter("MV_ARM251A", "", "001", "001", "001")
self.oHelper.AddParameter("MV_AGRTMPR", "", "010", "010", "010")
self.oHelper.AddParameter("MV_AGRTMPS", "", "001", "001", "001")
self.oHelper.AddParameter("MV_AGRTMRQ", "", "501", "501", "501")
self.oHelper.SetParameters()
self.oHelper.WaitShow("Romaneios com Pesagem")
self.oHelper.SetKey("F12")
self.oHelper.SetValue("MV_PAR01", "000001", name_attr=True)
self.oHelper.SetButton("Ok")
self.oHelper.SetButton("Incluir")
self.oHelper.SetButton("Ok")
self.oHelper.SetValue("Tipo", "1")
self.oHelper.SetValue("Cod.Entidade", "000001")
self.oHelper.SetValue("Loj.Entidade", "01")
self.oHelper.ClickFolder("Analise")
self.oHelper.SetValue("Cod. Safra", "1920")
self.oHelper.SetValue("Cod. Produto", "AGR-FARDAO-ALGODAO")
self.oHelper.SetValue("Local", "01")
self.oHelper.SetValue("Fazenda", "01")
self.oHelper.SetValue("Un. Benef.", "01")
self.oHelper.SetButton("Outras Ações", "Vincular Fardões")
self.oHelper.ClickBox("Código","000033", grid_number=1)
self.oHelper.SetButton(">>")
self.oHelper.SetButton("Salvar")
self.oHelper.SetButton("Outras Ações", "Pesagem")
self.oHelper.SetValue("nPeso", "1000", name_attr=True)
self.oHelper.SetButton("Confirmar")
self.oHelper.SetButton("Outras Ações", "Pesagem")
self.oHelper.SetValue("nPeso", "101000", name_attr=True)
self.oHelper.SetButton("Confirmar")
self.oHelper.SetButton("Confirmar")
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("Outras Ações", "Atualizar")
self.oHelper.SetButton("Sim")
self.oHelper.SetButton("Outras Ações", "Confirmar")
self.oHelper.WaitHide("Aguarde")
self.oHelper.SetButton("Visualizar")
self.oHelper.ClickFolder("Pesagem")
self.oHelper.CheckResult("NJJ_PSSUBT", "100.000,00")
self.oHelper.ClickFolder("Controle")
self.oHelper.CheckResult("NJJ_STATUS", "3 - Confirmado")
self.oHelper.AssertTrue()
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("x")
def test_OGA250_CT002(self):
#Cenário 002: Exclusão documento de saída e reabertura do romaneio (atualizado)
self.oHelper.SetLateralMenu("Atualizações > Gestão Agrícola > Faturamento > Exclus. Doc. Saída")
###self.oHelper.Program("MATA521A")
self.oHelper.AddParameter("MV_SIGAAGR","",".T.",".T.",".T.")
self.oHelper.SetParameters()
self.oHelper.SetValue("Modelo de Interface ?" ,"Marcacao")
self.oHelper.SetValue("Selecionar itens ?" ,"Nao")
self.oHelper.SetValue("Dt.Emissao de ?" ,"01/01/2000")
self.oHelper.SetValue("Dt.Emissao ate ?" ,"31/12/2045")
self.oHelper.SetValue("Serie de ?" ,"001")
self.oHelper.SetValue("Serie ate ?" ,"001")
self.oHelper.SetValue("Documento de ?" ,"T2501K")
self.oHelper.SetValue("Documento ate ?" ,"T2501K")
self.oHelper.SetButton("OK")
self.oHelper.ClickBox("Numero","T2501K")
self.oHelper.SetButton("Outras Ações","Excluir")
self.oHelper.SetButton("Sim")
self.oHelper.WaitProcessing("Estorno dos documentos de saida")
self.oHelper.SetButton("x")
self.oHelper.Program('OGA250')
self.oHelper.SearchBrowse("D MG 01 0000000135", "Filial+cod.romaneio")
self.oHelper.SetButton("Visualizar")
self.oHelper.ClickFolder("Controle")
self.oHelper.CheckResult("NJJ_STATUS", "2 - Atualizado")
self.oHelper.AssertTrue()
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("x")
def test_OGA250_CT003(self):
#Cenário 003: Exclusão documento de saída e reabertura do romaneio (atualizado)
self.oHelper.SetLateralMenu("Atualizações > Gestão Agrícola > Faturamento > Exclus. Doc. Saída")
#self.oHelper.Program("MATA521A")
self.oHelper.AddParameter("MV_SIGAAGR","",".T.",".T.",".T.")
self.oHelper.SetParameters()
self.oHelper.SetValue("Modelo de Interface ?" ,"Marcacao")
self.oHelper.SetValue("Selecionar itens ?" ,"Nao")
self.oHelper.SetValue("Dt.Emissao de ?" ,"01/01/2000")
self.oHelper.SetValue("Dt.Emissao ate ?" ,"31/12/2045")
self.oHelper.SetValue("Serie de ?" ,"001")
self.oHelper.SetValue("Serie ate ?" ,"001")
self.oHelper.SetValue("Documento de ?" ,"T2501L")
self.oHelper.SetValue("Documento ate ?" ,"T2501L")
self.oHelper.SetButton("OK")
self.oHelper.ClickBox("Numero","T2501L")
self.oHelper.SetButton("Outras Ações","Excluir")
self.oHelper.SetButton("Sim")
self.oHelper.WaitProcessing("Estorno dos documentos de saida")
self.oHelper.SetButton("x")
self.oHelper.Program('OGA250')
self.oHelper.SearchBrowse("D MG 01 0000000136", "Filial+cod.romaneio")
self.oHelper.SetButton("Visualizar")
self.oHelper.ClickFolder("Controle")
self.oHelper.CheckResult("NJJ_STATUS", "2 - Atualizado")
self.oHelper.AssertTrue()
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("x")
def test_OGA250_CT004(self): #cenario exclusão doc entrada
#Cenário 004: Exclusão documento de entrada e reabertura do romaneio (atualizado)
self.oHelper.Program('MATA103')
self.oHelper.AddParameter("MV_SIGAAGR","",".T.",".T.",".T.")
self.oHelper.SetParameters()
self.oHelper.SearchBrowse("D MG 01 T25023", "Filial+numero")
self.oHelper.SetButton('Outras Ações', "Excluir")
self.oHelper.SetButton("Confirmar")
self.oHelper.SetButton("x")
self.oHelper.Program('OGA250')
self.oHelper.SearchBrowse("D MG 01 0000000147", "Filial+cod.romaneio")
self.oHelper.SetButton("Visualizar")
self.oHelper.ClickFolder("Controle")
self.oHelper.CheckResult("NJJ_STATUS", "2 - Atualizado")
self.oHelper.AssertTrue()
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("x")
def test_OGA250_CT005(self):
#Cenário 005: Expedição Algodão sem IE - Novo Negócio
if self.oHelper.GetRelease() >= "12.1.028":
self.oHelper.Program('OGA250')
self.oHelper.AddParameter("MV_AGRA001", "", ".T.", ".T.", ".T.")
self.oHelper.AddParameter("MV_AGRO002", "", ".T.", ".T.", ".T.")
self.oHelper.AddParameter("MV_SIGAAGR", "", ".T.", ".T.", ".T.")
self.oHelper.AddParameter("MV_OGDECPS", "", "2", "2", "2")
self.oHelper.AddParameter("MV_AGRTMPP", "", "501", "501", "501")
self.oHelper.SetParameters()
self.oHelper.WaitShow("Romaneios com Pesagem")
self.oHelper.SearchBrowse("D MG 01 0000000154", "Filial+cod.romaneio")
self.oHelper.SetButton("Alterar")
self.oHelper.ClickFolder("Comercialização")
self.oHelper.SetValue("Intervalo", "001", grid=True)
self.oHelper.SetValue("Id Regra", "002", grid=True)
self.oHelper.LoadGrid()
self.oHelper.SetButton("Confirmar")
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("Outras Ações","Vincular Fardos")
self.oHelper.ClickBox("Filial",select_all=True, grid_number=1)
self.oHelper.SetButton(">>")
self.oHelper.SetButton("Salvar")
self.oHelper.SetButton("Outras Ações","Atualizar")
self.oHelper.SetButton("Sim")
self.oHelper.SetButton("Outras Ações","Confirmar")
self.oHelper.WaitShow("Serie / Notas")
self.oHelper.SetButton("Ok")
self.oHelper.SetButton("Ok")
self.oHelper.SetButton("Cancelar")
self.oHelper.SetButton("Visualizar")
self.oHelper.ClickFolder("Controle")
self.oHelper.CheckResult("NJJ_STATUS", "3 - Confirmado")
self.oHelper.AssertTrue()
self.oHelper.SetButton("Fechar")
self.oHelper.SetButton("x")
@classmethod
def tearDownClass(inst):
inst.oHelper.TearDown()
if __name__ == '__main__':
unittest.main()
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] |
Listeners/messageListener.py | Aggis15/T4NK0R | 0 | 126679 | <filename>Listeners/messageListener.py
import discord
from discord.ext import commands
import json
import random as r
from dotenv import load_dotenv
import os
import asyncpg
import logging
from PIL import Image, ImageFont, ImageDraw
from resizeimage import resizeimage
import requests
load_dotenv()
# Logging
logging.basicConfig(
filename="./logs/discordlogs.log",
filemode="w",
format="%(name)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
# Initiate json
file = open("config.json")
data = json.load(file)
# Public Vars
prefix = data["prefix"]
DB_HOST = os.environ.get("DB_HOST")
DB_USER = os.environ.get("DB_USER")
DB_PASS = <PASSWORD>("DB_<PASSWORD>")
DB_NAME = os.environ.get("DB_NAME")
DB_PORT = os.environ.get("DB_PORT")
LEVEL_TABLE_NAME = os.environ.get("LEVEL_TABLE_NAME")
startingXP = data["XP"]["startingXP"]
whitelistChannels = data["whitelistChannels"]
levelupChat = data["channelIDs"]["levelupChat"]
class MessageListener(commands.Cog):
def __init__(self, bot):
self.bot = bot
# Create cooldown bucket
self.cd_mapping = commands.CooldownMapping.from_cooldown(
100, 120, commands.BucketType.member
)
self.startingXP = startingXP
self.levelupChat = levelupChat
def xp(self):
return r.randint(1, 100)
@commands.Cog.listener()
async def on_message(self, message):
await self.bot.process_commands(message)
conn = await asyncpg.connect(
f"postgres://{DB_USER}:{DB_PASS}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
)
levelNameAfterLevelUp = ""
if message.author.bot:
return
elif message.channel.id in whitelistChannels:
return
else:
await self.bot.process_commands(message)
bucket = self.cd_mapping.get_bucket(message)
retry_after = bucket.update_rate_limit()
if not retry_after:
xp_val = self.xp()
await conn.execute(
f"INSERT INTO {LEVEL_TABLE_NAME} (username, userid, currentxp, neededxp) VALUES ('{message.author.name}', {message.author.id}, {xp_val}, {startingXP}) ON CONFLICT (userid) DO UPDATE SET currentxp = {LEVEL_TABLE_NAME}.currentxp + {xp_val}"
)
xp = await conn.fetchval(
f"SELECT currentxp FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
await conn.execute(
f"UPDATE {LEVEL_TABLE_NAME} SET neededxp = {LEVEL_TABLE_NAME}.neededxp - {xp_val} WHERE userid = {message.author.id}"
)
neededXP = await conn.fetchval(
f"SELECT neededxp FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
if neededXP <= 0:
await conn.execute(
f"UPDATE {LEVEL_TABLE_NAME} SET currentlevel = {LEVEL_TABLE_NAME}.currentlevel + 1 WHERE userid = {message.author.id}"
)
level = await conn.fetchval(
f"SELECT currentlevel FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
for levelNames in data["levelNames"]:
if level >= int(levelNames):
levelNameAfterLevelUp = data["levelNames"][levelNames]
break
await conn.execute(
f"UPDATE {LEVEL_TABLE_NAME} SET levelname = '{levelNameAfterLevelUp}' WHERE userid = {message.author.id}"
)
untilLevelUp = int(str(xp)[-2:])
untilLevelUp = startingXP * level - untilLevelUp
await conn.execute(
f"UPDATE {LEVEL_TABLE_NAME} SET neededxp = {untilLevelUp} WHERE userid = {message.author.id}"
)
notify = await conn.fetchval(
f"SELECT doNotify FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
if notify:
doOverrideLevel = await conn.fetchval(
f"SELECT dooverridelevelname FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
if doOverrideLevel is True:
levelNameAfterLevelUp = await conn.fetchval(
f"SELECT overridelevelname FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
untilLevelUp = await conn.fetchval(
f"SELECT neededxp FROM {LEVEL_TABLE_NAME} WHERE userid = {message.author.id}"
)
# Edit the default image to add the text then send it
defaultImage = Image.open("./Images/levelImage.png")
getAvatar = requests.get(message.author.avatar.url)
with open(
f"./Images/avatarCache/{message.author.id}.png", "wb"
) as outfile:
outfile.write(getAvatar.content)
avatarImage = Image.open(
f"./Images/avatarCache/{message.author.id}.png"
)
# Crop the avatar to make it a circle
width, height = avatarImage.size
x = width - height
img_cropped = avatarImage.crop((x, 0, x + height, height))
mask = Image.new("L", img_cropped.size)
mask_draw = ImageDraw.Draw(mask)
width, height = img_cropped.size
mask_draw.ellipse((0, 0, width, height), fill=255)
img_cropped.putalpha(mask)
img_cropped.save(
f"./Images/avatarCache/{message.author.id}.png"
)
# Resize the avatar to fit the image
resizeAvatar = Image.open(
f"./Images/avatarCache/{message.author.id}.png"
)
resizeAvatar = resizeimage.resize_width(resizeAvatar, 100)
resizeAvatar.save(
f"./Images/avatarCache/{message.author.id}.png",
resizeAvatar.format,
)
# Add the text
draw = ImageDraw.Draw(defaultImage)
levelFont = ImageFont.truetype("Bungee-Regular.ttf", 20)
xpFont = ImageFont.truetype("Bungee-Regular.ttf", 18)
untilLevelUpFont = ImageFont.truetype("Bungee-Regular.ttf", 16)
draw.text(
(234, 94),
f"{level} ({levelNameAfterLevelUp})",
(255, 255, 255),
font=levelFont,
)
draw.text((185, 55), str(xp), (255, 255, 255), font=xpFont)
draw.text(
(271, 31),
str(untilLevelUp),
(255, 255, 255),
font=untilLevelUpFont,
)
# Add the image, then save
resizedAvatar = Image.open(
f"./Images/avatarCache/{message.author.id}.png"
)
defaultImage.paste(resizedAvatar, (27, 25), resizedAvatar)
defaultImage.save("./Images/levelImageReady.png")
levelupChannel = self.bot.get_channel(self.levelupChat)
await levelupChannel.send(
f"{message.author.mention} You have reached the next level!",
file=discord.File("./Images/levelImageReady.png"),
)
logger.info(
f"{message.author.name} with ID: {message.author.id} has logged {xp_val} XP"
)
await conn.close()
def setup(bot):
bot.add_cog(MessageListener(bot))
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backend/unpp_api/apps/sanctionslist/migrations/0003_auto_20180615_1224.py | unicef/un-partner-portal | 6 | 147648 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.13 on 2018-06-15 12:24
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('partner', '0063_auto_20180612_0625'),
('sanctionslist', '0002_auto_20180612_1142'),
]
operations = [
migrations.AlterUniqueTogether(
name='sanctionednamematch',
unique_together=set([('name', 'partner')]),
),
]
| [
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exercise/2/2-5/test-all-code.py | toorevitimirp/SICP | 0 | 51480 | <reponame>toorevitimirp/SICP<gh_stars>0
import os
import subprocess
class Tester(object):
def __init__(self, dir='./'):
self.dir = dir
self.report_fn = "report.txt"
self.interpreter = "racket"
self.suffix = ".rkt"
def _program_files(self):
programs_list = []
all_files = os.listdir(self.dir)
for fn in all_files:
if os.path.isfile(fn) and \
os.path.splitext(fn)[-1]==".rkt":
programs_list.append(fn)
return programs_list
def run_programs(self):
self._empty_report()
fns = self._program_files()
print(fns)
for fn in fns:
cmd = self.interpreter + " " + fn
# print(fn,":",end='')
status, output = subprocess.getstatusoutput(cmd)
self._report(fn, status, output)
def _report(self, fn, status, output):
print("+"*70)
print(fn,":",status)
print(status)
print(output)
# with open(self.report_fn, 'w+') as f:
# f.writelines(fn+":"+str(status))
# f.writelines(output)
# f.writelines("+"*50)
def _empty_report(self):
pass
# with open(self.report_fn, 'r+') as f:
# f.seek(0)
# f.truncate()
if __name__ == "__main__":
t = Tester()
t.run_programs() | [
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396,
411,
1722,
29898,
1311,
29889,
12276,
29918,
9144,
29892,
525,
29878,
29974,
1495,
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285,
29901,
13,
4706,
396,
268,
285,
29889,
344,
1416,
29898,
29900,
29897,
13,
4706,
396,
268,
285,
29889,
509,
4661,
403,
580,
13,
13,
361,
4770,
978,
1649,
1275,
376,
1649,
3396,
1649,
1115,
13,
1678,
260,
353,
323,
4156,
580,
13,
1678,
260,
29889,
3389,
29918,
8860,
29879,
580,
2
] |
dp101/lcs.py | cp105/dynamic_programming_101 | 0 | 185949 | """
The longest common subsequence (LCS) problem is the problem of finding the longest subsequence common to
all sequences in a set of sequences (often just two sequences).
It differs from the longest common substring problem: unlike substrings, subsequences are not required to
occupy consecutive positions within the original sequences.
https://en.wikipedia.org/wiki/Longest_common_subsequence_problem
function "lcs(sq1, sq2)" takes in two sequences of numbers as arguments.
The function returns the longest common subsequence of sq1 and sq2.
"""
def lcs_(sq1, sq2, memo):
if (len(sq1), len(sq2)) in memo:
return memo[(len(sq1), len(sq2))]
elif not sq1 or not sq2:
return []
elif sq1[0] == sq2[0]:
return [sq1[0]] + lcs_(sq1[1:], sq2[1:], memo)
a = lcs_(sq1, sq2[1:], memo)
b = lcs_(sq1[1:], sq2, memo)
if len(a) > len(b):
memo[(len(sq1), len(sq2))] = a
return a
else:
memo[(len(sq1), len(sq2))] = b
return b
def lcs(sq1, sq2):
return lcs_(sq1, sq2, {})
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3044,
29896,
29892,
18074,
29906,
29892,
426,
1800,
13,
2
] |
sasquatch/error/exec.py | tmacro/s4 | 6 | 27433 | from .base import SQError, BaseErrorHelper
from .context import ContextAwareError
class ExecutionError(SQError):
'''raised when an error is encountered during script execution'''
class ExecErrorHelper(BaseErrorHelper):
_default = ExecutionError
@staticmethod
def throw(cls = ExecutionError, **kwargs):
if 'ctx' in kwargs and kwargs['ctx'] is not None:
ctx = kwargs.get('ctx')
return BaseErrorHelper.throw(cls, **ctx._asdict(), **kwargs)
class MissingKeywordError(ExecutionError):
'''Raised when a required keyword argument can not be collected'''
_msg = 'Unable to collect keyword {keyword}'
class InvalidFilePathError(ContextAwareError, ExecutionError):
'''Raised when a file path passed as input does not exist or is invalid'''
_msg = 'Path {filepath} is not a valid location'
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12,
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7645,
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525,
2605,
426,
1445,
2084,
29913,
338,
451,
263,
2854,
4423,
29915,
13,
2
] |
lib/rucio/core/transfer.py | ChristophAmes/rucio | 0 | 74440 | <gh_stars>0
# -*- coding: utf-8 -*-
# Copyright 2013-2021 CERN
#
# 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.
#
# Authors:
# - <NAME> <<EMAIL>>, 2013-2021
# - <NAME> <<EMAIL>>, 2017-2021
# - <NAME> <<EMAIL>>, 2017
# - <NAME> <<EMAIL>>, 2018
# - <NAME> <<EMAIL>>, 2018-2021
# - dciangot <<EMAIL>>, 2018
# - <NAME> <<EMAIL>>, 2018-2019
# - <NAME> <<EMAIL>>, 2018
# - <NAME> <<EMAIL>>, 2019
# - <NAME> <<EMAIL>>, 2019
# - <NAME> <<EMAIL>>, 2019-2021
# - <NAME>' <<EMAIL>>, 2019
# - <NAME> <<EMAIL>>, 2019-2020
# - <NAME> <<EMAIL>>, 2020
# - <NAME> <<EMAIL>>, 2020
# - <NAME> <<EMAIL>>, 2020
# - <NAME> <<EMAIL>>, 2020
# - <NAME> <<EMAIL>>, 2020
# - <NAME> <<EMAIL>>, 2021
# - <NAME> <<EMAIL>>, 2021
from __future__ import division
import copy
import datetime
import imp
import json
import logging
import re
import time
from typing import TYPE_CHECKING
from dogpile.cache import make_region
from dogpile.cache.api import NoValue
from sqlalchemy import and_
from sqlalchemy.exc import IntegrityError
from sqlalchemy.sql.expression import false
from rucio.common import constants
from rucio.common.config import config_get
from rucio.common.constants import SUPPORTED_PROTOCOLS, FTS_STATE
from rucio.common.exception import (InvalidRSEExpression, NoDistance,
RequestNotFound, RSEProtocolNotSupported,
RucioException, UnsupportedOperation)
from rucio.common.rse_attributes import get_rse_attributes
from rucio.common.types import InternalAccount
from rucio.common.utils import construct_surl
from rucio.core import did, message as message_core, request as request_core
from rucio.core.config import get as core_config_get
from rucio.core.monitor import record_counter, record_timer
from rucio.core.oidc import get_token_for_account_operation
from rucio.core.replica import add_replicas
from rucio.core.request import queue_requests, set_requests_state
from rucio.core.rse import get_rse_name, get_rse_vo, list_rses, get_rse_supported_checksums
from rucio.core.rse_expression_parser import parse_expression
from rucio.db.sqla import models, filter_thread_work
from rucio.db.sqla.constants import DIDType, RequestState, RSEType, RequestType, ReplicaState
from rucio.db.sqla.session import read_session, transactional_session
from rucio.rse import rsemanager as rsemgr
from rucio.transfertool.fts3 import FTS3Transfertool
from rucio.transfertool.mock import MockTransfertool
if TYPE_CHECKING:
from typing import List, Tuple
# Extra modules: Only imported if available
EXTRA_MODULES = {'globus_sdk': False}
for extra_module in EXTRA_MODULES:
try:
imp.find_module(extra_module)
EXTRA_MODULES[extra_module] = True
except ImportError:
EXTRA_MODULES[extra_module] = False
if EXTRA_MODULES['globus_sdk']:
from rucio.transfertool.globus import GlobusTransferTool # pylint: disable=import-error
"""
The core transfer.py is specifically for handling transfer-requests, thus requests
where the external_id is already known.
Requests accessed by request_id are covered in the core request.py
"""
REGION_SHORT = make_region().configure('dogpile.cache.memcached',
expiration_time=600,
arguments={'url': config_get('cache', 'url', False, '127.0.0.1:11211'), 'distributed_lock': True})
ALLOW_USER_OIDC_TOKENS = config_get('conveyor', 'allow_user_oidc_tokens', False, False)
REQUEST_OIDC_SCOPE = config_get('conveyor', 'request_oidc_scope', False, 'fts:submit-transfer')
REQUEST_OIDC_AUDIENCE = config_get('conveyor', 'request_oidc_audience', False, 'fts:example')
WEBDAV_TRANSFER_MODE = config_get('conveyor', 'webdav_transfer_mode', False, None)
def submit_bulk_transfers(external_host, files, transfertool='fts3', job_params={}, timeout=None, user_transfer_job=False, logger=logging.log):
"""
Submit transfer request to a transfertool.
:param external_host: External host name as string
:param files: List of Dictionary containing request file.
:param transfertool: Transfertool as a string.
:param job_params: Metadata key/value pairs for all files as a dictionary.
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
:returns: Transfertool external ID.
"""
record_counter('core.request.submit_transfer')
transfer_id = None
if transfertool == 'fts3':
start_time = time.time()
job_files = []
for file in files:
job_file = {}
for key in file:
if key == 'sources':
# convert sources from (src_rse, url, src_rse_id, rank) to url
job_file[key] = []
for source in file[key]:
job_file[key].append(source[1])
else:
job_file[key] = file[key]
job_files.append(job_file)
# getting info about account and OIDC support of the RSEs
use_oidc = job_params.get('use_oidc', False)
transfer_token = None
if use_oidc:
logger(logging.DEBUG, 'OAuth2/OIDC available at RSEs')
account = job_params.get('account', None)
getadmintoken = False
if ALLOW_USER_OIDC_TOKENS is False:
getadmintoken = True
logger(logging.DEBUG, 'Attempting to get a token for account %s. Admin token option set to %s' % (account, getadmintoken))
# find the appropriate OIDC token and exchange it (for user accounts) if necessary
token_dict = get_token_for_account_operation(account, req_audience=REQUEST_OIDC_AUDIENCE, req_scope=REQUEST_OIDC_SCOPE, admin=getadmintoken)
if token_dict is not None:
logger(logging.DEBUG, 'Access token has been granted.')
if 'token' in token_dict:
logger(logging.DEBUG, 'Access token used as transfer token.')
transfer_token = token_dict['token']
transfer_id = FTS3Transfertool(external_host=external_host, token=transfer_token).submit(files=job_files, job_params=job_params, timeout=timeout)
record_timer('core.request.submit_transfers_fts3', (time.time() - start_time) * 1000 / len(files))
elif transfertool == 'globus':
logger(logging.DEBUG, '... Starting globus xfer ...')
job_files = []
for file in files:
job_file = {}
for key in file:
if key == 'sources':
# convert sources from (src_rse, url, src_rse_id, rank) to url
job_file[key] = []
for source in file[key]:
job_file[key].append(source[1])
else:
job_file[key] = file[key]
job_files.append(job_file)
logger(logging.DEBUG, 'job_files: %s' % job_files)
transfer_id = GlobusTransferTool(external_host=None).bulk_submit(submitjob=job_files, timeout=timeout)
elif transfertool == 'mock':
transfer_id = MockTransfertool(external_host=None).submit(files, None)
return transfer_id
@transactional_session
def prepare_sources_for_transfers(transfers, session=None):
"""
Prepare the sources for transfers.
:param transfers: Dictionary containing request transfer info.
:param session: Database session to use.
"""
try:
for request_id in transfers:
rowcount = session.query(models.Request)\
.filter_by(id=request_id)\
.filter(models.Request.state == RequestState.QUEUED)\
.update({'state': transfers[request_id]['state'],
'external_id': transfers[request_id]['external_id'],
'external_host': transfers[request_id]['external_host'],
'dest_url': transfers[request_id]['dest_url'],
'submitted_at': datetime.datetime.utcnow()},
synchronize_session=False)
if rowcount == 0:
raise RequestNotFound("Failed to prepare transfer: request %s does not exist or is not in queued state" % (request_id))
if 'file' in transfers[request_id]:
file = transfers[request_id]['file']
for src_rse, src_url, src_rse_id, rank in file['sources']:
src_rowcount = session.query(models.Source)\
.filter_by(request_id=request_id)\
.filter(models.Source.rse_id == src_rse_id)\
.update({'is_using': True}, synchronize_session=False)
if src_rowcount == 0:
models.Source(request_id=file['metadata']['request_id'],
scope=file['metadata']['scope'],
name=file['metadata']['name'],
rse_id=src_rse_id,
dest_rse_id=file['metadata']['dest_rse_id'],
ranking=rank if rank else 0,
bytes=file['metadata']['filesize'],
url=src_url,
is_using=True).\
save(session=session, flush=False)
except IntegrityError as error:
raise RucioException(error.args)
@transactional_session
def set_transfers_state(transfers, submitted_at, session=None):
"""
Update the transfer info of a request.
:param transfers: Dictionary containing request transfer info.
:param session: Database session to use.
"""
try:
for request_id in transfers:
rowcount = session.query(models.Request)\
.filter_by(id=request_id)\
.filter(models.Request.state == RequestState.SUBMITTING)\
.update({'state': transfers[request_id]['state'],
'external_id': transfers[request_id]['external_id'],
'external_host': transfers[request_id]['external_host'],
'source_rse_id': transfers[request_id]['src_rse_id'],
'submitted_at': submitted_at},
synchronize_session=False)
if rowcount == 0:
raise RucioException("Failed to set requests %s tansfer %s: request doesn't exist or is not in SUBMITTING state" % (request_id, transfers[request_id]))
request_type = transfers[request_id].get('request_type', None)
msg = {'request-id': request_id,
'request-type': request_type,
'scope': transfers[request_id]['scope'].external,
'name': transfers[request_id]['name'],
'src-rse-id': transfers[request_id]['metadata'].get('src_rse_id', None),
'src-rse': transfers[request_id]['metadata'].get('src_rse', None),
'dst-rse-id': transfers[request_id]['metadata'].get('dst_rse_id', None),
'dst-rse': transfers[request_id]['metadata'].get('dst_rse', None),
'state': transfers[request_id]['state'],
'activity': transfers[request_id]['metadata'].get('activity', None),
'file-size': transfers[request_id]['metadata'].get('filesize', None),
'bytes': transfers[request_id]['metadata'].get('filesize', None),
'checksum-md5': transfers[request_id]['metadata'].get('md5', None),
'checksum-adler': transfers[request_id]['metadata'].get('adler32', None),
'external-id': transfers[request_id]['external_id'],
'external-host': transfers[request_id]['external_host'],
'queued_at': str(submitted_at)}
if transfers[request_id]['scope'].vo != 'def':
msg['vo'] = transfers[request_id]['scope'].vo
if msg['request-type']:
transfer_status = '%s-%s' % (msg['request-type'].name, msg['state'].name)
else:
transfer_status = 'transfer-%s' % msg['state']
transfer_status = transfer_status.lower()
message_core.add_message(transfer_status, msg, session=session)
except IntegrityError as error:
raise RucioException(error.args)
def bulk_query_transfers(request_host, transfer_ids, transfertool='fts3', timeout=None, logger=logging.log):
"""
Query the status of a transfer.
:param request_host: Name of the external host.
:param transfer_ids: List of (External-ID as a 32 character hex string)
:param transfertool: Transfertool name as a string.
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
:returns: Request status information as a dictionary.
"""
record_counter('core.request.bulk_query_transfers')
if transfertool == 'fts3':
try:
start_time = time.time()
fts_resps = FTS3Transfertool(external_host=request_host).bulk_query(transfer_ids=transfer_ids, timeout=timeout)
record_timer('core.request.bulk_query_transfers', (time.time() - start_time) * 1000 / len(transfer_ids))
except Exception:
raise
for transfer_id in transfer_ids:
if transfer_id not in fts_resps:
fts_resps[transfer_id] = Exception("Transfer id %s is not returned" % transfer_id)
if fts_resps[transfer_id] and not isinstance(fts_resps[transfer_id], Exception):
for request_id in fts_resps[transfer_id]:
if fts_resps[transfer_id][request_id]['file_state'] in (FTS_STATE.FAILED,
FTS_STATE.FINISHEDDIRTY,
FTS_STATE.CANCELED):
fts_resps[transfer_id][request_id]['new_state'] = RequestState.FAILED
elif fts_resps[transfer_id][request_id]['file_state'] in FTS_STATE.FINISHED:
fts_resps[transfer_id][request_id]['new_state'] = RequestState.DONE
return fts_resps
elif transfertool == 'globus':
try:
start_time = time.time()
logger(logging.DEBUG, 'transfer_ids: %s' % transfer_ids)
responses = GlobusTransferTool(external_host=None).bulk_query(transfer_ids=transfer_ids, timeout=timeout)
record_timer('core.request.bulk_query_transfers', (time.time() - start_time) * 1000 / len(transfer_ids))
except Exception:
raise
for k, v in responses.items():
if v == 'FAILED':
responses[k] = RequestState.FAILED
elif v == 'SUCCEEDED':
responses[k] = RequestState.DONE
else:
responses[k] = RequestState.SUBMITTED
return responses
else:
raise NotImplementedError
return None
@transactional_session
def set_transfer_update_time(external_host, transfer_id, update_time=datetime.datetime.utcnow(), session=None):
"""
Update the state of a request. Fails silently if the transfer_id does not exist.
:param external_host: Selected external host as string in format protocol://fqdn:port
:param transfer_id: External transfer job id as a string.
:param update_time: Time stamp.
:param session: Database session to use.
"""
record_counter('core.request.set_transfer_update_time')
try:
rowcount = session.query(models.Request).filter_by(external_id=transfer_id, state=RequestState.SUBMITTED).update({'updated_at': update_time}, synchronize_session=False)
except IntegrityError as error:
raise RucioException(error.args)
if not rowcount:
raise UnsupportedOperation("Transfer %s doesn't exist or its status is not submitted." % (transfer_id))
def query_latest(external_host, state, last_nhours=1, logger=logging.log):
"""
Query the latest transfers in last n hours with state.
:param external_host: FTS host name as a string.
:param state: FTS job state as a string or a dictionary.
:param last_nhours: Latest n hours as an integer.
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
:returns: Requests status information as a dictionary.
"""
record_counter('core.request.query_latest')
start_time = time.time()
resps = FTS3Transfertool(external_host=external_host).query_latest(state=state, last_nhours=last_nhours)
record_timer('core.request.query_latest_fts3.%s.%s_hours' % (external_host, last_nhours), (time.time() - start_time) * 1000)
if not resps:
return
ret_resps = []
for resp in resps:
if 'job_metadata' not in resp or resp['job_metadata'] is None or 'issuer' not in resp['job_metadata'] or resp['job_metadata']['issuer'] != 'rucio':
continue
if 'request_id' not in resp['job_metadata']:
# submitted by new submitter
try:
logger(logging.DEBUG, "Transfer %s on %s is %s, decrease its updated_at." % (resp['job_id'], external_host, resp['job_state']))
set_transfer_update_time(external_host, resp['job_id'], datetime.datetime.utcnow() - datetime.timedelta(hours=24))
except Exception as error:
logger(logging.DEBUG, "Exception happened when updating transfer updatetime: %s" % str(error).replace('\n', ''))
return ret_resps
@transactional_session
def touch_transfer(external_host, transfer_id, session=None):
"""
Update the timestamp of requests in a transfer. Fails silently if the transfer_id does not exist.
:param request_host: Name of the external host.
:param transfer_id: External transfer job id as a string.
:param session: Database session to use.
"""
record_counter('core.request.touch_transfer')
try:
# don't touch it if it's already touched in 30 seconds
session.query(models.Request).with_hint(models.Request, "INDEX(REQUESTS REQUESTS_EXTERNALID_UQ)", 'oracle')\
.filter_by(external_id=transfer_id)\
.filter(models.Request.state == RequestState.SUBMITTED)\
.filter(models.Request.updated_at < datetime.datetime.utcnow() - datetime.timedelta(seconds=30))\
.update({'updated_at': datetime.datetime.utcnow()}, synchronize_session=False)
except IntegrityError as error:
raise RucioException(error.args)
@transactional_session
def update_transfer_state(external_host, transfer_id, state, session=None, logger=logging.log):
"""
Used by poller to update the internal state of transfer,
after the response by the external transfertool.
:param request_host: Name of the external host.
:param transfer_id: External transfer job id as a string.
:param state: Request state as a string.
:param session: The database session to use.
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
:returns commit_or_rollback: Boolean.
"""
try:
if state == RequestState.LOST:
reqs = request_core.get_requests_by_transfer(external_host, transfer_id, session=session)
for req in reqs:
logger(logging.INFO, 'REQUEST %s OF TRANSFER %s ON %s STATE %s' % (str(req['request_id']), external_host, transfer_id, str(state)))
src_rse_id = req.get('source_rse_id', None)
dst_rse_id = req.get('dest_rse_id', None)
src_rse = None
dst_rse = None
if src_rse_id:
src_rse = get_rse_name(src_rse_id, session=session)
if dst_rse_id:
dst_rse = get_rse_name(dst_rse_id, session=session)
response = {'new_state': state,
'transfer_id': transfer_id,
'job_state': state,
'src_url': None,
'dst_url': req['dest_url'],
'duration': 0,
'reason': "The FTS job lost",
'scope': req.get('scope', None),
'name': req.get('name', None),
'src_rse': src_rse,
'dst_rse': dst_rse,
'request_id': req.get('request_id', None),
'activity': req.get('activity', None),
'src_rse_id': req.get('source_rse_id', None),
'dst_rse_id': req.get('dest_rse_id', None),
'previous_attempt_id': req.get('previous_attempt_id', None),
'adler32': req.get('adler32', None),
'md5': req.get('md5', None),
'filesize': req.get('filesize', None),
'external_host': external_host,
'job_m_replica': None,
'created_at': req.get('created_at', None),
'submitted_at': req.get('submitted_at', None),
'details': None,
'account': req.get('account', None)}
err_msg = request_core.get_transfer_error(response['new_state'], response['reason'] if 'reason' in response else None)
request_core.set_request_state(req['request_id'],
response['new_state'],
transfer_id=transfer_id,
src_rse_id=src_rse_id,
err_msg=err_msg,
session=session)
request_core.add_monitor_message(req, response, session=session)
else:
__set_transfer_state(external_host, transfer_id, state, session=session)
return True
except UnsupportedOperation as error:
logger(logging.WARNING, "Transfer %s on %s doesn't exist - Error: %s" % (transfer_id, external_host, str(error).replace('\n', '')))
return False
@transactional_session
def get_hops(source_rse_id, dest_rse_id, include_multihop=False, multihop_rses=None, limit_dest_schemes=None, session=None):
"""
Get a list of hops needed to transfer date from source_rse_id to dest_rse_id.
