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f736b12bd8cbb9754835b65f80a155dd4d218497 | 1,816 | py | Python | sec/test.py | pygongnlp/gramcorrector | 1b5b7f46f7185675b46341e40b2a866fd6d1d7ad | [
"MIT"
] | 5 | 2022-02-07T12:35:18.000Z | 2022-03-09T05:10:33.000Z | sec/test.py | pygongnlp/gramcorrector | 1b5b7f46f7185675b46341e40b2a866fd6d1d7ad | [
"MIT"
] | null | null | null | sec/test.py | pygongnlp/gramcorrector | 1b5b7f46f7185675b46341e40b2a866fd6d1d7ad | [
"MIT"
] | null | null | null | import argparse
import torch
import os
from transformers import AutoTokenizer, AutoModelForMaskedLM
from utils import load_data, write_to_file
from metric import compute_metrics
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", default="model/chinese_bert", type=str)
parser.add_argument("--save_path", default="./", type=str)
parser.add_argument("--test_file", default="data/sighan/test.json", type=str)
args = parser.parse_args()
assert os.path.exists(args.save_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
model = AutoModelForMaskedLM.from_pretrained(args.model_name_or_path)
checkpoint = torch.load(os.path.join(args.save_path, "model.tar"), map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
model = model.to(device)
src, trg = load_data(file_path=args.test_file, mode="test")
results = []
for s, t in zip(src, trg):
inputs = tokenizer(t, return_tensors="pt")
inputs = inputs.to(device)
outputs = model(**inputs)
logits = outputs.logits[0][1:-1] #filter [CLS] & [SEP]
predict = tokenizer.convert_ids_to_tokens(logits.argmax(-1).tolist())
s_tok = tokenizer.tokenize(s)
t_tok = tokenizer.tokenize(t)
assert len(s_tok) == len(t_tok) == len(predict)
results.append([s_tok, t_tok, predict])
metrics = compute_metrics(results)
print(f"{', '.join([f'{key}={value:.4f}' for key, value in metrics.items()])}")
write_to_file(file_path=os.path.join(args.save_path, "result_test.json"), results=results)
print(f"write to {os.path.join(args.save_path, 'result_test.json')}")
| 31.310345 | 94 | 0.693833 | import argparse
import torch
import os
from transformers import AutoTokenizer, AutoModelForMaskedLM
from utils import load_data, write_to_file
from metric import compute_metrics
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", default="model/chinese_bert", type=str)
parser.add_argument("--save_path", default="./", type=str)
parser.add_argument("--test_file", default="data/sighan/test.json", type=str)
args = parser.parse_args()
assert os.path.exists(args.save_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
model = AutoModelForMaskedLM.from_pretrained(args.model_name_or_path)
checkpoint = torch.load(os.path.join(args.save_path, "model.tar"), map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
model = model.to(device)
src, trg = load_data(file_path=args.test_file, mode="test")
results = []
for s, t in zip(src, trg):
inputs = tokenizer(t, return_tensors="pt")
inputs = inputs.to(device)
outputs = model(**inputs)
logits = outputs.logits[0][1:-1]
predict = tokenizer.convert_ids_to_tokens(logits.argmax(-1).tolist())
s_tok = tokenizer.tokenize(s)
t_tok = tokenizer.tokenize(t)
assert len(s_tok) == len(t_tok) == len(predict)
results.append([s_tok, t_tok, predict])
metrics = compute_metrics(results)
print(f"{', '.join([f'{key}={value:.4f}' for key, value in metrics.items()])}")
write_to_file(file_path=os.path.join(args.save_path, "result_test.json"), results=results)
print(f"write to {os.path.join(args.save_path, 'result_test.json')}")
| true | true |
f736b1fa87bbe89634f2f6829979f3e2c6684b78 | 10,309 | py | Python | rising/transforms/spatial.py | NKPmedia/rising | 2a580e9c74c8fb690e27e8bacf09ab97184ab1ee | [
"MIT"
] | null | null | null | rising/transforms/spatial.py | NKPmedia/rising | 2a580e9c74c8fb690e27e8bacf09ab97184ab1ee | [
"MIT"
] | null | null | null | rising/transforms/spatial.py | NKPmedia/rising | 2a580e9c74c8fb690e27e8bacf09ab97184ab1ee | [
"MIT"
] | null | null | null | # from __future__ import annotations
import torch
from itertools import combinations
from typing import Union, Sequence, Callable, Optional
from torch.multiprocessing import Value
from rising.random import AbstractParameter, DiscreteParameter
from rising.transforms.abstract import AbstractTransform, BaseTransform
from rising.transforms.functional.spatial import *
__all__ = ["Mirror", "Rot90", "ResizeNative",
"Zoom", "ProgressiveResize", "SizeStepScheduler"]
scheduler_type = Callable[[int], Union[int, Sequence[int]]]
class Mirror(BaseTransform):
"""Random mirror transform"""
def __init__(self,
dims: Union[int, DiscreteParameter,
Sequence[Union[int, DiscreteParameter]]],
keys: Sequence[str] = ('data',), grad: bool = False, **kwargs):
"""
Args:
dims: axes which should be mirrored
keys: keys which should be mirrored
prob: probability for mirror. If float value is provided,
it is used for all dims
grad: enable gradient computation inside transformation
**kwargs: keyword arguments passed to superclass
Examples:
>>> # Use mirror transform for augmentations
>>> from rising.random import DiscreteCombinationsParameter
>>> # We sample from all possible mirror combination for
>>> # volumetric data
>>> trafo = Mirror(DiscreteCombinationsParameter((0, 1, 2)))
"""
super().__init__(augment_fn=mirror, dims=dims, keys=keys, grad=grad,
property_names=('dims',), **kwargs)
class Rot90(AbstractTransform):
"""Rotate 90 degree around dims"""
def __init__(self, dims: Union[Sequence[int], DiscreteParameter],
keys: Sequence[str] = ('data',),
num_rots: Sequence[int] = (0, 1, 2, 3),
prob: float = 0.5, grad: bool = False, **kwargs):
"""
Args:
dims: dims/axis ro rotate. If more than two dims are
provided, 2 dimensions are randomly chosen at each call
keys: keys which should be rotated
num_rots: possible values for number of rotations
prob: probability for rotation
grad: enable gradient computation inside transformation
kwargs: keyword arguments passed to superclass
See Also:
:func:`torch.Tensor.rot90`
"""
super().__init__(grad=grad, **kwargs)
self.keys = keys
self.prob = prob
if not isinstance(dims, DiscreteParameter):
if len(dims) > 2:
dims = list(combinations(dims, 2))
else:
dims = (dims,)
dims = DiscreteParameter(dims)
self.register_sampler("dims", dims)
self.register_sampler("num_rots", DiscreteParameter(num_rots))
def forward(self, **data) -> dict:
"""
Apply transformation
Args:
data: dict with tensors
Returns:
dict: dict with augmented data
"""
if torch.rand(1) < self.prob:
num_rots = self.num_rots
rand_dims = self.dims
for key in self.keys:
data[key] = rot90(data[key], k=num_rots, dims=rand_dims)
return data
class ResizeNative(BaseTransform):
"""Resize data to given size"""
def __init__(self, size: Union[int, Sequence[int]], mode: str = 'nearest',
align_corners: Optional[bool] = None, preserve_range: bool = False,
keys: Sequence = ('data',), grad: bool = False, **kwargs):
"""
Args:
size: spatial output size (excluding batch size and
number of channels)
mode: one of ``nearest``, ``linear``, ``bilinear``, ``bicubic``,
``trilinear``, ``area`` (for more inforamtion see
:func:`torch.nn.functional.interpolate`)
align_corners: input and output tensors are aligned by the center \
points of their corners pixels, preserving the values at the
corner pixels.
preserve_range: output tensor has same range as input tensor
keys: keys which should be augmented
grad: enable gradient computation inside transformation
**kwargs: keyword arguments passed to augment_fn
"""
super().__init__(augment_fn=resize_native, size=size, mode=mode,
align_corners=align_corners, preserve_range=preserve_range,
keys=keys, grad=grad, **kwargs)
class Zoom(BaseTransform):
"""Apply augment_fn to keys. By default the scaling factor is sampled
from a uniform distribution with the range specified by
:attr:`random_args`
"""
def __init__(self, scale_factor: Union[Sequence, AbstractParameter] = (0.75, 1.25),
mode: str = 'nearest', align_corners: bool = None,
preserve_range: bool = False, keys: Sequence = ('data',),
grad: bool = False, **kwargs):
"""
Args:
scale_factor: positional arguments passed for random function.
If Sequence[Sequence] is provided, a random value for each item
in the outer Sequence is generated. This can be used to set
different ranges for different axis.
mode: one of `nearest`, `linear`, `bilinear`,
`bicubic`, `trilinear`, `area` (for more
inforamtion see :func:`torch.nn.functional.interpolate`)
align_corners: input and output tensors are aligned by the center
points of their corners pixels, preserving the values at the
corner pixels.
preserve_range: output tensor has same range as input tensor
keys: keys which should be augmented
grad: enable gradient computation inside transformation
**kwargs: keyword arguments passed to augment_fn
See Also:
:func:`random.uniform`, :func:`torch.nn.functional.interpolate`
"""
super().__init__(augment_fn=resize_native, scale_factor=scale_factor,
mode=mode, align_corners=align_corners,
preserve_range=preserve_range, keys=keys, grad=grad,
property_names=('scale_factor',), **kwargs)
class ProgressiveResize(ResizeNative):
"""Resize data to sizes specified by scheduler"""
def __init__(self, scheduler: scheduler_type, mode: str = 'nearest',
align_corners: bool = None, preserve_range: bool = False,
keys: Sequence = ('data',), grad: bool = False, **kwargs):
"""
Args:
scheduler: scheduler which determined the current size.
The scheduler is called with the current iteration of the
transform
mode: one of ``nearest``, ``linear``, ``bilinear``, ``bicubic``,
``trilinear``, ``area`` (for more inforamtion see
:func:`torch.nn.functional.interpolate`)
align_corners: input and output tensors are aligned by the center
points of their corners pixels, preserving the values at the
corner pixels.
preserve_range: output tensor has same range as input tensor
keys: keys which should be augmented
grad: enable gradient computation inside transformation
**kwargs: keyword arguments passed to augment_fn
Warnings:
When this transformations is used in combination with
multiprocessing, the step counter is not perfectly synchronized
between multiple processes.
As a result the step count my jump between values
in a range of the number of processes used.
"""
super().__init__(size=0, mode=mode, align_corners=align_corners,
preserve_range=preserve_range,
keys=keys, grad=grad, **kwargs)
self.scheduler = scheduler
self._step = Value('i', 0)
def reset_step(self) -> ResizeNative:
"""
Reset step to 0
Returns:
ResizeNative: returns self to allow chaining
"""
with self._step.get_lock():
self._step.value = 0
return self
def increment(self) -> ResizeNative:
"""
Increment step by 1
Returns:
ResizeNative: returns self to allow chaining
"""
with self._step.get_lock():
self._step.value += 1
return self
@property
def step(self) -> int:
"""
Current step
Returns:
int: number of steps
"""
return self._step.value
def forward(self, **data) -> dict:
"""
Resize data
Args:
**data: input batch
Returns:
dict: augmented batch
"""
self.kwargs["size"] = self.scheduler(self.step)
self.increment()
return super().forward(**data)
class SizeStepScheduler:
"""Scheduler return size when milestone is reached"""
def __init__(self, milestones: Sequence[int],
sizes: Union[Sequence[int], Sequence[Sequence[int]]]):
"""
Args:
milestones: contains number of iterations where size should be changed
sizes: sizes corresponding to milestones
"""
if len(milestones) != len(sizes) - 1:
raise TypeError("Sizes must include initial size and thus "
"has one element more than miltstones.")
self.targets = sorted(zip((0, *milestones), sizes), key=lambda x: x[0], reverse=True)
def __call__(self, step) -> Union[int, Sequence[int], Sequence[Sequence[int]]]:
"""
Return size with regard to milestones
Args:
step: current step
Returns:
Union[int, Sequence[int], Sequence[Sequence[int]]]: current size
"""
for t in self.targets:
if step >= t[0]:
return t[1]
return self.targets[-1][1]
| 38.181481 | 93 | 0.584829 |
import torch
from itertools import combinations
from typing import Union, Sequence, Callable, Optional
from torch.multiprocessing import Value
from rising.random import AbstractParameter, DiscreteParameter
from rising.transforms.abstract import AbstractTransform, BaseTransform
from rising.transforms.functional.spatial import *
__all__ = ["Mirror", "Rot90", "ResizeNative",
"Zoom", "ProgressiveResize", "SizeStepScheduler"]
scheduler_type = Callable[[int], Union[int, Sequence[int]]]
class Mirror(BaseTransform):
def __init__(self,
dims: Union[int, DiscreteParameter,
Sequence[Union[int, DiscreteParameter]]],
keys: Sequence[str] = ('data',), grad: bool = False, **kwargs):
super().__init__(augment_fn=mirror, dims=dims, keys=keys, grad=grad,
property_names=('dims',), **kwargs)
class Rot90(AbstractTransform):
def __init__(self, dims: Union[Sequence[int], DiscreteParameter],
keys: Sequence[str] = ('data',),
num_rots: Sequence[int] = (0, 1, 2, 3),
prob: float = 0.5, grad: bool = False, **kwargs):
super().__init__(grad=grad, **kwargs)
self.keys = keys
self.prob = prob
if not isinstance(dims, DiscreteParameter):
if len(dims) > 2:
dims = list(combinations(dims, 2))
else:
dims = (dims,)
dims = DiscreteParameter(dims)
self.register_sampler("dims", dims)
self.register_sampler("num_rots", DiscreteParameter(num_rots))
def forward(self, **data) -> dict:
if torch.rand(1) < self.prob:
num_rots = self.num_rots
rand_dims = self.dims
for key in self.keys:
data[key] = rot90(data[key], k=num_rots, dims=rand_dims)
return data
class ResizeNative(BaseTransform):
def __init__(self, size: Union[int, Sequence[int]], mode: str = 'nearest',
align_corners: Optional[bool] = None, preserve_range: bool = False,
keys: Sequence = ('data',), grad: bool = False, **kwargs):
super().__init__(augment_fn=resize_native, size=size, mode=mode,
align_corners=align_corners, preserve_range=preserve_range,
keys=keys, grad=grad, **kwargs)
class Zoom(BaseTransform):
def __init__(self, scale_factor: Union[Sequence, AbstractParameter] = (0.75, 1.25),
mode: str = 'nearest', align_corners: bool = None,
preserve_range: bool = False, keys: Sequence = ('data',),
grad: bool = False, **kwargs):
super().__init__(augment_fn=resize_native, scale_factor=scale_factor,
mode=mode, align_corners=align_corners,
preserve_range=preserve_range, keys=keys, grad=grad,
property_names=('scale_factor',), **kwargs)
class ProgressiveResize(ResizeNative):
def __init__(self, scheduler: scheduler_type, mode: str = 'nearest',
align_corners: bool = None, preserve_range: bool = False,
keys: Sequence = ('data',), grad: bool = False, **kwargs):
super().__init__(size=0, mode=mode, align_corners=align_corners,
preserve_range=preserve_range,
keys=keys, grad=grad, **kwargs)
self.scheduler = scheduler
self._step = Value('i', 0)
def reset_step(self) -> ResizeNative:
with self._step.get_lock():
self._step.value = 0
return self
def increment(self) -> ResizeNative:
with self._step.get_lock():
self._step.value += 1
return self
@property
def step(self) -> int:
return self._step.value
def forward(self, **data) -> dict:
self.kwargs["size"] = self.scheduler(self.step)
self.increment()
return super().forward(**data)
class SizeStepScheduler:
def __init__(self, milestones: Sequence[int],
sizes: Union[Sequence[int], Sequence[Sequence[int]]]):
if len(milestones) != len(sizes) - 1:
raise TypeError("Sizes must include initial size and thus "
"has one element more than miltstones.")
self.targets = sorted(zip((0, *milestones), sizes), key=lambda x: x[0], reverse=True)
def __call__(self, step) -> Union[int, Sequence[int], Sequence[Sequence[int]]]:
for t in self.targets:
if step >= t[0]:
return t[1]
return self.targets[-1][1]
| true | true |
f736b27b0ae813ad8751187037300ecb4adfc454 | 746 | py | Python | examples/jsonknife/library/customloader/loader.py | podhmo/dictknife | a172220c1adc8411b69f31646ea2154932d71516 | [
"MIT"
] | 13 | 2018-11-23T15:55:18.000Z | 2021-11-24T02:42:44.000Z | examples/jsonknife/library/customloader/loader.py | podhmo/dictknife | a172220c1adc8411b69f31646ea2154932d71516 | [
"MIT"
] | 105 | 2017-01-09T02:05:48.000Z | 2021-07-26T03:39:22.000Z | examples/jsonknife/library/customloader/loader.py | podhmo/dictknife | a172220c1adc8411b69f31646ea2154932d71516 | [
"MIT"
] | 4 | 2017-07-19T12:34:47.000Z | 2019-06-20T10:32:13.000Z | from dictknife import loading
from dictknife.jsonknife import get_resolver
from dictknife import DictWalker
def run(*, filename):
def onload(d, resolver, w=DictWalker(["$include"])):
for _, sd, in w.walk(d):
subresolver, jsref = resolver.resolve(sd.pop("$include"))
sd.update(subresolver.access_by_json_pointer(jsref))
resolver = get_resolver(filename, onload=onload)
loading.dumpfile(resolver.doc)
def main():
import argparse
# import logging
loading.setup()
# logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
return run(**vars(args))
if __name__ == "__main__":
main()
| 23.3125 | 69 | 0.680965 | from dictknife import loading
from dictknife.jsonknife import get_resolver
from dictknife import DictWalker
def run(*, filename):
def onload(d, resolver, w=DictWalker(["$include"])):
for _, sd, in w.walk(d):
subresolver, jsref = resolver.resolve(sd.pop("$include"))
sd.update(subresolver.access_by_json_pointer(jsref))
resolver = get_resolver(filename, onload=onload)
loading.dumpfile(resolver.doc)
def main():
import argparse
loading.setup()
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
return run(**vars(args))
if __name__ == "__main__":
main()
| true | true |
f736b2f22b1aa2eb400e9f17f05c8b763bc44569 | 3,046 | py | Python | src/HABApp/core/event_bus_listener.py | pailloM/HABApp | 3e0defd99ede9b91c164cb9d1ee011fd74e801c3 | [
"Apache-2.0"
] | 44 | 2018-12-13T08:46:44.000Z | 2022-03-07T03:23:21.000Z | src/HABApp/core/event_bus_listener.py | pailloM/HABApp | 3e0defd99ede9b91c164cb9d1ee011fd74e801c3 | [
"Apache-2.0"
] | 156 | 2019-03-02T20:53:31.000Z | 2022-03-23T13:13:58.000Z | src/HABApp/core/event_bus_listener.py | pailloM/HABApp | 3e0defd99ede9b91c164cb9d1ee011fd74e801c3 | [
"Apache-2.0"
] | 18 | 2019-03-08T07:13:21.000Z | 2022-03-22T19:52:31.000Z | import HABApp
from HABApp.core.events import AllEvents
from . import WrappedFunction
from typing import Optional, Any
class EventBusListener:
def __init__(self, topic, callback, event_type=AllEvents,
attr_name1: Optional[str] = None, attr_value1: Optional[Any] = None,
attr_name2: Optional[str] = None, attr_value2: Optional[Any] = None,
):
assert isinstance(topic, str), type(topic)
assert isinstance(callback, WrappedFunction)
assert attr_name1 is None or isinstance(attr_name1, str), attr_name1
assert attr_name2 is None or isinstance(attr_name2, str), attr_name2
self.topic: str = topic
self.func: WrappedFunction = callback
self.event_filter = event_type
# Property filters
self.attr_name1 = attr_name1
self.attr_value1 = attr_value1
self.attr_name2 = attr_name2
self.attr_value2 = attr_value2
self.__is_all: bool = self.event_filter is AllEvents
self.__is_single: bool = not isinstance(self.event_filter, (list, tuple, set))
def notify_listeners(self, event):
# We run always
if self.__is_all:
self.func.run(event)
return None
# single filter
if self.__is_single:
if isinstance(event, self.event_filter):
# If we have property filters wie only trigger when value is set accordingly
if self.attr_name1 is not None:
if getattr(event, self.attr_name1, None) != self.attr_value1:
return None
if self.attr_name2 is not None:
if getattr(event, self.attr_name2, None) != self.attr_value2:
return None
self.func.run(event)
return None
# Make it possible to specify multiple classes
for cls in self.event_filter:
if isinstance(event, cls):
# If we have property filters wie only trigger when value is set accordingly
if self.attr_name1 is not None:
if getattr(event, self.attr_name1, None) != self.attr_value1:
return None
if self.attr_name2 is not None:
if getattr(event, self.attr_name2, None) != self.attr_value2:
return None
self.func.run(event)
return None
def cancel(self):
"""Stop listening on the event bus"""
HABApp.core.EventBus.remove_listener(self)
def desc(self):
# return description
_type = str(self.event_filter)
if _type.startswith("<class '"):
_type = _type[8:-2].split('.')[-1]
_val = ''
if self.attr_name1 is not None:
_val += f', {self.attr_name1}=={self.attr_value1}'
if self.attr_name2 is not None:
_val += f', {self.attr_name2}=={self.attr_value2}'
return f'"{self.topic}" (type {_type}{_val})'
| 37.146341 | 92 | 0.588641 | import HABApp
from HABApp.core.events import AllEvents
from . import WrappedFunction
from typing import Optional, Any
class EventBusListener:
def __init__(self, topic, callback, event_type=AllEvents,
attr_name1: Optional[str] = None, attr_value1: Optional[Any] = None,
attr_name2: Optional[str] = None, attr_value2: Optional[Any] = None,
):
assert isinstance(topic, str), type(topic)
assert isinstance(callback, WrappedFunction)
assert attr_name1 is None or isinstance(attr_name1, str), attr_name1
assert attr_name2 is None or isinstance(attr_name2, str), attr_name2
self.topic: str = topic
self.func: WrappedFunction = callback
self.event_filter = event_type
self.attr_name1 = attr_name1
self.attr_value1 = attr_value1
self.attr_name2 = attr_name2
self.attr_value2 = attr_value2
self.__is_all: bool = self.event_filter is AllEvents
self.__is_single: bool = not isinstance(self.event_filter, (list, tuple, set))
def notify_listeners(self, event):
if self.__is_all:
self.func.run(event)
return None
if self.__is_single:
if isinstance(event, self.event_filter):
if self.attr_name1 is not None:
if getattr(event, self.attr_name1, None) != self.attr_value1:
return None
if self.attr_name2 is not None:
if getattr(event, self.attr_name2, None) != self.attr_value2:
return None
self.func.run(event)
return None
for cls in self.event_filter:
if isinstance(event, cls):
if self.attr_name1 is not None:
if getattr(event, self.attr_name1, None) != self.attr_value1:
return None
if self.attr_name2 is not None:
if getattr(event, self.attr_name2, None) != self.attr_value2:
return None
self.func.run(event)
return None
def cancel(self):
HABApp.core.EventBus.remove_listener(self)
def desc(self):
_type = str(self.event_filter)
if _type.startswith("<class '"):
_type = _type[8:-2].split('.')[-1]
_val = ''
if self.attr_name1 is not None:
_val += f', {self.attr_name1}=={self.attr_value1}'
if self.attr_name2 is not None:
_val += f', {self.attr_name2}=={self.attr_value2}'
return f'"{self.topic}" (type {_type}{_val})'
| true | true |
f736b3c641da105cd867f21cb3c29970c91b52bd | 18,928 | py | Python | contrib/python/office.py | dinisAbranches/nwchem | 21cb07ff634475600ab687882652b823cad8c0cd | [
"ECL-2.0"
] | 317 | 2017-11-20T21:29:11.000Z | 2022-03-28T11:48:24.000Z | contrib/python/office.py | dinisAbranches/nwchem | 21cb07ff634475600ab687882652b823cad8c0cd | [
"ECL-2.0"
] | 356 | 2017-12-05T01:38:12.000Z | 2022-03-31T02:28:21.000Z | contrib/python/office.py | dinisAbranches/nwchem | 21cb07ff634475600ab687882652b823cad8c0cd | [
"ECL-2.0"
] | 135 | 2017-11-19T18:36:44.000Z | 2022-03-31T02:28:49.000Z | '''
Stuff for driving MS office applications from Python using COM
Currently just Excel but Word will come soon.
'''
from win32com.client import Dispatch
from types import *
from string import uppercase
class Excel:
'''
Wrapper for MS Excel derived from that in Python Programming on Win32
'''
def __init__(self,filename=None):
'''
Open a new Excel spreadsheet optionally associated with a file
'''
self.xlApp = Dispatch("Excel.Application")
if filename:
self.filename = filename
self.xlBook = self.xlApp.Workbooks.Open(filename)
else:
self.xlBook = self.xlApp.Workbooks.Add()
self.filename = ''
self.open = 1
def save(self, newfilename=None):
'''
Save the workbook either to the default file, another file,
or let Excel query the user where to save it.
'''
if newfilename:
self.filename = newfilename
self.xlBook.SaveAs(newfilename)
else:
self.xlBook.Save()
def close(self):
self.xlBook.Close(SaveChanges=0)
del self.xlApp
self.open = 0
def getCell(self, row, col, sheet=1):
'''
Returns the value in cell (row,col) or None if it is blank.
'''
xlSheet = self.xlBook.Worksheets(sheet)
return xlSheet.Cells(row,col).Value
def getCellFormula(self, row, col, sheet=1):
'''
Returns the formula in cell (row,col) or the value if
there is no formula. If there is no value nor formula,
None is returned.
'''
xlSheet = self.xlBook.Worksheets(sheet)
result = xlSheet.Cells(row,col).Formula
if result == '': # A blank field seems to return a blank string
result = None
return result
def setCell(self, value, row, col, sheet=1):
'''
Sets the value in cell (row,col).
'''
xlSheet = self.xlBook.Worksheets(sheet)
xlSheet.Cells(row,col).Value = value
def getRange(self, row1, col1, row2=None, col2=None, sheet=1):
'''
Returns the data in the given range as a 2d array (i.e., as
a tuple of tuples). If the bottom corner is not specified
or is incompletely specified, assume a dimension of 1.
'''
if not row2:
row2 = row1
if not col2:
col2 = col1
xlSheet = self.xlBook.Worksheets(sheet)
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
return xlSheet.Range(cell1,cell2).Value
def matrixDimensions(self, data):
'''
Determine the dimemension of the matrix data which can be a
scalar, vector, or 2-D matrix. Allows for string data, or for
matrices in which the first row or column are strings labels
for series ... so look at the last row to determine the length
of a row (= number of columns). If the data is a vector then
it is taken as a row-vector in order to be consistent with how
the default extension happens when assigning a simple list or
vector into a rectangular range in Excel.
'''
last = None
n = m = 1
try:
n = len(data)
last = data[-1]
except TypeError:
n = m = 1 # We have a scalar
if last:
if type(last) == StringType:
m = n # Row-vector of strings
n = 1
else:
try:
m = len(last)
except TypeError:
m = n # Row-vector of scalars
n = 1
return (n,m)
def setRange(self, data, row1=1, col1=1, row2=None, col2=None, sheet=1):
'''
Set the range of cells to the given data.
If both corners of the range are specified, the corresponding
piece of data is copied into the range. If data is too small,
then it is mystically extended to fill the range. E.g., you
can fill a range from a scalar or a vector. Vectors are treated
as row-vectors when filling a rectangular region.
Optionally, you specify only the top-left corner of range in
row1, cell1 and specify row2<=0 - the other coordinate is figured
out from the dimension of the data. This can always be overridden by
specifying the full range coordinates.
If no coordinates are given, the data is put into the top left
of the spreadsheet.
Returns the range that was set.
'''
(n,m) = self.matrixDimensions(data)
if not row2:
row2 = row1 + n - 1
if not col2:
col2 = col1 + m - 1
xlSheet = self.xlBook.Worksheets(sheet)
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
xlSheet.Range(cell1,cell2).Value = data
return (row1, col1, row2, col2)
def getContiguousRange(self, row1, col1, sheet=1):
'''
Returns data in the range which forms a contiguous
block with top-left corner in cell (row1,col1).
Starting from the specified cell, scan down/across
the first column/row and identify the range bordered
by blank cells. Blanks within the region will be
set to None.
'''
xlSheet = self.xlBook.Worksheets(sheet)
row2 = row1
while xlSheet.Cells(row2+1,col1).Value not in [None,'']:
row2 = row2 + 1
col2 = col1
while xlSheet.Cells(row1,col2+1).Value not in [None,'']:
col2 = col2 + 1
return self.getRange(row1, col1, row2, col2, sheet=sheet)
def selectRange(self, row1, col1, row2=None, col2=None, sheet=1):
'''
Select the range of cells on the specified sheet. It also
has to select that sheet as the active worksheet.
'''
if not row2:
row2 = row1
if not col2:
col2 = col1
xlSheet = self.xlBook.Worksheets(sheet)
xlSheet.Select()
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
xlSheet.Range(cell1,cell2).Select()
def chartRange(self, row1, col1, row2, col2, sheet=1,
**keys):
'''
Chart the data in the specified range. Additional options
are processed by chartSelectedRange.
'''
self.selectRange(row1, col1, row2, col2, sheet=sheet)
keys['sheet'] = sheet
apply(self.chartSelectedRange, (), keys)
def chartSelectedRange(self,
title=None, xlabel=None, ylabel=None,
plotby='columns',
charttype='xy',
sheet=1,
xmin=None, xmax=None,
ymin=None, ymax=None,
xlog=0, ylog=0):
'''
The interface to Excel charts. Just a few of the capabilities
are exposed here.
[The first of a set of options is the default]
plotby = 'columns' ... data series run down columns
. = 'rows' ... across rows
charttype = 'xy' ... XY scatter plot with lines and points.
. First series is X. Others are y1, y2, etc.
. = 'surface' ... Surface plot of a scalar function of
. two variables. Data should be a grid of the function.
. = 'contour' or 'colorcontour' ... Contour plot of a scalar
. function of two variables. Data should be a grid of
. values.
xmin and xmax = min/max values of the x or category axis
. It defaults to autoscale by Excel. This only applies to
. XY plots (since the surface/contor plots do not use
. values for the category axes ... they use string labels)
ymin and ymax = min/max values of the y or value axis
. It defaults to auto by Excel. Applies to all charts.
xlog = 0 ... use a linear for x or category axis.
. = 1 ... use a log (values must be positive)
. This only applies to XY plots.
ylog = 0 ... use a linear for the value or Y axis
. = 1 ... use a log .
. Applies to all charts
If the first element of each data series is a string, it is
used to label the series. If this string is representable as
a numerical value you must precede it with a single quote to
force Excel to treat it as a string. Note that you must use
strings. If you use numbers it will be interpreted as data
and incorporated into the plot. For the 2-D plots (xy,
surface, contour) you can border the actual data on left and
on the top with strings to label axes.
'''
charttypes = {'xy':74, 'surface':83, 'colorcontour':85, 'contour':56}
try:
charttype = charttypes[charttype]
except KeyError:
print('Excel.chartSelectedRange: Unknown charttype', charttype, ' defaulting to XY')
charttype = charttypes['xy']
# Make the chart and set how the data will be interpreted
# Taking a reference to the active chart does not seemt to work???
self.xlApp.Charts.Add()
self.xlApp.ActiveChart.ChartType = charttype
xlRows=1
xlColumns=2
if plotby == 'rows':
self.xlApp.ActiveChart.PlotBy = xlRows
elif plotby == 'columns':
self.xlApp.ActiveChart.PlotBy = xlColumns
else:
print('Excel.chartSelectedRange: Unknown plotby', charttype, ' defaulting to columns')
self.xlApp.ActiveChart.PlotBy = xlColumns
# Set the title and axis labels
if title:
self.xlApp.ActiveChart.HasTitle = 1
self.xlApp.ActiveChart.ChartTitle.Characters.Text = title
xlCategory=1
xlValue=2
#xlSeries=3
xlPrimary=1
#xlSecondary=2
if xlabel:
self.xlApp.ActiveChart.Axes(xlCategory,xlPrimary).HasTitle = 1
self.xlApp.ActiveChart.Axes(xlCategory,xlPrimary).AxisTitle.Characters.Text = xlabel
if ylabel:
self.xlApp.ActiveChart.Axes(xlValue,xlPrimary).HasTitle = 1
self.xlApp.ActiveChart.Axes(xlValue,xlPrimary).AxisTitle.Characters.Text = ylabel
# Set the axis scale and log options
xlLinear = 0xffffefdc
xlLogarithmic=0xffffefdb
if ymin != None:
self.xlApp.ActiveChart.Axes(xlValue).MinimumScale = ymin
if ymax != None:
self.xlApp.ActiveChart.Axes(xlValue).MaximumScale = ymax
if ylog:
self.xlApp.ActiveChart.Axes(xlValue).ScaleType = xlLogarithmic
if charttype == charttypes['xy']:
if xmin != None:
self.xlApp.ActiveChart.Axes(xlCategory).MinimumScale = xmin
if xmax != None:
self.xlApp.ActiveChart.Axes(xlCategory).MaximumScale = xmax
if xlog:
self.xlApp.ActiveChart.Axes(xlCategory).ScaleType = xlLogarithmic
# A legend is kinda useful
self.xlApp.ActiveChart.HasLegend = 1
def chartData(self, data, row1=1, col1=1, sheet=1, **keys):
'''
Simplest interface for creating a chart. Data is a matrix
of data. Paste it into a sheet and plot it. All arguments
except the data can be defaulted. Optional arguments are passed
to the actual charting function.
'''
(n,m) = self.matrixDimensions(data)
row2 = row1 + n - 1
col2 = col1 + m - 1
self.setRange(data, row1, col1, row2, col2, sheet=sheet)
keys['sheet'] = sheet
apply(self.chartRange, (row1, col1, row2, col2), keys)
def a1(self, row, col, absrow=0, abscol=0):
'''
Return a string that may be used to address the cell in
a formula. The row and/or column address may be made absolute
by setting absrow/col to true values.
Internally we are addressing cells in the spreadsheet using
integers (row,col), which is what Excel calls R1C1 style
references. But, unless the user has turned-on R1C1 style
addressing (unlikely!) this will not work in formulae
so we must translate to the usual addressing style, called A1,
which uses letters for the columns and numbers for the rows,
writing the column index first.
E.g., A1 = R1C1 = (1,1), and B3 = R3C2 = (3,2).
Absolute addresses are preceded with a $ symbol.
'''
ar = ac = ''
if absrow: ar = '$'
if abscol: ac = '$'
if col < 1 or col > 256:
raise RangeError('column index must be in [1,256]')
(c1,c2) = divmod(col-1,26)
if c1:
c = uppercase[c1] + uppercase[c2]
else:
c = uppercase[c2]
r = str(row)
return ac + c + ar + r
def visible(self):
'''
Make the spreadsheet visible.
'''
self.xlApp.Visible = 1
def invisible(self):
'''
Make the spreadsheet invisible.
'''
self.xlApp.Visible = 0
def isvisible(self):
'''
Returns true if the spreadsheet is visible.
'''
return self.xlApp.Visible
def __del__(self):
'''
Destructor ... may be uncessary but it cannot hurt.
'''
if self.open:
self.close()
if __name__ == "__main__":
from math import *
import time
# Make a worksheet and test set/getCell
xls = Excel()
print(' Setting cell(2,2) to "Hi"')
xls.setCell("Hi", 2, 2)
print(xls.getCell(2,2))
print(' Setting cell(1,2) to "(1,2)"')
xls.setCell("(1,2)", 1, 2)
print(' Setting cell(2,1) to "(1,2)"')
xls.setCell("(2,1)", 2, 1)
xls.visible()
# Test setting a range to a scalar and getting contiguous range
print(' Setting 9,1,12,2 to 0')
xls.setRange(0,9,1,12,2)
print(' Getting same contiguous range back ... expecting matrix(4,2)=0')
value = xls.getContiguousRange(9,1)
print(value)
# Test setting/getting a range from/to a matrix
n = 3
m = 5
x = [0]*n
for i in range(n):
x[i] = [0]*m
for j in range(m):
x[i][j] = i + j
print(' Setting range (3:,4:) to ')
print(x)
xls.setRange(x,3,4) # Auto determination of the bottom corner
print(' Got back from same range ',3,3,3+n-1,4+m-1)
y = xls.getRange(3,4,3+n-1,4+m-1)
print(y)
# Add names for the series that will eventually become the chart
names = []
for i in range(m):
names.append("y%d" % i)
xls.setRange(names,2,4)
# Test selecting a range
print(' Selecting range ', 3,3,3+n-1,4+m-1)
xls.selectRange(3,4,3+n-1,4+m-1)
# Test general matrix
xls.setRange([[1,2],[5,6],["hi","bye"]],1,10,3,11)
# Test making an x-y plot (changes the range selection)
xls.chartRange(2,4,3+n-1,4+m-1,
title='THIS IS THE TITLE',
xlabel='XXXXX',
ylabel='YYYYY')
# Test making an x-y plot just from the data ... use a
# second sheet and the simple chart interface
print(' Creating chart of sin(x) and cos(x) using second sheet')
n = 20
m = 3
h = 2*pi/(n-1)
data = range(n+1)
data[0] = ['x', 'sin', 'cos']
for i in range(n):
x = i*h
data[i+1] = (x,sin(x),cos(x))
xls.chartData(data,sheet=2)
# Try using a formula to add up the absolute values of the data
# Use absolute values for the rows but not the columns to test
# reuse of the formula.
formula = '=sum('+xls.a1(2,2,absrow=1)+':'+xls.a1(21,2,absrow=1)+')'
print(' The formula is ', formula)
xls.setCell('Total',23,1,sheet=2)
xls.setCell(formula,23,2,sheet=2)
xls.setCell(formula,23,3,sheet=2)
# Getting the cell contents back will get the value not the formula
print(' The formula from the sheet is ', xls.getCellFormula(23,2,sheet=2))
print(' The value of the formula (sum of sin) is ', xls.getCell(23,2,sheet=2))
print(' The formula from where there is only the value "Total" is', xls.getCellFormula(23,1,sheet=2))
print(' The formula from where there is nothing ', xls.getCellFormula(23,4,sheet=2))
print(' The value from where there is nothing ', xls.getCell(23,4,sheet=2))
# Make a surface plot by creating a 2-D grid bordered on the
# left and top with strings to indicate the values. Note the
# use of a single quote before the value in the labels in
# order to force Excel to treat them as strings.
print(' Create surface chart of exp(-0.1*r*r)*cos(1.3*r)')
n = 10
h = 2*pi/(n-1)
data = range(n+1)
data[0] = range(n+1)
data[0][0] = ' '
for i in range(n):
x = i*h-pi
data[i+1] = range(n+1)
data[0][i+1] = data[i+1][0] = ("'%5.2f" % x)
for j in range(n):
y = j*h-pi
r = sqrt(x*x+y*y)
data[i+1][j+1] = exp(-0.1*r*r)*cos(1.3*r)
# Specify (row1,col1) to avoid overwriting the previous data
# Also, specify axis ranges to make the animation smoother.
xls.chartData(data,1,5,sheet=2,charttype='surface',
ymin=-1,ymax=1)
# Animate the chart by periodically updating the data range.
nloop = 60
for loop in range(1,nloop+1):
phase = loop*2*pi/(nloop-1)
for i in range(n):
x = i*h-pi
for j in range(n):
y = j*h-pi
r = sqrt(x*x+y*y)
data[i+1][j+1] = exp(-0.1*r*r)*cos(1.3*r+phase)
time.sleep(0.5)
xls.setRange(data,1,5,sheet=2)
# Finally make a chart with all options set
print(' Creating chart of sin(x) and cos(x) using second sheet')
n = 81
data = range(n+1)
data[0] = ['Age', 'Wisdom']
for i in range(n):
data[i+1] = [i+1, 1.0 + 100.0*exp(-((i-40)**2)/400.0)]
xls.chartData(data,1,1,sheet=3,plotby='columns',charttype='xy',
xmin=1,xmax=80,ymin=1,ymax=100,ylog=1,
title='Wisdom vs. Age', xlabel='Age/years',
ylabel='Wisdom')
| 38.238384 | 106 | 0.558273 |
from win32com.client import Dispatch
from types import *
from string import uppercase
class Excel:
def __init__(self,filename=None):
self.xlApp = Dispatch("Excel.Application")
if filename:
self.filename = filename
self.xlBook = self.xlApp.Workbooks.Open(filename)
else:
self.xlBook = self.xlApp.Workbooks.Add()
self.filename = ''
self.open = 1
def save(self, newfilename=None):
if newfilename:
self.filename = newfilename
self.xlBook.SaveAs(newfilename)
else:
self.xlBook.Save()
def close(self):
self.xlBook.Close(SaveChanges=0)
del self.xlApp
self.open = 0
def getCell(self, row, col, sheet=1):
xlSheet = self.xlBook.Worksheets(sheet)
return xlSheet.Cells(row,col).Value
def getCellFormula(self, row, col, sheet=1):
xlSheet = self.xlBook.Worksheets(sheet)
result = xlSheet.Cells(row,col).Formula
if result == '':
result = None
return result
def setCell(self, value, row, col, sheet=1):
xlSheet = self.xlBook.Worksheets(sheet)
xlSheet.Cells(row,col).Value = value
def getRange(self, row1, col1, row2=None, col2=None, sheet=1):
if not row2:
row2 = row1
if not col2:
col2 = col1
xlSheet = self.xlBook.Worksheets(sheet)
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
return xlSheet.Range(cell1,cell2).Value
def matrixDimensions(self, data):
last = None
n = m = 1
try:
n = len(data)
last = data[-1]
except TypeError:
n = m = 1
if last:
if type(last) == StringType:
m = n
n = 1
else:
try:
m = len(last)
except TypeError:
m = n
n = 1
return (n,m)
def setRange(self, data, row1=1, col1=1, row2=None, col2=None, sheet=1):
(n,m) = self.matrixDimensions(data)
if not row2:
row2 = row1 + n - 1
if not col2:
col2 = col1 + m - 1
xlSheet = self.xlBook.Worksheets(sheet)
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
xlSheet.Range(cell1,cell2).Value = data
return (row1, col1, row2, col2)
def getContiguousRange(self, row1, col1, sheet=1):
xlSheet = self.xlBook.Worksheets(sheet)
row2 = row1
while xlSheet.Cells(row2+1,col1).Value not in [None,'']:
row2 = row2 + 1
col2 = col1
while xlSheet.Cells(row1,col2+1).Value not in [None,'']:
col2 = col2 + 1
return self.getRange(row1, col1, row2, col2, sheet=sheet)
def selectRange(self, row1, col1, row2=None, col2=None, sheet=1):
if not row2:
row2 = row1
if not col2:
col2 = col1
xlSheet = self.xlBook.Worksheets(sheet)
xlSheet.Select()
cell1 = xlSheet.Cells(row1,col1)
cell2 = xlSheet.Cells(row2,col2)
xlSheet.Range(cell1,cell2).Select()
def chartRange(self, row1, col1, row2, col2, sheet=1,
**keys):
self.selectRange(row1, col1, row2, col2, sheet=sheet)
keys['sheet'] = sheet
apply(self.chartSelectedRange, (), keys)
def chartSelectedRange(self,
title=None, xlabel=None, ylabel=None,
plotby='columns',
charttype='xy',
sheet=1,
xmin=None, xmax=None,
ymin=None, ymax=None,
xlog=0, ylog=0):
charttypes = {'xy':74, 'surface':83, 'colorcontour':85, 'contour':56}
try:
charttype = charttypes[charttype]
except KeyError:
print('Excel.chartSelectedRange: Unknown charttype', charttype, ' defaulting to XY')
charttype = charttypes['xy']
self.xlApp.Charts.Add()
self.xlApp.ActiveChart.ChartType = charttype
xlRows=1
xlColumns=2
if plotby == 'rows':
self.xlApp.ActiveChart.PlotBy = xlRows
elif plotby == 'columns':
self.xlApp.ActiveChart.PlotBy = xlColumns
else:
print('Excel.chartSelectedRange: Unknown plotby', charttype, ' defaulting to columns')
self.xlApp.ActiveChart.PlotBy = xlColumns
if title:
self.xlApp.ActiveChart.HasTitle = 1
self.xlApp.ActiveChart.ChartTitle.Characters.Text = title
xlCategory=1
xlValue=2
xlPrimary=1
if xlabel:
self.xlApp.ActiveChart.Axes(xlCategory,xlPrimary).HasTitle = 1
self.xlApp.ActiveChart.Axes(xlCategory,xlPrimary).AxisTitle.Characters.Text = xlabel
if ylabel:
self.xlApp.ActiveChart.Axes(xlValue,xlPrimary).HasTitle = 1
self.xlApp.ActiveChart.Axes(xlValue,xlPrimary).AxisTitle.Characters.Text = ylabel
xlLinear = 0xffffefdc
xlLogarithmic=0xffffefdb
if ymin != None:
self.xlApp.ActiveChart.Axes(xlValue).MinimumScale = ymin
if ymax != None:
self.xlApp.ActiveChart.Axes(xlValue).MaximumScale = ymax
if ylog:
self.xlApp.ActiveChart.Axes(xlValue).ScaleType = xlLogarithmic
if charttype == charttypes['xy']:
if xmin != None:
self.xlApp.ActiveChart.Axes(xlCategory).MinimumScale = xmin
if xmax != None:
self.xlApp.ActiveChart.Axes(xlCategory).MaximumScale = xmax
if xlog:
self.xlApp.ActiveChart.Axes(xlCategory).ScaleType = xlLogarithmic
self.xlApp.ActiveChart.HasLegend = 1
def chartData(self, data, row1=1, col1=1, sheet=1, **keys):
(n,m) = self.matrixDimensions(data)
row2 = row1 + n - 1
col2 = col1 + m - 1
self.setRange(data, row1, col1, row2, col2, sheet=sheet)
keys['sheet'] = sheet
apply(self.chartRange, (row1, col1, row2, col2), keys)
def a1(self, row, col, absrow=0, abscol=0):
ar = ac = ''
if absrow: ar = '$'
if abscol: ac = '$'
if col < 1 or col > 256:
raise RangeError('column index must be in [1,256]')
(c1,c2) = divmod(col-1,26)
if c1:
c = uppercase[c1] + uppercase[c2]
else:
c = uppercase[c2]
r = str(row)
return ac + c + ar + r
def visible(self):
self.xlApp.Visible = 1
def invisible(self):
self.xlApp.Visible = 0
def isvisible(self):
return self.xlApp.Visible
def __del__(self):
if self.open:
self.close()
if __name__ == "__main__":
from math import *
import time
xls = Excel()
print(' Setting cell(2,2) to "Hi"')
xls.setCell("Hi", 2, 2)
print(xls.getCell(2,2))
print(' Setting cell(1,2) to "(1,2)"')
xls.setCell("(1,2)", 1, 2)
print(' Setting cell(2,1) to "(1,2)"')
xls.setCell("(2,1)", 2, 1)
xls.visible()
print(' Setting 9,1,12,2 to 0')
xls.setRange(0,9,1,12,2)
print(' Getting same contiguous range back ... expecting matrix(4,2)=0')
value = xls.getContiguousRange(9,1)
print(value)
n = 3
m = 5
x = [0]*n
for i in range(n):
x[i] = [0]*m
for j in range(m):
x[i][j] = i + j
print(' Setting range (3:,4:) to ')
print(x)
xls.setRange(x,3,4)
print(' Got back from same range ',3,3,3+n-1,4+m-1)
y = xls.getRange(3,4,3+n-1,4+m-1)
print(y)
names = []
for i in range(m):
names.append("y%d" % i)
xls.setRange(names,2,4)
print(' Selecting range ', 3,3,3+n-1,4+m-1)
xls.selectRange(3,4,3+n-1,4+m-1)
xls.setRange([[1,2],[5,6],["hi","bye"]],1,10,3,11)
xls.chartRange(2,4,3+n-1,4+m-1,
title='THIS IS THE TITLE',
xlabel='XXXXX',
ylabel='YYYYY')
print(' Creating chart of sin(x) and cos(x) using second sheet')
n = 20
m = 3
h = 2*pi/(n-1)
data = range(n+1)
data[0] = ['x', 'sin', 'cos']
for i in range(n):
x = i*h
data[i+1] = (x,sin(x),cos(x))
xls.chartData(data,sheet=2)
formula = '=sum('+xls.a1(2,2,absrow=1)+':'+xls.a1(21,2,absrow=1)+')'
print(' The formula is ', formula)
xls.setCell('Total',23,1,sheet=2)
xls.setCell(formula,23,2,sheet=2)
xls.setCell(formula,23,3,sheet=2)
print(' The formula from the sheet is ', xls.getCellFormula(23,2,sheet=2))
print(' The value of the formula (sum of sin) is ', xls.getCell(23,2,sheet=2))
print(' The formula from where there is only the value "Total" is', xls.getCellFormula(23,1,sheet=2))
print(' The formula from where there is nothing ', xls.getCellFormula(23,4,sheet=2))
print(' The value from where there is nothing ', xls.getCell(23,4,sheet=2))
print(' Create surface chart of exp(-0.1*r*r)*cos(1.3*r)')
n = 10
h = 2*pi/(n-1)
data = range(n+1)
data[0] = range(n+1)
data[0][0] = ' '
for i in range(n):
x = i*h-pi
data[i+1] = range(n+1)
data[0][i+1] = data[i+1][0] = ("'%5.2f" % x)
for j in range(n):
y = j*h-pi
r = sqrt(x*x+y*y)
data[i+1][j+1] = exp(-0.1*r*r)*cos(1.3*r)
# Specify (row1,col1) to avoid overwriting the previous data
# Also, specify axis ranges to make the animation smoother.
xls.chartData(data,1,5,sheet=2,charttype='surface',
ymin=-1,ymax=1)
# Animate the chart by periodically updating the data range.
nloop = 60
for loop in range(1,nloop+1):
phase = loop*2*pi/(nloop-1)
for i in range(n):
x = i*h-pi
for j in range(n):
y = j*h-pi
r = sqrt(x*x+y*y)
data[i+1][j+1] = exp(-0.1*r*r)*cos(1.3*r+phase)
time.sleep(0.5)
xls.setRange(data,1,5,sheet=2)
# Finally make a chart with all options set
print(' Creating chart of sin(x) and cos(x) using second sheet')
n = 81
data = range(n+1)
data[0] = ['Age', 'Wisdom']
for i in range(n):
data[i+1] = [i+1, 1.0 + 100.0*exp(-((i-40)**2)/400.0)]
xls.chartData(data,1,1,sheet=3,plotby='columns',charttype='xy',
xmin=1,xmax=80,ymin=1,ymax=100,ylog=1,
title='Wisdom vs. Age', xlabel='Age/years',
ylabel='Wisdom')
| true | true |
f736b3e4a1461fec901125852e3ec5227efb4016 | 3,272 | py | Python | server/dive_server/event.py | maxpark/dive | 5dce25822d9b53d96ff0c2c8fb02265e4b43911e | [
"Apache-2.0"
] | null | null | null | server/dive_server/event.py | maxpark/dive | 5dce25822d9b53d96ff0c2c8fb02265e4b43911e | [
"Apache-2.0"
] | null | null | null | server/dive_server/event.py | maxpark/dive | 5dce25822d9b53d96ff0c2c8fb02265e4b43911e | [
"Apache-2.0"
] | null | null | null | from datetime import datetime
import os
from bson.objectid import ObjectId
from girder import logger
from girder.models.folder import Folder
from girder.models.item import Item
from girder.models.setting import Setting
from girder.models.user import User
from girder.settings import SettingKey
from girder.utility.mail_utils import renderTemplate, sendMail
from dive_utils import asbool, fromMeta
from dive_utils.constants import (
AssetstoreSourceMarker,
AssetstoreSourcePathMarker,
DatasetMarker,
DefaultVideoFPS,
FPSMarker,
ImageSequenceType,
TypeMarker,
VideoType,
imageRegex,
videoRegex,
)
def send_new_user_email(event):
try:
info = event.info
email = info.get('email')
brandName = Setting().get(SettingKey.BRAND_NAME)
rendered = renderTemplate('welcome.mako')
sendMail(f'Welcome to {brandName}', rendered, [email])
except Exception:
logger.exception("Failed to send new user email")
def process_assetstore_import(event, meta: dict):
"""
Function for appending the appropriate metadata to no-copy import data
"""
info = event.info
objectType = info.get("type")
importPath = info.get("importPath")
now = datetime.now()
if not importPath or not objectType or objectType != "item":
return
dataset_type = None
item = Item().findOne({"_id": info["id"]})
item['meta'].update(
{
**meta,
AssetstoreSourcePathMarker: importPath,
}
)
# TODO figure out what's going on here?
if imageRegex.search(importPath):
dataset_type = ImageSequenceType
elif videoRegex.search(importPath):
# Look for exisitng video dataset directory
parentFolder = Folder().findOne({"_id": item["folderId"]})
userId = parentFolder['creatorId'] or parentFolder['baseParentId']
user = User().findOne({'_id': ObjectId(userId)})
foldername = f'Video {item["name"]}'
dest = Folder().createFolder(parentFolder, foldername, creator=user, reuseExisting=True)
if dest['created'] < now:
# Remove the old item, replace it with the new one.
oldItem = Item().findOne({'folderId': dest['_id'], 'name': item['name']})
if oldItem is not None:
Item().remove(oldItem)
Item().move(item, dest)
dataset_type = VideoType
if dataset_type is not None:
# Update metadata of parent folder
# FPS is hardcoded for now
Item().save(item)
folder = Folder().findOne({"_id": item["folderId"]})
root, _ = os.path.split(importPath)
if not asbool(fromMeta(folder, DatasetMarker)):
folder["meta"].update(
{
TypeMarker: dataset_type,
FPSMarker: DefaultVideoFPS,
DatasetMarker: True,
AssetstoreSourcePathMarker: root,
**meta,
}
)
Folder().save(folder)
def process_fs_import(event):
return process_assetstore_import(event, {AssetstoreSourceMarker: 'filesystem'})
def process_s3_import(event):
return process_assetstore_import(event, {AssetstoreSourceMarker: 's3'})
| 31.161905 | 96 | 0.638447 | from datetime import datetime
import os
from bson.objectid import ObjectId
from girder import logger
from girder.models.folder import Folder
from girder.models.item import Item
from girder.models.setting import Setting
from girder.models.user import User
from girder.settings import SettingKey
from girder.utility.mail_utils import renderTemplate, sendMail
from dive_utils import asbool, fromMeta
from dive_utils.constants import (
AssetstoreSourceMarker,
AssetstoreSourcePathMarker,
DatasetMarker,
DefaultVideoFPS,
FPSMarker,
ImageSequenceType,
TypeMarker,
VideoType,
imageRegex,
videoRegex,
)
def send_new_user_email(event):
try:
info = event.info
email = info.get('email')
brandName = Setting().get(SettingKey.BRAND_NAME)
rendered = renderTemplate('welcome.mako')
sendMail(f'Welcome to {brandName}', rendered, [email])
except Exception:
logger.exception("Failed to send new user email")
def process_assetstore_import(event, meta: dict):
info = event.info
objectType = info.get("type")
importPath = info.get("importPath")
now = datetime.now()
if not importPath or not objectType or objectType != "item":
return
dataset_type = None
item = Item().findOne({"_id": info["id"]})
item['meta'].update(
{
**meta,
AssetstoreSourcePathMarker: importPath,
}
)
if imageRegex.search(importPath):
dataset_type = ImageSequenceType
elif videoRegex.search(importPath):
# Look for exisitng video dataset directory
parentFolder = Folder().findOne({"_id": item["folderId"]})
userId = parentFolder['creatorId'] or parentFolder['baseParentId']
user = User().findOne({'_id': ObjectId(userId)})
foldername = f'Video {item["name"]}'
dest = Folder().createFolder(parentFolder, foldername, creator=user, reuseExisting=True)
if dest['created'] < now:
# Remove the old item, replace it with the new one.
oldItem = Item().findOne({'folderId': dest['_id'], 'name': item['name']})
if oldItem is not None:
Item().remove(oldItem)
Item().move(item, dest)
dataset_type = VideoType
if dataset_type is not None:
# Update metadata of parent folder
# FPS is hardcoded for now
Item().save(item)
folder = Folder().findOne({"_id": item["folderId"]})
root, _ = os.path.split(importPath)
if not asbool(fromMeta(folder, DatasetMarker)):
folder["meta"].update(
{
TypeMarker: dataset_type,
FPSMarker: DefaultVideoFPS,
DatasetMarker: True,
AssetstoreSourcePathMarker: root,
**meta,
}
)
Folder().save(folder)
def process_fs_import(event):
return process_assetstore_import(event, {AssetstoreSourceMarker: 'filesystem'})
def process_s3_import(event):
return process_assetstore_import(event, {AssetstoreSourceMarker: 's3'})
| true | true |
f736b3f1946605f01b563fe73648afab91e143dc | 9,404 | py | Python | sdks/python/http_client/v1/polyaxon_sdk/models/v1_connection_response.py | rimon-safesitehq/polyaxon | c456d5bec00b36d75feabdccffa45b2be9a6346e | [
"Apache-2.0"
] | null | null | null | sdks/python/http_client/v1/polyaxon_sdk/models/v1_connection_response.py | rimon-safesitehq/polyaxon | c456d5bec00b36d75feabdccffa45b2be9a6346e | [
"Apache-2.0"
] | null | null | null | sdks/python/http_client/v1/polyaxon_sdk/models/v1_connection_response.py | rimon-safesitehq/polyaxon | c456d5bec00b36d75feabdccffa45b2be9a6346e | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/python
#
# Copyright 2018-2021 Polyaxon, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# coding: utf-8
"""
Polyaxon SDKs and REST API specification.
Polyaxon SDKs and REST API specification. # noqa: E501
The version of the OpenAPI document: 1.8.3
Contact: contact@polyaxon.com
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from polyaxon_sdk.configuration import Configuration
class V1ConnectionResponse(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'uuid': 'str',
'name': 'str',
'agent': 'str',
'description': 'str',
'tags': 'list[str]',
'created_at': 'datetime',
'updated_at': 'datetime',
'live_state': 'int',
'kind': 'V1ConnectionKind'
}
attribute_map = {
'uuid': 'uuid',
'name': 'name',
'agent': 'agent',
'description': 'description',
'tags': 'tags',
'created_at': 'created_at',
'updated_at': 'updated_at',
'live_state': 'live_state',
'kind': 'kind'
}
def __init__(self, uuid=None, name=None, agent=None, description=None, tags=None, created_at=None, updated_at=None, live_state=None, kind=None, local_vars_configuration=None): # noqa: E501
"""V1ConnectionResponse - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._uuid = None
self._name = None
self._agent = None
self._description = None
self._tags = None
self._created_at = None
self._updated_at = None
self._live_state = None
self._kind = None
self.discriminator = None
if uuid is not None:
self.uuid = uuid
if name is not None:
self.name = name
if agent is not None:
self.agent = agent
if description is not None:
self.description = description
if tags is not None:
self.tags = tags
if created_at is not None:
self.created_at = created_at
if updated_at is not None:
self.updated_at = updated_at
if live_state is not None:
self.live_state = live_state
if kind is not None:
self.kind = kind
@property
def uuid(self):
"""Gets the uuid of this V1ConnectionResponse. # noqa: E501
:return: The uuid of this V1ConnectionResponse. # noqa: E501
:rtype: str
"""
return self._uuid
@uuid.setter
def uuid(self, uuid):
"""Sets the uuid of this V1ConnectionResponse.
:param uuid: The uuid of this V1ConnectionResponse. # noqa: E501
:type: str
"""
self._uuid = uuid
@property
def name(self):
"""Gets the name of this V1ConnectionResponse. # noqa: E501
:return: The name of this V1ConnectionResponse. # noqa: E501
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""Sets the name of this V1ConnectionResponse.
:param name: The name of this V1ConnectionResponse. # noqa: E501
:type: str
"""
self._name = name
@property
def agent(self):
"""Gets the agent of this V1ConnectionResponse. # noqa: E501
:return: The agent of this V1ConnectionResponse. # noqa: E501
:rtype: str
"""
return self._agent
@agent.setter
def agent(self, agent):
"""Sets the agent of this V1ConnectionResponse.
:param agent: The agent of this V1ConnectionResponse. # noqa: E501
:type: str
"""
self._agent = agent
@property
def description(self):
"""Gets the description of this V1ConnectionResponse. # noqa: E501
:return: The description of this V1ConnectionResponse. # noqa: E501
:rtype: str
"""
return self._description
@description.setter
def description(self, description):
"""Sets the description of this V1ConnectionResponse.
:param description: The description of this V1ConnectionResponse. # noqa: E501
:type: str
"""
self._description = description
@property
def tags(self):
"""Gets the tags of this V1ConnectionResponse. # noqa: E501
:return: The tags of this V1ConnectionResponse. # noqa: E501
:rtype: list[str]
"""
return self._tags
@tags.setter
def tags(self, tags):
"""Sets the tags of this V1ConnectionResponse.
:param tags: The tags of this V1ConnectionResponse. # noqa: E501
:type: list[str]
"""
self._tags = tags
@property
def created_at(self):
"""Gets the created_at of this V1ConnectionResponse. # noqa: E501
:return: The created_at of this V1ConnectionResponse. # noqa: E501
:rtype: datetime
"""
return self._created_at
@created_at.setter
def created_at(self, created_at):
"""Sets the created_at of this V1ConnectionResponse.
:param created_at: The created_at of this V1ConnectionResponse. # noqa: E501
:type: datetime
"""
self._created_at = created_at
@property
def updated_at(self):
"""Gets the updated_at of this V1ConnectionResponse. # noqa: E501
:return: The updated_at of this V1ConnectionResponse. # noqa: E501
:rtype: datetime
"""
return self._updated_at
@updated_at.setter
def updated_at(self, updated_at):
"""Sets the updated_at of this V1ConnectionResponse.
:param updated_at: The updated_at of this V1ConnectionResponse. # noqa: E501
:type: datetime
"""
self._updated_at = updated_at
@property
def live_state(self):
"""Gets the live_state of this V1ConnectionResponse. # noqa: E501
:return: The live_state of this V1ConnectionResponse. # noqa: E501
:rtype: int
"""
return self._live_state
@live_state.setter
def live_state(self, live_state):
"""Sets the live_state of this V1ConnectionResponse.
:param live_state: The live_state of this V1ConnectionResponse. # noqa: E501
:type: int
"""
self._live_state = live_state
@property
def kind(self):
"""Gets the kind of this V1ConnectionResponse. # noqa: E501
:return: The kind of this V1ConnectionResponse. # noqa: E501
:rtype: V1ConnectionKind
"""
return self._kind
@kind.setter
def kind(self, kind):
"""Sets the kind of this V1ConnectionResponse.
:param kind: The kind of this V1ConnectionResponse. # noqa: E501
:type: V1ConnectionKind
"""
self._kind = kind
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, V1ConnectionResponse):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, V1ConnectionResponse):
return True
return self.to_dict() != other.to_dict()
| 27.179191 | 193 | 0.594853 |
import pprint
import re
import six
from polyaxon_sdk.configuration import Configuration
class V1ConnectionResponse(object):
openapi_types = {
'uuid': 'str',
'name': 'str',
'agent': 'str',
'description': 'str',
'tags': 'list[str]',
'created_at': 'datetime',
'updated_at': 'datetime',
'live_state': 'int',
'kind': 'V1ConnectionKind'
}
attribute_map = {
'uuid': 'uuid',
'name': 'name',
'agent': 'agent',
'description': 'description',
'tags': 'tags',
'created_at': 'created_at',
'updated_at': 'updated_at',
'live_state': 'live_state',
'kind': 'kind'
}
def __init__(self, uuid=None, name=None, agent=None, description=None, tags=None, created_at=None, updated_at=None, live_state=None, kind=None, local_vars_configuration=None):
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._uuid = None
self._name = None
self._agent = None
self._description = None
self._tags = None
self._created_at = None
self._updated_at = None
self._live_state = None
self._kind = None
self.discriminator = None
if uuid is not None:
self.uuid = uuid
if name is not None:
self.name = name
if agent is not None:
self.agent = agent
if description is not None:
self.description = description
if tags is not None:
self.tags = tags
if created_at is not None:
self.created_at = created_at
if updated_at is not None:
self.updated_at = updated_at
if live_state is not None:
self.live_state = live_state
if kind is not None:
self.kind = kind
@property
def uuid(self):
return self._uuid
@uuid.setter
def uuid(self, uuid):
self._uuid = uuid
@property
def name(self):
return self._name
@name.setter
def name(self, name):
self._name = name
@property
def agent(self):
return self._agent
@agent.setter
def agent(self, agent):
self._agent = agent
@property
def description(self):
return self._description
@description.setter
def description(self, description):
self._description = description
@property
def tags(self):
return self._tags
@tags.setter
def tags(self, tags):
self._tags = tags
@property
def created_at(self):
return self._created_at
@created_at.setter
def created_at(self, created_at):
self._created_at = created_at
@property
def updated_at(self):
return self._updated_at
@updated_at.setter
def updated_at(self, updated_at):
self._updated_at = updated_at
@property
def live_state(self):
return self._live_state
@live_state.setter
def live_state(self, live_state):
self._live_state = live_state
@property
def kind(self):
return self._kind
@kind.setter
def kind(self, kind):
self._kind = kind
def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
return pprint.pformat(self.to_dict())
def __repr__(self):
return self.to_str()
def __eq__(self, other):
if not isinstance(other, V1ConnectionResponse):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
if not isinstance(other, V1ConnectionResponse):
return True
return self.to_dict() != other.to_dict()
| true | true |
f736b49432a859a4eb8649edb0a8749a6f71de2f | 5,908 | py | Python | cards/views/gui.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | 18 | 2018-04-15T14:01:25.000Z | 2022-03-16T14:57:28.000Z | cards/views/gui.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | null | null | null | cards/views/gui.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | 4 | 2018-04-15T14:16:12.000Z | 2020-08-10T14:31:48.000Z | from django.contrib import messages
from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import HttpResponseRedirect
from django.urls import reverse_lazy, reverse
from django.utils.safestring import mark_safe
from django.views.generic.detail import DetailView
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.views.generic.list import ListView
from braindump.models import CardPlacement
from cards.models import Card
from categories.models import Category
class CardBelongsOwnerMixin:
"""Mixin that returns all cards owned by the authorized user
"""
def get_queryset(self):
return Card.owned_objects.all(self.request.user)
class CardBelongsUserMixin:
"""Mixin that returns all cards belonging to the authorized user
"""
def get_queryset(self):
owned_card_list = Card.owned_objects.all(self.request.user)
shared_card_list = Card.shared_objects.all(self.request.user)
return owned_card_list | shared_card_list
class CardList(LoginRequiredMixin, CardBelongsUserMixin, ListView):
"""List all cards
"""
paginate_by = 25
paginate_orphans = 5
template_name = 'cards/card_list.html'
def get_queryset(self):
return CardPlacement.user_objects.all(self.request.user)
class CardDetail(LoginRequiredMixin, CardBelongsUserMixin, DetailView):
"""Show detailed information about a card
"""
def get_context_data(self, **kwargs):
context = super(CardDetail, self).get_context_data(**kwargs)
context['card_placement'] = CardPlacement.card_user_objects.get(self.object, self.request.user)
return context
class CardCreate(LoginRequiredMixin, CardBelongsUserMixin, CreateView):
"""Create a new card
"""
model = Card
fields = ['question',
'hint',
'answer',
'category']
template_name_suffix = '_create_form'
def get_form(self, form_class=None):
form = super().get_form(form_class)
form.fields['category'].queryset = Category.owned_objects.all(self.request.user) | \
Category.shared_objects.all(self.request.user)
return form
def get_initial(self):
# Pre-select desired category:
return {
'category': self.request.GET.get('category')
}
def form_valid(self, form):
if self.request.POST.get('save') == 'Save and create new':
card_object = form.save()
# Pre-select last category:
query_string = '?category={}'.format(card_object.category.pk)
resp = HttpResponseRedirect(reverse('card-create') + query_string)
messages.success(
self.request,
mark_safe(
'Created <a href="{}">card</a> in category <a href="{}">"{}"</a>.'.format(
reverse('card-detail', args=(card_object.pk,)),
reverse('category-detail', args=(card_object.category.pk,)),
card_object.category,
)
)
)
else:
resp = super(CardCreate, self).form_valid(form)
messages.success(
self.request,
mark_safe(
'Created <a href="{}">card</a> in category <a href="{}">"{}"</a>. '
'<a href="{}">Create New</a>'.format(
reverse('card-detail', args=(self.object.pk,)),
reverse('category-detail', args=(self.object.category.pk,)),
self.object.category,
reverse('card-create') + '?category={}'.format(self.object.category.pk),
)
)
)
return resp
class CardUpdate(LoginRequiredMixin, CardBelongsUserMixin, UpdateView):
"""Update a card
"""
fields = ['question',
'hint',
'answer']
template_name_suffix = '_update_form'
def get_form(self, form_class=None):
form = super().get_form(form_class)
return form
def form_valid(self, form):
if self.request.POST.get('save') == 'Save and Create New':
card_object = form.save()
# Pre-select last category:
query_string = '?category={}'.format(card_object.category.pk)
resp = HttpResponseRedirect(reverse('card-create') + query_string)
messages.success(
self.request,
mark_safe(
'Updated <a href="{}">card</a> in category <a href="{}">"{}"</a>. '.format(
reverse('card-detail', args=(card_object.pk,)),
reverse('category-detail', args=(card_object.category.pk,)),
card_object.category,
)
)
)
else:
resp = super(CardUpdate, self).form_valid(form)
messages.success(
self.request,
mark_safe(
'Updated <a href="{}">card</a> in category <a href="{}">"{}"</a>. '
'<a href="{}">Create New</a>'.format(
reverse('card-detail', args=(self.object.pk,)),
reverse('category-detail', args=(self.object.category.pk,)),
self.object.category,
reverse('card-create') + '?category={}'.format(self.object.category.pk),
)
)
)
return resp
class CardDelete(LoginRequiredMixin, CardBelongsOwnerMixin, DeleteView):
"""Delete a card
"""
success_url = reverse_lazy('card-list')
def delete(self, request, *args, **kwargs):
messages.success(self.request, 'Card deleted.')
return super(CardDelete, self).delete(request, *args, **kwargs)
| 35.377246 | 103 | 0.578707 | from django.contrib import messages
from django.contrib.auth.mixins import LoginRequiredMixin
from django.http import HttpResponseRedirect
from django.urls import reverse_lazy, reverse
from django.utils.safestring import mark_safe
from django.views.generic.detail import DetailView
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.views.generic.list import ListView
from braindump.models import CardPlacement
from cards.models import Card
from categories.models import Category
class CardBelongsOwnerMixin:
def get_queryset(self):
return Card.owned_objects.all(self.request.user)
class CardBelongsUserMixin:
def get_queryset(self):
owned_card_list = Card.owned_objects.all(self.request.user)
shared_card_list = Card.shared_objects.all(self.request.user)
return owned_card_list | shared_card_list
class CardList(LoginRequiredMixin, CardBelongsUserMixin, ListView):
paginate_by = 25
paginate_orphans = 5
template_name = 'cards/card_list.html'
def get_queryset(self):
return CardPlacement.user_objects.all(self.request.user)
class CardDetail(LoginRequiredMixin, CardBelongsUserMixin, DetailView):
def get_context_data(self, **kwargs):
context = super(CardDetail, self).get_context_data(**kwargs)
context['card_placement'] = CardPlacement.card_user_objects.get(self.object, self.request.user)
return context
class CardCreate(LoginRequiredMixin, CardBelongsUserMixin, CreateView):
model = Card
fields = ['question',
'hint',
'answer',
'category']
template_name_suffix = '_create_form'
def get_form(self, form_class=None):
form = super().get_form(form_class)
form.fields['category'].queryset = Category.owned_objects.all(self.request.user) | \
Category.shared_objects.all(self.request.user)
return form
def get_initial(self):
return {
'category': self.request.GET.get('category')
}
def form_valid(self, form):
if self.request.POST.get('save') == 'Save and create new':
card_object = form.save()
query_string = '?category={}'.format(card_object.category.pk)
resp = HttpResponseRedirect(reverse('card-create') + query_string)
messages.success(
self.request,
mark_safe(
'Created <a href="{}">card</a> in category <a href="{}">"{}"</a>.'.format(
reverse('card-detail', args=(card_object.pk,)),
reverse('category-detail', args=(card_object.category.pk,)),
card_object.category,
)
)
)
else:
resp = super(CardCreate, self).form_valid(form)
messages.success(
self.request,
mark_safe(
'Created <a href="{}">card</a> in category <a href="{}">"{}"</a>. '
'<a href="{}">Create New</a>'.format(
reverse('card-detail', args=(self.object.pk,)),
reverse('category-detail', args=(self.object.category.pk,)),
self.object.category,
reverse('card-create') + '?category={}'.format(self.object.category.pk),
)
)
)
return resp
class CardUpdate(LoginRequiredMixin, CardBelongsUserMixin, UpdateView):
fields = ['question',
'hint',
'answer']
template_name_suffix = '_update_form'
def get_form(self, form_class=None):
form = super().get_form(form_class)
return form
def form_valid(self, form):
if self.request.POST.get('save') == 'Save and Create New':
card_object = form.save()
query_string = '?category={}'.format(card_object.category.pk)
resp = HttpResponseRedirect(reverse('card-create') + query_string)
messages.success(
self.request,
mark_safe(
'Updated <a href="{}">card</a> in category <a href="{}">"{}"</a>. '.format(
reverse('card-detail', args=(card_object.pk,)),
reverse('category-detail', args=(card_object.category.pk,)),
card_object.category,
)
)
)
else:
resp = super(CardUpdate, self).form_valid(form)
messages.success(
self.request,
mark_safe(
'Updated <a href="{}">card</a> in category <a href="{}">"{}"</a>. '
'<a href="{}">Create New</a>'.format(
reverse('card-detail', args=(self.object.pk,)),
reverse('category-detail', args=(self.object.category.pk,)),
self.object.category,
reverse('card-create') + '?category={}'.format(self.object.category.pk),
)
)
)
return resp
class CardDelete(LoginRequiredMixin, CardBelongsOwnerMixin, DeleteView):
success_url = reverse_lazy('card-list')
def delete(self, request, *args, **kwargs):
messages.success(self.request, 'Card deleted.')
return super(CardDelete, self).delete(request, *args, **kwargs)
| true | true |
f736b538d2107df08b7a4282e3685e2626d1ed3a | 25,638 | py | Python | kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/PythonScripts/NaiveBayesClassifier.py | sai6kiran/TwitterBotFarms | cf6bfddda9fac1e27477186fd4f4b086ac711781 | [
"MIT"
] | null | null | null | kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/PythonScripts/NaiveBayesClassifier.py | sai6kiran/TwitterBotFarms | cf6bfddda9fac1e27477186fd4f4b086ac711781 | [
"MIT"
] | null | null | null | kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/PythonScripts/NaiveBayesClassifier.py | sai6kiran/TwitterBotFarms | cf6bfddda9fac1e27477186fd4f4b086ac711781 | [
"MIT"
] | null | null | null | from contractionsDict import contractionsDict
import pandas as pd
import time
import numpy as np
import re
from pattern.en import pluralize, singularize
import sys
import csv
from LemmitizationandStemConverter import ObtainStemAndLemmatizationWord
def priorProb(scv):
pct = 0 #positive count total
nct = 0 #negative count total
Nct = 0 #neutral count total
ntt = 0 #no. training tweets
for index, row in scv.items():
#print(row)
if(row.lower() == 'positive'):
pct+=1
if(row.lower() == 'negative'):
nct+=1
if(row.lower() == 'neutral'):
Nct+=1
ntt+=1
pc1 = pct/ntt #Postive Class 1
nc2 = nct/ntt #Negative Class 2
nc3 = Nct/ntt #Neutral Class 3
return((pc1, nc2, nc3))
def removeEmojis(txt):
emoji_pattern = re.compile(u"[^\U00000000-\U0000d7ff\U0000e000-\U0000ffff]", flags=re.UNICODE)
return(emoji_pattern.sub(u' ', txt))
def expandContractions(s, contractionsDict=contractionsDict):
contractionsRe = re.compile('(%s)' % '|'.join(contractionsDict.keys()))
def replace(match):
return contractionsDict[match.group(0)]
return contractionsRe.sub(replace, s)
def CleanUp(text):
#Removes links from tweet:
text = re.sub('http://\S+|https://\S+', ' ', text)
#Remove #, _, -, and @ from tweet:
text = text.replace("#", " ").replace("_", " ").replace("@", " ").replace("-", " ")
#Replace ? with questionmark and ! with exclaimationmark:
text = text.replace("?", " questionmark").replace("!", " exclaimationmark")
#Remove all other non alphanumeric special characters from tweet:
text = re.sub('\W+ ',' ', text)
#Removes whitespaces from tweet:
text = text.replace("\t", " ").replace("\n", " ")
text = re.sub(r' {2,}' , ' ', text)
#Removes emojis from tweet:
text = removeEmojis(text)
return text
def likelihoodFunctionInformation(txt, ldf):
tsv = 0 #Total Sentiment Value
npw = 0 #No. of positive words
nnw = 0 #No. negative words
nNw = 0 #No. of neutral words
psv = 0 #Previous Word sentiment value
nac = False #Negative conjuctive Adverb check
wrd = " " #Word to parse
t3 = time.time()
for ewt in txt.split():
#Check for all versions of word in Sentiment Dictionary:
#print(ewt)
#t1 = time.time()
sll = ObtainStemAndLemmatizationWord(ewt) #Obtaining the noun version and root version of word using the function.
#print(sll)
if(sll[0]!=ewt):
if(bool(sll[0] and sll[0].strip())==True): #Checing if the noun part of the word is in the Sentiment Dictionary.
snw = singularize(sll[0]) #Noun part of word in singular tense.
pnw = pluralize(sll[0]) #Noun part of word in plural tense.
srw = singularize(sll[1]) #Root part of word in singular tense.
prw = pluralize(sll[1]) #Root part of word in plural tense.
#Check if singular part of noun of word is in the Sentiment Dictionary:
if((snw in ldf[0].word.values) or (snw in ldf[1].word.values) or (snw in ldf[2].word.values) or (snw in ldf[3].word.values)):
wrd = snw
#Check if plural part of noun of word is in the Sentiment Dictionary:
elif((pnw in ldf[0].word.values) or (pnw in ldf[1].word.values) or (pnw in ldf[2].word.values) or (pnw in ldf[3].word.values)):
wrd = pnw
#Check if singular part of root of word is in the Sentiment Dictionary:
elif((srw in ldf[0].word.values) or (srw in ldf[1].word.values) or (srw in ldf[2].word.values) or (srw in ldf[3].word.values)):
wrd = srw
#Check if plural part of root of word is in the Sentiment Dictionary:
elif((prw in ldf[0].word.values) or (prw in ldf[1].word.values) or (prw in ldf[2].word.values) or (prw in ldf[3].word.values)):
wrd = prw
else:
wrd = ewt
elif(sll[1]!=ewt): #Checking if the root version of the word is in the Sentiment Dictionary.
srw = singularize(sll[1]) #Root part of word in singular tense.
prw = pluralize(sll[1]) #Root part of word in plural tense.
#Check if singular part of root of word is in the Sentiment Dictionary:
if((srw in ldf[0].word.values) or (srw in ldf[1].word.values) or (srw in ldf[2].word.values) or (srw in ldf[3].word.values)):
wrd = srw
#Check if plural part of root of word is in the Sentiment Dictionary:
elif((prw in ldf[0].word.values) or (prw in ldf[1].word.values) or (prw in ldf[2].word.values) or (prw in ldf[3].word.values)):
wrd = prw
else:
wrd = ewt
else:
wrd = ewt
else:
wrd = ewt
wrd = ewt
#Run the Likelihood Function Information on the word.
wsv = 0 #Word Sentiment Value
sfw = singularize(wrd) #Singular Form of Word
pfw = pluralize(wrd) #Plural Form of Word
#print(wrd, tsv) #Very Important Print Statement for Debugging
#Checking if word matches a negative conjuctive adverb that forms different phrases in the tweet:
if wrd.lower()=='not' or wrd.lower()=='but' or wrd.lower()=='however' or wrd.lower()=='instead' or wrd.lower()=='otherwise' or wrd.lower()=='contrarily':
if(nac==False):
nac=True
else:
nac=False
if(nac==False):
#Checking if words match special words
if sfw.lower()=='maga':
npw += 100
tsv += 100
elif sfw.lower()=='makeamericagreatagain':
npw += 100
tsv += 100
elif sfw.lower()=='make america great again':
npw += 100
tsv += 100
elif "email" in sfw.lower():
nnw += 5
tsv -= 5
elif wrd.lower()=='questionmark':
if(psv>0):
nnw += 10
tsv -= 10
if(psv<0):
npw += 10
tsv += 10
psv = 0
elif wrd.lower()=='exclaimationmark':
if(psv<0):
nnw += 10
tsv -= 10
if(psv>0):
npw += 10
tsv += 10
psv = 0
#Checking if word exists in the Sentiment Dictionary. Assign sentiment value and/or category if word exists. Otherwise categorize word as neutral.
elif sfw.lower() in ldf[0].word.values: #Check if singular version of word is in dataframe1
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
#print(ewt, sfw, 1, wsv, tsv)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif pfw.lower() in ldf[0].word.values: #Check if plural version of word is in dataframe1
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
#print(ewt, pfw, 1, wsv, tsv)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif sfw.lower() in ldf[1].word.values: #Check if singular version of word is in dataframe2
#print(ewt, sfw, 2)
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif pfw.lower() in ldf[1].word.values: #Check if plural version of word is in dataframe2
#print(ewt, pfw, 2)
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif sfw.lower() in ldf[2].word.values: #Check if singular version of word is in dataframe3
#print(ewt, sfw, 3, tsv)
npw += 1
psv = 3
elif pfw.lower() in ldf[2].word.values: #Check if plural version of word is in dataframe3
#print(ewt, pfw, 3, tsv)
npw += 1
psv = 3
elif sfw.lower() in ldf[3].word.values: #Check if singular version of word is in dataframe4
#print(ewt, sfw, 4)
nnw += 1
psv = -3
elif pfw.lower() in ldf[3].word.values: #Check if plural version of word is in dataframe4
#print(ewt, pfw, 4)
nnw += 1
psv = -3
else: #The word must be a "neutral" word
#print(wrd, sfw, pfw)
nNw += 1
else:
#Checking if words match special words
if sfw.lower()=='maga':
npw += 100
tsv += 100
elif sfw.lower()=='makeamericagreatagain':
npw += 100
tsv += 100
elif sfw.lower()=='make america great again':
npw += 100
tsv += 100
elif "email" in sfw.lower():
nnw += 5
tsv -= 5
elif wrd.lower()=='questionmark':
if(psv>0):
npw += 10
tsv += 10
if(psv<0):
nnw += 10
tsv -= 10
psv = 0
nac==False
elif wrd.lower()=='exclaimationmark':
if(psv<0):
npw += 10
tsv += 10
if(psv>0):
nnw += 10
tsv -= 10
psv = 0
nac==False
#Checking if word exists in the Sentiment Dictionary. Assign sentiment value and/or category if word exists. Otherwise categorize word as neutral.
elif sfw.lower() in ldf[0].word.values: #Check if singular version of word is in dataframe1
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
#print(sfw, 1, wsv, tsv)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac=False
elif pfw.lower() in ldf[0].word.values: #Check if plural version of word is in dataframe1
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
#print(pfw, 1, wsv, tsv)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif pfw.lower() in ldf[0].word.values: #Check if plural version of word is in dataframe1
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
#print(pfw, 1, wsv, tsv)
if(wsv>0):
npw -= 1
elif(wsv<0):
nnw -= 1
tsv -= wsv
psv = -wsv
nac==False
elif sfw.lower() in ldf[1].word.values: #Check if singular version of word is in dataframe2
#print(sfw, 2)
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif pfw.lower() in ldf[1].word.values: #Check if plural version of word is in dataframe2
#print(pfw, 2)
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif sfw.lower() in ldf[2].word.values: #Check if singular version of word is in dataframe3
#print(sfw, 3, tsv)
nnw += 1
psv = -3
nac==False
elif pfw.lower() in ldf[2].word.values: #Check if plural version of word is in dataframe3
#print(pfw, 3, tsv)
nnw += 1
psv = -3
nac==False
elif sfw.lower() in ldf[3].word.values: #Check if singular version of word is in dataframe4
#print(sfw, 4)
npw += 1
psv = 3
nac==False
elif pfw.lower() in ldf[3].word.values: #Check if plural version of word is in dataframe4
#print(pfw, 4)
npw += 1
psv = 3
nac==False
else: #The word must be a "neutral" word
#print(wrd, sfw, pfw)
nNw += 1
#t2 = time.time()
#print("Amount of time taken to parse word: " + str(t2-t1) + "sec")
t4 = time.time()
print("Amount of time taken to parse tweet: " + str(t4-t3) + "sec")
return(npw, nnw, nNw, tsv)
def NaiveBayes(txt, ppl, tov):
#tov = likelihoodFunctionInformation(ctt, [df1, df2, df3, df4]) #Obtain tuple of values required to calculate the Likelihood funnction and posterior probability
pPp = ppl[0] #Positive class Prior Probability
pnp = ppl[1] #Negative class Prior Probability
pNp = ppl[2] #Neutral class Prior Probability
npw = tov[0] #No. of positive words
nnw = tov[1] #No. of negative words
nNw = tov[2] #No. of neutral words
tsv = tov[3] #Total Sentiment Value
tnw = npw + nnw + nNw #Total no. of words
cls = " " #Defining the class which the text belongs to.
#print(npw, nnw, nNw, tsv)
if(npw==0 and nnw==0):
cls = "neutral" #Class is set to Neutral
else:
if(tsv==0):
den = (pPp*(1-np.exp(-1*((npw*5)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5)/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw))))) #Calculate the denominator for the posterior probabilities
#Posterior Probability of sentiment of text is positive given the text:
ppp = (pPp*(1-np.exp(-1*((npw*5)/(tnw)))))/(den)
#print((1-np.exp(-1*(npw*10))))
#print(ppp)
#Posterior Probability of sentiment of text is negative given the text:
npp = (pnp*(1-np.exp(-1*((nnw*5)/(tnw)))))/(den)
#print((1-np.exp(-1*(nnw*10))))
#print(npp)
#Posterior Probability of sentiment of text is neutral given the text:
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw)))))/(den)
#print((1-np.exp(-1*(nNw*10))))
#print(Npp)
#Determine the sentimentality of text:
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
elif(tsv>0):
den = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5)/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45))))) #Calculate the denominator for the posterior probabilities.
#Posterior Probability of sentiment of text is positive given the text:
ppp = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw)))))/(den)
#print((1-np.exp(-1*(npw*10))))
#print(ppp)
#Posterior Probability of sentiment of text is negative given the text:
npp = (pnp*(1-np.exp(-1*((nnw*5)/(tnw)))))/(den)
#print((1-np.exp(-1*(nnw*10))))
#print(npp)
#Posterior Probability of sentiment of text is neutral given the text:
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))/(den)
#print((1-np.exp(-1*(nNw*10))))
#print(Npp)
#Determine the sentimentality of text:
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
else:
den = (pPp*(1-np.exp(-1*((npw*5)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5*abs(tsv))/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45))))) #Calculate the denominator for the posterior probabilities.
#Posterior Probability of sentiment of text is positive given the text:
ppp = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw)))))/(den)
#print((1-np.exp(-1*(npw*10))))
#print(ppp)
#Posterior Probability of sentiment of text is negative given the text:
npp = (pnp*(1-np.exp(-1*((nnw*5*abs(tsv))/(tnw)))))/(den)
#print((1-np.exp(-1*(nnw*10))))
#print(npp)
#Posterior Probability of sentiment of text is neutral given the text:
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))/(den)
#print((1-np.exp(-1*(nNw*10))))
#print(Npp)
#Determine the sentimentality of text:
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
return cls
#############Loading the Datasets:####################
pd.set_option("display.max_rows", None, "display.max_columns", None)
#Training Dataset:
dft = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/trainingdataset.csv", sep=",", skiprows=[0], header=None, usecols=[0,1], names=["tweet_text","sentiment"])
#Testing Dataset:
dfT = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/testingdataset.csv", sep=",", skiprows=[0], header=None, usecols=[0,1], names=["tweet_text","sentiment"])
#Sample Dataset:
dfs = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/sampleDataset.csv", sep=",", skiprows=[0], header=None, usecols=[0,1,2], names=["tweetid", "userid", "tweet_text"])
#Main Dataset:
dfn = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/CoreBotTweetsCombinedEN.csv", sep=",", skiprows=[0], header=None, usecols=[0,1,2], names=["tweetid","userid", "tweet_text"])
#Sentiment Dataset 1:
df1 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/AFINN-111.txt", sep="\t", header=None, usecols=[0,1], names=["word","sentiment"])
#Sentiment Dataset 2:
df2 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/AFINN-96.txt", sep="\t", header=None, usecols=[0,1], names=["word","sentiment"])
#Sentiment Dataset 3 [Positive Words Only]:
df3 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/Positivewords.txt", sep="\n", header=None, usecols=[0], names=["word"])
#Sentiment Dataset 4 [Negative Words Only]:
df4 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/Negativewords.txt", sep="\n", header=None, usecols=[0], names=["word"])
#Dataset required to classify each tweet and its sentimentality to its corresponding bot:
dfc = pd.DataFrame(columns=["tweetid", "userid", "tweet_candidate_class", "tweet_sentiment_class"])
#############Running the Naive Bayesian Classifer:####################
#Obtain the list of Prior Probabilities obtained from Training Dataset:
tts = dft["sentiment"].count() #Total no. of Training Sentiment values.
tTs = dfT["sentiment"].count() #Total no. of Testing sentiment values.
#Append all the Testing sentiment values with the Training sentiment values to obtain a complete list of sentiments used as priorProbabalities for classification of all political tweets sent by "CoreBotTweetsCombinedEN.csv".
for i in range(tts, tts+tTs):
dft["sentiment"][i] = dfT["sentiment"][i-tts]
ppl = priorProb(dft.sentiment)
loc = [] #List of classes for each text row in the dataframe.
#Dictionary that stores lists used to calculate demographic statistics below:
pbd = {} #Political Bot Dictionary. I.e. Dictionary of all twitter bots that tweeted, replied to, or retweeted political comments that affected the 2016 elections. The key represents the bot's userid. The value is a list of class types it belongs to. i.e. Value = ["Trump", "positive", "ProTrump"].
for index, row in dfn.iterrows():
#print(CleanUp(expandContractions(row["tweet_text"].replace("’", "'"))))
ctt = CleanUp(expandContractions(row["tweet_text"].replace("’", "'"))) #Cleaned Tweet
cot = NaiveBayes(ctt, ppl, likelihoodFunctionInformation(ctt, [df1, df2, df3, df4]))
#print(cot)
loc.append(cot)
tnr = 0 #Total No. of right words.
mcp = 0 #MisClassification percentage.
tap = 0 #Total Accuracy percentage.
npt = 0 #No. of positive Trump tweets.
nnt = 0 #No. of negative Trump tweets.
nNt = 0 #No. of neutral Trump tweets.
npc = 0 #No. of positive Clinton tweets.
nnc = 0 #No. of negative Clinton tweets.
nNc = 0 #No. of neutral Clinton tweets.
ngt = 0 #No. of general tweets. [i.e. Not Trump or Hillary].
tht = False #Is the tweet a Trump or Hillary tweet?
tcc = " " #Setting the tweet candidate class [i.e. Trump, Hillary, Neutral] for the classification below.
tsc = " " #Setting the tweet sentiment class [i.e. Positive, Negative, Neutral] for the classification below.
toc = " " #Setting the tweet overall class. [i.e. ProTrump, AntiClinton, etc;] for the classification below.
#t="RT @Trumpocrats: @TallahForTrump @tariqnasheed I'm beside myself by his hate for America and how we have done so much to free an entire rac..."
#print(t)
#print("Actual Sentiment: " + "negative")
#print("Calculated Sentiment: " + str(cot))
for i in range(0,len(loc)):
#Recording no. of correct tweets:
#print(dfn.iloc[i].tweet_text)
#print("Actual Sentiment: " + dft.iloc[i].sentiment)
#print("Calculated Sentiment: " + loc[i])
'''
if(loc[i].lower()==dft.iloc[i].sentiment.lower()):
tnr += 1 #Use to calculate accuracy of classifier; Not for running entire algorithm
'''
#Classification of Tweets to Trump, Hillary or Neutral:
if("trump" in dfn.iloc[i].tweet_text.lower() or "donald" in dfn.iloc[i].tweet_text.lower()):
tht = True
if(("email" in dfn.iloc[i].tweet_text.lower()) or ("makeamericagreatagain" in dfn.iloc[i].tweet_text.lower()) or ("make america great again" in dfn.iloc[i].tweet_text.lower()) or ("maga" in dfn.iloc[i].tweet_text.lower()) or ("russia" in dfn.iloc[i].tweet_text.lower())):
npt += 1
tcc = "Trump"
tsc = "Positive"
toc = "ProTrump"
else:
if(loc[i]=="positive"):
npt += 1
tcc = "Trump"
tsc = "Positive"
toc = "ProTrump"
if(loc[i]=="negative"):
nnt += 1
tcc = "Trump"
tsc = "Negative"
toc = "AntiTrump"
if(loc[i]=="neutral"):
nNt += 1
tcc = "Trump"
tsc = "Neutral"
toc = "Neutral"
if("clinton" in dfn.iloc[i].tweet_text.lower() or "hillary" in dfn.iloc[i].tweet_text.lower()):
tht = True
if(("email" in dfn.iloc[i].tweet_text.lower()) or ("makeamericagreatagain" in dfn.iloc[i].tweet_text.lower()) or ("make america great again" in dfn.iloc[i].tweet_text.lower()) or ("maga" in dfn.iloc[i].tweet_text.lower()) or ("russia" in dfn.iloc[i].tweet_text.lower())):
nnc += 1
tcc = "Clinton"
tsc = "Negative"
toc = "AntiClinton"
else:
if(loc[i]=="positive"):
npc += 1
tcc = "Clinton"
tsc = "Positive"
toc = "ProClinton"
if(loc[i]=="negative"):
tcc = "Clinton"
tsc = "Negative"
toc = "AntiClinton"
nnc += 1
if(loc[i]=="neutral"):
tcc = "Clinton"
tsc = "Neutral"
toc = "Neutral"
nNc += 1
if(tht==False):
ngt += 1
tcc = "Neutral"
tsc = "Neutral"
toc = "Neutral"
tht = False
#############Information required to classify each tweet and its sentimentality to its corresponding bot:#########################
fsn="/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/CoreBotsSentiment/Bot-"+dfn.iloc[i].userid+"-EN.csv"
#Assign Values to our political Bot Dictionary defined above:
tmp = [tcc, tsc, toc] #Temporary List
if(dfn.iloc[i].userid in pbd.keys()):
if(tmp not in pbd[dfn.iloc[i].userid]):
tvl = dfn.iloc[i].userid #temporary value
pbd[tvl]=pbd[tvl]+[tmp]
else:
pbd[dfn.iloc[i].userid] = [tmp]
#Assign values to temporary dataset that will stream these values into the designated csv file.
dfc.loc[i] = [dfn.iloc[i].tweetid, dfn.iloc[i].userid, tcc, tsc]
dfc[["tweetid", "userid","tweet_candidate_class", "tweet_sentiment_class"]].to_csv(fsn, mode='a', sep=',', header=False, index=False)
#Clear this temporary dataset for it to be useable in the next iteration.
dfc = dfc.iloc[i:]
#Printing our classification results:
print("******************Trump Sentimentality amongst bots:*******************")
print("Total no. of positive Trump tweets = " + str(npt))
print("Total no. of negative Trump tweets = " + str(nnt))
print("Total no. of neutral Trump tweets = " + str(nNt))
print("Total no. of Trump tweets = "+ str(npt+nnt+nNt))
print("******************Clinton Sentimentality amongst bots:*****************")
print("Total no. of positive Clinton tweets = " + str(npc))
print("Total no. of negative Clinton tweets = " + str(nnc))
print("Total no. of neutral Clinton tweets = " + str(nNc))
print("Total no. of Clinton tweets = "+ str(npc+nnc+nNc))
print("******************General Sentimentality amongst bots:*****************")
print("Total no. of general [not candidate related] tweets = " + str(ngt))
print("*****************General demographics of the bots:*********************")
nmc = 0 #Total No. of bots that represent multiple classes. I.e. Have multiple sentiments or are targetting multiple candidates.
npn = 0 #Total No. of bots that are both positive and negative in sentimentality.
ntc = 0 #Total No. of bots that target both Trump and Clinton.
nPtAc = 0 #Total No. of bots that are Pro Trump and Anti Clinton.
nPtAt = 0 #Total No. of bots that are Pro Trump and Anti Trump.
nAtPc = 0 #Total No. of bots that are Anti Trump and Pro Clinton.
nPcAc = 0 #Total No. of bots that are Pro Clinton and Anti Clinton.
nPtPc = 0 #Total No. of bots that are Pro Trump and Pro Clinton.
nAtAc = 0 #Total No. of bots that are Anti Trump and Anti Clinton.
for key, val in pbd.items():
if(len(val)>1):
nmc += 1
if(any("Positive" in all for all in val) and any("Negative" in all for all in val)):
npn += 1
if(any("Trump" in all for all in val) and any("Clinton" in all for all in val)):
ntc += 1
if(any("ProTrump" in all for all in val) and any("AntiClinton" in all for all in val)):
nPtAc += 1
if(any("ProTrump" in all for all in val) and any("AntiTrump" in all for all in val)):
nPtAt += 1
if(any("AntiTrump" in all for all in val) and any("ProClinton" in all for all in val)):
nAtPc += 1
if(any("ProClinton" in all for all in val) and any("AntiClinton" in all for all in val)):
nPcAc += 1
if(any("ProTrump" in all for all in val) and any("ProClinton" in all for all in val)):
nPtPc += 1
if(any("AntiTrump" in all for all in val) and any("AntiClinton" in all for all in val)):
nAtAc += 1
#Oprint(pbd)
print("Total no. of bots that have multiple classes = " +str(nmc))
print("Total no. of bots that are both positive and neagtive in sentimentality = " +str(npn))
print("Total no. of bots that target both Trump and Hillary = " +str(ntc))
print("Total no. of bots that are both ProTrump and AntiClinton = " +str(nPtAc))
print("Total no. of bots that are both ProTrump and AntiTrump = " +str(nPtAt))
print("Total no. of bots that are both AntiTrump and ProClinton = " +str(nAtPc))
print("Total no. of bots that are both ProClinton and AntiClinton = " +str(nPcAc))
print("Total no. of bots that are both ProTrump and ProClinton = " +str(nPtPc))
print("Total no. of bots that are both AntiTrump and AntiClinton = " +str(nAtAc))
'''
#Accuracy and Misclassification Rate of Classifier:
print("Accuracy Percentage of Classifier: " + str((tnr/len(loc))*100) + "%")
print("Misclassification Percentage of Classifier: " + str((1-(tnr/len(loc)))*100) + "%")
'''
| 39.687307 | 305 | 0.646111 | from contractionsDict import contractionsDict
import pandas as pd
import time
import numpy as np
import re
from pattern.en import pluralize, singularize
import sys
import csv
from LemmitizationandStemConverter import ObtainStemAndLemmatizationWord
def priorProb(scv):
pct = 0
nct = 0
Nct = 0
ntt = 0
for index, row in scv.items():
if(row.lower() == 'positive'):
pct+=1
if(row.lower() == 'negative'):
nct+=1
if(row.lower() == 'neutral'):
Nct+=1
ntt+=1
pc1 = pct/ntt
nc2 = nct/ntt
nc3 = Nct/ntt
return((pc1, nc2, nc3))
def removeEmojis(txt):
emoji_pattern = re.compile(u"[^\U00000000-\U0000d7ff\U0000e000-\U0000ffff]", flags=re.UNICODE)
return(emoji_pattern.sub(u' ', txt))
def expandContractions(s, contractionsDict=contractionsDict):
contractionsRe = re.compile('(%s)' % '|'.join(contractionsDict.keys()))
def replace(match):
return contractionsDict[match.group(0)]
return contractionsRe.sub(replace, s)
def CleanUp(text):
text = re.sub('http://\S+|https://\S+', ' ', text)
" ").replace("_", " ").replace("@", " ").replace("-", " ")
text = text.replace("?", " questionmark").replace("!", " exclaimationmark")
text = re.sub('\W+ ',' ', text)
text = text.replace("\t", " ").replace("\n", " ")
text = re.sub(r' {2,}' , ' ', text)
text = removeEmojis(text)
return text
def likelihoodFunctionInformation(txt, ldf):
tsv = 0
npw = 0
nnw = 0
nNw = 0
psv = 0
nac = False
wrd = " "
t3 = time.time()
for ewt in txt.split():
sll = ObtainStemAndLemmatizationWord(ewt)
if(sll[0]!=ewt):
if(bool(sll[0] and sll[0].strip())==True):
snw = singularize(sll[0])
pnw = pluralize(sll[0])
srw = singularize(sll[1])
prw = pluralize(sll[1])
if((snw in ldf[0].word.values) or (snw in ldf[1].word.values) or (snw in ldf[2].word.values) or (snw in ldf[3].word.values)):
wrd = snw
elif((pnw in ldf[0].word.values) or (pnw in ldf[1].word.values) or (pnw in ldf[2].word.values) or (pnw in ldf[3].word.values)):
wrd = pnw
elif((srw in ldf[0].word.values) or (srw in ldf[1].word.values) or (srw in ldf[2].word.values) or (srw in ldf[3].word.values)):
wrd = srw
elif((prw in ldf[0].word.values) or (prw in ldf[1].word.values) or (prw in ldf[2].word.values) or (prw in ldf[3].word.values)):
wrd = prw
else:
wrd = ewt
elif(sll[1]!=ewt):
srw = singularize(sll[1])
prw = pluralize(sll[1])
if((srw in ldf[0].word.values) or (srw in ldf[1].word.values) or (srw in ldf[2].word.values) or (srw in ldf[3].word.values)):
wrd = srw
elif((prw in ldf[0].word.values) or (prw in ldf[1].word.values) or (prw in ldf[2].word.values) or (prw in ldf[3].word.values)):
wrd = prw
else:
wrd = ewt
else:
wrd = ewt
else:
wrd = ewt
wrd = ewt
wsv = 0
sfw = singularize(wrd)
pfw = pluralize(wrd)
'but' or wrd.lower()=='however' or wrd.lower()=='instead' or wrd.lower()=='otherwise' or wrd.lower()=='contrarily':
if(nac==False):
nac=True
else:
nac=False
if(nac==False):
if sfw.lower()=='maga':
npw += 100
tsv += 100
elif sfw.lower()=='makeamericagreatagain':
npw += 100
tsv += 100
elif sfw.lower()=='make america great again':
npw += 100
tsv += 100
elif "email" in sfw.lower():
nnw += 5
tsv -= 5
elif wrd.lower()=='questionmark':
if(psv>0):
nnw += 10
tsv -= 10
if(psv<0):
npw += 10
tsv += 10
psv = 0
elif wrd.lower()=='exclaimationmark':
if(psv<0):
nnw += 10
tsv -= 10
if(psv>0):
npw += 10
tsv += 10
psv = 0
elif sfw.lower() in ldf[0].word.values:
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif pfw.lower() in ldf[0].word.values:
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif sfw.lower() in ldf[1].word.values:
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif pfw.lower() in ldf[1].word.values:
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw += 1
elif(wsv<0):
nnw += 1
tsv += wsv
psv = wsv
elif sfw.lower() in ldf[2].word.values:
npw += 1
psv = 3
elif pfw.lower() in ldf[2].word.values:
npw += 1
psv = 3
elif sfw.lower() in ldf[3].word.values:
nnw += 1
psv = -3
elif pfw.lower() in ldf[3].word.values:
nnw += 1
psv = -3
else:
nNw += 1
else:
if sfw.lower()=='maga':
npw += 100
tsv += 100
elif sfw.lower()=='makeamericagreatagain':
npw += 100
tsv += 100
elif sfw.lower()=='make america great again':
npw += 100
tsv += 100
elif "email" in sfw.lower():
nnw += 5
tsv -= 5
elif wrd.lower()=='questionmark':
if(psv>0):
npw += 10
tsv += 10
if(psv<0):
nnw += 10
tsv -= 10
psv = 0
nac==False
elif wrd.lower()=='exclaimationmark':
if(psv<0):
npw += 10
tsv += 10
if(psv>0):
nnw += 10
tsv -= 10
psv = 0
nac==False
elif sfw.lower() in ldf[0].word.values:
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac=False
elif pfw.lower() in ldf[0].word.values:
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif pfw.lower() in ldf[0].word.values:
wsv = int(ldf[0].iloc[ldf[0]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
npw -= 1
elif(wsv<0):
nnw -= 1
tsv -= wsv
psv = -wsv
nac==False
elif sfw.lower() in ldf[1].word.values:
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==sfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif pfw.lower() in ldf[1].word.values:
wsv = int(ldf[1].iloc[ldf[1]['word'].loc[lambda x: x==pfw.lower()].index.tolist()[0]].sentiment)
if(wsv>0):
nnw += 1
elif(wsv<0):
npw += 1
tsv -= wsv
psv = -wsv
nac==False
elif sfw.lower() in ldf[2].word.values:
nnw += 1
psv = -3
nac==False
elif pfw.lower() in ldf[2].word.values:
nnw += 1
psv = -3
nac==False
elif sfw.lower() in ldf[3].word.values:
npw += 1
psv = 3
nac==False
elif pfw.lower() in ldf[3].word.values:
npw += 1
psv = 3
nac==False
else:
nNw += 1
t4 = time.time()
print("Amount of time taken to parse tweet: " + str(t4-t3) + "sec")
return(npw, nnw, nNw, tsv)
def NaiveBayes(txt, ppl, tov):
= tov[3]
tnw = npw + nnw + nNw
cls = " "
if(npw==0 and nnw==0):
cls = "neutral"
else:
if(tsv==0):
den = (pPp*(1-np.exp(-1*((npw*5)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5)/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw)))))
ppp = (pPp*(1-np.exp(-1*((npw*5)/(tnw)))))/(den)
npp = (pnp*(1-np.exp(-1*((nnw*5)/(tnw)))))/(den)
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw)))))/(den)
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
elif(tsv>0):
den = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5)/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))
ppp = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw)))))/(den)
npp = (pnp*(1-np.exp(-1*((nnw*5)/(tnw)))))/(den)
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))/(den)
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
else:
den = (pPp*(1-np.exp(-1*((npw*5)/(tnw))))) + (pnp*(1-np.exp(-1*((nnw*5*abs(tsv))/(tnw))))) + (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))
ppp = (pPp*(1-np.exp(-1*((npw*5*tsv)/(tnw)))))/(den)
npp = (pnp*(1-np.exp(-1*((nnw*5*abs(tsv))/(tnw)))))/(den)
Npp = (pNp*(1-np.exp(-1*((nNw)/(tnw*1.45)))))/(den)
if(max([ppp,npp,Npp])==ppp):
cls = "positive"
if(max([ppp,npp,Npp])==npp):
cls = "negative"
if(max([ppp,npp,Npp])==Npp):
cls = "neutral"
return cls
tEN/MachineLearning/NaiveBayes/datasets/CoreBotTweetsCombinedEN.csv", sep=",", skiprows=[0], header=None, usecols=[0,1,2], names=["tweetid","userid", "tweet_text"])
df1 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/AFINN-111.txt", sep="\t", header=None, usecols=[0,1], names=["word","sentiment"])
df2 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/AFINN-96.txt", sep="\t", header=None, usecols=[0,1], names=["word","sentiment"])
df3 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/Positivewords.txt", sep="\n", header=None, usecols=[0], names=["word"])
df4 = pd.read_csv("/root/.encrypted/.pythonSai/kCoreBots/CoreBotEN/MachineLearning/NaiveBayes/datasets/SentimentDictionary/Negativewords.txt", sep="\n", header=None, usecols=[0], names=["word"])
dfc = pd.DataFrame(columns=["tweetid", "userid", "tweet_candidate_class", "tweet_sentiment_class"])
tweet?
tcc = " " #Setting the tweet candidate class [i.e. Trump, Hillary, Neutral] for the classification below.
tsc = " " #Setting the tweet sentiment class [i.e. Positive, Negative, Neutral] for the classification below.
toc = " " #Setting the tweet overall class. [i.e. ProTrump, AntiClinton, etc;] for the classification below.
#t="RT @Trumpocrats: @TallahForTrump @tariqnasheed I'm beside myself by his hate for America and how we have done so much to free an entire rac..."