Ideally, the list will only include one item (dest_rse_id) since no hops are needed.
:param source_rse_id: Source RSE id of the transfer.
:param dest_rse_id: Dest RSE id of the transfer.
:param include_multihop: If no direct link can be made, also include multihop transfers.
:param multihop_rses: List of RSE ids that can be used for multihop.
:param limit_dest_schemes: List of destination schemes the matching scheme algorithm should be limited to for a single hop.
:returns: List of hops in the format [{'source_rse_id': source_rse_id, 'source_scheme': 'srm', 'source_scheme_priority': N, 'dest_rse_id': dest_rse_id, 'dest_scheme': 'srm', 'dest_scheme_priority': N}]
:raises: NoDistance
"""
if not limit_dest_schemes:
limit_dest_schemes = []
# Check if there is a cached result
result = REGION_SHORT.get('get_hops_%s_%s_%s' % (str(source_rse_id), str(dest_rse_id), ''.join(sorted(limit_dest_schemes))))
if not isinstance(result, NoValue):
return result
if multihop_rses is None:
multihop_rses = []
# TODO: Might be problematic to always load the distance_graph, since it might be expensiv
# Load the graph from the distances table
# distance_graph = __load_distance_graph(session=session)
distance_graph = {}
distance_graph[source_rse_id] = __load_outgoing_distances_node(rse_id=source_rse_id, session=session)
# 1. Check if there is a direct connection between source and dest:
if distance_graph.get(source_rse_id, {dest_rse_id: None}).get(dest_rse_id) is not None:
# Check if there is a protocol match between the two RSEs
try:
matching_scheme = rsemgr.find_matching_scheme(rse_settings_dest=__load_rse_settings(rse_id=dest_rse_id, session=session),
rse_settings_src=__load_rse_settings(rse_id=source_rse_id, session=session),
operation_src='third_party_copy',
operation_dest='third_party_copy',
domain='wan',
scheme=limit_dest_schemes if limit_dest_schemes else None)
path = [{'source_rse_id': source_rse_id,
'dest_rse_id': dest_rse_id,
'source_scheme': matching_scheme[1],
'dest_scheme': matching_scheme[0],
'source_scheme_priority': matching_scheme[3],
'dest_scheme_priority': matching_scheme[2]}]
REGION_SHORT.set('get_hops_%s_%s_%s' % (str(source_rse_id), str(dest_rse_id), ''.join(sorted(limit_dest_schemes))), path)
return path
except RSEProtocolNotSupported as error:
if include_multihop:
# Delete the edge from the graph
del distance_graph[source_rse_id][dest_rse_id]
else:
raise error
if not include_multihop:
raise NoDistance()
# 2. There is no connection or no scheme match --> Try a multi hop --> Dijkstra algorithm
HOP_PENALTY = core_config_get('transfers', 'hop_penalty', default=10, session=session) # Penalty to be applied to each further hop
# Check if the destination RSE is an island RSE:
if not __load_inbound_distances_node(rse_id=dest_rse_id, session=session):
raise NoDistance()
visited_nodes = {source_rse_id: {'distance': 0,
'path': []}} # Dijkstra already visisted nodes
# {rse_id: {'path': [{'source_rse_id':, 'dest_rse_id':, 'source_scheme', 'dest_scheme': }],
# 'distance': X}
# }
to_visit = [source_rse_id] # Nodes to visit, once list is empty, break loop
local_optimum = 9999 # Local optimum to accelerated search
while to_visit:
for current_node in copy.deepcopy(to_visit):
to_visit.remove(current_node)
current_distance = visited_nodes[current_node]['distance']
current_path = visited_nodes[current_node]['path']
if current_node not in distance_graph:
distance_graph[current_node] = __load_outgoing_distances_node(rse_id=current_node, session=session)
for out_v in distance_graph[current_node]:
# Check if the distance would be smaller
if distance_graph[current_node][out_v] is None:
continue
if visited_nodes.get(out_v, {'distance': 9999})['distance'] > current_distance + distance_graph[current_node][out_v] + HOP_PENALTY\
and local_optimum > current_distance + distance_graph[current_node][out_v] + HOP_PENALTY:
# Check if the intermediate RSE is enabled for multihop
if out_v != dest_rse_id and out_v not in multihop_rses:
continue
# Check if there is a compatible protocol pair
try:
matching_scheme = rsemgr.find_matching_scheme(rse_settings_dest=__load_rse_settings(rse_id=out_v, session=session),
rse_settings_src=__load_rse_settings(rse_id=current_node, session=session),
operation_src='third_party_copy',
operation_dest='third_party_copy',
domain='wan',
scheme=limit_dest_schemes if out_v == dest_rse_id and limit_dest_schemes else None)
visited_nodes[out_v] = {'distance': current_distance + distance_graph[current_node][out_v] + HOP_PENALTY,
'path': current_path + [{'source_rse_id': current_node,
'dest_rse_id': out_v,
'source_scheme': matching_scheme[1],
'dest_scheme': matching_scheme[0],
'source_scheme_priority': matching_scheme[3],
'dest_scheme_priority': matching_scheme[2]}]}
if out_v != dest_rse_id:
to_visit.append(out_v)
else:
local_optimum = current_distance + distance_graph[current_node][out_v] + HOP_PENALTY
except RSEProtocolNotSupported:
pass
if dest_rse_id in visited_nodes:
REGION_SHORT.set('get_hops_%s_%s_%s' % (str(source_rse_id), str(dest_rse_id), ''.join(sorted(limit_dest_schemes))), visited_nodes[dest_rse_id]['path'])
return visited_nodes[dest_rse_id]['path']
else:
raise NoDistance()
def get_attributes(attributes):
dict_attributes = {}
if attributes:
if isinstance(attributes, dict):
attr = json.loads(json.dumps(attributes))
else:
attr = json.loads(str(attributes))
# parse source expression
dict_attributes['source_replica_expression'] = attr["source_replica_expression"] if (attr and "source_replica_expression" in attr) else None
dict_attributes['allow_tape_source'] = attr["allow_tape_source"] if (attr and "allow_tape_source" in attr) else True
dict_attributes['dsn'] = attr["ds_name"] if (attr and "ds_name" in attr) else None
dict_attributes['lifetime'] = attr.get('lifetime', -1)
return dict_attributes
def get_dsn(scope, name, dsn):
if dsn:
return dsn
# select a containing dataset
for parent in did.list_parent_dids(scope, name):
if parent['type'] == DIDType.DATASET:
return parent['name']
return 'other'
def __build_dest_url(scope, name, protocol, dest_rse_attrs, dest_is_deterministic, dest_is_tape, dict_attributes, retry_count, activity):
"""
Private helper function to build destination URL when retrieving transfers to execute.
"""
if dest_is_deterministic:
dest_url = list(protocol.lfns2pfns(lfns={'scope': scope.external, 'name': name}).values())[0]
else:
# compute dest url in case of non deterministic
# naming convention, etc.
dsn = get_dsn(scope, name, dict_attributes.get('dsn', None))
# DQ2 path always starts with /, but prefix might not end with /
naming_convention = dest_rse_attrs.get('naming_convention', None)
dest_path = construct_surl(dsn, name, naming_convention)
if dest_is_tape:
if retry_count or activity == 'Recovery':
dest_path = '%s_%i' % (dest_path, int(time.time()))
dest_url = list(protocol.lfns2pfns(lfns={'scope': scope.external, 'name': name, 'path': dest_path}).values())[0]
return dest_url
def __rewrite_source_url(source_url, source_sign_url, dest_sign_url, source_scheme):
"""
Parametrize source url for some special cases of source and destination schemes
"""
if dest_sign_url == 'gcs':
if source_scheme in ['davs', 'https']:
source_url += '?copy_mode=push'
elif dest_sign_url == 's3':
if source_scheme in ['davs', 'https']:
source_url += '?copy_mode=push'
elif WEBDAV_TRANSFER_MODE:
if source_scheme in ['davs', 'https']:
source_url += '?copy_mode=%s' % WEBDAV_TRANSFER_MODE
source_sign_url_map = {'gcs': 'gclouds', 's3': 's3s'}
if source_sign_url in source_sign_url_map:
if source_url[:7] == 'davs://':
source_url = source_sign_url_map[source_sign_url] + source_url[4:]
if source_url[:8] == 'https://':
source_url = source_sign_url_map[source_sign_url] + source_url[5:]
if source_url[:12] == 'srm+https://':
source_url = 'srm' + source_url[10:]
return source_url
def __rewrite_dest_url(dest_url, dest_sign_url, dest_scheme):
"""
Parametrize destination url for some special cases of destination schemes
"""
if dest_sign_url == 'gcs':
dest_url = re.sub('davs', 'gclouds', dest_url)
dest_url = re.sub('https', 'gclouds', dest_url)
elif dest_sign_url == 's3':
dest_url = re.sub('davs', 's3s', dest_url)
dest_url = re.sub('https', 's3s', dest_url)
if dest_url[:12] == 'srm+https://':
dest_url = 'srm' + dest_url[10:]
return dest_url
@transactional_session
def get_transfer_requests_and_source_replicas(total_workers=0, worker_number=0, limit=None, activity=None, older_than=None, rses=None, schemes=None,
bring_online=43200, retry_other_fts=False, failover_schemes=None, transfertool=None, logger=logging.log, session=None):
"""
Get transfer requests and the associated source replicas
:param total_workers: Number of total workers.
:param worker_number: Id of the executing worker.
:param limit: Limit.
:param activity: Activity.
:param older_than: Get transfers older than.
:param rses: Include RSES.
:param schemes: Include schemes.
:param bring_online: Bring online timeout.
:param retry_other_fts: Retry other fts servers.
:param failover_schemes: Failover schemes.
:param transfertool: The transfer tool as specified in rucio.cfg.
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
:param session: The database session in use.
:returns: transfers, reqs_no_source, reqs_scheme_mismatch, reqs_only_tape_source
"""
req_sources = __list_transfer_requests_and_source_replicas(total_workers=total_workers,
worker_number=worker_number,
limit=limit,
activity=activity,
older_than=older_than,
rses=rses,
request_state=RequestState.QUEUED,
transfertool=transfertool,
session=session)
class _LocalContext:
def __init__(self, session):
self.session = session
self.rse_id_to_name_map = {}
self.rse_id_to_info_map = {}
self.rse_id_to_attrs_map = {}
self.protocols = {}
def _ensure_rse_loaded(self, rse_id):
if rse_id not in self.rse_id_to_name_map:
rse_name = get_rse_name(rse_id=rse_id, session=self.session)
self.rse_id_to_name_map[rse_id] = rse_name
self.rse_id_to_info_map[rse_id] = rsemgr.get_rse_info(rse=rse_name,
vo=get_rse_vo(rse_id=rse_id, session=self.session),
session=self.session)
self.rse_id_to_attrs_map[rse_id] = get_rse_attributes(rse_id, session=self.session)
def rse_name(self, rse_id):
self._ensure_rse_loaded(rse_id)
return self.rse_id_to_name_map[rse_id]
def rse_info(self, rse_id):
self._ensure_rse_loaded(rse_id)
return self.rse_id_to_info_map[rse_id]
def rse_attrs(self, rse_id):
self._ensure_rse_loaded(rse_id)
return self.rse_id_to_attrs_map[rse_id]
def is_tape_rse(self, rse_id):
_rse_info = self.rse_info(rse_id)
if _rse_info['rse_type'] == RSEType.TAPE or _rse_info['rse_type'] == 'TAPE':
return True
return False
def protocol(self, rse_id, scheme, operation):
protocol_key = '%s_%s_%s' % (operation, rse_id, scheme)
protocol = self.protocols.get(protocol_key)
if not protocol:
protocol = rsemgr.create_protocol(self.rse_info(rse_id), 'third_party_copy', scheme)
self.protocols[protocol_key] = protocol
return protocol
ctx = _LocalContext(session)
unavailable_read_rse_ids = __get_unavailable_rse_ids(operation='read', session=session)
unavailable_write_rse_ids = __get_unavailable_rse_ids(operation='write', session=session)
bring_online_local = bring_online
transfers, reqs_no_source, reqs_only_tape_source, reqs_scheme_mismatch = {}, [], [], []
multi_hop_dict = {}
multihop_rses = []
try:
multihop_rses = [rse['id'] for rse in parse_expression('available_for_multihop=true')]
except InvalidRSEExpression:
multihop_rses = []
for req_id, rule_id, scope, name, md5, adler32, bytes, activity, attributes, previous_attempt_id, dest_rse_id, account, source_rse_id, rse, deterministic, rse_type, path, retry_count, src_url, ranking, link_ranking in req_sources:
if ranking is None:
ranking = 0
multihop = False
# Add req to req_no_source list (Will be removed later if needed)
if req_id not in reqs_no_source:
reqs_no_source.append(req_id)
# source_rse_id will be None if no source replicas
# rse will be None if rse is staging area
if source_rse_id is None or rse is None:
continue
if rses and dest_rse_id not in rses:
continue
dest_rse_name = ctx.rse_name(dest_rse_id)
source_rse_name = ctx.rse_name(source_rse_id)
dict_attributes = get_attributes(attributes)
# Check if the source and destination are blocked
if source_rse_id in unavailable_read_rse_ids:
continue
if dest_rse_id in unavailable_write_rse_ids:
logger(logging.WARNING, 'RSE %s is blocked for write. Will skip the submission of new jobs', dest_rse_name)
continue
# parse source expression
source_replica_expression = dict_attributes.get('source_replica_expression', None)
if source_replica_expression:
try:
parsed_rses = parse_expression(source_replica_expression, session=session)
except InvalidRSEExpression as error:
logger(logging.ERROR, "Invalid RSE exception %s: %s", source_replica_expression, str(error))
continue
else:
allowed_rses = [x['id'] for x in parsed_rses]
if source_rse_id not in allowed_rses:
continue
# Call the get_hops function to create a list of RSEs used for the transfer
# In case the source_rse and the dest_rse are connected, the list contains only the destination RSE
# In case of non-connected, the list contains all the intermediary RSEs
list_hops = []
include_multihop = False
if transfertool in ['fts3', None]:
include_multihop = core_config_get('transfers', 'use_multihop', default=False, expiration_time=600, session=session)
try:
list_hops = get_hops(source_rse_id,
dest_rse_id,
include_multihop=include_multihop,
multihop_rses=multihop_rses,
limit_dest_schemes=transfers.get(req_id, {}).get('schemes', None),
session=session)
if len(list_hops) > 1:
logger(logging.DEBUG, 'From %s to %s requires multihop: %s', source_rse_id, dest_rse_id, list_hops)
multihop = True
multi_hop_dict[req_id] = (list_hops, dict_attributes, retry_count)
except NoDistance:
logger(logging.WARNING, "Request %s: no link from %s to %s", req_id, source_rse_name, dest_rse_name)
if req_id in reqs_scheme_mismatch:
reqs_scheme_mismatch.remove(req_id)
if req_id not in reqs_no_source:
reqs_no_source.append(req_id)
continue
except RSEProtocolNotSupported:
logger(logging.WARNING, "Request %s: no matching protocol between %s and %s", req_id, source_rse_name, dest_rse_name)
if req_id in reqs_no_source:
reqs_no_source.remove(req_id)
if req_id not in reqs_scheme_mismatch:
reqs_scheme_mismatch.append(req_id)
continue
source_scheme = list_hops[0]['source_scheme']
dest_scheme = list_hops[-1]['dest_scheme']
dest_scheme_priority = list_hops[-1]['dest_scheme_priority']
allow_tape_source = True
try:
# Get source protocol
source_protocol = ctx.protocol(source_rse_id, source_scheme, 'read')
source_sign_url = ctx.rse_attrs(source_rse_id).get('sign_url', None)
dest_sign_url = ctx.rse_attrs(dest_rse_id).get('sign_url', None)
# Compute the source URL
source_url = list(source_protocol.lfns2pfns(lfns={'scope': scope.external, 'name': name, 'path': path}).values())[0]
source_url = __rewrite_source_url(source_url, source_sign_url=source_sign_url, dest_sign_url=dest_sign_url, source_scheme=source_scheme)
# If the request_id is not already in the transfer dictionary, need to compute the destination URL
if req_id not in transfers:
# parse allow tape source expression, not finally version.
# allow_tape_source = attr["allow_tape_source"] if (attr and "allow_tape_source" in attr) else True
allow_tape_source = True
# Extend the metadata dictionary with request attributes
transfer_src_type = "DISK"
transfer_dst_type = "DISK"
overwrite, bring_online = True, None
if ctx.is_tape_rse(source_rse_id) or ctx.rse_attrs(source_rse_id).get('staging_required', False):
bring_online = bring_online_local
transfer_src_type = "TAPE"
if not allow_tape_source:
if req_id not in reqs_only_tape_source:
reqs_only_tape_source.append(req_id)
if req_id in reqs_no_source:
reqs_no_source.remove(req_id)
continue
if ctx.is_tape_rse(dest_rse_id):
overwrite = False
transfer_dst_type = "TAPE"
# Get destination protocol
dest_protocol = ctx.protocol(dest_rse_id, dest_scheme, 'write')
# Compute the destination url
dest_url = __build_dest_url(scope=scope, name=name,
protocol=dest_protocol,
dest_rse_attrs=ctx.rse_attrs(dest_rse_id),
dest_is_deterministic=ctx.rse_info(dest_rse_id)['deterministic'],
dest_is_tape=ctx.is_tape_rse(dest_rse_id),
dict_attributes=dict_attributes,
retry_count=retry_count,
activity=activity)
dest_url = __rewrite_dest_url(dest_url, dest_sign_url=dest_sign_url, dest_scheme=dest_scheme)
# Get dest space token
dest_spacetoken = None
if dest_protocol.attributes and 'extended_attributes' in dest_protocol.attributes and \
dest_protocol.attributes['extended_attributes'] and 'space_token' in dest_protocol.attributes['extended_attributes']:
dest_spacetoken = dest_protocol.attributes['extended_attributes']['space_token']
use_ipv4 = ctx.rse_attrs(source_rse_id).get('use_ipv4', False) or ctx.rse_attrs(dest_rse_id).get('use_ipv4', False)
# get external_host + strict_copy + archive timeout
strict_copy = ctx.rse_attrs(dest_rse_id).get('strict_copy', False)
fts_hosts = ctx.rse_attrs(dest_rse_id).get('fts', None)
archive_timeout = ctx.rse_attrs(dest_rse_id).get('archive_timeout', None)
if source_sign_url == 'gcs':
fts_hosts = ctx.rse_attrs(source_rse_id).get('fts', None)
source_globus_endpoint_id = ctx.rse_attrs(source_rse_id).get('globus_endpoint_id', None)
dest_globus_endpoint_id = ctx.rse_attrs(dest_rse_id).get('globus_endpoint_id', None)
if transfertool == 'fts3' and not fts_hosts:
logger(logging.ERROR, 'Destination RSE %s FTS attribute not defined - SKIP REQUEST %s', dest_rse_name, req_id)
continue
if transfertool == 'globus' and (not dest_globus_endpoint_id or not source_globus_endpoint_id):
logger(logging.ERROR, 'Destination RSE %s Globus endpoint attributes not defined - SKIP REQUEST %s', dest_rse_name, req_id)
continue
if retry_count is None:
retry_count = 0
external_host = ''
if fts_hosts:
fts_list = fts_hosts.split(",")
external_host = fts_list[0]
if retry_other_fts:
external_host = fts_list[retry_count % len(fts_list)]
# Get the checksum validation strategy (none, source, destination or both)
verify_checksum = 'both'
if not ctx.rse_attrs(dest_rse_id).get('verify_checksum', True):
if not ctx.rse_attrs(source_rse_id).get('verify_checksum', True):
verify_checksum = 'none'
else:
verify_checksum = 'source'
else:
if not ctx.rse_attrs(source_rse_id).get('verify_checksum', True):
verify_checksum = 'destination'
else:
verify_checksum = 'both'
source_rse_checksums = get_rse_supported_checksums(source_rse_id, session=session)
dest_rse_checksums = get_rse_supported_checksums(dest_rse_id, session=session)
common_checksum_names = set(source_rse_checksums).intersection(dest_rse_checksums)
if len(common_checksum_names) == 0:
logger(logging.INFO, 'No common checksum method. Verifying destination only.')
verify_checksum = 'destination'
# Fill the transfer dictionary including file_metadata
file_metadata = {'request_id': req_id,
'scope': scope,
'name': name,
'activity': activity,
'request_type': RequestType.TRANSFER,
'src_type': transfer_src_type,
'dst_type': transfer_dst_type,
'src_rse': source_rse_name,
'dst_rse': dest_rse_name,
'src_rse_id': source_rse_id,
'dest_rse_id': dest_rse_id,
'filesize': bytes,
'md5': md5,
'adler32': adler32,
'verify_checksum': verify_checksum,
'source_globus_endpoint_id': source_globus_endpoint_id,
'dest_globus_endpoint_id': dest_globus_endpoint_id}
if previous_attempt_id:
file_metadata['previous_attempt_id'] = previous_attempt_id
transfers[req_id] = {'request_id': req_id,
'schemes': __add_compatible_schemes(schemes=[dest_scheme], allowed_schemes=SUPPORTED_PROTOCOLS),
'account': account,
# 'src_urls': [source_url],
'sources': [(rse, source_url, source_rse_id, ranking, link_ranking)],
'dest_urls': [dest_url],
'src_spacetoken': None,
'dest_spacetoken': dest_spacetoken,
'overwrite': overwrite,
'bring_online': bring_online,
'copy_pin_lifetime': dict_attributes.get('lifetime', 172800),
'external_host': external_host,
'selection_strategy': 'auto',
'rule_id': rule_id,
'file_metadata': file_metadata,
'dest_scheme_priority': dest_scheme_priority}
if multihop:
transfers[req_id]['multihop'] = True
transfers[req_id]['initial_request_id'] = req_id
if strict_copy:
transfers[req_id]['strict_copy'] = strict_copy
if use_ipv4:
transfers[req_id]['use_ipv4'] = True
if archive_timeout and ctx.is_tape_rse(dest_rse_id):
try:
transfers[req_id]['archive_timeout'] = int(archive_timeout)
logger(logging.DEBUG, 'Added archive timeout to transfer.')
except ValueError:
logger(logging.WARNING, 'Could not set archive_timeout for %s. Must be integer.', dest_url)
pass
else:
# parse allow tape source expression, not finally version.
allow_tape_source = dict_attributes.get('allow_tape_source', None)
# No check yet if the previous one is a multihop or not.
# TODO : Check if the current transfer is better than the previous one
if multihop:
continue
# The transfer queued previously is a multihop, but this one is direct.
# Reset the sources, remove the multihop flag
if transfers[req_id].get('multihop', False):
transfers[req_id].pop('multihop', None)
transfers[req_id]['sources'] = []
current_source_is_tape = transfers[req_id]['bring_online']
new_source_is_tape = ctx.is_tape_rse(source_rse_id) or ctx.rse_attrs(source_rse_id).get('staging_required', False)
if new_source_is_tape and not allow_tape_source:
continue
if current_source_is_tape and not new_source_is_tape or \
new_source_is_tape and not current_source_is_tape:
# Tape and Disk sources must not be used at the same time.
# Either keep existing sources unchanged, or substitute all existing source with the new source.
# Find the best ranking among existing sources
avail_top_ranking = None
for founded_source in transfers[req_id]['sources']:
if avail_top_ranking is None:
avail_top_ranking = founded_source[3]
continue
if founded_source[3] is not None and founded_source[3] > avail_top_ranking:
avail_top_ranking = founded_source[3]
# If ranking of the new source is better. On equal ranking, prefer Disk over Tape.
if avail_top_ranking is None or (ranking > avail_top_ranking) or (ranking >= avail_top_ranking and current_source_is_tape):
transfers[req_id]['sources'] = []
transfers[req_id]['bring_online'] = bring_online_local if new_source_is_tape else None
transfers[req_id]['file_metadata']['src_type'] = 'TAPE' if new_source_is_tape else 'DISK'
transfers[req_id]['file_metadata']['src_rse'] = rse
else:
continue
if current_source_is_tape and new_source_is_tape:
# multiple Tape source replicas are not allowed in FTS3.
# Either keep the old source. Or substitute it with the new one.
if ranking > transfers[req_id]['sources'][0][3] or (ranking == transfers[req_id]['sources'][0][3] and link_ranking < transfers[req_id]['sources'][0][4]):
transfers[req_id]['sources'] = []
transfers[req_id]['bring_online'] = bring_online_local
transfers[req_id]['file_metadata']['src_rse'] = rse
else:
continue
transfers[req_id]['sources'].append((rse, source_url, source_rse_id, ranking, link_ranking))
# if one source has force IPv4, force IPv4 for the whole job
use_ipv4 = ctx.rse_attrs(source_rse_id).get('use_ipv4', False)
if use_ipv4:
transfers[req_id]['use_ipv4'] = True
except Exception:
logger(logging.CRITICAL, "Exception happened when trying to get transfer for request %s:" % (req_id), exc_info=True)
break
# checking OIDC AuthN/Z support per destination and soucre RSEs;
# assumes use of boolean 'oidc_support' RSE attribute
for req_id in transfers:
use_oidc = False
dest_rse_id = transfers[req_id]['file_metadata']['dest_rse_id']
if 'oidc_support' in ctx.rse_attrs(dest_rse_id):
use_oidc = ctx.rse_attrs(dest_rse_id)['oidc_support']
else:
transfers[req_id]['use_oidc'] = use_oidc
continue
for source in transfers[req_id]['sources']:
source_rse_id = source[2]
if 'oidc_support' in ctx.rse_attrs(source_rse_id):
use_oidc = use_oidc and ctx.rse_attrs(source_rse_id)['oidc_support']
else:
use_oidc = False
if not use_oidc:
break
# OIDC token will be requested for the account of this tranfer
transfers[req_id]['use_oidc'] = use_oidc
for req_id in copy.deepcopy(transfers):
# If the transfer is a multihop, need to create the intermediate replicas, intermediate requests and the transfers
if transfers[req_id].get('multihop', False):
parent_request = None
scope = transfers[req_id]['file_metadata']['scope']
name = transfers[req_id]['file_metadata']['name']
list_multihop, dict_attributes, retry_count = multi_hop_dict[req_id]
parent_requests = []
for hop in list_multihop:
# hop = {'source_rse_id': source_rse_id, 'source_scheme': 'srm', 'source_scheme_priority': N, 'dest_rse_id': dest_rse_id, 'dest_scheme': 'srm', 'dest_scheme_priority': N}
source_scheme = hop['source_scheme']
source_rse_id = hop['source_rse_id']
dest_rse_id = hop['dest_rse_id']
source_rse_name = ctx.rse_name(source_rse_id)
dest_rse_name = ctx.rse_name(dest_rse_id)
dest_rse_vo = get_rse_vo(rse_id=hop['dest_rse_id'], session=session)
transfer_src_type = "DISK"
transfer_dst_type = "DISK"
allow_tape_source = True
# Compute the source URL. We don't need to fill the rse_mapping and rse_attrs for the intermediate RSEs cause it has already been done before
source_protocol = ctx.protocol(source_rse_id, source_scheme, 'read')
source_url = list(source_protocol.lfns2pfns(lfns={'scope': scope.external, 'name': name, 'path': None}).values())[0]
if transfers[req_id]['file_metadata']['dest_rse_id'] != hop['dest_rse_id']:
files = [{'scope': scope,
'name': name,
'bytes': transfers[req_id]['file_metadata']['filesize'],
'adler32': transfers[req_id]['file_metadata']['adler32'],
'md5': transfers[req_id]['file_metadata']['md5'],
'state': 'C'}]
try:
add_replicas(rse_id=hop['dest_rse_id'],
files=files,
account=InternalAccount('root', vo=dest_rse_vo),
ignore_availability=False,
dataset_meta=None,
session=session)
except Exception as error:
logger(logging.ERROR, 'Problem adding replicas %s:%s on %s : %s', scope, name, dest_rse_name, str(error))
req_attributes = {'activity': transfers[req_id]['file_metadata']['activity'],
'source_replica_expression': None,
'lifetime': None,
'ds_scope': None,
'ds_name': None,
'bytes': transfers[req_id]['file_metadata']['filesize'],
'md5': transfers[req_id]['file_metadata']['md5'],
'adler32': transfers[req_id]['file_metadata']['adler32'],
'priority': None,
'allow_tape_source': True}
new_req = queue_requests(requests=[{'dest_rse_id': dest_rse_id,
'scope': scope,
'name': name,
'rule_id': '00000000000000000000000000000000', # Dummy Rule ID used for multihop. TODO: Replace with actual rule_id once we can flag intermediate requests
'attributes': req_attributes,
'request_type': RequestType.TRANSFER,
'retry_count': retry_count,
'account': InternalAccount('root', vo=dest_rse_vo),
'requested_at': datetime.datetime.now()}], session=session)
# If a request already exists, new_req will be an empty list.
if not new_req:
# Need to fail all the intermediate requests + the initial one and exit the multihop loop
logger(logging.WARNING, 'Multihop : A request already exists for the transfer between %s and %s. Will cancel all the parent requests', source_rse_name, dest_rse_name)
parent_requests.append(req_id)
try:
set_requests_state(request_ids=parent_requests, new_state=RequestState.FAILED, session=session)
except UnsupportedOperation:
logger(logging.ERROR, 'Multihop : Cannot cancel all the parent requests : %s', str(parent_requests))
# Remove from the transfer dictionary all the requests
for cur_req_id in parent_requests:
transfers.pop(cur_req_id, None)
break
new_req_id = new_req[0]['id']
parent_requests.append(new_req_id)
set_requests_state(request_ids=[new_req_id, ], new_state=RequestState.QUEUED, session=session)
logger(logging.DEBUG, 'New request created for the transfer between %s and %s : %s', source_rse_name, dest_rse_name, new_req_id)
# Here we will compute the destination URL
# Get destination protocol
dest_rse_id = hop['dest_rse_id']
dest_scheme = hop['dest_scheme']
dest_protocol = ctx.protocol(dest_rse_id, dest_scheme, 'write')
# Get dest space token
dest_spacetoken = None
if dest_protocol.attributes and 'extended_attributes' in dest_protocol.attributes and \
dest_protocol.attributes['extended_attributes'] and 'space_token' in dest_protocol.attributes['extended_attributes']:
dest_spacetoken = dest_protocol.attributes['extended_attributes']['space_token']
# Compute the destination url
dest_url = __build_dest_url(scope=scope, name=name,
protocol=dest_protocol,
dest_rse_attrs=ctx.rse_attrs(dest_rse_id),
dest_is_deterministic=ctx.rse_info(dest_rse_id)['deterministic'],
dest_is_tape=ctx.is_tape_rse(dest_rse_id),
dict_attributes=dict_attributes,
retry_count=retry_count,
activity=activity)
# Extend the metadata dictionary with request attributes
overwrite, bring_online = True, None
if ctx.is_tape_rse(source_rse_id):
bring_online = bring_online_local
transfer_src_type = "TAPE"
if not allow_tape_source:
if req_id not in reqs_only_tape_source:
reqs_only_tape_source.append(req_id)
if req_id in reqs_no_source:
reqs_no_source.remove(req_id)
continue
if ctx.is_tape_rse(dest_rse_id):
overwrite = False
transfer_dst_type = "TAPE"
file_metadata = {'request_id': new_req_id,
'scope': scope,
'name': name,
'activity': transfers[req_id]['file_metadata']['activity'],
'request_type': RequestType.TRANSFER,
'src_type': transfer_src_type,
'dst_type': transfer_dst_type,
'src_rse': source_rse_name,
'dst_rse': dest_rse_name,
'src_rse_id': source_rse_id,
'dest_rse_id': dest_rse_id,
'filesize': transfers[req_id]['file_metadata']['filesize'],
'md5': transfers[req_id]['file_metadata']['md5'],
'adler32': transfers[req_id]['file_metadata']['adler32'],
'verify_checksum': transfers[req_id]['file_metadata']['verify_checksum'],
'source_globus_endpoint_id': transfers[req_id]['file_metadata']['source_globus_endpoint_id'],
'dest_globus_endpoint_id': transfers[req_id]['file_metadata']['dest_globus_endpoint_id']}
transfers[new_req_id] = {'request_id': new_req_id,
'initial_request_id': req_id,
'parent_request': parent_request,
'account': InternalAccount('root'),
'schemes': __add_compatible_schemes(schemes=[dest_scheme], allowed_schemes=SUPPORTED_PROTOCOLS),
# 'src_urls': [source_url],
'sources': [(source_rse_name, source_url, source_rse_id, 0, 0)],
'dest_urls': [dest_url],
'src_spacetoken': None,
'dest_spacetoken': dest_spacetoken,
'overwrite': transfers[req_id]['overwrite'],
'bring_online': bring_online,
'copy_pin_lifetime': transfers[req_id]['copy_pin_lifetime'],
'external_host': transfers[req_id]['external_host'],
'selection_strategy': 'auto',
'rule_id': transfers[req_id]['rule_id'],
'multihop': True,
'file_metadata': file_metadata}
parent_request = new_req_id
else:
# For the last hop, we just need to correct the source
transfers[req_id]['parent_request'] = parent_request
transfers[req_id]['file_metadata']['src_rse_id'] = source_rse_id
transfers[req_id]['file_metadata']['src_rse'] = source_rse_name
# We make the assumption that the hop is never made through TAPE
transfers[req_id]['file_metadata']['src_type'] = 'DISK'
transfers[req_id]['sources'] = [(source_rse_name, source_url, source_rse_id, 0, 0)]
transfers[req_id]['bring_online'] = bring_online
if req_id in reqs_no_source:
reqs_no_source.remove(req_id)
if req_id in reqs_only_tape_source:
reqs_only_tape_source.remove(req_id)
if req_id in reqs_scheme_mismatch:
reqs_scheme_mismatch.remove(req_id)
return transfers, reqs_no_source, reqs_scheme_mismatch, reqs_only_tape_source
@read_session
def __list_transfer_requests_and_source_replicas(total_workers=0, worker_number=0, limit=None, activity=None,
older_than=None, rses=None, request_state=None, transfertool=None, session=None) -> "List[Tuple]":
"""
List requests with source replicas
:param total_workers: Number of total workers.
:param worker_number: Id of the executing worker.
:param limit: Integer of requests to retrieve.
:param activity: Activity to be selected.
:param older_than: Only select requests older than this DateTime.
:param rses: List of rse_id to select requests.
:param transfertool: The transfer tool as specified in rucio.cfg.
:param session: Database session to use.
:returns: List.
"""
if request_state is None:
request_state = RequestState.QUEUED
sub_requests = session.query(models.Request.id,
models.Request.rule_id,
models.Request.scope,
models.Request.name,
models.Request.md5,
models.Request.adler32,
models.Request.bytes,
models.Request.activity,
models.Request.attributes,
models.Request.previous_attempt_id,
models.Request.dest_rse_id,
models.Request.retry_count,
models.Request.account,
models.Request.created_at) \
.with_hint(models.Request, "INDEX(REQUESTS REQUESTS_TYP_STA_UPD_IDX)", 'oracle') \
.filter(models.Request.state == request_state) \
.filter(models.Request.request_type == RequestType.TRANSFER) \
.join(models.RSE, models.RSE.id == models.Request.dest_rse_id) \
.filter(models.RSE.deleted == false()) \
.order_by(models.Request.created_at) \
.filter(models.RSE.availability.in_((2, 3, 6, 7)))
if isinstance(older_than, datetime.datetime):
sub_requests = sub_requests.filter(models.Request.requested_at < older_than)
if activity:
sub_requests = sub_requests.filter(models.Request.activity == activity)
# if a transfertool is specified make sure to filter for those requests and apply related index
if transfertool:
sub_requests = sub_requests.filter(models.Request.transfertool == transfertool)
sub_requests = sub_requests.with_hint(models.Request, "INDEX(REQUESTS REQUESTS_TYP_STA_TRA_ACT_IDX)", 'oracle')
else:
sub_requests = sub_requests.with_hint(models.Request, "INDEX(REQUESTS REQUESTS_TYP_STA_UPD_IDX)", 'oracle')
sub_requests = filter_thread_work(session=session, query=sub_requests, total_threads=total_workers, thread_id=worker_number, hash_variable='requests.id')
if limit:
sub_requests = sub_requests.limit(limit)
sub_requests = sub_requests.subquery()
query = session.query(sub_requests.c.id,
sub_requests.c.rule_id,
sub_requests.c.scope,
sub_requests.c.name,
sub_requests.c.md5,
sub_requests.c.adler32,
sub_requests.c.bytes,
sub_requests.c.activity,
sub_requests.c.attributes,
sub_requests.c.previous_attempt_id,
sub_requests.c.dest_rse_id,
sub_requests.c.account,
models.RSEFileAssociation.rse_id,
models.RSE.rse,
models.RSE.deterministic,
models.RSE.rse_type,
models.RSEFileAssociation.path,
sub_requests.c.retry_count,
models.Source.url,
models.Source.ranking.label("source_ranking"),
models.Distance.ranking.label("distance_ranking")) \
.order_by(sub_requests.c.created_at) \
.outerjoin(models.RSEFileAssociation, and_(sub_requests.c.scope == models.RSEFileAssociation.scope,
sub_requests.c.name == models.RSEFileAssociation.name,
models.RSEFileAssociation.state == ReplicaState.AVAILABLE,
sub_requests.c.dest_rse_id != models.RSEFileAssociation.rse_id)) \
.with_hint(models.RSEFileAssociation, "+ index(replicas REPLICAS_PK)", 'oracle') \
.outerjoin(models.RSE, and_(models.RSE.id == models.RSEFileAssociation.rse_id,
models.RSE.deleted == false())) \
.outerjoin(models.Source, and_(sub_requests.c.id == models.Source.request_id,
models.RSE.id == models.Source.rse_id)) \
.with_hint(models.Source, "+ index(sources SOURCES_PK)", 'oracle') \
.outerjoin(models.Distance, and_(sub_requests.c.dest_rse_id == models.Distance.dest_rse_id,
models.RSEFileAssociation.rse_id == models.Distance.src_rse_id)) \
.with_hint(models.Distance, "+ index(distances DISTANCES_PK)", 'oracle')
# if transfertool specified, select only the requests where the source rses are set up for the transfer tool
if transfertool:
query = query.subquery()
query = session.query(query) \
.join(models.RSEAttrAssociation, models.RSEAttrAssociation.rse_id == query.c.rse_id) \
.filter(models.RSEAttrAssociation.key == 'transfertool',
models.RSEAttrAssociation.value.like('%' + transfertool + '%'))
if rses:
result = []
for item in query.all():
dest_rse_id = item[10]
if dest_rse_id in rses:
result.append(item)
return result
return query.all()
@transactional_session
def __set_transfer_state(external_host, transfer_id, new_state, session=None):
"""
Update the state of a transfer. Fails silently if the transfer_id does not exist.
:param external_host: Selected external host as string in format protocol://fqdn:port
:param transfer_id: External transfer job id as a string.
:param new_state: New state as string.
:param session: Database session to use.
"""
record_counter('core.request.set_transfer_state')
try:
rowcount = session.query(models.Request).filter_by(external_id=transfer_id).update({'state': new_state, 'updated_at': datetime.datetime.utcnow()}, synchronize_session=False)
except IntegrityError as error:
raise RucioException(error.args)
if not rowcount:
raise UnsupportedOperation("Transfer %s on %s state %s cannot be updated." % (transfer_id, external_host, new_state))
@read_session
def __get_unavailable_rse_ids(operation, session=None, logger=logging.log):
"""
:param logger: Optional decorated logger that can be passed from the calling daemons or servers.
Get unavailable rse ids for a given operation : read, write, delete
"""
if operation not in ['read', 'write', 'delete']:
logger(logging.ERROR, "Wrong operation specified : %s" % (operation))
return []
key = 'unavailable_%s_rse_ids' % operation
result = REGION_SHORT.get(key)
if isinstance(result, NoValue):
try:
logger(logging.DEBUG, "Refresh unavailable %s rses" % operation)
availability_key = 'availability_%s' % operation
unavailable_rses = list_rses(filters={availability_key: False}, session=session)
unavailable_rse_ids = [rse['id'] for rse in unavailable_rses]
REGION_SHORT.set(key, unavailable_rse_ids)
return unavailable_rse_ids
except Exception:
logger(logging.ERROR, "Failed to refresh unavailable %s rses, error" % (operation), exc_info=True)
return []
return result
def __add_compatible_schemes(schemes, allowed_schemes):
"""
Add the compatible schemes to a list of schemes
:param schemes: Schemes as input.
:param allowed_schemes: Allowed schemes, only these can be in the output.
:returns: List of schemes
"""
return_schemes = []
for scheme in schemes:
if scheme in allowed_schemes:
return_schemes.append(scheme)
for scheme_map_scheme in constants.SCHEME_MAP.get(scheme, []):
if scheme_map_scheme not in allowed_schemes:
continue
else:
return_schemes.append(scheme_map_scheme)
return list(set(return_schemes))
@transactional_session
def __load_inbound_distances_node(rse_id, session=None):
"""
Loads the inbound edges of the distance graph for one node.
:param rse_id: RSE id to load the edges for.
:param session: The DB Session to use.
:returns: Dictionary based graph object.
"""
result = REGION_SHORT.get('inbound_edges_%s' % str(rse_id))
if isinstance(result, NoValue):
inbound_edges = {}
for distance in session.query(models.Distance).join(models.RSE, models.RSE.id == models.Distance.src_rse_id) \
.filter(models.Distance.dest_rse_id == rse_id) \
.filter(models.RSE.deleted == false()).all():
if distance.ranking is None:
continue
ranking = distance.ranking if distance.ranking >= 0 else 0
inbound_edges[distance.src_rse_id] = ranking
REGION_SHORT.set('inbound_edges_%s' % str(rse_id), inbound_edges)
result = inbound_edges
return result
@transactional_session
def __load_outgoing_distances_node(rse_id, session=None):
"""
Loads the outgoing edges of the distance graph for one node.
:param rse_id: RSE id to load the edges for.
:param session: The DB Session to use.
:returns: Dictionary based graph object.
"""
result = REGION_SHORT.get('outgoing_edges_%s' % str(rse_id))
if isinstance(result, NoValue):
outgoing_edges = {}
for distance in session.query(models.Distance).join(models.RSE, models.RSE.id == models.Distance.dest_rse_id)\
.filter(models.Distance.src_rse_id == rse_id)\
.filter(models.RSE.deleted == false()).all():
if distance.ranking is None:
continue
ranking = distance.ranking if distance.ranking >= 0 else 0
outgoing_edges[distance.dest_rse_id] = ranking
REGION_SHORT.set('outgoing_edges_%s' % str(rse_id), outgoing_edges)
result = outgoing_edges
return result
@transactional_session
def __load_rse_settings(rse_id, session=None):
"""
Loads the RSE settings from cache.
:param rse_id: RSE id to load the settings from.
:param session: The DB Session to use.
:returns: Dict of RSE Settings
"""
result = REGION_SHORT.get('rse_settings_%s' % str(rse_id))
if isinstance(result, NoValue):
result = rsemgr.get_rse_info(rse=get_rse_name(rse_id=rse_id, session=session),
vo=get_rse_vo(rse_id=rse_id, session=session),
session=session)
REGION_SHORT.set('rse_settings_%s' % str(rse_id), result)
return result
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6213,
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278,
1023,
390,
1660,
29879,
13,
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1018,
29901,
13,
9651,
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29918,
816,
2004,
353,
364,
12846,
629,
29889,
2886,
29918,
4352,
292,
29918,
816,
2004,
29898,
29878,
344,
29918,
11027,
29918,
7854,
29922,
1649,
1359,
29918,
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344,
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11027,
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344,
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333,
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462,
3986,
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3986,
11380,
29922,
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13826,
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2731,
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344,
29918,
333,
29892,
13,
462,
268,
525,
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29918,
816,
2004,
2396,
9686,
29918,
816,
2004,
29961,
29896,
1402,
13,
462,
268,
525,
7854,
29918,
816,
2004,
2396,
9686,
29918,
816,
2004,
29961,
29900,
1402,
13,
462,
268,
525,
4993,
29918,
816,
2004,
29918,
29886,
21766,
2396,
9686,
29918,
816,
2004,
29961,
29941,
1402,
13,
462,
268,
525,
7854,
29918,
816,
2004,
29918,
29886,
21766,
2396,
9686,
29918,
816,
2004,
29961,
29906,
29962,
6525,
13,
9651,
5195,
29954,
2725,
29918,
7068,
8476,
29889,
842,
877,
657,
29918,
29882,
3554,
29918,
29995,
29879,
29918,
29995,
29879,
29918,
29995,
29879,
29915,
1273,
313,
710,
29898,
4993,
29918,
29878,
344,
29918,
333,
511,
851,
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29918,
29878,
344,
29918,
333,
511,
525,
4286,
7122,
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24582,
29898,
13400,
29918,
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816,
13826,
876,
511,
2224,
29897,
13,
9651,
736,
2224,
13,
4706,
5174,
390,
1660,
17830,
3664,
14039,
287,
408,
1059,
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13,
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565,
3160,
29918,
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4861,
459,
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13,
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396,
21267,
278,
7636,
515,
278,
3983,
13,
18884,
628,
5418,
29918,
4262,
29961,
4993,
29918,
29878,
344,
29918,
333,
3816,
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29918,
29878,
344,
29918,
333,
29962,
13,
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1683,
29901,
13,
18884,
12020,
1059,
13,
13,
1678,
565,
451,
3160,
29918,
4713,
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459,
29901,
13,
4706,
12020,
1939,
27469,
580,
13,
13,
1678,
396,
29871,
29906,
29889,
1670,
338,
694,
3957,
470,
694,
11380,
1993,
6660,
3967,
263,
2473,
8171,
6660,
360,
13535,
4151,
5687,
13,
1678,
379,
4590,
29918,
29925,
1430,
1964,
15631,
353,
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29918,
2917,
29918,
657,
877,
3286,
25534,
742,
525,
29882,
459,
29918,
2238,
18745,
742,
2322,
29922,
29896,
29900,
29892,
4867,
29922,
7924,
29897,
29871,
396,
7363,
18745,
304,
367,
7436,
304,
1269,
4340,
8171,
13,
13,
1678,
396,
5399,
565,
278,
12551,
390,
1660,
338,
385,
11359,
390,
1660,
29901,
13,
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565,
451,
4770,
1359,
29918,
262,
9917,
29918,
5721,
2925,
29918,
3177,
29898,
29878,
344,
29918,
333,
29922,
7854,
29918,
29878,
344,
29918,
333,
29892,
4867,
29922,
7924,
1125,
13,
4706,
12020,
1939,
27469,
580,
13,
13,
1678,
16669,
29918,
18010,
353,
426,
4993,
29918,
29878,
344,
29918,
333,
29901,
11117,
19244,
2396,
29871,
29900,
29892,
13,
462,
462,
268,
525,
2084,
2396,
5159,
930,
29871,
396,
360,
13535,
4151,
2307,
1998,
12652,
7573,
13,
1678,
396,
426,
29878,
344,
29918,
333,
29901,
11117,
2084,
2396,
518,
10998,
4993,
29918,
29878,
344,
29918,
333,
2396,
29892,
525,
7854,
29918,
29878,
344,
29918,
333,
2396,
29892,
525,
4993,
29918,
816,
2004,
742,
525,
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29918,
816,
2004,
2396,
500,
1402,
13,
1678,
396,
965,
525,
19244,
2396,
1060,
29913,
13,
1678,
396,
500,
13,
1678,
304,
29918,
1730,
277,
353,
518,
4993,
29918,
29878,
344,
29918,
333,
29962,
29871,
396,
405,
2631,
304,
6493,
29892,
2748,
1051,
338,
4069,
29892,
2867,
2425,
13,
1678,
1887,
29918,
3670,
12539,
353,
29871,
29929,
29929,
29929,
29929,
29871,
396,
9959,
5994,
398,
304,
15592,
630,
2740,
13,
13,
1678,
1550,
304,
29918,
1730,
277,
29901,
13,
4706,
363,
1857,
29918,
3177,
297,
3509,
29889,
24535,
8552,
29898,
517,
29918,
1730,
277,
1125,
13,
9651,
304,
29918,
1730,
277,
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5992,
29898,
3784,
29918,
3177,
29897,
13,
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1857,
29918,
19244,
353,
16669,
29918,
18010,
29961,
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29918,
3177,
22322,
19244,
2033,
13,
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1857,
29918,
2084,
353,
16669,
29918,
18010,
29961,
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29918,
3177,
22322,
2084,
2033,
13,
13,
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565,
1857,
29918,
3177,
451,
297,
5418,
29918,
4262,
29901,
13,
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5418,
29918,
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3784,
29918,
3177,
29962,
353,
4770,
1359,
29918,
449,
17696,
29918,
5721,
2925,
29918,
3177,
29898,
29878,
344,
29918,
333,
29922,
3784,
29918,
3177,
29892,
4867,
29922,
7924,
29897,
13,
13,
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363,
714,
29918,
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297,
5418,
29918,
4262,
29961,
3784,
29918,
3177,
5387,
13,
18884,
396,
5399,
565,
278,
5418,
723,
367,
7968,
13,
18884,
565,
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29918,
4262,
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3177,
3816,
449,
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338,
6213,
29901,
13,
462,
1678,
6773,
13,
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565,
16669,
29918,
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29889,
657,
29898,
449,
29918,
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29892,
11117,
19244,
2396,
29871,
29929,
29929,
29929,
29929,
1800,
1839,
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2033,
1405,
1857,
29918,
19244,
718,
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29961,
3784,
29918,
3177,
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449,
29918,
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718,
379,
4590,
29918,
29925,
1430,
1964,
15631,
29905,
13,
462,
259,
322,
1887,
29918,
3670,
12539,
1405,
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29918,
19244,
718,
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3177,
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379,
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1430,
1964,
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13,
462,
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396,
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565,
278,
19697,
390,
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338,
9615,
363,
2473,
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459,
13,
462,
1678,
565,
714,
29918,
29894,
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2731,
29918,
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344,
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333,
322,
714,
29918,
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451,
297,
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459,
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267,
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13,
462,
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13,
462,
1678,
396,
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565,
727,
338,
263,
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9608,
5101,
13,
462,
1678,
1018,
29901,
13,
462,
4706,
9686,
29918,
816,
2004,
353,
364,
12846,
629,
29889,
2886,
29918,
4352,
292,
29918,
816,
2004,
29898,
29878,
344,
29918,
11027,
29918,
7854,
29922,
1649,
1359,
29918,
29878,
344,
29918,
11027,
29898,
29878,
344,
29918,
333,
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449,
29918,
29894,
29892,
4867,
29922,
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511,
13,
462,
462,
462,
462,
418,
364,
344,
29918,
11027,
29918,
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29922,
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1359,
29918,
29878,
344,
29918,
11027,
29898,
29878,
344,
29918,
333,
29922,
3784,
29918,
3177,
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29922,
7924,
511,
13,
462,
462,
462,
462,
418,
5858,
29918,
4351,
2433,
22585,
29918,
22633,
29918,
8552,
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13,
462,
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462,
462,
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5354,
2433,
11440,
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13,
462,
462,
462,
462,
418,
11380,
29922,
13400,
29918,
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29918,
816,
13826,
565,
714,
29918,
29894,
1275,
2731,
29918,
29878,
344,
29918,
333,
322,
4046,
29918,
7854,
29918,
816,
13826,
1683,
6213,
29897,
13,
462,
4706,
16669,
29918,
18010,
29961,
449,
29918,
29894,
29962,
353,
11117,
19244,
2396,
1857,
29918,
19244,
718,
5418,
29918,
4262,
29961,
3784,
29918,
3177,
3816,
449,
29918,
29894,
29962,
718,
379,
4590,
29918,
29925,
1430,
1964,
15631,
29892,
13,
462,
462,
18884,
525,
2084,
2396,
1857,
29918,
2084,
718,
518,
10998,
4993,
29918,
29878,
344,
29918,
333,
2396,
1857,
29918,
3177,
29892,
13,
462,
462,
462,
462,
308,
525,
7854,
29918,
29878,
344,
29918,
333,
2396,
714,
29918,
29894,
29892,
13,
462,
462,
462,
462,
308,
525,
4993,
29918,
816,
2004,
2396,
9686,
29918,
816,
2004,
29961,
29896,
1402,
13,
462,
462,
462,
462,
308,
525,
7854,
29918,
816,
2004,
2396,
9686,
29918,
816,
2004,
29961,
29900,
1402,
13,
462,
462,
462,
462,
308,
525,
4993,
29918,
816,
2004,
29918,
29886,
21766,
2396,
9686,
29918,
816,
2004,
29961,
29941,
1402,
13,
462,
462,
462,
462,
308,
525,
7854,
29918,
816,
2004,
29918,
29886,
21766,
2396,
9686,
29918,
816,
2004,
29961,
29906,
29962,
6525,
29913,
13,
462,
4706,
565,
714,
29918,
29894,
2804,
2731,
29918,
29878,
344,
29918,
333,
29901,
13,
462,
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304,
29918,
1730,
277,
29889,
4397,
29898,
449,
29918,
29894,
29897,
13,
462,
4706,
1683,
29901,
13,
462,
9651,
1887,
29918,
3670,
12539,
353,
1857,
29918,
19244,
718,
5418,
29918,
4262,
29961,
3784,
29918,
3177,
3816,
449,
29918,
29894,
29962,
718,
379,
4590,
29918,
29925,
1430,
1964,
15631,
13,
462,
1678,
5174,
390,
1660,
17830,
3664,
14039,
287,
29901,
13,
462,
4706,
1209,
13,
1678,
565,
2731,
29918,
29878,
344,
29918,
333,
297,
16669,
29918,
18010,
29901,
13,
4706,
5195,
29954,
2725,
29918,
7068,
8476,
29889,
842,
877,
657,
29918,
29882,
3554,
29918,
29995,
29879,
29918,
29995,
29879,
29918,
29995,
29879,
29915,
1273,
313,
710,
29898,
4993,
29918,
29878,
344,
29918,
333,
511,
851,
29898,
7854,
29918,
29878,
344,
29918,
333,
511,
525,
4286,
7122,
29898,
24582,
29898,
13400,
29918,
7854,
29918,
816,
13826,
876,
511,
16669,
29918,
18010,
29961,
7854,
29918,
29878,
344,
29918,
333,
22322,
2084,
11287,
13,
4706,
736,
16669,
29918,
18010,
29961,
7854,
29918,
29878,
344,
29918,
333,
22322,
2084,
2033,
13,
1678,
1683,
29901,
13,
4706,
12020,
1939,
27469,
580,
13,
13,
13,
1753,
679,
29918,
15697,
29898,
15697,
1125,
13,
1678,
9657,
29918,
15697,
353,
6571,
13,
1678,
565,
8393,
29901,
13,
4706,
565,
338,
8758,
29898,
15697,
29892,
9657,
1125,
13,
9651,
12421,
353,
4390,
29889,
18132,
29898,
3126,
29889,
29881,
17204,
29898,
15697,
876,
13,
4706,
1683,
29901,
13,
9651,
12421,
353,
4390,
29889,
18132,
29898,
710,
29898,
15697,
876,
13,
1678,
396,
6088,
2752,
4603,
13,
1678,
9657,
29918,
15697,
1839,
4993,
29918,
3445,
10123,
29918,
17471,
2033,
353,
12421,
3366,
4993,
29918,
3445,
10123,
29918,
17471,
3108,
565,
313,
5552,
322,
376,
4993,
29918,
3445,
10123,
29918,
17471,
29908,
297,
12421,
29897,
1683,
6213,
13,
1678,
9657,
29918,
15697,
1839,
9536,
29918,
941,
412,
29918,
4993,
2033,
353,
12421,
3366,
9536,
29918,
941,
412,
29918,
4993,
3108,
565,
313,
5552,
322,
376,
9536,
29918,
941,
412,
29918,
4993,
29908,
297,
12421,
29897,
1683,
5852,
13,
1678,
9657,
29918,
15697,
1839,
6289,
29876,
2033,
353,
12421,
3366,
6289,
29918,
978,
3108,
565,
313,
5552,
322,
376,
6289,
29918,
978,
29908,
297,
12421,
29897,
1683,
6213,
13,
1678,
9657,
29918,
15697,
1839,
29880,
361,
5410,
2033,
353,
12421,
29889,
657,
877,
29880,
361,
5410,
742,
448,
29896,
29897,
13,
1678,
736,
9657,
29918,
15697,
13,
13,
13,
1753,
679,
29918,
6289,
29876,
29898,
6078,
29892,
1024,
29892,
270,
16586,
1125,
13,
1678,
565,
270,
16586,
29901,
13,
4706,
736,
270,
16586,
13,
1678,
396,
1831,
263,
6943,
8783,
13,
1678,
363,
3847,
297,
1258,
29889,
1761,
29918,
3560,
29918,
29881,
4841,
29898,
6078,
29892,
1024,
1125,
13,
4706,
565,
3847,
1839,
1853,
2033,
1275,
360,
1367,
1542,
29889,
25832,
8127,
29911,
29901,
13,
9651,
736,
3847,
1839,
978,
2033,
13,
1678,
736,
525,
1228,
29915,
13,
13,
13,
1753,
4770,
4282,
29918,
7854,
29918,
2271,
29898,
6078,
29892,
1024,
29892,
9608,
29892,
2731,
29918,
29878,
344,
29918,
5552,
29879,
29892,
2731,
29918,
275,
29918,
4801,
837,
262,
4695,
29892,
2731,
29918,
275,
29918,
941,
412,
29892,
9657,
29918,
15697,
29892,
337,
2202,
29918,
2798,
29892,
6354,
1125,
13,
1678,
9995,
13,
1678,
12230,
16876,
740,
304,
2048,
12551,
3988,
746,
5663,
15387,
1301,
25534,
304,
6222,
29889,
13,
1678,
9995,
13,
13,
1678,
565,
2731,
29918,
275,
29918,
4801,
837,
262,
4695,
29901,
13,
4706,
2731,
29918,
2271,
353,
1051,
29898,
20464,
29889,
29880,
29888,
1983,
29906,
7810,
1983,
29898,
29880,
29888,
1983,
3790,
29915,
6078,
2396,
6874,
29889,
23176,
29892,
525,
978,
2396,
1024,
7690,
5975,
3101,
29961,
29900,
29962,
13,
1678,
1683,
29901,
13,
4706,
396,
10272,
2731,
3142,
297,
1206,
310,
1661,
11806,
4695,
13,
4706,
396,
22006,
15687,
29892,
2992,
29889,
13,
4706,
270,
16586,
353,
679,
29918,
6289,
29876,
29898,
6078,
29892,
1024,
29892,
9657,
29918,
15697,
29889,
657,
877,
6289,
29876,
742,
6213,
876,
13,
4706,
396,
360,
29984,
29906,
2224,
2337,
8665,
411,
847,
29892,
541,
10944,
1795,
451,
1095,
411,
847,
13,
4706,
22006,
29918,
535,
7316,
353,
2731,
29918,
29878,
344,
29918,
5552,
29879,
29889,
657,
877,
8588,
292,
29918,
535,
7316,
742,
6213,
29897,
13,
4706,
2731,
29918,
2084,
353,
3386,
29918,
29879,
2271,
29898,
6289,
29876,
29892,
1024,
29892,
22006,
29918,
535,
7316,
29897,
13,
4706,
565,
2731,
29918,
275,
29918,
941,
412,
29901,
13,
9651,
565,
337,
2202,
29918,
2798,
470,
6354,
1275,
525,
4789,
22205,
2396,
13,
18884,
2731,
29918,
2084,
353,
14210,
29879,
29918,
29995,
29875,
29915,
1273,
313,
7854,
29918,
2084,
29892,
938,
29898,
2230,
29889,
2230,
22130,
13,
13,
4706,
2731,
29918,
2271,
353,
1051,
29898,
20464,
29889,
29880,
29888,
1983,
29906,
7810,
1983,
29898,
29880,
29888,
1983,
3790,
29915,
6078,
2396,
6874,
29889,
23176,
29892,
525,
978,
2396,
1024,
29892,
525,
2084,
2396,
2731,
29918,
2084,
7690,
5975,
3101,
29961,
29900,
29962,
13,
13,
1678,
736,
2731,
29918,
2271,
13,
13,
13,
1753,
4770,
23174,
29918,
4993,
29918,
2271,
29898,
4993,
29918,
2271,
29892,
2752,
29918,
4530,
29918,
2271,
29892,
2731,
29918,
4530,
29918,
2271,
29892,
2752,
29918,
816,
2004,
1125,
13,
1678,
9995,
13,
1678,
12662,
300,
374,
911,
2752,
3142,
363,
777,
4266,
4251,
310,
2752,
322,
12551,
27715,
13,
1678,
9995,
13,
1678,
565,
2731,
29918,
4530,
29918,
2271,
1275,
525,
29887,
2395,
2396,
13,
4706,
565,
2752,
29918,
816,
2004,
297,
6024,
29881,
485,
29879,
742,
525,
991,
2033,
29901,
13,
9651,
2752,
29918,
2271,
4619,
525,
29973,
8552,
29918,
8513,
29922,
5910,
29915,
13,
1678,
25342,
2731,
29918,
4530,
29918,
2271,
1275,
525,
29879,
29941,
2396,
13,
4706,
565,
2752,
29918,
816,
2004,
297,
6024,
29881,
485,
29879,
742,
525,
991,
2033,
29901,
13,
9651,
2752,
29918,
2271,
4619,
525,
29973,
8552,
29918,
8513,
29922,
5910,
29915,
13,
1678,
25342,
399,
25752,
29928,
7520,
29918,
26813,
20322,
1001,
29918,
20387,
29901,
13,
4706,
565,
2752,
29918,
816,
2004,
297,
6024,
29881,
485,
29879,
742,
525,
991,
2033,
29901,
13,
9651,
2752,
29918,
2271,
4619,
525,
29973,
8552,
29918,
8513,
16328,
29879,
29915,
1273,
399,
25752,
29928,
7520,
29918,
26813,
20322,
1001,
29918,
20387,
13,
13,
1678,
2752,
29918,
4530,
29918,
2271,
29918,
1958,
353,
11117,
29887,
2395,
2396,
525,
29887,
9274,
29879,
742,
525,
29879,
29941,
2396,
525,
29879,
29941,
29879,
10827,
13,
1678,
565,
2752,
29918,
4530,
29918,
2271,
297,
2752,
29918,
4530,
29918,
2271,
29918,
1958,
29901,
13,
4706,
565,
2752,
29918,
2271,
7503,
29955,
29962,
1275,
525,
29881,
485,
29879,
597,
2396,
13,
9651,
2752,
29918,
2271,
353,
2752,
29918,
4530,
29918,
2271,
29918,
1958,
29961,
4993,
29918,
4530,
29918,
2271,
29962,
718,
2752,
29918,
2271,
29961,
29946,
17531,
13,
4706,
565,
2752,
29918,
2271,
7503,
29947,
29962,
1275,
525,
991,
597,
2396,
13,
9651,
2752,
29918,
2271,
353,
2752,
29918,
4530,
29918,
2271,
29918,
1958,
29961,
4993,
29918,
4530,
29918,
2271,
29962,
718,
2752,
29918,
2271,
29961,
29945,
17531,
13,
13,
1678,
565,
2752,
29918,
2271,
7503,
29896,
29906,
29962,
1275,
525,
29879,
1758,
29974,
991,
597,
2396,
13,
4706,
2752,
29918,
2271,
353,
525,
29879,
1758,
29915,
718,
2752,
29918,
2271,
29961,
29896,
29900,
17531,
13,
1678,
736,
2752,
29918,
2271,
13,
13,
13,
1753,
4770,
23174,
29918,
7854,
29918,
2271,
29898,
7854,
29918,
2271,
29892,
2731,
29918,
4530,
29918,
2271,
29892,
2731,
29918,
816,
2004,
1125,
13,
1678,
9995,
13,
1678,
12662,
300,
374,
911,
12551,
3142,
363,
777,
4266,
4251,
310,
12551,
27715,
13,
1678,
9995,
13,
1678,
565,
2731,
29918,
4530,
29918,
2271,
1275,
525,
29887,
2395,
2396,
13,
4706,
2731,
29918,
2271,
353,
337,
29889,
1491,
877,
29881,
485,
29879,
742,
525,
29887,
9274,
29879,
742,
2731,
29918,
2271,
29897,
13,
4706,
2731,
29918,
2271,
353,
337,
29889,
1491,
877,
991,
742,
525,
29887,
9274,
29879,
742,
2731,
29918,
2271,
29897,
13,
1678,
25342,
2731,
29918,
4530,
29918,
2271,
1275,
525,
29879,
29941,
2396,
13,
4706,
2731,
29918,
2271,
353,
337,
29889,
1491,
877,
29881,
485,
29879,
742,
525,
29879,
29941,
29879,
742,
2731,
29918,
2271,
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13,
4706,
2731,
29918,
2271,
353,
337,
29889,
1491,
877,
991,
742,
525,
29879,
29941,
29879,
742,
2731,
29918,
2271,
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13,
13,
1678,
565,
2731,
29918,
2271,
7503,
29896,
29906,
29962,
1275,
525,
29879,
1758,
29974,
991,
597,
2396,
13,
4706,
2731,
29918,
2271,
353,
525,
29879,
1758,
29915,
718,
2731,
29918,
2271,
29961,
29896,
29900,
17531,
13,
1678,
736,
2731,
29918,
2271,
13,
13,
13,
29992,
20736,
284,
29918,
7924,
13,
1753,
679,
29918,
3286,
571,
29918,
24830,
29918,
392,
29918,
4993,
29918,
3445,
506,
294,
29898,
7827,
29918,
1287,
414,
29922,
29900,
29892,
15645,
29918,
4537,
29922,
29900,
29892,
4046,
29922,
8516,
29892,
6354,
29922,
8516,
29892,
9642,
29918,
27603,
29922,
8516,
29892,
20371,
267,
29922,
8516,
29892,
27715,
29922,
8516,
29892,
13,
462,
462,
795,
6963,
29918,
14627,
29922,
29946,
29941,
29906,
29900,
29900,
29892,
337,
2202,
29918,
1228,
29918,
615,
29879,
29922,
8824,
29892,
4418,
957,
29918,
816,
13826,
29922,
8516,
29892,
6782,
10154,
29922,
8516,
29892,
17927,
29922,
21027,
29889,
1188,
29892,
4867,
29922,
8516,
1125,
13,
1678,
9995,
13,
1678,
3617,
6782,
7274,
322,
278,
6942,
2752,
1634,
506,
294,
13,
1678,
584,
3207,
3001,
29918,
1287,
414,
29901,
308,
9681,
310,
3001,
17162,
29889,
13,
1678,
584,
3207,
15645,
29918,
4537,
29901,
308,
5163,
310,
278,
14012,
15645,
29889,
13,
1678,
584,
3207,
4046,
29901,
462,
9628,
277,
29889,
13,
1678,
584,
3207,
6354,
29901,
795,
13414,
29889,
13,
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29892,
278,
1051,
3743,
871,
278,
12551,
390,
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13,
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396,
512,
1206,
310,
1661,
29899,
18045,
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278,
1051,
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599,
278,
1006,
4210,
653,
390,
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267,
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4046,
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13826,
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3286,
25534,
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7971,
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13826,
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313,
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337,
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2798,
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1939,
27469,
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13,
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978,
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13,
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565,
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333,
297,
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2004,
29918,
29885,
1608,
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13,
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12428,
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29918,
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2004,
29918,
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7971,
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451,
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17830,
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287,
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333,
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13,
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565,
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333,
451,
297,
12428,
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29918,
816,
2004,
29918,
29885,
1608,
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29901,
13,
18884,
12428,
29879,
29918,
816,
2004,
29918,
29885,
1608,
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4397,
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7971,
29918,
333,
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13,
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6773,
13,
13,
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2752,
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2004,
353,
1051,
29918,
29882,
3554,
29961,
29900,
22322,
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816,
2004,
2033,
13,
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2731,
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816,
2004,
353,
1051,
29918,
29882,
3554,
14352,
29896,
22322,
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29918,
816,
2004,
2033,
13,
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2731,
29918,
816,
2004,
29918,
29886,
21766,
353,
1051,
29918,
29882,
3554,
14352,
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22322,
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29918,
816,
2004,
29918,
29886,
21766,
2033,
13,
13,
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2758,
29918,
941,
412,
29918,
4993,
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13,
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1018,
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13,
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396,
3617,
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13,
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2752,
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20464,
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12893,
29889,
20464,
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29918,
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344,
29918,
333,
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2752,
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2004,
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949,
1495,
13,
13,
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2752,
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4530,
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2271,
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5552,
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29906,
7810,
1983,
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1983,
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6078,
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23176,
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2084,
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4530,
29918,
2271,
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2752,
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2004,
29922,
4993,
29918,
816,
2004,
29897,
13,
13,
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396,
960,
278,
2009,
29918,
333,
338,
451,
2307,
297,
278,
6782,
8600,
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817,
304,
10272,
278,
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565,
12428,
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333,
451,
297,
1301,
25534,
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13,
13,
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260,
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12421,
3366,
9536,
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3108,
565,
313,
5552,
322,
376,
9536,
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412,
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297,
12421,
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1683,
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353,
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13,
13,
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396,
7338,
355,
278,
15562,
8600,
411,
2009,
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13,
18884,
6782,
29918,
4351,
29918,
1853,
353,
376,
23711,
29968,
29908,
13,
18884,
6782,
29918,
22992,
29918,
1853,
353,
376,
23711,
29968,
29908,
13,
18884,
26556,
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6963,
29918,
14627,
353,
5852,
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6213,
13,
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565,
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29889,
275,
29918,
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412,
29918,
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344,
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4993,
29918,
29878,
344,
29918,
333,
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470,
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344,
29918,
5552,
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29918,
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344,
29918,
333,
467,
657,
877,
303,
6751,
29918,
12403,
742,
7700,
1125,
13,
462,
1678,
6963,
29918,
14627,
353,
6963,
29918,
14627,
29918,
2997,
13,
462,
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6782,
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1853,
353,
376,
29911,
3301,
29923,
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13,
462,
1678,
565,
451,
2758,
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412,
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13,
462,
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565,
12428,
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333,
451,
297,
12428,
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6194,
29918,
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13,
462,
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12428,
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6194,
29918,
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7971,
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333,
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13,
462,
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565,
12428,
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333,
297,
12428,
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29918,
1217,
29918,
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29901,
13,
462,
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12428,
29879,
29918,
1217,
29918,
4993,
29889,
5992,
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7971,
29918,
333,
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13,
462,
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6773,
13,
13,
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565,
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275,
29918,
941,
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29878,
344,
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29918,
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344,
29918,
333,
1125,
13,
462,
1678,
26556,
353,
7700,
13,
462,
1678,
6782,
29918,
22992,
29918,
1853,
353,
376,
29911,
3301,
29923,
29908,
13,
13,
18884,
396,
3617,
12551,
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13,
18884,
2731,
29918,
20464,
353,
12893,
29889,
20464,
29898,
7854,
29918,
29878,
344,
29918,
333,
29892,
2731,
29918,
816,
2004,
29892,
525,
3539,
1495,
13,
13,
18884,
396,
11796,
29872,
278,
12551,
3142,
13,
18884,
2731,
29918,
2271,
353,
4770,
4282,
29918,
7854,
29918,
2271,
29898,
6078,
29922,
6078,
29892,
1024,
29922,
978,
29892,
13,
462,
462,
9651,
9608,
29922,
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29918,
20464,
29892,
13,
462,
462,
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2731,
29918,
29878,
344,
29918,
5552,
29879,
29922,
13073,
29889,
29878,
344,
29918,
5552,
29879,
29898,
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29918,
29878,
344,
29918,
333,
511,
13,
462,
462,
9651,
2731,
29918,
275,
29918,
4801,
837,
262,
4695,
29922,
13073,
29889,
29878,
344,
29918,
3888,
29898,
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29918,
29878,
344,
29918,
333,
29897,
1839,
4801,
837,
262,
4695,
7464,
13,
462,
462,
9651,
2731,
29918,
275,
29918,
941,
412,
29922,
13073,
29889,
275,
29918,
941,
412,
29918,
29878,
344,
29898,
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29918,
29878,
344,
29918,
333,
511,
13,
462,
462,
9651,
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29918,
15697,
29922,
8977,
29918,
15697,
29892,
13,
462,
462,
9651,
337,
2202,
29918,
2798,
29922,
276,
2202,
29918,
2798,
29892,
13,
462,
462,
9651,
6354,
29922,
10072,
29897,
13,
18884,
2731,
29918,
2271,
353,
4770,
23174,
29918,
7854,
29918,
2271,
29898,
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29918,
2271,
29892,
2731,
29918,
4530,
29918,
2271,
29922,
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29918,
4530,
29918,
2271,
29892,
2731,
29918,
816,
2004,
29922,
7854,
29918,
816,
2004,
29897,
13,
13,
18884,
396,
3617,
2731,
2913,
5993,
13,
18884,
2731,
29918,
1028,
562,
300,
4476,
353,
6213,
13,
18884,
565,
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29918,
20464,
29889,
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322,
525,
1062,
2760,
29918,
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29915,
297,
2731,
29918,
20464,
29889,
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322,
320,
13,
462,
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29918,
20464,
29889,
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1839,
1062,
2760,
29918,
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2033,
322,
525,
3493,
29918,
6979,
29915,
297,
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20464,
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1839,
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13,
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16215,
3493,
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6979,
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13,
13,
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671,
29918,
666,
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29946,
353,
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344,
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5552,
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344,
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333,
467,
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666,
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666,
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7029,
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13,
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285,
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23525,
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353,
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29889,
29878,
344,
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] |
official/vision/beta/projects/yolo/modeling/factory.py | mcasanova1445/models | 1 | 153961 | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains common factory functions yolo neural networks."""