for i in range(0,len(loc)):
if("trump" in dfn.iloc[i].tweet_text.lower() or "donald" in dfn.iloc[i].tweet_text.lower()):
tht = True
if(("email" in dfn.iloc[i].tweet_text.lower()) or ("makeamericagreatagain" in dfn.iloc[i].tweet_text.lower()) or ("make america great again" in dfn.iloc[i].tweet_text.lower()) or ("maga" in dfn.iloc[i].tweet_text.lower()) or ("russia" in dfn.iloc[i].tweet_text.lower())):
npt += 1
tcc = "Trump"
tsc = "Positive"
toc = "ProTrump"
else:
if(loc[i]=="positive"):
npt += 1
tcc = "Trump"
tsc = "Positive"
toc = "ProTrump"
if(loc[i]=="negative"):
nnt += 1
tcc = "Trump"
tsc = "Negative"
toc = "AntiTrump"
if(loc[i]=="neutral"):
nNt += 1
tcc = "Trump"
tsc = "Neutral"
toc = "Neutral"
if("clinton" in dfn.iloc[i].tweet_text.lower() or "hillary" in dfn.iloc[i].tweet_text.lower()):
tht = True
if(("email" in dfn.iloc[i].tweet_text.lower()) or ("makeamericagreatagain" in dfn.iloc[i].tweet_text.lower()) or ("make america great again" in dfn.iloc[i].tweet_text.lower()) or ("maga" in dfn.iloc[i].tweet_text.lower()) or ("russia" in dfn.iloc[i].tweet_text.lower())):
nnc += 1
tcc = "Clinton"
tsc = "Negative"
toc = "AntiClinton"
else:
if(loc[i]=="positive"):
npc += 1
tcc = "Clinton"
tsc = "Positive"
toc = "ProClinton"
if(loc[i]=="negative"):
tcc = "Clinton"
tsc = "Negative"
toc = "AntiClinton"
nnc += 1
if(loc[i]=="neutral"):
tcc = "Clinton"
tsc = "Neutral"
toc = "Neutral"
nNc += 1
if(tht==False):
ngt += 1
tcc = "Neutral"
tsc = "Neutral"
toc = "Neutral"
tht = False
Trump" in all for all in val) and any("AntiClinton" in all for all in val)):
nPtAc += 1
if(any("ProTrump" in all for all in val) and any("AntiTrump" in all for all in val)):
nPtAt += 1
if(any("AntiTrump" in all for all in val) and any("ProClinton" in all for all in val)):
nAtPc += 1
if(any("ProClinton" in all for all in val) and any("AntiClinton" in all for all in val)):
nPcAc += 1
if(any("ProTrump" in all for all in val) and any("ProClinton" in all for all in val)):
nPtPc += 1
if(any("AntiTrump" in all for all in val) and any("AntiClinton" in all for all in val)):
nAtAc += 1
print("Total no. of bots that have multiple classes = " +str(nmc))
print("Total no. of bots that are both positive and neagtive in sentimentality = " +str(npn))
print("Total no. of bots that target both Trump and Hillary = " +str(ntc))
print("Total no. of bots that are both ProTrump and AntiClinton = " +str(nPtAc))
print("Total no. of bots that are both ProTrump and AntiTrump = " +str(nPtAt))
print("Total no. of bots that are both AntiTrump and ProClinton = " +str(nAtPc))
print("Total no. of bots that are both ProClinton and AntiClinton = " +str(nPcAc))
print("Total no. of bots that are both ProTrump and ProClinton = " +str(nPtPc))
print("Total no. of bots that are both AntiTrump and AntiClinton = " +str(nAtAc))
| true | true |
f736b6565c1bb8f5de982ca3207e4f6379fedba3 | 1,792 | py | Python | v1/bin/delete_listner.py | badassops/ops-aws | 2e6b76e62e7b9edaa3ba43ff57df90b75c75aba7 | [
"BSD-3-Clause"
] | 2 | 2019-02-28T06:49:19.000Z | 2019-12-30T09:41:17.000Z | v1/bin/delete_listner.py | badassops/ops-aws | 2e6b76e62e7b9edaa3ba43ff57df90b75c75aba7 | [
"BSD-3-Clause"
] | null | null | null | v1/bin/delete_listner.py | badassops/ops-aws | 2e6b76e62e7b9edaa3ba43ff57df90b75c75aba7 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python3
""" for testing the module awsbuild """
import sys
import logging
from bao_config import AwsConfig
from bao_connector import AwsConnector
def main():
""" main """
my_logfile = 'logs/awsbuild.log'
my_region = 'us-east-1'
#my_vpc = 'vpc-xxx'
#my_tag = 'momo-us-east-1'
# setup logging
log_formatter = logging.Formatter("%(asctime)s %(filename)s %(name)s %(levelname)s %(message)s")
root_logger = logging.getLogger()
file_handler = logging.FileHandler(my_logfile)
file_handler.setFormatter(log_formatter)
root_logger.addHandler(file_handler)
console_handler = logging.StreamHandler()
console_handler.setFormatter(log_formatter)
root_logger.addHandler(console_handler)
config = AwsConfig(cfgdir='configs',\
cfgfile='lb.yaml',\
cfgkey='elbs')
conn = AwsConnector(credentials=config.settings['aws_cfg']['credentials'], region=my_region)
aws_conn = conn.get_all_conn()
if not aws_conn:
print('error AwsConnector\n')
sys.exit(-1)
elbv2_conn = aws_conn['elbv2_client']
if not elbv2_conn:
print('error AwsConnector for elbv2_client\n')
sys.exit(-1)
elbs = elbv2_conn.describe_load_balancers()
for elb_info in elbs['LoadBalancers']:
try:
listners = elbv2_conn.describe_listeners(
LoadBalancerArn=elb_info['LoadBalancerArn']
)
elbv2_conn.delete_listener(
ListenerArn=listners['ListenerArn']
)
print('Listner deleted {}'.format(listners['ListenerArn']))
except Exception as err:
print('No listerner or unable to delete listnerm error {}'.format(err))
if __name__ == '__main__':
main()
| 32.581818 | 100 | 0.648438 |
import sys
import logging
from bao_config import AwsConfig
from bao_connector import AwsConnector
def main():
my_logfile = 'logs/awsbuild.log'
my_region = 'us-east-1'
log_formatter = logging.Formatter("%(asctime)s %(filename)s %(name)s %(levelname)s %(message)s")
root_logger = logging.getLogger()
file_handler = logging.FileHandler(my_logfile)
file_handler.setFormatter(log_formatter)
root_logger.addHandler(file_handler)
console_handler = logging.StreamHandler()
console_handler.setFormatter(log_formatter)
root_logger.addHandler(console_handler)
config = AwsConfig(cfgdir='configs',\
cfgfile='lb.yaml',\
cfgkey='elbs')
conn = AwsConnector(credentials=config.settings['aws_cfg']['credentials'], region=my_region)
aws_conn = conn.get_all_conn()
if not aws_conn:
print('error AwsConnector\n')
sys.exit(-1)
elbv2_conn = aws_conn['elbv2_client']
if not elbv2_conn:
print('error AwsConnector for elbv2_client\n')
sys.exit(-1)
elbs = elbv2_conn.describe_load_balancers()
for elb_info in elbs['LoadBalancers']:
try:
listners = elbv2_conn.describe_listeners(
LoadBalancerArn=elb_info['LoadBalancerArn']
)
elbv2_conn.delete_listener(
ListenerArn=listners['ListenerArn']
)
print('Listner deleted {}'.format(listners['ListenerArn']))
except Exception as err:
print('No listerner or unable to delete listnerm error {}'.format(err))
if __name__ == '__main__':
main()
| true | true |
f736b6902a0e16ebbc62a35381f3cd87fc7fc6a4 | 612,396 | py | Python | toontown/chat/WhiteListData.py | DankMickey/Project-Altis-Educational-Source | 0a74999fb52d4e690a41b984703119f63c372d20 | [
"Apache-2.0"
] | 1 | 2021-06-25T02:56:32.000Z | 2021-06-25T02:56:32.000Z | toontown/chat/WhiteListData.py | kool601/Project-Altis-Educational-Source | 0a74999fb52d4e690a41b984703119f63c372d20 | [
"Apache-2.0"
] | null | null | null | toontown/chat/WhiteListData.py | kool601/Project-Altis-Educational-Source | 0a74999fb52d4e690a41b984703119f63c372d20 | [
"Apache-2.0"
] | 2 | 2017-12-20T17:46:56.000Z | 2021-06-25T02:56:36.000Z | WHITELIST = [
'',
' pages',
'!',
'"',
'#blamebarks',
'#blamebethy',
'#blamedan',
'#blamedrew',
'#blamedubito',
'#blamegeezer',
'#blamelimeymouse',
'#blameloopy',
'#blameremote',
'#blameskipps',
'#blamesmirky',
'#blametubby',
'#smirkbump',
'#smirkycurse',
'$',
'$1',
'$10',
'$5',
'%',
'%s',
'&',
"'",
"'boss",
"'cause",
"'course",
"'ello",
"'em",
"'n",
"'s",
'(',
'(:',
'(=<',
"(>'.')>",
'(>^.^)>',
')',
'):',
')=<',
'*scared',
'+',
',',
'-',
'-.-',
'-.-"',
"-.-'",
'-_-',
'-_-"',
"-_-'",
'-_-:',
'.',
'...',
'....',
'...?',
'/',
'/:',
'/=',
'0',
'0%',
'0.o',
'00',
'000',
'0:',
'0:)',
'0_0',
'0_o',
'1',
'1+',
'1.5',
'1.5x',
'1/10',
'10',
'10%',
'10+',
'10/10',
'100',
'100%',
'1000',
'10000',
'101',
'102',
'103',
'104',
'105',
'106',
'107',
'108',
'109',
'10s',
'10th',
'11',
'11+',
'110',
'110%',
'111',
'112',
'113',
'114',
'115',
'116',
'117',
'118',
'119',
'11s',
'12',
'12+',
'120',
'121',
'122',
'123',
'124',
'125',
'126',
'127',
'128',
'129',
'12s',
'13',
'13+',
'130',
'131',
'132',
'133',
'134',
'135',
'136',
'137',
'138',
'139',
'13s',
'14',
'14+',
'140',
'141',
'142',
'143',
'144',
'14400',
'145',
'146',
'147',
'148',
'149',
'14s',
'15',
'15+',
'150',
'151',
'152',
'153',
'154',
'155',
'156',
'157',
'158',
'159',
'15minutescansaveyou15percentormoreoncarinsurance',
'15s',
'16',
'16+',
'160',
'161',
'162',
'163',
'164',
'165',
'166',
'167',
'168',
'169',
'16s',
'17',
'17+',
'170',
'171',
'172',
'173',
'174',
'175',
'176',
'177',
'178',
'179',
'17s',
'18',
'18+',
'180',
'181',
'182',
'183',
'184',
'185',
'186',
'187',
'188',
'189',
'18s',
'19',
'190',
'191',
'192',
'193',
'194',
'195',
'196',
'197',
'198',
'199',
'1994',
'1995',
'1996',
'1997',
'1998',
'1999',
'1s',
'1st',
'2',
'2+',
'2.0',
'2.5',
'2.5x',
'2/10',
'20',
'20%',
'200',
'2000',
'2001',
'2002',
'2003',
'2004',
'2005',
'2006',
'2007',
'2008',
'2009',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015',
'2017',
'21',
'22',
'23',
'23300',
'24',
'25',
'25%',
'26',
'27',
'28',
'29',
'2d',
'2nd',
'2s',
'2x',
'3',
'3+',
'3.5',
'3.5x',
'3/10',
'30',
'30%',
'300',
'31',
'32',
'33',
'34',
'35',
'36',
'360',
'37',
'38',
'388',
'39',
'3d',
'3rd',
'3s',
'3x',
'4',
'4+',
'4/10',
'40',
'40%',
'400',
'41',
'42',
'43',
'44',
'45',
'46',
'47',
'48',
'49',
'4s',
'4th',
'4x',
'5',
'5%',
'5+',
'5/10',
'50',
'50%',
'500',
'51',
'52',
'53',
'54',
'55',
'5500',
'56',
'57',
'58',
'59',
'5s',
'5th',
'5x',
'6',
'6+',
'6/10',
'60',
'60%',
'600',
'61',
'62',
'63',
'64',
'65',
'66',
'67',
'68',
'6s',
'6th',
'6x',
'7',
'7+',
'7/10',
'70',
'70%',
'700',
'71',
'72',
'73',
'74',
'75',
'75%',
'76',
'77',
'776',
'78',
'79',
'7s',
'7th',
'8',
'8(',
'8)',
'8+',
'8/10',
'80',
'80%',
'800',
'81',
'82',
'83',
'84',
'85',
'85%',
'86',
'87',
'88',
'89',
'8900',
'8s',
'8th',
'9',
'9+',
'9/10',
'90',
'90%',
'900',
'91',
'92',
'93',
'94',
'95',
'96',
'97',
'98',
'99',
'9s',
'9th',
':',
":'(",
":')",
":'d",
":'o(",
':(',
':)',
':*',
':-(',
':-)',
':-o',
':/',
':0',
':3',
':::<',
':<',
':>',
':[',
':]',
':^)',
':_',
':b',
':c',
':d',
':i',
':j',
':l',
':o',
':o&',
':o)',
':p',
':s',
':v',
':x',
':y',
':|',
';',
';)',
';-)',
';-;',
';3',
';;',
';_;',
';c',
';d',
';p',
';x',
'<',
"<('.'<)",
'<(^.^<)',
'<.<',
'</3',
'<2',
'<3',
'<:',
'<_<',
'=',
'=(',
'=)',
'=-)',
'=.="',
'=/',
'==c',
'=[',
'=]',
'=d',
'=o)',
'=p',
'>',
'>.<',
'>.>',
'>8(',
'>:',
'>:(',
'>:)',
'>:c',
'>:u',
'>=(',
'>=)',
'>=d',
'>_<',
'>_>',
'>~<',
'?',
'@.@',
'@_@',
'@o@',
'[',
'\:',
'\=',
']',
'^',
'^.^',
'^^',
'^_^',
'_',
'a',
'a-hem',
'a-office',
'a-oo-oo-oo-ooo',
'aa',
'aacres',
'aah',
'aardvark',
"aardvark's",
'aardvarks',
'aarg',
'aargghh',
'aargh',
'aaron',
'aarrgghh',
'aarrgghhh',
'aarrm',
'aarrr',
'aarrrgg',
'aarrrgh',
'aarrrr',
'aarrrrggg',
'aarrrrr',
'aarrrrrr',
'aarrrrrrr',
'aarrrrrrrrr',
'aarrrrrrrrrghhhhh',
'aay',
'abacus',
'abaft',
'abalone-shell',
'abandon',
'abandoned',
'abandoner',
'abandoning',
'abandons',
'abassa',
'abay-ba-da',
'abbot',
'abbrev',
'abbreviate',
'abbreviated',
'abbreviation',
'abbreviations',
'abby',
'abcdefghijklmnopqrstuvwxyz',
'abe',
'abeam',
'aberrant',
'abhor',
'abhors',
'abide',
'abigail',
'abilities',
'ability',
"ability's",
'abira',
'able',
'able-bodied',
'abler',
'ablest',
'abnormal',
'aboard',
'abode',
'abominable',
'abound',
'abounds',
'about',
'above',
'abra',
'abracadabra',
'abraham',
'abrasive',
'abrose',
'abrupt',
'abruptly',
'absence',
"absence's",
'absences',
'absent',
'absent-minded',
'absented',
'absenting',
'absently',
'absents',
'absolute',
'absolutely',
'absolutes',
'absolution',
'absorb',
'absorbs',
'abstemious',
'absurd',
'absurdly',
'abu',
"abu's",
'abuela',
'abundant',
'abuse',
'academic',
'academics',
'academies',
'academy',
"academy's",
'acc',
'accelerate',
'accelerated',
'accelerates',
'acceleration',
'accelerator',
'accelerators',
'accent',
'accented',
'accents',
'accentuate',
'accentuates',
'accept',
'acceptable',
'acceptance',
'accepted',
'accepter',
"accepter's",
'accepters',
'accepting',
'acceptive',
'accepts',
'access',
'accessed',
'accesses',
'accessibility',
'accessing',
'accessories',
'accessorize',
'accessory',
'accident',
"accident's",
'accidental',
'accidentally',
'accidently',
'accidents',
'accolade',
'accompanies',
'accompany',
'accompanying',
'accomplice',
'accomplish',
'accomplished',
'accomplishes',
'accomplishing',
'accomplishment',
'accord',
'according',
'accordingly',
'accordion',
'accordions',
'account',
'accountable',
'accountant',
'accounted',
'accounting',
'accountings',
'accounts',
'accrue',
'acct',
"acct's",
'accts',
'accumulate',
'accumulated',
'accumulating',
'accumulator',
'accumulators',
'accuracy',
'accurate',
'accurately',
'accursed',
'accusation',
'accusations',
'accuse',
'accused',
'accuser',
'accusers',
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'beaniescooter',
'beaniescreech',
'beaniesmirk',
'beaniesnooker',
'beaniesnoop',
'beaniesnout',
'beaniesocks',
'beaniespeed',
'beaniespinner',
'beaniesplat',
'beaniesprinkles',
'beaniesticks',
'beaniestink',
'beanieswirl',
'beanieteeth',
'beaniethud',
'beanietoes',
'beanieton',
'beanietoon',
'beanietooth',
'beanietwist',
'beaniewhatsit',
'beaniewhip',
'beaniewig',
'beaniewoof',
'beaniezaner',
'beaniezap',
'beaniezapper',
'beaniezilla',
'beaniezoom',
'beanjinks',
'beanklunk',
'beanknees',
'beanmarble',
'beanmash',
'beanmonkey',
'beanmooch',
'beanmouth',
'beanmuddle',
'beanmuffin',
'beanmush',
'beannerd',
'beannoodle',
'beannose',
'beannugget',
'beanphew',
'beanphooey',
'beanpocket',
'beanpoof',
'beanpop',
'beanpounce',
'beanpow',
'beanpretzel',
'beanquack',
'beanroni',
'beans',
'beanscooter',
'beanscreech',
'beansmirk',
'beansnooker',
'beansnoop',
'beansnout',
'beansocks',
'beanspeed',
'beanspinner',
'beansplat',
'beansprinkles',
'beanstalks',
'beansticks',
'beanstink',
'beanswirl',
'beanteeth',
'beanthud',
'beantoes',
'beanton',
'beantoon',
'beantooth',
'beantwist',
'beanwhatsit',
'beanwhip',
'beanwig',
'beanwoof',
'beanzaner',
'beanzap',
'beanzapper',
'beanzilla',
'beanzoom',
'bear',
"bear's",
'beard',
"beard's",
'bearded',
'beardless',
'beardmonsters',
'beards',
'beardy',
'bearer',
'bearers',
'bearing',
'bearings',
'bearish',
'bears',
'beartrude',
'beast',
"beast's",
'beastie',
'beasties',
'beastings',
'beastly',
'beasts',
'beat',
'beatable',
'beau',
'beaucoup',
'beauteous',
'beauticians',
'beauties',
'beautiful',
'beautifully',
'beautifulness',
'beauty',
"beauty's",
'beaver',
'beawesome',
'became',
'because',
'beck',
"beck's",
'beckets',
'beckett',
"beckett's",
'beckoned',
'beckons',
'become',
'becomes',
'becoming',
'bed',
'bed-sized',
'bedazzle',
'bedazzled',
'bedazzler',
'bedazzles',
'bedclothes',
'bedding',
'bedhog',
'bedknobs',
"bedo's",
'bedroll',
'bedroom',
'bedrooms',
'beds',
'bedspread',
'bedspreads',
'bee',
"bee's",
'beedrill',
'beef',
'beefcake',
'beefed',
'beefs',
'beefy',
'beehive',
"beehive's",
'beehives',
'beeline',
'beemovie',
'been',
'beep',
'beeped',
'beepers',
'beeping',
'beeps',
'bees',
'beeswax',
'beet',
'beethoven',
'beetle',
"beetle's",
'beetles',
'beets',
'before',
'beforehand',
'befriend',
'befriended',
'befriending',
'befriends',
'befuddle',
'befuddled',
'beg',
'began',
'begat',
'begets',
'beggar',
'beggars',
'begged',
'begging',
'begin',
'beginner',
"beginner's",
'beginners',
'beginning',
"beginning's",
'beginnings',
'begins',
'begonia',
'begotten',
'begs',
'begun',
'behalf',
'behave',
'behaved',
'behaver',
'behaves',
'behaving',
'behavior',
'behavioral',
'behaviors',
'behemoth',
'behemoths',
'behind',
'behind-the-scenes',
'behinds',
'behold',
'beholden',
'beholding',
'behoove',
'behop',
'behr',
'beige',
"bein'",
'being',
"being's",
'beings',
'bejeweled',
'bela',
'belarus',
'belarusian',
'belated',
'belay',
'belch',
'belief',
"belief's",
'beliefs',
'believable',
'believe',
'believed',
'believer',
'believers',
'believes',
'believing',
'belittle',
'belittles',
'bell',
"bell's",
'bella',
"bella's",
'bellas',
'belle',
"belle's",
'belles',
"belles'",
'bellflower',
'bellhop',
'belli',
'bellied',
'bellies',
'bellossom',
'bellow',
'bellows',
'bells',
'bellsprout',
'belly',
'bellyache',
'belong',
'belonged',
'belonging',
'belongings',
'belongs',
'beloved',
'beloveds',
'below',
'belowdeck',
'belt',
'belts',
'bemuse',
'bemused',
'bemuses',
'bemusing',
'ben',
'benches',
'benchmark',
'benchwarmers',
'bend',
'bender',
'bending',
'bends',
'beneath',
'benedek',
"benedek's",
'beneficial',
'benefit',
'benefited',
'benefiting',
'benefits',
'benjamin',
'benjy',
'benne',
'benny',
"benny's",
'benroyko',
'bent',
'beppo',
'bequeath',
'bequeathed',
'bequeathing',
'bequeaths',
'bequermo',
'bequila',
'bereadytothrow',
'beret',
'berets',
'berg',
'beriths27th',
'bernadette',
'bernard',
'bernie',
'berried',
'berries',
'berry',
'berserk',
'bert',
"bert's",
'berth',
'bertha',
'berthed',
'berthing',
'berths',
'beruna',
'beseech',
'beside',
'besides',
'bespeak',
'bespeaking',
'bespeaks',
'bespoke',
'bespoken',
'bess',
"bess'",
"bess's",
'bess-statue',
'bessie',
'best',
'bested',
'bester',
'besting',
'bests',
'bet',
"bet's",
'beta',
"beta's",
'betas',
'betcha',
'beth',
'bethy',
'bethyisbest',
'betray',
'betrayal',
'betrayed',
'bets',
'betsy',
'better',
'bettered',
'bettering',
'betterment',
'betters',
'bettie',
'betting',
'betty',
'between',
'betwixt',
'bev',
'bevel',
'beverage',
'beverages',
'beware',
'bewilder',
'bewildered',
'bewildering',
'bewitch',
'bewitched',
'bewitches',
'bewitching',
'beyonce',
'beyond',
'bezerk',
'bff',
'bfs',
'bi-lingual',
'bias',
'biased',
'bib',
'bibbidi',
'bible',
'biblical',
'bicep',
'biceps',
'bickering',
'bicuspid',
'bicycle',
'bicycles',
'bid',
'bidder',
'bidding',
'biddlesmore',
'bide',
'bids',
'bien',
'bifocals',
'big',
'big-screen',
'biggen',
'biggenbee',
'biggenberry',
'biggenblabber',
'biggenbocker',
'biggenboing',
'biggenboom',
'biggenbounce',
'biggenbouncer',
'biggenbrains',
'biggenbubble',
'biggenbumble',
'biggenbump',
'biggenbumper',
'biggenburger',
'biggenchomp',
'biggencorn',
'biggencrash',
'biggencrumbs',
'biggencrump',
'biggencrunch',
'biggendoodle',
'biggendorf',
'biggenface',
'biggenfidget',
'biggenfink',
'biggenfish',
'biggenflap',
'biggenflapper',
'biggenflinger',
'biggenflip',
'biggenflipper',
'biggenfoot',
'biggenfuddy',
'biggenfussen',
'biggengadget',
'biggengargle',
'biggengloop',
'biggenglop',
'biggengoober',
'biggengoose',
'biggengrooven',
'biggenhoffer',
'biggenhopper',
'biggenjinks',
'biggenklunk',
'biggenknees',
'biggenmarble',
'biggenmash',
'biggenmonkey',
'biggenmooch',
'biggenmouth',
'biggenmuddle',
'biggenmuffin',
'biggenmush',
'biggennerd',
'biggennoodle',
'biggennose',
'biggennugget',
'biggenphew',
'biggenphooey',
'biggenpocket',
'biggenpoof',
'biggenpop',
'biggenpounce',
'biggenpow',
'biggenpretzel',
'biggenquack',
'biggenroni',
'biggenscooter',
'biggenscreech',
'biggensmirk',
'biggensnooker',
'biggensnoop',
'biggensnout',
'biggensocks',
'biggenspeed',
'biggenspinner',
'biggensplat',
'biggensprinkles',
'biggensticks',
'biggenstink',
'biggenswirl',
'biggenteeth',
'biggenthud',
'biggentoes',
'biggenton',
'biggentoon',
'biggentooth',
'biggentwist',
'biggenwhatsit',
'biggenwhip',
'biggenwig',
'biggenwoof',
'biggenzaner',
'biggenzap',
'biggenzapper',
'biggenzilla',
'biggenzoom',
'bigger',
'biggest',
'biggie',
'biggies',
'biggles',
'biggleton',
'bight',
'bigwig',
'bike',
"bike's",
'biked',
'biker',
'bikers',
'bikes',
'biking',
'bikini',
'bile',
'bilge',
'bilgepump',
'bilgerats',
'bilges',
'bilging',
'bilingual',
'bill',
"bill's",
'billed',
'biller',
'billers',
'billiards',
'billing',
'billings',
'billington',
'billion',
'billionaire',
'billions',
'billionth',
'billow',
'billows',
'billowy',
'bills',
'billy',
'billybob',
'bim',
'bimbim',
'bimonthly',
'bimos',
'bin',
'binary',
'binarybot',
'binarybots',
'bind',
'binding',
'bing',
'bingham',
'bingo',
"bingo's",
'bingos',
'binky',
'binnacle',
'binoculars',
'bins',
'bio',
'biog',
'biology',
'bionic',
'bionicle',
'biopsychology',
'bios',
'bip',
'birch-bark',
'bird',
"bird's",
'birdbrain',
'birddog',
'birder',
'birdhouse',
'birdie',
'birdies',
'birdman',
'birds',
'birdseed',
'birth',
'birthdates',
'birthday',
'birthdays',
'birthed',
'birthmark',
'birthmarks',
'birthplace',
'birthstone',
'biscotti',
'biscuit',
'biscuits',
'bishop',
'bishops',
'bison',
'bisque',
'bisquit',
'bistro',
'bit',
"bit's",
'bite',
'biter',
'biters',
'bites',
'biting',
'bits',
'bitsy',
'bitten',
'bitter',
'bitty',
'biweekly',
'biyearly',
'biz',
'bizarre',
'bizzenbee',
'bizzenberry',
'bizzenblabber',
'bizzenbocker',
'bizzenboing',
'bizzenboom',
'bizzenbounce',
'bizzenbouncer',
'bizzenbrains',
'bizzenbubble',
'bizzenbumble',
'bizzenbump',
'bizzenbumper',
'bizzenburger',
'bizzenchomp',
'bizzencorn',
'bizzencrash',
'bizzencrumbs',
'bizzencrump',
'bizzencrunch',
'bizzendoodle',
'bizzendorf',
'bizzenface',
'bizzenfidget',
'bizzenfink',
'bizzenfish',
'bizzenflap',
'bizzenflapper',
'bizzenflinger',
'bizzenflip',
'bizzenflipper',
'bizzenfoot',
'bizzenfuddy',
'bizzenfussen',
'bizzengadget',
'bizzengargle',
'bizzengloop',
'bizzenglop',
'bizzengoober',
'bizzengoose',
'bizzengrooven',
'bizzenhoffer',
'bizzenhopper',
'bizzenjinks',
'bizzenklunk',
'bizzenknees',
'bizzenmarble',
'bizzenmash',
'bizzenmonkey',
'bizzenmooch',
'bizzenmouth',
'bizzenmuddle',
'bizzenmuffin',
'bizzenmush',
'bizzennerd',
'bizzennoodle',
'bizzennose',
'bizzennugget',
'bizzenphew',
'bizzenphooey',
'bizzenpocket',
'bizzenpoof',
'bizzenpop',
'bizzenpounce',
'bizzenpow',
'bizzenpretzel',
'bizzenquack',
'bizzenroni',
'bizzenscooter',
'bizzenscreech',
'bizzensmirk',
'bizzensnooker',
'bizzensnoop',
'bizzensnout',
'bizzensocks',
'bizzenspeed',
'bizzenspinner',
'bizzensplat',
'bizzensprinkles',
'bizzensticks',
'bizzenstink',
'bizzenswirl',
'bizzenteeth',
'bizzenthud',
'bizzentoes',
'bizzenton',
'bizzentoon',
'bizzentooth',
'bizzentwist',
'bizzenwhatsit',
'bizzenwhip',
'bizzenwig',
'bizzenwoof',
'bizzenzaner',
'bizzenzap',
'bizzenzapper',
'bizzenzilla',
'bizzenzoom',
'bla',
'blabbing',
'black',
'black-eyed',
'blackbeard',
"blackbeard's",
'blackbeards',
'blackbeared',
'blackbelt',
'blackberries',
'blackberry',
'blackbird',
'blackboard',
'blackboards',
'blackdeath',
'blacked',
'blackened',
'blackest',
'blackguard',
'blackguards',
'blackhaerts',
'blackhawk',
'blackheads',
'blackheart',
"blackheart's",
'blackhearts',
'blacking',
'blackish',
'blackjack',
'blackjacks',
'blacklist',
'blacklisted',
'blacklisting',
'blackness',
'blackout',
'blackoutaviation',
'blackouts',
'blackrage',
'blackrose',
'blacksail',
'blacksmith',
"blacksmith's",
'blacksmithing',
'blacksmiths',
'blackthorn',
'blackwatch',
'blackwater',
'bladder',
'bladders',
"blade's",
'bladebreakerr',
'blademasters',
'blades',
'bladeskulls',
'bladestorm',
'blaggards',
'blah',
'blair',
'blaise',
'blake',
'blakeley',
"blakeley's",
'blame',
'blamed',
'blamer',
'blamers',
'blames',
'blaming',
'blanada',
'blanago',
'blanca',
"blanca's",
'blanche',
'bland',
'blank',
'blanked',
'blanket',
'blankets',
'blanking',
'blankly',
'blanks',
'blanos',
'blaring',
'blast',
"blast'em",
'blasted',
'blaster',
'blasters',
"blastin'",
'blasting',
'blastoise',
'blasts',
'blasty',
'blat',
'blaze',
'bld',
'bldg',
'bldgs',
'bleached',
'bleak',
'bleary',
'bled',
'bleep',
'bleeped',
'bleeper',
'bleepin',
'bleeping',
'bleeps',
'blend',
'blended',
'blender',
'blenders',
'blending',
'blends',
'blenny',
'bless',
'blessed',
'blesses',
'blessing',
'blessings',
'bleu',
'blew',
'bligh',
'blight',
'blighters',
'blimey',
'blimp',
'blind',
'blinded',
'blinder',
'blinders',
'blindfold',
'blindfolded',
'blinding',
'blindly',
'blindness',
'blinds',
'blindsided',
'bling',
'bling-bling',
'blingbling',
'blinged',
'blinging',
'blings',
'blink',
'blinked',
'blinker',
'blinkers',
'blinking',
'blinks',
'blinky',
'blip',
'blipping',
'bliss',
'blissey',
'blissfully',
'blister',
'blistering',
'blisters',
'blitz',
'blizzard',
'blizzards',
'bloat',
'bloated',
'bloats',
'blob',
'blobby',
'blobs',
'bloc',
'block',
"block's",
'blockade',
'blockader',
'blockades',
'blockading',
'blockbuster',
'blocked',
'blocker',
'blockers',
'blocking',
'blockout',
'blocks',
'blocky',
'bloke',
'blokes',
'blomma',
'blond',
'blonde',
"blonde's",
'blondes',
'blondie',
'blonds',
'bloodbrothers',
'bloodhounds',
'bloodless',
'bloodshot',
'bloodsucker',
'bloodsuckers',
'bloodthrushers',
'bloom',
'bloomers',
'blooming',
'blooms',
'bloop',
'bloopa',
'blooper',
'bloopers',
'blossom',
'blossoms',
'blossum',
'blot',
'blots',
'blouse',
'blowfish',
'blowy',
'blu-ray',
'blub',
'blubber',
'blubberbee',
'blubberberry',
'blubberblabber',
'blubberbocker',
'blubberboing',
'blubberboom',
'blubberbounce',
'blubberbouncer',
'blubberbrains',
'blubberbubble',
'blubberbumble',
'blubberbump',
'blubberbumper',
'blubberburger',
'blubberchomp',
'blubbercorn',
'blubbercrash',
'blubbercrumbs',
'blubbercrump',
'blubbercrunch',
'blubberdoodle',
'blubberdorf',
'blubberface',
'blubberfidget',
'blubberfink',
'blubberfish',
'blubberflap',
'blubberflapper',
'blubberflinger',
'blubberflip',
'blubberflipper',
'blubberfoot',
'blubberfuddy',
'blubberfussen',
'blubbergadget',
'blubbergargle',
'blubbergloop',
'blubberglop',
'blubbergoober',
'blubbergoose',
'blubbergrooven',
'blubberhoffer',
'blubberhopper',
'blubbering',
'blubberjinks',
'blubberklunk',
'blubberknees',
'blubbermarble',
'blubbermash',
'blubbermonkey',
'blubbermooch',
'blubbermouth',
'blubbermuddle',
'blubbermuffin',
'blubbermush',
'blubbernerd',
'blubbernoodle',
'blubbernose',
'blubbernugget',
'blubberphew',
'blubberphooey',
'blubberpocket',
'blubberpoof',
'blubberpop',
'blubberpounce',
'blubberpow',
'blubberpretzel',
'blubberquack',
'blubberroni',
'blubberscooter',
'blubberscreech',
'blubbersmirk',
'blubbersnooker',
'blubbersnoop',
'blubbersnout',
'blubbersocks',
'blubberspeed',
'blubberspinner',
'blubbersplat',
'blubbersprinkles',
'blubbersticks',
'blubberstink',
'blubberswirl',
'blubberteeth',
'blubberthud',
'blubbertoes',
'blubberton',
'blubbertoon',
'blubbertooth',
'blubbertwist',
'blubberwhatsit',
'blubberwhip',
'blubberwig',
'blubberwoof',
'blubberzaner',
'blubberzap',
'blubberzapper',
'blubberzilla',
'blubberzoom',
'bludgeon',
'bludgeoning',
'blue',
"blue's",
'bluebeards',
'bluebell',
'blueberries',
'blueberry',
'bluebird',
'bluebirds',
'blueblood',
'bluefishes',
'bluegrass',
'bluejay',
'blueprints',
'blues',
'bluff',
'bluffed',
'bluffer',
'bluffers',
'bluffing',
'bluffs',
'blunder',
'blundering',
'bluntly',
'blur',
'blurb',
'blurbs',
'blurred',
'blurry',
'blurs',
'blurting',
'blush',
'blushed',
'blushes',
'blushing',
'blustery',
'blut',
'blynken',
'bman',
'bo',
'boa',
'boar',
'board',
"board's",
'boardbot',
'boardbots',
'boarded',
'boarder',
'boarders',
'boarding',
'boards',
'boardwalk',
'boardwalks',
'boarhound',
'boars',
'boas',
'boast',
'boastful',
'boasting',
'boat',
"boat's",
'boated',
'boater',
'boaters',
'boathouse',
'boating',
'boatload',
'boatloads',
'boats',
'boatswain',
"boatswain's",
'boatswains',
'boatyard',
'bob',
"bob's",
'bobbed',
'bobber',
'bobbidi',
'bobble',
'bobbleheads',
'bobby',
"bobby's",
'bobbys',
'boberts',
'bobo',
'bobsled',
'bobsleded',
'bobsleding',
'bobsleds',
'bobsleigh',
'bobsleighes',
'bock',
'bodacious',
'bode',
'bodeguita',
'bodice',
'bodices',
'bodied',
'bodies',
'bodily',
'body',
"body's",
'bodyguard',
'bodyguards',
'boffo',
'bog',
'bogart',
'bogey',
'bogger',
'boggle',
'boggles',
'boggy',
'bogie',
'bogs',
'bogus',
'boi',
'boil',
'boiled',
'boiler',
'boiling',
'boils',
'boingenbee',
'boingenberry',
'boingenblabber',
'boingenbocker',
'boingenboing',
'boingenboom',
'boingenbounce',
'boingenbouncer',
'boingenbrains',
'boingenbubble',
'boingenbumble',
'boingenbump',
'boingenbumper',
'boingenburger',
'boingenchomp',
'boingencorn',
'boingencrash',
'boingencrumbs',
'boingencrump',
'boingencrunch',
'boingendoodle',
'boingendorf',
'boingenface',
'boingenfidget',
'boingenfink',
'boingenfish',
'boingenflap',
'boingenflapper',
'boingenflinger',
'boingenflip',
'boingenflipper',
'boingenfoot',
'boingenfuddy',
'boingenfussen',
'boingengadget',
'boingengargle',
'boingengloop',
'boingenglop',
'boingengoober',
'boingengoose',
'boingengrooven',
'boingenhoffer',
'boingenhopper',
'boingenjinks',
'boingenklunk',
'boingenknees',
'boingenmarble',
'boingenmash',
'boingenmonkey',
'boingenmooch',
'boingenmouth',
'boingenmuddle',
'boingenmuffin',
'boingenmush',
'boingennerd',
'boingennoodle',
'boingennose',
'boingennugget',
'boingenphew',
'boingenphooey',
'boingenpocket',
'boingenpoof',
'boingenpop',
'boingenpounce',
'boingenpow',
'boingenpretzel',
'boingenquack',
'boingenroni',
'boingenscooter',
'boingenscreech',
'boingensmirk',
'boingensnooker',
'boingensnoop',
'boingensnout',
'boingensocks',
'boingenspeed',
'boingenspinner',
'boingensplat',
'boingensprinkles',
'boingensticks',
'boingenstink',
'boingenswirl',
'boingenteeth',
'boingenthud',
'boingentoes',
'boingenton',
'boingentoon',
'boingentooth',
'boingentwist',
'boingenwhatsit',
'boingenwhip',
'boingenwig',
'boingenwoof',
'boingenzaner',
'boingenzap',
'boingenzapper',
'boingenzilla',
'boingenzoom',
'boingyboro',
'bokugeki',
'bokuji',
'bokuzama',
'bold',
'bolder',
'boldest',
'boldly',
'bole',
'bolivia',
'bollard',
'bologna',
'bolt',
"bolt's",
'bolted',
'bolton',
'bolts',
'boma',
'boma-boma',
'bombard',
'bombarding',
'bombardment',
'bombe',
'bombed',
'bomber',
'bombers',
'bombing',
'bombs',
'bombshell',
'bon',
'bonaam',
'bonanza',
'bonbons',
'bond',
'bonded',
'bonding',
'bonds',
'bondsman',
'bonehead',
'boneheads',
'boneless',
'boneyard',
'boneyards',
'bonfire',
'bonfires',
'bongo',
'bonita',
"bonita's",
'bonito',
'bonjour',
'bonkers',
'bonkl',
'bonnet',
'bonnets',
'bonney',
'bonnie',
'bonny',
'bono',
'bonsai',
'bonsoir',
'bonus',
'bonuses',
'bony',
'bonzo',
'boo',
"boo's",
'boo-yaa',
'booed',
'booger',
'boogers',
'boogey',
'boogie',
'boogie-woogie',
'boogied',
'boogies',
'boohoo',
'book',
"book's",
'bookball',
'bookcase',
'bookcases',
'booked',
'bookie',
'booking',
'bookings',
'bookkeeper',
'bookkeepers',
'bookkeeping',
'booklet',
'bookmaker',
'bookmakers',
'bookmark',
'bookmarked',
'books',
'bookshelf',
'bookshelves',
'bookstore',
'bookworm',
'boom',
'boomcrash',
'boomed',
'boomer',
'boomerang',
'boomers',
'booming',
'booms',
'boon',
'boonies',
'booo',
'boooo',
'booooh',
'boooom',
'booooo',
'booooom',
'booooomin',
'boooooo',
'boooooooooooooooooooo',
'boooooooooooooooooooooooooommm',
'boop',
'boor',
'boos',
'boost',
'boosted',
'booster',
'boosters',
'boosting',
'boosts',
'boot',
"boot'n'ears",
'booted',
'booth',
"booth's",
'booths',
'bootiful',
'booting',
'bootleggers',
'boots',
'bootstrap',
'bootstraps',
'bootsy',
'booyah',
'bop',
'bopper',
'bord',
'border',
"border's",
'bordered',
'borderer',
'bordering',
'borderings',
'borderline',
'borders',
'bore',
'borealis',
'bored',
'boredom',
'borer',
'bores',
'boring',
'boris',
'bork',
'borks',
'borksalot',
'born',
'borrow',
'borrowed',
'borrower',
'borrowers',
'borrowing',
'borrowings',
'borrows',
'bos',
'boss',
"boss'",
'bossbot',
'bossbots',
'bossed',
'bosses',
'bossily',
'bossing',
'bossy',
'bossyboots',
'bot',
"bot's",
'botched',
'both',
'bother',
'bothered',
'bothering',
'bothers',
'bothersome',
'bots',
'bottle',
"bottle's",
'bottled',
'bottleneck',
'bottler',
'bottlers',
'bottles',
'bottling',
'bottom',
'bottomed',
'bottomer',
'bottomfeeder',
'bottomfeeders',
'bottoming',
'bottomless',
'bottoms',
'bough',
'boughs',
'bought',
'boulder',
'boulders',
'boulevard',
"boulevard's",
'boulevards',
'bounce',
'bounced',
'bouncer',
'bouncers',
'bounces',
'bouncing',
'bouncy',
'bound',
'boundaries',
'boundary',
"boundary's",
'bounding',
'bounds',
'bounteous',
'bounties',
'bountiful',
'bounty',
'bountyhunter',
'bourbon',
'bourse',
'bout',
'boutique',
'bouts',
'bovel',
'bovine',
'bow',
"bow's",
'bowdash',
'bowed',
'bowers',
'bowie',
'bowing',
'bowl',
"bowl's",
'bowlegged',
'bowler',
'bowling',
'bowls',
'bowman',
'bows',
'bowser',
'box',
'boxcar',
'boxed',
'boxer',
'boxes',
'boxfish',
'boxing',
'boy',
"boy's",
'boycott',
'boycotting',
'boyfriend',
"boyfriend's",
'boyfriends',
'boyish',
'boys',
'boysenberry',
'bozo',
"bozo's",
'bozos',
'brace',
'bracelet',
'bracelets',
'braces',
'bracket',
'bracketing',
'brad',
"brad's",
'bradley',
'brady',
'brag',
'braggart',
'braggarts',
'bragged',
'bragger',
'braggers',
'bragging',
'brags',
'braid',
'braided',
'braiding',
'braids',
'brail',
'brain',
"brain's",
'brained',
'braining',
'brainless',
'brains',
'brainstorm',
'brainwash',
'brainwashed',
'brainy',
'brake',
'braken',
'brakes',
'braking',
'bran',
'branch',
'branched',
'branches',
'branching',
'branchings',
'brand',
'brandishing',
'brandon',
"brandon's",
'brands',
'brandy',
"brandy's",
'brantley',
'brash',
'brass',
'brat',
'brats',
'bratty',
'brave',
'braved',
'bravely',
'braver',
'bravery',
'braves',
'bravest',
'braving',
'bravo',
"bravo's",
'brawl',
'brawny',
'bray',
'brazen',
'brazil',
'brb',
'breach',
'breached',
'bread',
"bread's",
'bread-buttering',
'breadcrumbs',
'breaded',
'breading',
'breads',
'breadstick',
"breadstick's",
'breadth',
'break',
'breakable',
'breakdown',
'breaker',
'breakers',
'breakfast',
'breakfasted',
'breakfaster',
'breakfasters',
'breakfasting',
'breakfasts',
'breaking',
'breakout',
'breaks',
'breakup',
'breath',
'breathe',
'breathed',
'breather',
'breathers',
'breathes',
'breathing',
'breathless',
'breaths',
'breathtaking',
'bred',
'breech',
'breeches',
'breeze',
"breeze's",
'breezed',
'breezes',
'breezest',
'breezing',
'breezy',
'brenda',
"brenda's",
'brethern',
'brethren',
'brevrend',
'brew',
'brewed',
'brewer',
'brewers',
'brewing',
'brews',
'brian',
'briar',
'briars',
'briarstone',
'briarstones',
'bribe',
'bribed',
'bribery',
'bribes',
'bribing',
'brick',
'bricked',
'bricker',
'bricking',
'bricks',
'bridal',
'bride',
"bride's",
'brides',
'bridesmaid',
'bridge',
"bridge's",
'bridged',
'bridges',
'bridget',
'bridging',
'brie',
'brief',
'briefed',
'briefer',
'briefest',
'briefing',
'briefings',
'briefly',
'briefs',
'brig',
"brig's",
'brigad',
'brigade',
'brigadeers',
'brigades',
'brigadier',
'brigadiers',
'brigand',
'brigands',
'brigantine',
'brigantines',
'bright',
'brighten',
'brightens',
'brighter',
'brightest',
'brighting',
'brightly',
'brightness',
'brights',
'brigs',
'brilliance',
'brilliant',
'brilliantly',
'brim',
'brimming',
'brimstone',
'brine',
'briney',
'bring',
'bringer',
'bringers',
'bringing',
'brings',
'bringthemadness',
'brining',
'brink',
'brinks',
'briny',
'brio',
'briquettes',
'brisk',
'brisket',
'britannia',
'britches',
'british',
'bro',
"bro's",
'broached',
'broad',
'broadband',
'broadcast',
'broadcasts',
'broaden',
'broadens',
'broader',
'broadest',
'broadly',
'broads',
'broadside',
"broadside's",
'broadsided',
'broadsides',
'broadsword',
'broadway',
'broccoli',
'brochure',
'brock',
'brogan',
'brogans',
'broil',
'broiled',
'broke',
'broken',
'brokenly',
'broker',
'brokers',
'broking',
'bronco',
'broncos',
'bronze',
'bronzed',
'bronzy',
'brood',
'brook',
"brook's",
'brooks',
'broom',
"broom's",
'brooms',
'broomstick',
"broomstick's",
'broomsticks',
'bros',
'broth',
'brother',
"brother's",
'brotherhood',
"brotherhood's",
'brotherhoods',
'brothering',
'brotherly',
'brothers',
"brothers'",
'broths',
'brought',
'brouhaha',
'brow',
'brown',
'browncoats',
'browner',
'brownie',
'brownies',
'browning',
'brownish',
'browns',
'brows',
'browse',
'browsed',
'browser',
'browsers',
'browsing',
'brrr',
'brrrgh',
'brt',
'bruce',
"bruce's",
'bruckheimer',
'bruh',
'bruin',
'bruise',
'bruised',
'bruiser',
'bruises',
'bruising',
'brulee',
'brume',
'brunch',
'brunette',
'brunettes',
'brunt',
'brush',
'brushed',
'brusher',
'brushes',
'brushing',
'brushoff',
'brussels',
'brutal',
'brute',
'brutish',
'bryan',
'bryanna',
'bryson',
'btb',
'btl',
'btw',
'bubba',
'bubble',
"bubble's",
'bubbled',
'bubblegum',
'bubbles',
'bubbling',
'bubbloon',
'bubbly',
'bubo',
'bubs',
"buc's",
'bucaneer',
'bucanneers',
'buccaneer',
"buccaneer's",
'buccaneers',
'bucco',
'buckaroo',
"buckaroo's",
'buckaroos',
'bucket',
"bucket's",
'bucketed',
'bucketing',
'buckets',
'buckeye',
'buckeyes',
'buckle',
"buckle's",
'buckled',
'buckler',
'buckles',
'bucko',
"bucko's",
'buckos',
'bucks',
'buckshot',
'buckskin',
'buckwheat',
'bucs',
'bud',
"bud's",
'budd',
"budd's",
'buddies',
'buddy',
"buddy's",
'budge',
'budged',
'budget',
'budgeted',
'budgeter',
'budgeters',
'budgeting',
'budgets',
'budgie',
'budging',
'buds',
'buena',
'bueno',
'buff',
'buffalo',
"buffalo's",
'buffalos',
'buffed',
'buffer',
'buffet',
"buffet's",
'buffets',
'buffoon',
'buffoons',
'buffs',
'bug',
"bug's",
'bug-eye',
'bugalicious',
'bugariffic',
'bugbear',
'bugbears',
'bugeye',
'bugged',
'buggered',
'buggers',
'buggier',
'buggies',
'buggiest',
'bugging',
'buggy',
'bugle',
'bugles',
'bugs',
'bugsit',
'bugtastic',
'buh',
'build',
'builded',
'builder',
"builder's",
'builders',
'building',
"building's",
'buildings',
'builds',
'buildup',
'buillion',
'built',
'bulb',
"bulb's",
'bulbasaur',
'bulbs',
'bulge',
'bulging',
'bulgy',
'bulk',
'bulkhead',
'bulky',
'bull',
"bull's",
'bulldog',
'bulldogs',
'bulldozer',
'bulletin',
'bullfighters',
'bullfighting',
'bullied',
'bullies',
'bullion',
'bullpen',
'bulls',
'bullseye',
'bully',
"bully's",
'bullying',
'bulwark',
"bulwark's",
'bulwarks',
'bum-bum',
'bumberbee',
'bumberberry',
'bumberblabber',
'bumberbocker',
'bumberboing',
'bumberboom',
'bumberbounce',
'bumberbouncer',
'bumberbrains',
'bumberbubble',
'bumberbumble',
'bumberbump',
'bumberbumper',
'bumberburger',
'bumberchomp',
'bumbercorn',
'bumbercrash',
'bumbercrumbs',
'bumbercrump',
'bumbercrunch',
'bumberdoodle',
'bumberdorf',
'bumberface',
'bumberfidget',
'bumberfink',
'bumberfish',
'bumberflap',
'bumberflapper',
'bumberflinger',
'bumberflip',
'bumberflipper',
'bumberfoot',
'bumberfuddy',
'bumberfussen',
'bumbergadget',
'bumbergargle',
'bumbergloop',
'bumberglop',
'bumbergoober',
'bumbergoose',
'bumbergrooven',
'bumberhoffer',
'bumberhopper',
'bumberjinks',
'bumberklunk',
'bumberknees',
'bumbermarble',
'bumbermash',
'bumbermonkey',
'bumbermooch',
'bumbermouth',
'bumbermuddle',
'bumbermuffin',
'bumbermush',
'bumbernerd',
'bumbernoodle',
'bumbernose',
'bumbernugget',
'bumberphew',
'bumberphooey',
'bumberpocket',
'bumberpoof',
'bumberpop',
'bumberpounce',
'bumberpow',
'bumberpretzel',
'bumberquack',
'bumberroni',
'bumberscooter',
'bumberscreech',
'bumbersmirk',
'bumbersnooker',
'bumbersnoop',
'bumbersnout',
'bumbersocks',
'bumberspeed',
'bumberspinner',
'bumbersplat',
'bumbersprinkles',
'bumbersticks',
'bumberstink',
'bumberswirl',
'bumberteeth',
'bumberthud',
'bumbertoes',
'bumberton',
'bumbertoon',
'bumbertooth',
'bumbertwist',
'bumberwhatsit',
'bumberwhip',
'bumberwig',
'bumberwoof',
'bumberzaner',
'bumberzap',
'bumberzapper',
'bumberzilla',
'bumberzoom',
'bumble',
'bumblebee',
'bumblebehr',
'bumbleberry',
'bumbleblabber',
'bumblebocker',
'bumbleboing',
'bumbleboom',
'bumblebounce',
'bumblebouncer',
'bumblebrains',
'bumblebubble',
'bumblebumble',
'bumblebump',
'bumblebumper',
'bumbleburger',
'bumblechomp',
'bumblecorn',
'bumblecrash',
'bumblecrumbs',
'bumblecrump',
'bumblecrunch',
'bumbledoodle',
'bumbledorf',
'bumbleface',
'bumblefidget',
'bumblefink',
'bumblefish',
'bumbleflap',
'bumbleflapper',
'bumbleflinger',
'bumbleflip',
'bumbleflipper',
'bumblefoot',
'bumblefuddy',
'bumblefussen',
'bumblegadget',
'bumblegargle',
'bumblegloop',
'bumbleglop',
'bumblegoober',
'bumblegoose',
'bumblegrooven',
'bumblehoffer',
'bumblehopper',
'bumblejinks',
'bumbleklunk',
'bumbleknees',
'bumblemarble',
'bumblemash',
'bumblemonkey',
'bumblemooch',
'bumblemouth',
'bumblemuddle',
'bumblemuffin',
'bumblemush',
'bumblenerd',
'bumblenoodle',
'bumblenose',
'bumblenugget',
'bumblephew',
'bumblephooey',
'bumblepocket',
'bumblepoof',
'bumblepop',
'bumblepounce',
'bumblepow',
'bumblepretzel',
'bumblequack',
'bumbler',
"bumbler's",
'bumbleroni',
'bumblers',
'bumblescooter',
'bumblescreech',
'bumblesmirk',
'bumblesnooker',
'bumblesnoop',
'bumblesnout',
'bumblesocks',
'bumblesoup',
'bumblespeed',
'bumblespinner',
'bumblesplat',
'bumblesprinkles',
'bumblesticks',
'bumblestink',
'bumbleswirl',
'bumbleteeth',
'bumblethud',
'bumbletoes',
'bumbleton',
'bumbletoon',
'bumbletooth',
'bumbletwist',
'bumblewhatsit',
'bumblewhip',
'bumblewig',
'bumblewoof',
'bumblezaner',
'bumblezap',
'bumblezapper',
'bumblezilla',
'bumblezoom',
'bumm',
'bummed',
'bummer',
'bummers',
'bumming',
'bumms',
'bump',
'bumpenbee',
'bumpenberry',
'bumpenblabber',
'bumpenbocker',
'bumpenboing',
'bumpenboom',
'bumpenbounce',
'bumpenbouncer',
'bumpenbrains',
'bumpenbubble',
'bumpenbumble',
'bumpenbump',
'bumpenbumper',
'bumpenburger',
'bumpenchomp',
'bumpencorn',
'bumpencrash',
'bumpencrumbs',
'bumpencrump',
'bumpencrunch',
'bumpendoodle',
'bumpendorf',
'bumpenface',
'bumpenfidget',
'bumpenfink',
'bumpenfish',
'bumpenflap',
'bumpenflapper',
'bumpenflinger',
'bumpenflip',
'bumpenflipper',
'bumpenfoot',
'bumpenfuddy',
'bumpenfussen',
'bumpengadget',
'bumpengargle',
'bumpengloop',
'bumpenglop',
'bumpengoober',
'bumpengoose',
'bumpengrooven',
'bumpenhoffer',
'bumpenhopper',
'bumpenjinks',
'bumpenklunk',
'bumpenknees',
'bumpenmarble',
'bumpenmash',
'bumpenmonkey',
'bumpenmooch',
'bumpenmouth',
'bumpenmuddle',
'bumpenmuffin',
'bumpenmush',
'bumpennerd',
'bumpennoodle',
'bumpennose',
'bumpennugget',
'bumpenphew',
'bumpenphooey',
'bumpenpocket',
'bumpenpoof',
'bumpenpop',
'bumpenpounce',
'bumpenpow',
'bumpenpretzel',
'bumpenquack',
'bumpenroni',
'bumpenscooter',
'bumpenscreech',
'bumpensmirk',
'bumpensnooker',
'bumpensnoop',
'bumpensnout',
'bumpensocks',
'bumpenspeed',
'bumpenspinner',
'bumpensplat',
'bumpensprinkles',
'bumpensticks',
'bumpenstink',
'bumpenswirl',
'bumpenteeth',
'bumpenthud',
'bumpentoes',
'bumpenton',
'bumpentoon',
'bumpentooth',
'bumpentwist',
'bumpenwhatsit',
'bumpenwhip',
'bumpenwig',
'bumpenwoof',
'bumpenzaner',
'bumpenzap',
'bumpenzapper',
'bumpenzilla',
'bumpenzoom',
'bumpkin',
'bumpy',
'bun',
'bunch',
'bunched',
'bunches',
'bunching',
'bunchy',
'bund',
'bundle',
'bundled',
'bundleofsticks',
'bundler',
'bundles',
'bundling',
'bunions',
'bunk',
'bunked',
'bunker',
'bunking',
'bunks',
'bunnies',
'bunny',
'buns',
'bunsen',
'bunting',
'bunyan',
'buoy',
'buoys',
'bur',
'burden',
'burdened',
'bureau',
'bureaus',
'burg',
'burger',
"burger's",
'burgers',
'burghers',
'burglar',
'burgundy',
'burial',
'buried',
'burier',
'buries',
'burlesque',
'burly',
'burn',
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'butternut',
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'buzzword',
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'bwah',
'bwahaha',
'bwahahah',
'bwahahaha',
'bwahahahah',
'bwahahahaha',
'bwahahahahaha',
'bwahahha',
'bwhahahaha',
'by',
'bye',
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'bytes',
'c#',
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'c.a.r.t.e.r.',
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'c.j.',
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'cabeza',
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'certain',
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'cgc',
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"chad's",
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'chaining',
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"chair's",
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'changeable',
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"chant's",
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'cheapest',
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'cheapskates',
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"cheat's",
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'cheaters',
'cheating',
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"check's",
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'chemicals',
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"chernabog's",
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"cheshire's",
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'frinkelklunk',
'frinkelknees',
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'fuzzyzilla',
'fuzzyzoom',
'fyi',
"g'bye",
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'game',
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'guppy',
'gurl',
'gurth',
'guru',
'gus',
"gus's",
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'gusher',
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"gusteau's",
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'gutting',
'guy',
"guy's",
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'guyona',
'guyoso',
'guyros',
'guys',
'guytia',
'gwa',
'gwarsh',
'gwen',
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"gwinn's",
'gyarados',
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"gym's",
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'gyros',
'h-pos',
'h.g.',
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'ha',
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'hakaba',
'hake',
'hakuna',
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'halau',
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'haley',
"haley's",
'haleys',
'half',
'half-o-dil',
'half-palindrome',
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'hali',
'halibut',
'haling',
'hall',
"hall's",
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"halloween's",
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'hallway',
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'halo',
'halos',
'halt',
'halting',
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'halve',
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'ham',
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'hamburger',
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'hamilton',
'hamlet',
'hammer',
'hammerhead',
"hammerhead's",
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'hammering',
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'hammock',
'hammocks',
'hammy',
'hampshire',
'hams',
'hamster',
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'hamsterball',
'hamsters',
'hanaroni',
'hand',
"hand's",
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'handball',
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'handedly',
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'hander',
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'handing',
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'handlebar',
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'handling',
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'hanebakuon',
'hanegaku',
'haneoto',
'hang',
'hang-loose',
'hangnail',
'hangout',
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'hanker',
'hankering',
'hankie',
"hankie's",
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'hanky',
'hanna',
'hannah',
"hannah's",
'hannahs',
'hans',
"hans'",
'hanukkah',
"hao's",
'hap',
"hap'n",
'hapacha',
'hapaxion',
'hapazoa',
'happen',
'happened',
'happening',
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'happens',
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'happiest',
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'happiness',
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'harryyouareawizard',
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"hat's",
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"hawai'i",
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'hawthorne',
'haxby',
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'haymaker',
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'haywire',
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'hazard',
'hazardous',
'hazel',
'hazelnut',
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'hazy',
'hazzard',
'hbu',
'hd',
'he',
"he'd",
"he'll",
"he's",
'head',
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'header',
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'headhunters',
'heading',
"heading's",
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"headstone's",
'headstones',
'headstrong',
'headway',
'heal',
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'healer',
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'heals',
'health',
"health's",
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'healthiest',
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'healthy',
'heap',
'heaps',
'hear',
'heard',
'hearer',
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'hearest',
'hearing',
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'hears',
'hearsay',
'heart',
"heart's",
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'heartache',
'heartbeat',
'heartbreak',
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'heartbreaking',
'heartbreaks',
'heartbroken',
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'heartedly',
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'heartens',
'heartfelt',
'hearth',
"hearth's",
'hearties',
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'heartstea',
'heartthrob',
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'hearty',
"hearty's",
'heartys',
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'heating',
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'heavyweight',
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'heckle',
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'hedgehog',
"hedgehog's",
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'hedy',
'hee',
'heed',
'heeded',
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'heeled',
'heeler',
'heeling',
'heels',
'heffalump',
"heffalump's",
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'heh',
'hehe',
'heidi',
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'heinous',
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'helix',
'hello',
'hellos',
'helm',
"helm's",
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'helmets',
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'helmsman',
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'helmswoman',
'help',
'helpdesk',
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'helper',
"helper's",
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'helps',
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'hemisphere',
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'hen',
"hen's",
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'henhouse',
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'henrys',
'hens',
'her',
"her's",
'heracross',
'herbert',
'herbie',
"herbie's",
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'herbs',
'hercules',
"hercules'",
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'herded',
'herder',
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'herding',
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'here',
"here's",
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'herecomestheworld',
'herein',
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'heretic',
'hereunto',
'heritage',
'hermes',
'hermione',
'hermit',
"hermit's",
'hermits',
'hermoine',
'hernia',
'hero',
"hero's",
'heroes',
'heroic',
'heroism',
'heron',
'herons',
'heros',
'herp',
'herring',
'herrings',
'herro',
'hers',
'herself',
'hershey',
'hersheys',
'hes',
'hesitant',
'hesitate',
'hesitating',
'hesitation',
'hest',
'hewent',
'hey',
'heya',
'heyguys',
'heywood',
'hf',
'hi',
'hi-top',
'hibernate',
'hibernating',
'hibernation',
'hibiscus',
'hic',
'hiccup',
'hiccups',
'hick',
'hickory',
'hickorytip',
'hicks',
'hid',
'hidden',
'hide',
'hide-and-seek',
'hideaway',
'hideaways',
'hided',
'hideous',
'hideously',
'hideout',
'hideouts',
'hides',
'hiding',
'hierarchy',
'higglyball',
'higglytown',
'high',
'high-energy',
'high-flying',
'high-impact',
'high-octane',
'high-powered',
'high-seas',
'high-speed',
'high-strung',
'high-voltage',
'higher',
'highest',
'highest-rated',
'highjack',
'highlander',
'highlands',
'highlight',
'highlighted',
'highlighting',
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'highly',
'highness',
'highs',
'highsea',
'highseas',
'hightail',
'hightailed',
'highway',
'hihi',
'hiiii-yaaaaa',
'hike',
'hiker',
'hiking',
'hilarious',
'hilarity',
'hill',
"hill's",
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'hiller',
'hilling',
'hills',
'hillside',
'hilltop',
'hilly',
'hilt',
'him',
'hims',
'himself',
'himuro',
'hindered',
'hindering',
'hinds',
'hindsight',
'hinges',
'hint',
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'hinter',
'hinterseas',
'hinting',
'hints',
'hip',
'hip-hop',
"hip-hoppin'",
'hippie',
'hippies',
"hippies'",
'hippo',
"hippo's",
'hippos',
'hippy',
'hipster',
'hire',
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'hirer',
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'hiring',
'his',
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'hissing',
'hissy',
'histoire',
'historian',
'historic',
'historical',
'historically',
'histories',
'history',
"history's",
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"hit's",
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'hitmonlee',
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'hitop',
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'hitter',
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'hitting',
'hive',
"hive's",
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'hiya',
'hkdl',
'hmm',
'hmmm',
'hms',
'ho-o-o-o-orse',
'ho-oh',
'hoard',
'hoarder',
'hoarding',
'hoards',
'hoax',
'hob',
'hobbies',
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'hobbits',
'hobbles',
'hobbling',
'hobby',
'hoboken',
'hocked',
'hockey',
'hocks',
'hocus',
'hocus-focus',
'hodgepodge',
'hofflord',
'hog',
'hogg',
'hogge',
'hogged',
'hogger',
'hoggers',
'hogging',
'hogglestock',
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'hogwarts',
'hogwash',
'hoi',
'hoist',
'hoisting',
'hola',
'hold',
"hold'em",
"hold's",
'holdem',
'holden',
'holder',
'holders',
"holdin'",
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'hole',
'holey',
'holidas',
'holiday',
"holiday's",
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'holidays',
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'holler',
'hollered',
'hollering',
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'hollow',
"hollow's",
'hollowed-out',
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'holly',
"holly's",
'hollywood',
'hollywoods',
'holm',
'holme',
"holme's",
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'holmrepurpose',
'holms',
'holo',
'hologram',
'holograms',
'holt',
'holy',
'homage',
'hombre',
'hombres',
'home',
"home's",
'home-grown',
'home-made',
'homebound',
'homeboy',
'homecoming',
'homed',
'homedog',
'homegirl',
'homeland',
'homeless',
'homely',
'homemade',
'homeopathic',
'homepage',
'homeport',
'homer',
"homer's",
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'homespun',
'homestead',
'hometown',
'homework',
'homeworker',
"homeworker's",
'homeworkers',
'homey',
'homeys',
'homier',
'homies',
'homing',
'homograph',
'homographs',
'hon',
'honcho',
'honchos',
'honda',
'hone',
'honed',
'hones',
'honest',
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'honeycomb',
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'honeys',
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'honeysuckles',
'honing',
'honk',
'honor',
'honorable',
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'honoring',
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'hood',
"hood's",
'hoods',
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'hook',
"hook's",
'hooked',
'hooks',
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'hooligans',
'hoop',
'hoopla',
'hooplah',
'hoops',
'hoopster',
'hoopsters',
'hooray',
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'hope',
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'hopeful',
"hopeful's",
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'hoping',
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'hopping',
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'hoppy',
'hops',
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"horatio's",
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'hordes',
'horison',
'horizon',
"horizon's",
'horizons',
'horizontal',
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'hornet',
"hornet's",
'hornets',
'horns',
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'horoscope',
'horrendous',
'horrible',
'horribly',
'horrid',
'horridness',
'horrific',
'horrified',
'horrifying',
'horror',
"horror's",
'horrors',
'horse',
"horse's",
'horsea',
'horseman',
'horsepower',
'horses',
'horseshoe',
"horseshoe's",
'horseshoes',
'horseshow',
'horsey',
'horsing',
'hose',
'hosed',
'hoses',
'hosiery',
'hospital',
'hospitality',
'hospitalize',
'hospitals',
'host',
"host's",
'hosted',
'hostile',
'hostiles',
'hostility',
'hosting',
'hosts',
'hot',
'hot-tempered',
'hotel',
"hotel's",
'hotels',
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'hotkey',
'hotkeys',
'hotshot',
'hotspot',
'hotspots',
'hotter',
'hottest',
'hound',
'hounded',
'hounding',
'houndoom',
'houndour',
'hounds',
'hour',
"hour's",
'hourglass',
'hourly',
'hours',
'house',
"house's",
'housebroken',
'housecleaning',
'housed',
'houseful',
'household',
'housekeeper',
'housemates',
'houseplant',
'houser',
'houses',
'housewife',
'housewives',
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'housing',
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'hovel',
'hover',
'hovercraft',
'hovered',
'hovering',
'hovers',
'how',
"how'd",
"how're",
"how's",
"how've",
'how-to',
'how-to-video',
'how2',
'howard',
'howdy',
'however',
'howl',
'howled',
'howling',
'hows',
'hp',
"hp's",
'hq',
'hqofficerf',
'hqofficerm',
'hqs',
'hr',
'hr.',
'hrage',
'hsm',
"hsm-2's",
'hsm2',
'hsm3',
'html',
'hub',
"hub's",
'hubbies',
'hubby',
"hubby's",
'hubert',
'hubs',
'hucklebee',
'huckleberry',
'huckleblabber',
'hucklebocker',
'huckleboing',
'huckleboom',
'hucklebounce',
'hucklebouncer',
'hucklebrains',
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'hucklebumble',
'hucklebump',
'hucklebumper',
'huckleburger',
'hucklechomp',
'hucklecorn',
'hucklecrash',
'hucklecrumbs',
'hucklecrump',
'hucklecrunch',
'huckledoodle',
'huckledorf',
'huckleface',
'hucklefidget',
'hucklefink',
'hucklefish',
'huckleflap',
'huckleflapper',
'huckleflinger',
'huckleflip',
'huckleflipper',
'hucklefoot',
'hucklefuddy',
'hucklefussen',
'hucklegadget',
'hucklegargle',
'hucklegloop',
'huckleglop',
'hucklegoober',
'hucklegoose',
'hucklegrooven',
'hucklehoffer',
'hucklehopper',
'hucklejinks',
'huckleklunk',
'huckleknees',
'hucklemarble',
'hucklemash',
'hucklemonkey',
'hucklemooch',
'hucklemouth',
'hucklemuddle',
'hucklemuffin',
'hucklemush',
'hucklenerd',
'hucklenoodle',
'hucklenose',
'hucklenugget',
'hucklephew',
'hucklephooey',
'hucklepocket',
'hucklepoof',
'hucklepop',
'hucklepounce',
'hucklepow',
'hucklepretzel',
'hucklequack',
'huckleroni',
'hucklescooter',
'hucklescreech',
'hucklesmirk',
'hucklesnooker',
'hucklesnoop',
'hucklesnout',
'hucklesocks',
'hucklespeed',
'hucklespinner',
'hucklesplat',
'hucklesprinkles',
'hucklesticks',
'hucklestink',
'huckleswirl',
'huckleteeth',
'hucklethud',
'huckletoes',
'huckleton',
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'huckletooth',
'huckletwist',
'hucklewhatsit',
'hucklewhip',
'hucklewig',
'hucklewoof',
'hucklezaner',
'hucklezap',
'hucklezapper',
'hucklezilla',
'hucklezoom',
'huddle',
'huddled',
"hudgen's",
'hudgens',
'hudson',
"hudson's",
'hudsons',
'hue',
'hug',
"hug's",
'huge',
'hugely',
'huger',
'hugers',
'hugest',
'hugged',
'hugh',
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'jabbermush',
'jabbernerd',
'jabbernoodle',
'jabbernose',
'jabbernugget',
'jabberphew',
'jabberphooey',
'jabberpocket',
'jabberpoof',
'jabberpop',
'jabberpounce',
'jabberpow',
'jabberpretzel',
'jabberquack',
'jabberroni',
'jabberscooter',
'jabberscreech',
'jabbersmirk',
'jabbersnooker',
'jabbersnoop',
'jabbersnout',
'jabbersocks',
'jabberspeed',
'jabberspinner',
'jabbersplat',
'jabbersprinkles',
'jabbersticks',
'jabberstink',
'jabberswirl',
'jabberteeth',
'jabberthud',
'jabbertoes',
'jabberton',
'jabbertoon',
'jabbertooth',
'jabbertwist',
'jabberwhatsit',
'jabberwhip',
'jabberwig',
'jabberwoof',
'jabberzaner',
'jabberzap',
'jabberzapper',
'jabberzilla',
'jabberzoom',
'jace',
'jack',
"jack's",
'jackals',
'jacket',
"jacket's",
'jackets',
'jackfruit',
'jackie',
"jackie's",
'jackies',
'jackolantern',
"jackolantern's",
'jackolanterns',
'jackpie',
'jackpot',
"jackpot's",
'jackpots',
'jacks',
'jackson',
"jackson's",
'jacques',
'jade',
'jaded',
'jado',
'jafar',
"jafar's",
'jafars',
'jagged',
'jaguar',
'jail',
'jails',
'jak',
'jake',
"jake's",
'jakebooy',
'jakefromstatefarm',
'jakes',
'jalex',
'jam',
'jamada',
'jamago',
'jamble',
'jamboree',
'james',
"james'",
'jamie',
'jamigos',
'jamilles',
'jammania',
'jammed',
'jammer',
'jammin',
"jammin'",
"jammin's",
'jamming',
'jammy',
'jamoso',
'jams',
'jan',
'jane',
"jane's",
'janes',
'janet',
'janice',
'january',
"january's",
'januarys',
'japan',
'jar',
"jar's",
'jarax',
'jared',
'jargon',
'jars',
'jasmine',
"jasmine's",
'jasmines',
'jason',
"jason's",
'jasons',
'java',
'javascript',
'jaw',
"jaw's",
'jaw-dropper',
'jawed',
'jaws',
'jayson',
'jazz',
'jazzed',
'jazzy',
'jb',
'jbs',
'jealous',
'jealously',
'jealousy',
'jean',
"jean's",
'jeanie',
'jeanne',
'jeans',
'jed',
'jedi',
"jedi's",
'jedis',
'jeena',
'jeeperbee',
'jeeperberry',
'jeeperblabber',
'jeeperbocker',
'jeeperboing',
'jeeperboom',
'jeeperbounce',
'jeeperbouncer',
'jeeperbrains',
'jeeperbubble',
'jeeperbumble',
'jeeperbump',
'jeeperbumper',
'jeeperburger',
'jeeperchomp',
'jeepercorn',
'jeepercrash',
'jeepercrumbs',
'jeepercrump',
'jeepercrunch',
'jeeperdoodle',
'jeeperdorf',
'jeeperface',
'jeeperfidget',
'jeeperfink',
'jeeperfish',
'jeeperflap',
'jeeperflapper',
'jeeperflinger',
'jeeperflip',
'jeeperflipper',
'jeeperfoot',
'jeeperfuddy',
'jeeperfussen',
'jeepergadget',
'jeepergargle',
'jeepergloop',
'jeeperglop',
'jeepergoober',
'jeepergoose',
'jeepergrooven',
'jeeperhoffer',
'jeeperhopper',
'jeeperjinks',
'jeeperklunk',
'jeeperknees',
'jeepermarble',
'jeepermash',
'jeepermonkey',
'jeepermooch',
'jeepermouth',
'jeepermuddle',
'jeepermuffin',
'jeepermush',
'jeepernerd',
'jeepernoodle',
'jeepernose',
'jeepernugget',
'jeeperphew',
'jeeperphooey',
'jeeperpocket',
'jeeperpoof',
'jeeperpop',
'jeeperpounce',
'jeeperpow',
'jeeperpretzel',
'jeeperquack',
'jeeperroni',
'jeeperscooter',
'jeeperscreech',
'jeepersmirk',
'jeepersnooker',
'jeepersnoop',
'jeepersnout',
'jeepersocks',
'jeeperspeed',
'jeeperspinner',
'jeepersplat',
'jeepersprinkles',
'jeepersticks',
'jeeperstink',
'jeeperswirl',
'jeeperteeth',
'jeeperthud',
'jeepertoes',
'jeeperton',
'jeepertoon',
'jeepertooth',
'jeepertwist',
'jeeperwhatsit',
'jeeperwhip',
'jeeperwig',
'jeeperwoof',
'jeeperzaner',
'jeeperzap',
'jeeperzapper',
'jeeperzilla',
'jeeperzoom',
'jeff',
'jeffrey',
'jehan',
'jellies',
'jelly',
'jellybean',
"jellybean's",
'jellybeans',
'jellyfish',
"jellyfish's",
'jellyroll',
'jennifer',
'jenny',
'jeopardy',
'jeremiah',
'jeremy',
'jerome',
'jerry',
"jerry's",
'jerrys',
'jersey',
'jess',
'jessamine',
'jesse',
"jesse's",
'jesses',
'jest',
'jester',
"jester's",
'jesters',
'jests',
'jet',
"jet's",
'jethred',
'jetix',
'jetixtreme',
'jetpack',
'jets',
'jetsam',
"jetsam's",
'jetsams',
'jett',
"jett's",
'jetts',
'jeune',
"jewel's",
'jeweled',
'jeweler',
'jewelers',
'jewelry',
'jex',
'jig',
'jigglypuff',
'jigsaw',
'jill',
'jim',
'jima',
'jimmie',
'jimmyleg',
'jingle',
'jinglebells',
'jingles',
'jingly',
'jinks',
'jinx',
'jinxbee',
'jinxberry',
'jinxblabber',
'jinxbocker',
'jinxboing',
'jinxboom',
'jinxbounce',
'jinxbouncer',
'jinxbrains',
'jinxbubble',
'jinxbumble',
'jinxbump',
'jinxbumper',
'jinxburger',
'jinxchomp',
'jinxcorn',
'jinxcrash',
'jinxcrumbs',
'jinxcrump',
'jinxcrunch',
'jinxdoodle',
'jinxdorf',
'jinxed',
'jinxes',
'jinxface',
'jinxfidget',
'jinxfink',
'jinxfish',
'jinxflap',
'jinxflapper',
'jinxflinger',
'jinxflip',
'jinxflipper',
'jinxfoot',
'jinxfuddy',
'jinxfussen',
'jinxgadget',
'jinxgargle',
'jinxgloop',
'jinxglop',
'jinxgoober',
'jinxgoose',
'jinxgrooven',
'jinxhoffer',
'jinxhopper',
'jinxing',
'jinxjinks',
'jinxklunk',
'jinxknees',
'jinxmarble',
'jinxmash',
'jinxmonkey',
'jinxmooch',
'jinxmouth',
'jinxmuddle',
'jinxmuffin',
'jinxmush',
'jinxnerd',
'jinxnoodle',
'jinxnose',
'jinxnugget',
'jinxphew',
'jinxphooey',
'jinxpocket',
'jinxpoof',
'jinxpop',
'jinxpounce',
'jinxpow',
'jinxpretzel',
'jinxquack',
'jinxroni',
'jinxscooter',
'jinxscreech',
'jinxsmirk',
'jinxsnooker',
'jinxsnoop',
'jinxsnout',
'jinxsocks',
'jinxspeed',
'jinxspinner',
'jinxsplat',
'jinxsprinkles',
'jinxsticks',
'jinxstink',
'jinxswirl',
'jinxteeth',
'jinxthud',
'jinxtoes',
'jinxton',
'jinxtoon',
'jinxtooth',
'jinxtwist',
'jinxwhatsit',
'jinxwhip',
'jinxwig',
'jinxwoof',
'jinxzaner',
'jinxzap',
'jinxzapper',
'jinxzilla',
'jinxzoom',
'jitterbug',
'jittery',
'jive',
'jive_turkies',
"jivin'",
'jj',
'jjdinner',
'jjkoletar',
'jk',
'jo',
'joan',
'job',
'jobs',
'jocard',
'joe',
"joe's",
'joel',
'joes',
'joey',
'joff',
'joff-tchoff-tchoffo-tchoffo-tchoff',
'john',
'johnny',
"johnny's",
'johnnys',
'johns',
'johnson',
"johnson's",
'johnsons',
'johny',
'join',
'joined',
'joiner',
'joiners',
'joining',
'joins',
'jojo',
"jojo's",
'jojos',
'joke',
"joke's",
'joked',
'joker',
"joker's",
'jokers',
'jokes',
'jokey',
'joking',
'jolene',
'jollibee',
'jollibeefellowship',
'jollibeeinfinite',
'jollibeerewritten',
'jolly',
"jolly's",
'jollyroger',
"jollyroger's",
'jollyrogers',
'jolt',
'jolteon',
"jona's",
'jonas',
'jonathan',
'jones',
"jones'",
"jones's",
'jordan',
'jos',
'joseph',
'josh',
'joshamee',
'joshsora',
'joshua',
'joshuas',
'josie',
'journal',
'journals',
'journes',
'journey',
'journeyed',
'journeying',
'journeyings',
'joy',
"joy's",
'joybuzzer',
'joyce',
'joyful',
'joyous',
'joys',
'jpeg',
'jpn',
'jpop',
'jps',
'jr',
'jr.',
'js',
'juan',
'jubilee',
'judge',
"judge's",
'judged',
'judger',
'judges',
'judging',
'judgment',
'judy',
"judy's",
'judys',
'juels',
'juggernaut',
'juggernauts',
'juggle',
'juggler',
'juggles',
'juggling',
'juice',
'juiced',
'juju',
'jukebox',
'jul',
'julep',
'julie',
'juliet',
'julius',
'july',
"july's",
'julys',
'jumba',
"jumba's",
'jumble',
'jumblebee',
'jumbleberry',
'jumbleblabber',
'jumblebocker',
'jumbleboing',
'jumbleboom',
'jumblebounce',
'jumblebouncer',
'jumblebrains',
'jumblebubble',
'jumblebumble',
'jumblebump',
'jumblebumper',
'jumblechomp',
'jumblecorn',
'jumblecrash',
'jumblecrumbs',
'jumblecrump',
'jumblecrunch',
'jumbledoodle',
'jumbledorf',
'jumbleface',
'jumblefidget',
'jumblefink',
'jumblefish',
'jumbleflap',
'jumbleflapper',
'jumbleflinger',
'jumbleflip',
'jumbleflipper',
'jumblefoot',
'jumblefuddy',
'jumblefussen',
'jumblegadget',
'jumblegargle',
'jumblegloop',
'jumbleglop',
'jumblegoober',
'jumblegoose',
'jumblegrooven',
'jumblehoffer',
'jumblehopper',
'jumblejinks',
'jumbleklunk',
'jumbleknees',
'jumblemarble',
'jumblemash',
'jumblemonkey',
'jumblemooch',
'jumblemouth',
'jumblemuddle',
'jumblemuffin',
'jumblemush',
'jumblenerd',
'jumblenoodle',
'jumblenose',
'jumblenugget',
'jumblephew',
'jumblephooey',
'jumblepocket',
'jumblepoof',
'jumblepop',
'jumblepounce',
'jumblepow',
'jumblepretzel',
'jumblequack',
'jumbleroni',
'jumbles',
'jumblescooter',
'jumblescreech',
'jumblesmirk',
'jumblesnooker',
'jumblesnoop',
'jumblesnout',
'jumblesocks',
'jumblespeed',
'jumblespinner',
'jumblesplat',
'jumblesprinkles',
'jumblesticks',
'jumblestink',
'jumbleswirl',
'jumbleteeth',
'jumblethud',
'jumbletoes',
'jumbleton',
'jumbletoon',
'jumbletooth',
'jumbletwist',
'jumblewhatsit',
'jumblewhip',
'jumblewig',
'jumblewoof',
'jumblezaner',
'jumblezap',
'jumblezapper',
'jumblezilla',
'jumblezoom',
'jumbo',
'jump',
"jump's",
'jumped',
'jumper',
'jumpers',
'jumpin',
'jumping',
'jumpluff',
'jumps',
'jumpy',
'jun',
'june',
"june's",
'juneau',
'junebug',
'junehs',
'junes',
'jung',
'jungle',
"jungle's",
'jungled',
'jungles',
'junior',
"junior's",
'juniors',
'juniper',
'junk',
'jupiter',
"jupiter's",
'jupiters',
'jurassic',
'jury',
'just',
'just-waking-up',
'juster',
'justice',
'justin',
"justin's",
'justing',
'justins',
'justly',
'juvenile',
'juvenilia',
'jynx',
'k',
'ka',
'ka-boom',
'ka-ching',
'kabob',
'kaboomery',
'kabuto',
'kabutops',
'kadabra',
'kady',
'kagero',
'kahoot',
'kahoot.it',
'kai',
'kaitlin',
'kaitlyn',
'kaken',
'kakuna',
'kamakuri',
'kanaya',
'kang',
'kanga',
"kanga's",
'kangaroo',
'kangas',
'kangaskhan',
'kansas',
'kapahala',
'kappa',
'kappaross',
'karakuri',
'karaoke',
'karat',
'karate',
'karbay',
'karen',
'karin',
'karkat',
'karma',
'karnival',
'karo',
"karo's",
'kart',
'karts',
'kasumi',
'kasumire',
'kasumite',
'kat',
'katarina',
'kate',
"kate's",
'katelyn',
'kathy',
"katie's",
'katz',
'katzenstein',
'kawaii',
'kay',
'kazaam',
'kazoo',
'kazoology',
'kdf',
'keaton',
'keelgrin',
'keely',
"keely's",
'keelys',
'keen',
'keep',
'keeper',
"keeper's",
'keepers',
"keepin'",
'keeping',
'keeps',
'keepsake',
"keepsake's",
'keepsakes',
'kei',
'keira',
"keira's",
'keiras',
'kek',
'keke',
'kellogg',
"kellogg's",
'kelloggs',
'kelly',
"kelly's",
'kellys',
'kelp',
'kelp-jelly',
'kelsi',
"kelsi's",
'kelsis',
'kelvin',
'ken',
'kenai',
"kenai's",
'kenais',
'kennel',
'kenneth',
'kenny',
"kenny's",
'kennys',
'kent',
'kentucky',
'kept',
'kerchak',
"kerchak's",
'kerchaks',
'kermie',
'kermit',
'kes',
'kesha',
'kestred',
'ketchum',
'ketchup',
'ketobasu',
'kettle',
'kettles',
'kevin',
"kevin's",
'kevinh',
'kevins',
'kevman95',
'kewl',
'key',
"key's",
'keyboard',
"keyboard's",
'keyboarding',
'keyboards',
'keyhole-design',
'keys',
'keystone',
'keyword',
'kh',
'khaki',
"khaki's",
'khakis',
'khamsin',
'ki',
'kiba',
'kibatekka',
'kick',
'kickball',
'kicked',
'kicker',
'kickers',
'kickflip',
"kickin'",
'kicking',
'kicks',
'kid',
"kid's",
'kidd',
'kiddie',
'kidding',
'kids',
'kidstuff',
'kiely',
"kiely's",
'kielys',
'kiki',
"kiki's",
'kikis',
'kikyo',
'kill',
'killed',
'kim',
"kim's",
'kimchi',
'kimmunicator',
'kims',
'kind',
'kinda',
'kindergarten',
"kindergarten's",
'kindergartens',
'kindest',
'kindle',
'kindled',
'kindles',
'kindling',
'kindly',
'kindness',
'kinds',
'king',
"king's",
'king-sized',
'kingdedede',
'kingdom',
"kingdom's",
'kingdoms',
'kingdra',
'kingfisher',
'kingfishers',
'kingler',
'kingly',
'kingman',
"kingman's",
'kings',
'kingshead',
'kinna',
'kion',
'kiosk',
"kiosk's",
'kiosks',
'kipp',
'kippur',
'kirby',
'kiril',
'kirk',
'kirke',
'kit',
'kitchen',
"kitchen's",
'kitchener',
'kitchens',
'kite',
'kites',
'kitkat',
'kitt',
'kitten',
"kitten's",
'kittens',
'kitties',
'kitty',
"kitty's",
'kiwi',
'kk',
'klebba',
'klutz',
'klutzy',
'knap',
'knave',
'knee',
'kneecap',
'kneed',
'kneel',
'knees',
'knew',
'knghts',
'knight',
'knightley',
"knightley's",
'knights',
'knitting',
'knock',
'knockback',
'knockdown',
'knocked',
'knocking',
'knockoff',
'knocks',
'knoll',
'knot',
'knots',
'knotty',
'know',
'know-it-alls',
'knower',
'knowing',
'knowledge',
'knowledgeable',
'knowledges',
'known',
'knows',
'knowzone',
'knuckle',
'knucklehead',
'knuckles',
'koala',
"koala's",
'koalas',
'kobi',
'kobra',
'koda',
"koda's",
'kodas',
'kodiac',
'koffing',
'koha',
'kokeshi',
'koko',
'kokoago',
'kokoros',
'koleniko',
'koletar',
'kollin',
'komadoros',
'komainu',
'komanoto',
'kong',
"kong's",
'kongs',
'kooky',
'kookybee',
'kookyberry',
'kookyblabber',
'kookybocker',
'kookyboing',
'kookyboom',
'kookybounce',
'kookybouncer',
'kookybrains',
'kookybubble',
'kookybumble',
'kookybump',
'kookybumper',
'kookyburger',
'kookychomp',
'kookycorn',
'kookycrash',
'kookycrumbs',
'kookycrump',
'kookycrunch',
'kookydoodle',
'kookydorf',
'kookyface',
'kookyfidget',
'kookyfink',
'kookyfish',
'kookyflap',
'kookyflapper',
'kookyflinger',
'kookyflip',
'kookyflipper',
'kookyfoot',
'kookyfuddy',
'kookyfussen',
'kookygadget',
'kookygargle',
'kookygloop',
'kookyglop',
'kookygoober',
'kookygoose',
'kookygrooven',
'kookyhoffer',
'kookyhopper',
'kookyjinks',
'kookyklunk',
'kookyknees',
'kookymarble',
'kookymash',
'kookymonkey',
'kookymooch',
'kookymouth',
'kookymuddle',
'kookymuffin',
'kookymush',
'kookynerd',
'kookynoodle',
'kookynose',
'kookynugget',
'kookyphew',
'kookyphooey',
'kookypocket',
'kookypoof',
'kookypop',
'kookypounce',
'kookypow',
'kookypretzel',
'kookyquack',
'kookyroni',
'kookyscooter',
'kookyscreech',
'kookysmirk',
'kookysnooker',
'kookysnoop',
'kookysnout',
'kookysocks',
'kookyspeed',
'kookyspinner',
'kookysplat',
'kookysprinkles',
'kookysticks',
'kookystink',
'kookyswirl',
'kookyteeth',
'kookythud',
'kookytoes',
'kookyton',
'kookytoon',
'kookytooth',
'kookytwist',
'kookywhatsit',
'kookywhip',
'kookywig',
'kookywoof',
'kookyzaner',
'kookyzap',
'kookyzapper',
'kookyzilla',
'kookyzoom',
'kool',
'korogeki',
'koroko',
'korozama',
'korra',
'kosimi',
"kosmic's",
'kouki',
'kp',
"kp's",
'krab',
'krabby',
'kraken',
"kraken's",
'krakens',
'krawl',
'krazy',
'kreepers',
'krew',
'krewe',
'krispies',
'krissy',
'kristin',
'kristina',
'krogager',
'kronk',
"kronk's",
'krunklehorn',
'krusty',
'krux',
'krybots',
'kthx',
'ktta',
'kubaku',
'kuganon',
'kugaster',
'kumonn',
'kun',
'kung',
'kuzco',
'kwanzaa',
'kyle',
"kyle's",
'kyles',
'kyra',
'kyto',
"kyto's",
'l8r',
'la',
'lab',
'label',
'labeled',
'labeling',
'labelled',
'labelling',
'labels',
'labor',
'labradoodle',
'labradoodles',
'labs',
'labyrinth',
'lace',
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'thing',
'thingamabob',
'thingamabobs',
'thingamajigs',
'thingie',
'thingies',
'things',
'thingy',
'thinicetrobarrier',
'think',
'thinker',
'thinkers',
"thinkin'",
'thinking',
'thinkings',
'thinks',
'thinnamin',
'third',
'thirst',
'thirst-quenching',
'thirsted',
'thirster',
'thirstier',
'thirsts',
'thirsty',
'thirty',
'this',
'thisisasecretmessage',
'thismailboxismine',
'thistle',
'thistle-down',
'thnx',
'tho',
'thomas',
'thon',
'thonk',
'thonkang',
'thonkig',
'thor',
'thorhammer',
'thorn',
'those',
'though',
'thought',
"thought's",
'thoughtful',
'thoughts',
'thousand',
'thousands',
'thrashers',
'thread',
'threads',
'threats',
'three',
'threw',
'thrice',
'thrifty',
'thrill',
'thriller',
'thrilling',
'thrills',
'thrillseeker',
'thrives',
'throne',
'thrones',
'through',
'throughly',
'throughout',
'throw',
'thrower',
'throwing',
'throwitathimnotme',
'throwless',
'thrown',
'throws',
'thumb',
'thumbs',
'thumper',
'thunba',
'thunder',
'thunderbee',
'thunderberry',
'thunderblabber',
'thunderbocker',
'thunderboing',
'thunderbolt',
'thunderbolts',
'thunderboom',
'thunderbounce',
'thunderbouncer',
'thunderbrains',
'thunderbubble',
'thunderbumble',
'thunderbump',
'thunderbumper',
'thunderburger',
'thunderchomp',
'thundercorn',
'thundercrash',
'thundercrumbs',
'thundercrump',
'thundercrunch',
'thunderdoodle',
'thunderdorf',
'thunderface',
'thunderfidget',
'thunderfink',
'thunderfish',
'thunderflap',
'thunderflapper',
'thunderflinger',
'thunderflip',
'thunderflipper',
'thunderfoot',
'thunderfuddy',
'thunderfussen',
'thundergadget',
'thundergargle',
'thundergloop',
'thunderglop',
'thundergoober',
'thundergoose',
'thundergrooven',
'thunderhoffer',
'thunderhopper',
'thundering',
'thunderjinks',
'thunderklunk',
'thunderknees',
'thundermarble',
'thundermash',
'thundermonkey',
'thundermooch',
'thundermouth',
'thundermuddle',
'thundermuffin',
'thundermush',
'thundernerd',
'thundernoodle',
'thundernose',
'thundernugget',
'thunderphew',
'thunderphooey',
'thunderpocket',
'thunderpoof',
'thunderpop',
'thunderpounce',
'thunderpow',
'thunderpretzel',
'thunderquack',
'thunderroni',
'thunderscooter',
'thunderscreech',
'thundersmirk',
'thundersnooker',
'thundersnoop',
'thundersnout',
'thundersocks',
'thunderspeed',
'thunderspinner',
'thundersplat',
'thundersprinkles',
'thundersticks',
'thunderstink',
'thunderstorm',
'thunderstorms',
'thunderswirl',
'thunderteeth',
'thunderthud',
'thundertoes',
'thunderton',
'thundertoon',
'thundertooth',
'thundertwist',
'thunderwhatsit',
'thunderwhip',
'thunderwig',
'thunderwoof',
'thunderzaner',
'thunderzap',
'thunderzapper',
'thunderzilla',
'thunderzoom',
'thundor',
'thundora',
'thunkig',
'thunkong',
'thursday',
'thursdays',
'thus',
'thusly',
'thx',
'tia',
'tiana',
'tiara',
'tiazoa',
'tiberius',
'tibian',
'tic',
'tic-toc',
'tick',
'ticker',
'ticket',
"ticket's",
'ticketed',
'ticketing',
'tickets',
'ticking',
'tickle',
'tickled',
'tickles',
'tickling',
'ticklish',
'ticks',
'tidal',
'tidbits',
'tide',
'tidy',
'tie',
'tier',
'ties',
'tiffany',
'tiffens',
'tiger',
"tiger's",
'tigers',
'tigger',
"tigger's",
'tightwad',
'tightwads',
'tiki',
'tikis',
'til',
'tilde',
'tile',
'tiled',
'tiles',
'till',
'tilled',
'tiller',
'tillers',
'tilling',
'tills',
'tilly',
'tim',
'timberlake',
'timberleaf',
'timbers',
'timberwolves',
'timbre',
'time',
'timed',
'timeless',
'timelords',
'timely',
'timeout',
'timer',
'timers',
'times',
'timesnewroman',
'timing',
'timings',
'timon',
"timon's",
'timons',
'timothy',
'tin',
'tina',
"tina's",
'tindera',
'tink',
"tink's",
'tinker',
"tinker's",
'tinkerbell',
"tinkerbell's",
'tinkling',
'tinny',
'tinsel',
'tint',
'tinted',
'tints',
'tiny',
'tip',
'tip-top',
'tipped',
'tipping',
'tips',
'tipton',
'tire',
'tired',
'tires',
'tiresome',
'tiring',
'tis',
'tisdale',
"tish's",
'titan',
"titan's",
'titanic',
'titans',
'title',
'titles',
'tj',
'tkp',
'tl;dr',
'tlc',
'tm',
'tnt',
'tnts',
'to',
'toad',
'toads',
'toadstool',
'toadstool-drying',
'toadstools',
'toast',
'toasted',
'toaster',
'toasting',
'toasty',
'tobasu',
'tobias',
'toboggan',
'tobogganing',
'toboggans',
'toby',
'todas',
'today',
"today's",
'todd',
"todd's",
'toddler',
'toe',
"toe's",
'toed',
'toehooks',
'toes',
'tofu',
'togepi',
'together',
'togetic',
'toggle',
'toggler',
'token',
'tokens',
'told',
'tolerable',
'toll',
'tom',
"tom's",
"tom-tom's",
'tomas',
'tomato',
'tomboy',
'tomorrow',
"tomorrow's",
'tomorrowland',
"tomorrowland's",
'tomorrows',
'tomswordfish',
'ton',
'tone',
'toner',
'tones',
'tong',
'tongs',
'tonic',
'tonics',
'tonight',
'toning',
'tonite',
'tons',
'tony',
'too',
'toodles',
'took',
'tool',
'toolbar',
'toolbars',
'toolbelt',
'tooled',
'tooler',
'toolers',
'tooling',
'tools',
'toolshed',
'toon',
"toon's",
'toon-tastic',
'toon-torial',
'toon-up',
'toon-upless',
'toon-ups',
'toonacious',
'toonblr',
'tooncan',
'tooned',
'toonerific',
'toonfinite',
'toonhq.org',
'toonification',
'tooning',
'toonish',
'toonity',
'toonosaur',
'toons',
'toonscape',
'toontanic',
'toontask',
'toontasking',
'toontasks',
'toontastic',
'toonter',
'toontorial',
'toontown',
'toontrooper',
'toontroopers',
'toonup',
'toonupless',
'toonups',
'toony',
'tooth',
'toothless',
'toothpaste',
'toothpick',
'tootie',
'tootles',
'top',
'top-ranking',
'topaz',
'tophat',
'tophats',
'topiary',
'topic',
'topics',
'topkek',
'toppenbee',
'toppenberry',
'toppenblabber',
'toppenbocker',
'toppenboing',
'toppenboom',
'toppenbounce',
'toppenbouncer',
'toppenbrains',
'toppenbubble',
'toppenbumble',
'toppenbump',
'toppenbumper',
'toppenburger',
'toppenchomp',
'toppencorn',
'toppencrash',
'toppencrumbs',
'toppencrump',
'toppencrunch',
'toppendoodle',
'toppendorf',
'toppenface',
'toppenfidget',
'toppenfink',
'toppenfish',
'toppenflap',
'toppenflapper',
'toppenflinger',
'toppenflip',
'toppenflipper',
'toppenfoot',
'toppenfuddy',
'toppenfussen',
'toppengadget',
'toppengargle',
'toppengloop',
'toppenglop',
'toppengoober',
'toppengoose',
'toppengrooven',
'toppenhoffer',
'toppenhopper',
'toppenjinks',
'toppenklunk',
'toppenknees',
'toppenmarble',
'toppenmash',
'toppenmonkey',
'toppenmooch',
'toppenmouth',
'toppenmuddle',
'toppenmuffin',
'toppenmush',
'toppennerd',
'toppennoodle',
'toppennose',
'toppennugget',
'toppenphew',
'toppenphooey',
'toppenpocket',
'toppenpoof',
'toppenpop',
'toppenpounce',
'toppenpow',
'toppenpretzel',
'toppenquack',
'toppenroni',
'toppenscooter',
'toppenscreech',
'toppensmirk',
'toppensnooker',
'toppensnoop',
'toppensnout',
'toppensocks',
'toppenspeed',
'toppenspinner',
'toppensplat',
'toppensprinkles',
'toppensticks',
'toppenstink',
'toppenswirl',
'toppenteeth',
'toppenthud',
'toppentoes',
'toppenton',
'toppentoon',
'toppentooth',
'toppentwist',
'toppenwhatsit',
'toppenwhip',
'toppenwig',
'toppenwoof',
'toppenzaner',
'toppenzap',
'toppenzapper',
'toppenzilla',
'toppenzoom',
'topping',
'toppings',
'tops',
'topsy',
'topsy-turvy',
'toptoon',
'toptoons',
'tor',
'torga',
'torgallup',
'torgazar',
'tori',
'tormenta',
'torn',
'tortaba',
'tortaire',
'tortana',
'torth',
'tortoises',
'tortos',
'tory',
'tos',
'tosis',
'toss',
'tossed',
'tosses',
'tossing',
'total',
"total's",
'totaled',
'totaling',
'totally',
'totals',
'tote',
'totem',
'totems',
'tothestars',
'toti',
'totodile',
'tots',
'toucan',
'toucans',
'touch',
'touchdown',
'touchdowns',
'touge',
'tough',
'tough-skinned',
'toughest',
'toughness',
'toupee',
'toupees',
'tour',
'toured',
'tourer',
'tourguide',
'touring',
'tournament',
'tournaments',
'tours',
'tow',
'tow-mater',
'toward',
'towardly',
'towards',
'tower',
'towered',
'towering',
'towers',
'towing',
'town',
"town's",
'towner',
'towns',
'townsend',
'townsfolk',
'townsperson',
'tows',
'toxic',
'toxicmanager',
'toxicmanagers',
'toy',
'toyer',
'toying',
'toys',
'tp',
'tping',
'trace',
'track',
"track's",
'tracked',
'tracker',
'trackers',
'tracking',
'tracks',
'tracy',
'trade',
'tradeable',
'traded',
'trademark',
'trader',
'traders',
'trades',
'trading',
'tradition',
'traditions',
'traffic',
"traffic's",
'traffics',
'tragedy',
'trail',
'trailed',
'trailer',
'trailers',
'trailing',
'trailings',
'trails',
'train',
'trained',
'trainer',
'trainers',
'training',
'trains',
'trampoline',
'trampolines',
'tranquil',
'transfer',
"transfer's",
'transfered',
'transferred',
'transferring',
'transfers',
'translate',
'translated',
'translates',
'translating',
'transom',
'transparent',
'transport',
'transportation',
'transported',
'transporter',
'transporters',
'transporting',
'transports',
'trap',
"trap's",
'trapdoor',
'trapdoors',
'trapeze',
'trapless',
'trapped',
'traps',
'trash',
'trashcan',
"trashcan's",
'trashcans',
'travel',
'traveled',
'traveler',
'traveling',
'travelling',
'travels',
'travis',
'tray',
'treacherous',
'treachery',
"treachery's",
'tread',
'treaded',
'treading',
'treads',
'treasure',
'treasurechest',
'treasurechests',
'treasured',
'treasuremaps',
'treasurer',
'treasures',
'treasuring',
'treat',
'treated',
'treater',
'treaters',
'treating',
'treatment',
'treats',
'treble',
'tree',
'tree-bark-grading',
'tree-picking',
'tree-picking-talent',
'treehouse',
'treehouses',
'trees',
'treetop',
'trek',
'trellis',
'tremendous',
'tremor',
'trend',
'trending',
'trendinghobbit',
'trends',
'trent',
"trent's",
'tres',
'trespass',
'trespasser',
'treyarch',
'tri-barrel',
'tri-lock',
'triad',
'trial',
"trial's",
'trials',
'triangle',
'tribal',
'tribe',
'tribulation',
'tribulations',
'trick',
'tricked',
'tricked-out',
'tricker',
'trickery',
'tricking',
'tricks',
'tricky',
'trickybee',
'trickyberry',
'trickyblabber',
'trickybocker',
'trickyboing',
'trickyboom',
'trickybounce',
'trickybouncer',
'trickybrains',
'trickybubble',
'trickybumble',
'trickybump',
'trickybumper',
'trickyburger',
'trickychomp',
'trickycorn',
'trickycrash',
'trickycrumbs',
'trickycrump',
'trickycrunch',
'trickydoodle',
'trickydorf',
'trickyface',
'trickyfidget',
'trickyfink',
'trickyfish',
'trickyflap',
'trickyflapper',
'trickyflinger',
'trickyflip',
'trickyflipper',
'trickyfoot',
'trickyfuddy',
'trickyfussen',
'trickygadget',
'trickygargle',
'trickygloop',
'trickyglop',
'trickygoober',
'trickygoose',
'trickygrooven',
'trickyhoffer',
'trickyhopper',
'trickyjinks',
'trickyklunk',
'trickyknees',
'trickymarble',
'trickymash',
'trickymonkey',
'trickymooch',
'trickymouth',
'trickymuddle',
'trickymuffin',
'trickymush',
'trickynerd',
'trickynoodle',
'trickynose',
'trickynugget',
'trickyphew',
'trickyphooey',
'trickyplaystt',
'trickypocket',
'trickypoof',
'trickypop',
'trickypounce',
'trickypow',
'trickypretzel',
'trickyquack',
'trickyroni',
'trickyscooter',
'trickyscreech',
'trickysmirk',
'trickysnooker',
'trickysnoop',
'trickysnout',
'trickysocks',
'trickyspeed',
'trickyspinner',
'trickysplat',
'trickysprinkles',
'trickysticks',
'trickystink',
'trickyswirl',
'trickyteeth',
'trickythud',
'trickytoes',
'trickyton',
'trickytoon',
'trickytooth',
'trickytwist',
'trickywhatsit',
'trickywhip',
'trickywig',
'trickywoof',
'trickyzaner',
'trickyzap',
'trickyzapper',
'trickyzilla',
'trickyzoom',
'tried',
'trier',
'triers',
'tries',
'trifle',
'triforce',
'triggered',
'triggerfish',
'trike',
'trillion',
'trim',
'trims',
'trinity',
'trinket',
'trinkets',
'trinomial',
'trio',
'trioing',
'trip',
"trip's",
'triple',
'triplet',
'triplets',
'triply',
"trippin'",
'trippy',
'trips',
'triskaidekaphobia',
'tristam',
'tristan',
'triton',
"triton's",
'tritons',
'triumph',
'triumphant',
'trivia',
'trixie',
'trliable',
'troga',
'trogallup',
'trogazar',
'trogdor',
'troll',
'trolley',
'trolleys',
'trolls',
'trolly',
'tron',
'troop',
'trooper',
'troopers',
'troops',
'trophies',
'trophy',
'trophys',
'tropic',
"tropic's",
'tropical',
'tropically',
'tropics',
'trot',
'troubadours',
'trouble',
'troubled',
'troublemaker',
'troubler',
'troubles',
'troublesome',
'troubling',
'trough',
'troughes',
'trounce',
"trounce'em",
'trousers',
'trout',
'trove',
'trowels',
'troy',
'tru',
'truchemistry',
'truck',
'truckloads',
'trudy',
'true',
'trued',
"truehound's",
'truer',
'trues',
'truest',
'truetosilver',
'truffle',
'trufflehunter',
"trufflehunter's",
'trufflehunters',
'truffles',
'truigos',
'truing',
'truly',
'trump',
'trumpet',
'trumpets',
'trumpkin',
'trunk',
"trunk's",
'trunked',
'trunkfish',
'trunking',
'trunks',
'truscott',
'trust',
'trusted',
'truster',
'trusting',
'trusts',
'trustworthy',
'trusty',
'truth',
'truths',
'try',
'tryin',
'trying',
'tt',
'ttc',
'ttcentral',
'ttf',
'ttfn',
'ttfs',
'tthe',
'tti',
'tto',
'ttpa',
'ttpahq.pw',
'ttr',
'ttrl',
'ttyl',
'tu',
'tub',
'tuba',
'tubas',
'tubby',
'tube',
'tuber',
'tubes',
'tubs',
'tuesday',
'tuesdays',
'tuft',
'tufts',
'tug',
'tug-o-war',
'tug-of-war',
'tugs',
'tuless',
'tulip',
'tulips',
'tumble',
'tumbleweed',
'tumbleweeds',
'tumbly',
'tumnus',
'tuna',
'tunas',
'tundra',
'tune',
'tune-licious',
'tuned',
'tuner',
'tuners',
'tunes',
'tuning',
'tunnel',
'tunneled',
'tunneling',
'tunnels',
'turbo',
'turbojugend',
'turbonegro',
'turf',
'turk',
'turkey',
'turkish',
'turn',
'turnbull',
"turnbull's",
'turned',
'turner',
'turners',
'turning',
'turnings',
'turnip',
'turnover',
'turnovers',
'turns',
'turnstile',
'turnstiles',
'turqouise',
'turquoise',
'turret',
'turtle',
'turtles',
'turvey',
'tusk',
'tusked',
'tut',
'tutorial',
'tutorials',
'tutu',
'tutupia',
'tuxedo',
'tuxedos',
'tv',
"tv's",
'tvs',
'twain',
'tweebs',
'tweedlebee',
'tweedleberry',
'tweedleblabber',
'tweedlebocker',
'tweedleboing',
'tweedleboom',
'tweedlebounce',
'tweedlebouncer',
'tweedlebrains',
'tweedlebubble',
'tweedlebumble',
'tweedlebump',
'tweedlebumper',
'tweedleburger',
'tweedlechomp',
'tweedlecorn',
'tweedlecrash',
'tweedlecrumbs',
'tweedlecrump',
'tweedlecrunch',
'tweedledee',
'tweedledoodle',
'tweedledorf',
'tweedledum',
'tweedleface',
'tweedlefidget',
'tweedlefink',
'tweedlefish',
'tweedleflap',
'tweedleflapper',
'tweedleflinger',
'tweedleflip',
'tweedleflipper',
'tweedlefoot',
'tweedlefuddy',
'tweedlefussen',
'tweedlegadget',
'tweedlegargle',
'tweedlegloop',
'tweedleglop',
'tweedlegoober',
'tweedlegoose',
'tweedlegrooven',
'tweedlehoffer',
'tweedlehopper',
'tweedlejinks',
'tweedleklunk',
'tweedleknees',
'tweedlemarble',
'tweedlemash',
'tweedlemonkey',
'tweedlemooch',
'tweedlemouth',
'tweedlemuddle',
'tweedlemuffin',
'tweedlemush',
'tweedlenerd',
'tweedlenoodle',
'tweedlenose',
'tweedlenugget',
'tweedlephew',
'tweedlephooey',
'tweedlepocket',
'tweedlepoof',
'tweedlepop',
'tweedlepounce',
'tweedlepow',
'tweedlepretzel',
'tweedlequack',
'tweedleroni',
'tweedlescooter',
'tweedlescreech',
'tweedlesmirk',
'tweedlesnooker',
'tweedlesnoop',
'tweedlesnout',
'tweedlesocks',
'tweedlespeed',
'tweedlespinner',
'tweedlesplat',
'tweedlesprinkles',
'tweedlesticks',
'tweedlestink',
'tweedleswirl',
'tweedleteeth',
'tweedlethud',
'tweedletoes',
'tweedleton',
'tweedletoon',
'tweedletooth',
'tweedletwist',
'tweedlewhatsit',
'tweedlewhip',
'tweedlewig',
'tweedlewoof',
'tweedlezaner',
'tweedlezap',
'tweedlezapper',
'tweedlezilla',
'tweedlezoom',
'tween',
'tweet',
'tweety',
'twelve',
'twenty',
'twice',
'twiddlebee',
'twiddleberry',
'twiddleblabber',
'twiddlebocker',
'twiddleboing',
'twiddleboom',
'twiddlebounce',
'twiddlebouncer',
'twiddlebrains',
'twiddlebubble',
'twiddlebumble',
'twiddlebump',
'twiddlebumper',
'twiddleburger',
'twiddlechomp',
'twiddlecorn',
'twiddlecrash',
'twiddlecrumbs',
'twiddlecrump',
'twiddlecrunch',
'twiddledoodle',
'twiddledorf',
'twiddleface',
'twiddlefidget',
'twiddlefink',
'twiddlefish',
'twiddleflap',
'twiddleflapper',
'twiddleflinger',
'twiddleflip',
'twiddleflipper',
'twiddlefoot',
'twiddlefuddy',
'twiddlefussen',
'twiddlegadget',
'twiddlegargle',
'twiddlegloop',
'twiddleglop',
'twiddlegoober',
'twiddlegoose',
'twiddlegrooven',
'twiddlehoffer',
'twiddlehopper',
'twiddlejinks',
'twiddleklunk',
'twiddleknees',
'twiddlemarble',
'twiddlemash',
'twiddlemonkey',
'twiddlemooch',
'twiddlemouth',
'twiddlemuddle',
'twiddlemuffin',
'twiddlemush',
'twiddlenerd',
'twiddlenoodle',
'twiddlenose',
'twiddlenugget',
'twiddlephew',
'twiddlephooey',
'twiddlepocket',
'twiddlepoof',
'twiddlepop',
'twiddlepounce',
'twiddlepow',
'twiddlepretzel',
'twiddlequack',
'twiddleroni',
'twiddlescooter',
'twiddlescreech',
'twiddlesmirk',
'twiddlesnooker',
'twiddlesnoop',
'twiddlesnout',
'twiddlesocks',
'twiddlespeed',
'twiddlespinner',
'twiddlesplat',
'twiddlesprinkles',
'twiddlesticks',
'twiddlestink',
'twiddleswirl',
'twiddleteeth',
'twiddlethud',
'twiddletoes',
'twiddleton',
'twiddletoon',
'twiddletooth',
'twiddletwist',
'twiddlewhatsit',
'twiddlewhip',
'twiddlewig',
'twiddlewoof',
'twiddlezaner',
'twiddlezap',
'twiddlezapper',
'twiddlezilla',
'twiddlezoom',
'twig',
'twiggys',
'twight',
'twighty',
'twigs',
'twilight',
'twilightclan',
'twill',
'twin',
"twin's",
'twinkle',
'twinklebee',
'twinkleberry',
'twinkleblabber',
'twinklebocker',
'twinkleboing',
'twinkleboom',
'twinklebounce',
'twinklebouncer',
'twinklebrains',
'twinklebubble',
'twinklebumble',
'twinklebump',
'twinklebumper',
'twinkleburger',
'twinklechomp',
'twinklecorn',
'twinklecrash',
'twinklecrumbs',
'twinklecrump',
'twinklecrunch',
'twinkledoodle',
'twinkledorf',
'twinkleface',
'twinklefidget',
'twinklefink',
'twinklefish',
'twinkleflap',
'twinkleflapper',
'twinkleflinger',
'twinkleflip',
'twinkleflipper',
'twinklefoot',
'twinklefuddy',
'twinklefussen',
'twinklegadget',
'twinklegargle',
'twinklegloop',
'twinkleglop',
'twinklegoober',
'twinklegoose',
'twinklegrooven',
'twinklehoffer',
'twinklehopper',
'twinklejinks',
'twinkleklunk',
'twinkleknees',
'twinklemarble',
'twinklemash',
'twinklemonkey',
'twinklemooch',
'twinklemouth',
'twinklemuddle',
'twinklemuffin',
'twinklemush',
'twinklenerd',
'twinklenoodle',
'twinklenose',
'twinklenugget',
'twinklephew',
'twinklephooey',
'twinklepocket',
'twinklepoof',
'twinklepop',
'twinklepounce',
'twinklepow',
'twinklepretzel',
'twinklequack',
'twinkleroni',
'twinklescooter',
'twinklescreech',
'twinklesmirk',
'twinklesnooker',
'twinklesnoop',
'twinklesnout',
'twinklesocks',
'twinklespeed',
'twinklespinner',
'twinklesplat',
'twinklesprinkles',
'twinklesticks',
'twinklestink',
'twinkleswirl',
'twinkleteeth',
'twinklethud',
'twinkletoes',
'twinkleton',
'twinkletoon',
'twinkletooth',
'twinkletwist',
'twinklewhatsit',
'twinklewhip',
'twinklewig',
'twinklewoof',
'twinklezaner',
'twinklezap',
'twinklezapper',
'twinklezilla',
'twinklezoom',
'twinkling',
'twins',
'twire',
"twire's",
'twirl',
'twirls',
'twirly',
'twist',
'twistable',
'twisted',
'twister',
'twisters',
'twisting',
'twists',
'twisty',
'twitch',
'twitter',
'twizzler',
'twizzlers',
'two',
'two-face',
'txt',
'ty',
'tycho',
'tycoon',
'tyler',
'tyme',
'type',
"type's",
'typea',
'typed',
'typer',
'types',
'typhlosion',
'typhoon',
'typical',
'typing',
'typo',
'tyra',
'tyranitar',
'tyrannosaurus',
'tyranny',
'tyrant',
'tyrants',
'tyrogue',
'tyrone',
'tyrus',
'tysm',
'tyty',
'tyvm',
'u',
'uber',
'uberdog',
'ubuntu',
'uchiha',
'ues',
'ufo',
'ugh',
'ugly',
'uglybob',
'ugo',
'ugone',
'uh',
'uhh',
'uhhh',
'uhhhh',
'uk',
'ukelele',
'ukeleles',
'ukulele',
'ukuleles',
'ultimate',
'ultimately',
'ultimatum',
'ultimoose',
'ultra',
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'ultra-smart',
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'ultramix',
'ultron',
'um',
'umbrella',
'umbrellas',
'umbreon',
'un',
'un-ignore',
'unable',
'unafraid',
'unassuming',
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'unattune',
'unattuned',
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'uncles',
'uncomplicated',
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'uncool',
'uncopyrightable',
'und',
'undea',
'undecided',
'undefeated',
'undemocratic',
'under',
'undercover',
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'underdogs',
'underground',
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'understandingly',
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'understands',
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'undo',
'undoer',
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'undoing',
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'unforgettable',
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'unfortunately',
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'unfriend',
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'unger',
'ungrateful',
'unguildable',
'unguilded',
'unhappier',
'unhappiest',
'unhappily',
'unhappiness',
'unhappy',
'unheard',
'unicorn',
'unicorns',
'unicycle',
'unification',
'uniform',
'uniforms',
'unignore',
'unimportance',
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'unintentional',
'unintentionally',
'uninterested',
'uninteresting',
'union',
'unique',
'uniques',
'unit',
'unite',
'united',
'unites',
'unity',
'universal',
'universe',
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'unknown',
'unlearn',
'unlearned',
'unlearning',
'unlearns',
'unleash',
'unless',
'unlikely',
'unlimited',
'unload',
'unlock',
'unlocked',
'unlocking',
'unlocks',
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'unmeant',
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'uno',
'unown',
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'unreasonable',
'unsaid',
'unscramble',
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'unspent',
'unsteady',
'unstuck',
'untamed',
'until',
'untitled',
'untold',
'untouchable',
'untradeable',
'untried',
'untrustworthy',
'untruth',
'unused',
'unusual',
'unusually',
'unwritten',
'up',
'upbeat',
'upcoming',
'upd8',
'update',
'updated',
'updates',
'updating',
'upgrade',
'upgraded',
'upgrades',
'upgrading',
'uphill',
'uplay',
'upload',
'uploaded',
'uploading',
'upon',
'upper',
'uppity',
'upright',
'uproot',
'ups',
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'upsets',
'upsetting',
'upside-down',
'upstairs',
'upstream',
'upsy',
'uptick',
'ur',
'urban',
'uriah',
'urs',
'ursaring',
'ursatz',
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"ursula's",
'ursulas',
'us',
'usa',
'usable',
'use',
'used',
'useful',
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'usefulness',
'useless',
'user',
"user's",
'users',
'uses',
'usf',
'using',
'usual',
'usually',
'utah',
'utilities',
'utility',
'utmost',
'utopian',
'utter',
'uway',
'v-8',
'v-pos',
'v.p.',
'v.p.s',
'v2',
'v2.0',
'va',
'vacation',
'vacationed',
'vacationing',
'vacations',
'vachago',
'vachilles',
'vagabond',
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'vagrant',
'vaild',
'vain',
'vais',
'vale',
'valedictorian',
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"valentine's",
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"valentoon's",
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'valets',
'valheru',
'valiant',
'valid',
'validated',
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'vallenueva',
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'valor',
'valorie',
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'vampire',
"vampire's",
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'vanessa',
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'vanishing',
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'vaporeon',
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'variable',
"variable's",
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'varsity',
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'varying',
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'vase',
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'vast',
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'vastest',
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'vault',
'vedi',
'veer',
'vegas',
'vege-tables',
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'vegetables',
'vegetarian',
'veggie',
'veggies',
"veggin'",
'vehicle',
"vehicle's",
'vehicles',
'veil',
'velociraptor',
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'velvet-moss',
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'veni',
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'venomoth',
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'venusaur',
'verb',
'verbs',
'verdant',
'verify',
'vermin',
'vermont',
'vern',
'veronica',
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'versace',
'versatile',
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'version',
'versions',
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'vertical',
'vertigo',
'very',
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'vessels',
'vest',
'vests',
'vet',
'veteran',
'veterans',
'veterinarian',
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'vexation',
'via',
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'vicki',
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'victory',
"victory's",
'victreebel',
'vida',
'vidalia',
'video',
'videogame',
'videoriffic',
'videos',
'vidia',
"vidia's",
'vidy',
'vienna',
'vietnam',
'view',
'viewed',
'viewer',
'viewers',
'viewing',
'viewings',
'views',
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'vigor',
'viking',
'vikings',
'vil',
'vilakroma',
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'vilanox',
'vilar',
'vile',
'vileplume',
'village',
"village's",
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'vine',
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'vintage',
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'virginia',
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'visual',
'visualization',
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'vmk',
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'voidporo',
'volatile',
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'volleyball',
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'voltorn',
'volume',
"volume's",
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'voodoo',
'voona',
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'vortex',
'vote',
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'voy',
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'voyager',
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'voyages',
'vp',
"vp's",
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'vps',
'vr',
'vriska',
'vroom',
'vs',
'vs.',
'vu',
'vulpix',
'vulture',
'vultures',
'vw',
'w00t',
'w8',
'wa',
'wa-pa-pa-pa-pa-pa-pow',
'wacky',
'wackybee',
'wackyberry',
'wackyblabber',
'wackybocker',
'wackyboing',
'wackyboom',
'wackybounce',
'wackybouncer',
'wackybrains',
'wackybubble',
'wackybumble',
'wackybump',
'wackybumper',
'wackyburger',
'wackychomp',
'wackycorn',
'wackycrash',
'wackycrumbs',
'wackycrump',
'wackycrunch',
'wackydoodle',
'wackydorf',
'wackyface',
'wackyfidget',
'wackyfink',
'wackyfish',
'wackyflap',
'wackyflapper',
'wackyflinger',
'wackyflip',
'wackyflipper',
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'wackyfuddy',
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'wackyscreech',
'wackysmirk',
'wackysnooker',
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'wackywhatsit',
'wackywhip',
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'wackywoof',
'wackyzaner',
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'wackyzapper',
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'wackyzone',
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'waddle',
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"wagerin'",
"wagerin's",
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"wagner's",
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"wagon's",
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'wahoo',
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'waking',
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'waldo',
'waldorf',
'walk',
'walked',
'walker',
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'walking',
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"wall's",
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'wallaberries',
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'walled',
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'wallpapering',
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'walnut-drumming',
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'walruses',
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"walt's",
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"warship's",
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"warships'",
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'watchin',
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"water's",
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"waterfall's",
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'watering',
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'watermelon',
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"waterpark's",
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"waterslide's",
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'watery',
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'way',
"way's",
'waylon',
'wayne',
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'wayward',
'wazup',
'wb',
'wbu',
"wdig's",
'wdw',
'we',
"we'd",
"we'll",
"we're",
"we've",
'we-evil',
'weak',
'weaken',
'weakens',
'weaker',
'weakest',
'weakling',
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'weakness',
'wealthy',
'wear',
'wearenumberone',
'wearenumberonebuteveryoneisaltisbeingdown',
'wearenumberonebutsmirkycries',
'wearer',
'wearing',
'wears',
'weasel',
'weaselbee',
'weaselberry',
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'weaselbocker',
'weaselboing',
'weaselboom',
'weaselbounce',
'weaselbouncer',
'weaselbrains',
'weaselbubble',
'weaselbumble',
'weaselbump',
'weaselbumper',
'weaselburger',
'weaselchomp',
'weaselcorn',
'weaselcrash',
'weaselcrumbs',
'weaselcrump',
'weaselcrunch',
'weaseldoodle',
'weaseldorf',
'weaselface',
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'weaselfink',
'weaselfish',
'weaselflap',
'weaselflapper',
'weaselflinger',
'weaselflip',
'weaselflipper',
'weaselfoot',
'weaselfuddy',
'weaselfussen',
'weaselgadget',
'weaselgargle',
'weaselgloop',
'weaselglop',
'weaselgoober',
'weaselgoose',
'weaselgrooven',
'weaselhoffer',
'weaselhopper',
'weaseljinks',
'weaselklunk',
'weaselknees',
'weaselmarble',
'weaselmash',
'weaselmonkey',
'weaselmooch',
'weaselmouth',
'weaselmuddle',
'weaselmuffin',
'weaselmush',
'weaselnerd',
'weaselnoodle',
'weaselnose',
'weaselnugget',
'weaselphew',
'weaselphooey',
'weaselpocket',
'weaselpoof',
'weaselpop',
'weaselpounce',
'weaselpow',
'weaselpretzel',
'weaselquack',
'weaselroni',
'weasels',
'weaselscooter',
'weaselscreech',
'weaselsmirk',
'weaselsnooker',
'weaselsnoop',
'weaselsnout',
'weaselsocks',
'weaselspeed',
'weaselspinner',
'weaselsplat',
'weaselsprinkles',
'weaselsticks',
'weaselstink',
'weaselswirl',
'weaselteeth',
'weaselthud',
'weaseltoes',
'weaselton',
'weaseltoon',
'weaseltooth',
'weaseltwist',
'weaselwhatsit',
'weaselwhip',
'weaselwig',
'weaselwoof',
'weaselzaner',
'weaselzap',
'weaselzapper',
'weaselzilla',
'weaselzoom',
'weather',
'weathered',
'weatherer',
'weathering',
'weatherly',
'weathers',
'weave',
'weaves',
'weaving',
'web',
'webber',
'webepirates',
'websight',
'website',
'webster',
'wedding',
'weddings',
'wednesday',
'wednesdays',
'weeds',
'week',
"week's",
'weekdays',
'weekend',
'weekenders',
'weekly',
'weeks',
'weepinbell',
'weezing',
'wego',
'wei',
'weigh',
'weight',
'weights',
'weird',
'weirded',
'weirdings',
'weirdness',
'weirdo',
'weirdos',
'weiss',
'welch',
'welcome',
'welcomed',
'welcomely',
'welcomer',
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'welcoming',
'well',
'welled',
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'wells',
'welp',
'wenchs',
'went',
'were',
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'werent',
'west',
'wester',
'western',
'westerner',
'westerners',
'westing',
'westly',
'westward',
'wet',
'wew',
'wha',
'whaddya',
'whale',
'whalebone',
'whaler',
'whales',
'whaling',
'wham',
'whammo',
'what',
"what'cha",
"what's",
'what-in',
'what-the-hey',
'whatcha',
'whatever',
"whatever's",
'whats',
'wheat',
'whee',
'wheee',
'wheeee',
'wheeeee',
'wheeeeee',
'wheeeeeee',
'wheel',
'wheelbarrow',
'wheelbarrows',
'wheeled',
'wheeler',
'wheelers',
'wheeling',
'wheelings',
'wheels',
'wheezer',
'when',
"when's",
'whenever',
'whenisaygo',
'whens',
'where',
"where's",
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'wherever',
'whether',
'whew',
'which',
'whiff',
'whiffle',
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'while',
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'whiles',
'whiling',
'whimsical',
'whimsy',
'whining',
'whinnie',
"whinnie's",
'whiny',
'whiplash',
'whippersnapper',
'whirl',
'whirligig',
'whirling',
'whirlpool',
'whirlpools',
'whirly',
'whirr',
'whisk',
'whisked',
'whisker',
'whiskerbee',
'whiskerberry',
'whiskerblabber',
'whiskerbocker',
'whiskerboing',
'whiskerboom',
'whiskerbounce',
'whiskerbouncer',
'whiskerbrains',
'whiskerbubble',
'whiskerbumble',
'whiskerbump',
'whiskerbumper',
'whiskerburger',
'whiskerchomp',
'whiskercorn',
'whiskercrash',
'whiskercrumbs',
'whiskercrump',
'whiskercrunch',
'whiskerdoodle',
'whiskerdorf',
'whiskerface',
'whiskerfidget',
'whiskerfink',
'whiskerfish',
'whiskerflap',
'whiskerflapper',
'whiskerflinger',
'whiskerflip',
'whiskerflipper',
'whiskerfoot',
'whiskerfuddy',
'whiskerfussen',
'whiskergadget',
'whiskergargle',
'whiskergloop',
'whiskerglop',
'whiskergoober',
'whiskergoose',
'whiskergrooven',
'whiskerhoffer',
'whiskerhopper',
'whiskerjinks',
'whiskerklunk',
'whiskerknees',
'whiskermarble',
'whiskermash',
'whiskermonkey',
'whiskermooch',
'whiskermouth',
'whiskermuddle',
'whiskermuffin',
'whiskermush',
'whiskernerd',
'whiskernoodle',
'whiskernose',
'whiskernugget',
'whiskerphew',
'whiskerphooey',
'whiskerpocket',
'whiskerpoof',
'whiskerpop',
'whiskerpounce',
'whiskerpow',
'whiskerpretzel',
'whiskerquack',
'whiskerroni',
'whiskers',
'whiskerscooter',
'whiskerscreech',
'whiskersmirk',
'whiskersnooker',
'whiskersnoop',
'whiskersnout',
'whiskersocks',
'whiskerspeed',
'whiskerspinner',
'whiskersplat',
'whiskersprinkles',
'whiskersticks',
'whiskerstink',
'whiskerswirl',
'whiskerteeth',
'whiskerthud',
'whiskertoes',
'whiskerton',
'whiskertoon',
'whiskertooth',
'whiskertwist',
'whiskerwhatsit',
'whiskerwhip',
'whiskerwig',
'whiskerwoof',
'whiskerzaner',
'whiskerzap',
'whiskerzapper',
'whiskerzilla',
'whiskerzoom',
'whisper',
'whispered',
'whispering',
'whispers',
'whistle',
'whistlebee',
'whistleberry',
'whistleblabber',
'whistlebocker',
'whistleboing',
'whistleboom',
'whistlebounce',
'whistlebouncer',
'whistlebrains',
'whistlebubble',
'whistlebumble',
'whistlebump',
'whistlebumper',
'whistleburger',
'whistlechomp',
'whistlecorn',
'whistlecrash',
'whistlecrumbs',
'whistlecrump',
'whistlecrunch',
'whistled',
'whistledoodle',
'whistledorf',
'whistleface',
'whistlefidget',
'whistlefink',
'whistlefish',
'whistleflap',
'whistleflapper',
'whistleflinger',
'whistleflip',
'whistleflipper',
'whistlefoot',
'whistlefuddy',
'whistlefussen',
'whistlegadget',
'whistlegargle',
'whistlegloop',
'whistleglop',
'whistlegoober',
'whistlegoose',
'whistlegrooven',
'whistlehoffer',
'whistlehopper',
'whistlejinks',
'whistleklunk',
'whistleknees',
'whistlemarble',
'whistlemash',
'whistlemonkey',
'whistlemooch',
'whistlemouth',
'whistlemuddle',
'whistlemuffin',
'whistlemush',
'whistlenerd',
'whistlenoodle',
'whistlenose',
'whistlenugget',
'whistlephew',
'whistlephooey',
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'whistlepoof',
'whistlepop',
'whistlepounce',
'whistlepow',
'whistlepretzel',
'whistlequack',
"whistler's",
'whistleroni',
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'whistlescreech',
'whistlesmirk',
'whistlesnooker',
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'whistlespeed',
'whistlespinner',
'whistlesplat',
'whistlesprinkles',
'whistlesticks',
'whistlestink',
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'whistleteeth',
'whistlethud',
'whistletoes',
'whistleton',
'whistletoon',
'whistletooth',
'whistletwist',
'whistlewhatsit',
'whistlewhip',
'whistlewig',
'whistlewoof',
'whistlezaner',
'whistlezap',
'whistlezapper',
'whistlezilla',
'whistlezoom',
'whistling',
'white',
"white's",
'whiteboard',
'whiteboards',
'whitelist',
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'whitelisting',
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'whitewater',
'who',
"who'd",
"who's",
'whoa',
'whoah',
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'whoever',
'whoframedrogerrabbit',
'whogryps',
'whole',
'wholly',
'whom',
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'whoopee',
'whoopie',
'whoops',
'whoopsie',
'whoosh',
'whopper',
'whopping',
'whos',
'whose',
'why',
'wicked',
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'wider',
'widescreen',
'widest',
'widget',
'widgets',
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'wife',
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'wig',
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"wiggle's",
'wiggles',
'wigglytuff',
'wigs',
'wii',
'wiidburns',
'wikipedia',
'wilbur',
'wild',
'wild-n-crazy',
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'wildbocker',
'wildboing',
'wildboom',
'wildbounce',
'wildbouncer',
'wildbrains',
'wildbubble',
'wildbumble',
'wildbump',
'wildbumper',
'wildburger',
'wildburns',
'wildcat',
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'wildchomp',
'wildcorn',
'wildcrash',
'wildcrumbs',
'wildcrump',
'wildcrunch',
'wilddoodle',
'wilddorf',
'wilder',
'wilderness',
'wildest',
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'wildfink',
'wildfire',
'wildfish',
'wildflap',
'wildflapper',
'wildflinger',
'wildflip',
'wildflipper',
'wildfoot',
'wildfuddy',
'wildfussen',
'wildgadget',
'wildgargle',
'wildgloop',
'wildglop',
'wildgoober',
'wildgoose',
'wildgrooven',
'wildhoffer',
'wildhopper',
'wilding',
'wildjinks',
'wildklunk',
'wildknees',
'wildly',
'wildmarble',
'wildmash',
'wildmonkey',
'wildmooch',
'wildmouth',
'wildmuddle',
'wildmuffin',
'wildmush',
'wildnerd',
'wildnoodle',
'wildnose',
'wildnugget',
'wildphew',
'wildphooey',
'wildpocket',
'wildpoof',
'wildpop',
'wildpounce',
'wildpow',
'wildpretzel',
'wildquack',
'wildroni',
'wildscooter',
'wildscreech',
'wildsmirk',
'wildsnooker',
'wildsnoop',
'wildsnout',
'wildsocks',
'wildspeed',
'wildspinner',
'wildsplat',
'wildsprinkles',
'wildsticks',
'wildstink',
'wildswirl',
'wildteeth',
'wildthud',
'wildtoes',
'wildton',
'wildtoon',
'wildtooth',
'wildtwist',
'wildwhatsit',
'wildwhip',
'wildwig',
'wildwoods',
'wildwoof',
'wildzaner',
'wildzap',
'wildzapper',
'wildzilla',
'wildzoom',
'will',
'willa',
"willa's",
'willas',
'willed',
'willer',
'william',
'williams',
'willing',
'willings',
'willow',
'willows',
'willpower',
'wills',
'wilma',
'wilt',
'wilts',
'wimbleweather',
'win',
'winba',
'winbus',
'wind',
'wind-racer',
'windburn',
'windcatcher',
'winded',
'winder',
'winders',
'winding',
'windjammer',
'windjammers',
'windmane',
'windmill',
'windmills',
'windora',
'window',
"window's",
'windowed',
'windowing',
'windows',
'winds',
'windshadow',
'windsor',
'windswept',
'windward',
'windy',
'wing',
'wing-washing',
'winged',
'winger',
'wingers',
'winging',
'wings',
'wingtip',
'wingtips',
'wink',
'winkination',
'winkle',
"winkle's",
'winks',
'winky',
'winn',
'winner',
"winner's",
'winners',
'winnie',
"winnie's",
'winning',
'winnings',
'wins',
'winter',
"winter's",
'wintered',
'winterer',
'wintering',
'winterly',
'winters',
'wipeout',
'wire',
'wireframe',
'wireframer',
'wireframes',
'wires',
'wisconsin',
'wisdom',
'wise',
'wiseacre',
"wiseacre's",
'wiseacres',
'wisely',
'wish',
'wished',
'wisher',
'wishers',
'wishes',
'wishing',
'wispa',
'wit',
'witch',
"witch's",
'witches',
'witching',
'witchy',
'with',
'withdrawal',
'wither',
'withers',
'within',
'without',
'witness',
'witnessed',
'witnesses',
'witnessing',
'witty',
'wittybee',
'wittyberry',
'wittyblabber',
'wittybocker',
'wittyboing',
'wittyboom',
'wittybounce',
'wittybouncer',
'wittybrains',
'wittybubble',
'wittybumble',
'wittybump',
'wittybumper',
'wittyburger',
'wittychomp',
'wittycorn',
'wittycrash',
'wittycrumbs',
'wittycrump',
'wittycrunch',
'wittydoodle',
'wittydorf',
'wittyface',
'wittyfidget',
'wittyfink',
'wittyfish',
'wittyflap',
'wittyflapper',
'wittyflinger',
'wittyflip',
'wittyflipper',
'wittyfoot',
'wittyfuddy',
'wittyfussen',
'wittygadget',
'wittygargle',
'wittygloop',
'wittyglop',
'wittygoober',
'wittygoose',
'wittygrooven',
'wittyhoffer',
'wittyhopper',
'wittyjinks',
'wittyklunk',
'wittyknees',
'wittymarble',
'wittymash',
'wittymonkey',
'wittymooch',
'wittymouth',
'wittymuddle',
'wittymuffin',
'wittymush',
'wittynerd',
'wittynoodle',
'wittynose',
'wittynugget',
'wittyphew',
'wittyphooey',
'wittypocket',
'wittypoof',
'wittypop',
'wittypounce',
'wittypow',
'wittypretzel',
'wittyquack',
'wittyroni',
'wittyscooter',
'wittyscreech',
'wittysmirk',
'wittysnooker',
'wittysnoop',
'wittysnout',
'wittysocks',
'wittyspeed',
'wittyspinner',
'wittysplat',
'wittysprinkles',
'wittysticks',
'wittystink',
'wittyswirl',
'wittyteeth',
'wittythud',
'wittytoes',
'wittyton',
'wittytoon',
'wittytooth',
'wittytwist',
'wittywhatsit',
'wittywhip',
'wittywig',
'wittywoof',
'wittyzaner',
'wittyzap',
'wittyzapper',
'wittyzilla',
'wittyzoom',
'wiz',
'wizard',
"wizard's",
'wizards',
'wizrd',
'wo',
'woah',
'wobble',
'wobbled',
'wobbles',
'wobbling',
'wobbly',
'wobbuffet',
'wocka',
'woe',
'woeful',
'wok',
'woke',
'woks',
'wolf',
"wolf's",
'wolfbane',
'wolfe',
'wolfer',
'wolfes',
'wolfhearts',
'wolfie',
'wolfpack',
'wolfs',
'wolfsbane',
'wolfy',
'wolves',
'woman',
'women',
'womp',
'won',
"won't",
'wonder',
'wonderbee',
'wonderberry',
'wonderblabber',
'wonderblue',
'wonderbocker',
'wonderboing',
'wonderboom',
'wonderbounce',
'wonderbouncer',
'wonderbrains',
'wonderbubble',
'wonderbumble',
'wonderbump',
'wonderbumper',
'wonderburger',
'wonderchomp',
'wondercorn',
'wondercrash',
'wondercrumbs',
'wondercrump',
'wondercrunch',
'wonderdoodle',
'wonderdorf',
'wondered',
'wonderer',
'wonderers',
'wonderface',
'wonderfidget',
'wonderfink',
'wonderfish',
'wonderflap',
'wonderflapper',
'wonderflinger',
'wonderflip',
'wonderflipper',
'wonderfoot',
'wonderfuddy',
'wonderful',
'wonderfully',
'wonderfussen',
'wondergadget',
'wondergargle',
'wondergloop',
'wonderglop',
'wondergoober',
'wondergoose',
'wondergrooven',
'wonderhoffer',
'wonderhopper',
'wondering',
'wonderings',
'wonderjinks',
'wonderklunk',
'wonderknees',
'wonderland',
"wonderland's",
'wonderlands',
'wondermarble',
'wondermash',
'wondermonkey',
'wondermooch',
'wondermouth',
'wondermuddle',
'wondermuffin',
'wondermush',
'wondernerd',
'wondernoodle',
'wondernose',
'wondernugget',
'wonderphew',
'wonderphooey',
'wonderpocket',
'wonderpoof',
'wonderpop',
'wonderpounce',
'wonderpow',
'wonderpretzel',
'wonderquack',
'wonderroni',
'wonders',
'wonderscooter',
'wonderscreech',
'wondersmirk',
'wondersnooker',
'wondersnoop',
'wondersnout',
'wondersocks',
'wonderspeed',
'wonderspinner',
'wondersplat',
'wondersprinkles',
'wondersticks',
'wonderstink',
'wonderswirl',
'wonderteeth',
'wonderthud',
'wondertoes',
'wonderton',
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'wondertooth',
'wondertwist',
'wonderwhatsit',
'wonderwhip',
'wonderwig',
'wonderwoof',
'wonderzaner',
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'wonderzapper',
'wonderzilla',
'wonderzoom',
'wondrous',
'wont',
'woo',
'wood',
'wooded',
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'woodwashere',
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'woohoo',
'woop',
'wooper',
'woot',
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'word',
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'worded',
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'words',
"words'n'stuff",
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"work's",
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'working',
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"world's",
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"wright's",
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'wrinkleboom',
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'wrinklegadget',
'wrinklegargle',
'wrinklegloop',
'wrinkleglop',
'wrinklegoober',
'wrinklegoose',
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'wrinklehoffer',
'wrinklehopper',
'wrinklejinks',
'wrinkleklunk',
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'wrinklemonkey',
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'wrinklemouth',
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'wrinklenugget',
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'wrinklephooey',
'wrinklepocket',
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'wrinklepop',
'wrinklepounce',
'wrinklepow',
'wrinklepretzel',
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'wrinklescreech',
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'wrinklesnooker',
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'wrinklethud',
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'wrinklewhip',
'wrinklewig',
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'wrinklezilla',
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'writer',
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'wrongest',
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'beaniegrooven',
'beaniehoffer',
'beaniehopper',
'beaniejinks',
'beanieklunk',
'beanieknees',
'beaniemarble',
'beaniemash',
'beaniemonkey',
'beaniemooch',
'beaniemouth',
'beaniemuddle',
'beaniemuffin',
'beaniemush',
'beanienerd',
'beanienoodle',
'beanienose',
'beanienugget',
'beaniephew',
'beaniephooey',
'beaniepocket',
'beaniepoof',
'beaniepop',
'beaniepounce',
'beaniepow',
'beaniepretzel',
'beaniequack',
'beanieroni',
'beanies',
'beaniescooter',
'beaniescreech',
'beaniesmirk',
'beaniesnooker',
'beaniesnoop',
'beaniesnout',
'beaniesocks',
'beaniespeed',
'beaniespinner',
'beaniesplat',
'beaniesprinkles',
'beaniesticks',
'beaniestink',
'beanieswirl',
'beanieteeth',
'beaniethud',
'beanietoes',
'beanieton',
'beanietoon',
'beanietooth',
'beanietwist',
'beaniewhatsit',
'beaniewhip',
'beaniewig',
'beaniewoof',
'beaniezaner',
'beaniezap',
'beaniezapper',
'beaniezilla',
'beaniezoom',
'beanjinks',
'beanklunk',
'beanknees',
'beanmarble',
'beanmash',
'beanmonkey',
'beanmooch',
'beanmouth',
'beanmuddle',
'beanmuffin',
'beanmush',
'beannerd',
'beannoodle',
'beannose',
'beannugget',
'beanphew',
'beanphooey',
'beanpocket',
'beanpoof',
'beanpop',
'beanpounce',
'beanpow',
'beanpretzel',
'beanquack',
'beanroni',
'beans',
'beanscooter',
'beanscreech',
'beansmirk',
'beansnooker',
'beansnoop',
'beansnout',
'beansocks',
'beanspeed',
'beanspinner',
'beansplat',
'beansprinkles',
'beanstalks',
'beansticks',
'beanstink',
'beanswirl',
'beanteeth',
'beanthud',
'beantoes',
'beanton',
'beantoon',
'beantooth',
'beantwist',
'beanwhatsit',
'beanwhip',
'beanwig',
'beanwoof',
'beanzaner',
'beanzap',
'beanzapper',
'beanzilla',
'beanzoom',
'bear',
"bear's",
'beard',
"beard's",
'bearded',
'beardless',
'beardmonsters',
'beards',
'beardy',
'bearer',
'bearers',
'bearing',
'bearings',
'bearish',
'bears',
'beartrude',
'beast',
"beast's",
'beastie',
'beasties',
'beastings',
'beastly',
'beasts',
'beat',
'beatable',
'beau',
'beaucoup',
'beauteous',
'beauticians',
'beauties',
'beautiful',
'beautifully',
'beautifulness',
'beauty',
"beauty's",
'beaver',
'beawesome',
'became',
'because',
'beck',
"beck's",
'beckets',
'beckett',
"beckett's",
'beckoned',
'beckons',
'become',
'becomes',
'becoming',
'bed',
'bed-sized',
'bedazzle',
'bedazzled',
'bedazzler',
'bedazzles',
'bedclothes',
'bedding',
'bedhog',
'bedknobs',
"bedo's",
'bedroll',
'bedroom',
'bedrooms',
'beds',
'bedspread',
'bedspreads',
'bee',
"bee's",
'beedrill',
'beef',
'beefcake',
'beefed',
'beefs',
'beefy',
'beehive',
"beehive's",
'beehives',
'beeline',
'beemovie',
'been',
'beep',
'beeped',
'beepers',
'beeping',
'beeps',
'bees',
'beeswax',
'beet',
'beethoven',
'beetle',
"beetle's",
'beetles',
'beets',
'before',
'beforehand',
'befriend',
'befriended',
'befriending',
'befriends',
'befuddle',
'befuddled',
'beg',
'began',
'begat',
'begets',
'beggar',
'beggars',
'begged',
'begging',
'begin',
'beginner',
"beginner's",
'beginners',
'beginning',
"beginning's",
'beginnings',
'begins',
'begonia',
'begotten',
'begs',
'begun',
'behalf',
'behave',
'behaved',
'behaver',
'behaves',
'behaving',
'behavior',
'behavioral',
'behaviors',
'behemoth',
'behemoths',
'behind',
'behind-the-scenes',
'behinds',
'behold',
'beholden',
'beholding',
'behoove',
'behop',
'behr',
'beige',
"bein'",
'being',
"being's",
'beings',
'bejeweled',
'bela',
'belarus',
'belarusian',
'belated',
'belay',
'belch',
'belief',
"belief's",
'beliefs',
'believable',
'believe',
'believed',
'believer',
'believers',
'believes',
'believing',
'belittle',
'belittles',
'bell',
"bell's",
'bella',
"bella's",
'bellas',
'belle',
"belle's",
'belles',
"belles'",
'bellflower',
'bellhop',
'belli',
'bellied',
'bellies',
'bellossom',
'bellow',
'bellows',
'bells',
'bellsprout',
'belly',
'bellyache',
'belong',
'belonged',
'belonging',
'belongings',
'belongs',
'beloved',
'beloveds',
'below',
'belowdeck',
'belt',
'belts',
'bemuse',
'bemused',
'bemuses',
'bemusing',
'ben',
'benches',
'benchmark',
'benchwarmers',
'bend',
'bender',
'bending',
'bends',
'beneath',
'benedek',
"benedek's",
'beneficial',
'benefit',
'benefited',
'benefiting',
'benefits',
'benjamin',
'benjy',
'benne',
'benny',
"benny's",
'benroyko',
'bent',
'beppo',
'bequeath',
'bequeathed',
'bequeathing',
'bequeaths',
'bequermo',
'bequila',
'bereadytothrow',
'beret',
'berets',
'berg',
'beriths27th',
'bernadette',
'bernard',
'bernie',
'berried',
'berries',
'berry',
'berserk',
'bert',
"bert's",
'berth',
'bertha',
'berthed',
'berthing',
'berths',
'beruna',
'beseech',
'beside',
'besides',
'bespeak',
'bespeaking',
'bespeaks',
'bespoke',
'bespoken',
'bess',
"bess'",
"bess's",
'bess-statue',
'bessie',
'best',
'bested',
'bester',
'besting',
'bests',
'bet',
"bet's",
'beta',
"beta's",
'betas',
'betcha',
'beth',
'bethy',
'bethyisbest',
'betray',
'betrayal',
'betrayed',
'bets',
'betsy',
'better',
'bettered',
'bettering',
'betterment',
'betters',
'bettie',
'betting',
'betty',
'between',
'betwixt',
'bev',
'bevel',
'beverage',
'beverages',
'beware',
'bewilder',
'bewildered',
'bewildering',
'bewitch',
'bewitched',
'bewitches',
'bewitching',
'beyonce',
'beyond',
'bezerk',
'bff',
'bfs',
'bi-lingual',
'bias',
'biased',
'bib',
'bibbidi',
'bible',
'biblical',
'bicep',
'biceps',
'bickering',
'bicuspid',
'bicycle',
'bicycles',
'bid',
'bidder',
'bidding',
'biddlesmore',
'bide',
'bids',
'bien',
'bifocals',
'big',
'big-screen',
'biggen',
'biggenbee',
'biggenberry',
'biggenblabber',
'biggenbocker',
'biggenboing',
'biggenboom',
'biggenbounce',
'biggenbouncer',
'biggenbrains',
'biggenbubble',
'biggenbumble',
'biggenbump',
'biggenbumper',
'biggenburger',
'biggenchomp',
'biggencorn',
'biggencrash',
'biggencrumbs',
'biggencrump',
'biggencrunch',
'biggendoodle',
'biggendorf',
'biggenface',
'biggenfidget',
'biggenfink',
'biggenfish',
'biggenflap',
'biggenflapper',
'biggenflinger',
'biggenflip',
'biggenflipper',
'biggenfoot',
'biggenfuddy',
'biggenfussen',
'biggengadget',
'biggengargle',
'biggengloop',
'biggenglop',
'biggengoober',
'biggengoose',
'biggengrooven',
'biggenhoffer',
'biggenhopper',
'biggenjinks',
'biggenklunk',
'biggenknees',
'biggenmarble',
'biggenmash',
'biggenmonkey',
'biggenmooch',
'biggenmouth',
'biggenmuddle',
'biggenmuffin',
'biggenmush',
'biggennerd',
'biggennoodle',
'biggennose',
'biggennugget',
'biggenphew',
'biggenphooey',
'biggenpocket',
'biggenpoof',
'biggenpop',
'biggenpounce',
'biggenpow',
'biggenpretzel',
'biggenquack',
'biggenroni',
'biggenscooter',
'biggenscreech',
'biggensmirk',
'biggensnooker',
'biggensnoop',
'biggensnout',
'biggensocks',
'biggenspeed',
'biggenspinner',
'biggensplat',
'biggensprinkles',
'biggensticks',
'biggenstink',
'biggenswirl',
'biggenteeth',
'biggenthud',
'biggentoes',
'biggenton',
'biggentoon',
'biggentooth',
'biggentwist',
'biggenwhatsit',
'biggenwhip',
'biggenwig',
'biggenwoof',
'biggenzaner',
'biggenzap',
'biggenzapper',
'biggenzilla',
'biggenzoom',
'bigger',
'biggest',
'biggie',
'biggies',
'biggles',
'biggleton',
'bight',
'bigwig',
'bike',
"bike's",
'biked',
'biker',
'bikers',
'bikes',
'biking',
'bikini',
'bile',
'bilge',
'bilgepump',
'bilgerats',
'bilges',
'bilging',
'bilingual',
'bill',
"bill's",
'billed',
'biller',
'billers',
'billiards',
'billing',
'billings',
'billington',
'billion',
'billionaire',
'billions',
'billionth',
'billow',
'billows',
'billowy',
'bills',
'billy',
'billybob',
'bim',
'bimbim',
'bimonthly',
'bimos',
'bin',
'binary',
'binarybot',
'binarybots',
'bind',
'binding',
'bing',
'bingham',
'bingo',
"bingo's",
'bingos',
'binky',
'binnacle',
'binoculars',
'bins',
'bio',
'biog',
'biology',
'bionic',
'bionicle',
'biopsychology',
'bios',
'bip',
'birch-bark',
'bird',
"bird's",
'birdbrain',
'birddog',
'birder',
'birdhouse',
'birdie',
'birdies',
'birdman',
'birds',
'birdseed',
'birth',
'birthdates',
'birthday',
'birthdays',
'birthed',
'birthmark',
'birthmarks',
'birthplace',
'birthstone',
'biscotti',
'biscuit',
'biscuits',
'bishop',
'bishops',
'bison',
'bisque',
'bisquit',
'bistro',
'bit',
"bit's",
'bite',
'biter',
'biters',
'bites',
'biting',
'bits',
'bitsy',
'bitten',
'bitter',
'bitty',
'biweekly',
'biyearly',
'biz',
'bizarre',
'bizzenbee',
'bizzenberry',
'bizzenblabber',
'bizzenbocker',
'bizzenboing',
'bizzenboom',
'bizzenbounce',
'bizzenbouncer',
'bizzenbrains',
'bizzenbubble',
'bizzenbumble',
'bizzenbump',
'bizzenbumper',
'bizzenburger',
'bizzenchomp',
'bizzencorn',
'bizzencrash',
'bizzencrumbs',
'bizzencrump',
'bizzencrunch',
'bizzendoodle',
'bizzendorf',
'bizzenface',
'bizzenfidget',
'bizzenfink',
'bizzenfish',
'bizzenflap',
'bizzenflapper',
'bizzenflinger',
'bizzenflip',
'bizzenflipper',
'bizzenfoot',
'bizzenfuddy',
'bizzenfussen',
'bizzengadget',
'bizzengargle',
'bizzengloop',
'bizzenglop',
'bizzengoober',
'bizzengoose',
'bizzengrooven',
'bizzenhoffer',
'bizzenhopper',
'bizzenjinks',
'bizzenklunk',
'bizzenknees',
'bizzenmarble',
'bizzenmash',
'bizzenmonkey',
'bizzenmooch',
'bizzenmouth',
'bizzenmuddle',
'bizzenmuffin',
'bizzenmush',
'bizzennerd',
'bizzennoodle',
'bizzennose',
'bizzennugget',
'bizzenphew',
'bizzenphooey',
'bizzenpocket',
'bizzenpoof',
'bizzenpop',
'bizzenpounce',
'bizzenpow',
'bizzenpretzel',
'bizzenquack',
'bizzenroni',
'bizzenscooter',
'bizzenscreech',
'bizzensmirk',
'bizzensnooker',
'bizzensnoop',
'bizzensnout',
'bizzensocks',
'bizzenspeed',
'bizzenspinner',
'bizzensplat',
'bizzensprinkles',
'bizzensticks',
'bizzenstink',
'bizzenswirl',
'bizzenteeth',
'bizzenthud',
'bizzentoes',
'bizzenton',
'bizzentoon',
'bizzentooth',
'bizzentwist',
'bizzenwhatsit',
'bizzenwhip',
'bizzenwig',
'bizzenwoof',
'bizzenzaner',
'bizzenzap',
'bizzenzapper',
'bizzenzilla',
'bizzenzoom',
'bla',
'blabbing',
'black',
'black-eyed',
'blackbeard',
"blackbeard's",
'blackbeards',
'blackbeared',
'blackbelt',
'blackberries',
'blackberry',
'blackbird',
'blackboard',
'blackboards',
'blackdeath',
'blacked',
'blackened',
'blackest',
'blackguard',
'blackguards',
'blackhaerts',
'blackhawk',
'blackheads',
'blackheart',
"blackheart's",
'blackhearts',
'blacking',
'blackish',
'blackjack',
'blackjacks',
'blacklist',
'blacklisted',
'blacklisting',
'blackness',
'blackout',
'blackoutaviation',
'blackouts',
'blackrage',
'blackrose',
'blacksail',
'blacksmith',
"blacksmith's",
'blacksmithing',
'blacksmiths',
'blackthorn',
'blackwatch',
'blackwater',
'bladder',
'bladders',
"blade's",
'bladebreakerr',
'blademasters',
'blades',
'bladeskulls',
'bladestorm',
'blaggards',
'blah',
'blair',
'blaise',
'blake',
'blakeley',
"blakeley's",
'blame',
'blamed',
'blamer',
'blamers',
'blames',
'blaming',
'blanada',
'blanago',
'blanca',
"blanca's",
'blanche',
'bland',
'blank',
'blanked',
'blanket',
'blankets',
'blanking',
'blankly',
'blanks',
'blanos',
'blaring',
'blast',
"blast'em",
'blasted',
'blaster',
'blasters',
"blastin'",
'blasting',
'blastoise',
'blasts',
'blasty',
'blat',
'blaze',
'bld',
'bldg',
'bldgs',
'bleached',
'bleak',
'bleary',
'bled',
'bleep',
'bleeped',
'bleeper',
'bleepin',
'bleeping',
'bleeps',
'blend',
'blended',
'blender',
'blenders',
'blending',
'blends',
'blenny',
'bless',
'blessed',
'blesses',
'blessing',
'blessings',
'bleu',
'blew',
'bligh',
'blight',
'blighters',
'blimey',
'blimp',
'blind',
'blinded',
'blinder',
'blinders',
'blindfold',
'blindfolded',
'blinding',
'blindly',
'blindness',
'blinds',
'blindsided',
'bling',
'bling-bling',
'blingbling',
'blinged',
'blinging',
'blings',
'blink',
'blinked',
'blinker',
'blinkers',
'blinking',
'blinks',
'blinky',
'blip',
'blipping',
'bliss',
'blissey',
'blissfully',
'blister',
'blistering',
'blisters',
'blitz',
'blizzard',
'blizzards',
'bloat',
'bloated',
'bloats',
'blob',
'blobby',
'blobs',
'bloc',
'block',
"block's",
'blockade',
'blockader',
'blockades',
'blockading',
'blockbuster',
'blocked',
'blocker',
'blockers',
'blocking',
'blockout',
'blocks',
'blocky',
'bloke',
'blokes',
'blomma',
'blond',
'blonde',
"blonde's",
'blondes',
'blondie',
'blonds',
'bloodbrothers',
'bloodhounds',
'bloodless',
'bloodshot',
'bloodsucker',
'bloodsuckers',
'bloodthrushers',
'bloom',
'bloomers',
'blooming',
'blooms',
'bloop',
'bloopa',
'blooper',
'bloopers',
'blossom',
'blossoms',
'blossum',
'blot',
'blots',
'blouse',
'blowfish',
'blowy',
'blu-ray',
'blub',
'blubber',
'blubberbee',
'blubberberry',
'blubberblabber',
'blubberbocker',
'blubberboing',
'blubberboom',
'blubberbounce',
'blubberbouncer',
'blubberbrains',
'blubberbubble',
'blubberbumble',
'blubberbump',
'blubberbumper',
'blubberburger',
'blubberchomp',
'blubbercorn',
'blubbercrash',
'blubbercrumbs',
'blubbercrump',
'blubbercrunch',
'blubberdoodle',
'blubberdorf',
'blubberface',
'blubberfidget',
'blubberfink',
'blubberfish',
'blubberflap',
'blubberflapper',
'blubberflinger',
'blubberflip',
'blubberflipper',
'blubberfoot',
'blubberfuddy',
'blubberfussen',
'blubbergadget',
'blubbergargle',
'blubbergloop',
'blubberglop',
'blubbergoober',
'blubbergoose',
'blubbergrooven',
'blubberhoffer',
'blubberhopper',
'blubbering',
'blubberjinks',
'blubberklunk',
'blubberknees',
'blubbermarble',
'blubbermash',
'blubbermonkey',
'blubbermooch',
'blubbermouth',
'blubbermuddle',
'blubbermuffin',
'blubbermush',
'blubbernerd',
'blubbernoodle',
'blubbernose',
'blubbernugget',
'blubberphew',
'blubberphooey',
'blubberpocket',
'blubberpoof',
'blubberpop',
'blubberpounce',
'blubberpow',
'blubberpretzel',
'blubberquack',
'blubberroni',
'blubberscooter',
'blubberscreech',
'blubbersmirk',
'blubbersnooker',
'blubbersnoop',
'blubbersnout',
'blubbersocks',
'blubberspeed',
'blubberspinner',
'blubbersplat',
'blubbersprinkles',
'blubbersticks',
'blubberstink',
'blubberswirl',
'blubberteeth',
'blubberthud',
'blubbertoes',
'blubberton',
'blubbertoon',
'blubbertooth',
'blubbertwist',
'blubberwhatsit',
'blubberwhip',
'blubberwig',
'blubberwoof',
'blubberzaner',
'blubberzap',
'blubberzapper',
'blubberzilla',
'blubberzoom',
'bludgeon',
'bludgeoning',
'blue',
"blue's",
'bluebeards',
'bluebell',
'blueberries',
'blueberry',
'bluebird',
'bluebirds',
'blueblood',
'bluefishes',
'bluegrass',
'bluejay',
'blueprints',
'blues',
'bluff',
'bluffed',
'bluffer',
'bluffers',
'bluffing',
'bluffs',
'blunder',
'blundering',
'bluntly',
'blur',
'blurb',
'blurbs',
'blurred',
'blurry',
'blurs',
'blurting',
'blush',
'blushed',
'blushes',
'blushing',
'blustery',
'blut',
'blynken',
'bman',
'bo',
'boa',
'boar',
'board',
"board's",
'boardbot',
'boardbots',
'boarded',
'boarder',
'boarders',
'boarding',
'boards',
'boardwalk',
'boardwalks',
'boarhound',
'boars',
'boas',
'boast',
'boastful',
'boasting',
'boat',
"boat's",
'boated',
'boater',
'boaters',
'boathouse',
'boating',
'boatload',
'boatloads',
'boats',
'boatswain',
"boatswain's",
'boatswains',
'boatyard',
'bob',
"bob's",
'bobbed',
'bobber',
'bobbidi',
'bobble',
'bobbleheads',
'bobby',
"bobby's",
'bobbys',
'boberts',
'bobo',
'bobsled',
'bobsleded',
'bobsleding',
'bobsleds',
'bobsleigh',
'bobsleighes',
'bock',
'bodacious',
'bode',
'bodeguita',
'bodice',
'bodices',
'bodied',
'bodies',
'bodily',
'body',
"body's",
'bodyguard',
'bodyguards',
'boffo',
'bog',
'bogart',
'bogey',
'bogger',
'boggle',
'boggles',
'boggy',
'bogie',
'bogs',
'bogus',
'boi',
'boil',
'boiled',
'boiler',
'boiling',
'boils',
'boingenbee',
'boingenberry',
'boingenblabber',
'boingenbocker',
'boingenboing',
'boingenboom',
'boingenbounce',
'boingenbouncer',
'boingenbrains',
'boingenbubble',
'boingenbumble',
'boingenbump',
'boingenbumper',
'boingenburger',
'boingenchomp',
'boingencorn',
'boingencrash',
'boingencrumbs',
'boingencrump',
'boingencrunch',
'boingendoodle',
'boingendorf',
'boingenface',
'boingenfidget',
'boingenfink',
'boingenfish',
'boingenflap',
'boingenflapper',
'boingenflinger',
'boingenflip',
'boingenflipper',
'boingenfoot',
'boingenfuddy',
'boingenfussen',
'boingengadget',
'boingengargle',
'boingengloop',
'boingenglop',
'boingengoober',
'boingengoose',
'boingengrooven',
'boingenhoffer',
'boingenhopper',
'boingenjinks',
'boingenklunk',
'boingenknees',
'boingenmarble',
'boingenmash',
'boingenmonkey',
'boingenmooch',
'boingenmouth',
'boingenmuddle',
'boingenmuffin',
'boingenmush',
'boingennerd',
'boingennoodle',
'boingennose',
'boingennugget',
'boingenphew',
'boingenphooey',
'boingenpocket',
'boingenpoof',
'boingenpop',
'boingenpounce',
'boingenpow',
'boingenpretzel',
'boingenquack',
'boingenroni',
'boingenscooter',
'boingenscreech',
'boingensmirk',
'boingensnooker',
'boingensnoop',
'boingensnout',
'boingensocks',
'boingenspeed',
'boingenspinner',
'boingensplat',
'boingensprinkles',
'boingensticks',
'boingenstink',
'boingenswirl',
'boingenteeth',
'boingenthud',
'boingentoes',
'boingenton',
'boingentoon',
'boingentooth',
'boingentwist',
'boingenwhatsit',
'boingenwhip',
'boingenwig',
'boingenwoof',
'boingenzaner',
'boingenzap',
'boingenzapper',
'boingenzilla',
'boingenzoom',
'boingyboro',
'bokugeki',
'bokuji',
'bokuzama',
'bold',
'bolder',
'boldest',
'boldly',
'bole',
'bolivia',
'bollard',
'bologna',
'bolt',
"bolt's",
'bolted',
'bolton',
'bolts',
'boma',
'boma-boma',
'bombard',
'bombarding',
'bombardment',
'bombe',
'bombed',
'bomber',
'bombers',
'bombing',
'bombs',
'bombshell',
'bon',
'bonaam',
'bonanza',
'bonbons',
'bond',
'bonded',
'bonding',
'bonds',
'bondsman',
'bonehead',
'boneheads',
'boneless',
'boneyard',
'boneyards',
'bonfire',
'bonfires',
'bongo',
'bonita',
"bonita's",
'bonito',
'bonjour',
'bonkers',
'bonkl',
'bonnet',
'bonnets',
'bonney',
'bonnie',
'bonny',
'bono',
'bonsai',
'bonsoir',
'bonus',
'bonuses',
'bony',
'bonzo',
'boo',
"boo's",
'boo-yaa',
'booed',
'booger',
'boogers',
'boogey',
'boogie',
'boogie-woogie',
'boogied',
'boogies',
'boohoo',
'book',
"book's",
'bookball',
'bookcase',
'bookcases',
'booked',
'bookie',
'booking',
'bookings',
'bookkeeper',
'bookkeepers',
'bookkeeping',
'booklet',
'bookmaker',
'bookmakers',
'bookmark',
'bookmarked',
'books',
'bookshelf',
'bookshelves',
'bookstore',
'bookworm',
'boom',
'boomcrash',
'boomed',
'boomer',
'boomerang',
'boomers',
'booming',
'booms',
'boon',
'boonies',
'booo',
'boooo',
'booooh',
'boooom',
'booooo',
'booooom',
'booooomin',
'boooooo',
'boooooooooooooooooooo',
'boooooooooooooooooooooooooommm',
'boop',
'boor',
'boos',
'boost',
'boosted',
'booster',
'boosters',
'boosting',
'boosts',
'boot',
"boot'n'ears",
'booted',
'booth',
"booth's",
'booths',
'bootiful',
'booting',
'bootleggers',
'boots',
'bootstrap',
'bootstraps',
'bootsy',
'booyah',
'bop',
'bopper',
'bord',
'border',
"border's",
'bordered',
'borderer',
'bordering',
'borderings',
'borderline',
'borders',
'bore',
'borealis',
'bored',
'boredom',
'borer',
'bores',
'boring',
'boris',
'bork',
'borks',
'borksalot',
'born',
'borrow',
'borrowed',
'borrower',
'borrowers',
'borrowing',
'borrowings',
'borrows',
'bos',
'boss',
"boss'",
'bossbot',
'bossbots',
'bossed',
'bosses',
'bossily',
'bossing',
'bossy',
'bossyboots',
'bot',
"bot's",
'botched',
'both',
'bother',
'bothered',
'bothering',
'bothers',
'bothersome',
'bots',
'bottle',
"bottle's",
'bottled',
'bottleneck',
'bottler',
'bottlers',
'bottles',
'bottling',
'bottom',
'bottomed',
'bottomer',
'bottomfeeder',
'bottomfeeders',
'bottoming',
'bottomless',
'bottoms',
'bough',
'boughs',
'bought',
'boulder',
'boulders',
'boulevard',
"boulevard's",
'boulevards',
'bounce',
'bounced',
'bouncer',
'bouncers',
'bounces',
'bouncing',
'bouncy',
'bound',
'boundaries',
'boundary',
"boundary's",
'bounding',
'bounds',
'bounteous',
'bounties',
'bountiful',
'bounty',
'bountyhunter',
'bourbon',
'bourse',
'bout',
'boutique',
'bouts',
'bovel',
'bovine',
'bow',
"bow's",
'bowdash',
'bowed',
'bowers',
'bowie',
'bowing',
'bowl',
"bowl's",
'bowlegged',
'bowler',
'bowling',
'bowls',
'bowman',
'bows',
'bowser',
'box',
'boxcar',
'boxed',
'boxer',
'boxes',
'boxfish',
'boxing',
'boy',
"boy's",
'boycott',
'boycotting',
'boyfriend',
"boyfriend's",
'boyfriends',
'boyish',
'boys',
'boysenberry',
'bozo',
"bozo's",
'bozos',
'brace',
'bracelet',
'bracelets',
'braces',
'bracket',
'bracketing',
'brad',
"brad's",
'bradley',
'brady',
'brag',
'braggart',
'braggarts',
'bragged',
'bragger',
'braggers',
'bragging',
'brags',
'braid',
'braided',
'braiding',
'braids',
'brail',
'brain',
"brain's",
'brained',
'braining',
'brainless',
'brains',
'brainstorm',
'brainwash',
'brainwashed',
'brainy',
'brake',
'braken',
'brakes',
'braking',
'bran',
'branch',
'branched',
'branches',
'branching',
'branchings',
'brand',
'brandishing',
'brandon',
"brandon's",
'brands',
'brandy',
"brandy's",
'brantley',
'brash',
'brass',
'brat',
'brats',
'bratty',
'brave',
'braved',
'bravely',
'braver',
'bravery',
'braves',
'bravest',
'braving',
'bravo',
"bravo's",
'brawl',
'brawny',
'bray',
'brazen',
'brazil',
'brb',
'breach',
'breached',
'bread',
"bread's",
'bread-buttering',
'breadcrumbs',
'breaded',
'breading',
'breads',
'breadstick',
"breadstick's",
'breadth',
'break',
'breakable',
'breakdown',
'breaker',
'breakers',
'breakfast',
'breakfasted',
'breakfaster',
'breakfasters',
'breakfasting',
'breakfasts',
'breaking',
'breakout',
'breaks',
'breakup',
'breath',
'breathe',
'breathed',
'breather',
'breathers',
'breathes',
'breathing',
'breathless',
'breaths',
'breathtaking',
'bred',
'breech',
'breeches',
'breeze',
"breeze's",
'breezed',
'breezes',
'breezest',
'breezing',
'breezy',
'brenda',
"brenda's",
'brethern',
'brethren',
'brevrend',
'brew',
'brewed',
'brewer',
'brewers',
'brewing',
'brews',
'brian',
'briar',
'briars',
'briarstone',
'briarstones',
'bribe',
'bribed',
'bribery',
'bribes',
'bribing',
'brick',
'bricked',
'bricker',
'bricking',
'bricks',
'bridal',
'bride',
"bride's",
'brides',
'bridesmaid',
'bridge',
"bridge's",
'bridged',
'bridges',
'bridget',
'bridging',
'brie',
'brief',
'briefed',
'briefer',
'briefest',
'briefing',
'briefings',
'briefly',
'briefs',
'brig',
"brig's",
'brigad',
'brigade',
'brigadeers',
'brigades',
'brigadier',
'brigadiers',
'brigand',
'brigands',
'brigantine',
'brigantines',
'bright',
'brighten',
'brightens',
'brighter',
'brightest',
'brighting',
'brightly',
'brightness',
'brights',
'brigs',
'brilliance',
'brilliant',
'brilliantly',
'brim',
'brimming',
'brimstone',
'brine',
'briney',
'bring',
'bringer',
'bringers',
'bringing',
'brings',
'bringthemadness',
'brining',
'brink',
'brinks',
'briny',
'brio',
'briquettes',
'brisk',
'brisket',
'britannia',
'britches',
'british',
'bro',
"bro's",
'broached',
'broad',
'broadband',
'broadcast',
'broadcasts',
'broaden',
'broadens',
'broader',
'broadest',
'broadly',
'broads',
'broadside',
"broadside's",
'broadsided',
'broadsides',
'broadsword',
'broadway',
'broccoli',
'brochure',
'brock',
'brogan',
'brogans',
'broil',
'broiled',
'broke',
'broken',
'brokenly',
'broker',
'brokers',
'broking',
'bronco',
'broncos',
'bronze',
'bronzed',
'bronzy',
'brood',
'brook',
"brook's",
'brooks',
'broom',
"broom's",
'brooms',
'broomstick',
"broomstick's",
'broomsticks',
'bros',
'broth',
'brother',
"brother's",
'brotherhood',
"brotherhood's",
'brotherhoods',
'brothering',
'brotherly',
'brothers',
"brothers'",
'broths',
'brought',
'brouhaha',
'brow',
'brown',
'browncoats',
'browner',
'brownie',
'brownies',
'browning',
'brownish',
'browns',
'brows',
'browse',
'browsed',
'browser',
'browsers',
'browsing',
'brrr',
'brrrgh',
'brt',
'bruce',
"bruce's",
'bruckheimer',
'bruh',
'bruin',
'bruise',
'bruised',
'bruiser',
'bruises',
'bruising',
'brulee',
'brume',
'brunch',
'brunette',
'brunettes',
'brunt',
'brush',
'brushed',
'brusher',
'brushes',
'brushing',
'brushoff',
'brussels',
'brutal',
'brute',
'brutish',
'bryan',
'bryanna',
'bryson',
'btb',
'btl',
'btw',
'bubba',
'bubble',
"bubble's",
'bubbled',
'bubblegum',
'bubbles',
'bubbling',
'bubbloon',
'bubbly',
'bubo',
'bubs',
"buc's",
'bucaneer',
'bucanneers',
'buccaneer',
"buccaneer's",
'buccaneers',
'bucco',
'buckaroo',
"buckaroo's",
'buckaroos',
'bucket',
"bucket's",
'bucketed',
'bucketing',
'buckets',
'buckeye',
'buckeyes',
'buckle',
"buckle's",
'buckled',
'buckler',
'buckles',
'bucko',
"bucko's",
'buckos',
'bucks',
'buckshot',
'buckskin',
'buckwheat',