from absl import logging
from official.vision.beta.modeling.backbones import factory as backbone_factory
from official.vision.beta.modeling.decoders import factory as decoder_factory
from official.vision.beta.projects.yolo.configs import yolo
from official.vision.beta.projects.yolo.modeling import yolo_model
from official.vision.beta.projects.yolo.modeling.heads import yolo_head
from official.vision.beta.projects.yolo.modeling.layers import detection_generator
def build_yolo_detection_generator(model_config: yolo.Yolo, anchor_boxes):
"""Builds yolo detection generator."""
model = detection_generator.YoloLayer(
classes=model_config.num_classes,
anchors=anchor_boxes,
iou_thresh=model_config.detection_generator.iou_thresh,
nms_thresh=model_config.detection_generator.nms_thresh,
max_boxes=model_config.detection_generator.max_boxes,
pre_nms_points=model_config.detection_generator.pre_nms_points,
nms_type=model_config.detection_generator.nms_type,
box_type=model_config.detection_generator.box_type.get(),
path_scale=model_config.detection_generator.path_scales.get(),
scale_xy=model_config.detection_generator.scale_xy.get(),
label_smoothing=model_config.loss.label_smoothing,
use_scaled_loss=model_config.loss.use_scaled_loss,
update_on_repeat=model_config.loss.update_on_repeat,
truth_thresh=model_config.loss.truth_thresh.get(),
loss_type=model_config.loss.box_loss_type.get(),
max_delta=model_config.loss.max_delta.get(),
iou_normalizer=model_config.loss.iou_normalizer.get(),
cls_normalizer=model_config.loss.cls_normalizer.get(),
object_normalizer=model_config.loss.object_normalizer.get(),
ignore_thresh=model_config.loss.ignore_thresh.get(),
objectness_smooth=model_config.loss.objectness_smooth.get())
return model
def build_yolo_head(input_specs, model_config: yolo.Yolo, l2_regularization):
"""Builds yolo head."""
min_level = min(map(int, input_specs.keys()))
max_level = max(map(int, input_specs.keys()))
head = yolo_head.YoloHead(
min_level=min_level,
max_level=max_level,
classes=model_config.num_classes,
boxes_per_level=model_config.anchor_boxes.anchors_per_scale,
norm_momentum=model_config.norm_activation.norm_momentum,
norm_epsilon=model_config.norm_activation.norm_epsilon,
kernel_regularizer=l2_regularization,
smart_bias=model_config.head.smart_bias)
return head
def build_yolo(input_specs, model_config, l2_regularization):
"""Builds yolo model."""
backbone = model_config.backbone.get()
anchor_dict, _ = model_config.anchor_boxes.get(
backbone.min_level, backbone.max_level)
backbone = backbone_factory.build_backbone(input_specs, model_config.backbone,
model_config.norm_activation,
l2_regularization)
decoder = decoder_factory.build_decoder(backbone.output_specs, model_config,
l2_regularization)
head = build_yolo_head(decoder.output_specs, model_config, l2_regularization)
detection_generator_obj = build_yolo_detection_generator(model_config,
anchor_dict)
model = yolo_model.Yolo(
backbone=backbone,
decoder=decoder,
head=head,
detection_generator=detection_generator_obj)
model.build(input_specs.shape)
model.summary(print_fn=logging.info)
losses = detection_generator_obj.get_losses()
return model, losses
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1904,
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28495,
13,
2
] |
bagpy/__init__.py | jmscslgroup/rosbagpy | 107 | 53435 | <reponame>jmscslgroup/rosbagpy
# Initial Date: March 2, 2020
# Author: <NAME>
# Copyright (c) <NAME>, Arizona Board of Regents
# All rights reserved.
from .bagreader import bagreader
from .bagreader import animate_timeseries
from .bagreader import create_fig
| [
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] |
Escola/views.py | JoaoGuilhermeBNascimento/DjangoAPI | 0 | 105208 | from rest_framework import serializers, viewsets
from Escola.models import Aluno
from Escola.serializer import AlunoSerializer
class AlunosViewSet(viewsets.ModelViewSet):
queryset = Aluno.objects.all()
serializer_class = AlunoSerializer
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slicedimage/backends/_s3.py | ttung/slicedimage | 6 | 42305 | <gh_stars>1-10
import urllib.parse
from io import BytesIO
from pathlib import PurePosixPath
import boto3
from botocore import UNSIGNED
from botocore.config import Config
from ._base import Backend, verify_checksum
RETRY_STATUS_CODES = frozenset({500, 502, 503, 504})
class S3Backend(Backend):
CONFIG_UNSIGNED_REQUESTS_KEY = "unsigned-requests"
def __init__(self, baseurl, s3_config):
parsed = urllib.parse.urlparse(baseurl)
assert parsed[0].lower() == "s3"
self._bucket = parsed[1]
if parsed[2][0] == "/":
self._basepath = PurePosixPath(parsed[2][1:])
else:
self._basepath = PurePosixPath(parsed[2])
self._s3_config = s3_config
def read_contextmanager(self, name, checksum_sha256=None):
key = str(self._basepath / name)
return _S3ContextManager(self._bucket, key, checksum_sha256, self._s3_config)
class _S3ContextManager:
def __init__(self, s3_bucket, s3_key, checksum_sha256, s3_config):
self.s3_bucket = s3_bucket
self.s3_key = s3_key
self.checksum_sha256 = checksum_sha256
self.s3_config = s3_config
def __enter__(self):
unsigned_requests = self.s3_config.get(S3Backend.CONFIG_UNSIGNED_REQUESTS_KEY, False)
if unsigned_requests:
resource_config = Config(signature_version=UNSIGNED)
else:
resource_config = None
session = boto3.session.Session()
s3 = session.resource("s3", config=resource_config)
bucket = s3.Bucket(self.s3_bucket)
s3_obj = bucket.Object(self.s3_key)
self.buffer = BytesIO()
s3_obj.download_fileobj(self.buffer)
self.buffer.seek(0)
verify_checksum(self.buffer, self.checksum_sha256)
return self.buffer.__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
try:
return self.buffer.__exit__(exc_type, exc_val, exc_tb)
finally:
self.buffer = None
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] |
setup.py | zhammer/LibCST | 1 | 145415 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import importlib.util
from os import path
from typing import TYPE_CHECKING
import setuptools
if TYPE_CHECKING:
from importlib.machinery import ModuleSpec
from types import ModuleType
# Grab the readme so that our package stays in sync with github.
this_directory: str = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, "README.rst"), encoding="utf-8") as f:
long_description = f.read()
# Grab the version constant so that libcst.tool stays in sync with this package.
spec: "ModuleSpec" = importlib.util.spec_from_file_location(
"version", path.join(this_directory, "libcst/_version.py")
)
version: "ModuleType" = importlib.util.module_from_spec(spec)
# pyre-ignore Pyre doesn't know about importlib entirely.
spec.loader.exec_module(version)
# pyre-ignore Pyre has no way of knowing that this constant exists.
LIBCST_VERSION = version.LIBCST_VERSION
setuptools.setup(
name="libcst",
description="A concrete syntax tree with AST-like properties for Python 3.5, 3.6, 3.7 and 3.8 programs.",
long_description=long_description,
long_description_content_type="text/x-rst",
version=LIBCST_VERSION,
url="https://github.com/Instagram/LibCST",
license="MIT",
packages=setuptools.find_packages(),
package_data={
"libcst": ["py.typed"],
"libcst.tests.pyre": ["*"],
"libcst.codemod.tests": ["*"],
},
test_suite="libcst",
python_requires=">=3.6",
install_requires=[dep.strip() for dep in open("requirements.txt").readlines()],
extras_require={
"dev": [dep.strip() for dep in open("requirements-dev.txt").readlines() if "=" in dep],
},
classifiers=[
"License :: OSI Approved :: MIT License",
"Topic :: Software Development :: Libraries",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
],
zip_safe=False, # for mypy compatibility https://mypy.readthedocs.io/en/latest/installed_packages.html
)
| [
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] |
src/medius/mediuspackets/respondtoclaninvitation.py | Metroynome/robo | 8 | 191034 | from enums.enums import MediusEnum, CallbackStatus
from utils import utils
from medius.mediuspackets.respondtoclaninvitationresponse import RespondToClanInvitationResponseSerializer
class RespondToClanInvitationSerializer:
data_dict = [
{'name': 'mediusid', 'n_bytes': 2, 'cast': None},
{'name': 'message_id', 'n_bytes': MediusEnum.MESSAGEID_MAXLEN, 'cast': None},
{'name': 'session_key', 'n_bytes': MediusEnum.SESSIONKEY_MAXLEN, 'cast': None},
{'name': 'buf', 'n_bytes': 2, 'cast': None},
{'name': 'clan_invitation_id', 'n_bytes': 4, 'cast': utils.bytes_to_int_little},
{'name': 'response', 'n_bytes': 4, 'cast': utils.bytes_to_int_little},
]
class RespondToClanInvitationHandler:
def process(self, serialized, monolith, con):
client_manager = monolith.get_client_manager()
if serialized['response'] == 0: # undecided
pass
elif serialized['response'] == 1: # accept
client_manager.respond_clan_invite(serialized['clan_invitation_id'], True)
elif serialized['response'] == 2: # reject
client_manager.respond_clan_invite(serialized['clan_invitation_id'], False)
elif serialized['response'] == 3: # decline
client_manager.respond_clan_invite(serialized['clan_invitation_id'], False)
return [RespondToClanInvitationResponseSerializer.build(
serialized['message_id'],
CallbackStatus.SUCCESS
)]
| [
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13,
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13,
2
] |
anime downloaders/animeblkom downloader.py | badr286/anime-stuff | 0 | 15937 | <reponame>badr286/anime-stuff
from animeblkom import Animeblkom, animeblkom_episode
from downloader import blkom
from requests import get
download_list = []
anime_url = input('Anime Url: ') # ex. https://animeblkom.net/watch/one-piece
episodes = Animeblkom.get_anime_episodes(anime_url)
print(f'{len(episodes)} episodes found.')
episodes = episodes[ int(input('from: '))-1 : int(input('to: ')) ]
for episode in episodes:
print(episode.num)
episode_servers = episode.get_servers()
for i in range(len(episode_servers)):
curr_server = episode_servers[i]
print(f'{i}. {curr_server}')
choice = int(input('Choice: '))
download_list.append( episode_servers[choice].split(', ')[1].strip() )
print('\n')
for link in download_list:
file = blkom.download(link)
print(file.info)
file.save()
print('\n')
input('done')
| [
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2080,
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2
] |
lib/models/bn_helper.py | hongrui16/naic2020_B | 0 | 7960 | import torch
import functools
if torch.__version__.startswith('0'):
from .sync_bn.inplace_abn.bn import InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
BatchNorm2d_class = InPlaceABNSync
relu_inplace = False
else:
# BatchNorm2d_class = BatchNorm2d = torch.nn.SyncBatchNorm
BatchNorm2d_class = BatchNorm2d = torch.nn.BatchNorm2d
relu_inplace = True | [
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2
] |
tests/functional/Hydro/AcousticWave/CSPH_mod_package.py | jmikeowen/Spheral | 22 | 13083 | #-------------------------------------------------------------------------------
# A mock physics package to mess around with the CRKSPH corrections.
#-------------------------------------------------------------------------------
from Spheral1d import *
class CRKSPH_mod_package(Physics):
def __init__(self):
Physics.__init__(self)
return
def evaluateDerivatives(self, t, dt, db, state, derivs):
return
def dt(self, db, state, derivs, t):
return pair_double_string(1e100, "No vote")
def registerState(self, dt, state):
return
def registerDerivatives(self, db, derivs):
return
def label(self):
return "CRKSPH_mod_package"
def initialize(self, t, dt, db, state, derivs):
# Grab the CRKSPH arrays.
A0_fl = state.scalarFields(HydroFieldNames.A0_CRKSPH)
A_fl = state.scalarFields(HydroFieldNames.A_CRKSPH)
B_fl = state.vectorFields(HydroFieldNames.B_CRKSPH)
A0 = A0_fl[0]
A = A_fl[0]
B = B_fl[0]
print "A", A.internalValues()
return
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setup.py | deluxor/dcosdev | 0 | 133345 | <filename>setup.py
#!/usr/bin/env python
"""dcosdev project"""
from setuptools import find_packages, setup
REQUIRES = [
'docker==4.2.0',
'minio==5.0.8',
'requests==2.23.0',
'boto3==1.12.34',
"click==7.1.1",
"pyyaml==5.3.1",
]
setup(name='dcosdev',
version='0.0.1',
description='short description',
long_description='long description',
platforms=["Linux"],
author="...",
author_email="...",
url="...",
license="Apache 2",
packages=find_packages(),
entry_points={
'console_scripts': [
'dcosdev=dcosdev.commands:maingroup',
],
},
install_requires=REQUIRES,
zip_safe=False,
include_package_data=True,
)
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distributed/__init__.py | merlinarer/scrl | 102 | 148556 | <reponame>merlinarer/scrl
from .launch import launch
from .getters import get_dist_url, scatter_values
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sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_async_lro.py | dmarx/azure-sdk-for-python | 1 | 186490 | <filename>sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_async_lro.py
# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
from azure.core.polling.base_polling import OperationFailed, BadStatus
from azure.core.polling.async_base_polling import AsyncLROBasePolling
from azure.core.polling import AsyncLROPoller
from azure.core.polling._async_poller import PollingReturnType
_FINISHED = frozenset(["succeeded", "cancelled", "failed", "partiallysucceeded"])
_FAILED = frozenset(["failed"])
_SUCCEEDED = frozenset(["succeeded", "partiallysucceeded"])
class TextAnalyticsAsyncLROPollingMethod(AsyncLROBasePolling):
def finished(self):
"""Is this polling finished?
:rtype: bool
"""
return TextAnalyticsAsyncLROPollingMethod._finished(self.status())
@staticmethod
def _finished(status):
if hasattr(status, "value"):
status = status.value
return str(status).lower() in _FINISHED
@staticmethod
def _failed(status):
if hasattr(status, "value"):
status = status.value
return str(status).lower() in _FAILED
@staticmethod
def _raise_if_bad_http_status_and_method(response):
"""Check response status code is valid.
Must be 200, 201, 202, or 204.
:raises: BadStatus if invalid status.
"""
code = response.status_code
if code in {200, 201, 202, 204}:
return
raise BadStatus(
"Invalid return status {!r} for {!r} operation".format(
code, response.request.method
)
)
async def _poll(self): # pylint:disable=invalid-overridden-method
"""Poll status of operation so long as operation is incomplete and
we have an endpoint to query.
:param callable update_cmd: The function to call to retrieve the
latest status of the long running operation.
:raises: OperationFailed if operation status 'Failed' or 'Canceled'.
:raises: BadStatus if response status invalid.
:raises: BadResponse if response invalid.
"""
while not self.finished():
await self._delay()
await self.update_status()
if TextAnalyticsAsyncLROPollingMethod._failed(self.status()):
raise OperationFailed("Operation failed or canceled")
final_get_url = self._operation.get_final_get_url(self._pipeline_response)
if final_get_url:
self._pipeline_response = await self.request_status(final_get_url)
TextAnalyticsAsyncLROPollingMethod._raise_if_bad_http_status_and_method(
self._pipeline_response.http_response
)
class AsyncAnalyzeBatchActionsLROPollingMethod(TextAnalyticsAsyncLROPollingMethod):
@property
def _current_body(self):
from ._generated.v3_1_preview_3.models import JobMetadata
return JobMetadata.deserialize(self._pipeline_response)
@property
def created_on(self):
if not self._current_body:
return None
return self._current_body.created_date_time
@property
def display_name(self):
if not self._current_body:
return None
return self._current_body.display_name
@property
def expires_on(self):
if not self._current_body:
return None
return self._current_body.expiration_date_time
@property
def actions_failed_count(self):
if not self._current_body:
return None
return self._current_body.additional_properties['tasks']['failed']
@property
def actions_in_progress_count(self):
if not self._current_body:
return None
return self._current_body.additional_properties['tasks']['inProgress']
@property
def actions_succeeded_count(self):
if not self._current_body:
return None
return self._current_body.additional_properties['tasks']["completed"]
@property
def last_modified_on(self):
if not self._current_body:
return None
return self._current_body.last_update_date_time
@property
def total_actions_count(self):
if not self._current_body:
return None
return self._current_body.additional_properties['tasks']["total"]
@property
def id(self):
if not self._current_body:
return None
return self._current_body.job_id
class AsyncAnalyzeBatchActionsLROPoller(AsyncLROPoller[PollingReturnType]):
@property
def created_on(self):
return self._polling_method.created_on
@property
def display_name(self):
return self._polling_method.display_name
@property
def expires_on(self):
return self._polling_method.expires_on
@property
def actions_failed_count(self):
return self._polling_method.actions_failed_count
@property
def actions_in_progress_count(self):
return self._polling_method.actions_in_progress_count
@property
def actions_succeeded_count(self):
return self._polling_method.actions_succeeded_count
@property
def last_modified_on(self):
return self._polling_method.last_modified_on
@property
def total_actions_count(self):
return self._polling_method.total_actions_count
@property
def id(self):
return self._polling_method.id
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ctlearn/default_models/resnet_rnn.py | sgh14/ctlearn | 38 | 73496 | import importlib
import sys
import tensorflow as tf
LSTM_SIZE = 2048
def resnet_rnn_model(features, model_params, example_description, training):
# Get hyperparameters
dropout_rate = model_params['resnet_rnn'].get('dropout_rate', 0.5)
# Reshape inputs into proper dimensions
for (name, f), d in zip(features.items(), example_description):
if name.endswith('images'):
telescope_data = tf.reshape(f, [-1, *d['shape']])
num_telescopes = d['shape'][0]
if name.endswith('triggers'):
telescope_triggers = tf.cast(f, tf.float32)
# Transpose telescope_data from [batch_size,num_tel,length,width,channels]
# to [num_tel,batch_size,length,width,channels].
telescope_data = tf.transpose(telescope_data, perm=[1, 0, 2, 3, 4])
# Define the network being used. Each CNN block analyzes a single
# telescope. The outputs for non-triggering telescopes are zeroed out
# (effectively, those channels are dropped out).
# Unlike standard dropout, this zeroing-out procedure is performed both at
# training and test time since it encodes meaningful aspects of the data.
# The telescope outputs are then stacked into input for the array-level
# network, either into 1D feature vectors or into 3D convolutional
# feature maps, depending on the requirements of the network head.
# The array-level processing is then performed by the network head. The
# logits are returned and fed into a classifier.
# Load ResNet block model
sys.path.append(model_params['model_directory'])
resnet_block_module = importlib.import_module(model_params['resnet_rnn']['network']['module'])
resnet_block = getattr(resnet_block_module, model_params['resnet_rnn']['network']['function'])
trainable = model_params['resnet_rnn'].get('trainable_backbone', False)
#calculate number of valid images per event
num_tels_triggered = tf.to_int32(tf.reduce_sum(telescope_triggers,1))
telescope_outputs = []
for telescope_index in range(num_telescopes):
# Set all telescopes after the first to share weights
reuse = None if telescope_index == 0 else True
with tf.variable_scope("resnet_block"):
x = tf.gather(telescope_data, telescope_index)
# The original ResNet implementation use this padding, but we pad the images in the ImageMapper.
#x = tf.pad(telescope_data, tf.constant([[3, 3], [3, 3]]), name='conv1_pad')
init_layer = model_params['res_net'].get('init_layer', False)
if init_layer:
x = tf.layers.conv2d(x, filters=init_layer['filters'], kernel_size=init_layer['kernel_size'],
strides=init_layer['strides'], trainable=trainable, name='conv1_conv')
#x = tf.pad(x, tf.constant([[1, 1], [1, 1]]), name='pool1_pad')
init_max_pool = model_params['res_net'].get('init_max_pool', False)
if init_max_pool:
x = tf.layers.max_pooling2d(x, init_max_pool['size'], strides=init_max_pool['strides'], trainable=trainable, name='pool1_pool')
output = resnet_block(x, params=model_params, reuse=reuse, trainable=trainable)
output = tf.reduce_mean(output, axis=[1,2], name='global_avgpool')
if model_params['resnet_rnn']['pretrained_weights']:
tf.contrib.framework.init_from_checkpoint(model_params['resnet_rnn']['pretrained_weights'],{'Network/':'resnet_block/'})
#flatten output of embedding CNN to (batch_size, _)
image_embedding = tf.layers.flatten(output, name='image_embedding')
image_embedding_dropout = tf.layers.dropout(image_embedding, training=training)
telescope_outputs.append(image_embedding_dropout)
with tf.variable_scope("NetworkHead"):
#combine image embeddings (batch_size, num_tel, num_units_embedding)
embeddings = tf.stack(telescope_outputs,axis=1)
#implement attention mechanism with range num_tel (covering all timesteps)
#define LSTM cell size
rnn_cell = tf.nn.rnn_cell.LSTMCell(LSTM_SIZE)
outputs, _ = tf.nn.dynamic_rnn(
rnn_cell,
embeddings,
dtype=tf.float32,
swap_memory=True,
sequence_length=num_tels_triggered)
# (batch_size, max_num_tel * LSTM_SIZE)
outputs = tf.layers.flatten(outputs)
output_dropout = tf.layers.dropout(outputs, rate=dropout_rate,
training=training, name="rnn_output_dropout")
fc1 = tf.layers.dense(inputs=output_dropout, units=1024, kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.004), name="fc1")
dropout_1 = tf.layers.dropout(inputs=fc1, rate=dropout_rate,
training=training)
fc2 = tf.layers.dense(inputs=dropout_1, units=512, kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.004), name="fc2")
dropout_2 = tf.layers.dropout(inputs=fc2, rate=dropout_rate,
training=training)
return dropout_2
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] |
tests/python/pants_test/build_graph/test_build_file_address_mapper.py | qma/pants | 0 | 43466 | # coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
from textwrap import dedent
from pants.build_graph.address import Address, BuildFileAddress
from pants.build_graph.address_lookup_error import AddressLookupError
from pants.build_graph.build_file_address_mapper import BuildFileAddressMapper
from pants.build_graph.target import Target
from pants_test.base_test import BaseTest
# TODO(<NAME>) There are methods in BuildFileAddressMapper that are missing
# explicit unit tests: addresses_in_spec_path, spec_to_address, spec_to_addresses
class BuildFileAddressMapperTest(BaseTest):
def test_resolve(self):
build_file = self.add_to_build_file('BUILD', 'target(name="foo")')
address, addressable = self.address_mapper.resolve(Address.parse('//:foo'))
self.assertIsInstance(address, BuildFileAddress)
self.assertEqual(build_file, address.build_file)
self.assertEqual('foo', address.target_name)
self.assertEqual(address.target_name, addressable.addressed_name)
self.assertEqual(addressable.addressed_type, Target)
def test_resolve_spec(self):
self.add_to_build_file('BUILD', dedent("""
target(name='foozle')
target(name='baz')
"""))
with self.assertRaises(AddressLookupError):
self.address_mapper.resolve_spec('//:bad_spec')
dependencies_addressable = self.address_mapper.resolve_spec('//:foozle')
self.assertEqual(dependencies_addressable.addressed_type, Target)
def test_scan_addresses(self):
root_build_file = self.add_to_build_file('BUILD', 'target(name="foo")')
subdir_build_file = self.add_to_build_file('subdir/BUILD', 'target(name="bar")')
subdir_suffix_build_file = self.add_to_build_file('subdir/BUILD.suffix', 'target(name="baz")')
with open(os.path.join(self.build_root, 'BUILD.invalid.suffix'), 'w') as invalid_build_file:
invalid_build_file.write('target(name="foobar")')
self.assertEquals({BuildFileAddress(root_build_file, 'foo'),
BuildFileAddress(subdir_build_file, 'bar'),
BuildFileAddress(subdir_suffix_build_file, 'baz')},
self.address_mapper.scan_addresses())
def test_scan_addresses_with_excludes(self):
root_build_file = self.add_to_build_file('BUILD', 'target(name="foo")')
self.add_to_build_file('subdir/BUILD', 'target(name="bar")')
spec_excludes = [os.path.join(self.build_root, 'subdir')]
self.assertEquals({BuildFileAddress(root_build_file, 'foo')},
self.address_mapper.scan_addresses(spec_excludes=spec_excludes))
def test_scan_addresses_with_root(self):
self.add_to_build_file('BUILD', 'target(name="foo")')
subdir_build_file = self.add_to_build_file('subdir/BUILD', 'target(name="bar")')
subdir_suffix_build_file = self.add_to_build_file('subdir/BUILD.suffix', 'target(name="baz")')
subdir = os.path.join(self.build_root, 'subdir')
self.assertEquals({BuildFileAddress(subdir_build_file, 'bar'),
BuildFileAddress(subdir_suffix_build_file, 'baz')},
self.address_mapper.scan_addresses(root=subdir))
def test_scan_addresses_with_invalid_root(self):
with self.assertRaises(BuildFileAddressMapper.InvalidRootError):
self.address_mapper.scan_addresses(root='subdir')
def test_raises_invalid_build_file_reference(self):
# reference a BUILD file that doesn't exist
with self.assertRaisesRegexp(BuildFileAddressMapper.InvalidBuildFileReference,
'^BUILD file does not exist at: .*/non-existent-path'
'\s+when translating spec //non-existent-path:a'):
self.address_mapper.spec_to_address('//non-existent-path:a')
def test_raises_address_not_in_build_file(self):
self.add_to_build_file('BUILD', 'target(name="foo")')
# Create an address that doesn't exist in an existing BUILD file
address = Address.parse(':bar')
with self.assertRaises(BuildFileAddressMapper.AddressNotInBuildFile):
self.address_mapper.resolve(address)
def test_raises_address_invalid_address_error(self):
with self.assertRaises(BuildFileAddressMapper.InvalidAddressError):
self.address_mapper.resolve_spec("../foo")
def test_raises_empty_build_file_error(self):
self.add_to_build_file('BUILD', 'pass')
with self.assertRaises(BuildFileAddressMapper.EmptyBuildFileError):
self.address_mapper.resolve_spec('//:foo')
def test_address_lookup_error_hierarcy(self):
self.assertIsInstance(BuildFileAddressMapper.AddressNotInBuildFile(), AddressLookupError)
self.assertIsInstance(BuildFileAddressMapper.EmptyBuildFileError(), AddressLookupError)
self.assertIsInstance(BuildFileAddressMapper.InvalidBuildFileReference(), AddressLookupError)
self.assertIsInstance(BuildFileAddressMapper.InvalidAddressError(), AddressLookupError)
self.assertIsInstance(BuildFileAddressMapper.BuildFileScanError(), AddressLookupError)
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tests/test_basic.py | blue-yonder/cee_syslog_handler | 7 | 192216 | <reponame>blue-yonder/cee_syslog_handler<filename>tests/test_basic.py
from cee_syslog_handler import get_fields
class Record(object):
def __init__(self, value=None):
if value:
self.some_column = value
def check_single_value(value):
record = Record(value)
message_dict = get_fields({}, record)
assert message_dict == {"_some_column": value}
def test_get_fields_empty():
record = Record()
message_dict = get_fields({}, record)
assert message_dict == {}
def test_string_types():
check_single_value("some_text")
check_single_value(u"some_text")
check_single_value("1")
check_single_value("1.1")
def test_numeric_types():
check_single_value(1.1)
check_single_value(1)
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] |
network/evaluate_keypoints.py | mhsung/deep-functional-dictionaries | 41 | 3233 | # <NAME> (<EMAIL>)
# April 2018
import os, sys
BASE_DIR = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.join(BASE_DIR, '..'))
from datasets import *
from generate_outputs import *
from scipy.optimize import linear_sum_assignment
#import matplotlib.pyplot as plt
import numpy as np
def compute_all_keypoints(sess, net, data):
P = data.point_clouds
assert(P.shape[0] == data.n_data)
assert(P.shape[1] == data.n_points)
KP = data.keypoints
assert(KP.shape[0] == data.n_data)
assert(KP.shape[1] == data.n_labels)
A = predict_A(P, sess, net)
assert(A.shape[0] == data.n_data)
assert(A.shape[1] == data.n_points)
assert(A.shape[2] == net.K)
pred_KP = np.argmax(A, axis=1)
return P, KP, pred_KP
def evaluate_PCK(P, KP, pred_KP):
n_data = P.shape[0]
n_points = P.shape[1]
n_labels = KP.shape[1]
K = pred_KP.shape[1]
# dists_info: (point_cloud_index, label, basis_index, distance)
dists_info = []
for k in range(n_data):
# NOTE:
# Skip if the keypoint does not exist.
labels = [i for i in range(n_labels) if KP[k,i] >= 0]
# Find the closest prediction (w/o matching).
for i, label in enumerate(labels):
all_dists = np.zeros(K)
idx_i = KP[k,label]
assert(idx_i < n_points)
p_i = P[k,idx_i]
for j in range(K):
idx_j = pred_KP[k,j]
assert(idx_j < n_points)
p_j = P[k,idx_j]
all_dists[j] = np.linalg.norm(p_i - p_j)
j = np.argmin(all_dists)
dists_info.append((k, i, j, all_dists[j]))
dists_info = np.array(dists_info)
return dists_info
def evaluate_PCK_after_label_basis_matching(P, KP, pred_KP):
n_data = P.shape[0]
n_points = P.shape[1]
n_labels = KP.shape[1]
K = pred_KP.shape[1]
# Find the best mapping from labels to bases.
all_dists = np.zeros((n_data, n_labels, K))
label_counts = np.zeros(n_labels)
for k in range(n_data):
for i in range(n_labels):
# NOTE:
# Skip if the keypoint does not exist.
if KP[k,i] < 0: continue
idx_i = KP[k,i]
assert(idx_i < n_points)
p_i = P[k,idx_i]
label_counts[i] += 1.
for j in range(K):
idx_j = pred_KP[k,j]
assert(idx_j < n_points)
p_j = P[k,idx_j]
all_dists[k,i,j] += np.linalg.norm(p_i - p_j)
mean_dists = np.sum(all_dists, axis=0) / \
np.expand_dims(label_counts, axis=-1)
row_ind, col_ind = linear_sum_assignment(mean_dists)
# dists_info: (point_cloud_index, label, basis_index, distance)
dists_info = []
for k in range(n_data):
for (i, j) in zip(row_ind, col_ind):
if KP[k,i] < 0: continue
dists_info.append((k, i, j, all_dists[k,i,j]))
dists_info = np.array(dists_info)
return dists_info
def save_results(dists_info, out_dir, postfix=None):
# dists_info: (point_cloud_index, label, basis_index, distance)
dists = dists_info[:,3]
if postfix is not None:
out_file = os.path.join(out_dir, 'distances_{}.npy'.format(postfix))
else:
out_file = os.path.join(out_dir, 'distances.npy')
np.save(out_file, dists)
print("Saved '{}'.".format(out_file))
'''
# Draw plot.
n_matches = dists.size
x_list = np.linspace(0.0, 0.1, 20 + 1)
counts = np.zeros(x_list.size, dtype=int)
for i in range(x_list.size):
counts[i] = np.sum(dists <= x_list[i])
y_list = counts.astype(x_list.dtype) / float(n_matches)
plt.clf()
plt.plot(x_list, y_list)
plt.ylim(0., 1.)
plt.yticks(np.linspace(0., 1., 10 + 1))
if postfix is not None:
out_file = os.path.join(out_dir, 'pck_{}.png'.format(postfix))
else:
out_file = os.path.join(out_dir, 'pck.png')
plt.savefig(out_file)
print("Saved '{}'.".format(out_file))
'''
def evaluate(sess, net, data, out_dir):
if not os.path.exists(out_dir): os.makedirs(out_dir)
P, KP, pred_KP = compute_all_keypoints(sess, net, data)
dists = evaluate_PCK(P, KP, pred_KP)
save_results(dists, out_dir)
dists_after_matching = evaluate_PCK_after_label_basis_matching(
P, KP, pred_KP)
save_results(dists_after_matching, out_dir, postfix='after_matching')
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coursera/python_programming_basics/1_week_01.py | anklav24/Python-Education | 0 | 120175 | <reponame>anklav24/Python-Education<filename>coursera/python_programming_basics/1_week_01.py
print('Hello, world.')
print('Hello, Python!')
print(2 + 3)
print('2' * 3)
print(f'2 + 3 = {2 + 3}')
print('1', '2', '3', sep=' + ', end=' ')
print('=', 1 + 2 + 3, end='')
print('!')
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catkin_ws/src:/opt/ros/kinetic/lib/python2.7/dist-packages:/home/bala/duckietown/catkin_ws/src:/home/bala/duckietown/catkin_ws/src/lib/python2.7/site-packages/contracts/enabling.py | johnson880319/Software | 13 | 147230 | <reponame>johnson880319/Software<gh_stars>10-100
from . import logger
import os
class Switches:
# default to ENV variable
disable_all = os.environ.get('DISABLE_CONTRACTS', False)
def disable_all():
""" Disables all contracts checks. """
# print('disable_all()')
Switches.disable_all = True
logger.info('All contracts checking disabled.')
def enable_all():
"""
Enables all contracts checks.
Can be overridden by an environment variable.
"""
# print('enable_all()')
if not os.environ.get('DISABLE_CONTRACTS', False):
Switches.disable_all = False
logger.info('All contracts checking enabled.')
def all_disabled():
# print('all_Disabled? %s' % Switches.disable_all)
""" Returns true if all contracts checks are disabled. """
return Switches.disable_all
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google-challenge-problems/answer_Q2a.py | kevin-orr/Python | 0 | 89134 | __author__ = 'kevinorr'
"""
This was my attempt at the first part of Q2 in a Google challenge about maximising the number of bunnies in train cars...
It passed the 'verify' section - all the tests passed.
"""
from collections import Counter
def answer(x):
"""
sort the list and find out what the mode would be
Then pick first element in sorted list and take enough from last element to make first element = mode value
then repeat
:param x: a train of cars holding rabbits
:return: the max number of equal array elements
"""
number_of_bunnies = sum(x)
number_cars = len(x)
# get integral of how many we can squeeze into each car
estimate_per_car = number_of_bunnies / number_cars
x = sorted(x)
first_index = 0
end_index = len(x) - 1
# now run around the list
while first_index < end_index:
bunnies_in_this_car = x[first_index]
if bunnies_in_this_car == estimate_per_car:
# move to next in list
first_index += 1
continue
else:
# take enough bunnies to share with this car from the maximal one at the end of list
delta_to_add = estimate_per_car - x[first_index]
x[first_index] += delta_to_add
x[end_index] -= delta_to_add
x = sorted(x)
# I had to remove the IO line below when running against 'verify' - just left it in here for context
print x
# finally use Counter to get the frequency of the most common element
return Counter(x).most_common(1)[0][1]
if __name__ == "__main__":
train_full_of_bunnies = [0, 0, 0, 4]
# for the list above we expect to get back 4 as answer -> [1, 1, 1, 1]
expected_answer = 4
assert expected_answer == answer(train_full_of_bunnies)
train_full_of_bunnies = [1, 2, 3]
expected_answer = 3
assert expected_answer == answer(train_full_of_bunnies)
train_full_of_bunnies = [1, 4, 1]
expected_answer = 3
assert expected_answer == answer(train_full_of_bunnies)
train_full_of_bunnies = [1, 2, 2]
expected_answer = 2
assert expected_answer == answer(train_full_of_bunnies)
train_full_of_bunnies = [2, 3, 4, 1, 6, 3]
expected_answer = 5
assert expected_answer == answer(train_full_of_bunnies)
train_full_of_bunnies = [1, 0, 3, 3, 5, 10, 8, 12, 4, 15, 11, 2, 14, 5, 7, 13, 15, 5, 7, 10, 8, 12, 4, 15, 11, 2, 33, 5, 0, 44, 5, 7, 13, 15]
expected_answer = 33
assert expected_answer == answer(train_full_of_bunnies)
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Participants/Pixel Soup/src/frontend/networking/net_interface.py | python-discord/game-jam-2020 | 15 | 116166 | import socket
from pickle import loads, dumps
class Pipe:
def __init__(self, server: str, port: int) -> None:
self.server = server
self.port = port
self.tcp = socket.socket()
self.udp = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.connected = False
self.username = None
self.udp_password = None
self.game_port = None
def connect(self) -> bool:
try:
self.tcp.connect((self.server, self.port))
self.connected = True
return True
except (ConnectionResetError, ConnectionRefusedError):
return False
def login(self) -> bool:
if not self.connected:
success = self.connect()
if not success:
return False
self.tcp.send(f"play request,,1234509876".encode())
response = self.tcp.recv(100)
if response == b"Pended":
return True
else:
return False
def await_response(self) -> list:
try:
data = self.tcp.recv(1000)
data = data.decode().split("||")
full_data = []
for seg in data:
seg = seg.split(",,")
full_data += seg
if seg[0] == "Start":
self.username = seg[1]
self.game_port = int(seg[2])
return full_data
except ConnectionResetError:
return [False]
def transport(self, game_data: list) -> tuple:
game_data = [self.username] + game_data
game_data = (dumps(game_data)) + b"||||"
self.udp.sendto(game_data, (self.server, self.game_port))
data, _ = self.udp.recvfrom(100)
data = data.split(b"||||")
if len(data) > 1:
data = loads(data[0])
return True, data
else:
return False, None
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] |
pgoapi/protos/pogoprotos/data/telemetry/battle_party_telemetry_pb2.py | SkOODaT/Pgoapi | 1 | 167654 | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: pogoprotos/data/telemetry/battle_party_telemetry.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from pogoprotos.enums import telemetry_ids_pb2 as pogoprotos_dot_enums_dot_telemetry__ids__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='pogoprotos/data/telemetry/battle_party_telemetry.proto',
package='pogoprotos.data.telemetry',
syntax='proto3',
serialized_pb=_b('\n6pogoprotos/data/telemetry/battle_party_telemetry.proto\x12\x19pogoprotos.data.telemetry\x1a$pogoprotos/enums/telemetry_ids.proto\"\x99\x01\n\x14\x42\x61ttlePartyTelemetry\x12H\n\x15\x62\x61ttle_party_click_id\x18\x01 \x01(\x0e\x32).pogoprotos.enums.BattlePartyTelemetryIds\x12\x1a\n\x12\x62\x61ttle_party_count\x18\x02 \x01(\x05\x12\x1b\n\x13\x62\x61ttle_party_number\x18\x03 \x01(\x05\x62\x06proto3')
,
dependencies=[pogoprotos_dot_enums_dot_telemetry__ids__pb2.DESCRIPTOR,])
_BATTLEPARTYTELEMETRY = _descriptor.Descriptor(
name='BattlePartyTelemetry',
full_name='pogoprotos.data.telemetry.BattlePartyTelemetry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='battle_party_click_id', full_name='pogoprotos.data.telemetry.BattlePartyTelemetry.battle_party_click_id', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='battle_party_count', full_name='pogoprotos.data.telemetry.BattlePartyTelemetry.battle_party_count', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='battle_party_number', full_name='pogoprotos.data.telemetry.BattlePartyTelemetry.battle_party_number', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=124,
serialized_end=277,
)
_BATTLEPARTYTELEMETRY.fields_by_name['battle_party_click_id'].enum_type = pogoprotos_dot_enums_dot_telemetry__ids__pb2._BATTLEPARTYTELEMETRYIDS
DESCRIPTOR.message_types_by_name['BattlePartyTelemetry'] = _BATTLEPARTYTELEMETRY
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
BattlePartyTelemetry = _reflection.GeneratedProtocolMessageType('BattlePartyTelemetry', (_message.Message,), dict(
DESCRIPTOR = _BATTLEPARTYTELEMETRY,
__module__ = 'pogoprotos.data.telemetry.battle_party_telemetry_pb2'
# @@protoc_insertion_point(class_scope:pogoprotos.data.telemetry.BattlePartyTelemetry)
))
_sym_db.RegisterMessage(BattlePartyTelemetry)
# @@protoc_insertion_point(module_scope)
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landlab/components/steepness_index/channel_steepness.py | amanaster2/landlab | 4 | 1611473 | # -*- coding: utf-8 -*-
"""Created on Mon Oct 19.