'bucs',
'bud',
"bud's",
'budd',
"budd's",
'buddies',
'buddy',
"buddy's",
'budge',
'budged',
'budget',
'budgeted',
'budgeter',
'budgeters',
'budgeting',
'budgets',
'budgie',
'budging',
'buds',
'buena',
'bueno',
'buff',
'buffalo',
"buffalo's",
'buffalos',
'buffed',
'buffer',
'buffet',
"buffet's",
'buffets',
'buffoon',
'buffoons',
'buffs',
'bug',
"bug's",
'bug-eye',
'bugalicious',
'bugariffic',
'bugbear',
'bugbears',
'bugeye',
'bugged',
'buggered',
'buggers',
'buggier',
'buggies',
'buggiest',
'bugging',
'buggy',
'bugle',
'bugles',
'bugs',
'bugsit',
'bugtastic',
'buh',
'build',
'builded',
'builder',
"builder's",
'builders',
'building',
"building's",
'buildings',
'builds',
'buildup',
'buillion',
'built',
'bulb',
"bulb's",
'bulbasaur',
'bulbs',
'bulge',
'bulging',
'bulgy',
'bulk',
'bulkhead',
'bulky',
'bull',
"bull's",
'bulldog',
'bulldogs',
'bulldozer',
'bulletin',
'bullfighters',
'bullfighting',
'bullied',
'bullies',
'bullion',
'bullpen',
'bulls',
'bullseye',
'bully',
"bully's",
'bullying',
'bulwark',
"bulwark's",
'bulwarks',
'bum-bum',
'bumberbee',
'bumberberry',
'bumberblabber',
'bumberbocker',
'bumberboing',
'bumberboom',
'bumberbounce',
'bumberbouncer',
'bumberbrains',
'bumberbubble',
'bumberbumble',
'bumberbump',
'bumberbumper',
'bumberburger',
'bumberchomp',
'bumbercorn',
'bumbercrash',
'bumbercrumbs',
'bumbercrump',
'bumbercrunch',
'bumberdoodle',
'bumberdorf',
'bumberface',
'bumberfidget',
'bumberfink',
'bumberfish',
'bumberflap',
'bumberflapper',
'bumberflinger',
'bumberflip',
'bumberflipper',
'bumberfoot',
'bumberfuddy',
'bumberfussen',
'bumbergadget',
'bumbergargle',
'bumbergloop',
'bumberglop',
'bumbergoober',
'bumbergoose',
'bumbergrooven',
'bumberhoffer',
'bumberhopper',
'bumberjinks',
'bumberklunk',
'bumberknees',
'bumbermarble',
'bumbermash',
'bumbermonkey',
'bumbermooch',
'bumbermouth',
'bumbermuddle',
'bumbermuffin',
'bumbermush',
'bumbernerd',
'bumbernoodle',
'bumbernose',
'bumbernugget',
'bumberphew',
'bumberphooey',
'bumberpocket',
'bumberpoof',
'bumberpop',
'bumberpounce',
'bumberpow',
'bumberpretzel',
'bumberquack',
'bumberroni',
'bumberscooter',
'bumberscreech',
'bumbersmirk',
'bumbersnooker',
'bumbersnoop',
'bumbersnout',
'bumbersocks',
'bumberspeed',
'bumberspinner',
'bumbersplat',
'bumbersprinkles',
'bumbersticks',
'bumberstink',
'bumberswirl',
'bumberteeth',
'bumberthud',
'bumbertoes',
'bumberton',
'bumbertoon',
'bumbertooth',
'bumbertwist',
'bumberwhatsit',
'bumberwhip',
'bumberwig',
'bumberwoof',
'bumberzaner',
'bumberzap',
'bumberzapper',
'bumberzilla',
'bumberzoom',
'bumble',
'bumblebee',
'bumblebehr',
'bumbleberry',
'bumbleblabber',
'bumblebocker',
'bumbleboing',
'bumbleboom',
'bumblebounce',
'bumblebouncer',
'bumblebrains',
'bumblebubble',
'bumblebumble',
'bumblebump',
'bumblebumper',
'bumbleburger',
'bumblechomp',
'bumblecorn',
'bumblecrash',
'bumblecrumbs',
'bumblecrump',
'bumblecrunch',
'bumbledoodle',
'bumbledorf',
'bumbleface',
'bumblefidget',
'bumblefink',
'bumblefish',
'bumbleflap',
'bumbleflapper',
'bumbleflinger',
'bumbleflip',
'bumbleflipper',
'bumblefoot',
'bumblefuddy',
'bumblefussen',
'bumblegadget',
'bumblegargle',
'bumblegloop',
'bumbleglop',
'bumblegoober',
'bumblegoose',
'bumblegrooven',
'bumblehoffer',
'bumblehopper',
'bumblejinks',
'bumbleklunk',
'bumbleknees',
'bumblemarble',
'bumblemash',
'bumblemonkey',
'bumblemooch',
'bumblemouth',
'bumblemuddle',
'bumblemuffin',
'bumblemush',
'bumblenerd',
'bumblenoodle',
'bumblenose',
'bumblenugget',
'bumblephew',
'bumblephooey',
'bumblepocket',
'bumblepoof',
'bumblepop',
'bumblepounce',
'bumblepow',
'bumblepretzel',
'bumblequack',
'bumbler',
"bumbler's",
'bumbleroni',
'bumblers',
'bumblescooter',
'bumblescreech',
'bumblesmirk',
'bumblesnooker',
'bumblesnoop',
'bumblesnout',
'bumblesocks',
'bumblesoup',
'bumblespeed',
'bumblespinner',
'bumblesplat',
'bumblesprinkles',
'bumblesticks',
'bumblestink',
'bumbleswirl',
'bumbleteeth',
'bumblethud',
'bumbletoes',
'bumbleton',
'bumbletoon',
'bumbletooth',
'bumbletwist',
'bumblewhatsit',
'bumblewhip',
'bumblewig',
'bumblewoof',
'bumblezaner',
'bumblezap',
'bumblezapper',
'bumblezilla',
'bumblezoom',
'bumm',
'bummed',
'bummer',
'bummers',
'bumming',
'bumms',
'bump',
'bumpenbee',
'bumpenberry',
'bumpenblabber',
'bumpenbocker',
'bumpenboing',
'bumpenboom',
'bumpenbounce',
'bumpenbouncer',
'bumpenbrains',
'bumpenbubble',
'bumpenbumble',
'bumpenbump',
'bumpenbumper',
'bumpenburger',
'bumpenchomp',
'bumpencorn',
'bumpencrash',
'bumpencrumbs',
'bumpencrump',
'bumpencrunch',
'bumpendoodle',
'bumpendorf',
'bumpenface',
'bumpenfidget',
'bumpenfink',
'bumpenfish',
'bumpenflap',
'bumpenflapper',
'bumpenflinger',
'bumpenflip',
'bumpenflipper',
'bumpenfoot',
'bumpenfuddy',
'bumpenfussen',
'bumpengadget',
'bumpengargle',
'bumpengloop',
'bumpenglop',
'bumpengoober',
'bumpengoose',
'bumpengrooven',
'bumpenhoffer',
'bumpenhopper',
'bumpenjinks',
'bumpenklunk',
'bumpenknees',
'bumpenmarble',
'bumpenmash',
'bumpenmonkey',
'bumpenmooch',
'bumpenmouth',
'bumpenmuddle',
'bumpenmuffin',
'bumpenmush',
'bumpennerd',
'bumpennoodle',
'bumpennose',
'bumpennugget',
'bumpenphew',
'bumpenphooey',
'bumpenpocket',
'bumpenpoof',
'bumpenpop',
'bumpenpounce',
'bumpenpow',
'bumpenpretzel',
'bumpenquack',
'bumpenroni',
'bumpenscooter',
'bumpenscreech',
'bumpensmirk',
'bumpensnooker',
'bumpensnoop',
'bumpensnout',
'bumpensocks',
'bumpenspeed',
'bumpenspinner',
'bumpensplat',
'bumpensprinkles',
'bumpensticks',
'bumpenstink',
'bumpenswirl',
'bumpenteeth',
'bumpenthud',
'bumpentoes',
'bumpenton',
'bumpentoon',
'bumpentooth',
'bumpentwist',
'bumpenwhatsit',
'bumpenwhip',
'bumpenwig',
'bumpenwoof',
'bumpenzaner',
'bumpenzap',
'bumpenzapper',
'bumpenzilla',
'bumpenzoom',
'bumpkin',
'bumpy',
'bun',
'bunch',
'bunched',
'bunches',
'bunching',
'bunchy',
'bund',
'bundle',
'bundled',
'bundleofsticks',
'bundler',
'bundles',
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'bwahaha',
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'bwhahahaha',
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'bye',
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'bytes',
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'c.a.r.t.e.r.',
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'chemicals',
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"chernabog's",
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'frinkelchomp',
'frinkelcorn',
'frinkelcrash',
'frinkelcrumbs',
'frinkelcrump',
'frinkelcrunch',
'frinkeldoodle',
'frinkeldorf',
'frinkelface',
'frinkelfidget',
'frinkelfink',
'frinkelfish',
'frinkelflap',
'frinkelflapper',
'frinkelflinger',
'frinkelflip',
'frinkelflipper',
'frinkelfoot',
'frinkelfuddy',
'frinkelfussen',
'frinkelgadget',
'frinkelgargle',
'frinkelgloop',
'frinkelglop',
'frinkelgoober',
'frinkelgoose',
'frinkelgrooven',
'frinkelhoffer',
'frinkelhopper',
'frinkeljinks',
'frinkelklunk',
'frinkelknees',
'frinkelmarble',
'frinkelmash',
'frinkelmonkey',
'frinkelmooch',
'frinkelmouth',
'frinkelmuddle',
'frinkelmuffin',
'frinkelmush',
'frinkelnerd',
'frinkelnoodle',
'frinkelnose',
'frinkelnugget',
'frinkelphew',
'frinkelphooey',
'frinkelpocket',
'frinkelpoof',
'frinkelpop',
'frinkelpounce',
'frinkelpow',
'frinkelpretzel',
'frinkelquack',
'frinkelroni',
'frinkelscooter',
'frinkelscreech',
'frinkelsmirk',
'frinkelsnooker',
'frinkelsnoop',
'frinkelsnout',
'frinkelsocks',
'frinkelspeed',
'frinkelspinner',
'frinkelsplat',
'frinkelsprinkles',
'frinkelsticks',
'frinkelstink',
'frinkelswirl',
'frinkelteeth',
'frinkelthud',
'frinkeltoes',
'frinkelton',
'frinkeltoon',
'frinkeltooth',
'frinkeltwist',
'frinkelwhatsit',
'frinkelwhip',
'frinkelwig',
'frinkelwoof',
'frinkelzaner',
'frinkelzap',
'frinkelzapper',
'frinkelzilla',
'frinkelzoom',
'frisbee',
'frisbees',
'frit',
'frites',
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'frivolity',
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'frizzle',
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'fro',
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"frog's",
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'ftopayrespects',
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'fuchsia',
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'fuel',
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'fugitives',
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'fugue',
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'fulfilled',
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'full',
'full-length',
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"fuller's",
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'fullscreen',
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'fumblebocker',
'fumbleboing',
'fumbleboom',
'fumblebounce',
'fumblebouncer',
'fumblebrains',
'fumblebubble',
'fumblebumble',
'fumblebump',
'fumblebumper',
'fumbleburger',
'fumblechomp',
'fumblecorn',
'fumblecrash',
'fumblecrumbs',
'fumblecrump',
'fumblecrunch',
'fumbled',
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'fumbleflap',
'fumbleflapper',
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'fumbleflipper',
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'fumblegargle',
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'fumbleglop',
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'fun',
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'funnybumper',
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'funnycorn',
'funnycrash',
'funnycrumbs',
'funnycrump',
'funnycrunch',
'funnydoodle',
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'funnyface',
'funnyfidget',
'funnyfink',
'funnyfish',
'funnyflap',
'funnyflapper',
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'funnyflipper',
'funnyfoot',
'funnyfuddy',
'funnyfussen',
'funnygadget',
'funnygargle',
'funnygloop',
'funnyglop',
'funnygoober',
'funnygoose',
'funnygrooven',
'funnyhoffer',
'funnyhopper',
'funnyjinks',
'funnyklunk',
'funnyknees',
'funnymarble',
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'funnymonkey',
'funnymooch',
'funnymouth',
'funnymuddle',
'funnymuffin',
'funnymush',
'funnynerd',
'funnynoodle',
'funnynose',
'funnynugget',
'funnyphew',
'funnyphooey',
'funnypocket',
'funnypoof',
'funnypop',
'funnypounce',
'funnypow',
'funnypretzel',
'funnyquack',
'funnyroni',
'funnyscooter',
'funnyscreech',
'funnysmirk',
'funnysnooker',
'funnysnoop',
'funnysnout',
'funnysocks',
'funnyspeed',
'funnyspinner',
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'funnysprinkles',
'funnysticks',
'funnystink',
'funnyswirl',
'funnyteeth',
'funnythud',
'funnytoes',
'funnyton',
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'funnyzapper',
'funnyzilla',
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'funs',
'funscape',
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'funtime',
'funzone',
'fur',
'furball',
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'furiously',
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'fuzzybumble',
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'fuzzybumper',
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'fuzzychomp',
'fuzzycorn',
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'fuzzycrumbs',
'fuzzycrump',
'fuzzycrunch',
'fuzzydoodle',
'fuzzydorf',
'fuzzyface',
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'fuzzyfink',
'fuzzyfish',
'fuzzyflap',
'fuzzyflapper',
'fuzzyflinger',
'fuzzyflip',
'fuzzyflipper',
'fuzzyfoot',
'fuzzyfuddy',
'fuzzyfussen',
'fuzzygadget',
'fuzzygargle',
'fuzzygloop',
'fuzzyglop',
'fuzzygoober',
'fuzzygoose',
'fuzzygrooven',
'fuzzyhoffer',
'fuzzyhopper',
'fuzzyjinks',
'fuzzyklunk',
'fuzzyknees',
'fuzzymarble',
'fuzzymash',
'fuzzymonkey',
'fuzzymooch',
'fuzzymouth',
'fuzzymuddle',
'fuzzymuffin',
'fuzzymush',
'fuzzynerd',
'fuzzynoodle',
'fuzzynose',
'fuzzynugget',
'fuzzyphew',
'fuzzyphooey',
'fuzzypocket',
'fuzzypoof',
'fuzzypop',
'fuzzypounce',
'fuzzypow',
'fuzzypretzel',
'fuzzyquack',
'fuzzyroni',
'fuzzyscooter',
'fuzzyscreech',
'fuzzysmirk',
'fuzzysnooker',
'fuzzysnoop',
'fuzzysnout',
'fuzzysocks',
'fuzzyspeed',
'fuzzyspinner',
'fuzzysplat',
'fuzzysprinkles',
'fuzzysticks',
'fuzzystink',
'fuzzyswirl',
'fuzzyteeth',
'fuzzythud',
'fuzzytoes',
'fuzzyton',
'fuzzytoon',
'fuzzytooth',
'fuzzytwist',
'fuzzywhatsit',
'fuzzywhip',
'fuzzywig',
'fuzzywoof',
'fuzzyzaner',
'fuzzyzap',
'fuzzyzapper',
'fuzzyzilla',
'fuzzyzoom',
'fyi',
"g'bye",
"g'day",
"g'luck",
"g'night",
"g'nite",
"g'way",
'g2g',
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'g2store',
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'gabbing',
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"gabriella's",
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'gaby',
'gad',
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'gadgets',
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'gal',
"gal's",
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"galaxy's",
'gale',
'galeon',
'gall',
"gall's",
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'gallants',
'gallbladder',
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'galleons',
'galleria',
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'gallery',
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'gallium',
'gallon',
'gallons',
'galloon',
'galloons',
'galloping',
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'galoot',
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'galosh',
'galoshes',
'gals',
'gambit',
'game',
"game's",
'game-face',
'gamecards',
'gamecrashes',
'gamecube',
'gamed',
'gameface',
"gamekeeper's",
'gamekeepers',
'gamely',
'gamemaster',
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'gameplay',
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'gamerfriend',
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'gamerz',
'games',
'gamesboy',
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'gamesite',
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'gamestyle',
'gamesurge',
'gametap',
'gamin',
'gaming',
'gamma',
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'ganga',
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'gantu',
'gap',
'gaps',
'garage',
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'garcon',
'garden',
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'gastons',
'gate',
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'gateways',
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'gathered',
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'gender',
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"her's",
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'hercules',
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"hero's",
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'hey',
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'heyguys',
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'hidden',
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'higglyball',
'higglytown',
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'hightailed',
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'hiiii-yaaaaa',
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'hiking',
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'hilarity',
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"hill's",
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'him',
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'hinges',
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'hinting',
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"hip-hoppin'",
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"history's",
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"hit's",
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"hive's",
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'hkdl',
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'hmmm',
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'hoarder',
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'hobbies',
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'hoboken',
'hocked',
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'hocus-focus',
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'hofflord',
'hog',
'hogg',
'hogge',
'hogged',
'hogger',
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'hoist',
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'holidas',
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'holler',
'hollered',
'hollering',
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"hollow's",
'hollowed-out',
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"holly's",
'hollywood',
'hollywoods',
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'holme',
"holme's",
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'holograms',
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'holy',
'homage',
'hombre',
'hombres',
'home',
"home's",
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'home-made',
'homebound',
'homeboy',
'homecoming',
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'homedog',
'homegirl',
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'hometown',
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'homeworker',
"homeworker's",
'homeworkers',
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'homeys',
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'homing',
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'homographs',
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'honcho',
'honchos',
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'hones',
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"hood's",
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"hook's",
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'hooligans',
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'hooplah',
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'hoopsters',
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"hopeful's",
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'hops',
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"horatio's",
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"horizon's",
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"hornet's",
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'horrible',
'horribly',
'horrid',
'horridness',
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'horrified',
'horrifying',
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"horror's",
'horrors',
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"horse's",
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'horseman',
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'horses',
'horseshoe',
"horseshoe's",
'horseshoes',
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'horsing',
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'hosed',
'hoses',
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'hospitality',
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"host's",
'hosted',
'hostile',
'hostiles',
'hostility',
'hosting',
'hosts',
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'hot-tempered',
'hotel',
"hotel's",
'hotels',
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'hotkey',
'hotkeys',
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'hotspot',
'hotspots',
'hotter',
'hottest',
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'hounded',
'hounding',
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'houndour',
'hounds',
'hour',
"hour's",
'hourglass',
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'hours',
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"house's",
'housebroken',
'housecleaning',
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'houseful',
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'housekeeper',
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'houseplant',
'houser',
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'housewife',
'housewives',
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'hovercraft',
'hovered',
'hovering',
'hovers',
'how',
"how'd",
"how're",
"how's",
"how've",
'how-to',
'how-to-video',
'how2',
'howard',
'howdy',
'however',
'howl',
'howled',
'howling',
'hows',
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"hp's",
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'hqofficerf',
'hqofficerm',
'hqs',
'hr',
'hr.',
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'hsm',
"hsm-2's",
'hsm2',
'hsm3',
'html',
'hub',
"hub's",
'hubbies',
'hubby',
"hubby's",
'hubert',
'hubs',
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'huckleberry',
'huckleblabber',
'hucklebocker',
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'huckleboom',
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'hucklebouncer',
'hucklebrains',
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'hucklebumble',
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'hucklebumper',
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'hucklecorn',
'hucklecrash',
'hucklecrumbs',
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'hucklecrunch',
'huckledoodle',
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'huckleface',
'hucklefidget',
'hucklefink',
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'huckleflapper',
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'huckleflipper',
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'hucklegargle',
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'huckleglop',
'hucklegoober',
'hucklegoose',
'hucklegrooven',
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'hucklehopper',
'hucklejinks',
'huckleklunk',
'huckleknees',
'hucklemarble',
'hucklemash',
'hucklemonkey',
'hucklemooch',
'hucklemouth',
'hucklemuddle',
'hucklemuffin',
'hucklemush',
'hucklenerd',
'hucklenoodle',
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'hucklenugget',
'hucklephew',
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'hucklepop',
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'huckleroni',
'hucklescooter',
'hucklescreech',
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'hucklesocks',
'hucklespeed',
'hucklespinner',
'hucklesplat',
'hucklesprinkles',
'hucklesticks',
'hucklestink',
'huckleswirl',
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'jabberfidget',
'jabberfink',
'jabberfish',
'jabberflap',
'jabberflapper',
'jabberflinger',
'jabberflip',
'jabberflipper',
'jabberfoot',
'jabberfuddy',
'jabberfussen',
'jabbergadget',
'jabbergargle',
'jabbergloop',
'jabberglop',
'jabbergoober',
'jabbergoose',
'jabbergrooven',
'jabberhoffer',
'jabberhopper',
'jabberjinks',
'jabberklunk',
'jabberknees',
'jabbermarble',
'jabbermash',
'jabbermonkey',
'jabbermooch',
'jabbermouth',
'jabbermuddle',
'jabbermuffin',
'jabbermush',
'jabbernerd',
'jabbernoodle',
'jabbernose',
'jabbernugget',
'jabberphew',
'jabberphooey',
'jabberpocket',
'jabberpoof',
'jabberpop',
'jabberpounce',
'jabberpow',
'jabberpretzel',
'jabberquack',
'jabberroni',
'jabberscooter',
'jabberscreech',
'jabbersmirk',
'jabbersnooker',
'jabbersnoop',
'jabbersnout',
'jabbersocks',
'jabberspeed',
'jabberspinner',
'jabbersplat',
'jabbersprinkles',
'jabbersticks',
'jabberstink',
'jabberswirl',
'jabberteeth',
'jabberthud',
'jabbertoes',
'jabberton',
'jabbertoon',
'jabbertooth',
'jabbertwist',
'jabberwhatsit',
'jabberwhip',
'jabberwig',
'jabberwoof',
'jabberzaner',
'jabberzap',
'jabberzapper',
'jabberzilla',
'jabberzoom',
'jace',
'jack',
"jack's",
'jackals',
'jacket',
"jacket's",
'jackets',
'jackfruit',
'jackie',
"jackie's",
'jackies',
'jackolantern',
"jackolantern's",
'jackolanterns',
'jackpie',
'jackpot',
"jackpot's",
'jackpots',
'jacks',
'jackson',
"jackson's",
'jacques',
'jade',
'jaded',
'jado',
'jafar',
"jafar's",
'jafars',
'jagged',
'jaguar',
'jail',
'jails',
'jak',
'jake',
"jake's",
'jakebooy',
'jakefromstatefarm',
'jakes',
'jalex',
'jam',
'jamada',
'jamago',
'jamble',
'jamboree',
'james',
"james'",
'jamie',
'jamigos',
'jamilles',
'jammania',
'jammed',
'jammer',
'jammin',
"jammin'",
"jammin's",
'jamming',
'jammy',
'jamoso',
'jams',
'jan',
'jane',
"jane's",
'janes',
'janet',
'janice',
'january',
"january's",
'januarys',
'japan',
'jar',
"jar's",
'jarax',
'jared',
'jargon',
'jars',
'jasmine',
"jasmine's",
'jasmines',
'jason',
"jason's",
'jasons',
'java',
'javascript',
'jaw',
"jaw's",
'jaw-dropper',
'jawed',
'jaws',
'jayson',
'jazz',
'jazzed',
'jazzy',
'jb',
'jbs',
'jealous',
'jealously',
'jealousy',
'jean',
"jean's",
'jeanie',
'jeanne',
'jeans',
'jed',
'jedi',
"jedi's",
'jedis',
'jeena',
'jeeperbee',
'jeeperberry',
'jeeperblabber',
'jeeperbocker',
'jeeperboing',
'jeeperboom',
'jeeperbounce',
'jeeperbouncer',
'jeeperbrains',
'jeeperbubble',
'jeeperbumble',
'jeeperbump',
'jeeperbumper',
'jeeperburger',
'jeeperchomp',
'jeepercorn',
'jeepercrash',
'jeepercrumbs',
'jeepercrump',
'jeepercrunch',
'jeeperdoodle',
'jeeperdorf',
'jeeperface',
'jeeperfidget',
'jeeperfink',
'jeeperfish',
'jeeperflap',
'jeeperflapper',
'jeeperflinger',
'jeeperflip',
'jeeperflipper',
'jeeperfoot',
'jeeperfuddy',
'jeeperfussen',
'jeepergadget',
'jeepergargle',
'jeepergloop',
'jeeperglop',
'jeepergoober',
'jeepergoose',
'jeepergrooven',
'jeeperhoffer',
'jeeperhopper',
'jeeperjinks',
'jeeperklunk',
'jeeperknees',
'jeepermarble',
'jeepermash',
'jeepermonkey',
'jeepermooch',
'jeepermouth',
'jeepermuddle',
'jeepermuffin',
'jeepermush',
'jeepernerd',
'jeepernoodle',
'jeepernose',
'jeepernugget',
'jeeperphew',
'jeeperphooey',
'jeeperpocket',
'jeeperpoof',
'jeeperpop',
'jeeperpounce',
'jeeperpow',
'jeeperpretzel',
'jeeperquack',
'jeeperroni',
'jeeperscooter',
'jeeperscreech',
'jeepersmirk',
'jeepersnooker',
'jeepersnoop',
'jeepersnout',
'jeepersocks',
'jeeperspeed',
'jeeperspinner',
'jeepersplat',
'jeepersprinkles',
'jeepersticks',
'jeeperstink',
'jeeperswirl',
'jeeperteeth',
'jeeperthud',
'jeepertoes',
'jeeperton',
'jeepertoon',
'jeepertooth',
'jeepertwist',
'jeeperwhatsit',
'jeeperwhip',
'jeeperwig',
'jeeperwoof',
'jeeperzaner',
'jeeperzap',
'jeeperzapper',
'jeeperzilla',
'jeeperzoom',
'jeff',
'jeffrey',
'jehan',
'jellies',
'jelly',
'jellybean',
"jellybean's",
'jellybeans',
'jellyfish',
"jellyfish's",
'jellyroll',
'jennifer',
'jenny',
'jeopardy',
'jeremiah',
'jeremy',
'jerome',
'jerry',
"jerry's",
'jerrys',
'jersey',
'jess',
'jessamine',
'jesse',
"jesse's",
'jesses',
'jest',
'jester',
"jester's",
'jesters',
'jests',
'jet',
"jet's",
'jethred',
'jetix',
'jetixtreme',
'jetpack',
'jets',
'jetsam',
"jetsam's",
'jetsams',
'jett',
"jett's",
'jetts',
'jeune',
"jewel's",
'jeweled',
'jeweler',
'jewelers',
'jewelry',
'jex',
'jig',
'jigglypuff',
'jigsaw',
'jill',
'jim',
'jima',
'jimmie',
'jimmyleg',
'jingle',
'jinglebells',
'jingles',
'jingly',
'jinks',
'jinx',
'jinxbee',
'jinxberry',
'jinxblabber',
'jinxbocker',
'jinxboing',
'jinxboom',
'jinxbounce',
'jinxbouncer',
'jinxbrains',
'jinxbubble',
'jinxbumble',
'jinxbump',
'jinxbumper',
'jinxburger',
'jinxchomp',
'jinxcorn',
'jinxcrash',
'jinxcrumbs',
'jinxcrump',
'jinxcrunch',
'jinxdoodle',
'jinxdorf',
'jinxed',
'jinxes',
'jinxface',
'jinxfidget',
'jinxfink',
'jinxfish',
'jinxflap',
'jinxflapper',
'jinxflinger',
'jinxflip',
'jinxflipper',
'jinxfoot',
'jinxfuddy',
'jinxfussen',
'jinxgadget',
'jinxgargle',
'jinxgloop',
'jinxglop',
'jinxgoober',
'jinxgoose',
'jinxgrooven',
'jinxhoffer',
'jinxhopper',
'jinxing',
'jinxjinks',
'jinxklunk',
'jinxknees',
'jinxmarble',
'jinxmash',
'jinxmonkey',
'jinxmooch',
'jinxmouth',
'jinxmuddle',
'jinxmuffin',
'jinxmush',
'jinxnerd',
'jinxnoodle',
'jinxnose',
'jinxnugget',
'jinxphew',
'jinxphooey',
'jinxpocket',
'jinxpoof',
'jinxpop',
'jinxpounce',
'jinxpow',
'jinxpretzel',
'jinxquack',
'jinxroni',
'jinxscooter',
'jinxscreech',
'jinxsmirk',
'jinxsnooker',
'jinxsnoop',
'jinxsnout',
'jinxsocks',
'jinxspeed',
'jinxspinner',
'jinxsplat',
'jinxsprinkles',
'jinxsticks',
'jinxstink',
'jinxswirl',
'jinxteeth',
'jinxthud',
'jinxtoes',
'jinxton',
'jinxtoon',
'jinxtooth',
'jinxtwist',
'jinxwhatsit',
'jinxwhip',
'jinxwig',
'jinxwoof',
'jinxzaner',
'jinxzap',
'jinxzapper',
'jinxzilla',
'jinxzoom',
'jitterbug',
'jittery',
'jive',
'jive_turkies',
"jivin'",
'jj',
'jjdinner',
'jjkoletar',
'jk',
'jo',
'joan',
'job',
'jobs',
'jocard',
'joe',
"joe's",
'joel',
'joes',
'joey',
'joff',
'joff-tchoff-tchoffo-tchoffo-tchoff',
'john',
'johnny',
"johnny's",
'johnnys',
'johns',
'johnson',
"johnson's",
'johnsons',
'johny',
'join',
'joined',
'joiner',
'joiners',
'joining',
'joins',
'jojo',
"jojo's",
'jojos',
'joke',
"joke's",
'joked',
'joker',
"joker's",
'jokers',
'jokes',
'jokey',
'joking',
'jolene',
'jollibee',
'jollibeefellowship',
'jollibeeinfinite',
'jollibeerewritten',
'jolly',
"jolly's",
'jollyroger',
"jollyroger's",
'jollyrogers',
'jolt',
'jolteon',
"jona's",
'jonas',
'jonathan',
'jones',
"jones'",
"jones's",
'jordan',
'jos',
'joseph',
'josh',
'joshamee',
'joshsora',
'joshua',
'joshuas',
'josie',
'journal',
'journals',
'journes',
'journey',
'journeyed',
'journeying',
'journeyings',
'joy',
"joy's",
'joybuzzer',
'joyce',
'joyful',
'joyous',
'joys',
'jpeg',
'jpn',
'jpop',
'jps',
'jr',
'jr.',
'js',
'juan',
'jubilee',
'judge',
"judge's",
'judged',
'judger',
'judges',
'judging',
'judgment',
'judy',
"judy's",
'judys',
'juels',
'juggernaut',
'juggernauts',
'juggle',
'juggler',
'juggles',
'juggling',
'juice',
'juiced',
'juju',
'jukebox',
'jul',
'julep',
'julie',
'juliet',
'julius',
'july',
"july's",
'julys',
'jumba',
"jumba's",
'jumble',
'jumblebee',
'jumbleberry',
'jumbleblabber',
'jumblebocker',
'jumbleboing',
'jumbleboom',
'jumblebounce',
'jumblebouncer',
'jumblebrains',
'jumblebubble',
'jumblebumble',
'jumblebump',
'jumblebumper',
'jumblechomp',
'jumblecorn',
'jumblecrash',
'jumblecrumbs',
'jumblecrump',
'jumblecrunch',
'jumbledoodle',
'jumbledorf',
'jumbleface',
'jumblefidget',
'jumblefink',
'jumblefish',
'jumbleflap',
'jumbleflapper',
'jumbleflinger',
'jumbleflip',
'jumbleflipper',
'jumblefoot',
'jumblefuddy',
'jumblefussen',
'jumblegadget',
'jumblegargle',
'jumblegloop',
'jumbleglop',
'jumblegoober',
'jumblegoose',
'jumblegrooven',
'jumblehoffer',
'jumblehopper',
'jumblejinks',
'jumbleklunk',
'jumbleknees',
'jumblemarble',
'jumblemash',
'jumblemonkey',
'jumblemooch',
'jumblemouth',
'jumblemuddle',
'jumblemuffin',
'jumblemush',
'jumblenerd',
'jumblenoodle',
'jumblenose',
'jumblenugget',
'jumblephew',
'jumblephooey',
'jumblepocket',
'jumblepoof',
'jumblepop',
'jumblepounce',
'jumblepow',
'jumblepretzel',
'jumblequack',
'jumbleroni',
'jumbles',
'jumblescooter',
'jumblescreech',
'jumblesmirk',
'jumblesnooker',
'jumblesnoop',
'jumblesnout',
'jumblesocks',
'jumblespeed',
'jumblespinner',
'jumblesplat',
'jumblesprinkles',
'jumblesticks',
'jumblestink',
'jumbleswirl',
'jumbleteeth',
'jumblethud',
'jumbletoes',
'jumbleton',
'jumbletoon',
'jumbletooth',
'jumbletwist',
'jumblewhatsit',
'jumblewhip',
'jumblewig',
'jumblewoof',
'jumblezaner',
'jumblezap',
'jumblezapper',
'jumblezilla',
'jumblezoom',
'jumbo',
'jump',
"jump's",
'jumped',
'jumper',
'jumpers',
'jumpin',
'jumping',
'jumpluff',
'jumps',
'jumpy',
'jun',
'june',
"june's",
'juneau',
'junebug',
'junehs',
'junes',
'jung',
'jungle',
"jungle's",
'jungled',
'jungles',
'junior',
"junior's",
'juniors',
'juniper',
'junk',
'jupiter',
"jupiter's",
'jupiters',
'jurassic',
'jury',
'just',
'just-waking-up',
'juster',
'justice',
'justin',
"justin's",
'justing',
'justins',
'justly',
'juvenile',
'juvenilia',
'jynx',
'k',
'ka',
'ka-boom',
'ka-ching',
'kabob',
'kaboomery',
'kabuto',
'kabutops',
'kadabra',
'kady',
'kagero',
'kahoot',
'kahoot.it',
'kai',
'kaitlin',
'kaitlyn',
'kaken',
'kakuna',
'kamakuri',
'kanaya',
'kang',
'kanga',
"kanga's",
'kangaroo',
'kangas',
'kangaskhan',
'kansas',
'kapahala',
'kappa',
'kappaross',
'karakuri',
'karaoke',
'karat',
'karate',
'karbay',
'karen',
'karin',
'karkat',
'karma',
'karnival',
'karo',
"karo's",
'kart',
'karts',
'kasumi',
'kasumire',
'kasumite',
'kat',
'katarina',
'kate',
"kate's",
'katelyn',
'kathy',
"katie's",
'katz',
'katzenstein',
'kawaii',
'kay',
'kazaam',
'kazoo',
'kazoology',
'kdf',
'keaton',
'keelgrin',
'keely',
"keely's",
'keelys',
'keen',
'keep',
'keeper',
"keeper's",
'keepers',
"keepin'",
'keeping',
'keeps',
'keepsake',
"keepsake's",
'keepsakes',
'kei',
'keira',
"keira's",
'keiras',
'kek',
'keke',
'kellogg',
"kellogg's",
'kelloggs',
'kelly',
"kelly's",
'kellys',
'kelp',
'kelp-jelly',
'kelsi',
"kelsi's",
'kelsis',
'kelvin',
'ken',
'kenai',
"kenai's",
'kenais',
'kennel',
'kenneth',
'kenny',
"kenny's",
'kennys',
'kent',
'kentucky',
'kept',
'kerchak',
"kerchak's",
'kerchaks',
'kermie',
'kermit',
'kes',
'kesha',
'kestred',
'ketchum',
'ketchup',
'ketobasu',
'kettle',
'kettles',
'kevin',
"kevin's",
'kevinh',
'kevins',
'kevman95',
'kewl',
'key',
"key's",
'keyboard',
"keyboard's",
'keyboarding',
'keyboards',
'keyhole-design',
'keys',
'keystone',
'keyword',
'kh',
'khaki',
"khaki's",
'khakis',
'khamsin',
'ki',
'kiba',
'kibatekka',
'kick',
'kickball',
'kicked',
'kicker',
'kickers',
'kickflip',
"kickin'",
'kicking',
'kicks',
'kid',
"kid's",
'kidd',
'kiddie',
'kidding',
'kids',
'kidstuff',
'kiely',
"kiely's",
'kielys',
'kiki',
"kiki's",
'kikis',
'kikyo',
'kill',
'killed',
'kim',
"kim's",
'kimchi',
'kimmunicator',
'kims',
'kind',
'kinda',
'kindergarten',
"kindergarten's",
'kindergartens',
'kindest',
'kindle',
'kindled',
'kindles',
'kindling',
'kindly',
'kindness',
'kinds',
'king',
"king's",
'king-sized',
'kingdedede',
'kingdom',
"kingdom's",
'kingdoms',
'kingdra',
'kingfisher',
'kingfishers',
'kingler',
'kingly',
'kingman',
"kingman's",
'kings',
'kingshead',
'kinna',
'kion',
'kiosk',
"kiosk's",
'kiosks',
'kipp',
'kippur',
'kirby',
'kiril',
'kirk',
'kirke',
'kit',
'kitchen',
"kitchen's",
'kitchener',
'kitchens',
'kite',
'kites',
'kitkat',
'kitt',
'kitten',
"kitten's",
'kittens',
'kitties',
'kitty',
"kitty's",
'kiwi',
'kk',
'klebba',
'klutz',
'klutzy',
'knap',
'knave',
'knee',
'kneecap',
'kneed',
'kneel',
'knees',
'knew',
'knghts',
'knight',
'knightley',
"knightley's",
'knights',
'knitting',
'knock',
'knockback',
'knockdown',
'knocked',
'knocking',
'knockoff',
'knocks',
'knoll',
'knot',
'knots',
'knotty',
'know',
'know-it-alls',
'knower',
'knowing',
'knowledge',
'knowledgeable',
'knowledges',
'known',
'knows',
'knowzone',
'knuckle',
'knucklehead',
'knuckles',
'koala',
"koala's",
'koalas',
'kobi',
'kobra',
'koda',
"koda's",
'kodas',
'kodiac',
'koffing',
'koha',
'kokeshi',
'koko',
'kokoago',
'kokoros',
'koleniko',
'koletar',
'kollin',
'komadoros',
'komainu',
'komanoto',
'kong',
"kong's",
'kongs',
'kooky',
'kookybee',
'kookyberry',
'kookyblabber',
'kookybocker',
'kookyboing',
'kookyboom',
'kookybounce',
'kookybouncer',
'kookybrains',
'kookybubble',
'kookybumble',
'kookybump',
'kookybumper',
'kookyburger',
'kookychomp',
'kookycorn',
'kookycrash',
'kookycrumbs',
'kookycrump',
'kookycrunch',
'kookydoodle',
'kookydorf',
'kookyface',
'kookyfidget',
'kookyfink',
'kookyfish',
'kookyflap',
'kookyflapper',
'kookyflinger',
'kookyflip',
'kookyflipper',
'kookyfoot',
'kookyfuddy',
'kookyfussen',
'kookygadget',
'kookygargle',
'kookygloop',
'kookyglop',
'kookygoober',
'kookygoose',
'kookygrooven',
'kookyhoffer',
'kookyhopper',
'kookyjinks',
'kookyklunk',
'kookyknees',
'kookymarble',
'kookymash',
'kookymonkey',
'kookymooch',
'kookymouth',
'kookymuddle',
'kookymuffin',
'kookymush',
'kookynerd',
'kookynoodle',
'kookynose',
'kookynugget',
'kookyphew',
'kookyphooey',
'kookypocket',
'kookypoof',
'kookypop',
'kookypounce',
'kookypow',
'kookypretzel',
'kookyquack',
'kookyroni',
'kookyscooter',
'kookyscreech',
'kookysmirk',
'kookysnooker',
'kookysnoop',
'kookysnout',
'kookysocks',
'kookyspeed',
'kookyspinner',
'kookysplat',
'kookysprinkles',
'kookysticks',
'kookystink',
'kookyswirl',
'kookyteeth',
'kookythud',
'kookytoes',
'kookyton',
'kookytoon',
'kookytooth',
'kookytwist',
'kookywhatsit',
'kookywhip',
'kookywig',
'kookywoof',
'kookyzaner',
'kookyzap',
'kookyzapper',
'kookyzilla',
'kookyzoom',
'kool',
'korogeki',
'koroko',
'korozama',
'korra',
'kosimi',
"kosmic's",
'kouki',
'kp',
"kp's",
'krab',
'krabby',
'kraken',
"kraken's",
'krakens',
'krawl',
'krazy',
'kreepers',
'krew',
'krewe',
'krispies',
'krissy',
'kristin',
'kristina',
'krogager',
'kronk',
"kronk's",
'krunklehorn',
'krusty',
'krux',
'krybots',
'kthx',
'ktta',
'kubaku',
'kuganon',
'kugaster',
'kumonn',
'kun',
'kung',
'kuzco',
'kwanzaa',
'kyle',
"kyle's",
'kyles',
'kyra',
'kyto',
"kyto's",
'l8r',
'la',
'lab',
'label',
'labeled',
'labeling',
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'labelling',
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'labor',
'labradoodle',
'labradoodles',
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'lacrosse',
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'ladle',
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'laff-o-dil',
'laffer',
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'lag',
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'lagging',
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'lagoon',
"lagoon's",
'lagoons',
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'laid-back',
'laidel',
'lake',
"lake's",
'laker',
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'laking',
'lala',
'lalala',
'lamanai',
'lamb',
'lambda',
'lamberginias',
'lame',
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'lamely',
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'lana',
'lanai',
'lance',
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'lances',
'land',
'landa',
'landed',
'lander',
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'landing',
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'landlubber',
'landlubbers',
'landmark',
'lands',
'landscape',
'lane',
'lanes',
'language',
"language's",
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'lantern',
"lantern's",
'lanterns',
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'lapras',
'laptop',
'large',
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'larkspur',
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'larry',
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'lasso',
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'latvian',
'laugh',
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"laughin'",
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'laughter',
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'launch',
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'launching',
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'launchpad',
'laundry',
'laura',
'laurel',
'laurel-leaf',
'lauren',
'lava',
'lavendar',
'lavender',
'lavish',
'law',
"law's",
'lawbot',
"lawbot's",
'lawbots',
'lawful',
'lawless',
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"lawn's",
'lawns',
'lawrence',
'laws',
'lawyer',
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'lbhq',
'le',
'lead',
'leaded',
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'leader',
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'leaderboard',
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'leaf',
"leaf's",
'leaf-boat',
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'leafboarding',
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'leafing',
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'ledyba',
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'leed',
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'left',
'left-click',
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'leg',
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"legend's",
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"leibovitz's",
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"lizzie's",
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'llama',
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'llamas',
'lloyd',
"lloyd's",
'lloyds',
'lmho',
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'local',
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'lockpick',
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"their's",
'theirs',
'theives',
'thelma',
'thelonius',
'them',
'themaskedmeowth',
'theme',
"theme's",
'themes',
'themselves',
'then',
'thenights',
'theodore',
'therandomdog',
'there',
"there'll",
"there's",
'therefore',
'theres',
'theresa',
'thermos',
'thermoses',
'these',
'theses',
'theta',
'they',
"they'll",
"they're",
"they've",
'theyll',
'theyre',
'thicket',
'thief',
"thief's",
'thieves',
'thigh',
'thighs',
'thimble',
'thin',
'thing',
'thingamabob',
'thingamabobs',
'thingamajigs',
'thingie',
'thingies',
'things',
'thingy',
'thinicetrobarrier',
'think',
'thinker',
'thinkers',
"thinkin'",
'thinking',
'thinkings',
'thinks',
'thinnamin',
'third',
'thirst',
'thirst-quenching',
'thirsted',
'thirster',
'thirstier',
'thirsts',
'thirsty',
'thirty',
'this',
'thisisasecretmessage',
'thismailboxismine',
'thistle',
'thistle-down',
'thnx',
'tho',
'thomas',
'thon',
'thonk',
'thonkang',
'thonkig',
'thor',
'thorhammer',
'thorn',
'those',
'though',
'thought',
"thought's",
'thoughtful',
'thoughts',
'thousand',
'thousands',
'thrashers',
'thread',
'threads',
'threats',
'three',
'threw',
'thrice',
'thrifty',
'thrill',
'thriller',
'thrilling',
'thrills',
'thrillseeker',
'thrives',
'throne',
'thrones',
'through',
'throughly',
'throughout',
'throw',
'thrower',
'throwing',
'throwitathimnotme',
'throwless',
'thrown',
'throws',
'thumb',
'thumbs',
'thumper',
'thunba',
'thunder',
'thunderbee',
'thunderberry',
'thunderblabber',
'thunderbocker',
'thunderboing',
'thunderbolt',
'thunderbolts',
'thunderboom',
'thunderbounce',
'thunderbouncer',
'thunderbrains',
'thunderbubble',
'thunderbumble',
'thunderbump',
'thunderbumper',
'thunderburger',
'thunderchomp',
'thundercorn',
'thundercrash',
'thundercrumbs',
'thundercrump',
'thundercrunch',
'thunderdoodle',
'thunderdorf',
'thunderface',
'thunderfidget',
'thunderfink',
'thunderfish',
'thunderflap',
'thunderflapper',
'thunderflinger',
'thunderflip',
'thunderflipper',
'thunderfoot',
'thunderfuddy',
'thunderfussen',
'thundergadget',
'thundergargle',
'thundergloop',
'thunderglop',
'thundergoober',
'thundergoose',
'thundergrooven',
'thunderhoffer',
'thunderhopper',
'thundering',
'thunderjinks',
'thunderklunk',
'thunderknees',
'thundermarble',
'thundermash',
'thundermonkey',
'thundermooch',
'thundermouth',
'thundermuddle',
'thundermuffin',
'thundermush',
'thundernerd',
'thundernoodle',
'thundernose',
'thundernugget',
'thunderphew',
'thunderphooey',
'thunderpocket',
'thunderpoof',
'thunderpop',
'thunderpounce',
'thunderpow',
'thunderpretzel',
'thunderquack',
'thunderroni',
'thunderscooter',
'thunderscreech',
'thundersmirk',
'thundersnooker',
'thundersnoop',
'thundersnout',
'thundersocks',
'thunderspeed',
'thunderspinner',
'thundersplat',
'thundersprinkles',
'thundersticks',
'thunderstink',
'thunderstorm',
'thunderstorms',
'thunderswirl',
'thunderteeth',
'thunderthud',
'thundertoes',
'thunderton',
'thundertoon',
'thundertooth',
'thundertwist',
'thunderwhatsit',
'thunderwhip',
'thunderwig',
'thunderwoof',
'thunderzaner',
'thunderzap',
'thunderzapper',
'thunderzilla',
'thunderzoom',
'thundor',
'thundora',
'thunkig',
'thunkong',
'thursday',
'thursdays',
'thus',
'thusly',
'thx',
'tia',
'tiana',
'tiara',
'tiazoa',
'tiberius',
'tibian',
'tic',
'tic-toc',
'tick',
'ticker',
'ticket',
"ticket's",
'ticketed',
'ticketing',
'tickets',
'ticking',
'tickle',
'tickled',
'tickles',
'tickling',
'ticklish',
'ticks',
'tidal',
'tidbits',
'tide',
'tidy',
'tie',
'tier',
'ties',
'tiffany',
'tiffens',
'tiger',
"tiger's",
'tigers',
'tigger',
"tigger's",
'tightwad',
'tightwads',
'tiki',
'tikis',
'til',
'tilde',
'tile',
'tiled',
'tiles',
'till',
'tilled',
'tiller',
'tillers',
'tilling',
'tills',
'tilly',
'tim',
'timberlake',
'timberleaf',
'timbers',
'timberwolves',
'timbre',
'time',
'timed',
'timeless',
'timelords',
'timely',
'timeout',
'timer',
'timers',
'times',
'timesnewroman',
'timing',
'timings',
'timon',
"timon's",
'timons',
'timothy',
'tin',
'tina',
"tina's",
'tindera',
'tink',
"tink's",
'tinker',
"tinker's",
'tinkerbell',
"tinkerbell's",
'tinkling',
'tinny',
'tinsel',
'tint',
'tinted',
'tints',
'tiny',
'tip',
'tip-top',
'tipped',
'tipping',
'tips',
'tipton',
'tire',
'tired',
'tires',
'tiresome',
'tiring',
'tis',
'tisdale',
"tish's",
'titan',
"titan's",
'titanic',
'titans',
'title',
'titles',
'tj',
'tkp',
'tl;dr',
'tlc',
'tm',
'tnt',
'tnts',
'to',
'toad',
'toads',
'toadstool',
'toadstool-drying',
'toadstools',
'toast',
'toasted',
'toaster',
'toasting',
'toasty',
'tobasu',
'tobias',
'toboggan',
'tobogganing',
'toboggans',
'toby',
'todas',
'today',
"today's",
'todd',
"todd's",
'toddler',
'toe',
"toe's",
'toed',
'toehooks',
'toes',
'tofu',
'togepi',
'together',
'togetic',
'toggle',
'toggler',
'token',
'tokens',
'told',
'tolerable',
'toll',
'tom',
"tom's",
"tom-tom's",
'tomas',
'tomato',
'tomboy',
'tomorrow',
"tomorrow's",
'tomorrowland',
"tomorrowland's",
'tomorrows',
'tomswordfish',
'ton',
'tone',
'toner',
'tones',
'tong',
'tongs',
'tonic',
'tonics',
'tonight',
'toning',
'tonite',
'tons',
'tony',
'too',
'toodles',
'took',
'tool',
'toolbar',
'toolbars',
'toolbelt',
'tooled',
'tooler',
'toolers',
'tooling',
'tools',
'toolshed',
'toon',
"toon's",
'toon-tastic',
'toon-torial',
'toon-up',
'toon-upless',
'toon-ups',
'toonacious',
'toonblr',
'tooncan',
'tooned',
'toonerific',
'toonfinite',
'toonhq.org',
'toonification',
'tooning',
'toonish',
'toonity',
'toonosaur',
'toons',
'toonscape',
'toontanic',
'toontask',
'toontasking',
'toontasks',
'toontastic',
'toonter',
'toontorial',
'toontown',
'toontrooper',
'toontroopers',
'toonup',
'toonupless',
'toonups',
'toony',
'tooth',
'toothless',
'toothpaste',
'toothpick',
'tootie',
'tootles',
'top',
'top-ranking',
'topaz',
'tophat',
'tophats',
'topiary',
'topic',
'topics',
'topkek',
'toppenbee',
'toppenberry',
'toppenblabber',
'toppenbocker',
'toppenboing',
'toppenboom',
'toppenbounce',
'toppenbouncer',
'toppenbrains',
'toppenbubble',
'toppenbumble',
'toppenbump',
'toppenbumper',
'toppenburger',
'toppenchomp',
'toppencorn',
'toppencrash',
'toppencrumbs',
'toppencrump',
'toppencrunch',
'toppendoodle',
'toppendorf',
'toppenface',
'toppenfidget',
'toppenfink',
'toppenfish',
'toppenflap',
'toppenflapper',
'toppenflinger',
'toppenflip',
'toppenflipper',
'toppenfoot',
'toppenfuddy',
'toppenfussen',
'toppengadget',
'toppengargle',
'toppengloop',
'toppenglop',
'toppengoober',
'toppengoose',
'toppengrooven',
'toppenhoffer',
'toppenhopper',
'toppenjinks',
'toppenklunk',
'toppenknees',
'toppenmarble',
'toppenmash',
'toppenmonkey',
'toppenmooch',
'toppenmouth',
'toppenmuddle',
'toppenmuffin',
'toppenmush',
'toppennerd',
'toppennoodle',
'toppennose',
'toppennugget',
'toppenphew',
'toppenphooey',
'toppenpocket',
'toppenpoof',
'toppenpop',
'toppenpounce',
'toppenpow',
'toppenpretzel',
'toppenquack',
'toppenroni',
'toppenscooter',
'toppenscreech',
'toppensmirk',
'toppensnooker',
'toppensnoop',
'toppensnout',
'toppensocks',
'toppenspeed',
'toppenspinner',
'toppensplat',
'toppensprinkles',
'toppensticks',
'toppenstink',
'toppenswirl',
'toppenteeth',
'toppenthud',
'toppentoes',
'toppenton',
'toppentoon',
'toppentooth',
'toppentwist',
'toppenwhatsit',
'toppenwhip',
'toppenwig',
'toppenwoof',
'toppenzaner',
'toppenzap',
'toppenzapper',
'toppenzilla',
'toppenzoom',
'topping',
'toppings',
'tops',
'topsy',
'topsy-turvy',
'toptoon',
'toptoons',
'tor',
'torga',
'torgallup',
'torgazar',
'tori',
'tormenta',
'torn',
'tortaba',
'tortaire',
'tortana',
'torth',
'tortoises',
'tortos',
'tory',
'tos',
'tosis',
'toss',
'tossed',
'tosses',
'tossing',
'total',
"total's",
'totaled',
'totaling',
'totally',
'totals',
'tote',
'totem',
'totems',
'tothestars',
'toti',
'totodile',
'tots',
'toucan',
'toucans',
'touch',
'touchdown',
'touchdowns',
'touge',
'tough',
'tough-skinned',
'toughest',
'toughness',
'toupee',
'toupees',
'tour',
'toured',
'tourer',
'tourguide',
'touring',
'tournament',
'tournaments',
'tours',
'tow',
'tow-mater',
'toward',
'towardly',
'towards',
'tower',
'towered',
'towering',
'towers',
'towing',
'town',
"town's",
'towner',
'towns',
'townsend',
'townsfolk',
'townsperson',
'tows',
'toxic',
'toxicmanager',
'toxicmanagers',
'toy',
'toyer',
'toying',
'toys',
'tp',
'tping',
'trace',
'track',
"track's",
'tracked',
'tracker',
'trackers',
'tracking',
'tracks',
'tracy',
'trade',
'tradeable',
'traded',
'trademark',
'trader',
'traders',
'trades',
'trading',
'tradition',
'traditions',
'traffic',
"traffic's",
'traffics',
'tragedy',
'trail',
'trailed',
'trailer',
'trailers',
'trailing',
'trailings',
'trails',
'train',
'trained',
'trainer',
'trainers',
'training',
'trains',
'trampoline',
'trampolines',
'tranquil',
'transfer',
"transfer's",
'transfered',
'transferred',
'transferring',
'transfers',
'translate',
'translated',
'translates',
'translating',
'transom',
'transparent',
'transport',
'transportation',
'transported',
'transporter',
'transporters',
'transporting',
'transports',
'trap',
"trap's",
'trapdoor',
'trapdoors',
'trapeze',
'trapless',
'trapped',
'traps',
'trash',
'trashcan',
"trashcan's",
'trashcans',
'travel',
'traveled',
'traveler',
'traveling',
'travelling',
'travels',
'travis',
'tray',
'treacherous',
'treachery',
"treachery's",
'tread',
'treaded',
'treading',
'treads',
'treasure',
'treasurechest',
'treasurechests',
'treasured',
'treasuremaps',
'treasurer',
'treasures',
'treasuring',
'treat',
'treated',
'treater',
'treaters',
'treating',
'treatment',
'treats',
'treble',
'tree',
'tree-bark-grading',
'tree-picking',
'tree-picking-talent',
'treehouse',
'treehouses',
'trees',
'treetop',
'trek',
'trellis',
'tremendous',
'tremor',
'trend',
'trending',
'trendinghobbit',
'trends',
'trent',
"trent's",
'tres',
'trespass',
'trespasser',
'treyarch',
'tri-barrel',
'tri-lock',
'triad',
'trial',
"trial's",
'trials',
'triangle',
'tribal',
'tribe',
'tribulation',
'tribulations',
'trick',
'tricked',
'tricked-out',
'tricker',
'trickery',
'tricking',
'tricks',
'tricky',
'trickybee',
'trickyberry',
'trickyblabber',
'trickybocker',
'trickyboing',
'trickyboom',
'trickybounce',
'trickybouncer',
'trickybrains',
'trickybubble',
'trickybumble',
'trickybump',
'trickybumper',
'trickyburger',
'trickychomp',
'trickycorn',
'trickycrash',
'trickycrumbs',
'trickycrump',
'trickycrunch',
'trickydoodle',
'trickydorf',
'trickyface',
'trickyfidget',
'trickyfink',
'trickyfish',
'trickyflap',
'trickyflapper',
'trickyflinger',
'trickyflip',
'trickyflipper',
'trickyfoot',
'trickyfuddy',
'trickyfussen',
'trickygadget',
'trickygargle',
'trickygloop',
'trickyglop',
'trickygoober',
'trickygoose',
'trickygrooven',
'trickyhoffer',
'trickyhopper',
'trickyjinks',
'trickyklunk',
'trickyknees',
'trickymarble',
'trickymash',
'trickymonkey',
'trickymooch',
'trickymouth',
'trickymuddle',
'trickymuffin',
'trickymush',
'trickynerd',
'trickynoodle',
'trickynose',
'trickynugget',
'trickyphew',
'trickyphooey',
'trickyplaystt',
'trickypocket',
'trickypoof',
'trickypop',
'trickypounce',
'trickypow',
'trickypretzel',
'trickyquack',
'trickyroni',
'trickyscooter',
'trickyscreech',
'trickysmirk',
'trickysnooker',
'trickysnoop',
'trickysnout',
'trickysocks',
'trickyspeed',
'trickyspinner',
'trickysplat',
'trickysprinkles',
'trickysticks',
'trickystink',
'trickyswirl',
'trickyteeth',
'trickythud',
'trickytoes',
'trickyton',
'trickytoon',
'trickytooth',
'trickytwist',
'trickywhatsit',
'trickywhip',
'trickywig',
'trickywoof',
'trickyzaner',
'trickyzap',
'trickyzapper',
'trickyzilla',
'trickyzoom',
'tried',
'trier',
'triers',
'tries',
'trifle',
'triforce',
'triggered',
'triggerfish',
'trike',
'trillion',
'trim',
'trims',
'trinity',
'trinket',
'trinkets',
'trinomial',
'trio',
'trioing',
'trip',
"trip's",
'triple',
'triplet',
'triplets',
'triply',
"trippin'",
'trippy',
'trips',
'triskaidekaphobia',
'tristam',
'tristan',
'triton',
"triton's",
'tritons',
'triumph',
'triumphant',
'trivia',
'trixie',
'trliable',
'troga',
'trogallup',
'trogazar',
'trogdor',
'troll',
'trolley',
'trolleys',
'trolls',
'trolly',
'tron',
'troop',
'trooper',
'troopers',
'troops',
'trophies',
'trophy',
'trophys',
'tropic',
"tropic's",
'tropical',
'tropically',
'tropics',
'trot',
'troubadours',
'trouble',
'troubled',
'troublemaker',
'troubler',
'troubles',
'troublesome',
'troubling',
'trough',
'troughes',
'trounce',
"trounce'em",
'trousers',
'trout',
'trove',
'trowels',
'troy',
'tru',
'truchemistry',
'truck',
'truckloads',
'trudy',
'true',
'trued',
"truehound's",
'truer',
'trues',
'truest',
'truetosilver',
'truffle',
'trufflehunter',
"trufflehunter's",
'trufflehunters',
'truffles',
'truigos',
'truing',
'truly',
'trump',
'trumpet',
'trumpets',
'trumpkin',
'trunk',
"trunk's",
'trunked',
'trunkfish',
'trunking',
'trunks',
'truscott',
'trust',
'trusted',
'truster',
'trusting',
'trusts',
'trustworthy',
'trusty',
'truth',
'truths',
'try',
'tryin',
'trying',
'tt',
'ttc',
'ttcentral',
'ttf',
'ttfn',
'ttfs',
'tthe',
'tti',
'tto',
'ttpa',
'ttpahq.pw',
'ttr',
'ttrl',
'ttyl',
'tu',
'tub',
'tuba',
'tubas',
'tubby',
'tube',
'tuber',
'tubes',
'tubs',
'tuesday',
'tuesdays',
'tuft',
'tufts',
'tug',
'tug-o-war',
'tug-of-war',
'tugs',
'tuless',
'tulip',
'tulips',
'tumble',
'tumbleweed',
'tumbleweeds',
'tumbly',
'tumnus',
'tuna',
'tunas',
'tundra',
'tune',
'tune-licious',
'tuned',
'tuner',
'tuners',
'tunes',
'tuning',
'tunnel',
'tunneled',
'tunneling',
'tunnels',
'turbo',
'turbojugend',
'turbonegro',
'turf',
'turk',
'turkey',
'turkish',
'turn',
'turnbull',
"turnbull's",
'turned',
'turner',
'turners',
'turning',
'turnings',
'turnip',
'turnover',
'turnovers',
'turns',
'turnstile',
'turnstiles',
'turqouise',
'turquoise',
'turret',
'turtle',
'turtles',
'turvey',
'tusk',
'tusked',
'tut',
'tutorial',
'tutorials',
'tutu',
'tutupia',
'tuxedo',
'tuxedos',
'tv',
"tv's",
'tvs',
'twain',
'tweebs',
'tweedlebee',
'tweedleberry',
'tweedleblabber',
'tweedlebocker',
'tweedleboing',
'tweedleboom',
'tweedlebounce',
'tweedlebouncer',
'tweedlebrains',
'tweedlebubble',
'tweedlebumble',
'tweedlebump',
'tweedlebumper',
'tweedleburger',
'tweedlechomp',
'tweedlecorn',
'tweedlecrash',
'tweedlecrumbs',
'tweedlecrump',
'tweedlecrunch',
'tweedledee',
'tweedledoodle',
'tweedledorf',
'tweedledum',
'tweedleface',
'tweedlefidget',
'tweedlefink',
'tweedlefish',
'tweedleflap',
'tweedleflapper',
'tweedleflinger',
'tweedleflip',
'tweedleflipper',
'tweedlefoot',
'tweedlefuddy',
'tweedlefussen',
'tweedlegadget',
'tweedlegargle',
'tweedlegloop',
'tweedleglop',
'tweedlegoober',
'tweedlegoose',
'tweedlegrooven',
'tweedlehoffer',
'tweedlehopper',
'tweedlejinks',
'tweedleklunk',
'tweedleknees',
'tweedlemarble',
'tweedlemash',
'tweedlemonkey',
'tweedlemooch',
'tweedlemouth',
'tweedlemuddle',
'tweedlemuffin',
'tweedlemush',
'tweedlenerd',
'tweedlenoodle',
'tweedlenose',
'tweedlenugget',
'tweedlephew',
'tweedlephooey',
'tweedlepocket',
'tweedlepoof',
'tweedlepop',
'tweedlepounce',
'tweedlepow',
'tweedlepretzel',
'tweedlequack',
'tweedleroni',
'tweedlescooter',
'tweedlescreech',
'tweedlesmirk',
'tweedlesnooker',
'tweedlesnoop',
'tweedlesnout',
'tweedlesocks',
'tweedlespeed',
'tweedlespinner',
'tweedlesplat',
'tweedlesprinkles',
'tweedlesticks',
'tweedlestink',
'tweedleswirl',
'tweedleteeth',
'tweedlethud',
'tweedletoes',
'tweedleton',
'tweedletoon',
'tweedletooth',
'tweedletwist',
'tweedlewhatsit',
'tweedlewhip',
'tweedlewig',
'tweedlewoof',
'tweedlezaner',
'tweedlezap',
'tweedlezapper',
'tweedlezilla',
'tweedlezoom',
'tween',
'tweet',
'tweety',
'twelve',
'twenty',
'twice',
'twiddlebee',
'twiddleberry',
'twiddleblabber',
'twiddlebocker',
'twiddleboing',
'twiddleboom',
'twiddlebounce',
'twiddlebouncer',
'twiddlebrains',
'twiddlebubble',
'twiddlebumble',
'twiddlebump',
'twiddlebumper',
'twiddleburger',
'twiddlechomp',
'twiddlecorn',
'twiddlecrash',
'twiddlecrumbs',
'twiddlecrump',
'twiddlecrunch',
'twiddledoodle',
'twiddledorf',
'twiddleface',
'twiddlefidget',
'twiddlefink',
'twiddlefish',
'twiddleflap',
'twiddleflapper',
'twiddleflinger',
'twiddleflip',
'twiddleflipper',
'twiddlefoot',
'twiddlefuddy',
'twiddlefussen',
'twiddlegadget',
'twiddlegargle',
'twiddlegloop',
'twiddleglop',
'twiddlegoober',
'twiddlegoose',
'twiddlegrooven',
'twiddlehoffer',
'twiddlehopper',
'twiddlejinks',
'twiddleklunk',
'twiddleknees',
'twiddlemarble',
'twiddlemash',
'twiddlemonkey',
'twiddlemooch',
'twiddlemouth',
'twiddlemuddle',
'twiddlemuffin',
'twiddlemush',
'twiddlenerd',
'twiddlenoodle',
'twiddlenose',
'twiddlenugget',
'twiddlephew',
'twiddlephooey',
'twiddlepocket',
'twiddlepoof',
'twiddlepop',
'twiddlepounce',
'twiddlepow',
'twiddlepretzel',
'twiddlequack',
'twiddleroni',
'twiddlescooter',
'twiddlescreech',
'twiddlesmirk',
'twiddlesnooker',
'twiddlesnoop',
'twiddlesnout',
'twiddlesocks',
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'twiddlespinner',
'twiddlesplat',
'twiddlesprinkles',
'twiddlesticks',
'twiddlestink',
'twiddleswirl',
'twiddleteeth',
'twiddlethud',
'twiddletoes',
'twiddleton',
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'twiddletooth',
'twiddletwist',
'twiddlewhatsit',
'twiddlewhip',
'twiddlewig',
'twiddlewoof',
'twiddlezaner',
'twiddlezap',
'twiddlezapper',
'twiddlezilla',
'twiddlezoom',
'twig',
'twiggys',
'twight',
'twighty',
'twigs',
'twilight',
'twilightclan',
'twill',
'twin',
"twin's",
'twinkle',
'twinklebee',
'twinkleberry',
'twinkleblabber',
'twinklebocker',
'twinkleboing',
'twinkleboom',
'twinklebounce',
'twinklebouncer',
'twinklebrains',
'twinklebubble',
'twinklebumble',
'twinklebump',
'twinklebumper',
'twinkleburger',
'twinklechomp',
'twinklecorn',
'twinklecrash',
'twinklecrumbs',
'twinklecrump',
'twinklecrunch',
'twinkledoodle',
'twinkledorf',
'twinkleface',
'twinklefidget',
'twinklefink',
'twinklefish',
'twinkleflap',
'twinkleflapper',
'twinkleflinger',
'twinkleflip',
'twinkleflipper',
'twinklefoot',
'twinklefuddy',
'twinklefussen',
'twinklegadget',
'twinklegargle',
'twinklegloop',
'twinkleglop',
'twinklegoober',
'twinklegoose',
'twinklegrooven',
'twinklehoffer',
'twinklehopper',
'twinklejinks',
'twinkleklunk',
'twinkleknees',
'twinklemarble',
'twinklemash',
'twinklemonkey',
'twinklemooch',
'twinklemouth',
'twinklemuddle',
'twinklemuffin',
'twinklemush',
'twinklenerd',
'twinklenoodle',
'twinklenose',
'twinklenugget',
'twinklephew',
'twinklephooey',
'twinklepocket',
'twinklepoof',
'twinklepop',
'twinklepounce',
'twinklepow',
'twinklepretzel',
'twinklequack',
'twinkleroni',
'twinklescooter',
'twinklescreech',
'twinklesmirk',
'twinklesnooker',
'twinklesnoop',
'twinklesnout',
'twinklesocks',
'twinklespeed',
'twinklespinner',
'twinklesplat',
'twinklesprinkles',
'twinklesticks',
'twinklestink',
'twinkleswirl',
'twinkleteeth',
'twinklethud',
'twinkletoes',
'twinkleton',
'twinkletoon',
'twinkletooth',
'twinkletwist',
'twinklewhatsit',
'twinklewhip',
'twinklewig',
'twinklewoof',
'twinklezaner',
'twinklezap',
'twinklezapper',
'twinklezilla',
'twinklezoom',
'twinkling',
'twins',
'twire',
"twire's",
'twirl',
'twirls',
'twirly',
'twist',
'twistable',
'twisted',
'twister',
'twisters',
'twisting',
'twists',
'twisty',
'twitch',
'twitter',
'twizzler',
'twizzlers',
'two',
'two-face',
'txt',
'ty',
'tycho',
'tycoon',
'tyler',
'tyme',
'type',
"type's",
'typea',
'typed',
'typer',
'types',
'typhlosion',
'typhoon',
'typical',
'typing',
'typo',
'tyra',
'tyranitar',
'tyrannosaurus',
'tyranny',
'tyrant',
'tyrants',
'tyrogue',
'tyrone',
'tyrus',
'tysm',
'tyty',
'tyvm',
'u',
'uber',
'uberdog',
'ubuntu',
'uchiha',
'ues',
'ufo',
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'uhh',
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'umbrella',
'umbrellas',
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'uncopyrightable',
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'undea',
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'unger',
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'unhappiness',
'unhappy',
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'unicorn',
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'unicycle',
'unification',
'uniform',
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'uninteresting',
'union',
'unique',
'uniques',
'unit',
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'unites',
'unity',
'universal',
'universe',
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'unless',
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'unlocking',
'unlocks',
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'unstuck',
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'until',
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'untouchable',
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'untruth',
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'unusually',
'unwritten',
'up',
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'upd8',
'update',
'updated',
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'uploaded',
'uploading',
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'uptick',
'ur',
'urban',
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'ursaring',
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"ursula's",
'ursulas',
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'usa',
'usable',
'use',
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'user',
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'using',
'usual',
'usually',
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'utopian',
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'uway',
'v-8',
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'v.p.',
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'v2',
'v2.0',
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'vacation',
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'vachilles',
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"valentine's",
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"valentoon's",
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'vastest',
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'veer',
'vegas',
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'vegetables',
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'veggie',
'veggies',
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'vehicle',
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'venomoth',
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'venusaur',
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'verbs',
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'vermin',
'vermont',
'vern',
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'version',
'versions',
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'very',
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'vessels',
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'vests',
'vet',
'veteran',
'veterans',
'veterinarian',
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'vexation',
'via',
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"victory's",
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'vietnam',
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'viewed',
'viewer',
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'vikings',
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'vilakroma',
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"village's",
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'volleyball',
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"vp's",
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'vr',
'vriska',
'vroom',
'vs',
'vs.',
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'vulpix',
'vulture',
'vultures',
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'w00t',
'w8',
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'wa-pa-pa-pa-pa-pa-pow',
'wacky',
'wackybee',
'wackyberry',
'wackyblabber',
'wackybocker',
'wackyboing',
'wackyboom',
'wackybounce',
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'wackybrains',
'wackybubble',
'wackybumble',
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'wackybumper',
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'wackycorn',
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"wagerin's",
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'walked',
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'walking',
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"wall's",
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'walled',
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"walt's",
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"warship's",
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"warships'",
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"waterfall's",
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"waterslide's",
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"way's",
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'wayne',
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'wayward',
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'wb',
'wbu',
"wdig's",
'wdw',
'we',
"we'd",
"we'll",
"we're",
"we've",
'we-evil',
'weak',
'weaken',
'weakens',
'weaker',
'weakest',
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'weakness',
'wealthy',
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'wearenumberone',
'wearenumberonebuteveryoneisaltisbeingdown',
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'wearing',
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'weaselberry',
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'weaselboing',
'weaselboom',
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'weaselbumble',
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'weaselbumper',
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'weaselchomp',
'weaselcorn',
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'weaselcrumbs',
'weaselcrump',
'weaselcrunch',
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'weaselfish',
'weaselflap',
'weaselflapper',
'weaselflinger',
'weaselflip',
'weaselflipper',
'weaselfoot',
'weaselfuddy',
'weaselfussen',
'weaselgadget',
'weaselgargle',
'weaselgloop',
'weaselglop',
'weaselgoober',
'weaselgoose',
'weaselgrooven',
'weaselhoffer',
'weaselhopper',
'weaseljinks',
'weaselklunk',
'weaselknees',
'weaselmarble',
'weaselmash',
'weaselmonkey',
'weaselmooch',
'weaselmouth',
'weaselmuddle',
'weaselmuffin',
'weaselmush',
'weaselnerd',
'weaselnoodle',
'weaselnose',
'weaselnugget',
'weaselphew',
'weaselphooey',
'weaselpocket',
'weaselpoof',
'weaselpop',
'weaselpounce',
'weaselpow',
'weaselpretzel',
'weaselquack',
'weaselroni',
'weasels',
'weaselscooter',
'weaselscreech',
'weaselsmirk',
'weaselsnooker',
'weaselsnoop',
'weaselsnout',
'weaselsocks',
'weaselspeed',
'weaselspinner',
'weaselsplat',
'weaselsprinkles',
'weaselsticks',
'weaselstink',
'weaselswirl',
'weaselteeth',
'weaselthud',
'weaseltoes',
'weaselton',
'weaseltoon',
'weaseltooth',
'weaseltwist',
'weaselwhatsit',
'weaselwhip',
'weaselwig',
'weaselwoof',
'weaselzaner',
'weaselzap',
'weaselzapper',
'weaselzilla',
'weaselzoom',
'weather',
'weathered',
'weatherer',
'weathering',
'weatherly',
'weathers',
'weave',
'weaves',
'weaving',
'web',
'webber',
'webepirates',
'websight',
'website',
'webster',
'wedding',
'weddings',
'wednesday',
'wednesdays',
'weeds',
'week',
"week's",
'weekdays',
'weekend',
'weekenders',
'weekly',
'weeks',
'weepinbell',
'weezing',
'wego',
'wei',
'weigh',
'weight',
'weights',
'weird',
'weirded',
'weirdings',
'weirdness',
'weirdo',
'weirdos',
'weiss',
'welch',
'welcome',
'welcomed',
'welcomely',
'welcomer',
'welcomes',
'welcoming',
'well',
'welled',
'welling',
'wells',
'welp',
'wenchs',
'went',
'were',
"weren't",
'werent',
'west',
'wester',
'western',
'westerner',
'westerners',
'westing',
'westly',
'westward',
'wet',
'wew',
'wha',
'whaddya',
'whale',
'whalebone',
'whaler',
'whales',
'whaling',
'wham',
'whammo',
'what',
"what'cha",
"what's",
'what-in',
'what-the-hey',
'whatcha',
'whatever',
"whatever's",
'whats',
'wheat',
'whee',
'wheee',
'wheeee',
'wheeeee',
'wheeeeee',
'wheeeeeee',
'wheel',
'wheelbarrow',
'wheelbarrows',
'wheeled',
'wheeler',
'wheelers',
'wheeling',
'wheelings',
'wheels',
'wheezer',
'when',
"when's",
'whenever',
'whenisaygo',
'whens',
'where',
"where's",
'wheres',
'wherever',
'whether',
'whew',
'which',
'whiff',
'whiffle',
'whiffs',
'while',
'whiled',
'whiles',
'whiling',
'whimsical',
'whimsy',
'whining',
'whinnie',
"whinnie's",
'whiny',
'whiplash',
'whippersnapper',
'whirl',
'whirligig',
'whirling',
'whirlpool',
'whirlpools',
'whirly',
'whirr',
'whisk',
'whisked',
'whisker',
'whiskerbee',
'whiskerberry',
'whiskerblabber',
'whiskerbocker',
'whiskerboing',
'whiskerboom',
'whiskerbounce',
'whiskerbouncer',
'whiskerbrains',
'whiskerbubble',
'whiskerbumble',
'whiskerbump',
'whiskerbumper',
'whiskerburger',
'whiskerchomp',
'whiskercorn',
'whiskercrash',
'whiskercrumbs',
'whiskercrump',
'whiskercrunch',
'whiskerdoodle',
'whiskerdorf',
'whiskerface',
'whiskerfidget',
'whiskerfink',
'whiskerfish',
'whiskerflap',
'whiskerflapper',
'whiskerflinger',
'whiskerflip',
'whiskerflipper',
'whiskerfoot',
'whiskerfuddy',
'whiskerfussen',
'whiskergadget',
'whiskergargle',
'whiskergloop',
'whiskerglop',
'whiskergoober',
'whiskergoose',
'whiskergrooven',
'whiskerhoffer',
'whiskerhopper',
'whiskerjinks',
'whiskerklunk',
'whiskerknees',
'whiskermarble',
'whiskermash',
'whiskermonkey',
'whiskermooch',
'whiskermouth',
'whiskermuddle',
'whiskermuffin',
'whiskermush',
'whiskernerd',
'whiskernoodle',
'whiskernose',
'whiskernugget',
'whiskerphew',
'whiskerphooey',
'whiskerpocket',
'whiskerpoof',
'whiskerpop',
'whiskerpounce',
'whiskerpow',
'whiskerpretzel',
'whiskerquack',
'whiskerroni',
'whiskers',
'whiskerscooter',
'whiskerscreech',
'whiskersmirk',
'whiskersnooker',
'whiskersnoop',
'whiskersnout',
'whiskersocks',
'whiskerspeed',
'whiskerspinner',
'whiskersplat',
'whiskersprinkles',
'whiskersticks',
'whiskerstink',
'whiskerswirl',
'whiskerteeth',
'whiskerthud',
'whiskertoes',
'whiskerton',
'whiskertoon',
'whiskertooth',
'whiskertwist',
'whiskerwhatsit',
'whiskerwhip',
'whiskerwig',
'whiskerwoof',
'whiskerzaner',
'whiskerzap',
'whiskerzapper',
'whiskerzilla',
'whiskerzoom',
'whisper',
'whispered',
'whispering',
'whispers',
'whistle',
'whistlebee',
'whistleberry',
'whistleblabber',
'whistlebocker',
'whistleboing',
'whistleboom',
'whistlebounce',
'whistlebouncer',
'whistlebrains',
'whistlebubble',
'whistlebumble',
'whistlebump',
'whistlebumper',
'whistleburger',
'whistlechomp',
'whistlecorn',
'whistlecrash',
'whistlecrumbs',
'whistlecrump',
'whistlecrunch',
'whistled',
'whistledoodle',
'whistledorf',
'whistleface',
'whistlefidget',
'whistlefink',
'whistlefish',
'whistleflap',
'whistleflapper',
'whistleflinger',
'whistleflip',
'whistleflipper',
'whistlefoot',
'whistlefuddy',
'whistlefussen',
'whistlegadget',
'whistlegargle',
'whistlegloop',
'whistleglop',
'whistlegoober',
'whistlegoose',
'whistlegrooven',
'whistlehoffer',
'whistlehopper',
'whistlejinks',
'whistleklunk',
'whistleknees',
'whistlemarble',
'whistlemash',
'whistlemonkey',
'whistlemooch',
'whistlemouth',
'whistlemuddle',
'whistlemuffin',
'whistlemush',
'whistlenerd',
'whistlenoodle',
'whistlenose',
'whistlenugget',
'whistlephew',
'whistlephooey',
'whistlepocket',
'whistlepoof',
'whistlepop',
'whistlepounce',
'whistlepow',
'whistlepretzel',
'whistlequack',
"whistler's",
'whistleroni',
'whistles',
'whistlescooter',
'whistlescreech',
'whistlesmirk',
'whistlesnooker',
'whistlesnoop',
'whistlesnout',
'whistlesocks',
'whistlespeed',
'whistlespinner',
'whistlesplat',
'whistlesprinkles',
'whistlesticks',
'whistlestink',
'whistleswirl',
'whistleteeth',
'whistlethud',
'whistletoes',
'whistleton',
'whistletoon',
'whistletooth',
'whistletwist',
'whistlewhatsit',
'whistlewhip',
'whistlewig',
'whistlewoof',
'whistlezaner',
'whistlezap',
'whistlezapper',
'whistlezilla',
'whistlezoom',
'whistling',
'white',
"white's",
'whiteboard',
'whiteboards',
'whitelist',
'whitelisted',
'whitelisting',
'whitening',
'whitestar',
'whitewater',
'who',
"who'd",
"who's",
'whoa',
'whoah',
'whodunit',
'whoever',
'whoframedrogerrabbit',
'whogryps',
'whole',
'wholly',
'whom',
'whooo',
'whoop',
'whoopee',
'whoopie',
'whoops',
'whoopsie',
'whoosh',
'whopper',
'whopping',
'whos',
'whose',
'why',
'wicked',
'wicker',
'wide',
'widely',
'wider',
'widescreen',
'widest',
'widget',
'widgets',
'widow',
'width',
'wife',
'wiffle',
'wifi',
'wig',
'wigged',
'wiggle',
"wiggle's",
'wiggles',
'wigglytuff',
'wigs',
'wii',
'wiidburns',
'wikipedia',
'wilbur',
'wild',
'wild-n-crazy',
'wild7',
'wildbee',
'wildberry',
'wildblabber',
'wildbocker',
'wildboing',
'wildboom',
'wildbounce',
'wildbouncer',
'wildbrains',
'wildbubble',
'wildbumble',
'wildbump',
'wildbumper',
'wildburger',
'wildburns',
'wildcat',
'wildcats',
'wildchomp',
'wildcorn',
'wildcrash',
'wildcrumbs',
'wildcrump',
'wildcrunch',
'wilddoodle',
'wilddorf',
'wilder',
'wilderness',
'wildest',
'wildface',
'wildfidget',
'wildfink',
'wildfire',
'wildfish',
'wildflap',
'wildflapper',
'wildflinger',
'wildflip',
'wildflipper',
'wildfoot',
'wildfuddy',
'wildfussen',
'wildgadget',
'wildgargle',
'wildgloop',
'wildglop',
'wildgoober',
'wildgoose',
'wildgrooven',
'wildhoffer',
'wildhopper',
'wilding',
'wildjinks',
'wildklunk',
'wildknees',
'wildly',
'wildmarble',
'wildmash',
'wildmonkey',
'wildmooch',
'wildmouth',
'wildmuddle',
'wildmuffin',
'wildmush',
'wildnerd',
'wildnoodle',
'wildnose',
'wildnugget',
'wildphew',
'wildphooey',
'wildpocket',
'wildpoof',
'wildpop',
'wildpounce',
'wildpow',
'wildpretzel',
'wildquack',
'wildroni',
'wildscooter',
'wildscreech',
'wildsmirk',
'wildsnooker',
'wildsnoop',
'wildsnout',
'wildsocks',
'wildspeed',
'wildspinner',
'wildsplat',
'wildsprinkles',
'wildsticks',
'wildstink',
'wildswirl',
'wildteeth',
'wildthud',
'wildtoes',
'wildton',
'wildtoon',
'wildtooth',
'wildtwist',
'wildwhatsit',
'wildwhip',
'wildwig',
'wildwoods',
'wildwoof',
'wildzaner',
'wildzap',
'wildzapper',
'wildzilla',
'wildzoom',
'will',
'willa',
"willa's",
'willas',
'willed',
'willer',
'william',
'williams',
'willing',
'willings',
'willow',
'willows',
'willpower',
'wills',
'wilma',
'wilt',
'wilts',
'wimbleweather',
'win',
'winba',
'winbus',
'wind',
'wind-racer',
'windburn',
'windcatcher',
'winded',
'winder',
'winders',
'winding',
'windjammer',
'windjammers',
'windmane',
'windmill',
'windmills',
'windora',
'window',
"window's",
'windowed',
'windowing',
'windows',
'winds',
'windshadow',
'windsor',
'windswept',
'windward',
'windy',
'wing',
'wing-washing',
'winged',
'winger',
'wingers',
'winging',
'wings',
'wingtip',
'wingtips',
'wink',
'winkination',
'winkle',
"winkle's",
'winks',
'winky',
'winn',
'winner',
"winner's",
'winners',
'winnie',
"winnie's",
'winning',
'winnings',
'wins',
'winter',
"winter's",
'wintered',
'winterer',
'wintering',
'winterly',
'winters',
'wipeout',
'wire',
'wireframe',
'wireframer',
'wireframes',
'wires',
'wisconsin',
'wisdom',
'wise',
'wiseacre',
"wiseacre's",
'wiseacres',
'wisely',
'wish',
'wished',
'wisher',
'wishers',
'wishes',
'wishing',
'wispa',
'wit',
'witch',
"witch's",
'witches',
'witching',
'witchy',
'with',
'withdrawal',
'wither',
'withers',
'within',
'without',
'witness',
'witnessed',
'witnesses',
'witnessing',
'witty',
'wittybee',
'wittyberry',
'wittyblabber',
'wittybocker',
'wittyboing',
'wittyboom',
'wittybounce',
'wittybouncer',
'wittybrains',
'wittybubble',
'wittybumble',
'wittybump',
'wittybumper',
'wittyburger',
'wittychomp',
'wittycorn',
'wittycrash',
'wittycrumbs',
'wittycrump',
'wittycrunch',
'wittydoodle',
'wittydorf',
'wittyface',
'wittyfidget',
'wittyfink',
'wittyfish',
'wittyflap',
'wittyflapper',
'wittyflinger',
'wittyflip',
'wittyflipper',
'wittyfoot',
'wittyfuddy',
'wittyfussen',
'wittygadget',
'wittygargle',
'wittygloop',
'wittyglop',
'wittygoober',
'wittygoose',
'wittygrooven',
'wittyhoffer',
'wittyhopper',
'wittyjinks',
'wittyklunk',
'wittyknees',
'wittymarble',
'wittymash',
'wittymonkey',
'wittymooch',
'wittymouth',
'wittymuddle',
'wittymuffin',
'wittymush',
'wittynerd',
'wittynoodle',
'wittynose',
'wittynugget',
'wittyphew',
'wittyphooey',
'wittypocket',
'wittypoof',
'wittypop',
'wittypounce',
'wittypow',
'wittypretzel',
'wittyquack',
'wittyroni',
'wittyscooter',
'wittyscreech',
'wittysmirk',
'wittysnooker',
'wittysnoop',
'wittysnout',
'wittysocks',
'wittyspeed',
'wittyspinner',
'wittysplat',
'wittysprinkles',
'wittysticks',
'wittystink',
'wittyswirl',
'wittyteeth',
'wittythud',
'wittytoes',
'wittyton',
'wittytoon',
'wittytooth',
'wittytwist',
'wittywhatsit',
'wittywhip',
'wittywig',
'wittywoof',
'wittyzaner',
'wittyzap',
'wittyzapper',
'wittyzilla',
'wittyzoom',
'wiz',
'wizard',
"wizard's",
'wizards',
'wizrd',
'wo',
'woah',
'wobble',
'wobbled',
'wobbles',
'wobbling',
'wobbly',
'wobbuffet',
'wocka',
'woe',
'woeful',
'wok',
'woke',
'woks',
'wolf',
"wolf's",
'wolfbane',
'wolfe',
'wolfer',
'wolfes',
'wolfhearts',
'wolfie',
'wolfpack',
'wolfs',
'wolfsbane',
'wolfy',
'wolves',
'woman',
'women',
'womp',
'won',
"won't",
'wonder',
'wonderbee',
'wonderberry',
'wonderblabber',
'wonderblue',
'wonderbocker',
'wonderboing',
'wonderboom',
'wonderbounce',
'wonderbouncer',
'wonderbrains',
'wonderbubble',
'wonderbumble',
'wonderbump',
'wonderbumper',
'wonderburger',
'wonderchomp',
'wondercorn',
'wondercrash',
'wondercrumbs',
'wondercrump',
'wondercrunch',
'wonderdoodle',
'wonderdorf',
'wondered',
'wonderer',
'wonderers',
'wonderface',
'wonderfidget',
'wonderfink',
'wonderfish',
'wonderflap',
'wonderflapper',
'wonderflinger',
'wonderflip',
'wonderflipper',
'wonderfoot',
'wonderfuddy',
'wonderful',
'wonderfully',
'wonderfussen',
'wondergadget',
'wondergargle',
'wondergloop',
'wonderglop',
'wondergoober',
'wondergoose',
'wondergrooven',
'wonderhoffer',
'wonderhopper',
'wondering',
'wonderings',
'wonderjinks',
'wonderklunk',
'wonderknees',
'wonderland',
"wonderland's",
'wonderlands',
'wondermarble',
'wondermash',
'wondermonkey',
'wondermooch',
'wondermouth',
'wondermuddle',
'wondermuffin',
'wondermush',
'wondernerd',
'wondernoodle',
'wondernose',
'wondernugget',
'wonderphew',
'wonderphooey',
'wonderpocket',
'wonderpoof',
'wonderpop',
'wonderpounce',
'wonderpow',
'wonderpretzel',
'wonderquack',
'wonderroni',
'wonders',
'wonderscooter',
'wonderscreech',
'wondersmirk',
'wondersnooker',
'wondersnoop',
'wondersnout',
'wondersocks',
'wonderspeed',
'wonderspinner',
'wondersplat',
'wondersprinkles',
'wondersticks',
'wonderstink',
'wonderswirl',
'wonderteeth',
'wonderthud',
'wondertoes',
'wonderton',
'wondertoon',
'wondertooth',
'wondertwist',
'wonderwhatsit',
'wonderwhip',
'wonderwig',
'wonderwoof',
'wonderzaner',
'wonderzap',
'wonderzapper',
'wonderzilla',
'wonderzoom',
'wondrous',
'wont',
'woo',
'wood',
'wooded',
'woodland',
'woodruff',
'woods',
'woodwashere',
'woof',
'woohoo',
'woop',
'wooper',
'woot',
'woozy',
'word',
'word-licious',
'wordbelch',
'wordbug',
'wordburps',
'worddog',
'worded',
'wordeze',
'wordfly',
'wordglitch',
'wording',
'wordlo',
'wordmania',
'wordmeister',
'wordmist',
'wordpaths',
'words',
"words'n'stuff",
'wordseek',
'wordsmith',
'wordsmiths',
'wordstinkers',
'wordstuff',
'wordwings',
'wordworks',
'wordworms',
'woriors',
'work',
"work's",
'worked',
'worker',
'workers',
'workin',
'working',
'workings',
'workout',
'works',
'works-in-progress',
'workshop',
'workshops',
'world',
"world's",
'worlds',
'worm',
'worms',
'worn',
'worried',
'worrier',
'worriers',
'worries',
'worriors',
'worry',
'worrying',
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'worst',
'worth',
'worthing',
'worthy',
'wot',
'wough',
'would',
"would've",
'woulda',
'wouldest',
"wouldn't",
'wouldnt',
'wound',
'wound-up',
'wounded',
'wounding',
'wounds',
'woven',
'wow',
'wraith',
'wraiths',
'wrapper',
'wrath',
'wreath',
'wreathes',
'wreaths',
'wreck',
'wrecked',
'wrecking',
'wrecking-talents',
'wrecks',
'wrench',
'wrestling',
'wretch',
'wriggle',
'wright',
"wright's",
'wringling',
'wrinkle',
'wrinklebee',
'wrinkleberry',
'wrinkleblabber',
'wrinklebocker',
'wrinkleboing',
'wrinkleboom',
'wrinklebounce',
'wrinklebouncer',
'wrinklebrains',
'wrinklebubble',
'wrinklebumble',
'wrinklebump',
'wrinklebumper',
'wrinkleburger',
'wrinklechomp',
'wrinklecorn',
'wrinklecrash',
'wrinklecrumbs',
'wrinklecrump',
'wrinklecrunch',
'wrinkled',
'wrinkledoodle',
'wrinkledorf',
'wrinkleface',
'wrinklefidget',
'wrinklefink',
'wrinklefish',
'wrinkleflap',
'wrinkleflapper',
'wrinkleflinger',
'wrinkleflip',
'wrinkleflipper',
'wrinklefoot',
'wrinklefuddy',
'wrinklefussen',
'wrinklegadget',
'wrinklegargle',
'wrinklegloop',
'wrinkleglop',
'wrinklegoober',
'wrinklegoose',
'wrinklegrooven',
'wrinklehoffer',
'wrinklehopper',
'wrinklejinks',
'wrinkleklunk',
'wrinkleknees',
'wrinklemarble',
'wrinklemash',
'wrinklemonkey',
'wrinklemooch',
'wrinklemouth',
'wrinklemuddle',
'wrinklemuffin',
'wrinklemush',
'wrinklenerd',
'wrinklenoodle',
'wrinklenose',
'wrinklenugget',
'wrinklephew',
'wrinklephooey',
'wrinklepocket',
'wrinklepoof',
'wrinklepop',
'wrinklepounce',
'wrinklepow',
'wrinklepretzel',
'wrinklequack',
'wrinkleroni',
'wrinkles',
'wrinklescooter',
'wrinklescreech',
'wrinklesmirk',
'wrinklesnooker',
'wrinklesnoop',
'wrinklesnout',
'wrinklesocks',
'wrinklespeed',
'wrinklespinner',
'wrinklesplat',
'wrinklesprinkles',
'wrinklesticks',
'wrinklestink',
'wrinkleswirl',
'wrinkleteeth',
'wrinklethud',
'wrinkletoes',
'wrinkleton',
'wrinkletoon',
'wrinkletooth',
'wrinkletwist',
'wrinklewhatsit',
'wrinklewhip',
'wrinklewig',
'wrinklewoof',
'wrinklezaner',
'wrinklezap',
'wrinklezapper',
'wrinklezilla',
'wrinklezoom',
'wriot',
'write',
'writer',
'writers',
'writes',
'writing',
'writings',
'written',
'wrld',
'wrong',
'wronged',
'wronger',
'wrongest',
'wronging',
'wrongly',
'wrongs',
'wrote',
'wtg',
'wumbo',
'wumbology',
'wut',
'wwod',
"wyatt's",
'wyd',
'wyda',
'wynaut',
'wynken',
'wynn',
'wynne',
'wyoming',
'wysteria',
'x',
'x-shop',
'x-tremely',
'xanon',
'xatu',
'xavier',
'xbox',
'xd',
'xd-buy',
'xdash',
'xdeals',
'xder',
'xdibs',
'xdig',
'xdirect',
'xdoer',
'xdome',
'xdot',
'xdough',
'xdrive',
'xem',
'xenops',
"xiamen's",
'xii',
'xiii',
'xmas',
'xoxo',
'xp',
'xpedition',
'xpend',
'xpert',
'xpythonic',
'xsentials',
'xtra',
'xtraordinary',
'xtreme',
'xtremely',
'y',
"y'all",
"y'er",
'ya',
"ya'll",
'yaarrrgghh',
'yacht',
"yacht's",
'yachting',
'yachts',
'yackety-yak',
'yah',
'yalarad',
'yall',
'yang',
"yang's",
'yank',
'yankee',
'yankees',
'yanks',
'yanma',
'yanni',
'yapmme',
'yar',
'yard',
"yard's",
'yardage',
'yardarm',
'yarded',
'yarding',
'yards',
'yardwork',
'yarn',
'yarr',
'yarrow',
'yarrr',
'yas',
'yasmin',
'yasmine',
'yasss',
'yavn',
'yawn',
'yawner',
'yawning',
'yawns',
'yay',
'ye',
"ye'll",
"ye're",
"ye've",
'yea',
'yeah',
'year',
"year's",
'yearbook',
'years',
'yee',
'yee-haw',
'yeehah',
'yeehaw',
'yeet',
'yeh',
'yell',
'yelled',
'yeller',
'yelling',
'yellow',
'yellow-green',
'yellow-orange',
'yellow-shelled',
'yells',
'yelp',
'yensid',
"yensid's",
'yep',
'yeppers',
'yer',
'yerself',
'yes',
'yesbot',
'yesbots',
'yeses',
'yesman',
'yesmen',
'yess',
'yesss',
'yesterday',
'yet',
'yeti',
"yeti's",
'yetis',
'yets',
'yey',
'yield',
'yikes',
'yin',
'ying',
'yippee',
'yippie',
'yo',
'yodo',
'yoga',
'yogi',
'yogurt',
'yolo',
'yom',
'yoo',
'york',
'yoshi',
'you',
"you'd",
"you'll",
"you're",
"you've",
'youd',
'youll',
'young',
'youngster',
'your',
"your's",
'youre',
'youreawizardharry',
'yours',
'yourself',
'youth',
'youtube',
'youtuber',
'youve',
'yow',
'yowl',
'yoyo',
'yuck',
'yucks',
'yufalla',
'yuki',
"yuki's",
'yukon',
'yum',
'yummy',
'yup',
'yus',
'yw',
'yzma',
'z',
'z-fighting',
'z.z',
'z.z.z.',
'zaamaros',
'zaamaru',
'zaapi',
'zabuton',
'zabutons',
'zac',
'zach',
'zack',
"zack's",
'zamboni',
'zambonis',
'zan',
'zanes',
'zangetsu',
'zany',
'zanzibarbarians',
'zaoran',
'zap',
'zapdos',
'zapless',
'zapp',
'zari',
'zart',
'zazu',
"zazu's",
'zazus',
'zazzle',
'zealous',
'zebra',
"zebra's",
'zebras',
'zed',
'zedd',
'zeddars',
'zeke',
'zelda',
'zen',
'zenith',
'zeniths',
'zenon',
'zep',
'zephyr',
'zeppelin',
'zeragong',
"zerko's",
'zero',
'zero-gravity',
'zesty',
'zeus',
'zhilo',
'ziba',
'zigeunermusik',
'zigg',
'ziggs',
'ziggurat',
'zigguratxnaut',
'ziggy',
"ziggy's",
'zigzag',
'zillerbee',
'zillerberry',
'zillerblabber',
'zillerbocker',
'zillerboing',
'zillerboom',
'zillerbounce',
'zillerbouncer',
'zillerbrains',
'zillerbubble',
'zillerbumble',
'zillerbump',
'zillerbumper',
'zillerburger',
'zillerchomp',
'zillercorn',
'zillercrash',
'zillercrumbs',
'zillercrump',
'zillercrunch',
'zillerdoodle',
'zillerdorf',
'zillerface',
'zillerfidget',
'zillerfink',
'zillerfish',
'zillerflap',
'zillerflapper',
'zillerflinger',
'zillerflip',
'zillerflipper',
'zillerfoot',
'zillerfuddy',
'zillerfussen',
'zillergadget',
'zillergargle',
'zillergloop',
'zillerglop',
'zillergoober',
'zillergoose',
'zillergrooven',
'zillerhoffer',
'zillerhopper',
'zillerjinks',
'zillerklunk',
'zillerknees',
'zillermarble',
'zillermash',
'zillermonkey',
'zillermooch',
'zillermouth',
'zillermuddle',
'zillermuffin',
'zillermush',
'zillernerd',
'zillernoodle',
'zillernose',
'zillernugget',
'zillerphew',
'zillerphooey',
'zillerpocket',
'zillerpoof',
'zillerpop',
'zillerpounce',
'zillerpow',
'zillerpretzel',
'zillerquack',
'zillerroni',
'zillerscooter',
'zillerscreech',
'zillersmirk',
'zillersnooker',
'zillersnoop',
'zillersnout',
'zillersocks',
'zillerspeed',
'zillerspinner',
'zillersplat',
'zillersprinkles',
'zillersticks',
'zillerstink',
'zillerswirl',
'zillerteeth',
'zillerthud',
'zillertoes',
'zillerton',
'zillertoon',
'zillertooth',
'zillertwist',
'zillerwhatsit',
'zillerwhip',
'zillerwig',
'zillerwoof',
'zillerzaner',
'zillerzap',
'zillerzapper',
'zillerzilla',
'zillerzoom',
'zillion',
'zimmer',
'zing',
'zinger',
'zingers',
'zinnia',
'zinnias',
'zip-a-dee-doo-dah',
'zippenbee',
'zippenberry',
'zippenblabber',
'zippenbocker',
'zippenboing',
'zippenboom',
'zippenbounce',
'zippenbouncer',
'zippenbrains',
'zippenbubble',
'zippenbumble',
'zippenbump',
'zippenbumper',
'zippenburger',
'zippenchomp',
'zippencorn',
'zippencrash',
'zippencrumbs',
'zippencrump',
'zippencrunch',
'zippendoodle',
'zippendorf',
'zippenface',
'zippenfidget',
'zippenfink',
'zippenfish',
'zippenflap',
'zippenflapper',
'zippenflinger',
'zippenflip',
'zippenflipper',
'zippenfoot',
'zippenfuddy',
'zippenfussen',
'zippengadget',
'zippengargle',
'zippengloop',
'zippenglop',
'zippengoober',
'zippengoose',
'zippengrooven',
'zippenhoffer',
'zippenhopper',
'zippenjinks',
'zippenklunk',
'zippenknees',
'zippenmarble',
'zippenmash',
'zippenmonkey',
'zippenmooch',
'zippenmouth',
'zippenmuddle',
'zippenmuffin',
'zippenmush',
'zippennerd',
'zippennoodle',
'zippennose',
'zippennugget',
'zippenphew',
'zippenphooey',
'zippenpocket',
'zippenpoof',
'zippenpop',
'zippenpounce',
'zippenpow',
'zippenpretzel',
'zippenquack',
'zippenroni',
'zippenscooter',
'zippenscreech',
'zippensmirk',
'zippensnooker',
'zippensnoop',
'zippensnout',
'zippensocks',
'zippenspeed',
'zippenspinner',
'zippensplat',
'zippensprinkles',
'zippensticks',
'zippenstink',
'zippenswirl',
'zippenteeth',
'zippenthud',
'zippentoes',
'zippenton',
'zippentoon',
'zippentooth',
'zippentwist',
'zippenwhatsit',
'zippenwhip',
'zippenwig',
'zippenwoof',
'zippenzaner',
'zippenzap',
'zippenzapper',
'zippenzilla',
'zippenzoom',
'zippity',
'zippy',
"zippy's",
'zither',
'zithers',
'zizzle',
'zoidberg',
'zombats',
'zombie',
'zombies',
'zone',
'zoner',
'zones',
'zonk',
'zonked',
'zonks',
'zoo',
"zoo's",
'zooblebee',
'zoobleberry',
'zoobleblabber',
'zooblebocker',
'zoobleboing',
'zoobleboom',
'zooblebounce',
'zooblebouncer',
'zooblebrains',
'zooblebubble',
'zooblebumble',
'zooblebump',
'zooblebumper',
'zoobleburger',
'zooblechomp',
'zooblecorn',
'zooblecrash',
'zooblecrumbs',
'zooblecrump',
'zooblecrunch',
'zoobledoodle',
'zoobledorf',
'zoobleface',
'zooblefidget',
'zooblefink',
'zooblefish',
'zoobleflap',
'zoobleflapper',
'zoobleflinger',
'zoobleflip',
'zoobleflipper',
'zooblefoot',
'zooblefuddy',
'zooblefussen',
'zooblegadget',
'zooblegargle',
'zooblegloop',
'zoobleglop',
'zooblegoober',
'zooblegoose',
'zooblegrooven',
'zooblehoffer',
'zooblehopper',
'zooblejinks',
'zoobleklunk',
'zoobleknees',
'zooblemarble',
'zooblemash',
'zooblemonkey',
'zooblemooch',
'zooblemouth',
'zooblemuddle',
'zooblemuffin',
'zooblemush',
'zooblenerd',
'zooblenoodle',
'zooblenose',
'zooblenugget',
'zooblephew',
'zooblephooey',
'zooblepocket',
'zooblepoof',
'zooblepop',
'zooblepounce',
'zooblepow',
'zooblepretzel',
'zooblequack',
'zoobleroni',
'zooblescooter',
'zooblescreech',
'zooblesmirk',
'zooblesnooker',
'zooblesnoop',
'zooblesnout',
'zooblesocks',
'zooblespeed',
'zooblespinner',
'zooblesplat',
'zooblesprinkles',
'zooblesticks',
'zooblestink',
'zoobleswirl',
'zoobleteeth',
'zooblethud',
'zoobletoes',
'zoobleton',
'zoobletoon',
'zoobletooth',
'zoobletwist',
'zooblewhatsit',
'zooblewhip',
'zooblewig',
'zooblewoof',
'zooblezaner',
'zooblezap',
'zooblezapper',
'zooblezilla',
'zooblezoom',
'zooks',
'zoological',
'zoology',
'zoom',
'zoos',
'zoot',
'zorna',
'zorro',
'zowie',
'zoza',
'zozane',
'zozanero',
'zubat',
'zucchini',
'zulu',
'zurg',
'zuzu',
'zydeco',
'zyra',
'zyrdrake',
'zyrgazelle',
'zyyk',
'zz',
'zzz',
'zzzz',
'zzzzs',
'zzzzzs',
] | true | true |
f736b69cf3117e2bde9314a2d47c671dcdbc4263 | 1,722 | py | Python | userAuthentication/models.py | Jeremiahjacinth13/tradex | 80c0024017c35531bad4042ad199dc9dddcad54a | [
"MIT"
] | 1 | 2020-09-22T16:09:49.000Z | 2020-09-22T16:09:49.000Z | userAuthentication/models.py | Jeremiahjacinth13/tradex | 80c0024017c35531bad4042ad199dc9dddcad54a | [
"MIT"
] | null | null | null | userAuthentication/models.py | Jeremiahjacinth13/tradex | 80c0024017c35531bad4042ad199dc9dddcad54a | [
"MIT"
] | null | null | null | from django.db import models
from django.contrib.auth.models import AbstractUser
import json
# Create your models here.
class User(AbstractUser):
USERTYPE_CHOICES = (
('buyer', 'Buyer'),
('seller', 'Seller')
)
userType = models.CharField(choices=USERTYPE_CHOICES, default='buyer', max_length=9)
profile_picture = models.ImageField(blank = True, upload_to = 'profile_images', default = 'profile_images/avatar.jpg')
paypal_email_address = models.EmailField()
def getProducts(self):
if self.userType == 'buyer':
return self.cart.get().products.all()
return self.store.get().products.all()
def serialize(self):
data_to_return = {'id': self.id, 'userName': self.username, 'firstName': self.first_name, 'lastName': self.last_name, 'profilePicture': self.profile_picture.name, 'postsMade': [post.serialize() for post in self.posts.all()], 'userType': self.userType, 'accountDetails': self.account.get().serialize(), 'emailAddress': self.email, 'paypalEmail': self.paypal_email_address, 'profile': self.profile.serialize()}
if self.userType == 'buyer':
data_to_return['cart'] = self.cart.get().serialize()
else:
data_to_return['products'] = self.store.get().serialize()
return data_to_return
def __str__(self):
return self.username
class User_profile(models.Model):
user = models.OneToOneField(User, on_delete = models.CASCADE, related_name = 'profile')
bio = models.CharField(max_length = 200, default='About Me')
status = models.CharField(max_length = 60, default = 'Currently Available')
def __str__(self):
return f"{self.user} {self.status}"
def serialize(self):
return {'bio': self.bio,'status': self.status} | 41 | 412 | 0.704994 | from django.db import models
from django.contrib.auth.models import AbstractUser
import json
class User(AbstractUser):
USERTYPE_CHOICES = (
('buyer', 'Buyer'),
('seller', 'Seller')
)
userType = models.CharField(choices=USERTYPE_CHOICES, default='buyer', max_length=9)
profile_picture = models.ImageField(blank = True, upload_to = 'profile_images', default = 'profile_images/avatar.jpg')
paypal_email_address = models.EmailField()
def getProducts(self):
if self.userType == 'buyer':
return self.cart.get().products.all()
return self.store.get().products.all()
def serialize(self):
data_to_return = {'id': self.id, 'userName': self.username, 'firstName': self.first_name, 'lastName': self.last_name, 'profilePicture': self.profile_picture.name, 'postsMade': [post.serialize() for post in self.posts.all()], 'userType': self.userType, 'accountDetails': self.account.get().serialize(), 'emailAddress': self.email, 'paypalEmail': self.paypal_email_address, 'profile': self.profile.serialize()}
if self.userType == 'buyer':
data_to_return['cart'] = self.cart.get().serialize()
else:
data_to_return['products'] = self.store.get().serialize()
return data_to_return
def __str__(self):
return self.username
class User_profile(models.Model):
user = models.OneToOneField(User, on_delete = models.CASCADE, related_name = 'profile')
bio = models.CharField(max_length = 200, default='About Me')
status = models.CharField(max_length = 60, default = 'Currently Available')
def __str__(self):
return f"{self.user} {self.status}"
def serialize(self):
return {'bio': self.bio,'status': self.status} | true | true |
f736b6f61677ea8db1df2c211cae042863c5beca | 3,935 | py | Python | tests/easier68k/core/opcodes/test_subq.py | bpas247/Easier68k | 30a39883f1e73cd2bd848cf7bd356c96b8664ff4 | [
"MIT"
] | 16 | 2018-03-03T21:00:14.000Z | 2021-11-04T09:16:08.000Z | tests/easier68k/core/opcodes/test_subq.py | bpas247/Easier68k | 30a39883f1e73cd2bd848cf7bd356c96b8664ff4 | [
"MIT"
] | 99 | 2018-02-27T19:02:59.000Z | 2019-10-29T22:39:26.000Z | tests/easier68k/core/opcodes/test_subq.py | bpas247/Easier68k | 30a39883f1e73cd2bd848cf7bd356c96b8664ff4 | [
"MIT"
] | 5 | 2018-04-04T02:03:10.000Z | 2019-11-19T17:42:42.000Z | """
Test method for Sub opcode
"""
from easier68k.simulator.m68k import M68K
from easier68k.core.opcodes.subq import Subq
from easier68k.core.models.assembly_parameter import AssemblyParameter
from easier68k.core.enum.ea_mode import EAMode
from easier68k.core.enum.register import Register
from easier68k.core.enum.op_size import OpSize
from easier68k.core.models.memory_value import MemoryValue
from .test_opcode_helper import run_opcode_test
def test_subq():
"""
Test to see that it can subtract a number from another number.
Example case used:
MOVE.W #123,D0
SUBQ.W #8,D0
"""
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=123))
params = [AssemblyParameter(EAMode.IMM, 8), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.WORD) # SUBQ.W #8,D0
run_opcode_test(sim, subq, Register.D0, 0x73, [False, False, False, False, False], 2)
def test_subq_negative():
"""
Test to see that sub can handle negative values.
Example case used:
MOVE.L #-2,D2
SUBQ.L #1,D2
"""
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D2, MemoryValue(OpSize.LONG, signed_int=-2))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 2)]
subq = Subq(params, OpSize.LONG) # SUBQ.L #1,D2
run_opcode_test(sim, subq, Register.D2, 0xFFFFFFFD, [False, True, False, False, False], 2)
def test_subq_disassembles():
"""
Test to see that sub can be assembled from some input
Example case used:
MOVE.W #$123,D0
SUBQ.B #1,D0 - which results in 122
"""
data = bytearray.fromhex('5300') # SUBQ.B #1,D0
result = Subq.disassemble_instruction(data)
assert result is not None
sim = M68K()
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=0x123))
run_opcode_test(sim, result, Register.D0, 0x122, [False, False, False, False, False], 2)
def test_ccr_carry():
"""
Tests to see that the carry bit is set correctly
Example case used:
MOVE.W #$100,D0
SUBQ.B #1,D0
"""
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=0x100))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) # SUBQ.B #1,D0
run_opcode_test(sim, subq, Register.D0, 0x1FF, [True, True, False, False, True], 2)
def test_ccr_overflow():
"""
Tests to see that the overflow bit is set correctly
Example case used:
MOVE.L #-125,D0
SUBQ.B #4,D0
"""
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.LONG, signed_int=-125))
params = [AssemblyParameter(EAMode.IMM, 4), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) # SUBQ.B #4,D0
run_opcode_test(sim, subq, Register.D0, 0xFFFFFF7F, [False, False, False, True, False], 2)
def test_ccr_zero():
"""
Tests to see that the zero bit is set correctly
Example case used:
MOVE.L #1,D0
SUBQ.B #1,D0
"""
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.LONG, unsigned_int=1))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) # SUBQ.B #1,D0
run_opcode_test(sim, subq, Register.D0, 0x0, [False, False, True, False, False], 2)
def test_subq_assemble():
"""
Check that assembly is the same as the input
Example case used:
SUBQ.W #2,D1
"""
# SUBQ.W #2,D1
data = bytearray.fromhex('5541')
result = Subq.disassemble_instruction(data)
assm = result.assemble()
assert data == assm
| 23.993902 | 94 | 0.67014 |
from easier68k.simulator.m68k import M68K
from easier68k.core.opcodes.subq import Subq
from easier68k.core.models.assembly_parameter import AssemblyParameter
from easier68k.core.enum.ea_mode import EAMode
from easier68k.core.enum.register import Register
from easier68k.core.enum.op_size import OpSize
from easier68k.core.models.memory_value import MemoryValue
from .test_opcode_helper import run_opcode_test
def test_subq():
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=123))
params = [AssemblyParameter(EAMode.IMM, 8), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.WORD) run_opcode_test(sim, subq, Register.D0, 0x73, [False, False, False, False, False], 2)
def test_subq_negative():
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D2, MemoryValue(OpSize.LONG, signed_int=-2))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 2)]
subq = Subq(params, OpSize.LONG) run_opcode_test(sim, subq, Register.D2, 0xFFFFFFFD, [False, True, False, False, False], 2)
def test_subq_disassembles():
data = bytearray.fromhex('5300') result = Subq.disassemble_instruction(data)
assert result is not None
sim = M68K()
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=0x123))
run_opcode_test(sim, result, Register.D0, 0x122, [False, False, False, False, False], 2)
def test_ccr_carry():
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.WORD, unsigned_int=0x100))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) run_opcode_test(sim, subq, Register.D0, 0x1FF, [True, True, False, False, True], 2)
def test_ccr_overflow():
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.LONG, signed_int=-125))
params = [AssemblyParameter(EAMode.IMM, 4), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) run_opcode_test(sim, subq, Register.D0, 0xFFFFFF7F, [False, False, False, True, False], 2)
def test_ccr_zero():
sim = M68K()
sim.set_program_counter_value(0x1000)
sim.set_register(Register.D0, MemoryValue(OpSize.LONG, unsigned_int=1))
params = [AssemblyParameter(EAMode.IMM, 1), AssemblyParameter(EAMode.DRD, 0)]
subq = Subq(params, OpSize.BYTE) run_opcode_test(sim, subq, Register.D0, 0x0, [False, False, True, False, False], 2)
def test_subq_assemble():
data = bytearray.fromhex('5541')
result = Subq.disassemble_instruction(data)
assm = result.assemble()
assert data == assm
| true | true |
f736b6f6f60ebbb068ffdf173d88b321ee06190e | 880 | py | Python | ELAB09/09-04.py | tawanchaiii/01204111_63 | edf1174f287f5174d93729d9b5c940c74d3b6553 | [
"WTFPL"
] | null | null | null | ELAB09/09-04.py | tawanchaiii/01204111_63 | edf1174f287f5174d93729d9b5c940c74d3b6553 | [
"WTFPL"
] | null | null | null | ELAB09/09-04.py | tawanchaiii/01204111_63 | edf1174f287f5174d93729d9b5c940c74d3b6553 | [
"WTFPL"
] | null | null | null | class py_solution:
def __init__(self,L):
self.L = L
self.stack = []
self.A = ['(','[','{']
self.B = [')',']','}']
def is_valid_parentheses(self):
for i in self.L :
if i in self.A :
self.stack.append(i)
else :
if len(self.stack) == 0 : return False
else :
ind = self.re(self.stack[-1])
if (i == self.B[ind]) : self.stack.pop()
else : return False
if len(self.stack) != 0 : return False
return True
def re(self,q):
for i in range(len(self.B)):
if self.A[i] == q : return i
st = input('input: ')
ob = py_solution(st)
if ob.is_valid_parentheses() :
print("valid parentheses")
else :
print("invalid parentheses") | 31.428571 | 61 | 0.445455 | class py_solution:
def __init__(self,L):
self.L = L
self.stack = []
self.A = ['(','[','{']
self.B = [')',']','}']
def is_valid_parentheses(self):
for i in self.L :
if i in self.A :
self.stack.append(i)
else :
if len(self.stack) == 0 : return False
else :
ind = self.re(self.stack[-1])
if (i == self.B[ind]) : self.stack.pop()
else : return False
if len(self.stack) != 0 : return False
return True
def re(self,q):
for i in range(len(self.B)):
if self.A[i] == q : return i
st = input('input: ')
ob = py_solution(st)
if ob.is_valid_parentheses() :
print("valid parentheses")
else :
print("invalid parentheses") | true | true |
f736b814c11d6a2e7707ec1540b0664a45130c58 | 8,174 | py | Python | tests/test-upgrade.py | panlinux/microk8s | 500e5f48f1080cae8aab79619c8d7ae16d4111ca | [
"Apache-2.0"
] | 1 | 2020-08-09T18:58:38.000Z | 2020-08-09T18:58:38.000Z | tests/test-upgrade.py | thegirlintheroom613/microk8s | 56a91115b40378952f3b16b8bb9bd4b5ed37901c | [
"Apache-2.0"
] | 16 | 2020-08-25T17:02:09.000Z | 2022-03-02T03:03:55.000Z | tests/test-upgrade.py | thegirlintheroom613/microk8s | 56a91115b40378952f3b16b8bb9bd4b5ed37901c | [
"Apache-2.0"
] | null | null | null | import pytest
import os
import platform
import time
import requests
from validators import (
validate_dns_dashboard,
validate_storage,
validate_ingress,
validate_ambassador,
validate_gpu,
validate_registry,
validate_forward,
validate_metrics_server,
validate_prometheus,
validate_fluentd,
validate_jaeger,
validate_kubeflow,
validate_cilium,
validate_metallb_config,
validate_multus,
)
from subprocess import check_call, CalledProcessError, check_output
from utils import (
microk8s_enable,
wait_for_pod_state,
wait_for_installation,
run_until_success,
)
upgrade_from = os.environ.get('UPGRADE_MICROK8S_FROM', 'beta')
# Have UPGRADE_MICROK8S_TO point to a file to upgrade to that file
upgrade_to = os.environ.get('UPGRADE_MICROK8S_TO', 'edge')
under_time_pressure = os.environ.get('UNDER_TIME_PRESSURE', 'False')
class TestUpgrade(object):
"""
Validates a microk8s upgrade path
"""
def test_upgrade(self):
"""
Deploy, probe, upgrade, validate nothing broke.
"""
print("Testing upgrade from {} to {}".format(upgrade_from, upgrade_to))
cmd = "sudo snap install microk8s --classic --channel={}".format(upgrade_from)
run_until_success(cmd)
wait_for_installation()
if is_container():
# In some setups (eg LXC on GCE) the hashsize nf_conntrack file under
# sys is marked as rw but any update on it is failing causing kube-proxy
# to fail.
here = os.path.dirname(os.path.abspath(__file__))
apply_patch = os.path.join(here, "patch-kube-proxy.sh")
check_call("sudo {}".format(apply_patch).split())
# Run through the validators and
# select those that were valid for the original snap
test_matrix = {}
try:
enable = microk8s_enable("dns")
wait_for_pod_state("", "kube-system", "running", label="k8s-app=kube-dns")
assert "Nothing to do for" not in enable
enable = microk8s_enable("dashboard")
assert "Nothing to do for" not in enable
validate_dns_dashboard()
test_matrix['dns_dashboard'] = validate_dns_dashboard
except:
print('Will not test dns-dashboard')
try:
enable = microk8s_enable("storage")
assert "Nothing to do for" not in enable
validate_storage()
test_matrix['storage'] = validate_storage
except:
print('Will not test storage')
try:
enable = microk8s_enable("ingress")
assert "Nothing to do for" not in enable
validate_ingress()
test_matrix['ingress'] = validate_ingress
except:
print('Will not test ingress')
try:
enable = microk8s_enable("gpu")
assert "Nothing to do for" not in enable
validate_gpu()
test_matrix['gpu'] = validate_gpu
except:
print('Will not test gpu')
try:
enable = microk8s_enable("registry")
assert "Nothing to do for" not in enable
validate_registry()
test_matrix['registry'] = validate_registry
except:
print('Will not test registry')
try:
validate_forward()
test_matrix['forward'] = validate_forward
except:
print('Will not test port forward')
try:
enable = microk8s_enable("metrics-server")
assert "Nothing to do for" not in enable
validate_metrics_server()
test_matrix['metrics_server'] = validate_metrics_server
except:
print('Will not test the metrics server')
# AMD64 only tests
if platform.machine() == 'x86_64' and under_time_pressure == 'False':
'''
# Prometheus operator on our lxc is chashlooping disabling the test for now.
try:
enable = microk8s_enable("prometheus", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_prometheus()
test_matrix['prometheus'] = validate_prometheus
except:
print('Will not test the prometheus')
# The kubeflow deployment is huge. It will not fit comfortably
# with the rest of the addons on the same machine during an upgrade
# we will need to find another way to test it.
try:
enable = microk8s_enable("kubeflow", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_kubeflow()
test_matrix['kubeflow'] = validate_kubeflow
except:
print('Will not test kubeflow')
'''
try:
enable = microk8s_enable("fluentd", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_fluentd()
test_matrix['fluentd'] = validate_fluentd
except:
print('Will not test the fluentd')
try:
enable = microk8s_enable("jaeger", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_jaeger()
test_matrix['jaeger'] = validate_jaeger
except:
print('Will not test the jaeger addon')
try:
enable = microk8s_enable("cilium", timeout_insec=300)
assert "Nothing to do for" not in enable
validate_cilium()
test_matrix['cilium'] = validate_cilium
except:
print('Will not test the cilium addon')
try:
ip_ranges = (
"192.168.0.105-192.168.0.105,192.168.0.110-192.168.0.111,192.168.1.240/28"
)
enable = microk8s_enable("{}:{}".format("metallb", ip_ranges), timeout_insec=500)
assert "MetalLB is enabled" in enable and "Nothing to do for" not in enable
validate_metallb_config(ip_ranges)
test_matrix['metallb'] = validate_metallb_config
except:
print("Will not test the metallb addon")
try:
enable = microk8s_enable("multus", timeout_insec=150)
assert "Nothing to do for" not in enable
validate_multus()
test_matrix['multus'] = validate_multus
except:
print('Will not test the multus addon')
# Refresh the snap to the target
if upgrade_to.endswith('.snap'):
cmd = "sudo snap install {} --classic --dangerous".format(upgrade_to)
else:
cmd = "sudo snap refresh microk8s --channel={}".format(upgrade_to)
run_until_success(cmd)
# Allow for the refresh to be processed
time.sleep(10)
wait_for_installation()
# Test any validations that were valid for the original snap
for test, validation in test_matrix.items():
print("Testing {}".format(test))
validation()
if not is_container():
# On lxc umount docker overlay is not permitted.
check_call("sudo snap remove microk8s".split())
def is_container():
'''
Returns: True if the deployment is in a VM/container.
'''
try:
if os.path.isdir('/run/systemd/system'):
container = check_output('sudo systemd-detect-virt --container'.split())
print("Tests are running in {}".format(container))
return True
except CalledProcessError:
print("systemd-detect-virt did not detect a container")
if os.path.exists('/run/container_type'):
return True
try:
check_call("sudo grep -E (lxc|hypervisor) /proc/1/environ /proc/cpuinfo".split())
print("Tests are running in an undetectable container")
return True
except CalledProcessError:
print("no indication of a container in /proc")
return False
| 35.53913 | 97 | 0.589308 | import pytest
import os
import platform
import time
import requests
from validators import (
validate_dns_dashboard,
validate_storage,
validate_ingress,
validate_ambassador,
validate_gpu,
validate_registry,
validate_forward,
validate_metrics_server,
validate_prometheus,
validate_fluentd,
validate_jaeger,
validate_kubeflow,
validate_cilium,
validate_metallb_config,
validate_multus,
)
from subprocess import check_call, CalledProcessError, check_output
from utils import (
microk8s_enable,
wait_for_pod_state,
wait_for_installation,
run_until_success,
)
upgrade_from = os.environ.get('UPGRADE_MICROK8S_FROM', 'beta')
upgrade_to = os.environ.get('UPGRADE_MICROK8S_TO', 'edge')
under_time_pressure = os.environ.get('UNDER_TIME_PRESSURE', 'False')
class TestUpgrade(object):
def test_upgrade(self):
print("Testing upgrade from {} to {}".format(upgrade_from, upgrade_to))
cmd = "sudo snap install microk8s --classic --channel={}".format(upgrade_from)
run_until_success(cmd)
wait_for_installation()
if is_container():
here = os.path.dirname(os.path.abspath(__file__))
apply_patch = os.path.join(here, "patch-kube-proxy.sh")
check_call("sudo {}".format(apply_patch).split())
test_matrix = {}
try:
enable = microk8s_enable("dns")
wait_for_pod_state("", "kube-system", "running", label="k8s-app=kube-dns")
assert "Nothing to do for" not in enable
enable = microk8s_enable("dashboard")
assert "Nothing to do for" not in enable
validate_dns_dashboard()
test_matrix['dns_dashboard'] = validate_dns_dashboard
except:
print('Will not test dns-dashboard')
try:
enable = microk8s_enable("storage")
assert "Nothing to do for" not in enable
validate_storage()
test_matrix['storage'] = validate_storage
except:
print('Will not test storage')
try:
enable = microk8s_enable("ingress")
assert "Nothing to do for" not in enable
validate_ingress()
test_matrix['ingress'] = validate_ingress
except:
print('Will not test ingress')
try:
enable = microk8s_enable("gpu")
assert "Nothing to do for" not in enable
validate_gpu()
test_matrix['gpu'] = validate_gpu
except:
print('Will not test gpu')
try:
enable = microk8s_enable("registry")
assert "Nothing to do for" not in enable
validate_registry()
test_matrix['registry'] = validate_registry
except:
print('Will not test registry')
try:
validate_forward()
test_matrix['forward'] = validate_forward
except:
print('Will not test port forward')
try:
enable = microk8s_enable("metrics-server")
assert "Nothing to do for" not in enable
validate_metrics_server()
test_matrix['metrics_server'] = validate_metrics_server
except:
print('Will not test the metrics server')
if platform.machine() == 'x86_64' and under_time_pressure == 'False':
try:
enable = microk8s_enable("fluentd", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_fluentd()
test_matrix['fluentd'] = validate_fluentd
except:
print('Will not test the fluentd')
try:
enable = microk8s_enable("jaeger", timeout_insec=30)
assert "Nothing to do for" not in enable
validate_jaeger()
test_matrix['jaeger'] = validate_jaeger
except:
print('Will not test the jaeger addon')
try:
enable = microk8s_enable("cilium", timeout_insec=300)
assert "Nothing to do for" not in enable
validate_cilium()
test_matrix['cilium'] = validate_cilium
except:
print('Will not test the cilium addon')
try:
ip_ranges = (
"192.168.0.105-192.168.0.105,192.168.0.110-192.168.0.111,192.168.1.240/28"
)
enable = microk8s_enable("{}:{}".format("metallb", ip_ranges), timeout_insec=500)
assert "MetalLB is enabled" in enable and "Nothing to do for" not in enable
validate_metallb_config(ip_ranges)
test_matrix['metallb'] = validate_metallb_config
except:
print("Will not test the metallb addon")
try:
enable = microk8s_enable("multus", timeout_insec=150)
assert "Nothing to do for" not in enable
validate_multus()
test_matrix['multus'] = validate_multus
except:
print('Will not test the multus addon')
if upgrade_to.endswith('.snap'):
cmd = "sudo snap install {} --classic --dangerous".format(upgrade_to)
else:
cmd = "sudo snap refresh microk8s --channel={}".format(upgrade_to)
run_until_success(cmd)
time.sleep(10)
wait_for_installation()
for test, validation in test_matrix.items():
print("Testing {}".format(test))
validation()
if not is_container():
check_call("sudo snap remove microk8s".split())
def is_container():
try:
if os.path.isdir('/run/systemd/system'):
container = check_output('sudo systemd-detect-virt --container'.split())
print("Tests are running in {}".format(container))
return True
except CalledProcessError:
print("systemd-detect-virt did not detect a container")
if os.path.exists('/run/container_type'):
return True
try:
check_call("sudo grep -E (lxc|hypervisor) /proc/1/environ /proc/cpuinfo".split())
print("Tests are running in an undetectable container")
return True
except CalledProcessError:
print("no indication of a container in /proc")
return False
| true | true |
f736b989a20a38aa334c0842fad73b91270d1165 | 4,189 | py | Python | apps/operation/migrations/0001_initial.py | wusri66666/Demo_Study | 0d2e6c506fba90bc144b29e2c44555f7fe83c8ac | [
"Apache-2.0"
] | null | null | null | apps/operation/migrations/0001_initial.py | wusri66666/Demo_Study | 0d2e6c506fba90bc144b29e2c44555f7fe83c8ac | [
"Apache-2.0"
] | null | null | null | apps/operation/migrations/0001_initial.py | wusri66666/Demo_Study | 0d2e6c506fba90bc144b29e2c44555f7fe83c8ac | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2019-01-04 08:34
from __future__ import unicode_literals
import datetime
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('courses', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='CourseComments',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('comments', models.CharField(max_length=200, verbose_name='评论')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.Course', verbose_name='课程')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '课程评论',
'verbose_name_plural': '课程评论',
},
),
migrations.CreateModel(
name='UserAsk',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=20, verbose_name='姓名')),
('mobile', models.CharField(max_length=11, verbose_name='手机')),
('course_name', models.CharField(max_length=50, verbose_name='课程名')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
],
options={
'verbose_name': '用户咨询',
'verbose_name_plural': '用户咨询',
},
),
migrations.CreateModel(
name='UserCourse',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.Course', verbose_name='课程')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '用户课程',
'verbose_name_plural': '用户课程',
},
),
migrations.CreateModel(
name='UserFavorite',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('fav_id', models.IntegerField(default=0, verbose_name='数据id')),
('fav_type', models.IntegerField(choices=[(1, '课程'), (2, '课程机构'), (3, '讲师')], default=1, verbose_name='收藏类型')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '用户收藏',
'verbose_name_plural': '用户收藏',
},
),
migrations.CreateModel(
name='UserMessage',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('user', models.IntegerField(default=0, verbose_name='接收用户')),
('message', models.CharField(max_length=500, verbose_name='消息内容')),
('has_read', models.BooleanField(default=False, verbose_name='是否已读')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
],
options={
'verbose_name': '用户消息',
'verbose_name_plural': '用户消息',
},
),
]
| 46.032967 | 137 | 0.577226 |
from __future__ import unicode_literals
import datetime
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('courses', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='CourseComments',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('comments', models.CharField(max_length=200, verbose_name='评论')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.Course', verbose_name='课程')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '课程评论',
'verbose_name_plural': '课程评论',
},
),
migrations.CreateModel(
name='UserAsk',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=20, verbose_name='姓名')),
('mobile', models.CharField(max_length=11, verbose_name='手机')),
('course_name', models.CharField(max_length=50, verbose_name='课程名')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
],
options={
'verbose_name': '用户咨询',
'verbose_name_plural': '用户咨询',
},
),
migrations.CreateModel(
name='UserCourse',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='courses.Course', verbose_name='课程')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '用户课程',
'verbose_name_plural': '用户课程',
},
),
migrations.CreateModel(
name='UserFavorite',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('fav_id', models.IntegerField(default=0, verbose_name='数据id')),
('fav_type', models.IntegerField(choices=[(1, '课程'), (2, '课程机构'), (3, '讲师')], default=1, verbose_name='收藏类型')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='用户')),
],
options={
'verbose_name': '用户收藏',
'verbose_name_plural': '用户收藏',
},
),
migrations.CreateModel(
name='UserMessage',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('user', models.IntegerField(default=0, verbose_name='接收用户')),
('message', models.CharField(max_length=500, verbose_name='消息内容')),
('has_read', models.BooleanField(default=False, verbose_name='是否已读')),
('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')),
],
options={
'verbose_name': '用户消息',
'verbose_name_plural': '用户消息',
},
),
]
| true | true |
f736b9b9799e47cbbfa10c26164b065ef169814c | 584 | py | Python | Linux-Operation0605/app/utils/domain_session.py | zhouli121018/nodejsgm | 0ccbc8acf61badc812f684dd39253d55c99f08eb | [
"MIT"
] | null | null | null | Linux-Operation0605/app/utils/domain_session.py | zhouli121018/nodejsgm | 0ccbc8acf61badc812f684dd39253d55c99f08eb | [
"MIT"
] | 18 | 2020-06-05T18:17:40.000Z | 2022-03-11T23:25:21.000Z | Linux-Operation0605/app/utils/domain_session.py | zhouli121018/nodejsgm | 0ccbc8acf61badc812f684dd39253d55c99f08eb | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from app.core.models import Domain
def get_domainid_bysession(request):
""" 获取操作域名ID
:param request:
:return:
"""
try:
domain_id = int(request.session.get('domain_id', None))
except:
domain_id = 0
if not domain_id:
obj = Domain.objects.order_by('id').first()
if obj:
domain_id = obj.id
request.session['domain_id'] = domain_id
return domain_id
def get_session_domain(domain_id):
obj = Domain.objects.filter(id=domain_id).first()
return obj and obj.domain or None | 26.545455 | 63 | 0.623288 |
from app.core.models import Domain
def get_domainid_bysession(request):
try:
domain_id = int(request.session.get('domain_id', None))
except:
domain_id = 0
if not domain_id:
obj = Domain.objects.order_by('id').first()
if obj:
domain_id = obj.id
request.session['domain_id'] = domain_id
return domain_id
def get_session_domain(domain_id):
obj = Domain.objects.filter(id=domain_id).first()
return obj and obj.domain or None | true | true |
f736ba506e1db4cd229deabcd56464863bde4f78 | 132 | py | Python | src/home/admin.py | pandeydivesh15/item_sharing_portal | c814d5cf0a7b34d73d8155e508a0cf4f334af199 | [
"MIT"
] | 1 | 2019-11-04T16:45:27.000Z | 2019-11-04T16:45:27.000Z | src/home/admin.py | pandeydivesh15/item_sharing_portal | c814d5cf0a7b34d73d8155e508a0cf4f334af199 | [
"MIT"
] | null | null | null | src/home/admin.py | pandeydivesh15/item_sharing_portal | c814d5cf0a7b34d73d8155e508a0cf4f334af199 | [
"MIT"
] | null | null | null | from django.contrib import admin
# Register your models here.
from .models import feedback_data
admin.site.register(feedback_data)
| 22 | 34 | 0.825758 | from django.contrib import admin
from .models import feedback_data
admin.site.register(feedback_data)
| true | true |
f736ba8e434c0a446055bf225b513f600d7b732a | 3,173 | py | Python | app/models/users.py | larryTheGeek/ride_my_way_v2 | b5f20095308a9230dc4fc63fcacae278c8f8ed71 | [
"MIT"
] | null | null | null | app/models/users.py | larryTheGeek/ride_my_way_v2 | b5f20095308a9230dc4fc63fcacae278c8f8ed71 | [
"MIT"
] | 5 | 2021-03-18T20:54:03.000Z | 2022-03-11T23:27:31.000Z | app/models/users.py | LarryKarani/ride_my_way_v2 | b5f20095308a9230dc4fc63fcacae278c8f8ed71 | [
"MIT"
] | null | null | null | """This module contains the user model that regesters a new user """
import os
import datetime
from .db import Db
from werkzeug.security import generate_password_hash
class User():
def __init__(self, username, email, password, designation):
self.username = username
self.email = email
self.password = generate_password_hash(password.strip())
self.designation = designation
def __repr__(self):
return {'username': self.username,
'email': self.email,
'password': self.password,
'designation': self.designation}
@staticmethod
def check_user(username):
"""checks if a user has already been register"""
sql = "SELECT * FROM users WHERE users.username=\'%s\' "%(username)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchone()
return output
@staticmethod
def check_email(email):
"""checks if a user has already been register"""
sql = "SELECT * FROM users WHERE users.email_adress=\'%s\' "%(email)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchone()
return output
def register_user(self):
"""Regesters a new user into the database"""
sql = 'INSERT INTO users (username,\
pass_word,\
email_adress,\
user_type)\
VALUES(\'%s\', \'%s\', \'%s\',\'%s\');' % (
self.username,
# hash password
self.password,
self.email,
self.designation
)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
conn.commit()
@staticmethod
def get_a_user(id):
sql = f"SELECT * FROM users WHERE users.id={id}"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchall()
print(f"users with {id} is {output}")
@staticmethod
def update_user(id, username, email, designation):
sql = f"UPDATE users SET username = \'{username}\',\
email_adress =\'{email}\',\
user_type =\'{designation}\'\
WHERE ride_offer.id = {id}"
conn = Db.db_connection(os.environ.get('config_name'))
cur = conn.cursor()
cur.execute(sql)
conn.commit()
print("update successful")
@staticmethod
def delete_user(id):
sql = f"DELETE FROM ride_offer WHERE users.id ={id}"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
conn.commit()
print(f'succesfuly deleted user with id {id}')
@staticmethod
def get_all_usesrs():
sql = f"SELECT * FROM users"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchall()
print(f'output is {output}')
return output
| 31.415842 | 76 | 0.532934 | import os
import datetime
from .db import Db
from werkzeug.security import generate_password_hash
class User():
def __init__(self, username, email, password, designation):
self.username = username
self.email = email
self.password = generate_password_hash(password.strip())
self.designation = designation
def __repr__(self):
return {'username': self.username,
'email': self.email,
'password': self.password,
'designation': self.designation}
@staticmethod
def check_user(username):
sql = "SELECT * FROM users WHERE users.username=\'%s\' "%(username)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchone()
return output
@staticmethod
def check_email(email):
sql = "SELECT * FROM users WHERE users.email_adress=\'%s\' "%(email)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchone()
return output
def register_user(self):
sql = 'INSERT INTO users (username,\
pass_word,\
email_adress,\
user_type)\
VALUES(\'%s\', \'%s\', \'%s\',\'%s\');' % (
self.username,
self.password,
self.email,
self.designation
)
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
conn.commit()
@staticmethod
def get_a_user(id):
sql = f"SELECT * FROM users WHERE users.id={id}"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchall()
print(f"users with {id} is {output}")
@staticmethod
def update_user(id, username, email, designation):
sql = f"UPDATE users SET username = \'{username}\',\
email_adress =\'{email}\',\
user_type =\'{designation}\'\
WHERE ride_offer.id = {id}"
conn = Db.db_connection(os.environ.get('config_name'))
cur = conn.cursor()
cur.execute(sql)
conn.commit()
print("update successful")
@staticmethod
def delete_user(id):
sql = f"DELETE FROM ride_offer WHERE users.id ={id}"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
conn.commit()
print(f'succesfuly deleted user with id {id}')
@staticmethod
def get_all_usesrs():
sql = f"SELECT * FROM users"
conn = Db.db_connection()
cur = conn.cursor()
cur.execute(sql)
output = cur.fetchall()
print(f'output is {output}')
return output
| true | true |
f736bf59d0f7748a75d7f375e3061bb8208a79b4 | 6,221 | py | Python | sentinel2_processing/georasteR_converter.py | JonathanLehner/cassini_2021_nature_discoverer | 41e1e7ec01400d16bd34baf0763adce0383f3841 | [
"Apache-2.0"
] | 1 | 2021-06-20T14:04:14.000Z | 2021-06-20T14:04:14.000Z | sentinel2_processing/georasteR_converter.py | JonathanLehner/cassini_2021_nature_discoverer | 41e1e7ec01400d16bd34baf0763adce0383f3841 | [
"Apache-2.0"
] | null | null | null | sentinel2_processing/georasteR_converter.py | JonathanLehner/cassini_2021_nature_discoverer | 41e1e7ec01400d16bd34baf0763adce0383f3841 | [
"Apache-2.0"
] | 2 | 2021-06-23T15:27:10.000Z | 2021-06-28T20:48:22.000Z | """
now using
https://towardsdatascience.com/reading-and-visualizing-geotiff-images-with-python-8dcca7a74510
https://github.com/GeoUtils/georaster/blob/master/georaster/georaster.py
https://rasterio.readthedocs.io/en/latest/topics/color.html
"""
import os
import pprint as pp
import time
from datetime import datetime
from os import listdir
from os.path import join, isfile
import georaster
from osgeo import gdal
import matplotlib.pyplot as plt
import wordninja
from cleantext import clean
from natsort import natsorted
from tqdm import tqdm
tif_dir_path = str(input("Enter path to folder with geotiff files -->"))
# -----------------------------------------------------------------
output_folder_name = "georasteR_conversion"
output_path_full = os.path.join(tif_dir_path, output_folder_name)
if not os.path.isdir(output_path_full):
os.mkdir(output_path_full)
# make a place to store outputs if one does not exist
print("outputs will be in: \n", output_path_full)
# -----------------------------------------------------------------
def cleantxt_wrap(ugly_text):
# a wrapper for clean text with options different than default
# https://pypi.org/project/clean-text/
cleaned_text = clean(ugly_text,
fix_unicode=True, # fix various unicode errors
to_ascii=True, # transliterate to closest ASCII representation
lower=True, # lowercase text
no_line_breaks=True, # fully strip line breaks as opposed to only normalizing them
no_urls=True, # replace all URLs with a special token
no_emails=True, # replace all email addresses with a special token
no_phone_numbers=True, # replace all phone numbers with a special token
no_numbers=False, # replace all numbers with a special token
no_digits=False, # replace all digits with a special token
no_currency_symbols=True, # replace all currency symbols with a special token
no_punct=True, # remove punctuations
replace_with_punct="", # instead of removing punctuations you may replace them
replace_with_url="<URL>",
replace_with_email="<EMAIL>",
replace_with_phone_number="<PHONE>",
replace_with_number="<NUM>",
replace_with_digit="0",
replace_with_currency_symbol="<CUR>",
lang="en" # set to 'de' for German special handling
)
return cleaned_text
def beautify_filename(filename, num_words=20, start_reverse=False,
word_separator="_"):
# takes a filename stored as text, removes extension, separates into X words ...
# and returns a nice filename with the words separateed by
# useful for when you are reading files, doing things to them, and making new files
filename = str(filename)
index_file_Ext = filename.rfind('.')
current_name = str(filename)[:index_file_Ext] # get rid of extension
clean_name = cleantxt_wrap(current_name) # wrapper with custom defs
file_words = wordninja.split(clean_name)
# splits concatenated text into a list of words based on common word freq
if len(file_words) <= num_words:
num_words = len(file_words)
if start_reverse:
t_file_words = file_words[-num_words:]
else:
t_file_words = file_words[:num_words]
pretty_name = word_separator.join(t_file_words) # see function argument
# NOTE IT DOES NOT RETURN THE EXTENSION
return pretty_name[: (len(pretty_name) - 1)] # there is a space always at the end, so -1
# ----------------------------------------------------------------------------
def convert_tiff_to_png_georasters(input_path, output_path, verbose=False):
# Use SingleBandRaster() if image has only one band
img = georaster.MultiBandRaster(input_path)
# img.r gives the raster in [height, width, band] format
# band no. starts from 0
plt.imshow(img.r[:, :, 2], interpolation='spline36')
plt.title(os.path.basename(input_path))
plt.savefig(output_path, bbox_inches='tight', dpi=200)
if verbose:
# For no. of bands and resolution
gd_img = gdal.Open(input_path, gdal.GA_ReadOnly)
print("\n data on rasters from gdal:")
gd_img.RasterCount, gd_img.RasterXSize, gd_img.RasterYSize
gd_img.GetStatistics(True, True)
# stats about image
img.GetStatistics(True, True)
# ----------------------------------------------------------------------------
# load files
files_to_munch = natsorted([f for f in listdir(tif_dir_path) if isfile(os.path.join(tif_dir_path, f))])
total_files_1 = len(files_to_munch)
removed_count_1 = 0
approved_files = []
# remove non-tif_image files
for prefile in files_to_munch:
if prefile.endswith(".tif"):
approved_files.append(prefile)
else:
files_to_munch.remove(prefile)
removed_count_1 += 1
print("out of {0:3d} file(s) originally in the folder, ".format(total_files_1),
"{0:3d} non-tif_image files were removed".format(removed_count_1))
print('\n {0:3d} tif_image file(s) in folder will be transcribed.'.format(len(approved_files)))
pp.pprint(approved_files)
# ----------------------------------------------------------------------------
# loop
st = time.time()
for tif_file in tqdm(approved_files, total=len(approved_files),
desc="Resizing tif_images"):
index_pos = approved_files.index(tif_file)
out_name = beautify_filename(tif_file) + "converted_nr_{}_".format(index_pos) + ".png"
this_input_path = join(tif_dir_path, tif_file)
this_output_path = join(output_path_full, out_name)
convert_tiff_to_png_georasters(this_input_path, this_output_path)
rt = round((time.time() - st) / 60, 2)
print("\n\nfinished converting all tif_images - ", datetime.now())
print("Converted {} tif_images in {} minutes".format(len(approved_files), rt))
print("they are located in: \n", output_path_full)
| 40.660131 | 108 | 0.634142 |
import os
import pprint as pp
import time
from datetime import datetime
from os import listdir
from os.path import join, isfile
import georaster
from osgeo import gdal
import matplotlib.pyplot as plt
import wordninja
from cleantext import clean
from natsort import natsorted
from tqdm import tqdm
tif_dir_path = str(input("Enter path to folder with geotiff files -->"))
output_folder_name = "georasteR_conversion"
output_path_full = os.path.join(tif_dir_path, output_folder_name)
if not os.path.isdir(output_path_full):
os.mkdir(output_path_full)
print("outputs will be in: \n", output_path_full)
def cleantxt_wrap(ugly_text):
cleaned_text = clean(ugly_text,
fix_unicode=True,
to_ascii=True,
lower=True,
no_line_breaks=True,
no_urls=True,
no_emails=True,
no_phone_numbers=True,
no_numbers=False,
no_digits=False,
no_currency_symbols=True,
no_punct=True,
replace_with_punct="",
replace_with_url="<URL>",
replace_with_email="<EMAIL>",
replace_with_phone_number="<PHONE>",
replace_with_number="<NUM>",
replace_with_digit="0",
replace_with_currency_symbol="<CUR>",
lang="en"
)
return cleaned_text
def beautify_filename(filename, num_words=20, start_reverse=False,
word_separator="_"):
filename = str(filename)
index_file_Ext = filename.rfind('.')
current_name = str(filename)[:index_file_Ext]
clean_name = cleantxt_wrap(current_name)
file_words = wordninja.split(clean_name)
if len(file_words) <= num_words:
num_words = len(file_words)
if start_reverse:
t_file_words = file_words[-num_words:]
else:
t_file_words = file_words[:num_words]
pretty_name = word_separator.join(t_file_words)
return pretty_name[: (len(pretty_name) - 1)]
def convert_tiff_to_png_georasters(input_path, output_path, verbose=False):
img = georaster.MultiBandRaster(input_path)
plt.imshow(img.r[:, :, 2], interpolation='spline36')
plt.title(os.path.basename(input_path))
plt.savefig(output_path, bbox_inches='tight', dpi=200)
if verbose:
gd_img = gdal.Open(input_path, gdal.GA_ReadOnly)
print("\n data on rasters from gdal:")
gd_img.RasterCount, gd_img.RasterXSize, gd_img.RasterYSize
gd_img.GetStatistics(True, True)
img.GetStatistics(True, True)
files_to_munch = natsorted([f for f in listdir(tif_dir_path) if isfile(os.path.join(tif_dir_path, f))])
total_files_1 = len(files_to_munch)
removed_count_1 = 0
approved_files = []
for prefile in files_to_munch:
if prefile.endswith(".tif"):
approved_files.append(prefile)
else:
files_to_munch.remove(prefile)
removed_count_1 += 1
print("out of {0:3d} file(s) originally in the folder, ".format(total_files_1),
"{0:3d} non-tif_image files were removed".format(removed_count_1))
print('\n {0:3d} tif_image file(s) in folder will be transcribed.'.format(len(approved_files)))
pp.pprint(approved_files)
st = time.time()
for tif_file in tqdm(approved_files, total=len(approved_files),
desc="Resizing tif_images"):
index_pos = approved_files.index(tif_file)
out_name = beautify_filename(tif_file) + "converted_nr_{}_".format(index_pos) + ".png"
this_input_path = join(tif_dir_path, tif_file)
this_output_path = join(output_path_full, out_name)
convert_tiff_to_png_georasters(this_input_path, this_output_path)
rt = round((time.time() - st) / 60, 2)
print("\n\nfinished converting all tif_images - ", datetime.now())
print("Converted {} tif_images in {} minutes".format(len(approved_files), rt))
print("they are located in: \n", output_path_full)
| true | true |
f736c2f98e04eecb89ffda818d0aed23fa7d8694 | 85 | py | Python | api/immfly/environments/local_settings.py | pollitosabroson/fly | 2f0846d26e4482f723a990bbd18b43220b3c4521 | [
"Apache-2.0"
] | null | null | null | api/immfly/environments/local_settings.py | pollitosabroson/fly | 2f0846d26e4482f723a990bbd18b43220b3c4521 | [
"Apache-2.0"
] | null | null | null | api/immfly/environments/local_settings.py | pollitosabroson/fly | 2f0846d26e4482f723a990bbd18b43220b3c4521 | [
"Apache-2.0"
] | null | null | null | from immfly.settings import * # NOQA
INSTALLED_APPS += [ # NOQA
'drf_yasg',
]
| 14.166667 | 37 | 0.635294 | from immfly.settings import *
INSTALLED_APPS += [
'drf_yasg',
]
| true | true |
f736c33e21dacfcd6131ef5e31914f56c754e7d8 | 747 | py | Python | terra_qry_discord_bot.py | ngmisl/terra-python-query | b42d87bd806f6fcd275152b4e4c4c769eb0c2cd9 | [
"MIT"
] | 3 | 2022-01-23T06:59:55.000Z | 2022-01-23T09:33:59.000Z | terra_qry_discord_bot.py | ngmisl/terra-python-query | b42d87bd806f6fcd275152b4e4c4c769eb0c2cd9 | [
"MIT"
] | null | null | null | terra_qry_discord_bot.py | ngmisl/terra-python-query | b42d87bd806f6fcd275152b4e4c4c769eb0c2cd9 | [
"MIT"
] | 2 | 2022-01-23T06:59:57.000Z | 2022-01-23T09:34:33.000Z | import discord
import requests
import os
my_secret = os.environ['TOKEN']
glow = 'terra1tu9yjssxslh3fd6fe908ntkquf3nd3xt8kp2u2'
client = discord.Client()
@client.event
async def on_ready():
print('We have logged in as {0.user}'.format(client))
@client.event
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('$tickets'):
query_msg = '{"state":{"contract_addr":"' + glow + '"}}'
response = requests.get("https://lcd.terra.dev/wasm/contracts/" + glow + "/store",
params={"query_msg": query_msg},
).json()
total_tickets = response['result']['total_tickets']
await message.channel.send('Total Tickets Locked: ' + total_tickets)
client.run(my_secret)
| 24.096774 | 87 | 0.69344 | import discord
import requests
import os
my_secret = os.environ['TOKEN']
glow = 'terra1tu9yjssxslh3fd6fe908ntkquf3nd3xt8kp2u2'
client = discord.Client()
@client.event
async def on_ready():
print('We have logged in as {0.user}'.format(client))
@client.event
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('$tickets'):
query_msg = '{"state":{"contract_addr":"' + glow + '"}}'
response = requests.get("https://lcd.terra.dev/wasm/contracts/" + glow + "/store",
params={"query_msg": query_msg},
).json()
total_tickets = response['result']['total_tickets']
await message.channel.send('Total Tickets Locked: ' + total_tickets)
client.run(my_secret)
| true | true |
f736c4711122603da5cb790791c0fc789c9401ee | 103,746 | py | Python | research/object_detection/core/preprocessor.py | imcwx/models | 523ff5d0d50c3181329e62509270d4d778734000 | [
"Apache-2.0"
] | null | null | null | research/object_detection/core/preprocessor.py | imcwx/models | 523ff5d0d50c3181329e62509270d4d778734000 | [
"Apache-2.0"
] | null | null | null | research/object_detection/core/preprocessor.py | imcwx/models | 523ff5d0d50c3181329e62509270d4d778734000 | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 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.
# ==============================================================================
"""Preprocess images and bounding boxes for detection.
We perform two sets of operations in preprocessing stage:
(a) operations that are applied to both training and testing data,
(b) operations that are applied only to training data for the purpose of
data augmentation.
A preprocessing function receives a set of inputs,
e.g. an image and bounding boxes,
performs an operation on them, and returns them.
Some examples are: randomly cropping the image, randomly mirroring the image,
randomly changing the brightness, contrast, hue and
randomly jittering the bounding boxes.
The preprocess function receives a tensor_dict which is a dictionary that maps
different field names to their tensors. For example,
tensor_dict[fields.InputDataFields.image] holds the image tensor.
The image is a rank 4 tensor: [1, height, width, channels] with
dtype=tf.float32. The groundtruth_boxes is a rank 2 tensor: [N, 4] where
in each row there is a box with [ymin xmin ymax xmax].
Boxes are in normalized coordinates meaning
their coordinate values range in [0, 1]
Important Note: In tensor_dict, images is a rank 4 tensor, but preprocessing
functions receive a rank 3 tensor for processing the image. Thus, inside the
preprocess function we squeeze the image to become a rank 3 tensor and then
we pass it to the functions. At the end of the preprocess we expand the image
back to rank 4.
"""
import sys
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import control_flow_ops
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import keypoint_ops
from object_detection.core import standard_fields as fields
def _apply_with_random_selector(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: input Tensor.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
return control_flow_ops.merge([func(
control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case)
for case in range(num_cases)])[0]
def _apply_with_random_selector_tuples(x, func, num_cases):
"""Computes func(x, sel), with sel sampled from [0...num_cases-1].
Args:
x: A tuple of input tensors.
func: Python function to apply.
num_cases: Python int32, number of cases to sample sel from.
Returns:
The result of func(x, sel), where func receives the value of the
selector as a python integer, but sel is sampled dynamically.
"""
num_inputs = len(x)
rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
# Pass the real x only to one of the func calls.
tuples = [list() for t in x]
for case in range(num_cases):
new_x = [control_flow_ops.switch(t, tf.equal(rand_sel, case))[1] for t in x]
output = func(tuple(new_x), case)
for j in range(num_inputs):
tuples[j].append(output[j])
for i in range(num_inputs):
tuples[i] = control_flow_ops.merge(tuples[i])[0]
return tuple(tuples)
def _random_integer(minval, maxval, seed):
"""Returns a random 0-D tensor between minval and maxval.
Args:
minval: minimum value of the random tensor.
maxval: maximum value of the random tensor.
seed: random seed.
Returns:
A random 0-D tensor between minval and maxval.
"""
return tf.random_uniform(
[], minval=minval, maxval=maxval, dtype=tf.int32, seed=seed)
def normalize_image(image, original_minval, original_maxval, target_minval,
target_maxval):
"""Normalizes pixel values in the image.
Moves the pixel values from the current [original_minval, original_maxval]
range to a the [target_minval, target_maxval] range.
Args:
image: rank 3 float32 tensor containing 1
image -> [height, width, channels].
original_minval: current image minimum value.
original_maxval: current image maximum value.
target_minval: target image minimum value.
target_maxval: target image maximum value.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('NormalizeImage', values=[image]):
original_minval = float(original_minval)
original_maxval = float(original_maxval)
target_minval = float(target_minval)
target_maxval = float(target_maxval)
image = tf.to_float(image)
image = tf.subtract(image, original_minval)
image = tf.multiply(image, (target_maxval - target_minval) /
(original_maxval - original_minval))
image = tf.add(image, target_minval)
return image
def retain_boxes_above_threshold(boxes,
labels,
label_scores,
masks=None,
keypoints=None,
threshold=0.0):
"""Retains boxes whose label score is above a given threshold.
If the label score for a box is missing (represented by NaN), the box is
retained. The boxes that don't pass the threshold will not appear in the
returned tensor.
Args:
boxes: float32 tensor of shape [num_instance, 4] representing boxes
location in normalized coordinates.
labels: rank 1 int32 tensor of shape [num_instance] containing the object
classes.
label_scores: float32 tensor of shape [num_instance] representing the
score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks are of
the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
coordinates.
threshold: scalar python float.
Returns:
retained_boxes: [num_retained_instance, 4]
retianed_labels: [num_retained_instance]
retained_label_scores: [num_retained_instance]
If masks, or keypoints are not None, the function also returns:
retained_masks: [num_retained_instance, height, width]
retained_keypoints: [num_retained_instance, num_keypoints, 2]
"""
with tf.name_scope('RetainBoxesAboveThreshold',
values=[boxes, labels, label_scores]):
indices = tf.where(
tf.logical_or(label_scores > threshold, tf.is_nan(label_scores)))
indices = tf.squeeze(indices, axis=1)
retained_boxes = tf.gather(boxes, indices)
retained_labels = tf.gather(labels, indices)
retained_label_scores = tf.gather(label_scores, indices)
result = [retained_boxes, retained_labels, retained_label_scores]
if masks is not None:
retained_masks = tf.gather(masks, indices)
result.append(retained_masks)
if keypoints is not None:
retained_keypoints = tf.gather(keypoints, indices)
result.append(retained_keypoints)
return result
def _flip_boxes_left_right(boxes):
"""Left-right flip the boxes.