@author: dejh
"""
import numpy as np
from landlab import Component
class SteepnessFinder(Component):
"""This component calculates steepness indices, sensu Wobus et al. 2006,
for a Landlab landscape. Follows broadly the approach used in
GeomorphTools, geomorphtools.org.
Examples
--------
>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator, FastscapeEroder
>>> from landlab.components import SteepnessFinder
>>> mg = RasterModelGrid((3, 10), xy_spacing=100.)
>>> for nodes in (mg.nodes_at_right_edge, mg.nodes_at_bottom_edge,
... mg.nodes_at_top_edge):
... mg.status_at_node[nodes] = mg.BC_NODE_IS_CLOSED
>>> _ = mg.add_zeros("topographic__elevation", at="node")
>>> mg.at_node['topographic__elevation'][mg.core_nodes] = mg.node_x[
... mg.core_nodes]/1000.
>>> fr = FlowAccumulator(mg, flow_director='D8')
>>> sp = FastscapeEroder(mg, K_sp=0.01)
>>> sf = SteepnessFinder(mg, min_drainage_area=10000.)
>>> for i in range(10):
... mg.at_node['topographic__elevation'][mg.core_nodes] += 10.
... _ = fr.run_one_step()
... sp.run_one_step(1000.)
>>> sf.calculate_steepnesses()
>>> mg.at_node['channel__steepness_index'].reshape((3, 10))[1, :]
array([ 0. , 29.28427125, 1. , 1. ,
1. , 1. , 1. , 1. ,
0.99999997, 0. ])
>>> sf.hillslope_mask
array([ True, True, True, True, True, True, True, True, True,
True, False, False, False, False, False, False, False, False,
False, True, True, True, True, True, True, True, True,
True, True, True], dtype=bool)
>>> sf = SteepnessFinder(mg, min_drainage_area=10000., discretization_length=350.)
>>> sf.calculate_steepnesses()
>>> mg.at_node['channel__steepness_index'].reshape((3, 10))[1, :]
array([ 0. , 3.08232295, 3.08232295, 3.08232295, 1. ,
1. , 1. , 1. , 0. , 0. ])
>>> sf = SteepnessFinder(mg, min_drainage_area=10000., elev_step=1.5)
>>> sf.calculate_steepnesses()
>>> mg.at_node['channel__steepness_index'].reshape((3, 10))[1, :]
array([ 0. , 1.22673541, 1.2593727 , 1.27781936, 1.25659369,
1.12393156, 0.97335328, 0.79473963, 0.56196578, 0. ])
References
----------
**Required Software Citation(s) Specific to this Component**
None Listed
**Additional References**
<NAME>., <NAME>., <NAME>., <NAME>., <NAME>.,
<NAME>., <NAME>., and <NAME>.: Tectonics from topography:
Procedures, promise, and pitfalls, in: Tectonics, Climate, and Landscape
Evolution, edited by: <NAME>., <NAME>., <NAME>., and
<NAME>., Geological Society of America Special Paper 398, Geological
Society of America, Boulder, CO, USA, 55–74, 2006.
"""
_name = "SteepnessFinder"
_unit_agnostic = True
_info = {
"channel__steepness_index": {
"dtype": float,
"intent": "out",
"optional": False,
"units": "variable",
"mapping": "node",
"doc": "the local steepness index",
},
"drainage_area": {
"dtype": float,
"intent": "in",
"optional": False,
"units": "m**2",
"mapping": "node",
"doc": "Upstream accumulated surface area contributing to the node's discharge",
},
"flow__link_to_receiver_node": {
"dtype": int,
"intent": "in",
"optional": False,
"units": "-",
"mapping": "node",
"doc": "ID of link downstream of each node, which carries the discharge",
},
"flow__receiver_node": {
"dtype": int,
"intent": "in",
"optional": False,
"units": "-",
"mapping": "node",
"doc": "Node array of receivers (node that receives flow from current node)",
},
"flow__upstream_node_order": {
"dtype": int,
"intent": "in",
"optional": False,
"units": "-",
"mapping": "node",
"doc": "Node array containing downstream-to-upstream ordered list of node IDs",
},
"topographic__elevation": {
"dtype": float,
"intent": "in",
"optional": False,
"units": "m",
"mapping": "node",
"doc": "Land surface topographic elevation",
},
"topographic__steepest_slope": {
"dtype": float,
"intent": "in",
"optional": False,
"units": "-",
"mapping": "node",
"doc": "The steepest *downhill* slope",
},
}
def __init__(
self,
grid,
reference_concavity=0.5,
min_drainage_area=1.0e6,
elev_step=0.0,
discretization_length=0.0,
):
"""
Parameters
----------
grid : RasterModelGrid
A landlab RasterModelGrid.
reference_concavity : float
The reference concavity to use in the calculation.
min_drainage_area : float (m**2; default 1.e6)
The minimum drainage area above which steepness indices are
calculated.
Defaults to 1.e6 m**2, per Wobus et al. 2006.
elev_step : float (m; default 0.)
If >0., becomes a vertical elevation change step to use to
discretize the data (per Wobus). If 0., all nodes are used and
no discretization happens.
discretization_length : float (m; default 0.)
If >0., becomes the lengthscale over which to segment the profiles -
i.e., one different steepness index value is calculated every
discretization_length. If only one (or no) points are present in a
segment, it will be lumped together with the next segment.
If zero, one value is assigned to each channel node.
"""
super().__init__(grid)
if grid.at_node["flow__receiver_node"].size != grid.size("node"):
msg = (
"A route-to-multiple flow director has been "
"run on this grid. The landlab development team has not "
"verified that SteepnessFinder is compatible with "
"route-to-multiple methods. Please open a GitHub Issue "
"to start this process."
)
raise NotImplementedError(msg)
self._reftheta = reference_concavity
self._min_drainage = min_drainage_area
assert elev_step >= 0.0, "elev_step must be >= 0!"
self._elev_step = elev_step
self._discretization = discretization_length
self._ksn = self._grid.add_zeros(
"channel__steepness_index", at="node", clobber=True
)
self._mask = self._grid.ones("node", dtype=bool)
# this one needs modifying if smooth_elev
self._elev = self._grid.at_node["topographic__elevation"]
def calculate_steepnesses(self):
"""This is the main method. Call it to calculate local steepness
indices at all points with drainage areas greater than
*min_drainage_area*.
This "run" method can optionally take the same parameter set as
provided at instantiation. If they are provided, they will override
the existing values from instantiation.
Normalized steepness of any node without a defined value is reported
as 0. These nodes are also identified in the mask retrieved with
:func:`hillslope_mask`.
"""
self._mask.fill(True)
self._ksn.fill(0.0)
reftheta = self._reftheta
min_drainage = self._min_drainage
elev_step = self._elev_step
discretization_length = self._discretization
upstr_order = self._grid.at_node["flow__upstream_node_order"]
# get an array of only nodes with A above threshold:
valid_dstr_order = (
upstr_order[
self._grid.at_node["drainage_area"][upstr_order] >= min_drainage
]
)[::-1]
# note elevs are guaranteed to be in order, UNLESS a fill
# algorithm has been used.
nodes_incorporated = self._grid.zeros("node", dtype=bool)
# now do each poss channel in turn
# get the head of the first (longest!) channel:
for dstr_order_index in range(valid_dstr_order.size):
this_ch_top_node = valid_dstr_order[dstr_order_index] # top node
if not nodes_incorporated[this_ch_top_node]:
nodes_incorporated[this_ch_top_node] = True
nodes_in_channel = [this_ch_top_node]
penultimate_node = this_ch_top_node
current_node_incorporated = False
while not current_node_incorporated:
next_node = self._grid.at_node["flow__receiver_node"][
penultimate_node
]
if next_node == penultimate_node: # end of flow path
break
nodes_in_channel.append(next_node)
current_node_incorporated = nodes_incorporated[next_node]
# ^ this is a COPY op, so we're free to update the array
nodes_incorporated[next_node] = True
penultimate_node = next_node
# by here, we have a full, unique reach in nodes_in_channel
# it incorporates a single, duplicate node at the lower end
# Now, if this segment long enough?
if elev_step:
top_elev = self._elev[nodes_in_channel[0]]
base_elev = self._elev[nodes_in_channel[-1]]
# work up the channel from the base to make new interp pts
interp_pt_elevs = np.arange(base_elev, top_elev, elev_step)
if interp_pt_elevs.size <= 1:
# <1 step; bail on this whole segment
break
# now we can fairly closely follow the Geomorphtools
# algorithm:
ch_nodes = np.array(nodes_in_channel)
# ^ this is top-to-bottom
ch_A = self._grid.at_node["drainage_area"][ch_nodes]
ch_dists = self.channel_distances_downstream(ch_nodes)
ch_S = self.interpolate_slopes_with_step(
ch_nodes, ch_dists, interp_pt_elevs
)
else:
# all the nodes; much easier as links work
ch_nodes = np.array(nodes_in_channel)
ch_dists = self.channel_distances_downstream(ch_nodes)
ch_A = self._grid.at_node["drainage_area"][ch_nodes]
ch_S = self._grid.at_node["topographic__steepest_slope"][ch_nodes]
assert np.all(ch_S >= 0.0)
# if we're doing spatial discretization, do it here:
if discretization_length:
ch_ksn = self.calc_ksn_discretized(
ch_dists, ch_A, ch_S, reftheta, discretization_length
)
else: # not discretized
# also chopping off the final node, as above
log_A = np.log10(ch_A[:-1])
log_S = np.log10(ch_S[:-1])
# we're potentially propagating nans here if S<=0
log_ksn = log_S + reftheta * log_A
ch_ksn = 10.0 ** log_ksn
# save the answers into the main arrays:
assert np.all(self._mask[ch_nodes[:-1]])
# Final node gets trimmed off...
self._ksn[ch_nodes[:-1]] = ch_ksn
self._mask[ch_nodes] = False
# now a final sweep to remove any undefined ksn values:
self._mask[self._ksn == -1.0] = True
self._ksn[self._ksn == -1.0] = 0.0
def channel_distances_downstream(self, ch_nodes):
"""Calculates distances downstream from top node of a defined flowpath.
Parameters
----------
ch_nodes : array of ints
The nodes along a single defined flow path, starting upstream.
Returns
-------
ch_dists : array of floats
Distances downstream from top node of ch_nodes.
Examples
--------
>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> mg = RasterModelGrid((4,5), xy_spacing=(10., 5.))
>>> for nodes in (mg.nodes_at_right_edge, mg.nodes_at_bottom_edge,
... mg.nodes_at_top_edge):
... mg.status_at_node[nodes] = mg.BC_NODE_IS_CLOSED
>>> mg.status_at_node[[6, 12, 13, 14]] = mg.BC_NODE_IS_CLOSED
>>> _ = mg.add_field("topographic__elevation", mg.node_x, at="node")
>>> fr = FlowAccumulator(mg, flow_director='D8')
>>> sf = SteepnessFinder(mg)
>>> _ = fr.run_one_step()
>>> ch_nodes = np.array([8, 7, 11, 10])
>>> sf.channel_distances_downstream(ch_nodes)
array([ 0. , 10. , 21.18033989, 31.18033989])
"""
ch_links = self._grid.at_node["flow__link_to_receiver_node"][ch_nodes]
ch_dists = np.empty_like(ch_nodes, dtype=float)
# dists from ch head, NOT drainage divide
ch_dists[0] = 0.0
np.cumsum(self._grid.length_of_d8[ch_links[:-1]], out=ch_dists[1:])
return ch_dists
def interpolate_slopes_with_step(self, ch_nodes, ch_dists, interp_pt_elevs):
"""Maps slopes to nodes, interpolating withing defined vertical
intervals.
This follows Geomorphtools' discretization methods. It is essentially a
downwind map of the slopes.
Parameters
----------
ch_nodes : array of ints
The nodes along a single defined flow path, starting upstream.
ch_dists : array of floats
Distances downstream from top node of ch_nodes.
interp_pt_elevs : array of floats
Elevations at the discretizing points along the profile, in order
of increasing elevation.
Returns
-------
ch_S : array of floats
Interpolated slopes at each node in the flowpath (always positive).
Examples
--------
>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> mg = RasterModelGrid((3,10), xy_spacing=(10., 5.))
>>> for nodes in (mg.nodes_at_right_edge, mg.nodes_at_bottom_edge,
... mg.nodes_at_top_edge):
... mg.status_at_node[nodes] = mg.BC_NODE_IS_CLOSED
>>> _ = mg.add_field("topographic__elevation", mg.node_x**1.1, at="node")
>>> fr = FlowAccumulator(mg, flow_director='D8')
>>> sf = SteepnessFinder(mg)
>>> _ = fr.run_one_step()
>>> ch_nodes = np.arange(18, 9, -1)
>>> ch_dists = sf.channel_distances_downstream(ch_nodes)
>>> interp_pt_elevs = np.array([0., 30., 60., 90., 120.])
>>> sf.interpolate_slopes_with_step(ch_nodes, ch_dists,
... interp_pt_elevs)
array([ 1.67970205, 1.67970205, 1.67970205, 1.65129294, 1.62115336,
1.5811951 , 1.53157521, 1.44240187, 1.36442227])
>>> mg.at_node['topographic__steepest_slope'][ch_nodes]
array([ 1.69383001, 1.66972677, 1.64200694, 1.60928598, 1.56915472,
1.51678178, 1.43964028, 1.25892541, 0. ])
>>> mg.at_node['topographic__elevation'][:] = mg.node_x
>>> interp_pt_elevs = np.array([0., 25., 50., 75., 80.])
>>> sf.interpolate_slopes_with_step(ch_nodes, ch_dists,
... interp_pt_elevs)
array([ 1., 1., 1., 1., 1., 1., 1., 1., 1.])
"""
ch_z = self._grid.at_node["topographic__elevation"][ch_nodes]
assert (
ch_z[0] >= interp_pt_elevs[-1]
), "Highest interp_pt_elev must be below top channel node"
interp_pt_x = np.interp(interp_pt_elevs, ch_z[::-1], ch_dists[::-1])
interp_pt_S = np.empty_like(interp_pt_elevs)
# now a downwind map of the slopes onto the nodes
# slopes are defined positive
z_diff = interp_pt_elevs[:-1] - interp_pt_elevs[1:]
x_diff = interp_pt_x[1:] - interp_pt_x[:-1]
np.divide(z_diff, x_diff, out=interp_pt_S[:-1])
interp_pt_S[-1] = interp_pt_S[-2]
# Map S back onto nodes
ch_S = np.interp(ch_z, interp_pt_elevs, interp_pt_S)
return ch_S
def calc_ksn_discretized(
self, ch_dists, ch_A, ch_S, ref_theta, discretization_length
):
"""Calculate normalized steepness index on defined channel segments.
Every segment must have at least 2 nodes along it. If not, segments
will be automatically merged to achieve this. The channel will be
segmented starting at the *downstream* end.
NB: The final node in the channel does not receive an index, as it
either belongs to a longer, existing flow path, or it is a boundary
node with S = 0. Neither works.
Parameters
----------
ch_dists : array of floats
Distances downstream from top node of a single stream path.
ch_A : array of floats
Drainage areas at each node in the flowpath.
ch_S : array of floats
Slope at each node in the flowpath (defined as positive).
ref_theta : float
The reference concavity; must be positive.
discretization_length : float (m)
The streamwise length of each segment.
Returns
-------
ch_ksn : array of floats
The normalized steepness index at each node in the flowpath,
EXCEPT THE LAST. (i.e., length is (ch_dists.size - 1)). Values
will be the same within each defined segment.
Examples
--------
>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from landlab.components import SteepnessFinder
>>> mg = RasterModelGrid((3,10), xy_spacing=(10., 5.))
>>> for nodes in (mg.nodes_at_right_edge, mg.nodes_at_bottom_edge,
... mg.nodes_at_top_edge):
... mg.status_at_node[nodes] = mg.BC_NODE_IS_CLOSED
>>> _ = mg.add_field("topographic__elevation", mg.node_x, at="node")
>>> fr = FlowAccumulator(mg, flow_director='D8')
>>> sf = SteepnessFinder(mg)
>>> _ = fr.run_one_step()
>>> ch_nodes = np.arange(18, 9, -1)
>>> ch_dists = sf.channel_distances_downstream(ch_nodes)
>>> ch_A = mg.at_node['drainage_area'][ch_nodes]
>>> ch_S = mg.at_node['topographic__steepest_slope'][ch_nodes]
>>> ksn_25 = sf.calc_ksn_discretized(ch_dists, ch_A, ch_S, 0.5, 25.)
>>> ksn_25.size == ch_dists.size - 1
True
>>> ksn_25
array([ -1. , 11.0668192 , 11.0668192 , 15.70417802,
15.70417802, 15.70417802, 19.3433642 , 19.3433642 ])
>>> ksn_10 = sf.calc_ksn_discretized(ch_dists, ch_A, ch_S, 0.5, 10.)
>>> ksn_10
array([ 8.40896415, 8.40896415, 13.16074013, 13.16074013,
16.5487546 , 16.5487546 , 19.3433642 , 19.3433642 ])
>>> ch_ksn_overdiscretized = sf.calc_ksn_discretized(
... ch_dists, ch_A, ch_S, 0.5, 10.)
>>> np.allclose(ch_ksn_overdiscretized, ksn_10)
True
"""
ch_ksn = np.empty_like(ch_A)
# need to remove the influence of the final node in the seg,
# as it reflects either the edge of the grid (S=0) or a point
# after a confluence - hence the 0.000001
seg_ends = np.arange(ch_dists[-1] - 0.000001, 0.0, -discretization_length)[::-1]
# ^ counts up from 0, but terminates at the far end cleanly
pts_in_each_seg = np.searchsorted(seg_ends, ch_dists)
num_segs = pts_in_each_seg[-1]
i = num_segs - 1 # the final pt is no longer included
while i >= 0:
old_i = i
pts_in_seg = pts_in_each_seg == i
num_pts_in_seg = int(pts_in_seg.sum())
# if i == num_segs:
# true_pts_in_seg = pts_in_each_seg.copy()
# pts_in_each_seg[-1] = False
# else:
# true_pts_in_seg = pts_in_each_seg
# make sure there's always 2 pts in the seg...
while num_pts_in_seg < 2:
i -= 1
pts_in_seg = np.logical_and(
pts_in_each_seg <= old_i, pts_in_each_seg >= i
)
num_pts_in_seg = int(pts_in_seg.sum())
if i < 0:
break
if num_pts_in_seg < 2:
# must be at the end of the seg...
# nodes in invalid segs at the end get ksn = -1.
ch_ksn[pts_in_seg] = -1.0
break
seg_A = ch_A[pts_in_seg]
seg_S = ch_S[pts_in_seg]
logseg_A = np.log10(seg_A)
logseg_S = np.log10(seg_S)
meanlogseg_A = np.mean(logseg_A)
meanlogseg_S = np.mean(logseg_S)
logseg_ksn = meanlogseg_S + ref_theta * meanlogseg_A
ch_ksn[pts_in_seg] = 10.0 ** logseg_ksn
i -= 1
return ch_ksn[:-1]
@property
def steepness_indices(self):
"""Return the array of channel steepness indices.
Nodes not in the channel receive zeros.
"""
return self._ksn
@property
def hillslope_mask(self):
"""Return a boolean array, False where steepness indices exist."""
return self._mask
@property
def masked_steepness_indices(self):
"""Returns a masked array version of the 'channel__steepness_index'
field. This enables easier plotting of the values with.
:func:`landlab.imshow_grid_at_node` or similar.
Examples
--------
Make a topographic map with an overlay of steepness values:
>>> from landlab import imshow_grid_at_node
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator, FastscapeEroder
>>> from landlab.components import SteepnessFinder
>>> mg = RasterModelGrid((5, 5), xy_spacing=100.)
>>> for nodes in (mg.nodes_at_right_edge, mg.nodes_at_bottom_edge,
... mg.nodes_at_top_edge):
... mg.status_at_node[nodes] = mg.BC_NODE_IS_CLOSED
>>> _ = mg.add_zeros("topographic__elevation", at="node")
>>> mg.at_node['topographic__elevation'][mg.core_nodes] = mg.node_x[
... mg.core_nodes]/1000.
>>> np.random.seed(0)
>>> mg.at_node['topographic__elevation'][
... mg.core_nodes] += np.random.rand(mg.number_of_core_nodes)
>>> fr = FlowAccumulator(mg, flow_director='D8')
>>> sp = FastscapeEroder(mg, K_sp=0.01)
>>> cf = SteepnessFinder(mg, min_drainage_area=20000.)
>>> for i in range(10):
... mg.at_node['topographic__elevation'][mg.core_nodes] += 10.
... _ = fr.run_one_step()
... sp.run_one_step(1000.)
>>> _ = fr.run_one_step()
>>> cf.calculate_steepnesses()
>>> imshow_grid_at_node(mg, 'topographic__elevation',
... allow_colorbar=False)
>>> imshow_grid_at_node(mg, cf.masked_steepness_indices,
... color_for_closed=None, cmap='winter')
"""
return np.ma.array(self.steepness_indices, mask=self.hillslope_mask)
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DailyProgrammer/DP20160926A.py | DayGitH/Python-Challenges | 2 | 105053 | <filename>DailyProgrammer/DP20160926A.py
"""
[2016-09-26] Challenge #285 [Easy] Cross Platform/Language Data Encoding part 1
https://www.reddit.com/r/dailyprogrammer/comments/54lu54/20160926_challenge_285_easy_cross/
We will make a binary byte oriented encoding of data that is self describing and extensible, and aims to solve the
following problems:
* portability between 32 and 64 (and any other) bit systems, and languages, and endian-ness.
* type system independent of underlying language.
* Allow heterogeneous arrays (differing types of array elements) where the underlying language has poor support for
them.
* leverage power of homogeneous arrays in a language.
* support records regardless of underlying language (array of records is homogeneous, even though a record is a
heterogeneous list of fields)
* Allow ragged arrays (a table where each row is a list, but the rows do not have a uniform size (or shape))
* Provide basic in memory compression. Allow deferred decoding of partial data.
# 1. base64 encoding (used in later challenges)
To read and write binary data on reddit, we will use base64 encoding,
https://www.reddit.com/r/dailyprogrammer/comments/4xy6i1/20160816_challenge_279_easy_uuencoding/
# 2. Extendible byte base.
Any size integer can be coded into a variable byte array by using the maximum byte value as a marker to add the next
byte value to decode the total.
This is useful for coding numbers that you think can be limited to around 255 or close to it, without being "hard
constrained" by that limit. "256 possible op codes (or characters) ought to be enough for everyone forever thinking"
**unsigned byte input**
12
255
256
510
512 44 1024
last input is a list of 3 integers to encode
**sample outputs**
12
255 0
255 1
255 255 0
255 255 2 44 255 255 255 255 4
every element that is not 255 marks the end of "that integer" in a list. You should also write a decoder that
transforms output into input.
# 3. multibyte and variable byte encodings
Instead of a single byte target encoding, 2,4,8 and variable defined byte sizes are also desirable to cover integers
with larger ranges. An account balance might have a 40 bit practical limit, but you might not guarantee it forever.
64 bits might not be enough for Zimbabwe currency balances for example.
For compressing a list of numbers, often it is useful to set the whole list to one "byte size". Other choices include,
* setting an enum/table of possible byte size codings of 1 2 4 8 sizes, and then encoding, the number of elements, the
table/enum size and definition, and then 2 lists (enum key, data items)
* interleave bytesize, data
The latter will often be longer for long lists, but does not encode the table so is simpler to encode/decode.
**Encoding format for table definition:**
1. 4 bytes: first 30 bits - length of list. last 2 bits: key into 1 2 4 8. If first 30 bits are max value, then
following 4 bytes are added to count until a non-max value is taken. Similar to challenge #2.
2. list of byte lengths defined by key in 1. If last 2 bits of 1 are 3 (signifies up to 8 distinct integer sizes),
then this list has 8 items. If there only 6 distinct integer size codings, then the last 2 items in this list would be
ignored and set to 0. Values over 255 are encoded as in challenge 2.
3. list of ordered data encodings in boolean form, if there are more than 1. 1 bit for 2, 2 bits for 4, 3 bits for 8.
4. list of data elements.
**challenges**
encode list of integers from 0 to 1025 using 8 or 16 bit variable encoding. With the shortest encoding that will
contain the number. Just print the sum of all the bytes as result for output brevity.
**solution**
1. first 4 bytes are (1025 * 4) + 1 (leading 0 bytes for smaller than "full size" numbers)
2. 2 byte list: 1 2
3. 0 for first 256 bits, 1 for remaining bits (total 1032 bits long with padding)
4. 256 + (769 * 2) bytes long encoding of the numbers.
# 4. balanced signed numbers
Some numbers are negative. The common computer encoding for signed number ranges is to subtract half the max power of
2 from the value. A signed byte has range -128 to 127, where a 0 value corresponds to -128 (in our encoding).
For numbers outside this range encoded in a single byte, the process is to take the first byte to determine the sign,
and then following bytes add or subtract up to 255 per byte until a non 255 value is reached.
# 5. unbalanced signed numbers
Instead of the midpoint marking 0, a byte can encode a value within any defined range.
Another important application is to use "negative" numbers as codes of some sort. These include:
* An expectation that negative numbers are less frequent and smaller relative to 0
* coding special values such as null, infinity, undeterminable (0/0)
* Using codes to hint at extended byte encodings and sign of the number, or even data type
**sample 0 index codes** (for 16 reserved codes) (new paragraph for multiline explained codes)
Null
Infinity
Negative Infinity
Negative 1 byte
Negative 2 bytes
Negative 4 bytes
Negative 8 bytes
Negative custom byte length (value is encoded into 2 numbers. First is byte length (in 255 terminated bytes, followed
by that number of bytes to represent the number)
Positive 1 byte (first number indicates range of 468 to 723). 467 could have been encoded as 255 254 without this
special code.
Positive 2 byte
Positive 4 byte
Positive 8 byte
Positive 16 byte
Positive 64 byte
Positive custom byte length (3 to 262 excluding other defined lengths)
Positive custom 2 byte length (16 bit unsigned number defines byte length of number, followed by encoded number)
**sample inputs**
10
123123
-55
Null
**sample output**
26
9 123123
3 54 (minimum range value is -1)
0
**challenge input**
192387198237192837192837192387123817239182737 _44 981237123
array of 3 numbers (_44 is -44) to be encoded
"""
def main():
pass
if __name__ == "__main__":
main()
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flask_web_app.py | mrsixw/git-statistics | 0 | 58777 | #!/usr/bin/env python
import sqlite3
import os
import plotly
import json
from flask import Flask, render_template, session, g
from flask_bootstrap import Bootstrap
from git_insight_generator import generate_branch_insight, generate_monthly_commit_data, generate_month_change_data, generate_commit_time_of_day
import locale
DATABASE = './database/git_repo_data.db'
app = Flask(__name__)
Bootstrap(app)
def print_local_number(number):
locale.setlocale(locale.LC_ALL,'en_GB')
return locale.format("%d", number, grouping=True)
app.jinja_env.filters['local_number'] = print_local_number
def get_base_url():
if app.config['SERVER_NAME'] is not None:
base = app.config['SERVER_NAME']
#print app.config
if app.config['APPLICATION_ROOT'] is not None:
base += app.config['APPLICATION_ROOT']
else:
base = '127.0.0.1:5000'
#print base
return base
def make_dicts(cursor, row):
return dict((cursor.description[idx][0], value)
for idx, value in enumerate(row))
def get_db():
db = getattr(g, '_database', None)
if db is None:
db = g._database = sqlite3.connect(DATABASE)
db.row_factory = make_dicts
# hack
g._baseurl = get_base_url()
#print g._baseurl
return db
def query_db(query, args=(), one=False):
cur = get_db().execute(query, args)
rv = cur.fetchall()
cur.close()
return (rv[0] if rv else None) if one else rv
def get_branches():
return query_db("SELECT branch_name FROM git_branches")
@app.teardown_appcontext
def close_connection(exception):
print "Teardown DB"
db = getattr(g, '_database', None)
if db is not None:
db.close()
def commits_over_time(branch):
cpm = generate_monthly_commit_data(query_db, branch)
changes = generate_month_change_data(query_db, branch)
commit_tod = generate_commit_time_of_day(query_db, branch)
week_days = ['Monday',
'Tuesday',
'Wednesday',
'Thursday',
'Friday',
'Saturday',
'Sunday']
graphs = [
dict(
data=[
dict(
x=[x for x in sorted(cpm.keys())],
y=[cpm[x] for x in sorted(cpm.keys())],
type='bar'
),
],
layout=dict(
title='Commits Over Time'
)
),
dict(
data=[
dict(
x = [x for x in sorted(changes.keys())],
y = [changes[x]['additions'] for x in sorted(changes.keys())],
type ='bar',
name = 'Code Additions',
marker=dict(color='rgb(0,255,0)')
),
dict(
x=[x for x in sorted(changes.keys())],
y=[changes[x]['deletions'] for x in sorted(changes.keys())],
type='bar',
name = 'Code Deletions',
marker=dict(color='rgb(255,0,0)')
)
],
layout=dict(
title='Changes Over Time',
barmode='stack'
)
),
dict(
data = [
dict(
x = week_days,
y = [commit_tod[x]['0-1'] for x in week_days],
name = '0am-2am',
type = 'bar'
),
dict(
x=week_days,
y=[commit_tod[x]['2-3'] for x in week_days],
name='2am-4am',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['4-5'] for x in week_days],
name='4am-6am',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['6-7'] for x in week_days],
name='6am-8am',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['8-9'] for x in week_days],
name='8am-10am',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['10-11'] for x in week_days],
name='10am-12pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['12-13'] for x in week_days],
name='12pm-2pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['14-15'] for x in week_days],
name='2pm-4pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['16-17'] for x in week_days],
name='4pm-6pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['18-19'] for x in week_days],
name='6pm-8pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['20-21'] for x in week_days],
name='8pm-10pm',
type='bar'
),
dict(
x=week_days,
y=[commit_tod[x]['22-23'] for x in week_days],
name='10pm-12am',
type='bar'
),
],
layout=dict(
title='Commit Punchcard',
barmode='stack'
)
)
]
# Add "ids" to each of the graphs to pass up to the client
# for templating
ids = ['Commits over time','Changes over time', 'Commit Punchcard']
# Convert the figures to JSON
# PlotlyJSONEncoder appropriately converts pandas, datetime, etc
# objects to their JSON equivalents
graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
return ids,graphJSON
@app.route('/branch/<branch>')
def branch_index(branch):
branches = get_branches()
ids, graphJSON = commits_over_time(branch)
return render_template('branch.html', branches = branches,
branch = branch,
branch_insight = generate_branch_insight(query_db, branch),
ids = ids,
graphJSON = graphJSON)
@app.route('/')
def index():
db = get_db()
g._baseurl = get_base_url()
branches = get_branches()
return render_template('index.html', branches = branches)
if __name__ == '__main__':
app.secret_key = os.urandom(24)
print app.json_encoder
app.run(debug=True) | [
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registrations/tasks.py | praekeltfoundation/nurseconnect-registration | 0 | 117983 | from datetime import datetime
from urllib.parse import urljoin
import requests
from celery.exceptions import SoftTimeLimitExceeded
from django.conf import settings
from requests.exceptions import RequestException
from temba_client.exceptions import TembaException
from temba_client.utils import format_iso8601
from nurseconnect_registration.celery import app
from registrations.utils import (
get_rapidpro_contact,
get_rapidpro_flow_by_name,
tembaclient,
)
openhim_session = requests.Session()
openhim_session.auth = settings.OPENHIM_AUTH
openhim_session.headers.update({"User-Agent": "NurseConnectRegistration"})
@app.task(
autoretry_for=(RequestException, SoftTimeLimitExceeded),
retry_backoff=True,
max_retries=15,
acks_late=True,
soft_time_limit=10,
time_limit=15,
)
def send_registration_to_openhim(
contact, referral_msisdn, channel, clinic_code, persal, sanc, timestamp, eid
):
msisdn = contact[0]
uuid = contact[1]
response = openhim_session.post(
url=urljoin(settings.OPENHIM_URL, "nc/subscription"),
json={
"mha": 1,
"swt": 7 if channel == "WhatsApp" else 1,
"type": 7,
"dmsisdn": referral_msisdn or msisdn,
"cmsisdn": msisdn,
"rmsisdn": None,
"faccode": clinic_code,
"id": "{}^^^ZAF^TEL".format(msisdn.lstrip("+")),
"dob": None,
"persal": persal,
"sanc": sanc,
"encdate": datetime.utcfromtimestamp(timestamp).strftime("%Y%m%d%H%M%S"),
"sid": uuid,
"eid": eid,
},
)
response.raise_for_status()
return (response.status_code, response.headers, response.content)
@app.task(
autoretry_for=(RequestException, SoftTimeLimitExceeded, TembaException),
retry_backoff=True,
max_retries=15,
acks_late=True,
soft_time_limit=10,
time_limit=15,
)
def send_registration_to_rapidpro(
contact, msisdn, referral_msisdn, channel, clinic_code, timestamp
):
# Create/Update contact
contact_data = {
"preferred_channel": channel.lower(),
"registered_by": referral_msisdn or msisdn,
"facility_code": clinic_code,
"registration_date": format_iso8601(datetime.fromtimestamp(timestamp)),
"reg_source": "mobi-site",
}
contact = get_rapidpro_contact(msisdn) # Refresh contact so we don't recreate it
if contact:
uuid = contact.get("uuid")
contact = tembaclient.update_contact(uuid, fields=contact_data)
else:
urns = ["tel:%s" % msisdn]
if channel == "WhatsApp":
urns.append("whatsapp:%s" % msisdn.replace("+", ""))
contact = tembaclient.create_contact(urns=urns, fields=contact_data)
# Start the contact on the registration flow
flow = get_rapidpro_flow_by_name("post registration")
tembaclient.create_flow_start(flow.uuid, contacts=[contact.uuid])
return (msisdn, contact.uuid)
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openbook_posts/migrations/0036_auto_20190620_1957.py | TamaraAbells/okuna-api | 164 | 181159 | # Generated by Django 2.2.2 on 2019-06-20 17:57
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('openbook_common', '0013_language'),
('openbook_posts', '0035_auto_20190620_1357'),
]
operations = [
migrations.AddField(
model_name='postcomment',
name='language',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='post_comments', to='openbook_common.Language'),
),
migrations.AlterField(
model_name='postcomment',
name='text',
field=models.TextField(max_length=1500, verbose_name='text'),
),
]
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1024,
2433,
726,
742,
13,
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9794,
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29918,
978,
2433,
726,
5477,
13,
4706,
10353,
13,
1678,
4514,
13,
2
] |
src/spring-cloud/azext_spring_cloud/_validators_enterprise.py | SanyaKochhar/azure-cli-extensions | 2 | 16247 | <gh_stars>1-10
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# pylint: disable=too-few-public-methods, unused-argument, redefined-builtin
from azure.cli.core.azclierror import ClientRequestError
from ._util_enterprise import is_enterprise_tier
def only_support_enterprise(cmd, namespace):
if namespace.resource_group and namespace.service and not is_enterprise_tier(cmd, namespace.resource_group, namespace.service):
raise ClientRequestError("'{}' only supports for Enterprise tier Spring instance.".format(namespace.command))
def not_support_enterprise(cmd, namespace):
if namespace.resource_group and namespace.service and is_enterprise_tier(cmd, namespace.resource_group, namespace.service):
raise ClientRequestError("'{}' doesn't support for Enterprise tier Spring instance.".format(namespace.command))
| [
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1213,
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4830,
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29889,
6519,
876,
13,
2
] |
lista_par_impar.py | isaberamos/Programinhas | 1 | 172490 | princ = [[], []]
valor = 0
for c in range(1,8):
valor = int(input(f"Digite o {c}º valor: "))
if valor % 2 == 0:
princ[0].append(valor)
else:
if valor % 2 != 0:
princ[1].append(valor)
princ[0].sort()
princ[1].sort()
print(f"Os números pares são: {princ[0]}")
print(f"Os números ímpares são: {princ[1]}")
| [
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] |
fancylog/tools/git.py | stephenlenzi/fancylog | 3 | 174050 | """
git
===============
Wrappers around gitpython to return information about the git repository
for debugging
"""
class GitPythonError(Exception):
"""
Exception if gitpython cannot be found (Typical in production
environments).