Args:
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
Returns:
Flipped boxes.
"""
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
flipped_xmin = tf.subtract(1.0, xmax)
flipped_xmax = tf.subtract(1.0, xmin)
flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)
return flipped_boxes
def _flip_boxes_up_down(boxes):
"""Up-down flip the boxes.
Args:
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
Returns:
Flipped boxes.
"""
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
flipped_ymin = tf.subtract(1.0, ymax)
flipped_ymax = tf.subtract(1.0, ymin)
flipped_boxes = tf.concat([flipped_ymin, xmin, flipped_ymax, xmax], 1)
return flipped_boxes
def _rot90_boxes(boxes):
"""Rotate boxes counter-clockwise by 90 degrees.
Args:
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
Returns:
Rotated boxes.
"""
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
rotated_ymin = tf.subtract(1.0, xmax)
rotated_ymax = tf.subtract(1.0, xmin)
rotated_xmin = ymin
rotated_xmax = ymax
rotated_boxes = tf.concat(
[rotated_ymin, rotated_xmin, rotated_ymax, rotated_xmax], 1)
return rotated_boxes
def _flip_masks_left_right(masks):
"""Left-right flip masks.
Args:
masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
Returns:
flipped masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
"""
return masks[:, :, ::-1]
def _flip_masks_up_down(masks):
"""Up-down flip masks.
Args:
masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
Returns:
flipped masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
"""
return masks[:, ::-1, :]
def _rot90_masks(masks):
"""Rotate masks counter-clockwise by 90 degrees.
Args:
masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
Returns:
rotated masks: rank 3 float32 tensor with shape
[num_instances, height, width] representing instance masks.
"""
masks = tf.transpose(masks, [0, 2, 1])
return masks[:, ::-1, :]
def random_horizontal_flip(image,
boxes=None,
masks=None,
keypoints=None,
keypoint_flip_permutation=None,
seed=None):
"""Randomly flips the image and detections horizontally.
The probability of flipping the image is 50%.
Args:
image: rank 3 float32 tensor with shape [height, width, channels].
boxes: (optional) rank 2 float32 tensor with shape [N, 4]
containing the bounding boxes.
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip
permutation.
seed: random seed
Returns:
image: image which is the same shape as input image.
If boxes, masks, keypoints, and keypoint_flip_permutation are not None,
the function also returns the following tensors.
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
Raises:
ValueError: if keypoints are provided but keypoint_flip_permutation is not.
"""
def _flip_image(image):
# flip image
image_flipped = tf.image.flip_left_right(image)
return image_flipped
if keypoints is not None and keypoint_flip_permutation is None:
raise ValueError(
'keypoints are provided but keypoints_flip_permutation is not provided')
with tf.name_scope('RandomHorizontalFlip', values=[image, boxes]):
result = []
# random variable defining whether to do flip or not
do_a_flip_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
# flip image
image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
result.append(image)
# flip boxes
if boxes is not None:
boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_left_right(boxes),
lambda: boxes)
result.append(boxes)
# flip masks
if masks is not None:
masks = tf.cond(do_a_flip_random, lambda: _flip_masks_left_right(masks),
lambda: masks)
result.append(masks)
# flip keypoints
if keypoints is not None and keypoint_flip_permutation is not None:
permutation = keypoint_flip_permutation
keypoints = tf.cond(
do_a_flip_random,
lambda: keypoint_ops.flip_horizontal(keypoints, 0.5, permutation),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_vertical_flip(image,
boxes=None,
masks=None,
keypoints=None,
keypoint_flip_permutation=None,
seed=None):
"""Randomly flips the image and detections vertically.
The probability of flipping the image is 50%.
Args:
image: rank 3 float32 tensor with shape [height, width, channels].
boxes: (optional) rank 2 float32 tensor with shape [N, 4]
containing the bounding boxes.
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
keypoint_flip_permutation: rank 1 int32 tensor containing the keypoint flip
permutation.
seed: random seed
Returns:
image: image which is the same shape as input image.
If boxes, masks, keypoints, and keypoint_flip_permutation are not None,
the function also returns the following tensors.
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
Raises:
ValueError: if keypoints are provided but keypoint_flip_permutation is not.
"""
def _flip_image(image):
# flip image
image_flipped = tf.image.flip_up_down(image)
return image_flipped
if keypoints is not None and keypoint_flip_permutation is None:
raise ValueError(
'keypoints are provided but keypoints_flip_permutation is not provided')
with tf.name_scope('RandomVerticalFlip', values=[image, boxes]):
result = []
# random variable defining whether to do flip or not
do_a_flip_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
# flip image
image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
result.append(image)
# flip boxes
if boxes is not None:
boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_up_down(boxes),
lambda: boxes)
result.append(boxes)
# flip masks
if masks is not None:
masks = tf.cond(do_a_flip_random, lambda: _flip_masks_up_down(masks),
lambda: masks)
result.append(masks)
# flip keypoints
if keypoints is not None and keypoint_flip_permutation is not None:
permutation = keypoint_flip_permutation
keypoints = tf.cond(
do_a_flip_random,
lambda: keypoint_ops.flip_vertical(keypoints, 0.5, permutation),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_rotation90(image,
boxes=None,
masks=None,
keypoints=None,
seed=None):
"""Randomly rotates the image and detections 90 degrees counter-clockwise.
The probability of rotating the image is 50%. This can be combined with
random_horizontal_flip and random_vertical_flip to produce an output with a
uniform distribution of the eight possible 90 degree rotation / reflection
combinations.
Args:
image: rank 3 float32 tensor with shape [height, width, channels].
boxes: (optional) rank 2 float32 tensor with shape [N, 4]
containing the bounding boxes.
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
seed: random seed
Returns:
image: image which is the same shape as input image.
If boxes, masks, and keypoints, are not None,
the function also returns the following tensors.
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
def _rot90_image(image):
# flip image
image_rotated = tf.image.rot90(image)
return image_rotated
with tf.name_scope('RandomRotation90', values=[image, boxes]):
result = []
# random variable defining whether to rotate by 90 degrees or not
do_a_rot90_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
# flip image
image = tf.cond(do_a_rot90_random, lambda: _rot90_image(image),
lambda: image)
result.append(image)
# flip boxes
if boxes is not None:
boxes = tf.cond(do_a_rot90_random, lambda: _rot90_boxes(boxes),
lambda: boxes)
result.append(boxes)
# flip masks
if masks is not None:
masks = tf.cond(do_a_rot90_random, lambda: _rot90_masks(masks),
lambda: masks)
result.append(masks)
# flip keypoints
if keypoints is not None:
keypoints = tf.cond(
do_a_rot90_random,
lambda: keypoint_ops.rot90(keypoints),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_pixel_value_scale(image, minval=0.9, maxval=1.1, seed=None):
"""Scales each value in the pixels of the image.
This function scales each pixel independent of the other ones.
For each value in image tensor, draws a random number between
minval and maxval and multiples the values with them.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
minval: lower ratio of scaling pixel values.
maxval: upper ratio of scaling pixel values.
seed: random seed.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('RandomPixelValueScale', values=[image]):
image = tf.convert_to_tensor(image, name='image')
# Remember original dtype to so we can convert back if needed
orig_dtype = image.dtype
image = tf.image.convert_image_dtype(image, tf.float32)
color_coef = tf.random_uniform(
tf.shape(image),
minval=minval,
maxval=maxval,
dtype=tf.float32,
seed=seed)
image = tf.multiply(image, color_coef)
return tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
return image
def random_image_scale(image,
masks=None,
min_scale_ratio=0.5,
max_scale_ratio=2.0,
seed=None):
"""Scales the image size.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels].
masks: (optional) rank 3 float32 tensor containing masks with
size [height, width, num_masks]. The value is set to None if there are no
masks.
min_scale_ratio: minimum scaling ratio.
max_scale_ratio: maximum scaling ratio.
seed: random seed.
Returns:
image: image which is the same rank as input image.
masks: If masks is not none, resized masks which are the same rank as input
masks will be returned.
"""
with tf.name_scope('RandomImageScale', values=[image]):
result = []
image_shape = tf.shape(image)
image_height = image_shape[0]
image_width = image_shape[1]
size_coef = tf.random_uniform([],
minval=min_scale_ratio,
maxval=max_scale_ratio,
dtype=tf.float32, seed=seed)
image_newysize = tf.to_int32(
tf.multiply(tf.to_float(image_height), size_coef))
image_newxsize = tf.to_int32(
tf.multiply(tf.to_float(image_width), size_coef))
image = tf.image.resize_images(
image, [image_newysize, image_newxsize], align_corners=True)
result.append(image)
if masks:
masks = tf.image.resize_nearest_neighbor(
masks, [image_newysize, image_newxsize], align_corners=True)
result.append(masks)
return tuple(result)
def random_rgb_to_gray(image, probability=0.1, seed=None):
"""Changes the image from RGB to Grayscale with the given probability.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
probability: the probability of returning a grayscale image.
The probability should be a number between [0, 1].
seed: random seed.
Returns:
image: image which is the same shape as input image.
"""
def _image_to_gray(image):
image_gray1 = tf.image.rgb_to_grayscale(image)
image_gray3 = tf.image.grayscale_to_rgb(image_gray1)
return image_gray3
with tf.name_scope('RandomRGBtoGray', values=[image]):
# random variable defining whether to do flip or not
do_gray_random = tf.random_uniform([], seed=seed)
image = tf.cond(
tf.greater(do_gray_random, probability), lambda: image,
lambda: _image_to_gray(image))
return image
def random_adjust_brightness(image, max_delta=0.2):
"""Randomly adjusts brightness.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
max_delta: how much to change the brightness. A value between [0, 1).
Returns:
image: image which is the same shape as input image.
boxes: boxes which is the same shape as input boxes.
"""
with tf.name_scope('RandomAdjustBrightness', values=[image]):
image = tf.image.random_brightness(image, max_delta)
return image
def random_adjust_contrast(image, min_delta=0.8, max_delta=1.25):
"""Randomly adjusts contrast.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
min_delta: see max_delta.
max_delta: how much to change the contrast. Contrast will change with a
value between min_delta and max_delta. This value will be
multiplied to the current contrast of the image.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('RandomAdjustContrast', values=[image]):
image = tf.image.random_contrast(image, min_delta, max_delta)
return image
def random_adjust_hue(image, max_delta=0.02):
"""Randomly adjusts hue.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
max_delta: change hue randomly with a value between 0 and max_delta.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('RandomAdjustHue', values=[image]):
image = tf.image.random_hue(image, max_delta)
return image
def random_adjust_saturation(image, min_delta=0.8, max_delta=1.25):
"""Randomly adjusts saturation.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
min_delta: see max_delta.
max_delta: how much to change the saturation. Saturation will change with a
value between min_delta and max_delta. This value will be
multiplied to the current saturation of the image.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('RandomAdjustSaturation', values=[image]):
image = tf.image.random_saturation(image, min_delta, max_delta)
return image
def random_distort_color(image, color_ordering=0):
"""Randomly distorts color.
Randomly distorts color using a combination of brightness, hue, contrast
and saturation changes.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
color_ordering: Python int, a type of distortion (valid values: 0, 1).
Returns:
image: image which is the same shape as input image.
Raises:
ValueError: if color_ordering is not in {0, 1}.
"""
with tf.name_scope('RandomDistortColor', values=[image]):
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
else:
raise ValueError('color_ordering must be in {0, 1}')
return image
def random_jitter_boxes(boxes, ratio=0.05, seed=None):
"""Randomly jitter boxes in image.
Args:
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
ratio: The ratio of the box width and height that the corners can jitter.
For example if the width is 100 pixels and ratio is 0.05,
the corners can jitter up to 5 pixels in the x direction.
seed: random seed.
Returns:
boxes: boxes which is the same shape as input boxes.
"""
def random_jitter_box(box, ratio, seed):
"""Randomly jitter box.
Args:
box: bounding box [1, 1, 4].
ratio: max ratio between jittered box and original box,
a number between [0, 0.5].
seed: random seed.
Returns:
jittered_box: jittered box.
"""
rand_numbers = tf.random_uniform(
[1, 1, 4], minval=-ratio, maxval=ratio, dtype=tf.float32, seed=seed)
box_width = tf.subtract(box[0, 0, 3], box[0, 0, 1])
box_height = tf.subtract(box[0, 0, 2], box[0, 0, 0])
hw_coefs = tf.stack([box_height, box_width, box_height, box_width])
hw_rand_coefs = tf.multiply(hw_coefs, rand_numbers)
jittered_box = tf.add(box, hw_rand_coefs)
jittered_box = tf.clip_by_value(jittered_box, 0.0, 1.0)
return jittered_box
with tf.name_scope('RandomJitterBoxes', values=[boxes]):
# boxes are [N, 4]. Lets first make them [N, 1, 1, 4]
boxes_shape = tf.shape(boxes)
boxes = tf.expand_dims(boxes, 1)
boxes = tf.expand_dims(boxes, 2)
distorted_boxes = tf.map_fn(
lambda x: random_jitter_box(x, ratio, seed), boxes, dtype=tf.float32)
distorted_boxes = tf.reshape(distorted_boxes, boxes_shape)
return distorted_boxes
def _strict_random_crop_image(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=1.0,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.1, 1.0),
overlap_thresh=0.3):
"""Performs random crop.
Note: boxes will be clipped to the crop. Keypoint coordinates that are
outside the crop will be set to NaN, which is consistent with the original
keypoint encoding for non-existing keypoints. This function always crops
the image and is supposed to be used by `random_crop_image` function which
sometimes returns image unchanged.
Args:
image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes with shape
[num_instances, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: (optional) float32 tensor of shape [num_instances]
representing the score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If label_scores, masks, or keypoints is not None, the function also returns:
label_scores: rank 1 float32 tensor with shape [num_instances].
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
with tf.name_scope('RandomCropImage', values=[image, boxes]):
image_shape = tf.shape(image)
# boxes are [N, 4]. Lets first make them [N, 1, 4].
boxes_expanded = tf.expand_dims(
tf.clip_by_value(
boxes, clip_value_min=0.0, clip_value_max=1.0), 1)
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
image_shape,
bounding_boxes=boxes_expanded,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=100,
use_image_if_no_bounding_boxes=True)
im_box_begin, im_box_size, im_box = sample_distorted_bounding_box
new_image = tf.slice(image, im_box_begin, im_box_size)
new_image.set_shape([None, None, image.get_shape()[2]])
# [1, 4]
im_box_rank2 = tf.squeeze(im_box, squeeze_dims=[0])
# [4]
im_box_rank1 = tf.squeeze(im_box)
boxlist = box_list.BoxList(boxes)
boxlist.add_field('labels', labels)
if label_scores is not None:
boxlist.add_field('label_scores', label_scores)
im_boxlist = box_list.BoxList(im_box_rank2)
# remove boxes that are outside cropped image
boxlist, inside_window_ids = box_list_ops.prune_completely_outside_window(
boxlist, im_box_rank1)
# remove boxes that are outside image
# overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes(
# boxlist, im_boxlist, overlap_thresh)
# remove boxes that are outside image AND GET BLACKBOXLIST
overlapping_boxlist, keep_ids, black_boxlist = box_list_ops.prune_non_overlapping_boxes_custom(
boxlist, im_boxlist, overlap_thresh)
# change the coordinate of the remaining boxes
new_labels = overlapping_boxlist.get_field('labels')
new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
im_box_rank1)
####################################################################
# Change coordinate of boxes to be blacked
black_boxlist = box_list_ops.change_coordinate_frame(black_boxlist,
im_box_rank1)
blackbox = black_boxlist.get()
new_image = tf.expand_dims(new_image, 0)
blackbox = tf.expand_dims(blackbox, 0)
new_image = tf.image.draw_bounding_boxes(new_image, blackbox, fill=True)
new_image = tf.squeeze(new_image)
blackbox = tf.squeeze(blackbox)
#####################################################################
new_boxes = new_boxlist.get()
new_boxes = tf.clip_by_value(
new_boxes, clip_value_min=0.0, clip_value_max=1.0)
result = [new_image, new_boxes, new_labels]
if label_scores is not None:
new_label_scores = overlapping_boxlist.get_field('label_scores')
result.append(new_label_scores)
if masks is not None:
masks_of_boxes_inside_window = tf.gather(masks, inside_window_ids)
masks_of_boxes_completely_inside_window = tf.gather(
masks_of_boxes_inside_window, keep_ids)
masks_box_begin = [0, im_box_begin[0], im_box_begin[1]]
masks_box_size = [-1, im_box_size[0], im_box_size[1]]
new_masks = tf.slice(
masks_of_boxes_completely_inside_window,
masks_box_begin, masks_box_size)
result.append(new_masks)
if keypoints is not None:
keypoints_of_boxes_inside_window = tf.gather(keypoints, inside_window_ids)
keypoints_of_boxes_completely_inside_window = tf.gather(
keypoints_of_boxes_inside_window, keep_ids)
new_keypoints = keypoint_ops.change_coordinate_frame(
keypoints_of_boxes_completely_inside_window, im_box_rank1)
new_keypoints = keypoint_ops.prune_outside_window(new_keypoints,
[0.0, 0.0, 1.0, 1.0])
result.append(new_keypoints)
return tuple(result)
def random_crop_image(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=0.5,
aspect_ratio_range=(0.60, 0.90),
area_range=(0.3, 1.0),
overlap_thresh=0.3,
random_coef=0.0,
seed=None):
# min_object_covered=1.0,
# aspect_ratio_range=(0.75, 1.33),
# area_range=(0.1, 1.0),
# for trng
# min_object_covered=0.5,
# aspect_ratio_range=(0.60, 0.90),
# area_range=(0.5, 1.0)
"""Randomly crops the image.
Given the input image and its bounding boxes, this op randomly
crops a subimage. Given a user-provided set of input constraints,
the crop window is resampled until it satisfies these constraints.
If within 100 trials it is unable to find a valid crop, the original
image is returned. See the Args section for a description of the input
constraints. Both input boxes and returned Boxes are in normalized
form (e.g., lie in the unit square [0, 1]).
This function will return the original image with probability random_coef.
Note: boxes will be clipped to the crop. Keypoint coordinates that are
outside the crop will be set to NaN, which is consistent with the original
keypoint encoding for non-existing keypoints.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes with shape
[num_instances, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: (optional) float32 tensor of shape [num_instances].
representing the score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
seed: random seed.
Returns:
image: Image shape will be [new_height, new_width, channels].
boxes: boxes which is the same rank as input boxes. Boxes are in normalized
form.
labels: new labels.
If label_scores, masks, or keypoints are not None, the function also
returns:
label_scores: new scores.
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
def strict_random_crop_image_fn():
return _strict_random_crop_image(
image,
boxes,
labels,
label_scores=label_scores,
masks=masks,
keypoints=keypoints,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
overlap_thresh=overlap_thresh)
# avoids tf.cond to make faster RCNN training on borg. See b/140057645.
if random_coef < sys.float_info.min:
result = strict_random_crop_image_fn()
else:
do_a_crop_random = tf.random_uniform([], seed=seed)
do_a_crop_random = tf.greater(do_a_crop_random, random_coef)
outputs = [image, boxes, labels]
if label_scores is not None:
outputs.append(label_scores)
if masks is not None:
outputs.append(masks)
if keypoints is not None:
outputs.append(keypoints)
result = tf.cond(do_a_crop_random, strict_random_crop_image_fn,
lambda: tuple(outputs))
return result
def random_pad_image(image,
boxes,
min_image_size=None,
max_image_size=None,
pad_color=None,
seed=None):
"""Randomly pads the image.
This function randomly pads the image with zeros. The final size of the
padded image will be between min_image_size and max_image_size.
if min_image_size is smaller than the input image size, min_image_size will
be set to the input image size. The same for max_image_size. The input image
will be located at a uniformly random location inside the padded image.
The relative location of the boxes to the original image will remain the same.
Args:
image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
min_image_size: a tensor of size [min_height, min_width], type tf.int32.
If passed as None, will be set to image size
[height, width].
max_image_size: a tensor of size [max_height, max_width], type tf.int32.
If passed as None, will be set to twice the
image [height * 2, width * 2].
pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
if set as None, it will be set to average color of the input
image.
seed: random seed.
Returns:
image: Image shape will be [new_height, new_width, channels].
boxes: boxes which is the same rank as input boxes. Boxes are in normalized
form.
"""
# if pad_color is None:
# pad_color = tf.reduce_mean(image, axis=[0, 1])
image_shape = tf.shape(image)
image_height = image_shape[0]
image_width = image_shape[1]
if max_image_size is None:
max_image_size = tf.stack([image_height * 2, image_width * 2])
max_image_size = tf.maximum(max_image_size,
tf.stack([image_height, image_width]))
if min_image_size is None:
min_image_size = tf.stack([image_height, image_width])
min_image_size = tf.maximum(min_image_size,
tf.stack([image_height, image_width]))
target_height = tf.cond(
max_image_size[0] > min_image_size[0],
lambda: _random_integer(min_image_size[0], max_image_size[0], seed),
lambda: max_image_size[0])
target_width = tf.cond(
max_image_size[1] > min_image_size[1],
lambda: _random_integer(min_image_size[1], max_image_size[1], seed),
lambda: max_image_size[1])
offset_height = tf.cond(
target_height > image_height,
lambda: _random_integer(0, target_height - image_height, seed),
lambda: tf.constant(0, dtype=tf.int32))
offset_width = tf.cond(
target_width > image_width,
lambda: _random_integer(0, target_width - image_width, seed),
lambda: tf.constant(0, dtype=tf.int32))
new_image = tf.image.pad_to_bounding_box(
image,
offset_height=offset_height,
offset_width=offset_width,
target_height=target_height,
target_width=target_width)
# Setting color of the padded pixels
# image_ones = tf.ones_like(image)
# image_ones_padded = tf.image.pad_to_bounding_box(
# image_ones,
# offset_height=offset_height,
# offset_width=offset_width,
# target_height=target_height,
# target_width=target_width)
# image_color_padded = (1.0 - image_ones_padded) * pad_color
# new_image += image_color_padded
# setting boxes
new_window = tf.to_float(
tf.stack([
-offset_height, -offset_width, target_height - offset_height,
target_width - offset_width
]))
new_window /= tf.to_float(
tf.stack([image_height, image_width, image_height, image_width]))
boxlist = box_list.BoxList(boxes)
new_boxlist = box_list_ops.change_coordinate_frame(boxlist, new_window)
new_boxes = new_boxlist.get()
return new_image, new_boxes
def random_crop_pad_image(image,
boxes,
labels,
label_scores=None,
min_object_covered=0.5,
aspect_ratio_range=(0.75/1.1, 0.75*1.1),
area_range=(0.2, 1.0),
overlap_thresh=0.7,
random_coef=0.0,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(1.75, 1.75),
pad_color=None,
seed=None):
# orig
# min_object_covered=1.0,
# aspect_ratio_range=(0.75, 1.33),
# area_range=(0.1, 1.0),
# max_padded_size_ratio=(2.0, 2.0),
# overlap_thresh=0.3,
# pmi_ukraine
# min_object_covered=0.4,
# aspect_ratio_range=(0.60, 0.90),
"""Randomly crops and pads the image.
Given an input image and its bounding boxes, this op first randomly crops
the image and then randomly pads the image with background values. Parameters
min_padded_size_ratio and max_padded_size_ratio, determine the range of the
final output image size. Specifically, the final image size will have a size
in the range of min_padded_size_ratio * tf.shape(image) and
max_padded_size_ratio * tf.shape(image). Note that these ratios are with
respect to the size of the original image, so we can't capture the same
effect easily by independently applying RandomCropImage
followed by RandomPadImage.
Args:
image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: rank 1 float32 containing the label scores.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
min_padded_size_ratio: min ratio of padded image height and width to the
input image's height and width.
max_padded_size_ratio: max ratio of padded image height and width to the
input image's height and width.
pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
if set as None, it will be set to average color of the randomly
cropped image.
seed: random seed.
Returns:
padded_image: padded image.
padded_boxes: boxes which is the same rank as input boxes. Boxes are in
normalized form.
cropped_labels: cropped labels.
if label_scores is not None also returns:
cropped_label_scores: cropped label scores.
"""
# np.random.seed(123) # crop and pad
# np.random.seed(1234) # crop
# np.random.seed(12345) # none, orig
# np.random.seed(1) # pad
rand = np.random.random_sample()
# rand = 0.95
crop, pad = False, False
if rand < 0.70:
crop = True
elif rand < 0.80:
pad = True
else: # rand < 0.90:
crop = True
pad = True
# else:
# # return orig
# pass
# print("The random number generated is: " + str(rand))
# print("It will crop: " + str(crop))
# print("It will pad: " + str(pad))
image_size = tf.shape(image)
image_height = image_size[0]
image_width = image_size[1]
if crop and pad:
result = random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=label_scores,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
overlap_thresh=overlap_thresh,
random_coef=random_coef,
seed=seed)
cropped_image, cropped_boxes, cropped_labels = result[:3]
min_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
min_padded_size_ratio)
max_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
max_padded_size_ratio)
padded_image, padded_boxes = random_pad_image(
cropped_image,
cropped_boxes,
min_image_size=min_image_size,
max_image_size=max_image_size,
pad_color=pad_color,
seed=seed)
cropped_padded_output = (padded_image, padded_boxes, cropped_labels)
if label_scores is not None:
cropped_label_scores = result[3]
cropped_padded_output += (cropped_label_scores,)
elif crop:
result = random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=label_scores,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
overlap_thresh=overlap_thresh,
random_coef=random_coef,
seed=seed)
cropped_image, cropped_boxes, cropped_labels = result[:3]
cropped_padded_output = (cropped_image, cropped_boxes, cropped_labels)
if label_scores is not None:
cropped_label_scores = result[3]
cropped_padded_output += (cropped_label_scores,)
elif pad:
min_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
min_padded_size_ratio)
max_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
max_padded_size_ratio)
padded_image, padded_boxes = random_pad_image(
image,
boxes,
min_image_size=min_image_size,
max_image_size=max_image_size,
pad_color=pad_color,
seed=seed)
cropped_padded_output = (padded_image, padded_boxes, labels)
if label_scores is not None:
cropped_padded_output += (label_scores,)
else:
# image = tf.expand_dims(image, 0)
# boxes = tf.expand_dims(boxes, 0)
# image = tf.image.draw_bounding_boxes(image, boxes, fill=True)
# image = tf.squeeze(image)
# boxes = tf.squeeze(boxes)
cropped_padded_output = (image, boxes, labels)
return cropped_padded_output
def random_crop_to_aspect_ratio(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
aspect_ratio=1.0,
overlap_thresh=0.3,
seed=None):
"""Randomly crops an image to the specified aspect ratio.
Randomly crops the a portion of the image such that the crop is of the
specified aspect ratio, and the crop is as large as possible. If the specified
aspect ratio is larger than the aspect ratio of the image, this op will
randomly remove rows from the top and bottom of the image. If the specified
aspect ratio is less than the aspect ratio of the image, this op will randomly
remove cols from the left and right of the image. If the specified aspect
ratio is the same as the aspect ratio of the image, this op will return the
image.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: (optional) float32 tensor of shape [num_instances]
representing the score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
aspect_ratio: the aspect ratio of cropped image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
seed: random seed.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If label_scores, masks, or keypoints is not None, the function also returns:
label_scores: new label scores.
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
Raises:
ValueError: If image is not a 3D tensor.
"""
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('RandomCropToAspectRatio', values=[image]):
image_shape = tf.shape(image)
orig_height = image_shape[0]
orig_width = image_shape[1]
orig_aspect_ratio = tf.to_float(orig_width) / tf.to_float(orig_height)
new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
def target_height_fn():
return tf.to_int32(tf.round(tf.to_float(orig_width) / new_aspect_ratio))
target_height = tf.cond(orig_aspect_ratio >= new_aspect_ratio,
lambda: orig_height, target_height_fn)
def target_width_fn():
return tf.to_int32(tf.round(tf.to_float(orig_height) * new_aspect_ratio))
target_width = tf.cond(orig_aspect_ratio <= new_aspect_ratio,
lambda: orig_width, target_width_fn)
# either offset_height = 0 and offset_width is randomly chosen from
# [0, offset_width - target_width), or else offset_width = 0 and
# offset_height is randomly chosen from [0, offset_height - target_height)
offset_height = _random_integer(0, orig_height - target_height + 1, seed)
offset_width = _random_integer(0, orig_width - target_width + 1, seed)
new_image = tf.image.crop_to_bounding_box(
image, offset_height, offset_width, target_height, target_width)
im_box = tf.stack([
tf.to_float(offset_height) / tf.to_float(orig_height),
tf.to_float(offset_width) / tf.to_float(orig_width),
tf.to_float(offset_height + target_height) / tf.to_float(orig_height),
tf.to_float(offset_width + target_width) / tf.to_float(orig_width)
])
boxlist = box_list.BoxList(boxes)
boxlist.add_field('labels', labels)
if label_scores is not None:
boxlist.add_field('label_scores', label_scores)
im_boxlist = box_list.BoxList(tf.expand_dims(im_box, 0))
# remove boxes whose overlap with the image is less than overlap_thresh
overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes(
boxlist, im_boxlist, overlap_thresh)
# change the coordinate of the remaining boxes
new_labels = overlapping_boxlist.get_field('labels')
new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
im_box)
new_boxlist = box_list_ops.clip_to_window(new_boxlist,
tf.constant([0.0, 0.0, 1.0, 1.0],
tf.float32))
new_boxes = new_boxlist.get()
result = [new_image, new_boxes, new_labels]
if label_scores is not None:
new_label_scores = overlapping_boxlist.get_field('label_scores')
result.append(new_label_scores)
if masks is not None:
masks_inside_window = tf.gather(masks, keep_ids)
masks_box_begin = tf.stack([0, offset_height, offset_width])
masks_box_size = tf.stack([-1, target_height, target_width])
new_masks = tf.slice(masks_inside_window, masks_box_begin, masks_box_size)
result.append(new_masks)
if keypoints is not None:
keypoints_inside_window = tf.gather(keypoints, keep_ids)
new_keypoints = keypoint_ops.change_coordinate_frame(
keypoints_inside_window, im_box)
new_keypoints = keypoint_ops.prune_outside_window(new_keypoints,
[0.0, 0.0, 1.0, 1.0])
result.append(new_keypoints)
return tuple(result)
def random_pad_to_aspect_ratio(image,
boxes,
masks=None,
keypoints=None,
aspect_ratio=1.0,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(2.0, 2.0),
seed=None):
# aspect_ratio=1.0,
# aspect_ratio=800.0/1080.0,
"""Randomly zero pads an image to the specified aspect ratio.
Pads the image so that the resulting image will have the specified aspect
ratio without scaling less than the min_padded_size_ratio or more than the
max_padded_size_ratio. If the min_padded_size_ratio or max_padded_size_ratio
is lower than what is possible to maintain the aspect ratio, then this method
will use the least padding to achieve the specified aspect ratio.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
aspect_ratio: aspect ratio of the final image.
min_padded_size_ratio: min ratio of padded image height and width to the
input image's height and width.
max_padded_size_ratio: max ratio of padded image height and width to the
input image's height and width.
seed: random seed.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If label_scores, masks, or keypoints is not None, the function also returns:
label_scores: new label scores.
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
Raises:
ValueError: If image is not a 3D tensor.
"""
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('RandomPadToAspectRatio', values=[image]):
image_shape = tf.shape(image)
image_height = tf.to_float(image_shape[0])
image_width = tf.to_float(image_shape[1])
image_aspect_ratio = image_width / image_height
new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
target_height = tf.cond(
image_aspect_ratio <= new_aspect_ratio,
lambda: image_height,
lambda: image_width / new_aspect_ratio)
target_width = tf.cond(
image_aspect_ratio >= new_aspect_ratio,
lambda: image_width,
lambda: image_height * new_aspect_ratio)
min_height = tf.maximum(
min_padded_size_ratio[0] * image_height, target_height)
min_width = tf.maximum(
min_padded_size_ratio[1] * image_width, target_width)
max_height = tf.maximum(
max_padded_size_ratio[0] * image_height, target_height)
max_width = tf.maximum(
max_padded_size_ratio[1] * image_width, target_width)
min_scale = tf.maximum(min_height / target_height, min_width / target_width)
max_scale = tf.minimum(max_height / target_height, max_width / target_width)
scale = tf.random_uniform([], min_scale, max_scale, seed=seed)
target_height = scale * target_height
target_width = scale * target_width
new_image = tf.image.pad_to_bounding_box(
image, 0, 0, tf.to_int32(target_height), tf.to_int32(target_width))
im_box = tf.stack([
0.0,
0.0,
target_height / image_height,
target_width / image_width
])
boxlist = box_list.BoxList(boxes)
new_boxlist = box_list_ops.change_coordinate_frame(boxlist, im_box)
new_boxes = new_boxlist.get()
result = [new_image, new_boxes]
if masks is not None:
new_masks = tf.expand_dims(masks, -1)
new_masks = tf.image.pad_to_bounding_box(new_masks, 0, 0,
tf.to_int32(target_height),
tf.to_int32(target_width))
new_masks = tf.squeeze(new_masks, [-1])
result.append(new_masks)
if keypoints is not None:
new_keypoints = keypoint_ops.change_coordinate_frame(keypoints, im_box)
result.append(new_keypoints)
return tuple(result)
def random_black_patches(image,
max_black_patches=10,
probability=0.5,
size_to_image_ratio=0.1,
random_seed=None):
"""Randomly adds some black patches to the image.
This op adds up to max_black_patches square black patches of a fixed size
to the image where size is specified via the size_to_image_ratio parameter.
Args:
image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
max_black_patches: number of times that the function tries to add a
black box to the image.
probability: at each try, what is the chance of adding a box.
size_to_image_ratio: Determines the ratio of the size of the black patches
to the size of the image.
box_size = size_to_image_ratio *
min(image_width, image_height)
random_seed: random seed.
Returns:
image
"""
def add_black_patch_to_image(image):
"""Function for adding one patch to the image.
Args:
image: image
Returns:
image with a randomly added black box
"""
image_shape = tf.shape(image)
image_height = image_shape[0]
image_width = image_shape[1]
box_size = tf.to_int32(
tf.multiply(
tf.minimum(tf.to_float(image_height), tf.to_float(image_width)),
size_to_image_ratio))
normalized_y_min = tf.random_uniform(
[], minval=0.0, maxval=(1.0 - size_to_image_ratio), seed=random_seed)
normalized_x_min = tf.random_uniform(
[], minval=0.0, maxval=(1.0 - size_to_image_ratio), seed=random_seed)
y_min = tf.to_int32(normalized_y_min * tf.to_float(image_height))
x_min = tf.to_int32(normalized_x_min * tf.to_float(image_width))
black_box = tf.ones([box_size, box_size, 3], dtype=tf.float32)
mask = 1.0 - tf.image.pad_to_bounding_box(black_box, y_min, x_min,
image_height, image_width)
image = tf.multiply(image, mask)
return image
with tf.name_scope('RandomBlackPatchInImage', values=[image]):
for _ in range(max_black_patches):
random_prob = tf.random_uniform(
[], minval=0.0, maxval=1.0, dtype=tf.float32, seed=random_seed)
image = tf.cond(
tf.greater(random_prob, probability), lambda: image,
lambda: add_black_patch_to_image(image))
return image
def image_to_float(image):
"""Used in Faster R-CNN. Casts image pixel values to float.
Args:
image: input image which might be in tf.uint8 or sth else format
Returns:
image: image in tf.float32 format.
"""
with tf.name_scope('ImageToFloat', values=[image]):
image = tf.to_float(image)
return image
def random_resize_method(image, target_size):
"""Uses a random resize method to resize the image to target size.
Args:
image: a rank 3 tensor.
target_size: a list of [target_height, target_width]
Returns:
resized image.
"""
resized_image = _apply_with_random_selector(
image,
lambda x, method: tf.image.resize_images(x, target_size, method),
num_cases=4)
return resized_image
def _compute_new_static_size(image, min_dimension, max_dimension):
"""Compute new static shape for resize_to_range method."""
image_shape = image.get_shape().as_list()
orig_height = image_shape[0]
orig_width = image_shape[1]
orig_min_dim = min(orig_height, orig_width)
# Calculates the larger of the possible sizes
large_scale_factor = min_dimension / float(orig_min_dim)
# Scaling orig_(height|width) by large_scale_factor will make the smaller
# dimension equal to min_dimension, save for floating point rounding errors.
# For reasonably-sized images, taking the nearest integer will reliably
# eliminate this error.
large_height = int(round(orig_height * large_scale_factor))
large_width = int(round(orig_width * large_scale_factor))
large_size = [large_height, large_width]
if max_dimension:
# Calculates the smaller of the possible sizes, use that if the larger
# is too big.
orig_max_dim = max(orig_height, orig_width)
small_scale_factor = max_dimension / float(orig_max_dim)
# Scaling orig_(height|width) by small_scale_factor will make the larger
# dimension equal to max_dimension, save for floating point rounding
# errors. For reasonably-sized images, taking the nearest integer will
# reliably eliminate this error.
small_height = int(round(orig_height * small_scale_factor))
small_width = int(round(orig_width * small_scale_factor))
small_size = [small_height, small_width]
new_size = large_size
if max(large_size) > max_dimension:
new_size = small_size
else:
new_size = large_size
return tf.constant(new_size)
def _compute_new_dynamic_size(image, min_dimension, max_dimension):
"""Compute new dynamic shape for resize_to_range method."""
image_shape = tf.shape(image)
orig_height = tf.to_float(image_shape[0])
orig_width = tf.to_float(image_shape[1])
orig_min_dim = tf.minimum(orig_height, orig_width)
# Calculates the larger of the possible sizes
min_dimension = tf.constant(min_dimension, dtype=tf.float32)
large_scale_factor = min_dimension / orig_min_dim
# Scaling orig_(height|width) by large_scale_factor will make the smaller
# dimension equal to min_dimension, save for floating point rounding errors.
# For reasonably-sized images, taking the nearest integer will reliably
# eliminate this error.
large_height = tf.to_int32(tf.round(orig_height * large_scale_factor))
large_width = tf.to_int32(tf.round(orig_width * large_scale_factor))
large_size = tf.stack([large_height, large_width])
if max_dimension:
# Calculates the smaller of the possible sizes, use that if the larger
# is too big.
orig_max_dim = tf.maximum(orig_height, orig_width)
max_dimension = tf.constant(max_dimension, dtype=tf.float32)
small_scale_factor = max_dimension / orig_max_dim
# Scaling orig_(height|width) by small_scale_factor will make the larger
# dimension equal to max_dimension, save for floating point rounding
# errors. For reasonably-sized images, taking the nearest integer will
# reliably eliminate this error.
small_height = tf.to_int32(tf.round(orig_height * small_scale_factor))
small_width = tf.to_int32(tf.round(orig_width * small_scale_factor))
small_size = tf.stack([small_height, small_width])
new_size = tf.cond(
tf.to_float(tf.reduce_max(large_size)) > max_dimension,
lambda: small_size, lambda: large_size)
else:
new_size = large_size
return new_size
def resize_to_range(image,
masks=None,
min_dimension=None,
max_dimension=None,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False):
"""Resizes an image so its dimensions are within the provided value.
The output size can be described by two cases:
1. If the image can be rescaled so its minimum dimension is equal to the
provided value without the other dimension exceeding max_dimension,
then do so.
2. Otherwise, resize so the largest dimension is equal to max_dimension.
Args:
image: A 3D tensor of shape [height, width, channels]
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks.
min_dimension: (optional) (scalar) desired size of the smaller image
dimension.
max_dimension: (optional) (scalar) maximum allowed size
of the larger image dimension.
method: (optional) interpolation method used in resizing. Defaults to
BILINEAR.
align_corners: bool. If true, exactly align all 4 corners of the input
and output. Defaults to False.
Returns:
A 3D tensor of shape [new_height, new_width, channels],
where the image has been resized (with bilinear interpolation) so that
min(new_height, new_width) == min_dimension or
max(new_height, new_width) == max_dimension.
If masks is not None, also outputs masks:
A 3D tensor of shape [num_instances, new_height, new_width]
Raises:
ValueError: if the image is not a 3D tensor.
"""
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('ResizeToRange', values=[image, min_dimension]):
if image.get_shape().is_fully_defined():
new_size = _compute_new_static_size(image, min_dimension, max_dimension)
else:
new_size = _compute_new_dynamic_size(image, min_dimension, max_dimension)
new_image = tf.image.resize_images(
image, new_size, method=method, align_corners=align_corners)
result = new_image
if masks is not None:
new_masks = tf.expand_dims(masks, 3)
new_masks = tf.image.resize_nearest_neighbor(
new_masks, new_size, align_corners=align_corners)
new_masks = tf.squeeze(new_masks, 3)
result = [new_image, new_masks]
return result
# TODO: Make sure the static shapes are preserved.
def resize_to_min_dimension(image, masks=None, min_dimension=600):
"""Resizes image and masks given the min size maintaining the aspect ratio.
If one of the image dimensions is smaller that min_dimension, it will scale
the image such that its smallest dimension is equal to min_dimension.
Otherwise, will keep the image size as is.
Args:
image: a tensor of size [height, width, channels].
masks: (optional) a tensors of size [num_instances, height, width].
min_dimension: minimum image dimension.
Returns:
a tuple containing the following:
Resized image. A tensor of size [new_height, new_width, channels].
(optional) Resized masks. A tensor of
size [num_instances, new_height, new_width].
Raises:
ValueError: if the image is not a 3D tensor.
"""
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('ResizeGivenMinDimension', values=[image, min_dimension]):
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
min_image_dimension = tf.minimum(image_height, image_width)
min_target_dimension = tf.maximum(min_image_dimension, min_dimension)
target_ratio = tf.to_float(min_target_dimension) / tf.to_float(
min_image_dimension)
target_height = tf.to_int32(tf.to_float(image_height) * target_ratio)
target_width = tf.to_int32(tf.to_float(image_width) * target_ratio)
image = tf.image.resize_bilinear(
tf.expand_dims(image, axis=0),
size=[target_height, target_width],
align_corners=True)
result = tf.squeeze(image, axis=0)
if masks is not None:
masks = tf.image.resize_nearest_neighbor(
tf.expand_dims(masks, axis=3),
size=[target_height, target_width],
align_corners=True)
result = (result, tf.squeeze(masks, axis=3))
return result
def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None):
"""Scales boxes from normalized to pixel coordinates.
Args:
image: A 3D float32 tensor of shape [height, width, channels].
boxes: A 2D float32 tensor of shape [num_boxes, 4] containing the bounding
boxes in normalized coordinates. Each row is of the form
[ymin, xmin, ymax, xmax].
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x normalized
coordinates.
Returns:
image: unchanged input image.
scaled_boxes: a 2D float32 tensor of shape [num_boxes, 4] containing the
bounding boxes in pixel coordinates.
scaled_keypoints: a 3D float32 tensor with shape
[num_instances, num_keypoints, 2] containing the keypoints in pixel
coordinates.
"""
boxlist = box_list.BoxList(boxes)
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get()
result = [image, scaled_boxes]
if keypoints is not None:
scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width)
result.append(scaled_keypoints)
return tuple(result)
# pylint: disable=g-doc-return-or-yield
def resize_image(image,
masks=None,
new_height=600,
new_width=1024,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False):
"""See `tf.image.resize_images` for detailed doc."""
with tf.name_scope(
'ResizeImage',
values=[image, new_height, new_width, method, align_corners]):
new_image = tf.image.resize_images(
image, [new_height, new_width],
method=method,
align_corners=align_corners)
result = new_image
if masks is not None:
num_instances = tf.shape(masks)[0]
new_size = tf.constant([new_height, new_width], dtype=tf.int32)
def resize_masks_branch():
new_masks = tf.expand_dims(masks, 3)
new_masks = tf.image.resize_nearest_neighbor(
new_masks, new_size, align_corners=align_corners)
new_masks = tf.squeeze(new_masks, axis=3)
return new_masks
def reshape_masks_branch():
new_masks = tf.reshape(masks, [0, new_size[0], new_size[1]])
return new_masks
masks = tf.cond(num_instances > 0, resize_masks_branch,
reshape_masks_branch)
result = [new_image, masks]
return result
def subtract_channel_mean(image, means=None):
"""Normalizes an image by subtracting a mean from each channel.
Args:
image: A 3D tensor of shape [height, width, channels]
means: float list containing a mean for each channel
Returns:
normalized_images: a tensor of shape [height, width, channels]
Raises:
ValueError: if images is not a 4D tensor or if the number of means is not
equal to the number of channels.
"""
with tf.name_scope('SubtractChannelMean', values=[image, means]):
if len(image.get_shape()) != 3:
raise ValueError('Input must be of size [height, width, channels]')
if len(means) != image.get_shape()[-1]:
raise ValueError('len(means) must match the number of channels')
return image - [[means]]
def one_hot_encoding(labels, num_classes=None):
"""One-hot encodes the multiclass labels.
Example usage:
labels = tf.constant([1, 4], dtype=tf.int32)
one_hot = OneHotEncoding(labels, num_classes=5)
one_hot.eval() # evaluates to [0, 1, 0, 0, 1]
Args:
labels: A tensor of shape [None] corresponding to the labels.
num_classes: Number of classes in the dataset.
Returns:
onehot_labels: a tensor of shape [num_classes] corresponding to the one hot
encoding of the labels.
Raises:
ValueError: if num_classes is not specified.
"""
with tf.name_scope('OneHotEncoding', values=[labels]):
if num_classes is None:
raise ValueError('num_classes must be specified')
labels = tf.one_hot(labels, num_classes, 1, 0)
return tf.reduce_max(labels, 0)
def rgb_to_gray(image):
"""Converts a 3 channel RGB image to a 1 channel grayscale image.
Args:
image: Rank 3 float32 tensor containing 1 image -> [height, width, 3]
with pixel values varying between [0, 1].
Returns:
image: A single channel grayscale image -> [image, height, 1].
"""
return tf.image.rgb_to_grayscale(image)
def ssd_random_crop(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio_range=((0.5, 2.0),) * 7,
#aspect_ratio_range=((0.75, 1.25),) * 7,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
#overlap_thresh=(0.0, 0.75, 0.8, 0.8, 0.85, 0.95, 1.0),
random_coef=(0.15,) * 7,
seed=None):
"""Random crop preprocessing with default parameters as in SSD paper.
Liu et al., SSD: Single shot multibox detector.
For further information on random crop preprocessing refer to RandomCrop
function above.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: rank 1 float32 tensor containing the scores.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
seed: random seed.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If label_scores, masks, or keypoints is not None, the function also returns:
label_scores: new label scores.
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
def random_crop_selector(selected_result, index):
"""Applies random_crop_image to selected result.
Args:
selected_result: A tuple containing image, boxes, labels, keypoints (if
not None), and masks (if not None).
index: The index that was randomly selected.
Returns: A tuple containing image, boxes, labels, keypoints (if not None),
and masks (if not None).
"""
i = 3
image, boxes, labels = selected_result[:i]
selected_label_scores = None
selected_masks = None
selected_keypoints = None
if label_scores is not None:
selected_label_scores = selected_result[i]
i += 1
if masks is not None:
selected_masks = selected_result[i]
i += 1
if keypoints is not None:
selected_keypoints = selected_result[i]
return random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=selected_label_scores,
masks=selected_masks,
keypoints=selected_keypoints,
min_object_covered=min_object_covered[index],
aspect_ratio_range=aspect_ratio_range[index],
area_range=area_range[index],
overlap_thresh=overlap_thresh[index],
random_coef=random_coef[index],
seed=seed)
result = _apply_with_random_selector_tuples(
tuple(
t for t in (image, boxes, labels, label_scores, masks, keypoints)
if t is not None),
random_crop_selector,
num_cases=len(min_object_covered))
return result
def ssd_random_crop_pad(image,
boxes,
labels,
label_scores=None,
min_object_covered=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio_range=((0.5, 2.0),) * 6,
#aspect_ratio_range=((0.75, 1.25),) * 6,
area_range=((0.1, 1.0),) * 6,
overlap_thresh=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
#overlap_thresh=(0.7, 0.75, 0.8, 0.8, 0.85, 0.95, 1.0),
random_coef=(0.15,) * 6,
min_padded_size_ratio=((1.0, 1.0),) * 6,
max_padded_size_ratio=((2.0, 2.0),) * 6,
pad_color=(None,) * 6,
seed=None):
"""Random crop preprocessing with default parameters as in SSD paper.
Liu et al., SSD: Single shot multibox detector.
For further information on random crop preprocessing refer to RandomCrop
function above.
Args:
image: rank 3 float32 tensor containing 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: float32 tensor of shape [num_instances] representing the
score for each box.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
min_padded_size_ratio: min ratio of padded image height and width to the
input image's height and width.
max_padded_size_ratio: max ratio of padded image height and width to the
input image's height and width.
pad_color: padding color. A rank 1 tensor of [3] with dtype=tf.float32.
if set as None, it will be set to average color of the randomly
cropped image.
seed: random seed.
Returns:
image: Image shape will be [new_height, new_width, channels].
boxes: boxes which is the same rank as input boxes. Boxes are in normalized
form.
new_labels: new labels.
new_label_scores: new label scores.
"""
def random_crop_pad_selector(image_boxes_labels, index):
i = 3
image, boxes, labels = image_boxes_labels[:i]
selected_label_scores = None
if label_scores is not None:
selected_label_scores = image_boxes_labels[i]
return random_crop_pad_image(
image,
boxes,
labels,
selected_label_scores,
min_object_covered=min_object_covered[index],
aspect_ratio_range=aspect_ratio_range[index],
area_range=area_range[index],
overlap_thresh=overlap_thresh[index],
random_coef=random_coef[index],
min_padded_size_ratio=min_padded_size_ratio[index],
max_padded_size_ratio=max_padded_size_ratio[index],
pad_color=pad_color[index],
seed=seed)
return _apply_with_random_selector_tuples(
tuple(t for t in (image, boxes, labels, label_scores) if t is not None),
random_crop_pad_selector,
num_cases=len(min_object_covered))
def ssd_random_crop_fixed_aspect_ratio(
image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio=1.0,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 7,
seed=None):
"""Random crop preprocessing with default parameters as in SSD paper.
Liu et al., SSD: Single shot multibox detector.
For further information on random crop preprocessing refer to RandomCrop
function above.
The only difference is that the aspect ratio of the crops are fixed.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: (optional) float32 tensor of shape [num_instances]
representing the score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio: aspect ratio of the cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
seed: random seed.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If masks or keypoints is not None, the function also returns:
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
aspect_ratio_range = ((aspect_ratio, aspect_ratio),) * len(area_range)
crop_result = ssd_random_crop(
image, boxes, labels, label_scores, masks, keypoints, min_object_covered,
aspect_ratio_range, area_range, overlap_thresh, random_coef, seed)
i = 3
new_image, new_boxes, new_labels = crop_result[:i]
new_label_scores = None
new_masks = None
new_keypoints = None
if label_scores is not None:
new_label_scores = crop_result[i]
i += 1
if masks is not None:
new_masks = crop_result[i]
i += 1
if keypoints is not None:
new_keypoints = crop_result[i]
result = random_crop_to_aspect_ratio(
new_image,
new_boxes,
new_labels,
new_label_scores,
new_masks,
new_keypoints,
aspect_ratio=aspect_ratio,
seed=seed)
return result
def ssd_random_crop_pad_fixed_aspect_ratio(
image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio=1.0,
aspect_ratio_range=((0.5, 2.0),) * 7,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 7,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(2.0, 2.0),
seed=None):
"""Random crop and pad preprocessing with default parameters as in SSD paper.
Liu et al., SSD: Single shot multibox detector.
For further information on random crop preprocessing refer to RandomCrop
function above.
The only difference is that after the initial crop, images are zero-padded
to a fixed aspect ratio instead of being resized to that aspect ratio.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
boxes: rank 2 float32 tensor containing the bounding boxes -> [N, 4].
Boxes are in normalized form meaning their coordinates vary
between [0, 1].
Each row is in the form of [ymin, xmin, ymax, xmax].
labels: rank 1 int32 tensor containing the object classes.
label_scores: (optional) float32 tensor of shape [num_instances]
representing the score for each box.
masks: (optional) rank 3 float32 tensor with shape
[num_instances, height, width] containing instance masks. The masks
are of the same height, width as the input `image`.
keypoints: (optional) rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]. The keypoints are in y-x
normalized coordinates.
min_object_covered: the cropped image must cover at least this fraction of
at least one of the input bounding boxes.
aspect_ratio: the final aspect ratio to pad to.
aspect_ratio_range: allowed range for aspect ratio of cropped image.
area_range: allowed range for area ratio between cropped image and the
original image.
overlap_thresh: minimum overlap thresh with new cropped
image to keep the box.
random_coef: a random coefficient that defines the chance of getting the
original image. If random_coef is 0, we will always get the
cropped image, and if it is 1.0, we will always get the
original image.
min_padded_size_ratio: min ratio of padded image height and width to the
input image's height and width.
max_padded_size_ratio: max ratio of padded image height and width to the
input image's height and width.
seed: random seed.
Returns:
image: image which is the same rank as input image.
boxes: boxes which is the same rank as input boxes.
Boxes are in normalized form.
labels: new labels.
If masks or keypoints is not None, the function also returns:
masks: rank 3 float32 tensor with shape [num_instances, height, width]
containing instance masks.
keypoints: rank 3 float32 tensor with shape
[num_instances, num_keypoints, 2]
"""
crop_result = ssd_random_crop(
image, boxes, labels, label_scores, masks, keypoints, min_object_covered,
aspect_ratio_range, area_range, overlap_thresh, random_coef, seed)
i = 3
new_image, new_boxes, new_labels = crop_result[:i]
new_label_scores = None
new_masks = None
new_keypoints = None
if label_scores is not None:
new_label_scores = crop_result[i]
i += 1
if masks is not None:
new_masks = crop_result[i]
i += 1
if keypoints is not None:
new_keypoints = crop_result[i]
result = random_pad_to_aspect_ratio(
new_image,
new_boxes,
new_masks,
new_keypoints,
aspect_ratio=aspect_ratio,
min_padded_size_ratio=min_padded_size_ratio,
max_padded_size_ratio=max_padded_size_ratio,
seed=seed)
result = list(result)
if new_label_scores is not None:
result.insert(2, new_label_scores)
result.insert(2, new_labels)
result = tuple(result)
return result
def get_default_func_arg_map(include_label_scores=False,
include_instance_masks=False,
include_keypoints=False):
"""Returns the default mapping from a preprocessor function to its args.
Args:
include_label_scores: If True, preprocessing functions will modify the
label scores, too.
include_instance_masks: If True, preprocessing functions will modify the
instance masks, too.
include_keypoints: If True, preprocessing functions will modify the
keypoints, too.
Returns:
A map from preprocessing functions to the arguments they receive.
"""
groundtruth_label_scores = None
if include_label_scores:
groundtruth_label_scores = (fields.InputDataFields.groundtruth_label_scores)
groundtruth_instance_masks = None
if include_instance_masks:
groundtruth_instance_masks = (
fields.InputDataFields.groundtruth_instance_masks)
groundtruth_keypoints = None
if include_keypoints:
groundtruth_keypoints = fields.InputDataFields.groundtruth_keypoints
prep_func_arg_map = {
normalize_image: (fields.InputDataFields.image,),
random_horizontal_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_vertical_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_rotation90: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pixel_value_scale: (fields.InputDataFields.image,),
random_image_scale: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
random_rgb_to_gray: (fields.InputDataFields.image,),
random_adjust_brightness: (fields.InputDataFields.image,),
random_adjust_contrast: (fields.InputDataFields.image,),
random_adjust_hue: (fields.InputDataFields.image,),
random_adjust_saturation: (fields.InputDataFields.image,),
random_distort_color: (fields.InputDataFields.image,),
random_jitter_boxes: (fields.InputDataFields.groundtruth_boxes,),
random_crop_image: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes),
random_crop_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores),
random_crop_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pad_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_black_patches: (fields.InputDataFields.image,),
retain_boxes_above_threshold: (
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
image_to_float: (fields.InputDataFields.image,),
random_resize_method: (fields.InputDataFields.image,),
resize_to_range: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
resize_to_min_dimension: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
scale_boxes_to_pixel_coordinates: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_keypoints,),
resize_image: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
subtract_channel_mean: (fields.InputDataFields.image,),
one_hot_encoding: (fields.InputDataFields.groundtruth_image_classes,),
rgb_to_gray: (fields.InputDataFields.image,),
ssd_random_crop: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
ssd_random_crop_pad: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores),
ssd_random_crop_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
ssd_random_crop_pad_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
}
return prep_func_arg_map
def preprocess(tensor_dict, preprocess_options, func_arg_map=None):
"""Preprocess images and bounding boxes.
Various types of preprocessing (to be implemented) based on the
preprocess_options dictionary e.g. "crop image" (affects image and possibly
boxes), "white balance image" (affects only image), etc. If self._options
is None, no preprocessing is done.
Args:
tensor_dict: dictionary that contains images, boxes, and can contain other
things as well.
images-> rank 4 float32 tensor contains
1 image -> [1, height, width, 3].
with pixel values varying between [0, 1]
boxes-> rank 2 float32 tensor containing
the bounding boxes -> [N, 4].
Boxes are in normalized form meaning
their coordinates vary between [0, 1].
Each row is in the form
of [ymin, xmin, ymax, xmax].
preprocess_options: It is a list of tuples, where each tuple contains a
function and a dictionary that contains arguments and
their values.
func_arg_map: mapping from preprocessing functions to arguments that they
expect to receive and return.
Returns:
tensor_dict: which contains the preprocessed images, bounding boxes, etc.
Raises:
ValueError: (a) If the functions passed to Preprocess
are not in func_arg_map.
(b) If the arguments that a function needs
do not exist in tensor_dict.
(c) If image in tensor_dict is not rank 4
"""
if func_arg_map is None:
func_arg_map = get_default_func_arg_map()
# changes the images to image (rank 4 to rank 3) since the functions
# receive rank 3 tensor for image
if fields.InputDataFields.image in tensor_dict:
images = tensor_dict[fields.InputDataFields.image]
if len(images.get_shape()) != 4:
raise ValueError('images in tensor_dict should be rank 4')
image = tf.squeeze(images, squeeze_dims=[0])
tensor_dict[fields.InputDataFields.image] = image
# Preprocess inputs based on preprocess_options
for option in preprocess_options:
func, params = option
if func not in func_arg_map:
raise ValueError('The function %s does not exist in func_arg_map' %
(func.__name__))
arg_names = func_arg_map[func]
for a in arg_names:
if a is not None and a not in tensor_dict:
raise ValueError('The function %s requires argument %s' %
(func.__name__, a))
def get_arg(key):
return tensor_dict[key] if key is not None else None
args = [get_arg(a) for a in arg_names]
results = func(*args, **params)
if not isinstance(results, (list, tuple)):
results = (results,)
# Removes None args since the return values will not contain those.
arg_names = [arg_name for arg_name in arg_names if arg_name is not None]
for res, arg_name in zip(results, arg_names):
tensor_dict[arg_name] = res
# changes the image to images (rank 3 to rank 4) to be compatible to what
# we received in the first place
if fields.InputDataFields.image in tensor_dict:
image = tensor_dict[fields.InputDataFields.image]
images = tf.expand_dims(image, 0)
tensor_dict[fields.InputDataFields.image] = images
return tensor_dict
| 39.031603 | 99 | 0.668623 |
import sys
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import control_flow_ops
from object_detection.core import box_list
from object_detection.core import box_list_ops
from object_detection.core import keypoint_ops
from object_detection.core import standard_fields as fields
def _apply_with_random_selector(x, func, num_cases):
rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
return control_flow_ops.merge([func(
control_flow_ops.switch(x, tf.equal(rand_sel, case))[1], case)
for case in range(num_cases)])[0]
def _apply_with_random_selector_tuples(x, func, num_cases):
num_inputs = len(x)
rand_sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
tuples = [list() for t in x]
for case in range(num_cases):
new_x = [control_flow_ops.switch(t, tf.equal(rand_sel, case))[1] for t in x]
output = func(tuple(new_x), case)
for j in range(num_inputs):
tuples[j].append(output[j])
for i in range(num_inputs):
tuples[i] = control_flow_ops.merge(tuples[i])[0]
return tuple(tuples)
def _random_integer(minval, maxval, seed):
return tf.random_uniform(
[], minval=minval, maxval=maxval, dtype=tf.int32, seed=seed)
def normalize_image(image, original_minval, original_maxval, target_minval,
target_maxval):
with tf.name_scope('NormalizeImage', values=[image]):
original_minval = float(original_minval)
original_maxval = float(original_maxval)
target_minval = float(target_minval)
target_maxval = float(target_maxval)
image = tf.to_float(image)
image = tf.subtract(image, original_minval)
image = tf.multiply(image, (target_maxval - target_minval) /
(original_maxval - original_minval))
image = tf.add(image, target_minval)
return image
def retain_boxes_above_threshold(boxes,
labels,
label_scores,
masks=None,
keypoints=None,
threshold=0.0):
with tf.name_scope('RetainBoxesAboveThreshold',
values=[boxes, labels, label_scores]):
indices = tf.where(
tf.logical_or(label_scores > threshold, tf.is_nan(label_scores)))
indices = tf.squeeze(indices, axis=1)
retained_boxes = tf.gather(boxes, indices)
retained_labels = tf.gather(labels, indices)
retained_label_scores = tf.gather(label_scores, indices)
result = [retained_boxes, retained_labels, retained_label_scores]
if masks is not None:
retained_masks = tf.gather(masks, indices)
result.append(retained_masks)
if keypoints is not None:
retained_keypoints = tf.gather(keypoints, indices)
result.append(retained_keypoints)
return result
def _flip_boxes_left_right(boxes):
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
flipped_xmin = tf.subtract(1.0, xmax)
flipped_xmax = tf.subtract(1.0, xmin)
flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)
return flipped_boxes
def _flip_boxes_up_down(boxes):
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
flipped_ymin = tf.subtract(1.0, ymax)
flipped_ymax = tf.subtract(1.0, ymin)
flipped_boxes = tf.concat([flipped_ymin, xmin, flipped_ymax, xmax], 1)
return flipped_boxes
def _rot90_boxes(boxes):
ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)
rotated_ymin = tf.subtract(1.0, xmax)
rotated_ymax = tf.subtract(1.0, xmin)
rotated_xmin = ymin
rotated_xmax = ymax
rotated_boxes = tf.concat(
[rotated_ymin, rotated_xmin, rotated_ymax, rotated_xmax], 1)
return rotated_boxes
def _flip_masks_left_right(masks):
return masks[:, :, ::-1]
def _flip_masks_up_down(masks):
return masks[:, ::-1, :]
def _rot90_masks(masks):
masks = tf.transpose(masks, [0, 2, 1])
return masks[:, ::-1, :]
def random_horizontal_flip(image,
boxes=None,
masks=None,
keypoints=None,
keypoint_flip_permutation=None,
seed=None):
def _flip_image(image):
image_flipped = tf.image.flip_left_right(image)
return image_flipped
if keypoints is not None and keypoint_flip_permutation is None:
raise ValueError(
'keypoints are provided but keypoints_flip_permutation is not provided')
with tf.name_scope('RandomHorizontalFlip', values=[image, boxes]):
result = []
do_a_flip_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
result.append(image)
if boxes is not None:
boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_left_right(boxes),
lambda: boxes)
result.append(boxes)
if masks is not None:
masks = tf.cond(do_a_flip_random, lambda: _flip_masks_left_right(masks),
lambda: masks)
result.append(masks)
if keypoints is not None and keypoint_flip_permutation is not None:
permutation = keypoint_flip_permutation
keypoints = tf.cond(
do_a_flip_random,
lambda: keypoint_ops.flip_horizontal(keypoints, 0.5, permutation),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_vertical_flip(image,
boxes=None,
masks=None,
keypoints=None,
keypoint_flip_permutation=None,
seed=None):
def _flip_image(image):
image_flipped = tf.image.flip_up_down(image)
return image_flipped
if keypoints is not None and keypoint_flip_permutation is None:
raise ValueError(
'keypoints are provided but keypoints_flip_permutation is not provided')
with tf.name_scope('RandomVerticalFlip', values=[image, boxes]):
result = []
do_a_flip_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
image = tf.cond(do_a_flip_random, lambda: _flip_image(image), lambda: image)
result.append(image)
if boxes is not None:
boxes = tf.cond(do_a_flip_random, lambda: _flip_boxes_up_down(boxes),
lambda: boxes)
result.append(boxes)
if masks is not None:
masks = tf.cond(do_a_flip_random, lambda: _flip_masks_up_down(masks),
lambda: masks)
result.append(masks)
if keypoints is not None and keypoint_flip_permutation is not None:
permutation = keypoint_flip_permutation
keypoints = tf.cond(
do_a_flip_random,
lambda: keypoint_ops.flip_vertical(keypoints, 0.5, permutation),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_rotation90(image,
boxes=None,
masks=None,
keypoints=None,
seed=None):
def _rot90_image(image):
image_rotated = tf.image.rot90(image)
return image_rotated
with tf.name_scope('RandomRotation90', values=[image, boxes]):
result = []
do_a_rot90_random = tf.greater(tf.random_uniform([], seed=seed), 0.5)
image = tf.cond(do_a_rot90_random, lambda: _rot90_image(image),
lambda: image)
result.append(image)
if boxes is not None:
boxes = tf.cond(do_a_rot90_random, lambda: _rot90_boxes(boxes),
lambda: boxes)
result.append(boxes)
if masks is not None:
masks = tf.cond(do_a_rot90_random, lambda: _rot90_masks(masks),
lambda: masks)
result.append(masks)
if keypoints is not None:
keypoints = tf.cond(
do_a_rot90_random,
lambda: keypoint_ops.rot90(keypoints),
lambda: keypoints)
result.append(keypoints)
return tuple(result)
def random_pixel_value_scale(image, minval=0.9, maxval=1.1, seed=None):
with tf.name_scope('RandomPixelValueScale', values=[image]):
image = tf.convert_to_tensor(image, name='image')
orig_dtype = image.dtype
image = tf.image.convert_image_dtype(image, tf.float32)
color_coef = tf.random_uniform(
tf.shape(image),
minval=minval,
maxval=maxval,
dtype=tf.float32,
seed=seed)
image = tf.multiply(image, color_coef)
return tf.image.convert_image_dtype(image, orig_dtype, saturate=True)
return image
def random_image_scale(image,
masks=None,
min_scale_ratio=0.5,
max_scale_ratio=2.0,
seed=None):
with tf.name_scope('RandomImageScale', values=[image]):
result = []
image_shape = tf.shape(image)
image_height = image_shape[0]
image_width = image_shape[1]
size_coef = tf.random_uniform([],
minval=min_scale_ratio,
maxval=max_scale_ratio,
dtype=tf.float32, seed=seed)
image_newysize = tf.to_int32(
tf.multiply(tf.to_float(image_height), size_coef))
image_newxsize = tf.to_int32(
tf.multiply(tf.to_float(image_width), size_coef))
image = tf.image.resize_images(
image, [image_newysize, image_newxsize], align_corners=True)
result.append(image)
if masks:
masks = tf.image.resize_nearest_neighbor(
masks, [image_newysize, image_newxsize], align_corners=True)
result.append(masks)
return tuple(result)
def random_rgb_to_gray(image, probability=0.1, seed=None):
def _image_to_gray(image):
image_gray1 = tf.image.rgb_to_grayscale(image)
image_gray3 = tf.image.grayscale_to_rgb(image_gray1)
return image_gray3
with tf.name_scope('RandomRGBtoGray', values=[image]):
do_gray_random = tf.random_uniform([], seed=seed)
image = tf.cond(
tf.greater(do_gray_random, probability), lambda: image,
lambda: _image_to_gray(image))
return image
def random_adjust_brightness(image, max_delta=0.2):
with tf.name_scope('RandomAdjustBrightness', values=[image]):
image = tf.image.random_brightness(image, max_delta)
return image
def random_adjust_contrast(image, min_delta=0.8, max_delta=1.25):
with tf.name_scope('RandomAdjustContrast', values=[image]):
image = tf.image.random_contrast(image, min_delta, max_delta)
return image
def random_adjust_hue(image, max_delta=0.02):
with tf.name_scope('RandomAdjustHue', values=[image]):
image = tf.image.random_hue(image, max_delta)
return image
def random_adjust_saturation(image, min_delta=0.8, max_delta=1.25):
with tf.name_scope('RandomAdjustSaturation', values=[image]):
image = tf.image.random_saturation(image, min_delta, max_delta)
return image
def random_distort_color(image, color_ordering=0):
with tf.name_scope('RandomDistortColor', values=[image]):
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
else:
raise ValueError('color_ordering must be in {0, 1}')
return image
def random_jitter_boxes(boxes, ratio=0.05, seed=None):
def random_jitter_box(box, ratio, seed):
rand_numbers = tf.random_uniform(
[1, 1, 4], minval=-ratio, maxval=ratio, dtype=tf.float32, seed=seed)
box_width = tf.subtract(box[0, 0, 3], box[0, 0, 1])
box_height = tf.subtract(box[0, 0, 2], box[0, 0, 0])
hw_coefs = tf.stack([box_height, box_width, box_height, box_width])
hw_rand_coefs = tf.multiply(hw_coefs, rand_numbers)
jittered_box = tf.add(box, hw_rand_coefs)
jittered_box = tf.clip_by_value(jittered_box, 0.0, 1.0)
return jittered_box
with tf.name_scope('RandomJitterBoxes', values=[boxes]):
boxes_shape = tf.shape(boxes)
boxes = tf.expand_dims(boxes, 1)
boxes = tf.expand_dims(boxes, 2)
distorted_boxes = tf.map_fn(
lambda x: random_jitter_box(x, ratio, seed), boxes, dtype=tf.float32)
distorted_boxes = tf.reshape(distorted_boxes, boxes_shape)
return distorted_boxes
def _strict_random_crop_image(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=1.0,
aspect_ratio_range=(0.75, 1.33),
area_range=(0.1, 1.0),
overlap_thresh=0.3):
with tf.name_scope('RandomCropImage', values=[image, boxes]):
image_shape = tf.shape(image)
boxes_expanded = tf.expand_dims(
tf.clip_by_value(
boxes, clip_value_min=0.0, clip_value_max=1.0), 1)
sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
image_shape,
bounding_boxes=boxes_expanded,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
max_attempts=100,
use_image_if_no_bounding_boxes=True)
im_box_begin, im_box_size, im_box = sample_distorted_bounding_box
new_image = tf.slice(image, im_box_begin, im_box_size)
new_image.set_shape([None, None, image.get_shape()[2]])
im_box_rank2 = tf.squeeze(im_box, squeeze_dims=[0])
im_box_rank1 = tf.squeeze(im_box)
boxlist = box_list.BoxList(boxes)
boxlist.add_field('labels', labels)
if label_scores is not None:
boxlist.add_field('label_scores', label_scores)
im_boxlist = box_list.BoxList(im_box_rank2)
boxlist, inside_window_ids = box_list_ops.prune_completely_outside_window(
boxlist, im_box_rank1)
overlapping_boxlist, keep_ids, black_boxlist = box_list_ops.prune_non_overlapping_boxes_custom(
boxlist, im_boxlist, overlap_thresh)
new_labels = overlapping_boxlist.get_field('labels')
new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
im_box_rank1)
,
lambda: tf.constant(0, dtype=tf.int32))
new_image = tf.image.pad_to_bounding_box(
image,
offset_height=offset_height,
offset_width=offset_width,
target_height=target_height,
target_width=target_width)
new_window = tf.to_float(
tf.stack([
-offset_height, -offset_width, target_height - offset_height,
target_width - offset_width
]))
new_window /= tf.to_float(
tf.stack([image_height, image_width, image_height, image_width]))
boxlist = box_list.BoxList(boxes)
new_boxlist = box_list_ops.change_coordinate_frame(boxlist, new_window)
new_boxes = new_boxlist.get()
return new_image, new_boxes
def random_crop_pad_image(image,
boxes,
labels,
label_scores=None,
min_object_covered=0.5,
aspect_ratio_range=(0.75/1.1, 0.75*1.1),
area_range=(0.2, 1.0),
overlap_thresh=0.7,
random_coef=0.0,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(1.75, 1.75),
pad_color=None,
seed=None):
ample()
crop, pad = False, False
if rand < 0.70:
crop = True
elif rand < 0.80:
pad = True
else:
crop = True
pad = True
image_size = tf.shape(image)
image_height = image_size[0]
image_width = image_size[1]
if crop and pad:
result = random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=label_scores,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
overlap_thresh=overlap_thresh,
random_coef=random_coef,
seed=seed)
cropped_image, cropped_boxes, cropped_labels = result[:3]
min_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
min_padded_size_ratio)
max_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
max_padded_size_ratio)
padded_image, padded_boxes = random_pad_image(
cropped_image,
cropped_boxes,
min_image_size=min_image_size,
max_image_size=max_image_size,
pad_color=pad_color,
seed=seed)
cropped_padded_output = (padded_image, padded_boxes, cropped_labels)
if label_scores is not None:
cropped_label_scores = result[3]
cropped_padded_output += (cropped_label_scores,)
elif crop:
result = random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=label_scores,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range,
area_range=area_range,
overlap_thresh=overlap_thresh,
random_coef=random_coef,
seed=seed)
cropped_image, cropped_boxes, cropped_labels = result[:3]
cropped_padded_output = (cropped_image, cropped_boxes, cropped_labels)
if label_scores is not None:
cropped_label_scores = result[3]
cropped_padded_output += (cropped_label_scores,)
elif pad:
min_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
min_padded_size_ratio)
max_image_size = tf.to_int32(
tf.to_float(tf.stack([image_height, image_width])) *
max_padded_size_ratio)
padded_image, padded_boxes = random_pad_image(
image,
boxes,
min_image_size=min_image_size,
max_image_size=max_image_size,
pad_color=pad_color,
seed=seed)
cropped_padded_output = (padded_image, padded_boxes, labels)
if label_scores is not None:
cropped_padded_output += (label_scores,)
else:
cropped_padded_output = (image, boxes, labels)
return cropped_padded_output
def random_crop_to_aspect_ratio(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
aspect_ratio=1.0,
overlap_thresh=0.3,
seed=None):
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('RandomCropToAspectRatio', values=[image]):
image_shape = tf.shape(image)
orig_height = image_shape[0]
orig_width = image_shape[1]
orig_aspect_ratio = tf.to_float(orig_width) / tf.to_float(orig_height)
new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
def target_height_fn():
return tf.to_int32(tf.round(tf.to_float(orig_width) / new_aspect_ratio))
target_height = tf.cond(orig_aspect_ratio >= new_aspect_ratio,
lambda: orig_height, target_height_fn)
def target_width_fn():
return tf.to_int32(tf.round(tf.to_float(orig_height) * new_aspect_ratio))
target_width = tf.cond(orig_aspect_ratio <= new_aspect_ratio,
lambda: orig_width, target_width_fn)
offset_height = _random_integer(0, orig_height - target_height + 1, seed)
offset_width = _random_integer(0, orig_width - target_width + 1, seed)
new_image = tf.image.crop_to_bounding_box(
image, offset_height, offset_width, target_height, target_width)
im_box = tf.stack([
tf.to_float(offset_height) / tf.to_float(orig_height),
tf.to_float(offset_width) / tf.to_float(orig_width),
tf.to_float(offset_height + target_height) / tf.to_float(orig_height),
tf.to_float(offset_width + target_width) / tf.to_float(orig_width)
])
boxlist = box_list.BoxList(boxes)
boxlist.add_field('labels', labels)
if label_scores is not None:
boxlist.add_field('label_scores', label_scores)
im_boxlist = box_list.BoxList(tf.expand_dims(im_box, 0))
overlapping_boxlist, keep_ids = box_list_ops.prune_non_overlapping_boxes(
boxlist, im_boxlist, overlap_thresh)
new_labels = overlapping_boxlist.get_field('labels')
new_boxlist = box_list_ops.change_coordinate_frame(overlapping_boxlist,
im_box)
new_boxlist = box_list_ops.clip_to_window(new_boxlist,
tf.constant([0.0, 0.0, 1.0, 1.0],
tf.float32))
new_boxes = new_boxlist.get()
result = [new_image, new_boxes, new_labels]
if label_scores is not None:
new_label_scores = overlapping_boxlist.get_field('label_scores')
result.append(new_label_scores)
if masks is not None:
masks_inside_window = tf.gather(masks, keep_ids)
masks_box_begin = tf.stack([0, offset_height, offset_width])
masks_box_size = tf.stack([-1, target_height, target_width])
new_masks = tf.slice(masks_inside_window, masks_box_begin, masks_box_size)
result.append(new_masks)
if keypoints is not None:
keypoints_inside_window = tf.gather(keypoints, keep_ids)
new_keypoints = keypoint_ops.change_coordinate_frame(
keypoints_inside_window, im_box)
new_keypoints = keypoint_ops.prune_outside_window(new_keypoints,
[0.0, 0.0, 1.0, 1.0])
result.append(new_keypoints)
return tuple(result)
def random_pad_to_aspect_ratio(image,
boxes,
masks=None,
keypoints=None,
aspect_ratio=1.0,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(2.0, 2.0),
seed=None):
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('RandomPadToAspectRatio', values=[image]):
image_shape = tf.shape(image)
image_height = tf.to_float(image_shape[0])
image_width = tf.to_float(image_shape[1])
image_aspect_ratio = image_width / image_height
new_aspect_ratio = tf.constant(aspect_ratio, dtype=tf.float32)
target_height = tf.cond(
image_aspect_ratio <= new_aspect_ratio,
lambda: image_height,
lambda: image_width / new_aspect_ratio)
target_width = tf.cond(
image_aspect_ratio >= new_aspect_ratio,
lambda: image_width,
lambda: image_height * new_aspect_ratio)
min_height = tf.maximum(
min_padded_size_ratio[0] * image_height, target_height)
min_width = tf.maximum(
min_padded_size_ratio[1] * image_width, target_width)
max_height = tf.maximum(
max_padded_size_ratio[0] * image_height, target_height)
max_width = tf.maximum(
max_padded_size_ratio[1] * image_width, target_width)
min_scale = tf.maximum(min_height / target_height, min_width / target_width)
max_scale = tf.minimum(max_height / target_height, max_width / target_width)
scale = tf.random_uniform([], min_scale, max_scale, seed=seed)
target_height = scale * target_height
target_width = scale * target_width
new_image = tf.image.pad_to_bounding_box(
image, 0, 0, tf.to_int32(target_height), tf.to_int32(target_width))
im_box = tf.stack([
0.0,
0.0,
target_height / image_height,
target_width / image_width
])
boxlist = box_list.BoxList(boxes)
new_boxlist = box_list_ops.change_coordinate_frame(boxlist, im_box)
new_boxes = new_boxlist.get()
result = [new_image, new_boxes]
if masks is not None:
new_masks = tf.expand_dims(masks, -1)
new_masks = tf.image.pad_to_bounding_box(new_masks, 0, 0,
tf.to_int32(target_height),
tf.to_int32(target_width))
new_masks = tf.squeeze(new_masks, [-1])
result.append(new_masks)
if keypoints is not None:
new_keypoints = keypoint_ops.change_coordinate_frame(keypoints, im_box)
result.append(new_keypoints)
return tuple(result)
def random_black_patches(image,
max_black_patches=10,
probability=0.5,
size_to_image_ratio=0.1,
random_seed=None):
def add_black_patch_to_image(image):
image_shape = tf.shape(image)
image_height = image_shape[0]
image_width = image_shape[1]
box_size = tf.to_int32(
tf.multiply(
tf.minimum(tf.to_float(image_height), tf.to_float(image_width)),
size_to_image_ratio))
normalized_y_min = tf.random_uniform(
[], minval=0.0, maxval=(1.0 - size_to_image_ratio), seed=random_seed)
normalized_x_min = tf.random_uniform(
[], minval=0.0, maxval=(1.0 - size_to_image_ratio), seed=random_seed)
y_min = tf.to_int32(normalized_y_min * tf.to_float(image_height))
x_min = tf.to_int32(normalized_x_min * tf.to_float(image_width))
black_box = tf.ones([box_size, box_size, 3], dtype=tf.float32)
mask = 1.0 - tf.image.pad_to_bounding_box(black_box, y_min, x_min,
image_height, image_width)
image = tf.multiply(image, mask)
return image
with tf.name_scope('RandomBlackPatchInImage', values=[image]):
for _ in range(max_black_patches):
random_prob = tf.random_uniform(
[], minval=0.0, maxval=1.0, dtype=tf.float32, seed=random_seed)
image = tf.cond(
tf.greater(random_prob, probability), lambda: image,
lambda: add_black_patch_to_image(image))
return image
def image_to_float(image):
with tf.name_scope('ImageToFloat', values=[image]):
image = tf.to_float(image)
return image
def random_resize_method(image, target_size):
resized_image = _apply_with_random_selector(
image,
lambda x, method: tf.image.resize_images(x, target_size, method),
num_cases=4)
return resized_image
def _compute_new_static_size(image, min_dimension, max_dimension):
image_shape = image.get_shape().as_list()
orig_height = image_shape[0]
orig_width = image_shape[1]
orig_min_dim = min(orig_height, orig_width)
large_scale_factor = min_dimension / float(orig_min_dim)
large_height = int(round(orig_height * large_scale_factor))
large_width = int(round(orig_width * large_scale_factor))
large_size = [large_height, large_width]
if max_dimension:
orig_max_dim = max(orig_height, orig_width)
small_scale_factor = max_dimension / float(orig_max_dim)
small_height = int(round(orig_height * small_scale_factor))
small_width = int(round(orig_width * small_scale_factor))
small_size = [small_height, small_width]
new_size = large_size
if max(large_size) > max_dimension:
new_size = small_size
else:
new_size = large_size
return tf.constant(new_size)
def _compute_new_dynamic_size(image, min_dimension, max_dimension):
image_shape = tf.shape(image)
orig_height = tf.to_float(image_shape[0])
orig_width = tf.to_float(image_shape[1])
orig_min_dim = tf.minimum(orig_height, orig_width)
min_dimension = tf.constant(min_dimension, dtype=tf.float32)
large_scale_factor = min_dimension / orig_min_dim
large_height = tf.to_int32(tf.round(orig_height * large_scale_factor))
large_width = tf.to_int32(tf.round(orig_width * large_scale_factor))
large_size = tf.stack([large_height, large_width])
if max_dimension:
orig_max_dim = tf.maximum(orig_height, orig_width)
max_dimension = tf.constant(max_dimension, dtype=tf.float32)
small_scale_factor = max_dimension / orig_max_dim
small_height = tf.to_int32(tf.round(orig_height * small_scale_factor))
small_width = tf.to_int32(tf.round(orig_width * small_scale_factor))
small_size = tf.stack([small_height, small_width])
new_size = tf.cond(
tf.to_float(tf.reduce_max(large_size)) > max_dimension,
lambda: small_size, lambda: large_size)
else:
new_size = large_size
return new_size
def resize_to_range(image,
masks=None,
min_dimension=None,
max_dimension=None,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False):
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('ResizeToRange', values=[image, min_dimension]):
if image.get_shape().is_fully_defined():
new_size = _compute_new_static_size(image, min_dimension, max_dimension)
else:
new_size = _compute_new_dynamic_size(image, min_dimension, max_dimension)
new_image = tf.image.resize_images(
image, new_size, method=method, align_corners=align_corners)
result = new_image
if masks is not None:
new_masks = tf.expand_dims(masks, 3)
new_masks = tf.image.resize_nearest_neighbor(
new_masks, new_size, align_corners=align_corners)
new_masks = tf.squeeze(new_masks, 3)
result = [new_image, new_masks]
return result
def resize_to_min_dimension(image, masks=None, min_dimension=600):
if len(image.get_shape()) != 3:
raise ValueError('Image should be 3D tensor')
with tf.name_scope('ResizeGivenMinDimension', values=[image, min_dimension]):
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
min_image_dimension = tf.minimum(image_height, image_width)
min_target_dimension = tf.maximum(min_image_dimension, min_dimension)
target_ratio = tf.to_float(min_target_dimension) / tf.to_float(
min_image_dimension)
target_height = tf.to_int32(tf.to_float(image_height) * target_ratio)
target_width = tf.to_int32(tf.to_float(image_width) * target_ratio)
image = tf.image.resize_bilinear(
tf.expand_dims(image, axis=0),
size=[target_height, target_width],
align_corners=True)
result = tf.squeeze(image, axis=0)
if masks is not None:
masks = tf.image.resize_nearest_neighbor(
tf.expand_dims(masks, axis=3),
size=[target_height, target_width],
align_corners=True)
result = (result, tf.squeeze(masks, axis=3))
return result
def scale_boxes_to_pixel_coordinates(image, boxes, keypoints=None):
boxlist = box_list.BoxList(boxes)
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
scaled_boxes = box_list_ops.scale(boxlist, image_height, image_width).get()
result = [image, scaled_boxes]
if keypoints is not None:
scaled_keypoints = keypoint_ops.scale(keypoints, image_height, image_width)
result.append(scaled_keypoints)
return tuple(result)
def resize_image(image,
masks=None,
new_height=600,
new_width=1024,
method=tf.image.ResizeMethod.BILINEAR,
align_corners=False):
with tf.name_scope(
'ResizeImage',
values=[image, new_height, new_width, method, align_corners]):
new_image = tf.image.resize_images(
image, [new_height, new_width],
method=method,
align_corners=align_corners)
result = new_image
if masks is not None:
num_instances = tf.shape(masks)[0]
new_size = tf.constant([new_height, new_width], dtype=tf.int32)
def resize_masks_branch():
new_masks = tf.expand_dims(masks, 3)
new_masks = tf.image.resize_nearest_neighbor(
new_masks, new_size, align_corners=align_corners)
new_masks = tf.squeeze(new_masks, axis=3)
return new_masks
def reshape_masks_branch():
new_masks = tf.reshape(masks, [0, new_size[0], new_size[1]])
return new_masks
masks = tf.cond(num_instances > 0, resize_masks_branch,
reshape_masks_branch)
result = [new_image, masks]
return result
def subtract_channel_mean(image, means=None):
with tf.name_scope('SubtractChannelMean', values=[image, means]):
if len(image.get_shape()) != 3:
raise ValueError('Input must be of size [height, width, channels]')
if len(means) != image.get_shape()[-1]:
raise ValueError('len(means) must match the number of channels')
return image - [[means]]
def one_hot_encoding(labels, num_classes=None):
with tf.name_scope('OneHotEncoding', values=[labels]):
if num_classes is None:
raise ValueError('num_classes must be specified')
labels = tf.one_hot(labels, num_classes, 1, 0)
return tf.reduce_max(labels, 0)
def rgb_to_gray(image):
return tf.image.rgb_to_grayscale(image)
def ssd_random_crop(image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio_range=((0.5, 2.0),) * 7,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 7,
seed=None):
def random_crop_selector(selected_result, index):
i = 3
image, boxes, labels = selected_result[:i]
selected_label_scores = None
selected_masks = None
selected_keypoints = None
if label_scores is not None:
selected_label_scores = selected_result[i]
i += 1
if masks is not None:
selected_masks = selected_result[i]
i += 1
if keypoints is not None:
selected_keypoints = selected_result[i]
return random_crop_image(
image=image,
boxes=boxes,
labels=labels,
label_scores=selected_label_scores,
masks=selected_masks,
keypoints=selected_keypoints,
min_object_covered=min_object_covered[index],
aspect_ratio_range=aspect_ratio_range[index],
area_range=area_range[index],
overlap_thresh=overlap_thresh[index],
random_coef=random_coef[index],
seed=seed)
result = _apply_with_random_selector_tuples(
tuple(
t for t in (image, boxes, labels, label_scores, masks, keypoints)
if t is not None),
random_crop_selector,
num_cases=len(min_object_covered))
return result
def ssd_random_crop_pad(image,
boxes,
labels,
label_scores=None,
min_object_covered=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio_range=((0.5, 2.0),) * 6,
area_range=((0.1, 1.0),) * 6,
overlap_thresh=(0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 6,
min_padded_size_ratio=((1.0, 1.0),) * 6,
max_padded_size_ratio=((2.0, 2.0),) * 6,
pad_color=(None,) * 6,
seed=None):
def random_crop_pad_selector(image_boxes_labels, index):
i = 3
image, boxes, labels = image_boxes_labels[:i]
selected_label_scores = None
if label_scores is not None:
selected_label_scores = image_boxes_labels[i]
return random_crop_pad_image(
image,
boxes,
labels,
selected_label_scores,
min_object_covered=min_object_covered[index],
aspect_ratio_range=aspect_ratio_range[index],
area_range=area_range[index],
overlap_thresh=overlap_thresh[index],
random_coef=random_coef[index],
min_padded_size_ratio=min_padded_size_ratio[index],
max_padded_size_ratio=max_padded_size_ratio[index],
pad_color=pad_color[index],
seed=seed)
return _apply_with_random_selector_tuples(
tuple(t for t in (image, boxes, labels, label_scores) if t is not None),
random_crop_pad_selector,
num_cases=len(min_object_covered))
def ssd_random_crop_fixed_aspect_ratio(
image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio=1.0,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 7,
seed=None):
aspect_ratio_range = ((aspect_ratio, aspect_ratio),) * len(area_range)
crop_result = ssd_random_crop(
image, boxes, labels, label_scores, masks, keypoints, min_object_covered,
aspect_ratio_range, area_range, overlap_thresh, random_coef, seed)
i = 3
new_image, new_boxes, new_labels = crop_result[:i]
new_label_scores = None
new_masks = None
new_keypoints = None
if label_scores is not None:
new_label_scores = crop_result[i]
i += 1
if masks is not None:
new_masks = crop_result[i]
i += 1
if keypoints is not None:
new_keypoints = crop_result[i]
result = random_crop_to_aspect_ratio(
new_image,
new_boxes,
new_labels,
new_label_scores,
new_masks,
new_keypoints,
aspect_ratio=aspect_ratio,
seed=seed)
return result
def ssd_random_crop_pad_fixed_aspect_ratio(
image,
boxes,
labels,
label_scores=None,
masks=None,
keypoints=None,
min_object_covered=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
aspect_ratio=1.0,
aspect_ratio_range=((0.5, 2.0),) * 7,
area_range=((0.1, 1.0),) * 7,
overlap_thresh=(0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0),
random_coef=(0.15,) * 7,
min_padded_size_ratio=(1.0, 1.0),
max_padded_size_ratio=(2.0, 2.0),
seed=None):
crop_result = ssd_random_crop(
image, boxes, labels, label_scores, masks, keypoints, min_object_covered,
aspect_ratio_range, area_range, overlap_thresh, random_coef, seed)
i = 3
new_image, new_boxes, new_labels = crop_result[:i]
new_label_scores = None
new_masks = None
new_keypoints = None
if label_scores is not None:
new_label_scores = crop_result[i]
i += 1
if masks is not None:
new_masks = crop_result[i]
i += 1
if keypoints is not None:
new_keypoints = crop_result[i]
result = random_pad_to_aspect_ratio(
new_image,
new_boxes,
new_masks,
new_keypoints,
aspect_ratio=aspect_ratio,
min_padded_size_ratio=min_padded_size_ratio,
max_padded_size_ratio=max_padded_size_ratio,
seed=seed)
result = list(result)
if new_label_scores is not None:
result.insert(2, new_label_scores)
result.insert(2, new_labels)
result = tuple(result)
return result
def get_default_func_arg_map(include_label_scores=False,
include_instance_masks=False,
include_keypoints=False):
groundtruth_label_scores = None
if include_label_scores:
groundtruth_label_scores = (fields.InputDataFields.groundtruth_label_scores)
groundtruth_instance_masks = None
if include_instance_masks:
groundtruth_instance_masks = (
fields.InputDataFields.groundtruth_instance_masks)
groundtruth_keypoints = None
if include_keypoints:
groundtruth_keypoints = fields.InputDataFields.groundtruth_keypoints
prep_func_arg_map = {
normalize_image: (fields.InputDataFields.image,),
random_horizontal_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_vertical_flip: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_rotation90: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pixel_value_scale: (fields.InputDataFields.image,),
random_image_scale: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
random_rgb_to_gray: (fields.InputDataFields.image,),
random_adjust_brightness: (fields.InputDataFields.image,),
random_adjust_contrast: (fields.InputDataFields.image,),
random_adjust_hue: (fields.InputDataFields.image,),
random_adjust_saturation: (fields.InputDataFields.image,),
random_distort_color: (fields.InputDataFields.image,),
random_jitter_boxes: (fields.InputDataFields.groundtruth_boxes,),
random_crop_image: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes),
random_crop_pad_image: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores),
random_crop_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_pad_to_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_instance_masks,
groundtruth_keypoints,),
random_black_patches: (fields.InputDataFields.image,),
retain_boxes_above_threshold: (
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
image_to_float: (fields.InputDataFields.image,),
random_resize_method: (fields.InputDataFields.image,),
resize_to_range: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
resize_to_min_dimension: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
scale_boxes_to_pixel_coordinates: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
groundtruth_keypoints,),
resize_image: (
fields.InputDataFields.image,
groundtruth_instance_masks,),
subtract_channel_mean: (fields.InputDataFields.image,),
one_hot_encoding: (fields.InputDataFields.groundtruth_image_classes,),
rgb_to_gray: (fields.InputDataFields.image,),
ssd_random_crop: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
ssd_random_crop_pad: (fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores),
ssd_random_crop_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
ssd_random_crop_pad_fixed_aspect_ratio: (
fields.InputDataFields.image,
fields.InputDataFields.groundtruth_boxes,
fields.InputDataFields.groundtruth_classes,
groundtruth_label_scores,
groundtruth_instance_masks,
groundtruth_keypoints,),
}
return prep_func_arg_map
def preprocess(tensor_dict, preprocess_options, func_arg_map=None):
if func_arg_map is None:
func_arg_map = get_default_func_arg_map()
if fields.InputDataFields.image in tensor_dict:
images = tensor_dict[fields.InputDataFields.image]
if len(images.get_shape()) != 4:
raise ValueError('images in tensor_dict should be rank 4')
image = tf.squeeze(images, squeeze_dims=[0])
tensor_dict[fields.InputDataFields.image] = image
for option in preprocess_options:
func, params = option
if func not in func_arg_map:
raise ValueError('The function %s does not exist in func_arg_map' %
(func.__name__))
arg_names = func_arg_map[func]
for a in arg_names:
if a is not None and a not in tensor_dict:
raise ValueError('The function %s requires argument %s' %
(func.__name__, a))
def get_arg(key):
return tensor_dict[key] if key is not None else None
args = [get_arg(a) for a in arg_names]
results = func(*args, **params)
if not isinstance(results, (list, tuple)):
results = (results,)
arg_names = [arg_name for arg_name in arg_names if arg_name is not None]
for res, arg_name in zip(results, arg_names):
tensor_dict[arg_name] = res
if fields.InputDataFields.image in tensor_dict:
image = tensor_dict[fields.InputDataFields.image]
images = tf.expand_dims(image, 0)
tensor_dict[fields.InputDataFields.image] = images
return tensor_dict
| true | true |
f736c558fdfca3270d670899f7bfc0b45578046f | 2,718 | py | Python | src/gdalos/__util__.py | talos-gis/gdalos | 8ba3d8c512c9c13adee2287d0f254f35b3b74b57 | [
"MIT"
] | null | null | null | src/gdalos/__util__.py | talos-gis/gdalos | 8ba3d8c512c9c13adee2287d0f254f35b3b74b57 | [
"MIT"
] | null | null | null | src/gdalos/__util__.py | talos-gis/gdalos | 8ba3d8c512c9c13adee2287d0f254f35b3b74b57 | [
"MIT"
] | null | null | null | from functools import wraps
from inspect import Parameter, signature
from itertools import chain
from typing import Mapping, Sequence
class CallParamDict:
def __init__(
self,
func,
args: tuple,
kwargs: dict,
pos_param_names: Sequence[str],
all_params: Mapping[str, Parameter],
):
self.func = func
self.args = args
self.kwargs = kwargs
self.pos_param_names = pos_param_names
self.all_params = all_params
self.all_arguments = dict(zip(pos_param_names, args))
self.all_arguments.update(kwargs)
def __str__(self):
return f"""{self.func.__name__}({
",".join(chain(
(repr(a) for a in self.args),
(f'{k}= {v!r}' for (k,v) in self.kwargs.items()))
)
})"""
def __getitem__(self, item):
if item in self.all_arguments:
return self.all_arguments[item]
p = self.all_params[item]
if p.default != Parameter.empty:
return p.default
raise KeyError(item)
def __setitem__(self, key, value):
prev = key in self.all_arguments
self.all_arguments[key] = value
if prev:
# value was default before, nothing more needs changing
return
def with_param_dict(kwarg_name="_params"):
def decorator(func):
all_parameters: Mapping[str, Parameter] = signature(func).parameters
dest_param = all_parameters.get(kwarg_name)
if not dest_param or dest_param.kind != Parameter.KEYWORD_ONLY:
raise NameError(
"function must contain keyword-only parameter named " + kwarg_name
)
pos_names = []
for n, p in all_parameters.items():
if p.kind == Parameter.VAR_POSITIONAL:
raise TypeError(
f"with_param_dict can't a variadic argument parameter ({p})"
)
if p.kind not in (
Parameter.POSITIONAL_ONLY,
Parameter.POSITIONAL_OR_KEYWORD,
):
break
pos_names.append(n)
@wraps(func)
def wrapper(*args, **kwargs):
if kwarg_name in kwargs:
raise TypeError(f"{kwarg_name} cannot be specified outside the wrapper")
params = dict(zip(pos_names, args))
params.update(kwargs)
for k, p in all_parameters.items():
if k == kwarg_name:
continue
params.setdefault(k, p.default)
kwargs[kwarg_name] = params
return func(*args, **kwargs)
return wrapper
return decorator
| 28.914894 | 88 | 0.566225 | from functools import wraps
from inspect import Parameter, signature
from itertools import chain
from typing import Mapping, Sequence
class CallParamDict:
def __init__(
self,
func,
args: tuple,
kwargs: dict,
pos_param_names: Sequence[str],
all_params: Mapping[str, Parameter],
):
self.func = func
self.args = args
self.kwargs = kwargs
self.pos_param_names = pos_param_names
self.all_params = all_params
self.all_arguments = dict(zip(pos_param_names, args))
self.all_arguments.update(kwargs)
def __str__(self):
return f"""{self.func.__name__}({
",".join(chain(
(repr(a) for a in self.args),
(f'{k}= {v!r}' for (k,v) in self.kwargs.items()))
)
})"""
def __getitem__(self, item):
if item in self.all_arguments:
return self.all_arguments[item]
p = self.all_params[item]
if p.default != Parameter.empty:
return p.default
raise KeyError(item)
def __setitem__(self, key, value):
prev = key in self.all_arguments
self.all_arguments[key] = value
if prev:
return
def with_param_dict(kwarg_name="_params"):
def decorator(func):
all_parameters: Mapping[str, Parameter] = signature(func).parameters
dest_param = all_parameters.get(kwarg_name)
if not dest_param or dest_param.kind != Parameter.KEYWORD_ONLY:
raise NameError(
"function must contain keyword-only parameter named " + kwarg_name
)
pos_names = []
for n, p in all_parameters.items():
if p.kind == Parameter.VAR_POSITIONAL:
raise TypeError(
f"with_param_dict can't a variadic argument parameter ({p})"
)
if p.kind not in (
Parameter.POSITIONAL_ONLY,
Parameter.POSITIONAL_OR_KEYWORD,
):
break
pos_names.append(n)
@wraps(func)
def wrapper(*args, **kwargs):
if kwarg_name in kwargs:
raise TypeError(f"{kwarg_name} cannot be specified outside the wrapper")
params = dict(zip(pos_names, args))
params.update(kwargs)
for k, p in all_parameters.items():
if k == kwarg_name:
continue
params.setdefault(k, p.default)
kwargs[kwarg_name] = params
return func(*args, **kwargs)
return wrapper
return decorator
| true | true |
f736c660effd8b5895693520368dddb51a4ea7b9 | 4,020 | py | Python | setup.py | llvm-mirror/lnt | 8c57bba3687ada10de5653ae46c537e957525bdb | [
"Apache-2.0"
] | 12 | 2015-10-29T19:28:02.000Z | 2020-02-04T21:25:32.000Z | setup.py | llvm-mirror/lnt | 8c57bba3687ada10de5653ae46c537e957525bdb | [
"Apache-2.0"
] | 3 | 2017-03-04T14:23:14.000Z | 2019-11-02T21:56:51.000Z | setup.py | llvm-mirror/lnt | 8c57bba3687ada10de5653ae46c537e957525bdb | [
"Apache-2.0"
] | 14 | 2015-04-03T03:36:06.000Z | 2019-10-23T14:09:08.000Z | try:
from pip.req import parse_requirements
except ImportError:
# The req module has been moved to pip._internal in the 10 release.
from pip._internal.req import parse_requirements
import lnt
import os
from sys import platform as _platform
import sys
from setuptools import setup, find_packages, Extension
if sys.version_info < (2, 7):
raise RuntimeError("Python 2.7 or higher required.")
cflags = []
if _platform == "darwin":
os.environ["CC"] = "xcrun --sdk macosx clang"
os.environ["CXX"] = "xcrun --sdk macosx clang"
cflags += ['-stdlib=libc++', '-mmacosx-version-min=10.7']
# setuptools expects to be invoked from within the directory of setup.py, but
# it is nice to allow:
# python path/to/setup.py install
# to work (for scripts, etc.)
os.chdir(os.path.dirname(os.path.abspath(__file__)))
cPerf = Extension('lnt.testing.profile.cPerf',
sources=['lnt/testing/profile/cPerf.cpp'],
extra_compile_args=['-std=c++11'] + cflags)
if "--server" in sys.argv:
sys.argv.remove("--server")
req_file = "requirements.server.txt"
else:
req_file = "requirements.client.txt"
try:
install_reqs = parse_requirements(req_file, session=False)
except TypeError:
# In old PIP the session flag cannot be passed.
install_reqs = parse_requirements(req_file)
reqs = [str(ir.req) for ir in install_reqs]
setup(
name="LNT",
version=lnt.__version__,
author=lnt.__author__,
author_email=lnt.__email__,
url='http://llvm.org',
license = 'Apache-2.0 with LLVM exception',
description="LLVM Nightly Test Infrastructure",
keywords='web testing performance development llvm',
long_description="""\
*LNT*
+++++
About
=====
*LNT* is an infrastructure for performance testing. The software itself
consists of two main parts, a web application for accessing and visualizing
performance data, and command line utilities to allow users to generate and
submit test results to the server.
The package was originally written for use in testing LLVM compiler
technologies, but is designed to be usable for the performance testing of any
software.
Documentation
=============
The official *LNT* documentation is available online at:
http://llvm.org/docs/lnt
Source
======
The *LNT* source is available in the LLVM SVN repository:
http://llvm.org/svn/llvm-project/lnt/trunk
""",
classifiers=[
'Development Status :: 4 - Beta',
'Environment :: Web Environment',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache-2.0 with LLVM exception',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Software Development :: Quality Assurance',
'Topic :: Software Development :: Testing',
],
zip_safe=False,
# Additional resource extensions we use.
package_data={'lnt.server.ui': ['static/*.ico',
'static/*.js',
'static/*.css',
'static/*.svg',
'static/bootstrap/css/*.css',
'static/bootstrap/js/*.js',
'static/bootstrap/img/*.png',
'static/flot/*.min.js',
'static/d3/*.min.js',
'static/jquery/**/*.min.js',
'templates/*.html',
'templates/reporting/*.html',
'templates/reporting/*.txt'],
'lnt.server.db': ['migrations/*.py'],
},
packages=find_packages(),
test_suite='tests.test_all',
entry_points={
'console_scripts': [
'lnt = lnt.lnttool:main',
],
},
install_requires=reqs,
ext_modules=[cPerf],
python_requires='>=2.7',
)
| 30.225564 | 77 | 0.597512 | try:
from pip.req import parse_requirements
except ImportError:
from pip._internal.req import parse_requirements
import lnt
import os
from sys import platform as _platform
import sys
from setuptools import setup, find_packages, Extension
if sys.version_info < (2, 7):
raise RuntimeError("Python 2.7 or higher required.")
cflags = []
if _platform == "darwin":
os.environ["CC"] = "xcrun --sdk macosx clang"
os.environ["CXX"] = "xcrun --sdk macosx clang"
cflags += ['-stdlib=libc++', '-mmacosx-version-min=10.7']
os.chdir(os.path.dirname(os.path.abspath(__file__)))
cPerf = Extension('lnt.testing.profile.cPerf',
sources=['lnt/testing/profile/cPerf.cpp'],
extra_compile_args=['-std=c++11'] + cflags)
if "--server" in sys.argv:
sys.argv.remove("--server")
req_file = "requirements.server.txt"
else:
req_file = "requirements.client.txt"
try:
install_reqs = parse_requirements(req_file, session=False)
except TypeError:
install_reqs = parse_requirements(req_file)
reqs = [str(ir.req) for ir in install_reqs]
setup(
name="LNT",
version=lnt.__version__,
author=lnt.__author__,
author_email=lnt.__email__,
url='http://llvm.org',
license = 'Apache-2.0 with LLVM exception',
description="LLVM Nightly Test Infrastructure",
keywords='web testing performance development llvm',
long_description="""\
*LNT*
+++++
About
=====
*LNT* is an infrastructure for performance testing. The software itself
consists of two main parts, a web application for accessing and visualizing
performance data, and command line utilities to allow users to generate and
submit test results to the server.
The package was originally written for use in testing LLVM compiler
technologies, but is designed to be usable for the performance testing of any
software.
Documentation
=============
The official *LNT* documentation is available online at:
http://llvm.org/docs/lnt
Source
======
The *LNT* source is available in the LLVM SVN repository:
http://llvm.org/svn/llvm-project/lnt/trunk
""",
classifiers=[
'Development Status :: 4 - Beta',
'Environment :: Web Environment',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache-2.0 with LLVM exception',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Software Development :: Quality Assurance',
'Topic :: Software Development :: Testing',
],
zip_safe=False,
package_data={'lnt.server.ui': ['static/*.ico',
'static/*.js',
'static/*.css',
'static/*.svg',
'static/bootstrap/css/*.css',
'static/bootstrap/js/*.js',
'static/bootstrap/img/*.png',
'static/flot/*.min.js',
'static/d3/*.min.js',
'static/jquery/**/*.min.js',
'templates/*.html',
'templates/reporting/*.html',
'templates/reporting/*.txt'],
'lnt.server.db': ['migrations/*.py'],
},
packages=find_packages(),
test_suite='tests.test_all',
entry_points={
'console_scripts': [
'lnt = lnt.lnttool:main',
],
},
install_requires=reqs,
ext_modules=[cPerf],
python_requires='>=2.7',
)
| true | true |
f736c670b872bb5fafda18c985b34fa9f433811e | 447 | py | Python | test/test.py | huangjunxiong11/Change-Grounp | f265df1141a951bc2f7d4a87fadaad247d7114e3 | [
"MIT"
] | 2 | 2020-12-23T15:58:38.000Z | 2020-12-23T15:59:25.000Z | test/test.py | huangjunxiong11/Change-Grounp | f265df1141a951bc2f7d4a87fadaad247d7114e3 | [
"MIT"
] | null | null | null | test/test.py | huangjunxiong11/Change-Grounp | f265df1141a951bc2f7d4a87fadaad247d7114e3 | [
"MIT"
] | 1 | 2020-11-04T15:05:15.000Z | 2020-11-04T15:05:15.000Z | from moviepy.editor import VideoFileClip
import os
def setbitrate(inputvideo, bitrate):
"""
改变视频码率，降低码率也可以实现对视频大小的最优化压缩
:param inputvideo:
:param bitrate: 例如600k
:return:
"""
path, _ = os.path.splitext(inputvideo)
a = '设置码率{}.mp4'.format(bitrate)
name = path + a
cmd = 'ffmpeg -i ' + inputvideo + ' -b:v ' + str(bitrate) + 'k ' + name
# 执行cmd命令
os.system(cmd)
setbitrate('output有音频视频.mp4', 600)
| 21.285714 | 75 | 0.626398 | from moviepy.editor import VideoFileClip
import os
def setbitrate(inputvideo, bitrate):
path, _ = os.path.splitext(inputvideo)
a = '设置码率{}.mp4'.format(bitrate)
name = path + a
cmd = 'ffmpeg -i ' + inputvideo + ' -b:v ' + str(bitrate) + 'k ' + name
os.system(cmd)
setbitrate('output有音频视频.mp4', 600)
| true | true |
f736c6794b84f1e36bf7856de283b96e698d0546 | 136 | py | Python | src/scenery/model/SceneFile.py | Dachaz/scenery | e64199eb064b192bbbad2aa3944f136c24048866 | [
"MIT"
] | 9 | 2019-02-18T23:07:08.000Z | 2020-08-24T02:17:35.000Z | src/scenery/model/SceneFile.py | Dachaz/scenery | e64199eb064b192bbbad2aa3944f136c24048866 | [
"MIT"
] | 1 | 2020-08-08T16:52:12.000Z | 2020-08-08T16:52:12.000Z | src/scenery/model/SceneFile.py | Dachaz/scenery | e64199eb064b192bbbad2aa3944f136c24048866 | [
"MIT"
] | null | null | null | class SceneFile:
def __init__(self, fileName, root):
self.root = root
self.file = fileName
self.meta = None
| 22.666667 | 39 | 0.595588 | class SceneFile:
def __init__(self, fileName, root):
self.root = root
self.file = fileName
self.meta = None
| true | true |
f736c7ee25f80c9c01427982f19ab5291706222f | 1,241 | py | Python | src/ethereum/frontier/vm/error.py | norswap/execution-specs | c2274790e8ac2d637c7dbe092477a1b21243916c | [
"CC0-1.0"
] | 1 | 2021-09-07T21:30:14.000Z | 2021-09-07T21:30:14.000Z | src/ethereum/frontier/vm/error.py | KenMan79/execution-specs | ac8e65a422032a8ebb4077068627bbfb2fce6eda | [
"CC0-1.0"
] | null | null | null | src/ethereum/frontier/vm/error.py | KenMan79/execution-specs | ac8e65a422032a8ebb4077068627bbfb2fce6eda | [
"CC0-1.0"
] | null | null | null | """
Ethereum Virtual Machine (EVM) Errors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. contents:: Table of Contents
:backlinks: none
:local:
Introduction
------------
Errors which cause the EVM to halt exceptionally.
"""
class StackUnderflowError(Exception):
"""
Occurs when a pop is executed on an empty stack.
"""
pass
class StackOverflowError(Exception):
"""
Occurs when a push is executed on a stack at max capacity.
"""
pass
class OutOfGasError(Exception):
"""
Occurs when an operation costs more than the amount of gas left in the
frame.
"""
pass
class InvalidOpcode(Exception):
"""
Raised when an invalid opcode is encountered.
"""
pass
class InvalidJumpDestError(Exception):
"""
Occurs when the destination of a jump operation doesn't meet any of the
following criteria:
* The jump destination is less than the length of the code.
* The jump destination should have the `JUMPDEST` opcode (0x5B).
* The jump destination shouldn't be part of the data corresponding to
`PUSH-N` opcodes.
"""
class StackDepthLimitError(Exception):
"""
Raised when the message depth is greater than `1024`
"""
pass
| 18.80303 | 75 | 0.641418 |
class StackUnderflowError(Exception):
pass
class StackOverflowError(Exception):
pass
class OutOfGasError(Exception):
pass
class InvalidOpcode(Exception):
pass
class InvalidJumpDestError(Exception):
class StackDepthLimitError(Exception):
pass
| true | true |
f736c82e74565020361811d21f1cd08ec4a3f895 | 465 | py | Python | src/setup.py | mr-niels-christensen/pyrules | 807b2d9afe0fb540ecb5a2302cc1bcdc9733a89e | [
"MIT"
] | null | null | null | src/setup.py | mr-niels-christensen/pyrules | 807b2d9afe0fb540ecb5a2302cc1bcdc9733a89e | [
"MIT"
] | 19 | 2015-08-26T13:59:50.000Z | 2016-05-16T15:27:44.000Z | src/setup.py | mr-niels-christensen/pyrules | 807b2d9afe0fb540ecb5a2302cc1bcdc9733a89e | [
"MIT"
] | null | null | null | from distutils.core import setup
setup(
name='pyrules',
version='0.2',
packages=['pyrules2'],
package_dir={'': 'src'},
url='https://github.com/mr-niels-christensen/pyrules',
license='MIT',
author='Niels Christensen',
author_email='nhc@mayacs.com',
description='pyrules is a pure-Python library for implementing discrete rule-based models. ',
install_requires=[
'frozendict==0.5',
'googlemaps==2.4',
]
)
| 25.833333 | 98 | 0.63871 | from distutils.core import setup
setup(
name='pyrules',
version='0.2',
packages=['pyrules2'],
package_dir={'': 'src'},
url='https://github.com/mr-niels-christensen/pyrules',
license='MIT',
author='Niels Christensen',
author_email='nhc@mayacs.com',
description='pyrules is a pure-Python library for implementing discrete rule-based models. ',
install_requires=[
'frozendict==0.5',
'googlemaps==2.4',
]
)
| true | true |
f736c82fdd13410b5006180443ed9fb5d2275d3b | 496 | py | Python | examples/cc/h2o_ccsd_t.py | seunghoonlee89/pyscf-ecCC-TCC | 2091566fb83c1474e40bf74f271be2ce4611f60c | [
"Apache-2.0"
] | 2 | 2021-09-17T06:10:17.000Z | 2022-01-22T23:37:22.000Z | examples/cc/h2o_ccsd_t.py | seunghoonlee89/pyscf-ecCC-TCC | 2091566fb83c1474e40bf74f271be2ce4611f60c | [
"Apache-2.0"
] | null | null | null | examples/cc/h2o_ccsd_t.py | seunghoonlee89/pyscf-ecCC-TCC | 2091566fb83c1474e40bf74f271be2ce4611f60c | [
"Apache-2.0"
] | 2 | 2021-09-16T23:37:42.000Z | 2021-10-14T23:00:39.000Z | #!/usr/bin/env python
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
A simple example to run CCSD(T) and UCCSD(T) calculation.
'''
import pyscf
mol = pyscf.M(
atom = 'O -0.26677564 -0.27872083 0.00000000;\
H -0.26677564 0.82127917 0.00000000;\
H -0.26677564 -0.64538753 1.03708994',
basis = 'ccpvtz')
mf = mol.RHF().run()
mycc = mf.CCSD().run()
et = mycc.ccsd_t()
print('CCSD(T) correlation energy', mycc.e_corr + et)
| 21.565217 | 63 | 0.574597 |
import pyscf
mol = pyscf.M(
atom = 'O -0.26677564 -0.27872083 0.00000000;\
H -0.26677564 0.82127917 0.00000000;\
H -0.26677564 -0.64538753 1.03708994',
basis = 'ccpvtz')
mf = mol.RHF().run()
mycc = mf.CCSD().run()
et = mycc.ccsd_t()
print('CCSD(T) correlation energy', mycc.e_corr + et)
| true | true |
f736c918bf41087689867b9a502ccd0940fcd521 | 1,294 | py | Python | fileSystem/school-projects/development/softwaredesignandcomputerlogiccis122/cis122lab3/python/testresponse.py | nomad-mystic/nomadmystic | 7814c1f7c1a45464df5896d03dd3c3bed0f763d0 | [
"MIT"
] | 1 | 2016-06-15T08:36:56.000Z | 2016-06-15T08:36:56.000Z | fileSystem/school-projects/development/softwaredesignandcomputerlogiccis122/cis122lab3/python/testresponse.py | nomad-mystic/nomadmystic | 7814c1f7c1a45464df5896d03dd3c3bed0f763d0 | [
"MIT"
] | 1 | 2016-06-08T13:05:41.000Z | 2016-06-08T13:06:07.000Z | fileSystem/school-projects/development/softwaredesignandcomputerlogiccis122/cis122lab3/python/testresponse.py | nomad-mystic/nomadmystic | 7814c1f7c1a45464df5896d03dd3c3bed0f763d0 | [
"MIT"
] | null | null | null | __author__ = 'pather'
import urllib.request
import re
africa_url_response = urllib.request.urlopen('http://worldpopulationreview.com/continents/africa-population/')
africa_url_html = africa_url_response.read()
africa_url_text = africa_url_html.decode('UTF-8')
africa_current_population = re.search('<span>([^<]*)', africa_url_text).group(1)
current_population = africa_current_population
# africa_future_population = current_population
print(africa_current_population)
# this wasn't need in the lab 3
def chosen_continent_pop_finder(name_of_continent):
if name_of_continent == 'Asia' or name_of_continent == 'asia':
return name_of_continent
elif name_of_continent == 'Africa' or name_of_continent == 'africa':
return name_of_continent
elif name_of_continent == 'Europe' or name_of_continent == 'europe':
return name_of_continent
elif name_of_continent == 'South America' or name_of_continent == 'south america':
return name_of_continent
elif name_of_continent == 'North America' or name_of_continent == 'north america':
return name_of_continent
elif name_of_continent == 'Oceania' or name_of_continent == 'oceania':
return name_of_continent
else:
print("Whoops! Let's try this again")
main() | 34.052632 | 110 | 0.742658 | __author__ = 'pather'
import urllib.request
import re
africa_url_response = urllib.request.urlopen('http://worldpopulationreview.com/continents/africa-population/')
africa_url_html = africa_url_response.read()
africa_url_text = africa_url_html.decode('UTF-8')
africa_current_population = re.search('<span>([^<]*)', africa_url_text).group(1)
current_population = africa_current_population
print(africa_current_population)
def chosen_continent_pop_finder(name_of_continent):
if name_of_continent == 'Asia' or name_of_continent == 'asia':
return name_of_continent
elif name_of_continent == 'Africa' or name_of_continent == 'africa':
return name_of_continent
elif name_of_continent == 'Europe' or name_of_continent == 'europe':
return name_of_continent
elif name_of_continent == 'South America' or name_of_continent == 'south america':
return name_of_continent
elif name_of_continent == 'North America' or name_of_continent == 'north america':
return name_of_continent
elif name_of_continent == 'Oceania' or name_of_continent == 'oceania':
return name_of_continent
else:
print("Whoops! Let's try this again")
main() | true | true |
f736ca658d4562bd6ffedbbd40c676eef04de781 | 8,708 | py | Python | sdk/python/pulumi_azure_native/apimanagement/latest/api_issue_comment.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/apimanagement/latest/api_issue_comment.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/apimanagement/latest/api_issue_comment.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = ['ApiIssueComment']
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""", DeprecationWarning)
class ApiIssueComment(pulumi.CustomResource):
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""", DeprecationWarning)
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
api_id: Optional[pulumi.Input[str]] = None,
comment_id: Optional[pulumi.Input[str]] = None,
created_date: Optional[pulumi.Input[str]] = None,
issue_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
service_name: Optional[pulumi.Input[str]] = None,
text: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
Issue Comment Contract details.
Latest API Version: 2019-12-01.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] api_id: API identifier. Must be unique in the current API Management service instance.
:param pulumi.Input[str] comment_id: Comment identifier within an Issue. Must be unique in the current Issue.
:param pulumi.Input[str] created_date: Date and time when the comment was created.
:param pulumi.Input[str] issue_id: Issue identifier. Must be unique in the current API Management service instance.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
:param pulumi.Input[str] service_name: The name of the API Management service.
:param pulumi.Input[str] text: Comment text.
:param pulumi.Input[str] user_id: A resource identifier for the user who left the comment.
"""
pulumi.log.warn("""ApiIssueComment is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""")
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if api_id is None and not opts.urn:
raise TypeError("Missing required property 'api_id'")
__props__['api_id'] = api_id
__props__['comment_id'] = comment_id
__props__['created_date'] = created_date
if issue_id is None and not opts.urn:
raise TypeError("Missing required property 'issue_id'")
__props__['issue_id'] = issue_id
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
if service_name is None and not opts.urn:
raise TypeError("Missing required property 'service_name'")
__props__['service_name'] = service_name
if text is None and not opts.urn:
raise TypeError("Missing required property 'text'")
__props__['text'] = text
if user_id is None and not opts.urn:
raise TypeError("Missing required property 'user_id'")
__props__['user_id'] = user_id
__props__['name'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:apimanagement/latest:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20170301:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20170301:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20180101:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20180101:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20180601preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20180601preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20190101:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20190101:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20191201:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20191201:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20191201preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20191201preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20200601preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20200601preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20201201:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20201201:ApiIssueComment")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ApiIssueComment, __self__).__init__(
'azure-native:apimanagement/latest:ApiIssueComment',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ApiIssueComment':
"""
Get an existing ApiIssueComment resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["created_date"] = None
__props__["name"] = None
__props__["text"] = None
__props__["type"] = None
__props__["user_id"] = None
return ApiIssueComment(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="createdDate")
def created_date(self) -> pulumi.Output[Optional[str]]:
"""
Date and time when the comment was created.
"""
return pulumi.get(self, "created_date")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Resource name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def text(self) -> pulumi.Output[str]:
"""
Comment text.
"""
return pulumi.get(self, "text")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Resource type for API Management resource.
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="userId")
def user_id(self) -> pulumi.Output[str]:
"""
A resource identifier for the user who left the comment.
"""
return pulumi.get(self, "user_id")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 52.775758 | 1,526 | 0.673404 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = ['ApiIssueComment']
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""", DeprecationWarning)
class ApiIssueComment(pulumi.CustomResource):
warnings.warn("""The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""", DeprecationWarning)
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
api_id: Optional[pulumi.Input[str]] = None,
comment_id: Optional[pulumi.Input[str]] = None,
created_date: Optional[pulumi.Input[str]] = None,
issue_id: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
service_name: Optional[pulumi.Input[str]] = None,
text: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
pulumi.log.warn("""ApiIssueComment is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:apimanagement:ApiIssueComment'.""")
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if api_id is None and not opts.urn:
raise TypeError("Missing required property 'api_id'")
__props__['api_id'] = api_id
__props__['comment_id'] = comment_id
__props__['created_date'] = created_date
if issue_id is None and not opts.urn:
raise TypeError("Missing required property 'issue_id'")
__props__['issue_id'] = issue_id
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
if service_name is None and not opts.urn:
raise TypeError("Missing required property 'service_name'")
__props__['service_name'] = service_name
if text is None and not opts.urn:
raise TypeError("Missing required property 'text'")
__props__['text'] = text
if user_id is None and not opts.urn:
raise TypeError("Missing required property 'user_id'")
__props__['user_id'] = user_id
__props__['name'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:apimanagement/latest:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20170301:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20170301:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20180101:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20180101:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20180601preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20180601preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20190101:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20190101:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20191201:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20191201:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20191201preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20191201preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20200601preview:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20200601preview:ApiIssueComment"), pulumi.Alias(type_="azure-native:apimanagement/v20201201:ApiIssueComment"), pulumi.Alias(type_="azure-nextgen:apimanagement/v20201201:ApiIssueComment")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ApiIssueComment, __self__).__init__(
'azure-native:apimanagement/latest:ApiIssueComment',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ApiIssueComment':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["created_date"] = None
__props__["name"] = None
__props__["text"] = None
__props__["type"] = None
__props__["user_id"] = None
return ApiIssueComment(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="createdDate")
def created_date(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "created_date")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter
def text(self) -> pulumi.Output[str]:
return pulumi.get(self, "text")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
return pulumi.get(self, "type")
@property
@pulumi.getter(name="userId")
def user_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "user_id")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| true | true |
f736caa58345d44b679f5da0f0f74b98382e2f08 | 5,473 | py | Python | spotty/providers/gcp/gcp_resources/instance.py | Inculus/spotty | 56863012668a6c13ad13c2a04f900047e229fbe6 | [
"MIT"
] | 1 | 2020-07-17T07:02:09.000Z | 2020-07-17T07:02:09.000Z | spotty/providers/gcp/gcp_resources/instance.py | Inculus/spotty | 56863012668a6c13ad13c2a04f900047e229fbe6 | [
"MIT"
] | null | null | null | spotty/providers/gcp/gcp_resources/instance.py | Inculus/spotty | 56863012668a6c13ad13c2a04f900047e229fbe6 | [
"MIT"
] | null | null | null | from datetime import datetime
from spotty.providers.gcp.helpers.ce_client import CEClient
class Instance(object):
def __init__(self, ce: CEClient, data: dict):
"""
Args:
data (dict): Example:
{'canIpForward': False,
'cpuPlatform': 'Intel Haswell',
'creationTimestamp': '2019-04-20T16:21:49.536-07:00',
'deletionProtection': False,
'description': '',
'disks': [{'autoDelete': True,
'boot': True,
'deviceName': 'instance-1',
'guestOsFeatures': [{'type': 'VIRTIO_SCSI_MULTIQUEUE'}],
'index': 0,
'interface': 'SCSI',
'kind': 'compute#attachedDisk',
'licenses': ['https://www.googleapis.com/compute/v1/projects/debian-cloud/global/licenses/debian-9-stretch'],
'mode': 'READ_WRITE',
'source': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/zones/us-east1-b/disks/instance-1',
'type': 'PERSISTENT'}],
'id': '928537266896639843',
'kind': 'compute#instance',
'labelFingerprint': '42WmSpB8rSM=',
'machineType': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/zones/us-east1-b/machineTypes/n1-standard-1',
'metadata': {'fingerprint': 'IoRxXrApBlw=',
'kind': 'compute#metadata'},
'name': 'instance-1',
'networkInterfaces': [{'accessConfigs': [{'kind': 'compute#accessConfig',
'name': 'External NAT',
'natIP': '34.73.140.188',
'networkTier': 'PREMIUM',
'type': 'ONE_TO_ONE_NAT'}],
'fingerprint': 'COAWpxIgZx0=',
'kind': 'compute#networkInterface',
'name': 'nic0',
'network': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/global/networks/default',
'networkIP': '10.142.0.2',
'subnetwork': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/regions/us-east1/subnetworks/default'}],
'scheduling': {'automaticRestart': False,
'onHostMaintenance': 'TERMINATE',
'preemptible': True},
'selfLink': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/zones/us-east1-b/instances/instance-1',
'serviceAccounts': [{'email': '293101887402-compute@developer.gserviceaccount.com',
'scopes': ['https://www.googleapis.com/auth/devstorage.read_only',
'https://www.googleapis.com/auth/logging.write',
'https://www.googleapis.com/auth/monitoring.write',
'https://www.googleapis.com/auth/servicecontrol',
'https://www.googleapis.com/auth/service.management.readonly',
'https://www.googleapis.com/auth/trace.append']}],
'startRestricted': False,
'status': 'RUNNING',
'tags': {'fingerprint': '42WmSpB8rSM='},
'zone': 'https://www.googleapis.com/compute/v1/projects/spotty-221422/zones/us-east1-b'}
"""
self._ce = ce
self._data = data
@staticmethod
def get_by_name(ce: CEClient, machine_name: str):
"""Returns an instance by its stack name."""
res = ce.list_instances(machine_name)
if not res:
return None
return Instance(ce, res[0])
@property
def name(self) -> str:
return self._data['name']
@property
def is_running(self) -> bool:
return self.status == 'RUNNING'
@property
def is_terminated(self) -> bool:
# see Instance Life Cycle: https://cloud.google.com/compute/docs/instances/instance-life-cycle
return self.status == 'TERMINATED'
@property
def public_ip_address(self) -> str:
return self._data['networkInterfaces'][0]['accessConfigs'][0].get('natIP')
@property
def status(self) -> str:
return self._data['status']
@property
def machine_type(self) -> str:
return self._data['machineType'].split('/')[-1]
@property
def zone(self) -> str:
return self._data['zone'].split('/')[-1]
@property
def creation_timestamp(self) -> str:
# fix the format: '2019-04-20T16:21:49.536-07:00' -> '2019-04-20T16:21:49-0700'
time_str = self._data['creationTimestamp'][:-10] + \
self._data['creationTimestamp'][-6:-3] + \
self._data['creationTimestamp'][-2:]
return datetime.strptime(time_str, '%Y-%m-%dT%H:%M:%S%z')
@property
def is_preemtible(self) -> bool:
return self._data['scheduling']['preemptible']
| 48.433628 | 155 | 0.494975 | from datetime import datetime
from spotty.providers.gcp.helpers.ce_client import CEClient
class Instance(object):
def __init__(self, ce: CEClient, data: dict):
self._ce = ce
self._data = data
@staticmethod
def get_by_name(ce: CEClient, machine_name: str):
res = ce.list_instances(machine_name)
if not res:
return None
return Instance(ce, res[0])
@property
def name(self) -> str:
return self._data['name']
@property
def is_running(self) -> bool:
return self.status == 'RUNNING'
@property
def is_terminated(self) -> bool:
return self.status == 'TERMINATED'
@property
def public_ip_address(self) -> str:
return self._data['networkInterfaces'][0]['accessConfigs'][0].get('natIP')
@property
def status(self) -> str:
return self._data['status']
@property
def machine_type(self) -> str:
return self._data['machineType'].split('/')[-1]
@property
def zone(self) -> str:
return self._data['zone'].split('/')[-1]
@property
def creation_timestamp(self) -> str:
time_str = self._data['creationTimestamp'][:-10] + \
self._data['creationTimestamp'][-6:-3] + \
self._data['creationTimestamp'][-2:]
return datetime.strptime(time_str, '%Y-%m-%dT%H:%M:%S%z')
@property
def is_preemtible(self) -> bool:
return self._data['scheduling']['preemptible']
| true | true |
f736cb81e16217a84536bf98af9ceaf87c65324b | 4,902 | py | Python | src/dcm/agent/tests/unit/test_pubsub.py | JPWKU/unix-agent | 8f1278fc8c2768a8d4d54af642a881bace43652f | [
"Apache-2.0"
] | null | null | null | src/dcm/agent/tests/unit/test_pubsub.py | JPWKU/unix-agent | 8f1278fc8c2768a8d4d54af642a881bace43652f | [
"Apache-2.0"
] | 22 | 2015-09-15T20:52:34.000Z | 2016-03-11T22:44:24.000Z | src/dcm/agent/tests/unit/test_pubsub.py | JPWKU/unix-agent | 8f1278fc8c2768a8d4d54af642a881bace43652f | [
"Apache-2.0"
] | 3 | 2015-09-11T20:21:33.000Z | 2016-09-30T08:30:19.000Z | #
# Copyright (C) 2014 Dell, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import unittest
import uuid
import dcm.agent.events.callback as events
import dcm.agent.events.pubsub as pubsub
import dcm.agent.tests.utils.general as test_utils
class TestPubSub(unittest.TestCase):
@classmethod
def setUpClass(cls):
test_utils.connect_to_debugger()
def setUp(self):
self._event_space = events.EventSpace()
self._pub_sub = pubsub.PubSubEvent(self._event_space)
def test_simple_publish(self):
topic = str(uuid.uuid4())
x_val = 1
y_val = []
apple_val = "sauce"
def test_callback(x_param, y_param, apple_param=None):
self.assertEqual(x_param, x_val)
self.assertEqual(y_param, y_val)
self.assertEqual(apple_param, apple_val)
y_val.append("called")
self._pub_sub.subscribe(topic, test_callback)
self._pub_sub.publish(topic,
topic_args=(x_val, y_val),
topic_kwargs={'apple_param': apple_val})
self._event_space.poll(timeblock=0.0)
self.assertEqual(len(y_val), 1)
def test_multiple_subscribe(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
x_param.append(2)
def test_callback3(x_param):
x_param.append(3)
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic, topic_args=(x_val,))
self._event_space.poll(timeblock=0.0)
self.assertEqual(len(x_val), 3)
self.assertIn(1, x_val)
self.assertIn(2, x_val)
self.assertIn(3, x_val)
def test_public_empty(self):
topic = str(uuid.uuid4())
self._pub_sub.publish(topic)
self._event_space.poll(timeblock=0.0)
def test_unsubscribe(self):
topic = str(uuid.uuid4())
def test_callback():
pass
self._pub_sub.subscribe(topic, test_callback)
self._pub_sub.unsubscribe(topic, test_callback)
try:
self._pub_sub.unsubscribe(topic, test_callback)
passes = False
except KeyError:
passes = True
self.assertTrue(passes)
def test_done_callback(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
x_param.append(2)
def test_callback3(x_param):
x_param.append(3)
def done_cb(topic_error, x_param=None):
self.assertEqual(len(x_param), 3)
self.assertIn(1, x_param)
self.assertIn(2, x_param)
self.assertIn(3, x_param)
self.assertIsNone(topic_error)
x_param.append("done")
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic,
topic_args=(x_val,),
done_cb=done_cb,
done_kwargs={'x_param': x_val})
self._event_space.poll(timeblock=0.0)
self.assertIn('done', x_val)
def test_done_error_callback(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
raise Exception("error")
def test_callback3(x_param):
x_param.append(3)
def done_cb(topic_error, x_param=None):
self.assertLess(len(x_param), 3)
self.assertIsNotNone(topic_error)
x_param.append("done")
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic,
topic_args=(x_val,),
done_cb=done_cb,
done_kwargs={'x_param': x_val})
self._event_space.poll(timeblock=0.0)
self.assertIn('done', x_val)
| 31.025316 | 74 | 0.612607 |
import unittest
import uuid
import dcm.agent.events.callback as events
import dcm.agent.events.pubsub as pubsub
import dcm.agent.tests.utils.general as test_utils
class TestPubSub(unittest.TestCase):
@classmethod
def setUpClass(cls):
test_utils.connect_to_debugger()
def setUp(self):
self._event_space = events.EventSpace()
self._pub_sub = pubsub.PubSubEvent(self._event_space)
def test_simple_publish(self):
topic = str(uuid.uuid4())
x_val = 1
y_val = []
apple_val = "sauce"
def test_callback(x_param, y_param, apple_param=None):
self.assertEqual(x_param, x_val)
self.assertEqual(y_param, y_val)
self.assertEqual(apple_param, apple_val)
y_val.append("called")
self._pub_sub.subscribe(topic, test_callback)
self._pub_sub.publish(topic,
topic_args=(x_val, y_val),
topic_kwargs={'apple_param': apple_val})
self._event_space.poll(timeblock=0.0)
self.assertEqual(len(y_val), 1)
def test_multiple_subscribe(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
x_param.append(2)
def test_callback3(x_param):
x_param.append(3)
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic, topic_args=(x_val,))
self._event_space.poll(timeblock=0.0)
self.assertEqual(len(x_val), 3)
self.assertIn(1, x_val)
self.assertIn(2, x_val)
self.assertIn(3, x_val)
def test_public_empty(self):
topic = str(uuid.uuid4())
self._pub_sub.publish(topic)
self._event_space.poll(timeblock=0.0)
def test_unsubscribe(self):
topic = str(uuid.uuid4())
def test_callback():
pass
self._pub_sub.subscribe(topic, test_callback)
self._pub_sub.unsubscribe(topic, test_callback)
try:
self._pub_sub.unsubscribe(topic, test_callback)
passes = False
except KeyError:
passes = True
self.assertTrue(passes)
def test_done_callback(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
x_param.append(2)
def test_callback3(x_param):
x_param.append(3)
def done_cb(topic_error, x_param=None):
self.assertEqual(len(x_param), 3)
self.assertIn(1, x_param)
self.assertIn(2, x_param)
self.assertIn(3, x_param)
self.assertIsNone(topic_error)
x_param.append("done")
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic,
topic_args=(x_val,),
done_cb=done_cb,
done_kwargs={'x_param': x_val})
self._event_space.poll(timeblock=0.0)
self.assertIn('done', x_val)
def test_done_error_callback(self):
topic = str(uuid.uuid4())
x_val = []
def test_callback1(x_param):
x_param.append(1)
def test_callback2(x_param):
raise Exception("error")
def test_callback3(x_param):
x_param.append(3)
def done_cb(topic_error, x_param=None):
self.assertLess(len(x_param), 3)
self.assertIsNotNone(topic_error)
x_param.append("done")
self._pub_sub.subscribe(topic, test_callback1)
self._pub_sub.subscribe(topic, test_callback2)
self._pub_sub.subscribe(topic, test_callback3)
self._pub_sub.publish(topic,
topic_args=(x_val,),
done_cb=done_cb,
done_kwargs={'x_param': x_val})
self._event_space.poll(timeblock=0.0)
self.assertIn('done', x_val)
| true | true |
f736cc8d15031f273b4f5f5ae9a12e5c83c1cc27 | 10,189 | py | Python | articles/migrations/0060_auto_20150925_2012.py | losolio/website | 5b983e9dfaf604212aab87c51d8904ffc29527a3 | [
"MIT"
] | null | null | null | articles/migrations/0060_auto_20150925_2012.py | losolio/website | 5b983e9dfaf604212aab87c51d8904ffc29527a3 | [
"MIT"
] | null | null | null | articles/migrations/0060_auto_20150925_2012.py | losolio/website | 5b983e9dfaf604212aab87c51d8904ffc29527a3 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import modelcluster.fields
import wagtail.wagtailcore.blocks
import wagtail.wagtailcore.fields
import wagtail.wagtailembeds.blocks
import wagtail.wagtailimages.blocks
from django.db import migrations, models
import articles.fields
import interactives.models
class Migration(migrations.Migration):
dependencies = [
('articles', '0059_auto_20150923_1524'),
]
operations = [
migrations.CreateModel(
name='Citation',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('sort_order', models.IntegerField(null=True, editable=False, blank=True)),
('text', wagtail.wagtailcore.fields.RichTextField()),
],
options={
'ordering': ['sort_order'],
'abstract': False,
},
),
migrations.CreateModel(
name='EndNote',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('sort_order', models.IntegerField(null=True, editable=False, blank=True)),
('text', wagtail.wagtailcore.fields.RichTextField()),
],
options={
'ordering': ['sort_order'],
'abstract': False,
},
),
migrations.RemoveField(
model_name='chapteredarticlepage',
name='chapters',
),
migrations.AddField(
model_name='articlepage',
name='chapters',
field=articles.fields.ChapterField([('chapter', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock()), (b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul')), (b'Overflow', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul')), (b'ColumnedContent', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul'))], required=False))]))], required=False))])), (b'ColumnedContent', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul'))], required=False))]))], required=False))]))], null=True, blank=True),
),
migrations.AddField(
model_name='articlepage',
name='citations_heading',
field=models.TextField(default='Works Cited', blank=True),
),
migrations.AddField(
model_name='articlepage',
name='endnote_identifier_style',
field=models.CharField(default='roman-lower', max_length=20, choices=[('roman-lower', 'Roman Numerals - Lowercase'), ('roman-upper', 'Roman Numerals - Uppercase'), ('numbers', 'Numbers')]),
),
migrations.AddField(
model_name='articlepage',
name='endnotes_heading',
field=models.TextField(default='End Notes', blank=True),
),
migrations.AddField(
model_name='articlepage',
name='table_of_contents_heading',
field=models.TextField(default='Table of Contents', blank=True),
),
migrations.AddField(
model_name='endnote',
name='article',
field=modelcluster.fields.ParentalKey(related_name='endnote_links', to='articles.ArticlePage'),
),
migrations.AddField(
model_name='citation',
name='article',
field=modelcluster.fields.ParentalKey(related_name='citation_links', to='articles.ArticlePage'),
),
]
| 118.476744 | 7,174 | 0.702228 |
from __future__ import unicode_literals
import modelcluster.fields
import wagtail.wagtailcore.blocks
import wagtail.wagtailcore.fields
import wagtail.wagtailembeds.blocks
import wagtail.wagtailimages.blocks
from django.db import migrations, models
import articles.fields
import interactives.models
class Migration(migrations.Migration):
dependencies = [
('articles', '0059_auto_20150923_1524'),
]
operations = [
migrations.CreateModel(
name='Citation',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('sort_order', models.IntegerField(null=True, editable=False, blank=True)),
('text', wagtail.wagtailcore.fields.RichTextField()),
],
options={
'ordering': ['sort_order'],
'abstract': False,
},
),
migrations.CreateModel(
name='EndNote',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('sort_order', models.IntegerField(null=True, editable=False, blank=True)),
('text', wagtail.wagtailcore.fields.RichTextField()),
],
options={
'ordering': ['sort_order'],
'abstract': False,
},
),
migrations.RemoveField(
model_name='chapteredarticlepage',
name='chapters',
),
migrations.AddField(
model_name='articlepage',
name='chapters',
field=articles.fields.ChapterField([('chapter', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock()), (b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul')), (b'Overflow', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul')), (b'ColumnedContent', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul'))], required=False))]))], required=False))])), (b'ColumnedContent', wagtail.wagtailcore.blocks.StructBlock([(b'body', wagtail.wagtailcore.blocks.StreamBlock([(b'Heading', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.CharBlock()), (b'heading_level', wagtail.wagtailcore.blocks.ChoiceBlock(default=2, choices=[(2, '2'), (3, '3'), (4, '4'), (5, '5'), (6, '6')]))])), (b'Paragraph', wagtail.wagtailcore.blocks.StructBlock([(b'text', wagtail.wagtailcore.blocks.RichTextBlock()), (b'use_dropcap', wagtail.wagtailcore.blocks.BooleanBlock(required=False))])), (b'Image', wagtail.wagtailcore.blocks.StructBlock([(b'image', wagtail.wagtailimages.blocks.ImageChooserBlock()), (b'placement', wagtail.wagtailcore.blocks.ChoiceBlock(default='full', choices=[('left', 'Left Aligned'), ('right', 'Right Aligned'), ('full', 'Full Width'), ('editorial', 'Full Width Editorial')])), (b'expandable', wagtail.wagtailcore.blocks.BooleanBlock(default=False, required=False)), (b'label', wagtail.wagtailcore.blocks.CharBlock(help_text='Additional label to be displayed with the image.', required=False))])), (b'Embed', wagtail.wagtailembeds.blocks.EmbedBlock(icon='site')), (b'List', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.RichTextBlock(label='item'), icon='list-ul')), (b'Sharable', articles.fields.SharableBlock()), (b'PullQuote', articles.fields.PullQuoteBlock()), (b'Quote', articles.fields.SimpleQuoteBlock()), (b'Interactive', articles.fields.InteractiveBlock(interactives.models.Interactive)), (b'RelatedItems', wagtail.wagtailcore.blocks.StructBlock([(b'heading', wagtail.wagtailcore.blocks.CharBlock(default='Related')), (b'items', wagtail.wagtailcore.blocks.ListBlock(wagtail.wagtailcore.blocks.PageChooserBlock(label='item')))], icon='list-ul'))], required=False))]))], required=False))]))], null=True, blank=True),
),
migrations.AddField(
model_name='articlepage',
name='citations_heading',
field=models.TextField(default='Works Cited', blank=True),
),
migrations.AddField(
model_name='articlepage',
name='endnote_identifier_style',
field=models.CharField(default='roman-lower', max_length=20, choices=[('roman-lower', 'Roman Numerals - Lowercase'), ('roman-upper', 'Roman Numerals - Uppercase'), ('numbers', 'Numbers')]),
),
migrations.AddField(
model_name='articlepage',
name='endnotes_heading',
field=models.TextField(default='End Notes', blank=True),
),
migrations.AddField(
model_name='articlepage',
name='table_of_contents_heading',
field=models.TextField(default='Table of Contents', blank=True),
),
migrations.AddField(
model_name='endnote',
name='article',
field=modelcluster.fields.ParentalKey(related_name='endnote_links', to='articles.ArticlePage'),
),
migrations.AddField(
model_name='citation',
name='article',
field=modelcluster.fields.ParentalKey(related_name='citation_links', to='articles.ArticlePage'),
),
]
| true | true |
f736ccf2709cf9a412754bf299213e98f6cdd5cd | 60 | py | Python | src/python/project/model.py | metee1996/ds-example-project | 8d43b8786711a69779adb7fd6a830fe63fe30909 | [
"Apache-2.0"
] | 4 | 2018-02-22T14:15:50.000Z | 2019-04-04T20:12:51.000Z | src/python/project/model.py | metee1996/ds-example-project | 8d43b8786711a69779adb7fd6a830fe63fe30909 | [
"Apache-2.0"
] | null | null | null | src/python/project/model.py | metee1996/ds-example-project | 8d43b8786711a69779adb7fd6a830fe63fe30909 | [
"Apache-2.0"
] | 11 | 2018-02-23T00:26:44.000Z | 2020-04-08T11:32:31.000Z | import numpy as np
def power(x):
return np.power(x, 2)
| 12 | 25 | 0.65 | import numpy as np
def power(x):
return np.power(x, 2)
| true | true |
f736cd1bb79f65fb5483c56a4c4fc07056e754eb | 2,246 | py | Python | tools/convert_video2frames.py | RexBarker/VideoObjectRemoval | 26b8648645044389e9b7311f609c04b41f92f0b7 | [
"MIT"
] | 10 | 2020-11-09T03:05:11.000Z | 2022-03-15T08:17:18.000Z | tools/convert_video2frames.py | RexBarker/VideoObjectRemoval | 26b8648645044389e9b7311f609c04b41f92f0b7 | [
"MIT"
] | 1 | 2020-12-14T11:33:10.000Z | 2020-12-15T02:27:24.000Z | tools/convert_video2frames.py | RexBarker/VideoObjectRemoval | 26b8648645044389e9b7311f609c04b41f92f0b7 | [
"MIT"
] | 3 | 2021-02-17T09:14:31.000Z | 2021-11-10T01:55:51.000Z | import argparse
import cv2
import os
import numpy as np
from math import log10, ceil
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input_file', type=str, required=True, default=None,
help="input video file (.avi, .mp4, .mkv, mov)")
parser.add_argument('--rotate_right', action='store_true', help="Rotate image by 90 deg clockwise")
parser.add_argument('--rotate_left', action='store_true', help="Rotate image by 90 deg anticlockwise")
parser.add_argument('--image_type', type=str, default='png', help="output frame file type (def=png)")
parser.add_argument('--output_dir', type=str, default=None,
help="name of output directory (default = base of input file name")
args = parser.parse_args()
return args
def video_to_frames(inputfile,outputdir,imagetype='png'):
if not os.path.exists(outputdir):
dout = '.'
for din in outputdir.split('/'):
dout = dout + '/' + din
if not os.path.exists(dout):
os.mkdir(dout)
cap = cv2.VideoCapture(inputfile)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
padlength = ceil(log10(length))
n = 0
while True:
ret, frame = cap.read()
if not ret: break
if args.rotate_left:
frame = cv2.rotate(frame,cv2.ROTATE_90_COUNTERCLOCKWISE)
elif args.rotate_right:
frame = cv2.rotate(frame,cv2.ROTATE_90_CLOCKWISE)
fname = str(n).rjust(padlength,'0') + '.' + imagetype
cv2.imwrite(os.path.join(outputdir,fname),frame)
n += 1
return n # number of frames processed
if __name__ == '__main__':
args = parse_args()
assert os.path.exists(args.input_file), f"Could not find input file = {args.input_file}"
inputfile = args.input_file
currdir = os.path.abspath(os.curdir)
if args.output_dir is not None:
outputdir = args.output_dir
else:
outputdir = os.path.basename(inputdir).split('.')[0]
outputdir = os.path.join(currdir,outputdir + "_frames")
n = video_to_frames(inputfile,outputdir,imagetype=args.image_type)
print(f"\nCompleted successfully, processed {n} frames")
| 31.194444 | 106 | 0.64114 | import argparse
import cv2
import os
import numpy as np
from math import log10, ceil
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input_file', type=str, required=True, default=None,
help="input video file (.avi, .mp4, .mkv, mov)")
parser.add_argument('--rotate_right', action='store_true', help="Rotate image by 90 deg clockwise")
parser.add_argument('--rotate_left', action='store_true', help="Rotate image by 90 deg anticlockwise")
parser.add_argument('--image_type', type=str, default='png', help="output frame file type (def=png)")
parser.add_argument('--output_dir', type=str, default=None,
help="name of output directory (default = base of input file name")
args = parser.parse_args()
return args
def video_to_frames(inputfile,outputdir,imagetype='png'):
if not os.path.exists(outputdir):
dout = '.'
for din in outputdir.split('/'):
dout = dout + '/' + din
if not os.path.exists(dout):
os.mkdir(dout)
cap = cv2.VideoCapture(inputfile)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
padlength = ceil(log10(length))
n = 0
while True:
ret, frame = cap.read()
if not ret: break
if args.rotate_left:
frame = cv2.rotate(frame,cv2.ROTATE_90_COUNTERCLOCKWISE)
elif args.rotate_right:
frame = cv2.rotate(frame,cv2.ROTATE_90_CLOCKWISE)
fname = str(n).rjust(padlength,'0') + '.' + imagetype
cv2.imwrite(os.path.join(outputdir,fname),frame)
n += 1
return n
if __name__ == '__main__':
args = parse_args()
assert os.path.exists(args.input_file), f"Could not find input file = {args.input_file}"
inputfile = args.input_file
currdir = os.path.abspath(os.curdir)
if args.output_dir is not None:
outputdir = args.output_dir
else:
outputdir = os.path.basename(inputdir).split('.')[0]
outputdir = os.path.join(currdir,outputdir + "_frames")
n = video_to_frames(inputfile,outputdir,imagetype=args.image_type)
print(f"\nCompleted successfully, processed {n} frames")
| true | true |
f736cd719f269335aea5d9fd217a88184c2f3c71 | 10,680 | py | Python | cloudpathlib/s3/s3client.py | ElucidataInc/cloudpathlib | 9c11c6af2b0ac713ddcd950123d1db3b17515efa | [
"MIT"
] | null | null | null | cloudpathlib/s3/s3client.py | ElucidataInc/cloudpathlib | 9c11c6af2b0ac713ddcd950123d1db3b17515efa | [
"MIT"
] | null | null | null | cloudpathlib/s3/s3client.py | ElucidataInc/cloudpathlib | 9c11c6af2b0ac713ddcd950123d1db3b17515efa | [
"MIT"
] | null | null | null | import os
from pathlib import Path, PurePosixPath
from typing import Any, Dict, Iterable, Optional, Union
from ..client import Client, register_client_class
from ..cloudpath import implementation_registry
from .s3path import S3Path
try:
from boto3.session import Session
from boto3.s3.transfer import TransferConfig
from botocore.config import Config
from botocore.exceptions import ClientError
import botocore.session
except ModuleNotFoundError:
implementation_registry["s3"].dependencies_loaded = False
@register_client_class("s3")
class S3Client(Client):
"""Client class for AWS S3 which handles authentication with AWS for [`S3Path`](../s3path/)
instances. See documentation for the [`__init__` method][cloudpathlib.s3.s3client.S3Client.__init__]
for detailed authentication options."""
def __init__(
self,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
no_sign_request: Optional[bool] = False,
botocore_session: Optional["botocore.session.Session"] = None,
profile_name: Optional[str] = None,
boto3_session: Optional["Session"] = None,
local_cache_dir: Optional[Union[str, os.PathLike]] = None,
endpoint_url: Optional[str] = None,
boto3_transfer_config: Optional["TransferConfig"] = None,
):
"""Class constructor. Sets up a boto3 [`Session`](
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html).