"""
pass
class GitEnvironmentError(Exception):
"""
Exception if gitpython fails (Typical in production environments).
"""
pass
class GitHead:
"""
Class to parse a repo.head.commit object from gitpython, and return
more informative properties
"""
def __init__(self, head_commit):
self.hash = head_commit.hexsha
self.committer_name = head_commit.committer.name
self.committer_email = head_commit.committer.email
self.message = head_commit.summary
self.datetime = head_commit.authored_datetime.strftime(
"Date: %Y-%m-%d, Time: %H-%M-%S"
)
class GitInfo:
"""
Class to parse a repo object from gitpython, and return more informative
properties
"""
def __init__(self, repo):
self.head = GitHead(repo.head.commit)
def get_git_info(repo_path):
"""
Returns a class with useful information about the git repository.
(if there is one). Will only work with "dev" installs (otherwise
gitpython is not installed)
:return:
"""
try:
import git
except ImportError:
raise GitPythonError
return None
try:
repo = git.Repo(repo_path)
return GitInfo(repo)
except git.InvalidGitRepositoryError:
raise GitEnvironmentError
return None
| [
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12020,
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18649,
2392,
13,
4706,
736,
6213,
13,
2
] |
project/acorta/models.py | cborao/Django-url-shortener | 0 | 135088 | <filename>project/acorta/models.py
from django.db import models
class Content(models.Model):
key = models.CharField(max_length=200)
url = models.CharField(max_length=200)
def __str__(self):
return self.key + ": " + self.url
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1,
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users/tokens.py | maks-nurgazy/diploma-project | 0 | 27165 | <reponame>maks-nurgazy/diploma-project
from rest_framework_simplejwt.tokens import RefreshToken
def get_jwt_tokens_for_user(user, **kwargs):
"""
Generates a refresh token for the valid user
"""
refresh = RefreshToken.for_user(user)
return str(refresh), str(refresh.access_token)
| [
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src/database/academics_db.py | git-vish/CodeSpaceAPI | 1 | 119835 | <filename>src/database/academics_db.py<gh_stars>1-10
"""Database operations for Academics API.
"""
# Author Info
__author__ = '<NAME>'
__date__ = '10/07/21'
__email__ = '<EMAIL>'
# Library Imports
from typing import Dict, List, Optional
# Own Imports
from src.settings import BASE_SUBJECT, BASE_LAB, BASE_RESOURCE
def get_subject_of_sem(semester: int) -> Optional[List[Dict]]:
"""Fetch subjects with of specified semester.
Arguments:
---------
semester: Semester [3...8].
Returns:
---------
List of subject dictionaries.
"""
try:
return next(BASE_SUBJECT.fetch(query={'semester': semester}))
except Exception:
return None
def get_subjects(year: Optional[int] = 0) -> Optional[List[Dict]]:
"""Fetch list of subjects.
Fetch subjects for specified year if year is 0, fetch all.
Arguments:
---------
year: Academics year.
Returns:
---------
List of Dictionaries for each subject.
"""
if not year:
return next(BASE_SUBJECT.fetch())
if year not in {2, 3, 4}:
return None
return next(BASE_SUBJECT.fetch(query={'year': year}))
def get_labs(year: Optional[int] = 0) -> Optional[List[Dict]]:
"""Fetch list of labs.
Fetch labs for specified year if year is 0, fetch all.
Arguments:
---------
year: Academics year.
Returns:
---------
List of Dictionaries for each lab.
"""
if not year:
return next(BASE_LAB.fetch())
if year not in {2, 3, 4}:
return None
return next(BASE_LAB.fetch(query={'year': year}))
def create_resource(subject: str, title: str, category: str, user: str, url: str) -> bool:
"""Create new resource.
Arguments:
---------
subject: Subject code.
title: Resource title.
category: Type of resource. [library, exam]
user: User's database key.
url: Resource file url.
Returns:
---------
True if resource gets created else False.
"""
resource = {
'subject': subject,
'title': title,
'category': category,
'user': user,
'url': url
}
try:
BASE_RESOURCE.put(resource)
except Exception as e:
print(e)
return False
return True
def get_resources(subject: str) -> List[Dict]:
"""Fetch resources for a subject.
Arguments:
---------
subject: Subject code.
Returns:
---------
List of resource dictionaries with matching subject code.
"""
return next(BASE_RESOURCE.fetch(query={'subject': subject}))
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inkcut-master/inkcut/device/protocols/debug.py | ilnanny/Inkscape-addons | 3 | 27189 | # -*- coding: utf-8 -*-
'''
Created on Oct 23, 2015
@author: jrm
'''
from inkcut.device.plugin import DeviceProtocol
from inkcut.core.utils import async_sleep, log
class DebugProtocol(DeviceProtocol):
""" A protocol that just logs what is called """
def connection_made(self):
log.debug("protocol.connectionMade()")
def move(self, x, y, z, absolute=True):
log.debug("protocol.move({x},{y},{z})".format(x=x, y=y, z=z))
#: Wait some time before we get there
return async_sleep(0.1)
def set_pen(self, p):
log.debug("protocol.set_pen({p})".format(p=p))
def set_velocity(self, v):
log.debug("protocol.set_velocity({v})".format(v=v))
def set_force(self, f):
log.debug("protocol.set_force({f})".format(f=f))
def data_received(self, data):
log.debug("protocol.data_received({}".format(data))
def connection_lost(self):
log.debug("protocol.connection_lost()") | [
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sym_executor.py | zhangzhenghsy/fiber | 0 | 12614 | #!/usr/bin/python
import angr,simuvex
import sys,os
import time
from utils_sig import *
from sym_tracer import Sym_Tracer
from sig_recorder import Sig_Recorder
#This class is responsible for performing symbolic execution.
class Sym_Executor(object):
def __init__(self,options=None,dbg_out=False):
self.tracer = None
self.recorder = None
self.dbg_out = dbg_out
self._whitelist = set()
self._all_bbs = set()
self._num_find = 10
self.options = options
def _get_initial_state(self,proj,start,targetfunc=None):
if proj is None:
return None
st = proj.factory.blank_state(addr=start,symbolic_sp=True)
# print st.arch.registers.keys()
# We can customize the symbolic execution by setting various options in the state
# for a full list of available options:
# https://github.com/angr/simuvex/blob/master/simuvex/s_options.py
# E.g. st.options.add(simuvex.o.LAZY_SOLVES) ('options' is a set)
# CALLLESS to do intra-procedure analysis
st.options.add(simuvex.o.CALLLESS)
if targetfunc is not None:
st.options.add(str(hex(targetfunc)))
# To prevent the engine from discarding log history
st.options.add(simuvex.o.TRACK_ACTION_HISTORY)
if self.options.get('simplify_ast',True):
st.options.add(simuvex.o.SIMPLIFY_EXPRS)
st.options.add(simuvex.o.SIMPLIFY_MEMORY_READS)
st.options.add(simuvex.o.SIMPLIFY_MEMORY_WRITES)
st.options.add(simuvex.o.SIMPLIFY_EXIT_GUARD)
#TODO: Find a way to deal with function side-effect (i.e. a function call will output to a parameter, then the parameter will be used in a condition later)
st.options.add(simuvex.o.IGNORE_EXIT_GUARDS)
st.options.add(simuvex.o.IGNORE_MERGE_CONDITIONS)
st.options.add(simuvex.o.DONT_MERGE_UNCONSTRAINED)
#Use customized addr conc strategy
st.memory.read_strategies = [angr.concretization_strategies.SimConcretizationStrategyHZ(limit=3)]
st.memory.write_strategies = [angr.concretization_strategies.SimConcretizationStrategyHZ(limit=3)]
#print st.options
return st
#Include all the BBs along the path from start to ends in the cfg into the whitelist.
#The CFG here is CFGAcc.
def _prep_whitelist(self,cfg,cfg_bounds,ends,start=None,proj=None,sym_tab=None,cfg2=None,cfg_bounds2=None,ends2=None,start2=None,func_cfg=None):
#print "cfg:", [hex(n.addr) for n in cfg.nodes()]
#print cfg.functions[cfg_bounds[0]]
if cfg is None or cfg_bounds is None or len(cfg_bounds) < 2:
print '_prep_whitelist(): Incomplete CFG information'
return
#for addr in cfg2.functions:
# print cfg2.functions[addr]
if cfg2 is not None:
func_cfg2 = get_func_cfg(cfg2,cfg_bounds2[0],proj=proj,sym_tab=sym_tab)
if func_cfg is None:
print 'No func_cfg is available at %x' % cfg_bounds[0]
return
start = cfg_bounds[0]
self._all_bbs = set([x.addr for x in func_cfg.nodes()])
#print '_all_bbs: ' + str([hex(x) for x in list(self._all_bbs)])
#print '_all_bbs2: '+str([hex(x) for x in list(set([x.addr for x in func_cfg2.nodes()]))])
if cfg2 is not None:
self._all_bbs = self._all_bbs.union(set([x.addr for x in func_cfg2.nodes()]))
self._whitelist = get_node_addrs_between(func_cfg,start,ends,from_func_start=(start == cfg_bounds[0]))
if cfg2 is not None:
self._whitelist= self._whitelist.union(get_node_addrs_between(func_cfg2,start2,ends2,from_func_start=(start2 == cfg_bounds2[0])))
l = list(self._whitelist)
l.sort()
#print 'whitelist: ' + str([hex(x) for x in l])
l = list(self._all_bbs)
l.sort()
#print '_all_bbs: ' + str([hex(x) for x in l])
if self.dbg_out:
l = list(self._whitelist)
l.sort()
print 'whitelist: ' + str([hex(x) for x in l])
return
#Why we put a absolutely 'False' find_func here:
#(1)We rely on an accurate whitelist and all the nodes in the list should be explored, so we don't want
#to stop at a certain node.
#(2)With this find_func, basically we will have no states in the 'found' stash in the end, but that's OK
#because all the things we want to do will be done along the symbolic execution process.
def _find_func(self,p):
return False
def _avoid_func(self,p):
#print 'avoid_func: ' + str(hex(p.addr)) + ' ' + str(p.addr in whitelist)
#One problem is that, sometimes p.addr is in the middle of a certain BB, while in whitelist we only have start addresses of BBs.
#Currently for these cases, we will let it continue to execute because it will align to the BB starts later.
with open('testexplorenodes','a') as f:
f.write(str(hex(p.addr))+'\n')
return False if p.addr not in self._all_bbs else (not p.addr in self._whitelist)
#This is basically the 'hook_complete' used in 'explorer' technique, simply deciding whether num_find has been reached.
def _vt_terminator(self,smg):
return len(smg.stashes['found']) >= self._num_find
def _prep_veritesting_options(self,find=None,avoid=None,num_find=10):
if find is None:
find = self._find_func
if avoid is None:
avoid = self._avoid_func
#We need to construct an 'explorer' as an 'exploration_technique' used in the internal SimManager of Veritesting,
#which is basically the same one as used in normal DSE SimManager (by invoking 'explore()' method)
#NOTE that the Veritesting mode will use a separate SimManager, so we have to make TWO 'explorer'.
exp_tech = angr.exploration_techniques.Explorer(find=find,avoid=avoid,num_find=num_find)
veritesting_options = {}
#NOTE: 'loop_unrolling_limit' is compared and considered as 'passed' with '>=' instead of '>', that means if we use '1', no loops will be even entered.
#However we want exactly ONE loop execution, so we should should use '2' here actually.
veritesting_options['loop_unrolling_limit'] = 2
veritesting_options['tech'] = exp_tech
#NOTE that original 'explorer' technique will set a 'hook_complete' in SimManager, which will be passed from 'run()' to 'step()'
#as a 'until_func', however, Veritesting will not invoke 'run()', instead, it calls 'step()' directly, so this hook is basically
#invalidated. To deal with this, we provide a 'terminator' to Veritesting, which will terminate Veritesting when len(stashes[found]) > num_find
veritesting_options['terminator'] = self._vt_terminator
return veritesting_options
#Do the symbolic execution on the given CFG, from start to target, with Veritesting and Whitelist mechanisms.
#Params:
#proj: the angr project.
#states: if it's None, creates a default initial state@start, if start is None, then @cfg_bounds[0].
#cfg: cfg_accurate.
#cfg_bounds: a 2-element list, specifying the area of the target function (to be executed) in the cfg.
#start: Where to start the symbolic execution? Must be within the cfg_bounds.
#targets: Where to end the symbolic execution? Must be within the cfg_bounds. Can specify multiple targets in a list.
#Ret:
#The resulting SimManager.
def try_sym_exec(self,proj,cfg,cfg_bounds,targets,states=None,start=None,new_tracer=False,tracer=None,new_recorder=False,recorder=None,sym_tab=None,sigs=None,cfg2=None,cfg_bounds2=None,targets2=None, start2=None,func_cfg=None,num_find=10):
#print "start1: ", hex(start)
#print "start2: ", hex(start2)
if cfg is None or cfg_bounds is None or len(cfg_bounds) < 2:
print 'No CFG information available for sym exec.'
return None
#This is the start point of sym exec.
st = start if start is not None else cfg_bounds[0]
if start2 is not None:
st=start2
#Fill initial state.
#print 'hex(start)', hex(start)
#print 'str(hex(start))', str(hex(start))
if states is None:
if start2 is not None:
init_state = self._get_initial_state(proj,st,start)
#init_state = self._get_initial_state(proj,start)
else:
init_state = self._get_initial_state(proj,st)
states = [init_state]
#Whether we need to create a new Sym_Tracer to trace the symbolic execution
if new_tracer:
self.tracer = Sym_Tracer(symbol_table=sym_tab,dbg_out=self.dbg_out)
#for example:<class 'sym_tracer.Sym_Tracer'>: {'addr_collision': False, 'dbg_out': True, 'symbol_table': <sym_table.Sym_Table object at 0x7fffeba54890>, '_addr_conc_buf': [], '_sym_map': {}}
#Clear any remaining breakpoints
self.tracer.stop_trace(states)
self.tracer.trace(states)
else:
self.tracer = tracer
#Whether we need to create a new Sig_Recorder
if new_recorder:
if sigs is None:
print 'You must provide sigs if you want to use new recorder'
return
if self.tracer is None:
print 'You must provide tracer or specify new_tracer flag if you want to use new recorder'
return
self.recorder = Sig_Recorder(sigs,self.tracer,dbg_out=dbg_out)
#Clear any remaining breakpoints
self.recorder.stop_record(states)
#Record structural information (nodes and their relationships) and semantic information of 'root'
#instructions with per-instruction breakpoint, the structural information has already been partly recorded in the initial signature.
self.recorder.record(states)
else:
self.recorder = recorder
#Set the whitelist of basic blocks, we only want to include the BBs that along the paths from st to targets.
self._prep_whitelist(cfg,cfg_bounds,targets,start,proj=proj,sym_tab=sym_tab,cfg2=cfg2,cfg_bounds2=cfg_bounds2,ends2=targets2,start2=start2,func_cfg=func_cfg)
self._num_find = num_find
#Set the VeriTesting options
veritesting_options = self._prep_veritesting_options(num_find=self._num_find)
#Construct the simulation execution manager
smg = proj.factory.simgr(thing=states, veritesting=True, veritesting_options=veritesting_options)
#TODO: Do we still need to use loop limiter for the main DSE SimManager since Veritesting has already got a built-in loop limiter?
#limiter = angr.exploration_techniques.looplimiter.LoopLimiter(count=0, discard_stash='spinning')
#smg.use_technique(limiter)
t0 = time.time()
smg.explore(find=self._find_func, avoid=self._avoid_func, num_find=self._num_find)
print ['%s:%d ' % (name,len(stash)) for name, stash in smg.stashes.items()]
print 'Time elapsed: ' + str(time.time() - t0)
return smg
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src/ebay_rest/rates.py | gbm001/ebay_rest | 0 | 100077 | # Standard library imports
from datetime import timedelta
import threading
import logging
import math
import time
# Local imports
from .date_time import DateTime
from .error import Error
from .multiton import Multiton
@Multiton # return the same object when the __init__ params are identical
class Rates:
""" Manages call limit and utilization data for an eBay application.
https://developer.ebay.com/api-docs/developer/analytics/resources/rate_limit/methods/getRateLimits
"""
def __init__(self, app_id):
"""
Maintain a set of daily limits for each app_id. Be lazy about it when throttling is not used.
:param app_id: eBay keeps a set of daily limits for each app_id.
"""
self._app_id = app_id # save because it eases debugging
self._lock = threading.Lock() # secure this lock before updating or reading class variables
self._refresh_date_time = None # the soonest it is advisable to refresh rates data from eBay
self._cache = None # cache of the most recent rates re-organized to expedite lookups
def decrement_rate(self, base_path: str, rate_keys: list) -> None:
"""
Decrement the remaining count of calls associated with a name.
Warning, avoid endless recursion, don't merge this with the throttled version of the method.
:param
base_path (str) :
:param
rate_keys (list) : Strings, keys used to lookup a rate
:return: None
"""
with self._lock:
rate_dict = self._find_rate_dict(base_path, rate_keys)
if rate_dict:
if rate_dict['remaining'] > 0:
rate_dict['remaining'] -= 1
def decrement_rate_throttled(self, base_path: str, rate_keys: list, timeout: float) -> None:
"""
Decrement the remaining count of calls associated with a name.
:param
base_path (str) :
:param
rate_keys (list) : Strings, keys used to lookup a rate
:param
timeout (float) : When invoked with the floating-point timeout argument set to a positive value,
throttle for at most the number of seconds specified by timeout and as below the prorated call limit. A timeout
argument of -1 specifies an unbounded wait.
:return: None
"""
# The algorithm relies upon the geometrical properties of right-angled triangles.
# Threshold is a line that extends from the height of the limit at the period start to zero at the end.
# Further, imagine lowering the threshold half when throttled.
# If not throttled, imagine lowering the threshold to 1.
# It is OK to proceed when the remaining count is above the threshold.
# If we need to wait, wait in proportion to how far the threshold is out of reach or until period end.
timeout_used = 0
redo = True
while redo:
self._lock.acquire()
rate_dict = self._find_rate_dict(base_path, rate_keys)
if rate_dict is None:
redo = False
else:
limit = rate_dict['limit']
reset = rate_dict['reset']
time_window = rate_dict['time_window']
delta = abs((DateTime.now() - reset).total_seconds()) # abs covers small clock errors
threshold = ((delta * limit) / time_window) * 0.5
if threshold < 1.0:
threshold = 1.0 # 1 is as low is it should go, protect against rounding errors
remaining = rate_dict['remaining']
if remaining >= math.ceil(threshold):
if remaining > 0:
rate_dict['remaining'] = remaining - 1
self._lock.release()
redo = False
else:
# if there are no calls left in the current period
if remaining <= 0:
# then wait until the end of the period
wait_seconds = abs((DateTime.now() - reset).total_seconds())
else:
# otherwise wait for the remaining-threshold delta proportioned by remaining time
wait_seconds = ((threshold - remaining) * time_window) / limit
if timeout != -1.0:
timeout_remaining = timeout - timeout_used
if timeout_remaining <= 0:
raise Error(number=2, reason="Throttle timeout.")
if wait_seconds > timeout_remaining: # don't wait any longer than the caller wants
wait_seconds = timeout_remaining
self._lock.release()
time.sleep(wait_seconds)
timeout_used += wait_seconds
def need_refresh(self) -> bool:
""" Return True if the rates need refreshing. """
with self._lock:
if self._refresh_date_time:
if self._refresh_date_time > DateTime.now():
result = False
else:
result = True
else:
result = True
return result
def refresh_developer_analytics(self, rate_limits) -> None:
""" Refresh the local Developer Analytics values and when the next refresh is recommended. """
if not rate_limits:
cache = None
refresh_date_time = None
else:
cache = dict() # stores the flattened rates records
resets = set() # stores unique reset date-times
for rate_limit in rate_limits:
base_path = '/'.join([rate_limit['api_context'], rate_limit['api_name'], rate_limit['api_version']])
base_path = '/' + base_path.lower()
for resource in rate_limit['resources']:
if resource['rates']:
if resource['rates'][0]:
if resource['rates'][0]['limit']:
key = base_path + '|' + resource['name']
rates = resource['rates'][0]
reset = DateTime.from_string(rates['reset'])
rates['reset'] = reset
resets.add(reset)
cache[key] = rates
now = DateTime.now()
# Another program may also be using up calls, so periodically synchronizing with eBay's counts.
periodic = now + timedelta(minutes=15)
# find the soonest reset
soonest_reset = periodic # safety, just in case they are no useful resets date-times
resets = list(resets)
resets.sort()
for reset in resets:
if reset >= now: # skip when eBay is late to act on a reset
soonest_reset = reset
break
# use which ever is sooner
if periodic <= soonest_reset:
refresh_date_time = periodic
else:
refresh_date_time = soonest_reset
with self._lock:
self._cache = cache
self._refresh_date_time = refresh_date_time
def _find_rate_dict(self, base_path: str, rate_keys: list) -> dict or None:
"""
Get the index so the rate object associated with a name.
The caller must have a lock.
https://developer.ebay.com/api-docs/developer/analytics/resources/rate_limit/methods/getRateLimits
:param
base_path (str) :
:param
rate_keys (list) : Strings, keys used to lookup a rate
:return: a rates dict or None
"""
cache = self._cache
if not cache:
return None
else:
[resource_name_base, resource_name_module] = rate_keys
key = base_path + '|' + resource_name_base
cache = self._cache
if key in cache:
result = cache[key]
else:
key = key + resource_name_module
if key in cache:
result = cache[key]
else:
logging.debug('Unable to find rates for: ' + key)
result = None
return result
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tests/test_bus.py | MaLiN2223/py_proj_transport | 0 | 157475 | """
This file contains test for Bus class
"""
import sys
import pytest
from simulation import Bus
from simulation.line import Line, LineStop
from simulation.passenger_group import PassengersGroup
from utils.TestsBase import TestBase
from utils.helpers import get_full_class_name
if sys.version_info[0] >= 3:
import unittest.mock as mock
from unittest.mock import PropertyMock
else:
import mock
from mock import PropertyMock
# region
def get_empty_line():
"""Creates empty line"""
return Line({'id': 0, 'bus_capacity': 0, "frequency1": 0, "frequency2": 0}, [LineStop('', 0)], [LineStop('', 0)])
def passenger_group_equality(first_group, second_group):
"""
Checks if two passenger groups are equal
:param first_group: first group
:param second_group: second group
:return: True if are equal False if not
:rtype: bool
"""
return first_group.count == second_group.count and first_group.destination == second_group.destination
def get_group(count, name="!"):
"""
Creates PassengersGroup with parameters
:param count: group count
:param name: group name
:return: Passengers Group
:rtype: PassengersGroup
"""
group = PassengersGroup(name, count)
group.name = name
group.count = count
return group
LINE_NAME = get_full_class_name(Line)
def test_create():
"""
Checks if bus is created properly
"""
with mock.patch(LINE_NAME + ".routes", new_callable=PropertyMock) as mocked_routes:
mocked_routes.return_value = [[LineStop('A', 0), LineStop('', 0)], [LineStop('B', 0), LineStop('', 0)]]
line = get_empty_line()
bus = Bus(line, 0)
bus2 = Bus(line, 1)
assert bus.passengers == []
assert bus.line == line
assert bus.time_to_next_stop == 0
assert bus.current_stop_name == 'A'
assert bus.current_stop == 0
assert bus2.passengers == []
assert bus2.line == line
assert bus2.time_to_next_stop == 0
assert bus2.current_stop_name == 'B'
assert bus2.current_stop == 0
N_RANGE = 7
COUNTS = [[i] * ((2 * i) + N_RANGE + 1) for i in range(2, N_RANGE)]
TEST_ARRAY = []
for _count in COUNTS:
TEST_ARRAY.append(list(zip([i for i in range(1, len(_count) - 1)], _count)))
TEST_ARRAY = [item for sublist in TEST_ARRAY for item in sublist]
@pytest.mark.parametrize(("steps", "stops"), TEST_ARRAY)
def test_move(steps, stops):
"""
Tests if bus is moving properly
:param steps: steps to move
:param stops: stops count on the line
"""
with mock.patch(LINE_NAME + ".routes", new_callable=PropertyMock) as mocked_routes:
lines_left = [LineStop('l' + str(i), 2) for i in range(stops)]
lines_left = [LineStop('P', 1)] + lines_left
lines_right = [LineStop('r' + str(i), 2) for i in range(stops)]
lines_right = [LineStop('P', 1)] + lines_right
mocked_routes.return_value = [
lines_left,
lines_right
]
line = get_empty_line()
bus = Bus(line, 0)
bus2 = Bus(line, 1)
bus.move()
bus2.move()
stop = 0
for i in range(min(3 * stops - 4, steps)): # 4 because of 4 = 2 ticks from end + 2 ticks from start
bus.move()
bus2.move()
if 2 - (i % 3) == 0:
stop += 1
assert bus.time_to_next_stop == 2 - (i % 3), "i = {}".format(i)
assert bus.current_stop_name == 'l' + str(stop)
assert bus.current_stop == (i // 3) + 1, "i = {}".format(i)
assert bus2.time_to_next_stop == 2 - (i % 3), "i = {}".format(i)
assert bus2.current_stop_name == 'r' + str(stop)
assert bus2.current_stop == (i // 3) + 1, "i = {}".format(i)
if steps >= 3 * stops:
for i in range(steps - 3 * stops + 1):
bus2.move()
bus.move()
assert bus.next_stop_name == "None"
assert bus2.next_stop_name == "None"
else:
assert bus.next_stop_name != "None"
assert bus2.next_stop_name != "None"
class TestFill(TestBase):
"""
Group for filling tests
"""
def test_basic(self):
"""
Fills bus with one group without overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 10
group = get_group(10)
bus = Bus(get_empty_line(), 0)
assert bus.passengers == []
assert [] == bus.fill([group])
assert len(bus.passengers) == 1
assert isinstance(bus.passengers[0], PassengersGroup)
assert bus.passengers[0].count == 10
def test_overflow_basic(self):
"""
Fills bus with one group with overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 10
group = get_group(30, "A")
bus = Bus(get_empty_line(), 0)
self.are_lists_equal(bus.passengers, [])
after_fill = bus.fill([group])
self.are_lists_equal([PassengersGroup("A", 20)], after_fill, passenger_group_equality)
assert len(bus.passengers) == 1
assert bus.count == 10
assert isinstance(bus.passengers[0], PassengersGroup)
self.are_equal(bus.passengers[0], PassengersGroup("A", 10), passenger_group_equality)
def test_two_different_in_one(self):
"""
Fills bus with two groups (as one list) without overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 20
group1 = get_group(10, "A")
group2 = get_group(10, "B")
bus = Bus(get_empty_line(), 0)
self.are_lists_equal(bus.passengers, [])
after_fill = bus.fill([group1, group2])
assert after_fill == []
assert bus.count == 20
assert len(bus.passengers) == 2
assert group1 in bus.passengers
assert group2 in bus.passengers
assert bus.passengers[0] != bus.passengers[1]
def test_two_different_in_one_overflow(self):
"""
Fills bus with two groupss (as one list) with overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 20
group1 = get_group(10, "A")
group2 = get_group(30, "B")
bus = Bus(get_empty_line(), 0)
self.are_lists_equal(bus.passengers, [])
after_fill = bus.fill([group1, group2])
assert sum([i.count for i in after_fill]) == 20
assert after_fill != []
assert bus.count == 20
assert len(bus.passengers) == 2
assert bus.passengers[0] != bus.passengers[1]
def test_two_different_in_two_overflow(self):
"""
Fills bus with two groups one by one with overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 20
group1 = get_group(10, "A")
group2 = get_group(30, "B")
bus = Bus(get_empty_line(), 0)
self.are_lists_equal(bus.passengers, [])
after_fill = bus.fill([group1])
assert after_fill == []
assert bus.count == 10
assert len(bus.passengers) == 1
after_fill = bus.fill([group2])
self.are_lists_equal(after_fill, [get_group(20, "B")], passenger_group_equality)
assert bus.count == 20
assert len(bus.passengers) == 2
def test_add_the_same_overflow(self):
"""
Fills bus with two identical groupss with overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 20
group1 = get_group(10, "A")
group2 = get_group(30, "A")
bus = Bus(get_empty_line(), 0)
self.are_lists_equal(bus.passengers, [])
after_fill = bus.fill([group1])
assert bus.count == 10
assert len(bus.passengers) == 1
after_fill = bus.fill([group2])
self.are_lists_equal(after_fill, [get_group(20, "A")], passenger_group_equality)
assert bus.count == 20
assert len(bus.passengers) == 1
self.are_equal(bus.passengers[0], get_group(20, "A"), passenger_group_equality)
def test_add_the_same(self):
"""
Fills bus with two identical groupss without overflow
"""
with mock.patch(LINE_NAME + ".bus_capacity", new_callable=PropertyMock) as mocked_bus_capacity:
mocked_bus_capacity.return_value = 20
group1 = get_group(10, "A")
group2 = get_group(9, "A")
bus = Bus(get_empty_line(), 0)
assert bus.passengers == []
after_fill = bus.fill([group1])
assert after_fill == []
assert bus.count == 10
assert len(bus.passengers) == 1
after_fill = bus.fill([group2])
assert after_fill == []
assert bus.count == 19
assert len(bus.passengers) == 1
self.are_equal(bus.passengers[0], get_group(19, "A"), passenger_group_equality)
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5041,
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18521,
29918,
5813,
718,
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613,
716,
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13,
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13,
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29909,
4968,
28507,
29918,
2972,
29918,
13895,
29897,
13,
2
] |
coding/learn_pytest/bak/test_anothersmtp.py | yatao91/learning_road | 3 | 57478 | <filename>coding/learn_pytest/bak/test_anothersmtp.py
# -*- coding: utf-8 -*-
smtpserver = "smtp.qq.com" # will be read by smtp fixture
def test_showhelo(smtp_connection):
assert 0, smtp_connection.helo() | [
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9392,
29918,
9965,
29889,
3952,
29877,
580,
2
] |
utils.py | sWizad/HashNeRF-pytorch | 0 | 12007 | import json
import numpy as np
import pdb
import torch
from ray_utils import get_rays, get_ray_directions, get_ndc_rays
BOX_OFFSETS = torch.tensor([[[i,j,k] for i in [0, 1] for j in [0, 1] for k in [0, 1]]],
device='cuda')
SQR_OFFSETS = torch.tensor([[[i,j] for i in [0, 1] for j in [0, 1] ]], device='cuda')
def hash(coords, log2_hashmap_size):
'''
coords: 3D coordinates. B x 3
log2T: logarithm of T w.r.t 2
'''
x, y, z = coords[..., 0], coords[..., 1], coords[..., 2]
return torch.tensor((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663 ^ z*83492791)
#return ((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663 ^ z*83492791)
def hash2d(coords, log2_hashmap_size):
'''
coords: 2D coordinates. B x 3
log2T: logarithm of T w.r.t 2
'''
x, y = coords[..., 0], coords[..., 1]
return torch.tensor((1<<log2_hashmap_size)-1) & (x*73856093 ^ y*19349663)
def xy2index(xy,resolution):
return xy[...,0]+xy[...,1]*resolution
def get_bbox3d_for_blenderobj(camera_transforms, H, W, near=2.0, far=6.0):
camera_angle_x = float(camera_transforms['camera_angle_x'])
focal = 0.5*W/np.tan(0.5 * camera_angle_x)
# ray directions in camera coordinates
directions = get_ray_directions(H, W, focal)
min_bound = [100, 100, 100]
max_bound = [-100, -100, -100]
points = []
for frame in camera_transforms["frames"]:
c2w = torch.FloatTensor(frame["transform_matrix"])
rays_o, rays_d = get_rays(directions, c2w)
def find_min_max(pt):
for i in range(3):
if(min_bound[i] > pt[i]):
min_bound[i] = pt[i]
if(max_bound[i] < pt[i]):
max_bound[i] = pt[i]
return
for i in [0, W-1, H*W-W, H*W-1]:
min_point = rays_o[i] + near*rays_d[i]
max_point = rays_o[i] + far*rays_d[i]
points += [min_point, max_point]
find_min_max(min_point)
find_min_max(max_point)
return (torch.tensor(min_bound)-torch.tensor([1.0,1.0,1.0]), torch.tensor(max_bound)+torch.tensor([1.0,1.0,1.0]))
def get_bbox3d_for_llff(poses, hwf, near=0.0, far=1.0):
H, W, focal = hwf
H, W = int(H), int(W)
# ray directions in camera coordinates
directions = get_ray_directions(H, W, focal)
min_bound = [100, 100, 100]
max_bound = [-100, -100, -100]
points = []
poses = torch.FloatTensor(poses)
for pose in poses:
rays_o, rays_d = get_rays(directions, pose)
rays_o, rays_d = get_ndc_rays(H, W, focal, 1.0, rays_o, rays_d)
def find_min_max(pt):
for i in range(3):
if(min_bound[i] > pt[i]):
min_bound[i] = pt[i]
if(max_bound[i] < pt[i]):
max_bound[i] = pt[i]
return
for i in [0, W-1, H*W-W, H*W-1]:
min_point = rays_o[i] + near*rays_d[i]
max_point = rays_o[i] + far*rays_d[i]
points += [min_point, max_point]
find_min_max(min_point)
find_min_max(max_point)
return (torch.tensor(min_bound)-torch.tensor([0.1,0.1,0.0001]), torch.tensor(max_bound)+torch.tensor([0.1,0.1,0.0001]))
def get_voxel_vertices(xyz, bounding_box, resolution, log2_hashmap_size):
'''
xyz: 3D coordinates of samples. B x 3
bounding_box: min and max x,y,z coordinates of object bbox
resolution: number of voxels per axis
'''
box_min, box_max = bounding_box
if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min):
# print("ALERT: some points are outside bounding box. Clipping them!")
pdb.set_trace()
xyz = torch.clamp(xyz, min=box_min, max=box_max)
grid_size = (box_max-box_min)/resolution
bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int()
voxel_min_vertex = bottom_left_idx*grid_size + box_min
voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size
# hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111
# for i in [0, 1]:
# for j in [0, 1]:
# for k in [0, 1]:
# vertex_idx = bottom_left_idx + torch.tensor([i,j,k])
# # vertex = bottom_left + torch.tensor([i,j,k])*grid_size
# hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size))
voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS
hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size)
return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices
def get_plane_vertices_old(xyz, bounding_box, resolution, log2_hashmap_size):
'''
xyz: 3D coordinates of samples. B x 3
bounding_box: min and max x,y,z coordinates of object bbox
resolution: number of voxels per axis
'''
def box2plane(input):
in_xy = input[:,:2]#.unsqueeze(1)
in_xz = input[:,::2]#.unsqueeze(1)
in_yz = input[:,-2:]#.unsqueeze(1)
return [in_xy,in_xz,in_yz]
box_min, box_max = bounding_box
if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min):
# print("ALERT: some points are outside bounding box. Clipping them!")
pdb.set_trace()
xyz = torch.clamp(xyz, min=box_min, max=box_max)
grid_size = (box_max-box_min)/resolution
bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int() #(B, 3)
voxel_min_vertex = bottom_left_idx*grid_size + box_min
voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size
# hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111
# for i in [0, 1]:
# for j in [0, 1]:
# for k in [0, 1]:
# vertex_idx = bottom_left_idx + torch.tensor([i,j,k])
# # vertex = bottom_left + torch.tensor([i,j,k])*grid_size
# hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size))
#voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS #(B, 8, 3)
#hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) #(B, 8)
voxel_indices_xy = bottom_left_idx[:,:2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
voxel_indices_xz = bottom_left_idx[:,::2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
voxel_indices_yz = bottom_left_idx[:,-2:].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
hashed_voxel_indices_xy = hash2d(voxel_indices_xy, log2_hashmap_size) #(B, 4)
hashed_voxel_indices_xz = hash2d(voxel_indices_xz, log2_hashmap_size) #(B, 4)
hashed_voxel_indices_yz = hash2d(voxel_indices_yz, log2_hashmap_size) #(B, 4)
hashed_voxel_indices = [hashed_voxel_indices_xy,
hashed_voxel_indices_xz,
hashed_voxel_indices_yz]
voxel_min_vertex = box2plane(voxel_min_vertex)
voxel_max_vertex = box2plane(voxel_max_vertex)
#pdb.set_trace()
return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices
def get_plane_vertices(xyz, bounding_box, resolution, log2_hashmap_size):
'''
xyz: 3D coordinates of samples. B x 3
bounding_box: min and max x,y,z coordinates of object bbox
resolution: number of voxels per axis
'''
def box2plane(input):
in_xy = input[:,:2]#.unsqueeze(1)
in_xz = input[:,::2]#.unsqueeze(1)
in_yz = input[:,-2:]#.unsqueeze(1)
return [in_xy,in_xz,in_yz]
box_min, box_max = bounding_box
if not torch.all(xyz <= box_max) or not torch.all(xyz >= box_min):
# print("ALERT: some points are outside bounding box. Clipping them!")
pdb.set_trace()
xyz = torch.clamp(xyz, min=box_min, max=box_max)
grid_size = (box_max-box_min)/resolution
bottom_left_idx = torch.floor((xyz-box_min)/grid_size).int() #(B, 3)
voxel_min_vertex = bottom_left_idx*grid_size + box_min
voxel_max_vertex = voxel_min_vertex + torch.tensor([1.0,1.0,1.0])*grid_size
# hashed_voxel_indices = [] # B x 8 ... 000,001,010,011,100,101,110,111
# for i in [0, 1]:
# for j in [0, 1]:
# for k in [0, 1]:
# vertex_idx = bottom_left_idx + torch.tensor([i,j,k])
# # vertex = bottom_left + torch.tensor([i,j,k])*grid_size
# hashed_voxel_indices.append(hash(vertex_idx, log2_hashmap_size))
#voxel_indices = bottom_left_idx.unsqueeze(1) + BOX_OFFSETS #(B, 8, 3)
#hashed_voxel_indices = hash(voxel_indices, log2_hashmap_size) #(B, 8)
voxel_indices_xy = bottom_left_idx[:,:2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
voxel_indices_xz = bottom_left_idx[:,::2].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
voxel_indices_yz = bottom_left_idx[:,-2:].unsqueeze(1) + SQR_OFFSETS #(B, 4, 2)
#hashed_voxel_indices_xy = hash2d(voxel_indices_xy, log2_hashmap_size) #(B, 4)
#hashed_voxel_indices_xz = hash2d(voxel_indices_xz, log2_hashmap_size) #(B, 4)
#hashed_voxel_indices_yz = hash2d(voxel_indices_yz, log2_hashmap_size) #(B, 4)
hashed_voxel_indices_xy = xy2index(voxel_indices_xy,resolution) #(B, 4)
hashed_voxel_indices_xz = xy2index(voxel_indices_xz,resolution) #(B, 4)
hashed_voxel_indices_yz = xy2index(voxel_indices_yz,resolution) #(B, 4)
#print(hashed_voxel_indices_yz.shape)
#pdb.set_trace()
hashed_voxel_indices = [hashed_voxel_indices_xy,
hashed_voxel_indices_xz,
hashed_voxel_indices_yz]
voxel_min_vertex = box2plane(voxel_min_vertex)
voxel_max_vertex = box2plane(voxel_max_vertex)
return voxel_min_vertex, voxel_max_vertex, hashed_voxel_indices
if __name__=="__main__":
with open("data/nerf_synthetic/chair/transforms_train.json", "r") as f:
camera_transforms = json.load(f)
bounding_box = get_bbox3d_for_blenderobj(camera_transforms, 800, 800)
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] |
models/discriminator.py | arminalgln/graphpmu | 0 | 144930 | <reponame>arminalgln/graphpmu
import torch
import torch.nn as nn
import torch.nn.functional as F
class Discriminator(nn.Module):
def __init__(self, n_feat, n_hidden1, n_hidden2):
super(Discriminator, self).__init__()
self.fc1 = nn.Linear(n_feat, n_hidden1)
self.fc2 = nn.Linear(n_hidden1, n_hidden2)
self.fc3 = nn.Linear(n_hidden2, 1)
self.init_emb()
def init_emb(self):
for m in self.modules():
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.fill_(0.0)
def forward(self, x):
x = F.leaky_relu(self.fc1(x))
x = F.leaky_relu(self.fc2(x))
x = torch.sigmoid(self.fc3(x))
# x = torch.sigmoid(self.fc1(x))
return x
# #%%
# x = torch.rand(100,8)
# disc = Discriminator(8, 16, 32)
# y = disc(x)
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DeepLearningExamples/TensorFlow/LanguageModeling/BERT/run_classifier.py | puririshi98/benchmark | 0 | 208 | # coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import horovod.tensorflow as hvd
import time
from utils.utils import LogEvalRunHook, LogTrainRunHook, setup_xla_flags
from utils.gpu_affinity import set_affinity
import utils.dllogger_class
from dllogger import Verbosity
from utils.create_glue_data import *
import numpy as np
import tf_metrics
flags = tf.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("task_name", None, "The name of the task to train.")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"dllog_path", "/results/bert_dllog.json",
"filename where dllogger writes to")
flags.DEFINE_string(
"optimizer_type", "lamb",
"Optimizer type : adam or lamb")
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
flags.DEFINE_bool(
"do_predict", False,
"Whether to run the model in inference mode on the test set.")