Directly supports the same authentication interface, as well as the same environment
variables supported by boto3. See [boto3 Session documentation](
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/session.html).
If no authentication arguments or environment variables are provided, then the client will
be instantiated as anonymous, which will only have access to public buckets.
Args:
aws_access_key_id (Optional[str]): AWS access key ID.
aws_secret_access_key (Optional[str]): AWS secret access key.
aws_session_token (Optional[str]): Session key for your AWS account. This is only
needed when you are using temporarycredentials.
no_sign_request: (Optional[bool]): If `True`, credentials are not looked for and we use unsigned
requests to fetch resources. This will only allow access to public resources. This is equivalent
to `--no-sign-request` in the AWS CLI (https://docs.aws.amazon.com/cli/latest/reference/).
botocore_session (Optional[botocore.session.Session]): An already instantiated botocore
Session.
profile_name (Optional[str]): Profile name of a profile in a shared credentials file.
boto3_session (Optional[Session]): An already instantiated boto3 Session.
local_cache_dir (Optional[Union[str, os.PathLike]]): Path to directory to use as cache
for downloaded files. If None, will use a temporary directory.
endpoint_url (Optional[str]): S3 server endpoint URL to use for the constructed boto3 S3 resource and client.
Parameterize it to access a customly deployed S3-compatible object store such as MinIO, Ceph or any other.
boto3_transfer_config (Optional[dict]): Instantiated TransferConfig for managing s3 transfers.
(https://boto3.amazonaws.com/v1/documentation/api/latest/reference/customizations/s3.html#boto3.s3.transfer.TransferConfig)
"""
endpoint_url = endpoint_url or os.getenv("AWS_ENDPOINT_URL")
if boto3_session is not None:
self.sess = boto3_session
else:
self.sess = Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
botocore_session=botocore_session,
profile_name=profile_name,
)
if no_sign_request:
self.s3 = self.sess.resource(
"s3",
endpoint_url=endpoint_url,
config=Config(signature_version=botocore.session.UNSIGNED),
)
self.client = self.sess.client(
"s3",
endpoint_url=endpoint_url,
config=Config(signature_version=botocore.session.UNSIGNED),
)
else:
self.s3 = self.sess.resource("s3", endpoint_url=endpoint_url)
self.client = self.sess.client("s3", endpoint_url=endpoint_url)
self.boto3_transfer_config = boto3_transfer_config
super().__init__(local_cache_dir=local_cache_dir)
def _get_metadata(self, cloud_path: S3Path) -> Dict[str, Any]:
data = self.s3.ObjectSummary(cloud_path.bucket, cloud_path.key).get()
return {
"last_modified": data["LastModified"],
"size": data["ContentLength"],
"etag": data["ETag"],
"mime": data["ContentType"],
"extra": data["Metadata"],
}
def _download_file(self, cloud_path: S3Path, local_path: Union[str, os.PathLike]) -> Path:
local_path = Path(local_path)
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.download_file(str(local_path), Config=self.boto3_transfer_config)
return local_path
def _is_file_or_dir(self, cloud_path: S3Path) -> Optional[str]:
# short-circuit the root-level bucket
if not cloud_path.key:
return "dir"
# get first item by listing at least one key
s3_obj = self._s3_file_query(cloud_path)
if s3_obj is None:
return None
# since S3 only returns files when filtering objects:
# if the first item key is equal to the path key, this is a file
if s3_obj.key == cloud_path.key:
# "fake" directories on S3 can be created in the console UI
# these are 0-size keys that end in `/`
# Ref: https://github.com/boto/boto3/issues/377
if s3_obj.key.endswith("/") and s3_obj.content_length == 0:
return "dir"
else:
return "file"
else:
return "dir"
def _exists(self, cloud_path: S3Path) -> bool:
return self._s3_file_query(cloud_path) is not None
def _s3_file_query(self, cloud_path: S3Path):
"""Boto3 query used for quick checks of existence and if path is file/dir"""
# first check if this is an object that we can access directly
try:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.load()
return obj
# else, confirm it is a dir by filtering to the first item under the prefix
except ClientError:
return next(
(
obj
for obj in (
self.s3.Bucket(cloud_path.bucket)
.objects.filter(Prefix=cloud_path.key)
.limit(1)
)
),
None,
)
def _list_dir(self, cloud_path: S3Path, recursive=False) -> Iterable[S3Path]:
bucket = self.s3.Bucket(cloud_path.bucket)
prefix = cloud_path.key
if prefix and not prefix.endswith("/"):
prefix += "/"
yielded_dirs = set()
if recursive:
for o in bucket.objects.filter(Prefix=prefix):
# get directory from this path
for parent in PurePosixPath(o.key[len(prefix) :]).parents:
# if we haven't surfaced their directory already
if parent not in yielded_dirs and str(parent) != ".":
yield self.CloudPath(f"s3://{cloud_path.bucket}/{prefix}{parent}")
yielded_dirs.add(parent)
yield self.CloudPath(f"s3://{o.bucket_name}/{o.key}")
else:
# non recursive is best done with old client API rather than resource
paginator = self.client.get_paginator("list_objects")
for result in paginator.paginate(
Bucket=cloud_path.bucket, Prefix=prefix, Delimiter="/"
):
# sub directory names
for result_prefix in result.get("CommonPrefixes", []):
yield self.CloudPath(f"s3://{cloud_path.bucket}/{result_prefix.get('Prefix')}")
# files in the directory
for result_key in result.get("Contents", []):
if result_key.get('Size') > 0:
yield self.CloudPath(f"s3://{cloud_path.bucket}/{result_key.get('Key')}")
def _move_file(self, src: S3Path, dst: S3Path, remove_src: bool = True) -> S3Path:
# just a touch, so "REPLACE" metadata
if src == dst:
o = self.s3.Object(src.bucket, src.key)
o.copy_from(
CopySource={"Bucket": src.bucket, "Key": src.key},
Metadata=self._get_metadata(src).get("extra", {}),
MetadataDirective="REPLACE",
)
else:
target = self.s3.Object(dst.bucket, dst.key)
target.copy({"Bucket": src.bucket, "Key": src.key})
if remove_src:
self._remove(src)
return dst
def _remove(self, cloud_path: S3Path) -> None:
try:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
# will throw if not a file
obj.load()
resp = obj.delete()
assert resp.get("ResponseMetadata").get("HTTPStatusCode") == 204
except ClientError:
# try to delete as a direcotry instead
bucket = self.s3.Bucket(cloud_path.bucket)
prefix = cloud_path.key
if prefix and not prefix.endswith("/"):
prefix += "/"
resp = bucket.objects.filter(Prefix=prefix).delete()
# ensure directory deleted; if cloud_path did not exist at all
# resp will be [], so no need to check success
if resp:
assert resp[0].get("ResponseMetadata").get("HTTPStatusCode") == 200
def _upload_file(self, local_path: Union[str, os.PathLike], cloud_path: S3Path) -> S3Path:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.upload_file(str(local_path), Config=self.boto3_transfer_config)
return cloud_path
S3Client.S3Path = S3Client.CloudPath # type: ignore
| 42.047244 | 139 | 0.61264 | import os
from pathlib import Path, PurePosixPath
from typing import Any, Dict, Iterable, Optional, Union
from ..client import Client, register_client_class
from ..cloudpath import implementation_registry
from .s3path import S3Path
try:
from boto3.session import Session
from boto3.s3.transfer import TransferConfig
from botocore.config import Config
from botocore.exceptions import ClientError
import botocore.session
except ModuleNotFoundError:
implementation_registry["s3"].dependencies_loaded = False
@register_client_class("s3")
class S3Client(Client):
def __init__(
self,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
no_sign_request: Optional[bool] = False,
botocore_session: Optional["botocore.session.Session"] = None,
profile_name: Optional[str] = None,
boto3_session: Optional["Session"] = None,
local_cache_dir: Optional[Union[str, os.PathLike]] = None,
endpoint_url: Optional[str] = None,
boto3_transfer_config: Optional["TransferConfig"] = None,
):
endpoint_url = endpoint_url or os.getenv("AWS_ENDPOINT_URL")
if boto3_session is not None:
self.sess = boto3_session
else:
self.sess = Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
botocore_session=botocore_session,
profile_name=profile_name,
)
if no_sign_request:
self.s3 = self.sess.resource(
"s3",
endpoint_url=endpoint_url,
config=Config(signature_version=botocore.session.UNSIGNED),
)
self.client = self.sess.client(
"s3",
endpoint_url=endpoint_url,
config=Config(signature_version=botocore.session.UNSIGNED),
)
else:
self.s3 = self.sess.resource("s3", endpoint_url=endpoint_url)
self.client = self.sess.client("s3", endpoint_url=endpoint_url)
self.boto3_transfer_config = boto3_transfer_config
super().__init__(local_cache_dir=local_cache_dir)
def _get_metadata(self, cloud_path: S3Path) -> Dict[str, Any]:
data = self.s3.ObjectSummary(cloud_path.bucket, cloud_path.key).get()
return {
"last_modified": data["LastModified"],
"size": data["ContentLength"],
"etag": data["ETag"],
"mime": data["ContentType"],
"extra": data["Metadata"],
}
def _download_file(self, cloud_path: S3Path, local_path: Union[str, os.PathLike]) -> Path:
local_path = Path(local_path)
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.download_file(str(local_path), Config=self.boto3_transfer_config)
return local_path
def _is_file_or_dir(self, cloud_path: S3Path) -> Optional[str]:
if not cloud_path.key:
return "dir"
s3_obj = self._s3_file_query(cloud_path)
if s3_obj is None:
return None
if s3_obj.key == cloud_path.key:
if s3_obj.key.endswith("/") and s3_obj.content_length == 0:
return "dir"
else:
return "file"
else:
return "dir"
def _exists(self, cloud_path: S3Path) -> bool:
return self._s3_file_query(cloud_path) is not None
def _s3_file_query(self, cloud_path: S3Path):
try:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.load()
return obj
except ClientError:
return next(
(
obj
for obj in (
self.s3.Bucket(cloud_path.bucket)
.objects.filter(Prefix=cloud_path.key)
.limit(1)
)
),
None,
)
def _list_dir(self, cloud_path: S3Path, recursive=False) -> Iterable[S3Path]:
bucket = self.s3.Bucket(cloud_path.bucket)
prefix = cloud_path.key
if prefix and not prefix.endswith("/"):
prefix += "/"
yielded_dirs = set()
if recursive:
for o in bucket.objects.filter(Prefix=prefix):
for parent in PurePosixPath(o.key[len(prefix) :]).parents:
if parent not in yielded_dirs and str(parent) != ".":
yield self.CloudPath(f"s3://{cloud_path.bucket}/{prefix}{parent}")
yielded_dirs.add(parent)
yield self.CloudPath(f"s3://{o.bucket_name}/{o.key}")
else:
# non recursive is best done with old client API rather than resource
paginator = self.client.get_paginator("list_objects")
for result in paginator.paginate(
Bucket=cloud_path.bucket, Prefix=prefix, Delimiter="/"
):
# sub directory names
for result_prefix in result.get("CommonPrefixes", []):
yield self.CloudPath(f"s3://{cloud_path.bucket}/{result_prefix.get('Prefix')}")
# files in the directory
for result_key in result.get("Contents", []):
if result_key.get('Size') > 0:
yield self.CloudPath(f"s3://{cloud_path.bucket}/{result_key.get('Key')}")
def _move_file(self, src: S3Path, dst: S3Path, remove_src: bool = True) -> S3Path:
# just a touch, so "REPLACE" metadata
if src == dst:
o = self.s3.Object(src.bucket, src.key)
o.copy_from(
CopySource={"Bucket": src.bucket, "Key": src.key},
Metadata=self._get_metadata(src).get("extra", {}),
MetadataDirective="REPLACE",
)
else:
target = self.s3.Object(dst.bucket, dst.key)
target.copy({"Bucket": src.bucket, "Key": src.key})
if remove_src:
self._remove(src)
return dst
def _remove(self, cloud_path: S3Path) -> None:
try:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
# will throw if not a file
obj.load()
resp = obj.delete()
assert resp.get("ResponseMetadata").get("HTTPStatusCode") == 204
except ClientError:
# try to delete as a direcotry instead
bucket = self.s3.Bucket(cloud_path.bucket)
prefix = cloud_path.key
if prefix and not prefix.endswith("/"):
prefix += "/"
resp = bucket.objects.filter(Prefix=prefix).delete()
# ensure directory deleted; if cloud_path did not exist at all
# resp will be [], so no need to check success
if resp:
assert resp[0].get("ResponseMetadata").get("HTTPStatusCode") == 200
def _upload_file(self, local_path: Union[str, os.PathLike], cloud_path: S3Path) -> S3Path:
obj = self.s3.Object(cloud_path.bucket, cloud_path.key)
obj.upload_file(str(local_path), Config=self.boto3_transfer_config)
return cloud_path
S3Client.S3Path = S3Client.CloudPath # type: ignore
| true | true |
f736ce1759f8b8e50d2262ffb1bb2caacf4afcc4 | 276 | py | Python | emailhub/backends/smtp.py | h3/django-emailhub | a618256096b3faa55479c46b6313861cfd898a9f | [
"MIT"
] | null | null | null | emailhub/backends/smtp.py | h3/django-emailhub | a618256096b3faa55479c46b6313861cfd898a9f | [
"MIT"
] | null | null | null | emailhub/backends/smtp.py | h3/django-emailhub | a618256096b3faa55479c46b6313861cfd898a9f | [
"MIT"
] | null | null | null | from django.core.mail.backends.smtp import SMTPEmailBackend
from emailhub.utils.email import process_outgoing_email
class EmailBackend(SMTPEmailBackend):
def _send(self, message):
process_outgoing_email(message)
super(EmailBackend, self)._send(message)
| 27.6 | 59 | 0.782609 | from django.core.mail.backends.smtp import SMTPEmailBackend
from emailhub.utils.email import process_outgoing_email
class EmailBackend(SMTPEmailBackend):
def _send(self, message):
process_outgoing_email(message)
super(EmailBackend, self)._send(message)
| true | true |
f736ce4d698e7428c6ecb081ca7d309648cc2aa3 | 5,434 | py | Python | dev_test_cex_full_non_stop.py | Bosma/unicorn-binance-websocket-api | e8bfe08125ff0afb8f780c3970a6ba6ec6ba6c54 | [
"MIT"
] | null | null | null | dev_test_cex_full_non_stop.py | Bosma/unicorn-binance-websocket-api | e8bfe08125ff0afb8f780c3970a6ba6ec6ba6c54 | [
"MIT"
] | null | null | null | dev_test_cex_full_non_stop.py | Bosma/unicorn-binance-websocket-api | e8bfe08125ff0afb8f780c3970a6ba6ec6ba6c54 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# File: dev_test_cex_full_non_stop.py
#
# Part of ‘UNICORN Binance WebSocket API’
# Project website: https://github.com/oliver-zehentleitner/unicorn-binance-websocket-api
# Documentation: https://oliver-zehentleitner.github.io/unicorn-binance-websocket-api
# PyPI: https://pypi.org/project/unicorn-binance-websocket-api/
#
# Author: Oliver Zehentleitner
# https://about.me/oliver-zehentleitner
#
# Copyright (c) 2019-2020, Oliver Zehentleitner
# All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish, dis-
# tribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the fol-
# lowing conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABIL-
# ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
# SHALL THE AUTHOR BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
from unicorn_binance_websocket_api.unicorn_binance_websocket_api_manager import BinanceWebSocketApiManager
import logging
import math
import os
import requests
import sys
import time
import threading
try:
from binance.client import Client
except ImportError:
print("Please install `python-binance`! https://pypi.org/project/python-binance/#description")
sys.exit(1)
binance_api_key = ""
binance_api_secret = ""
channels = {'aggTrade', 'trade', 'kline_1m', 'kline_5m', 'kline_15m', 'kline_30m', 'kline_1h', 'kline_2h', 'kline_4h',
'kline_6h', 'kline_8h', 'kline_12h', 'kline_1d', 'kline_3d', 'kline_1w', 'kline_1M', 'miniTicker',
'ticker', 'bookTicker', 'depth5', 'depth10', 'depth20', 'depth', 'depth@100ms'}
arr_channels = {'!miniTicker', '!ticker', '!bookTicker'}
# https://docs.python.org/3/library/logging.html#logging-levels
logging.basicConfig(level=logging.INFO,
filename=os.path.basename(__file__) + '.log',
format="{asctime} [{levelname:8}] {process} {thread} {module}: {message}",
style="{")
def print_stream_data_from_stream_buffer(binance_websocket_api_manager):
time.sleep(30)
while True:
if binance_websocket_api_manager.is_manager_stopping():
exit(0)
oldest_stream_data_from_stream_buffer = binance_websocket_api_manager.pop_stream_data_from_stream_buffer()
if oldest_stream_data_from_stream_buffer is False:
time.sleep(0.01)
# create instance of BinanceWebSocketApiManager
#binance_websocket_api_manager = BinanceWebSocketApiManager(throw_exception_if_unrepairable=True)
binance_websocket_api_manager = BinanceWebSocketApiManager(throw_exception_if_unrepairable=False)
print("starting monitoring api!")
binance_websocket_api_manager.start_monitoring_api()
try:
binance_rest_client = Client(binance_api_key, binance_api_secret)
binance_websocket_api_manager = BinanceWebSocketApiManager()
except requests.exceptions.ConnectionError:
print("No internet connection?")
sys.exit(1)
# start a worker process to move the received stream_data from the stream_buffer to a print function
worker_thread = threading.Thread(target=print_stream_data_from_stream_buffer, args=(binance_websocket_api_manager,))
worker_thread.start()
markets = []
data = binance_rest_client.get_all_tickers()
for item in data:
markets.append(item['symbol'])
private_stream_id = binance_websocket_api_manager.create_stream(["!userData"],
["arr"],
api_key=binance_api_key,
api_secret=binance_api_secret,
stream_label="userData stream!")
binance_websocket_api_manager.create_stream(arr_channels, "arr", stream_label="`arr` channels")
divisor = math.ceil(len(markets) / binance_websocket_api_manager.get_limit_of_subscriptions_per_stream())
max_subscriptions = math.ceil(len(markets) / divisor)
for channel in channels:
if len(markets) <= max_subscriptions:
binance_websocket_api_manager.create_stream(channel, markets, stream_label=channel)
else:
loops = 1
i = 1
markets_sub = []
for market in markets:
markets_sub.append(market)
if i == max_subscriptions or loops * max_subscriptions + i == len(markets):
binance_websocket_api_manager.create_stream(channel, markets_sub,
stream_label=str(channel + "_" + str(i)))
markets_sub = []
i = 1
loops += 1
i += 1
while True:
binance_websocket_api_manager.print_summary()
time.sleep(1)
| 42.124031 | 118 | 0.698197 |
from unicorn_binance_websocket_api.unicorn_binance_websocket_api_manager import BinanceWebSocketApiManager
import logging
import math
import os
import requests
import sys
import time
import threading
try:
from binance.client import Client
except ImportError:
print("Please install `python-binance`! https://pypi.org/project/python-binance/#description")
sys.exit(1)
binance_api_key = ""
binance_api_secret = ""
channels = {'aggTrade', 'trade', 'kline_1m', 'kline_5m', 'kline_15m', 'kline_30m', 'kline_1h', 'kline_2h', 'kline_4h',
'kline_6h', 'kline_8h', 'kline_12h', 'kline_1d', 'kline_3d', 'kline_1w', 'kline_1M', 'miniTicker',
'ticker', 'bookTicker', 'depth5', 'depth10', 'depth20', 'depth', 'depth@100ms'}
arr_channels = {'!miniTicker', '!ticker', '!bookTicker'}
onfig(level=logging.INFO,
filename=os.path.basename(__file__) + '.log',
format="{asctime} [{levelname:8}] {process} {thread} {module}: {message}",
style="{")
def print_stream_data_from_stream_buffer(binance_websocket_api_manager):
time.sleep(30)
while True:
if binance_websocket_api_manager.is_manager_stopping():
exit(0)
oldest_stream_data_from_stream_buffer = binance_websocket_api_manager.pop_stream_data_from_stream_buffer()
if oldest_stream_data_from_stream_buffer is False:
time.sleep(0.01)
binance_websocket_api_manager = BinanceWebSocketApiManager(throw_exception_if_unrepairable=False)
print("starting monitoring api!")
binance_websocket_api_manager.start_monitoring_api()
try:
binance_rest_client = Client(binance_api_key, binance_api_secret)
binance_websocket_api_manager = BinanceWebSocketApiManager()
except requests.exceptions.ConnectionError:
print("No internet connection?")
sys.exit(1)
worker_thread = threading.Thread(target=print_stream_data_from_stream_buffer, args=(binance_websocket_api_manager,))
worker_thread.start()
markets = []
data = binance_rest_client.get_all_tickers()
for item in data:
markets.append(item['symbol'])
private_stream_id = binance_websocket_api_manager.create_stream(["!userData"],
["arr"],
api_key=binance_api_key,
api_secret=binance_api_secret,
stream_label="userData stream!")
binance_websocket_api_manager.create_stream(arr_channels, "arr", stream_label="`arr` channels")
divisor = math.ceil(len(markets) / binance_websocket_api_manager.get_limit_of_subscriptions_per_stream())
max_subscriptions = math.ceil(len(markets) / divisor)
for channel in channels:
if len(markets) <= max_subscriptions:
binance_websocket_api_manager.create_stream(channel, markets, stream_label=channel)
else:
loops = 1
i = 1
markets_sub = []
for market in markets:
markets_sub.append(market)
if i == max_subscriptions or loops * max_subscriptions + i == len(markets):
binance_websocket_api_manager.create_stream(channel, markets_sub,
stream_label=str(channel + "_" + str(i)))
markets_sub = []
i = 1
loops += 1
i += 1
while True:
binance_websocket_api_manager.print_summary()
time.sleep(1)
| true | true |
f736ce7112493f04302c25a6d3cf1105623bba16 | 5,028 | py | Python | Python/empire/structs/_struct_util.py | Tombmyst/Empire | f28782787c5fa9127e353549b73ec90d3c82c003 | [
"Apache-2.0"
] | null | null | null | Python/empire/structs/_struct_util.py | Tombmyst/Empire | f28782787c5fa9127e353549b73ec90d3c82c003 | [
"Apache-2.0"
] | null | null | null | Python/empire/structs/_struct_util.py | Tombmyst/Empire | f28782787c5fa9127e353549b73ec90d3c82c003 | [
"Apache-2.0"
] | null | null | null | from empire import *
from empire.strings.casing import *
from empire.structs.abstract_struct import AbstractStruct
from empire.structs.struct_serializable import StructSerializable
from empire.util.log import *
from collections import OrderedDict
from fuzzywuzzy import fuzz
class Assignment:
@staticmethod
def determine_struct_field_name(the_struct: AbstractStruct, key_from_raw: str, casing: WordCasing, public_fields: JSON, dynamic_fields_option_enabled: bool) -> Tuple[str, bool]:
is_dynamically_added: bool = False
struct_field_name: str = CasingTransform.transform(key_from_raw, casing.casing_type, casing.spacing_type)
if struct_field_name not in public_fields and '_' + struct_field_name in public_fields:
struct_field_name = '_' + struct_field_name
elif struct_field_name not in public_fields:
if dynamic_fields_option_enabled:
setattr(the_struct, key_from_raw, None)
is_dynamically_added = True
struct_field_name = key_from_raw
else:
struct_field_name = Assignment._try_to_find_field(public_fields, struct_field_name)
return struct_field_name, is_dynamically_added
@staticmethod
def bind_struct_field_to_raw_field(field_mapping: JSON, struct_field_name: str, raw_key: str) -> bool:
if not struct_field_name:
Log.severe('Unable to find a correspondence for "{}" in struct'.format(raw_key), 'Struct', '_assign_values_from_dict')
return False
else:
field_mapping[struct_field_name] = raw_key
return True
@staticmethod
def assign_value(the_struct: AbstractStruct, struct_field_name: str, raw_value: Any):
struct_field_value: Any = getattr(the_struct, struct_field_name)
if issubclass(type(struct_field_value), AbstractStruct):
if type(raw_value) is not dict and not issubclass(type(raw_value), AbstractStruct):
raise TypeError('Mismatching types: Field {} is a Struct and can only be assigned with a dict or a Struct, not a {}'.format(struct_field_name, str(type(raw_value))))
if issubclass(type(raw_value), AbstractStruct):
struct_field_value.copy_from(raw_value)
else:
struct_field_value.assign_values(raw_value)
elif issubclass(type(struct_field_value), StructSerializable):
setattr(the_struct, struct_field_name, struct_field_value.struct_unserialize(raw_value))
elif type(struct_field_value) is tuple:
setattr(the_struct, struct_field_name, tuple(raw_value))
elif type(struct_field_value) is set:
setattr(the_struct, struct_field_name, set(raw_value))
elif type(struct_field_value) is OrderedDict:
setattr(the_struct, struct_field_name, OrderedDict(raw_value))
else:
setattr(the_struct, struct_field_name, raw_value)
@staticmethod
def _try_to_find_field(public_fields: JSON, name: str) -> Union[str, None]:
highest = (0, '')
for field in public_fields:
ratio = fuzz.token_sort_ratio(name, field)
# Log.trace('Field "{}" has ratio {}'.format(field, ratio), __file__, get_function_name())
if ratio > highest[0]:
highest = (ratio, field)
# Log.trace('Fuzz found field "{}" to be similar to "{}"'.format(highest[1], name), __file__, get_function_name())
return highest[1] if highest[1] else None
class Dictionify:
@staticmethod
def get_effective_field_mapping(field_mapping: JSON, dict_obj: JSON) -> Tuple[JSON, bool]:
if len(field_mapping.keys()) > 0:
return field_mapping, True
else:
return dict_obj, False
@staticmethod
def to_dict_recursive(the_dict: JSON) -> Dict:
dic = {}
for subkey in the_dict.keys():
value: Any = the_dict[subkey]
if issubclass(type(value), AbstractStruct):
dic[subkey] = value.to_dict()
elif type(value) is dict:
dic[subkey] = Dictionify.to_dict_recursive(value)
elif type(value) in [list, tuple, set]:
dic[subkey] = Dictionify.to_dict_from_list_recursive(value)
else:
dic[subkey] = value
return dic
@staticmethod
def to_dict_from_list_recursive(the_list: Union[List, Set, Tuple]) -> List[JSON]:
result: List[JSON] = []
for element in the_list:
if issubclass(type(element), AbstractStruct):
result.append(element.to_dict())
elif type(element) is dict:
result.append(Dictionify.to_dict_recursive(element))
elif type(element) in [list, tuple, set]:
result.append(Dictionify.to_dict_from_list_recursive(element))
else:
result.append(element)
return result
| 44.892857 | 181 | 0.655529 | from empire import *
from empire.strings.casing import *
from empire.structs.abstract_struct import AbstractStruct
from empire.structs.struct_serializable import StructSerializable
from empire.util.log import *
from collections import OrderedDict
from fuzzywuzzy import fuzz
class Assignment:
@staticmethod
def determine_struct_field_name(the_struct: AbstractStruct, key_from_raw: str, casing: WordCasing, public_fields: JSON, dynamic_fields_option_enabled: bool) -> Tuple[str, bool]:
is_dynamically_added: bool = False
struct_field_name: str = CasingTransform.transform(key_from_raw, casing.casing_type, casing.spacing_type)
if struct_field_name not in public_fields and '_' + struct_field_name in public_fields:
struct_field_name = '_' + struct_field_name
elif struct_field_name not in public_fields:
if dynamic_fields_option_enabled:
setattr(the_struct, key_from_raw, None)
is_dynamically_added = True
struct_field_name = key_from_raw
else:
struct_field_name = Assignment._try_to_find_field(public_fields, struct_field_name)
return struct_field_name, is_dynamically_added
@staticmethod
def bind_struct_field_to_raw_field(field_mapping: JSON, struct_field_name: str, raw_key: str) -> bool:
if not struct_field_name:
Log.severe('Unable to find a correspondence for "{}" in struct'.format(raw_key), 'Struct', '_assign_values_from_dict')
return False
else:
field_mapping[struct_field_name] = raw_key
return True
@staticmethod
def assign_value(the_struct: AbstractStruct, struct_field_name: str, raw_value: Any):
struct_field_value: Any = getattr(the_struct, struct_field_name)
if issubclass(type(struct_field_value), AbstractStruct):
if type(raw_value) is not dict and not issubclass(type(raw_value), AbstractStruct):
raise TypeError('Mismatching types: Field {} is a Struct and can only be assigned with a dict or a Struct, not a {}'.format(struct_field_name, str(type(raw_value))))
if issubclass(type(raw_value), AbstractStruct):
struct_field_value.copy_from(raw_value)
else:
struct_field_value.assign_values(raw_value)
elif issubclass(type(struct_field_value), StructSerializable):
setattr(the_struct, struct_field_name, struct_field_value.struct_unserialize(raw_value))
elif type(struct_field_value) is tuple:
setattr(the_struct, struct_field_name, tuple(raw_value))
elif type(struct_field_value) is set:
setattr(the_struct, struct_field_name, set(raw_value))
elif type(struct_field_value) is OrderedDict:
setattr(the_struct, struct_field_name, OrderedDict(raw_value))
else:
setattr(the_struct, struct_field_name, raw_value)
@staticmethod
def _try_to_find_field(public_fields: JSON, name: str) -> Union[str, None]:
highest = (0, '')
for field in public_fields:
ratio = fuzz.token_sort_ratio(name, field)
if ratio > highest[0]:
highest = (ratio, field)
return highest[1] if highest[1] else None
class Dictionify:
@staticmethod
def get_effective_field_mapping(field_mapping: JSON, dict_obj: JSON) -> Tuple[JSON, bool]:
if len(field_mapping.keys()) > 0:
return field_mapping, True
else:
return dict_obj, False
@staticmethod
def to_dict_recursive(the_dict: JSON) -> Dict:
dic = {}
for subkey in the_dict.keys():
value: Any = the_dict[subkey]
if issubclass(type(value), AbstractStruct):
dic[subkey] = value.to_dict()
elif type(value) is dict:
dic[subkey] = Dictionify.to_dict_recursive(value)
elif type(value) in [list, tuple, set]:
dic[subkey] = Dictionify.to_dict_from_list_recursive(value)
else:
dic[subkey] = value
return dic
@staticmethod
def to_dict_from_list_recursive(the_list: Union[List, Set, Tuple]) -> List[JSON]:
result: List[JSON] = []
for element in the_list:
if issubclass(type(element), AbstractStruct):
result.append(element.to_dict())
elif type(element) is dict:
result.append(Dictionify.to_dict_recursive(element))
elif type(element) in [list, tuple, set]:
result.append(Dictionify.to_dict_from_list_recursive(element))
else:
result.append(element)
return result
| true | true |
f736ceb3ead779a6e9d124918d709c125750bfce | 7,107 | py | Python | examples/run_time_series_prediction_example.py | ohtu-projekti-dataproblemsemulator/dataproblemsemulator | 58170716b44c4e4dee639451977f352b3c68053c | [
"MIT"
] | 2 | 2019-12-13T09:58:49.000Z | 2020-02-10T10:37:17.000Z | examples/run_time_series_prediction_example.py | ohtu-projekti-dataproblemsemulator/dataproblemsemulator | 58170716b44c4e4dee639451977f352b3c68053c | [
"MIT"
] | 199 | 2019-05-19T17:48:39.000Z | 2022-03-11T23:56:15.000Z | examples/run_time_series_prediction_example.py | thalvari/dpEmu-AutoML | b24eac686fae4147264c1ccc8169fd96b1875577 | [
"MIT"
] | 5 | 2019-10-02T23:14:05.000Z | 2020-05-28T16:23:22.000Z | # MIT License
#
# Copyright (c) 2019 Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import random as rn
import sys
from math import sqrt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from keras import backend
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from dpemu import pg_utils
from dpemu import runner
from dpemu.filters.time_series import Gap
from dpemu.nodes import Array
from dpemu.plotting_utils import print_results_by_model, visualize_scores, visualize_time_series_prediction
def get_data(argv):
dataset_name = argv[1]
n_data = int(argv[2])
if dataset_name == "passengers":
data = pd.read_csv("data/passengers.csv", header=0, usecols=["passengers"])[:n_data].values.astype(float)
n_period = 12
else:
data = pd.read_csv("data/temperature.csv", header=0, usecols=[dataset_name])[:n_data].values.astype(float)
n_period = 24
data = data[~np.isnan(data)]
n_data = len(data)
n_test = int(n_data * .2)
return data[:-n_test], data[-n_test:], n_data, n_period, dataset_name
def get_err_root_node():
err_root_node = Array()
# err_root_node.addfilter(GaussianNoise("mean", "std"))
# err_root_node.addfilter(SensorDrift("magnitude"))
err_root_node.addfilter(Gap("prob_break", "prob_recover", "missing_value"))
return err_root_node
def get_err_params_list():
# err_params_list = [{"mean": 0, "std": std} for std in np.linspace(0, 35, 8)]
# err_params_list = [{"magnitude": m} for m in range(8)]
err_params_list = [{"prob_break": p, "prob_recover": .5, "missing_value": np.nan} for p in np.linspace(0, .14, 8)]
return err_params_list
class Preprocessor:
def run(self, train_data, test_data, params):
return train_data, test_data, {}
class LSTMModel:
def __init__(self):
seed = 42
rn.seed(seed)
np.random.seed(seed)
tf.set_random_seed(seed)
conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
session = tf.Session(graph=tf.get_default_graph(), config=conf)
backend.set_session(session)
@staticmethod
def __get_periodic_diffs(data, n_period):
return np.array([data[i] - data[i - n_period] for i in range(n_period, len(data))])
@staticmethod
def __get_rmse(test_pred, test):
return sqrt(mean_squared_error(test_pred, test))
def run(self, train_data, _, params):
n_period = params["n_period"]
clean_test = params["clean_test"]
n_test = clean_test.shape[0]
train_data = train_data[~np.isnan(train_data)]
train_data = np.reshape(train_data, (len(train_data), 1))
n_features = 1
n_steps = 3 * n_period
n_nodes = 100
n_epochs = 200
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_train = scaler.fit_transform(train_data)
train_periodic_diffs = self.__get_periodic_diffs(scaled_train, n_period)
train_periodic_diffs = pg_utils.to_time_series_x_y(train_periodic_diffs, n_steps)
model = Sequential()
model.add(LSTM(n_nodes, activation="relu", input_shape=(n_steps, n_features)))
model.add(Dense(n_nodes, activation="relu"))
model.add(Dense(1))
model.compile(loss="mse", optimizer="adam")
model.fit(train_periodic_diffs[0], train_periodic_diffs[1], epochs=n_epochs)
train_with_test_pred = scaled_train
for _ in range(n_test):
x_cur = self.__get_periodic_diffs(train_with_test_pred, n_period)[-n_steps:]
x_cur = np.reshape(x_cur, (1, n_steps, n_features))
y_cur = model.predict(x_cur) + train_with_test_pred[-n_period]
train_with_test_pred = np.concatenate([train_with_test_pred, y_cur], axis=0)
train_with_test_pred = scaler.inverse_transform(train_with_test_pred)
test_pred = train_with_test_pred[-n_test:]
rmse = self.__get_rmse(test_pred, clean_test)
return {
"rmse": rmse,
"err_train": train_with_test_pred[:-n_test],
"test_pred": test_pred
}
def get_model_params_dict_list(test_data, n_period):
return [{"model": LSTMModel, "params_list": [{"clean_test": test_data, "n_period": n_period}]}]
def visualize(df, data, n_data, dataset_name):
visualize_scores(
df,
score_names=["rmse"],
is_higher_score_better=[False],
# err_param_name="std",
# err_param_name="magnitude",
err_param_name="prob_break",
title=f"Prediction scores for {dataset_name} dataset (n={n_data}) with added error"
)
visualize_time_series_prediction(
df,
data,
score_name="rmse",
is_higher_score_better=False,
# err_param_name="std",
# err_param_name="magnitude",
err_param_name="prob_break",
model_name="LSTM",
err_train_column="err_train",
test_pred_column="test_pred",
title=f"Predictions for {dataset_name} dataset (n={n_data}) with added error"
)
plt.show()
def main(argv):
if len(argv) != 3 or argv[1] not in ["passengers", "Jerusalem", "Eilat", "Miami", "Tel Aviv District"]:
exit(0)
train_data, test_data, n_data, n_period, dataset_name = get_data(argv)
df = runner.run(
train_data=train_data,
test_data=test_data,
preproc=Preprocessor,
preproc_params={},
err_root_node=get_err_root_node(),
err_params_list=get_err_params_list(),
model_params_dict_list=get_model_params_dict_list(test_data, n_period),
)
print_results_by_model(df, dropped_columns=["err_train", "test_pred", "clean_test", "n_period"])
visualize(df, np.concatenate([train_data, test_data], axis=0), n_data, dataset_name)
if __name__ == "__main__":
main(sys.argv)
| 36.823834 | 118 | 0.693964 |
import random as rn
import sys
from math import sqrt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from keras import backend
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from dpemu import pg_utils
from dpemu import runner
from dpemu.filters.time_series import Gap
from dpemu.nodes import Array
from dpemu.plotting_utils import print_results_by_model, visualize_scores, visualize_time_series_prediction
def get_data(argv):
dataset_name = argv[1]
n_data = int(argv[2])
if dataset_name == "passengers":
data = pd.read_csv("data/passengers.csv", header=0, usecols=["passengers"])[:n_data].values.astype(float)
n_period = 12
else:
data = pd.read_csv("data/temperature.csv", header=0, usecols=[dataset_name])[:n_data].values.astype(float)
n_period = 24
data = data[~np.isnan(data)]
n_data = len(data)
n_test = int(n_data * .2)
return data[:-n_test], data[-n_test:], n_data, n_period, dataset_name
def get_err_root_node():
err_root_node = Array()
err_root_node.addfilter(Gap("prob_break", "prob_recover", "missing_value"))
return err_root_node
def get_err_params_list():
err_params_list = [{"prob_break": p, "prob_recover": .5, "missing_value": np.nan} for p in np.linspace(0, .14, 8)]
return err_params_list
class Preprocessor:
def run(self, train_data, test_data, params):
return train_data, test_data, {}
class LSTMModel:
def __init__(self):
seed = 42
rn.seed(seed)
np.random.seed(seed)
tf.set_random_seed(seed)
conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
session = tf.Session(graph=tf.get_default_graph(), config=conf)
backend.set_session(session)
@staticmethod
def __get_periodic_diffs(data, n_period):
return np.array([data[i] - data[i - n_period] for i in range(n_period, len(data))])
@staticmethod
def __get_rmse(test_pred, test):
return sqrt(mean_squared_error(test_pred, test))
def run(self, train_data, _, params):
n_period = params["n_period"]
clean_test = params["clean_test"]
n_test = clean_test.shape[0]
train_data = train_data[~np.isnan(train_data)]
train_data = np.reshape(train_data, (len(train_data), 1))
n_features = 1
n_steps = 3 * n_period
n_nodes = 100
n_epochs = 200
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_train = scaler.fit_transform(train_data)
train_periodic_diffs = self.__get_periodic_diffs(scaled_train, n_period)
train_periodic_diffs = pg_utils.to_time_series_x_y(train_periodic_diffs, n_steps)
model = Sequential()
model.add(LSTM(n_nodes, activation="relu", input_shape=(n_steps, n_features)))
model.add(Dense(n_nodes, activation="relu"))
model.add(Dense(1))
model.compile(loss="mse", optimizer="adam")
model.fit(train_periodic_diffs[0], train_periodic_diffs[1], epochs=n_epochs)
train_with_test_pred = scaled_train
for _ in range(n_test):
x_cur = self.__get_periodic_diffs(train_with_test_pred, n_period)[-n_steps:]
x_cur = np.reshape(x_cur, (1, n_steps, n_features))
y_cur = model.predict(x_cur) + train_with_test_pred[-n_period]
train_with_test_pred = np.concatenate([train_with_test_pred, y_cur], axis=0)
train_with_test_pred = scaler.inverse_transform(train_with_test_pred)
test_pred = train_with_test_pred[-n_test:]
rmse = self.__get_rmse(test_pred, clean_test)
return {
"rmse": rmse,
"err_train": train_with_test_pred[:-n_test],
"test_pred": test_pred
}
def get_model_params_dict_list(test_data, n_period):
return [{"model": LSTMModel, "params_list": [{"clean_test": test_data, "n_period": n_period}]}]
def visualize(df, data, n_data, dataset_name):
visualize_scores(
df,
score_names=["rmse"],
is_higher_score_better=[False],
err_param_name="prob_break",
title=f"Prediction scores for {dataset_name} dataset (n={n_data}) with added error"
)
visualize_time_series_prediction(
df,
data,
score_name="rmse",
is_higher_score_better=False,
err_param_name="prob_break",
model_name="LSTM",
err_train_column="err_train",
test_pred_column="test_pred",
title=f"Predictions for {dataset_name} dataset (n={n_data}) with added error"
)
plt.show()
def main(argv):
if len(argv) != 3 or argv[1] not in ["passengers", "Jerusalem", "Eilat", "Miami", "Tel Aviv District"]:
exit(0)
train_data, test_data, n_data, n_period, dataset_name = get_data(argv)
df = runner.run(
train_data=train_data,
test_data=test_data,
preproc=Preprocessor,
preproc_params={},
err_root_node=get_err_root_node(),
err_params_list=get_err_params_list(),
model_params_dict_list=get_model_params_dict_list(test_data, n_period),
)
print_results_by_model(df, dropped_columns=["err_train", "test_pred", "clean_test", "n_period"])
visualize(df, np.concatenate([train_data, test_data], axis=0), n_data, dataset_name)
if __name__ == "__main__":
main(sys.argv)
| true | true |
f736ced9fe989d1f7a96d0a6e6b7572447a7fcad | 1,004 | py | Python | setup.py | aws-samples/monorepo-multi-pipeline-trigger | a1997ebdcb1151930017115f6fe5049e7912b747 | [
"MIT-0"
] | 4 | 2022-02-02T23:27:41.000Z | 2022-02-25T10:18:32.000Z | setup.py | aws-samples/monorepo-multi-pipeline-trigger | a1997ebdcb1151930017115f6fe5049e7912b747 | [
"MIT-0"
] | null | null | null | setup.py | aws-samples/monorepo-multi-pipeline-trigger | a1997ebdcb1151930017115f6fe5049e7912b747 | [
"MIT-0"
] | null | null | null | import setuptools
with open("README.md") as fp:
long_description = fp.read()
setuptools.setup(
name="monorepo_codepipeline_trigger",
version="0.0.1",
description="An empty CDK Python app",
long_description=long_description,
long_description_content_type="text/markdown",
author="author",
package_dir={"": "."},
packages=setuptools.find_packages(where="monorepo_codepipeline_trigger"),
install_requires=[
"aws-cdk.core==1.113.0",
],
python_requires=">=3.6",
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: JavaScript",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Topic :: Software Development :: Code Generators",
"Topic :: Utilities",
"Typing :: Typed",
],
)
| 22.818182 | 77 | 0.614542 | import setuptools
with open("README.md") as fp:
long_description = fp.read()
setuptools.setup(
name="monorepo_codepipeline_trigger",
version="0.0.1",
description="An empty CDK Python app",
long_description=long_description,
long_description_content_type="text/markdown",
author="author",
package_dir={"": "."},
packages=setuptools.find_packages(where="monorepo_codepipeline_trigger"),
install_requires=[
"aws-cdk.core==1.113.0",
],
python_requires=">=3.6",
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Programming Language :: JavaScript",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Topic :: Software Development :: Code Generators",
"Topic :: Utilities",
"Typing :: Typed",
],
)
| true | true |
f736cf726afef35131e4d0be105fa63ba0439d19 | 735 | py | Python | gurobi/examples_from_gurobi/tune.py | cdeil/python-discrete-optimization-examples | 34e97ee221a6e431e9885609817e10b6f07284b3 | [
"MIT"
] | 6 | 2020-04-09T21:56:09.000Z | 2021-02-25T02:57:33.000Z | gurobi/examples_from_gurobi/tune.py | cdeil/python-discrete-optimization-examples | 34e97ee221a6e431e9885609817e10b6f07284b3 | [
"MIT"
] | 1 | 2020-04-10T13:02:02.000Z | 2020-04-10T15:27:17.000Z | gurobi/examples_from_gurobi/tune.py | cdeil/python-discrete-optimization-examples | 34e97ee221a6e431e9885609817e10b6f07284b3 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3.7
# Copyright 2020, Gurobi Optimization, LLC
# This example reads a model from a file and tunes it.
# It then writes the best parameter settings to a file
# and solves the model using these parameters.
import sys
import gurobipy as gp
if len(sys.argv) < 2:
print('Usage: tune.py filename')
sys.exit(0)
# Read the model
model = gp.read(sys.argv[1])
# Set the TuneResults parameter to 1
model.Params.tuneResults = 1
# Tune the model
model.tune()
if model.tuneResultCount > 0:
# Load the best tuned parameters into the model
model.getTuneResult(0)
# Write tuned parameters to a file
model.write('tune.prm')
# Solve the model using the tuned parameters
model.optimize()
| 21 | 55 | 0.708844 |
import sys
import gurobipy as gp
if len(sys.argv) < 2:
print('Usage: tune.py filename')
sys.exit(0)
model = gp.read(sys.argv[1])
model.Params.tuneResults = 1
model.tune()
if model.tuneResultCount > 0:
model.getTuneResult(0)
model.write('tune.prm')
model.optimize()
| true | true |
f736d124d90b215faa2ede1c2ae8f98d471eec64 | 3,345 | py | Python | aliyun-python-sdk-mts/aliyunsdkmts/request/v20140618/UpdateTemplateRequest.py | yndu13/aliyun-openapi-python-sdk | 12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5 | [
"Apache-2.0"
] | 1,001 | 2015-07-24T01:32:41.000Z | 2022-03-25T01:28:18.000Z | aliyun-python-sdk-mts/aliyunsdkmts/request/v20140618/UpdateTemplateRequest.py | yndu13/aliyun-openapi-python-sdk | 12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5 | [
"Apache-2.0"
] | 363 | 2015-10-20T03:15:00.000Z | 2022-03-08T12:26:19.000Z | aliyun-python-sdk-mts/aliyunsdkmts/request/v20140618/UpdateTemplateRequest.py | yndu13/aliyun-openapi-python-sdk | 12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5 | [
"Apache-2.0"
] | 682 | 2015-09-22T07:19:02.000Z | 2022-03-22T09:51:46.000Z | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdkmts.endpoint import endpoint_data
class UpdateTemplateRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'Mts', '2014-06-18', 'UpdateTemplate','mts')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_Container(self): # String
return self.get_query_params().get('Container')
def set_Container(self, Container): # String
self.add_query_param('Container', Container)
def get_ResourceOwnerId(self): # Long
return self.get_query_params().get('ResourceOwnerId')
def set_ResourceOwnerId(self, ResourceOwnerId): # Long
self.add_query_param('ResourceOwnerId', ResourceOwnerId)
def get_Video(self): # String
return self.get_query_params().get('Video')
def set_Video(self, Video): # String
self.add_query_param('Video', Video)
def get_TransConfig(self): # String
return self.get_query_params().get('TransConfig')
def set_TransConfig(self, TransConfig): # String
self.add_query_param('TransConfig', TransConfig)
def get_Audio(self): # String
return self.get_query_params().get('Audio')
def set_Audio(self, Audio): # String
self.add_query_param('Audio', Audio)
def get_ResourceOwnerAccount(self): # String
return self.get_query_params().get('ResourceOwnerAccount')
def set_ResourceOwnerAccount(self, ResourceOwnerAccount): # String
self.add_query_param('ResourceOwnerAccount', ResourceOwnerAccount)
def get_OwnerAccount(self): # String
return self.get_query_params().get('OwnerAccount')
def set_OwnerAccount(self, OwnerAccount): # String
self.add_query_param('OwnerAccount', OwnerAccount)
def get_MuxConfig(self): # String
return self.get_query_params().get('MuxConfig')
def set_MuxConfig(self, MuxConfig): # String
self.add_query_param('MuxConfig', MuxConfig)
def get_OwnerId(self): # Long
return self.get_query_params().get('OwnerId')
def set_OwnerId(self, OwnerId): # Long
self.add_query_param('OwnerId', OwnerId)
def get_TemplateId(self): # String
return self.get_query_params().get('TemplateId')
def set_TemplateId(self, TemplateId): # String
self.add_query_param('TemplateId', TemplateId)
def get_Name(self): # String
return self.get_query_params().get('Name')
def set_Name(self, Name): # String
self.add_query_param('Name', Name)
| 37.58427 | 74 | 0.750374 |
from aliyunsdkcore.request import RpcRequest
from aliyunsdkmts.endpoint import endpoint_data
class UpdateTemplateRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'Mts', '2014-06-18', 'UpdateTemplate','mts')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_Container(self):
return self.get_query_params().get('Container')
def set_Container(self, Container):
self.add_query_param('Container', Container)
def get_ResourceOwnerId(self):
return self.get_query_params().get('ResourceOwnerId')
def set_ResourceOwnerId(self, ResourceOwnerId):
self.add_query_param('ResourceOwnerId', ResourceOwnerId)
def get_Video(self):
return self.get_query_params().get('Video')
def set_Video(self, Video):
self.add_query_param('Video', Video)
def get_TransConfig(self):
return self.get_query_params().get('TransConfig')
def set_TransConfig(self, TransConfig):
self.add_query_param('TransConfig', TransConfig)
def get_Audio(self):
return self.get_query_params().get('Audio')
def set_Audio(self, Audio):
self.add_query_param('Audio', Audio)
def get_ResourceOwnerAccount(self):
return self.get_query_params().get('ResourceOwnerAccount')
def set_ResourceOwnerAccount(self, ResourceOwnerAccount):
self.add_query_param('ResourceOwnerAccount', ResourceOwnerAccount)
def get_OwnerAccount(self):
return self.get_query_params().get('OwnerAccount')
def set_OwnerAccount(self, OwnerAccount):
self.add_query_param('OwnerAccount', OwnerAccount)
def get_MuxConfig(self):
return self.get_query_params().get('MuxConfig')
def set_MuxConfig(self, MuxConfig):
self.add_query_param('MuxConfig', MuxConfig)
def get_OwnerId(self):
return self.get_query_params().get('OwnerId')
def set_OwnerId(self, OwnerId):
self.add_query_param('OwnerId', OwnerId)
def get_TemplateId(self):
return self.get_query_params().get('TemplateId')
def set_TemplateId(self, TemplateId):
self.add_query_param('TemplateId', TemplateId)
def get_Name(self):
return self.get_query_params().get('Name')
def set_Name(self, Name):
self.add_query_param('Name', Name)
| true | true |
f736d1a0133b765be2d913c5a9cd2625d47b2fb3 | 8,771 | py | Python | cs15211/KClosestPointstoOrigin.py | JulyKikuAkita/PythonPrac | 0ba027d9b8bc7c80bc89ce2da3543ce7a49a403c | [
"Apache-2.0"
] | 1 | 2021-07-05T01:53:30.000Z | 2021-07-05T01:53:30.000Z | cs15211/KClosestPointstoOrigin.py | JulyKikuAkita/PythonPrac | 0ba027d9b8bc7c80bc89ce2da3543ce7a49a403c | [
"Apache-2.0"
] | null | null | null | cs15211/KClosestPointstoOrigin.py | JulyKikuAkita/PythonPrac | 0ba027d9b8bc7c80bc89ce2da3543ce7a49a403c | [
"Apache-2.0"
] | 1 | 2018-01-08T07:14:08.000Z | 2018-01-08T07:14:08.000Z | __source__ = 'https://leetcode.com/problems/k-closest-points-to-origin/'
# Time: O(NLogN ~ N)
# Space: O(N)
#
# Quick Select: K-problem
# Description: Leetcode # 973. K Closest Points to Origin
#
# We have a list of points on the plane. Find the K closest points to the origin (0, 0).
#
# (Here, the distance between two points on a plane is the Euclidean distance.)
#
# You may return the answer in any order.
# The answer is guaranteed to be unique (except for the order that it is in.)
#
# Example 1:
#
# Input: points = [[1,3],[-2,2]], K = 1
# Output: [[-2,2]]
# Explanation:
# The distance between (1, 3) and the origin is sqrt(10).
# The distance between (-2, 2) and the origin is sqrt(8).
# Since sqrt(8) < sqrt(10), (-2, 2) is closer to the origin.
# We only want the closest K = 1 points from the origin, so the answer is just [[-2,2]].
# Example 2:
#
# Input: points = [[3,3],[5,-1],[-2,4]], K = 2
# Output: [[3,3],[-2,4]]
# (The answer [[-2,4],[3,3]] would also be accepted.)
#
#
# Note:
#
# 1 <= K <= points.length <= 10000
# -10000 < points[i][0] < 10000
# -10000 < points[i][1] < 10000
#
import unittest
import random
# 428ms 99.89%
class Solution(object):
def kClosest(self, points, K):
"""
:type points: List[List[int]]
:type K: int
:rtype: List[List[int]]
"""
dist = lambda i: points[i][0]**2 + points[i][1]**2
def work(i, j, K):
if i >= j: return
oi, oj = i, j
pivot = dist(random.randint(i, j))
while i < j:
while i < j and dist(i) < pivot: i += 1
while i < j and dist(j) > pivot: j -= 1
points[i], points[j] = points[j], points[i]
if K <= i - oi + 1:
work(oi, i, K)
else:
work(i+1, oj, K - (i - oi + 1))
work(0, len(points) - 1, K)
return points[:K]
class TestMethods(unittest.TestCase):
def test_Local(self):
self.assertEqual(1, 1)
if __name__ == '__main__':
unittest.main()
Java = '''
# Thought: https://leetcode.com/problems/k-closest-points-to-origin/solution/
# Approach 1: Sort
# Complexity Analysis
# Time Complexity: O(NlogN), where N is the length of points.
# Space Complexity: O(N)
# 71ms 62.57%
class Solution {
public int[][] kClosest(int[][] points, int K) {
Arrays.sort(points, (int[] a, int[] b) -> (a[0] * a[0] + a[1] * a[1]) - (b[0]* b[0] + b[1]* b[1]));
int[][] res = new int[K][];
for (int i = 0; i < K; i++) {
res[i] = points[i];
}
return res;
}
}
# Approach 2: Divide and Conquer
# Complexity Analysis
# Time Complexity: O(N) in average case complexity, where N is the length of points.
# Space Complexity: O(N)
# 11ms 100%
import java.util.concurrent.ThreadLocalRandom;
class Solution {
int[][] points;
public int[][] kClosest(int[][] points, int K) {
this.points = points;
work(0, points.length - 1, K);
return Arrays.copyOfRange(points, 0, K);
}
public void work(int i, int j, int K) {
if (i >= j) return;
int oi = i, oj = j;
int pivot = dist(ThreadLocalRandom.current().nextInt(i, j));
while (i < j) {
while (i < j && dist(i) < pivot) i++;
while (i < j && dist(j) > pivot) j--;
swap(i, j);
}
if (K <= i - oi + 1) {
work(oi, i, K);
} else {
work(i + 1, oj, K - (i - oi + 1));
}
}
public int dist(int i) {
return points[i][0] * points[i][0] + points[i][1] * points[i][1];
}
public void swap(int i, int j) {
int t0 = points[i][0], t1 = points[i][1];
points[i][0] = points[j][0];
points[i][1] = points[j][1];
points[j][0] = t0;
points[j][1] = t1;
}
}
# https://leetcode.com/problems/k-closest-points-to-origin/discuss/220235/Java-Three-solutions-to-this-classical-K-th-problem.
# This is a very classical problem, so-called K-th problem.
# Here I will share some summaries and some classical solutions to this kind of problem.
#
# I. The very naive and simple solution is sorting the all points by their distance to the origin point directly,
# then get the top k closest points. We can use the sort function and the code is very short.
#
# Theoretically, the time complexity is O(NlogN), pratically, the real time it takes on leetcode is 104ms.
#
# The advantages of this solution are short, intuitive and easy to implement.
# The disadvantages of this solution are not very efficient and have to know all of the points previously,
# and it is unable to deal with real-time(online) case, it is an off-line solution.
#
# The short code shows as follows:
# 72ms 61.85%
class Solution {
public int[][] kClosest(int[][] points, int K) {
Arrays.sort(points, (p1, p2) -> p1[0] * p1[0] + p1[1] * p1[1] - p2[0] * p2[0] - p2[1] * p2[1]);
return Arrays.copyOfRange(points, 0, K);
}
}
# II. The second solution is based on the first one. We don't have to sort all points.
# Instead, we can maintain a max-heap with size K. Then for each point, we add it to the heap.
# Once the size of the heap is greater than K,
# we are supposed to extract one from the max heap to ensure the size of the heap is always K.
# Thus, the max heap is always maintain top K smallest elements from the first one to crruent one.
# Once the size of the heap is over its maximum capacity, it will exclude the maximum element in it,
# since it can not be the proper candidate anymore.
#
# Theoretically, the time complexity is O(NlogK), but practically, the real time it takes on leetcode is 134ms.
#
# The advantage of this solution is it can deal with real-time(online) stream data.
# It does not have to know the size of the data previously.
# The disadvantage of this solution is it is not the most efficient solution.
#
# The short code shows as follows:
# 79ms 56.66%
class Solution {
public int[][] kClosest(int[][] points, int K) {
PriorityQueue<int[]> pq
= new PriorityQueue<int[]>((p1, p2) -> p2[0] * p2[0] + p2[1] * p2[1] - p1[0] * p1[0] - p1[1] * p1[1]);
for (int[] p : points) {
pq.offer(p);
if (pq.size() > K) {
pq.poll();
}
}
int[][] res = new int[K][2];
while (K > 0) {
res[--K] = pq.poll();
}
return res;
}
}
# III. The last solution is based on quick sort, we can also call it quick select.
# In the quick sort, we will always choose a pivot to compare with other elements.
# After one iteration, we will get an array that all elements smaller than the pivot are on the left side of the pivot
# and all elements greater than the pivot are on the right side of the pivot
# (assuming we sort the array in ascending order).
# So, inspired from this, each iteration, we choose a pivot and then find the position p the pivot should be.
# Then we compare p with the K, if the p is smaller than the K,
# meaning the all element on the left of the pivot are all proper candidates but it is not adequate,
# we have to do the same thing on right side, and vice versa.
# If the p is exactly equal to the K, meaning that we've found the K-th position.
# Therefore, we just return the first K elements, since they are not greater than the pivot.
#
# Theoretically, the average time complexity is O(N) , but just like quick sort,
# in the worst case, this solution would be degenerated to O(N^2), and practically,
# the real time it takes on leetcode is 15ms.
#
# The advantage of this solution is it is very efficient.
# The disadvantage of this solution are it is neither an online solution nor a stable one.
# And the K elements closest are not sorted in ascending order.
#
# The short code shows as follows:
#
# 8ms 100%
class Solution {
public int[][] kClosest(int[][] points, int K) {
int len = points.length, l = 0, r = len - 1;
while (l <= r) {
int mid = helper(points, l, r);
if (mid == K) break;
if (mid < K) l = mid + 1;
else {
r = mid - 1;
}
}
return Arrays.copyOfRange(points, 0, K);
}
private int helper(int[][] A, int l, int r) {
int[] pivot = A[l];
while (l < r) {
while (l < r && compare(A[r], pivot) >= 0) r--;
A[l] = A[r];
while (l < r && compare(A[l], pivot) <= 0) l++;
A[r] = A[l];
}
A[l] = pivot;
return l;
}
private int compare(int[] p1, int[] p2) {
return p1[0] * p1[0] + p1[1] * p1[1] - p2[0] * p2[0] - p2[1] * p2[1];
}
}
'''
| 34.944223 | 126 | 0.588416 | __source__ = 'https://leetcode.com/problems/k-closest-points-to-origin/'
port unittest
import random
class Solution(object):
def kClosest(self, points, K):
dist = lambda i: points[i][0]**2 + points[i][1]**2
def work(i, j, K):
if i >= j: return
oi, oj = i, j
pivot = dist(random.randint(i, j))
while i < j:
while i < j and dist(i) < pivot: i += 1
while i < j and dist(j) > pivot: j -= 1
points[i], points[j] = points[j], points[i]
if K <= i - oi + 1:
work(oi, i, K)
else:
work(i+1, oj, K - (i - oi + 1))
work(0, len(points) - 1, K)
return points[:K]
class TestMethods(unittest.TestCase):
def test_Local(self):
self.assertEqual(1, 1)
if __name__ == '__main__':
unittest.main()
Java = '''
# Thought: https://leetcode.com/problems/k-closest-points-to-origin/solution/
# Approach 1: Sort
# Complexity Analysis
# Time Complexity: O(NlogN), where N is the length of points.
# Space Complexity: O(N)
# 71ms 62.57%
class Solution {
public int[][] kClosest(int[][] points, int K) {
Arrays.sort(points, (int[] a, int[] b) -> (a[0] * a[0] + a[1] * a[1]) - (b[0]* b[0] + b[1]* b[1]));
int[][] res = new int[K][];
for (int i = 0; i < K; i++) {
res[i] = points[i];
}
return res;
}
}
# Approach 2: Divide and Conquer
# Complexity Analysis
# Time Complexity: O(N) in average case complexity, where N is the length of points.
# Space Complexity: O(N)
# 11ms 100%
import java.util.concurrent.ThreadLocalRandom;
class Solution {
int[][] points;
public int[][] kClosest(int[][] points, int K) {
this.points = points;
work(0, points.length - 1, K);
return Arrays.copyOfRange(points, 0, K);
}
public void work(int i, int j, int K) {
if (i >= j) return;
int oi = i, oj = j;
int pivot = dist(ThreadLocalRandom.current().nextInt(i, j));
while (i < j) {
while (i < j && dist(i) < pivot) i++;
while (i < j && dist(j) > pivot) j--;
swap(i, j);
}
if (K <= i - oi + 1) {
work(oi, i, K);
} else {
work(i + 1, oj, K - (i - oi + 1));
}
}
public int dist(int i) {
return points[i][0] * points[i][0] + points[i][1] * points[i][1];
}
public void swap(int i, int j) {
int t0 = points[i][0], t1 = points[i][1];
points[i][0] = points[j][0];
points[i][1] = points[j][1];
points[j][0] = t0;
points[j][1] = t1;
}
}
# https://leetcode.com/problems/k-closest-points-to-origin/discuss/220235/Java-Three-solutions-to-this-classical-K-th-problem.
# This is a very classical problem, so-called K-th problem.
# Here I will share some summaries and some classical solutions to this kind of problem.
#
# I. The very naive and simple solution is sorting the all points by their distance to the origin point directly,
# then get the top k closest points. We can use the sort function and the code is very short.
#
# Theoretically, the time complexity is O(NlogN), pratically, the real time it takes on leetcode is 104ms.
#
# The advantages of this solution are short, intuitive and easy to implement.
# The disadvantages of this solution are not very efficient and have to know all of the points previously,
# and it is unable to deal with real-time(online) case, it is an off-line solution.
#
# The short code shows as follows:
# 72ms 61.85%
class Solution {
public int[][] kClosest(int[][] points, int K) {
Arrays.sort(points, (p1, p2) -> p1[0] * p1[0] + p1[1] * p1[1] - p2[0] * p2[0] - p2[1] * p2[1]);
return Arrays.copyOfRange(points, 0, K);
}
}
# II. The second solution is based on the first one. We don't have to sort all points.
# Instead, we can maintain a max-heap with size K. Then for each point, we add it to the heap.
# Once the size of the heap is greater than K,
# we are supposed to extract one from the max heap to ensure the size of the heap is always K.
# Thus, the max heap is always maintain top K smallest elements from the first one to crruent one.
# Once the size of the heap is over its maximum capacity, it will exclude the maximum element in it,
# since it can not be the proper candidate anymore.
#
# Theoretically, the time complexity is O(NlogK), but practically, the real time it takes on leetcode is 134ms.
#
# The advantage of this solution is it can deal with real-time(online) stream data.
# It does not have to know the size of the data previously.
# The disadvantage of this solution is it is not the most efficient solution.
#
# The short code shows as follows:
# 79ms 56.66%
class Solution {
public int[][] kClosest(int[][] points, int K) {
PriorityQueue<int[]> pq
= new PriorityQueue<int[]>((p1, p2) -> p2[0] * p2[0] + p2[1] * p2[1] - p1[0] * p1[0] - p1[1] * p1[1]);
for (int[] p : points) {
pq.offer(p);
if (pq.size() > K) {
pq.poll();
}
}
int[][] res = new int[K][2];
while (K > 0) {
res[--K] = pq.poll();
}
return res;
}
}
# III. The last solution is based on quick sort, we can also call it quick select.
# In the quick sort, we will always choose a pivot to compare with other elements.
# After one iteration, we will get an array that all elements smaller than the pivot are on the left side of the pivot
# and all elements greater than the pivot are on the right side of the pivot
# (assuming we sort the array in ascending order).
# So, inspired from this, each iteration, we choose a pivot and then find the position p the pivot should be.
# Then we compare p with the K, if the p is smaller than the K,
# meaning the all element on the left of the pivot are all proper candidates but it is not adequate,
# we have to do the same thing on right side, and vice versa.
# If the p is exactly equal to the K, meaning that we've found the K-th position.
# Therefore, we just return the first K elements, since they are not greater than the pivot.
#
# Theoretically, the average time complexity is O(N) , but just like quick sort,
# in the worst case, this solution would be degenerated to O(N^2), and practically,
# the real time it takes on leetcode is 15ms.
#
# The advantage of this solution is it is very efficient.
# The disadvantage of this solution are it is neither an online solution nor a stable one.
# And the K elements closest are not sorted in ascending order.
#
# The short code shows as follows:
#
# 8ms 100%
class Solution {
public int[][] kClosest(int[][] points, int K) {
int len = points.length, l = 0, r = len - 1;
while (l <= r) {
int mid = helper(points, l, r);
if (mid == K) break;
if (mid < K) l = mid + 1;
else {
r = mid - 1;
}
}
return Arrays.copyOfRange(points, 0, K);
}
private int helper(int[][] A, int l, int r) {
int[] pivot = A[l];
while (l < r) {
while (l < r && compare(A[r], pivot) >= 0) r--;
A[l] = A[r];
while (l < r && compare(A[l], pivot) <= 0) l++;
A[r] = A[l];
}
A[l] = pivot;
return l;
}
private int compare(int[] p1, int[] p2) {
return p1[0] * p1[0] + p1[1] * p1[1] - p2[0] * p2[0] - p2[1] * p2[1];
}
}
'''
| true | true |
f736d1d8cec2bc49af25e8390134efa91fea5bf9 | 3,697 | py | Python | source/lambda/firehose_topic_proxy/util/topic.py | knihit/discovering-hot-topics-using-machine-learning | a7d2d87bedee54d18d6885d472f758b0bacf9db8 | [
"Apache-2.0"
] | null | null | null | source/lambda/firehose_topic_proxy/util/topic.py | knihit/discovering-hot-topics-using-machine-learning | a7d2d87bedee54d18d6885d472f758b0bacf9db8 | [
"Apache-2.0"
] | 21 | 2021-07-22T19:02:25.000Z | 2022-02-14T16:28:18.000Z | source/lambda/firehose_topic_proxy/util/topic.py | aassadza-org/discovering-hot-topics-using-machine-learning | ef5f9d00a14b6b2024c9e0f9dfb915a6e632074d | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
######################################################################################################################
# Copyright 2020-2021 Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at #
# #
# http://www.apache.org/licenses/LICENSE-2.0 #
# #
# or in the 'license' file accompanying this file. This file is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES #
# OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions #
# and limitations under the License. #
######################################################################################################################
import datetime
import json
import os
from datetime import datetime
import boto3
from botocore import config
from shared_util import custom_boto_config, custom_logging
logger = custom_logging.get_logger(__name__)
firehose = boto3.client("firehose", config=custom_boto_config.init())
def store_topics(data):
for key in data:
for record in data[key]:
logger.debug("Record information for writing to Firehose is " + json.dumps(record))
response = firehose.put_record(
DeliveryStreamName=os.environ["TOPICS_FIREHOSE"],
Record={
"Data": json.dumps(
{
"job_id": record["job_id"],
"job_timestamp": datetime.strftime(
datetime.strptime(record["job_timestamp"], "%Y-%m-%dT%H:%M:%S.%fZ"),
"%Y-%m-%d %H:%M:%S.%f",
),
"topic": record["topic"],
"term": record["term"],
"weight": record["weight"],
}
)
+ "\n"
},
)
logger.debug("Response for record " + record["job_id"] + "is " + json.dumps(response))
def store_mappings(data):
logger.debug("Data received is " + json.dumps(data))
response = firehose.put_record(
DeliveryStreamName=os.environ["TOPIC_MAPPINGS_FIREHOSE"],
Record={
"Data": json.dumps(
{
"platform": data["platform"],
"job_id": data["job_id"],
"job_timestamp": datetime.strftime(
datetime.strptime(data["job_timestamp"], "%Y-%m-%dT%H:%M:%S.%fZ"), "%Y-%m-%d %H:%M:%S.%f"
),
"topic": data["topic"],
"id_str": data["id_str"],
}
)
+ "\n"
},
)
logger.debug(
"Response for record "
+ json.dumps({"platform": data["platform"], "topic": data["topic"], "id_str": data["id_str"]})
+ "is "
+ json.dumps(response)
)
| 46.797468 | 118 | 0.405193 | true | true | |
f736d37413434b6c7e43bb19558ae19a7db09c56 | 5,914 | py | Python | pyslet/qtiv1/assessment.py | cidermole/pyslet | b30e9a439f6c0f0e2d01f1ac80986944bed7427b | [
"BSD-3-Clause"
] | 62 | 2015-02-05T19:51:16.000Z | 2022-03-17T22:36:39.000Z | pyslet/qtiv1/assessment.py | cidermole/pyslet | b30e9a439f6c0f0e2d01f1ac80986944bed7427b | [
"BSD-3-Clause"
] | 64 | 2015-01-04T15:43:28.000Z | 2022-02-22T08:54:20.000Z | pyslet/qtiv1/assessment.py | cidermole/pyslet | b30e9a439f6c0f0e2d01f1ac80986944bed7427b | [
"BSD-3-Clause"
] | 39 | 2015-10-24T08:31:38.000Z | 2021-11-01T19:50:13.000Z | #! /usr/bin/env python
import itertools
from . import common
from . import core
from ..xml import structures as xml
class Assessment(common.QTICommentContainer):
"""The Assessment data structure is used to contain the exchange of test
data structures. It will always contain at least one Section and may
contain meta-data, objectives, rubric control switches, assessment-level
processing, feedback and selection and sequencing information for
sections::
<!ELEMENT assessment (qticomment? ,
duration? ,
qtimetadata* ,
objectives* ,
assessmentcontrol* ,
rubric* ,
presentation_material? ,
outcomes_processing* ,
assessproc_extension? ,
assessfeedback* ,
selection_ordering? ,
reference? ,
(sectionref | section)+
)>
<!ATTLIST assessment ident CDATA #REQUIRED
%I_Title;
xml:lang CDATA
#IMPLIED >"""
XMLNAME = "assessment"
XMLATTR_ident = 'ident'
XMLATTR_title = 'title'
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
self.ident = None
self.title = None
self.Duration = None
self.QTIMetadata = []
self.Objectives = []
self.AssessmentControl = []
self.Rubric = []
self.PresentationMaterial = None
self.QTIOutcomesProcessing = []
self.AssessProcExtension = None
self.AssessFeedback = []
self.QTISelectionOrdering = None
self.QTIReference = None
self.SectionMixin = []
def get_children(self):
for child in itertools.chain(
common.QTIComment.get_children(self),
self.QTIMetadata,
self.Objectives,
self.AssessmentControl,
self.Rubric):
yield child
if self.PresentationMaterial:
yield self.PresentationMaterial
for child in self.QTIOutcomesProcessing:
yield child
if self.QTIAssessProcExtension:
yield self.QTIAssessProcExtension
for child in self.AssessFeedback:
yield child
if self.QTISelectionOrdering:
yield self.QTISelectionOrdering
if self.QTIReference:
yield self.QTIReference
for child in self.SectionMixin:
yield child
def migrate_to_v2(self, output):
"""Converts this assessment to QTI v2
For details, see QuesTestInterop.migrate_to_v2."""
for obj in self.SectionMixin:
obj.migrate_to_v2(output)
class AssessmentControl(common.QTICommentContainer):
"""The control switches that are used to enable or disable the display of
hints, solutions and feedback within the Assessment::
<!ELEMENT assessmentcontrol (qticomment?)>
<!ATTLIST assessmentcontrol
hintswitch (Yes | No ) 'Yes'
solutionswitch (Yes | No ) 'Yes'
view (All | Administrator | AdminAuthority | Assessor |
Author | Candidate | InvigilatorProctor |
Psychometrician | Scorer | Tutor ) 'All'
feedbackswitch (Yes | No ) 'Yes' >"""
XMLNAME = 'assessmentcontrol'
XMLATTR_hintswitch = ('hintSwitch', core.ParseYesNo, core.FormatYesNo)
XMLATTR_solutionswitch = (
'solutionSwitch', core.ParseYesNo, core.FormatYesNo)
XMLATTR_view = ('view', core.View.from_str_lower, core.View.to_str)
XMLATTR_feedbackswitch = (
'feedbackSwitch', core.ParseYesNo, core.FormatYesNo)
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
self.view = core.View.DEFAULT
self.hintSwitch = True
self.solutionSwitch = True
self.feedbackSwitch = True
class AssessProcExtension(core.QTIElement):
"""This is used to contain proprietary alternative Assessment-level
processing functionality::
<!ELEMENT assessproc_extension ANY>"""
XMLNAME = "assessproc_extension"
XMLCONTENT = xml.XMLMixedContent
class AssessFeedback(common.ContentMixin, common.QTICommentContainer):
"""The container for the Assessment-level feedback that is to be presented
as a result of Assessment-level processing of the user responses::
<!ELEMENT assessfeedback (qticomment? , (material+ | flow_mat+))>
<!ATTLIST assessfeedback
view (All | Administrator | AdminAuthority | Assessor |
Author | Candidate | InvigilatorProctor |
Psychometrician | Scorer | Tutor ) 'All'
ident CDATA #REQUIRED
title CDATA #IMPLIED >"""
XMLNAME = 'assessfeedback'
XMLATTR_view = ('view', core.View.from_str_lower, core.View.to_str)
XMLATTR_ident = 'ident'
XMLATTR_title = 'title'
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
common.ContentMixin.__init__(self)
self.view = core.View.DEFAULT
self.ident = None
self.title = None
def get_children(self):
return itertools.chain(
common.QTICommentContainer.get_children(self),
common.ContentMixin.get_content_children(self))
def content_child(self, child_class):
return child_class in (common.Material, common.QTIFlowMat)
| 36.732919 | 78 | 0.596212 |
import itertools
from . import common
from . import core
from ..xml import structures as xml
class Assessment(common.QTICommentContainer):
XMLNAME = "assessment"
XMLATTR_ident = 'ident'
XMLATTR_title = 'title'
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
self.ident = None
self.title = None
self.Duration = None
self.QTIMetadata = []
self.Objectives = []
self.AssessmentControl = []
self.Rubric = []
self.PresentationMaterial = None
self.QTIOutcomesProcessing = []
self.AssessProcExtension = None
self.AssessFeedback = []
self.QTISelectionOrdering = None
self.QTIReference = None
self.SectionMixin = []
def get_children(self):
for child in itertools.chain(
common.QTIComment.get_children(self),
self.QTIMetadata,
self.Objectives,
self.AssessmentControl,
self.Rubric):
yield child
if self.PresentationMaterial:
yield self.PresentationMaterial
for child in self.QTIOutcomesProcessing:
yield child
if self.QTIAssessProcExtension:
yield self.QTIAssessProcExtension
for child in self.AssessFeedback:
yield child
if self.QTISelectionOrdering:
yield self.QTISelectionOrdering
if self.QTIReference:
yield self.QTIReference
for child in self.SectionMixin:
yield child
def migrate_to_v2(self, output):
for obj in self.SectionMixin:
obj.migrate_to_v2(output)
class AssessmentControl(common.QTICommentContainer):
XMLNAME = 'assessmentcontrol'
XMLATTR_hintswitch = ('hintSwitch', core.ParseYesNo, core.FormatYesNo)
XMLATTR_solutionswitch = (
'solutionSwitch', core.ParseYesNo, core.FormatYesNo)
XMLATTR_view = ('view', core.View.from_str_lower, core.View.to_str)
XMLATTR_feedbackswitch = (
'feedbackSwitch', core.ParseYesNo, core.FormatYesNo)
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
self.view = core.View.DEFAULT
self.hintSwitch = True
self.solutionSwitch = True
self.feedbackSwitch = True
class AssessProcExtension(core.QTIElement):
XMLNAME = "assessproc_extension"
XMLCONTENT = xml.XMLMixedContent
class AssessFeedback(common.ContentMixin, common.QTICommentContainer):
XMLNAME = 'assessfeedback'
XMLATTR_view = ('view', core.View.from_str_lower, core.View.to_str)
XMLATTR_ident = 'ident'
XMLATTR_title = 'title'
XMLCONTENT = xml.ElementContent
def __init__(self, parent):
common.QTICommentContainer.__init__(self, parent)
common.ContentMixin.__init__(self)
self.view = core.View.DEFAULT
self.ident = None
self.title = None
def get_children(self):
return itertools.chain(
common.QTICommentContainer.get_children(self),
common.ContentMixin.get_content_children(self))
def content_child(self, child_class):
return child_class in (common.Material, common.QTIFlowMat)
| true | true |
f736d450e7abb7fcaf2575338ff93dd8b19da9aa | 2,209 | py | Python | tests/components/input_number/test_recorder.py | mib1185/core | b17d4ac65cde9a27ff6032d70b148792e5eba8df | [
"Apache-2.0"
] | 30,023 | 2016-04-13T10:17:53.000Z | 2020-03-02T12:56:31.000Z | tests/components/input_number/test_recorder.py | mib1185/core | b17d4ac65cde9a27ff6032d70b148792e5eba8df | [
"Apache-2.0"
] | 24,710 | 2016-04-13T08:27:26.000Z | 2020-03-02T12:59:13.000Z | tests/components/input_number/test_recorder.py | mib1185/core | b17d4ac65cde9a27ff6032d70b148792e5eba8df | [
"Apache-2.0"
] | 11,956 | 2016-04-13T18:42:31.000Z | 2020-03-02T09:32:12.000Z | """The tests for recorder platform."""
from __future__ import annotations
from datetime import timedelta
from homeassistant.components.input_number import (
ATTR_MAX,
ATTR_MIN,
ATTR_MODE,
ATTR_STEP,
DOMAIN,
)
from homeassistant.components.recorder.db_schema import StateAttributes, States
from homeassistant.components.recorder.util import session_scope
from homeassistant.const import ATTR_EDITABLE
from homeassistant.core import HomeAssistant, State
from homeassistant.setup import async_setup_component
from homeassistant.util import dt as dt_util
from tests.common import async_fire_time_changed
from tests.components.recorder.common import async_wait_recording_done
async def test_exclude_attributes(
hass: HomeAssistant, recorder_mock, enable_custom_integrations: None
):
"""Test attributes to be excluded."""
assert await async_setup_component(
hass, DOMAIN, {DOMAIN: {"test": {"min": 0, "max": 100}}}
)
state = hass.states.get("input_number.test")
assert state
assert state.attributes[ATTR_EDITABLE] is False
assert state.attributes[ATTR_MIN] == 0
assert state.attributes[ATTR_MAX] == 100
assert state.attributes[ATTR_STEP] == 1
assert state.attributes[ATTR_MODE] == "slider"
await hass.async_block_till_done()
async_fire_time_changed(hass, dt_util.utcnow() + timedelta(minutes=5))
await hass.async_block_till_done()
await async_wait_recording_done(hass)
def _fetch_states() -> list[State]:
with session_scope(hass=hass) as session:
native_states = []
for db_state, db_state_attributes in session.query(States, StateAttributes):
state = db_state.to_native()
state.attributes = db_state_attributes.to_native()
native_states.append(state)
return native_states
states: list[State] = await hass.async_add_executor_job(_fetch_states)
assert len(states) == 1
assert ATTR_EDITABLE not in states[0].attributes
assert ATTR_MIN not in states[0].attributes
assert ATTR_MAX not in states[0].attributes
assert ATTR_STEP not in states[0].attributes
assert ATTR_MODE not in states[0].attributes
| 36.213115 | 88 | 0.738796 | from __future__ import annotations
from datetime import timedelta
from homeassistant.components.input_number import (
ATTR_MAX,
ATTR_MIN,
ATTR_MODE,
ATTR_STEP,
DOMAIN,
)
from homeassistant.components.recorder.db_schema import StateAttributes, States
from homeassistant.components.recorder.util import session_scope
from homeassistant.const import ATTR_EDITABLE
from homeassistant.core import HomeAssistant, State
from homeassistant.setup import async_setup_component
from homeassistant.util import dt as dt_util
from tests.common import async_fire_time_changed
from tests.components.recorder.common import async_wait_recording_done
async def test_exclude_attributes(
hass: HomeAssistant, recorder_mock, enable_custom_integrations: None
):
assert await async_setup_component(
hass, DOMAIN, {DOMAIN: {"test": {"min": 0, "max": 100}}}
)
state = hass.states.get("input_number.test")
assert state
assert state.attributes[ATTR_EDITABLE] is False
assert state.attributes[ATTR_MIN] == 0
assert state.attributes[ATTR_MAX] == 100
assert state.attributes[ATTR_STEP] == 1
assert state.attributes[ATTR_MODE] == "slider"
await hass.async_block_till_done()
async_fire_time_changed(hass, dt_util.utcnow() + timedelta(minutes=5))
await hass.async_block_till_done()
await async_wait_recording_done(hass)
def _fetch_states() -> list[State]:
with session_scope(hass=hass) as session:
native_states = []
for db_state, db_state_attributes in session.query(States, StateAttributes):
state = db_state.to_native()
state.attributes = db_state_attributes.to_native()
native_states.append(state)
return native_states
states: list[State] = await hass.async_add_executor_job(_fetch_states)
assert len(states) == 1
assert ATTR_EDITABLE not in states[0].attributes
assert ATTR_MIN not in states[0].attributes
assert ATTR_MAX not in states[0].attributes
assert ATTR_STEP not in states[0].attributes
assert ATTR_MODE not in states[0].attributes
| true | true |
f736d6621e8e5e359af4649892dc9ba5ec8c8b04 | 15,463 | py | Python | gestion/models.py | nanoy42/coope | 3f970fe199f4ad8672cbb3a3200b0c4d110c9847 | [
"MIT"
] | 3 | 2019-06-27T21:08:41.000Z | 2019-09-07T17:20:39.000Z | gestion/models.py | nanoy42/coope | 3f970fe199f4ad8672cbb3a3200b0c4d110c9847 | [
"MIT"
] | 21 | 2019-06-09T10:56:53.000Z | 2021-06-10T21:35:12.000Z | gestion/models.py | nanoy42/coope | 3f970fe199f4ad8672cbb3a3200b0c4d110c9847 | [
"MIT"
] | null | null | null | from django.db import models
from django.contrib.auth.models import User
from simple_history.models import HistoricalRecords
from django.core.validators import MinValueValidator
from preferences.models import PaymentMethod
from django.core.exceptions import ValidationError
class Category(models.Model):
"""
A product category
"""
class Meta:
verbose_name="Catégorie"
name = models.CharField(max_length=100, verbose_name="Nom", unique=True)
order = models.IntegerField(default=0)
"""
The name of the category
"""
def __str__(self):
return self.name
@property
def active_products(self):
"""
Return active producs of this category
"""
return self.product_set.filter(is_active=True)
@property
def active_stock_products(self):
"""
Return active products that use stocks
"""
return self.product_set.filter(is_active=True).filter(use_stocks=True)
class Product(models.Model):
"""
Stores a product.
"""
DRAFT_NONE = 0
DRAFT_PINTE = 1
DRAFT_DEMI = 2
DRAFT_GALOPIN = 3
DRAFT_TYPES = (
(DRAFT_NONE, "Pas une bière pression"),
(DRAFT_PINTE, "Pinte"),
(DRAFT_DEMI, "Demi"),
(DRAFT_GALOPIN, "Galopin"),
)
class Meta:
verbose_name = "Produit"
name = models.CharField(max_length=255, verbose_name="Nom", unique=True)
"""
The name of the product.
"""
amount = models.DecimalField(max_digits=5, decimal_places=2, verbose_name="Prix de vente", validators=[MinValueValidator(0)])
"""
The price of the product.
"""
stock = models.IntegerField(default=0, verbose_name="Stock")
"""
Number of product
"""
category = models.ForeignKey('Category', on_delete=models.PROTECT, verbose_name="Catégorie")
"""
The category of the product
"""
needQuantityButton = models.BooleanField(default=False, verbose_name="Bouton quantité")
"""
If True, a javascript quantity button will be displayed
"""
is_active = models.BooleanField(default=True, verbose_name="Actif")
"""
If True, will be displayed on the :func:`gestion.views.manage` view.
"""
volume = models.PositiveIntegerField(default=0)
"""
The volume, if relevant, of the product
"""
deg = models.DecimalField(default=0,max_digits=5, decimal_places=2, verbose_name="Degré", validators=[MinValueValidator(0)])
"""
Degree of alcohol, if relevant
"""
adherentRequired = models.BooleanField(default=True, verbose_name="Adhérent requis")
"""
If True, only adherents will be able to buy this product
"""
showingMultiplier = models.PositiveIntegerField(default=1)
"""
On the graphs on :func:`users.views.profile` view, the number of total consumptions is divised by the showingMultiplier
"""
draft_category = models.IntegerField(choices=DRAFT_TYPES, default=DRAFT_NONE, verbose_name="Type de pression")
use_stocks = models.BooleanField(default=True, verbose_name="Utiliser les stocks ?")
history = HistoricalRecords()
def __str__(self):
if self.draft_category == self.DRAFT_NONE:
return self.name + " (" + str(self.amount) + " €)"
else:
return self.name + " (" + str(self.amount) + " €, " + str(self.deg) + "°)"
def user_ranking(self, pk):
"""
Return the user ranking for the product
"""
user = User.objects.get(pk=pk)
consumptions = Consumption.objects.filter(customer=user).filter(product=self)
if consumptions:
return (user, consumptions[0].quantity)
else:
return (user, 0)
@property
def ranking(self):
"""
Get the first 25 users with :func:`~gestion.models.user_ranking`
"""
users = User.objects.all()
ranking = [self.user_ranking(user.pk) for user in users]
ranking.sort(key=lambda x:x[1], reverse=True)
return ranking[0:25]
def isPinte(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_PINTE:
raise ValidationError(
('%(product)s n\'est pas une pinte'),
params={'product': product},
)
def isDemi(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_DEMI:
raise ValidationError(
('%(product)s n\'est pas un demi'),
params={'product': product},
)
def isGalopin(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_GALOPIN:
raise ValidationError(
('%(product)s n\'est pas un galopin'),
params={'product': product},
)
class Keg(models.Model):
"""
Stores a keg.
"""
class Meta:
verbose_name = "Fût"
permissions = (
("open_keg", "Peut percuter les fûts"),
("close_keg", "Peut fermer les fûts")
)
name = models.CharField(max_length=255, unique=True, verbose_name="Nom")
"""
The name of the keg.
"""
stockHold = models.IntegerField(default=0, verbose_name="Stock en soute")
"""
The number of this keg in the hold.
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Prix du fût", validators=[MinValueValidator(0)])
"""
The price of the keg.
"""
capacity = models.IntegerField(default=30, verbose_name="Capacité (L)")
"""
The capacity, in liters, of the keg.
"""
pinte = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futp", validators=[isPinte])
"""
The related :class:`~gestion.models.Product` for pint.
"""
demi = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futd", validators=[isDemi])
"""
The related :class:`~gestion.models.Product` for demi.
"""
galopin = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futg", validators=[isGalopin],null=True, blank=True)
"""
The related :class:`~gestion.models.Product` for galopin.
"""
is_active = models.BooleanField(default=False, verbose_name="Actif")
"""
If True, will be displayed on :func:`~gestion.views.manage` view
"""
deg = models.DecimalField(default=0,max_digits=5, decimal_places=2, verbose_name="Degré", validators=[MinValueValidator(0)])
history = HistoricalRecords()
def __str__(self):
return self.name
class KegHistory(models.Model):
"""
Stores a keg history, related to :class:`~gestion.models.Keg`.
"""
class Meta:
verbose_name = "Historique de fût"
keg = models.ForeignKey(Keg, on_delete=models.PROTECT, verbose_name="Fût")
"""
The :class:`~gestion.models.Keg` instance.
"""
openingDate = models.DateTimeField(auto_now_add=True, verbose_name="Date ouverture")
"""
The date when the keg was opened.
"""
quantitySold = models.DecimalField(decimal_places=2, max_digits=5, default=0, verbose_name="Quantité vendue")
"""
The quantity, in liters, sold.
"""
amountSold = models.DecimalField(decimal_places=2, max_digits=5, default=0, verbose_name="Somme vendue")
"""
The quantity, in euros, sold.
"""
closingDate = models.DateTimeField(null=True, blank=True, verbose_name="Date fermeture")
"""
The date when the keg was closed
"""
isCurrentKegHistory = models.BooleanField(default=True, verbose_name="Actuel")
"""
If True, it corresponds to the current Keg history of :class:`~gestion.models.Keg` instance.
"""
history = HistoricalRecords()
def __str__(self):
res = "Fût de " + str(self.keg) + " (" + str(self.openingDate) + " - "
if(self.closingDate):
res += str(self.closingDate) + ")"
else:
res += "?)"
return res
class Reload(models.Model):
"""
Stores reloads.
"""
class Meta:
verbose_name = "Rechargement"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="reload_taken", verbose_name="Client")
"""
Client (:class:`django.contrib.auth.models.User`).
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
"""
Amount of the reload.
"""
PaymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
"""
:class:`Payment Method <preferences.models.PaymentMethod>` of the reload.
"""
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="reload_realized")
"""
Coopeman (:class:`django.contrib.auth.models.User`) who collected the reload.
"""
date = models.DateTimeField(auto_now_add=True)
"""
Date of the reload.
"""
history = HistoricalRecords()
def __str__(self):
return "Rechargement effectue par {0} le {1} ({2} euros, coopeman : {3})".format(self.customer, self.date, self.amount, self.coopeman)
class Refund(models.Model):
"""
Stores refunds.
"""
class Meta:
verbose_name = "Remboursement"
date = models.DateTimeField(auto_now_add=True)
"""
Date of the refund
"""
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="refund_taken", verbose_name="Client")
"""
Client (:class:`django.contrib.auth.models.User`).
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
"""
Amount of the refund.
"""
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="refund_realized")
"""
Coopeman (:class:`django.contrib.auth.models.User`) who realized the refund.
"""
history = HistoricalRecords()
def __str__(self):
return "{0} remboursé de {1} le {2} (effectué par {3})".format(self.customer, self.amount, self.date, self.coopeman)
class Menu(models.Model):
"""
Stores menus.
"""
name = models.CharField(max_length=255, verbose_name="Nom")
"""
Name of the menu.
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
"""
Price of the menu.
"""
articles = models.ManyToManyField(Product, verbose_name="Produits")
"""
Stores :class:`Products <gestion.models.Product>` contained in the menu
"""
is_active = models.BooleanField(default=False, verbose_name="Actif")
"""
If True, the menu will be displayed on the :func:`gestion.views.manage` view
"""
history = HistoricalRecords()
def __str__(self):
return self.name
@property
def adherent_required(self):
"""
Test if the menu contains a restricted :class:`~gestion.models.Product`
"""
res = False
for article in self.articles.all():
res = res or article.adherentRequired
return res
class MenuHistory(models.Model):
"""
Stores MenuHistory related to :class:`~gestion.models.Menu`.
"""
class Meta:
verbose_name = "Historique de menu"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="menu_taken", verbose_name="Client")
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
"""
Client (:class:`django.contrib.auth.models.User`).
"""
paymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
"""
:class:`Payment Method <preferences.models.PaymentMethod>` of the Menu purchased.
"""
date = models.DateTimeField(auto_now_add=True)
"""
Date of the purhcase.
"""
menu = models.ForeignKey(Menu, on_delete=models.PROTECT)
"""
:class:`gestion.models.Menu` purchased.
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, default=0, verbose_name="Montant")
"""
Price of the purchase.
"""
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="menu_selled")
"""
Coopeman (:class:django.contrib.auth.models.User`) who collected the money.
"""
history = HistoricalRecords()
def __str__(self):
return "{2} a consommé {0} {1}".format(self.quantity, self.menu, self.customer)
class ConsumptionHistory(models.Model):
"""
Stores consumption history related to Product
"""
class Meta:
verbose_name = "Consommation"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_taken", verbose_name="Client")
"""
Client (:class:`django.contrib.auth.models.User`).
"""
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
"""
Quantity of :attr:`gestion.models.ConsumptionHistory.product` taken.
"""
paymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
"""
:class:`Payment Method <preferences.models.PaymentMethod>` of the product purchased.
"""
date = models.DateTimeField(auto_now_add=True)
"""
Date of the purhcase.
"""
product = models.ForeignKey(Product, on_delete=models.PROTECT, verbose_name="Produit")
"""
:class:`gestion.models.product` purchased.
"""
amount = models.DecimalField(max_digits=7, decimal_places=2, default=0, verbose_name="Montant")
"""
Price of the purchase.
"""
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_selled")
"""
Coopeman (:class:django.contrib.auth.models.User`) who collected the money.
"""
history = HistoricalRecords()
def __str__(self):
return "{0} {1} consommé par {2} le {3} (encaissé par {4})".format(self.quantity, self.product, self.customer, self.date, self.coopeman)
class Consumption(models.Model):
"""
Stores total consumptions.
"""
class Meta:
verbose_name = "Consommation totale"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_global_taken", verbose_name="Client")
"""
Client (:class:`django.contrib.auth.models.User`).
"""
product = models.ForeignKey(Product, on_delete=models.PROTECT, verbose_name="Produit")
"""
A :class:`gestion.models.Product` instance.
"""
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
"""
The total number of :attr:`gestion.models.Consumption.product` consumed by the :attr:`gestion.models.Consumption.consumer`.
"""
history = HistoricalRecords()
def __str__(self):
return "Consommation de " + str(self.customer) + " concernant le produit " + str(self.product)
class Pinte(models.Model):
"""
Stores a physical pinte
"""
current_owner = models.ForeignKey(User, on_delete=models.PROTECT, null=True, default=None, related_name="pintes_owned_currently")
"""
The current owner (:class:`django.contrib.auth.models.User`).
"""
previous_owner = models.ForeignKey(User, on_delete=models.PROTECT, null=True, default=None, related_name="pintes_owned_previously")
"""
The previous owner (:class:`django.contrib.auth.models.User`).
"""
last_update_date = models.DateTimeField(auto_now=True)
"""
The last update date
"""
history = HistoricalRecords()
| 33.688453 | 144 | 0.65466 | from django.db import models
from django.contrib.auth.models import User
from simple_history.models import HistoricalRecords
from django.core.validators import MinValueValidator
from preferences.models import PaymentMethod
from django.core.exceptions import ValidationError
class Category(models.Model):
class Meta:
verbose_name="Catégorie"
name = models.CharField(max_length=100, verbose_name="Nom", unique=True)
order = models.IntegerField(default=0)
def __str__(self):
return self.name
@property
def active_products(self):
return self.product_set.filter(is_active=True)
@property
def active_stock_products(self):
return self.product_set.filter(is_active=True).filter(use_stocks=True)
class Product(models.Model):
DRAFT_NONE = 0
DRAFT_PINTE = 1
DRAFT_DEMI = 2
DRAFT_GALOPIN = 3
DRAFT_TYPES = (
(DRAFT_NONE, "Pas une bière pression"),
(DRAFT_PINTE, "Pinte"),
(DRAFT_DEMI, "Demi"),
(DRAFT_GALOPIN, "Galopin"),
)
class Meta:
verbose_name = "Produit"
name = models.CharField(max_length=255, verbose_name="Nom", unique=True)
amount = models.DecimalField(max_digits=5, decimal_places=2, verbose_name="Prix de vente", validators=[MinValueValidator(0)])
stock = models.IntegerField(default=0, verbose_name="Stock")
category = models.ForeignKey('Category', on_delete=models.PROTECT, verbose_name="Catégorie")
needQuantityButton = models.BooleanField(default=False, verbose_name="Bouton quantité")
is_active = models.BooleanField(default=True, verbose_name="Actif")
volume = models.PositiveIntegerField(default=0)
deg = models.DecimalField(default=0,max_digits=5, decimal_places=2, verbose_name="Degré", validators=[MinValueValidator(0)])
adherentRequired = models.BooleanField(default=True, verbose_name="Adhérent requis")
showingMultiplier = models.PositiveIntegerField(default=1)
draft_category = models.IntegerField(choices=DRAFT_TYPES, default=DRAFT_NONE, verbose_name="Type de pression")
use_stocks = models.BooleanField(default=True, verbose_name="Utiliser les stocks ?")
history = HistoricalRecords()
def __str__(self):
if self.draft_category == self.DRAFT_NONE:
return self.name + " (" + str(self.amount) + " €)"
else:
return self.name + " (" + str(self.amount) + " €, " + str(self.deg) + "°)"
def user_ranking(self, pk):
user = User.objects.get(pk=pk)
consumptions = Consumption.objects.filter(customer=user).filter(product=self)
if consumptions:
return (user, consumptions[0].quantity)
else:
return (user, 0)
@property
def ranking(self):
users = User.objects.all()
ranking = [self.user_ranking(user.pk) for user in users]
ranking.sort(key=lambda x:x[1], reverse=True)
return ranking[0:25]
def isPinte(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_PINTE:
raise ValidationError(
('%(product)s n\'est pas une pinte'),
params={'product': product},
)
def isDemi(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_DEMI:
raise ValidationError(
('%(product)s n\'est pas un demi'),
params={'product': product},
)
def isGalopin(id):
product = Product.objects.get(id=id)
if product.draft_category != Product.DRAFT_GALOPIN:
raise ValidationError(
('%(product)s n\'est pas un galopin'),
params={'product': product},
)
class Keg(models.Model):
class Meta:
verbose_name = "Fût"
permissions = (
("open_keg", "Peut percuter les fûts"),
("close_keg", "Peut fermer les fûts")
)
name = models.CharField(max_length=255, unique=True, verbose_name="Nom")
stockHold = models.IntegerField(default=0, verbose_name="Stock en soute")
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Prix du fût", validators=[MinValueValidator(0)])
capacity = models.IntegerField(default=30, verbose_name="Capacité (L)")
pinte = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futp", validators=[isPinte])
demi = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futd", validators=[isDemi])
galopin = models.ForeignKey(Product, on_delete=models.PROTECT, related_name="futg", validators=[isGalopin],null=True, blank=True)
is_active = models.BooleanField(default=False, verbose_name="Actif")
deg = models.DecimalField(default=0,max_digits=5, decimal_places=2, verbose_name="Degré", validators=[MinValueValidator(0)])
history = HistoricalRecords()
def __str__(self):
return self.name
class KegHistory(models.Model):
class Meta:
verbose_name = "Historique de fût"
keg = models.ForeignKey(Keg, on_delete=models.PROTECT, verbose_name="Fût")
openingDate = models.DateTimeField(auto_now_add=True, verbose_name="Date ouverture")
quantitySold = models.DecimalField(decimal_places=2, max_digits=5, default=0, verbose_name="Quantité vendue")
amountSold = models.DecimalField(decimal_places=2, max_digits=5, default=0, verbose_name="Somme vendue")
closingDate = models.DateTimeField(null=True, blank=True, verbose_name="Date fermeture")
isCurrentKegHistory = models.BooleanField(default=True, verbose_name="Actuel")
history = HistoricalRecords()
def __str__(self):
res = "Fût de " + str(self.keg) + " (" + str(self.openingDate) + " - "
if(self.closingDate):
res += str(self.closingDate) + ")"
else:
res += "?)"
return res
class Reload(models.Model):
class Meta:
verbose_name = "Rechargement"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="reload_taken", verbose_name="Client")
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
PaymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="reload_realized")
date = models.DateTimeField(auto_now_add=True)
history = HistoricalRecords()
def __str__(self):
return "Rechargement effectue par {0} le {1} ({2} euros, coopeman : {3})".format(self.customer, self.date, self.amount, self.coopeman)
class Refund(models.Model):
class Meta:
verbose_name = "Remboursement"
date = models.DateTimeField(auto_now_add=True)
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="refund_taken", verbose_name="Client")
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="refund_realized")
history = HistoricalRecords()
def __str__(self):
return "{0} remboursé de {1} le {2} (effectué par {3})".format(self.customer, self.amount, self.date, self.coopeman)
class Menu(models.Model):
name = models.CharField(max_length=255, verbose_name="Nom")
amount = models.DecimalField(max_digits=7, decimal_places=2, verbose_name="Montant", validators=[MinValueValidator(0)])
articles = models.ManyToManyField(Product, verbose_name="Produits")
is_active = models.BooleanField(default=False, verbose_name="Actif")
history = HistoricalRecords()
def __str__(self):
return self.name
@property
def adherent_required(self):
res = False
for article in self.articles.all():
res = res or article.adherentRequired
return res
class MenuHistory(models.Model):
class Meta:
verbose_name = "Historique de menu"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="menu_taken", verbose_name="Client")
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
paymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
date = models.DateTimeField(auto_now_add=True)
menu = models.ForeignKey(Menu, on_delete=models.PROTECT)
amount = models.DecimalField(max_digits=7, decimal_places=2, default=0, verbose_name="Montant")
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="menu_selled")
history = HistoricalRecords()
def __str__(self):
return "{2} a consommé {0} {1}".format(self.quantity, self.menu, self.customer)
class ConsumptionHistory(models.Model):
class Meta:
verbose_name = "Consommation"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_taken", verbose_name="Client")
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
paymentMethod = models.ForeignKey(PaymentMethod, on_delete=models.PROTECT, verbose_name="Moyen de paiement")
date = models.DateTimeField(auto_now_add=True)
product = models.ForeignKey(Product, on_delete=models.PROTECT, verbose_name="Produit")
amount = models.DecimalField(max_digits=7, decimal_places=2, default=0, verbose_name="Montant")
coopeman = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_selled")
history = HistoricalRecords()
def __str__(self):
return "{0} {1} consommé par {2} le {3} (encaissé par {4})".format(self.quantity, self.product, self.customer, self.date, self.coopeman)
class Consumption(models.Model):
class Meta:
verbose_name = "Consommation totale"
customer = models.ForeignKey(User, on_delete=models.PROTECT, related_name="consumption_global_taken", verbose_name="Client")
product = models.ForeignKey(Product, on_delete=models.PROTECT, verbose_name="Produit")
quantity = models.PositiveIntegerField(default=0, verbose_name="Quantité")
history = HistoricalRecords()
def __str__(self):
return "Consommation de " + str(self.customer) + " concernant le produit " + str(self.product)
class Pinte(models.Model):
current_owner = models.ForeignKey(User, on_delete=models.PROTECT, null=True, default=None, related_name="pintes_owned_currently")
previous_owner = models.ForeignKey(User, on_delete=models.PROTECT, null=True, default=None, related_name="pintes_owned_previously")
last_update_date = models.DateTimeField(auto_now=True)
history = HistoricalRecords()
| true | true |
f736d6f5ee422b782da9d1678bdfa6988a5e901a | 7,639 | py | Python | Dome9-SG-LookUp/SGAnalyzerAppDir/SGAnalyzerApp/SGAnalyzerApp.py | ntnshrm87/Dome9SGAnalyzerApp | 29b6ac41bd073f727bc81cd4ea3aeeaf0d5f9c14 | [
"MIT"
] | null | null | null | Dome9-SG-LookUp/SGAnalyzerAppDir/SGAnalyzerApp/SGAnalyzerApp.py | ntnshrm87/Dome9SGAnalyzerApp | 29b6ac41bd073f727bc81cd4ea3aeeaf0d5f9c14 | [
"MIT"
] | null | null | null | Dome9-SG-LookUp/SGAnalyzerAppDir/SGAnalyzerApp/SGAnalyzerApp.py | ntnshrm87/Dome9SGAnalyzerApp | 29b6ac41bd073f727bc81cd4ea3aeeaf0d5f9c14 | [
"MIT"
] | null | null | null | # SGAnalyzerApp - gui
# importing modules required to run
from tkinter import *
import json
import time
from SGAnalyzerAppDir.SGAnalyzerApp import Dome9SG
def app():
# creating root object
root = Tk()
root.geometry("840x270")
root.title("Dome9-SG-LookUp")
root.resizable(0, 0)
Tops = Frame(root, width=180, relief=RIDGE)
Tops.pack(side=TOP)
f1 = Frame(root, width=170, height=150, relief=RIDGE)
f1.pack(side=LEFT)
# Set String Vars
external_Id = StringVar()
security_Group_Name = StringVar()
description = StringVar()
vpc_Id = StringVar()
region_Id = StringVar()
cloud_Account_Name = StringVar()
AWS_AC_ID = StringVar()
# Function to reset the window
def reset():
external_Id.set("")
security_Group_Name.set("")
description.set("")
vpc_Id.set("")
region_Id.set("")
cloud_Account_Name.set("")
AWS_AC_ID.set("")
# Function for not getting SG
def not_found():
security_Group_Name.set("Not Found")
description.set("Not Found")
vpc_Id.set("Not Found")
region_Id.set("Not Found")
cloud_Account_Name.set("Not Found")
AWS_AC_ID.set("Not Found")
def get_sg_details():
""" Get SG Details in GUI box when only SGID is provided """
check = external_Id.get()
if not check.strip():
external_Id.set("Enter Valid string!!!")
else:
obj = Dome9SG(sg_id=external_Id.get())
sg_details_json = obj.get_sg_by_id()
if "Result" in sg_details_json.keys():
not_found()
else:
security_Group_Name.set(sg_details_json["securityGroupName"])
description.set(sg_details_json["description"])
vpc_Id.set(sg_details_json["vpcId"])
region_Id.set(sg_details_json["regionId"])
cloud_Account_Name.set(sg_details_json["cloudAccountName"])
AWS_AC_ID.set(obj.aws_dome9map[sg_details_json["cloudAccountId"]])
def get_sg_rules():
""" Download SGRules in JSON when only SGID is provided """
""" Format: SGRules_sg-abcd1234_FriAug21922112019"""
check = external_Id.get()
if not check.strip():
external_Id.set("Enter Valid string!!!")
else:
sg_details_json = Dome9SG(sg_id=external_Id.get()).get_sg_by_id()
if "Result" in sg_details_json.keys():
not_found()
else:
service_rules = sg_details_json["services"]
filename = 'SGRules_' + sg_details_json["externalId"] + "_" + \
str(time.asctime(time.localtime(time.time()))).replace(":", "").replace(" ", "") \
+ '.json'
with open(filename, 'w', encoding='utf-8') as j:
json.dump(service_rules, j, ensure_ascii=False, indent=4)
def get_sg_by_name():
""" Download all SGs with SGDetails in json when SGName is provided """
""" Format: SGMatches_SG-DC_FriAug21923162019.json """
check = security_Group_Name.get()
if not check.strip():
security_Group_Name.set("Enter Valid string!!!")
else:
sg_details_json = Dome9SG(sg_name=security_Group_Name.get()).get_all_sg_by_name()
if len(sg_details_json) == 0:
not_found()
filename = 'SGMatches_' + security_Group_Name.get() + "_" + \
str(time.asctime(time.localtime(time.time()))).replace(":", "").replace(" ", "") \
+ '.json'
with open(filename, 'w', encoding='utf-8') as j:
json.dump(sg_details_json, j, ensure_ascii=False, indent=4)
# Title
lblTitle = Label(Tops, font=('helvetica', 10, 'bold'), text="Sec Ops SG Analyzer",
fg="Black", bd=5, anchor='w')
lblTitle.grid(row=0, column=2)
lblTitle2 = Label(Tops, font=('arial', 10, 'bold'), text=time.asctime(time.localtime(time.time())),
fg="Steel Blue", bd=5, anchor='w')
lblTitle2.grid(row=1, column=2)
# SGId
lblSGId = Label(f1, font=('arial', 10, 'bold'), text="SGID (Required):", bd=5, anchor="w")
lblSGId.grid(row=0, column=2)
txtSGId = Entry(f1, font=('arial', 10, 'bold'), textvariable=external_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtSGId.grid(row=0, column=3)
# AccountNumber
lblACNo = Label(f1, font=('arial', 10, 'bold'), text="Account Number:", bd=5, anchor="w")
lblACNo.grid(row=0, column=0)
txtACNo = Entry(f1, font=('arial', 10, 'bold'), textvariable=AWS_AC_ID, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtACNo.grid(row=0, column=1)
# RegionId
lblRegId = Label(f1, font=('arial', 10, 'bold'), text="Region :", bd=5, anchor="w")
lblRegId.grid(row=0, column=4)
txtRegId = Entry(f1, font=('arial', 10, 'bold'), textvariable=region_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtRegId.grid(row=0, column=5)
# SGName
lblSGName = Label(f1, font=('arial', 10, 'bold'), text="SGName(Required):", bd=10, anchor="w")
lblSGName.grid(row=1, column=2)
txtSGName = Entry(f1, font=('arial', 10, 'bold'), textvariable=security_Group_Name, bd=5, insertwidth=4,
bg="powder blue", justify='right')
txtSGName.grid(row=1, column=3)
# AccountName
lblACN = Label(f1, font=('arial', 10, 'bold'), text="Account Name:", bd=10, anchor="w")
lblACN.grid(row=1, column=0)
txtACN = Entry(f1, font=('arial', 10, 'bold'),
textvariable=cloud_Account_Name, bd=5, insertwidth=4,
bg="powder blue", justify='right')
txtACN.grid(row=1, column=1)
# VPC-ID
lblVPCId = Label(f1, font=('arial', 10, 'bold'), text="VPC-ID:", bd=10, anchor="w")
lblVPCId.grid(row=1, column=4)
txtVPCId = Entry(f1, font=('arial', 10, 'bold'), textvariable=vpc_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtVPCId.grid(row=1, column=5)
# SGDescription
lblSGDesc = Label(f1, font=('arial', 10, 'bold'), text="SG Description:", bd=10, anchor="w")
lblSGDesc.grid(row=2, column=2)
txtSGDesc = Entry(f1, font=('arial', 10, 'bold'), textvariable=description, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtSGDesc.grid(row=2, column=3)
# Show Details
btnSD = Button(f1, padx=6, pady=6, bd=8, fg="black", font=('arial', 10, 'bold'), width=10, text="Details by SGID",
bg="thistle", command=get_sg_details).grid(row=9, column=1)
# Reset button
btnReset = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="Reset", bg="thistle",
command=reset).grid(row=9, column=2)
# Show Rules
btnRules = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="Rules by SGID", bg="thistle",
command=get_sg_rules).grid(row=9, column=3)
# Show matches of SGs
btnMatchSG = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="SGs by Name", bg="thistle",
command=get_sg_by_name).grid(row=9, column=4)
root.mainloop()
def main():
app()
if __name__ == "__main__":
main()
| 39.174359 | 118 | 0.57429 |
from tkinter import *
import json
import time
from SGAnalyzerAppDir.SGAnalyzerApp import Dome9SG
def app():
root = Tk()
root.geometry("840x270")
root.title("Dome9-SG-LookUp")
root.resizable(0, 0)
Tops = Frame(root, width=180, relief=RIDGE)
Tops.pack(side=TOP)
f1 = Frame(root, width=170, height=150, relief=RIDGE)
f1.pack(side=LEFT)
external_Id = StringVar()
security_Group_Name = StringVar()
description = StringVar()
vpc_Id = StringVar()
region_Id = StringVar()
cloud_Account_Name = StringVar()
AWS_AC_ID = StringVar()
def reset():
external_Id.set("")
security_Group_Name.set("")
description.set("")
vpc_Id.set("")
region_Id.set("")
cloud_Account_Name.set("")
AWS_AC_ID.set("")
def not_found():
security_Group_Name.set("Not Found")
description.set("Not Found")
vpc_Id.set("Not Found")
region_Id.set("Not Found")
cloud_Account_Name.set("Not Found")
AWS_AC_ID.set("Not Found")
def get_sg_details():
check = external_Id.get()
if not check.strip():
external_Id.set("Enter Valid string!!!")
else:
obj = Dome9SG(sg_id=external_Id.get())
sg_details_json = obj.get_sg_by_id()
if "Result" in sg_details_json.keys():
not_found()
else:
security_Group_Name.set(sg_details_json["securityGroupName"])
description.set(sg_details_json["description"])
vpc_Id.set(sg_details_json["vpcId"])
region_Id.set(sg_details_json["regionId"])
cloud_Account_Name.set(sg_details_json["cloudAccountName"])
AWS_AC_ID.set(obj.aws_dome9map[sg_details_json["cloudAccountId"]])
def get_sg_rules():
check = external_Id.get()
if not check.strip():
external_Id.set("Enter Valid string!!!")
else:
sg_details_json = Dome9SG(sg_id=external_Id.get()).get_sg_by_id()
if "Result" in sg_details_json.keys():
not_found()
else:
service_rules = sg_details_json["services"]
filename = 'SGRules_' + sg_details_json["externalId"] + "_" + \
str(time.asctime(time.localtime(time.time()))).replace(":", "").replace(" ", "") \
+ '.json'
with open(filename, 'w', encoding='utf-8') as j:
json.dump(service_rules, j, ensure_ascii=False, indent=4)
def get_sg_by_name():
check = security_Group_Name.get()
if not check.strip():
security_Group_Name.set("Enter Valid string!!!")
else:
sg_details_json = Dome9SG(sg_name=security_Group_Name.get()).get_all_sg_by_name()
if len(sg_details_json) == 0:
not_found()
filename = 'SGMatches_' + security_Group_Name.get() + "_" + \
str(time.asctime(time.localtime(time.time()))).replace(":", "").replace(" ", "") \
+ '.json'
with open(filename, 'w', encoding='utf-8') as j:
json.dump(sg_details_json, j, ensure_ascii=False, indent=4)
lblTitle = Label(Tops, font=('helvetica', 10, 'bold'), text="Sec Ops SG Analyzer",
fg="Black", bd=5, anchor='w')
lblTitle.grid(row=0, column=2)
lblTitle2 = Label(Tops, font=('arial', 10, 'bold'), text=time.asctime(time.localtime(time.time())),
fg="Steel Blue", bd=5, anchor='w')
lblTitle2.grid(row=1, column=2)
lblSGId = Label(f1, font=('arial', 10, 'bold'), text="SGID (Required):", bd=5, anchor="w")
lblSGId.grid(row=0, column=2)
txtSGId = Entry(f1, font=('arial', 10, 'bold'), textvariable=external_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtSGId.grid(row=0, column=3)
lblACNo = Label(f1, font=('arial', 10, 'bold'), text="Account Number:", bd=5, anchor="w")
lblACNo.grid(row=0, column=0)
txtACNo = Entry(f1, font=('arial', 10, 'bold'), textvariable=AWS_AC_ID, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtACNo.grid(row=0, column=1)
lblRegId = Label(f1, font=('arial', 10, 'bold'), text="Region :", bd=5, anchor="w")
lblRegId.grid(row=0, column=4)
txtRegId = Entry(f1, font=('arial', 10, 'bold'), textvariable=region_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtRegId.grid(row=0, column=5)
lblSGName = Label(f1, font=('arial', 10, 'bold'), text="SGName(Required):", bd=10, anchor="w")
lblSGName.grid(row=1, column=2)
txtSGName = Entry(f1, font=('arial', 10, 'bold'), textvariable=security_Group_Name, bd=5, insertwidth=4,
bg="powder blue", justify='right')
txtSGName.grid(row=1, column=3)
lblACN = Label(f1, font=('arial', 10, 'bold'), text="Account Name:", bd=10, anchor="w")
lblACN.grid(row=1, column=0)
txtACN = Entry(f1, font=('arial', 10, 'bold'),
textvariable=cloud_Account_Name, bd=5, insertwidth=4,
bg="powder blue", justify='right')
txtACN.grid(row=1, column=1)
lblVPCId = Label(f1, font=('arial', 10, 'bold'), text="VPC-ID:", bd=10, anchor="w")
lblVPCId.grid(row=1, column=4)
txtVPCId = Entry(f1, font=('arial', 10, 'bold'), textvariable=vpc_Id, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtVPCId.grid(row=1, column=5)
lblSGDesc = Label(f1, font=('arial', 10, 'bold'), text="SG Description:", bd=10, anchor="w")
lblSGDesc.grid(row=2, column=2)
txtSGDesc = Entry(f1, font=('arial', 10, 'bold'), textvariable=description, bd=5, insertwidth=4, bg="powder blue",
justify='right')
txtSGDesc.grid(row=2, column=3)
btnSD = Button(f1, padx=6, pady=6, bd=8, fg="black", font=('arial', 10, 'bold'), width=10, text="Details by SGID",
bg="thistle", command=get_sg_details).grid(row=9, column=1)
btnReset = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="Reset", bg="thistle",
command=reset).grid(row=9, column=2)
btnRules = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="Rules by SGID", bg="thistle",
command=get_sg_rules).grid(row=9, column=3)
btnMatchSG = Button(f1, padx=6, pady=6, bd=8,
fg="black", font=('arial', 10, 'bold'),
width=10, text="SGs by Name", bg="thistle",
command=get_sg_by_name).grid(row=9, column=4)
root.mainloop()
def main():
app()
if __name__ == "__main__":
main()
| true | true |
f736d90d0be426c4a74dfddcc419aa32132a7458 | 1,288 | py | Python | train.py | DipeshAggarwal/wgan-gp-keras | 7a70192cdd26726ee981107299a7fa6e21cbe84b | [
"Unlicense"
] | null | null | null | train.py | DipeshAggarwal/wgan-gp-keras | 7a70192cdd26726ee981107299a7fa6e21cbe84b | [
"Unlicense"
] | null | null | null | train.py | DipeshAggarwal/wgan-gp-keras | 7a70192cdd26726ee981107299a7fa6e21cbe84b | [
"Unlicense"
] | null | null | null | from core.loss import d_wasserstein_loss
from core.loss import g_wasserstein_loss
from core.nn.conv.wgan import generator
from core.nn.conv.wgan import critic
from core.callbacks import GANMonitor
from core.model import WGAN_GP
import tensorflow as tf
import numpy as np
import config
train_images = tf.keras.utils.image_dataset_from_directory(
"dataset/images/", label_mode=None, image_size=(config.IMAGE_WIDTH, config.IMAGE_HEIGHT), batch_size=config.BATCH_SIZE
)
train_images = train_images.map(lambda x: (x - 127.5) / 127.5)
generator = generator(config.LATENT_DIM, tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02), channels=config.CHANNELS)
critic = critic(config.IMAGE_HEIGHT, config.IMAGE_WIDTH, config.CHANNELS)
wgan = WGAN_GP(critic=critic, generator=generator, latent_dim=config.LATENT_DIM, critic_extra_steps=config.EXTRA_STEPS)
d_opt = tf.keras.optimizers.Adam(learning_rate=config.LR, beta_1=0.5, beta_2=0.9)
g_opt = tf.keras.optimizers.Adam(learning_rate=config.LR, beta_1=0.5, beta_2=0.9)
wgan.compile(
d_optimiser=d_opt,
g_optimiser=g_opt,
d_loss_fn=d_wasserstein_loss,
g_loss_fn=g_wasserstein_loss,
)
callback = [GANMonitor(num_images=16, latent_dim=config.LATENT_DIM)]
wgan.fit(train_images, epochs=config.EPOCHS, callbacks=callback)
| 37.882353 | 125 | 0.801242 | from core.loss import d_wasserstein_loss
from core.loss import g_wasserstein_loss
from core.nn.conv.wgan import generator
from core.nn.conv.wgan import critic
from core.callbacks import GANMonitor
from core.model import WGAN_GP
import tensorflow as tf
import numpy as np
import config
train_images = tf.keras.utils.image_dataset_from_directory(
"dataset/images/", label_mode=None, image_size=(config.IMAGE_WIDTH, config.IMAGE_HEIGHT), batch_size=config.BATCH_SIZE
)
train_images = train_images.map(lambda x: (x - 127.5) / 127.5)
generator = generator(config.LATENT_DIM, tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02), channels=config.CHANNELS)
critic = critic(config.IMAGE_HEIGHT, config.IMAGE_WIDTH, config.CHANNELS)
wgan = WGAN_GP(critic=critic, generator=generator, latent_dim=config.LATENT_DIM, critic_extra_steps=config.EXTRA_STEPS)
d_opt = tf.keras.optimizers.Adam(learning_rate=config.LR, beta_1=0.5, beta_2=0.9)
g_opt = tf.keras.optimizers.Adam(learning_rate=config.LR, beta_1=0.5, beta_2=0.9)
wgan.compile(
d_optimiser=d_opt,
g_optimiser=g_opt,
d_loss_fn=d_wasserstein_loss,
g_loss_fn=g_wasserstein_loss,
)
callback = [GANMonitor(num_images=16, latent_dim=config.LATENT_DIM)]
wgan.fit(train_images, epochs=config.EPOCHS, callbacks=callback)
| true | true |
f736da03f6e8b422ea606c28b9e083ff524e8b8d | 10,961 | py | Python | python_dashboard/caf_opendata_dictionnary_mapping.py | sduprey/open_data_platform | a20b5ab6a1ca99ae8aef11ac74a314114144d291 | [
"MIT"
] | 2 | 2019-12-03T13:35:31.000Z | 2019-12-03T13:35:37.000Z | python_dashboard/caf_opendata_dictionnary_mapping.py | sduprey/open_data_platform | a20b5ab6a1ca99ae8aef11ac74a314114144d291 | [
"MIT"
] | null | null | null | python_dashboard/caf_opendata_dictionnary_mapping.py | sduprey/open_data_platform | a20b5ab6a1ca99ae8aef11ac74a314114144d291 | [
"MIT"
] | null | null | null | #CAF,INDICATEUR SUR LA PART DES PRESTATIONS DANS LES RESSOURCES DES FOYERS ALLOCATAIRES PAR COMMUNE,
#DependancePrestaCom,http://data.caf.fr/dataset/indicateur-sur-la-part-des-prestations-dans-les-ressources-des-foyers-allocataires-par-commune
#CAF,BENEFICIAIRES BAS REVENUS,
#BasrevnuCom,http://data.caf.fr/dataset/beneficiaire-bas-revenus
#CAF,POPULATION DES FOYERS ALLOCATAIRES PAR COMMUNE,
#NIVCOMTOTAL2009,http://data.caf.fr/dataset/population-des-foyers-allocataires-par-commune
#CAF,REPARTITION DES FOYERS ALLOCATAIRES SELON LE TYPE DE FAMILLE PAR COMMUNE,
#ConfigFamiliale,http://data.caf.fr/dataset/repartition-des-foyers-allocataires-selon-le-type-de-famille-par-commune
#CAF,DENOMBREMENT ET REPARTITION DES FOYERS ALLOCATAIRES SELON L'AGE DU RESPONSABLE,
#TrancheAge,http://data.caf.fr/dataset/denombrement-et-repartition-des-foyers-allocataires-selon-l-age-du-responsable
#CAF,POPULATION DES FOYERS ALLOCATAIRES PERCEVANT UNE AIDE PERSONNELLE AU LOGEMENT,
#LOGCom,http://data.caf.fr/dataset/population-des-foyers-allocataires-percevant-une-aide-personnelle-au-logement
#CAF, FOYERS ALLOCATAIRES PERCEVANT LA PRESTATION D'ACCUEIL DU JEUNE ENFANT PAJE PAR COMMUNE,
#PAJECom,http://data.caf.fr/dataset/foyers-allocataires-percevant-la-prestation-d-accueil-du-jeune-enfant-paje-par-commune
#CAF, POPULATION DES FOYERS ALLOCATAIRES PERCEVANT LE REVENU DE SOLIDARITE,
#RSAPersCom,http://data.caf.fr/dataset/population-des-foyers-allocataires-percevant-le-revenu-de-solidarite
#CAF, PERSONNES AYANT UN DROIT VERSABLE A L'ALLOCATION AUX ADULTES HANDICAPES,
#AAHCom,http://data.caf.fr/dataset/personnes-ayant-un-droit-versable-a-l-allocation-aux-adultes-handicapes
#CAF,REPARTITION PAR TRANCHE D'AGE DES ENFANTS COUVERTS PAR DES PRESTATIONS CAF,
#EnfantAgeCom,http://data.caf.fr/dataset/repartition-par-tranche-d-age-des-enfants-couverts-par-des-prestations-caf
#CAF,NOMBRE D'ENFANTS COUVERTS PAR L'ALLOCATION D'EDUCATION DE L'ENFANT HANDICAPE AEEH PAR COMMUNE
#enfantAEEH,http://data.caf.fr/dataset/nombre-d-enfants-couverts-par-l-allocation-d-education-de-l-enfant-handicape-aeeh-par-commune
#CAF,FOYERS ALLOCATAIRES PERCEVANT LE REVENU DE SOLIDARITE ACTIVE RSA PAR COMMUNE,
#RSACom,http://data.caf.fr/dataset/foyers-allocataires-percevant-le-revenu-de-solidarite-active-rsa-par-commune
#CAF,FOYERS ALLOCATAIRES PERCEVANT UNE PRESTATION ENFANCE ET JEUNESSE AF CF ASF AEEH et ARS par commune,
#EJCom,http://data.caf.fr/dataset/foyers-allocataires-percevant-une-prestation-enfance-et-jeunesse-af-cf-asf-aeeh-et-ars-par-comm
#CAF,POPULATION DES FOYERS ALLOCATAIRES,
#LogPersPrestaCom,http://data.caf.fr/dataset/population-des-foyers-allocatair
#CAF,NOMBRE D'ENFANTS COUVERTS PAR L'ALLOCATION DE RENTREE SCOLAIRE ARS PAR COMMUNE
#EnfantARS,http://data.caf.fr/dataset/nombre-denfants-couverts-par-l-allocation-de-rentree-scolaire-ars-par-commune
caf_open_dictionnary = {
"NB_allocataires" : "NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"TR50PFRB" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LES RESSOURCES SONT CONSTITUEES A 50% OU PLUS DES PRESTATIONS CAF",
"TR100PFRB" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LES RESSOURCES SONT CONSTITUEES A 100% DES PRESTATIONS CAF",
"NB_allocataires_ress":"NOMBRE DE FOYERS ALLOCATAIRES DE REFERENCE POUR CALCUL DES BAS REVENUS",
"NB_pers_couv_ress":"NOMBRE DE PERSONNES COUVERTES DE REFERENCE POUR CALCUL DES BAS REVENUS",
"ALL_bas_revenu":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BAS REVENUS",
"Pers_bas_revenu":"NOMBRE TOTAL DE PERSONNES COUVERTES PAR LES FOYERS BAS REVENUS",
"NB_Pers_par_Foyer_Alloc":"NOMBRE DE PERSONNES COUVERTES PAR LA BRANCHE FAMILLE",
"NB_Enfants":"NOMBRE D'ENFANTS COUVERTS",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"COUP_0_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES SANS ENFANT",
"COUP_1_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 1 ENFANT",
"COUP_2_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 2 ENFANTS",
"COUP_3_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 3 ENFANTS",
"COUP_4plus_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 4 A X ENFANTS",
"Homme_Isole":"NOMBRE DE FOYERS ALLOCATAIRES MESSIEURS ISOLES",
"Femme_Isolee":"NOMBRE DE FOYERS ALLOCATAIRES MESDAMES ISOLEES",
"MONO_1_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 1 ENFANT",
"MONO_2_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 2 ENFANTS",
"MONO_3_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 3 ENFANTS",
"MONO_4plus_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 4 A X ENFANTS",
"NB_Allocataires" : "NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL0A19" :"NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE MOINS DE 20 ANS",
"ALL20A24" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 20 A 24 ANS",
"ALL25A29" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 25 A 29 ANS",
"ALL30A39" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 30 A 39 ANS",
"ALL40A49" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 40 A 49 ANS",
"ALL50A54*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 50 A 54 ANS",
"ALL55A59*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 55 A 59 ANS",
"ALL60A64*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 60 A 64 ANS",
"ALL65A69*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 65 A 69 ANS",
"ALL70AX*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 70 ANS OU PLUS",
"ALLAGEX" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST D'AGE INCONNU",
"total_allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"total_allocataires_logement":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"total_ALF":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'ALF",
"total_ALS":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'ALS",
"total_APL":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'APL",
"locataire_ALF":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'ALF",
"locataire_ALS":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'ALS",
"locataire_APL":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'APL",
"proprietaire_ALF":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"proprietaire_ALS":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"proprietaire_APL":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"total_locataire":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"total_proprietaire":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_PAJE":"NOMBRE DE FOYERS ALLOCATAIRES PAJE VERSABLE",
"ALL_PRIM":"NOMBRE DE FOYERS ALLOCATAIRES PRIME NAISSANCE OU ADOPTION VERSEES",
"ALL_BASEP":"NOMBRE DE FOYERS ALLOCATAIRES AVEC DROIT BASE PAJE VERSABLE",
"ALL_CMG":"NOMBRE DE FOYERS ALLOCATAIRES CMG VERSABLE",
"ALL_CMG_ASMA":"NOMBRE DE FOYERS ALLOCATAIRES CMG ASSISTANTE MATERNELLE VERSABLE",
"ALL_CMG_DOM":"NOMBRE DE FOYERS ALLOCATAIRES CMG GARDE A DOMICILE VERSABLE",
"ALL_CMG_A":"NOMBRE DE FOYERS ALLOCATAIRES CMG STRUCTURE (ENTREPRISE OU ASSOCIATION) VERSABLE",
"ALL_Clca":"NOMBRE DE FOYERS ALLOCATAIRES CLCA VERSABLE",
"NB_Pers_par_Foyer_Alloc":"NOMBRE DE PERSONNES COUVERTES PAR UNE PRESTATION DE LA BRANCHE FAMILLE",
"NB_Pers_couv_RSA":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA",
"RSA_SOCLE_non_Majore_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA SOCLE SANS MAJORATION VERSABLE",
"RSA_SOCLE_Majore_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA SOCLE AVEC MAJORATION VERSABLE",
"RSA_activite_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA ACTIVITE VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_AAH":"NOMBRE D'ALLOCATAIRES AVEC AAH VERSABLE",
"NB_enfants":"NOMBRE D'ENFANTS BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_0_2_ans":"NOMBRE D'ENFANTS DE 0 A 2 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_3_5_ans":"NOMBRE D'ENFANTS DE 3 A 5 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_6_11_ans":"NOMBRE D'ENFANTS DE 6 A 11 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_12_15_ans":"NOMBRE D'ENFANTS DE 12 A 15 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_16_17_ans":"NOMBRE D'ENFANTS DE 16 A 17 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_18_19_ans":"NOMBRE D'ENFANTS DE 18 A 19 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_20_24_ans":"NOMBRE D'ENFANTS DE 20 A 24 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_enfant_AEEH":"NOMBRE D'ENFANTS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_0A2":"NOMBRE D'ENFANTS DE 0 A 2 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_3A5":"NOMBRE D'ENFANTS DE 3 A 5 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_6A11":"NOMBRE D'ENFANTS DE 6 A 11 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_12A15":"NOMBRE D'ENFANTS DE 12 A 15 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_16A17":"NOMBRE D'ENFANTS DE 16 A 17 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_18A20":"NOMBRE D'ENFANTS DE 18 A 20 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"NB_allocataire_RSA":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES RSA",
"Dont_RSA_jeune":"NOMBRE DE FOYERS ALLOCATAIRES RSA JEUNE",
"RSA_SOCLE_non_Majore":"NOMBRE DE FOYERS ALLOCATAIRES RSA SOCLE SANS MAJORATION VERSABLE",
"RSA_SOCLE_Majore":"NOMBRE DE FOYERS ALLOCATAIRES RSA SOCLE AVEC MAJORATION VERSABLE",
"RSA_activite":"NOMBRE DE FOYERS ALLOCATAIRES RSA ACTIVITE VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_AF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC AF VERSABLE",
"ALL_CF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC CF VERSABLE",
"ALL_ARS":"NOMBRE DE FOYERS ALLOCATAIRES AVEC ARS VERSABLE",
"ALL_ASF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC ASF VERSABLE",
"ALL_AEEH":"NOMBRE DE FOYERS ALLOCATAIRES AVEC AEEH VERSABLE",
"NB_Pers_Couv_Al":"NOMBRE DE PERSONNES COUVERTES PAR UNE AIDE AU LOGEMENT VERSABLE",
"Pers_Couv_Al_ALF":"NOMBRE DE PERSONNES COUVERTES PAR ALF VERSABLE",
"Pers_Couv_Al_ALS":"NOMBRE DE PERSONNES COUVERTES PAR ALS VERSABLE",
"Pers_Couv_Al_APL":"NOMBRE DE PERSONNES COUVERTES PAR APL VERSABLE",
"NB_enfant_ARS":"NOMBRE D'ENFANTS OUVRANT DROIT A L'ARS VERSABLE",
"ARS_5A10":"NOMBRE D'ENFANTS DE 5 A 10 ANS, OUVRANT DROIT A L'ARS VERSABLE",
"ARS_11A14":"NOMBRE D'ENFANTS DE 11 A 14 ANS, OUVRANT DROIT A L'ARS VERSABLE",
"ARS_15A17":"NOMBRE D'ENFANTS DE 15 A 17 ANS, OUVRANT DROIT A L'ARS VERSABLE"
} | 74.564626 | 142 | 0.801204 |
#TrancheAge,http://data.caf.fr/dataset/denombrement-et-repartition-des-foyers-allocataires-selon-l-age-du-responsable
#CAF,POPULATION DES FOYERS ALLOCATAIRES PERCEVANT UNE AIDE PERSONNELLE AU LOGEMENT,
#LOGCom,http://data.caf.fr/dataset/population-des-foyers-allocataires-percevant-une-aide-personnelle-au-logement
#CAF, FOYERS ALLOCATAIRES PERCEVANT LA PRESTATION D'ACCUEIL DU JEUNE ENFANT PAJE PAR COMMUNE,
#AAHCom,http://data.caf.fr/dataset/personnes-ayant-un-droit-versable-a-l-allocation-aux-adultes-handicapes
#CAF,REPARTITION PAR TRANCHE D'AGE DES ENFANTS COUVERTS PAR DES PRESTATIONS CAF,
caf_open_dictionnary = {
"NB_allocataires" : "NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"TR50PFRB" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LES RESSOURCES SONT CONSTITUEES A 50% OU PLUS DES PRESTATIONS CAF",
"TR100PFRB" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LES RESSOURCES SONT CONSTITUEES A 100% DES PRESTATIONS CAF",
"NB_allocataires_ress":"NOMBRE DE FOYERS ALLOCATAIRES DE REFERENCE POUR CALCUL DES BAS REVENUS",
"NB_pers_couv_ress":"NOMBRE DE PERSONNES COUVERTES DE REFERENCE POUR CALCUL DES BAS REVENUS",
"ALL_bas_revenu":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BAS REVENUS",
"Pers_bas_revenu":"NOMBRE TOTAL DE PERSONNES COUVERTES PAR LES FOYERS BAS REVENUS",
"NB_Pers_par_Foyer_Alloc":"NOMBRE DE PERSONNES COUVERTES PAR LA BRANCHE FAMILLE",
"NB_Enfants":"NOMBRE D'ENFANTS COUVERTS",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"COUP_0_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES SANS ENFANT",
"COUP_1_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 1 ENFANT",
"COUP_2_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 2 ENFANTS",
"COUP_3_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 3 ENFANTS",
"COUP_4plus_ENF":"NOMBRE DE FOYERS ALLOCATAIRES COUPLES AVEC 4 A X ENFANTS",
"Homme_Isole":"NOMBRE DE FOYERS ALLOCATAIRES MESSIEURS ISOLES",
"Femme_Isolee":"NOMBRE DE FOYERS ALLOCATAIRES MESDAMES ISOLEES",
"MONO_1_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 1 ENFANT",
"MONO_2_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 2 ENFANTS",
"MONO_3_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 3 ENFANTS",
"MONO_4plus_ENF":"NOMBRE DE FOYERS ALLOCATAIRES MONOPARENTS AVEC 4 A X ENFANTS",
"NB_Allocataires" : "NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL0A19" :"NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE MOINS DE 20 ANS",
"ALL20A24" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 20 A 24 ANS",
"ALL25A29" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 25 A 29 ANS",
"ALL30A39" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 30 A 39 ANS",
"ALL40A49" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 40 A 49 ANS",
"ALL50A54*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 50 A 54 ANS",
"ALL55A59*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 55 A 59 ANS",
"ALL60A64*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 60 A 64 ANS",
"ALL65A69*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 65 A 69 ANS",
"ALL70AX*" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST AGE DE 70 ANS OU PLUS",
"ALLAGEX" : "NOMBRE DE FOYERS ALLOCATAIRES DONT LE TITUTAILRE DU DOSSIER EST D'AGE INCONNU",
"total_allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"total_allocataires_logement":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"total_ALF":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'ALF",
"total_ALS":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'ALS",
"total_APL":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES BENEFICIAIRE DE L'APL",
"locataire_ALF":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'ALF",
"locataire_ALS":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'ALS",
"locataire_APL":"NOMBRE DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE DE L'APL",
"proprietaire_ALF":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"proprietaire_ALS":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"proprietaire_APL":"NOMBRE DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE DE L'ALF",
"total_locataire":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES EN LOCATION OU EN FOYER BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"total_proprietaire":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES PROPRIETAIRE BENEFICIAIRE D'UNE AIDE AU LOGEMENT",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_PAJE":"NOMBRE DE FOYERS ALLOCATAIRES PAJE VERSABLE",
"ALL_PRIM":"NOMBRE DE FOYERS ALLOCATAIRES PRIME NAISSANCE OU ADOPTION VERSEES",
"ALL_BASEP":"NOMBRE DE FOYERS ALLOCATAIRES AVEC DROIT BASE PAJE VERSABLE",
"ALL_CMG":"NOMBRE DE FOYERS ALLOCATAIRES CMG VERSABLE",
"ALL_CMG_ASMA":"NOMBRE DE FOYERS ALLOCATAIRES CMG ASSISTANTE MATERNELLE VERSABLE",
"ALL_CMG_DOM":"NOMBRE DE FOYERS ALLOCATAIRES CMG GARDE A DOMICILE VERSABLE",
"ALL_CMG_A":"NOMBRE DE FOYERS ALLOCATAIRES CMG STRUCTURE (ENTREPRISE OU ASSOCIATION) VERSABLE",
"ALL_Clca":"NOMBRE DE FOYERS ALLOCATAIRES CLCA VERSABLE",
"NB_Pers_par_Foyer_Alloc":"NOMBRE DE PERSONNES COUVERTES PAR UNE PRESTATION DE LA BRANCHE FAMILLE",
"NB_Pers_couv_RSA":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA",
"RSA_SOCLE_non_Majore_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA SOCLE SANS MAJORATION VERSABLE",
"RSA_SOCLE_Majore_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA SOCLE AVEC MAJORATION VERSABLE",
"RSA_activite_Pers_couv":"NOMBRE DE PERSONNES COUVERTES PAR LE RSA ACTIVITE VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_AAH":"NOMBRE D'ALLOCATAIRES AVEC AAH VERSABLE",
"NB_enfants":"NOMBRE D'ENFANTS BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_0_2_ans":"NOMBRE D'ENFANTS DE 0 A 2 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_3_5_ans":"NOMBRE D'ENFANTS DE 3 A 5 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_6_11_ans":"NOMBRE D'ENFANTS DE 6 A 11 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_12_15_ans":"NOMBRE D'ENFANTS DE 12 A 15 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_16_17_ans":"NOMBRE D'ENFANTS DE 16 A 17 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_18_19_ans":"NOMBRE D'ENFANTS DE 18 A 19 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_Enfants_20_24_ans":"NOMBRE D'ENFANTS DE 20 A 24 ANS, BENEFICIAIRES D'AU MOINS UNE PRESTATION CAF VERSABLE",
"NB_enfant_AEEH":"NOMBRE D'ENFANTS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_0A2":"NOMBRE D'ENFANTS DE 0 A 2 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_3A5":"NOMBRE D'ENFANTS DE 3 A 5 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_6A11":"NOMBRE D'ENFANTS DE 6 A 11 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_12A15":"NOMBRE D'ENFANTS DE 12 A 15 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_16A17":"NOMBRE D'ENFANTS DE 16 A 17 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"AEEH_18A20":"NOMBRE D'ENFANTS DE 18 A 20 ANS BENEFICIAIRES DE L'AEEH VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"NB_allocataire_RSA":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES RSA",
"Dont_RSA_jeune":"NOMBRE DE FOYERS ALLOCATAIRES RSA JEUNE",
"RSA_SOCLE_non_Majore":"NOMBRE DE FOYERS ALLOCATAIRES RSA SOCLE SANS MAJORATION VERSABLE",
"RSA_SOCLE_Majore":"NOMBRE DE FOYERS ALLOCATAIRES RSA SOCLE AVEC MAJORATION VERSABLE",
"RSA_activite":"NOMBRE DE FOYERS ALLOCATAIRES RSA ACTIVITE VERSABLE",
"NB_Allocataires":"NOMBRE TOTAL DE FOYERS ALLOCATAIRES DE LA BRANCHE FAMILLE",
"ALL_AF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC AF VERSABLE",
"ALL_CF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC CF VERSABLE",
"ALL_ARS":"NOMBRE DE FOYERS ALLOCATAIRES AVEC ARS VERSABLE",
"ALL_ASF":"NOMBRE DE FOYERS ALLOCATAIRES AVEC ASF VERSABLE",
"ALL_AEEH":"NOMBRE DE FOYERS ALLOCATAIRES AVEC AEEH VERSABLE",
"NB_Pers_Couv_Al":"NOMBRE DE PERSONNES COUVERTES PAR UNE AIDE AU LOGEMENT VERSABLE",
"Pers_Couv_Al_ALF":"NOMBRE DE PERSONNES COUVERTES PAR ALF VERSABLE",
"Pers_Couv_Al_ALS":"NOMBRE DE PERSONNES COUVERTES PAR ALS VERSABLE",
"Pers_Couv_Al_APL":"NOMBRE DE PERSONNES COUVERTES PAR APL VERSABLE",
"NB_enfant_ARS":"NOMBRE D'ENFANTS OUVRANT DROIT A L'ARS VERSABLE",
"ARS_5A10":"NOMBRE D'ENFANTS DE 5 A 10 ANS, OUVRANT DROIT A L'ARS VERSABLE",
"ARS_11A14":"NOMBRE D'ENFANTS DE 11 A 14 ANS, OUVRANT DROIT A L'ARS VERSABLE",
"ARS_15A17":"NOMBRE D'ENFANTS DE 15 A 17 ANS, OUVRANT DROIT A L'ARS VERSABLE"
} | true | true |
f736da1803f1d6c5b5da0b027eb1a91f5e27d665 | 2,509 | py | Python | cathie/cats_api.py | TCKACHIKSIS/lms | fd06eb7a2baa9b9f82caa5223c86ba500f88333c | [
"MIT"
] | null | null | null | cathie/cats_api.py | TCKACHIKSIS/lms | fd06eb7a2baa9b9f82caa5223c86ba500f88333c | [
"MIT"
] | null | null | null | cathie/cats_api.py | TCKACHIKSIS/lms | fd06eb7a2baa9b9f82caa5223c86ba500f88333c | [
"MIT"
] | null | null | null | import json
import re
import requests
from django.conf import settings
from cathie.exceptions import CatsAnswerCodeException
from cathie import authorization
def cats_check_status():
pass
@authorization.check_authorization_for_cats
def cats_submit_solution(source_text: str, problem_id: int, de_id: int, source=None):
# ToDo обработать повторную отправку решения
url = f'{settings.CATS_URL}main.pl?f=api_submit_problem;json=1;'
url += f'sid={authorization.cats_sid()}'
data = {
'de_id': de_id,
'source_text': source_text,
'problem_id': problem_id
}
r = requests.post(url, data=data)
if r.status_code != 200:
raise CatsAnswerCodeException(r)
r_content = json.loads(r.content.decode('utf-8'))
req_ids = None
if r_content.get('href_run_details'):
req_ids = re.search(r'(?<=rid=)\d+', r_content['href_run_details']).group()
if req_ids.isdigit():
req_ids = int(req_ids)
return req_ids, r_content
def cats_submit_problem():
pass
@authorization.check_authorization_for_cats
def cats_check_solution_status(req_ids: int):
url = f'{settings.CATS_URL}main.pl?f=api_get_request_state;req_ids={req_ids};json=1;'
url += f'sid={authorization.cats_sid()}'
r = requests.get(url)
if r.status_code != 200:
raise CatsAnswerCodeException(r)
data = r.json()
if data:
return data[0]['verdict'], data
@authorization.check_authorization_for_cats
def cats_get_problems_from_contest(contest_id):
url = f'{settings.CATS_URL}?f=problems;json=1;cid={contest_id};'
url += f'sid={authorization.cats_sid()}'
answer = requests.get(url)
if answer.status_code != 200:
raise CatsAnswerCodeException(answer)
data = json.loads(answer.content.decode('utf-8'))
# course_problems = CatsProblemSerializer(data=data.problems, many=True)
return data['problems']
def cats_get_problem_description_by_url(description_url):
url = f'{settings.CATS_URL}{description_url.lstrip("./")}'
headers = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/89.0.4356.6 Safari/537.36'
}
request = requests.request(method='get', url=url, headers=headers)
if request.status_code != 200:
raise CatsAnswerCodeException(request)
data = request.content.decode('utf-8')
return data
# def cats_get_problem_by_id(cats_id, user):
# pass
| 31.3625 | 89 | 0.684735 | import json
import re
import requests
from django.conf import settings
from cathie.exceptions import CatsAnswerCodeException
from cathie import authorization
def cats_check_status():
pass
@authorization.check_authorization_for_cats
def cats_submit_solution(source_text: str, problem_id: int, de_id: int, source=None):
url = f'{settings.CATS_URL}main.pl?f=api_submit_problem;json=1;'
url += f'sid={authorization.cats_sid()}'
data = {
'de_id': de_id,
'source_text': source_text,
'problem_id': problem_id
}
r = requests.post(url, data=data)
if r.status_code != 200:
raise CatsAnswerCodeException(r)
r_content = json.loads(r.content.decode('utf-8'))
req_ids = None
if r_content.get('href_run_details'):
req_ids = re.search(r'(?<=rid=)\d+', r_content['href_run_details']).group()
if req_ids.isdigit():
req_ids = int(req_ids)
return req_ids, r_content
def cats_submit_problem():
pass
@authorization.check_authorization_for_cats
def cats_check_solution_status(req_ids: int):
url = f'{settings.CATS_URL}main.pl?f=api_get_request_state;req_ids={req_ids};json=1;'
url += f'sid={authorization.cats_sid()}'
r = requests.get(url)
if r.status_code != 200:
raise CatsAnswerCodeException(r)
data = r.json()
if data:
return data[0]['verdict'], data
@authorization.check_authorization_for_cats
def cats_get_problems_from_contest(contest_id):
url = f'{settings.CATS_URL}?f=problems;json=1;cid={contest_id};'
url += f'sid={authorization.cats_sid()}'
answer = requests.get(url)
if answer.status_code != 200:
raise CatsAnswerCodeException(answer)
data = json.loads(answer.content.decode('utf-8'))
return data['problems']
def cats_get_problem_description_by_url(description_url):
url = f'{settings.CATS_URL}{description_url.lstrip("./")}'
headers = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/89.0.4356.6 Safari/537.36'
}
request = requests.request(method='get', url=url, headers=headers)
if request.status_code != 200:
raise CatsAnswerCodeException(request)
data = request.content.decode('utf-8')
return data
| true | true |
f736daf5a4ce1a12769452079bb4c85885389586 | 4,176 | py | Python | python/object_generator.py | matteovol/Cool-features | ca9969e23ba9bcc9a68cae64770cff90532d2368 | [
"MIT"
] | 2 | 2020-02-05T09:36:43.000Z | 2020-02-05T09:52:20.000Z | python/object_generator.py | matteovol/Cool-features | ca9969e23ba9bcc9a68cae64770cff90532d2368 | [
"MIT"
] | null | null | null | python/object_generator.py | matteovol/Cool-features | ca9969e23ba9bcc9a68cae64770cff90532d2368 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
from argparse import ArgumentParser
from os import path, listdir, stat
from os.path import isfile, join
from time import sleep
import subprocess
import threading
class Shell():
def __init__(self, objFolder, name, link):
self.__objFolder = objFolder
self.__name = name
self.__link = link
def run(self):
while True:
i = input("> ")
if i == "exit":
break
if i == "comp":
try:
subprocess.run(self.__genCommand())
except Exception:
pass
def __genCommand(self) -> [str]:
files = [self.__objFolder + "/" + f for f in listdir(self.__objFolder) if isfile(join(self.__objFolder, f)) and f.endswith(".cpp.o")]
s = "g++ -o " + self.__name + " " + " ".join(files) + " -l"
s += " -l".join(self.__link)
print(s)
return s.split(" ")
class ConfReader():
def __init__(self, file):
self.__file = file
self.__name = None
self.__link = None
self.__include = None
self.__options = None
self.__parse()
def __parse(self):
with open(self.__file) as fd:
lines = fd.readlines()
for l in lines:
t = l.strip().split(":")
if t[0] == "name":
self.__name = t[1]
elif t[0] == "link":
self.__link = t[1].split(",")
elif t[0] == "include":
self.__include = t[1].split(",")
elif t[0] == "options":
self.__options = t[1].split(",")
# print(self.__link)
# print(self.__include)
# print(self.__options)
def __bool__(self) -> bool:
return self.__link != None and self.__include != None and self.__options != None and self.__name != None
def getInfos(self) -> (str, str, str, str):
return self.__name, self.__link, self.__include, self.__options
class Compile():
def __init__(self, srcFolder, objFolder, link, include, options):
self.__srcFolder = srcFolder
self.__objFolder = objFolder
self.__link = link
self.__include = include
self.__options = options
self.__sources = self.__getSources()
#print(self.__sources)
def __getSources(self):
files = [f for f in listdir(self.__srcFolder) if isfile(join(self.__srcFolder, f)) and f.endswith(".cpp")]
dates = [int(stat(join(self.__srcFolder, f)).st_mtime) for f in files]
return dict(zip(files, dates))
def reload(self, e: threading.Event):
while not e.isSet():
for old, new in zip(self.__sources.items(), self.__getSources().items()):
name, oldStamp = old
_, newStamp = new
if newStamp > oldStamp:
subprocess.run(self.__createCommand(name))
self.__sources[name] = newStamp
sleep(1)
def __createCommand(self, name):
s = f"g++ -c {join(self.__srcFolder, name)}"
s += " -o " + join(self.__objFolder, name + ".o ")
s += " ".join(self.__options) + " -I"
s += " -I".join(self.__include) + " -l"
s += " -l".join(self.__link)
#print(s.split(" "))
return s.split(" ")
def main():
parse = ArgumentParser("Hot reloading for C++ files")
parse.add_argument("src", help="folder where sources are")
parse.add_argument("obj", help="folder where objects fille will be")
parse.add_argument("config", help="configuration file")
args = parse.parse_args()
srcFolder = path.abspath(args.src)
objFolder = path.abspath(args.obj)
c = ConfReader(args.config)
if c:
name, link, include, options = c.getInfos()
comp = Compile(srcFolder, objFolder, link, include, options)
e = threading.Event()
t = threading.Thread(target=comp.reload, args=(e,))
t.start()
s = Shell(objFolder, name, link)
s.run()
e.set()
t.join()
else:
print("Encule")
exit(84)
if __name__ == "__main__":
main() | 29.828571 | 141 | 0.54909 |
from argparse import ArgumentParser
from os import path, listdir, stat
from os.path import isfile, join
from time import sleep
import subprocess
import threading
class Shell():
def __init__(self, objFolder, name, link):
self.__objFolder = objFolder
self.__name = name
self.__link = link
def run(self):
while True:
i = input("> ")
if i == "exit":
break
if i == "comp":
try:
subprocess.run(self.__genCommand())
except Exception:
pass
def __genCommand(self) -> [str]:
files = [self.__objFolder + "/" + f for f in listdir(self.__objFolder) if isfile(join(self.__objFolder, f)) and f.endswith(".cpp.o")]
s = "g++ -o " + self.__name + " " + " ".join(files) + " -l"
s += " -l".join(self.__link)
print(s)
return s.split(" ")
class ConfReader():
def __init__(self, file):
self.__file = file
self.__name = None
self.__link = None
self.__include = None
self.__options = None
self.__parse()
def __parse(self):
with open(self.__file) as fd:
lines = fd.readlines()
for l in lines:
t = l.strip().split(":")
if t[0] == "name":
self.__name = t[1]
elif t[0] == "link":
self.__link = t[1].split(",")
elif t[0] == "include":
self.__include = t[1].split(",")
elif t[0] == "options":
self.__options = t[1].split(",")
def __bool__(self) -> bool:
return self.__link != None and self.__include != None and self.__options != None and self.__name != None
def getInfos(self) -> (str, str, str, str):
return self.__name, self.__link, self.__include, self.__options
class Compile():
def __init__(self, srcFolder, objFolder, link, include, options):
self.__srcFolder = srcFolder
self.__objFolder = objFolder
self.__link = link
self.__include = include
self.__options = options
self.__sources = self.__getSources()
def __getSources(self):
files = [f for f in listdir(self.__srcFolder) if isfile(join(self.__srcFolder, f)) and f.endswith(".cpp")]
dates = [int(stat(join(self.__srcFolder, f)).st_mtime) for f in files]
return dict(zip(files, dates))
def reload(self, e: threading.Event):
while not e.isSet():
for old, new in zip(self.__sources.items(), self.__getSources().items()):
name, oldStamp = old
_, newStamp = new
if newStamp > oldStamp:
subprocess.run(self.__createCommand(name))
self.__sources[name] = newStamp
sleep(1)
def __createCommand(self, name):
s = f"g++ -c {join(self.__srcFolder, name)}"
s += " -o " + join(self.__objFolder, name + ".o ")
s += " ".join(self.__options) + " -I"
s += " -I".join(self.__include) + " -l"
s += " -l".join(self.__link)
return s.split(" ")
def main():
parse = ArgumentParser("Hot reloading for C++ files")
parse.add_argument("src", help="folder where sources are")
parse.add_argument("obj", help="folder where objects fille will be")
parse.add_argument("config", help="configuration file")
args = parse.parse_args()
srcFolder = path.abspath(args.src)
objFolder = path.abspath(args.obj)
c = ConfReader(args.config)
if c:
name, link, include, options = c.getInfos()
comp = Compile(srcFolder, objFolder, link, include, options)
e = threading.Event()
t = threading.Thread(target=comp.reload, args=(e,))
t.start()
s = Shell(objFolder, name, link)
s.run()
e.set()
t.join()
else:
print("Encule")
exit(84)
if __name__ == "__main__":
main() | true | true |
f736db9961a6cd142b3495a9b8d293727ac74e18 | 2,955 | py | Python | tests/test_write_metrics_reports.py | dylanbuchi/MONAI | 1651f1b003b0ffae8b615d191952ad65ad091277 | [
"Apache-2.0"
] | 2,971 | 2019-10-16T23:53:16.000Z | 2022-03-31T20:58:24.000Z | tests/test_write_metrics_reports.py | dylanbuchi/MONAI | 1651f1b003b0ffae8b615d191952ad65ad091277 | [
"Apache-2.0"
] | 2,851 | 2020-01-10T16:23:44.000Z | 2022-03-31T22:14:53.000Z | tests/test_write_metrics_reports.py | dylanbuchi/MONAI | 1651f1b003b0ffae8b615d191952ad65ad091277 | [
"Apache-2.0"
] | 614 | 2020-01-14T19:18:01.000Z | 2022-03-31T14:06:14.000Z | # Copyright 2020 - 2021 MONAI Consortium
# 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 csv
import os
import tempfile
import unittest
import torch
from monai.handlers.utils import write_metrics_reports
class TestWriteMetricsReports(unittest.TestCase):
def test_content(self):
with tempfile.TemporaryDirectory() as tempdir:
write_metrics_reports(
save_dir=tempdir,
images=["filepath1", "filepath2"],
metrics={"metric1": 1, "metric2": 2},
metric_details={"metric3": torch.tensor([[1, 2], [2, 3]]), "metric4": torch.tensor([[5, 6], [7, 8]])},
summary_ops=["mean", "median", "max", "90percentile"],
deli="\t",
output_type="csv",
)
# check the metrics.csv and content
self.assertTrue(os.path.exists(os.path.join(tempdir, "metrics.csv")))
with open(os.path.join(tempdir, "metrics.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
self.assertEqual(row, [f"metric{i + 1}\t{i + 1}"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric3_raw.csv")))
# check the metric_raw.csv and content
with open(os.path.join(tempdir, "metric3_raw.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
if i > 0:
self.assertEqual(row, [f"filepath{i}\t{float(i)}\t{float(i + 1)}\t{i + 0.5}"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric3_summary.csv")))
# check the metric_summary.csv and content
with open(os.path.join(tempdir, "metric3_summary.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
if i == 1:
self.assertEqual(row, ["class0\t1.5000\t1.5000\t2.0000\t1.9000"])
elif i == 2:
self.assertEqual(row, ["class1\t2.5000\t2.5000\t3.0000\t2.9000"])
elif i == 3:
self.assertEqual(row, ["mean\t2.0000\t2.0000\t2.5000\t2.4000"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_raw.csv")))
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_summary.csv")))
if __name__ == "__main__":
unittest.main()
| 45.461538 | 118 | 0.589848 |
import csv
import os
import tempfile
import unittest
import torch
from monai.handlers.utils import write_metrics_reports
class TestWriteMetricsReports(unittest.TestCase):
def test_content(self):
with tempfile.TemporaryDirectory() as tempdir:
write_metrics_reports(
save_dir=tempdir,
images=["filepath1", "filepath2"],
metrics={"metric1": 1, "metric2": 2},
metric_details={"metric3": torch.tensor([[1, 2], [2, 3]]), "metric4": torch.tensor([[5, 6], [7, 8]])},
summary_ops=["mean", "median", "max", "90percentile"],
deli="\t",
output_type="csv",
)
self.assertTrue(os.path.exists(os.path.join(tempdir, "metrics.csv")))
with open(os.path.join(tempdir, "metrics.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
self.assertEqual(row, [f"metric{i + 1}\t{i + 1}"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric3_raw.csv")))
with open(os.path.join(tempdir, "metric3_raw.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
if i > 0:
self.assertEqual(row, [f"filepath{i}\t{float(i)}\t{float(i + 1)}\t{i + 0.5}"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric3_summary.csv")))
with open(os.path.join(tempdir, "metric3_summary.csv")) as f:
f_csv = csv.reader(f)
for i, row in enumerate(f_csv):
if i == 1:
self.assertEqual(row, ["class0\t1.5000\t1.5000\t2.0000\t1.9000"])
elif i == 2:
self.assertEqual(row, ["class1\t2.5000\t2.5000\t3.0000\t2.9000"])
elif i == 3:
self.assertEqual(row, ["mean\t2.0000\t2.0000\t2.5000\t2.4000"])
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_raw.csv")))
self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_summary.csv")))
if __name__ == "__main__":
unittest.main()
| true | true |
f736dbd7d95de4c652c34910da36f5ef978e97aa | 1,608 | py | Python | qlib/tests/__init__.py | guoqianyou/qlib | 184ce34a347123bf2cdd0bb48e2e110df9fe2722 | [
"MIT"
] | null | null | null | qlib/tests/__init__.py | guoqianyou/qlib | 184ce34a347123bf2cdd0bb48e2e110df9fe2722 | [
"MIT"
] | null | null | null | qlib/tests/__init__.py | guoqianyou/qlib | 184ce34a347123bf2cdd0bb48e2e110df9fe2722 | [
"MIT"
] | null | null | null | import unittest
from .data import GetData
from .. import init
from ..constant import REG_CN
class TestAutoData(unittest.TestCase):
_setup_kwargs = {}
provider_uri = "~/.qlib/qlib_data/cn_data_simple" # target_dir
provider_uri_1day = "~/.qlib/qlib_data/cn_data" # target_dir
provider_uri_1min = "~/.qlib/qlib_data/cn_data_1min"
@classmethod
def setUpClass(cls, enable_1d_type="simple", enable_1min=False) -> None:
# use default data
if enable_1d_type == "simple":
provider_uri_day = cls.provider_uri
name_day = "qlib_data_simple"
elif enable_1d_type == "full":
provider_uri_day = cls.provider_uri_1day
name_day = "qlib_data"
else:
raise NotImplementedError(f"This type of input is not supported")
GetData().qlib_data(
name=name_day,
region=REG_CN,
interval="1d",
target_dir=provider_uri_day,
delete_old=False,
exists_skip=True,
)
if enable_1min:
GetData().qlib_data(
name="qlib_data",
region=REG_CN,
interval="1min",
target_dir=cls.provider_uri_1min,
delete_old=False,
exists_skip=True,
)
provider_uri_map = {"1min": cls.provider_uri_1min, "day": provider_uri_day}
init(
provider_uri=provider_uri_map,
region=REG_CN,
expression_cache=None,
dataset_cache=None,
**cls._setup_kwargs,
)
| 29.777778 | 83 | 0.58209 | import unittest
from .data import GetData
from .. import init
from ..constant import REG_CN
class TestAutoData(unittest.TestCase):
_setup_kwargs = {}
provider_uri = "~/.qlib/qlib_data/cn_data_simple"
provider_uri_1day = "~/.qlib/qlib_data/cn_data"
provider_uri_1min = "~/.qlib/qlib_data/cn_data_1min"
@classmethod
def setUpClass(cls, enable_1d_type="simple", enable_1min=False) -> None:
if enable_1d_type == "simple":
provider_uri_day = cls.provider_uri
name_day = "qlib_data_simple"
elif enable_1d_type == "full":
provider_uri_day = cls.provider_uri_1day
name_day = "qlib_data"
else:
raise NotImplementedError(f"This type of input is not supported")
GetData().qlib_data(
name=name_day,
region=REG_CN,
interval="1d",
target_dir=provider_uri_day,
delete_old=False,
exists_skip=True,
)
if enable_1min:
GetData().qlib_data(
name="qlib_data",
region=REG_CN,
interval="1min",
target_dir=cls.provider_uri_1min,
delete_old=False,
exists_skip=True,
)
provider_uri_map = {"1min": cls.provider_uri_1min, "day": provider_uri_day}
init(
provider_uri=provider_uri_map,
region=REG_CN,
expression_cache=None,
dataset_cache=None,
**cls._setup_kwargs,
)
| true | true |
f736dc5818b4fe79384dd209aeee90e8c93232f9 | 7,764 | py | Python | indico/modules/events/surveys/forms.py | aiforrural/Digital-Events-Example | 628aaa8727b259b9367ac0ae1c5ba8e9e95eca82 | [
"MIT"
] | 1 | 2021-02-08T09:34:27.000Z | 2021-02-08T09:34:27.000Z | indico/modules/events/surveys/forms.py | pamirk/indico | c3b4e06b11cc21ad497f74d0b2ca901bc1b2a768 | [
"MIT"
] | null | null | null | indico/modules/events/surveys/forms.py | pamirk/indico | c3b4e06b11cc21ad497f74d0b2ca901bc1b2a768 | [
"MIT"
] | null | null | null | # This file is part of Indico.
# Copyright (C) 2002 - 2020 CERN
#
# Indico is free software; you can redistribute it and/or
# modify it under the terms of the MIT License; see the
# LICENSE file for more details.
from datetime import time
from flask import request
from markupsafe import escape
from wtforms.fields import BooleanField, HiddenField, SelectField, StringField, TextAreaField
from wtforms.fields.html5 import IntegerField
from wtforms.validators import DataRequired, NumberRange, Optional
from indico.core.db import db
from indico.modules.events.surveys.models.surveys import Survey
from indico.util.i18n import _
from indico.util.placeholders import get_missing_placeholders, render_placeholder_info
from indico.web.forms.base import IndicoForm
from indico.web.forms.fields import EmailListField, FileField, IndicoDateTimeField
from indico.web.forms.validators import HiddenUnless, LinkedDateTime, UsedIf, ValidationError
from indico.web.forms.widgets import CKEditorWidget, SwitchWidget
class SurveyForm(IndicoForm):
_notification_fields = ('notifications_enabled', 'notify_participants', 'start_notification_emails',
'new_submission_emails')
title = StringField(_("Title"), [DataRequired()], description=_("The title of the survey"))
introduction = TextAreaField(_("Introduction"), description=_("An introduction to be displayed before the survey"))
anonymous = BooleanField(_("Anonymous submissions"), widget=SwitchWidget(),
description=_("User information will not be attached to submissions"))
require_user = BooleanField(_("Only logged-in users"), [HiddenUnless('anonymous')], widget=SwitchWidget(),
description=_("Require users to be logged in for submitting the survey"))
limit_submissions = BooleanField(_("Limit submissions"), widget=SwitchWidget(),
description=_("Whether there is a submission cap"))
submission_limit = IntegerField(_("Capacity"),
[HiddenUnless('limit_submissions'), DataRequired(), NumberRange(min=1)],
description=_("Maximum number of submissions accepted"))
private = BooleanField(_("Private survey"), widget=SwitchWidget(),
description=_("Only selected people can answer the survey."))
partial_completion = BooleanField(_('Partial completion'), widget=SwitchWidget(),
description=_('Allow to save answers without submitting the survey.'))
notifications_enabled = BooleanField(_('Enabled'), widget=SwitchWidget(),
description=_('Send email notifications for specific events related to the '
'survey.'))
notify_participants = BooleanField(_('Participants'), [HiddenUnless('notifications_enabled', preserve_data=True)],
widget=SwitchWidget(),
description=_('Notify participants of the event when this survey starts.'))
start_notification_emails = EmailListField(_('Start notification recipients'),
[HiddenUnless('notifications_enabled', preserve_data=True)],
description=_('Email addresses to notify about the start of the survey'))
new_submission_emails = EmailListField(_('New submission notification recipients'),
[HiddenUnless('notifications_enabled', preserve_data=True)],
description=_('Email addresses to notify when a new submission is made'))
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
def validate_title(self, field):
query = (Survey.query.with_parent(self.event)
.filter(db.func.lower(Survey.title) == field.data.lower(),
Survey.title != field.object_data,
~Survey.is_deleted))
if query.count():
raise ValidationError(_('There is already a survey named "{}" on this event'.format(escape(field.data))))
def post_validate(self):
if not self.anonymous.data:
self.require_user.data = True
class ScheduleSurveyForm(IndicoForm):
start_dt = IndicoDateTimeField(_("Start"), [UsedIf(lambda form, field: form.allow_reschedule_start), Optional()],
default_time=time(0, 0),
description=_("Moment when the survey will open for submissions"))
end_dt = IndicoDateTimeField(_("End"), [Optional(), LinkedDateTime('start_dt')],
default_time=time(23, 59),
description=_("Moment when the survey will close"))
resend_start_notification = BooleanField(_('Resend start notification'), widget=SwitchWidget(),
description=_("Resend the survey start notification."))
def __init__(self, *args, **kwargs):
survey = kwargs.pop('survey')
self.allow_reschedule_start = kwargs.pop('allow_reschedule_start')
self.timezone = survey.event.timezone
super().__init__(*args, **kwargs)
if not survey.start_notification_sent or not self.allow_reschedule_start:
del self.resend_start_notification
class SectionForm(IndicoForm):
display_as_section = BooleanField(_("Display as section"), widget=SwitchWidget(), default=True,
description=_("Whether this is going to be displayed as a section or standalone"))
title = StringField(_('Title'), [HiddenUnless('display_as_section', preserve_data=True), DataRequired()],
description=_("The title of the section."))
description = TextAreaField(_('Description'), [HiddenUnless('display_as_section', preserve_data=True)],
description=_("The description text of the section."))
class TextForm(IndicoForm):
description = TextAreaField(_('Text'),
description=_("The text that should be displayed."))
class ImportQuestionnaireForm(IndicoForm):
json_file = FileField(_('File'), accepted_file_types="application/json,.json",
description=_("Choose a previously exported survey content to import. "
"Existing sections will be preserved."))
class InvitationForm(IndicoForm):
from_address = SelectField(_('From'), [DataRequired()])
subject = StringField(_('Subject'), [DataRequired()])
body = TextAreaField(_('Email body'), [DataRequired()], widget=CKEditorWidget(simple=True))
recipients = EmailListField(_('Recipients'), [DataRequired()], description=_('One email address per line.'))
copy_for_sender = BooleanField(_('Send copy to me'), widget=SwitchWidget())
submitted = HiddenField()
def __init__(self, *args, **kwargs):
event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.from_address.choices = list(event.get_allowed_sender_emails().items())
self.body.description = render_placeholder_info('survey-link-email', event=None, survey=None)
def is_submitted(self):
return super().is_submitted() and 'submitted' in request.form
def validate_body(self, field):
missing = get_missing_placeholders('survey-link-email', field.data, event=None, survey=None)
if missing:
raise ValidationError(_('Missing placeholders: {}').format(', '.join(missing)))
| 57.511111 | 120 | 0.646316 |
from datetime import time
from flask import request
from markupsafe import escape
from wtforms.fields import BooleanField, HiddenField, SelectField, StringField, TextAreaField
from wtforms.fields.html5 import IntegerField
from wtforms.validators import DataRequired, NumberRange, Optional
from indico.core.db import db
from indico.modules.events.surveys.models.surveys import Survey
from indico.util.i18n import _
from indico.util.placeholders import get_missing_placeholders, render_placeholder_info
from indico.web.forms.base import IndicoForm
from indico.web.forms.fields import EmailListField, FileField, IndicoDateTimeField
from indico.web.forms.validators import HiddenUnless, LinkedDateTime, UsedIf, ValidationError
from indico.web.forms.widgets import CKEditorWidget, SwitchWidget
class SurveyForm(IndicoForm):
_notification_fields = ('notifications_enabled', 'notify_participants', 'start_notification_emails',
'new_submission_emails')
title = StringField(_("Title"), [DataRequired()], description=_("The title of the survey"))
introduction = TextAreaField(_("Introduction"), description=_("An introduction to be displayed before the survey"))
anonymous = BooleanField(_("Anonymous submissions"), widget=SwitchWidget(),
description=_("User information will not be attached to submissions"))
require_user = BooleanField(_("Only logged-in users"), [HiddenUnless('anonymous')], widget=SwitchWidget(),
description=_("Require users to be logged in for submitting the survey"))
limit_submissions = BooleanField(_("Limit submissions"), widget=SwitchWidget(),
description=_("Whether there is a submission cap"))
submission_limit = IntegerField(_("Capacity"),
[HiddenUnless('limit_submissions'), DataRequired(), NumberRange(min=1)],
description=_("Maximum number of submissions accepted"))
private = BooleanField(_("Private survey"), widget=SwitchWidget(),
description=_("Only selected people can answer the survey."))
partial_completion = BooleanField(_('Partial completion'), widget=SwitchWidget(),
description=_('Allow to save answers without submitting the survey.'))
notifications_enabled = BooleanField(_('Enabled'), widget=SwitchWidget(),
description=_('Send email notifications for specific events related to the '
'survey.'))
notify_participants = BooleanField(_('Participants'), [HiddenUnless('notifications_enabled', preserve_data=True)],
widget=SwitchWidget(),
description=_('Notify participants of the event when this survey starts.'))
start_notification_emails = EmailListField(_('Start notification recipients'),
[HiddenUnless('notifications_enabled', preserve_data=True)],
description=_('Email addresses to notify about the start of the survey'))
new_submission_emails = EmailListField(_('New submission notification recipients'),
[HiddenUnless('notifications_enabled', preserve_data=True)],
description=_('Email addresses to notify when a new submission is made'))
def __init__(self, *args, **kwargs):
self.event = kwargs.pop('event')
super().__init__(*args, **kwargs)
def validate_title(self, field):
query = (Survey.query.with_parent(self.event)
.filter(db.func.lower(Survey.title) == field.data.lower(),
Survey.title != field.object_data,
~Survey.is_deleted))
if query.count():
raise ValidationError(_('There is already a survey named "{}" on this event'.format(escape(field.data))))
def post_validate(self):
if not self.anonymous.data:
self.require_user.data = True
class ScheduleSurveyForm(IndicoForm):
start_dt = IndicoDateTimeField(_("Start"), [UsedIf(lambda form, field: form.allow_reschedule_start), Optional()],
default_time=time(0, 0),
description=_("Moment when the survey will open for submissions"))
end_dt = IndicoDateTimeField(_("End"), [Optional(), LinkedDateTime('start_dt')],
default_time=time(23, 59),
description=_("Moment when the survey will close"))
resend_start_notification = BooleanField(_('Resend start notification'), widget=SwitchWidget(),
description=_("Resend the survey start notification."))
def __init__(self, *args, **kwargs):
survey = kwargs.pop('survey')
self.allow_reschedule_start = kwargs.pop('allow_reschedule_start')
self.timezone = survey.event.timezone
super().__init__(*args, **kwargs)
if not survey.start_notification_sent or not self.allow_reschedule_start:
del self.resend_start_notification
class SectionForm(IndicoForm):
display_as_section = BooleanField(_("Display as section"), widget=SwitchWidget(), default=True,
description=_("Whether this is going to be displayed as a section or standalone"))
title = StringField(_('Title'), [HiddenUnless('display_as_section', preserve_data=True), DataRequired()],
description=_("The title of the section."))
description = TextAreaField(_('Description'), [HiddenUnless('display_as_section', preserve_data=True)],
description=_("The description text of the section."))
class TextForm(IndicoForm):
description = TextAreaField(_('Text'),
description=_("The text that should be displayed."))
class ImportQuestionnaireForm(IndicoForm):
json_file = FileField(_('File'), accepted_file_types="application/json,.json",
description=_("Choose a previously exported survey content to import. "
"Existing sections will be preserved."))
class InvitationForm(IndicoForm):
from_address = SelectField(_('From'), [DataRequired()])
subject = StringField(_('Subject'), [DataRequired()])
body = TextAreaField(_('Email body'), [DataRequired()], widget=CKEditorWidget(simple=True))
recipients = EmailListField(_('Recipients'), [DataRequired()], description=_('One email address per line.'))
copy_for_sender = BooleanField(_('Send copy to me'), widget=SwitchWidget())
submitted = HiddenField()
def __init__(self, *args, **kwargs):
event = kwargs.pop('event')
super().__init__(*args, **kwargs)
self.from_address.choices = list(event.get_allowed_sender_emails().items())
self.body.description = render_placeholder_info('survey-link-email', event=None, survey=None)
def is_submitted(self):
return super().is_submitted() and 'submitted' in request.form
def validate_body(self, field):
missing = get_missing_placeholders('survey-link-email', field.data, event=None, survey=None)
if missing:
raise ValidationError(_('Missing placeholders: {}').format(', '.join(missing)))
| true | true |
f736dc6eda5cdf2f372f771c251bcac5aeaeded3 | 199 | py | Python | tensorflow/compiler/xla/libceed/example_program.py | DiffeoInvariant/tensorflow | 9ba7472c978176cba92dde8bf4d96434536c645c | [
"Apache-2.0"
] | null | null | null | tensorflow/compiler/xla/libceed/example_program.py | DiffeoInvariant/tensorflow | 9ba7472c978176cba92dde8bf4d96434536c645c | [
"Apache-2.0"
] | null | null | null | tensorflow/compiler/xla/libceed/example_program.py | DiffeoInvariant/tensorflow | 9ba7472c978176cba92dde8bf4d96434536c645c | [
"Apache-2.0"
] | null | null | null | from jax import numpy as jnp
import jax
def cr_fn(x, y):
return x @ x + y * y
cr_jac = jax.jacfwd(cr_fn, argnums=(0, 1))
cr_jac(jnp.eye(3), jnp.eye(3))
def cr_j(x, y):
return cr_jac(x, y)
| 16.583333 | 42 | 0.623116 | from jax import numpy as jnp
import jax
def cr_fn(x, y):
return x @ x + y * y
cr_jac = jax.jacfwd(cr_fn, argnums=(0, 1))
cr_jac(jnp.eye(3), jnp.eye(3))
def cr_j(x, y):
return cr_jac(x, y)
| true | true |
f736dc96e0e4f7315128230d31d8082642034b93 | 909 | py | Python | tests/__init__.py | Klaws--/pronounceable | c59fd0f6b392f40df68089628740f2edc418bad2 | [
"MIT"
] | 6 | 2019-02-20T04:46:04.000Z | 2021-07-26T09:23:08.000Z | tests/__init__.py | Klaws--/pronounceable | c59fd0f6b392f40df68089628740f2edc418bad2 | [
"MIT"
] | 1 | 2020-06-01T09:57:05.000Z | 2020-06-01T09:57:05.000Z | tests/__init__.py | Klaws--/pronounceable | c59fd0f6b392f40df68089628740f2edc418bad2 | [
"MIT"
] | 3 | 2020-06-19T09:32:22.000Z | 2021-09-09T21:18:18.000Z | from time import time
from functools import partial
def timeit(func, validator=lambda x: True, rep=50):
time_record = []
i = 0
try:
for i in range(rep):
print('Running test {} of {}'.format(i+1, rep))
start = time()
x = func()
if validator(x):
time_record.append(time() - start)
else:
print('Test failed!')
except KeyboardInterrupt:
pass
print('Success {} of {}'.format(len(time_record), i+1))
if len(time_record) > 0:
average = sum(time_record)/len(time_record)
if isinstance(func, partial):
function_name = func.func.__qualname__
elif callable(func):
function_name = func.__qualname__
else:
function_name = ''
print('{:.4f} seconds per {}'.format(average, function_name))
return time_record
| 28.40625 | 69 | 0.555556 | from time import time
from functools import partial
def timeit(func, validator=lambda x: True, rep=50):
time_record = []
i = 0
try:
for i in range(rep):
print('Running test {} of {}'.format(i+1, rep))
start = time()
x = func()
if validator(x):
time_record.append(time() - start)
else:
print('Test failed!')
except KeyboardInterrupt:
pass
print('Success {} of {}'.format(len(time_record), i+1))
if len(time_record) > 0:
average = sum(time_record)/len(time_record)
if isinstance(func, partial):
function_name = func.func.__qualname__
elif callable(func):
function_name = func.__qualname__
else:
function_name = ''
print('{:.4f} seconds per {}'.format(average, function_name))
return time_record
| true | true |
f736dd047f681606305f1e14dd89e2e3cc8192ff | 2,141 | py | Python | users/migrations/0001_initial.py | Surveyor-Jr/zimaps | d4def072b50c7018e9f7800a36c2050f28791cc2 | [
"CC-BY-4.0"
] | null | null | null | users/migrations/0001_initial.py | Surveyor-Jr/zimaps | d4def072b50c7018e9f7800a36c2050f28791cc2 | [
"CC-BY-4.0"
] | null | null | null | users/migrations/0001_initial.py | Surveyor-Jr/zimaps | d4def072b50c7018e9f7800a36c2050f28791cc2 | [
"CC-BY-4.0"
] | null | null | null | # Generated by Django 3.1.6 on 2021-02-23 17:50
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(blank=True, max_length=50, null=True)),
('last_name', models.CharField(blank=True, max_length=50, null=True)),
('gender', models.CharField(blank=True, choices=[('female', 'Female'), ('male', 'Male'), ('prefer_not_to_say', 'Prefer Not To Say')], max_length=50, null=True)),
('date_of_birth', models.DateField(blank=True, help_text='in the format: YYYY-MM-DD', null=True)),
('bio', models.TextField(default='Just a freelancer looking to earn some extra money by offering services', help_text='Help people get to know you better. Remember a good bio attracts me clients!')),
('phone_number', models.IntegerField(blank=True, help_text='Enter your phone number. E.g 776887606', null=True)),
('twitter', models.CharField(blank=True, help_text="Enter your Twitter username without including the '@' character. E.g surveyor_jr", max_length=20, null=True)),
('facebook', models.URLField(blank=True, help_text='Paste in your Facebook profile URL here. Navigate to your profile and copy the URL displayed', null=True)),
('linkedin', models.URLField(blank=True, help_text='Paste in your LinkedIn Profile URL. Navigate to your profile and copy the URL displayed', null=True)),
('image', models.ImageField(default='default.png', upload_to='profile_pics')),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)),
],
),
]
| 61.171429 | 215 | 0.663241 |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(blank=True, max_length=50, null=True)),
('last_name', models.CharField(blank=True, max_length=50, null=True)),
('gender', models.CharField(blank=True, choices=[('female', 'Female'), ('male', 'Male'), ('prefer_not_to_say', 'Prefer Not To Say')], max_length=50, null=True)),
('date_of_birth', models.DateField(blank=True, help_text='in the format: YYYY-MM-DD', null=True)),
('bio', models.TextField(default='Just a freelancer looking to earn some extra money by offering services', help_text='Help people get to know you better. Remember a good bio attracts me clients!')),
('phone_number', models.IntegerField(blank=True, help_text='Enter your phone number. E.g 776887606', null=True)),
('twitter', models.CharField(blank=True, help_text="Enter your Twitter username without including the '@' character. E.g surveyor_jr", max_length=20, null=True)),
('facebook', models.URLField(blank=True, help_text='Paste in your Facebook profile URL here. Navigate to your profile and copy the URL displayed', null=True)),
('linkedin', models.URLField(blank=True, help_text='Paste in your LinkedIn Profile URL. Navigate to your profile and copy the URL displayed', null=True)),
('image', models.ImageField(default='default.png', upload_to='profile_pics')),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='profile', to=settings.AUTH_USER_MODEL)),
],
),
]
| true | true |
f736dd7b4f444de8ee37a4d4038f6af0457bba19 | 1,629 | py | Python | Linguaggio Python/03 - Strutture Dati/b_list_comprehension.py | anhelus/informatica-dm-uniba-ex | 5d67cdd3c7c8b8955723f1787e26637d59358daa | [
"MIT"
] | null | null | null | Linguaggio Python/03 - Strutture Dati/b_list_comprehension.py | anhelus/informatica-dm-uniba-ex | 5d67cdd3c7c8b8955723f1787e26637d59358daa | [
"MIT"
] | null | null | null | Linguaggio Python/03 - Strutture Dati/b_list_comprehension.py | anhelus/informatica-dm-uniba-ex | 5d67cdd3c7c8b8955723f1787e26637d59358daa | [
"MIT"
] | null | null | null | def estrai_classico(lista, lettera):
output = []
for l in lista:
if l[0] == lettera:
output.append(l)
return output
def quadrati(val_massimo):
output = []
for v in range(val_massimo):
output.append(v ** 2)
return output
def quadrato(numero):
return numero ** 2
def costruisci_pari(i):
return "{} è pari".format(i)
if __name__ == "__main__":
lista_nomi = ["Jax Teller", "Walter White", "Billy Butcher", "Luke Skywalker", "Bobby Singer", "Johnny Lawrence"]
# Trovo tutti i nomi che iniziano per "J" con il metodo classico
lista_nomi_j = estrai_classico(lista_nomi, "J")
# print(lista_nomi_j)
# Trovo tutti i nomi che iniziano per "B" con una list comprehension
lista_nomi_b = [nome for nome in lista_nomi if nome[0] == "B"]
# print(lista_nomi_b)
# print("Lista dei nomi: {}".format(lista_nomi))
# print("Lista dei nomi che iniziano con J (con metodo classico): {}".format(lista_nomi_j))
# print("Lista dei nomi che iniziano con B (con list comprehension): {}".format(lista_nomi_b))
# print("Lista dei quadrati fino a 10 calcolata col metodo classico: {}".format(quadrati(10)))
# print("Lista dei quadrati calcolata con list comprehension: {}".format([i ** 2 for i in range(10)]))
# # Mostro "pari" per i numeri pari, e "dispari" per i numeri dispari; utile per l'if/else
lista_pari_dispari = [costruisci_pari(i) if i % 2 == 0 else "{} è dispari".format(i) for i in range(1, 10)]
print("Lista pari e dispari: {}".format(lista_pari_dispari))
# # Esempio di assignment expression
# fib = [0, 1]
# fib += [(fib := [fib[1], fib[0] + fib[1]]) and fib[1] for i in range(10)]
# print(fib) | 33.244898 | 114 | 0.683855 | def estrai_classico(lista, lettera):
output = []
for l in lista:
if l[0] == lettera:
output.append(l)
return output
def quadrati(val_massimo):
output = []
for v in range(val_massimo):
output.append(v ** 2)
return output
def quadrato(numero):
return numero ** 2
def costruisci_pari(i):
return "{} è pari".format(i)
if __name__ == "__main__":
lista_nomi = ["Jax Teller", "Walter White", "Billy Butcher", "Luke Skywalker", "Bobby Singer", "Johnny Lawrence"]
lista_nomi_j = estrai_classico(lista_nomi, "J")
lista_nomi_b = [nome for nome in lista_nomi if nome[0] == "B"]
or i in range(1, 10)]
print("Lista pari e dispari: {}".format(lista_pari_dispari))
# # Esempio di assignment expression
# fib = [0, 1]
# fib += [(fib := [fib[1], fib[0] + fib[1]]) and fib[1] for i in range(10)]
# print(fib) | true | true |
f736dd7bf6aba110680f6c323784ae0f64055193 | 28,248 | py | Python | ansible/library/kolla_docker.py | aliate/kolla-ansible | c93ec09dc7d31ecec74eedac493592a22ee395d2 | [
"Apache-2.0"
] | 2 | 2020-12-20T01:17:48.000Z | 2021-01-02T15:06:19.000Z | ansible/library/kolla_docker.py | aliate/kolla-ansible | c93ec09dc7d31ecec74eedac493592a22ee395d2 | [
"Apache-2.0"
] | null | null | null | ansible/library/kolla_docker.py | aliate/kolla-ansible | c93ec09dc7d31ecec74eedac493592a22ee395d2 | [
"Apache-2.0"
] | 2 | 2020-08-28T19:08:16.000Z | 2021-01-02T15:06:21.000Z | #!/usr/bin/env python
# Copyright 2015 Sam Yaple
#
# 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.
DOCUMENTATION = '''
---
module: kolla_docker
short_description: Module for controlling Docker
description:
- A module targeting at controlling Docker as used by Kolla.
options:
common_options:
description:
- A dict containing common params such as login info
required: False
type: dict
default: dict()
action:
description:
- The action the module should take
required: True
type: str
choices:
- compare_container
- compare_image
- create_volume
- get_container_env
- get_container_state
- pull_image
- remove_container
- remove_volume
- recreate_or_restart_container
- restart_container
- start_container
- stop_container
api_version:
description:
- The version of the api for docker-py to use when contacting docker
required: False
type: str
default: auto
auth_email:
description:
- The email address used to authenticate
required: False
type: str
auth_password:
description:
- The password used to authenticate
required: False
type: str
auth_registry:
description:
- The registry to authenticate
required: False
type: str
auth_username:
description:
- The username used to authenticate
required: False
type: str
detach:
description:
- Detach from the container after it is created
required: False
default: True
type: bool
name:
description:
- Name of the container or volume to manage
required: False
type: str
environment:
description:
- The environment to set for the container
required: False
type: dict
image:
description:
- Name of the docker image
required: False
type: str
ipc_mode:
description:
- Set docker ipc namespace
required: False
type: str
default: None
choices:
- host
cap_add:
description:
- Add capabilities to docker container
required: False
type: list
default: list()
security_opt:
description:
- Set container security profile
required: False
type: list
default: list()
labels:
description:
- List of labels to apply to container
required: False
type: dict
default: dict()
pid_mode:
description:
- Set docker pid namespace
required: False
type: str
default: None
choices:
- host
privileged:
description:
- Set the container to privileged
required: False
default: False
type: bool
remove_on_exit:
description:
- When not detaching from container, remove on successful exit
required: False
default: True
type: bool
restart_policy:
description:
- Determine what docker does when the container exits
required: False
type: str
choices:
- never
- on-failure
- always
- unless-stopped
restart_retries:
description:
- How many times to attempt a restart if restart_policy is set
type: int
default: 10
volumes:
description:
- Set volumes for docker to use
required: False
type: list
volumes_from:
description:
- Name or id of container(s) to use volumes from
required: True
type: list
state:
description:
- Check container status
required: False
type: str
choices:
- running
- exited
- paused
author: Sam Yaple
'''
EXAMPLES = '''
- hosts: kolla_docker
tasks:
- name: Start container
kolla_docker:
image: ubuntu
name: test_container
action: start_container
- name: Remove container
kolla_docker:
name: test_container
action: remove_container
- name: Pull image without starting container
kolla_docker:
action: pull_image
image: private-registry.example.com:5000/ubuntu
- name: Create named volume
action: create_volume
name: name_of_volume
- name: Remove named volume
action: remove_volume
name: name_of_volume
'''
import json
import os
import traceback
import docker
def get_docker_client():
return docker.APIClient
class DockerWorker(object):
def __init__(self, module):
self.module = module
self.params = self.module.params
self.changed = False
# TLS not fully implemented
# tls_config = self.generate_tls()
options = {
'version': self.params.get('api_version')
}
self.dc = get_docker_client()(**options)
def generate_tls(self):
tls = {'verify': self.params.get('tls_verify')}
tls_cert = self.params.get('tls_cert'),
tls_key = self.params.get('tls_key'),
tls_cacert = self.params.get('tls_cacert')
if tls['verify']:
if tls_cert:
self.check_file(tls_cert)
self.check_file(tls_key)
tls['client_cert'] = (tls_cert, tls_key)
if tls_cacert:
self.check_file(tls_cacert)
tls['verify'] = tls_cacert
return docker.tls.TLSConfig(**tls)
def check_file(self, path):
if not os.path.isfile(path):
self.module.fail_json(
failed=True,
msg='There is no file at "{}"'.format(path)
)
if not os.access(path, os.R_OK):
self.module.fail_json(
failed=True,
msg='Permission denied for file at "{}"'.format(path)
)
def check_image(self):
find_image = ':'.join(self.parse_image())
for image in self.dc.images():
repo_tags = image.get('RepoTags')
if not repo_tags:
continue
for image_name in repo_tags:
if image_name == find_image:
return image
def check_volume(self):
for vol in self.dc.volumes()['Volumes'] or list():
if vol['Name'] == self.params.get('name'):
return vol
def check_container(self):
find_name = '/{}'.format(self.params.get('name'))
for cont in self.dc.containers(all=True):
if find_name in cont['Names']:
return cont
def get_container_info(self):
container = self.check_container()
if not container:
return None
return self.dc.inspect_container(self.params.get('name'))
def compare_container(self):
container = self.check_container()
if not container or self.check_container_differs():
self.changed = True
return self.changed
def check_container_differs(self):
container_info = self.get_container_info()
return (
self.compare_cap_add(container_info) or
self.compare_security_opt(container_info) or
self.compare_image(container_info) or
self.compare_ipc_mode(container_info) or
self.compare_labels(container_info) or
self.compare_privileged(container_info) or
self.compare_pid_mode(container_info) or
self.compare_volumes(container_info) or
self.compare_volumes_from(container_info) or
self.compare_environment(container_info) or
self.compare_container_state(container_info)
)
def compare_ipc_mode(self, container_info):
new_ipc_mode = self.params.get('ipc_mode')
current_ipc_mode = container_info['HostConfig'].get('IpcMode')
if not current_ipc_mode:
current_ipc_mode = None
# only check IPC mode if it is specified
if new_ipc_mode is not None and new_ipc_mode != current_ipc_mode:
return True
return False
def compare_cap_add(self, container_info):
new_cap_add = self.params.get('cap_add', list())
current_cap_add = container_info['HostConfig'].get('CapAdd',
list())
if not current_cap_add:
current_cap_add = list()
if set(new_cap_add).symmetric_difference(set(current_cap_add)):
return True
def compare_security_opt(self, container_info):
ipc_mode = self.params.get('ipc_mode')
pid_mode = self.params.get('pid_mode')
privileged = self.params.get('privileged', False)
# NOTE(jeffrey4l) security opt is disabled when using host ipc mode or
# host pid mode or privileged. So no need to compare security opts
if ipc_mode == 'host' or pid_mode == 'host' or privileged:
return False
new_sec_opt = self.params.get('security_opt', list())
current_sec_opt = container_info['HostConfig'].get('SecurityOpt',
list())
if not current_sec_opt:
current_sec_opt = list()
if set(new_sec_opt).symmetric_difference(set(current_sec_opt)):
return True
def compare_pid_mode(self, container_info):
new_pid_mode = self.params.get('pid_mode')
current_pid_mode = container_info['HostConfig'].get('PidMode')
if not current_pid_mode:
current_pid_mode = None
if new_pid_mode != current_pid_mode:
return True
def compare_privileged(self, container_info):
new_privileged = self.params.get('privileged')
current_privileged = container_info['HostConfig']['Privileged']
if new_privileged != current_privileged:
return True
def compare_image(self, container_info=None):
container_info = container_info or self.get_container_info()
parse_repository_tag = docker.utils.parse_repository_tag
if not container_info:
return True
new_image = self.check_image()
current_image = container_info['Image']
if not new_image:
return True
if new_image['Id'] != current_image:
return True
# NOTE(Jeffrey4l) when new image and the current image have
# the same id, but the tag name different.
elif (parse_repository_tag(container_info['Config']['Image']) !=
parse_repository_tag(self.params.get('image'))):
return True
def compare_labels(self, container_info):
new_labels = self.params.get('labels')
current_labels = container_info['Config'].get('Labels', dict())
image_labels = self.check_image().get('Labels', dict())
for k, v in image_labels.items():
if k in new_labels:
if v != new_labels[k]:
return True
else:
del current_labels[k]
if new_labels != current_labels:
return True
def compare_volumes_from(self, container_info):
new_vols_from = self.params.get('volumes_from')
current_vols_from = container_info['HostConfig'].get('VolumesFrom')
if not new_vols_from:
new_vols_from = list()
if not current_vols_from:
current_vols_from = list()
if set(current_vols_from).symmetric_difference(set(new_vols_from)):
return True
def compare_volumes(self, container_info):
volumes, binds = self.generate_volumes()
current_vols = container_info['Config'].get('Volumes')
current_binds = container_info['HostConfig'].get('Binds')
if not volumes:
volumes = list()
if not current_vols:
current_vols = list()
if not current_binds:
current_binds = list()
if set(volumes).symmetric_difference(set(current_vols)):
return True
new_binds = list()
if binds:
for k, v in binds.items():
new_binds.append("{}:{}:{}".format(k, v['bind'], v['mode']))
if set(new_binds).symmetric_difference(set(current_binds)):
return True
def compare_environment(self, container_info):
if self.params.get('environment'):
current_env = dict()
for kv in container_info['Config'].get('Env', list()):
k, v = kv.split('=', 1)
current_env.update({k: v})
for k, v in self.params.get('environment').items():
if k not in current_env:
return True
if current_env[k] != v:
return True
def compare_container_state(self, container_info):
new_state = self.params.get('state')
current_state = container_info['State'].get('Status')
if new_state != current_state:
return True
def parse_image(self):
full_image = self.params.get('image')
if '/' in full_image:
registry, image = full_image.split('/', 1)
else:
image = full_image
if ':' in image:
return full_image.rsplit(':', 1)
else:
return full_image, 'latest'
def get_image_id(self):
full_image = self.params.get('image')
image = self.dc.images(name=full_image, quiet=True)
return image[0] if len(image) == 1 else None
def pull_image(self):
if self.params.get('auth_username'):
self.dc.login(
username=self.params.get('auth_username'),
password=self.params.get('auth_password'),
registry=self.params.get('auth_registry'),
email=self.params.get('auth_email')
)
image, tag = self.parse_image()
old_image_id = self.get_image_id()
statuses = [
json.loads(line.strip().decode('utf-8')) for line in self.dc.pull(
repository=image, tag=tag, stream=True
)
]
for status in reversed(statuses):
if 'error' in status:
if status['error'].endswith('not found'):
self.module.fail_json(
msg="The requested image does not exist: {}:{}".format(
image, tag),
failed=True
)
else:
self.module.fail_json(
msg="Unknown error message: {}".format(
status['error']),
failed=True
)
new_image_id = self.get_image_id()
self.changed = old_image_id != new_image_id
def remove_container(self):
if self.check_container():
self.changed = True
# NOTE(jeffrey4l): in some case, docker failed to remove container
# filesystem and raise error. But the container info is
# disappeared already. If this happens, assume the container is
# removed.
try:
self.dc.remove_container(
container=self.params.get('name'),
force=True
)
except docker.errors.APIError:
if self.check_container():
raise
def generate_volumes(self):
volumes = self.params.get('volumes')
if not volumes:
return None, None
vol_list = list()
vol_dict = dict()
for vol in volumes:
if len(vol) == 0:
continue
if ':' not in vol:
vol_list.append(vol)
continue
split_vol = vol.split(':')
if (len(split_vol) == 2
and ('/' not in split_vol[0] or '/' in split_vol[1])):
split_vol.append('rw')
vol_list.append(split_vol[1])
vol_dict.update({
split_vol[0]: {
'bind': split_vol[1],
'mode': split_vol[2]
}
})
return vol_list, vol_dict
def build_host_config(self, binds):
options = {
'network_mode': 'host',
'ipc_mode': self.params.get('ipc_mode'),
'cap_add': self.params.get('cap_add'),
'security_opt': self.params.get('security_opt'),
'pid_mode': self.params.get('pid_mode'),
'privileged': self.params.get('privileged'),
'volumes_from': self.params.get('volumes_from')
}
if self.params.get('restart_policy') in ['on-failure',
'always',
'unless-stopped']:
policy = {'Name': self.params.get('restart_policy')}
# NOTE(Jeffrey4l): MaximumRetryCount is only needed for on-failure
# policy
if self.params.get('restart_policy') == 'on-failure':
retries = self.params.get('restart_retries')
policy['MaximumRetryCount'] = retries
options['restart_policy'] = policy
if binds:
options['binds'] = binds
return self.dc.create_host_config(**options)
def _inject_env_var(self, environment_info):
newenv = {
'KOLLA_SERVICE_NAME': self.params.get('name').replace('_', '-')
}
environment_info.update(newenv)
return environment_info
def _format_env_vars(self):
env = self._inject_env_var(self.params.get('environment'))
return {k: "" if env[k] is None else env[k] for k in env}
def build_container_options(self):
volumes, binds = self.generate_volumes()
return {
'detach': self.params.get('detach'),
'environment': self._format_env_vars(),
'host_config': self.build_host_config(binds),
'labels': self.params.get('labels'),
'image': self.params.get('image'),
'name': self.params.get('name'),
'volumes': volumes,
'tty': True
}
def create_container(self):
self.changed = True
options = self.build_container_options()
self.dc.create_container(**options)
def recreate_or_restart_container(self):
self.changed = True
container = self.check_container()
# get config_strategy from env
environment = self.params.get('environment')
config_strategy = environment.get('KOLLA_CONFIG_STRATEGY')
if not container:
self.start_container()
return
# If config_strategy is COPY_ONCE or container's parameters are
# changed, try to start a new one.
if config_strategy == 'COPY_ONCE' or self.check_container_differs():
self.stop_container()
self.remove_container()
self.start_container()
elif config_strategy == 'COPY_ALWAYS':
self.restart_container()
def start_container(self):
if not self.check_image():
self.pull_image()
container = self.check_container()
if container and self.check_container_differs():
self.stop_container()
self.remove_container()
container = self.check_container()
if not container:
self.create_container()
container = self.check_container()
if not container['Status'].startswith('Up '):
self.changed = True
self.dc.start(container=self.params.get('name'))
# We do not want to detach so we wait around for container to exit
if not self.params.get('detach'):
rc = self.dc.wait(self.params.get('name'))
# NOTE(jeffrey4l): since python docker package 3.0, wait return a
# dict all the time.
if isinstance(rc, dict):
rc = rc['StatusCode']
if rc != 0:
self.module.fail_json(
failed=True,
changed=True,
msg="Container exited with non-zero return code %s" % rc
)
if self.params.get('remove_on_exit'):
self.stop_container()
self.remove_container()
def get_container_env(self):
name = self.params.get('name')
info = self.get_container_info()
if not info:
self.module.fail_json(msg="No such container: {}".format(name))
else:
envs = dict()
for env in info['Config']['Env']:
if '=' in env:
key, value = env.split('=', 1)
else:
key, value = env, ''
envs[key] = value
self.module.exit_json(**envs)
def get_container_state(self):
name = self.params.get('name')
info = self.get_container_info()
if not info:
self.module.fail_json(msg="No such container: {}".format(name))
else:
self.module.exit_json(**info['State'])
def stop_container(self):
name = self.params.get('name')
graceful_timeout = self.params.get('graceful_timeout')
if not graceful_timeout:
graceful_timeout = 10
container = self.check_container()
if not container:
self.module.fail_json(
msg="No such container: {} to stop".format(name))
elif not container['Status'].startswith('Exited '):
self.changed = True
self.dc.stop(name, timeout=graceful_timeout)
def restart_container(self):
name = self.params.get('name')
graceful_timeout = self.params.get('graceful_timeout')
if not graceful_timeout:
graceful_timeout = 10
info = self.get_container_info()
if not info:
self.module.fail_json(
msg="No such container: {}".format(name))
else:
self.changed = True
self.dc.stop(name, timeout=graceful_timeout)
self.dc.start(name)
def create_volume(self):
if not self.check_volume():
self.changed = True
self.dc.create_volume(name=self.params.get('name'), driver='local')
def remove_volume(self):
if self.check_volume():
self.changed = True
try:
self.dc.remove_volume(name=self.params.get('name'))
except docker.errors.APIError as e:
if e.response.status_code == 409:
self.module.fail_json(
failed=True,
msg="Volume named '{}' is currently in-use".format(
self.params.get('name')
)
)
raise
def generate_module():
# NOTE(jeffrey4l): add empty string '' to choices let us use
# pid_mode: "{{ service.pid_mode | default ('') }}" in yaml
argument_spec = dict(
common_options=dict(required=False, type='dict', default=dict()),
action=dict(required=True, type='str',
choices=['compare_container', 'compare_image',
'create_volume', 'get_container_env',
'get_container_state', 'pull_image',
'recreate_or_restart_container',
'remove_container', 'remove_volume',
'restart_container', 'start_container',
'stop_container']),
api_version=dict(required=False, type='str', default='auto'),
auth_email=dict(required=False, type='str'),
auth_password=dict(required=False, type='str', no_log=True),
auth_registry=dict(required=False, type='str'),
auth_username=dict(required=False, type='str'),
detach=dict(required=False, type='bool', default=True),
labels=dict(required=False, type='dict', default=dict()),
name=dict(required=False, type='str'),
environment=dict(required=False, type='dict'),
image=dict(required=False, type='str'),
ipc_mode=dict(required=False, type='str', choices=['',
'host',
'private',
'shareable']),
cap_add=dict(required=False, type='list', default=list()),
security_opt=dict(required=False, type='list', default=list()),
pid_mode=dict(required=False, type='str', choices=['host', '']),
privileged=dict(required=False, type='bool', default=False),
graceful_timeout=dict(required=False, type='int', default=10),
remove_on_exit=dict(required=False, type='bool', default=True),
restart_policy=dict(required=False, type='str', choices=[
'no',
'never',
'on-failure',
'always',
'unless-stopped']),
restart_retries=dict(required=False, type='int', default=10),
state=dict(required=False, type='str', default='running',
choices=['running',
'exited',
'paused']),
tls_verify=dict(required=False, type='bool', default=False),
tls_cert=dict(required=False, type='str'),
tls_key=dict(required=False, type='str'),
tls_cacert=dict(required=False, type='str'),
volumes=dict(required=False, type='list'),
volumes_from=dict(required=False, type='list')
)
required_if = [
['action', 'pull_image', ['image']],
['action', 'start_container', ['image', 'name']],
['action', 'compare_container', ['name']],
['action', 'compare_image', ['name']],
['action', 'create_volume', ['name']],
['action', 'get_container_env', ['name']],
['action', 'get_container_state', ['name']],
['action', 'recreate_or_restart_container', ['name']],
['action', 'remove_container', ['name']],
['action', 'remove_volume', ['name']],
['action', 'restart_container', ['name']],
['action', 'stop_container', ['name']]
]
module = AnsibleModule(
argument_spec=argument_spec,
required_if=required_if,
bypass_checks=False
)
new_args = module.params.pop('common_options', dict())
# NOTE(jeffrey4l): merge the environment
env = module.params.pop('environment', dict())
if env:
new_args['environment'].update(env)
for key, value in module.params.items():
if key in new_args and value is None:
continue
new_args[key] = value
# if pid_mode = ""/None/False, remove it
if not new_args.get('pid_mode', False):
new_args.pop('pid_mode', None)
# if ipc_mode = ""/None/False, remove it
if not new_args.get('ipc_mode', False):
new_args.pop('ipc_mode', None)
module.params = new_args
return module
def main():
module = generate_module()
try:
dw = DockerWorker(module)
# TODO(inc0): We keep it bool to have ansible deal with consistent
# types. If we ever add method that will have to return some
# meaningful data, we need to refactor all methods to return dicts.
result = bool(getattr(dw, module.params.get('action'))())
module.exit_json(changed=dw.changed, result=result)
except Exception:
module.exit_json(failed=True, changed=True,
msg=repr(traceback.format_exc()))
# import module snippets
from ansible.module_utils.basic import * # noqa
if __name__ == '__main__':
main()
| 33.708831 | 79 | 0.574448 |
DOCUMENTATION = '''
---
module: kolla_docker
short_description: Module for controlling Docker
description:
- A module targeting at controlling Docker as used by Kolla.
options:
common_options:
description:
- A dict containing common params such as login info
required: False
type: dict
default: dict()
action:
description:
- The action the module should take
required: True
type: str
choices:
- compare_container
- compare_image
- create_volume
- get_container_env
- get_container_state
- pull_image
- remove_container
- remove_volume
- recreate_or_restart_container
- restart_container
- start_container
- stop_container
api_version:
description:
- The version of the api for docker-py to use when contacting docker
required: False
type: str
default: auto
auth_email:
description:
- The email address used to authenticate
required: False
type: str
auth_password:
description:
- The password used to authenticate
required: False
type: str
auth_registry:
description:
- The registry to authenticate
required: False
type: str
auth_username:
description:
- The username used to authenticate
required: False
type: str
detach:
description:
- Detach from the container after it is created
required: False
default: True
type: bool
name:
description:
- Name of the container or volume to manage
required: False
type: str
environment:
description:
- The environment to set for the container
required: False
type: dict
image:
description:
- Name of the docker image
required: False
type: str
ipc_mode:
description:
- Set docker ipc namespace
required: False
type: str
default: None
choices:
- host
cap_add:
description:
- Add capabilities to docker container
required: False
type: list
default: list()
security_opt:
description:
- Set container security profile
required: False
type: list
default: list()
labels:
description:
- List of labels to apply to container
required: False
type: dict
default: dict()
pid_mode:
description:
- Set docker pid namespace
required: False
type: str
default: None
choices:
- host
privileged:
description:
- Set the container to privileged
required: False
default: False
type: bool
remove_on_exit:
description:
- When not detaching from container, remove on successful exit
required: False
default: True
type: bool
restart_policy:
description:
- Determine what docker does when the container exits
required: False
type: str
choices:
- never
- on-failure
- always
- unless-stopped
restart_retries:
description:
- How many times to attempt a restart if restart_policy is set
type: int
default: 10
volumes:
description:
- Set volumes for docker to use
required: False
type: list
volumes_from:
description:
- Name or id of container(s) to use volumes from
required: True
type: list
state:
description:
- Check container status
required: False
type: str
choices:
- running
- exited
- paused
author: Sam Yaple
'''
EXAMPLES = '''
- hosts: kolla_docker
tasks:
- name: Start container
kolla_docker:
image: ubuntu
name: test_container
action: start_container
- name: Remove container
kolla_docker:
name: test_container
action: remove_container
- name: Pull image without starting container
kolla_docker:
action: pull_image
image: private-registry.example.com:5000/ubuntu
- name: Create named volume
action: create_volume
name: name_of_volume
- name: Remove named volume
action: remove_volume
name: name_of_volume
'''
import json
import os
import traceback
import docker
def get_docker_client():
return docker.APIClient
class DockerWorker(object):
def __init__(self, module):
self.module = module
self.params = self.module.params
self.changed = False
options = {
'version': self.params.get('api_version')
}
self.dc = get_docker_client()(**options)
def generate_tls(self):
tls = {'verify': self.params.get('tls_verify')}
tls_cert = self.params.get('tls_cert'),
tls_key = self.params.get('tls_key'),
tls_cacert = self.params.get('tls_cacert')
if tls['verify']:
if tls_cert:
self.check_file(tls_cert)
self.check_file(tls_key)
tls['client_cert'] = (tls_cert, tls_key)
if tls_cacert:
self.check_file(tls_cacert)
tls['verify'] = tls_cacert
return docker.tls.TLSConfig(**tls)
def check_file(self, path):
if not os.path.isfile(path):
self.module.fail_json(
failed=True,
msg='There is no file at "{}"'.format(path)
)
if not os.access(path, os.R_OK):
self.module.fail_json(
failed=True,
msg='Permission denied for file at "{}"'.format(path)
)
def check_image(self):
find_image = ':'.join(self.parse_image())
for image in self.dc.images():
repo_tags = image.get('RepoTags')
if not repo_tags:
continue
for image_name in repo_tags:
if image_name == find_image:
return image
def check_volume(self):
for vol in self.dc.volumes()['Volumes'] or list():
if vol['Name'] == self.params.get('name'):
return vol
def check_container(self):
find_name = '/{}'.format(self.params.get('name'))
for cont in self.dc.containers(all=True):
if find_name in cont['Names']:
return cont
def get_container_info(self):
container = self.check_container()
if not container:
return None
return self.dc.inspect_container(self.params.get('name'))
def compare_container(self):
container = self.check_container()
if not container or self.check_container_differs():
self.changed = True
return self.changed
def check_container_differs(self):
container_info = self.get_container_info()
return (
self.compare_cap_add(container_info) or
self.compare_security_opt(container_info) or
self.compare_image(container_info) or
self.compare_ipc_mode(container_info) or
self.compare_labels(container_info) or
self.compare_privileged(container_info) or
self.compare_pid_mode(container_info) or
self.compare_volumes(container_info) or
self.compare_volumes_from(container_info) or
self.compare_environment(container_info) or
self.compare_container_state(container_info)
)
def compare_ipc_mode(self, container_info):
new_ipc_mode = self.params.get('ipc_mode')
current_ipc_mode = container_info['HostConfig'].get('IpcMode')
if not current_ipc_mode:
current_ipc_mode = None
if new_ipc_mode is not None and new_ipc_mode != current_ipc_mode:
return True
return False
def compare_cap_add(self, container_info):
new_cap_add = self.params.get('cap_add', list())
current_cap_add = container_info['HostConfig'].get('CapAdd',
list())
if not current_cap_add:
current_cap_add = list()
if set(new_cap_add).symmetric_difference(set(current_cap_add)):
return True
def compare_security_opt(self, container_info):
ipc_mode = self.params.get('ipc_mode')
pid_mode = self.params.get('pid_mode')
privileged = self.params.get('privileged', False)
if ipc_mode == 'host' or pid_mode == 'host' or privileged:
return False
new_sec_opt = self.params.get('security_opt', list())
current_sec_opt = container_info['HostConfig'].get('SecurityOpt',
list())
if not current_sec_opt:
current_sec_opt = list()
if set(new_sec_opt).symmetric_difference(set(current_sec_opt)):
return True
def compare_pid_mode(self, container_info):
new_pid_mode = self.params.get('pid_mode')
current_pid_mode = container_info['HostConfig'].get('PidMode')
if not current_pid_mode:
current_pid_mode = None
if new_pid_mode != current_pid_mode:
return True
def compare_privileged(self, container_info):
new_privileged = self.params.get('privileged')
current_privileged = container_info['HostConfig']['Privileged']
if new_privileged != current_privileged:
return True
def compare_image(self, container_info=None):
container_info = container_info or self.get_container_info()
parse_repository_tag = docker.utils.parse_repository_tag
if not container_info:
return True
new_image = self.check_image()
current_image = container_info['Image']
if not new_image:
return True
if new_image['Id'] != current_image:
return True
elif (parse_repository_tag(container_info['Config']['Image']) !=
parse_repository_tag(self.params.get('image'))):
return True
def compare_labels(self, container_info):
new_labels = self.params.get('labels')
current_labels = container_info['Config'].get('Labels', dict())
image_labels = self.check_image().get('Labels', dict())
for k, v in image_labels.items():
if k in new_labels:
if v != new_labels[k]:
return True
else:
del current_labels[k]
if new_labels != current_labels:
return True
def compare_volumes_from(self, container_info):
new_vols_from = self.params.get('volumes_from')
current_vols_from = container_info['HostConfig'].get('VolumesFrom')
if not new_vols_from:
new_vols_from = list()
if not current_vols_from:
current_vols_from = list()
if set(current_vols_from).symmetric_difference(set(new_vols_from)):
return True
def compare_volumes(self, container_info):
volumes, binds = self.generate_volumes()
current_vols = container_info['Config'].get('Volumes')
current_binds = container_info['HostConfig'].get('Binds')
if not volumes:
volumes = list()
if not current_vols:
current_vols = list()
if not current_binds:
current_binds = list()
if set(volumes).symmetric_difference(set(current_vols)):
return True
new_binds = list()
if binds:
for k, v in binds.items():
new_binds.append("{}:{}:{}".format(k, v['bind'], v['mode']))
if set(new_binds).symmetric_difference(set(current_binds)):
return True
def compare_environment(self, container_info):
if self.params.get('environment'):
current_env = dict()
for kv in container_info['Config'].get('Env', list()):
k, v = kv.split('=', 1)
current_env.update({k: v})
for k, v in self.params.get('environment').items():
if k not in current_env:
return True
if current_env[k] != v:
return True
def compare_container_state(self, container_info):
new_state = self.params.get('state')
current_state = container_info['State'].get('Status')
if new_state != current_state:
return True
def parse_image(self):
full_image = self.params.get('image')
if '/' in full_image:
registry, image = full_image.split('/', 1)
else:
image = full_image
if ':' in image:
return full_image.rsplit(':', 1)
else:
return full_image, 'latest'
def get_image_id(self):
full_image = self.params.get('image')
image = self.dc.images(name=full_image, quiet=True)
return image[0] if len(image) == 1 else None
def pull_image(self):
if self.params.get('auth_username'):
self.dc.login(
username=self.params.get('auth_username'),
password=self.params.get('auth_password'),
registry=self.params.get('auth_registry'),
email=self.params.get('auth_email')
)
image, tag = self.parse_image()
old_image_id = self.get_image_id()
statuses = [
json.loads(line.strip().decode('utf-8')) for line in self.dc.pull(
repository=image, tag=tag, stream=True
)
]
for status in reversed(statuses):
if 'error' in status:
if status['error'].endswith('not found'):
self.module.fail_json(
msg="The requested image does not exist: {}:{}".format(
image, tag),
failed=True
)
else:
self.module.fail_json(
msg="Unknown error message: {}".format(
status['error']),
failed=True
)
new_image_id = self.get_image_id()
self.changed = old_image_id != new_image_id
def remove_container(self):
if self.check_container():
self.changed = True
try:
self.dc.remove_container(
container=self.params.get('name'),
force=True
)
except docker.errors.APIError:
if self.check_container():
raise
def generate_volumes(self):
volumes = self.params.get('volumes')
if not volumes:
return None, None
vol_list = list()
vol_dict = dict()
for vol in volumes:
if len(vol) == 0:
continue
if ':' not in vol:
vol_list.append(vol)
continue
split_vol = vol.split(':')
if (len(split_vol) == 2
and ('/' not in split_vol[0] or '/' in split_vol[1])):
split_vol.append('rw')
vol_list.append(split_vol[1])
vol_dict.update({
split_vol[0]: {
'bind': split_vol[1],
'mode': split_vol[2]
}
})
return vol_list, vol_dict
def build_host_config(self, binds):
options = {
'network_mode': 'host',
'ipc_mode': self.params.get('ipc_mode'),
'cap_add': self.params.get('cap_add'),
'security_opt': self.params.get('security_opt'),
'pid_mode': self.params.get('pid_mode'),
'privileged': self.params.get('privileged'),
'volumes_from': self.params.get('volumes_from')
}
if self.params.get('restart_policy') in ['on-failure',
'always',
'unless-stopped']:
policy = {'Name': self.params.get('restart_policy')}
if self.params.get('restart_policy') == 'on-failure':
retries = self.params.get('restart_retries')
policy['MaximumRetryCount'] = retries
options['restart_policy'] = policy
if binds:
options['binds'] = binds
return self.dc.create_host_config(**options)
def _inject_env_var(self, environment_info):
newenv = {
'KOLLA_SERVICE_NAME': self.params.get('name').replace('_', '-')
}
environment_info.update(newenv)
return environment_info
def _format_env_vars(self):
env = self._inject_env_var(self.params.get('environment'))
return {k: "" if env[k] is None else env[k] for k in env}
def build_container_options(self):
volumes, binds = self.generate_volumes()
return {
'detach': self.params.get('detach'),
'environment': self._format_env_vars(),
'host_config': self.build_host_config(binds),
'labels': self.params.get('labels'),
'image': self.params.get('image'),
'name': self.params.get('name'),
'volumes': volumes,
'tty': True
}
def create_container(self):
self.changed = True
options = self.build_container_options()
self.dc.create_container(**options)
def recreate_or_restart_container(self):
self.changed = True
container = self.check_container()
environment = self.params.get('environment')
config_strategy = environment.get('KOLLA_CONFIG_STRATEGY')
if not container:
self.start_container()
return
# changed, try to start a new one.
if config_strategy == 'COPY_ONCE' or self.check_container_differs():
self.stop_container()
self.remove_container()
self.start_container()
elif config_strategy == 'COPY_ALWAYS':
self.restart_container()
def start_container(self):
if not self.check_image():
self.pull_image()
container = self.check_container()
if container and self.check_container_differs():
self.stop_container()
self.remove_container()
container = self.check_container()
if not container:
self.create_container()
container = self.check_container()
if not container['Status'].startswith('Up '):
self.changed = True
self.dc.start(container=self.params.get('name'))
# We do not want to detach so we wait around for container to exit
if not self.params.get('detach'):
rc = self.dc.wait(self.params.get('name'))
# NOTE(jeffrey4l): since python docker package 3.0, wait return a
# dict all the time.
if isinstance(rc, dict):
rc = rc['StatusCode']
if rc != 0:
self.module.fail_json(
failed=True,
changed=True,
msg="Container exited with non-zero return code %s" % rc
)
if self.params.get('remove_on_exit'):
self.stop_container()
self.remove_container()
def get_container_env(self):
name = self.params.get('name')
info = self.get_container_info()
if not info:
self.module.fail_json(msg="No such container: {}".format(name))
else:
envs = dict()
for env in info['Config']['Env']:
if '=' in env:
key, value = env.split('=', 1)
else:
key, value = env, ''
envs[key] = value
self.module.exit_json(**envs)
def get_container_state(self):
name = self.params.get('name')
info = self.get_container_info()
if not info:
self.module.fail_json(msg="No such container: {}".format(name))
else:
self.module.exit_json(**info['State'])
def stop_container(self):
name = self.params.get('name')
graceful_timeout = self.params.get('graceful_timeout')
if not graceful_timeout:
graceful_timeout = 10
container = self.check_container()
if not container:
self.module.fail_json(
msg="No such container: {} to stop".format(name))
elif not container['Status'].startswith('Exited '):
self.changed = True
self.dc.stop(name, timeout=graceful_timeout)
def restart_container(self):
name = self.params.get('name')
graceful_timeout = self.params.get('graceful_timeout')
if not graceful_timeout:
graceful_timeout = 10
info = self.get_container_info()
if not info:
self.module.fail_json(
msg="No such container: {}".format(name))
else:
self.changed = True
self.dc.stop(name, timeout=graceful_timeout)
self.dc.start(name)
def create_volume(self):
if not self.check_volume():
self.changed = True
self.dc.create_volume(name=self.params.get('name'), driver='local')
def remove_volume(self):
if self.check_volume():
self.changed = True
try:
self.dc.remove_volume(name=self.params.get('name'))
except docker.errors.APIError as e:
if e.response.status_code == 409:
self.module.fail_json(
failed=True,
msg="Volume named '{}' is currently in-use".format(
self.params.get('name')
)
)
raise
def generate_module():
# NOTE(jeffrey4l): add empty string '' to choices let us use
# pid_mode: "{{ service.pid_mode | default ('') }}" in yaml
argument_spec = dict(
common_options=dict(required=False, type='dict', default=dict()),
action=dict(required=True, type='str',
choices=['compare_container', 'compare_image',
'create_volume', 'get_container_env',
'get_container_state', 'pull_image',
'recreate_or_restart_container',
'remove_container', 'remove_volume',
'restart_container', 'start_container',
'stop_container']),
api_version=dict(required=False, type='str', default='auto'),
auth_email=dict(required=False, type='str'),
auth_password=dict(required=False, type='str', no_log=True),
auth_registry=dict(required=False, type='str'),
auth_username=dict(required=False, type='str'),
detach=dict(required=False, type='bool', default=True),
labels=dict(required=False, type='dict', default=dict()),
name=dict(required=False, type='str'),
environment=dict(required=False, type='dict'),
image=dict(required=False, type='str'),
ipc_mode=dict(required=False, type='str', choices=['',
'host',
'private',
'shareable']),
cap_add=dict(required=False, type='list', default=list()),
security_opt=dict(required=False, type='list', default=list()),
pid_mode=dict(required=False, type='str', choices=['host', '']),
privileged=dict(required=False, type='bool', default=False),
graceful_timeout=dict(required=False, type='int', default=10),
remove_on_exit=dict(required=False, type='bool', default=True),
restart_policy=dict(required=False, type='str', choices=[
'no',
'never',
'on-failure',
'always',
'unless-stopped']),
restart_retries=dict(required=False, type='int', default=10),
state=dict(required=False, type='str', default='running',
choices=['running',
'exited',
'paused']),
tls_verify=dict(required=False, type='bool', default=False),
tls_cert=dict(required=False, type='str'),
tls_key=dict(required=False, type='str'),
tls_cacert=dict(required=False, type='str'),
volumes=dict(required=False, type='list'),
volumes_from=dict(required=False, type='list')
)
required_if = [
['action', 'pull_image', ['image']],
['action', 'start_container', ['image', 'name']],
['action', 'compare_container', ['name']],
['action', 'compare_image', ['name']],
['action', 'create_volume', ['name']],
['action', 'get_container_env', ['name']],
['action', 'get_container_state', ['name']],
['action', 'recreate_or_restart_container', ['name']],
['action', 'remove_container', ['name']],
['action', 'remove_volume', ['name']],
['action', 'restart_container', ['name']],
['action', 'stop_container', ['name']]
]
module = AnsibleModule(
argument_spec=argument_spec,
required_if=required_if,
bypass_checks=False
)
new_args = module.params.pop('common_options', dict())
# NOTE(jeffrey4l): merge the environment
env = module.params.pop('environment', dict())
if env:
new_args['environment'].update(env)
for key, value in module.params.items():
if key in new_args and value is None:
continue
new_args[key] = value
# if pid_mode = ""/None/False, remove it
if not new_args.get('pid_mode', False):
new_args.pop('pid_mode', None)
# if ipc_mode = ""/None/False, remove it
if not new_args.get('ipc_mode', False):
new_args.pop('ipc_mode', None)
module.params = new_args
return module
def main():
module = generate_module()
try:
dw = DockerWorker(module)
# TODO(inc0): We keep it bool to have ansible deal with consistent
# types. If we ever add method that will have to return some
# meaningful data, we need to refactor all methods to return dicts.
result = bool(getattr(dw, module.params.get('action'))())
module.exit_json(changed=dw.changed, result=result)
except Exception:
module.exit_json(failed=True, changed=True,
msg=repr(traceback.format_exc()))
# import module snippets
from ansible.module_utils.basic import * # noqa
if __name__ == '__main__':
main()
| true | true |
f736ddb5af589c7e9907ddc383214ea30188f1df | 1,492 | py | Python | refinery/units/formats/xml.py | bronxc/refinery | 9448facf48a0008f27861dd1a5ee8f5218e6bb86 | [
"BSD-3-Clause"
] | null | null | null | refinery/units/formats/xml.py | bronxc/refinery | 9448facf48a0008f27861dd1a5ee8f5218e6bb86 | [
"BSD-3-Clause"
] | null | null | null | refinery/units/formats/xml.py | bronxc/refinery | 9448facf48a0008f27861dd1a5ee8f5218e6bb86 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from collections import defaultdict
from refinery.lib.structures import MemoryFile
from refinery.lib import xml
from refinery.units.sinks.ppxml import ppxml
from refinery.units.formats import PathExtractorUnit, UnpackResult
class xtxml(PathExtractorUnit):
"""
Extract values from an XML document.
"""
_strict_path_matching = True
def unpack(self, data):
def walk(node: xml.XMLNode, *path: str):
def extract(node: xml.XMLNode = node):
if not node.children:
return node.content.encode(self.codec)
with MemoryFile() as stream:
node.write(stream)
return bytes(stream.getbuffer() | ppxml)
children_by_tag = defaultdict(list)
for child in node.children:
children_by_tag[child.tag].append(child)
yield UnpackResult('/'.join(path), extract, **node.attributes)
for tag, children in children_by_tag.items():
if len(children) == 1:
yield from walk(children[0], *path, tag)
continue
width = len(F'{len(children):X}')
for k, child in enumerate(children):
yield from walk(child, *path, F'{tag}[0x{k:0{width}X}]')
root = xml.parse(data)
name = root.tag or 'xml'
yield from walk(root, name)
| 38.25641 | 77 | 0.569035 |
from collections import defaultdict
from refinery.lib.structures import MemoryFile
from refinery.lib import xml
from refinery.units.sinks.ppxml import ppxml
from refinery.units.formats import PathExtractorUnit, UnpackResult
class xtxml(PathExtractorUnit):
_strict_path_matching = True
def unpack(self, data):
def walk(node: xml.XMLNode, *path: str):
def extract(node: xml.XMLNode = node):
if not node.children:
return node.content.encode(self.codec)
with MemoryFile() as stream:
node.write(stream)
return bytes(stream.getbuffer() | ppxml)
children_by_tag = defaultdict(list)
for child in node.children:
children_by_tag[child.tag].append(child)
yield UnpackResult('/'.join(path), extract, **node.attributes)
for tag, children in children_by_tag.items():
if len(children) == 1:
yield from walk(children[0], *path, tag)
continue
width = len(F'{len(children):X}')
for k, child in enumerate(children):
yield from walk(child, *path, F'{tag}[0x{k:0{width}X}]')
root = xml.parse(data)
name = root.tag or 'xml'
yield from walk(root, name)
| true | true |
f736ded3c5229af4007f2fc775635fd77c17beaa | 662 | py | Python | tmc/queries/q_get_adversaries_x_industry.py | fierytermite/tmc | fde0d09e5a9566042fdfa56585f04c073104ad1b | [
"Unlicense"
] | 17 | 2020-12-11T18:18:57.000Z | 2022-03-22T23:04:26.000Z | tmc/queries/q_get_adversaries_x_industry.py | fierytermite/tmc | fde0d09e5a9566042fdfa56585f04c073104ad1b | [
"Unlicense"
] | 16 | 2020-07-23T05:13:45.000Z | 2021-08-10T00:13:39.000Z | tmc/queries/q_get_adversaries_x_industry.py | fierytermite/tmc | fde0d09e5a9566042fdfa56585f04c073104ad1b | [
"Unlicense"
] | 2 | 2020-12-11T18:09:14.000Z | 2022-03-22T23:04:30.000Z | from flask import ( g, redirect, url_for )
from tmc.db import get_db, make_dicts
# Get list of all adversaries per industry available in the database.
def get_adversaries_x_industry():
db = get_db()
try:
db.row_factory = make_dicts
#db.row_factory = lambda cursor, row: {row: row[0]}
query = db.execute(
'SELECT adversary_id as ID, adversary_name as Name, adversary_identifiers as Identifiers, adversary_description as Description FROM adversaries ORDER BY Name').fetchall()
return query
except TypeError:
#embed()
return False #Change this for something more meaningful -- warning/alert | 41.375 | 182 | 0.700906 | from flask import ( g, redirect, url_for )
from tmc.db import get_db, make_dicts
def get_adversaries_x_industry():
db = get_db()
try:
db.row_factory = make_dicts
query = db.execute(
'SELECT adversary_id as ID, adversary_name as Name, adversary_identifiers as Identifiers, adversary_description as Description FROM adversaries ORDER BY Name').fetchall()
return query
except TypeError:
return False | true | true |
f736defdb74f74e4c79404d083e37c6c2a3e9141 | 9,945 | py | Python | lineFollowerArucoROS-checkpoint3.py | SR42-dev/line-following-robot-with-aruco-markers-obstacle-detection-and-turtlesim-publisher | d7dae86a4f1fdc56ab80193c218e25243e44e487 | [
"CC0-1.0"
] | null | null | null | lineFollowerArucoROS-checkpoint3.py | SR42-dev/line-following-robot-with-aruco-markers-obstacle-detection-and-turtlesim-publisher | d7dae86a4f1fdc56ab80193c218e25243e44e487 | [
"CC0-1.0"
] | null | null | null | lineFollowerArucoROS-checkpoint3.py | SR42-dev/line-following-robot-with-aruco-markers-obstacle-detection-and-turtlesim-publisher | d7dae86a4f1fdc56ab80193c218e25243e44e487 | [
"CC0-1.0"
] | null | null | null | import sys
import cv2
import math
import time
import rospy
import serial
import argparse
import numpy as np
from std_srvs.srv import Empty
from turtlesim.msg import Pose
from geometry_msgs.msg import Twist
# ROS movement global variables and function definitions
x = 0
y = 0
z = 0
yaw = 0
def poseCallback(pose_message):
global x, y, z, yaw
x = pose_message.x
y = pose_message.y
yaw = pose_message.theta
def move(speed, distance, is_forward):
velocity_message = Twist()
global x, y
x0 = x
y0 = y
if is_forward:
velocity_message.linear.x = abs(speed)
else:
velocity_message.linear.x = -abs(speed)
distance_moved = 0.0
loop_rate = rospy.Rate(10)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
while True:
rospy.loginfo('Turtlesim linear movement')
velocity_publisher.publish(velocity_message)
loop_rate.sleep()
distance_moved = distance_moved + abs(0.5 * math.sqrt(((x - x0) * 2) + ((y - y0) * 2)))
if not (distance_moved < distance):
rospy.loginfo("----Reached----")
break
velocity_message.linear.x = 0
velocity_publisher.publish(velocity_message)
def rotate(angular_speed_degree, relative_angle_degree, clockwise):
global yaw
velocity_message = Twist()
velocity_message.linear.x = 0
velocity_message.linear.y = 0
velocity_message.linear.z = 0
velocity_message.angular.x = 0
velocity_message.angular.y = 0
velocity_message.angular.z = 0
theta0 = yaw
angular_speed = math.radians(abs(angular_speed_degree))
if clockwise:
velocity_message.angular.z = -abs(angular_speed)
else:
velocity_message.angular.z = abs(angular_speed)
angle_moved = 0.0
loop_rate = rospy.Rate(10)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
t0 = rospy.Time.now().to_sec()
while True:
rospy.loginfo('Turtlesim rotation')
velocity_publisher.publish(velocity_message)
t1 = rospy.Time.now().to_sec()
current_angle_degree = (t1 - t0) * angular_speed_degree
loop_rate.sleep()
if current_angle_degree > relative_angle_degree:
rospy.loginfo('----Reached----')
break
velocity_message.angular.z = 0
velocity_publisher.publish(velocity_message)
rospy.init_node('turtlesim_motion_pose', anonymous=True)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
position_topic = '/turtle1/pose'
pose_subscriber = rospy.Subscriber(position_topic, Pose, poseCallback)
time.sleep(2)
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--type", type=str,
default="DICT_ARUCO_ORIGINAL",
help="type of ArUCo tag to detect")
args = vars(ap.parse_args())
# define names of each possible ArUco tag OpenCV supports
ARUCO_DICT = {
"DICT_4X4_50": cv2.aruco.DICT_4X4_50,
"DICT_4X4_100": cv2.aruco.DICT_4X4_100,
"DICT_4X4_250": cv2.aruco.DICT_4X4_250,
"DICT_4X4_1000": cv2.aruco.DICT_4X4_1000,
"DICT_5X5_50": cv2.aruco.DICT_5X5_50,
"DICT_5X5_100": cv2.aruco.DICT_5X5_100,
"DICT_5X5_250": cv2.aruco.DICT_5X5_250,
"DICT_5X5_1000": cv2.aruco.DICT_5X5_1000,
"DICT_6X6_50": cv2.aruco.DICT_6X6_50,
"DICT_6X6_100": cv2.aruco.DICT_6X6_100,
"DICT_6X6_250": cv2.aruco.DICT_6X6_250,
"DICT_6X6_1000": cv2.aruco.DICT_6X6_1000,
"DICT_7X7_50": cv2.aruco.DICT_7X7_50,
"DICT_7X7_100": cv2.aruco.DICT_7X7_100,
"DICT_7X7_250": cv2.aruco.DICT_7X7_250,
"DICT_7X7_1000": cv2.aruco.DICT_7X7_1000,
"DICT_ARUCO_ORIGINAL": cv2.aruco.DICT_ARUCO_ORIGINAL,
"DICT_APRILTAG_16h5": cv2.aruco.DICT_APRILTAG_16h5,
"DICT_APRILTAG_25h9": cv2.aruco.DICT_APRILTAG_25h9,
"DICT_APRILTAG_36h10": cv2.aruco.DICT_APRILTAG_36h10,
"DICT_APRILTAG_36h11": cv2.aruco.DICT_APRILTAG_36h11
}
# verify that the supplied ArUCo tag exists and is supported by
# OpenCV
if ARUCO_DICT.get(args["type"], None) is None:
print("[INFO] ArUCo tag of '{}' is not supported".format(
args["type"]))
sys.exit(0)
# load the ArUCo dictionary and grab the ArUCo parameters
print("[INFO] detecting '{}' tags...".format(args["type"]))
arucoDict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_4X4_250)
arucoParams = cv2.aruco.DetectorParameters_create()
# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
cap = cv2.VideoCapture(2)
c1 = 0
linecolor = (100, 215, 255)
lwr_red = np.array([0, 0, 0])
upper_red = np.array([179, 65, 55])
countl = False
countr = False
Ser = serial.Serial("/dev/ttyUSB0", baudrate=9600)
Ser.flush()
width = cap.get(3)
while True:
ret, frame = cap.read()
if not ret:
_, frame = cap.read()
# detect ArUco markers in the input frame
(corners, ids, rejected) = cv2.aruco.detectMarkers(frame,
arucoDict, parameters=arucoParams)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
kernel = np.ones((5, 5), np.uint8)
mask = cv2.inRange(hsv, lwr_red, upper_red)
mask = cv2.dilate(mask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=mask)
cnts, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
center = None
# verify at least one ArUco marker was detected
if len(corners) > 0:
# flatten the ArUco IDs list
ids = ids.flatten()
# loop over the detected ArUCo corners
for (markerCorner, markerID) in zip(corners, ids):
# extract the marker corners (which are always returned
# in top-left, top-right, bottom-right, and bottom-left
# order)
corners = markerCorner.reshape((4, 2))
(topLeft, topRight, bottomRight, bottomLeft) = corners
# convert each of the (x, y)-coordinate pairs to integers
topRight = (int(topRight[0]), int(topRight[1]))
bottomRight = (int(bottomRight[0]), int(bottomRight[1]))
bottomLeft = (int(bottomLeft[0]), int(bottomLeft[1]))
topLeft = (int(topLeft[0]), int(topLeft[1]))
# draw the bounding box of the ArUCo detection
cv2.line(frame, topLeft, topRight, (0, 255, 0), 2)
cv2.line(frame, topRight, bottomRight, (0, 255, 0), 2)
cv2.line(frame, bottomRight, bottomLeft, (0, 255, 0), 2)
cv2.line(frame, bottomLeft, topLeft, (0, 255, 0), 2)
# compute and draw the center (x, y)-coordinates of the
# ArUco marker
cX = int((topLeft[0] + bottomRight[0]) / 2.0)
cY = int((topLeft[1] + bottomRight[1]) / 2.0)
cv2.circle(frame, (cX, cY), 4, (0, 0, 255), -1)
# draw the ArUco marker ID on the frame
cv2.putText(frame, str(markerID),
(topLeft[0], topLeft[1] - 15),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
if markerID == 0:
if not countl:
countr = False
countl=True
i='f'
for lp in range(12):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
cv2.putText(frame, '<--', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
print("Left")
i = 'l' # left turn
for lp in range(6):
Ser.write(i.encode())
rotate(30, 10, False)
time.sleep(0.5)
i='f'
for lp in range(7):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
elif markerID == 1:
if not countr:
countl = False
countr=True
i='f'
for lp in range(8):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
i = 'r' # left turn
cv2.putText(frame, '-->', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
print("Right")
for lp in range(6):
Ser.write(i.encode())
rotate(30, 10, True)
time.sleep(0.5)
else:
i = 'x'
Ser.write(i.encode())
print("Invalid")
if len(cnts) > 0:
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius > 3:
# cv2.circle(frame, (int(x), int(y)), int(radius), (255, 255, 255), 2)
cv2.circle(frame, center, 5, linecolor, -1)
if (x > 0.25 * width and x <= 0.75 * width):
print('Forward')
cv2.putText(frame, '^', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
Ser.write(b'f')
move(1, 1, True)
# time.sleep(0.01)
else:
print("Track Not Visible")
c1 += 1
if (c1 == 5):
print("Backward")
cv2.putText(frame, 'V', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
Ser.write(b'b')
move(1, 1, False)
c1 = 0
time.sleep(0.2)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cap.release()
Ser.close()
cv2.destroyAllWindows()
break | 34.53125 | 109 | 0.583409 | import sys
import cv2
import math
import time
import rospy
import serial
import argparse
import numpy as np
from std_srvs.srv import Empty
from turtlesim.msg import Pose
from geometry_msgs.msg import Twist
x = 0
y = 0
z = 0
yaw = 0
def poseCallback(pose_message):
global x, y, z, yaw
x = pose_message.x
y = pose_message.y
yaw = pose_message.theta
def move(speed, distance, is_forward):
velocity_message = Twist()
global x, y
x0 = x
y0 = y
if is_forward:
velocity_message.linear.x = abs(speed)
else:
velocity_message.linear.x = -abs(speed)
distance_moved = 0.0
loop_rate = rospy.Rate(10)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
while True:
rospy.loginfo('Turtlesim linear movement')
velocity_publisher.publish(velocity_message)
loop_rate.sleep()
distance_moved = distance_moved + abs(0.5 * math.sqrt(((x - x0) * 2) + ((y - y0) * 2)))
if not (distance_moved < distance):
rospy.loginfo("----Reached----")
break
velocity_message.linear.x = 0
velocity_publisher.publish(velocity_message)
def rotate(angular_speed_degree, relative_angle_degree, clockwise):
global yaw
velocity_message = Twist()
velocity_message.linear.x = 0
velocity_message.linear.y = 0
velocity_message.linear.z = 0
velocity_message.angular.x = 0
velocity_message.angular.y = 0
velocity_message.angular.z = 0
theta0 = yaw
angular_speed = math.radians(abs(angular_speed_degree))
if clockwise:
velocity_message.angular.z = -abs(angular_speed)
else:
velocity_message.angular.z = abs(angular_speed)
angle_moved = 0.0
loop_rate = rospy.Rate(10)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
t0 = rospy.Time.now().to_sec()
while True:
rospy.loginfo('Turtlesim rotation')
velocity_publisher.publish(velocity_message)
t1 = rospy.Time.now().to_sec()
current_angle_degree = (t1 - t0) * angular_speed_degree
loop_rate.sleep()
if current_angle_degree > relative_angle_degree:
rospy.loginfo('----Reached----')
break
velocity_message.angular.z = 0
velocity_publisher.publish(velocity_message)
rospy.init_node('turtlesim_motion_pose', anonymous=True)
cmd_vel_topic = '/turtle1/cmd_vel'
velocity_publisher = rospy.Publisher(cmd_vel_topic, Twist, queue_size=10)
position_topic = '/turtle1/pose'
pose_subscriber = rospy.Subscriber(position_topic, Pose, poseCallback)
time.sleep(2)
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--type", type=str,
default="DICT_ARUCO_ORIGINAL",
help="type of ArUCo tag to detect")
args = vars(ap.parse_args())
ARUCO_DICT = {
"DICT_4X4_50": cv2.aruco.DICT_4X4_50,
"DICT_4X4_100": cv2.aruco.DICT_4X4_100,
"DICT_4X4_250": cv2.aruco.DICT_4X4_250,
"DICT_4X4_1000": cv2.aruco.DICT_4X4_1000,
"DICT_5X5_50": cv2.aruco.DICT_5X5_50,
"DICT_5X5_100": cv2.aruco.DICT_5X5_100,
"DICT_5X5_250": cv2.aruco.DICT_5X5_250,
"DICT_5X5_1000": cv2.aruco.DICT_5X5_1000,
"DICT_6X6_50": cv2.aruco.DICT_6X6_50,
"DICT_6X6_100": cv2.aruco.DICT_6X6_100,
"DICT_6X6_250": cv2.aruco.DICT_6X6_250,
"DICT_6X6_1000": cv2.aruco.DICT_6X6_1000,
"DICT_7X7_50": cv2.aruco.DICT_7X7_50,
"DICT_7X7_100": cv2.aruco.DICT_7X7_100,
"DICT_7X7_250": cv2.aruco.DICT_7X7_250,
"DICT_7X7_1000": cv2.aruco.DICT_7X7_1000,
"DICT_ARUCO_ORIGINAL": cv2.aruco.DICT_ARUCO_ORIGINAL,
"DICT_APRILTAG_16h5": cv2.aruco.DICT_APRILTAG_16h5,
"DICT_APRILTAG_25h9": cv2.aruco.DICT_APRILTAG_25h9,
"DICT_APRILTAG_36h10": cv2.aruco.DICT_APRILTAG_36h10,
"DICT_APRILTAG_36h11": cv2.aruco.DICT_APRILTAG_36h11
}
if ARUCO_DICT.get(args["type"], None) is None:
print("[INFO] ArUCo tag of '{}' is not supported".format(
args["type"]))
sys.exit(0)
print("[INFO] detecting '{}' tags...".format(args["type"]))
arucoDict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_4X4_250)
arucoParams = cv2.aruco.DetectorParameters_create()
print("[INFO] starting video stream...")
cap = cv2.VideoCapture(2)
c1 = 0
linecolor = (100, 215, 255)
lwr_red = np.array([0, 0, 0])
upper_red = np.array([179, 65, 55])
countl = False
countr = False
Ser = serial.Serial("/dev/ttyUSB0", baudrate=9600)
Ser.flush()
width = cap.get(3)
while True:
ret, frame = cap.read()
if not ret:
_, frame = cap.read()
(corners, ids, rejected) = cv2.aruco.detectMarkers(frame,
arucoDict, parameters=arucoParams)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
kernel = np.ones((5, 5), np.uint8)
mask = cv2.inRange(hsv, lwr_red, upper_red)
mask = cv2.dilate(mask, kernel, iterations=1)
res = cv2.bitwise_and(frame, frame, mask=mask)
cnts, _ = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
center = None
if len(corners) > 0:
ids = ids.flatten()
for (markerCorner, markerID) in zip(corners, ids):
corners = markerCorner.reshape((4, 2))
(topLeft, topRight, bottomRight, bottomLeft) = corners
topRight = (int(topRight[0]), int(topRight[1]))
bottomRight = (int(bottomRight[0]), int(bottomRight[1]))
bottomLeft = (int(bottomLeft[0]), int(bottomLeft[1]))
topLeft = (int(topLeft[0]), int(topLeft[1]))
cv2.line(frame, topLeft, topRight, (0, 255, 0), 2)
cv2.line(frame, topRight, bottomRight, (0, 255, 0), 2)
cv2.line(frame, bottomRight, bottomLeft, (0, 255, 0), 2)
cv2.line(frame, bottomLeft, topLeft, (0, 255, 0), 2)
cX = int((topLeft[0] + bottomRight[0]) / 2.0)
cY = int((topLeft[1] + bottomRight[1]) / 2.0)
cv2.circle(frame, (cX, cY), 4, (0, 0, 255), -1)
cv2.putText(frame, str(markerID),
(topLeft[0], topLeft[1] - 15),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
if markerID == 0:
if not countl:
countr = False
countl=True
i='f'
for lp in range(12):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
cv2.putText(frame, '<--', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
print("Left")
i = 'l'
for lp in range(6):
Ser.write(i.encode())
rotate(30, 10, False)
time.sleep(0.5)
i='f'
for lp in range(7):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
elif markerID == 1:
if not countr:
countl = False
countr=True
i='f'
for lp in range(8):
Ser.write(i.encode())
move(1, 1, True)
time.sleep(0.1)
i = 'r'
cv2.putText(frame, '-->', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
print("Right")
for lp in range(6):
Ser.write(i.encode())
rotate(30, 10, True)
time.sleep(0.5)
else:
i = 'x'
Ser.write(i.encode())
print("Invalid")
if len(cnts) > 0:
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
if radius > 3:
cv2.circle(frame, center, 5, linecolor, -1)
if (x > 0.25 * width and x <= 0.75 * width):
print('Forward')
cv2.putText(frame, '^', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
Ser.write(b'f')
move(1, 1, True)
else:
print("Track Not Visible")
c1 += 1
if (c1 == 5):
print("Backward")
cv2.putText(frame, 'V', (5, 50), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2, cv2.LINE_AA)
Ser.write(b'b')
move(1, 1, False)
c1 = 0
time.sleep(0.2)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
cap.release()
Ser.close()
cv2.destroyAllWindows()
break | true | true |
f736df3039c7a5c7ccaaae53caf0915bb68e6580 | 5,910 | py | Python | research/object_detection/core/anchor_generator.py | jdavidagudelo/tensorflow-models | 6f019beec73b01861363bf717706e27f4210b979 | [
"Apache-2.0"
] | 1 | 2021-05-17T01:42:29.000Z | 2021-05-17T01:42:29.000Z | research/object_detection/core/anchor_generator.py | jdavidagudelo/tensorflow-models | 6f019beec73b01861363bf717706e27f4210b979 | [
"Apache-2.0"
] | null | null | null | research/object_detection/core/anchor_generator.py | jdavidagudelo/tensorflow-models | 6f019beec73b01861363bf717706e27f4210b979 | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 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.
# ==============================================================================
"""Base anchor generator.
The job of the anchor generator is to create (or load) a collection
of bounding boxes to be used as anchors.
Generated anchors are assumed to match some convolutional grid or list of grid
shapes. For example, we might want to generate anchors matching an 8x8
feature map and a 4x4 feature map. If we place 3 anchors per grid location
on the first feature map and 6 anchors per grid location on the second feature
map, then 3*8*8 + 6*4*4 = 288 anchors are generated in total.
To support fully convolutional settings, feature map shapes are passed
dynamically at generation time. The number of anchors to place at each location
is static --- implementations of AnchorGenerator must always be able return
the number of anchors that it uses per location for each feature map.
"""
from abc import ABCMeta
from abc import abstractmethod
import tensorflow as tf
class AnchorGenerator(object):
"""Abstract base class for anchor generators."""
__metaclass__ = ABCMeta
@abstractmethod
def name_scope(self):
"""Name scope.
Must be defined by implementations.
Returns:
a string representing the name scope of the anchor generation operation.
"""
pass
@property
def check_num_anchors(self):
"""Whether to dynamically check the number of anchors generated.
Can be overridden by implementations that would like to disable this
behavior.
Returns:
a boolean controlling whether the Generate function should dynamically
check the number of anchors generated against the mathematically
expected number of anchors.
"""
return True
@abstractmethod
def num_anchors_per_location(self):
"""Returns the number of anchors per spatial location.
Returns:
a list of integers, one for each expected feature map to be passed to
the `generate` function.
"""
pass
def generate(self, feature_map_shape_list, **params):
"""Generates a collection of bounding boxes to be used as anchors.
TODO(rathodv): remove **params from argument list and make stride and
offsets (for multiple_grid_anchor_generator) constructor arguments.
Args:
feature_map_shape_list: list of (height, width) pairs in the format
[(height_0, width_0), (height_1, width_1), ...] that the generated
anchors must align with. Pairs can be provided as 1-dimensional
integer tensors of length 2 or simply as tuples of integers.
**params: parameters for anchor generation op
Returns:
boxes_list: a list of BoxLists each holding anchor boxes corresponding to
the input feature map shapes.
Raises:
ValueError: if the number of feature map shapes does not match the length
of NumAnchorsPerLocation.
"""
if self.check_num_anchors and (
len(feature_map_shape_list) != len(self.num_anchors_per_location())):
raise ValueError('Number of feature maps is expected to equal the length '
'of `num_anchors_per_location`.')
with tf.name_scope(self.name_scope()):
anchors_list = self._generate(feature_map_shape_list, **params)
if self.check_num_anchors:
with tf.control_dependencies([
self._assert_correct_number_of_anchors(
anchors_list, feature_map_shape_list)]):
for item in anchors_list:
item.set(tf.identity(item.get()))
return anchors_list
@abstractmethod
def _generate(self, feature_map_shape_list, **params):
"""To be overridden by implementations.
Args:
feature_map_shape_list: list of (height, width) pairs in the format
[(height_0, width_0), (height_1, width_1), ...] that the generated
anchors must align with.
**params: parameters for anchor generation op
Returns:
boxes_list: a list of BoxList, each holding a collection of N anchor
boxes.
"""
pass
def _assert_correct_number_of_anchors(self, anchors_list,
feature_map_shape_list):
"""Assert that correct number of anchors was generated.
Args:
anchors_list: A list of box_list.BoxList object holding anchors generated.
feature_map_shape_list: list of (height, width) pairs in the format
[(height_0, width_0), (height_1, width_1), ...] that the generated
anchors must align with.
Returns:
Op that raises InvalidArgumentError if the number of anchors does not
match the number of expected anchors.
"""
expected_num_anchors = 0
actual_num_anchors = 0
for num_anchors_per_location, feature_map_shape, anchors in zip(
self.num_anchors_per_location(), feature_map_shape_list, anchors_list):
expected_num_anchors += (num_anchors_per_location
* feature_map_shape[0]
* feature_map_shape[1])
actual_num_anchors += anchors.num_boxes()
return tf.assert_equal(expected_num_anchors, actual_num_anchors)
| 39.4 | 87 | 0.676481 |
from abc import ABCMeta
from abc import abstractmethod
import tensorflow as tf
class AnchorGenerator(object):
__metaclass__ = ABCMeta
@abstractmethod
def name_scope(self):
pass
@property
def check_num_anchors(self):
return True
@abstractmethod
def num_anchors_per_location(self):
pass
def generate(self, feature_map_shape_list, **params):
if self.check_num_anchors and (
len(feature_map_shape_list) != len(self.num_anchors_per_location())):
raise ValueError('Number of feature maps is expected to equal the length '
'of `num_anchors_per_location`.')
with tf.name_scope(self.name_scope()):
anchors_list = self._generate(feature_map_shape_list, **params)
if self.check_num_anchors:
with tf.control_dependencies([
self._assert_correct_number_of_anchors(
anchors_list, feature_map_shape_list)]):
for item in anchors_list:
item.set(tf.identity(item.get()))
return anchors_list
@abstractmethod
def _generate(self, feature_map_shape_list, **params):
pass
def _assert_correct_number_of_anchors(self, anchors_list,
feature_map_shape_list):
expected_num_anchors = 0
actual_num_anchors = 0
for num_anchors_per_location, feature_map_shape, anchors in zip(
self.num_anchors_per_location(), feature_map_shape_list, anchors_list):
expected_num_anchors += (num_anchors_per_location
* feature_map_shape[0]
* feature_map_shape[1])
actual_num_anchors += anchors.num_boxes()
return tf.assert_equal(expected_num_anchors, actual_num_anchors)
| true | true |
f736dfb2e85552b321403c961da517f3b3efb100 | 911 | py | Python | python/paddle/v2/fluid/tests/test_uniform_random_op.py | QingshuChen/Paddle | 25a92be3e123ed21fd98c7be6bd7e3a6320756a3 | [
"Apache-2.0"
] | 3 | 2018-04-16T23:35:32.000Z | 2019-08-12T01:01:07.000Z | python/paddle/v2/fluid/tests/test_uniform_random_op.py | hero9968/PaddlePaddle-book | 1ff47b284c565d030b198705d5f18b4bd4ce53e5 | [
"Apache-2.0"
] | 9 | 2017-09-13T07:39:31.000Z | 2017-10-18T05:58:23.000Z | python/paddle/v2/fluid/tests/test_uniform_random_op.py | QingshuChen/Paddle | 25a92be3e123ed21fd98c7be6bd7e3a6320756a3 | [
"Apache-2.0"
] | 2 | 2020-11-04T08:07:46.000Z | 2020-11-06T08:33:24.000Z | import unittest
from paddle.v2.fluid.op import Operator
import paddle.v2.fluid.core as core
import numpy
class TestUniformRandomOp(unittest.TestCase):
def test_uniform_random_cpu(self):
self.uniform_random_test(place=core.CPUPlace())
def test_uniform_random_gpu(self):
if core.is_compile_gpu():
self.uniform_random_test(place=core.GPUPlace(0))
def uniform_random_test(self, place):
scope = core.Scope()
scope.var('X').get_tensor()
op = Operator(
"uniform_random",
Out='X',
shape=[1000, 784],
min=-5.0,
max=10.0,
seed=10)
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
tensor = numpy.array(scope.find_var('X').get_tensor())
self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
if __name__ == "__main__":
unittest.main()
| 26.028571 | 62 | 0.618002 | import unittest
from paddle.v2.fluid.op import Operator
import paddle.v2.fluid.core as core
import numpy
class TestUniformRandomOp(unittest.TestCase):
def test_uniform_random_cpu(self):
self.uniform_random_test(place=core.CPUPlace())
def test_uniform_random_gpu(self):
if core.is_compile_gpu():
self.uniform_random_test(place=core.GPUPlace(0))
def uniform_random_test(self, place):
scope = core.Scope()
scope.var('X').get_tensor()
op = Operator(
"uniform_random",
Out='X',
shape=[1000, 784],
min=-5.0,
max=10.0,
seed=10)
ctx = core.DeviceContext.create(place)
op.run(scope, ctx)
tensor = numpy.array(scope.find_var('X').get_tensor())
self.assertAlmostEqual(tensor.mean(), 2.5, delta=0.1)
if __name__ == "__main__":
unittest.main()
| true | true |
f736e0ceb6108928fd6db088c5e192170fd909de | 6,026 | py | Python | build/lib/axehelper/axehelper_bkg.py | bkornpob/axehelper | d89407f73f92e140a5cc9a76c643b9a8656e8b0f | [
"MIT"
] | null | null | null | build/lib/axehelper/axehelper_bkg.py | bkornpob/axehelper | d89407f73f92e140a5cc9a76c643b9a8656e8b0f | [
"MIT"
] | null | null | null | build/lib/axehelper/axehelper_bkg.py | bkornpob/axehelper | d89407f73f92e140a5cc9a76c643b9a8656e8b0f | [
"MIT"
] | null | null | null | # Kornpob Bhirombhakdi
import numpy as np
import matplotlib.pyplot as plt
import copy,glob,os
from astropy.io import fits
from math import pi
class AXEhelper_BKG:
def __init__(self,axeflist=None,fltflist=None,
padxleft=5,padxright=5,
padylow=10,halfdy=3,padyup=10,
adjusty=1.2
):
self.axeflist = axeflist
self.fltflist = fltflist
self.params = (padxleft,padxright,padylow,halfdy,padyup,adjusty)
self.headerall = self._headerall()
####################
####################
####################
def make_poly2d(self):
OBJ = {}
fltflist = self.fltflist
axeflist = self.axeflist
HEADERALL = self.headerall
for ii,i in enumerate(fltflist):
# image
tmp = fits.open(i)
tmpdata = tmp[1].data.copy()
tmpdq = tmp['DQ'].data.copy()
# header & prep
tmpheader = HEADERALL[axeflist[ii]]
xref,yref = tmpheader['XYREF']
pixx = tmpheader['PIXX']
pixy = tmpheader['PIXY']
cc0x,cc0y,cc1x,cc1y = tmpheader['CC']
sectx = tmpheader['SECTX']
secty = tmpheader['SECTY']
# Polynomial2D
x1 = np.arange(cc0x,cc1x)
x2 = np.arange(cc0y,cc1y)
x1,x2 = np.meshgrid(x1,x2)
obj = Polynomial2D()
obj.data['X1'] = x1.copy()
obj.data['X2'] = x2.copy()
obj.data['Y'] = tmpdata[cc0y:cc1y,cc0x:cc1x]
# print(x1.shape,x2.shape,obj.data['Y'].shape)
# data['MASK']
tmp = np.full_like(tmpdq,True,dtype=bool)
m = np.where(tmpdq==0)
tmp[m] = False
obj.data['MASK'] = tmp[cc0y:cc1y,cc0x:cc1x]
# print(obj.data['Y'].shape,obj.data['MASK'].shape)
OBJ[i] = copy.deepcopy(obj)
return OBJ
####################
####################
####################
def _headerall(self):
axeflist = self.axeflist
fltflist = self.fltflist
padxleft,padxright,padylow,halfdy,padyup,adjusty = self.params
tmp = {}
for i in axeflist:
# read from header
HEADER = copy.deepcopy(fits.open(i)[1].header)
xref,yref = HEADER['REFPNTX'],HEADER['REFPNTY']
bb0x,bb1x = HEADER['BB0X'],HEADER['BB1X']
orient = HEADER['ORIENT']
cpointx,cpointy = HEADER['CPOINTX'],HEADER['CPOINTY']
dldx0,dldx1 = HEADER['DLDX0'],HEADER['DLDX1']
# manually adjust offset
yref += adjusty
# trace and wavelength
fny = lambda x : np.tan((90.+orient)*pi/180.) * (x - cpointx) + yref
fnw = lambda x : dldx1 * (x - cpointx) + dldx0
pixx = np.array([round(xref),round(bb1x)],dtype=int)
pixy = np.round(fny(pixx)).astype(int)
ww = fnw(pixx)
# section
pixywidth = pixy[-1] - pixy[0] + 1
sectx = (padxleft,round(bb0x-xref),round(bb1x-bb0x),padxright)
secty = (padylow,halfdy,pixywidth,halfdy,padyup)
# cut box
cc0x = round(xref)-padxleft
cc1x = round(bb1x)+padxright
cc0y = int(fny(cc0x))-halfdy-padylow
cc1y = int(fny(cc1x))+halfdy+padyup
# output
tmp[i] = {}
tmp[i]['XYREF'] = (xref,yref)
tmp[i]['DLDX'] = (dldx0,dldx1)
tmp[i]['BBX'] = (bb0x,bb1x)
tmp[i]['PIXX'] = pixx.copy()
tmp[i]['PIXY'] = pixy.copy()
tmp[i]['WW'] = ww.copy()
tmp[i]['SECTX'] = copy.deepcopy(sectx)
tmp[i]['SECTY'] = copy.deepcopy(secty)
tmp[i]['CC'] = (cc0x,cc0y,cc1x,cc1y)
return copy.deepcopy(tmp)
####################
####################
####################
def show(self,save=False,savefname='default'):
fltflist = self.fltflist
axeflist = self.axeflist
HEADERALL = self.headerall
for ii,i in enumerate(fltflist):
tmp = fits.open(i)
tmpdata = tmp[1].data.copy()
tmpheader = HEADERALL[axeflist[ii]]
xref,yref = tmpheader['XYREF']
pixx = tmpheader['PIXX']
pixy = tmpheader['PIXY']
ww = tmpheader['WW']
cc0x,cc0y,cc1x,cc1y = tmpheader['CC']
sectx = tmpheader['SECTX']
secty = tmpheader['SECTY']
fig,ax = plt.subplots(2,1,sharex=True)
fig.tight_layout()
m = np.where(np.isfinite(tmpdata))
vmin,vmax = np.percentile(tmpdata[m],5.),np.percentile(tmpdata[m],99.)
ax[0].imshow(tmpdata,origin='lower',cmap='viridis',vmin=vmin,vmax=vmax)
ax[0].scatter(xref,yref,s=30,facecolor='red',edgecolor='None')
ax[0].plot(pixx,pixy,'r-')
ax[0].set_xlim(cc0x,cc1x)
ax[0].set_ylim(cc0y,cc1y)
ax[0].set_title('{0}'.format(i.split('/')[-1].split('_')[0]),fontsize=20)
ax[0].set_ylabel('pixY',fontsize=20)
bb0x = cc0x+sectx[0]+sectx[1]
bb1x = bb0x+sectx[2]
bb0y = cc0y+secty[0]
bb1y = bb0y+secty[1]+secty[2]+secty[3]
tmpx = [bb0x,bb1x,bb1x,bb0x,bb0x]
tmpy = [bb0y,bb0y,bb1y,bb1y,bb0y]
ax[0].plot(tmpx,tmpy,'r-')
ax[1].plot(pixx,ww)
ax[1].set_xlabel('pixX',fontsize=20)
ax[1].set_ylabel('obs. wavelength (A)',fontsize=20)
ax[1].grid()
if save:
if savefname=='default':
string = '/'.join(axeflist[ii].split('/')[0:-1])
string += '/{0}_axehelperbkg.png'.format(axeflist[ii].split('/')[-1].split('.')[0])
else:
string = savefname
fig.savefig(string,bbox_inches='tight')
| 35.656805 | 103 | 0.491371 |
import numpy as np
import matplotlib.pyplot as plt
import copy,glob,os
from astropy.io import fits
from math import pi
class AXEhelper_BKG:
def __init__(self,axeflist=None,fltflist=None,
padxleft=5,padxright=5,
padylow=10,halfdy=3,padyup=10,
adjusty=1.2
):
self.axeflist = axeflist
self.fltflist = fltflist
self.params = (padxleft,padxright,padylow,halfdy,padyup,adjusty)
self.headerall = self._headerall()
= tmpheader['SECTX']
secty = tmpheader['SECTY']
x1 = np.arange(cc0x,cc1x)
x2 = np.arange(cc0y,cc1y)
x1,x2 = np.meshgrid(x1,x2)
obj = Polynomial2D()
obj.data['X1'] = x1.copy()
obj.data['X2'] = x2.copy()
obj.data['Y'] = tmpdata[cc0y:cc1y,cc0x:cc1x]
tmp = np.full_like(tmpdq,True,dtype=bool)
m = np.where(tmpdq==0)
tmp[m] = False
obj.data['MASK'] = tmp[cc0y:cc1y,cc0x:cc1x]
OBJ[i] = copy.deepcopy(obj)
return OBJ
X1']
yref += adjusty
fny = lambda x : np.tan((90.+orient)*pi/180.) * (x - cpointx) + yref
fnw = lambda x : dldx1 * (x - cpointx) + dldx0
pixx = np.array([round(xref),round(bb1x)],dtype=int)
pixy = np.round(fny(pixx)).astype(int)
ww = fnw(pixx)
pixywidth = pixy[-1] - pixy[0] + 1
sectx = (padxleft,round(bb0x-xref),round(bb1x-bb0x),padxright)
secty = (padylow,halfdy,pixywidth,halfdy,padyup)
cc0x = round(xref)-padxleft
cc1x = round(bb1x)+padxright
cc0y = int(fny(cc0x))-halfdy-padylow
cc1y = int(fny(cc1x))+halfdy+padyup
tmp[i] = {}
tmp[i]['XYREF'] = (xref,yref)
tmp[i]['DLDX'] = (dldx0,dldx1)
tmp[i]['BBX'] = (bb0x,bb1x)
tmp[i]['PIXX'] = pixx.copy()
tmp[i]['PIXY'] = pixy.copy()
tmp[i]['WW'] = ww.copy()
tmp[i]['SECTX'] = copy.deepcopy(sectx)
tmp[i]['SECTY'] = copy.deepcopy(secty)
tmp[i]['CC'] = (cc0x,cc0y,cc1x,cc1y)
return copy.deepcopy(tmp)
secty = tmpheader['SECTY']
fig,ax = plt.subplots(2,1,sharex=True)
fig.tight_layout()
m = np.where(np.isfinite(tmpdata))
vmin,vmax = np.percentile(tmpdata[m],5.),np.percentile(tmpdata[m],99.)
ax[0].imshow(tmpdata,origin='lower',cmap='viridis',vmin=vmin,vmax=vmax)
ax[0].scatter(xref,yref,s=30,facecolor='red',edgecolor='None')
ax[0].plot(pixx,pixy,'r-')
ax[0].set_xlim(cc0x,cc1x)
ax[0].set_ylim(cc0y,cc1y)
ax[0].set_title('{0}'.format(i.split('/')[-1].split('_')[0]),fontsize=20)
ax[0].set_ylabel('pixY',fontsize=20)
bb0x = cc0x+sectx[0]+sectx[1]
bb1x = bb0x+sectx[2]
bb0y = cc0y+secty[0]
bb1y = bb0y+secty[1]+secty[2]+secty[3]
tmpx = [bb0x,bb1x,bb1x,bb0x,bb0x]
tmpy = [bb0y,bb0y,bb1y,bb1y,bb0y]
ax[0].plot(tmpx,tmpy,'r-')
ax[1].plot(pixx,ww)
ax[1].set_xlabel('pixX',fontsize=20)
ax[1].set_ylabel('obs. wavelength (A)',fontsize=20)
ax[1].grid()
if save:
if savefname=='default':
string = '/'.join(axeflist[ii].split('/')[0:-1])
string += '/{0}_axehelperbkg.png'.format(axeflist[ii].split('/')[-1].split('.')[0])
else:
string = savefname
fig.savefig(string,bbox_inches='tight')
| true | true |
f736e0f8b35d46a91144a34fafd0d2244270bc45 | 4,330 | py | Python | train.py | markytools/eeedeeplearning-finalProj | 6a06d73091262fb996c990302692cff7d9eed3b1 | [
"BSD-2-Clause"
] | null | null | null | train.py | markytools/eeedeeplearning-finalProj | 6a06d73091262fb996c990302692cff7d9eed3b1 | [
"BSD-2-Clause"
] | 5 | 2021-03-19T00:52:48.000Z | 2022-01-13T01:15:21.000Z | train.py | markytools/eeedeeplearning-finalProj | 6a06d73091262fb996c990302692cff7d9eed3b1 | [
"BSD-2-Clause"
] | null | null | null | import sys
from optparse import OptionParser
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.autograd import Variable
from eval import eval_net
from models.unet import UNet
from utils import *
def train_net(net, epochs=100, batch_size=2, lr=0.02, val_percent=0.1,
cp=True, gpu=False):
dir_img = '/media/markytools/503f6b96-90ca-4bfb-a99e-35f774205c77/EEE298/eee298deeplearning-finalproject-withdataset/LABELS_ONE_X/'
dir_mask = '/media/markytools/503f6b96-90ca-4bfb-a99e-35f774205c77/EEE298/eee298deeplearning-finalproject-withdataset/LABELS_ONE_Y/'
dir_checkpoint = './checkpoints'
ids = get_ids(dir_img)
ids = split_ids(ids)
iddataset = split_train_val(ids, val_percent)
print('''
Starting training:
Epochs: {}
Batch size: {}
Learning rate: {}
Training size: {}
Validation size: {}
Checkpoints: {}
CUDA: {}
'''.format(epochs, batch_size, lr, len(iddataset['train']),
len(iddataset['val']), str(cp), str(gpu)))
N_train = len(iddataset['train'])
optimizer = optim.Adam(net.parameters(),lr=lr,betas=(0.9,0.99))
criterion = nn.BCELoss()
for epoch in range(epochs):
print('Starting epoch {}/{}.'.format(epoch + 1, epochs))
# reset the generators
train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask)
val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask)
epoch_loss = 0
if 1:
val_dice = eval_net(net, val, gpu)
print('Validation Dice Coeff: {}'.format(val_dice))
for i, b in enumerate(batch(train, batch_size)):
X = np.array([i[0] for i in b])
y = np.array([i[1] for i in b])
X = torch.FloatTensor(X)
y = torch.ByteTensor(y)
if gpu:
X = Variable(X).cuda()
y = Variable(y).cuda()
else:
X = Variable(X)
y = Variable(y)
y_pred = net(X)
probs = F.sigmoid(y_pred)
probs_flat = probs.view(-1)
y_flat = y.view(-1)
loss = criterion(probs_flat, y_flat.float())
epoch_loss += loss.data[0]
print('{0:.4f} --- loss: {1:.6f}'.format(i * batch_size / N_train,
loss.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch finished ! Loss: {}'.format(epoch_loss / i))
if cp:
torch.save(net.state_dict(),
dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
print('Checkpoint {} saved !'.format(epoch + 1))
if __name__ == '__main__':
parser = OptionParser()
parser.add_option('-e', '--epochs', dest='epochs', default=300, type='int',
help='number of epochs')
parser.add_option('-b', '--batch-size', dest='batchsize', default=2,
type='int', help='batch size')
parser.add_option('-l', '--learning-rate', dest='lr', default=0.001,
type='float', help='learning rate')
parser.add_option('-g', '--gpu', action='store_true', dest='gpu',
default=False, help='use cuda')
parser.add_option('-m', '--model', dest='model', default=1,
type='int', help='select model (int): (1-Unet, )')
parser.add_option('-c', '--load', dest='load',
default=False, help='load file model')
(options, args) = parser.parse_args()
if (options.model == 1):
net = UNet(3, 1)
if options.load:
net.load_state_dict(torch.load(options.load))
print('Model loaded from {}'.format(options.load))
if options.gpu:
net.cuda()
cudnn.benchmark = True
try:
train_net(net, options.epochs, options.batchsize, options.lr,
gpu=options.gpu)
except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
print('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)
| 32.313433 | 136 | 0.555427 | import sys
from optparse import OptionParser
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.autograd import Variable
from eval import eval_net
from models.unet import UNet
from utils import *
def train_net(net, epochs=100, batch_size=2, lr=0.02, val_percent=0.1,
cp=True, gpu=False):
dir_img = '/media/markytools/503f6b96-90ca-4bfb-a99e-35f774205c77/EEE298/eee298deeplearning-finalproject-withdataset/LABELS_ONE_X/'
dir_mask = '/media/markytools/503f6b96-90ca-4bfb-a99e-35f774205c77/EEE298/eee298deeplearning-finalproject-withdataset/LABELS_ONE_Y/'
dir_checkpoint = './checkpoints'
ids = get_ids(dir_img)
ids = split_ids(ids)
iddataset = split_train_val(ids, val_percent)
print('''
Starting training:
Epochs: {}
Batch size: {}
Learning rate: {}
Training size: {}
Validation size: {}
Checkpoints: {}
CUDA: {}
'''.format(epochs, batch_size, lr, len(iddataset['train']),
len(iddataset['val']), str(cp), str(gpu)))
N_train = len(iddataset['train'])
optimizer = optim.Adam(net.parameters(),lr=lr,betas=(0.9,0.99))
criterion = nn.BCELoss()
for epoch in range(epochs):
print('Starting epoch {}/{}.'.format(epoch + 1, epochs))
train = get_imgs_and_masks(iddataset['train'], dir_img, dir_mask)
val = get_imgs_and_masks(iddataset['val'], dir_img, dir_mask)
epoch_loss = 0
if 1:
val_dice = eval_net(net, val, gpu)
print('Validation Dice Coeff: {}'.format(val_dice))
for i, b in enumerate(batch(train, batch_size)):
X = np.array([i[0] for i in b])
y = np.array([i[1] for i in b])
X = torch.FloatTensor(X)
y = torch.ByteTensor(y)
if gpu:
X = Variable(X).cuda()
y = Variable(y).cuda()
else:
X = Variable(X)
y = Variable(y)
y_pred = net(X)
probs = F.sigmoid(y_pred)
probs_flat = probs.view(-1)
y_flat = y.view(-1)
loss = criterion(probs_flat, y_flat.float())
epoch_loss += loss.data[0]
print('{0:.4f} --- loss: {1:.6f}'.format(i * batch_size / N_train,
loss.data[0]))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch finished ! Loss: {}'.format(epoch_loss / i))
if cp:
torch.save(net.state_dict(),
dir_checkpoint + 'CP{}.pth'.format(epoch + 1))
print('Checkpoint {} saved !'.format(epoch + 1))
if __name__ == '__main__':
parser = OptionParser()
parser.add_option('-e', '--epochs', dest='epochs', default=300, type='int',
help='number of epochs')
parser.add_option('-b', '--batch-size', dest='batchsize', default=2,
type='int', help='batch size')
parser.add_option('-l', '--learning-rate', dest='lr', default=0.001,
type='float', help='learning rate')
parser.add_option('-g', '--gpu', action='store_true', dest='gpu',
default=False, help='use cuda')
parser.add_option('-m', '--model', dest='model', default=1,
type='int', help='select model (int): (1-Unet, )')
parser.add_option('-c', '--load', dest='load',
default=False, help='load file model')
(options, args) = parser.parse_args()
if (options.model == 1):
net = UNet(3, 1)
if options.load:
net.load_state_dict(torch.load(options.load))
print('Model loaded from {}'.format(options.load))
if options.gpu:
net.cuda()
cudnn.benchmark = True
try:
train_net(net, options.epochs, options.batchsize, options.lr,
gpu=options.gpu)
except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth')
print('Saved interrupt')
try:
sys.exit(0)
except SystemExit:
os._exit(0)
| true | true |
f736e1baacc7a543fbd9d94caba50163109b9ccc | 4,701 | py | Python | api/programs/model.py | kacunningham413/PlasmoCount | 0213c63add92c8df1a53526af394bc9692ca4a62 | [
"BSD-3-Clause"
] | 2 | 2021-02-26T01:32:31.000Z | 2021-03-01T19:48:07.000Z | api/programs/model.py | kacunningham413/PlasmoCount | 0213c63add92c8df1a53526af394bc9692ca4a62 | [
"BSD-3-Clause"
] | 5 | 2021-03-08T21:13:30.000Z | 2021-04-27T20:16:07.000Z | api/programs/model.py | kacunningham413/PlasmoCount | 0213c63add92c8df1a53526af394bc9692ca4a62 | [
"BSD-3-Clause"
] | 5 | 2021-03-08T21:31:54.000Z | 2021-11-16T10:27:43.000Z | from pathlib import Path
import pandas as pd
from PIL import Image as PILImage
import torch
from torchvision import transforms, ops
from fastai.basic_train import load_learner
from fastai.vision import Image
from fastai.core import FloatItem
import matplotlib.pyplot as plt
from scipy import stats
class Model:
def __init__(self,
model_path='./models',
od_model='faster-rcnn.pt',
class_model='class_resnet.pkl',
ls_model='ls_resnet.pkl',
gam_model='gam_resnet.pkl',
cutoffs=[1.5, 2.5]):
model_path = Path(model_path)
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
self.od_model = torch.load(str(model_path / od_model), device)
self.od_model.eval()
self.class_model = load_learner(path=model_path, file=class_model)
self.ls_model = load_learner(path=model_path, file=ls_model)
self.gam_model = load_learner(path=model_path, file=gam_model)
self.cutoffs = cutoffs
def load_image(self, fileName):
self.fileName = fileName
img = PILImage.open(self.fileName).convert("RGB")
tensor = transforms.ToTensor()(img)
self.img = tensor
return tensor
def predict(self, has_gams):
with torch.no_grad():
prediction = self.od_model([self.img])[0]
prediction = self.post_processing(prediction)
# get crops for class detection
classes = []
life_stages = []
for bbox in prediction['boxes']:
x0, y0, x1, y1 = bbox.int()
bbox_img = Image(self.img[:, y0:y1, x0:x1])
bbox_pred = self.class_model.predict(bbox_img)
if str(bbox_pred[0]) == 'infected':
if has_gams:
gam_pred = self.gam_model.predict(bbox_img)
if str(gam_pred[0]) == 'asexual':
ls_pred = self.ls_model.predict(bbox_img)
else:
ls_pred = [FloatItem(-1)]
else:
ls_pred = self.ls_model.predict(bbox_img)
life_stages.append(ls_pred)
else:
life_stages.append(None)
classes.append(bbox_pred)
# format predictions
result = {}
result['boxes'] = pd.Series(prediction['boxes'].tolist())
result['p_boxes'] = pd.Series(prediction['scores'].tolist())
result = pd.DataFrame.from_dict(result)
result[['classes', 'p_classes']] = pd.Series(classes).apply(
lambda x: pd.Series([str(x[0]), (x[2][x[1]]).item()]))
result['life_stage'] = pd.Series(life_stages).apply(
lambda x: float(x[0].data) if x is not None else None)
result['life_stage_c'] = result['life_stage'].apply(
lambda x: self.calc_life_stages(x))
return result
def post_processing(self,
pred,
score_thresh=0.9,
iou_thresh=0.5,
z_thresh=4):
pred = self.apply_score_filter(pred, score_thresh)
pred = self.apply_nms(pred, iou_thresh)
pred = self.apply_size_filter(pred, z_thresh)
return pred
def apply_nms(self, pred, iou_thresh):
idx = ops.nms(pred["boxes"], pred["scores"], iou_thresh)
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def apply_score_filter(self, pred, thresh):
idx = [i for i, score in enumerate(pred['scores']) if score > thresh]
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def calc_area(self, coods):
return abs((coods[:, 2] - coods[:, 0]) * (coods[:, 3] - coods[:, 1]))
def apply_size_filter(self, pred, z_thresh):
area = self.calc_area(pred['boxes'])
zscores = stats.zscore(area)
idx = [i for i, score in enumerate(zscores) if abs(score) < z_thresh]
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def calc_life_stages(self, x):
RT_cutoff, TS_cutoff = self.cutoffs
if not x:
return 'uninfected'
elif (x >= 0) & (x <= RT_cutoff):
return 'ring'
elif (x > RT_cutoff) & (x <= TS_cutoff):
return 'trophozoite'
elif (x > TS_cutoff):
return 'schizont'
elif (x == -1):
return 'gametocyte'
else:
return 'uninfected' | 38.219512 | 77 | 0.55201 | from pathlib import Path
import pandas as pd
from PIL import Image as PILImage
import torch
from torchvision import transforms, ops
from fastai.basic_train import load_learner
from fastai.vision import Image
from fastai.core import FloatItem
import matplotlib.pyplot as plt
from scipy import stats
class Model:
def __init__(self,
model_path='./models',
od_model='faster-rcnn.pt',
class_model='class_resnet.pkl',
ls_model='ls_resnet.pkl',
gam_model='gam_resnet.pkl',
cutoffs=[1.5, 2.5]):
model_path = Path(model_path)
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
self.od_model = torch.load(str(model_path / od_model), device)
self.od_model.eval()
self.class_model = load_learner(path=model_path, file=class_model)
self.ls_model = load_learner(path=model_path, file=ls_model)
self.gam_model = load_learner(path=model_path, file=gam_model)
self.cutoffs = cutoffs
def load_image(self, fileName):
self.fileName = fileName
img = PILImage.open(self.fileName).convert("RGB")
tensor = transforms.ToTensor()(img)
self.img = tensor
return tensor
def predict(self, has_gams):
with torch.no_grad():
prediction = self.od_model([self.img])[0]
prediction = self.post_processing(prediction)
classes = []
life_stages = []
for bbox in prediction['boxes']:
x0, y0, x1, y1 = bbox.int()
bbox_img = Image(self.img[:, y0:y1, x0:x1])
bbox_pred = self.class_model.predict(bbox_img)
if str(bbox_pred[0]) == 'infected':
if has_gams:
gam_pred = self.gam_model.predict(bbox_img)
if str(gam_pred[0]) == 'asexual':
ls_pred = self.ls_model.predict(bbox_img)
else:
ls_pred = [FloatItem(-1)]
else:
ls_pred = self.ls_model.predict(bbox_img)
life_stages.append(ls_pred)
else:
life_stages.append(None)
classes.append(bbox_pred)
result = {}
result['boxes'] = pd.Series(prediction['boxes'].tolist())
result['p_boxes'] = pd.Series(prediction['scores'].tolist())
result = pd.DataFrame.from_dict(result)
result[['classes', 'p_classes']] = pd.Series(classes).apply(
lambda x: pd.Series([str(x[0]), (x[2][x[1]]).item()]))
result['life_stage'] = pd.Series(life_stages).apply(
lambda x: float(x[0].data) if x is not None else None)
result['life_stage_c'] = result['life_stage'].apply(
lambda x: self.calc_life_stages(x))
return result
def post_processing(self,
pred,
score_thresh=0.9,
iou_thresh=0.5,
z_thresh=4):
pred = self.apply_score_filter(pred, score_thresh)
pred = self.apply_nms(pred, iou_thresh)
pred = self.apply_size_filter(pred, z_thresh)
return pred
def apply_nms(self, pred, iou_thresh):
idx = ops.nms(pred["boxes"], pred["scores"], iou_thresh)
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def apply_score_filter(self, pred, thresh):
idx = [i for i, score in enumerate(pred['scores']) if score > thresh]
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def calc_area(self, coods):
return abs((coods[:, 2] - coods[:, 0]) * (coods[:, 3] - coods[:, 1]))
def apply_size_filter(self, pred, z_thresh):
area = self.calc_area(pred['boxes'])
zscores = stats.zscore(area)
idx = [i for i, score in enumerate(zscores) if abs(score) < z_thresh]
for i in ["boxes", "labels", "scores"]:
pred[i] = pred[i][idx]
return pred
def calc_life_stages(self, x):
RT_cutoff, TS_cutoff = self.cutoffs
if not x:
return 'uninfected'
elif (x >= 0) & (x <= RT_cutoff):
return 'ring'
elif (x > RT_cutoff) & (x <= TS_cutoff):
return 'trophozoite'
elif (x > TS_cutoff):
return 'schizont'
elif (x == -1):
return 'gametocyte'
else:
return 'uninfected' | true | true |
f736e1eac82af52cacebbc9e9fe706f8dec0f9b4 | 1,254 | py | Python | Admin-Scripts/Video4.py | vijayshankarrealdeal/Java | 2dff1a79c91782bf2aeb1bee057b19c41cafd2a1 | [
"MIT"
] | 3 | 2021-03-07T16:29:35.000Z | 2021-03-22T07:41:04.000Z | Admin-Scripts/Video4.py | vijayshankarrealdeal/Java | 2dff1a79c91782bf2aeb1bee057b19c41cafd2a1 | [
"MIT"
] | null | null | null | Admin-Scripts/Video4.py | vijayshankarrealdeal/Java | 2dff1a79c91782bf2aeb1bee057b19c41cafd2a1 | [
"MIT"
] | 2 | 2021-03-08T06:12:52.000Z | 2021-03-14T05:01:19.000Z | import firebase_admin
from firebase_admin import credentials,firestore
from firebase_admin import storage
cred = credentials.Certificate("./adminKey.json")
firebase_admin.initialize_app(cred, {
'storageBucket': 'women-e598c.appspot.com'
})
#Database Methods
db = firestore.client()
#discrip = ""
title = "Plight of Women"
cloudStorageLink = "https://firebasestorage.googleapis.com/v0/b/women-e598c.appspot.com/o/y2mate.com%20-%20The%20plight%20of%20women%20in%20India_360p.mp4?alt=media&token=3633254b-9fee-4f0c-9fa3-057e0616545c"
name = "CNN"
source = "YouTube"
sourceLink = "https://www.youtube.com/watch?v=XtHgTf67hzc"
discription = "CNN's Sumnima Udas examines the cycle of discrimination against women in India."
viewsOnVideo = 637,57,144340
socialHandle = " "
webpage = ""
if(len(title)!=0 and len(cloudStorageLink)!=0):
videsoWrite = db.collection("adminContent").document("Videos").collection("data").document().set({
"title":title,
"name":name,
"source":source,
"sourceLink":sourceLink,
"discription":discription,
"viewsOnVideo":viewsOnVideo,
"socialHandle":socialHandle,
"webpage":webpage,
"cloudStorageLink":cloudStorageLink
})
else:
print("Error")
| 33 | 208 | 0.716906 | import firebase_admin
from firebase_admin import credentials,firestore
from firebase_admin import storage
cred = credentials.Certificate("./adminKey.json")
firebase_admin.initialize_app(cred, {
'storageBucket': 'women-e598c.appspot.com'
})
db = firestore.client()
title = "Plight of Women"
cloudStorageLink = "https://firebasestorage.googleapis.com/v0/b/women-e598c.appspot.com/o/y2mate.com%20-%20The%20plight%20of%20women%20in%20India_360p.mp4?alt=media&token=3633254b-9fee-4f0c-9fa3-057e0616545c"
name = "CNN"
source = "YouTube"
sourceLink = "https://www.youtube.com/watch?v=XtHgTf67hzc"
discription = "CNN's Sumnima Udas examines the cycle of discrimination against women in India."