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_bool("use_trt", False, "Whether to use TF-TRT")
flags.DEFINE_float("num_train_epochs", 3.0,
"Total number of training epochs to perform.")
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
flags.DEFINE_integer("save_checkpoints_steps", 1000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("display_loss_steps", 10,
"How often to print loss from estimator")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_integer("num_accumulation_steps", 1,
"Number of accumulation steps before gradient update"
"Global batch size = num_accumulation_steps * train_batch_size")
flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.")
flags.DEFINE_bool("use_xla", True, "Whether to enable XLA JIT compilation.")
flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
flags.DEFINE_bool(
"verbose_logging", False,
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
def file_based_input_fn_builder(input_file, batch_size, seq_length, is_training,
drop_remainder, hvd=None):
"""Creates an `input_fn` closure to be passed to Estimator."""
name_to_features = {
"input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.io.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn():
"""The actual input function."""
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
compute_type=tf.float32)
# In the demo, we are doing a simple classification task on the entire
# segment.
#
# If you want to use the token-level output, use model.get_sequence_output()
# instead.
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias, name='cls_logits')
probabilities = tf.nn.softmax(logits, axis=-1, name='cls_probabilities')
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1, name='cls_per_example_loss')
loss = tf.reduce_mean(per_example_loss, name='cls_loss')
return (loss, per_example_loss, logits, probabilities)
def get_frozen_tftrt_model(bert_config, shape, num_labels, use_one_hot_embeddings, init_checkpoint):
tf_config = tf.compat.v1.ConfigProto()
tf_config.gpu_options.allow_growth = True
output_node_names = ['loss/cls_loss', 'loss/cls_per_example_loss', 'loss/cls_logits', 'loss/cls_probabilities']
with tf.Session(config=tf_config) as tf_sess:
input_ids = tf.placeholder(tf.int32, shape, 'input_ids')
input_mask = tf.placeholder(tf.int32, shape, 'input_mask')
segment_ids = tf.placeholder(tf.int32, shape, 'segment_ids')
label_ids = tf.placeholder(tf.int32, (None), 'label_ids')
create_model(bert_config, False, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf_sess.run(tf.global_variables_initializer())
print("LOADED!")
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
else:
init_string = ", *NOTTTTTTTTTTTTTTTTTTTTT"
tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string)
frozen_graph = tf.graph_util.convert_variables_to_constants(tf_sess,
tf_sess.graph.as_graph_def(), output_node_names)
num_nodes = len(frozen_graph.node)
print('Converting graph using TensorFlow-TensorRT...')
from tensorflow.python.compiler.tensorrt import trt_convert as trt
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=output_node_names,
max_workspace_size_bytes=(4096 << 20) - 1000,
precision_mode = "FP16" if FLAGS.amp else "FP32",
minimum_segment_size=4,
is_dynamic_op=True,
maximum_cached_engines=1000
)
frozen_graph = converter.convert()
print('Total node count before and after TF-TRT conversion:',
num_nodes, '->', len(frozen_graph.node))
print('TRT node count:',
len([1 for n in frozen_graph.node if str(n.op) == 'TRTEngineOp']))
with tf.io.gfile.GFile("frozen_modelTRT.pb", "wb") as f:
f.write(frozen_graph.SerializeToString())
return frozen_graph
def model_fn_builder(task_name, bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps,
use_one_hot_embeddings, hvd=None):
"""Returns `model_fn` closure for Estimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for Estimator."""
def metric_fn(per_example_loss, label_ids, logits):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
if task_name == "cola":
FN, FN_op = tf.metrics.false_negatives(labels=label_ids, predictions=predictions)
FP, FP_op = tf.metrics.false_positives(labels=label_ids, predictions=predictions)
TP, TP_op = tf.metrics.true_positives(labels=label_ids, predictions=predictions)
TN, TN_op = tf.metrics.true_negatives(labels=label_ids, predictions=predictions)
MCC = (TP * TN - FP * FN) / ((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)) ** 0.5
MCC_op = tf.group(FN_op, TN_op, TP_op, FP_op, tf.identity(MCC, name="MCC"))
return {"MCC": (MCC, MCC_op)}
elif task_name == "mrpc":
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions)
loss = tf.metrics.mean(values=per_example_loss)
f1 = tf_metrics.f1(labels=label_ids, predictions=predictions, num_classes=2, pos_indices=[1])
return {
"eval_accuracy": accuracy,
"eval_f1": f1,
"eval_loss": loss,
}
else:
accuracy = tf.metrics.accuracy(
labels=label_ids, predictions=predictions)
loss = tf.metrics.mean(values=per_example_loss)
return {
"eval_accuracy": accuracy,
"eval_loss": loss,
}
tf.compat.v1.logging.info("*** Features ***")
tf.compat.v1.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if not is_training and FLAGS.use_trt:
trt_graph = get_frozen_tftrt_model(bert_config, input_ids.shape, num_labels, use_one_hot_embeddings, init_checkpoint)
(total_loss, per_example_loss, logits, probabilities) = tf.import_graph_def(trt_graph,
input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids, 'label_ids':label_ids},
return_elements=['loss/cls_loss:0', 'loss/cls_per_example_loss:0', 'loss/cls_logits:0', 'loss/cls_probabilities:0'],
name='')
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {"probabilities": probabilities}
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metric_ops)
return output_spec
(total_loss, per_example_loss, logits, probabilities) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
if init_checkpoint and (hvd is None or hvd.rank() == 0):
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
if FLAGS.verbose_logging:
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps,
hvd, False, FLAGS.amp, FLAGS.num_accumulation_steps, FLAGS.optimizer_type)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.EVAL:
dummy_op = tf.no_op()
# Need to call mixed precision graph rewrite if fp16 to enable graph rewrite
if FLAGS.amp:
loss_scaler = tf.train.experimental.FixedLossScale(1)
dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimization.LAMBOptimizer(learning_rate=0.0), loss_scaler)
eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metric_ops)
else:
dummy_op = tf.no_op()
# Need to call mixed precision graph rewrite if fp16 to enable graph rewrite
if FLAGS.amp:
dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimization.LAMBOptimizer(learning_rate=0.0))
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=probabilities)
return output_spec
return model_fn
# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def input_fn_builder(features, batch_size, seq_length, is_training, drop_remainder, hvd=None):
"""Creates an `input_fn` closure to be passed to Estimator."""
all_input_ids = []
all_input_mask = []
all_segment_ids = []
all_label_ids = []
for feature in features:
all_input_ids.append(feature.input_ids)
all_input_mask.append(feature.input_mask)
all_segment_ids.append(feature.segment_ids)
all_label_ids.append(feature.label_id)
def input_fn():
"""The actual input function."""
num_examples = len(features)
# This is for demo purposes and does NOT scale to large data sets. We do
# not use Dataset.from_generator() because that uses tf.py_func which is
# not TPU compatible. The right way to load data is with TFRecordReader.
d = tf.data.Dataset.from_tensor_slices({
"input_ids":
tf.constant(
all_input_ids, shape=[num_examples, seq_length],
dtype=tf.int32),
"input_mask":
tf.constant(
all_input_mask,
shape=[num_examples, seq_length],
dtype=tf.int32),
"segment_ids":
tf.constant(
all_segment_ids,
shape=[num_examples, seq_length],
dtype=tf.int32),
"label_ids":
tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
})
if is_training:
if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
return d
return input_fn
def main(_):
setup_xla_flags()
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
dllogging = utils.dllogger_class.dllogger_class(FLAGS.dllog_path)
if FLAGS.horovod:
hvd.init()
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"xnli": XnliProcessor,
}
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.io.gfile.makedirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
master_process = True
training_hooks = []
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps
hvd_rank = 0
config = tf.compat.v1.ConfigProto()
if FLAGS.horovod:
tf.compat.v1.logging.info("Multi-GPU training with TF Horovod")
tf.compat.v1.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank())
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps * hvd.size()
master_process = (hvd.rank() == 0)
hvd_rank = hvd.rank()
config.gpu_options.visible_device_list = str(hvd.local_rank())
set_affinity(hvd.local_rank())
if hvd.size() > 1:
training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
if FLAGS.use_xla:
config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1
if FLAGS.amp:
tf.enable_resource_variables()
run_config = tf.estimator.RunConfig(
model_dir=FLAGS.output_dir if master_process else None,
session_config=config,
save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else None,
save_summary_steps=FLAGS.save_checkpoints_steps if master_process else None,
log_step_count_steps=FLAGS.display_loss_steps,
keep_checkpoint_max=1)
if master_process:
tf.compat.v1.logging.info("***** Configuaration *****")
for key in FLAGS.__flags.keys():
tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
tf.compat.v1.logging.info("**************************")
train_examples = None
num_train_steps = None
num_warmup_steps = None
training_hooks.append(LogTrainRunHook(global_batch_size, hvd_rank, FLAGS.save_checkpoints_steps, num_steps_ignore_xla=25))
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / global_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
start_index = 0
end_index = len(train_examples)
tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")]
if FLAGS.horovod:
tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(hvd.size())]
num_examples_per_rank = len(train_examples) // hvd.size()
remainder = len(train_examples) % hvd.size()
if hvd.rank() < remainder:
start_index = hvd.rank() * (num_examples_per_rank+1)
end_index = start_index + num_examples_per_rank + 1
else:
start_index = hvd.rank() * num_examples_per_rank + remainder
end_index = start_index + (num_examples_per_rank)
model_fn = model_fn_builder(
task_name=task_name,
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate if not FLAGS.horovod else FLAGS.learning_rate * hvd.size(),
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_one_hot_embeddings=False,
hvd=None if not FLAGS.horovod else hvd)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
file_based_convert_examples_to_features(
train_examples[start_index:end_index], label_list, FLAGS.max_seq_length, tokenizer, tmp_filenames[hvd_rank])
tf.compat.v1.logging.info("***** Running training *****")
tf.compat.v1.logging.info(" Num examples = %d", len(train_examples))
tf.compat.v1.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.compat.v1.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=tmp_filenames,
batch_size=FLAGS.train_batch_size,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
hvd=None if not FLAGS.horovod else hvd)
train_start_time = time.time()
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps, hooks=training_hooks)
train_time_elapsed = time.time() - train_start_time
train_time_wo_overhead = training_hooks[-1].total_time
avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_elapsed
ss_sentences_per_second = (training_hooks[-1].count - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead
if master_process:
tf.compat.v1.logging.info("-----------------------------")
tf.compat.v1.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed,
num_train_steps * global_batch_size)
tf.compat.v1.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead,
(training_hooks[-1].count - training_hooks[-1].skipped) * global_batch_size)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
tf.compat.v1.logging.info("-----------------------------")
if FLAGS.do_eval and master_process:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.compat.v1.logging.info("***** Running evaluation *****")
tf.compat.v1.logging.info(" Num examples = %d", len(eval_examples))
tf.compat.v1.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_drop_remainder = False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
batch_size=FLAGS.eval_batch_size,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
eval_hooks = [LogEvalRunHook(FLAGS.eval_batch_size)]
eval_start_time = time.time()
result = estimator.evaluate(input_fn=eval_input_fn, hooks=eval_hooks)
eval_time_elapsed = time.time() - eval_start_time
time_list = eval_hooks[-1].time_list
time_list.sort()
# Removing outliers (init/warmup) in throughput computation.
eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.8)])
num_sentences = (int(len(time_list) * 0.8)) * FLAGS.eval_batch_size
avg = np.mean(time_list)
cf_50 = max(time_list[:int(len(time_list) * 0.50)])
cf_90 = max(time_list[:int(len(time_list) * 0.90)])
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead
tf.compat.v1.logging.info("-----------------------------")
tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
eval_hooks[-1].count * FLAGS.eval_batch_size)
tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead,
num_sentences)
tf.compat.v1.logging.info("Summary Inference Statistics on EVAL set")
tf.compat.v1.logging.info("Batch size = %d", FLAGS.eval_batch_size)
tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32")
tf.compat.v1.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
tf.compat.v1.logging.info("Latency Average (ms) = %0.2f", avg * 1000)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
tf.compat.v1.logging.info("-----------------------------")
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.io.gfile.GFile(output_eval_file, "w") as writer:
tf.compat.v1.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
dllogging.logger.log(step=(), data={key: float(result[key])}, verbosity=Verbosity.DEFAULT)
tf.compat.v1.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict and master_process:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.compat.v1.logging.info("***** Running prediction*****")
tf.compat.v1.logging.info(" Num examples = %d", len(predict_examples))
tf.compat.v1.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
batch_size=FLAGS.predict_batch_size,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
predict_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)]
predict_start_time = time.time()
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
with tf.io.gfile.GFile(output_predict_file, "w") as writer:
tf.compat.v1.logging.info("***** Predict results *****")
for prediction in estimator.predict(input_fn=predict_input_fn, hooks=predict_hooks,
yield_single_examples=False):
output_line = "\t".join(
str(class_probability) for class_probability in prediction) + "\n"
writer.write(output_line)
predict_time_elapsed = time.time() - predict_start_time
time_list = predict_hooks[-1].time_list
time_list.sort()
# Removing outliers (init/warmup) in throughput computation.
predict_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.8)])
num_sentences = (int(len(time_list) * 0.8)) * FLAGS.predict_batch_size
avg = np.mean(time_list)
cf_50 = max(time_list[:int(len(time_list) * 0.50)])
cf_90 = max(time_list[:int(len(time_list) * 0.90)])
cf_95 = max(time_list[:int(len(time_list) * 0.95)])
cf_99 = max(time_list[:int(len(time_list) * 0.99)])
cf_100 = max(time_list[:int(len(time_list) * 1)])
ss_sentences_per_second = num_sentences * 1.0 / predict_time_wo_overhead
tf.compat.v1.logging.info("-----------------------------")
tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", predict_time_elapsed,
predict_hooks[-1].count * FLAGS.predict_batch_size)
tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", predict_time_wo_overhead,
num_sentences)
tf.compat.v1.logging.info("Summary Inference Statistics on TEST SET")
tf.compat.v1.logging.info("Batch size = %d", FLAGS.predict_batch_size)
tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32")
tf.compat.v1.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
tf.compat.v1.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
tf.compat.v1.logging.info("Latency Average (ms) = %0.2f", avg * 1000)
tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
tf.compat.v1.logging.info("-----------------------------")
if __name__ == "__main__":
flags.mark_flag_as_required("data_dir")
flags.mark_flag_as_required("task_name")
flags.mark_flag_as_required("vocab_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
tf.compat.v1.app.run()
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main.py | Spferical/matrix-megahal | 7 | 193354 | #!/usr/bin/env python3
import time
from matrix_client.client import MatrixClient
from matrix_client.api import MatrixRequestError
from requests.exceptions import ConnectionError, Timeout
import argparse
import random
from configparser import ConfigParser
import re
import traceback
import urllib.parse
import logging
import os
import sys
import signal
import queue
import codecs
from database import MarkovDatabaseBrain
COMMANDS = [
'!rate'
]
def sigterm_handler(_signo, _stack_frame):
"""Raises SystemExit(0), causing everything to cleanly shut down."""
sys.exit(0)
class ConfigParser(ConfigParser):
# allow case-sensitive option names
# needed for saving per-room response rates
optionxform = str
class Backend(object):
"""Interface for chat backends."""
def __init__(self, brain_file):
pass
def train_file(self, filename):
"""Trains the chat backend on the given file."""
with codecs.open(filename, encoding='utf8') as train_file:
for line in train_file:
self.learn(line)
def learn(self, line):
"""Updates the chat backend based on the given line of input."""
pass
def save(self):
"""Saves the backend to disk, if needed."""
pass
def reply(self, message):
"""Generates a reply to the given message."""
return "(dummy response)"
class MarkovBackend(Backend):
"""Chat backend using markov chains."""
def __init__(self, brain_file):
self.brain = MarkovDatabaseBrain(brain_file)
def sanitize(self, word):
"""Removes any awkward whitespace characters from the given word.
Removes '\n', '\r', and '\\u2028' (unicode newline character)."""
return word.replace('\n', '').replace('\r', '').replace('\u2028', '')
def train_file(self, filename):
with codecs.open(filename, encoding='utf8') as train_file:
for line in train_file:
self.learn(line)
self.save()
def learn(self, line):
line = line.strip()
words = line.split(' ')
words = [self.sanitize(word) for word in words]
for i in range(len(words) - 2):
prefix = words[i], words[i + 1]
follow = words[i + 2]
self.brain.add(prefix, follow)
def save(self):
self.brain.save()
def get_random_next_link(self, word1, word2):
"""Gives a word that could come after the two provided.
Words that follow the two given words are weighted by how frequently
they appear after them.
"""
possibilities = self.brain.get_followers((word1, word2))
if not possibilities:
return None
total = 0
for p in possibilities:
total += possibilities[p]
num = random.randint(1, total)
total = 0
for p in possibilities:
total += possibilities[p]
if total >= num:
break
return p
def reply(self, message):
if self.brain.is_empty():
return ''
seed = None
# try to seed reply from the message
possible_seed_words = message.split()
while seed is None and possible_seed_words:
message_word = random.choice(possible_seed_words)
seeds = list(self.brain.get_pairs_containing_word_ignoring_case(
message_word))
if seeds:
seed = random.choice(seeds)
else:
possible_seed_words.remove(message_word)
# we couldn't seed the reply from the input
# fall back to random seed
if seed is None:
seed = self.brain.get_three_random_words()
words = list(seed)
while self.brain.contains_pair((words[-2], words[-1])) and \
len(words) < 100:
word = self.get_random_next_link(words[-2], words[-1])
words.append(word)
return ' '.join(words)
class Config(object):
def __init__(self, cfgparser):
self.backend = cfgparser.get('General', 'backend')
self.display_name = cfgparser.get('General', 'display name')
self.learning = cfgparser.getboolean('General', 'learning')
self.username = cfgparser.get('Login', 'username')
self.password = cfgparser.get('Login', 'password')
self.server = cfgparser.get('Login', 'server')
self.default_response_rate = cfgparser.getfloat(
'General', 'default response rate')
self.response_rates = {}
for room_id, rate in cfgparser.items('Response Rates'):
room_id = room_id.replace('-colon-', ':')
self.response_rates[room_id] = float(rate)
def get_response_rate(self, room_id):
"""Returns our response rate for the room with the given room id."""
if room_id in self.response_rates:
return self.response_rates[room_id]
else:
return self.default_response_rate
def write(self):
"""Writes this config back to the file, with any changes reflected."""
cfgparser = ConfigParser()
cfgparser.add_section('General')
cfgparser.set('General', 'default response rate',
str(self.default_response_rate))
cfgparser.set('General', 'backend', self.backend)
cfgparser.set('General', 'display name', self.display_name)
cfgparser.set('General', 'learning', str(self.learning))
cfgparser.add_section('Login')
cfgparser.set('Login', 'username', self.username)
cfgparser.set('Login', 'password', self.password)
cfgparser.set('Login', 'server', self.server)
cfgparser.add_section('Response Rates')
for room_id, rate in list(self.response_rates.items()):
# censor colons because they are a configparser special
# character
room_id = room_id.replace(':', '-colon-')
cfgparser.set('Response Rates', room_id, str(rate))
with open('config.cfg', 'wt') as configfile:
cfgparser.write(configfile)
def get_default_configparser():
"""Returns a ConfigParser object for the default config file."""
config = ConfigParser(allow_no_value=True)
config.add_section('General')
config.set('General', 'default response rate', "0.10")
config.set('General', 'backend', 'markov')
config.set('General', 'display name', 'Markov')
config.set('General', 'learning', 'on')
config.add_section('Login')
config.set('Login', 'username', 'username')
config.set('Login', 'password', 'password')
config.set('Login', 'server', 'http://matrix.org')
config.add_section('Response Rates')
return config
class Bot(object):
"""Handles everything that the bot does."""
def __init__(self, config, chat_backend):
self.config = config
self.client = None
self.chat_backend = chat_backend
self.event_queue = queue.Queue()
self.invite_queue = queue.Queue()
def login(self):
"""Logs onto the server."""
client = MatrixClient(self.config.server)
client.login_with_password_no_sync(
self.config.username, self.config.password)
self.client = client
def get_room(self, event):
"""Returns the room the given event took place in."""
return self.client.rooms[event['room_id']]
def handle_command(self, event, command, args):
"""Handles the given command, possibly sending a reply to it."""
command = command.lower()
if command == '!rate':
if args:
num = re.match(r'[0-9]*(\.[0-9]+)?(%|)', args[0]).group()
if not num:
self.reply(event, "Error: Could not parse number.")
return
if num[-1] == '%':
rate = float(num[:-1]) / 100
else:
rate = float(num)
self.config.response_rates[event['room_id']] = rate
self.reply(event, "Response rate set to %f." % rate)
else:
rate = self.config.get_response_rate(event['room_id'])
self.reply(
event, "Response rate set to %f in this room." % rate)
def reply(self, event, message):
"""Replies to the given event with the provided message."""
room = self.get_room(event)
logging.info("Reply: %s" % message)
room.send_notice(message)
def is_name_in_message(self, message):
"""Returns whether the message contains the bot's name.
Considers both display name and username.
"""
regex = "({}|{})".format(
self.config.display_name, self.config.username)
return re.search(regex, message, flags=re.IGNORECASE)
def handle_invite(self, room_id, invite_state):
# join rooms if invited
try:
self.client.join_room(room_id)
logging.info('Joined room: %s' % room_id)
except MatrixRequestError as e:
if e.code == 404:
# room was deleted after invite or something; ignore it
logging.info('invited to nonexistent room {}'.format(room_id))
elif e.code in range(500, 600):
# synapse v0.99.1 500s if it cannot locate a room sometimes
# (when there are federation issues)
logging.warning('got 500 trying to join room we were invited to')
else:
raise(e)
def handle_event(self, event):
"""Handles the given event.
Joins a room if invited, learns from messages, and possibly responds to
messages.
"""
if event['type'] == 'm.room.message':
# only care about text messages by other people
if event['sender'] != self.client.user_id and \
event['content']['msgtype'] == 'm.text':
message = str(event['content']['body'])
# lowercase message so we can search it
# case-insensitively
logging.info("Handling message: %s" % message)
command_found = False
for command in COMMANDS:
match = re.search(command, message, flags=re.IGNORECASE)
if match and (match.start() == 0 or
self.is_name_in_message(message)):
command_found = True
args = message[match.start():].split(' ')
self.handle_command(event, args[0], args[1:])
break
if not command_found:
room = self.get_room(event)
response_rate = self.config.get_response_rate(room.room_id)
if self.is_name_in_message(message) or \
random.random() < response_rate:
# remove name from message and respond to it
message_no_name = re.sub(
' *' + re.escape(self.get_display_name()) + ' *',
' ', message, flags=re.IGNORECASE)
response = self.chat_backend.reply(message_no_name)
self.reply(event, response)
if self.config.learning:
self.chat_backend.learn(message)
self.send_read_receipt(event)
def set_display_name(self, display_name):
"""Sets the bot's display name on the server."""
self.client.api.set_display_name(self.client.user_id, display_name)
def get_display_name(self):
"""Gets the bot's display name from the server."""
return self.client.api.get_display_name(self.client.user_id)
def run(self):
"""Indefinitely listens for messages and handles all that come."""
current_display_name = self.get_display_name()
if current_display_name != self.config.display_name:
self.set_display_name(self.config.display_name)
last_save = time.time()
# listen for invites, including initial sync invites
self.client.add_invite_listener(
lambda room_id, state: self.invite_queue.put((room_id, state)))
# get rid of initial event sync
logging.info("initial event stream")
self.client.listen_for_events()
# listen to events and add them all to the event queue
# for handling in this thread
self.client.add_listener(self.event_queue.put)
def exception_handler(e):
if isinstance(e, Timeout):
logging.warning("listener thread timed out.")
logging.error("exception in listener thread:")
traceback.print_exc()
# start listen thread
logging.info("starting listener thread")
self.client.start_listener_thread(exception_handler=exception_handler)
try:
while True:
time.sleep(1)
# handle any queued events
while not self.event_queue.empty():
event = self.event_queue.get_nowait()
self.handle_event(event)
while not self.invite_queue.empty():
room_id, invite_state = self.invite_queue.get_nowait()
self.handle_invite(room_id, invite_state)
# save every 10 minutes or so
if time.time() - last_save > 60 * 10:
self.chat_backend.save()
last_save = time.time()
finally:
logging.info("stopping listener thread")
self.client.stop_listener_thread()
def send_read_receipt(self, event):
"""Sends a read receipt for the given event."""
if "room_id" in event and "event_id" in event:
room_id = urllib.parse.quote(event['room_id'])
event_id = urllib.parse.quote(event['event_id'])
self.client.api._send("POST", "/rooms/" + room_id +
"/receipt/m.read/" + event_id,
api_path="/_matrix/client/r0")
def train(backend, train_file):
"""Trains the given chat backend on the given train_file & saves it."""
print("Training...")
backend.train_file(train_file)
print("Training complete!")
backend.save()
def main():
argparser = argparse.ArgumentParser(
description="A chatbot for Matrix (matrix.org)")
argparser.add_argument("--debug",
help="Print out way more things.",
action="store_true")
argparser.add_argument("--train", metavar="train.txt", type=str,
help="Train the bot with a file of text.")
argparser.add_argument("--config", metavar="config.cfg", type=str,
help="Bot's config file (must be read-writable)")
argparser.add_argument("--brain", metavar="brain.db", type=str,
help="Bot's brain file (must be read-writable)")
args = vars(argparser.parse_args())
debug = args['debug']
# suppress logs of libraries
logging.getLogger("requests").setLevel(logging.WARNING)
logging.getLogger("urllib3").setLevel(logging.WARNING)
log_level = logging.DEBUG if debug else logging.INFO
logging.basicConfig(level=log_level,
format='%(asctime)s %(name)s '
'%(levelname)s %(message)s')
train_path = args['train']
config_path = args['config'] if args['config'] \
else os.getenv('MATRIX_CHATBOT_CONFIG', 'config.cfg')
brain_path = args['brain'] if args['brain'] \
else os.getenv('MATRIX_CHATBOT_BRAIN', 'brain.db')
cfgparser = ConfigParser()
success = cfgparser.read(config_path)
if not success:
cfgparser = get_default_configparser()
with open(config_path, 'wt') as configfile:
cfgparser.write(configfile)
print("A config has been generated. "
"Please set your bot's username, password, and homeserver "
"in " + config_path + " then run this again.")
return
config = Config(cfgparser)
backends = {'markov': MarkovBackend}
backend = backends[config.backend](brain_path)
logging.info("loading brain")
if train_path:
train(backend, train_path)
else:
signal.signal(signal.SIGTERM, sigterm_handler)
while True:
try:
bot = Bot(config, backend)
bot.login()
bot.run()
except (MatrixRequestError, ConnectionError):
traceback.print_exc()
logging.warning("disconnected. Waiting a minute to see if"
" the problem resolves itself...")
time.sleep(60)
finally:
backend.save()
logging.info('Saving config...')
config.write()
if __name__ == '__main__':
main()
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13,
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263,
5716,
565,
23610,
29892,
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VCD/utils/data_utils.py | Xingyu-Lin/VCD | 25 | 170657 | <reponame>Xingyu-Lin/VCD<filename>VCD/utils/data_utils.py<gh_stars>10-100
import numpy as np
import torch
from torch_geometric.data import Data
import torch_geometric
class PrivilData(Data):
"""
Encapsulation of multi-graphs for multi-step training
ind: 0-(hor-1), type: vsbl or full
Each graph contain:
edge_index_{type}_{ind},
x_{type}_{ind},
edge_attr_{type}_{ind},
gt_rwd_{type}_{ind}
gt_accel_{type}_{ind}
mesh_mapping_{type}_{ind}
"""
def __init__(self, has_part=False, has_full=False, **kwargs):
super(PrivilData, self).__init__(**kwargs)
self.has_part = has_part
self.has_full = has_full
def __inc__(self, key, value, *args, **kwargs):
if 'edge_index' in key:
x = key.replace('edge_index', 'x')
return self[x].size(0)
elif 'mesh_mapping' in key:
# add index of mesh matching by
x = key.replace('partial_pc_mapped_idx', 'x')
return self[x].size(0)
else:
return super().__inc__(key, value)
class AggDict(dict):
def __init__(self, is_detach=True):
"""
Aggregate numpy arrays or pytorch tensors
:param is_detach: Whether to save numpy arrays in stead of torch tensors
"""
super(AggDict).__init__()
self.is_detach = is_detach
def __getitem__(self, item):
return self.get(item, 0)
def add_item(self, key, value):
if self.is_detach and torch.is_tensor(value):
value = value.detach().cpu().numpy()
if not isinstance(value, torch.Tensor):
if isinstance(value, np.ndarray) or isinstance(value, np.number):
assert value.size == 1
else:
assert isinstance(value, int) or isinstance(value, float)
if key not in self.keys():
self[key] = value
else:
self[key] += value
def update_by_add(self, src_dict):
for key, value in src_dict.items():
self.add_item(key, value)
def get_mean(self, prefix, count=1):
avg_dict = {}
for k, v in self.items():
avg_dict[prefix + k] = v / count
return avg_dict
def updateDictByAdd(dict1, dict2):
'''
update dict1 by dict2
'''
for k1, v1 in dict2.items():
for k2, v2 in v1.items():
dict1[k1][k2] += v2.cpu().item()
return dict1
def get_index_before_padding(graph_sizes):
ins_len = graph_sizes.max()
pad_len = ins_len * graph_sizes.size(0)
valid_len = graph_sizes.sum()
accum = torch.zeros(1).cuda()
out = []
for gs in graph_sizes:
new_ind = torch.range(0, gs - 1).cuda() + accum
out.append(new_ind)
accum += ins_len
final_ind = torch.cat(out, dim=0)
return final_ind.long()
class MyDataParallel(torch_geometric.nn.DataParallel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getattr__(self, name):
if name == 'module':
return self._modules['module']
else:
return getattr(self.module, name)
def retrieve_data(data, key):
"""
vsbl: [vsbl], full: [full], dual :[vsbl, full]
"""
if isinstance(data, dict):
identifier = '_{}'.format(key)
out_data = {k.replace(identifier, ''): v for k, v in data.items() if identifier in k}
return out_data
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] |
spectroscope/__init__.py | peterblockman/spectroscope | 0 | 70985 | <reponame>peterblockman/spectroscope
import logging
def log():
logging.basicConfig(level=logging.DEBUG)
return logging.getLogger("spectroscope")
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backend/optimizer.py | SINTEF-SE/PySSMic | 0 | 1600045 | import logging
import math
from collections import defaultdict
from enum import Enum
from itertools import chain
from random import randint, random
from typing import List
import numpy as np
from scipy import optimize
import util.optimizer_utils as utils
class Algorithm(Enum):
"""The currently possible algorithms."""