viewsOnVideo = 637,57,144340
socialHandle = " "
webpage = ""
if(len(title)!=0 and len(cloudStorageLink)!=0):
videsoWrite = db.collection("adminContent").document("Videos").collection("data").document().set({
"title":title,
"name":name,
"source":source,
"sourceLink":sourceLink,
"discription":discription,
"viewsOnVideo":viewsOnVideo,
"socialHandle":socialHandle,
"webpage":webpage,
"cloudStorageLink":cloudStorageLink
})
else:
print("Error")
| true | true |
f736e207f6d1b0adfa6d87bad60d9ddd424e2578 | 34,600 | py | Python | apps/bluebutton/cms_parser.py | ekivemark/BlueButtonDev | c751a5c52a83df6b97ef2c653a4492d959610c42 | [
"Apache-2.0"
] | 1 | 2020-10-29T07:29:49.000Z | 2020-10-29T07:29:49.000Z | apps/bluebutton/cms_parser.py | ekivemark/BlueButtonUser | 096cf439cd0c4ccb3d16b0efebf1c34fd3fb8939 | [
"Apache-2.0"
] | 7 | 2020-02-11T23:03:55.000Z | 2021-12-13T19:42:01.000Z | apps/bluebutton/cms_parser.py | ekivemark/BlueButtonUser | 096cf439cd0c4ccb3d16b0efebf1c34fd3fb8939 | [
"Apache-2.0"
] | 2 | 2018-10-06T21:45:51.000Z | 2020-10-10T16:10:36.000Z | #!/usr/bin/env python
"""
python-bluebutton
FILE: cms_parser
Created: 3/3/15 12:16 PM
convert CMS BlueButton text to json
"""
__author__ = 'Mark Scrimshire:@ekivemark'
import json
import re
import os, sys
from collections import OrderedDict
from apps.bluebutton.cms_parser_utilities import *
from apps.bluebutton.cms_custom import *
# DBUG = False
divider = "----------"
def cms_file_read(inPath):
# Read file and save in OrderedDict
# Identify Headings and set them as level 0
# Everything else assign as Level 1
# Add in claimNumber value to line_dict to simplify detail
# downstream processing of lines
DBUG = False
ln_cntr = 0
blank_ln = 0
f_lines = []
set_level = 0
line_type = "BODY"
header_line = False
set_header = "HEADER"
current_segment = ""
claim_number = ""
kvs = {}
line_dict = {"key": 0,
"level": 0,
"line": "",
"type": "",
"claimNumber": "",
"category": ""}
with open(inPath, 'r') as f:
# get the line from the input file
# print("Processing:",)
for i, l in enumerate(f):
# reset the dictionary
line_dict = {}
# Read each line in file
l = l.rstrip()
# remove white space from end of line
# if (i % 10) == 0:
# print ".",
# Show progress every 10 steps
if len(l) < 1:
# skip blank lines
blank_ln += 1
continue
if line_type == "BODY" and (divider in l):
header_line = True
get_title = True
line_type = "HEADER"
blank_ln += 1
continue
elif line_type == "HEADER" and header_line and get_title:
# Get the title line
# print "we found title:",l
# print i, "[About to set Seg:", l, "]"
# Save the current_segment before we overwrite it
if not (divider in l):
if len(l.strip()) > 0:
# print "title length:", len(l.strip())
# Remove : from Title - for Claims LineNumber:
titleline = l.split(":")
tl = titleline[0].rstrip()
set_header = line_type
current_segment = tl
get_title = False
if "CLAIM LINES FOR CLAIM NUMBER" in l.upper():
# we have to account for Part D Claims
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
set_level = 1
else:
set_level = 0
else:
# we didn't find a title
# So set a default
# Only claim summary title segments are blank
# save current_segment
previous_segment = current_segment
current_segment = "claim Header"
# print "set title to", current_segment
# print i,"We never got a title line...",
# current_segment
set_level = 1
header_line = False
if current_segment == "claim Header":
set_header = "HEADER"
else:
set_header = "HEADER"
line_type = "BODY"
line_dict = {"key": ln_cntr,
"level": set_level,
"line": current_segment,
"type": set_header,
"claimNumber": claim_number}
elif line_type == "HEADER" and not get_title:
# we got a second divider
if divider in l:
set_header = "BODY"
line_type = "BODY"
header_line = False
blank_ln += 1
continue
else:
line_type = "BODY"
set_header = line_type
if "CLAIM NUMBER" in l.upper():
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
if "CLAIM TYPE: PART D" in l.upper():
# We need to re-write the previous f_lines entry
prev_line = f_lines[ln_cntr - 1]
if DBUG:
do_DBUG("prev_line:", prev_line)
if prev_line[ln_cntr - 1]["line"].upper() == "CLAIM LINES FOR CLAIM NUMBER":
prev_line[ln_cntr - 1]["line"] = "Part D Claims"
f_lines[ln_cntr - 1] = prev_line
if DBUG:
do_DBUG("re-wrote f_lines:",
f_lines[ln_cntr - 1])
line_dict = {"key": ln_cntr,
"level": set_level + 1,
"line": l,
"type": set_header,
"claimNumber": claim_number}
f_lines.append({ln_cntr: line_dict})
ln_cntr += 1
f.close()
# print(i+1, "records")
# print(ln_cntr, "written.")
# print(blank_ln, "skipped")
# print(f_lines)
# print("")
return f_lines
def cms_text_read(inText):
# Read textfield and save in OrderedDict
# Identify Headings and set them as level 0
# Everything else assign as Level 1
# Add in claimNumber value to line_dict to simplify detail
# downstream processing of lines
DBUG = False
ln_cntr = 0
blank_ln = 0
f_lines = []
set_level = 0
line_type = "BODY"
header_line = False
set_header = "HEADER"
current_segment = ""
claim_number = ""
kvs = {}
line_dict = {"key": 0,
"level": 0,
"line": "",
"type": "",
"claimNumber": "",
"category": ""}
if DBUG:
print("In apps.bluebutton.cms_parser.cms_text_read")
i = 0
for l in inText.split('\n'):
# reset the dictionary
line_dict = {}
# Read each line in file
l = l.rstrip()
# remove white space from end of line
# if (i % 10) == 0:
# print ".",
# Show progress every 10 steps
if len(l) < 1:
# skip blank lines
blank_ln += 1
continue
if line_type == "BODY" and (divider in l):
header_line = True
get_title = True
line_type = "HEADER"
blank_ln += 1
continue
elif line_type == "HEADER" and header_line and get_title:
# Get the title line
# print "we found title:",l
# print i, "[About to set Seg:", l, "]"
# Save the current_segment before we overwrite it
if not (divider in l):
if len(l.strip()) > 0:
# print "title length:", len(l.strip())
# Remove : from Title - for Claims LineNumber:
titleline = l.split(":")
tl = titleline[0].rstrip()
set_header = line_type
current_segment = tl
get_title = False
if "CLAIM LINES FOR CLAIM NUMBER" in l.upper():
# we have to account for Part D Claims
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
set_level = 1
else:
set_level = 0
else:
# we didn't find a title
# So set a default
# Only claim summary title segments are blank
# save current_segment
previous_segment = current_segment
current_segment = "claim Header"
# print "set title to", current_segment
# print i,"We never got a title line...",
# current_segment
set_level = 1
header_line = False
if current_segment == "claim Header":
set_header = "HEADER"
else:
set_header = "HEADER"
line_type = "BODY"
line_dict = {"key": ln_cntr,
"level": set_level,
"line": current_segment,
"type": set_header,
"claimNumber": claim_number}
elif line_type == "HEADER" and not get_title:
# we got a second divider
if divider in l:
set_header = "BODY"
line_type = "BODY"
header_line = False
blank_ln += 1
continue
else:
line_type = "BODY"
set_header = line_type
if "CLAIM NUMBER" in l.upper():
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
if "CLAIM TYPE: PART D" in l.upper():
# We need to re-write the previous f_lines entry
prev_line = f_lines[ln_cntr - 1]
if DBUG:
do_DBUG("prev_line:", prev_line)
if prev_line[ln_cntr - 1][
"line"].upper() == "CLAIM LINES FOR CLAIM NUMBER":
prev_line[ln_cntr - 1]["line"] = "Part D Claims"
f_lines[ln_cntr - 1] = prev_line
if DBUG:
do_DBUG("re-wrote f_lines:",
f_lines[ln_cntr - 1])
line_dict = {"key": ln_cntr,
"level": set_level + 1,
"line": l,
"type": set_header,
"claimNumber": claim_number}
f_lines.append({ln_cntr: line_dict})
ln_cntr += 1
i += 1
# print(i+1, "records")
# print(ln_cntr, "written.")
# print(blank_ln, "skipped")
# print(f_lines)
# print("")
return f_lines
def parse_lines(ln_list):
# Receive list created in cms_file_read
# Build the final Json dict
# Use SEG_DEF to control JSON construction
# set variables
DBUG = False
if DBUG:
to_json(ln_list)
ln = {}
ln_ctrl = {}
hdr_lk_up = ""
seg_match_exact = True
# Pass to get_segment for an exact match
match_ln = [None, None, None, None, None, None, None, None, None, None]
segment_dict = collections.OrderedDict()
out_dict = collections.OrderedDict()
# Set starting point in list
if DBUG:
print("Initializing Working Storage Arrays...",)
block_limit = 9
block = collections.OrderedDict()
n = 0
while n <= block_limit:
block[n] = collections.OrderedDict()
n += 1
if DBUG:
print("Done.")
i = 0
# while i <= 44: #(len(ln_list)-1):
while i <= (len(ln_list) - 1):
# process each line in the list until end of list
# We need to deal with an empty dict
ln = get_line_dict(ln_list, i)
if ln == {}:
if DBUG:
do_DBUG("Empty Ln", "Line(i):", i,
"ln:", ln)
i += 1
# increment counter and go back to top of while loop
continue
wrk_lvl = ln["level"]
match_ln = update_match(wrk_lvl,
headlessCamel(ln["line"]),
match_ln)
match_hdr = combined_match(wrk_lvl, match_ln)
hdr_lk_up = headlessCamel(ln["line"])
if DBUG:
do_DBUG("Line(i):", i, "ln:", ln,
"hdr_lk_up:", hdr_lk_up)
# lookup ln in SEG_DEF
if find_segment(hdr_lk_up, seg_match_exact):
ln_ctrl = get_segment(hdr_lk_up, seg_match_exact)
wrk_lvl = adjusted_level(ln["level"], match_ln)
# We found a match in SEG_DEF
# So we use SEG_DEF to tailor how we write the line and
# section since a SEG_DEF defines special processing
if DBUG:
do_DBUG("CALLING PROCESS_HEADER===========================",
"i:", i,
"Match_ln:", match_ln,
"ln-ctrl:", to_json(ln_ctrl),
"ln_lvl:", ln["level"],
"wrk_lvl:", wrk_lvl)
i, sub_seg, seg_name = process_header(i, ln_ctrl,
wrk_lvl,
ln_list)
# Now load the info returned from process_header in out_dict
out_dict[seg_name] = sub_seg[seg_name]
if DBUG: # or True:
do_DBUG("=============== RETURNED FROM PROCESS_HEADER",
"line:", i,
"ln_control:", ln_ctrl,
"seg_name:", seg_name,
"custom processing:", key_value("custom", ln_ctrl),
"sub_seg:", to_json(sub_seg))
if key_value("custom", ln_ctrl) == "":
# No custom processing required
i, block_seg, block_name = process_subseg(i, # + 1,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
elif key_value("custom", ln_ctrl) == "family_history":
#custom processing required (ln_ctrl["custom"] is set)
i, block_seg, block_name = custom_family_history(i, # + 1,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
elif key_value("custom", ln_ctrl) == "claim_summary":
#custom processing required (ln_ctrl["custom"] is set)
i, block_seg, block_name = process_subseg(i, # + 1,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
if DBUG:
do_DBUG("---------------- RETURNED FROM PROCESS_BLOCK",
"ctr: i:", i, "block_name:",block_name,
"block_seg:", to_json(block_seg),
)
# if check_type(block_seg) != "DICT":
# if DBUG:
# do_DBUG("((((((((((((((((((",
# "check_type:",
# check_type(block_seg),
# "["+block_name+"]:",
# block_seg[0],
# "))))))))))))))))))")
if check_type(block_seg) == "LIST":
if DBUG:
do_DBUG("LIST returned",
block_seg)
if not block_seg == []:
out_dict[block_name] = block_seg[0]
else:
if DBUG:
do_DBUG("Not List",
block_seg)
out_dict[block_name] = block_seg
if DBUG:
do_DBUG("out_dict["+ block_name + "]:", to_json(out_dict))
# if (i + 1) <= (len(ln_list) - 1):
# We are not passed the end of the list
# so increment the i counter in the call to
# process_segment
# i, sub_seg, seg_name = process_segment(i + 1, ln_ctrl,
# match_ln,
# ln["level"],
# ln_list)
if DBUG:
do_DBUG("============================",
"seg_name:", seg_name,
"segment returned:", sub_seg,
"Returned with counter-i:", i,
"----------------------------",
"out_dict[" + seg_name + "]",
to_json(out_dict[seg_name]),
"block_name:", block_name,
"block_seg:", block_seg)
if DBUG:
do_DBUG("====================END of LOOP",
"line number(i):", i,
"out_dict", to_json(out_dict),
"===============================")
# increment line counter
i += 1
if DBUG:
do_DBUG("End of list:", i,
"out_dict", to_json(out_dict))
return out_dict
def cms_file_parse2(inPath):
# Parse a CMS BlueButton file (inPath)
result = cms_file_read(inPath)
# Set default variables on entry
k = ""
v = ""
items = collections.OrderedDict()
first_header = True
header_line = True
get_title = False
skip = False
line_type = "Body"
multi = False
skip = False
segment_source = ""
match_key = {}
match_string = ""
current_segment = ""
previous_segment = current_segment
header_block = {}
block_info = {}
line_list = []
segment_dict = collections.OrderedDict()
sub_segment_dict = collections.OrderedDict()
sub_segment_list = []
# Open the file for reading
with open(inPath, 'r') as f:
# get the line from the input file
for i, l in enumerate(f):
# reset line_dict
# line_dict = collections.OrderedDict()
# remove blanks from end of line
l = l.rstrip()
# print("![", i, ":", line_type, ":", l, "]")
if line_type == "Body" and (divider in l):
# This should be the first divider line in the header
header_line = True
get_title = True
line_type = "Header"
if not first_header:
# we want to write out the previous segment
# print(i, ":Write Previous Segment")
# print(i, ":1st Divider:", l)
####################
# Write segment here
####################
# print(i, "Cur_Seg:", current_segment, ":", multi)
# if multi:
# print(line_list)
# write source: segment_source to segment_dict
segment_dict["source"] = segment_source
#
items, segment_dict, line_list = write_segment(items, current_segment, segment_dict, line_list, multi)
# Reset the Dict
segment_dict = collections.OrderedDict()
line_list = []
multi = False
####################
first_header = False
else:
# at top of document so no previous segment
first_header = False
# print(i,":1st Divider:",l)
elif line_type == "Header" and header_line and get_title:
# Get the title line
# print("we found title:",l)
# print(i, "[About to set Seg:", l, "]")
# Save the current_segment before we overwrite it
if not divider in l:
if len(l.strip()) > 0:
# print("title length:", len(l.strip()))
# Remove : from Title - for Claims LineNumber:
titleline = l.split(":")
tl = titleline[0].rstrip()
previous_segment = current_segment
header_block = get_segment(headlessCamel(tl))
# print headlessCamel(l), "translated:",
# header_block
if len(header_block) > 1:
current_segment = header_block["name"]
else:
current_segment = headlessCamel(tl)
if find_segment(headlessCamel(tl)):
# print("Segment list: %s FOUND" % l)
# Get a dict for this segment
header_block = get_segment(headlessCamel(tl))
multi = multi_item(header_block)
header_block_level = get_header_block_level(header_block)
# update the match_key
match_key[header_block_level] = current_segment
line_list = []
k = header_block["name"]
# print(i, k, ":Multi:", multi)
# print("k set to [%s]" % k)
current_segment, segment_dict = segment_prefill(header_block)
# print("Current_Segment:", current_segment)
# print("%s%s%s" % ('"', headlessCamel(l), '"'))
get_title = False
# print(i, ":Set segment:", current_segment, "]")
else:
# we didn't find a title
# So set a default
# Only claim summary title segments are blank
# save current_segment
previous_segment = current_segment
current_segment = "claimHeader"
# print("set title to", current_segment)
# print(i,"We never got a title line...",
# current_segment)
header_line = False
line_type = "Body"
# Write the last segment and reset
elif line_type == "Header" and (divider in l):
# this should be the closing divider line
# print("Closing Divider")
header_line = False
line_type = "Body"
else:
line_type = "Body"
# split on the : in to key and value
line = l.split(":")
if len(line) > 1:
# Assign line[0] to k and format as headlessCamel
k = headlessCamel(line[0])
v = line[1].lstrip()
v = v.rstrip()
#
# Now we deal with some special items.
# The Date and time in the header section
if k[2] == "/":
v = {"value": parse_time(l)}
k = "effectiveTime"
# print(i, ":", l)
# print(i, "got date for:",)
# current_segment, k, ":", v
segment_dict[k] = v
elif k.upper() == "SOURCE":
segment_source=set_source(segment_source, k, v)
# Apply headlessCamelCase to K
k = headlessCamel(k)
v = segment_source
# print(i, "set source in:",)
# current_segment, ":", k, ":", v
segment_dict[k] = v
else:
# match key against segment
match_string = current_segment + "." + k
print("Match:", match_string)
if find_segment(match_string):
# Get info about how to treat this key
# first we need to construct the field key to
# lookup in seg list
block_info = get_segment(match_string)
k = block_info["name"]
# print(i, ":k:", k, ":", block_info)
if block_info["mode"] == "block":
skip = True
if block_info["type"] == "dict":
sub_segment_dict[block_info["name"]] = v
elif block_info["type"] == "list":
sub_segment_list.append({k: v})
else:
sub_segment_dict[block_info["name"]] = v
elif block_info["mode"] == "close":
skip = True
if block_info["type"] == "dict":
sub_segment_dict[block_info["name"]] = v
segment_dict[block_info["dict_name"]] = sub_segment_dict
sub_segment_dict = collections.OrderedDict()
elif block_info["type"] == "list":
sub_segment_list.append({k: v})
segment_dict[block_info["dict_name"]] = sub_segment_list
sub_segment_list = []
else:
segment_dict[block_info["name"]] = v
if multi:
# Add Source value to each block
segment_dict["source"] = segment_source
# print("Line_List:[", line_list, "]")
# print("Segment_dict:[", segment_dict, "]")
if k in segment_dict:
line_list.append(segment_dict)
segment_dict = collections.OrderedDict()
if not skip:
segment_dict[k] = v
skip = False
# print("B[", i, ":", line_type, ":", l, "]")
# ===================
# Temporary Insertion
# if i > 80:
# break
# end of temporary insertion
# ===================
f.close()
# write the last segment
# print("Writing the last segment")
items, segment_dict, line_list = write_segment(items,
current_segment,
segment_dict,
line_list,
multi)
return items
def cms_file_parse(inPath):
# Parse a CMS BlueButton file (inPath)
# Using a redefined Parsing process
# Set default variables on entry
k = ""
v = ""
items = collections.OrderedDict()
first_header = True
header_line = True
get_title = False
line_type = "Header"
segment_dict = collections.OrderedDict()
current_segment = ""
segment_source = ""
previous_segment = current_segment
line_dict = collections.OrderedDict()
# Open the file for reading
with open(inPath, 'r') as f:
# get the line from the input file
for i, l in enumerate(f):
l = l.rstrip()
line = l.split(":")
if len(line) > 1:
k = line[0]
v = line[1].lstrip()
v = v.rstrip()
if len(l) <= 1 and header_line is False:
# The line is a detail line and is empty
# so ignore it and move on to next line
# print "empty line %s[%s] - skipping to next line" % (i,l)
continue
if header_line:
line_type = "Header"
else:
line_type = "Body"
# From now on We are dealing with a non-blank line
# Segment titles are wrapped by lines of minus signs (divider)
# So let's check if we have found a divider
if (divider in l) and not header_line:
# We have a divider. Is it an open or closing divider?
header_line = True
get_title = True
# First we need to write the old segment out
if first_header:
# file starts with a header line but
# there is nothing to write
first_header = False
# print("First Header - Nothing to write")
continue
else:
# not the first header so we should write the segment
# print("Not First Header - Write segment")
print(i, "writing segment",)
items, segment_dict = write_segment(items,
current_segment,
segment_dict)
# Then we can continue
continue
#print("HL/GT:",header_line,get_title)
if header_line and get_title:
if not divider in l:
previous_segment = current_segment
# assign title to current_segment
current_segment = k.lower().replace(" ", "_")
get_title = False
else:
# blank lines for title were skipped so we hit divider
# before setting current_segment = title
# So set to "claim_summary"
# since this is only unnamed segment
current_segment = "claim_summary"
get_title = False
# print("Header:",current_segment)
# now match the title in seg["key"]
# and write any prefill information to the segment
if find_segment(k):
# Check the seq list for a match
# print("Segment list: %s FOUND" % l)
seg_returned = get_segment(k)
k = seg_returned["name"]
# print("k set to [%s]" % k)
current_segment, segment_dict = segment_prefill(seg_returned)
# print("segment_dict: %s" % segment_dict)
else:
# We didn't find a match so let's set it to "Other"
current_segment = k.lower().replace(" ", "_")
segment_dict = collections.OrderedDict()
segment_dict[current_segment] = {}
print("%s:Current_Segment: %s" % (i, current_segment))
# print("Header Line:",header_line)
# go to next line in file
continue
print("[%s:CSeg:%s|%s L:[%s]" % (i,current_segment,
line_type,l))
# print("%s:Not a Heading Line" % i)
######################################
# Lines below are detail lines
# Need to lookup line in fld_tx to translate k to preferred string
# if no match in fld_tx then force to lower().replace(" ","_")
# Need to evaluate content of line to determine if
# dict, list or text needs to be processed
# add dict, list or text with key to segment_dict
# Let's check for Source and set that up
# ========================
# temporary insertion to skip detail lines
#continue
# ========================
if current_segment == "header":
# Now we deal with some special items.
# The Date and time in the header section
if k[2] == "/":
# print("got the date line")
v = {"value": parse_time(l)}
k = "effectiveTime"
segment_dict[current_segment] = {k: v}
continue
segment_source = set_source(segment_source, k, v)
if k.upper() == "SOURCE":
k = k.lower()
v = segment_source
segment_dict[current_segment] = {k: v}
continue
line_dict[k] = v
# print("line_dict:", current_segment,":", line_dict)
segment_dict[current_segment] = line_dict
# reset the line_dict
line_dict = collections.OrderedDict()
# end of for loop
f.close()
# write the last segment
# print("Writing the last segment")
items, segment_dict = write_segment(items,
current_segment,
segment_dict)
return items
def set_header_line(hl):
# flip header_line value. received as hl (True or False)
return (not hl)
def multi_item(seg):
# check for "multi" in seg dict
# If multi line = "True" set to True
# use line_list instead of dict to allow multiple entries
multi = False
if "multi" in seg:
if seg["multi"] == "True":
multi = True
# print "Multi:", multi
return multi
def build_key(mk, bi):
# update make_key using content of build_info
lvl = bi["level"]
mk[lvl] = bi["name"]
return mk
def get_header_block_level(header_block):
lvl = 0
if "level" in header_block:
lvl = header_block["level"]
return lvl
| 35.45082 | 122 | 0.431908 |
__author__ = 'Mark Scrimshire:@ekivemark'
import json
import re
import os, sys
from collections import OrderedDict
from apps.bluebutton.cms_parser_utilities import *
from apps.bluebutton.cms_custom import *
divider = "----------"
def cms_file_read(inPath):
DBUG = False
ln_cntr = 0
blank_ln = 0
f_lines = []
set_level = 0
line_type = "BODY"
header_line = False
set_header = "HEADER"
current_segment = ""
claim_number = ""
kvs = {}
line_dict = {"key": 0,
"level": 0,
"line": "",
"type": "",
"claimNumber": "",
"category": ""}
with open(inPath, 'r') as f:
for i, l in enumerate(f):
line_dict = {}
l = l.rstrip()
if len(l) < 1:
blank_ln += 1
continue
if line_type == "BODY" and (divider in l):
header_line = True
get_title = True
line_type = "HEADER"
blank_ln += 1
continue
elif line_type == "HEADER" and header_line and get_title:
if not (divider in l):
if len(l.strip()) > 0:
titleline = l.split(":")
tl = titleline[0].rstrip()
set_header = line_type
current_segment = tl
get_title = False
if "CLAIM LINES FOR CLAIM NUMBER" in l.upper():
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
set_level = 1
else:
set_level = 0
else:
# So set a default
# Only claim summary title segments are blank
# save current_segment
previous_segment = current_segment
current_segment = "claim Header"
# print "set title to", current_segment
# print i,"We never got a title line...",
# current_segment
set_level = 1
header_line = False
if current_segment == "claim Header":
set_header = "HEADER"
else:
set_header = "HEADER"
line_type = "BODY"
line_dict = {"key": ln_cntr,
"level": set_level,
"line": current_segment,
"type": set_header,
"claimNumber": claim_number}
elif line_type == "HEADER" and not get_title:
# we got a second divider
if divider in l:
set_header = "BODY"
line_type = "BODY"
header_line = False
blank_ln += 1
continue
else:
line_type = "BODY"
set_header = line_type
if "CLAIM NUMBER" in l.upper():
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
if "CLAIM TYPE: PART D" in l.upper():
# We need to re-write the previous f_lines entry
prev_line = f_lines[ln_cntr - 1]
if DBUG:
do_DBUG("prev_line:", prev_line)
if prev_line[ln_cntr - 1]["line"].upper() == "CLAIM LINES FOR CLAIM NUMBER":
prev_line[ln_cntr - 1]["line"] = "Part D Claims"
f_lines[ln_cntr - 1] = prev_line
if DBUG:
do_DBUG("re-wrote f_lines:",
f_lines[ln_cntr - 1])
line_dict = {"key": ln_cntr,
"level": set_level + 1,
"line": l,
"type": set_header,
"claimNumber": claim_number}
f_lines.append({ln_cntr: line_dict})
ln_cntr += 1
f.close()
# print(i+1, "records")
# print(ln_cntr, "written.")
# print(blank_ln, "skipped")
# print(f_lines)
# print("")
return f_lines
def cms_text_read(inText):
# Read textfield and save in OrderedDict
# Identify Headings and set them as level 0
# Everything else assign as Level 1
# Add in claimNumber value to line_dict to simplify detail
# downstream processing of lines
DBUG = False
ln_cntr = 0
blank_ln = 0
f_lines = []
set_level = 0
line_type = "BODY"
header_line = False
set_header = "HEADER"
current_segment = ""
claim_number = ""
kvs = {}
line_dict = {"key": 0,
"level": 0,
"line": "",
"type": "",
"claimNumber": "",
"category": ""}
if DBUG:
print("In apps.bluebutton.cms_parser.cms_text_read")
i = 0
for l in inText.split('\n'):
# reset the dictionary
line_dict = {}
# Read each line in file
l = l.rstrip()
# remove white space from end of line
# if (i % 10) == 0:
# print ".",
# Show progress every 10 steps
if len(l) < 1:
# skip blank lines
blank_ln += 1
continue
if line_type == "BODY" and (divider in l):
header_line = True
get_title = True
line_type = "HEADER"
blank_ln += 1
continue
elif line_type == "HEADER" and header_line and get_title:
# Get the title line
# print "we found title:",l
# print i, "[About to set Seg:", l, "]"
# Save the current_segment before we overwrite it
if not (divider in l):
if len(l.strip()) > 0:
# print "title length:", len(l.strip())
# Remove : from Title - for Claims LineNumber:
titleline = l.split(":")
tl = titleline[0].rstrip()
set_header = line_type
current_segment = tl
get_title = False
if "CLAIM LINES FOR CLAIM NUMBER" in l.upper():
# we have to account for Part D Claims
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
set_level = 1
else:
set_level = 0
else:
# we didn't find a title
previous_segment = current_segment
current_segment = "claim Header"
set_level = 1
header_line = False
if current_segment == "claim Header":
set_header = "HEADER"
else:
set_header = "HEADER"
line_type = "BODY"
line_dict = {"key": ln_cntr,
"level": set_level,
"line": current_segment,
"type": set_header,
"claimNumber": claim_number}
elif line_type == "HEADER" and not get_title:
if divider in l:
set_header = "BODY"
line_type = "BODY"
header_line = False
blank_ln += 1
continue
else:
line_type = "BODY"
set_header = line_type
if "CLAIM NUMBER" in l.upper():
kvs = assign_simple_key(l, kvs)
claim_number = kvs["v"]
if "CLAIM TYPE: PART D" in l.upper():
prev_line = f_lines[ln_cntr - 1]
if DBUG:
do_DBUG("prev_line:", prev_line)
if prev_line[ln_cntr - 1][
"line"].upper() == "CLAIM LINES FOR CLAIM NUMBER":
prev_line[ln_cntr - 1]["line"] = "Part D Claims"
f_lines[ln_cntr - 1] = prev_line
if DBUG:
do_DBUG("re-wrote f_lines:",
f_lines[ln_cntr - 1])
line_dict = {"key": ln_cntr,
"level": set_level + 1,
"line": l,
"type": set_header,
"claimNumber": claim_number}
f_lines.append({ln_cntr: line_dict})
ln_cntr += 1
i += 1
return f_lines
def parse_lines(ln_list):
DBUG = False
if DBUG:
to_json(ln_list)
ln = {}
ln_ctrl = {}
hdr_lk_up = ""
seg_match_exact = True
match_ln = [None, None, None, None, None, None, None, None, None, None]
segment_dict = collections.OrderedDict()
out_dict = collections.OrderedDict()
if DBUG:
print("Initializing Working Storage Arrays...",)
block_limit = 9
block = collections.OrderedDict()
n = 0
while n <= block_limit:
block[n] = collections.OrderedDict()
n += 1
if DBUG:
print("Done.")
i = 0
en(ln_list) - 1):
ln = get_line_dict(ln_list, i)
if ln == {}:
if DBUG:
do_DBUG("Empty Ln", "Line(i):", i,
"ln:", ln)
i += 1
continue
wrk_lvl = ln["level"]
match_ln = update_match(wrk_lvl,
headlessCamel(ln["line"]),
match_ln)
match_hdr = combined_match(wrk_lvl, match_ln)
hdr_lk_up = headlessCamel(ln["line"])
if DBUG:
do_DBUG("Line(i):", i, "ln:", ln,
"hdr_lk_up:", hdr_lk_up)
if find_segment(hdr_lk_up, seg_match_exact):
ln_ctrl = get_segment(hdr_lk_up, seg_match_exact)
wrk_lvl = adjusted_level(ln["level"], match_ln)
if DBUG:
do_DBUG("CALLING PROCESS_HEADER===========================",
"i:", i,
"Match_ln:", match_ln,
"ln-ctrl:", to_json(ln_ctrl),
"ln_lvl:", ln["level"],
"wrk_lvl:", wrk_lvl)
i, sub_seg, seg_name = process_header(i, ln_ctrl,
wrk_lvl,
ln_list)
out_dict[seg_name] = sub_seg[seg_name]
if DBUG:
do_DBUG("=============== RETURNED FROM PROCESS_HEADER",
"line:", i,
"ln_control:", ln_ctrl,
"seg_name:", seg_name,
"custom processing:", key_value("custom", ln_ctrl),
"sub_seg:", to_json(sub_seg))
if key_value("custom", ln_ctrl) == "":
i, block_seg, block_name = process_subseg(i,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
elif key_value("custom", ln_ctrl) == "family_history":
i, block_seg, block_name = custom_family_history(i,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
elif key_value("custom", ln_ctrl) == "claim_summary":
i, block_seg, block_name = process_subseg(i,
ln_ctrl,
match_ln,
wrk_lvl,
ln_list,
sub_seg,
seg_name)
if DBUG:
do_DBUG("---------------- RETURNED FROM PROCESS_BLOCK",
"ctr: i:", i, "block_name:",block_name,
"block_seg:", to_json(block_seg),
)
if check_type(block_seg) == "LIST":
if DBUG:
do_DBUG("LIST returned",
block_seg)
if not block_seg == []:
out_dict[block_name] = block_seg[0]
else:
if DBUG:
do_DBUG("Not List",
block_seg)
out_dict[block_name] = block_seg
if DBUG:
do_DBUG("out_dict["+ block_name + "]:", to_json(out_dict))
if DBUG:
do_DBUG("============================",
"seg_name:", seg_name,
"segment returned:", sub_seg,
"Returned with counter-i:", i,
"----------------------------",
"out_dict[" + seg_name + "]",
to_json(out_dict[seg_name]),
"block_name:", block_name,
"block_seg:", block_seg)
if DBUG:
do_DBUG("====================END of LOOP",
"line number(i):", i,
"out_dict", to_json(out_dict),
"===============================")
i += 1
if DBUG:
do_DBUG("End of list:", i,
"out_dict", to_json(out_dict))
return out_dict
def cms_file_parse2(inPath):
result = cms_file_read(inPath)
k = ""
v = ""
items = collections.OrderedDict()
first_header = True
header_line = True
get_title = False
skip = False
line_type = "Body"
multi = False
skip = False
segment_source = ""
match_key = {}
match_string = ""
current_segment = ""
previous_segment = current_segment
header_block = {}
block_info = {}
line_list = []
segment_dict = collections.OrderedDict()
sub_segment_dict = collections.OrderedDict()
sub_segment_list = []
with open(inPath, 'r') as f:
for i, l in enumerate(f):
l = l.rstrip()
if line_type == "Body" and (divider in l):
header_line = True
get_title = True
line_type = "Header"
if not first_header:
dict = collections.OrderedDict()
line_list = []
multi = False
header_line and get_title:
if not divider in l:
if len(l.strip()) > 0:
titleline = l.split(":")
tl = titleline[0].rstrip()
previous_segment = current_segment
header_block = get_segment(headlessCamel(tl))
if len(header_block) > 1:
current_segment = header_block["name"]
else:
current_segment = headlessCamel(tl)
if find_segment(headlessCamel(tl)):
header_block = get_segment(headlessCamel(tl))
multi = multi_item(header_block)
header_block_level = get_header_block_level(header_block)
match_key[header_block_level] = current_segment
line_list = []
k = header_block["name"]
current_segment, segment_dict = segment_prefill(header_block)
get_title = False
else:
# So set a default
# Only claim summary title segments are blank
# save current_segment
previous_segment = current_segment
current_segment = "claimHeader"
# print("set title to", current_segment)
# print(i,"We never got a title line...",
# current_segment)
header_line = False
line_type = "Body"
# Write the last segment and reset
elif line_type == "Header" and (divider in l):
# this should be the closing divider line
# print("Closing Divider")
header_line = False
line_type = "Body"
else:
line_type = "Body"
# split on the : in to key and value
line = l.split(":")
if len(line) > 1:
# Assign line[0] to k and format as headlessCamel
k = headlessCamel(line[0])
v = line[1].lstrip()
v = v.rstrip()
#
# Now we deal with some special items.
# The Date and time in the header section
if k[2] == "/":
v = {"value": parse_time(l)}
k = "effectiveTime"
# print(i, ":", l)
# print(i, "got date for:",)
# current_segment, k, ":", v
segment_dict[k] = v
elif k.upper() == "SOURCE":
segment_source=set_source(segment_source, k, v)
# Apply headlessCamelCase to K
k = headlessCamel(k)
v = segment_source
# print(i, "set source in:",)
# current_segment, ":", k, ":", v
segment_dict[k] = v
else:
# match key against segment
match_string = current_segment + "." + k
print("Match:", match_string)
if find_segment(match_string):
# Get info about how to treat this key
# first we need to construct the field key to
# lookup in seg list
block_info = get_segment(match_string)
k = block_info["name"]
# print(i, ":k:", k, ":", block_info)
if block_info["mode"] == "block":
skip = True
if block_info["type"] == "dict":
sub_segment_dict[block_info["name"]] = v
elif block_info["type"] == "list":
sub_segment_list.append({k: v})
else:
sub_segment_dict[block_info["name"]] = v
elif block_info["mode"] == "close":
skip = True
if block_info["type"] == "dict":
sub_segment_dict[block_info["name"]] = v
segment_dict[block_info["dict_name"]] = sub_segment_dict
sub_segment_dict = collections.OrderedDict()
elif block_info["type"] == "list":
sub_segment_list.append({k: v})
segment_dict[block_info["dict_name"]] = sub_segment_list
sub_segment_list = []
else:
segment_dict[block_info["name"]] = v
if multi:
# Add Source value to each block
segment_dict["source"] = segment_source
# print("Line_List:[", line_list, "]")
# print("Segment_dict:[", segment_dict, "]")
if k in segment_dict:
line_list.append(segment_dict)
segment_dict = collections.OrderedDict()
if not skip:
segment_dict[k] = v
skip = False
# print("B[", i, ":", line_type, ":", l, "]")
# ===================
# Temporary Insertion
# if i > 80:
# break
# end of temporary insertion
# ===================
f.close()
# write the last segment
# print("Writing the last segment")
items, segment_dict, line_list = write_segment(items,
current_segment,
segment_dict,
line_list,
multi)
return items
def cms_file_parse(inPath):
# Parse a CMS BlueButton file (inPath)
# Using a redefined Parsing process
# Set default variables on entry
k = ""
v = ""
items = collections.OrderedDict()
first_header = True
header_line = True
get_title = False
line_type = "Header"
segment_dict = collections.OrderedDict()
current_segment = ""
segment_source = ""
previous_segment = current_segment
line_dict = collections.OrderedDict()
# Open the file for reading
with open(inPath, 'r') as f:
# get the line from the input file
for i, l in enumerate(f):
l = l.rstrip()
line = l.split(":")
if len(line) > 1:
k = line[0]
v = line[1].lstrip()
v = v.rstrip()
if len(l) <= 1 and header_line is False:
# The line is a detail line and is empty
# so ignore it and move on to next line
# print "empty line %s[%s] - skipping to next line" % (i,l)
continue
if header_line:
line_type = "Header"
else:
line_type = "Body"
# From now on We are dealing with a non-blank line
# Segment titles are wrapped by lines of minus signs (divider)
# So let's check if we have found a divider
if (divider in l) and not header_line:
header_line = True
get_title = True
if first_header:
first_header = False
continue
else:
print(i, "writing segment",)
items, segment_dict = write_segment(items,
current_segment,
segment_dict)
continue
if header_line and get_title:
if not divider in l:
previous_segment = current_segment
current_segment = k.lower().replace(" ", "_")
get_title = False
else:
current_segment = "claim_summary"
get_title = False
if find_segment(k):
seg_returned = get_segment(k)
k = seg_returned["name"]
current_segment, segment_dict = segment_prefill(seg_returned)
else:
current_segment = k.lower().replace(" ", "_")
segment_dict = collections.OrderedDict()
segment_dict[current_segment] = {}
print("%s:Current_Segment: %s" % (i, current_segment))
continue
print("[%s:CSeg:%s|%s L:[%s]" % (i,current_segment,
line_type,l))
rce(segment_source, k, v)
if k.upper() == "SOURCE":
k = k.lower()
v = segment_source
segment_dict[current_segment] = {k: v}
continue
line_dict[k] = v
# print("line_dict:", current_segment,":", line_dict)
segment_dict[current_segment] = line_dict
# reset the line_dict
line_dict = collections.OrderedDict()
# end of for loop
f.close()
# write the last segment
# print("Writing the last segment")
items, segment_dict = write_segment(items,
current_segment,
segment_dict)
return items
def set_header_line(hl):
# flip header_line value. received as hl (True or False)
return (not hl)
def multi_item(seg):
# check for "multi" in seg dict
# If multi line = "True" set to True
# use line_list instead of dict to allow multiple entries
multi = False
if "multi" in seg:
if seg["multi"] == "True":
multi = True
# print "Multi:", multi
return multi
def build_key(mk, bi):
# update make_key using content of build_info
lvl = bi["level"]
mk[lvl] = bi["name"]
return mk
def get_header_block_level(header_block):
lvl = 0
if "level" in header_block:
lvl = header_block["level"]
return lvl
| true | true |
f736e2f47ca31eb39e92dd13c5650b78cfe8a360 | 2,732 | py | Python | 4_test_google_privacy.py | imls-measuring-up/library-privacy | 45a82d1cffc0b176f811c2bccaeb5a7a8d8770ad | [
"MIT"
] | 1 | 2022-01-30T04:36:37.000Z | 2022-01-30T04:36:37.000Z | 4_test_google_privacy.py | imls-measuring-up/library-privacy | 45a82d1cffc0b176f811c2bccaeb5a7a8d8770ad | [
"MIT"
] | null | null | null | 4_test_google_privacy.py | imls-measuring-up/library-privacy | 45a82d1cffc0b176f811c2bccaeb5a7a8d8770ad | [
"MIT"
] | 1 | 2018-11-07T00:07:28.000Z | 2018-11-07T00:07:28.000Z | """
project = "Protecting Patron Privacy on the Web: A Study of HTTPS and Google Analytics Implementation in Academic Library Websites"
name = "4_test_google_privacy.py",
version = "1.0",
author = "Patrick OBrien",
date = "07/25/2018"
author_email = "patrick@revxcorp.com",
description = ("Audit tests for unique research library home pages returned by the study population web servers."),
license = "[MIT license](https://opensource.org/licenses/mit-license.php)",
keywords = "IMLS Measuring Up, Digital Repositories, Research Library Privacy",
url = "https://github.com/imls-measuring-up/library-privacy",
"""
import codecs
import csv
# create Org, School, Library and Member
dir_name = '_data/analysis/'
data_file = dir_name + 'unique-request-uuid.txt'
file = 0
with open(data_file, 'r') as f:
reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
count_us, count_uk, count_ca, count_int, count_except = 0, 0, 0, 0, 0
# unique text used to determine the use of Google Analytics (GA) and Tag Manager
testing = ['google-analytics.com', 'analytics.js', 'ga.js', 'googletagmanager', 'gtm.js'] # GA & Tag Manager
# testing = ['anonymizeip',] # GA IP anonymizer
# testing = ['forcessl',] # GA SSL
for row in reader:
old_name = row['Request_UUID']
new_name = row['fileRename']
test_file = dir_name + new_name + '.html'
file += 1
google_true = 0
country = new_name.split('-')[0]
with codecs.open(test_file, 'r') as fh:
try:
line_num = 0
for line in fh:
if google_true > 0:
break
check = line.lower().strip()
line_num += 1
for test in testing:
if test in check:
if country == 'us':
count_us += 1
if country == 'ca':
count_ca += 1
if country == 'uk':
count_uk += 1
if country == 'International':
count_int += 1
print('file {} line {} positive {} '.format(file, line_num, new_name))
google_true += 1
break
else:
continue
except:
print("syntax error {} {} {}".format(file, new_name, old_name))
count_except += 1
print('us', count_us)
print('ca', count_ca)
print('uk', count_uk)
print('int', count_int)
print('exceptions', count_except)
| 37.424658 | 131 | 0.533309 |
import codecs
import csv
dir_name = '_data/analysis/'
data_file = dir_name + 'unique-request-uuid.txt'
file = 0
with open(data_file, 'r') as f:
reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
count_us, count_uk, count_ca, count_int, count_except = 0, 0, 0, 0, 0
testing = ['google-analytics.com', 'analytics.js', 'ga.js', 'googletagmanager', 'gtm.js']
er:
old_name = row['Request_UUID']
new_name = row['fileRename']
test_file = dir_name + new_name + '.html'
file += 1
google_true = 0
country = new_name.split('-')[0]
with codecs.open(test_file, 'r') as fh:
try:
line_num = 0
for line in fh:
if google_true > 0:
break
check = line.lower().strip()
line_num += 1
for test in testing:
if test in check:
if country == 'us':
count_us += 1
if country == 'ca':
count_ca += 1
if country == 'uk':
count_uk += 1
if country == 'International':
count_int += 1
print('file {} line {} positive {} '.format(file, line_num, new_name))
google_true += 1
break
else:
continue
except:
print("syntax error {} {} {}".format(file, new_name, old_name))
count_except += 1
print('us', count_us)
print('ca', count_ca)
print('uk', count_uk)
print('int', count_int)
print('exceptions', count_except)
| true | true |
f736e3ebaa26d30804abcb8ec44299e7e1d2b296 | 341 | py | Python | src/sima/riflex/combinedloadingapproach.py | SINTEF/simapy | 650b8c2f15503dad98e2bfc0d0788509593822c7 | [
"MIT"
] | null | null | null | src/sima/riflex/combinedloadingapproach.py | SINTEF/simapy | 650b8c2f15503dad98e2bfc0d0788509593822c7 | [
"MIT"
] | null | null | null | src/sima/riflex/combinedloadingapproach.py | SINTEF/simapy | 650b8c2f15503dad98e2bfc0d0788509593822c7 | [
"MIT"
] | null | null | null | # Generated with CombinedLoadingApproach
#
from enum import Enum
from enum import auto
class CombinedLoadingApproach(Enum):
""""""
LRFD = auto()
WSD = auto()
def label(self):
if self == CombinedLoadingApproach.LRFD:
return "LRFD"
if self == CombinedLoadingApproach.WSD:
return "WSD" | 22.733333 | 48 | 0.630499 |
from enum import Enum
from enum import auto
class CombinedLoadingApproach(Enum):
LRFD = auto()
WSD = auto()
def label(self):
if self == CombinedLoadingApproach.LRFD:
return "LRFD"
if self == CombinedLoadingApproach.WSD:
return "WSD" | true | true |
f736e45b678665adc297ce013d0439aa1dc29cf3 | 6,104 | py | Python | utils/generate_dataset.py | milinddeore/pytorch-vsumm-reinforce | c3ca731c9a7f00282c8460deb47f34658cfc0522 | [
"MIT"
] | null | null | null | utils/generate_dataset.py | milinddeore/pytorch-vsumm-reinforce | c3ca731c9a7f00282c8460deb47f34658cfc0522 | [
"MIT"
] | null | null | null | utils/generate_dataset.py | milinddeore/pytorch-vsumm-reinforce | c3ca731c9a7f00282c8460deb47f34658cfc0522 | [
"MIT"
] | null | null | null | """
Generate Dataset
1. Converting video to frames
2. Extracting features
3. Getting change points
4. User Summary ( for evaluation )
"""
import os, sys
sys.path.append('../')
from networks.CNN import ResNet
from utils.KTS.cpd_auto import cpd_auto
from tqdm import tqdm
import math
import cv2
import numpy as np
import h5py
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--path', type=str, required=True, help="path of video file, whos h5 needs to generate.")
parser.add_argument('--h5_gen', type=str, required=True, help="path to h5 generated file")
args = parser.parse_args()
class Generate_Dataset:
def __init__(self, video_path, save_path):
self.resnet = ResNet()
self.dataset = {}
self.video_list = []
self.video_path = ''
self.frame_root_path = './frames'
self.h5_file = h5py.File(save_path, 'w')
self._set_video_list(video_path)
print('Video path : {} H5 autogen path : {}'.format(video_path, save_path))
def _set_video_list(self, video_path):
if os.path.isdir(video_path):
self.video_path = video_path
self.video_list = os.listdir(video_path)
self.video_list.sort()
else:
self.video_path = ''
self.video_list.append(video_path)
for idx, file_name in enumerate(self.video_list):
self.dataset['video_{}'.format(idx+1)] = {}
self.h5_file.create_group('video_{}'.format(idx+1))
def _extract_feature(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (224, 224))
res_pool5 = self.resnet(frame)
frame_feat = res_pool5.cpu().data.numpy().flatten()
return frame_feat
def _get_change_points(self, video_feat, n_frame, fps):
print('n_frame {} fps {}'.format(n_frame, fps))
n = n_frame / math.ceil(fps)
m = int(math.ceil(n/2.0))
K = np.dot(video_feat, video_feat.T)
change_points, _ = cpd_auto(K, m, 1)
change_points = np.concatenate(([0], change_points, [n_frame-1]))
temp_change_points = []
for idx in range(len(change_points)-1):
segment = [change_points[idx], change_points[idx+1]-1]
if idx == len(change_points)-2:
segment = [change_points[idx], change_points[idx+1]]
temp_change_points.append(segment)
change_points = np.array(list(temp_change_points))
temp_n_frame_per_seg = []
for change_points_idx in range(len(change_points)):
n_frame = change_points[change_points_idx][1] - change_points[change_points_idx][0]
temp_n_frame_per_seg.append(n_frame)
n_frame_per_seg = np.array(list(temp_n_frame_per_seg))
return change_points, n_frame_per_seg
# TODO : save dataset
def _save_dataset(self):
pass
def generate_dataset(self):
for video_idx, video_filename in enumerate(tqdm(self.video_list)):
video_path = video_filename
if os.path.isdir(self.video_path):
video_path = os.path.join(self.video_path, video_filename)
video_basename = os.path.basename(video_path).split('.')[0]
if not os.path.exists(os.path.join(self.frame_root_path, video_basename)):
os.mkdir(os.path.join(self.frame_root_path, video_basename))
video_capture = cv2.VideoCapture(video_path)
fps = video_capture.get(cv2.CAP_PROP_FPS)
n_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
#frame_list = []
picks = []
video_feat = None
video_feat_for_train = None
for frame_idx in tqdm(range(n_frames-1)):
success, frame = video_capture.read()
if success:
frame_feat = self._extract_feature(frame)
if frame_idx % 15 == 0:
picks.append(frame_idx)
if video_feat_for_train is None:
video_feat_for_train = frame_feat
else:
video_feat_for_train = np.vstack((video_feat_for_train, frame_feat))
if video_feat is None:
video_feat = frame_feat
else:
video_feat = np.vstack((video_feat, frame_feat))
img_filename = "{}.jpg".format(str(frame_idx).zfill(5))
cv2.imwrite(os.path.join(self.frame_root_path, video_basename, img_filename), frame)
else:
break
video_capture.release()
change_points, n_frame_per_seg = self._get_change_points(video_feat, n_frames, fps)
# self.dataset['video_{}'.format(video_idx+1)]['frames'] = list(frame_list)
# self.dataset['video_{}'.format(video_idx+1)]['features'] = list(video_feat)
# self.dataset['video_{}'.format(video_idx+1)]['picks'] = np.array(list(picks))
# self.dataset['video_{}'.format(video_idx+1)]['n_frames'] = n_frames
# self.dataset['video_{}'.format(video_idx+1)]['fps'] = fps
# self.dataset['video_{}'.format(video_idx+1)]['change_points'] = change_points
# self.dataset['video_{}'.format(video_idx+1)]['n_frame_per_seg'] = n_frame_per_seg
self.h5_file['video_{}'.format(video_idx+1)]['features'] = list(video_feat_for_train)
self.h5_file['video_{}'.format(video_idx+1)]['picks'] = np.array(list(picks))
self.h5_file['video_{}'.format(video_idx+1)]['n_frames'] = n_frames
self.h5_file['video_{}'.format(video_idx+1)]['fps'] = fps
self.h5_file['video_{}'.format(video_idx+1)]['change_points'] = change_points
self.h5_file['video_{}'.format(video_idx+1)]['n_frame_per_seg'] = n_frame_per_seg
if __name__ == "__main__":
gen = Generate_Dataset(args.path, args.h5_gen)
gen.generate_dataset()
gen.h5_file.close()
| 38.878981 | 115 | 0.6096 | import os, sys
sys.path.append('../')
from networks.CNN import ResNet
from utils.KTS.cpd_auto import cpd_auto
from tqdm import tqdm
import math
import cv2
import numpy as np
import h5py
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-p', '--path', type=str, required=True, help="path of video file, whos h5 needs to generate.")
parser.add_argument('--h5_gen', type=str, required=True, help="path to h5 generated file")
args = parser.parse_args()
class Generate_Dataset:
def __init__(self, video_path, save_path):
self.resnet = ResNet()
self.dataset = {}
self.video_list = []
self.video_path = ''
self.frame_root_path = './frames'
self.h5_file = h5py.File(save_path, 'w')
self._set_video_list(video_path)
print('Video path : {} H5 autogen path : {}'.format(video_path, save_path))
def _set_video_list(self, video_path):
if os.path.isdir(video_path):
self.video_path = video_path
self.video_list = os.listdir(video_path)
self.video_list.sort()
else:
self.video_path = ''
self.video_list.append(video_path)
for idx, file_name in enumerate(self.video_list):
self.dataset['video_{}'.format(idx+1)] = {}
self.h5_file.create_group('video_{}'.format(idx+1))
def _extract_feature(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = cv2.resize(frame, (224, 224))
res_pool5 = self.resnet(frame)
frame_feat = res_pool5.cpu().data.numpy().flatten()
return frame_feat
def _get_change_points(self, video_feat, n_frame, fps):
print('n_frame {} fps {}'.format(n_frame, fps))
n = n_frame / math.ceil(fps)
m = int(math.ceil(n/2.0))
K = np.dot(video_feat, video_feat.T)
change_points, _ = cpd_auto(K, m, 1)
change_points = np.concatenate(([0], change_points, [n_frame-1]))
temp_change_points = []
for idx in range(len(change_points)-1):
segment = [change_points[idx], change_points[idx+1]-1]
if idx == len(change_points)-2:
segment = [change_points[idx], change_points[idx+1]]
temp_change_points.append(segment)
change_points = np.array(list(temp_change_points))
temp_n_frame_per_seg = []
for change_points_idx in range(len(change_points)):
n_frame = change_points[change_points_idx][1] - change_points[change_points_idx][0]
temp_n_frame_per_seg.append(n_frame)
n_frame_per_seg = np.array(list(temp_n_frame_per_seg))
return change_points, n_frame_per_seg
def _save_dataset(self):
pass
def generate_dataset(self):
for video_idx, video_filename in enumerate(tqdm(self.video_list)):
video_path = video_filename
if os.path.isdir(self.video_path):
video_path = os.path.join(self.video_path, video_filename)
video_basename = os.path.basename(video_path).split('.')[0]
if not os.path.exists(os.path.join(self.frame_root_path, video_basename)):
os.mkdir(os.path.join(self.frame_root_path, video_basename))
video_capture = cv2.VideoCapture(video_path)
fps = video_capture.get(cv2.CAP_PROP_FPS)
n_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
picks = []
video_feat = None
video_feat_for_train = None
for frame_idx in tqdm(range(n_frames-1)):
success, frame = video_capture.read()
if success:
frame_feat = self._extract_feature(frame)
if frame_idx % 15 == 0:
picks.append(frame_idx)
if video_feat_for_train is None:
video_feat_for_train = frame_feat
else:
video_feat_for_train = np.vstack((video_feat_for_train, frame_feat))
if video_feat is None:
video_feat = frame_feat
else:
video_feat = np.vstack((video_feat, frame_feat))
img_filename = "{}.jpg".format(str(frame_idx).zfill(5))
cv2.imwrite(os.path.join(self.frame_root_path, video_basename, img_filename), frame)
else:
break
video_capture.release()
change_points, n_frame_per_seg = self._get_change_points(video_feat, n_frames, fps)
self.h5_file['video_{}'.format(video_idx+1)]['features'] = list(video_feat_for_train)
self.h5_file['video_{}'.format(video_idx+1)]['picks'] = np.array(list(picks))
self.h5_file['video_{}'.format(video_idx+1)]['n_frames'] = n_frames
self.h5_file['video_{}'.format(video_idx+1)]['fps'] = fps
self.h5_file['video_{}'.format(video_idx+1)]['change_points'] = change_points
self.h5_file['video_{}'.format(video_idx+1)]['n_frame_per_seg'] = n_frame_per_seg
if __name__ == "__main__":
gen = Generate_Dataset(args.path, args.h5_gen)
gen.generate_dataset()
gen.h5_file.close()
| true | true |
f736e4c1655e40b2f95896208acb95a003bdc7e2 | 12,147 | py | Python | tensorflow/python/kernel_tests/slice_op_test.py | jhabikal21/tensorflow | 98d20962172301385aae694141801a375debd2bc | [
"Apache-2.0"
] | null | null | null | tensorflow/python/kernel_tests/slice_op_test.py | jhabikal21/tensorflow | 98d20962172301385aae694141801a375debd2bc | [
"Apache-2.0"
] | null | null | null | tensorflow/python/kernel_tests/slice_op_test.py | jhabikal21/tensorflow | 98d20962172301385aae694141801a375debd2bc | [
"Apache-2.0"
] | null | null | null | # Copyright 2015 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.
# ==============================================================================
"""Functional tests for slice op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.platform import test
class SliceTest(test.TestCase):
def testEmpty(self):
inp = np.random.rand(4, 4).astype("f")
for k in xrange(4):
with self.test_session(use_gpu=True):
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testInt32(self):
inp = np.random.rand(4, 4).astype("i")
for k in xrange(4):
with self.test_session(use_gpu=True):
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.int32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testInt64Slicing(self):
with self.test_session(use_gpu=True):
a = constant_op.constant([0, 1, 2], dtype=dtypes.int64)
# Slice using int64 Tensor.
i = constant_op.constant(1, dtype=dtypes.int64)
slice_t = a[i]
slice_val = slice_t.eval()
self.assertAllEqual(1, slice_val)
slice_t = a[i:i+1]
slice_val = slice_t.eval()
self.assertAllEqual([1], slice_val)
# Slice using int64 integer.
i = np.asarray(1).astype(np.int64)
slice_t = a[i]
slice_val = slice_t.eval()
self.assertAllEqual(1, slice_val)
slice_t = a[i:i+1]
slice_val = slice_t.eval()
self.assertAllEqual([1], slice_val)
def testSelectAll(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 4, 4, 4).astype("f")
a = constant_op.constant(inp, shape=[4, 4, 4, 4], dtype=dtypes.float32)
slice_explicit_t = array_ops.slice(a, [0, 0, 0, 0], [-1, -1, -1, -1])
slice_implicit_t = a[:, :, :, :]
self.assertAllEqual(inp, slice_explicit_t.eval())
self.assertAllEqual(inp, slice_implicit_t.eval())
self.assertEqual(inp.shape, slice_explicit_t.get_shape())
self.assertEqual(inp.shape, slice_implicit_t.get_shape())
def testSingleDimension(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(10).astype("f")
a = constant_op.constant(inp, shape=[10], dtype=dtypes.float32)
hi = np.random.randint(0, 9)
scalar_t = a[hi]
scalar_val = scalar_t.eval()
self.assertAllEqual(scalar_val, inp[hi])
if hi > 0:
lo = np.random.randint(0, hi)
else:
lo = 0
slice_t = a[lo:hi]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[lo:hi])
def testScalarInput(self):
input_val = 0
with self.test_session() as sess:
# Test with constant input; shape inference fails.
with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
constant_op.constant(input_val)[:].get_shape()
# Test evaluating with non-constant input; kernel execution fails.
input_t = array_ops.placeholder(dtypes.int32)
slice_t = input_t[:]
with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
"out of range"):
sess.run([slice_t], feed_dict={input_t: input_val})
def testInvalidIndex(self):
input_val = [1, 2]
with self.test_session() as sess:
# Test with constant input; shape inference fails.
with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
constant_op.constant(input_val)[1:, 1:].get_shape()
# Test evaluating with non-constant input; kernel execution fails.
input_t = array_ops.placeholder(dtypes.int32)
slice_t = input_t[1:, 1:]
with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
"out of range"):
sess.run([slice_t], feed_dict={input_t: input_val})
def _testSliceMatrixDim0(self, x, begin, size):
with self.test_session(use_gpu=True):
tf_ans = array_ops.slice(x, [begin, 0], [size, x.shape[1]]).eval()
np_ans = x[begin:begin + size, :]
self.assertAllEqual(tf_ans, np_ans)
def testSliceMatrixDim0(self):
x = np.random.rand(8, 4).astype("f")
self._testSliceMatrixDim0(x, 1, 2)
self._testSliceMatrixDim0(x, 3, 3)
y = np.random.rand(8, 7).astype("f") # 7 * sizeof(float) is not aligned
self._testSliceMatrixDim0(y, 1, 2)
self._testSliceMatrixDim0(y, 3, 3)
def testSingleElementAll(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 4).astype("f")
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)
x, y = np.random.randint(0, 3, size=2).tolist()
slice_t = a[x, 0:y]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[x, 0:y])
def testSimple(self):
with self.test_session(use_gpu=True) as sess:
inp = np.random.rand(4, 4).astype("f")
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=[4, 4],
dtype=dtypes.float32)
slice_t = array_ops.slice(a, [0, 0], [2, 2])
slice2_t = a[:2, :2]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
self.assertAllEqual(slice_val, inp[:2, :2])
self.assertAllEqual(slice2_val, inp[:2, :2])
self.assertEqual(slice_val.shape, slice_t.get_shape())
self.assertEqual(slice2_val.shape, slice2_t.get_shape())
def testComplex(self):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 10, 10, 4).astype("f")
a = constant_op.constant(inp, dtype=dtypes.float32)
x = np.random.randint(0, 9)
z = np.random.randint(0, 9)
if z > 0:
y = np.random.randint(0, z)
else:
y = 0
slice_t = a[:, x, y:z, :]
self.assertAllEqual(slice_t.eval(), inp[:, x, y:z, :])
def testRandom(self):
# Random dims of rank 6
input_shape = np.random.randint(0, 20, size=6)
inp = np.random.rand(*input_shape).astype("f")
with self.test_session(use_gpu=True) as sess:
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=input_shape,
dtype=dtypes.float32)
indices = [0 if x == 0 else np.random.randint(x) for x in input_shape]
sizes = [
np.random.randint(0, input_shape[i] - indices[i] + 1)
for i in range(6)
]
slice_t = array_ops.slice(a, indices, sizes)
slice2_t = a[indices[0]:indices[0] + sizes[0], indices[1]:indices[
1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[3]
+ sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:
indices[5] + sizes[5]]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
expected_val = inp[indices[0]:indices[0] + sizes[0], indices[1]:indices[
1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[
3] + sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:indices[
5] + sizes[5]]
self.assertAllEqual(slice_val, expected_val)
self.assertAllEqual(slice2_val, expected_val)
self.assertEqual(expected_val.shape, slice_t.get_shape())
self.assertEqual(expected_val.shape, slice2_t.get_shape())
def testPartialShapeInference(self):
z = array_ops.zeros((1, 2, 3))
self.assertAllEqual(z.get_shape().as_list(), [1, 2, 3])
m1 = array_ops.slice(z, [0, 0, 0], [-1, -1, -1])
self.assertAllEqual(m1.get_shape().as_list(), [1, 2, 3])
m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1])
self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None])
def _testGradientSlice(self, input_shape, slice_begin, slice_size):
with self.test_session(use_gpu=True):
num_inputs = np.prod(input_shape)
num_grads = np.prod(slice_size)
inp = np.random.rand(num_inputs).astype("f").reshape(input_shape)
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=input_shape,
dtype=dtypes.float32)
slice_t = array_ops.slice(a, slice_begin, slice_size)
grads = np.random.rand(num_grads).astype("f").reshape(slice_size)
grad_tensor = constant_op.constant(grads)
grad = gradients_impl.gradients(slice_t, [a], grad_tensor)[0]
result = grad.eval()
# Create a zero tensor of the input shape ane place
# the grads into the right location to compare against TensorFlow.
np_ans = np.zeros(input_shape)
slices = []
for i in xrange(len(input_shape)):
slices.append(slice(slice_begin[i], slice_begin[i] + slice_size[i]))
np_ans[slices] = grads
self.assertAllClose(np_ans, result)
def _testGradientVariableSize(self):
with self.test_session(use_gpu=True):
inp = constant_op.constant([1.0, 2.0, 3.0], name="in")
out = array_ops.slice(inp, [1], [-1])
grad_actual = gradients_impl.gradients(out, inp)[0].eval()
self.assertAllClose([0., 1., 1.], grad_actual)
def testGradientsAll(self):
# Slice the middle square out of a 4x4 input
self._testGradientSlice([4, 4], [1, 1], [2, 2])
# Slice the upper left square out of a 4x4 input
self._testGradientSlice([4, 4], [0, 0], [2, 2])
# Slice a non-square input starting from (2,1)
self._testGradientSlice([4, 4], [2, 1], [1, 2])
# Slice a 3D tensor
self._testGradientSlice([3, 3, 3], [0, 1, 0], [2, 1, 1])
# Use -1 as a slice dimension.
self._testGradientVariableSize()
def testNotIterable(self):
# NOTE (mrry): If we register __getitem__ as an overloaded id:2995 gh:2996
# operator, Python will valiantly attempt to iterate over the
# Tensor from 0 to infinity. This test ensures that this
# unintended behavior is prevented.
c = constant_op.constant(5.0)
with self.assertRaisesWithPredicateMatch(
TypeError, lambda e: "`Tensor` objects are not iterable" in str(e)):
for _ in c:
pass
def testComputedShape(self):
# NOTE (mrry): We cannot currently handle partially-known values, id:3496 gh:3497
# because `tf.slice()` uses -1 to specify a wildcard size, and
# this can't be handled using the
# `tensor_util.constant_value_as_shape()` trick.
a = constant_op.constant([[1, 2, 3], [4, 5, 6]])
begin = constant_op.constant(0)
size = constant_op.constant(1)
b = array_ops.slice(a, [begin, 0], [size, 2])
self.assertEqual([1, 2], b.get_shape())
begin = array_ops.placeholder(dtypes.int32, shape=())
c = array_ops.slice(a, [begin, 0], [-1, 2])
self.assertEqual([None, 2], c.get_shape().as_list())
def testSliceOfSlice(self):
with self.test_session(use_gpu=True):
a = constant_op.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
b = a[1:, :]
c = b[:-1, :]
d = c[1, :]
res = 2 * d - c[1, :] + a[2, :] - 2 * b[-2, :]
self.assertAllEqual([0, 0, 0], res.eval())
if __name__ == "__main__":
test.main()
| 38.318612 | 85 | 0.635959 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.platform import test
class SliceTest(test.TestCase):
def testEmpty(self):
inp = np.random.rand(4, 4).astype("f")
for k in xrange(4):
with self.test_session(use_gpu=True):
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testInt32(self):
inp = np.random.rand(4, 4).astype("i")
for k in xrange(4):
with self.test_session(use_gpu=True):
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.int32)
slice_t = a[2, k:k]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[2, k:k])
def testInt64Slicing(self):
with self.test_session(use_gpu=True):
a = constant_op.constant([0, 1, 2], dtype=dtypes.int64)
i = constant_op.constant(1, dtype=dtypes.int64)
slice_t = a[i]
slice_val = slice_t.eval()
self.assertAllEqual(1, slice_val)
slice_t = a[i:i+1]
slice_val = slice_t.eval()
self.assertAllEqual([1], slice_val)
i = np.asarray(1).astype(np.int64)
slice_t = a[i]
slice_val = slice_t.eval()
self.assertAllEqual(1, slice_val)
slice_t = a[i:i+1]
slice_val = slice_t.eval()
self.assertAllEqual([1], slice_val)
def testSelectAll(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 4, 4, 4).astype("f")
a = constant_op.constant(inp, shape=[4, 4, 4, 4], dtype=dtypes.float32)
slice_explicit_t = array_ops.slice(a, [0, 0, 0, 0], [-1, -1, -1, -1])
slice_implicit_t = a[:, :, :, :]
self.assertAllEqual(inp, slice_explicit_t.eval())
self.assertAllEqual(inp, slice_implicit_t.eval())
self.assertEqual(inp.shape, slice_explicit_t.get_shape())
self.assertEqual(inp.shape, slice_implicit_t.get_shape())
def testSingleDimension(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(10).astype("f")
a = constant_op.constant(inp, shape=[10], dtype=dtypes.float32)
hi = np.random.randint(0, 9)
scalar_t = a[hi]
scalar_val = scalar_t.eval()
self.assertAllEqual(scalar_val, inp[hi])
if hi > 0:
lo = np.random.randint(0, hi)
else:
lo = 0
slice_t = a[lo:hi]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[lo:hi])
def testScalarInput(self):
input_val = 0
with self.test_session() as sess:
with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
constant_op.constant(input_val)[:].get_shape()
input_t = array_ops.placeholder(dtypes.int32)
slice_t = input_t[:]
with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
"out of range"):
sess.run([slice_t], feed_dict={input_t: input_val})
def testInvalidIndex(self):
input_val = [1, 2]
with self.test_session() as sess:
with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
constant_op.constant(input_val)[1:, 1:].get_shape()
input_t = array_ops.placeholder(dtypes.int32)
slice_t = input_t[1:, 1:]
with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
"out of range"):
sess.run([slice_t], feed_dict={input_t: input_val})
def _testSliceMatrixDim0(self, x, begin, size):
with self.test_session(use_gpu=True):
tf_ans = array_ops.slice(x, [begin, 0], [size, x.shape[1]]).eval()
np_ans = x[begin:begin + size, :]
self.assertAllEqual(tf_ans, np_ans)
def testSliceMatrixDim0(self):
x = np.random.rand(8, 4).astype("f")
self._testSliceMatrixDim0(x, 1, 2)
self._testSliceMatrixDim0(x, 3, 3)
y = np.random.rand(8, 7).astype("f")
self._testSliceMatrixDim0(y, 1, 2)
self._testSliceMatrixDim0(y, 3, 3)
def testSingleElementAll(self):
for _ in range(10):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 4).astype("f")
a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)
x, y = np.random.randint(0, 3, size=2).tolist()
slice_t = a[x, 0:y]
slice_val = slice_t.eval()
self.assertAllEqual(slice_val, inp[x, 0:y])
def testSimple(self):
with self.test_session(use_gpu=True) as sess:
inp = np.random.rand(4, 4).astype("f")
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=[4, 4],
dtype=dtypes.float32)
slice_t = array_ops.slice(a, [0, 0], [2, 2])
slice2_t = a[:2, :2]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
self.assertAllEqual(slice_val, inp[:2, :2])
self.assertAllEqual(slice2_val, inp[:2, :2])
self.assertEqual(slice_val.shape, slice_t.get_shape())
self.assertEqual(slice2_val.shape, slice2_t.get_shape())
def testComplex(self):
with self.test_session(use_gpu=True):
inp = np.random.rand(4, 10, 10, 4).astype("f")
a = constant_op.constant(inp, dtype=dtypes.float32)
x = np.random.randint(0, 9)
z = np.random.randint(0, 9)
if z > 0:
y = np.random.randint(0, z)
else:
y = 0
slice_t = a[:, x, y:z, :]
self.assertAllEqual(slice_t.eval(), inp[:, x, y:z, :])
def testRandom(self):
input_shape = np.random.randint(0, 20, size=6)
inp = np.random.rand(*input_shape).astype("f")
with self.test_session(use_gpu=True) as sess:
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=input_shape,
dtype=dtypes.float32)
indices = [0 if x == 0 else np.random.randint(x) for x in input_shape]
sizes = [
np.random.randint(0, input_shape[i] - indices[i] + 1)
for i in range(6)
]
slice_t = array_ops.slice(a, indices, sizes)
slice2_t = a[indices[0]:indices[0] + sizes[0], indices[1]:indices[
1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[3]
+ sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:
indices[5] + sizes[5]]
slice_val, slice2_val = sess.run([slice_t, slice2_t])
expected_val = inp[indices[0]:indices[0] + sizes[0], indices[1]:indices[
1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[
3] + sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:indices[
5] + sizes[5]]
self.assertAllEqual(slice_val, expected_val)
self.assertAllEqual(slice2_val, expected_val)
self.assertEqual(expected_val.shape, slice_t.get_shape())
self.assertEqual(expected_val.shape, slice2_t.get_shape())
def testPartialShapeInference(self):
z = array_ops.zeros((1, 2, 3))
self.assertAllEqual(z.get_shape().as_list(), [1, 2, 3])
m1 = array_ops.slice(z, [0, 0, 0], [-1, -1, -1])
self.assertAllEqual(m1.get_shape().as_list(), [1, 2, 3])
m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1])
self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None])
def _testGradientSlice(self, input_shape, slice_begin, slice_size):
with self.test_session(use_gpu=True):
num_inputs = np.prod(input_shape)
num_grads = np.prod(slice_size)
inp = np.random.rand(num_inputs).astype("f").reshape(input_shape)
a = constant_op.constant(
[float(x) for x in inp.ravel(order="C")],
shape=input_shape,
dtype=dtypes.float32)
slice_t = array_ops.slice(a, slice_begin, slice_size)
grads = np.random.rand(num_grads).astype("f").reshape(slice_size)
grad_tensor = constant_op.constant(grads)
grad = gradients_impl.gradients(slice_t, [a], grad_tensor)[0]
result = grad.eval()
np_ans = np.zeros(input_shape)
slices = []
for i in xrange(len(input_shape)):
slices.append(slice(slice_begin[i], slice_begin[i] + slice_size[i]))
np_ans[slices] = grads
self.assertAllClose(np_ans, result)
def _testGradientVariableSize(self):
with self.test_session(use_gpu=True):
inp = constant_op.constant([1.0, 2.0, 3.0], name="in")
out = array_ops.slice(inp, [1], [-1])
grad_actual = gradients_impl.gradients(out, inp)[0].eval()
self.assertAllClose([0., 1., 1.], grad_actual)
def testGradientsAll(self):
self._testGradientSlice([4, 4], [1, 1], [2, 2])
self._testGradientSlice([4, 4], [0, 0], [2, 2])
self._testGradientSlice([4, 4], [2, 1], [1, 2])
self._testGradientSlice([3, 3, 3], [0, 1, 0], [2, 1, 1])
self._testGradientVariableSize()
def testNotIterable(self):
c = constant_op.constant(5.0)
with self.assertRaisesWithPredicateMatch(
TypeError, lambda e: "`Tensor` objects are not iterable" in str(e)):
for _ in c:
pass
def testComputedShape(self):
# `tensor_util.constant_value_as_shape()` trick.
a = constant_op.constant([[1, 2, 3], [4, 5, 6]])
begin = constant_op.constant(0)
size = constant_op.constant(1)
b = array_ops.slice(a, [begin, 0], [size, 2])
self.assertEqual([1, 2], b.get_shape())
begin = array_ops.placeholder(dtypes.int32, shape=())
c = array_ops.slice(a, [begin, 0], [-1, 2])
self.assertEqual([None, 2], c.get_shape().as_list())
def testSliceOfSlice(self):
with self.test_session(use_gpu=True):
a = constant_op.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
b = a[1:, :]
c = b[:-1, :]
d = c[1, :]
res = 2 * d - c[1, :] + a[2, :] - 2 * b[-2, :]
self.assertAllEqual([0, 0, 0], res.eval())
if __name__ == "__main__":
test.main()
| true | true |
f736e4c7956805611287953150f73298a8b1389b | 314 | py | Python | web/run_script.py | alexander-jaferey/dominos | 9802d97208058a01791259f851d51a3fd88981a0 | [
"MIT"
] | 1 | 2018-04-19T02:36:37.000Z | 2018-04-19T02:36:37.000Z | web/run_script.py | alexander-jaferey/dominos | 9802d97208058a01791259f851d51a3fd88981a0 | [
"MIT"
] | null | null | null | web/run_script.py | alexander-jaferey/dominos | 9802d97208058a01791259f851d51a3fd88981a0 | [
"MIT"
] | 1 | 2018-08-10T02:45:02.000Z | 2018-08-10T02:45:02.000Z |
import json
import subprocess
def run_script():
data = json.load(open('store.json', 'r'))
script = data['script_to_run']
if script == "":
# Nothing will be run
return 1
subprocess.call(['/usr/bin/python', script])
return 0
if __name__ == "__main__":
run_script() | 15.7 | 48 | 0.592357 |
import json
import subprocess
def run_script():
data = json.load(open('store.json', 'r'))
script = data['script_to_run']
if script == "":
return 1
subprocess.call(['/usr/bin/python', script])
return 0
if __name__ == "__main__":
run_script() | true | true |
f736e66675c4c1243bcc374bfc4a221aeb75ecb6 | 7,943 | py | Python | scripts/EA_A_03_2LFact_Data.py | EloyRD/ThesisExp | dfb890708e95d23cc68ff79b0858630c12aa940d | [
"Unlicense"
] | null | null | null | scripts/EA_A_03_2LFact_Data.py | EloyRD/ThesisExp | dfb890708e95d23cc68ff79b0858630c12aa940d | [
"Unlicense"
] | null | null | null | scripts/EA_A_03_2LFact_Data.py | EloyRD/ThesisExp | dfb890708e95d23cc68ff79b0858630c12aa940d | [
"Unlicense"
] | null | null | null | # -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,scripts//py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.1.6
# kernelspec:
# display_name: Python [conda env:thesis] *
# language: python
# name: conda-env-thesis-py
# ---
# %% [raw]
# \author{Eloy Ruiz-Donayre}
# \title{TESTCASE A - 2-Level 6-Factor Full Factorial (With 30 replicates) - Data Generation}
# \date{\today}
# \maketitle
# %% [raw]
# \tableofcontents
# %% [markdown]
# # Preliminaries
# %% [markdown]
# Importing python packages and setting display parameters
# %%
import numpy as np
import pandas as pd
import itertools as it
import scipy.stats as stats
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import thesis_EAfunc as EAf
import thesis_visfunc as EAv
# %%
plt.style.use("bmh")
# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
pd.set_option("display.latex.repr", True)
pd.set_option("display.latex.longtable", True)
# %% [markdown] {"toc-hr-collapsed": false}
# # Fitness Landscape Definition
# %%
# Problem domain
x_min = -15
x_max = 15
y_min = -15
y_max = 15
# Known minimum
x_point = -1
y_point = -1
domain = (x_min, x_max, y_min, y_max)
point = (x_point, y_point)
img_size = (8.5, 4.25)
# Problem definition
def f(x, y):
D = 2
alpha = 1 / 8
x = (x - 5) / 6
y = (y - 5) / 6
a = np.abs(x ** 2 + y ** 2 - D) ** (alpha * D)
b = (0.5 * (x ** 2 + y ** 2) + (x + y)) / D
return a + b + 0.5
# %%
# Testing the minimum
print(f(-1, -1))
# %%
# Testing the function
print(f(-1.0, -1.0), f(-11.0, -9.0), f(11.0, 3.0), f(-6.0, 9.0))
# %% [markdown] {"toc-hr-collapsed": false}
# # Setting up the experiment
# 64 Experiments
# >L-> In each experiment, one set of parameters is used.
# >>L-> 40 Replicates per experiment.
# >>>L-> Each replicate is different due to randomness effects.
# %%
# starting seed
np.random.seed(42)
# %% [markdown]
# ## Initializing data storage
# %%
mult_fit_cols = (
["exp"]
+ ["pop_s"]
+ ["b"]
+ ["mut_p"]
+ ["mut_s"]
+ ["p_sel"]
+ ["s_sel"]
+ ["run", "generation", "fitness_min", "fitness_max", "fitness_mean", "fitness_std"]
)
multi_fit = pd.DataFrame(columns=mult_fit_cols)
multi_fit = multi_fit.infer_objects()
# %% [markdown] {"toc-hr-collapsed": false}
# ## Parameter space for the experiment
# %% [markdown]
# ### Initializing
# %%
# Algorithm parameters
# Number of replicates, and generations per experiment
rep_n = 30
gen_f = 200
# Population size
pop_s = [10, 160]
# Parent subpopulation's selection method and size
par_selection = ["uniform", "tournament_k3"]
b = [0.5, 5]
par_s = [z * y for z in pop_s for y in b]
# Progeny subpopulation's size
prog_s = par_s
# Crossover Method
crossover = "uniform"
# Mutation method, probability and size
mutation = "random_all_gau_dis"
mut_p = [0.1, 0.9]
mut_s = [0.5, 5]
# New population selection method
sur_selection = ["fitness_proportional_selection", "uniform"]
# %% [markdown]
# ### 2-Level Factors encoded values
# %%
inputs_labels = {
"pop_s": "Population size",
"b": "Progeny-to-population ratio",
"mut_p": "Mutation Probability",
"mut_s": "Mutation size",
"p_sel": "Parent selection",
"s_sel": "Survivor selection method",
}
dat = [
("pop_s", 10, 160, -1, 1, "Numerical"),
("b", 0.5, 5, -1, 1, "Numerical"),
("mut_p", 0.1, 0.9, -1, 1, "Numerical (<1)"),
("mut_s", 0.5, 5, -1, 1, "Numerical"),
("p_sel", "uniform", "tournament k3", -1, 1, "Categorical"),
("s_sel", "fitness proportional", "uniform", -1, 1, "Categorical"),
]
inputs_df = pd.DataFrame(
dat,
columns=[
"Factor",
"Value_low",
"Value_high",
"encoded_low",
"encoded_high",
"Variable type",
],
)
inputs_df = inputs_df.set_index(["Factor"])
inputs_df["Label"] = inputs_df.index.map(lambda z: inputs_labels[z])
inputs_df = inputs_df[
["Label", "Variable type", "Value_low", "Value_high", "encoded_low", "encoded_high"]
]
inputs_df
# %% [markdown]
# ### Combining the 2-level Factors
# %% [markdown]
# We create a list with all the possible combinations of the 2-level factors
# %%
exp_par = list(it.product(pop_s, b, mut_p, mut_s, par_selection, sur_selection))
print('Cantidad de combinaciones de parametros en "exp_par" :' + str(len(exp_par)))
print()
print('Primera y última combinación de parametros en "exp_par":')
print("Secuencia (pop_s, b, mut_p, mut_s, p_sel, s_sel)")
print(exp_par[0])
print(exp_par[63])
# %% [markdown]
# # Experiment execution
# %%
# %%time
exp_n = 1
for (zz, yy, xx, vv, uu, tt) in exp_par:
sur_selection = tt
par_selection = uu
mut_s = vv
mut_p = xx
b = yy
pop_s = zz
prog_s = int(b * pop_s)
par_s = prog_s
fitness_res = EAf.EA_exp_only_fitness(
rep_n,
gen_f,
f,
domain,
pop_s,
par_s,
prog_s,
mut_p,
mut_s,
par_selection,
crossover,
mutation,
sur_selection,
)
fitness_res.insert(0, "s_sel", tt)
fitness_res.insert(0, "p_sel", uu)
fitness_res.insert(0, "mut_s", vv)
fitness_res.insert(0, "mut_p", xx)
fitness_res.insert(0, "b", yy)
fitness_res.insert(0, "pop_s", zz)
fitness_res.insert(0, "exp", exp_n)
multi_fit = multi_fit.append(fitness_res, ignore_index=True, sort=False)
multi_fit = multi_fit.infer_objects()
exp_n += 1
# %% [markdown]
# ## Data storage
# %% [markdown]
# Writing the Data Frame to a pickle file
# %%
multi_fit.to_pickle("./Data/TEST_A_2L_FitData.gz", compression="gzip")
# %% [markdown]
# Reading the Data Frame from a pickle file
# %%
multi_fit = pd.read_pickle("./Data/TEST_A_2L_FitData.gz", compression="gzip")
# %%
multi_fit.tail()
# %% [markdown]
# # Processing data for DOE Analysis
# %% [markdown]
# Storing the latest generation's population of each replicate
# %%
query = multi_fit["generation"] == gen_f
multi_final_fitness_res = multi_fit[query]
# %% [markdown]
# Reordering columns
# %%
multi_final_fitness_res = multi_final_fitness_res.drop(
["exp", "generation", "run", "seed"], axis=1
)
multi_final_fitness_res.columns = [
"pop_s",
"b",
"mut_p",
"mut_s",
"p_sel",
"s_sel",
"f_min",
"f_max",
"f_mean",
"f_std",
]
multi_final_fitness_res = multi_final_fitness_res[
[
"pop_s",
"b",
"mut_p",
"mut_s",
"p_sel",
"s_sel",
"f_min",
"f_max",
"f_mean",
"f_std",
]
]
multi_final_fitness_res = multi_final_fitness_res.reset_index(drop=True)
# %% [markdown]
# Encoding values for DOE's Factors
# %%
multi_final_fitness_res["pop_s"] = (
multi_final_fitness_res["pop_s"].replace([10, 160], [-1, 1]).infer_objects()
)
multi_final_fitness_res["b"] = (
multi_final_fitness_res["b"].replace([0.5, 5], [-1, 1]).infer_objects()
)
multi_final_fitness_res["mut_p"] = (
multi_final_fitness_res["mut_p"].replace([0.1, 0.9], [-1, 1]).infer_objects()
)
multi_final_fitness_res["mut_s"] = (
multi_final_fitness_res["mut_s"].replace([0.5, 5], [-1, 1]).infer_objects()
)
multi_final_fitness_res["p_sel"] = (
multi_final_fitness_res["p_sel"]
.replace(["uniform", "tournament_k3"], [-1, 1])
.infer_objects()
)
multi_final_fitness_res["s_sel"] = (
multi_final_fitness_res["s_sel"]
.replace(["fitness_proportional_selection", "uniform"], [-1, 1])
.infer_objects()
)
# %% [markdown]
# Exploring the Data Frame
# %%
multi_final_fitness_res.head()
# %%
multi_final_fitness_res.tail()
# %% [markdown]
# Storing the Factor Coding and DOE results Data Frames
# %%
inputs_df.to_pickle("./Data/TEST_A_DOE_code.gz", compression="gzip")
multi_final_fitness_res.to_pickle("./Data/TEST_A_DOE_data.gz", compression="gzip")
# %%
| 21.881543 | 93 | 0.631122 |
mpy as np
import pandas as pd
import itertools as it
import scipy.stats as stats
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import thesis_EAfunc as EAf
import thesis_visfunc as EAv
plt.style.use("bmh")
pd.set_option("display.latex.repr", True)
pd.set_option("display.latex.longtable", True)
in = -15
y_max = 15
x_point = -1
y_point = -1
domain = (x_min, x_max, y_min, y_max)
point = (x_point, y_point)
img_size = (8.5, 4.25)
def f(x, y):
D = 2
alpha = 1 / 8
x = (x - 5) / 6
y = (y - 5) / 6
a = np.abs(x ** 2 + y ** 2 - D) ** (alpha * D)
b = (0.5 * (x ** 2 + y ** 2) + (x + y)) / D
return a + b + 0.5
print(f(-1, -1))
print(f(-1.0, -1.0), f(-11.0, -9.0), f(11.0, 3.0), f(-6.0, 9.0))
["b"]
+ ["mut_p"]
+ ["mut_s"]
+ ["p_sel"]
+ ["s_sel"]
+ ["run", "generation", "fitness_min", "fitness_max", "fitness_mean", "fitness_std"]
)
multi_fit = pd.DataFrame(columns=mult_fit_cols)
multi_fit = multi_fit.infer_objects()
[z * y for z in pop_s for y in b]
# Progeny subpopulation's size
prog_s = par_s
crossover = "uniform"
mutation = "random_all_gau_dis"
mut_p = [0.1, 0.9]
mut_s = [0.5, 5]
sur_selection = ["fitness_proportional_selection", "uniform"]
ut_p": "Mutation Probability",
"mut_s": "Mutation size",
"p_sel": "Parent selection",
"s_sel": "Survivor selection method",
}
dat = [
("pop_s", 10, 160, -1, 1, "Numerical"),
("b", 0.5, 5, -1, 1, "Numerical"),
("mut_p", 0.1, 0.9, -1, 1, "Numerical (<1)"),
("mut_s", 0.5, 5, -1, 1, "Numerical"),
("p_sel", "uniform", "tournament k3", -1, 1, "Categorical"),
("s_sel", "fitness proportional", "uniform", -1, 1, "Categorical"),
]
inputs_df = pd.DataFrame(
dat,
columns=[
"Factor",
"Value_low",
"Value_high",
"encoded_low",
"encoded_high",
"Variable type",
],
)
inputs_df = inputs_df.set_index(["Factor"])
inputs_df["Label"] = inputs_df.index.map(lambda z: inputs_labels[z])
inputs_df = inputs_df[
["Label", "Variable type", "Value_low", "Value_high", "encoded_low", "encoded_high"]
]
inputs_df
ntidad de combinaciones de parametros en "exp_par" :' + str(len(exp_par)))
print()
print('Primera y última combinación de parametros en "exp_par":')
print("Secuencia (pop_s, b, mut_p, mut_s, p_sel, s_sel)")
print(exp_par[0])
print(exp_par[63])
yy, xx, vv, uu, tt) in exp_par:
sur_selection = tt
par_selection = uu
mut_s = vv
mut_p = xx
b = yy
pop_s = zz
prog_s = int(b * pop_s)
par_s = prog_s
fitness_res = EAf.EA_exp_only_fitness(
rep_n,
gen_f,
f,
domain,
pop_s,
par_s,
prog_s,
mut_p,
mut_s,
par_selection,
crossover,
mutation,
sur_selection,
)
fitness_res.insert(0, "s_sel", tt)
fitness_res.insert(0, "p_sel", uu)
fitness_res.insert(0, "mut_s", vv)
fitness_res.insert(0, "mut_p", xx)
fitness_res.insert(0, "b", yy)
fitness_res.insert(0, "pop_s", zz)
fitness_res.insert(0, "exp", exp_n)
multi_fit = multi_fit.append(fitness_res, ignore_index=True, sort=False)
multi_fit = multi_fit.infer_objects()
exp_n += 1
Data/TEST_A_2L_FitData.gz", compression="gzip")
multi_fit = pd.read_pickle("./Data/TEST_A_2L_FitData.gz", compression="gzip")
multi_fit.tail()
ation"] == gen_f
multi_final_fitness_res = multi_fit[query]
# %% [markdown]
# Reordering columns
# %%
multi_final_fitness_res = multi_final_fitness_res.drop(
["exp", "generation", "run", "seed"], axis=1
)
multi_final_fitness_res.columns = [
"pop_s",
"b",
"mut_p",
"mut_s",
"p_sel",
"s_sel",
"f_min",
"f_max",
"f_mean",
"f_std",
]
multi_final_fitness_res = multi_final_fitness_res[
[
"pop_s",
"b",
"mut_p",
"mut_s",
"p_sel",
"s_sel",
"f_min",
"f_max",
"f_mean",
"f_std",
]
]
multi_final_fitness_res = multi_final_fitness_res.reset_index(drop=True)
# %% [markdown]
# Encoding values for DOE's Factors
multi_final_fitness_res["pop_s"] = (
multi_final_fitness_res["pop_s"].replace([10, 160], [-1, 1]).infer_objects()
)
multi_final_fitness_res["b"] = (
multi_final_fitness_res["b"].replace([0.5, 5], [-1, 1]).infer_objects()
)
multi_final_fitness_res["mut_p"] = (
multi_final_fitness_res["mut_p"].replace([0.1, 0.9], [-1, 1]).infer_objects()
)
multi_final_fitness_res["mut_s"] = (
multi_final_fitness_res["mut_s"].replace([0.5, 5], [-1, 1]).infer_objects()
)
multi_final_fitness_res["p_sel"] = (
multi_final_fitness_res["p_sel"]
.replace(["uniform", "tournament_k3"], [-1, 1])
.infer_objects()
)
multi_final_fitness_res["s_sel"] = (
multi_final_fitness_res["s_sel"]
.replace(["fitness_proportional_selection", "uniform"], [-1, 1])
.infer_objects()
)
multi_final_fitness_res.head()
multi_final_fitness_res.tail()
inputs_df.to_pickle("./Data/TEST_A_DOE_code.gz", compression="gzip")
multi_final_fitness_res.to_pickle("./Data/TEST_A_DOE_data.gz", compression="gzip")
| true | true |
f736e67d0d50de0c5ff2caaf470ee5c389e17f43 | 17,828 | py | Python | fairseq_cli/train.py | monofo/fairseq | 335a4cbd403543ece43e24b41abbe53fc54b5f36 | [
"MIT"
] | 115 | 2021-08-25T14:58:12.000Z | 2022-03-21T11:25:36.000Z | fairseq_cli/train.py | monofo/fairseq | 335a4cbd403543ece43e24b41abbe53fc54b5f36 | [
"MIT"
] | 5 | 2021-09-13T10:48:28.000Z | 2021-12-21T13:52:25.000Z | fairseq_cli/train.py | monofo/fairseq | 335a4cbd403543ece43e24b41abbe53fc54b5f36 | [
"MIT"
] | 11 | 2021-08-25T16:22:07.000Z | 2021-11-24T16:26:20.000Z | #!/usr/bin/env python3 -u
# 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.
"""
Train a new model on one or across multiple GPUs.
"""
import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators
from fairseq.data.plasma_utils import PlasmaStore
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics, progress_bar
from fairseq.model_parallel.megatron_trainer import MegatronTrainer
from fairseq.trainer import Trainer
from omegaconf import DictConfig, OmegaConf
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.train")
def main(cfg: FairseqConfig) -> None:
if isinstance(cfg, argparse.Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
utils.import_user_module(cfg.common)
if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
# make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))
assert (
cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
), "Must specify batch size either with --max-tokens or --batch-size"
metrics.reset()
np.random.seed(cfg.common.seed)
utils.set_torch_seed(cfg.common.seed)
if distributed_utils.is_master(cfg.distributed_training):
checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
# Print args
logger.info(cfg)
if cfg.checkpoint.write_checkpoints_asynchronously:
try:
import iopath # noqa: F401
except ImportError:
logging.exception(
"Asynchronous checkpoint writing is specified but iopath is "
"not installed: `pip install iopath`"
)
return
# Setup task, e.g., translation, language modeling, etc.
task = tasks.setup_task(cfg.task)
assert cfg.criterion, "Please specify criterion to train a model"
# Build model and criterion
if cfg.distributed_training.ddp_backend == "fully_sharded":
with fsdp_enable_wrap(cfg.distributed_training):
model = fsdp_wrap(task.build_model(cfg.model))
else:
model = task.build_model(cfg.model)
criterion = task.build_criterion(cfg.criterion)
logger.info(model)
logger.info("task: {}".format(task.__class__.__name__))
logger.info("model: {}".format(model.__class__.__name__))
logger.info("criterion: {}".format(criterion.__class__.__name__))
logger.info(
"num. shared model params: {:,} (num. trained: {:,})".format(
sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)),
sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad)
)
)
logger.info(
"num. expert model params: {} (num. trained: {})".format(
sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad),
)
)
# Load valid dataset (we load training data below, based on the latest checkpoint)
# We load the valid dataset AFTER building the model
for valid_sub_split in cfg.dataset.valid_subset.split(","):
task.load_dataset(valid_sub_split, combine=False, epoch=1)
# (optionally) Configure quantization
if cfg.common.quantization_config_path is not None:
quantizer = quantization_utils.Quantizer(
config_path=cfg.common.quantization_config_path,
max_epoch=cfg.optimization.max_epoch,
max_update=cfg.optimization.max_update,
)
else:
quantizer = None
# Build trainer
if cfg.common.model_parallel_size == 1:
trainer = Trainer(cfg, task, model, criterion, quantizer)
else:
trainer = MegatronTrainer(cfg, task, model, criterion)
logger.info(
"training on {} devices (GPUs/TPUs)".format(
cfg.distributed_training.distributed_world_size
)
)
logger.info(
"max tokens per device = {} and max sentences per device = {}".format(
cfg.dataset.max_tokens,
cfg.dataset.batch_size,
)
)
# Load the latest checkpoint if one is available and restore the
# corresponding train iterator
extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
cfg.checkpoint,
trainer,
# don't cache epoch iterators for sharded datasets
disable_iterator_cache=task.has_sharded_data("train"),
)
if cfg.common.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("load_checkpoint") # wait for all workers
max_epoch = cfg.optimization.max_epoch or math.inf
lr = trainer.get_lr()
train_meter = meters.StopwatchMeter()
train_meter.start()
while epoch_itr.next_epoch_idx <= max_epoch:
if lr <= cfg.optimization.stop_min_lr:
logger.info(
f"stopping training because current learning rate ({lr}) is smaller "
"than or equal to minimum learning rate "
f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
)
break
# train for one epoch
valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
if should_stop:
break
# only use first validation loss to update the learning rate
lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
epoch_itr = trainer.get_train_iterator(
epoch_itr.next_epoch_idx,
# sharded data: get train iterator for next epoch
load_dataset=task.has_sharded_data("train"),
# don't cache epoch iterators for sharded datasets
disable_iterator_cache=task.has_sharded_data("train"),
)
train_meter.stop()
logger.info("done training in {:.1f} seconds".format(train_meter.sum))
# ioPath implementation to wait for all asynchronous file writes to complete.
if cfg.checkpoint.write_checkpoints_asynchronously:
logger.info(
"ioPath PathManager waiting for all asynchronous checkpoint "
"writes to finish."
)
PathManager.async_close()
logger.info("ioPath PathManager finished waiting.")
def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
# skip check if no validation was done in the current epoch
if valid_loss is None:
return False
if cfg.checkpoint.patience <= 0:
return False
def is_better(a, b):
return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
prev_best = getattr(should_stop_early, "best", None)
if prev_best is None or is_better(valid_loss, prev_best):
should_stop_early.best = valid_loss
should_stop_early.num_runs = 0
return False
else:
should_stop_early.num_runs += 1
if should_stop_early.num_runs >= cfg.checkpoint.patience:
logger.info(
"early stop since valid performance hasn't improved for last {} runs".format(
cfg.checkpoint.patience
)
)
return True
else:
return False
@metrics.aggregate("train")
def train(
cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
) -> Tuple[List[Optional[float]], bool]:
"""Train the model for one epoch and return validation losses."""
# Initialize data iterator
itr = epoch_itr.next_epoch_itr(
fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
)
update_freq = (
cfg.optimization.update_freq[epoch_itr.epoch - 1]
if epoch_itr.epoch <= len(cfg.optimization.update_freq)
else cfg.optimization.update_freq[-1]
)
itr = iterators.GroupedIterator(itr, update_freq)
if cfg.common.tpu:
itr = utils.tpu_data_loader(itr)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
epoch=epoch_itr.epoch,
tensorboard_logdir=(
cfg.common.tensorboard_logdir
if distributed_utils.is_master(cfg.distributed_training)
else None
),
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
wandb_project=(
cfg.common.wandb_project
if distributed_utils.is_master(cfg.distributed_training)
else None
),
wandb_run_name=os.environ.get(
"WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
),
azureml_logging=(
cfg.common.azureml_logging
if distributed_utils.is_master(cfg.distributed_training)
else False
),
)
progress.update_config(_flatten_config(cfg))
trainer.begin_epoch(epoch_itr.epoch)
valid_subsets = cfg.dataset.valid_subset.split(",")
should_stop = False
num_updates = trainer.get_num_updates()
logger.info("Start iterating over samples")
for i, samples in enumerate(progress):
with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
"train_step-%d" % i
):
log_output = trainer.train_step(samples)
if log_output is not None: # not OOM, overflow, ...
# log mid-epoch stats
num_updates = trainer.get_num_updates()
if num_updates % cfg.common.log_interval == 0:
stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
progress.log(stats, tag="train_inner", step=num_updates)
# reset mid-epoch stats after each log interval
# the end-of-epoch stats will still be preserved
metrics.reset_meters("train_inner")
end_of_epoch = not itr.has_next()
valid_losses, should_stop = validate_and_save(
cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
)
if should_stop:
break
# log end-of-epoch stats
logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
stats = get_training_stats(metrics.get_smoothed_values("train"))
progress.print(stats, tag="train", step=num_updates)
# reset epoch-level meters
metrics.reset_meters("train")
return valid_losses, should_stop
def _flatten_config(cfg: DictConfig):
config = OmegaConf.to_container(cfg)
# remove any legacy Namespaces and replace with a single "args"
namespace = None
for k, v in list(config.items()):
if isinstance(v, argparse.Namespace):
namespace = v
del config[k]
if namespace is not None:
config["args"] = vars(namespace)
return config
def validate_and_save(
cfg: DictConfig,
trainer: Trainer,
task: tasks.FairseqTask,
epoch_itr,
valid_subsets: List[str],
end_of_epoch: bool,
) -> Tuple[List[Optional[float]], bool]:
num_updates = trainer.get_num_updates()
max_update = cfg.optimization.max_update or math.inf
# Stopping conditions (and an additional one based on validation loss later
# on)
should_stop = False
if num_updates >= max_update:
should_stop = True
logger.info(
f"Stopping training due to "
f"num_updates: {num_updates} >= max_update: {max_update}"
)
training_time_hours = trainer.cumulative_training_time() / (60 * 60)
if (
cfg.optimization.stop_time_hours > 0
and training_time_hours > cfg.optimization.stop_time_hours
):
should_stop = True
logger.info(
f"Stopping training due to "
f"cumulative_training_time: {training_time_hours} > "
f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
)
do_save = (
(end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
or should_stop
or (
cfg.checkpoint.save_interval_updates > 0
and num_updates > 0
and num_updates % cfg.checkpoint.save_interval_updates == 0
and num_updates >= cfg.dataset.validate_after_updates
)
)
do_validate = (
(not end_of_epoch and do_save) # validate during mid-epoch saves
or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
or should_stop
or (
cfg.dataset.validate_interval_updates > 0
and num_updates > 0
and num_updates % cfg.dataset.validate_interval_updates == 0
)
) and not cfg.dataset.disable_validation
# Validate
valid_losses = [None]
if do_validate:
valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
should_stop |= should_stop_early(cfg, valid_losses[0])
# Save checkpoint
if do_save or should_stop:
checkpoint_utils.save_checkpoint(
cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
)
return valid_losses, should_stop
def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
return stats
def validate(
cfg: DictConfig,
trainer: Trainer,
task: tasks.FairseqTask,
epoch_itr,
subsets: List[str],
) -> List[Optional[float]]:
"""Evaluate the model on the validation set(s) and return the losses."""
if cfg.dataset.fixed_validation_seed is not None:
# set fixed seed for every validation
utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
trainer.begin_valid_epoch(epoch_itr.epoch)
valid_losses = []
for subset in subsets:
logger.info('begin validation on "{}" subset'.format(subset))
# Initialize data iterator
itr = trainer.get_valid_iterator(subset).next_epoch_itr(
shuffle=False, set_dataset_epoch=False # use a fixed valid set
)
if cfg.common.tpu:
itr = utils.tpu_data_loader(itr)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
epoch=epoch_itr.epoch,
prefix=f"valid on '{subset}' subset",
tensorboard_logdir=(
cfg.common.tensorboard_logdir
if distributed_utils.is_master(cfg.distributed_training)
else None
),
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
wandb_project=(
cfg.common.wandb_project
if distributed_utils.is_master(cfg.distributed_training)
else None
),
wandb_run_name=os.environ.get(
"WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
),
)
# create a new root metrics aggregator so validation metrics
# don't pollute other aggregators (e.g., train meters)
with metrics.aggregate(new_root=True) as agg:
for i, sample in enumerate(progress):
if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
break
trainer.valid_step(sample)
# log validation stats
stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values())
progress.print(stats, tag=subset, step=trainer.get_num_updates())
valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
return valid_losses
def get_valid_stats(
cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
) -> Dict[str, Any]:
stats["num_updates"] = trainer.get_num_updates()
if hasattr(checkpoint_utils.save_checkpoint, "best"):
key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
stats[key] = best_function(
checkpoint_utils.save_checkpoint.best,
stats[cfg.checkpoint.best_checkpoint_metric],
)
return stats
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
cfg = convert_namespace_to_omegaconf(args)
if cfg.common.use_plasma_view:
server = PlasmaStore(path=cfg.common.plasma_path)
logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
# if cfg.common.use_plasma_view:
# server.server.kill()
if __name__ == "__main__":
cli_main()
| 35.727455 | 109 | 0.658234 |
import argparse
import logging
import math
import os
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable
import numpy as np
import torch
from fairseq import (
checkpoint_utils,
options,
quantization_utils,
tasks,
utils,
)
from fairseq.data import iterators
from fairseq.data.plasma_utils import PlasmaStore
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics, progress_bar
from fairseq.model_parallel.megatron_trainer import MegatronTrainer
from fairseq.trainer import Trainer
from omegaconf import DictConfig, OmegaConf
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.train")
def main(cfg: FairseqConfig) -> None:
if isinstance(cfg, argparse.Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
utils.import_user_module(cfg.common)
if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
g.job_logging_cfg))
assert (
cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
), "Must specify batch size either with --max-tokens or --batch-size"
metrics.reset()
np.random.seed(cfg.common.seed)
utils.set_torch_seed(cfg.common.seed)
if distributed_utils.is_master(cfg.distributed_training):
checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
logger.info(cfg)
if cfg.checkpoint.write_checkpoints_asynchronously:
try:
import iopath
except ImportError:
logging.exception(
"Asynchronous checkpoint writing is specified but iopath is "
"not installed: `pip install iopath`"
)
return
task = tasks.setup_task(cfg.task)
assert cfg.criterion, "Please specify criterion to train a model"
if cfg.distributed_training.ddp_backend == "fully_sharded":
with fsdp_enable_wrap(cfg.distributed_training):
model = fsdp_wrap(task.build_model(cfg.model))
else:
model = task.build_model(cfg.model)
criterion = task.build_criterion(cfg.criterion)
logger.info(model)
logger.info("task: {}".format(task.__class__.__name__))
logger.info("model: {}".format(model.__class__.__name__))
logger.info("criterion: {}".format(criterion.__class__.__name__))
logger.info(
"num. shared model params: {:,} (num. trained: {:,})".format(
sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)),
sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad)
)
)
logger.info(
"num. expert model params: {} (num. trained: {})".format(
sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad),
)
)
for valid_sub_split in cfg.dataset.valid_subset.split(","):
task.load_dataset(valid_sub_split, combine=False, epoch=1)
if cfg.common.quantization_config_path is not None:
quantizer = quantization_utils.Quantizer(
config_path=cfg.common.quantization_config_path,
max_epoch=cfg.optimization.max_epoch,
max_update=cfg.optimization.max_update,
)
else:
quantizer = None
if cfg.common.model_parallel_size == 1:
trainer = Trainer(cfg, task, model, criterion, quantizer)
else:
trainer = MegatronTrainer(cfg, task, model, criterion)
logger.info(
"training on {} devices (GPUs/TPUs)".format(
cfg.distributed_training.distributed_world_size
)
)
logger.info(
"max tokens per device = {} and max sentences per device = {}".format(
cfg.dataset.max_tokens,
cfg.dataset.batch_size,
)
)
extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
cfg.checkpoint,
trainer,
disable_iterator_cache=task.has_sharded_data("train"),
)
if cfg.common.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("load_checkpoint") # wait for all workers
max_epoch = cfg.optimization.max_epoch or math.inf
lr = trainer.get_lr()
train_meter = meters.StopwatchMeter()
train_meter.start()
while epoch_itr.next_epoch_idx <= max_epoch:
if lr <= cfg.optimization.stop_min_lr:
logger.info(
f"stopping training because current learning rate ({lr}) is smaller "
"than or equal to minimum learning rate "
f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
)
break
# train for one epoch
valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
if should_stop:
break
# only use first validation loss to update the learning rate
lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
epoch_itr = trainer.get_train_iterator(
epoch_itr.next_epoch_idx,
# sharded data: get train iterator for next epoch
load_dataset=task.has_sharded_data("train"),
# don't cache epoch iterators for sharded datasets
disable_iterator_cache=task.has_sharded_data("train"),
)
train_meter.stop()
logger.info("done training in {:.1f} seconds".format(train_meter.sum))
if cfg.checkpoint.write_checkpoints_asynchronously:
logger.info(
"ioPath PathManager waiting for all asynchronous checkpoint "
"writes to finish."
)
PathManager.async_close()
logger.info("ioPath PathManager finished waiting.")
def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
if valid_loss is None:
return False
if cfg.checkpoint.patience <= 0:
return False
def is_better(a, b):
return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
prev_best = getattr(should_stop_early, "best", None)
if prev_best is None or is_better(valid_loss, prev_best):
should_stop_early.best = valid_loss
should_stop_early.num_runs = 0
return False
else:
should_stop_early.num_runs += 1
if should_stop_early.num_runs >= cfg.checkpoint.patience:
logger.info(
"early stop since valid performance hasn't improved for last {} runs".format(
cfg.checkpoint.patience
)
)
return True
else:
return False
@metrics.aggregate("train")
def train(
cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
) -> Tuple[List[Optional[float]], bool]:
# Initialize data iterator
itr = epoch_itr.next_epoch_itr(
fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
)
update_freq = (
cfg.optimization.update_freq[epoch_itr.epoch - 1]
if epoch_itr.epoch <= len(cfg.optimization.update_freq)
else cfg.optimization.update_freq[-1]
)
itr = iterators.GroupedIterator(itr, update_freq)
if cfg.common.tpu:
itr = utils.tpu_data_loader(itr)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
epoch=epoch_itr.epoch,
tensorboard_logdir=(
cfg.common.tensorboard_logdir
if distributed_utils.is_master(cfg.distributed_training)
else None
),
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
wandb_project=(
cfg.common.wandb_project
if distributed_utils.is_master(cfg.distributed_training)
else None
),
wandb_run_name=os.environ.get(
"WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
),
azureml_logging=(
cfg.common.azureml_logging
if distributed_utils.is_master(cfg.distributed_training)
else False
),
)
progress.update_config(_flatten_config(cfg))
trainer.begin_epoch(epoch_itr.epoch)
valid_subsets = cfg.dataset.valid_subset.split(",")
should_stop = False
num_updates = trainer.get_num_updates()
logger.info("Start iterating over samples")
for i, samples in enumerate(progress):
with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
"train_step-%d" % i
):
log_output = trainer.train_step(samples)
if log_output is not None: # not OOM, overflow, ...
# log mid-epoch stats
num_updates = trainer.get_num_updates()
if num_updates % cfg.common.log_interval == 0:
stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
progress.log(stats, tag="train_inner", step=num_updates)
# reset mid-epoch stats after each log interval
# the end-of-epoch stats will still be preserved
metrics.reset_meters("train_inner")
end_of_epoch = not itr.has_next()
valid_losses, should_stop = validate_and_save(
cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
)
if should_stop:
break
# log end-of-epoch stats
logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
stats = get_training_stats(metrics.get_smoothed_values("train"))
progress.print(stats, tag="train", step=num_updates)
# reset epoch-level meters
metrics.reset_meters("train")
return valid_losses, should_stop
def _flatten_config(cfg: DictConfig):
config = OmegaConf.to_container(cfg)
# remove any legacy Namespaces and replace with a single "args"
namespace = None
for k, v in list(config.items()):
if isinstance(v, argparse.Namespace):
namespace = v
del config[k]
if namespace is not None:
config["args"] = vars(namespace)
return config
def validate_and_save(
cfg: DictConfig,
trainer: Trainer,
task: tasks.FairseqTask,
epoch_itr,
valid_subsets: List[str],
end_of_epoch: bool,
) -> Tuple[List[Optional[float]], bool]:
num_updates = trainer.get_num_updates()
max_update = cfg.optimization.max_update or math.inf
# Stopping conditions (and an additional one based on validation loss later
# on)
should_stop = False
if num_updates >= max_update:
should_stop = True
logger.info(
f"Stopping training due to "
f"num_updates: {num_updates} >= max_update: {max_update}"
)
training_time_hours = trainer.cumulative_training_time() / (60 * 60)
if (
cfg.optimization.stop_time_hours > 0
and training_time_hours > cfg.optimization.stop_time_hours
):
should_stop = True
logger.info(
f"Stopping training due to "
f"cumulative_training_time: {training_time_hours} > "
f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
)
do_save = (
(end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
or should_stop
or (
cfg.checkpoint.save_interval_updates > 0
and num_updates > 0
and num_updates % cfg.checkpoint.save_interval_updates == 0
and num_updates >= cfg.dataset.validate_after_updates
)
)
do_validate = (
(not end_of_epoch and do_save) # validate during mid-epoch saves
or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
or should_stop
or (
cfg.dataset.validate_interval_updates > 0
and num_updates > 0
and num_updates % cfg.dataset.validate_interval_updates == 0
)
) and not cfg.dataset.disable_validation
# Validate
valid_losses = [None]
if do_validate:
valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
should_stop |= should_stop_early(cfg, valid_losses[0])
# Save checkpoint
if do_save or should_stop:
checkpoint_utils.save_checkpoint(
cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
)
return valid_losses, should_stop
def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
return stats
def validate(
cfg: DictConfig,
trainer: Trainer,
task: tasks.FairseqTask,
epoch_itr,
subsets: List[str],
) -> List[Optional[float]]:
if cfg.dataset.fixed_validation_seed is not None:
# set fixed seed for every validation
utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
trainer.begin_valid_epoch(epoch_itr.epoch)
valid_losses = []
for subset in subsets:
logger.info('begin validation on "{}" subset'.format(subset))
# Initialize data iterator
itr = trainer.get_valid_iterator(subset).next_epoch_itr(
shuffle=False, set_dataset_epoch=False # use a fixed valid set
)
if cfg.common.tpu:
itr = utils.tpu_data_loader(itr)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
epoch=epoch_itr.epoch,
prefix=f"valid on '{subset}' subset",
tensorboard_logdir=(
cfg.common.tensorboard_logdir
if distributed_utils.is_master(cfg.distributed_training)
else None
),
default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
wandb_project=(
cfg.common.wandb_project
if distributed_utils.is_master(cfg.distributed_training)
else None
),
wandb_run_name=os.environ.get(
"WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
),
)
# create a new root metrics aggregator so validation metrics
# don't pollute other aggregators (e.g., train meters)
with metrics.aggregate(new_root=True) as agg:
for i, sample in enumerate(progress):
if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
break
trainer.valid_step(sample)
stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values())
progress.print(stats, tag=subset, step=trainer.get_num_updates())
valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
return valid_losses
def get_valid_stats(
cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
) -> Dict[str, Any]:
stats["num_updates"] = trainer.get_num_updates()
if hasattr(checkpoint_utils.save_checkpoint, "best"):
key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
stats[key] = best_function(
checkpoint_utils.save_checkpoint.best,
stats[cfg.checkpoint.best_checkpoint_metric],
)
return stats
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
cfg = convert_namespace_to_omegaconf(args)
if cfg.common.use_plasma_view:
server = PlasmaStore(path=cfg.common.plasma_path)
logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
if __name__ == "__main__":
cli_main()
| true | true |
f736e6c3b6d7f063a22d5c7b5693cdc7a2d0b599 | 7,723 | py | Python | skyhook/skyhook/plugins/common_spider_step_executor.py | wfgydbu/skyhook | 8577d903bef90110646860fa6c59543c6a60d053 | [
"BSD-2-Clause"
] | 2 | 2020-04-20T15:36:08.000Z | 2020-07-15T00:50:04.000Z | skyhook/skyhook/plugins/common_spider_step_executor.py | wfgydbu/skyhook | 8577d903bef90110646860fa6c59543c6a60d053 | [
"BSD-2-Clause"
] | 1 | 2021-03-31T19:44:32.000Z | 2021-03-31T19:44:32.000Z | skyhook/skyhook/plugins/common_spider_step_executor.py | wfgydbu/skyhook | 8577d903bef90110646860fa6c59543c6a60d053 | [
"BSD-2-Clause"
] | 2 | 2020-04-28T01:37:20.000Z | 2020-07-02T04:47:37.000Z | import logging
import re
from skyhook.plugins.extractor import Extractor
from skyhook.items import CommonItem
from skyhook.plugins.processor.general import General
class CommonSpiderStepExecutor(object):
def __init__(self, response, parsed_list, policy_depth, rule, spider, item=None):
self.response = response
self.parsed_list = parsed_list
self.policy_depth = policy_depth
self.rule = rule
self.spider = spider
self.cur_policy = rule['meta']['policies'][self.policy_depth]
self.phase = self.cur_policy['phase']
self.item = item
def execute(self):
if self.phase == 'node':
return self.parse_node()
elif self.phase == 'field':
return self.parse_field()
else:
logging.error('unknown phase [{}]'.format(self.phase))
return SkipResult('skip because unknown phase {}'.format(self.phase))
def parse_node(self):
data = self.extract_path(extract_info=self.cur_policy, item=self.item)
if len(data) > 1:
limit = self.cur_policy.get('limit', [0, None])
start, end = self.get_list_range(limit=limit)
data = data[start:end]
return NodeStepResult(data=data)
def parse_field(self):
if not self.item: # step=1
logging.info('node phase: totally get {} nodes.'.format(len(self.parsed_list)))
items = []
for parsed_text in self.parsed_list:
item = CommonItem()
item['spider'] = self.rule['spider']
item['category'] = self.rule['category']
item['meta'] = self.rule['meta']
item['gfwBlocked'] = self.rule.get('gfwBlocked', False)
item['ruleId'] = str(self.rule['_id'])
fill_result = self.fill_fields(item=item, parsed_text=parsed_text)
if not isinstance(fill_result, SkipResult):
items.append(item)
else:
logging.info(fill_result.msg)
return NodePhaseResult(items)
else:
fill_result = self.fill_fields(item=self.item)
if not isinstance(fill_result, SkipResult):
return FieldStepResult(skip=False, item=self.item)
else:
logging.debug(fill_result.msg)
return FieldStepResult(skip=True)
def fill_fields(self, item, parsed_text=None):
try:
if 'fields' in self.cur_policy and self.cur_policy['fields']:
fields = self.cur_policy['fields']
for idx, field in enumerate(fields):
if field.get('type', '') == 'template':
# 先不处理template类型的字段
continue
logging.debug(field)
data = self.extract_path(extract_info=field, item=item, parsed_text=parsed_text)
cur_field = field['tag']
logging.debug(data)
# TODO: 这里应该是可以优化的
if field['tag'] in self.spider.settings['COMMON_SPIDER_FIELDS_TAG']['multi_value']:
item[cur_field] = [e for e in data]
elif field['tag'] in self.spider.settings['COMMON_SPIDER_FIELDS_TAG']['single_value']:
if field['tag'] == 'url':
if data:
item['url'] = data[0].decode('utf-8') if not isinstance(data[0], str) else data[0]
else:
item['url'] = self.rule['meta']['startUrl']
else:
item[cur_field] = ''.join([e.decode('utf-8') if not isinstance(e, str) else e for e in data])
else:
item.set_field(cur_field, ''.join([e.decode('utf-8') if not isinstance(e, str) else e for e in data]))
# 处理template类型的
template_fields = []
for field in fields:
if field.get('type', '') == 'template':
template_fields.append(field)
for field in template_fields:
cur_field = field['tag']
template_str = field['path']
template_field_name = re.findall(re.compile(r"\$\{(.*?)\}"), template_str)
for field_name in template_field_name:
template_str = template_str.replace("${%s}" % field_name, item.get_field(field_name))
aft_fn = field.get("aft_fn", "")
if aft_fn:
func = getattr(General, aft_fn[0], None)
logging.debug('execute function: {}'.format(aft_fn))
this = {'spider': self.spider, 'rule': self.rule, 'item': item, 'data': data}
if len(aft_fn) > 1 and aft_fn[1]:
data = func(this, **aft_fn[1])
else:
data = func(this)
template_str = "".join([e for e in data])
# logging.info('fill template field: {}, content: {}'.format(field, template_str))
try:
item[cur_field] = template_str
except Exception:
if "extras" in item.keys():
item["extras"][cur_field] = template_str
else:
item["extras"] = {cur_field: template_str}
return
except Exception:
logging.exception('fill_fields failed.')
return SkipResult("skip because fill_fields failed.")
def extract_path(self, extract_info, item, parsed_text=None):
parsed_list = [parsed_text] if parsed_text else self.parsed_list
path = extract_info.get('path', '')
path_type = extract_info.get('type', '')
aft_fn = extract_info.get('aft_fn', '')
data = []
try:
extractor = Extractor()
data = extractor.extract(parsed_list[0], path, path_type)
if not path:
logging.info('can not get extracted data because path is null, so use the current parsed_text as data.')
data = parsed_list
if aft_fn:
func = getattr(General, aft_fn[0], None)
logging.debug('execute function: {}'.format(aft_fn))
this = {'spider': self.spider, 'rule': self.rule, 'item': item, 'data': data}
if len(aft_fn) > 1 and aft_fn[1]:
data = func(this, **aft_fn[1])
else:
data = func(this)
return data
except Exception:
logging.exception('extract_path error, rule: {}'.format(self.rule))
return []
def get_list_range(self, limit=[0, None]):
if len(limit) <= 1:
start = int(limit[1]) if len(limit) == 1 else 0
end = None
else:
start = int(limit[0])
end = int(limit[1]) if limit[1] is not None else None
return start, end
class NodePhaseResult(object):
def __init__(self, items):
self.nodes = []
for idx, item in enumerate(items):
self.nodes.append({'item': item})
class FieldStepResult(object):
def __init__(self, skip=False, item=None):
self.skip = skip
self.item = item
class NodeStepResult(object):
def __init__(self, data=None, skip=False):
self.data = data
class SkipResult(object):
def __init__(self, msg):
self.msg = msg
| 41.079787 | 126 | 0.523372 | import logging
import re
from skyhook.plugins.extractor import Extractor
from skyhook.items import CommonItem
from skyhook.plugins.processor.general import General
class CommonSpiderStepExecutor(object):
def __init__(self, response, parsed_list, policy_depth, rule, spider, item=None):
self.response = response
self.parsed_list = parsed_list
self.policy_depth = policy_depth
self.rule = rule
self.spider = spider
self.cur_policy = rule['meta']['policies'][self.policy_depth]
self.phase = self.cur_policy['phase']
self.item = item
def execute(self):
if self.phase == 'node':
return self.parse_node()
elif self.phase == 'field':
return self.parse_field()
else:
logging.error('unknown phase [{}]'.format(self.phase))
return SkipResult('skip because unknown phase {}'.format(self.phase))
def parse_node(self):
data = self.extract_path(extract_info=self.cur_policy, item=self.item)
if len(data) > 1:
limit = self.cur_policy.get('limit', [0, None])
start, end = self.get_list_range(limit=limit)
data = data[start:end]
return NodeStepResult(data=data)
def parse_field(self):
if not self.item:
logging.info('node phase: totally get {} nodes.'.format(len(self.parsed_list)))
items = []
for parsed_text in self.parsed_list:
item = CommonItem()
item['spider'] = self.rule['spider']
item['category'] = self.rule['category']
item['meta'] = self.rule['meta']
item['gfwBlocked'] = self.rule.get('gfwBlocked', False)
item['ruleId'] = str(self.rule['_id'])
fill_result = self.fill_fields(item=item, parsed_text=parsed_text)
if not isinstance(fill_result, SkipResult):
items.append(item)
else:
logging.info(fill_result.msg)
return NodePhaseResult(items)
else:
fill_result = self.fill_fields(item=self.item)
if not isinstance(fill_result, SkipResult):
return FieldStepResult(skip=False, item=self.item)
else:
logging.debug(fill_result.msg)
return FieldStepResult(skip=True)
def fill_fields(self, item, parsed_text=None):
try:
if 'fields' in self.cur_policy and self.cur_policy['fields']:
fields = self.cur_policy['fields']
for idx, field in enumerate(fields):
if field.get('type', '') == 'template':
continue
logging.debug(field)
data = self.extract_path(extract_info=field, item=item, parsed_text=parsed_text)
cur_field = field['tag']
logging.debug(data)
if field['tag'] in self.spider.settings['COMMON_SPIDER_FIELDS_TAG']['multi_value']:
item[cur_field] = [e for e in data]
elif field['tag'] in self.spider.settings['COMMON_SPIDER_FIELDS_TAG']['single_value']:
if field['tag'] == 'url':
if data:
item['url'] = data[0].decode('utf-8') if not isinstance(data[0], str) else data[0]
else:
item['url'] = self.rule['meta']['startUrl']
else:
item[cur_field] = ''.join([e.decode('utf-8') if not isinstance(e, str) else e for e in data])
else:
item.set_field(cur_field, ''.join([e.decode('utf-8') if not isinstance(e, str) else e for e in data]))
template_fields = []
for field in fields:
if field.get('type', '') == 'template':
template_fields.append(field)
for field in template_fields:
cur_field = field['tag']
template_str = field['path']
template_field_name = re.findall(re.compile(r"\$\{(.*?)\}"), template_str)
for field_name in template_field_name:
template_str = template_str.replace("${%s}" % field_name, item.get_field(field_name))
aft_fn = field.get("aft_fn", "")
if aft_fn:
func = getattr(General, aft_fn[0], None)
logging.debug('execute function: {}'.format(aft_fn))
this = {'spider': self.spider, 'rule': self.rule, 'item': item, 'data': data}
if len(aft_fn) > 1 and aft_fn[1]:
data = func(this, **aft_fn[1])
else:
data = func(this)
template_str = "".join([e for e in data])
try:
item[cur_field] = template_str
except Exception:
if "extras" in item.keys():
item["extras"][cur_field] = template_str
else:
item["extras"] = {cur_field: template_str}
return
except Exception:
logging.exception('fill_fields failed.')
return SkipResult("skip because fill_fields failed.")
def extract_path(self, extract_info, item, parsed_text=None):
parsed_list = [parsed_text] if parsed_text else self.parsed_list
path = extract_info.get('path', '')
path_type = extract_info.get('type', '')
aft_fn = extract_info.get('aft_fn', '')
data = []
try:
extractor = Extractor()
data = extractor.extract(parsed_list[0], path, path_type)
if not path:
logging.info('can not get extracted data because path is null, so use the current parsed_text as data.')
data = parsed_list
if aft_fn:
func = getattr(General, aft_fn[0], None)
logging.debug('execute function: {}'.format(aft_fn))
this = {'spider': self.spider, 'rule': self.rule, 'item': item, 'data': data}
if len(aft_fn) > 1 and aft_fn[1]:
data = func(this, **aft_fn[1])
else:
data = func(this)
return data
except Exception:
logging.exception('extract_path error, rule: {}'.format(self.rule))
return []
def get_list_range(self, limit=[0, None]):
if len(limit) <= 1:
start = int(limit[1]) if len(limit) == 1 else 0
end = None
else:
start = int(limit[0])
end = int(limit[1]) if limit[1] is not None else None
return start, end
class NodePhaseResult(object):
def __init__(self, items):
self.nodes = []
for idx, item in enumerate(items):
self.nodes.append({'item': item})
class FieldStepResult(object):
def __init__(self, skip=False, item=None):
self.skip = skip
self.item = item
class NodeStepResult(object):
def __init__(self, data=None, skip=False):
self.data = data
class SkipResult(object):
def __init__(self, msg):
self.msg = msg
| true | true |
f736e74094878a57a5020e2019cd8e0dafb16f79 | 8,529 | py | Python | neural_deprojection/models/TwoD_to_2d_dVAE_GCD/graph_networks.py | Joshuaalbert/neural_deprojection | 5f7859bfd514efe1707a61e2a5e7fc6d949f85ce | [
"Apache-2.0"
] | null | null | null | neural_deprojection/models/TwoD_to_2d_dVAE_GCD/graph_networks.py | Joshuaalbert/neural_deprojection | 5f7859bfd514efe1707a61e2a5e7fc6d949f85ce | [
"Apache-2.0"
] | 35 | 2020-09-08T14:13:22.000Z | 2021-10-18T20:52:38.000Z | neural_deprojection/models/TwoD_to_2d_dVAE_GCD/graph_networks.py | Joshuaalbert/neural_deprojection | 5f7859bfd514efe1707a61e2a5e7fc6d949f85ce | [
"Apache-2.0"
] | null | null | null | import sys
sys.path.insert(1, '/data/s2675544/git/neural_deprojection/')
sys.path.insert(1, '/home/matthijs/git/neural_deprojection/')
from graph_nets import blocks
from graph_nets.utils_tf import concat
import tensorflow as tf
import sonnet as snt
from graph_nets.graphs import GraphsTuple
from graph_nets.utils_tf import fully_connect_graph_dynamic, fully_connect_graph_static
from neural_deprojection.graph_net_utils import AbstractModule, histogramdd, get_shape
import tensorflow_probability as tfp
from neural_deprojection.models.openai_dvae_modules.modules import Encoder, Decoder
class DiscreteImageVAE(AbstractModule):
def __init__(self,
hidden_size: int = 64,
embedding_dim: int = 64,
num_embedding: int = 1024,
num_token_samples: int = 32,
num_channels=1,
name=None):
super(DiscreteImageVAE, self).__init__(name=name)
# (num_embedding, embedding_dim)
self.num_channels=num_channels
self.embeddings = tf.Variable(initial_value=tf.random.truncated_normal((num_embedding, embedding_dim)),
name='embeddings')
self.num_token_samples = num_token_samples
self.num_embedding = num_embedding
self.embedding_dim = embedding_dim
self.temperature = tf.Variable(initial_value=tf.constant(1.), name='temperature', trainable=False)
self.beta = tf.Variable(initial_value=tf.constant(6.6), name='beta', trainable=False)
self.encoder = Encoder(hidden_size=hidden_size, num_embeddings=num_embedding, name='EncoderImage')
self.decoder = Decoder(hidden_size=hidden_size, num_channels=num_channels, name='DecoderImage')
def set_beta(self, beta):
self.beta.assign(beta)
def set_temperature(self, temperature):
self.temperature.assign(temperature)
@tf.function(input_signature=[tf.TensorSpec([None, None, None, None], dtype=tf.float32)])
def sample_encoder(self, img):
return self.encoder(img)
@tf.function(input_signature=[tf.TensorSpec([None, None, None, None], dtype=tf.float32),
tf.TensorSpec([], dtype=tf.float32),
tf.TensorSpec([], dtype=tf.float32)])
def sample_decoder(self, img_logits, temperature, num_samples):
[batch, H, W, _] = get_shape(img_logits)
logits = tf.reshape(img_logits, [batch * H * W, self.num_embedding]) # [batch*H*W, num_embeddings]
reduce_logsumexp = tf.math.reduce_logsumexp(logits, axis=-1) # [batch*H*W]
reduce_logsumexp = tf.tile(reduce_logsumexp[:, None], [1, self.num_embedding]) # [batch*H*W, num_embedding]
logits -= reduce_logsumexp # [batch*H*W, num_embeddings]
token_distribution = tfp.distributions.RelaxedOneHotCategorical(temperature, logits=logits)
token_samples_onehot = token_distribution.sample((num_samples,),
name='token_samples') # [S, batch*H*W, num_embeddings]
def _single_decode(token_sample_onehot):
# [batch*H*W, num_embeddings] @ [num_embeddings, embedding_dim]
token_sample = tf.matmul(token_sample_onehot, self.embeddings) # [batch*H*W, embedding_dim] # = z ~ q(z|x)
latent_img = tf.reshape(token_sample, [batch, H, W, self.embedding_dim]) # [batch, H, W, embedding_dim]
decoded_img = self.decoder(latent_img) # [batch, H', W', C*2]
return decoded_img
decoded_ims = tf.vectorized_map(_single_decode, token_samples_onehot) # [S, batch, H', W', C*2]
decoded_im = tf.reduce_mean(decoded_ims, axis=0) # [batch, H', W', C*2]
return decoded_im
def log_likelihood(self, img, mu, logb):
"""
Log-Laplace distribution.
Args:
img: [...,c] assumes of the form log(maximum(1e-5, img))
mu: [...,c]
logb: [...,c]
Returns:
log_prob [...]
"""
log_prob = - tf.math.abs(img - mu) / tf.math.exp(logb) \
- tf.math.log(2.) - img - logb
return tf.reduce_sum(log_prob, axis=-1)
def _build(self, img, **kwargs) -> dict:
"""
Args:
img: [batch, H', W', num_channel]
**kwargs:
Returns:
"""
encoded_img_logits = self.encoder(img) # [batch, H, W, num_embedding]
[batch, H, W, _] = get_shape(encoded_img_logits)
logits = tf.reshape(encoded_img_logits, [batch*H*W, self.num_embedding]) # [batch*H*W, num_embeddings]
reduce_logsumexp = tf.math.reduce_logsumexp(logits, axis=-1) # [batch*H*W]
reduce_logsumexp = tf.tile(reduce_logsumexp[:, None], [1, self.num_embedding]) # [batch*H*W, num_embedding]
logits -= reduce_logsumexp # [batch*H*W, num_embeddings]
temperature = tf.maximum(0.1, tf.cast(1. - 0.1/(self.step/1000), tf.float32))
token_distribution = tfp.distributions.RelaxedOneHotCategorical(temperature, logits=logits)
token_samples_onehot = token_distribution.sample((self.num_token_samples,), name='token_samples') # [S, batch*H*W, num_embeddings]
def _single_decode(token_sample_onehot):
#[batch*H*W, num_embeddings] @ [num_embeddings, embedding_dim]
token_sample = tf.matmul(token_sample_onehot, self.embeddings) # [batch*H*W, embedding_dim] # = z ~ q(z|x)
latent_img = tf.reshape(token_sample, [batch, H, W, self.embedding_dim]) # [batch, H, W, embedding_dim]
decoded_img = self.decoder(latent_img) # [batch, H', W', C*2]
# print('decod shape', decoded_img)
img_mu = decoded_img[..., :self.num_channels] #[batch, H', W', C]
# print('mu shape', img_mu)
img_logb = decoded_img[..., self.num_channels:]
# print('logb shape', img_logb)
log_likelihood = self.log_likelihood(img, img_mu, img_logb)#[batch, H', W', C]
log_likelihood = tf.reduce_sum(log_likelihood, axis=[-3,-2,-1]) # [batch]
sum_selected_logits = tf.math.reduce_sum(token_sample_onehot * logits, axis=-1) # [batch*H*W]
sum_selected_logits = tf.reshape(sum_selected_logits, [batch, H, W])
kl_term = tf.reduce_sum(sum_selected_logits, axis=[-2,-1])#[batch]
return log_likelihood, kl_term, decoded_img
#num_samples, batch
log_likelihood_samples, kl_term_samples, decoded_ims = tf.vectorized_map(_single_decode, token_samples_onehot) # [S, batch], [S, batch]
if self.step % 50 == 0:
img_mu_0 = tf.reduce_mean(decoded_ims, axis=0)[..., :self.num_channels]
img_mu_0 -= tf.reduce_min(img_mu_0)
img_mu_0 /= tf.reduce_max(img_mu_0)
tf.summary.image('mu', img_mu_0, step=self.step)
smoothed_img = img[..., self.num_channels:]
smoothed_img = (smoothed_img - tf.reduce_min(smoothed_img)) / (
tf.reduce_max(smoothed_img) - tf.reduce_min(smoothed_img))
tf.summary.image(f'img_before_autoencoder', smoothed_img, step=self.step)
var_exp = tf.reduce_mean(log_likelihood_samples, axis=0) # [batch]
kl_div = tf.reduce_mean(kl_term_samples, axis=0) # [batch]
elbo = var_exp - kl_div # batch
loss = - tf.reduce_mean(elbo) # scalar
entropy = -tf.reduce_sum(logits * tf.math.exp(logits), axis=-1) # [batch*H*W]
perplexity = 2. ** (-entropy / tf.math.log(2.)) # [batch*H*W]
mean_perplexity = tf.reduce_mean(perplexity) # scalar
if self.step % 2 == 0:
logits = tf.nn.softmax(logits, axis=-1) # [batch*H*W, num_embedding]
logits -= tf.reduce_min(logits)
logits /= tf.reduce_max(logits)
logits = tf.reshape(logits, [batch, H*W, self.num_embedding])[0] # [H*W, num_embedding]
# tf.repeat(tf.repeat(logits, 16*[4], axis=0), 512*[4], axis=1)
tf.summary.image('logits', logits[None, :, :, None], step=self.step)
tf.summary.scalar('perplexity', mean_perplexity, step=self.step)
tf.summary.scalar('var_exp', tf.reduce_mean(var_exp), step=self.step)
tf.summary.scalar('kl_div', tf.reduce_mean(kl_div), step=self.step)
return dict(loss=loss,
metrics=dict(var_exp=var_exp,
kl_div=kl_div,
mean_perplexity=mean_perplexity))
| 50.467456 | 144 | 0.62692 | import sys
sys.path.insert(1, '/data/s2675544/git/neural_deprojection/')
sys.path.insert(1, '/home/matthijs/git/neural_deprojection/')
from graph_nets import blocks
from graph_nets.utils_tf import concat
import tensorflow as tf
import sonnet as snt
from graph_nets.graphs import GraphsTuple
from graph_nets.utils_tf import fully_connect_graph_dynamic, fully_connect_graph_static
from neural_deprojection.graph_net_utils import AbstractModule, histogramdd, get_shape
import tensorflow_probability as tfp
from neural_deprojection.models.openai_dvae_modules.modules import Encoder, Decoder
class DiscreteImageVAE(AbstractModule):
def __init__(self,
hidden_size: int = 64,
embedding_dim: int = 64,
num_embedding: int = 1024,
num_token_samples: int = 32,
num_channels=1,
name=None):
super(DiscreteImageVAE, self).__init__(name=name)
self.num_channels=num_channels
self.embeddings = tf.Variable(initial_value=tf.random.truncated_normal((num_embedding, embedding_dim)),
name='embeddings')
self.num_token_samples = num_token_samples
self.num_embedding = num_embedding
self.embedding_dim = embedding_dim
self.temperature = tf.Variable(initial_value=tf.constant(1.), name='temperature', trainable=False)
self.beta = tf.Variable(initial_value=tf.constant(6.6), name='beta', trainable=False)
self.encoder = Encoder(hidden_size=hidden_size, num_embeddings=num_embedding, name='EncoderImage')
self.decoder = Decoder(hidden_size=hidden_size, num_channels=num_channels, name='DecoderImage')
def set_beta(self, beta):
self.beta.assign(beta)
def set_temperature(self, temperature):
self.temperature.assign(temperature)
@tf.function(input_signature=[tf.TensorSpec([None, None, None, None], dtype=tf.float32)])
def sample_encoder(self, img):
return self.encoder(img)
@tf.function(input_signature=[tf.TensorSpec([None, None, None, None], dtype=tf.float32),
tf.TensorSpec([], dtype=tf.float32),
tf.TensorSpec([], dtype=tf.float32)])
def sample_decoder(self, img_logits, temperature, num_samples):
[batch, H, W, _] = get_shape(img_logits)
logits = tf.reshape(img_logits, [batch * H * W, self.num_embedding])
reduce_logsumexp = tf.math.reduce_logsumexp(logits, axis=-1)
reduce_logsumexp = tf.tile(reduce_logsumexp[:, None], [1, self.num_embedding])
logits -= reduce_logsumexp
token_distribution = tfp.distributions.RelaxedOneHotCategorical(temperature, logits=logits)
token_samples_onehot = token_distribution.sample((num_samples,),
name='token_samples')
def _single_decode(token_sample_onehot):
token_sample = tf.matmul(token_sample_onehot, self.embeddings) atent_img = tf.reshape(token_sample, [batch, H, W, self.embedding_dim])
decoded_img = self.decoder(latent_img)
return decoded_img
decoded_ims = tf.vectorized_map(_single_decode, token_samples_onehot)
decoded_im = tf.reduce_mean(decoded_ims, axis=0)
return decoded_im
def log_likelihood(self, img, mu, logb):
log_prob = - tf.math.abs(img - mu) / tf.math.exp(logb) \
- tf.math.log(2.) - img - logb
return tf.reduce_sum(log_prob, axis=-1)
def _build(self, img, **kwargs) -> dict:
encoded_img_logits = self.encoder(img)
[batch, H, W, _] = get_shape(encoded_img_logits)
logits = tf.reshape(encoded_img_logits, [batch*H*W, self.num_embedding])
reduce_logsumexp = tf.math.reduce_logsumexp(logits, axis=-1)
reduce_logsumexp = tf.tile(reduce_logsumexp[:, None], [1, self.num_embedding])
logits -= reduce_logsumexp
temperature = tf.maximum(0.1, tf.cast(1. - 0.1/(self.step/1000), tf.float32))
token_distribution = tfp.distributions.RelaxedOneHotCategorical(temperature, logits=logits)
token_samples_onehot = token_distribution.sample((self.num_token_samples,), name='token_samples')
def _single_decode(token_sample_onehot):
token_sample = tf.matmul(token_sample_onehot, self.embeddings) atent_img = tf.reshape(token_sample, [batch, H, W, self.embedding_dim])
decoded_img = self.decoder(latent_img)
img_mu = decoded_img[..., :self.num_channels]
img_logb = decoded_img[..., self.num_channels:]
log_likelihood = self.log_likelihood(img, img_mu, img_logb)
log_likelihood = tf.reduce_sum(log_likelihood, axis=[-3,-2,-1])
sum_selected_logits = tf.math.reduce_sum(token_sample_onehot * logits, axis=-1)
sum_selected_logits = tf.reshape(sum_selected_logits, [batch, H, W])
kl_term = tf.reduce_sum(sum_selected_logits, axis=[-2,-1])
return log_likelihood, kl_term, decoded_img
log_likelihood_samples, kl_term_samples, decoded_ims = tf.vectorized_map(_single_decode, token_samples_onehot)
if self.step % 50 == 0:
img_mu_0 = tf.reduce_mean(decoded_ims, axis=0)[..., :self.num_channels]
img_mu_0 -= tf.reduce_min(img_mu_0)
img_mu_0 /= tf.reduce_max(img_mu_0)
tf.summary.image('mu', img_mu_0, step=self.step)
smoothed_img = img[..., self.num_channels:]
smoothed_img = (smoothed_img - tf.reduce_min(smoothed_img)) / (
tf.reduce_max(smoothed_img) - tf.reduce_min(smoothed_img))
tf.summary.image(f'img_before_autoencoder', smoothed_img, step=self.step)
var_exp = tf.reduce_mean(log_likelihood_samples, axis=0)
kl_div = tf.reduce_mean(kl_term_samples, axis=0)
elbo = var_exp - kl_div
loss = - tf.reduce_mean(elbo)
entropy = -tf.reduce_sum(logits * tf.math.exp(logits), axis=-1)
perplexity = 2. ** (-entropy / tf.math.log(2.))
mean_perplexity = tf.reduce_mean(perplexity)
if self.step % 2 == 0:
logits = tf.nn.softmax(logits, axis=-1)
logits -= tf.reduce_min(logits)
logits /= tf.reduce_max(logits)
logits = tf.reshape(logits, [batch, H*W, self.num_embedding])[0]
tf.summary.image('logits', logits[None, :, :, None], step=self.step)
tf.summary.scalar('perplexity', mean_perplexity, step=self.step)
tf.summary.scalar('var_exp', tf.reduce_mean(var_exp), step=self.step)
tf.summary.scalar('kl_div', tf.reduce_mean(kl_div), step=self.step)
return dict(loss=loss,
metrics=dict(var_exp=var_exp,
kl_div=kl_div,
mean_perplexity=mean_perplexity))
| true | true |
f736e7677c1f294581b6a8c7926ae057f9c75a6c | 6,779 | py | Python | elvis/set_up_infrastructure.py | dailab/elvis | 9b808d0777b014ee0c2d79e4b9a2390bea4841e6 | [
"MIT"
] | 5 | 2021-08-23T13:37:35.000Z | 2022-02-28T02:29:49.000Z | elvis/set_up_infrastructure.py | dailab/elvis | 9b808d0777b014ee0c2d79e4b9a2390bea4841e6 | [
"MIT"
] | 2 | 2021-05-18T15:57:48.000Z | 2022-02-24T03:37:13.000Z | elvis/set_up_infrastructure.py | dailab/elvis | 9b808d0777b014ee0c2d79e4b9a2390bea4841e6 | [
"MIT"
] | 1 | 2022-02-28T02:29:20.000Z | 2022-02-28T02:29:20.000Z | """Create infrastructure: connect charging points to transformer and charging points to
charging stations."""
from copy import deepcopy
from elvis.charging_station import ChargingStation
from elvis.charging_point import ChargingPoint
from elvis.infrastructure_node import Transformer, Storage
from elvis.battery import StationaryBattery
def set_up_infrastructure(infrastructure):
"""Reads in infrastructure layout as a dict and converts it into a tree shaped infrastructure
design with nodes representing the hardware components:
transformer: :obj: `infrastructure_node.Transformer`
charging station: :obj: `charging_station.ChargingStation`
charging point: :obj: `charging_point.ChargingPoint`
Returns all charging points.
Args:
infrastructure: (dict): Contains more nested dicts with the values to initialise the
infrastructure nodes.
Dict design:
transformer: {min_power(float), max_power(float), infrastructure(list)}
For each instance of charging station in infrastructure:
charging sation: {min_power(float), max_power(float), charging_points(list)}
For each instance of charging_point in charging_stations:
charging_point: {min_power(float), max_power(float)}
Returns:
charging_points: (list): Contains n instances of :obj: `charging_point.ChargingPoint`.
"""
charging_points = []
transformer = None
# build infrastructure and create node instances
# Add transformer
for __transformer in infrastructure['transformers']:
transformer = Transformer(__transformer['min_power'], __transformer['max_power'])
# Add all charging points and their charging points
for __charging_station in __transformer['charging_stations']:
charging_station = ChargingStation(__charging_station['min_power'],
__charging_station['max_power'],
transformer)
# Add all charging points of current charging point
for __charging_point in __charging_station['charging_points']:
__charging_point = ChargingPoint(__charging_point['min_power'],
__charging_point['max_power'],
charging_station)
charging_points.append(__charging_point)
if 'storage' in __transformer.keys():
__battery = __transformer['storage']
if 'efficiency' in __battery.keys():
bat_eff = __battery['efficiency']
else:
bat_eff = 1
battery = StationaryBattery(capacity=__battery['capacity'],
max_charge_power=__battery['max_power'],
min_charge_power=__battery['min_power'],
efficiency=bat_eff)
storage = Storage(battery, transformer)
assert isinstance(transformer, Transformer), 'Invalid infrastructure dict.'
transformer.set_up_leafs()
# transformer.draw_infrastructure()
return charging_points
def wallbox_infrastructure(num_cp, power_cp, num_cp_per_cs=1, power_cs=None,
power_transformer=None, min_power_cp=0, min_power_cs=0,
min_power_transformer=0):
"""Builds an Elvis conform infrastructure.
Args:
num_cp: (int): Number of charging points.
num_cp_per_cs: (int) Number of charging points per charging station.
power_cp: (int or float): Max power per charging point.
power_cs: (int or float): Max power of the charging station.
power_transformer: (int or float): Max power of the transformer.
min_power_cp: (int or float): Minimum power (if not 0) for the charging point.
min_power_cs: (int or float): Minimum power (if not 0) for the charging station.
min_power_transformer: (int or float) : Minimum power (if not 0) for the charging station.
Returns:
infrastructure: (dict)
"""
# Validate Input
assert isinstance(num_cp_per_cs, int), 'Only integers are allowed for the number of ' \
'charging points per charging station.'
num_cs = int(num_cp / num_cp_per_cs)
assert num_cp % num_cp_per_cs == 0, 'Only integers are allowed for the number of charging ' \
'stations. The passed number of charging points and the ' \
'number of charging points per charging station are not ' \
'realisable.'
num_cs = int(num_cp / num_cp_per_cs)
msg_power_numeric = 'The power assigned to the infrastructure elements must be of type int or' \
'float. The power must be bigger than 0.'
if power_cs is None:
power_cs = power_cp * num_cp_per_cs
assert isinstance(power_cp, (int, float)), msg_power_numeric
assert isinstance(power_cs, (int, float)), msg_power_numeric
if power_transformer is None:
power_transformer = num_cs * num_cp_per_cs * power_cp
assert isinstance(power_transformer, (int, float)), msg_power_numeric
assert isinstance(min_power_cp, (int, float)), msg_power_numeric
assert isinstance(min_power_transformer, (int, float)), msg_power_numeric
assert isinstance(min_power_cs, (int, float)), msg_power_numeric
msg_min_max = 'The min power assigned to a infrastructure node must be lower than the ' \
'max power.'
assert power_cp >= min_power_cp, msg_min_max
assert power_cs >= min_power_cs, msg_min_max
assert power_transformer >= min_power_transformer, msg_min_max
transformer = {'charging_stations': [], 'id': 'transformer1',
'min_power': min_power_transformer, 'max_power': power_transformer}
charging_point = {'min_power': min_power_cp, 'max_power': power_cp}
charging_station = {'min_power': min_power_cs, 'max_power': power_cs,
'charging_points': []}
for i in range(num_cs):
charging_station_temp = deepcopy(charging_station)
charging_station_temp['id'] = 'cs' + str(i + 1)
for j in range(num_cp_per_cs):
charging_point_temp = deepcopy(charging_point)
charging_point_temp['id'] = 'cp' + str(i * num_cp_per_cs + j+1)
charging_station_temp['charging_points'].append(charging_point_temp)
transformer['charging_stations'].append(charging_station_temp)
infrastructure = {'transformers': [transformer]}
return infrastructure
| 48.078014 | 100 | 0.646408 |
from copy import deepcopy
from elvis.charging_station import ChargingStation
from elvis.charging_point import ChargingPoint
from elvis.infrastructure_node import Transformer, Storage
from elvis.battery import StationaryBattery
def set_up_infrastructure(infrastructure):
charging_points = []
transformer = None
for __transformer in infrastructure['transformers']:
transformer = Transformer(__transformer['min_power'], __transformer['max_power'])
for __charging_station in __transformer['charging_stations']:
charging_station = ChargingStation(__charging_station['min_power'],
__charging_station['max_power'],
transformer)
for __charging_point in __charging_station['charging_points']:
__charging_point = ChargingPoint(__charging_point['min_power'],
__charging_point['max_power'],
charging_station)
charging_points.append(__charging_point)
if 'storage' in __transformer.keys():
__battery = __transformer['storage']
if 'efficiency' in __battery.keys():
bat_eff = __battery['efficiency']
else:
bat_eff = 1
battery = StationaryBattery(capacity=__battery['capacity'],
max_charge_power=__battery['max_power'],
min_charge_power=__battery['min_power'],
efficiency=bat_eff)
storage = Storage(battery, transformer)
assert isinstance(transformer, Transformer), 'Invalid infrastructure dict.'
transformer.set_up_leafs()
return charging_points
def wallbox_infrastructure(num_cp, power_cp, num_cp_per_cs=1, power_cs=None,
power_transformer=None, min_power_cp=0, min_power_cs=0,
min_power_transformer=0):
assert isinstance(num_cp_per_cs, int), 'Only integers are allowed for the number of ' \
'charging points per charging station.'
num_cs = int(num_cp / num_cp_per_cs)
assert num_cp % num_cp_per_cs == 0, 'Only integers are allowed for the number of charging ' \
'stations. The passed number of charging points and the ' \
'number of charging points per charging station are not ' \
'realisable.'
num_cs = int(num_cp / num_cp_per_cs)
msg_power_numeric = 'The power assigned to the infrastructure elements must be of type int or' \
'float. The power must be bigger than 0.'
if power_cs is None:
power_cs = power_cp * num_cp_per_cs
assert isinstance(power_cp, (int, float)), msg_power_numeric
assert isinstance(power_cs, (int, float)), msg_power_numeric
if power_transformer is None:
power_transformer = num_cs * num_cp_per_cs * power_cp
assert isinstance(power_transformer, (int, float)), msg_power_numeric
assert isinstance(min_power_cp, (int, float)), msg_power_numeric
assert isinstance(min_power_transformer, (int, float)), msg_power_numeric
assert isinstance(min_power_cs, (int, float)), msg_power_numeric
msg_min_max = 'The min power assigned to a infrastructure node must be lower than the ' \
'max power.'
assert power_cp >= min_power_cp, msg_min_max
assert power_cs >= min_power_cs, msg_min_max
assert power_transformer >= min_power_transformer, msg_min_max
transformer = {'charging_stations': [], 'id': 'transformer1',
'min_power': min_power_transformer, 'max_power': power_transformer}
charging_point = {'min_power': min_power_cp, 'max_power': power_cp}
charging_station = {'min_power': min_power_cs, 'max_power': power_cs,
'charging_points': []}
for i in range(num_cs):
charging_station_temp = deepcopy(charging_station)
charging_station_temp['id'] = 'cs' + str(i + 1)
for j in range(num_cp_per_cs):
charging_point_temp = deepcopy(charging_point)
charging_point_temp['id'] = 'cp' + str(i * num_cp_per_cs + j+1)
charging_station_temp['charging_points'].append(charging_point_temp)
transformer['charging_stations'].append(charging_station_temp)
infrastructure = {'transformers': [transformer]}
return infrastructure
| true | true |
f736e77e09591ce7566c4aed05b4eaffb4713c13 | 260 | py | Python | problems/find-pivot-index/solution-1.py | MleMoe/LeetCode-1 | 14f275ba3c8079b820808da17c4952fcf9c8253c | [
"MIT"
] | 2 | 2021-03-25T01:58:55.000Z | 2021-08-06T12:47:13.000Z | problems/find-pivot-index/solution-1.py | MleMoe/LeetCode-1 | 14f275ba3c8079b820808da17c4952fcf9c8253c | [
"MIT"
] | 3 | 2019-08-27T13:25:42.000Z | 2021-08-28T17:49:34.000Z | problems/find-pivot-index/solution-1.py | MleMoe/LeetCode-1 | 14f275ba3c8079b820808da17c4952fcf9c8253c | [
"MIT"
] | 1 | 2021-08-14T08:49:39.000Z | 2021-08-14T08:49:39.000Z | class Solution:
def pivotIndex(self, nums: [int]) -> int:
sum_nums = sum(nums)
left = 0
for i in range(len(nums)):
if left * 2 == sum_nums - nums[i]:
return i
left += nums[i]
return -1 | 28.888889 | 46 | 0.465385 | class Solution:
def pivotIndex(self, nums: [int]) -> int:
sum_nums = sum(nums)
left = 0
for i in range(len(nums)):
if left * 2 == sum_nums - nums[i]:
return i
left += nums[i]
return -1 | true | true |
f736e7ee09b45a5b152d00338c9d3462e7087e79 | 1,634 | py | Python | src/wechaty_plugin_contrib/matchers/contact_matcher.py | xinyu3ru/python-wechaty-plugin-contrib | 2fc6ee19438ac29266acb133613f83d3aee3c7c3 | [
"Apache-2.0"
] | 11 | 2020-06-11T15:11:27.000Z | 2022-03-06T01:19:22.000Z | src/wechaty_plugin_contrib/matchers/contact_matcher.py | xinyu3ru/python-wechaty-plugin-contrib | 2fc6ee19438ac29266acb133613f83d3aee3c7c3 | [
"Apache-2.0"
] | 6 | 2020-12-01T14:26:36.000Z | 2021-09-29T03:05:29.000Z | src/wechaty_plugin_contrib/matchers/contact_matcher.py | xinyu3ru/python-wechaty-plugin-contrib | 2fc6ee19438ac29266acb133613f83d3aee3c7c3 | [
"Apache-2.0"
] | 5 | 2020-06-25T11:31:39.000Z | 2021-08-05T05:34:02.000Z | """Contact Matcher to match the specific contact"""
import re
from re import Pattern
import inspect
from wechaty_plugin_contrib.config import (
get_logger,
Contact,
)
from .matcher import Matcher
logger = get_logger("ContactMatcher")
class ContactMatcher(Matcher):
async def match(self, target: Contact) -> bool:
"""match the room"""
logger.info(f'ContactMatcher match({target})')
if not isinstance(target, Contact):
return False
for option in self.options:
if isinstance(option, Pattern):
# match the room with regex pattern
contact_alias = await target.alias()
is_match = re.match(option, target.name) is not None or \
re.match(option, contact_alias) is not None
elif isinstance(option, str):
# make sure that the contact is ready
await target.ready()
is_match = target.contact_id == option or option == target.name
# elif hasattr(option, '__call__'):
# """check the type of the function
# refer: https://stackoverflow.com/a/56240578/6894382
# """
# if inspect.iscoroutinefunction(option):
# is_match = await option(target)
# else:
# is_match = option(target)
elif isinstance(option, bool):
return option
else:
raise ValueError(f'unknown type option: {option}')
if is_match:
return True
return False
| 30.259259 | 79 | 0.5612 | import re
from re import Pattern
import inspect
from wechaty_plugin_contrib.config import (
get_logger,
Contact,
)
from .matcher import Matcher
logger = get_logger("ContactMatcher")
class ContactMatcher(Matcher):
async def match(self, target: Contact) -> bool:
logger.info(f'ContactMatcher match({target})')
if not isinstance(target, Contact):
return False
for option in self.options:
if isinstance(option, Pattern):
contact_alias = await target.alias()
is_match = re.match(option, target.name) is not None or \
re.match(option, contact_alias) is not None
elif isinstance(option, str):
await target.ready()
is_match = target.contact_id == option or option == target.name
# refer: https://stackoverflow.com/a/56240578/6894382
# """
elif isinstance(option, bool):
return option
else:
raise ValueError(f'unknown type option: {option}')
if is_match:
return True
return False
| true | true |
f736e92f05076b49c835c2eee4dc92b17ab35a81 | 19,943 | py | Python | gamestonk_terminal/prediction_techniques/neural_networks.py | bfxavier/GamestonkTerminal | b0a685cacaca1f06fc41d8041bcae5492216dc52 | [
"MIT"
] | null | null | null | gamestonk_terminal/prediction_techniques/neural_networks.py | bfxavier/GamestonkTerminal | b0a685cacaca1f06fc41d8041bcae5492216dc52 | [
"MIT"
] | null | null | null | gamestonk_terminal/prediction_techniques/neural_networks.py | bfxavier/GamestonkTerminal | b0a685cacaca1f06fc41d8041bcae5492216dc52 | [
"MIT"
] | null | null | null | import argparse
import os
from warnings import simplefilter
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from TimeSeriesCrossValidation import splitTrain
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, SimpleRNN, Dense, Dropout
from gamestonk_terminal.helper_funcs import (
check_positive,
get_next_stock_market_days,
parse_known_args_and_warn,
print_pretty_prediction,
)
from gamestonk_terminal import config_neural_network_models as cfg_nn_models
register_matplotlib_converters()
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
simplefilter(action="ignore", category=FutureWarning)
# ----------------------------------------------------------------------------------------------------
def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
model = Sequential()
for idx_layer, d_layer in enumerate(Recurrent_Neural_Network):
# Recurrent Neural Network
if str(*d_layer) == "SimpleRNN":
# Is this the input layer? If so, define input_shape
if idx_layer == 0:
model.add(SimpleRNN(**d_layer["SimpleRNN"], input_shape=(n_inputs, 1)))
# Is this the last output layer? If so, set units to prediction days
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(SimpleRNN(**d_layer["SimpleRNN"], units=n_days))
else:
model.add(SimpleRNN(**d_layer["SimpleRNN"]))
# Long-Short Term-Memory
elif str(*d_layer) == "LSTM":
# Is this the input layer? If so, define input_shape
if idx_layer == 0:
model.add(LSTM(**d_layer["LSTM"], input_shape=(n_inputs, 1)))
# Is this the last output layer? If so, set units to prediction days
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(LSTM(**d_layer["LSTM"], units=n_days))
else:
model.add(LSTM(**d_layer["LSTM"]))
# Dense (Simple Neuron)
elif str(*d_layer) == "Dense":
# Is this the input layer? If so, define input_shape
if idx_layer == 0:
model.add(Dense(**d_layer["Dense"], input_dim=n_inputs))
# Is this the last output layer? If so, set units to prediction days
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(Dense(**d_layer["Dense"], units=n_days))
else:
model.add(Dense(**d_layer["Dense"]))
# Dropout (Regularization)
elif str(*d_layer) == "Dropout":
model.add(Dropout(**d_layer["Dropout"]))
else:
print(f"Incorrect neuron type: {str(*d_layer)}")
return model
def mlp(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="mlp", description="""Multilayer Perceptron. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days.",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
help="optimization technique.",
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
# Pre-process data
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else: # No pre-processing
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
# Split training data for the neural network
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1]))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1]))
# Build Neural Network model
model = build_neural_network_model(
cfg_nn_models.MultiLayer_Perceptron, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
# Train our model
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
# Prediction
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs),
verbose=0,
)
# Re-scale the data back
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"MLP on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
# Print prediction data
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
def rnn(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="rnn", description="""Recurrent Neural Network. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days.",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
help="optimizer technique",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
# Pre-process data
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else: # No pre-processing
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
# Split training data for the neural network
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
# Build Neural Network model
model = build_neural_network_model(
cfg_nn_models.Recurrent_Neural_Network, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
# Train our model
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
# Prediction
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
verbose=0,
)
# Re-scale the data back
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"RNN on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
# Print prediction data
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
def lstm(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="lstm", description="""Long-Short Term Memory. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
help="optimization technique.",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
# Pre-process data
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else: # No pre-processing
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
# Split training data for the neural network
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
# Build Neural Network model
model = build_neural_network_model(
cfg_nn_models.Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
# Train our model
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
# Prediction
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
verbose=0,
)
# Re-scale the data back
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
# Plotting
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"LSTM on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
# Print prediction data
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
| 30.871517 | 102 | 0.545304 | import argparse
import os
from warnings import simplefilter
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from TimeSeriesCrossValidation import splitTrain
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, SimpleRNN, Dense, Dropout
from gamestonk_terminal.helper_funcs import (
check_positive,
get_next_stock_market_days,
parse_known_args_and_warn,
print_pretty_prediction,
)
from gamestonk_terminal import config_neural_network_models as cfg_nn_models
register_matplotlib_converters()
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
simplefilter(action="ignore", category=FutureWarning)
def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
model = Sequential()
for idx_layer, d_layer in enumerate(Recurrent_Neural_Network):
if str(*d_layer) == "SimpleRNN":
if idx_layer == 0:
model.add(SimpleRNN(**d_layer["SimpleRNN"], input_shape=(n_inputs, 1)))
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(SimpleRNN(**d_layer["SimpleRNN"], units=n_days))
else:
model.add(SimpleRNN(**d_layer["SimpleRNN"]))
elif str(*d_layer) == "LSTM":
if idx_layer == 0:
model.add(LSTM(**d_layer["LSTM"], input_shape=(n_inputs, 1)))
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(LSTM(**d_layer["LSTM"], units=n_days))
else:
model.add(LSTM(**d_layer["LSTM"]))
elif str(*d_layer) == "Dense":
if idx_layer == 0:
model.add(Dense(**d_layer["Dense"], input_dim=n_inputs))
elif idx_layer == (len(Recurrent_Neural_Network) - 1):
model.add(Dense(**d_layer["Dense"], units=n_days))
else:
model.add(Dense(**d_layer["Dense"]))
elif str(*d_layer) == "Dropout":
model.add(Dropout(**d_layer["Dropout"]))
else:
print(f"Incorrect neuron type: {str(*d_layer)}")
return model
def mlp(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="mlp", description="""Multilayer Perceptron. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days.",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
help="optimization technique.",
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else:
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1]))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1]))
model = build_neural_network_model(
cfg_nn_models.MultiLayer_Perceptron, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs),
verbose=0,
)
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"MLP on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
def rnn(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="rnn", description="""Recurrent Neural Network. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days.",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
help="optimizer technique",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else:
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
model = build_neural_network_model(
cfg_nn_models.Recurrent_Neural_Network, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
verbose=0,
)
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"RNN on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
def lstm(l_args, s_ticker, df_stock):
parser = argparse.ArgumentParser(
add_help=False, prog="lstm", description="""Long-Short Term Memory. """
)
parser.add_argument(
"-d",
"--days",
action="store",
dest="n_days",
type=check_positive,
default=5,
help="prediction days",
)
parser.add_argument(
"-i",
"--input",
action="store",
dest="n_inputs",
type=check_positive,
default=40,
help="number of days to use for prediction.",
)
parser.add_argument(
"-e",
"--epochs",
action="store",
dest="n_epochs",
type=check_positive,
default=200,
help="number of training epochs.",
)
parser.add_argument(
"-j",
"--jumps",
action="store",
dest="n_jumps",
type=check_positive,
default=1,
help="number of jumps in training data.",
)
parser.add_argument(
"-p",
"--pp",
action="store",
dest="s_preprocessing",
default="normalization",
choices=["normalization", "standardization", "none"],
help="pre-processing data.",
)
parser.add_argument(
"-o",
"--optimizer",
action="store",
dest="s_optimizer",
default="adam",
help="optimization technique.",
choices=[
"adam",
"adagrad",
"adadelta",
"adamax",
"ftrl",
"nadam",
"optimizer",
"rmsprop",
"sgd",
],
)
parser.add_argument(
"-l",
"--loss",
action="store",
dest="s_loss",
default="mae",
choices=["mae", "mape", "mse", "msle"],
help="loss function.",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
if not ns_parser:
return
if ns_parser.s_preprocessing == "standardization":
scaler = StandardScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
elif ns_parser.s_preprocessing == "normalization":
scaler = MinMaxScaler()
stock_train_data = scaler.fit_transform(
np.array(df_stock["5. adjusted close"].values.reshape(-1, 1))
)
else:
stock_train_data = np.array(
df_stock["5. adjusted close"].values.reshape(-1, 1)
)
stock_x, stock_y = splitTrain.split_train(
stock_train_data,
ns_parser.n_inputs,
ns_parser.n_days,
numJumps=ns_parser.n_jumps,
)
stock_x = np.array(stock_x)
stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
stock_y = np.array(stock_y)
stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
model = build_neural_network_model(
cfg_nn_models.Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days
)
model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
model.fit(stock_x, stock_y, epochs=ns_parser.n_epochs, verbose=1)
print("")
print(model.summary())
print("")
yhat = model.predict(
stock_train_data[-ns_parser.n_inputs :].reshape(1, ns_parser.n_inputs, 1),
verbose=0,
)
if (ns_parser.s_preprocessing == "standardization") or (
ns_parser.s_preprocessing == "normalization"
):
y_pred_test_t = scaler.inverse_transform(yhat.tolist())
else:
y_pred_test_t = yhat
l_pred_days = get_next_stock_market_days(
last_stock_day=df_stock["5. adjusted close"].index[-1],
n_next_days=ns_parser.n_days,
)
df_pred = pd.Series(y_pred_test_t[0].tolist(), index=l_pred_days, name="Price")
plt.figure()
plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=3)
plt.title(f"LSTM on {s_ticker} - {ns_parser.n_days} days prediction")
plt.xlim(
df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1]
)
plt.xlabel("Time")
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
plt.plot(
[df_stock.index[-1], df_pred.index[0]],
[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
lw=1,
c="tab:green",
linestyle="--",
)
plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
plt.axvspan(
df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
)
_, _, ymin, ymax = plt.axis()
plt.vlines(
df_stock.index[-1],
ymin,
ymax,
colors="k",
linewidth=3,
linestyle="--",
color="k",
)
plt.ion()
plt.show()
print_pretty_prediction(df_pred, df_stock["5. adjusted close"].values[-1])
print("")
except Exception as e:
print(e)
print("")
| true | true |
f736ece3ad2fa1b2e684a633a5dcf8758c1fc91d | 6,113 | py | Python | salt/salt/returners/influxdb_return.py | smallyear/linuxLearn | 342e5020bf24b5fac732c4275a512087b47e578d | [
"Apache-2.0"
] | 1 | 2017-11-21T16:57:27.000Z | 2017-11-21T16:57:27.000Z | salt/salt/returners/influxdb_return.py | smallyear/linuxLearn | 342e5020bf24b5fac732c4275a512087b47e578d | [
"Apache-2.0"
] | null | null | null | salt/salt/returners/influxdb_return.py | smallyear/linuxLearn | 342e5020bf24b5fac732c4275a512087b47e578d | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
'''
Return data to an influxdb server.
.. versionadded:: 2015.8.0
To enable this returner the minion will need the python client for influxdb
installed and the following values configured in the minion or master
config, these are the defaults:
.. code-block:: yaml
influxdb.db: 'salt'
influxdb.user: 'salt'
influxdb.password: 'salt'
influxdb.host: 'localhost'
influxdb.port: 8086
Alternative configuration values can be used by prefacing the configuration.
Any values not found in the alternative configuration will be pulled from
the default location:
.. code-block:: yaml
alternative.influxdb.db: 'salt'
alternative.influxdb.user: 'salt'
alternative.influxdb.password: 'salt'
alternative.influxdb.host: 'localhost'
alternative.influxdb.port: 6379
To use the influxdb returner, append '--return influxdb' to the salt command.
.. code-block:: bash
salt '*' test.ping --return influxdb
To use the alternative configuration, append '--return_config alternative' to the salt command.
.. code-block:: bash
salt '*' test.ping --return influxdb --return_config alternative
'''
from __future__ import absolute_import
# Import python libs
import json
import logging
# Import Salt libs
import salt.utils.jid
import salt.returners
# Import third party libs
try:
import influxdb.influxdb08
HAS_INFLUXDB = True
except ImportError:
HAS_INFLUXDB = False
log = logging.getLogger(__name__)
# Define the module's virtual name
__virtualname__ = 'influxdb'
def __virtual__():
if not HAS_INFLUXDB:
return False
return __virtualname__
def _get_options(ret=None):
'''
Get the influxdb options from salt.
'''
attrs = {'host': 'host',
'port': 'port',
'db': 'db',
'user': 'user',
'password': 'password'}
_options = salt.returners.get_returner_options(__virtualname__,
ret,
attrs,
__salt__=__salt__,
__opts__=__opts__)
return _options
def _get_serv(ret=None):
'''
Return an influxdb client object
'''
_options = _get_options(ret)
host = _options.get('host')
port = _options.get('port')
database = _options.get('db')
user = _options.get('user')
password = _options.get('password')
return influxdb.influxdb08.InfluxDBClient(host=host,
port=port,
username=user,
password=password,
database=database)
def returner(ret):
'''
Return data to a influxdb data store
'''
serv = _get_serv(ret)
req = [
{
'name': 'returns',
'columns': ['fun', 'id', 'jid', 'return', 'full_ret'],
'points': [
[ret['fun'], ret['id'], ret['jid'], json.dumps(ret['return']), json.dumps(ret)]
],
}
]
try:
serv.write_points(req)
except Exception as ex:
log.critical('Failed to store return with InfluxDB returner: {0}'.format(ex))
def save_load(jid, load):
'''
Save the load to the specified jid
'''
serv = _get_serv(ret=None)
req = [
{
'name': 'jids',
'columns': ['jid', 'load'],
'points': [
[jid, json.dumps(load)]
],
}
]
try:
serv.write_points(req)
except Exception as ex:
log.critical('Failed to store load with InfluxDB returner: {0}'.format(ex))
def save_minions(jid, minions): # pylint: disable=unused-argument
'''
Included for API consistency
'''
pass
def get_load(jid):
'''
Return the load data that marks a specified jid
'''
serv = _get_serv(ret=None)
sql = "select load from jids where jid = '{0}'".format(jid)
log.debug(">> Now in get_load {0}".format(jid))
data = serv.query(sql)
log.debug(">> Now Data: {0}".format(data))
if data:
return data
return {}
def get_jid(jid):
'''
Return the information returned when the specified job id was executed
'''
serv = _get_serv(ret=None)
sql = "select id, full_ret from returns where jid = '{0}'".format(jid)
data = serv.query(sql)
ret = {}
if data:
points = data[0]['points']
for point in points:
ret[point[3]] = json.loads(point[2])
return ret
def get_fun(fun):
'''
Return a dict of the last function called for all minions
'''
serv = _get_serv(ret=None)
sql = '''select first(id) as fid, first(full_ret) as fret
from returns
where fun = '{0}'
group by fun, id
'''.format(fun)
data = serv.query(sql)
ret = {}
if data:
points = data[0]['points']
for point in points:
ret[point[1]] = json.loads(point[2])
return ret
def get_jids():
'''
Return a list of all job ids
'''
serv = _get_serv(ret=None)
sql = "select distinct(jid) from jids"
# [{u'points': [[0, u'saltdev']], u'name': u'returns', u'columns': [u'time', u'distinct']}]
data = serv.query(sql)
ret = []
if data:
for jid in data[0]['points']:
ret.append(jid[1])
return ret
def get_minions():
'''
Return a list of minions
'''
serv = _get_serv(ret=None)
sql = "select distinct(id) from returns"
data = serv.query(sql)
ret = []
if data:
for jid in data[0]['points']:
ret.append(jid[1])
return ret
def prep_jid(nocache=False, passed_jid=None): # pylint: disable=unused-argument
'''
Do any work necessary to prepare a JID, including sending a custom id
'''
return passed_jid if passed_jid is not None else salt.utils.jid.gen_jid()
| 24.257937 | 99 | 0.565516 |
from __future__ import absolute_import
import json
import logging
import salt.utils.jid
import salt.returners
try:
import influxdb.influxdb08
HAS_INFLUXDB = True
except ImportError:
HAS_INFLUXDB = False
log = logging.getLogger(__name__)
__virtualname__ = 'influxdb'
def __virtual__():
if not HAS_INFLUXDB:
return False
return __virtualname__
def _get_options(ret=None):
attrs = {'host': 'host',
'port': 'port',
'db': 'db',
'user': 'user',
'password': 'password'}
_options = salt.returners.get_returner_options(__virtualname__,
ret,
attrs,
__salt__=__salt__,
__opts__=__opts__)
return _options
def _get_serv(ret=None):
_options = _get_options(ret)
host = _options.get('host')
port = _options.get('port')
database = _options.get('db')
user = _options.get('user')
password = _options.get('password')
return influxdb.influxdb08.InfluxDBClient(host=host,
port=port,
username=user,
password=password,
database=database)
def returner(ret):
serv = _get_serv(ret)
req = [
{
'name': 'returns',
'columns': ['fun', 'id', 'jid', 'return', 'full_ret'],
'points': [
[ret['fun'], ret['id'], ret['jid'], json.dumps(ret['return']), json.dumps(ret)]
],
}
]
try:
serv.write_points(req)
except Exception as ex:
log.critical('Failed to store return with InfluxDB returner: {0}'.format(ex))
def save_load(jid, load):
serv = _get_serv(ret=None)
req = [
{
'name': 'jids',
'columns': ['jid', 'load'],
'points': [
[jid, json.dumps(load)]
],
}
]
try:
serv.write_points(req)
except Exception as ex:
log.critical('Failed to store load with InfluxDB returner: {0}'.format(ex))
def save_minions(jid, minions): # pylint: disable=unused-argument
pass
def get_load(jid):
serv = _get_serv(ret=None)
sql = "select load from jids where jid = '{0}'".format(jid)
log.debug(">> Now in get_load {0}".format(jid))
data = serv.query(sql)
log.debug(">> Now Data: {0}".format(data))
if data:
return data
return {}
def get_jid(jid):
serv = _get_serv(ret=None)
sql = "select id, full_ret from returns where jid = '{0}'".format(jid)
data = serv.query(sql)
ret = {}
if data:
points = data[0]['points']
for point in points:
ret[point[3]] = json.loads(point[2])
return ret
def get_fun(fun):
serv = _get_serv(ret=None)
sql = '''select first(id) as fid, first(full_ret) as fret
from returns
where fun = '{0}'
group by fun, id
'''.format(fun)
data = serv.query(sql)
ret = {}
if data:
points = data[0]['points']
for point in points:
ret[point[1]] = json.loads(point[2])
return ret
def get_jids():
serv = _get_serv(ret=None)
sql = "select distinct(jid) from jids"
# [{u'points': [[0, u'saltdev']], u'name': u'returns', u'columns': [u'time', u'distinct']}]
data = serv.query(sql)
ret = []
if data:
for jid in data[0]['points']:
ret.append(jid[1])
return ret
def get_minions():
serv = _get_serv(ret=None)
sql = "select distinct(id) from returns"
data = serv.query(sql)
ret = []
if data:
for jid in data[0]['points']:
ret.append(jid[1])
return ret
def prep_jid(nocache=False, passed_jid=None): # pylint: disable=unused-argument
return passed_jid if passed_jid is not None else salt.utils.jid.gen_jid()
| true | true |
f736ecf14908a77f2315408c7fa6a9fb34cfc340 | 3,425 | py | Python | Portfolio/views.py | Shreya549/AchieveVIT | 4623f80a4e38914f2d759fc0c3591bd642486a5b | [
"MIT"
] | 3 | 2020-08-29T20:23:27.000Z | 2021-05-20T05:44:01.000Z | Portfolio/views.py | Shreya549/AchieveVIT | 4623f80a4e38914f2d759fc0c3591bd642486a5b | [
"MIT"
] | 1 | 2020-09-29T16:28:24.000Z | 2020-09-29T16:28:24.000Z | Portfolio/views.py | Shreya549/AchieveVIT | 4623f80a4e38914f2d759fc0c3591bd642486a5b | [
"MIT"
] | null | null | null | from django.shortcuts import render
from rest_framework import viewsets, permissions, generics
from .serializers import EducationSerializer, WorkExperienceSerializer, AchievementsSerializer
from .models import Education, WorkExperience, Achievements
from Feed.models import Feed
from Profile.models import FacultyProfile
# Create your views here.
class EducationViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = EducationSerializer
def get_queryset(self):
profile = EducationSerializer.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Education'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class WorkExperienceViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = WorkExperienceSerializer
def get_queryset(self):
profile = WorkExperience.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Experience'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class AchievementsViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = AchievementsSerializer
def get_queryset(self):
profile = Achievements.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Achievements'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class EducationRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = EducationSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = Education.objects.filter(owner = owner)
return query
class WorkExperienceRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = WorkExperienceSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = WorkExperience.objects.filter(owner = owner)
return query
class AchievementsRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = AchievementsSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = Achievements.objects.filter(owner = owner)
return query
| 32.311321 | 94 | 0.689635 | from django.shortcuts import render
from rest_framework import viewsets, permissions, generics
from .serializers import EducationSerializer, WorkExperienceSerializer, AchievementsSerializer
from .models import Education, WorkExperience, Achievements
from Feed.models import Feed
from Profile.models import FacultyProfile
class EducationViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = EducationSerializer
def get_queryset(self):
profile = EducationSerializer.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Education'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class WorkExperienceViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = WorkExperienceSerializer
def get_queryset(self):
profile = WorkExperience.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Experience'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class AchievementsViewSet(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = AchievementsSerializer
def get_queryset(self):
profile = Achievements.objects.get(owner = self.request.user)
return profile
def perform_create(self, serializer):
serializer.save(owner = self.request.user)
feed = Feed.objects.create(
fk = serializer.data['uuid'],
type = 'Achievements'
)
feed.save()
def partial_update(self, request, *args, **kwargs):
kwargs['partial'] = True
return self.update(request, *args, **kwargs)
class EducationRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = EducationSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = Education.objects.filter(owner = owner)
return query
class WorkExperienceRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = WorkExperienceSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = WorkExperience.objects.filter(owner = owner)
return query
class AchievementsRetrieveView(viewsets.ModelViewSet):
permission_classes = [permissions.IsAuthenticated]
serializer_class = AchievementsSerializer
def get_queryset(self):
fk = self.request.GET.get('empid')
owner = FacultyProfile.objects.get(pk = fk).owner
query = Achievements.objects.filter(owner = owner)
return query
| true | true |
f736ecfba5a360a245116b0ceb05df6302148c46 | 546 | py | Python | gdb/gdbroot.py | marcostolosa/CVE-2021-3156 | bcf829627eea364a3abc41a6537fbf543e974ff8 | [
"BSD-3-Clause"
] | 473 | 2021-03-15T19:33:48.000Z | 2022-03-30T03:13:15.000Z | gdb/gdbroot.py | loneicewolf/CVE-2021-3156 | ee2506c68ebecc22228c4e5f8a5eaa7544496961 | [
"BSD-3-Clause"
] | 16 | 2021-03-23T06:59:40.000Z | 2022-01-24T07:11:03.000Z | gdb/gdbroot.py | loneicewolf/CVE-2021-3156 | ee2506c68ebecc22228c4e5f8a5eaa7544496961 | [
"BSD-3-Clause"
] | 128 | 2021-03-16T10:23:46.000Z | 2022-03-29T14:14:40.000Z | #!/usr/bin/python3
'''
Server for tracing patched sudo. run it with sudo or as root.
Requires 'cmds' file as gdb comamnds in current directory.
Running command:
python gdbroot.py
'''
import os
import time
FIFO_PATH = "/tmp/gdbsudo"
try:
os.unlink(FIFO_PATH)
except:
pass
os.umask(0)
os.mkfifo(FIFO_PATH, 0o666)
while True:
fifo = open(FIFO_PATH, "r")
pid = int(fifo.read())
fifo.close()
cmd = 'gdb -q -p %d < cmds > log 2>&1' % pid
print('\n=== got: %d. sleep 0.5s' % pid)
#time.sleep(0.5)
print(cmd)
os.system(cmd)
print('done')
| 17.0625 | 61 | 0.668498 |
import os
import time
FIFO_PATH = "/tmp/gdbsudo"
try:
os.unlink(FIFO_PATH)
except:
pass
os.umask(0)
os.mkfifo(FIFO_PATH, 0o666)
while True:
fifo = open(FIFO_PATH, "r")
pid = int(fifo.read())
fifo.close()
cmd = 'gdb -q -p %d < cmds > log 2>&1' % pid
print('\n=== got: %d. sleep 0.5s' % pid)
print(cmd)
os.system(cmd)
print('done')
| true | true |
f736ecfdcc047a0ef504e6d1932c8782c4c382d1 | 4,461 | py | Python | hackeriet/web/brusweb/__init__.py | jsdelivrbot/pyhackeriet | f96fec0a10680d491f846466af8d2d2bbd4cbb6b | [
"Apache-2.0"
] | null | null | null | hackeriet/web/brusweb/__init__.py | jsdelivrbot/pyhackeriet | f96fec0a10680d491f846466af8d2d2bbd4cbb6b | [
"Apache-2.0"
] | null | null | null | hackeriet/web/brusweb/__init__.py | jsdelivrbot/pyhackeriet | f96fec0a10680d491f846466af8d2d2bbd4cbb6b | [
"Apache-2.0"
] | null | null | null | from flask import Flask, request, Response, render_template, g, redirect, url_for, send_file, jsonify
from functools import wraps
import stripe
import os, uuid, json
from hackeriet.web.brusweb import brusdb, members
from hackeriet.mqtt import MQTT
# teste stripe
# lage bruker for gratis brus
# brus/error virker ikke
# descriptions virker settes ikke
# fikse graf
# sende mail
# Stripe ids
stripe_keys = {
'secret_key': os.environ.get('SECRET_KEY', ""),
'publishable_key': os.environ.get('PUBLISHABLE_KEY', "")
}
stripe.api_key = stripe_keys['secret_key']
app = Flask(__name__)
def mqtt_handler(mosq,obj,msg):
if msg.topic == "brus/sell":
args = json.loads(msg.payload.decode())
authorise_sale(**args)
if msg.topic == "hackeriet/reload_users":
members.load()
members.load()
mqtt = MQTT(mqtt_handler)
mqtt.subscribe("brus/sell",0)
mqtt.subscribe("hackeriet/reload_users",0)
def check_auth(username, password):
return members.authenticate(username, password)
def check_admin(username, password):
return members.authenticate_admin(username, password)
def authenticate():
return Response('L33t hax0rz only\n',401,
{'WWW-Authenticate': 'Basic realm="Hackeriet"'})
def requires_auth(f):
@wraps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth or not check_auth(auth.username, auth.password):
return authenticate()
return f(*args, **kwargs)
return decorated
def requires_admin(f):
@wraps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth or not check_admin(auth.username, auth.password):
return authenticate()
return f(*args, **kwargs)
return decorated
@app.route("/")
def hello():
return redirect(url_for('index'))
@app.route("/brus/sales.json")
def stats():
r = []
st = brusdb.get_outgoing_transactions()
for d in {e for (t,v,e) in st}:
if len([t for (t,v,e) in st if e==d]) > 4:
r += [{"key": d, "values": [[int(t)*1000,-v] if e==d else [int(t)*1000,0] for (t,v,e) in st]}]
return json.dumps(r)
@app.route('/brus/')
def index():
return render_template('index.html')
@app.route("/brus/account")
@requires_auth
def account():
user=request.authorization.username
return render_template('account.html', username=user,
history=brusdb.transaction_history(user),
balance=brusdb.balance(user),
key=stripe_keys['publishable_key'])
@app.route("/brus/withdraw", methods=['POST'])
def manual_subtract():
user=request.authorization.username
if brusdb.subtract_funds(user, int(request.form['value']),
request.form['desc'], True):
return redirect(url_for('account'))
else:
return "Insufficient funds"
@app.route("/brus/admin")
@requires_admin
def admin():
user=request.authorization.username
return render_template('admin.html', username=user,
users=members.list_users())
@app.route("/brus/admin/add", methods=['POST'])
@requires_admin
def admin_add():
brusdb.add_funds(request.form['user'], int(request.form['value']),
request.form['desc'])
return 'ok'
@app.route("/brus/charge", methods=['POST'])
@requires_auth
def charge():
# Amount in cents
amount = request.form['amountt']
user=request.authorization.username
stripe_id = None #brusdb.get_stripe_id(user)
if not stripe_id:
customer = stripe.Customer.create(
email=members.get_email(user),
card=request.form['stripeToken']
)
stripe_id = customer.id
#brusdb.set_stripe_id(user, stripe_id)
charge = stripe.Charge.create(
customer=stripe_id,
amount=amount,
currency='NOK',
description='Hackeriet'
)
brusdb.add_funds(user, int(amount)/100, "Transfer with Stripe.")
return render_template('charge.html', amount=int(amount)/100)
def authorise_sale(slot, card_data):
price, desc = brusdb.get_product_price_descr(brusdb.getproduct("brusautomat", slot))
if brusdb.subtract_funds(members.username_from_card(card_data), price, desc):
mqtt("brus/dispense", slot)
mqtt("brus/error", "Insufficient funds")
if __name__ == "__main__":
main()
def main():
app.debug = False
app.run()
| 28.414013 | 106 | 0.650303 | from flask import Flask, request, Response, render_template, g, redirect, url_for, send_file, jsonify
from functools import wraps
import stripe
import os, uuid, json
from hackeriet.web.brusweb import brusdb, members
from hackeriet.mqtt import MQTT
stripe_keys = {
'secret_key': os.environ.get('SECRET_KEY', ""),
'publishable_key': os.environ.get('PUBLISHABLE_KEY', "")
}
stripe.api_key = stripe_keys['secret_key']
app = Flask(__name__)
def mqtt_handler(mosq,obj,msg):
if msg.topic == "brus/sell":
args = json.loads(msg.payload.decode())
authorise_sale(**args)
if msg.topic == "hackeriet/reload_users":
members.load()
members.load()
mqtt = MQTT(mqtt_handler)
mqtt.subscribe("brus/sell",0)
mqtt.subscribe("hackeriet/reload_users",0)
def check_auth(username, password):
return members.authenticate(username, password)
def check_admin(username, password):
return members.authenticate_admin(username, password)
def authenticate():
return Response('L33t hax0rz only\n',401,
{'WWW-Authenticate': 'Basic realm="Hackeriet"'})
def requires_auth(f):
@wraps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth or not check_auth(auth.username, auth.password):
return authenticate()
return f(*args, **kwargs)
return decorated
def requires_admin(f):
@wraps(f)
def decorated(*args, **kwargs):
auth = request.authorization
if not auth or not check_admin(auth.username, auth.password):
return authenticate()
return f(*args, **kwargs)
return decorated
@app.route("/")
def hello():
return redirect(url_for('index'))
@app.route("/brus/sales.json")
def stats():
r = []
st = brusdb.get_outgoing_transactions()
for d in {e for (t,v,e) in st}:
if len([t for (t,v,e) in st if e==d]) > 4:
r += [{"key": d, "values": [[int(t)*1000,-v] if e==d else [int(t)*1000,0] for (t,v,e) in st]}]
return json.dumps(r)
@app.route('/brus/')
def index():
return render_template('index.html')
@app.route("/brus/account")
@requires_auth
def account():
user=request.authorization.username
return render_template('account.html', username=user,
history=brusdb.transaction_history(user),
balance=brusdb.balance(user),
key=stripe_keys['publishable_key'])
@app.route("/brus/withdraw", methods=['POST'])
def manual_subtract():
user=request.authorization.username
if brusdb.subtract_funds(user, int(request.form['value']),
request.form['desc'], True):
return redirect(url_for('account'))
else:
return "Insufficient funds"
@app.route("/brus/admin")
@requires_admin
def admin():
user=request.authorization.username
return render_template('admin.html', username=user,
users=members.list_users())
@app.route("/brus/admin/add", methods=['POST'])
@requires_admin
def admin_add():
brusdb.add_funds(request.form['user'], int(request.form['value']),
request.form['desc'])
return 'ok'
@app.route("/brus/charge", methods=['POST'])
@requires_auth
def charge():
amount = request.form['amountt']
user=request.authorization.username
stripe_id = None
if not stripe_id:
customer = stripe.Customer.create(
email=members.get_email(user),
card=request.form['stripeToken']
)
stripe_id = customer.id
charge = stripe.Charge.create(
customer=stripe_id,
amount=amount,
currency='NOK',
description='Hackeriet'
)
brusdb.add_funds(user, int(amount)/100, "Transfer with Stripe.")
return render_template('charge.html', amount=int(amount)/100)
def authorise_sale(slot, card_data):
price, desc = brusdb.get_product_price_descr(brusdb.getproduct("brusautomat", slot))
if brusdb.subtract_funds(members.username_from_card(card_data), price, desc):
mqtt("brus/dispense", slot)
mqtt("brus/error", "Insufficient funds")
if __name__ == "__main__":
main()
def main():
app.debug = False
app.run()
| true | true |
f736ed591c705009f410e5f41e8edef29949cfa3 | 138 | py | Python | test/api_test.py | adi4387/titanic | d45a124781ba1cb2b194c179725f856faea8c082 | [
"MIT"
] | null | null | null | test/api_test.py | adi4387/titanic | d45a124781ba1cb2b194c179725f856faea8c082 | [
"MIT"
] | null | null | null | test/api_test.py | adi4387/titanic | d45a124781ba1cb2b194c179725f856faea8c082 | [
"MIT"
] | null | null | null | import json
import requests
data = json.dumps({'name':'Aditya'})
res = requests.post('http://127.0.0.1:10001/api', data)
print(res.text)
| 19.714286 | 55 | 0.695652 | import json
import requests
data = json.dumps({'name':'Aditya'})
res = requests.post('http://127.0.0.1:10001/api', data)
print(res.text)
| true | true |
f736ee8816ef15543c88cd7f9323f3af4f9ff2b6 | 1,872 | py | Python | C15/main.py | ybalenko/Data-Analysis | cda706d66346d5a7a81fbc7f2146b7da43ea5cdc | [
"MIT"
] | null | null | null | C15/main.py | ybalenko/Data-Analysis | cda706d66346d5a7a81fbc7f2146b7da43ea5cdc | [
"MIT"
] | null | null | null | C15/main.py | ybalenko/Data-Analysis | cda706d66346d5a7a81fbc7f2146b7da43ea5cdc | [
"MIT"
] | null | null | null | import pandas
from pandas import DataFrame
import building_energy_data as bed
def main():
reporter = bed.BuidingEnergyReporter('file.csv')
# Q1: What was the name of the building that had the largest NumberofFloors?
building_name = reporter.max_number_of_floors()
print('Question 1: Name of the building that had the largest NumberOfFloors', building_name)
# Q2: How many buildings had an ENERGYSTARScore of at least 97?
buildings_with_energyscore = reporter.energy_score_buildings(97)
total_rows = len(buildings_with_energyscore.index)
print('Question 2: There are', total_rows,
'buildings that had an ENERGYSTARScore of at least 97')
# Q3: What is the median of the Site Energy Use Index (the SiteEUI(kBtu/sf) column) among all buildings that used natural gas?
median = reporter.median_SiteEUI_gas()
print('Question 3: The median of the Site Energy Use Index (the SiteEUI(kBtu/sf) column) among all buildings that used natural gas is', median)
# Q4: Within the Ballard neighborhood, which buildings used more electricity than BIOMED FAIRVIEW RESEARCH CENTER in 2018?
electricity = reporter.ballard_building_used_electricity_more_threshold()
print('Question 4: The following buildings used more electricity than BIOMED FAIRVIEW RESEARCH CENTER in 2018 within the Ballard neighborhood: \n', electricity)
# Q5: Which properties have a larger property gross floor area for their buildings greater than 15 football fields (NFL) and are not offices or hospitals?
not_offices_not_hospitals_more_15_NFL = reporter.buildings_floor_area_more_15_NFL()
print('Question 5: The properties that have a larger property gross floor area for their buildings greater than 15 football fields (NFL) and are not offices or hospitals: ',
not_offices_not_hospitals_more_15_NFL)
if __name__ == '__main__':
main()
| 52 | 177 | 0.777244 | import pandas
from pandas import DataFrame
import building_energy_data as bed
def main():
reporter = bed.BuidingEnergyReporter('file.csv')
building_name = reporter.max_number_of_floors()
print('Question 1: Name of the building that had the largest NumberOfFloors', building_name)
buildings_with_energyscore = reporter.energy_score_buildings(97)
total_rows = len(buildings_with_energyscore.index)
print('Question 2: There are', total_rows,
'buildings that had an ENERGYSTARScore of at least 97')
median = reporter.median_SiteEUI_gas()
print('Question 3: The median of the Site Energy Use Index (the SiteEUI(kBtu/sf) column) among all buildings that used natural gas is', median)
electricity = reporter.ballard_building_used_electricity_more_threshold()
print('Question 4: The following buildings used more electricity than BIOMED FAIRVIEW RESEARCH CENTER in 2018 within the Ballard neighborhood: \n', electricity)
not_offices_not_hospitals_more_15_NFL = reporter.buildings_floor_area_more_15_NFL()
print('Question 5: The properties that have a larger property gross floor area for their buildings greater than 15 football fields (NFL) and are not offices or hospitals: ',
not_offices_not_hospitals_more_15_NFL)
if __name__ == '__main__':
main()
| true | true |
f736eeac450d1ff8d9e88665aa56c3f3474104df | 2,597 | py | Python | dgp/core/context.py | dataspot/dgp | 553a255a4884b935cf2efecdc761050232f0f066 | [
"MIT"
] | 1 | 2019-07-17T11:34:27.000Z | 2019-07-17T11:34:27.000Z | dgp/core/context.py | datahq/dgp | f39592ce20ba67b73b08188f14585b6eb3d43f96 | [
"MIT"
] | 2 | 2019-04-30T12:32:32.000Z | 2019-04-30T12:35:26.000Z | dgp/core/context.py | dataspot/dgp | 553a255a4884b935cf2efecdc761050232f0f066 | [
"MIT"
] | null | null | null | import copy
import tabulator
import requests
from .config import Config
from ..config.log import logger
from ..config.consts import CONFIG_SKIP_ROWS, CONFIG_TAXONOMY_ID, CONFIG_FORMAT, CONFIG_ALLOW_INSECURE_TLS
from ..taxonomies import TaxonomyRegistry, Taxonomy
_workbook_cache = {}
def trimmer(extended_rows):
for row_number, headers, row in extended_rows:
if headers:
row = row[:len(headers)]
if len(row) < len(headers):
continue
yield (row_number, headers, row)
class Context():
def __init__(self, config: Config, taxonomies: TaxonomyRegistry):
self.config = config
self.taxonomies: TaxonomyRegistry = taxonomies
self._stream = None
self.enricher_dir = None
def _structure_params(self):
skip_rows = self.config.get(CONFIG_SKIP_ROWS) if CONFIG_SKIP_ROWS in self.config else None
fmt = self.config.get(CONFIG_FORMAT)
return dict(
headers=skip_rows + 1 if skip_rows is not None else None,
ignore_blank_headers=fmt in ('csv', 'xlsx', 'xls'),
post_parse=[trimmer]
)
def reset_stream(self):
self._stream = None
def http_session(self):
http_session = requests.Session()
http_session.headers.update(tabulator.config.HTTP_HEADERS)
if self.config.get(CONFIG_ALLOW_INSECURE_TLS):
http_session.verify = False
return http_session
@property
def stream(self):
if self._stream is None:
source = copy.deepcopy(self.config._unflatten().get('source', {}))
structure = self._structure_params()
try:
path = source.pop('path')
if not path:
return None
logger.info('Opening stream %s', path)
if 'workbook_cache' in source:
source['workbook_cache'] = _workbook_cache
self._stream = tabulator.Stream(path, **source, **structure, http_session=self.http_session()).open()
for k in source.keys():
self.config.get('source.' + k)
for k in structure.keys():
self.config.get('structure.' + k)
except Exception:
logger.exception('Failed to open URL, source=%r, structure=%r', source, structure)
raise
return self._stream
@property
def taxonomy(self) -> Taxonomy:
if CONFIG_TAXONOMY_ID in self.config:
return self.taxonomies.get(self.config[CONFIG_TAXONOMY_ID])
| 34.171053 | 117 | 0.613015 | import copy
import tabulator
import requests
from .config import Config
from ..config.log import logger
from ..config.consts import CONFIG_SKIP_ROWS, CONFIG_TAXONOMY_ID, CONFIG_FORMAT, CONFIG_ALLOW_INSECURE_TLS
from ..taxonomies import TaxonomyRegistry, Taxonomy
_workbook_cache = {}
def trimmer(extended_rows):
for row_number, headers, row in extended_rows:
if headers:
row = row[:len(headers)]
if len(row) < len(headers):
continue
yield (row_number, headers, row)
class Context():
def __init__(self, config: Config, taxonomies: TaxonomyRegistry):
self.config = config
self.taxonomies: TaxonomyRegistry = taxonomies
self._stream = None
self.enricher_dir = None
def _structure_params(self):
skip_rows = self.config.get(CONFIG_SKIP_ROWS) if CONFIG_SKIP_ROWS in self.config else None
fmt = self.config.get(CONFIG_FORMAT)
return dict(
headers=skip_rows + 1 if skip_rows is not None else None,
ignore_blank_headers=fmt in ('csv', 'xlsx', 'xls'),
post_parse=[trimmer]
)
def reset_stream(self):
self._stream = None
def http_session(self):
http_session = requests.Session()
http_session.headers.update(tabulator.config.HTTP_HEADERS)
if self.config.get(CONFIG_ALLOW_INSECURE_TLS):
http_session.verify = False
return http_session
@property
def stream(self):
if self._stream is None:
source = copy.deepcopy(self.config._unflatten().get('source', {}))
structure = self._structure_params()
try:
path = source.pop('path')
if not path:
return None
logger.info('Opening stream %s', path)
if 'workbook_cache' in source:
source['workbook_cache'] = _workbook_cache
self._stream = tabulator.Stream(path, **source, **structure, http_session=self.http_session()).open()
for k in source.keys():
self.config.get('source.' + k)
for k in structure.keys():
self.config.get('structure.' + k)
except Exception:
logger.exception('Failed to open URL, source=%r, structure=%r', source, structure)
raise
return self._stream
@property
def taxonomy(self) -> Taxonomy:
if CONFIG_TAXONOMY_ID in self.config:
return self.taxonomies.get(self.config[CONFIG_TAXONOMY_ID])
| true | true |
f736ef2c2ed6fc9b43f055f0d0b027e9ae9f7e07 | 8,040 | py | Python | maskrcnn_benchmark/modeling/rrpn/inference.py | markson14/RRPN_pytorch | 90acc619fbabb64e772e5abc5e2d0bc368425450 | [
"MIT"
] | 294 | 2019-05-03T08:40:20.000Z | 2022-03-25T06:12:11.000Z | maskrcnn_benchmark/modeling/rrpn/inference.py | chenjun2hao/RRPN_pytorch | f30c6180c44c2d6cc65ce4521a3cf839b5215089 | [
"MIT"
] | 62 | 2019-05-07T03:48:45.000Z | 2022-03-02T03:23:34.000Z | maskrcnn_benchmark/modeling/rrpn/inference.py | chenjun2hao/RRPN_pytorch | f30c6180c44c2d6cc65ce4521a3cf839b5215089 | [
"MIT"
] | 67 | 2019-05-06T02:30:17.000Z | 2021-08-30T12:45:22.000Z | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.rbox_coder import RBoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, RBoxList
from maskrcnn_benchmark.structures.rboxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.rboxlist_ops import boxlist_nms #
from maskrcnn_benchmark.structures.rboxlist_ops import remove_small_boxes
from ..utils import cat
class RPNPostProcessor(torch.nn.Module):
"""
Performs post-processing on the outputs of the RPN boxes, before feeding the
proposals to the heads
"""
def __init__(
self,
pre_nms_top_n,
post_nms_top_n,
nms_thresh,
min_size,
box_coder=None,
fpn_post_nms_top_n=None,
):
"""
Arguments:
pre_nms_top_n (int)
post_nms_top_n (int)
nms_thresh (float)
min_size (int)
box_coder (BoxCoder)
fpn_post_nms_top_n (int)
"""
super(RPNPostProcessor, self).__init__()
self.pre_nms_top_n = pre_nms_top_n
self.post_nms_top_n = post_nms_top_n
self.nms_thresh = nms_thresh
self.min_size = min_size
if box_coder is None:
box_coder = RBoxCoder(weights=(1.0, 1.0, 1.0, 1.0, 1.0))
self.box_coder = box_coder
if fpn_post_nms_top_n is None:
fpn_post_nms_top_n = post_nms_top_n
self.fpn_post_nms_top_n = fpn_post_nms_top_n
def add_gt_proposals(self, proposals, targets):
"""
Arguments:
proposals: list[BoxList]
targets: list[BoxList]
"""
# Get the device we're operating on
device = proposals[0].bbox.device
gt_boxes = [target.copy_with_fields([]) for target in targets]
# later cat of bbox requires all fields to be present for all bbox
# so we need to add a dummy for objectness that's missing
for gt_box in gt_boxes:
gt_box.add_field("objectness", torch.ones(len(gt_box), device=device))
proposals = [
cat_boxlist((proposal, gt_box))
for proposal, gt_box in zip(proposals, gt_boxes)
]
# print('rrpn_proposal:', proposals[0].bbox.size(), proposals[0].bbox[:, 2:4])
return proposals
# proposal_target_layer
def forward_for_single_feature_map(self, anchors, objectness, box_regression):
"""
Arguments:
anchors: list[BoxList]
objectness: tensor of size N, A, H, W
box_regression: tensor of size N, A * 5, H, W
"""
device = objectness.device
N, A, H, W = objectness.shape
# put in the same format as anchors
objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
objectness = objectness.sigmoid()
box_regression = box_regression.view(N, -1, 5, H, W).permute(0, 3, 4, 1, 2)
box_regression = box_regression.reshape(N, -1, 5)
num_anchors = A * H * W
pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
batch_idx = torch.arange(N, device=device)[:, None]
box_regression = box_regression[batch_idx, topk_idx]
image_shapes = [box.size for box in anchors]
concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
concat_anchors = concat_anchors.reshape(N, -1, 5)[batch_idx, topk_idx]
# print('concat_anchors:', concat_anchors.size(), concat_anchors[:, 2:4])
proposals = self.box_coder.decode(
box_regression.view(-1, 5), concat_anchors.view(-1, 5)
)
proposals = proposals.view(N, -1, 5)
# print('outsider:', proposals.size(), proposals[:, 2:4], 'box_regression:', box_regression)
#-------
result = []
for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
boxlist = RBoxList(proposal, im_shape, mode="xywha")
# print('before nms:', boxlist.bbox.size(), boxlist.bbox[:, 2:4])
boxlist.add_field("objectness", score)
# boxlist = boxlist.clip_to_image(remove_empty=False)
boxlist = remove_small_boxes(boxlist, self.min_size)
boxlist = boxlist_nms(
boxlist,
self.nms_thresh,
max_proposals=self.post_nms_top_n,
score_field="objectness",
)
# print('after nms:', boxlist.bbox.size(), boxlist.bbox[:, 2:4])
result.append(boxlist)
return result
def forward(self, anchors, objectness, box_regression, targets=None):
"""
Arguments:
anchors: list[list[BoxList]]
objectness: list[tensor]
box_regression: list[tensor]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
"""
sampled_boxes = []
num_levels = len(objectness)
anchors = list(zip(*anchors))
for a, o, b in zip(anchors, objectness, box_regression):
sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
if num_levels > 1:
boxlists = self.select_over_all_levels(boxlists)
# append ground-truth bboxes to proposals
if self.training and targets is not None:
boxlists = self.add_gt_proposals(boxlists, targets)
return boxlists
def select_over_all_levels(self, boxlists):
num_images = len(boxlists)
# different behavior during training and during testing:
# during training, post_nms_top_n is over *all* the proposals combined, while
# during testing, it is over the proposals for each image
# TODO resolve this difference and make it consistent. It should be per image,
# and not per batch
if self.training:
objectness = torch.cat(
[boxlist.get_field("objectness") for boxlist in boxlists], dim=0
)
box_sizes = [len(boxlist) for boxlist in boxlists]
post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
_, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True)
inds_mask = torch.zeros_like(objectness, dtype=torch.uint8)
inds_mask[inds_sorted] = 1
inds_mask = inds_mask.split(box_sizes)
for i in range(num_images):
boxlists[i] = boxlists[i][inds_mask[i]]
else:
for i in range(num_images):
objectness = boxlists[i].get_field("objectness")
post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
_, inds_sorted = torch.topk(
objectness, post_nms_top_n, dim=0, sorted=True
)
boxlists[i] = boxlists[i][inds_sorted]
return boxlists
def make_rpn_postprocessor(config, rpn_box_coder, is_train):
fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN
if not is_train:
fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST
pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TRAIN
post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TRAIN
if not is_train:
pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TEST
post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TEST
nms_thresh = config.MODEL.RPN.NMS_THRESH
min_size = config.MODEL.RPN.MIN_SIZE
box_selector = RPNPostProcessor(
pre_nms_top_n=pre_nms_top_n,
post_nms_top_n=post_nms_top_n,
nms_thresh=nms_thresh,
min_size=min_size,
box_coder=rpn_box_coder,
fpn_post_nms_top_n=fpn_post_nms_top_n,
)
return box_selector
| 37.395349 | 100 | 0.631592 |
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.modeling.rbox_coder import RBoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList, RBoxList
from maskrcnn_benchmark.structures.rboxlist_ops import cat_boxlist
from maskrcnn_benchmark.structures.rboxlist_ops import boxlist_nms
from maskrcnn_benchmark.structures.rboxlist_ops import remove_small_boxes
from ..utils import cat
class RPNPostProcessor(torch.nn.Module):
def __init__(
self,
pre_nms_top_n,
post_nms_top_n,
nms_thresh,
min_size,
box_coder=None,
fpn_post_nms_top_n=None,
):
super(RPNPostProcessor, self).__init__()
self.pre_nms_top_n = pre_nms_top_n
self.post_nms_top_n = post_nms_top_n
self.nms_thresh = nms_thresh
self.min_size = min_size
if box_coder is None:
box_coder = RBoxCoder(weights=(1.0, 1.0, 1.0, 1.0, 1.0))
self.box_coder = box_coder
if fpn_post_nms_top_n is None:
fpn_post_nms_top_n = post_nms_top_n
self.fpn_post_nms_top_n = fpn_post_nms_top_n
def add_gt_proposals(self, proposals, targets):
device = proposals[0].bbox.device
gt_boxes = [target.copy_with_fields([]) for target in targets]
# later cat of bbox requires all fields to be present for all bbox
# so we need to add a dummy for objectness that's missing
for gt_box in gt_boxes:
gt_box.add_field("objectness", torch.ones(len(gt_box), device=device))
proposals = [
cat_boxlist((proposal, gt_box))
for proposal, gt_box in zip(proposals, gt_boxes)
]
return proposals
def forward_for_single_feature_map(self, anchors, objectness, box_regression):
device = objectness.device
N, A, H, W = objectness.shape
objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
objectness = objectness.sigmoid()
box_regression = box_regression.view(N, -1, 5, H, W).permute(0, 3, 4, 1, 2)
box_regression = box_regression.reshape(N, -1, 5)
num_anchors = A * H * W
pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
batch_idx = torch.arange(N, device=device)[:, None]
box_regression = box_regression[batch_idx, topk_idx]
image_shapes = [box.size for box in anchors]
concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
concat_anchors = concat_anchors.reshape(N, -1, 5)[batch_idx, topk_idx]
proposals = self.box_coder.decode(
box_regression.view(-1, 5), concat_anchors.view(-1, 5)
)
proposals = proposals.view(N, -1, 5)
result = []
for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
boxlist = RBoxList(proposal, im_shape, mode="xywha")
boxlist.add_field("objectness", score)
boxlist = remove_small_boxes(boxlist, self.min_size)
boxlist = boxlist_nms(
boxlist,
self.nms_thresh,
max_proposals=self.post_nms_top_n,
score_field="objectness",
)
result.append(boxlist)
return result
def forward(self, anchors, objectness, box_regression, targets=None):
sampled_boxes = []
num_levels = len(objectness)
anchors = list(zip(*anchors))
for a, o, b in zip(anchors, objectness, box_regression):
sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))
boxlists = list(zip(*sampled_boxes))
boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]
if num_levels > 1:
boxlists = self.select_over_all_levels(boxlists)
if self.training and targets is not None:
boxlists = self.add_gt_proposals(boxlists, targets)
return boxlists
def select_over_all_levels(self, boxlists):
num_images = len(boxlists)
if self.training:
objectness = torch.cat(
[boxlist.get_field("objectness") for boxlist in boxlists], dim=0
)
box_sizes = [len(boxlist) for boxlist in boxlists]
post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
_, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True)
inds_mask = torch.zeros_like(objectness, dtype=torch.uint8)
inds_mask[inds_sorted] = 1
inds_mask = inds_mask.split(box_sizes)
for i in range(num_images):
boxlists[i] = boxlists[i][inds_mask[i]]
else:
for i in range(num_images):
objectness = boxlists[i].get_field("objectness")
post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
_, inds_sorted = torch.topk(
objectness, post_nms_top_n, dim=0, sorted=True
)
boxlists[i] = boxlists[i][inds_sorted]
return boxlists
def make_rpn_postprocessor(config, rpn_box_coder, is_train):
fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN
if not is_train:
fpn_post_nms_top_n = config.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST
pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TRAIN
post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TRAIN
if not is_train:
pre_nms_top_n = config.MODEL.RPN.PRE_NMS_TOP_N_TEST
post_nms_top_n = config.MODEL.RPN.POST_NMS_TOP_N_TEST
nms_thresh = config.MODEL.RPN.NMS_THRESH
min_size = config.MODEL.RPN.MIN_SIZE
box_selector = RPNPostProcessor(
pre_nms_top_n=pre_nms_top_n,
post_nms_top_n=post_nms_top_n,
nms_thresh=nms_thresh,
min_size=min_size,
box_coder=rpn_box_coder,
fpn_post_nms_top_n=fpn_post_nms_top_n,
)
return box_selector
| true | true |
f736ef7c4d019cb0d74a1b07bd56a8165aa017c5 | 760 | py | Python | setup.py | Zingeon/payrun-python | 1fbac0ee2556641840bf0b34d6da44437d91dc80 | [
"MIT"
] | null | null | null | setup.py | Zingeon/payrun-python | 1fbac0ee2556641840bf0b34d6da44437d91dc80 | [
"MIT"
] | null | null | null | setup.py | Zingeon/payrun-python | 1fbac0ee2556641840bf0b34d6da44437d91dc80 | [
"MIT"
] | null | null | null | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="payrun", # Replace with your own username
version="0.0.1",
author="Andrii Pushkar",
author_email="zingeon1@gmail.com",
description="A Python SDK for PayRun API",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/Zingeon/payrun-python",
download_url = "https://codeload.github.com/Zingeon/payrun-python/tar.gz/0.0.2",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
) | 33.043478 | 84 | 0.672368 | import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="payrun",
version="0.0.1",
author="Andrii Pushkar",
author_email="zingeon1@gmail.com",
description="A Python SDK for PayRun API",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/Zingeon/payrun-python",
download_url = "https://codeload.github.com/Zingeon/payrun-python/tar.gz/0.0.2",
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
) | true | true |
f736f009216a5b613c1c38e28616fc29d1e3caf3 | 3,938 | py | Python | treebudy/nodes.py | drafter250/treebudy | f01bdfc24cf5e44e16a58e6a9af6574920fa8083 | [
"BSD-3-Clause"
] | 1 | 2017-07-14T01:51:52.000Z | 2017-07-14T01:51:52.000Z | treebudy/nodes.py | treebudy/treebudy | f01bdfc24cf5e44e16a58e6a9af6574920fa8083 | [
"BSD-3-Clause"
] | 1 | 2016-12-12T03:17:50.000Z | 2016-12-12T03:17:50.000Z | treebudy/nodes.py | drafter250/treebudy | f01bdfc24cf5e44e16a58e6a9af6574920fa8083 | [
"BSD-3-Clause"
] | null | null | null | # -*- coding: utf-8 -*-
from collections.abc import MutableSequence, MutableMapping
from collections import OrderedDict
from itertools import chain
class Snode(MutableSequence):
"""A sequence object that knows it's parent"""
# this will allow easy subclassing to extend the container types that can
# be parsed
STYPES = (list, tuple)
MTYPES = (dict, OrderedDict)
def __init__(self, nodes=None, parent=None):
self.__children = list()
self._parent = None
self.parent = parent
if nodes:
self.extend(nodes)
@property
def parent(self):
return self._parent
@parent.setter
def parent(self, parent):
if parent is None:
self._parent = None
elif isinstance(parent, (Snode, Mnode)):
self._parent = parent
else:
raise TypeError("Type({}) cannot be a parent of \
Type({})".format(type(parent), type(self)))
def __getitem__(self, index):
return self.__children[index]
def __setitem__(self, index, node):
if isinstance(node, (Snode, Mnode)):
node.parent = self
self.__children[index] = node
elif isinstance(node, self.MTYPES):
self.__children[index] = Mnode(node, parent=self)
elif isinstance(node, self.STYPES):
self.__children[index] = Snode(node, parent=self)
else:
self.__children[index] = node
def __delitem__(self, index):
del self.__children[index]
def __len__(self):
return len(self.__children)
def insert(self, index, node):
"""insert something as a child of this node. If that something derives
from MutableSequence it will be converted into an Snode
"""
if isinstance(node, (Snode, Mnode)):
node.parent = self
self.__children.insert(index, node)
elif isinstance(node, self.MTYPES):
self.__children.insert(index, Mnode(node, parent=self))
elif isinstance(node, self.STYPES):
self.__children.insert(index, Snode(node, parent=self))
else:
self.__children.insert(index, node)
class Mnode(MutableMapping):
"""A mapping object that knows it's parent
Parameters
----------
nodes : mapping
parent: Mnode or Snode
"""
STYPES = (list, tuple)
MTYPES = (dict, OrderedDict)
def __init__(self, nodes=None, parent=None):
self.__children = OrderedDict()
self._parent = None
if parent:
self.parent = parent
if nodes:
self.update(nodes)
def __repr__(self):
return repr(self.__children)
@property
def parent(self):
return self._parent
@parent.setter
def parent(self, parent):
if parent is None:
self._parent = None
elif isinstance(parent, (Snode, Mnode)):
self._parent = parent
else:
raise TypeError("Type({}) cannot be a parent of \
Type({})".format(type(parent), type(self)))
def __iter__(self):
for node_name in self.__children:
yield node_name
def __getitem__(self, key):
return self.__children[key]
def __setitem__(self, key, node):
if isinstance(node, (Mnode, Snode)):
node.parent = self
self.__children[key] = node
elif isinstance(node, self.MTYPES):
self.__children[key] = Mnode(node, parent=self)
elif isinstance(node, self.STYPES):
self.__children[key] = Snode(node, parent=self)
else:
self.__children[key] = node
def __delitem__(self, key):
del self.__children[key]
def __len__(self):
return len(self.__children)
def update(self, mapping):
for key, node in mapping.items():
self[key] = node
| 28.330935 | 78 | 0.589893 |
from collections.abc import MutableSequence, MutableMapping
from collections import OrderedDict
from itertools import chain
class Snode(MutableSequence):
STYPES = (list, tuple)
MTYPES = (dict, OrderedDict)
def __init__(self, nodes=None, parent=None):
self.__children = list()
self._parent = None
self.parent = parent
if nodes:
self.extend(nodes)
@property
def parent(self):
return self._parent
@parent.setter
def parent(self, parent):
if parent is None:
self._parent = None
elif isinstance(parent, (Snode, Mnode)):
self._parent = parent
else:
raise TypeError("Type({}) cannot be a parent of \
Type({})".format(type(parent), type(self)))
def __getitem__(self, index):
return self.__children[index]
def __setitem__(self, index, node):
if isinstance(node, (Snode, Mnode)):
node.parent = self
self.__children[index] = node
elif isinstance(node, self.MTYPES):
self.__children[index] = Mnode(node, parent=self)
elif isinstance(node, self.STYPES):
self.__children[index] = Snode(node, parent=self)
else:
self.__children[index] = node
def __delitem__(self, index):
del self.__children[index]
def __len__(self):
return len(self.__children)
def insert(self, index, node):
if isinstance(node, (Snode, Mnode)):
node.parent = self
self.__children.insert(index, node)
elif isinstance(node, self.MTYPES):
self.__children.insert(index, Mnode(node, parent=self))
elif isinstance(node, self.STYPES):
self.__children.insert(index, Snode(node, parent=self))
else:
self.__children.insert(index, node)
class Mnode(MutableMapping):
STYPES = (list, tuple)
MTYPES = (dict, OrderedDict)
def __init__(self, nodes=None, parent=None):
self.__children = OrderedDict()
self._parent = None
if parent:
self.parent = parent
if nodes:
self.update(nodes)
def __repr__(self):
return repr(self.__children)
@property
def parent(self):
return self._parent
@parent.setter
def parent(self, parent):
if parent is None:
self._parent = None
elif isinstance(parent, (Snode, Mnode)):
self._parent = parent
else:
raise TypeError("Type({}) cannot be a parent of \
Type({})".format(type(parent), type(self)))
def __iter__(self):
for node_name in self.__children:
yield node_name
def __getitem__(self, key):
return self.__children[key]
def __setitem__(self, key, node):
if isinstance(node, (Mnode, Snode)):
node.parent = self
self.__children[key] = node
elif isinstance(node, self.MTYPES):
self.__children[key] = Mnode(node, parent=self)
elif isinstance(node, self.STYPES):
self.__children[key] = Snode(node, parent=self)
else:
self.__children[key] = node
def __delitem__(self, key):
del self.__children[key]
def __len__(self):
return len(self.__children)
def update(self, mapping):
for key, node in mapping.items():
self[key] = node
| true | true |
f736f0bb7425af4457ff805eabb818748149d97e | 1,232 | py | Python | src/python/tests/unittests/test_web/test_serialize/test_serialize_auto_queue.py | AlekLT/seedsync | d370a94253384e1e6e5caa5fcd44692f1d1f1ce3 | [
"Apache-2.0"
] | 255 | 2017-12-25T00:53:40.000Z | 2022-03-27T10:29:21.000Z | src/python/tests/unittests/test_web/test_serialize/test_serialize_auto_queue.py | AlekLT/seedsync | d370a94253384e1e6e5caa5fcd44692f1d1f1ce3 | [
"Apache-2.0"
] | 111 | 2018-01-04T10:35:49.000Z | 2022-03-29T15:12:52.000Z | src/python/tests/unittests/test_web/test_serialize/test_serialize_auto_queue.py | AlekLT/seedsync | d370a94253384e1e6e5caa5fcd44692f1d1f1ce3 | [
"Apache-2.0"
] | 53 | 2017-12-25T09:34:19.000Z | 2022-03-15T17:53:27.000Z | # Copyright 2017, Inderpreet Singh, All rights reserved.
import unittest
import json
from controller import AutoQueuePattern
from web.serialize import SerializeAutoQueue
class TestSerializeConfig(unittest.TestCase):
def test_is_list(self):
patterns = [
AutoQueuePattern(pattern="one"),
AutoQueuePattern(pattern="two"),
AutoQueuePattern(pattern="three")
]
out = SerializeAutoQueue.patterns(patterns)
out_list = json.loads(out)
self.assertIsInstance(out_list, list)
self.assertEqual(3, len(out_list))
def test_patterns(self):
patterns = [
AutoQueuePattern(pattern="one"),
AutoQueuePattern(pattern="tw o"),
AutoQueuePattern(pattern="th'ree"),
AutoQueuePattern(pattern="fo\"ur"),
AutoQueuePattern(pattern="fi=ve")
]
out = SerializeAutoQueue.patterns(patterns)
out_list = json.loads(out)
self.assertEqual(5, len(out_list))
self.assertEqual([
{"pattern": "one"},
{"pattern": "tw o"},
{"pattern": "th'ree"},
{"pattern": "fo\"ur"},
{"pattern": "fi=ve"},
], out_list)
| 30.8 | 56 | 0.593344 |
import unittest
import json
from controller import AutoQueuePattern
from web.serialize import SerializeAutoQueue
class TestSerializeConfig(unittest.TestCase):
def test_is_list(self):
patterns = [
AutoQueuePattern(pattern="one"),
AutoQueuePattern(pattern="two"),
AutoQueuePattern(pattern="three")
]
out = SerializeAutoQueue.patterns(patterns)
out_list = json.loads(out)
self.assertIsInstance(out_list, list)
self.assertEqual(3, len(out_list))
def test_patterns(self):
patterns = [
AutoQueuePattern(pattern="one"),
AutoQueuePattern(pattern="tw o"),
AutoQueuePattern(pattern="th'ree"),
AutoQueuePattern(pattern="fo\"ur"),
AutoQueuePattern(pattern="fi=ve")
]
out = SerializeAutoQueue.patterns(patterns)
out_list = json.loads(out)
self.assertEqual(5, len(out_list))
self.assertEqual([
{"pattern": "one"},
{"pattern": "tw o"},
{"pattern": "th'ree"},
{"pattern": "fo\"ur"},
{"pattern": "fi=ve"},
], out_list)
| true | true |
f736f17981501e31f64226a407c61c5295a0d3e7 | 3,831 | py | Python | scripts/migrate-users.py | tacerus/matrix-appservice-irc | db89870478e414ce4db3fa7796e5a83399389dc6 | [
"Apache-2.0"
] | 425 | 2015-07-29T19:30:26.000Z | 2022-03-27T18:08:23.000Z | scripts/migrate-users.py | tacerus/matrix-appservice-irc | db89870478e414ce4db3fa7796e5a83399389dc6 | [
"Apache-2.0"
] | 1,225 | 2015-04-13T14:01:16.000Z | 2022-03-29T17:36:55.000Z | scripts/migrate-users.py | tacerus/matrix-appservice-irc | db89870478e414ce4db3fa7796e5a83399389dc6 | [
"Apache-2.0"
] | 145 | 2015-05-13T09:15:43.000Z | 2022-03-20T12:17:56.000Z | #!/usr/bin/env python
from __future__ import print_function
import argparse
from datetime import datetime
import sys
import json
import yaml
import urllib
import requests
import time
import re
# import logging
# import httplib as http_client
# http_client.HTTPConnection.debuglevel = 1
# logging.basicConfig()
# logging.getLogger().setLevel(logging.DEBUG)
# requests_log = logging.getLogger("requests.packages.urllib3")
# requests_log.setLevel(logging.DEBUG)
# requests_log.propagate = True
# If you're running into M_NOT_JSON issues, python-requests strips the body of request on 301 redirects. Make sure you're using the direct url of your homeserver.
# See https://github.com/requests/requests/issues/2590
def get_appservice_token(reg):
with open(reg, "r") as f:
reg_yaml = yaml.load(f)
return reg_yaml["as_token"]
def get_users(homeserver, room_id, token, user_prefix, name_suffix):
res = requests.get(homeserver + "/_matrix/client/r0/rooms/" + urllib.quote(room_id) + "/joined_members?access_token=" + token)
joined = res.json().get("joined", None)
user_ids = [user_id for user_id in joined if user_id.startswith(user_prefix) and (joined.get(user_id).get("display_name") or "").endswith(name_suffix) ]
return { uid: joined.get(uid).get("display_name") for uid in user_ids }
def get_rooms(homeserver, token):
res = requests.get(homeserver + "/_matrix/client/r0/joined_rooms?access_token=" + token).json()
room_ids = []
for room_id in res["joined_rooms"]:
room_ids.append(room_id)
return room_ids
def migrate_displayname(uid, oldname, suffix, homeserver, token):
newname = re.sub(re.escape(suffix)+'$', "", oldname).rstrip()
print("Migrating %s from %s to %s" % (uid, oldname, newname))
headers = { 'Content-Type': 'application/json' }
res = requests.put(homeserver + "/_matrix/client/r0/profile/" + urllib.quote(uid) + "/displayname?access_token=" + token + "&user_id=" + urllib.quote(uid),
data = json.dumps({ 'displayname': newname }), headers=headers)
if res.json():
print(res.json())
if 'M_NOT_JSON' in str(res.json()):
print("python-requests strips the body of the request on 301 redirects (https://github.com/requests/requests/issues/2590). Make sure you're using the direct url of your homeserver.")
def main(registration, homeserver, prefix, suffix):
token = get_appservice_token(registration)
if not token:
raise Exception("Cannot read as_token from registration file")
rooms = get_rooms(homeserver, token)
per_room_users = [get_users(homeserver, room, token, prefix, suffix) for room in rooms]
merged_users = { k: v for d in per_room_users for k,v in d.items() }
for uid, display in merged_users.iteritems():
migrate_displayname(uid, display, suffix, homeserver, token)
time.sleep(0.1)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Remove (ircserver) suffix from users")
parser.add_argument("-r", "--registration", help="The path to the AS registration file", required=True)
parser.add_argument("-u", "--url", help="Base homeserver URL eg 'https://matrix.org'", required=True)
parser.add_argument("-p", "--prefix", help="User prefix to determine which users to check. E.g. @freenode_", required=True)
parser.add_argument("-s", "--suffix", help="Suffix to remove. E.g. (irc.freenode.net)", required=True)
args = parser.parse_args()
if not args.registration or not args.url or not args.prefix or not args.suffix:
parser.print_help()
sys.exit(1)
if args.prefix[0] != "@":
parser.print_help()
print("--prefix must start with '@'")
sys.exit(1)
main(registration=args.registration, homeserver=args.url, prefix=args.prefix, suffix=args.suffix)
| 46.719512 | 194 | 0.701906 |
from __future__ import print_function
import argparse
from datetime import datetime
import sys
import json
import yaml
import urllib
import requests
import time
import re
def get_appservice_token(reg):
with open(reg, "r") as f:
reg_yaml = yaml.load(f)
return reg_yaml["as_token"]
def get_users(homeserver, room_id, token, user_prefix, name_suffix):
res = requests.get(homeserver + "/_matrix/client/r0/rooms/" + urllib.quote(room_id) + "/joined_members?access_token=" + token)
joined = res.json().get("joined", None)
user_ids = [user_id for user_id in joined if user_id.startswith(user_prefix) and (joined.get(user_id).get("display_name") or "").endswith(name_suffix) ]
return { uid: joined.get(uid).get("display_name") for uid in user_ids }
def get_rooms(homeserver, token):
res = requests.get(homeserver + "/_matrix/client/r0/joined_rooms?access_token=" + token).json()
room_ids = []
for room_id in res["joined_rooms"]:
room_ids.append(room_id)
return room_ids
def migrate_displayname(uid, oldname, suffix, homeserver, token):
newname = re.sub(re.escape(suffix)+'$', "", oldname).rstrip()
print("Migrating %s from %s to %s" % (uid, oldname, newname))
headers = { 'Content-Type': 'application/json' }
res = requests.put(homeserver + "/_matrix/client/r0/profile/" + urllib.quote(uid) + "/displayname?access_token=" + token + "&user_id=" + urllib.quote(uid),
data = json.dumps({ 'displayname': newname }), headers=headers)
if res.json():
print(res.json())
if 'M_NOT_JSON' in str(res.json()):
print("python-requests strips the body of the request on 301 redirects (https://github.com/requests/requests/issues/2590). Make sure you're using the direct url of your homeserver.")
def main(registration, homeserver, prefix, suffix):
token = get_appservice_token(registration)
if not token:
raise Exception("Cannot read as_token from registration file")
rooms = get_rooms(homeserver, token)
per_room_users = [get_users(homeserver, room, token, prefix, suffix) for room in rooms]
merged_users = { k: v for d in per_room_users for k,v in d.items() }
for uid, display in merged_users.iteritems():
migrate_displayname(uid, display, suffix, homeserver, token)
time.sleep(0.1)
if __name__ == "__main__":
parser = argparse.ArgumentParser("Remove (ircserver) suffix from users")
parser.add_argument("-r", "--registration", help="The path to the AS registration file", required=True)
parser.add_argument("-u", "--url", help="Base homeserver URL eg 'https://matrix.org'", required=True)
parser.add_argument("-p", "--prefix", help="User prefix to determine which users to check. E.g. @freenode_", required=True)
parser.add_argument("-s", "--suffix", help="Suffix to remove. E.g. (irc.freenode.net)", required=True)
args = parser.parse_args()
if not args.registration or not args.url or not args.prefix or not args.suffix:
parser.print_help()
sys.exit(1)
if args.prefix[0] != "@":
parser.print_help()
print("--prefix must start with '@'")
sys.exit(1)
main(registration=args.registration, homeserver=args.url, prefix=args.prefix, suffix=args.suffix)
| true | true |
f736f1a2c55e61043977fbbd9c2c42cb6bc75c56 | 41,183 | py | Python | pydm/tests/widgets/test_drawing.py | cristinasewell/pydm | 30a830ba5614400ca31dbbf5d5268ea35add178d | [
"BSD-3-Clause-LBNL"
] | 2 | 2018-02-08T10:30:43.000Z | 2020-01-05T14:14:26.000Z | pydm/tests/widgets/test_drawing.py | cristinasewell/pydm | 30a830ba5614400ca31dbbf5d5268ea35add178d | [
"BSD-3-Clause-LBNL"
] | 1 | 2018-09-20T17:39:14.000Z | 2018-09-20T17:39:14.000Z | pydm/tests/widgets/test_drawing.py | cristinasewell/pydm | 30a830ba5614400ca31dbbf5d5268ea35add178d | [
"BSD-3-Clause-LBNL"
] | null | null | null | # Unit Tests for the PyDM drawing widgets
import os
from logging import ERROR
import pytest
from qtpy.QtGui import QColor, QBrush, QPixmap
from qtpy.QtWidgets import QApplication
from qtpy.QtCore import Property, Qt, QPoint, QSize
from qtpy.QtDesigner import QDesignerFormWindowInterface
from ...widgets.base import PyDMWidget
from ...widgets.drawing import (deg_to_qt, qt_to_deg, PyDMDrawing,
PyDMDrawingLine, PyDMDrawingImage,
PyDMDrawingRectangle, PyDMDrawingTriangle,
PyDMDrawingEllipse,
PyDMDrawingCircle, PyDMDrawingArc,
PyDMDrawingPie, PyDMDrawingChord,
PyDMDrawingPolygon, PyDMDrawingPolyline)
from ...utilities.stylesheet import apply_stylesheet
# --------------------
# POSITIVE TEST CASES
# --------------------
# # -------------
# # PyDMDrawing
# # -------------
@pytest.mark.parametrize("deg, expected_qt_deg", [
(0, 0),
(1, 16),
(-1, -16),
])
def test_deg_to_qt(deg, expected_qt_deg):
"""
Test the conversion from degrees to Qt degrees.
Expectations:
The angle measurement in degrees is converted correctly to Qt degrees, which are 16 times more than the degree
value, i.e. 1 degree = 16 Qt degrees.
Parameters
----------
deg : int, float
The angle value in degrees
expected_qt_deg : int, floag
The expected Qt degrees after the conversion
"""
assert deg_to_qt(deg) == expected_qt_deg
@pytest.mark.parametrize("qt_deg, expected_deg", [
(0, 0),
(16, 1),
(-16, -1),
(-32.0, -2),
(16.16, 1.01)
])
def test_qt_to_deg(qt_deg, expected_deg):
"""
Test the conversion from Qt degrees to degrees.
Expectations:
The angle measurement in Qt degrees is converted correctly to degrees, which are 16 times less than the Qt degree
value, i.e. 1 Qt degree = 1/16 degree
Parameters
----------
qt_deg : int, float
The angle value in Qt degrees
expected_deg : int, floag
The expected degrees after the conversion
"""
assert qt_to_deg(qt_deg) == expected_deg
def test_pydmdrawing_construct(qtbot):
"""
Test the construction of a PyDM base object.
Expectations:
Attributes are assigned with the appropriate default values.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.alarmSensitiveBorder is False
assert pydm_drawing._rotation == 0.0
assert pydm_drawing._brush.style() == Qt.SolidPattern
assert pydm_drawing._painter
assert pydm_drawing._pen.style() == pydm_drawing._pen_style == Qt.NoPen
assert pydm_drawing._pen_width == 0
assert pydm_drawing._pen_color == QColor(0, 0, 0)
def test_pydmdrawing_sizeHint(qtbot):
"""
Test the default size of the widget.
Expectations:
The size hint is a fixed size.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.sizeHint() == QSize(100, 100)
@pytest.mark.parametrize("alarm_sensitive_content", [
True,
False,
])
def test_pydmdrawing_paintEvent(qtbot, signals, alarm_sensitive_content):
"""
Test the paintEvent handling of the widget. This test method will also execute PyDMDrawing alarm_severity_changed
and draw_item().
Expectations:
The paintEvent will be triggered, and the widget's brush color is correctly set.
NOTE: This test depends on the default stylesheet having different values for 'qproperty-brush' for different alarm states of PyDMDrawing.
Parameters
----------
qtbot : fixture
Window for widget testing
signals : fixture
The signals fixture, which provides access signals to be bound to the appropriate slots
alarm_sensitive_content : bool
True if the widget will be redraw with a different color if an alarm is triggered; False otherwise.
"""
QApplication.instance().make_main_window()
main_window = QApplication.instance().main_window
qtbot.addWidget(main_window)
pydm_drawing = PyDMDrawing(parent=main_window, init_channel='fake://tst')
qtbot.addWidget(pydm_drawing)
pydm_drawing.alarmSensitiveContent = alarm_sensitive_content
brush_before = pydm_drawing.brush.color().name()
signals.new_severity_signal.connect(pydm_drawing.alarmSeverityChanged)
signals.new_severity_signal.emit(PyDMWidget.ALARM_MAJOR)
brush_after = pydm_drawing.brush.color().name()
if alarm_sensitive_content:
assert brush_before != brush_after
else:
assert brush_before == brush_after
@pytest.mark.parametrize("widget_width, widget_height, expected_results", [
(4.0, 4.0, (2.0, 2.0)),
(1.0, 1.0, (0.5, 0.5)),
(0, 0, (0, 0))
])
def test_pydmdrawing_get_center(qtbot, monkeypatch, widget_width, widget_height,
expected_results):
"""
Test the calculation of the widget's center from its width and height.
Expectations:
The center of the widget is correctly calculated.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override default attribute values
widget_width : int, float
The width of the widget
widget_height : int, float
The height of the widget
expected_results : tuple
The location of the center. This is a tuple of the distance from the width and that from the height.
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: widget_width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: widget_height)
assert pydm_drawing.get_center() == expected_results
@pytest.mark.parametrize(
"width, height, rotation_deg, pen_width, has_border, max_size, force_no_pen, expected",
[
# Zero rotation, with typical width, height, pen_width, and variable max_size, has_border, and force_no_pen
# width > height
(25.53, 10.35, 0.0, 2, True, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, True, True, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 0.0, 2, True, False, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, True, False, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 0.0, 2, False, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, False, True, False,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, False, False, True,
(-12.765, -5.175, 25.53, 10.35)),
# width < height
(10.35, 25.53, 0.0, 2, True, True, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, True, True, False,
(-3.175, -10.765, 6.35, 21.53)),
(10.35, 25.53, 0.0, 2, True, False, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, True, False, False,
(-3.175, -10.765, 6.35, 21.53)),
(10.35, 25.53, 0.0, 2, False, True, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, False, True, False,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, False, False, True,
(-5.175, -12.765, 10.35, 25.53)),
# width == height
(
10.35, 10.35, 0.0, 2, True, True, True, (-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, True, True, False, (-3.175, -3.175, 6.35, 6.35)),
(10.35, 10.35, 0.0, 2, True, False, True,
(-5.175, -5.175, 10.35, 10.35)),
(
10.35, 10.35, 0.0, 2, True, False, False, (-3.175, -3.175, 6.35, 6.35)),
(10.35, 10.35, 0.0, 2, False, True, True,
(-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, False, True, False,
(-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, False, False, True,
(-5.175, -5.175, 10.35, 10.35)),
# Variable rotation, max_size, and force_no_pen, has_border is True
(25.53, 10.35, 45.0, 2, True, True, True,
(-5.207, -2.111, 10.415, 4.222)),
(25.53, 10.35, 145.0, 2, True, True, True,
(-5.714, -2.316, 11.428, 4.633)),
(25.53, 10.35, 90.0, 2, True, True, False,
(-3.175, -0.098, 6.35, 0.196)),
(25.53, 10.35, 180.0, 2, True, False, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 270.0, 2, True, False, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 360.0, 2, False, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.72, 2, False, True, False,
(-12.382, -5.02, 24.764, 10.04)),
(25.53, 10.35, 71.333, 2, False, False, True,
(-12.765, -5.175, 25.53, 10.35)),
])
def test_pydmdrawing_get_bounds(qtbot, monkeypatch, width, height, rotation_deg,
pen_width, has_border, max_size,
force_no_pen, expected):
"""
Test the useful area calculations and compare the resulted tuple to the expected one.
Expectations:
The drawable area boundaries are correctly calculated.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override default attribute values
max_size : bool
If True, draw the widget within the maximum rectangular dimensions given by ```get_inner_max```. If False,
draw the widget within the user-provided width and height
force_no_pen : bool
If True, consider the pen width while calculating the bounds. If False, do not take into account the pen width
expected : tuple
The (x, y) coordinates of the starting point, and the maximum width and height of the rendered image
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
pydm_drawing._pen_width = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
if has_border:
monkeypatch.setattr(PyDMDrawing, "has_border", lambda *args: True)
else:
monkeypatch.setattr(PyDMDrawing, "has_border", lambda *args: False)
calculated_bounds = pydm_drawing.get_bounds(max_size, force_no_pen)
calculated_bounds = tuple(
[round(x, 3) if isinstance(x, float) else x for x in calculated_bounds])
assert calculated_bounds == expected
@pytest.mark.parametrize("pen_style, pen_width, expected_result", [
(Qt.NoPen, 0, False),
(Qt.NoPen, 1, False),
(Qt.SolidLine, 0, False),
(Qt.DashLine, 0, False),
(Qt.SolidLine, 1, True),
(Qt.DashLine, 10, True)
])
def test_pydmdrawing_has_border(qtbot, pen_style, pen_width, expected_result):
"""
Test the determination whether the widget will be drawn with a border, taking into account the pen style and width
Expectations:
The widget has a border if the pen style is not Qt.NoPen, and the pen width is greater than 0.
Parameters
----------
qtbot : fixture
Window for widget testing
pen_style : PenStyle
The style (patterns) of the pen
pen_width : int
The thickness of the pen's lines
expected_result : bool
True if the widget has a border, False otherwise
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing.penStyle = pen_style
pydm_drawing.penWidth = pen_width
assert pydm_drawing.has_border() == expected_result
@pytest.mark.parametrize("width, height, expected_result", [
(10, 15, False),
(10.5, 22.333, False),
(-10.333, -10.332, False),
(10.333, 10.333, True),
(-20.777, -20.777, True),
(70, 70, True),
])
def test_pydmdrawing_is_square(qtbot, monkeypatch, width, height,
expected_result):
"""
Check if the widget has the same width and height values.
Expectations:
The widget's squareness checking returns True if its width and height are the same; False otherwise.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override dialog behaviors
width : int, float
The width of the widget
height : int, float
The height of a widget
expected_result
True if the widget has equal width and height; False otherwise
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
assert pydm_drawing.is_square() == expected_result
@pytest.mark.parametrize("width, height, rotation_deg, expected", [
(25.53, 10.35, 0.0, (25.53, 10.35)),
(10.35, 25.53, 0.0, (10.35, 25.53)),
(25.53, 10.35, 45.0, (10.415, 4.222)),
(10.35, 25.53, 45.0, (4.222, 10.415)),
(10.35, 25.53, 360.0, (10.35, 25.53)),
(10.35, 25.53, -45.0, (4.222, 10.415)),
(10.35, 25.53, -270.0, (4.196, 10.35)),
(10.35, 25.53, -360.0, (10.35, 25.53)),
])
def test_pydmdrawing_get_inner_max(qtbot, monkeypatch, width, height,
rotation_deg, expected):
"""
Test the calculation of the inner rectangle in a rotated rectangle.
Expectations:
The returned inner rectangle's width and height are in a tuple, and must match with the values expected.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override existing method behaviors
width : int, float
The width of the rotated rectangle
height : int, float
The height of the rotated rectangle
rotation_deg : float
The rectangle's rotation angle (in degrees)
expected : tuple
The tuple containing the width and height of the inner rectangle
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
calculated_inner_max = pydm_drawing.get_inner_max()
calculated_inner_max = tuple(
[round(x, 3) if isinstance(x, float) else x for x in
calculated_inner_max])
assert calculated_inner_max == expected
def test_pydmdrawing_properties_and_setters(qtbot):
"""
Test the PyDMDrawing base class properties and setters.
Expectations:
Attribute values are to be retained and retrieved correctly.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.penWidth == 0
assert pydm_drawing.penColor == QColor(0, 0, 0)
assert pydm_drawing.rotation == 0.0
assert pydm_drawing._brush.style() == Qt.SolidPattern
assert pydm_drawing.penStyle == Qt.NoPen
assert pydm_drawing.penCapStyle == Qt.SquareCap
assert pydm_drawing.penJoinStyle == Qt.MiterJoin
# The pen width will retain the previously set value if a negative value
# is attempted to be assigned to it
pydm_drawing.penWidth = -1
assert pydm_drawing.penWidth == 0
pydm_drawing.penWidth = 5
pydm_drawing.penWidth = -1
assert pydm_drawing.penWidth == 5
pydm_drawing.penJoinStyle = Qt.RoundJoin
assert pydm_drawing.penJoinStyle == Qt.RoundJoin
pydm_drawing.penCapStyle = Qt.RoundCap
assert pydm_drawing.penCapStyle == Qt.RoundCap
pydm_drawing.penColor = QColor(255, 0, 0)
pydm_drawing.rotation = 99.99
pydm_drawing.brush = QBrush(Qt.Dense3Pattern)
assert pydm_drawing.penColor == QColor(255, 0, 0)
assert pydm_drawing.rotation == 99.99
assert pydm_drawing._brush.style() == Qt.Dense3Pattern
# # ----------------
# # PyDMDrawingLine
# # ----------------
@pytest.mark.parametrize("alarm_sensitive_content", [
True,
False,
])
def test_pydmdrawingline_draw_item(qtbot, signals, alarm_sensitive_content):
"""
Test PyDMDrawingLine base class drawing handling.
Expectations:
The focus manipulation of the base widget object triggers the draw_item() method of the PyDMDrawLine object.
Parameters
----------
qtbot : fixture
Window for widget testing
signals : fixture
To emit the alarm severity change signal in order to make an appearance change for the widget, thus triggering
a redraw
alarm_sensitive_content : bool
True if the widget will be redraw with a different color if an alarm is triggered; False otherwise
"""
pydm_drawingline = PyDMDrawingLine(init_channel='fake://tst')
qtbot.addWidget(pydm_drawingline)
pydm_drawingline.alarmSensitiveContent = alarm_sensitive_content
signals.new_severity_signal.connect(pydm_drawingline.alarmSeverityChanged)
signals.new_severity_signal.emit(PyDMWidget.ALARM_MAJOR)
with qtbot.waitExposed(pydm_drawingline):
pydm_drawingline.show()
qtbot.waitUntil(lambda: pydm_drawingline.isEnabled(), timeout=5000)
pydm_drawingline.setFocus()
def wait_focus():
return pydm_drawingline.hasFocus()
qtbot.waitUntil(wait_focus, timeout=5000)
# # -----------------
# # PyDMDrawingImage
# # -----------------
def test_pydmdrawingimage_construct(qtbot):
"""
Test the construct of a PyDMDrawingImage object.
Expectations:
The default attribute values are correctly set.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
assert pydm_drawingimage._pixmap is not None
assert pydm_drawingimage._aspect_ratio_mode == Qt.KeepAspectRatio
assert pydm_drawingimage.filename == ""
base_path = os.path.dirname(__file__)
test_file = os.path.join(base_path, '..', '..', '..', 'examples', 'drawing',
'SLAC_logo.jpeg')
pydm_drawingimage2 = PyDMDrawingImage(filename=test_file)
qtbot.addWidget(pydm_drawingimage2)
pydm_drawingimage3 = PyDMDrawingImage(filename=os.path.abspath(test_file))
qtbot.addWidget(pydm_drawingimage3)
pydm_drawingimage4 = PyDMDrawingImage(filename="foo")
qtbot.addWidget(pydm_drawingimage4)
test_gif = os.path.join(base_path, '..', '..', '..', 'examples', 'drawing',
'test.gif')
pydm_drawingimage5 = PyDMDrawingImage(filename=test_gif)
pydm_drawingimage5.movie_finished()
qtbot.addWidget(pydm_drawingimage5)
pydm_drawingimage5.filename = test_file
pydm_drawingimage5.movie_frame_changed(-1)
pydm_drawingimage5.movie_finished()
def test_pydmdrawingimage_get_designer_window(qtbot):
"""
Test getting the designer window that owns the widget. Currently, only test with the parent window being None.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
parent = None
pydm_drawingimage = PyDMDrawingImage(parent=parent)
qtbot.addWidget(pydm_drawingimage)
designer_window = pydm_drawingimage.get_designer_window()
if parent is None:
assert designer_window is None
elif isinstance(parent, QDesignerFormWindowInterface):
assert designer_window == parent
else:
assert designer_window == parent.parent()
def test_pydmdrawingimage_test_properties_and_setters(qtbot):
"""
Test the PyDMDrawing base class properties and setters.
Expectations:
Attribute values are to be retained and retrieved correctly.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
pydm_drawingimage.aspectRatioMode = Qt.KeepAspectRatioByExpanding
assert pydm_drawingimage.aspectRatioMode == Qt.KeepAspectRatioByExpanding
@pytest.mark.parametrize("is_pixmap_empty", [
True,
False,
])
def test_pydmdrawingimage_size_hint(qtbot, monkeypatch, is_pixmap_empty):
"""
Test the size hint of a PyDMDrawingImage object.
Expectations:
If the image is empty, the widget assumes the default size of width == height == 100 pixels. If not, the widget
takes the size from the off screen image presentation (QPixmap).
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
is_pixmap_empty : bool
True if the image presentation is empty; False otherwise
"""
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
if is_pixmap_empty:
monkeypatch.setattr(QSize, "isEmpty", lambda *args: True)
else:
monkeypatch.setattr(QPixmap, "size", lambda *args: QSize(125, 125))
size_hint = pydm_drawingimage.sizeHint()
assert size_hint == QSize(100,
100) if is_pixmap_empty else size_hint == pydm_drawingimage._pixmap.size()
@pytest.mark.parametrize("width, height, pen_width", [
(7.7, 10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingimage_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
"""
Test the rendering of a PyDMDrawingImage object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width : int
The width of the pen stroke
"""
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
pydm_drawingimage.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingimage.show()
# # ---------------------
# # PyDMDrawingRectangle
# # ---------------------
@pytest.mark.parametrize("width, height, pen_width", [
(7.7, 10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingrectangle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
"""
Test the rendering of a PyDMDrawingRectangle object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width : int
The width of the pen stroke
"""
pydm_drawingrectangle = PyDMDrawingRectangle()
qtbot.addWidget(pydm_drawingrectangle)
pydm_drawingrectangle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingrectangle.show()
# # ---------------------
# # PyDMDrawingTriangle
# # ---------------------
@pytest.mark.parametrize("x, y, width, height, expected_points", [
(0.0, 0.0, 7.7, 10.2, [QPoint(0, 5), QPoint(0, 0), QPoint(3, 0.0)]),
(10.3, 0, 7.7, 10.2, [QPoint(10, 5), QPoint(10, 0), QPoint(3, 0)]),
(10.3, 56.7, 7.7, 10.2, [QPoint(10, 5), QPoint(10, 56), QPoint(3, 56)]),
(0.0, 10.75, 7.7, 10.2, [QPoint(0, 5), QPoint(0, 10), QPoint(3, 10)]),
(-10.23, 0, 7.7, 10.2, [QPoint(-10, 5), QPoint(-10, 0), QPoint(3, 0)]),
(0.0, -10.23, 7.7, 10.2, [QPoint(0, 5), QPoint(0, -10), QPoint(3, -10)]),
(-60.23, -87.25, 7.7, 10.2,
[QPoint(-60, 5), QPoint(-60, -87), QPoint(3, -87)]),
(1, 2, 5.0, 5.0, [QPoint(1, 2), QPoint(1, 2), QPoint(2, 2)]),
])
def test_pydmdrawingtriangle_calculate_drawing_points(qtbot, x, y, width,
height, expected_points):
"""
Test the calculations of the point coordinates of a PyDMDrawingTriangle widget.
Expectations:
The calculations match with the expected values.
Parameters
----------
qtbot : fixture
Window for widget testing
x : int, float
The x-coordinate of the top of the triangle
y: int, float
The y-coordinate of the top of the triangle
width : int, float
The base measurement of the triangle
height : int, float
The height measurement of the triangle
expected_points : tuple
The collection of the three x and y coordinate sets of the triangle to draw
"""
pydm_drawingtriangle = PyDMDrawingTriangle()
qtbot.addWidget(pydm_drawingtriangle)
calculated_points = pydm_drawingtriangle._calculate_drawing_points(x, y,
width,
height)
assert calculated_points == expected_points
@pytest.mark.parametrize("width, height, pen_width", [
(7.7, 10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingtriangle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
"""
Test the rendering of a PyDMDrawingTriangle object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width : int
The width of the pen stroke
"""
pydm_drawingtriangle = PyDMDrawingTriangle()
qtbot.addWidget(pydm_drawingtriangle)
pydm_drawingtriangle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingtriangle.show()
# # -------------------
# # PyDMDrawingEclipse
# # -------------------
@pytest.mark.parametrize("width, height, pen_width", [
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingeclipse_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
"""
Test the rendering of a PyDMDrawingEclipse object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width : int
The width of the pen stroke
"""
pydm_dymdrawingeclipse = PyDMDrawingEllipse()
qtbot.addWidget(pydm_dymdrawingeclipse)
pydm_dymdrawingeclipse.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_dymdrawingeclipse.show()
# # ------------------
# # PyDMDrawingCircle
# # ------------------
@pytest.mark.parametrize("width, height, expected_radius", [
(5.0, 5.0, 2.5),
(10.25, 10.25, 5.125),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingcircle_calculate_radius(qtbot, width, height,
expected_radius):
"""
Test the calculation of a PyDMDrawingCircle's radius.
Expectations:
Given the width and height of the circle, the calculated radius will match with the expected value.
Parameters
----------
qtbot : fixture
Window for widget testing
width : int, float
The width to the widget
height : int, float
The height of the widget
expected_radius : int, float
The expected radius calculated from the given width and height
"""
pydm_dymdrawingcircle = PyDMDrawingCircle()
qtbot.addWidget(pydm_dymdrawingcircle)
calculated_radius = pydm_dymdrawingcircle._calculate_radius(width, height)
assert calculated_radius == expected_radius
@pytest.mark.parametrize("width, height, pen_width", [
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingcircle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
"""
Test the rendering of a PyDMDrawingCircle object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width : int
The width of the pen stroke
"""
pydm_dymdrawingcircle = PyDMDrawingCircle()
qtbot.addWidget(pydm_dymdrawingcircle)
pydm_dymdrawingcircle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_dymdrawingcircle.show()
# # ---------------
# # PyDMDrawingArc
# # ---------------
def test_pydmdrawingarc_construct(qtbot):
"""
Test the construct of a PyDMDrawingArc widget.
Expectations:
The default attribute values are as expected.
Parameters
----------
qtbot : fixture
Window for widget testing
"""
pydm_drawingarc = PyDMDrawingArc()
qtbot.addWidget(pydm_drawingarc)
assert pydm_drawingarc._pen_style == Qt.SolidLine
assert pydm_drawingarc._pen_width == 1.0
assert pydm_drawingarc._start_angle == 0
assert pydm_drawingarc._span_angle == deg_to_qt(90)
@pytest.mark.parametrize("width, height, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0),
(10.333, 10.333, 0, 0),
(10.333, 10.333, 0, 45),
(10.333, 11.777, 0, 45),
(10.333, 11.777, 0, -35),
(10.333, 11.777, 11, 45),
(10.333, 11.777, -11, -25),
])
def test_pydmdrawingarc_draw_item(qapp, qtbot, monkeypatch, width, height,
start_angle_deg, span_angle_deg):
"""
Test the rendering of a PyDMDrawingArc object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
start_angle_deg : int
The start angle in degrees
span_angle_deg : int
The span angle in degrees
"""
pydm_drawingarc = PyDMDrawingArc()
qtbot.addWidget(pydm_drawingarc)
pydm_drawingarc.startAngle = start_angle_deg
pydm_drawingarc.spanAngle = span_angle_deg
assert pydm_drawingarc.startAngle == start_angle_deg
assert pydm_drawingarc.spanAngle == span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingarc.show()
# # ---------------
# # PyDMDrawingPie
# # ---------------
@pytest.mark.parametrize(
"width, height, pen_width, rotation_deg, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0, 0, 0),
(10.333, 10.333, 0, 0, 0, 0),
(10.333, 11.777, 0, 0, 0, 45),
(10.333, 11.777, 0, 0, 0, -35),
(10.333, 11.777, 3, 15.333, 0, 0),
(10.333, 11.777, 3, 15.333, 0, 45),
(10.333, 11.777, 3, 15.333, 0, -35),
(10.333, 11.777, 3, 15.333, 11, 45),
(10.333, 11.777, 3, 15.333, -11, -25),
])
def test_pydmdrawingpie_draw_item(qapp, qtbot, monkeypatch, width, height, pen_width,
rotation_deg, start_angle_deg,
span_angle_deg):
"""
Test the rendering of a PyDMDrawingPie object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width: int
The thickness of the pen stroke
rotation_deg : int
The rotation in degrees
start_angle_deg : int
The start angle in degrees
span_angle_deg : int
The span angle in degrees
"""
pydm_drawingpie = PyDMDrawingPie()
qtbot.addWidget(pydm_drawingpie)
pydm_drawingpie._pen_width = pen_width
pydm_drawingpie._rotation = rotation_deg
pydm_drawingpie._start_angle = start_angle_deg
pydm_drawingpie._span_angle = span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingpie.show()
# # -----------------
# # PyDMDrawingChord
# # -----------------
@pytest.mark.parametrize(
"width, height, pen_width, rotation_deg, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0, 0, 0),
(10.333, 10.333, 0, 0, 0, 0),
(10.333, 11.777, 0, 0, 0, 45),
(10.333, 11.777, 0, 0, 0, -35),
(10.333, 11.777, 3, 15.333, 0, 0),
(10.333, 11.777, 3, 15.333, 0, 45),
(10.333, 11.777, 3, 15.333, 0, -35),
(10.333, 11.777, 3, 15.333, 11, 45),
(10.333, 11.777, 3, 15.333, -11, -25),
])
def test_pydmdrawingchord_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width, rotation_deg, start_angle_deg,
span_angle_deg):
"""
Test the rendering of a PyDMDrawingChord object.
Expectations:
The drawing of the object takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
pen_width: int
The thickness of the pen stroke
rotation_deg : int
The rotation in degrees
start_angle_deg : int
The start angle in degrees
span_angle_deg : int
The span angle in degrees
"""
pydm_drawingchord = PyDMDrawingChord()
qtbot.addWidget(pydm_drawingchord)
pydm_drawingchord._pen_width = pen_width
pydm_drawingchord._rotation = rotation_deg
pydm_drawingchord._start_angle = start_angle_deg
pydm_drawingchord._span_angle = span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingchord.show()
# # ---------------------
# # PyDMDrawingPolygon
# # ---------------------
@pytest.mark.parametrize("x, y, width, height, num_points, expected_points", [
(0, 0, 100, 100, 3, [(50.0, 0),(-25, 43.3012),(-25, -43.3012)]),
(0, 0, 100, 100, 4, [(50.0, 0), (0, 50.0), (-50.0, 0), (0, -50.0)])
])
def test_pydmdrawingpolygon_calculate_drawing_points(qapp, qtbot, x, y, width,
height, num_points,
expected_points):
"""
Test the calculations of the point coordinates of a PyDMDrawingTriangle widget.
Expectations:
The calculations match with the expected values.
Parameters
----------
qtbot : fixture
Window for widget testing
x : int, float
The x-coordinate
y: int, float
The y-coordinate
width : int, float
The base measurement
height : int, float
The height measurement
num_points : int
The number of points in the polygon
expected_points : tuple
The collection of the x and y coordinate sets
"""
drawing = PyDMDrawingPolygon()
qtbot.addWidget(drawing)
drawing.numberOfPoints = num_points
assert drawing.numberOfPoints == num_points
calculated_points = drawing._calculate_drawing_points(x, y,
width,
height)
for idx, p in enumerate(calculated_points):
assert p.x() == pytest.approx(expected_points[idx][0], 0.1)
assert p.y() == pytest.approx(expected_points[idx][1], 0.1)
drawing.show()
# # ---------------------
# # PyDMDrawingPolyline
# # ---------------------
@pytest.mark.parametrize("x, y, width, height, num_points, expected_points", [
(-1, 27, 389, 3, 2, [(-2, -2),(384, -2)]),
(301, 230, 99, 20, 3, [(-1, 18),(-1, -1),(97, -1)])
])
def test_pydmdrawingpolyline_getPoints(qapp, qtbot, x, y, width,
height, num_points,
expected_points):
"""
Test the calculations of the point coordinates of a PyDMDrawingPolyline widget.
Expectations:
The calculations match with the expected values.
Parameters
----------
qtbot : fixture
Window for widget testing
x : int, float
The x-coordinate
y: int, float
The y-coordinate
width : int, float
The base measurement
height : int, float
The height measurement
num_points : int
The number of points in the polygon
expected_points : tuple
The collection of the x and y coordinate sets
"""
drawing = PyDMDrawingPolyline()
qtbot.addWidget(drawing)
drawing.numberOfPoints = num_points
assert drawing.numberOfPoints == num_points
the_points = drawing.getPoints()
for idx, p in enumerate(the_points):
assert p.x() == pytest.approx(expected_points[idx][0], 0.1)
assert p.y() == pytest.approx(expected_points[idx][1], 0.1)
drawing.show()
@pytest.mark.parametrize("width, height", [
(99, 20)
])
def test_pydmdrawingpolyline_setPoints(qapp, qtbot, monkeypatch, width, height):
"""
Test the rendering of a PyDMDrawingPolyline widget.
Expectations:
The drawing of the widget takes place without any problems.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
width : int, float
The width to the widget
height : int, float
The height of the widget
"""
drawing = PyDMDrawingPolyline()
qtbot.addWidget(drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
drawing.setPoints(["-1, 18","-1, -1","97, -1"])
drawing.show()
# --------------------
# NEGATIVE TEST CASES
# --------------------
# # -------------
# # PyDMDrawing
# # -------------
@pytest.mark.parametrize("width, height, rotation_deg", [
(0, 10.35, 0.0),
(10.35, 0, 0.0),
(0, 0, 45.0),
(-10.5, 10.35, 15.0),
(10.35, -5, 17.5),
(-10.7, -10, 45.50),
])
def test_get_inner_max_neg(qtbot, monkeypatch, caplog, width, height,
rotation_deg):
"""
Test the handling of invalid width and/or height value during the inner rectangle calculations.
Expectations:
Invalid values will be logged as errors.
Parameters
----------
qtbot : fixture
Window for widget testing
monkeypatch : fixture
To override attribute values
caplog : fixture
To capture the error logging
width : int, float
The width of the widget
height : int, float
The height of the widget
rotation_deg : int, float
The widget's rotation, in degrees
"""
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawing.get_inner_max()
for record in caplog.records:
assert record.levelno == ERROR
if width == 0:
assert "Invalid width. The value must be greater than 0" in caplog.text
elif height == 0:
assert "Invalid height. The value must be greater than 0" in caplog.text
| 31.999223 | 142 | 0.626885 |
import os
from logging import ERROR
import pytest
from qtpy.QtGui import QColor, QBrush, QPixmap
from qtpy.QtWidgets import QApplication
from qtpy.QtCore import Property, Qt, QPoint, QSize
from qtpy.QtDesigner import QDesignerFormWindowInterface
from ...widgets.base import PyDMWidget
from ...widgets.drawing import (deg_to_qt, qt_to_deg, PyDMDrawing,
PyDMDrawingLine, PyDMDrawingImage,
PyDMDrawingRectangle, PyDMDrawingTriangle,
PyDMDrawingEllipse,
PyDMDrawingCircle, PyDMDrawingArc,
PyDMDrawingPie, PyDMDrawingChord,
PyDMDrawingPolygon, PyDMDrawingPolyline)
from ...utilities.stylesheet import apply_stylesheet
qt_deg", [
(0, 0),
(1, 16),
(-1, -16),
])
def test_deg_to_qt(deg, expected_qt_deg):
assert deg_to_qt(deg) == expected_qt_deg
@pytest.mark.parametrize("qt_deg, expected_deg", [
(0, 0),
(16, 1),
(-16, -1),
(-32.0, -2),
(16.16, 1.01)
])
def test_qt_to_deg(qt_deg, expected_deg):
assert qt_to_deg(qt_deg) == expected_deg
def test_pydmdrawing_construct(qtbot):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.alarmSensitiveBorder is False
assert pydm_drawing._rotation == 0.0
assert pydm_drawing._brush.style() == Qt.SolidPattern
assert pydm_drawing._painter
assert pydm_drawing._pen.style() == pydm_drawing._pen_style == Qt.NoPen
assert pydm_drawing._pen_width == 0
assert pydm_drawing._pen_color == QColor(0, 0, 0)
def test_pydmdrawing_sizeHint(qtbot):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.sizeHint() == QSize(100, 100)
@pytest.mark.parametrize("alarm_sensitive_content", [
True,
False,
])
def test_pydmdrawing_paintEvent(qtbot, signals, alarm_sensitive_content):
QApplication.instance().make_main_window()
main_window = QApplication.instance().main_window
qtbot.addWidget(main_window)
pydm_drawing = PyDMDrawing(parent=main_window, init_channel='fake://tst')
qtbot.addWidget(pydm_drawing)
pydm_drawing.alarmSensitiveContent = alarm_sensitive_content
brush_before = pydm_drawing.brush.color().name()
signals.new_severity_signal.connect(pydm_drawing.alarmSeverityChanged)
signals.new_severity_signal.emit(PyDMWidget.ALARM_MAJOR)
brush_after = pydm_drawing.brush.color().name()
if alarm_sensitive_content:
assert brush_before != brush_after
else:
assert brush_before == brush_after
@pytest.mark.parametrize("widget_width, widget_height, expected_results", [
(4.0, 4.0, (2.0, 2.0)),
(1.0, 1.0, (0.5, 0.5)),
(0, 0, (0, 0))
])
def test_pydmdrawing_get_center(qtbot, monkeypatch, widget_width, widget_height,
expected_results):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: widget_width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: widget_height)
assert pydm_drawing.get_center() == expected_results
@pytest.mark.parametrize(
"width, height, rotation_deg, pen_width, has_border, max_size, force_no_pen, expected",
[
(25.53, 10.35, 0.0, 2, True, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, True, True, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 0.0, 2, True, False, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, True, False, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 0.0, 2, False, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, False, True, False,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.0, 2, False, False, True,
(-12.765, -5.175, 25.53, 10.35)),
(10.35, 25.53, 0.0, 2, True, True, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, True, True, False,
(-3.175, -10.765, 6.35, 21.53)),
(10.35, 25.53, 0.0, 2, True, False, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, True, False, False,
(-3.175, -10.765, 6.35, 21.53)),
(10.35, 25.53, 0.0, 2, False, True, True,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, False, True, False,
(-5.175, -12.765, 10.35, 25.53)),
(10.35, 25.53, 0.0, 2, False, False, True,
(-5.175, -12.765, 10.35, 25.53)),
(
10.35, 10.35, 0.0, 2, True, True, True, (-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, True, True, False, (-3.175, -3.175, 6.35, 6.35)),
(10.35, 10.35, 0.0, 2, True, False, True,
(-5.175, -5.175, 10.35, 10.35)),
(
10.35, 10.35, 0.0, 2, True, False, False, (-3.175, -3.175, 6.35, 6.35)),
(10.35, 10.35, 0.0, 2, False, True, True,
(-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, False, True, False,
(-5.175, -5.175, 10.35, 10.35)),
(10.35, 10.35, 0.0, 2, False, False, True,
(-5.175, -5.175, 10.35, 10.35)),
(25.53, 10.35, 45.0, 2, True, True, True,
(-5.207, -2.111, 10.415, 4.222)),
(25.53, 10.35, 145.0, 2, True, True, True,
(-5.714, -2.316, 11.428, 4.633)),
(25.53, 10.35, 90.0, 2, True, True, False,
(-3.175, -0.098, 6.35, 0.196)),
(25.53, 10.35, 180.0, 2, True, False, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 270.0, 2, True, False, False,
(-10.765, -3.175, 21.53, 6.35)),
(25.53, 10.35, 360.0, 2, False, True, True,
(-12.765, -5.175, 25.53, 10.35)),
(25.53, 10.35, 0.72, 2, False, True, False,
(-12.382, -5.02, 24.764, 10.04)),
(25.53, 10.35, 71.333, 2, False, False, True,
(-12.765, -5.175, 25.53, 10.35)),
])
def test_pydmdrawing_get_bounds(qtbot, monkeypatch, width, height, rotation_deg,
pen_width, has_border, max_size,
force_no_pen, expected):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
pydm_drawing._pen_width = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
if has_border:
monkeypatch.setattr(PyDMDrawing, "has_border", lambda *args: True)
else:
monkeypatch.setattr(PyDMDrawing, "has_border", lambda *args: False)
calculated_bounds = pydm_drawing.get_bounds(max_size, force_no_pen)
calculated_bounds = tuple(
[round(x, 3) if isinstance(x, float) else x for x in calculated_bounds])
assert calculated_bounds == expected
@pytest.mark.parametrize("pen_style, pen_width, expected_result", [
(Qt.NoPen, 0, False),
(Qt.NoPen, 1, False),
(Qt.SolidLine, 0, False),
(Qt.DashLine, 0, False),
(Qt.SolidLine, 1, True),
(Qt.DashLine, 10, True)
])
def test_pydmdrawing_has_border(qtbot, pen_style, pen_width, expected_result):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing.penStyle = pen_style
pydm_drawing.penWidth = pen_width
assert pydm_drawing.has_border() == expected_result
@pytest.mark.parametrize("width, height, expected_result", [
(10, 15, False),
(10.5, 22.333, False),
(-10.333, -10.332, False),
(10.333, 10.333, True),
(-20.777, -20.777, True),
(70, 70, True),
])
def test_pydmdrawing_is_square(qtbot, monkeypatch, width, height,
expected_result):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
assert pydm_drawing.is_square() == expected_result
@pytest.mark.parametrize("width, height, rotation_deg, expected", [
(25.53, 10.35, 0.0, (25.53, 10.35)),
(10.35, 25.53, 0.0, (10.35, 25.53)),
(25.53, 10.35, 45.0, (10.415, 4.222)),
(10.35, 25.53, 45.0, (4.222, 10.415)),
(10.35, 25.53, 360.0, (10.35, 25.53)),
(10.35, 25.53, -45.0, (4.222, 10.415)),
(10.35, 25.53, -270.0, (4.196, 10.35)),
(10.35, 25.53, -360.0, (10.35, 25.53)),
])
def test_pydmdrawing_get_inner_max(qtbot, monkeypatch, width, height,
rotation_deg, expected):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
calculated_inner_max = pydm_drawing.get_inner_max()
calculated_inner_max = tuple(
[round(x, 3) if isinstance(x, float) else x for x in
calculated_inner_max])
assert calculated_inner_max == expected
def test_pydmdrawing_properties_and_setters(qtbot):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
assert pydm_drawing.penWidth == 0
assert pydm_drawing.penColor == QColor(0, 0, 0)
assert pydm_drawing.rotation == 0.0
assert pydm_drawing._brush.style() == Qt.SolidPattern
assert pydm_drawing.penStyle == Qt.NoPen
assert pydm_drawing.penCapStyle == Qt.SquareCap
assert pydm_drawing.penJoinStyle == Qt.MiterJoin
pydm_drawing.penWidth = -1
assert pydm_drawing.penWidth == 0
pydm_drawing.penWidth = 5
pydm_drawing.penWidth = -1
assert pydm_drawing.penWidth == 5
pydm_drawing.penJoinStyle = Qt.RoundJoin
assert pydm_drawing.penJoinStyle == Qt.RoundJoin
pydm_drawing.penCapStyle = Qt.RoundCap
assert pydm_drawing.penCapStyle == Qt.RoundCap
pydm_drawing.penColor = QColor(255, 0, 0)
pydm_drawing.rotation = 99.99
pydm_drawing.brush = QBrush(Qt.Dense3Pattern)
assert pydm_drawing.penColor == QColor(255, 0, 0)
assert pydm_drawing.rotation == 99.99
assert pydm_drawing._brush.style() == Qt.Dense3Pattern
, [
True,
False,
])
def test_pydmdrawingline_draw_item(qtbot, signals, alarm_sensitive_content):
pydm_drawingline = PyDMDrawingLine(init_channel='fake://tst')
qtbot.addWidget(pydm_drawingline)
pydm_drawingline.alarmSensitiveContent = alarm_sensitive_content
signals.new_severity_signal.connect(pydm_drawingline.alarmSeverityChanged)
signals.new_severity_signal.emit(PyDMWidget.ALARM_MAJOR)
with qtbot.waitExposed(pydm_drawingline):
pydm_drawingline.show()
qtbot.waitUntil(lambda: pydm_drawingline.isEnabled(), timeout=5000)
pydm_drawingline.setFocus()
def wait_focus():
return pydm_drawingline.hasFocus()
qtbot.waitUntil(wait_focus, timeout=5000)
drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
assert pydm_drawingimage._pixmap is not None
assert pydm_drawingimage._aspect_ratio_mode == Qt.KeepAspectRatio
assert pydm_drawingimage.filename == ""
base_path = os.path.dirname(__file__)
test_file = os.path.join(base_path, '..', '..', '..', 'examples', 'drawing',
'SLAC_logo.jpeg')
pydm_drawingimage2 = PyDMDrawingImage(filename=test_file)
qtbot.addWidget(pydm_drawingimage2)
pydm_drawingimage3 = PyDMDrawingImage(filename=os.path.abspath(test_file))
qtbot.addWidget(pydm_drawingimage3)
pydm_drawingimage4 = PyDMDrawingImage(filename="foo")
qtbot.addWidget(pydm_drawingimage4)
test_gif = os.path.join(base_path, '..', '..', '..', 'examples', 'drawing',
'test.gif')
pydm_drawingimage5 = PyDMDrawingImage(filename=test_gif)
pydm_drawingimage5.movie_finished()
qtbot.addWidget(pydm_drawingimage5)
pydm_drawingimage5.filename = test_file
pydm_drawingimage5.movie_frame_changed(-1)
pydm_drawingimage5.movie_finished()
def test_pydmdrawingimage_get_designer_window(qtbot):
parent = None
pydm_drawingimage = PyDMDrawingImage(parent=parent)
qtbot.addWidget(pydm_drawingimage)
designer_window = pydm_drawingimage.get_designer_window()
if parent is None:
assert designer_window is None
elif isinstance(parent, QDesignerFormWindowInterface):
assert designer_window == parent
else:
assert designer_window == parent.parent()
def test_pydmdrawingimage_test_properties_and_setters(qtbot):
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
pydm_drawingimage.aspectRatioMode = Qt.KeepAspectRatioByExpanding
assert pydm_drawingimage.aspectRatioMode == Qt.KeepAspectRatioByExpanding
@pytest.mark.parametrize("is_pixmap_empty", [
True,
False,
])
def test_pydmdrawingimage_size_hint(qtbot, monkeypatch, is_pixmap_empty):
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
if is_pixmap_empty:
monkeypatch.setattr(QSize, "isEmpty", lambda *args: True)
else:
monkeypatch.setattr(QPixmap, "size", lambda *args: QSize(125, 125))
size_hint = pydm_drawingimage.sizeHint()
assert size_hint == QSize(100,
100) if is_pixmap_empty else size_hint == pydm_drawingimage._pixmap.size()
@pytest.mark.parametrize("width, height, pen_width", [
(7.7, 10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingimage_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
pydm_drawingimage = PyDMDrawingImage()
qtbot.addWidget(pydm_drawingimage)
pydm_drawingimage.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingimage.show()
10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingrectangle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
pydm_drawingrectangle = PyDMDrawingRectangle()
qtbot.addWidget(pydm_drawingrectangle)
pydm_drawingrectangle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingrectangle.show()
[
(0.0, 0.0, 7.7, 10.2, [QPoint(0, 5), QPoint(0, 0), QPoint(3, 0.0)]),
(10.3, 0, 7.7, 10.2, [QPoint(10, 5), QPoint(10, 0), QPoint(3, 0)]),
(10.3, 56.7, 7.7, 10.2, [QPoint(10, 5), QPoint(10, 56), QPoint(3, 56)]),
(0.0, 10.75, 7.7, 10.2, [QPoint(0, 5), QPoint(0, 10), QPoint(3, 10)]),
(-10.23, 0, 7.7, 10.2, [QPoint(-10, 5), QPoint(-10, 0), QPoint(3, 0)]),
(0.0, -10.23, 7.7, 10.2, [QPoint(0, 5), QPoint(0, -10), QPoint(3, -10)]),
(-60.23, -87.25, 7.7, 10.2,
[QPoint(-60, 5), QPoint(-60, -87), QPoint(3, -87)]),
(1, 2, 5.0, 5.0, [QPoint(1, 2), QPoint(1, 2), QPoint(2, 2)]),
])
def test_pydmdrawingtriangle_calculate_drawing_points(qtbot, x, y, width,
height, expected_points):
pydm_drawingtriangle = PyDMDrawingTriangle()
qtbot.addWidget(pydm_drawingtriangle)
calculated_points = pydm_drawingtriangle._calculate_drawing_points(x, y,
width,
height)
assert calculated_points == expected_points
@pytest.mark.parametrize("width, height, pen_width", [
(7.7, 10.2, 0),
(10.2, 7.7, 0),
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
(100.0, 10.25, 5.125),
])
def test_pydmdrawingtriangle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
pydm_drawingtriangle = PyDMDrawingTriangle()
qtbot.addWidget(pydm_drawingtriangle)
pydm_drawingtriangle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingtriangle.show()
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingeclipse_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
pydm_dymdrawingeclipse = PyDMDrawingEllipse()
qtbot.addWidget(pydm_dymdrawingeclipse)
pydm_dymdrawingeclipse.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_dymdrawingeclipse.show()
", [
(5.0, 5.0, 2.5),
(10.25, 10.25, 5.125),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingcircle_calculate_radius(qtbot, width, height,
expected_radius):
pydm_dymdrawingcircle = PyDMDrawingCircle()
qtbot.addWidget(pydm_dymdrawingcircle)
calculated_radius = pydm_dymdrawingcircle._calculate_radius(width, height)
assert calculated_radius == expected_radius
@pytest.mark.parametrize("width, height, pen_width", [
(5.0, 5.0, 0),
(10.25, 10.25, 1.5),
(10.25, 100.0, 5.125),
])
def test_pydmdrawingcircle_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width):
pydm_dymdrawingcircle = PyDMDrawingCircle()
qtbot.addWidget(pydm_dymdrawingcircle)
pydm_dymdrawingcircle.penWidth = pen_width
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_dymdrawingcircle.show()
ydm_drawingarc = PyDMDrawingArc()
qtbot.addWidget(pydm_drawingarc)
assert pydm_drawingarc._pen_style == Qt.SolidLine
assert pydm_drawingarc._pen_width == 1.0
assert pydm_drawingarc._start_angle == 0
assert pydm_drawingarc._span_angle == deg_to_qt(90)
@pytest.mark.parametrize("width, height, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0),
(10.333, 10.333, 0, 0),
(10.333, 10.333, 0, 45),
(10.333, 11.777, 0, 45),
(10.333, 11.777, 0, -35),
(10.333, 11.777, 11, 45),
(10.333, 11.777, -11, -25),
])
def test_pydmdrawingarc_draw_item(qapp, qtbot, monkeypatch, width, height,
start_angle_deg, span_angle_deg):
pydm_drawingarc = PyDMDrawingArc()
qtbot.addWidget(pydm_drawingarc)
pydm_drawingarc.startAngle = start_angle_deg
pydm_drawingarc.spanAngle = span_angle_deg
assert pydm_drawingarc.startAngle == start_angle_deg
assert pydm_drawingarc.spanAngle == span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingarc.show()
en_width, rotation_deg, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0, 0, 0),
(10.333, 10.333, 0, 0, 0, 0),
(10.333, 11.777, 0, 0, 0, 45),
(10.333, 11.777, 0, 0, 0, -35),
(10.333, 11.777, 3, 15.333, 0, 0),
(10.333, 11.777, 3, 15.333, 0, 45),
(10.333, 11.777, 3, 15.333, 0, -35),
(10.333, 11.777, 3, 15.333, 11, 45),
(10.333, 11.777, 3, 15.333, -11, -25),
])
def test_pydmdrawingpie_draw_item(qapp, qtbot, monkeypatch, width, height, pen_width,
rotation_deg, start_angle_deg,
span_angle_deg):
pydm_drawingpie = PyDMDrawingPie()
qtbot.addWidget(pydm_drawingpie)
pydm_drawingpie._pen_width = pen_width
pydm_drawingpie._rotation = rotation_deg
pydm_drawingpie._start_angle = start_angle_deg
pydm_drawingpie._span_angle = span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingpie.show()
th, rotation_deg, start_angle_deg, span_angle_deg", [
(10.333, 11.777, 0, 0, 0, 0),
(10.333, 10.333, 0, 0, 0, 0),
(10.333, 11.777, 0, 0, 0, 45),
(10.333, 11.777, 0, 0, 0, -35),
(10.333, 11.777, 3, 15.333, 0, 0),
(10.333, 11.777, 3, 15.333, 0, 45),
(10.333, 11.777, 3, 15.333, 0, -35),
(10.333, 11.777, 3, 15.333, 11, 45),
(10.333, 11.777, 3, 15.333, -11, -25),
])
def test_pydmdrawingchord_draw_item(qapp, qtbot, monkeypatch, width, height,
pen_width, rotation_deg, start_angle_deg,
span_angle_deg):
pydm_drawingchord = PyDMDrawingChord()
qtbot.addWidget(pydm_drawingchord)
pydm_drawingchord._pen_width = pen_width
pydm_drawingchord._rotation = rotation_deg
pydm_drawingchord._start_angle = start_angle_deg
pydm_drawingchord._span_angle = span_angle_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawingchord.show()
cted_points", [
(0, 0, 100, 100, 3, [(50.0, 0),(-25, 43.3012),(-25, -43.3012)]),
(0, 0, 100, 100, 4, [(50.0, 0), (0, 50.0), (-50.0, 0), (0, -50.0)])
])
def test_pydmdrawingpolygon_calculate_drawing_points(qapp, qtbot, x, y, width,
height, num_points,
expected_points):
drawing = PyDMDrawingPolygon()
qtbot.addWidget(drawing)
drawing.numberOfPoints = num_points
assert drawing.numberOfPoints == num_points
calculated_points = drawing._calculate_drawing_points(x, y,
width,
height)
for idx, p in enumerate(calculated_points):
assert p.x() == pytest.approx(expected_points[idx][0], 0.1)
assert p.y() == pytest.approx(expected_points[idx][1], 0.1)
drawing.show()
ted_points", [
(-1, 27, 389, 3, 2, [(-2, -2),(384, -2)]),
(301, 230, 99, 20, 3, [(-1, 18),(-1, -1),(97, -1)])
])
def test_pydmdrawingpolyline_getPoints(qapp, qtbot, x, y, width,
height, num_points,
expected_points):
drawing = PyDMDrawingPolyline()
qtbot.addWidget(drawing)
drawing.numberOfPoints = num_points
assert drawing.numberOfPoints == num_points
the_points = drawing.getPoints()
for idx, p in enumerate(the_points):
assert p.x() == pytest.approx(expected_points[idx][0], 0.1)
assert p.y() == pytest.approx(expected_points[idx][1], 0.1)
drawing.show()
@pytest.mark.parametrize("width, height", [
(99, 20)
])
def test_pydmdrawingpolyline_setPoints(qapp, qtbot, monkeypatch, width, height):
drawing = PyDMDrawingPolyline()
qtbot.addWidget(drawing)
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
drawing.setPoints(["-1, 18","-1, -1","97, -1"])
drawing.show()
rotation_deg", [
(0, 10.35, 0.0),
(10.35, 0, 0.0),
(0, 0, 45.0),
(-10.5, 10.35, 15.0),
(10.35, -5, 17.5),
(-10.7, -10, 45.50),
])
def test_get_inner_max_neg(qtbot, monkeypatch, caplog, width, height,
rotation_deg):
pydm_drawing = PyDMDrawing()
qtbot.addWidget(pydm_drawing)
pydm_drawing._rotation = rotation_deg
monkeypatch.setattr(PyDMDrawing, "width", lambda *args: width)
monkeypatch.setattr(PyDMDrawing, "height", lambda *args: height)
pydm_drawing.get_inner_max()
for record in caplog.records:
assert record.levelno == ERROR
if width == 0:
assert "Invalid width. The value must be greater than 0" in caplog.text
elif height == 0:
assert "Invalid height. The value must be greater than 0" in caplog.text
| true | true |
f736f1fd69008ba9d3e5c7488b46ef0b76ea3267 | 8,447 | py | Python | mlmi/participant.py | iwan933/mlmi-federated-learning | e148664304dd7fbbc2cc2a6a34567533748c1720 | [
"MIT"
] | 2 | 2021-09-07T12:52:44.000Z | 2021-09-30T09:35:53.000Z | mlmi/participant.py | iwan933/mlmi-federated-learning | e148664304dd7fbbc2cc2a6a34567533748c1720 | [
"MIT"
] | null | null | null | mlmi/participant.py | iwan933/mlmi-federated-learning | e148664304dd7fbbc2cc2a6a34567533748c1720 | [
"MIT"
] | 1 | 2021-03-02T12:35:28.000Z | 2021-03-02T12:35:28.000Z | import copy
from pathlib import Path
from typing import Dict, List, Optional, Union
import torch
from pytorch_lightning.metrics import Accuracy
from torch import Tensor, optim
from torch.utils import data
import pytorch_lightning as pl
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.callbacks.base import Callback
from mlmi.structs import OptimizerArgs, TrainArgs, ModelArgs
from mlmi.log import getLogger
from mlmi.settings import CHECKPOINT_DIR
logger = getLogger(__name__)
def optimizer_state_dict_to_cpu(optimizer_state_dict):
c = copy.deepcopy(optimizer_state_dict)
o = {}
state_dict = c.get('state')
r = {}
for key, state in state_dict.items():
s = {}
for k, v in state.items():
if torch.is_tensor(v):
s[k] = v.cpu()
else:
s[k] = v
r[key] = s
o['state'] = r
o['param_groups'] = c.get('param_groups')
return o
class BaseParticipant(object):
def __init__(self, participant_name: str, model_args: ModelArgs, context):
assert participant_name is not None, 'A participant name is required to load and save logs'
assert model_args is not None, 'Model args are required to initialize a model for the participant'
assert context is not None, 'Experiment context is required for participant'
self._name = participant_name
self._cluster_id = None
self._experiment_context = context
participant_model_kwargs = self.get_model_kwargs()
if participant_model_kwargs is not None:
self._model = model_args(participant_name=participant_name, **participant_model_kwargs)
else:
self._model = model_args(participant_name=participant_name)
self._model_args = model_args
def get_model_kwargs(self) -> Optional[Dict]:
return None
@property
def model(self) -> Union[pl.LightningModule, 'BaseParticipantModel']:
"""
The model to train
:return: The model
"""
return self._model
@property
def cluster_id(self) -> str:
return self._cluster_id
@cluster_id.setter
def cluster_id(self, value: str):
self._cluster_id = value
def overwrite_model_state(self, model_state: Dict[str, Tensor]):
"""
Loads the model state into the current model instance
:param model_state: The model state to load
"""
self._model.load_state_dict(model_state, strict=False)
def load_model_state_from_checkpoint(self):
"""
Load the model state from an existing saved checkpoint
"""
self._model = self._model_args.model_class.load_from_checkpoint(
checkpoint_path=str(self.get_checkpoint_path().absolute()))
def get_checkpoint_path(self, suffix: Union[str, None] = None) -> Path:
"""
Constructs a checkpoint path based on
:return:
"""
str_suffix = '' if suffix is None else '_' + suffix
filename = (self._name + str_suffix + '.ckpt')
return CHECKPOINT_DIR / self._experiment_context.name / filename
def save_model_state(self):
"""
Saves the model state of the aggregated model
:param target_path: The path to save the model at
:return:
"""
path = self.get_checkpoint_path()
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(self._model.state_dict(), path)
class BaseTrainingParticipant(BaseParticipant):
def __init__(self, client_id: str, model_args: ModelArgs, context,
train_dataloader: data.DataLoader, num_train_samples: int,
test_dataloader: data.DataLoader, num_test_samples: int,
lightning_logger: LightningLoggerBase, *args, **kwargs):
self._train_dataloader = train_dataloader
self._test_dataloader = test_dataloader
self._num_train_samples = sum([len(y) for x, y in train_dataloader])
self._num_test_samples = num_test_samples
self._lightning_logger = lightning_logger
self._callbacks = None
self._model_state = None
self._trainer = None
super().__init__(client_id, model_args, context)
def create_trainer(self, enable_logging=True, **kwargs) -> pl.Trainer:
"""
Creates a new trainer instance for each training round.
:param kwargs: additional keyword arguments to send to the trainer for configuration
:return: a pytorch lightning trainer instance
"""
_kwargs = kwargs.copy()
_kwargs['logger'] = self.logger
_kwargs['checkpoint_callback'] = False
if torch.cuda.is_available():
_kwargs['gpus'] = 1
return pl.Trainer(callbacks=self._callbacks, limit_val_batches=0.0, **_kwargs)
def set_trainer_callbacks(self, callbacks: List[Callback]):
self._callbacks = callbacks
@property
def logger(self) -> LightningLoggerBase:
"""
Gets the logger to use for the training in later stage.
:return: The lightning logger to use
"""
return self._lightning_logger
@property
def train_data_loader(self) -> data.DataLoader:
return self._train_dataloader
@property
def test_data_loader(self) -> data.DataLoader:
return self._test_dataloader
@property
def num_train_samples(self) -> int:
return self._num_train_samples
@property
def num_test_samples(self) -> int:
return self._num_test_samples
def train(self, training_args: TrainArgs, *args, **kwargs):
"""
Implement the training routine.
:param training_args:
:param args:
:param kwargs:
:return:
"""
trainer = self.create_trainer(enable_logging=False, **training_args.kwargs)
train_dataloader = self.train_data_loader
trainer.fit(self.model, train_dataloader)
del self.model.trainer
def test(self, model: Optional[torch.nn.Module] = None, use_local_model: bool = False):
"""
Test the model state on this clients data.
:param
:param model_state: The model state to evaluate
:return: The output loss
"""
assert use_local_model or model is not None
trainer = self.create_trainer(enable_logging=False, progress_bar_refresh_rate=0)
if use_local_model:
result = trainer.test(model=self.model, test_dataloaders=self.test_data_loader, verbose=False)
self._model = self._model.cpu()
del self._model.trainer
else:
result = trainer.test(model=model, test_dataloaders=self.test_data_loader, verbose=False)
return result
class BaseAggregatorParticipant(BaseParticipant):
def __init__(self, participant_name: str, model_args: ModelArgs, context):
super().__init__(participant_name, model_args, context)
def aggregate(self, participants: List['BaseTrainingParticipant'], *args, **kwargs):
"""
Aggregate the models of other participants with their models.
:param participants: Participants to apply the model changes from
:return:
"""
raise NotImplementedError()
class BaseParticipantModel(object):
def __init__(self, *args, participant_name=None, optimizer_args: Optional[OptimizerArgs]=None,
model=None, **kwargs):
assert participant_name is not None, 'Please provide a participant name parameter in model args to identify' \
'your model in logging'
assert optimizer_args is not None, 'Optimizer args not set!'
assert model is not None, 'Model not passed!'
self.participant_name = participant_name
self.optimizer_args = optimizer_args
super().__init__(*args, **kwargs)
self.model = model
self._optimizer_state = None
@property
def optimizer_state(self):
return self._optimizer_state
@optimizer_state.setter
def optimizer_state(self, value):
self._optimizer_state = value
def configure_optimizers(self):
return self.optimizer_args(self.model.parameters())
"""
Do not restore state
if self.optimizer_state is not None:
optimizer.load_state_dict(self.optimizer_state)
return optimizer
"""
| 34.904959 | 118 | 0.66059 | import copy
from pathlib import Path
from typing import Dict, List, Optional, Union
import torch
from pytorch_lightning.metrics import Accuracy
from torch import Tensor, optim
from torch.utils import data
import pytorch_lightning as pl
from pytorch_lightning.loggers import LightningLoggerBase
from pytorch_lightning.callbacks.base import Callback
from mlmi.structs import OptimizerArgs, TrainArgs, ModelArgs
from mlmi.log import getLogger
from mlmi.settings import CHECKPOINT_DIR
logger = getLogger(__name__)
def optimizer_state_dict_to_cpu(optimizer_state_dict):
c = copy.deepcopy(optimizer_state_dict)
o = {}
state_dict = c.get('state')
r = {}
for key, state in state_dict.items():
s = {}
for k, v in state.items():
if torch.is_tensor(v):
s[k] = v.cpu()
else:
s[k] = v
r[key] = s
o['state'] = r
o['param_groups'] = c.get('param_groups')
return o
class BaseParticipant(object):
def __init__(self, participant_name: str, model_args: ModelArgs, context):
assert participant_name is not None, 'A participant name is required to load and save logs'
assert model_args is not None, 'Model args are required to initialize a model for the participant'
assert context is not None, 'Experiment context is required for participant'
self._name = participant_name
self._cluster_id = None
self._experiment_context = context
participant_model_kwargs = self.get_model_kwargs()
if participant_model_kwargs is not None:
self._model = model_args(participant_name=participant_name, **participant_model_kwargs)
else:
self._model = model_args(participant_name=participant_name)
self._model_args = model_args
def get_model_kwargs(self) -> Optional[Dict]:
return None
@property
def model(self) -> Union[pl.LightningModule, 'BaseParticipantModel']:
return self._model
@property
def cluster_id(self) -> str:
return self._cluster_id
@cluster_id.setter
def cluster_id(self, value: str):
self._cluster_id = value
def overwrite_model_state(self, model_state: Dict[str, Tensor]):
self._model.load_state_dict(model_state, strict=False)
def load_model_state_from_checkpoint(self):
self._model = self._model_args.model_class.load_from_checkpoint(
checkpoint_path=str(self.get_checkpoint_path().absolute()))
def get_checkpoint_path(self, suffix: Union[str, None] = None) -> Path:
str_suffix = '' if suffix is None else '_' + suffix
filename = (self._name + str_suffix + '.ckpt')
return CHECKPOINT_DIR / self._experiment_context.name / filename
def save_model_state(self):
path = self.get_checkpoint_path()
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(self._model.state_dict(), path)
class BaseTrainingParticipant(BaseParticipant):
def __init__(self, client_id: str, model_args: ModelArgs, context,
train_dataloader: data.DataLoader, num_train_samples: int,
test_dataloader: data.DataLoader, num_test_samples: int,
lightning_logger: LightningLoggerBase, *args, **kwargs):
self._train_dataloader = train_dataloader
self._test_dataloader = test_dataloader
self._num_train_samples = sum([len(y) for x, y in train_dataloader])
self._num_test_samples = num_test_samples
self._lightning_logger = lightning_logger
self._callbacks = None
self._model_state = None
self._trainer = None
super().__init__(client_id, model_args, context)
def create_trainer(self, enable_logging=True, **kwargs) -> pl.Trainer:
_kwargs = kwargs.copy()
_kwargs['logger'] = self.logger
_kwargs['checkpoint_callback'] = False
if torch.cuda.is_available():
_kwargs['gpus'] = 1
return pl.Trainer(callbacks=self._callbacks, limit_val_batches=0.0, **_kwargs)
def set_trainer_callbacks(self, callbacks: List[Callback]):
self._callbacks = callbacks
@property
def logger(self) -> LightningLoggerBase:
return self._lightning_logger
@property
def train_data_loader(self) -> data.DataLoader:
return self._train_dataloader
@property
def test_data_loader(self) -> data.DataLoader:
return self._test_dataloader
@property
def num_train_samples(self) -> int:
return self._num_train_samples
@property
def num_test_samples(self) -> int:
return self._num_test_samples
def train(self, training_args: TrainArgs, *args, **kwargs):
trainer = self.create_trainer(enable_logging=False, **training_args.kwargs)
train_dataloader = self.train_data_loader
trainer.fit(self.model, train_dataloader)
del self.model.trainer
def test(self, model: Optional[torch.nn.Module] = None, use_local_model: bool = False):
assert use_local_model or model is not None
trainer = self.create_trainer(enable_logging=False, progress_bar_refresh_rate=0)
if use_local_model:
result = trainer.test(model=self.model, test_dataloaders=self.test_data_loader, verbose=False)
self._model = self._model.cpu()
del self._model.trainer
else:
result = trainer.test(model=model, test_dataloaders=self.test_data_loader, verbose=False)
return result
class BaseAggregatorParticipant(BaseParticipant):
def __init__(self, participant_name: str, model_args: ModelArgs, context):
super().__init__(participant_name, model_args, context)
def aggregate(self, participants: List['BaseTrainingParticipant'], *args, **kwargs):
raise NotImplementedError()
class BaseParticipantModel(object):
def __init__(self, *args, participant_name=None, optimizer_args: Optional[OptimizerArgs]=None,
model=None, **kwargs):
assert participant_name is not None, 'Please provide a participant name parameter in model args to identify' \
'your model in logging'
assert optimizer_args is not None, 'Optimizer args not set!'
assert model is not None, 'Model not passed!'
self.participant_name = participant_name
self.optimizer_args = optimizer_args
super().__init__(*args, **kwargs)
self.model = model
self._optimizer_state = None
@property
def optimizer_state(self):
return self._optimizer_state
@optimizer_state.setter
def optimizer_state(self, value):
self._optimizer_state = value
def configure_optimizers(self):
return self.optimizer_args(self.model.parameters())
| true | true |
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