fifty_fifty = 'fifty_fifty'
L_BFGS_B = 'L-BFGS-B'
SLSQP = 'SLSQP'
TNC = 'TNC'
class Optimizer:
def __init__(self, producer, options):
self.producer = producer
self.options = options
self.logger = logging.getLogger("src.Optimizer")
self.differentiated_loads = []
self.min_objective_value = float('inf')
self.penalty_factor = float(self.options["pen"]) if "pen" in self.options else 1.0
self.algorithm = Algorithm[options["algo"]]
# The estimated total produced energy is the last entry of the producer's prediction profile
self.objection_value_offset = 0
self.cache = defaultdict(lambda: np.inf)
def optimize(self):
if self.algorithm in [Algorithm.L_BFGS_B, Algorithm.SLSQP, Algorithm.TNC]:
schedule_times, should_keep = self.basinhopping()
elif self.algorithm == Algorithm.fifty_fifty:
schedule_times, should_keep = self.fifty_fifty()
else:
should_keep = [True] * len(self.producer.schedule)
schedule_times = [s['job'].scheduled_time for s in self.producer.schedule]
self._reset_cache()
return schedule_times, should_keep
def basinhopping(self):
self.reset_and_differentiate_loads()
self.objection_value_offset = self.producer.prediction.values[-1]
tol = self.options["tol"] if "tol" in self.options else 1000.0
eps = self.options["eps"] if "eps" in self.options else 0.001
now = self.producer.manager.clock.now
bounds = [(max(s['job'].est, now), s['job'].lst) for s in self.producer.schedule]
x0 = np.array([randint(b[0], b[1]) for b in bounds])
kwargs = dict(method=self.algorithm.value, bounds=bounds, options=dict(eps=eps), tol=tol)
# The Basin-hopping algorithm is good at finding global minimums.
result = optimize.basinhopping(func=self.objective_function, x0=x0, minimizer_kwargs=kwargs)
objective_value = result.fun
schedule_times = list([int(round(e)) for e in result.x])
should_keep = [True] * len(self.producer.schedule)
strictly_positive = self.strictly_positive()
if np.isinf(self.min_objective_value) and not strictly_positive:
should_keep[-1] = False
elif objective_value < self.min_objective_value or strictly_positive:
self.min_objective_value = objective_value
else:
should_keep[-1] = False
return schedule_times, should_keep
def fifty_fifty(self):
should_keep = [True] * len(self.producer.schedule)
need_grid_power = not self.strictly_positive()
if random() < 0.5 or need_grid_power:
should_keep[-1] = False
schedule_times = [s['job'].scheduled_time for s in self.producer.schedule]
return schedule_times, should_keep
def objective_function(self, s: List[float]):
"""The function we want to minimize. schedule is a List of start times for the jobs in the schedule."""
schedule = [utils.round_to_nearest_60(x) for x in s]
cache = self._get_cached_value(schedule)
if cache < np.inf:
return cache
consumed = defaultdict(float)
for i, load in enumerate(self.differentiated_loads):
for t, p in load.items():
if not math.isnan(schedule[i]):
start_time = schedule[i]
# Add power, p(t), to consumed at time 'start_time' plus 't'
consumed[int(round(start_time + t))] += p
# Differentiate and interpolate production profile on the indices found in consumer
ts = list(consumed.keys())
produced = utils.differentiate_and_interpolate(self.producer.prediction, ts)
ts = sorted(list(set(ts)))
diff = 0
penalty = 0
for i, t in enumerate(ts):
if i == len(ts) - 1:
delta = 0
else:
delta = abs(ts[i + 1] - ts[i])
p = produced[t]
c = consumed[t]
# Multiply difference between produced and consumed in time 't' by the distance to the previous time index.
# This way, differences are weighted based on their time span.
diff += abs((p - c) * delta)
# If consumed is greater than produced, we have negative amount of production power.
if c > p:
diff_t = abs(p - c) * delta
penalty += diff_t
# Return the difference between produced and consumed energy, in addition to the penalty for having negative
# power levels. Finally, subtract this from the objection_value_offset, i.e. the sum of the produced power. This
# is so that the theoretically minimal value is equal to zero (if we consume all produced energy).
score = diff + penalty * self.penalty_factor
self._cache_value(schedule, score)
return score
def strictly_positive(self):
"""Checks if the current schedules yields positive power in every time step (i.e. we always have more produced
energy than consumed energy. Mostly the same as the objective function, but terminates faster, and returns a
boolean value indicating whether the excess energy function is always positive, given the current schedule."""
schedule = [s['job'].scheduled_time for s in self.producer.schedule]
self.reset_and_differentiate_loads()
consumed = defaultdict(float)
for i, load in enumerate(self.differentiated_loads):
for t, p in load.items():
offset = schedule[i]
consumed[int(round(t + offset))] += p
ts = list(consumed.keys())
produced = utils.differentiate_and_interpolate(self.producer.prediction, ts)
ts.extend(list(produced.index.values))
ts = sorted(ts)
for i, t in enumerate(ts):
if consumed[t] > produced[t]:
return False
return True
#
def reset_and_differentiate_loads(self):
"""Re-compute the differentiated and interpolated load profiles"""
indices = list(set(chain.from_iterable(
map(lambda x: self.producer.schedule[x]['job'].load_profile.index.values.tolist(),
range(0, len(self.producer.schedule))))))
indices.sort()
# Interpolate and differentiate load profiles before optimization.
self.differentiated_loads = []
for s in self.producer.schedule:
self.differentiated_loads.append(utils.differentiate(s['job'].load_profile))
def _reset_cache(self):
self.cache = defaultdict(lambda: np.inf)
def _key(self, schedule):
return "".join([str(int(round(f))) + ";" for f in schedule])
def _get_cached_value(self, schedule):
return self.cache[self._key(schedule)]
def _cache_value(self, schedule, value):
self.cache[self._key(schedule)] = value
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] |
tests/IntegrationTest.py | CBurbidge/Cloudfigure | 1 | 1611540 | import unittest
import cloudfigure as cf
from unittest import mock
import os, json
from os import listdir
from os.path import isfile, join
class MockCfn:
def __init__(self, directory_path):
self.calls = {}
describe_files = [f for f in listdir(directory_path) if f.startswith("DescribeStack_")]
for file_name in describe_files:
name = file_name[len("DescribeStack_"):(len(file_name) - len(".json"))]
self.calls[name] = json.loads(cf.read_all_text(join(directory_path, file_name)))
def describe_stacks(self, StackName):
if not StackName in self.calls:
raise Exception("Doesnt exist")
return self.calls[StackName]
class MockKms:
def __init__(self, directory_path):
kms_calls_text = cf.read_all_text(os.path.join(directory_path, "KmsCalls.json"))
self.calls = json.loads(kms_calls_text)
for call_key in self.calls.keys():
to_change = self.calls[call_key]["Plaintext"]
self.calls[call_key]["Plaintext"] = to_change.encode("utf-8")
def decrypt(self, CiphertextBlob):
blob = CiphertextBlob.decode("utf-8")
if not blob in self.calls:
raise Exception("Doesnt exist")
return self.calls[blob]
class MockBoto:
def __init__(self, kms, cfn):
self.kms = kms
self.cfn = cfn
def client(self, service):
if service == "cloudformation":
return self.cfn
if service == "kms":
return self.kms
raise Exception("shouldn't be asking for this")
class IntegrationTest(unittest.TestCase):
def __init__(self, directory_path):
self.working_dir = directory_path
self.cfn = MockCfn(directory_path)
self.kms = MockKms(directory_path)
stack_ids_text = cf.read_all_text(os.path.join(directory_path, "StackIds.json"))
self.stack_ids = json.loads(stack_ids_text)
config_path = os.path.join(directory_path, "Cloudfigure.json")
self.cloudfigure_config = cf.read_all_text(config_path)
# assert against substituted files
expected_files = {}
expected_files_in_dir = [f for f in listdir(directory_path) if f.startswith("ExpectedFile_")]
for file_name in expected_files_in_dir:
name = file_name[len("ExpectedFile_"):]
path = join(directory_path, name)
expected_files[path] = cf.read_all_text(join(directory_path, file_name))
self.expected_files = expected_files
self.writes = {}
cf.write_all_text = self.mock_write_all_text
super().__init__()
def mock_write_all_text(self, path, content):
self.writes[path] = content
def run(self):
self.result = cf.run_cloudfigure(MockBoto(self.kms, self.cfn), self.cloudfigure_config, self.stack_ids, self.working_dir)
return self.result
def assert_expected_files(self):
for expected_write in self.expected_files.keys():
if expected_write not in self.writes:
self.assertFalse(True, "file was written to which was not expected " + expected_write)
actual = self.writes[expected_write]
expected = self.expected_files[expected_write]
self.assertEqual(actual, expected)
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3539,
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497,
29918,
726,
13,
13,
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2344,
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497,
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726,
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13,
13,
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822,
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363,
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297,
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13,
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565,
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451,
297,
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267,
29901,
13,
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376,
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471,
3971,
304,
607,
471,
451,
3806,
376,
718,
3806,
29918,
3539,
29897,
13,
9651,
3935,
353,
1583,
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8231,
267,
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9684,
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3539,
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29961,
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13,
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1583,
29889,
9294,
9843,
29898,
19304,
29892,
3806,
29897,
13,
13,
13,
2
] |
ww/api/admin.py | basilpork/watchword | 1 | 76661 | from django.contrib import admin
from ww.api.models import Watch, Ping, Flare, Launch
@admin.register(Watch)
class WatchAdmin(admin.ModelAdmin):
list_display = ('id', 'name', 'created', 'word', 'state', 'status', 'last_ping',)
@admin.register(Ping)
class PingAdmin(admin.ModelAdmin):
list_display = ('id', 'created', 'watch_name', 'method', 'user_agent', 'remote_addr',)
def watch_name(self, ping):
return ping.watch.name
@admin.register(Flare)
class FlareAdmin(admin.ModelAdmin):
list_display = ('id', 'signal', 'config',)
@admin.register(Launch)
class LaunchAdmin(admin.ModelAdmin):
list_display = ('id', 'created', 'flare', 'watch', 'trigger_state', 'message',)
| [
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525,
21001,
29918,
3859,
742,
525,
4906,
742,
29897,
13,
2
] |
tests/test_token.py | codingjerk/visiology-py | 1 | 134071 | from datetime import datetime
from typing import Callable
from unittest.mock import Mock, patch
import visiology_py as vi
from tests.fixtures import *
def test_token_expires(
expire_date: datetime,
before_expire_date: datetime,
after_expire_date: datetime,
fixed_datetime: Callable[[datetime], Mock],
) -> None:
token = vi.AuthorizationToken(
type="Something",
secret="Anything",
expires_at=expire_date,
)
with patch(
"visiology_py.authorization_token.datetime",
fixed_datetime(after_expire_date),
):
assert token.is_expired()
with patch(
"visiology_py.authorization_token.datetime",
fixed_datetime(before_expire_date),
):
assert not token.is_expired()
| [
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268,
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13,
4706,
4974,
451,
5993,
29889,
275,
29918,
4548,
2859,
580,
13,
2
] |
modules/mbox/uatg_mbox_divu_insts_01.py | incoresemi/chromite_uatg_tests | 3 | 86789 | from random import choice, getrandbits
from typing import Dict, List, Union, Any
from uatg.instruction_constants import base_reg_file, mext_instructions
from yapsy.IPlugin import IPlugin
class uatg_mbox_divu_insts_01(IPlugin):
"""
The class contains the division instructions ( div, divu, divw, divuw,
rem, remu, remuw, remw)
"""
def __init__(self) -> None:
super().__init__()
self.isa = 'RV32I'
self.isa_bit = 'rv32'
self.offset_inc = 4
self.xlen = 32
self.num_rand_var = 100
def execute(self, core_yaml, isa_yaml) -> bool:
self.isa = isa_yaml['hart0']['ISA']
if 'RV32' in self.isa:
self.isa_bit = 'rv32'
self.xlen = 32
self.offset_inc = 4
else:
self.isa_bit = 'rv64'
self.xlen = 64
self.offset_inc = 8
if 'M' in self.isa or 'Zmmul' in self.isa:
return True
else:
return False
def generate_asm(self) -> List[Dict[str, Union[Union[str, list], Any]]]:
"""
Generates the ASM instructions for divider and stores both quotient
and remainder in rd and rd1 reg respectively.
It creates asm for the following instructions based upon ISA
div[w], divu[w], rem[w], remu[w]
"""
# rd, rs1, rs2 iterate through all the 32 register combinations for
# every instruction in m_extension_instructions
doc_string = 'Test generates the operation of div, divu, divuw ' \
'instructions'
reg_file = base_reg_file.copy()
reg_file.remove('x0')
instructions = []
if 'M' in self.isa:
instructions += mext_instructions[f'{self.isa_bit}-div']
instruction_list = [x for x in instructions if 'u' in x]
for inst in instruction_list:
for rs1 in reg_file:
asm_code = '#' * 5 + ' divu[w]/remu[w] reg, reg, reg ' \
+ '#' * 5 + '\n'
# initial register to use as signature pointer
swreg = 'x31'
# initialize swreg to point to signature_start label
asm_code += f'RVTEST_SIGBASE({swreg}, signature_start)\n'
# initial offset to with respect to signature label
offset = 0
# variable to hold the total number of signature bytes to be
# used.
sig_bytes = 0
inst_count = 0
for rd in reg_file:
for rd1 in reg_file:
for rs2 in reg_file:
rs1_val = hex(getrandbits(self.xlen))
rs2_val = hex(getrandbits(self.xlen))
# if signature register needs to be used for ops
# then first choose a new signature pointer and move
# the value to it.
if swreg in [rd, rd1, rs1, rs2]:
new_swreg = choice([
x for x in reg_file
if x not in [rd, rd1, rs1, rs2, 'x0']
])
asm_code += f'mv {new_swreg}, {swreg}\n'
swreg = new_swreg
if rd1 in [rd, swreg, rs1, rs2]:
new_rd1 = choice([
x for x in reg_file
if x not in [rd, swreg, rs2, rs1]
])
rd1 = new_rd1
if rd in [rs1, rd1, rs2, swreg]:
new_rd = choice([
x for x in reg_file
if x not in [rd1, swreg, rs2, rs1]
])
rd = new_rd
# perform the required assembly operation
asm_code += f'\ninst_{inst_count}:'
asm_code += f'\n#operation: {inst}, rs1={rs1}' \
f', rs2={rs2}, rd={rd}\n' \
f'TEST_RR_OP({inst}, {rd}, {rs1}' \
f', {rs2}, 0, {rs1_val}, ' \
f'{rs2_val}, {swreg}, {offset}' \
f', x0)\n'
if f'{inst}' == 'div':
asm_code += f'TEST_RR_OP(rem, {rd1}, ' \
f'{rs1}, {rs2}, 0, {rs1_val}' \
f', {rs2_val}, {swreg}, ' \
f'{offset}, x0)\n'
elif f'{inst}' == 'divu':
asm_code += f'TEST_RR_OP(remu, {rd1}, ' \
f'{rs1}, {rs2}, 0, {rs1_val}' \
f', {rs2_val}, {swreg}, ' \
f'{offset}, x0)\n'
elif f'{inst}' == 'divuw':
asm_code += f'TEST_RR_OP(remuw, {rd1}, ' \
f'{rs1}, {rs2}, 0, {rs1_val}' \
f', {rs2_val}, {swreg}, ' \
f'{offset}, x0)\n'
elif f'{inst}' == 'divw':
asm_code += f'TEST_RR_OP(remw, {rd1}, ' \
f'{rs1}, {rs2}, 0, {rs1_val}' \
f', {rs2_val}, {swreg}, ' \
f'{offset}, x0)\n'
# adjust the offset. reset to 0 if it crosses 2048
# and increment the current signature pointer with
# the current offset value
if offset + self.offset_inc >= 2048:
asm_code += f'addi {swreg}, {swreg}, {offset}\n'
offset = 0
# increment offset by the amount of bytes updated in
# signature by each test-macro.
offset = offset + self.offset_inc
# keep track of the total number of signature bytes
# used so far.
sig_bytes = sig_bytes + self.offset_inc
inst_count += 1
# asm code to populate the signature region
sig_code = 'signature_start:\n'
sig_code += ' .fill {0}, 4, 0xdeadbeef\n'.format(
int(sig_bytes / 4))
# compile macros for the test
compile_macros = []
# return asm_code and sig_code
yield ({
'asm_code': asm_code,
'asm_data': '',
'asm_sig': sig_code,
'compile_macros': compile_macros,
'name_postfix': f'{inst}_rs1_{rs1}',
'doc_string': doc_string
})
| [
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] |
data_processor.py | ZouJoshua/deeptext_project | 2 | 46507 | #!/usr/bin/env python
# coding:utf8
# Copyright (c) 2018, Tencent. All rights reserved
# This file contain DataProcessor class which can
# 1. Generate dict from train text data:
# Text format: label\t[(token )+]\t[(char )+]\t[(custom_feature )+].
# Label could be flattened or hierarchical which is separated by "--".
# 2. Convert data to tfrecord according to dict and config.
# 3. Provide input_fn for estimator train and serving.
import codecs
import json
import os
import sys
from collections import Counter
import tensorflow as tf
import util
from config import Config
class DataProcessor(object):
VOCAB_UNKNOWN = "_UNK"
VOCAB_PADDING = "_PAD"
# Text format: label\t[(token )+]\t[(char )+]\t[(custom_feature )+].
LINE_SPLIT_NUMBER = 4
# Index of line, also index in dict list
LABEL_INDEX = 0
TOKEN_INDEX = 1
CHAR_INDEX = 2
CUSTOM_FEATURE_INDEX = 3
USE_SPECIAL_LABEL = False
SPECIAL_LABEL_LIST = ["社会", "时政", "国际", "军事"]
SPECIAL_LABEL = "|".join(SPECIAL_LABEL_LIST)
# TODO(marvinmu): check config
def __init__(self, config, logger=None):
self.config = config
if logger:
self.logger = logger
else:
self.logger = util.Logger(config)
self.dict_names = ["label", "token", "char", "custom_feature",
"token_ngram", "char_ngram", "char_in_token"]
self.dict_files = []
for dict_name in self.dict_names:
self.dict_files.append(
self.config.data.dict_dir + "/" + dict_name + ".dict")
self.label_dict_file = self.dict_files[0]
# Should keep all labels
self.min_count = [0, self.config.feature_common.min_token_count,
self.config.feature_common.min_char_count,
self.config.var_len_feature.min_custom_feature_count,
self.config.var_len_feature.min_token_ngram_count,
self.config.var_len_feature.min_char_ngram_count,
self.config.feature_common.min_char_count_in_token]
# Should keep all labels
self.max_dict_size = \
[1000 * 1000, self.config.feature_common.max_token_dict_size,
self.config.feature_common.max_char_dict_size,
self.config.var_len_feature.max_custom_feature_dict_size,
self.config.var_len_feature.max_token_ngram_dict_size,
self.config.var_len_feature.max_char_ngram_dict_size,
self.config.feature_common.max_char_in_token_dict_size]
# Label and custom feature has no max_sequence_length.
self.max_sequence_length = \
[0, self.config.fixed_len_feature.max_token_sequence_length,
self.config.fixed_len_feature.max_char_sequence_length, 0]
# Label and custom feature has no ngram.
self.ngram_list = [0, self.config.var_len_feature.token_ngram,
self.config.var_len_feature.char_ngram, 0]
self.label_map = dict()
self.token_map = dict()
self.char_map = dict()
self.custom_feature_map = dict()
self.token_gram_map = dict()
self.char_gram_map = dict()
self.char_in_token_map = dict()
self.dict_list = [self.label_map, self.token_map, self.char_map,
self.custom_feature_map, self.token_gram_map,
self.char_gram_map, self.char_in_token_map]
self.id_to_label_map = dict()
self.id_to_token_map = dict()
self.id_to_char_map = dict()
self.id_to_custom_feature_map = dict()
self.id_to_token_gram_map = dict()
self.id_to_char_gram_map = dict()
self.id_to_char_in_token_map = dict()
self.id_to_vocab_dict_list = [
self.id_to_label_map, self.id_to_token_map,
self.id_to_char_map, self.id_to_custom_feature_map,
self.id_to_token_gram_map, self.id_to_char_gram_map,
self.id_to_char_in_token_map]
self.train_text_file, self.validate_text_file, self.test_text_file = \
self.config.data.train_text_file, \
self.config.data.validate_text_file, \
self.config.data.test_text_file
self.tfrecord_files = [
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.train_text_file) + ".tfrecord",
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.validate_text_file) + ".tfrecord",
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.test_text_file) + ".tfrecord"]
self.train_file, self.validate_file, self.test_file = \
self.tfrecord_files
self.feature_files = [
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.train_text_file) + ".feature",
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.validate_text_file) + ".feature",
self.config.data.tfrecord_dir + "/" + os.path.basename(
self.test_text_file) + ".feature"]
(self.train_feature_file, self.validate_feature_file,
self.test_feature_file) = self.feature_files
self.pretrained_embedding_files = [
"", config.feature_common.token_pretrained_embedding_file,
config.feature_common.char_pretrained_embedding_file,
config.var_len_feature.custom_feature_pretrained_embedding_file, ]
self.int_list_column = ["fixed_len_token", "var_len_token",
"char_in_token", "char_in_token_real_len",
"fixed_len_char", "var_len_char",
"var_len_token_ngram", "var_len_char_ngram",
"var_len_custom_feature"]
self.int_column = ["token_fixed_real_len", "char_fixed_real_len"]
self.float_column = ["token_var_real_len", "char_var_real_len",
"token_ngram_var_real_len",
"char_ngram_var_real_len",
"custom_feature_var_real_len"]
def _save_dict(self, dict_file, counter, name):
"""Save all vocab to file.
Args:
dict_file: File to save to.
counter: Vocab counts.
name: Dict name.
"""
dict_list = counter.most_common()
dict_file = codecs.open(dict_file, "w", encoding=util.CHARSET)
# Save _UNK for vocab not in dict and _PAD for padding
count = 1000 * 1000 # Must bigger than min count
if name != "label":
dict_list = [(self.VOCAB_PADDING, count),
(self.VOCAB_UNKNOWN, count)] + dict_list
for vocab in dict_list:
dict_file.write("%s\t%d\n" % (vocab[0], vocab[1]))
dict_file.close()
self.logger.info("Total count of %s: %d" % (name, len(dict_list)))
def _load_dict(self, dict_map, id_to_vocab_dict_map, dict_file, min_count,
max_dict_size, name):
"""Load dict according to params.
Args:
dict_map: Vocab dict map.
id_to_vocab_dict_map: Id to vocab dict map.
dict_file: File to load.
min_count: Vocab whose count is equal or greater than min_count
will be loaded.
max_dict_size: Load top max_dict_size vocabs sorted by count.
name: Dict name.
Returns:
dict.
"""
if not os.path.exists(dict_file):
self.logger.warn("Not exists %s for %s" % (dict_file, name))
else:
for line in codecs.open(dict_file, "r", encoding=util.CHARSET):
vocab = line.strip("\n").split("\t")
try:
temp = vocab[1]
except IndexError:
continue
if int(temp) >= min_count:
index = len(dict_map)
dict_map[vocab[0]] = index
id_to_vocab_dict_map[index] = vocab[0]
if len(dict_map) >= max_dict_size:
self.logger.warn(
"Reach the max size(%d) of %s, ignore the rest" % (
max_dict_size, name))
break
self.logger.info("Load %d vocab of %s" % (len(dict_map), name))
def load_all_dict(self):
"""Load all dict.
"""
for i, dict_name in enumerate(self.dict_names):
self._load_dict(self.dict_list[i], self.id_to_vocab_dict_list[i],
self.dict_files[i], self.min_count[i],
self.max_dict_size[i], dict_name)
def _generate_dict(self, text_file_list):
"""Generate dict and label dict given train text file.
Save all vocab to files and load dicts.
Text format: label\t[(token )+]\t[(char )+]\t[(feature )+].
Label could be flattened or hierarchical which is separated by "--".
Args:
text_file_list:
Text file list, usually only contain train text file.
"""
sample_size = 0
label_counter = Counter()
token_counter = Counter()
char_in_token_counter = Counter()
token_ngram_counter = Counter()
char_counter = Counter()
char_ngram_counter = Counter()
custom_feature_counter = Counter()
counters = [label_counter, token_counter, char_counter,
custom_feature_counter, token_ngram_counter,
char_ngram_counter, char_in_token_counter]
for text_file in text_file_list:
self.logger.info("Generate dict using text file %s" % text_file)
for line in codecs.open(text_file, "r", encoding='utf8'):
content = line.strip("\n").split('\t')
if len(content) != self.LINE_SPLIT_NUMBER:
self.logger.error("Wrong line: %s" % line)
continue
sample_size += 1
for i, _ in enumerate(content):
vocabs = content[i].strip().split(" ")
counters[i].update(vocabs)
if i == self.LABEL_INDEX:
# 增加特殊类处理
if vocabs[0] in self.SPECIAL_LABEL_LIST and self.USE_SPECIAL_LABEL:
vocabs = [self.SPECIAL_LABEL]
counters[i].update(vocabs)
# If vocab is token, extract char info of each token
if i == self.TOKEN_INDEX:
char_in_token = []
for vocab in vocabs:
char_in_token.extend(vocab)
char_in_token_counter.update(char_in_token)
if self.ngram_list[i] > 1:
ngram_list = []
for j in range(2, self.ngram_list[i] + 1):
ngram_list.extend(["".join(vocabs[k:k + j]) for k in
range(len(vocabs) - j + 1)])
counters[i + 3].update(ngram_list)
for counter in counters:
if "" in counter.keys():
counter.pop("")
self.logger.info("sample size: %d" % sample_size)
for i, dict_name in enumerate(self.dict_names):
self._save_dict(self.dict_files[i], counters[i], self.dict_names[i])
def _get_vocab_id_list(self, dict_map, vocabs, ngram, sequence_length,
max_var_length, ngram_dict_map=None,
char_in_token_map=None,
max_char_sequence_length_per_token=-1):
"""Convert vocab string list to vocab id list.
Args:
dict_map: Dict used to map string to id.
vocabs: Vocab string list.
ngram: Ngram to use (if bigger than 1).
sequence_length: List length for fixed length vocab id list.
max_var_length: List length for var length vocab id list.
ngram_dict_map: Ngram dict map.
max_char_sequence_length_per_token:
Useful when using char to get token embedding.
Returns:
fixed length vocab id list,
real length of fixed vocab id list,
var length vocab id list,
ngram string list.
"""
if len(dict_map) == 0 or len(vocabs) == 0:
return [], 0, [], [], [], []
vocabs_iter = [x for x in vocabs if x in dict_map]
var_len_vocabs = [dict_map[x] for x in vocabs_iter]
if len(var_len_vocabs) > max_var_length:
var_len_vocabs = var_len_vocabs[0:max_var_length]
if not var_len_vocabs:
var_len_vocabs.append(dict_map[self.VOCAB_UNKNOWN])
if len(vocabs) > sequence_length:
vocabs = vocabs[0:sequence_length]
fixed_len_vocabs = []
fixed_len_vocabs.extend(
[dict_map[x] if x in dict_map else dict_map[self.VOCAB_UNKNOWN]
for x in vocabs])
fixed_real_len = len(fixed_len_vocabs)
if fixed_real_len < sequence_length:
fixed_len_vocabs.extend([dict_map[self.VOCAB_PADDING]] * (
sequence_length - len(fixed_len_vocabs)))
ngram_list = []
if ngram > 1:
ngram_list_str = []
for i in range(2, ngram + 1):
ngram_list_str.extend(["".join(vocabs[j:j + i]) for j in
range(len(vocabs) - i + 1)])
ngram_iter = [x for x in ngram_list_str if x in ngram_dict_map]
ngram_list = [ngram_dict_map[x] for x in ngram_iter]
if not ngram_list:
ngram_list.append(ngram_dict_map[self.VOCAB_UNKNOWN])
char_in_token = []
char_in_token_real_len = []
if max_char_sequence_length_per_token > 0:
length = 0
for vocab in vocabs:
length += 1
chars = []
chars.extend(
[char_in_token_map[x] if x in char_in_token_map else
char_in_token_map[self.VOCAB_UNKNOWN] for x in vocab])
if len(chars) > max_char_sequence_length_per_token:
chars = chars[0:max_char_sequence_length_per_token]
char_in_token_real_len.append(len(chars))
if len(chars) < max_char_sequence_length_per_token:
chars.extend([char_in_token_map[self.VOCAB_PADDING]] * (
max_char_sequence_length_per_token - len(chars)))
char_in_token.extend(chars)
while length < sequence_length:
length += 1
char_in_token.extend(
[char_in_token_map[self.VOCAB_PADDING]] *
max_char_sequence_length_per_token)
char_in_token_real_len.append(0)
return (fixed_len_vocabs, fixed_real_len, var_len_vocabs, ngram_list,
char_in_token, char_in_token_real_len)
def _get_features_from_text(self, text, has_label=True):
"""Parse text to features that model can use.
Args:
text: Input text
has_label: If true, result will contain label.
Returns:
Features that model can use.
"""
content = text.split('\t')
if len(content) != self.LINE_SPLIT_NUMBER:
self.logger.error("Wrong format line: %s" % text)
return None
label_string = content[self.LABEL_INDEX]
if has_label and label_string not in self.label_map:
self.logger.error("Wrong label of line: %s" % text)
return None
token = content[self.TOKEN_INDEX].strip().split(" ")
(fixed_len_token, token_fixed_real_len, var_len_token,
var_len_token_ngram, char_in_token, char_in_token_real_len) = \
self._get_vocab_id_list(
self.token_map, token, self.config.var_len_feature.token_ngram,
self.config.fixed_len_feature.max_token_sequence_length,
self.config.var_len_feature.max_var_token_length,
self.token_gram_map, self.char_in_token_map,
self.config.fixed_len_feature.max_char_length_per_token)
chars = content[self.CHAR_INDEX].strip().split(" ")
(fixed_len_char, char_fixed_real_len, var_len_char, var_len_char_ngram,
_, _) = self._get_vocab_id_list(
self.char_map, chars, self.config.var_len_feature.char_ngram,
self.config.fixed_len_feature.max_char_sequence_length,
self.config.var_len_feature.max_var_char_length,
self.char_gram_map)
custom_features = content[self.CUSTOM_FEATURE_INDEX].strip().split(
" ")
_, _, var_len_custom_feature, _, _, _ = self._get_vocab_id_list(
self.custom_feature_map, custom_features, 0, 0, 0,
self.config.var_len_feature.max_var_custom_feature_length, None)
feature_sample = dict({
"fixed_len_token": fixed_len_token,
"token_fixed_real_len": token_fixed_real_len,
"var_len_token": var_len_token,
"token_var_real_len": len(var_len_token),
"char_in_token": char_in_token,
"char_in_token_real_len": char_in_token_real_len,
"var_len_token_ngram": var_len_token_ngram,
"token_ngram_var_real_len": len(var_len_token_ngram),
"fixed_len_char": fixed_len_char,
"char_fixed_real_len": char_fixed_real_len,
"var_len_char": var_len_char,
"char_var_real_len": len(var_len_char),
"var_len_char_ngram": var_len_char_ngram,
"char_ngram_var_real_len": len(var_len_char_ngram),
"var_len_custom_feature": var_len_custom_feature,
"custom_feature_var_real_len": len(var_len_custom_feature)
})
if has_label:
label = self.label_map[content[0]]
if content[self.LABEL_INDEX] in self.SPECIAL_LABEL_LIST and self.USE_SPECIAL_LABEL:
label = self.label_map[self.SPECIAL_LABEL]
feature_sample["label"] = label
return feature_sample
def _convert_features_to_tfexample(self, feature_sample,
has_label=True):
"""Convert feature sample to tf.example
Args:
feature_sample: Feature sample.
has_label: If true, result will contain label
Returns:
tf.example
"""
if not feature_sample:
return None
tfexample = tf.train.Example()
for name in self.int_list_column:
tfexample.features.feature[name].int64_list.value.extend(
feature_sample[name])
for name in self.int_column:
tfexample.features.feature[name].int64_list.value.append(
feature_sample[name])
for name in self.float_column:
tfexample.features.feature[name].float_list.value.append(
feature_sample[name])
if has_label:
tfexample.features.feature["label"].int64_list.value.append(
feature_sample["label"])
return tfexample
def get_tfexample_from_text(self, text, has_label=True):
feature_sample = self._get_features_from_text(text, has_label)
tfexample = self._convert_features_to_tfexample(feature_sample,
has_label)
return tfexample, feature_sample
def _get_tfrecord_from_text_file(self, text_file, tfrecord_file,
feature_file):
"""Get tfrecord from text file.
Text format: label\t[(token )+]\t[(char )+]\t[(feature )+].
Label could be flattened or hierarchical which is separated by "--".
Args:
text_file: Text file.
tfrecord_file: Tfrecord file to write.
feature_file: Feature file, will save feature sample for debug.
For validate and test evaluation
"""
self.logger.info("Get tfrecord from text file %s" % text_file)
writer = tf.python_io.TFRecordWriter(tfrecord_file)
sample_size = 0
with codecs.open(feature_file, "w",
encoding=util.CHARSET) as label_file:
for line in codecs.open(text_file, "r", encoding='utf8'):
tfexample, feature_sample = self.get_tfexample_from_text(line)
if tfexample is not None:
feature_str = json.dumps(feature_sample, ensure_ascii=False)
label_file.write(
self.id_to_label_map[feature_sample["label"]] + "\t" +
feature_str + "\n")
writer.write(tfexample.SerializeToString())
sample_size += 1
writer.close()
self.logger.info(
"Text file %s has sample %d" % (text_file, sample_size))
def process_from_text_file(self, use_exists_dict=False):
"""Process text data to tfrecord for training and generate dicts.
"""
if not os.path.exists(self.config.data.tfrecord_dir):
os.makedirs(self.config.data.tfrecord_dir)
if not os.path.exists(self.config.data.dict_dir):
os.makedirs(self.config.data.dict_dir)
if use_exists_dict:
self.load_all_dict()
else:
self._generate_dict([self.config.data.train_text_file])
# If using pretrained embedding, dict can be generated by all text file.
# when repeating the result in the paper of textcnn, the following code
# should be used.
# self._generate_dict([self.config.data.train_text_file,
# self.config.data.validate_text_file,
# self.config.data.test_text_file])
self.load_all_dict()
text_files = [self.config.data.train_text_file,
self.config.data.validate_text_file,
self.config.data.test_text_file]
for i, text_file in enumerate(text_files):
self._get_tfrecord_from_text_file(text_file, self.tfrecord_files[i],
self.feature_files[i])
@staticmethod
def _get_feature_spec(has_label):
"""Feature map to parse tf.example
Args:
has_label: If true, feature map include label
Return:
feature map
"""
feature_spec = dict({
"fixed_len_token": tf.VarLenFeature(dtype=tf.int64),
"token_fixed_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.int64),
"var_len_token": tf.VarLenFeature(dtype=tf.int64),
"token_var_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.float32),
"char_in_token": tf.VarLenFeature(dtype=tf.int64),
"char_in_token_real_len": tf.VarLenFeature(dtype=tf.int64),
"var_len_token_ngram": tf.VarLenFeature(dtype=tf.int64),
"token_ngram_var_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.float32),
"fixed_len_char": tf.VarLenFeature(dtype=tf.int64),
"char_fixed_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.int64),
"var_len_char": tf.VarLenFeature(dtype=tf.int64),
"char_var_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.float32),
"var_len_char_ngram": tf.VarLenFeature(dtype=tf.int64),
"char_ngram_var_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.float32),
"var_len_custom_feature": tf.VarLenFeature(dtype=tf.int64),
"custom_feature_var_real_len": tf.FixedLenFeature(shape=(1,),
dtype=tf.float32),
})
if has_label:
feature_spec["label"] = tf.FixedLenFeature(shape=(1,),
dtype=tf.int64)
return feature_spec
def check_tfrecord(self, file_names, field_name, dtype=tf.int32):
"""Check one field of tfrecord
Args:
file_names: List of file names.
field_name: Field to check.
dtype: Field data type.
"""
filename_queue = tf.train.string_input_producer(file_names,
shuffle=False)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
self._get_feature_spec(True))
feature = tf.cast(features[field_name], dtype)
check_file = codecs.open("tf_check.txt", "w", encoding=util.CHARSET)
with tf.Session(config=tf.ConfigProto(
device_count={"CPU":12},
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
gpu_options=gpu_options,
)) as sess:
#with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
check_file.write(feature.eval())
coord.request_stop()
coord.join(threads)
check_file.close()
def _parse_tfexample(self, example, mode=tf.estimator.ModeKeys.TRAIN):
"""Parse input example.
Args:
example: Tf.example.
mode: Estimator mode.
Return:
parsed feature and label.
"""
parsed = tf.parse_single_example(example, self._get_feature_spec(True))
parsed = self._sparse_to_dense(parsed)
label = None
if mode != tf.estimator.ModeKeys.PREDICT:
label = parsed.pop("label")
return parsed, label
def _sparse_to_dense(self, parsed_example):
for key in self.int_list_column:
if "var" not in key:
parsed_example[key] = tf.sparse_tensor_to_dense(
parsed_example[key])
return parsed_example
def dataset_input_fn(self, mode, input_file, batch_size, num_epochs=1):
"""Input function using tf.dataset for estimator
Args:
mode: input mode of tf.estimator.ModeKeys.{TRAIN, EVAL, PREDICT}.
input_file: Input tfrecord file.
batch_size: Batch size for model.
num_epochs: Number epoch.
Returns:
tf.dataset
"""
dataset = tf.data.TFRecordDataset(input_file)
dataset = dataset.map(self._parse_tfexample)
if mode != tf.estimator.ModeKeys.PREDICT:
dataset = dataset.shuffle(
buffer_size=self.config.data.shuffle_buffer)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
return dataset
def serving_input_receiver_fn(self):
"""Input function for session server
Returns:
input_receiver_fn for session server
"""
serialized_tf_example = tf.placeholder(
dtype=tf.string, name='input_example_tensor')
receiver_tensors = {'examples': serialized_tf_example}
parsed_example = tf.parse_example(serialized_tf_example,
self._get_feature_spec(False))
parsed_example = self._sparse_to_dense(parsed_example)
return tf.estimator.export.ServingInputReceiver(parsed_example,
receiver_tensors)
def main(_):
config = Config(config_file=sys.argv[1])
data_processor = DataProcessor(config)
data_processor.process_from_text_file()
if __name__ == '__main__':
tf.app.run()
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] |
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